Import 9 alphaear finance skills

- alphaear-deepear-lite: DeepEar Lite API integration
- alphaear-logic-visualizer: Draw.io XML finance diagrams
- alphaear-news: Real-time finance news (10+ sources)
- alphaear-predictor: Kronos time-series forecasting
- alphaear-reporter: Professional financial reports
- alphaear-search: Web search + local RAG
- alphaear-sentiment: FinBERT/LLM sentiment analysis
- alphaear-signal-tracker: Signal evolution tracking
- alphaear-stock: A-Share/HK/US stock data

Updates:
- All scripts updated to use universal .env path
- Added JINA_API_KEY, LLM_*, DEEPSEEK_API_KEY to .env.example
- Updated load_dotenv() to use ~/.config/opencode/.env

skills/alphaear-predictor/SKILL.md

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+---
+name: alphaear-predictor
+description: Market prediction skill using Kronos. Use when user needs finance market time-series forecasting or news-aware finance market adjustments.
+---
+
+# AlphaEar Predictor Skill
+
+## Overview
+
+This skill utilizes the Kronos model (via `KronosPredictorUtility`) to perform time-series forecasting and adjust predictions based on news sentiment.
+
+## Capabilities
+
+### 1. Forecast Market Trends
+
+### 1. Forecast Market Trends
+
+**Workflow:**
+1.  **Generate Base Forecast**: Use `scripts/kronos_predictor.py` (via `KronosPredictorUtility`) to generate the technical/quantitative forecast.
+2.  **Adjust Forecast (Agentic)**: Use the **Forecast Adjustment Prompt** in `references/PROMPTS.md` to subjectively adjust the numbers based on latest news/logic.
+
+**Key Tools:**
+-   `KronosPredictorUtility.get_base_forecast(df, lookback, pred_len, news_text)`: Returns `List[KLinePoint]`.
+
+**Example Usage (Python):**
+
+```python
+from scripts.utils.kronos_predictor import KronosPredictorUtility
+from scripts.utils.database_manager import DatabaseManager
+
+db = DatabaseManager()
+predictor = KronosPredictorUtility()
+
+# Forecast
+forecast = predictor.predict("600519", horizon="7d")
+print(forecast)
+```
+
+
+## Configuration
+
+This skill requires the **Kronos** model and an embedding model.
+
+1.  **Kronos Model**:
+    -   Ensure `exports/models` directory exists in the project root.
+    -   Place trained news projector weights (e.g., `kronos_news_v1.pt`) in `exports/models/`.
+    -   Or depend on the base model (automatically downloaded).
+
+2.  **Environment Variables**:
+    -   `EMBEDDING_MODEL`: Path or name of the embedding model (default: `sentence-transformers/all-MiniLM-L6-v2`).
+    -   `KRONOS_MODEL_PATH`: Optional path to override model loading.
+
+## Dependencies
+
+-   `torch`
+-   `transformers`
+-   `sentence-transformers`
+-   `pandas`
+-   `numpy`
+-   `scikit-learn`

skills/alphaear-predictor/references/PROMPTS.md

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+# AlphaEar Predictor Prompts
+
+## Forecast Adjustment (Analyst)
+
+**Prompt:**
+
+```markdown
+You are a senior quantitative strategy analyst.
+Your task is to subjectively/logically adjust the given [Kronos Model Forecast] based on the [Latest Intelligence/News Context].
+
+Ticker: {ticker}
+
+【Kronos Base Forecast (OHLC)】:
+{forecast_str}
+
+【Latest Intelligence Context】:
+{news_context}
+
+**Adjustment Principles:**
+1. Base forecast is technical-only.
+2. Context may contain a "Quantitative Correction" from a news-aware model. **Highly respect** this unless logic is flawed.
+3. Use qualitative analysis (news logic) to verify or fine-tune.
+4. If no quantitative correction exists, verify trend manually against news sentiment.
+
+**Output (Strict JSON):**
+```json
+{
+  "adjusted_forecast": [
+    {
+      "date": "YYYY-MM-DD",
+      "open": <float>,
+      "high": <float>,
+      "low": <float>,
+      "close": <float>,
+      "volume": <float>
+    },
+    ...
+  ],
+  "rationale": "Detailed logic..."
+}
+```
+Ensure same number of data points as base forecast.
+```

skills/alphaear-predictor/scripts/forecast_agent.py

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+import json
+from typing import List, Optional, Dict, Any
+from datetime import datetime
+from loguru import logger
+import pandas as pd
+
+from .kronos_predictor import KronosPredictorUtility
+from .utils.database_manager import DatabaseManager
+from .schema.models import ForecastResult, KLinePoint, InvestmentSignal
+
+class ForecastUtils:
+    """
+    预测辅助工具 (ForecastUtils)
+    提供数据准备、基础模型预测等功能。
+    LLM 调整逻辑已移交 Agent 执行 (参考 scripts/prompts/PROMPTS.md)。
+    """
+    
+    def __init__(self, db: DatabaseManager):
+        self.db = db
+        self.predictor_util = KronosPredictorUtility() # Singleton
+
+    def get_base_forecast(
+        self,
+        ticker: str,
+        signals: List[Dict] = None,
+        lookback: int = 20,
+        pred_len: int = 5,
+    ) -> Optional[List[KLinePoint]]:
+        """
+        获取基础预测数据 (技术面 + 新闻模型定量修正)。
+        Agent 应随后使用 PROMPTS.md 中的指令进行定性调整。
+        """
+        logger.info(f"🔮 Generating base forecast for {ticker}...")
+        
+        # 1. 获取历史数据
+        from .stock_tools import StockTools
+        stock_tools = StockTools(self.db, auto_update=False)
+        
+        end_date = datetime.now().strftime("%Y-%m-%d")
+        # 宽放一点时间以确保有足够的交易日
+        start_date = (datetime.now() - pd.Timedelta(days=max(lookback * 4, 90))).strftime("%Y-%m-%d")
+        df = stock_tools.get_stock_price(ticker, start_date=start_date, end_date=end_date)
+
+        if df.empty or len(df) < lookback:
+            # Try force sync
+            df = stock_tools.get_stock_price(ticker, start_date=start_date, end_date=end_date, force_sync=True)
+
+        if df.empty:
+            logger.warning(f"⚠️ No history data for {ticker}")
+            return None
+
+        effective_lookback = lookback
+        if len(df) < lookback:
+            if len(df) < 10:
+                logger.warning(f"⚠️ Insufficient history for {ticker}")
+                return None
+            effective_lookback = len(df)
+
+        # 2. 准备信号上下文
+        signal_lines = []
+        for s in (signals or []):
+            try:
+                title = s.get('title', '') if isinstance(s, dict) else getattr(s, 'title', '')
+                summary = s.get('summary', '') if isinstance(s, dict) else getattr(s, 'summary', '')
+                if title or summary:
+                    signal_lines.append(f"- {title}: {summary}")
+            except Exception:
+                continue
+
+        signals_context = "\n".join(signal_lines).strip()
+        
+        # 3. 模型预测 (News-Adjusted if context exists)
+        if signals_context:
+            return self.predictor_util.get_base_forecast(df, lookback=effective_lookback, pred_len=pred_len, news_text=signals_context)
+        else:
+            return self.predictor_util.get_base_forecast(df, lookback=effective_lookback, pred_len=pred_len, news_text=None)

skills/alphaear-predictor/scripts/json_utils.py

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+import ast
+import json
+import re
+from typing import Optional, Any
+from loguru import logger
+
+def _strip_comments(text: str) -> str:
+    """
+    Safely remove C-style comments (// and /* */) from JSON-like text,
+    preserving strings (including URLs like http://).
+    """
+    result = []
+    i = 0
+    n = len(text)
+    in_string = False
+    escape = False
+    
+    while i < n:
+        char = text[i]
+        
+        if in_string:
+            if char == '\\':
+                escape = not escape
+            elif char == '"' and not escape:
+                in_string = False
+            else:
+                escape = False
+            result.append(char)
+            i += 1
+            continue
+            
+        # Not in string
+        if char == '"':
+            in_string = True
+            result.append(char)
+            i += 1
+            continue
+            
+        # Check for // comment
+        if i + 1 < n and text[i:i+2] == '//':
+            i += 2
+            while i < n and text[i] != '\n':
+                i += 1
+            continue
+            
+        # Check for /* comment
+        if i + 1 < n and text[i:i+2] == '/*':
+            i += 2
+            while i + 1 < n and text[i:i+2] != '*/':
+                i += 1
+            i += 2
+            continue
+            
+        result.append(char)
+        i += 1
+        
+    return ''.join(result)
+
+def extract_json(text: str) -> Optional[Any]:
+    """
+    更加鲁棒的 JSON 提取工具。
+    处理:
+    1. Markdown 代码块 (```json ... ```)
+    2. 首尾多余字符
+    3. 同一个文本中多个 JSON 对象 (仅提取第一个)
+    4. 简单的 JSON 修复 (末尾逗号等)
+    5. C 风格注释 (// 和 /* */)
+    """
+    if not text:
+        return None
+    
+    # 1. 清理明显的 Markdown 包装
+    text = text.strip()
+    
+    # 先尝试精确匹配 ```json ... ``` 或 ```...```
+    md_match = re.search(r'```(?:json)?\s*\n?(.*?)\n?```', text, re.DOTALL)
+    if md_match:
+        text = md_match.group(1).strip()
+    elif text.startswith("```"):
+        # 回退：如果开头有 ``` 但没完整匹配
+        text = re.sub(r'^```[a-z]*\n?', '', text)
+        text = re.sub(r'\n?```\s*$', '', text)
+    
+    # 2. 寻找第一个 JSON 起始符 { 或 [
+    start_brace = text.find('{')
+    start_bracket = text.find('[')
+    
+    if start_brace == -1 and start_bracket == -1:
+        return None
+        
+    start_idx = start_brace if (start_bracket == -1 or (start_brace != -1 and start_brace < start_bracket)) else start_bracket
+    
+    # 2.5 预处理：修复一些极其常见的 LLM 错误
+    potential_json = text[start_idx:].strip()
+    
+    # remove comments safely
+    potential_json = _strip_comments(potential_json)
+    
+    # b. 修复缺失开头引号的键:  nodes": [  -> "nodes": [
+    # 匹配模式: (空白或换行) 单词 紧跟引号和冒号
+    potential_json = re.sub(r'([\{\,]\s*)([a-zA-Z_]\w*)\"\s*:', r'\1"\2":', potential_json)
+    
+    # c. 修复缺失末尾引号的键:  "nodes: [ -> "nodes": [
+    potential_json = re.sub(r'([\{\,]\s*)\"([a-zA-Z_]\w*)\s*:', r'\1"\2":', potential_json)
+
+    # d. 修复完全缺失引号的键: nodes: [ -> "nodes": [
+    # 注意避免匹配到像 http:// 这种内容，所以限定在 { 或 , 之后
+    potential_json = re.sub(r'([\{\,]\s*)([a-zA-Z_]\w*)\s*:', r'\1"\2":', potential_json)
+    
+    # 3. 使用 raw_decode 尝试解析
+    decoder = json.JSONDecoder()
+    
+    # 首先尝试直接解析（不做任何预处理）
+    try:
+        obj = json.loads(potential_json)
+        return obj
+    except json.JSONDecodeError:
+        pass
+    
+    # 简单预处理：移除对象/列表末位多余逗号
+    processed_json = re.sub(r',\s*([\]}])', r'\1', potential_json)
+    
+    try:
+        obj, end_pos = decoder.raw_decode(processed_json)
+        return obj
+    except json.JSONDecodeError:
+        pass
+    
+    # e. 修复未终止的字符串字面量问题：移除值中的实际换行符
+    # LLM 可能在字符串值中生成包含真实 newline 的内容，导致 JSON 非法
+    def fix_multiline_strings(s):
+        # 简单策略：将字符串值内的换行替换为空格
+        lines = s.split('\n')
+        result = []
+        in_string = False
+        for line in lines:
+            # 计算未转义的引号数
+            quote_count = line.count('"') - line.count('\\"')
+            if in_string:
+                result[-1] += ' ' + line.strip()
+            else:
+                result.append(line)
+            
+            if quote_count % 2 == 1:
+                in_string = not in_string
+        return '\n'.join(result)
+    
+    fixed_json = fix_multiline_strings(processed_json)
+    
+    try:
+        obj, end_pos = decoder.raw_decode(fixed_json)
+        return obj
+    except json.JSONDecodeError:
+        try:
+            # 4. 尝试处理单引号问题 (JSON 规范要求双引号，但 LLM 常输出单引号)
+            # 这是一个简单的替换技巧，仅针对像 {'key': 'value'} 这样的结构
+            # 注意：这可能会破坏包含单引号的字符串值，所以作为较后的回退
+            fix_quotes = re.sub(r"'(.*?)':", r'"\1":', processed_json) # 修复键
+            fix_quotes = re.sub(r":\s*'(.*?)'", r': "\1"', fix_quotes)   # 修复简单值
+            obj, end_pos = decoder.raw_decode(fix_quotes)
+            return obj
+        except (json.JSONDecodeError, TypeError):
+            try:
+                # 5. 使用 ast.literal_eval 作为终极回退 (处理 Python 字典格式)
+                # 提取第一个匹配的括号对内容
+                # 寻找匹配的 { }
+                stack = []
+                for i, char in enumerate(potential_json):
+                    if char == '{': stack.append('{')
+                    elif char == '}':
+                        if stack: stack.pop()
+                        if not stack:
+                            content = potential_json[:i+1]
+                            return ast.literal_eval(content)
+            except (ValueError, SyntaxError, MemoryError) as e:
+                logger.warning(f"All JSON extraction attempts failed: {e}")
+            except Exception as e:
+                logger.error(f"Unexpected error during JSON extraction: {e}")
+    
+    return None

skills/alphaear-predictor/scripts/kronos_predictor.py

@@ -0,0 +1,219 @@
+import torch
+import pandas as pd
+import numpy as np
+from datetime import datetime
+from typing import List, Optional
+from loguru import logger
+from pandas.tseries.offsets import BusinessDay
+import os
+from dotenv import load_dotenv
+
+load_dotenv(os.path.expanduser("~/.config/opencode/.env"))
+
+# Fix for Kronos internal imports
+import sys
+
+KRONOS_DIR = os.path.join(os.path.dirname(__file__), "predictor")
+if KRONOS_DIR not in sys.path:
+    sys.path.append(KRONOS_DIR)
+
+import glob
+from sentence_transformers import SentenceTransformer
+
+from .predictor.model import Kronos, KronosTokenizer, KronosPredictor
+from .schema.models import KLinePoint
+
+
+class KronosPredictorUtility:
+    """
+    Kronos 时序预测工具类
+    负责模型加载、推理以及数据结构转换
+    """
+
+    _instance = None
+    _predictor = None
+
+    def __new__(cls, *args, **kwargs):
+        if not cls._instance:
+            cls._instance = super(KronosPredictorUtility, cls).__new__(cls)
+        return cls._instance
+
+    def __init__(self, device: Optional[str] = None):
+        if self._predictor is not None:
+            return
+
+        try:
+            if not device:
+                device = (
+                    "cuda"
+                    if torch.cuda.is_available()
+                    else "mps"
+                    if torch.backends.mps.is_available()
+                    else "cpu"
+                )
+
+            logger.info(f"🔮 Loading Kronos Model on {device}...")
+
+            # 1. Load Embedder (SentenceTransformer)
+            model_name = os.getenv(
+                "EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2"
+            )  # Match training
+            try:
+                self.embedder = SentenceTransformer(
+                    model_name, device=device, local_files_only=True
+                )
+            except Exception:
+                logger.warning(
+                    f"⚠️ Local embedder {model_name} not found. Downloading..."
+                )
+                self.embedder = SentenceTransformer(model_name, device=device)
+
+            # 2. Load Kronos Base
+            try:
+                tokenizer = KronosTokenizer.from_pretrained(
+                    "NeoQuasar/Kronos-Tokenizer-base", local_files_only=True
+                )
+                model = Kronos.from_pretrained(
+                    "NeoQuasar/Kronos-base", local_files_only=True
+                )
+            except Exception:
+                logger.warning(
+                    "⚠️ Local Kronos cache not found. Attempting to download..."
+                )
+                tokenizer = KronosTokenizer.from_pretrained(
+                    "NeoQuasar/Kronos-Tokenizer-base"
+                )
+                model = Kronos.from_pretrained("NeoQuasar/Kronos-base")
+
+            # 3. Load Trained News Projector Weights
+            # Check predictor/exports/models directory
+            models_dir = os.path.join(KRONOS_DIR, "exports/models")
+            model_files = glob.glob(os.path.join(models_dir, "*.pt"))
+
+            if model_files:
+                latest_model = max(model_files, key=os.path.getctime)
+                logger.info(f"🔄 Loading trained news weights from {latest_model}...")
+                try:
+                    checkpoint = torch.load(latest_model, map_location=device)
+                    # The checkpoint contains 'news_proj_state_dict'
+                    if "news_proj_state_dict" in checkpoint:
+                        if not hasattr(model, "news_proj") or model.news_proj is None:
+                            import torch.nn as nn
+
+                            news_dim = checkpoint.get("news_dim", 384)
+                            model.news_proj = nn.Linear(news_dim, model.d_model).to(
+                                device
+                            )
+
+                        model.news_proj.load_state_dict(
+                            checkpoint["news_proj_state_dict"]
+                        )
+                        logger.success("✅ News-Aware Projection Layer loaded!")
+                        self.has_news_model = True
+                    else:
+                        logger.warning(
+                            "⚠️ Checkpoint found but missing 'news_proj_state_dict'. Using base model."
+                        )
+                        self.has_news_model = False
+                except Exception as e:
+                    logger.error(
+                        f"❌ Failed to load trained weights: {e}. Using base model."
+                    )
+                    self.has_news_model = False
+            else:
+                logger.info("ℹ️ No trained news models found. Using base model.")
+                self.has_news_model = False
+
+            tokenizer = tokenizer.to(device)
+            model = model.to(device)
+
+            self._predictor = KronosPredictor(
+                model, tokenizer, device=device, max_context=512
+            )
+            logger.info("✅ Kronos Model loaded successfully.")
+        except Exception as e:
+            logger.error(f"❌ Failed to load Kronos Model: {e}")
+            self._predictor = None
+            self.has_news_model = False
+
+    def get_base_forecast(
+        self,
+        df: pd.DataFrame,
+        lookback: int = 20,
+        pred_len: int = 5,
+        news_text: Optional[str] = None,
+    ) -> List[KLinePoint]:
+        """
+        生成原始模型预测
+        """
+        if self._predictor is None:
+            logger.error("Predictor not initialized.")
+            return []
+
+        if len(df) < lookback:
+            logger.warning(
+                f"Insufficient historical data ({len(df)}) for lookback ({lookback})."
+            )
+            return []
+
+        # 获取最后 lookback 条数据
+        x_df = df.iloc[-lookback:].copy()
+        x_timestamp = pd.to_datetime(x_df["date"])  # Ensure datetime
+        last_date = x_timestamp.iloc[-1]
+
+        # 生成未来时间戳
+        future_dates = pd.date_range(
+            start=last_date + BusinessDay(1), periods=pred_len, freq="B"
+        )
+        y_timestamp = pd.Series(future_dates)
+
+        # Embedding News if available
+        news_emb = None
+        if (
+            news_text
+            and getattr(self, "has_news_model", False)
+            and hasattr(self, "embedder")
+        ):
+            try:
+                # Truncate to avoid too long text
+                emb = self.embedder.encode(news_text[:1000])
+                news_emb = emb  # KronosPredictor expects numpy array or tensor
+            except Exception as e:
+                logger.error(f"Failed to encode news: {e}")
+
+        try:
+            # 预测所需的列
+            cols = ["open", "high", "low", "close", "volume"]
+            pred_df = self._predictor.predict(
+                df=x_df[cols],
+                x_timestamp=x_timestamp,
+                y_timestamp=y_timestamp,
+                pred_len=pred_len,
+                T=1.0,
+                top_p=0.9,
+                sample_count=1,
+                verbose=False,
+                news_emb=news_emb,
+            )
+
+            # 转换为 KLinePoint
+            results = []
+            for date, row in pred_df.iterrows():
+                results.append(
+                    KLinePoint(
+                        date=date.strftime("%Y-%m-%d"),
+                        open=float(row["open"]),
+                        high=float(row["high"]),
+                        low=float(row["low"]),
+                        close=float(row["close"]),
+                        volume=float(row["volume"]),
+                    )
+                )
+            return results
+        except Exception as e:
+            logger.error(f"Forecast generation failed: {e}")
+            return []
+
+
+# Singleton instance for easy access
+# Usage: predictor = KronosPredictorUtility()

skills/alphaear-predictor/scripts/predictor/model/__init__.py

@@ -0,0 +1,16 @@
+from .kronos import KronosTokenizer, Kronos, KronosPredictor
+
+model_dict = {
+    'kronos_tokenizer': KronosTokenizer,
+    'kronos': Kronos,
+    'kronos_predictor': KronosPredictor
+}
+
+
+def get_model_class(model_name):
+    if model_name in model_dict:
+        return model_dict[model_name]
+    else:
+        print(f"Model {model_name} not found in model_dict")
+        raise NotImplementedError
+

skills/alphaear-predictor/scripts/predictor/model/kronos.py

@@ -0,0 +1,676 @@
+import numpy as np
+import pandas as pd
+import torch
+from huggingface_hub import PyTorchModelHubMixin
+import sys
+
+from tqdm import trange
+
+sys.path.append("../")
+from model.module import *
+
+
+class KronosTokenizer(nn.Module, PyTorchModelHubMixin):
+    """
+    KronosTokenizer module for tokenizing input data using a hybrid quantization approach.
+
+    This tokenizer utilizes a combination of encoder and decoder Transformer blocks
+    along with the Binary Spherical Quantization (BSQuantizer) to compress and decompress input data.
+
+    Args:
+           d_in (int): Input dimension.
+           d_model (int): Model dimension.
+           n_heads (int): Number of attention heads.
+           ff_dim (int): Feed-forward dimension.
+           n_enc_layers (int): Number of encoder layers.
+           n_dec_layers (int): Number of decoder layers.
+           ffn_dropout_p (float): Dropout probability for feed-forward networks.
+           attn_dropout_p (float): Dropout probability for attention mechanisms.
+           resid_dropout_p (float): Dropout probability for residual connections.
+           s1_bits (int): Number of bits for the pre token in BSQuantizer.
+           s2_bits (int): Number of bits for the post token in BSQuantizer.
+           beta (float): Beta parameter for BSQuantizer.
+           gamma0 (float): Gamma0 parameter for BSQuantizer.
+           gamma (float): Gamma parameter for BSQuantizer.
+           zeta (float): Zeta parameter for BSQuantizer.
+           group_size (int): Group size parameter for BSQuantizer.
+
+    """
+
+    def __init__(self, d_in, d_model, n_heads, ff_dim, n_enc_layers, n_dec_layers, ffn_dropout_p, attn_dropout_p, resid_dropout_p, s1_bits, s2_bits, beta, gamma0, gamma, zeta, group_size):
+
+        super().__init__()
+        self.d_in = d_in
+        self.d_model = d_model
+        self.n_heads = n_heads
+        self.ff_dim = ff_dim
+        self.enc_layers = n_enc_layers
+        self.dec_layers = n_dec_layers
+        self.ffn_dropout_p = ffn_dropout_p
+        self.attn_dropout_p = attn_dropout_p
+        self.resid_dropout_p = resid_dropout_p
+
+        self.s1_bits = s1_bits
+        self.s2_bits = s2_bits
+        self.codebook_dim = s1_bits + s2_bits # Total dimension of the codebook after quantization
+        self.embed = nn.Linear(self.d_in, self.d_model)
+        self.head = nn.Linear(self.d_model, self.d_in)
+
+        # Encoder Transformer Blocks
+        self.encoder = nn.ModuleList([
+            TransformerBlock(self.d_model, self.n_heads, self.ff_dim, self.ffn_dropout_p, self.attn_dropout_p, self.resid_dropout_p)
+            for _ in range(self.enc_layers - 1)
+        ])
+        # Decoder Transformer Blocks
+        self.decoder = nn.ModuleList([
+            TransformerBlock(self.d_model, self.n_heads, self.ff_dim, self.ffn_dropout_p, self.attn_dropout_p, self.resid_dropout_p)
+            for _ in range(self.dec_layers - 1)
+        ])
+        self.quant_embed = nn.Linear(in_features=self.d_model, out_features=self.codebook_dim) # Linear layer before quantization
+        self.post_quant_embed_pre = nn.Linear(in_features=self.s1_bits, out_features=self.d_model) # Linear layer after quantization (pre part - s1 bits)
+        self.post_quant_embed = nn.Linear(in_features=self.codebook_dim, out_features=self.d_model) # Linear layer after quantization (full codebook)
+        self.tokenizer = BSQuantizer(self.s1_bits, self.s2_bits, beta, gamma0, gamma, zeta, group_size) # BSQuantizer module
+
+    def forward(self, x):
+        """
+        Forward pass of the KronosTokenizer.
+
+        Args:
+            x (torch.Tensor): Input tensor of shape (batch_size, seq_len, d_in).
+
+        Returns:
+            tuple: A tuple containing:
+                - tuple: (z_pre, z) - Reconstructed outputs from decoder with s1_bits and full codebook respectively,
+                         both of shape (batch_size, seq_len, d_in).
+                - torch.Tensor: bsq_loss - Loss from the BSQuantizer.
+                - torch.Tensor: quantized - Quantized representation from BSQuantizer.
+                - torch.Tensor: z_indices - Indices from the BSQuantizer.
+        """
+        z = self.embed(x)
+
+        for layer in self.encoder:
+            z = layer(z)
+
+        z = self.quant_embed(z) # (B, T, codebook)
+
+        bsq_loss, quantized, z_indices = self.tokenizer(z)
+
+        quantized_pre = quantized[:, :, :self.s1_bits] # Extract the first part of quantized representation (s1_bits)
+        z_pre = self.post_quant_embed_pre(quantized_pre)
+
+        z = self.post_quant_embed(quantized)
+
+        # Decoder layers (for pre part - s1 bits)
+        for layer in self.decoder:
+            z_pre = layer(z_pre)
+        z_pre = self.head(z_pre)
+
+        # Decoder layers (for full codebook)
+        for layer in self.decoder:
+            z = layer(z)
+        z = self.head(z)
+
+        return (z_pre, z), bsq_loss, quantized, z_indices
+
+    def indices_to_bits(self, x, half=False):
+        """
+        Converts indices to bit representations and scales them.
+
+        Args:
+            x (torch.Tensor): Indices tensor.
+            half (bool, optional): Whether to process only half of the codebook dimension. Defaults to False.
+
+        Returns:
+            torch.Tensor: Bit representation tensor.
+        """
+        if half:
+            x1 = x[0] # Assuming x is a tuple of indices if half is True
+            x2 = x[1]
+            mask = 2 ** torch.arange(self.codebook_dim//2, device=x1.device, dtype=torch.long) # Create a mask for bit extraction
+            x1 = (x1.unsqueeze(-1) & mask) != 0 # Extract bits for the first half
+            x2 = (x2.unsqueeze(-1) & mask) != 0 # Extract bits for the second half
+            x = torch.cat([x1, x2], dim=-1) # Concatenate the bit representations
+        else:
+            mask = 2 ** torch.arange(self.codebook_dim, device=x.device, dtype=torch.long) # Create a mask for bit extraction
+            x = (x.unsqueeze(-1) & mask) != 0 # Extract bits
+
+        x = x.float() * 2 - 1 # Convert boolean to bipolar (-1, 1)
+        q_scale = 1. / (self.codebook_dim ** 0.5) # Scaling factor
+        x = x * q_scale
+        return x
+
+    def encode(self, x, half=False):
+        """
+        Encodes the input data into quantized indices.
+
+        Args:
+            x (torch.Tensor): Input tensor of shape (batch_size, seq_len, d_in).
+            half (bool, optional): Whether to use half quantization in BSQuantizer. Defaults to False.
+
+        Returns:
+            torch.Tensor: Quantized indices from BSQuantizer.
+        """
+        z = self.embed(x)
+        for layer in self.encoder:
+            z = layer(z)
+        z = self.quant_embed(z)
+
+        bsq_loss, quantized, z_indices = self.tokenizer(z, half=half, collect_metrics=False)
+        return z_indices
+
+    def decode(self, x, half=False):
+        """
+        Decodes quantized indices back to the input data space.
+
+        Args:
+            x (torch.Tensor): Quantized indices tensor.
+            half (bool, optional): Whether the indices were generated with half quantization. Defaults to False.
+
+        Returns:
+            torch.Tensor: Reconstructed output tensor of shape (batch_size, seq_len, d_in).
+        """
+        quantized = self.indices_to_bits(x, half)
+        z = self.post_quant_embed(quantized)
+        for layer in self.decoder:
+            z = layer(z)
+        z = self.head(z)
+        return z
+
+
+class Kronos(nn.Module, PyTorchModelHubMixin):
+    """
+    Kronos Model.
+
+    Args:
+        s1_bits (int): Number of bits for pre tokens.
+        s2_bits (int): Number of bits for post tokens.
+        n_layers (int): Number of Transformer blocks.
+        d_model (int): Dimension of the model's embeddings and hidden states.
+        n_heads (int): Number of attention heads in the MultiheadAttention layers.
+        ff_dim (int): Dimension of the feedforward network in the Transformer blocks.
+        ffn_dropout_p (float): Dropout probability for the feedforward network.
+        attn_dropout_p (float): Dropout probability for the attention layers.
+        resid_dropout_p (float): Dropout probability for residual connections.
+        token_dropout_p (float): Dropout probability for token embeddings.
+        learn_te (bool): Whether to use learnable temporal embeddings.
+    """
+
+    def __init__(self, s1_bits, s2_bits, n_layers, d_model, n_heads, ff_dim, ffn_dropout_p, attn_dropout_p, resid_dropout_p, token_dropout_p, learn_te, news_dim=None):
+        super().__init__()
+        self.s1_bits = s1_bits
+        self.s2_bits = s2_bits
+        self.n_layers = n_layers
+        self.d_model = d_model
+        self.n_heads = n_heads
+        self.learn_te = learn_te
+        self.ff_dim = ff_dim
+        self.ffn_dropout_p = ffn_dropout_p
+        self.attn_dropout_p = attn_dropout_p
+        self.resid_dropout_p = resid_dropout_p
+        self.token_dropout_p = token_dropout_p
+        self.news_dim = news_dim
+
+        self.s1_vocab_size = 2 ** self.s1_bits
+        self.token_drop = nn.Dropout(self.token_dropout_p)
+        self.embedding = HierarchicalEmbedding(self.s1_bits, self.s2_bits, self.d_model)
+        self.time_emb = TemporalEmbedding(self.d_model, self.learn_te)
+        self.transformer = nn.ModuleList([
+            TransformerBlock(self.d_model, self.n_heads, self.ff_dim, self.ffn_dropout_p, self.attn_dropout_p, self.resid_dropout_p)
+            for _ in range(self.n_layers)
+        ])
+        self.norm = RMSNorm(self.d_model)
+        self.dep_layer = DependencyAwareLayer(self.d_model)
+        self.head = DualHead(self.s1_bits, self.s2_bits, self.d_model)
+
+        if self.news_dim is not None:
+            self.news_proj = nn.Linear(self.news_dim, self.d_model)
+        else:
+            self.news_proj = None
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+
+        if isinstance(module, nn.Linear):
+            nn.init.xavier_normal_(module.weight)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, mean=0, std=self.embedding.d_model ** -0.5)
+        elif isinstance(module, nn.LayerNorm):
+            nn.init.ones_(module.weight)
+            nn.init.zeros_(module.bias)
+        elif isinstance(module, RMSNorm):
+            nn.init.ones_(module.weight)
+
+    def forward(self, s1_ids, s2_ids, stamp=None, padding_mask=None, use_teacher_forcing=False, s1_targets=None, news_emb=None):
+        """
+        Args:
+            s1_ids (torch.Tensor): Input tensor of s1 token IDs. Shape: [batch_size, seq_len]
+            s2_ids (torch.Tensor): Input tensor of s2 token IDs. Shape: [batch_size, seq_len]
+            stamp (torch.Tensor, optional): Temporal stamp tensor. Shape: [batch_size, seq_len]. Defaults to None.
+            padding_mask (torch.Tensor, optional): Mask for padding tokens. Shape: [batch_size, seq_len]. Defaults to None.
+            use_teacher_forcing (bool, optional): Whether to use teacher forcing for s1 decoding. Defaults to False.
+            s1_targets (torch.Tensor, optional): Target s1 token IDs for teacher forcing. Shape: [batch_size, seq_len]. Defaults to None.
+            news_emb (torch.Tensor, optional): News embedding tensor. Shape: [batch_size, news_dim]. Defaults to None.
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor]:
+                - s1 logits: Logits for s1 token predictions. Shape: [batch_size, seq_len, s1_vocab_size]
+                - s2_logits: Logits for s2 token predictions, conditioned on s1. Shape: [batch_size, seq_len, s2_vocab_size]
+        """
+        x = self.embedding([s1_ids, s2_ids])
+        if stamp is not None:
+            time_embedding = self.time_emb(stamp)
+            x = x + time_embedding
+        x = self.token_drop(x)
+
+        for layer in self.transformer:
+            x = layer(x, key_padding_mask=padding_mask)
+
+        x = self.norm(x)
+
+        if news_emb is not None and self.news_proj is not None:
+            news_bias = self.news_proj(news_emb).unsqueeze(1) # [B, 1, d_model]
+            x = x + news_bias
+
+        s1_logits = self.head(x)
+
+        if use_teacher_forcing:
+            sibling_embed = self.embedding.emb_s1(s1_targets)
+        else:
+            s1_probs = F.softmax(s1_logits.detach(), dim=-1)
+            sample_s1_ids = torch.multinomial(s1_probs.view(-1, self.s1_vocab_size), 1).view(s1_ids.shape)
+            sibling_embed = self.embedding.emb_s1(sample_s1_ids)
+
+        x2 = self.dep_layer(x, sibling_embed, key_padding_mask=padding_mask) # Dependency Aware Layer: Condition on s1 embeddings
+        s2_logits = self.head.cond_forward(x2)
+        return s1_logits, s2_logits
+
+    def decode_s1(self, s1_ids, s2_ids, stamp=None, padding_mask=None, news_emb=None):
+        """
+        Decodes only the s1 tokens.
+
+        This method performs a forward pass to predict only s1 tokens. It returns the s1 logits
+        and the context representation from the Transformer, which can be used for subsequent s2 decoding.
+
+        Args:
+            s1_ids (torch.Tensor): Input tensor of s1 token IDs. Shape: [batch_size, seq_len]
+            s2_ids (torch.Tensor): Input tensor of s2 token IDs. Shape: [batch_size, seq_len]
+            stamp (torch.Tensor, optional): Temporal stamp tensor. Shape: [batch_size, seq_len]. Defaults to None.
+            padding_mask (torch.Tensor, optional): Mask for padding tokens. Shape: [batch_size, seq_len]. Defaults to None.
+            news_emb (torch.Tensor, optional): News embedding tensor. Shape: [batch_size, news_dim]. Defaults to None.
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor]:
+                - s1 logits: Logits for s1 token predictions. Shape: [batch_size, seq_len, s1_vocab_size]
+                - context: Context representation from the Transformer. Shape: [batch_size, seq_len, d_model]
+        """
+        x = self.embedding([s1_ids, s2_ids])
+        if stamp is not None:
+            time_embedding = self.time_emb(stamp)
+            x = x + time_embedding
+        x = self.token_drop(x)
+
+        for layer in self.transformer:
+            x = layer(x, key_padding_mask=padding_mask)
+
+        x = self.norm(x)
+
+        if news_emb is not None and self.news_proj is not None:
+            news_bias = self.news_proj(news_emb).unsqueeze(1) # [B, 1, d_model]
+            x = x + news_bias
+
+        s1_logits = self.head(x)
+        return s1_logits, x
+
+    def decode_s2(self, context, s1_ids, padding_mask=None):
+        """
+        Decodes the s2 tokens, conditioned on the context and s1 tokens.
+
+        This method decodes s2 tokens based on a pre-computed context representation (typically from `decode_s1`)
+        and the s1 token IDs. It uses the dependency-aware layer and the conditional s2 head to predict s2 tokens.
+
+        Args:
+            context (torch.Tensor): Context representation from the transformer (output of decode_s1).
+                                     Shape: [batch_size, seq_len, d_model]
+            s1_ids (torch.torch.Tensor): Input tensor of s1 token IDs. Shape: [batch_size, seq_len]
+            padding_mask (torch.Tensor, optional): Mask for padding tokens. Shape: [batch_size, seq_len]. Defaults to None.
+
+        Returns:
+            torch.Tensor: s2 logits. Shape: [batch_size, seq_len, s2_vocab_size]
+        """
+        sibling_embed = self.embedding.emb_s1(s1_ids)
+        x2 = self.dep_layer(context, sibling_embed, key_padding_mask=padding_mask)
+        return self.head.cond_forward(x2)
+
+
+def top_k_top_p_filtering(
+        logits,
+        top_k: int = 0,
+        top_p: float = 1.0,
+        filter_value: float = -float("Inf"),
+        min_tokens_to_keep: int = 1,
+):
+    """Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
+    Args:
+        logits: logits distribution shape (batch size, vocabulary size)
+        if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
+        if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
+            Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
+        Make sure we keep at least min_tokens_to_keep per batch example in the output
+    From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
+    """
+    if top_k > 0:
+        top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1))  # Safety check
+        # Remove all tokens with a probability less than the last token of the top-k
+        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+        logits[indices_to_remove] = filter_value
+        return logits
+
+    if top_p < 1.0:
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+        cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+
+        # Remove tokens with cumulative probability above the threshold (token with 0 are kept)
+        sorted_indices_to_remove = cumulative_probs > top_p
+        if min_tokens_to_keep > 1:
+            # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
+            sorted_indices_to_remove[..., :min_tokens_to_keep] = 0
+        # Shift the indices to the right to keep also the first token above the threshold
+        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+        sorted_indices_to_remove[..., 0] = 0
+
+        # scatter sorted tensors to original indexing
+        indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+        logits[indices_to_remove] = filter_value
+        return logits
+
+
+def sample_from_logits(logits, temperature=1.0, top_k=None, top_p=None, sample_logits=True):
+    logits = logits / temperature
+    if top_k is not None or top_p is not None:
+        if top_k > 0 or top_p < 1.0:
+            logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
+
+    probs = F.softmax(logits, dim=-1)
+
+    if not sample_logits:
+        _, x = top_k(probs, k=1, dim=-1)
+    else:
+        x = torch.multinomial(probs, num_samples=1)
+
+    return x
+
+
+def auto_regressive_inference(tokenizer, model, x, x_stamp, y_stamp, max_context, pred_len, clip=5, T=1.0, top_k=0, top_p=0.99, sample_count=5, verbose=False, news_emb=None):
+    with torch.no_grad():
+        x = torch.clip(x, -clip, clip)
+
+        device = x.device
+        x = x.unsqueeze(1).repeat(1, sample_count, 1, 1).reshape(-1, x.size(1), x.size(2)).to(device)
+        x_stamp = x_stamp.unsqueeze(1).repeat(1, sample_count, 1, 1).reshape(-1, x_stamp.size(1), x_stamp.size(2)).to(device)
+        y_stamp = y_stamp.unsqueeze(1).repeat(1, sample_count, 1, 1).reshape(-1, y_stamp.size(1), y_stamp.size(2)).to(device)
+
+        x_token = tokenizer.encode(x, half=True)
+        
+        initial_seq_len = x.size(1)
+        batch_size = x_token[0].size(0)
+        total_seq_len = initial_seq_len + pred_len
+        full_stamp = torch.cat([x_stamp, y_stamp], dim=1)
+
+        generated_pre = x_token[0].new_empty(batch_size, pred_len)
+        generated_post = x_token[1].new_empty(batch_size, pred_len)
+
+        pre_buffer = x_token[0].new_zeros(batch_size, max_context)
+        post_buffer = x_token[1].new_zeros(batch_size, max_context)
+        buffer_len = min(initial_seq_len, max_context)
+        if buffer_len > 0:
+            start_idx = max(0, initial_seq_len - max_context)
+            pre_buffer[:, :buffer_len] = x_token[0][:, start_idx:start_idx + buffer_len]
+            post_buffer[:, :buffer_len] = x_token[1][:, start_idx:start_idx + buffer_len]
+
+        if verbose:
+            ran = trange
+        else:
+            ran = range
+        for i in ran(pred_len):
+            current_seq_len = initial_seq_len + i
+            window_len = min(current_seq_len, max_context)
+
+            if current_seq_len <= max_context:
+                input_tokens = [
+                    pre_buffer[:, :window_len],
+                    post_buffer[:, :window_len]
+                ]
+            else:
+                input_tokens = [pre_buffer, post_buffer]
+
+            context_end = current_seq_len
+            context_start = max(0, context_end - max_context)
+            current_stamp = full_stamp[:, context_start:context_end, :].contiguous()
+
+            s1_logits, context = model.decode_s1(input_tokens[0], input_tokens[1], current_stamp, news_emb=news_emb)
+            s1_logits = s1_logits[:, -1, :]
+            sample_pre = sample_from_logits(s1_logits, temperature=T, top_k=top_k, top_p=top_p, sample_logits=True)
+
+            s2_logits = model.decode_s2(context, sample_pre)
+            s2_logits = s2_logits[:, -1, :]
+            sample_post = sample_from_logits(s2_logits, temperature=T, top_k=top_k, top_p=top_p, sample_logits=True)
+
+            generated_pre[:, i] = sample_pre.squeeze(-1)
+            generated_post[:, i] = sample_post.squeeze(-1)
+
+            if current_seq_len < max_context:
+                pre_buffer[:, current_seq_len] = sample_pre.squeeze(-1)
+                post_buffer[:, current_seq_len] = sample_post.squeeze(-1)
+            else:
+                pre_buffer.copy_(torch.roll(pre_buffer, shifts=-1, dims=1))
+                post_buffer.copy_(torch.roll(post_buffer, shifts=-1, dims=1))
+                pre_buffer[:, -1] = sample_pre.squeeze(-1)
+                post_buffer[:, -1] = sample_post.squeeze(-1)
+
+        full_pre = torch.cat([x_token[0], generated_pre], dim=1)
+        full_post = torch.cat([x_token[1], generated_post], dim=1)
+
+        context_start = max(0, total_seq_len - max_context)
+        input_tokens = [
+            full_pre[:, context_start:total_seq_len].contiguous(),
+            full_post[:, context_start:total_seq_len].contiguous()
+        ]
+        z = tokenizer.decode(input_tokens, half=True)
+        z = z.reshape(-1, sample_count, z.size(1), z.size(2))
+        preds = z.cpu().numpy()
+        preds = np.mean(preds, axis=1)
+
+        return preds
+
+
+def calc_time_stamps(x_timestamp):
+    time_df = pd.DataFrame()
+    time_df['minute'] = x_timestamp.dt.minute
+    time_df['hour'] = x_timestamp.dt.hour
+    time_df['weekday'] = x_timestamp.dt.weekday
+    time_df['day'] = x_timestamp.dt.day
+    time_df['month'] = x_timestamp.dt.month
+    return time_df
+
+
+class KronosPredictor:
+
+    def __init__(self, model, tokenizer, device="cuda:0", max_context=512, clip=5):
+        self.tokenizer = tokenizer
+        self.model = model
+        self.max_context = max_context
+        self.clip = clip
+        self.price_cols = ['open', 'high', 'low', 'close']
+        self.vol_col = 'volume'
+        self.amt_vol = 'amount'
+        self.time_cols = ['minute', 'hour', 'weekday', 'day', 'month']
+        self.device = device
+
+        self.tokenizer = self.tokenizer.to(self.device)
+        self.model = self.model.to(self.device)
+
+    def generate(self, x, x_stamp, y_stamp, pred_len, T, top_k, top_p, sample_count, verbose, news_emb=None):
+
+        x_tensor = torch.from_numpy(np.array(x).astype(np.float32)).to(self.device)
+        x_stamp_tensor = torch.from_numpy(np.array(x_stamp).astype(np.float32)).to(self.device)
+        y_stamp_tensor = torch.from_numpy(np.array(y_stamp).astype(np.float32)).to(self.device)
+
+        preds = auto_regressive_inference(self.tokenizer, self.model, x_tensor, x_stamp_tensor, y_stamp_tensor, self.max_context, pred_len,
+                                          self.clip, T, top_k, top_p, sample_count, verbose, news_emb=news_emb)
+        preds = preds[:, -pred_len:, :]
+        return preds
+
+    def predict(self, df, x_timestamp, y_timestamp, pred_len, T=1.0, top_k=0, top_p=0.9, sample_count=1, verbose=True, news_emb=None):
+
+        if not isinstance(df, pd.DataFrame):
+            raise ValueError("Input must be a pandas DataFrame.")
+
+        if not all(col in df.columns for col in self.price_cols):
+            raise ValueError(f"Price columns {self.price_cols} not found in DataFrame.")
+
+        df = df.copy()
+        if self.vol_col not in df.columns:
+            df[self.vol_col] = 0.0  # Fill missing volume with zeros
+            df[self.amt_vol] = 0.0  # Fill missing amount with zeros
+        if self.amt_vol not in df.columns and self.vol_col in df.columns:
+            df[self.amt_vol] = df[self.vol_col] * df[self.price_cols].mean(axis=1)
+
+        if df[self.price_cols + [self.vol_col, self.amt_vol]].isnull().values.any():
+            raise ValueError("Input DataFrame contains NaN values in price or volume columns.")
+
+        x_time_df = calc_time_stamps(x_timestamp)
+        y_time_df = calc_time_stamps(y_timestamp)
+
+        x = df[self.price_cols + [self.vol_col, self.amt_vol]].values.astype(np.float32)
+        x_stamp = x_time_df.values.astype(np.float32)
+        y_stamp = y_time_df.values.astype(np.float32)
+
+        x_mean, x_std = np.mean(x, axis=0), np.std(x, axis=0)
+
+        x = (x - x_mean) / (x_std + 1e-5)
+        x = np.clip(x, -self.clip, self.clip)
+
+        x = x[np.newaxis, :]
+        x_stamp = x_stamp[np.newaxis, :]
+        y_stamp = y_stamp[np.newaxis, :]
+
+        if news_emb is not None:
+            news_emb_tensor = torch.from_numpy(np.array(news_emb).astype(np.float32)).to(self.device)
+            # Ensure batch dimension for news_emb if only one sample
+            if news_emb_tensor.ndim == 1:
+                news_emb_tensor = news_emb_tensor.unsqueeze(0)
+        else:
+            news_emb_tensor = None
+
+        preds = self.generate(x, x_stamp, y_stamp, pred_len, T, top_k, top_p, sample_count, verbose, news_emb=news_emb_tensor)
+
+        preds = preds.squeeze(0)
+        preds = preds * (x_std + 1e-5) + x_mean
+
+        pred_df = pd.DataFrame(preds, columns=self.price_cols + [self.vol_col, self.amt_vol], index=y_timestamp)
+        return pred_df
+
+
+    def predict_batch(self, df_list, x_timestamp_list, y_timestamp_list, pred_len, T=1.0, top_k=0, top_p=0.9, sample_count=1, verbose=True):
+        """
+        Perform parallel (batch) prediction on multiple time series. All series must have the same historical length and prediction length (pred_len).
+
+        Args:
+            df_list (List[pd.DataFrame]): List of input DataFrames, each containing price columns and optional volume/amount columns.
+            x_timestamp_list (List[pd.DatetimeIndex or Series]): List of timestamps corresponding to historical data, length should match the number of rows in each DataFrame.
+            y_timestamp_list (List[pd.DatetimeIndex or Series]): List of future prediction timestamps, length should equal pred_len.
+            pred_len (int): Number of prediction steps.
+            T (float): Sampling temperature.
+            top_k (int): Top-k filtering threshold.
+            top_p (float): Top-p (nucleus sampling) threshold.
+            sample_count (int): Number of parallel samples per series, automatically averaged internally.
+            verbose (bool): Whether to display autoregressive progress.
+
+        Returns:
+            List[pd.DataFrame]: List of prediction results in the same order as input, each DataFrame contains
+                                `open, high, low, close, volume, amount` columns, indexed by corresponding `y_timestamp`.
+        """
+        # Basic validation
+        if not isinstance(df_list, (list, tuple)) or not isinstance(x_timestamp_list, (list, tuple)) or not isinstance(y_timestamp_list, (list, tuple)):
+            raise ValueError("df_list, x_timestamp_list, y_timestamp_list must be list or tuple types.")
+        if not (len(df_list) == len(x_timestamp_list) == len(y_timestamp_list)):
+            raise ValueError("df_list, x_timestamp_list, y_timestamp_list must have consistent lengths.")
+
+        num_series = len(df_list)
+
+        x_list = []
+        x_stamp_list = []
+        y_stamp_list = []
+        means = []
+        stds = []
+        seq_lens = []
+        y_lens = []
+
+        for i in range(num_series):
+            df = df_list[i]
+            if not isinstance(df, pd.DataFrame):
+                raise ValueError(f"Input at index {i} is not a pandas DataFrame.")
+            if not all(col in df.columns for col in self.price_cols):
+                raise ValueError(f"DataFrame at index {i} is missing price columns {self.price_cols}.")
+
+            df = df.copy()
+            if self.vol_col not in df.columns:
+                df[self.vol_col] = 0.0
+                df[self.amt_vol] = 0.0
+            if self.amt_vol not in df.columns and self.vol_col in df.columns:
+                df[self.amt_vol] = df[self.vol_col] * df[self.price_cols].mean(axis=1)
+
+            if df[self.price_cols + [self.vol_col, self.amt_vol]].isnull().values.any():
+                raise ValueError(f"DataFrame at index {i} contains NaN values in price or volume columns.")
+
+            x_timestamp = x_timestamp_list[i]
+            y_timestamp = y_timestamp_list[i]
+
+            x_time_df = calc_time_stamps(x_timestamp)
+            y_time_df = calc_time_stamps(y_timestamp)
+
+            x = df[self.price_cols + [self.vol_col, self.amt_vol]].values.astype(np.float32)
+            x_stamp = x_time_df.values.astype(np.float32)
+            y_stamp = y_time_df.values.astype(np.float32)
+
+            if x.shape[0] != x_stamp.shape[0]:
+                raise ValueError(f"Inconsistent lengths at index {i}: x has {x.shape[0]} vs x_stamp has {x_stamp.shape[0]}.")
+            if y_stamp.shape[0] != pred_len:
+                raise ValueError(f"y_timestamp length at index {i} should equal pred_len={pred_len}, got {y_stamp.shape[0]}.")
+
+            x_mean, x_std = np.mean(x, axis=0), np.std(x, axis=0)
+            x_norm = (x - x_mean) / (x_std + 1e-5)
+            x_norm = np.clip(x_norm, -self.clip, self.clip)
+
+            x_list.append(x_norm)
+            x_stamp_list.append(x_stamp)
+            y_stamp_list.append(y_stamp)
+            means.append(x_mean)
+            stds.append(x_std)
+
+            seq_lens.append(x_norm.shape[0])
+            y_lens.append(y_stamp.shape[0])
+
+        # Require all series to have consistent historical and prediction lengths for batch processing
+        if len(set(seq_lens)) != 1:
+            raise ValueError(f"Parallel prediction requires all series to have consistent historical lengths, got: {seq_lens}")
+        if len(set(y_lens)) != 1:
+            raise ValueError(f"Parallel prediction requires all series to have consistent prediction lengths, got: {y_lens}")
+
+        x_batch = np.stack(x_list, axis=0).astype(np.float32)           # (B, seq_len, feat)
+        x_stamp_batch = np.stack(x_stamp_list, axis=0).astype(np.float32) # (B, seq_len, time_feat)
+        y_stamp_batch = np.stack(y_stamp_list, axis=0).astype(np.float32) # (B, pred_len, time_feat)
+
+        preds = self.generate(x_batch, x_stamp_batch, y_stamp_batch, pred_len, T, top_k, top_p, sample_count, verbose)
+        # preds: (B, pred_len, feat)
+
+        pred_dfs = []
+        for i in range(num_series):
+            preds_i = preds[i] * (stds[i] + 1e-5) + means[i]
+            pred_df = pd.DataFrame(preds_i, columns=self.price_cols + [self.vol_col, self.amt_vol], index=y_timestamp_list[i])
+            pred_dfs.append(pred_df)
+
+        return pred_dfs

skills/alphaear-predictor/scripts/predictor/model/module.py

@@ -0,0 +1,562 @@
+import math
+
+from einops import rearrange, reduce
+import torch
+import torch.nn as nn
+from torch.autograd import Function
+import torch.nn.functional as F
+
+
+class DifferentiableEntropyFunction(Function):
+    @staticmethod
+    def forward(ctx, zq, basis, K, eps):
+        zb = (zq + 1) / 2
+        zi = ((zb * basis).sum(-1)).to(torch.int64)
+        cnt = torch.scatter_reduce(torch.zeros(2 ** K, device=zq.device, dtype=zq.dtype),
+                                   0,
+                                   zi.flatten(),
+                                   torch.ones_like(zi.flatten()).to(zq.dtype),
+                                   'sum')
+        prob = (cnt + eps) / (cnt + eps).sum()
+        H = -(prob * torch.log(prob)).sum()
+        ctx.save_for_backward(zq, zi, prob)
+        ctx.K = K
+        return H
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        zq, zi, prob = ctx.saved_tensors
+        grad_array = -grad_output * (torch.log(prob) + 1) / zi.numel() / ctx.K
+        reord_grad = grad_array[zi.flatten()].reshape(zi.shape)
+        grad_input = reord_grad.unsqueeze(-1) * zq
+        return grad_input, None, None, None, None
+
+
+def codebook_entropy(zq, basis, K, eps=1e-4):
+    return DifferentiableEntropyFunction.apply(zq, basis, K, eps)
+
+
+class BinarySphericalQuantizer(nn.Module):
+    def __init__(self, embed_dim, beta, gamma0, gamma, zeta,
+                 input_format='bchw',
+                 soft_entropy=True, group_size=9,
+                 persample_entropy_compute='analytical',
+                 cb_entropy_compute='group',
+                 l2_norm=True,
+                 inv_temperature=1):
+        """
+        Paper link: https://arxiv.org/pdf/2406.07548.pdf
+        Here we use the official implementation of the BinarySphericalQuantizer.
+        """
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.beta = beta  # loss weight for commit loss
+        self.gamma0 = gamma0  # loss weight for entropy penalty
+        self.gamma = gamma  # loss weight for entropy penalty
+        self.zeta = zeta  # loss weight for entire entropy penalty
+        self.input_format = input_format
+        assert self.embed_dim % group_size == 0, "embed_dim must be divisible by group_size"
+        self.num_groups = self.embed_dim // group_size
+        self.group_size = group_size
+        assert persample_entropy_compute in ['group', 'analytical'], "persample_entropy_compute must be either 'group' or 'analytical'"
+        assert cb_entropy_compute in ['group', 'nce'], "cb_entropy_compute must be either 'group' or 'nce'"
+        self.persample_entropy_compute = persample_entropy_compute
+        self.cb_entropy_compute = cb_entropy_compute
+        self.l2_norm = l2_norm
+        self.inv_temperature = inv_temperature
+
+        self.register_buffer('basis', 2 ** torch.arange(embed_dim - 1, -1, -1))
+        self.register_buffer('group_basis', 2 ** torch.arange(group_size - 1, -1, -1))
+
+        self.num_dimensions = 2 ** embed_dim
+        self.bits_per_index = embed_dim
+
+        # we only need to keep the codebook portion up to the group size
+        # because we approximate the H loss with this subcode
+        group_codes = torch.arange(2 ** self.group_size)
+        group_codebook = self.indexes_to_codes(group_codes).float()[:, -group_size:]
+        self.register_buffer('group_codebook', group_codebook, persistent=False)
+
+        self.soft_entropy = soft_entropy  # soft_entropy: Sec 3.2 of https://arxiv.org/pdf/1911.05894.pdf
+
+    def quantize(self, z):
+        assert z.shape[-1] == self.embed_dim, f"Expected {self.embed_dim} dimensions, got {z.shape[-1]}"
+
+        zhat = torch.where(z > 0,
+                           torch.tensor(1, dtype=z.dtype, device=z.device),
+                           torch.tensor(-1, dtype=z.dtype, device=z.device))
+        return z + (zhat - z).detach()
+
+    def forward(self, z, collect_metrics=True):
+        # if self.input_format == 'bchw':
+        #     z = rearrange(z, 'b c h w -> b h w c')
+        zq = self.quantize(z)
+
+        q_scale = 1. / (self.embed_dim ** 0.5) if self.l2_norm else 1.
+
+        zq = zq * q_scale
+
+        if not collect_metrics:
+            return zq, zq.new_zeros(()), {}
+
+        indices = self.codes_to_indexes(zq.detach())
+        group_indices = self.codes_to_group_indexes(zq.detach())
+        if not self.training:
+            used_codes = torch.unique(indices, return_counts=False)
+        else:
+            used_codes = None
+
+        if self.soft_entropy:
+            persample_entropy, cb_entropy, avg_prob = self.soft_entropy_loss(z)
+            entropy_penalty = self.gamma0 * persample_entropy - self.gamma * cb_entropy
+        else:
+            zb_by_sample = ((zq + 1) / 2).reshape(z.shape[0], -1, z.shape[-1]).to(torch.float32)
+            persample_entropy = self.get_hard_per_sample_entropy(zb_by_sample)
+            cb_entropy = codebook_entropy(zq, self.basis, self.embed_dim)
+            entropy_penalty = self.gamma0 * persample_entropy - self.gamma * cb_entropy
+
+        # commit loss
+        commit_loss = self.beta * torch.mean(((zq.detach() - z) ** 2).sum(dim=-1))
+
+        # if self.input_format == 'bchw':
+        #     zq = rearrange(zq, 'b h w c -> b c h w')
+
+        return (
+            zq,
+            commit_loss + self.zeta * entropy_penalty / self.inv_temperature,
+            {"H": cb_entropy, "used_codes": used_codes, "indices": indices, "group_indices": group_indices,
+             "avg_prob": avg_prob}
+        )
+
+    def soft_entropy_loss(self, z):
+        # if we divide the code in subgroups of size group_size, the codebook will be of size 2 ** group_size
+        # the sub-code is the last group_size bits of the full code
+        group_code_book = self.group_codebook / (self.embed_dim ** 0.5 if self.l2_norm else 1)
+        divided_z = rearrange(z, '... (g c) -> ... g c', c=self.group_size)
+
+        # we calculate the distance between the divided_z and the codebook for each subgroup
+        distance = - 2 * torch.einsum('... g c, d c ->... g d', divided_z, group_code_book)
+        prob = (-distance * self.inv_temperature).softmax(dim=-1)
+        if self.persample_entropy_compute == 'analytical':
+            if self.l2_norm:
+                p = torch.sigmoid(-4 * z / (self.embed_dim ** 0.5) * self.inv_temperature)
+            else:
+                p = torch.sigmoid(-4 * z * self.inv_temperature)
+            prob = torch.stack([p, 1 - p], dim=-1)
+            per_sample_entropy = self.get_entropy(prob, dim=-1, normalize=False).sum(dim=-1).mean()
+        else:
+            per_sample_entropy = self.get_entropy(prob, dim=-1, normalize=False).sum(dim=-1).mean()
+
+        # macro average of the probability of each subgroup
+        avg_prob = reduce(prob, '... g d ->g d', 'mean')
+        codebook_entropy = self.get_entropy(avg_prob, dim=-1, normalize=False)
+
+        # the approximation of the entropy is the sum of the entropy of each subgroup
+        return per_sample_entropy, codebook_entropy.sum(), avg_prob
+
+    def get_hard_per_sample_entropy(self, zb_by_sample):
+        probs_per_dim = zb_by_sample.sum(1) / zb_by_sample.shape[1]
+        persample_entropy = - probs_per_dim * torch.log(probs_per_dim + 1e-8) - (1 - probs_per_dim) * torch.log(1 - probs_per_dim + 1e-8)
+        persample_entropy = persample_entropy.sum(-1)
+        return persample_entropy.mean()
+
+    def codes_to_indexes(self, zhat):
+        """Converts a `code` to an index in the codebook.
+        Args:
+            zhat: A tensor of shape (B, ..., C) containing the codes. must be in {-1, 1}
+        """
+        assert zhat.shape[-1] == self.embed_dim, f"Expected {self.embed_dim} dimensions, got {zhat.shape[-1]}"
+        return ((zhat + 1) / 2 * self.basis).sum(axis=-1).to(torch.int64)
+
+    def codes_to_group_indexes(self, zhat):
+        """Converts a `code` to a list of indexes (in groups) in the codebook.
+        Args:
+            zhat: A tensor of shape (B, ..., C) containing the codes. must be in {-1, 1}
+        """
+        zhat_in_group = rearrange(zhat, 'b ... (g c) -> b ... g c', c=self.group_size)
+        return ((zhat_in_group + 1) / 2 * self.group_basis).sum(axis=-1).to(torch.int64)
+
+    def indexes_to_codes(self, indices):
+        """Inverse of `indexes_to_codes`."""
+        indices = indices.unsqueeze(-1)
+        codes_non_centered = torch.remainder(
+            torch.floor_divide(indices, self.basis), 2
+        )
+        return codes_non_centered * 2 - 1
+
+    def group_indexes_to_codes(self, group_indices):
+        """Inverse of `group_indexes_to_codes`."""
+        group_indices = group_indices.unsqueeze(-1)
+        codes_non_centered = torch.remainder(
+            torch.floor_divide(group_indices, self.group_basis), 2
+        )
+        codes_non_centered = rearrange(codes_non_centered, 'b ... g c -> b ... (g c)')
+        return codes_non_centered * 2 - 1
+
+    def get_entropy(self, count, dim=-1, eps=1e-4, normalize=True):
+        if normalize:
+            probs = (count + eps) / (count + eps).sum(dim=dim, keepdim=True)
+        else:
+            probs = count
+        H = -(probs * torch.log(probs + 1e-8)).sum(dim=dim)
+        return H
+
+    def get_group_codebook_entry(self, group_indices):
+        z_q = self.group_indexes_to_codes(group_indices)
+        q_scale = 1. / (self.embed_dim ** 0.5) if self.l2_norm else 1.
+        z_q = z_q * q_scale
+        if self.input_format == 'bchw':
+            h, w = int(z_q.shape[1] ** 0.5)
+            assert h * w == z_q.shape[1], 'Invalid sequence length'
+            z_q = rearrange(z_q, 'b (h w) c -> b c h w', h=h)
+        return z_q
+
+    def get_codebook_entry(self, indices):
+        z_q = self.indexes_to_codes(indices)
+        q_scale = 1. / (self.embed_dim ** 0.5) if self.l2_norm else 1.
+        z_q = z_q * q_scale
+        if self.input_format == 'bchw':
+            h, w = int(z_q.shape[1] ** 0.5)
+            assert h * w == z_q.shape[1], 'Invalid sequence length'
+            z_q = rearrange(z_q, 'b (h w) c -> b c h w', h=h)
+        return z_q
+
+
+class BSQuantizer(nn.Module):
+
+    def __init__(self, s1_bits, s2_bits, beta, gamma0, gamma, zeta, group_size):
+        super().__init__()
+        self.codebook_dim = s1_bits + s2_bits
+        self.s1_bits = s1_bits
+        self.s2_bits = s2_bits
+        self.bsq = BinarySphericalQuantizer(self.codebook_dim, beta, gamma0, gamma, zeta, group_size=group_size)
+
+    def bits_to_indices(self, bits):
+        bits = (bits >= 0).to(torch.long)
+        indices = 2 ** torch.arange(
+            0,
+            bits.shape[-1],
+            1,
+            dtype=torch.long,
+            device=bits.device,
+        )
+        return (bits * indices).sum(-1)
+
+    def forward(self, z, half=False, collect_metrics=True):
+        z = F.normalize(z, dim=-1)
+        quantized, bsq_loss, metrics = self.bsq(z, collect_metrics=collect_metrics)
+        if half:
+            q_pre = quantized[:, :, :self.s1_bits]
+            q_post = quantized[:, :, self.s1_bits:]
+            z_indices = [self.bits_to_indices(q_pre), self.bits_to_indices(q_post)]
+        else:
+            z_indices = self.bits_to_indices(quantized)
+        return bsq_loss, quantized, z_indices
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-5):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight
+
+
+class FeedForward(nn.Module):
+    def __init__(self, d_model, ff_dim, ffn_dropout_p=0.0):
+        super().__init__()
+
+        self.w1 = nn.Linear(d_model, ff_dim, bias=False)
+        self.w3 = nn.Linear(d_model, ff_dim, bias=False)
+        self.w2 = nn.Linear(ff_dim, d_model, bias=False)
+        self.ffn_dropout = nn.Dropout(ffn_dropout_p)
+
+    def forward(self, x):
+        return self.ffn_dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
+
+
+class RotaryPositionalEmbedding(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq)
+        self.seq_len_cached = None
+        self.cos_cached = None
+        self.sin_cached = None
+
+    def _update_cos_sin_cache(self, x, seq_len):
+        if seq_len != self.seq_len_cached:
+            self.seq_len_cached = seq_len
+            t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq)
+            freqs = torch.einsum('i,j->ij', t, self.inv_freq)
+            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
+            self.cos_cached = emb.cos()[None, None, :, :]
+            self.sin_cached = emb.sin()[None, None, :, :]
+        return self.cos_cached, self.sin_cached
+
+    def forward(self, q, k):
+        cos, sin = self._update_cos_sin_cache(q, q.shape[-2])
+        return (
+            (q * cos) + (self._rotate_half(q) * sin),
+            (k * cos) + (self._rotate_half(k) * sin),
+        )
+
+    def _rotate_half(self, x):
+        x1, x2 = x.chunk(2, dim=-1)
+        return torch.cat((-x2, x1), dim=-1)
+
+
+class MultiHeadAttentionWithRoPE(nn.Module):
+    def __init__(self, d_model, n_heads, attn_dropout_p=0.0, resid_dropout_p=0.0):
+        super().__init__()
+        self.d_model = d_model
+        self.n_heads = n_heads
+        self.head_dim = d_model // n_heads
+
+        self.q_proj = nn.Linear(d_model, d_model)
+        self.k_proj = nn.Linear(d_model, d_model)
+        self.v_proj = nn.Linear(d_model, d_model)
+        self.out_proj = nn.Linear(d_model, d_model)
+        self.rotary = RotaryPositionalEmbedding(self.head_dim)
+        self.attn_dropout_p = attn_dropout_p
+        self.resid_dropout = nn.Dropout(resid_dropout_p)
+
+    def forward(self, x, key_padding_mask=None):
+        batch_size, seq_len, _ = x.shape
+
+        q = self.q_proj(x).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
+        k = self.k_proj(x).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
+        v = self.v_proj(x).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
+
+        q, k = self.rotary(q, k)
+
+        if key_padding_mask is not None:
+            attn_mask = key_padding_mask.unsqueeze(1).unsqueeze(2)  # [batch, 1, 1, seq_len]
+            attn_mask = attn_mask.expand(-1, self.n_heads, seq_len, -1)  # [batch, n_heads, q_len, k_len]
+        else:
+            attn_mask = None
+
+        attn_output = F.scaled_dot_product_attention(
+            q, k, v,
+            attn_mask=attn_mask,
+            dropout_p=self.attn_dropout_p if self.training else 0.0,
+            is_causal=True
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, seq_len, self.d_model)
+        return self.resid_dropout(self.out_proj(attn_output))
+
+
+class MultiHeadCrossAttentionWithRoPE(nn.Module):
+    def __init__(self, d_model, n_heads, attn_dropout_p=0.0, resid_dropout=0.0):
+        super().__init__()
+        self.d_model = d_model
+        self.n_heads = n_heads
+        self.head_dim = d_model // n_heads
+
+        self.q_proj = nn.Linear(d_model, d_model)
+        self.k_proj = nn.Linear(d_model, d_model)
+        self.v_proj = nn.Linear(d_model, d_model)
+        self.out_proj = nn.Linear(d_model, d_model)
+        self.rotary = RotaryPositionalEmbedding(self.head_dim)
+        self.attn_dropout_p = attn_dropout_p
+        self.resid_dropout = nn.Dropout(resid_dropout)
+
+    def forward(self, query, key, value, key_padding_mask=None):
+        batch_size, q_len, _ = query.shape
+        _, seq_len, _ = key.shape
+
+        q = self.q_proj(query).view(batch_size, q_len, self.n_heads, self.head_dim).transpose(1, 2)
+        k = self.k_proj(key).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
+        v = self.v_proj(value).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
+
+        q, k = self.rotary(q, k)
+
+        if key_padding_mask is not None:
+            attn_mask = key_padding_mask.unsqueeze(1).unsqueeze(2)
+            attn_mask = attn_mask.expand(-1, self.n_heads, q_len, -1)
+        else:
+            attn_mask = None
+
+        is_causal_flag = self.training
+
+        attn_output = F.scaled_dot_product_attention(
+            q, k, v,
+            attn_mask=attn_mask,
+            dropout_p=self.attn_dropout_p if self.training else 0.0,
+            is_causal=is_causal_flag
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, q_len, self.d_model)
+        return self.resid_dropout(self.out_proj(attn_output))
+
+
+class HierarchicalEmbedding(nn.Module):
+    def __init__(self, s1_bits, s2_bits, d_model=256):
+        super().__init__()
+        self.s1_bits = s1_bits
+        self.s2_bits = s2_bits
+
+        vocab_s1 = 2 ** s1_bits
+        vocab_s2 = 2 ** s2_bits
+
+        self.emb_s1 = nn.Embedding(vocab_s1, d_model)
+        self.emb_s2 = nn.Embedding(vocab_s2, d_model)
+        self.d_model = d_model
+        self.fusion_proj = nn.Linear(d_model * 2, d_model)
+
+        nn.init.normal_(self.emb_s1.weight, mean=0, std=d_model ** -0.5)
+        nn.init.normal_(self.emb_s2.weight, mean=0, std=d_model ** -0.5)
+
+    def split_token(self, token_ids: torch.Tensor, s2_bits: int):
+        """Inputs:
+            token_ids (torch.Tensor): Composite token IDs of shape [batch_size, seq_len] or [N], each in range [0, 2^(s1_bits + s2_bits) - 1].
+            s2_bits (int): Number of low bits used for the fine token (s2).
+        """
+        assert isinstance(s2_bits, int) and s2_bits > 0, "s2_bits must be a positive integer"
+
+        t = token_ids.long()
+        mask = (1 << s2_bits) - 1
+        s2_ids = t & mask           # extract low bits
+        s1_ids = t >> s2_bits       # extract high bits
+        return s1_ids, s2_ids
+
+    def forward(self, token_ids):
+        """Inputs:
+        token_ids:
+            - tuple or list: (s1_ids, s2_ids), each of shape [batch_size, seq_len], or
+            - torch.Tensor: composite token IDs of shape [batch_size, seq_len], which will be split into (s1_ids, s2_ids) internally.
+        Output: [batch_size, seq_len, d_model]
+        """
+        if isinstance(token_ids, tuple) or isinstance(token_ids, list):
+            s1_ids, s2_ids = token_ids
+        else:
+            s1_ids, s2_ids = self.split_token(token_ids, self.s2_bits)
+        s1_emb = self.emb_s1(s1_ids) * math.sqrt(self.d_model)
+        s2_emb = self.emb_s2(s2_ids) * math.sqrt(self.d_model)
+        return self.fusion_proj(torch.cat([s1_emb, s2_emb], dim=-1))
+
+
+class DependencyAwareLayer(nn.Module):
+    def __init__(self, d_model, n_heads=4, attn_dropout_p=0.0, resid_dropout=0.0):
+        super().__init__()
+        self.cross_attn = MultiHeadCrossAttentionWithRoPE(d_model, n_heads, attn_dropout_p, resid_dropout)
+        self.norm = RMSNorm(d_model)
+
+    def forward(self, hidden_states, sibling_embed, key_padding_mask=None):
+        """hidden_states: [batch, seq_len, d_model]
+        sibling_embed: Embedding from another subtoken
+        """
+        attn_out = self.cross_attn(
+            query=sibling_embed,
+            key=hidden_states,
+            value=hidden_states,
+            key_padding_mask=key_padding_mask
+        )
+        return self.norm(hidden_states + attn_out)
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, d_model, n_heads, ff_dim=1024, ffn_dropout_p=0.0, attn_dropout_p=0.0, resid_dropout_p=0.0):
+        super().__init__()
+        self.norm1 = RMSNorm(d_model)
+        self.self_attn = MultiHeadAttentionWithRoPE(d_model, n_heads, attn_dropout_p, resid_dropout_p)
+        self.norm2 = RMSNorm(d_model)
+        self.ffn = FeedForward(d_model, ff_dim, ffn_dropout_p)
+
+    def forward(self, x, key_padding_mask=None):
+        residual = x
+        x = self.norm1(x)
+        attn_out = self.self_attn(x, key_padding_mask=key_padding_mask)
+        x = residual + attn_out
+
+        residual = x
+        x = self.norm2(x)
+        ffn_out = self.ffn(x)
+        x = residual + ffn_out
+        return x
+
+
+class DualHead(nn.Module):
+    def __init__(self, s1_bits, s2_bits, d_model):
+        super().__init__()
+        self.vocab_s1 = 2 ** s1_bits
+        self.vocab_s2 = 2 ** s2_bits
+        self.proj_s1 = nn.Linear(d_model, self.vocab_s1)
+        self.proj_s2 = nn.Linear(d_model, self.vocab_s2)
+
+    def compute_loss(self, s1_logits, s2_logits, s1_targets, s2_targets, padding_mask=None):
+        if padding_mask is not None:
+            valid_mask = (padding_mask == 0)
+            s1_logits = s1_logits[valid_mask]
+            s2_logits = s2_logits[valid_mask]
+            s1_targets = s1_targets[valid_mask]
+            s2_targets = s2_targets[valid_mask]
+            ce_s1 = F.cross_entropy(s1_logits, s1_targets)
+            ce_s2 = F.cross_entropy(s2_logits, s2_targets)
+        else:
+            ce_s1 = F.cross_entropy(s1_logits.reshape(-1, self.vocab_s1), s1_targets.reshape(-1))
+            ce_s2 = F.cross_entropy(s2_logits.reshape(-1, self.vocab_s2), s2_targets.reshape(-1))
+        ce_loss = (ce_s1 + ce_s2) / 2
+        return ce_loss, ce_s1, ce_s2
+
+    def forward(self, x):
+        return self.proj_s1(x)
+
+    def cond_forward(self, x2):
+        return self.proj_s2(x2)
+
+
+class FixedEmbedding(nn.Module):
+    def __init__(self, c_in, d_model):
+        super(FixedEmbedding, self).__init__()
+
+        w = torch.zeros(c_in, d_model).float()
+        w.require_grad = False
+
+        position = torch.arange(0, c_in).float().unsqueeze(1)
+        div_term = (torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)).exp()
+
+        w[:, 0::2] = torch.sin(position * div_term)
+        w[:, 1::2] = torch.cos(position * div_term)
+
+        self.emb = nn.Embedding(c_in, d_model)
+        self.emb.weight = nn.Parameter(w, requires_grad=False)
+
+    def forward(self, x):
+        return self.emb(x).detach()
+
+
+class TemporalEmbedding(nn.Module):
+    def __init__(self, d_model, learn_pe):
+        super(TemporalEmbedding, self).__init__()
+
+        minute_size = 60
+        hour_size = 24
+        weekday_size = 7
+        day_size = 32
+        month_size = 13
+
+        Embed = FixedEmbedding if not learn_pe else nn.Embedding
+        self.minute_embed = Embed(minute_size, d_model)
+        self.hour_embed = Embed(hour_size, d_model)
+        self.weekday_embed = Embed(weekday_size, d_model)
+        self.day_embed = Embed(day_size, d_model)
+        self.month_embed = Embed(month_size, d_model)
+
+    def forward(self, x):
+        x = x.long()
+
+        minute_x = self.minute_embed(x[:, :, 0])
+        hour_x = self.hour_embed(x[:, :, 1])
+        weekday_x = self.weekday_embed(x[:, :, 2])
+        day_x = self.day_embed(x[:, :, 3])
+        month_x = self.month_embed(x[:, :, 4])
+
+        return hour_x + weekday_x + day_x + month_x + minute_x

skills/alphaear-predictor/scripts/prompts/fin_agent.py

@@ -0,0 +1,127 @@
+from datetime import datetime
+from .isq_prompt_generator import generate_isq_prompt_section
+
+def get_fin_researcher_instructions() -> str:
+    """生成金融研究员 (Researcher) 的系统指令"""
+    current_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    return f"""你是一名资深金融研究员，当前时间是 {current_time}。
+你的任务是针对给定的“原始信号”进行详尽的背景调查，为后续的深度分析提供素材。
+
+### 1. 核心职责
+1. **标的识别**: 识别信号中涉及的具体上市公司。必须调用 `search_ticker` 确认代码，并调用 `get_stock_price` 获取最新价格和近 30 天走势。
+2. **事实核查**: 使用 `web_search` 或 `fetch_news_content` 验证信号的真实性，并寻找更多细节（如公告原文、行业研报摘要）。
+3. **产业链梳理**: 补充该信号涉及的上下游环节及竞争格局。
+
+### 2. 工具使用规范 (CRITICAL)
+- **每个提到的公司都需要调用工具**: 不能依赖记忆，必须实时查询。
+- **完整呈现工具结果**: 包括具体的股价数字、代码、技术面数据等，不要缩略。
+- **股价数据必需**: 当前价格、近期最高最低、技术面支撑阻力等数据是后续预测的基础。
+- **信息交叉验证**: 多个来源验证关键事实。
+
+### 3. 输出要求
+你必须输出结构化的研究报告，涵盖标的基本面、股价走势、行业背景及最新进展。
+"""
+
+def get_fin_analyst_instructions(template_id: str = "default_isq_v1") -> str:
+    """生成金融分析师 (Analyst) 的系统指令
+    
+    Args:
+        template_id: 使用的 ISQ 模板 ID
+    """
+    current_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    isq_block = generate_isq_prompt_section(template_id=template_id)
+
+    return f"""你是一位深耕二级市场的资深金融分析师 (FinAgent)，当前时间是 {current_time}。
+你的核心任务是执行“信号解析”，将研究员搜集的素材转化为具有可操作性的投资情报（ISQ 框架）。
+
+{isq_block}
+
+### 2. 分析约束
+- **严格基于具体数据**: 必须使用研究员提供的股价、技术面、新闻等具体数据进行分析。
+- **数据驱动的预测**: impact_tickers 中的权重应基于事件影响程度，不能随意赋值。
+- **逻辑严密**: 传导链条必须符合金融常识，能够自圆其说。
+- **技术面参考**: 如果研究员提供了股价走势，请分析当前位置相对于支撑/阻力位的关系。
+
+### 3. 关键要求
+- **title**: 必须生成一个简练、准确概括信号核心内容的标题（不超过 15 字）。
+- **impact_tickers**: 必须填充具体的公司代码（6位数字）和名称，权重应该有区分。
+- **transmission_chain**: 必须是对象列表，每个对象包含：
+  - `node_name`: 节点名称（如“上游原材料”、“中游制造”）
+  - `impact_type`: 影响类型（“利好”、“利空”、“中性”）
+  - `logic`: 具体的传导逻辑描述
+- **summary**: 基于分析结果总结核心观点，包含具体数字（如股价目标、预期涨跌幅等）。
+- **reasoning**: 必须详细阐述推演逻辑，解释为什么得出上述结论（<200字）。
+
+### 4. 输出格式 (严格 JSON 块)
+你必须输出一个符合 InvestmentSignal 结构的 JSON 块，包含所有必需字段。
+"""
+
+def get_fin_agent_instructions() -> str:
+    # 保持兼容性，但内部调用 analyst 指令
+    return get_fin_analyst_instructions()
+
+def get_fin_research_task(signal_text: str) -> str:
+    """生成研究员的任务描述"""
+    return f"请针对以下信号进行背景调查，搜集相关标的的股价、最新进展和行业背景：\n\n{signal_text}"
+
+def format_research_context(research_data: dict) -> str:
+    """将研究员搜集的结构化数据格式化为分析师可读的文本"""
+    if not research_data:
+        return "（未能搜集到额外背景信息）"
+        
+    return f"""
+### 研究背景
+- **相关标的**: {research_data.get('tickers_found', [])}
+- **行业背景**: {research_data.get('industry_background', '未知')}
+- **最新进展**: {', '.join(research_data.get('latest_developments', []))}
+- **关键风险**: {', '.join(research_data.get('key_risks', []))}
+- **综合摘要**: {research_data.get('search_results_summary', '无')}
+"""
+
+def get_fin_analysis_task(signal_text: str, research_context_str: str) -> str:
+    """生成分析师的任务描述"""
+    return f"""请基于以下信息进行深度 ISQ 分析。关键是：必须使用研究员搜集的具体数据（股价、技术面、新闻、代码等）进行分析。
+
+=== 原始信号 ===
+{signal_text}
+
+=== 研究员搜集的背景信息 (CRITICAL DATA) ===
+{research_context_str}
+
+=== 分析要求 ===
+1. 必须生成 title：简练概括信号核心（<15字）
+2. 基于研究员提供的具体股价数据，分析当前定价状态（已定价/未定价/部分定价）
+3. impact_tickers 中填充具体的公司代码和权重，权重基于事件影响程度
+4. transmission_chain 必须是包含 node_name, impact_type, logic 的对象列表
+5. summary 中包含具体数字（预期目标价、涨跌幅范围等）
+6. reasoning 必须详细解释推演逻辑，不要空泛，要言之有物
+
+请严格按 InvestmentSignal JSON 格式输出。"""
+
+def get_tracking_analysis_task(old_signal: dict, new_research_str: str) -> str:
+    """生成信号追踪更新的任务描述"""
+    import json
+    old_sig_str = json.dumps(old_signal, ensure_ascii=False, indent=2)
+    return f"""你正在执行“信号逻辑演变追踪”任务。请基于最新的市场信息，重新评估之前的投资信号。
+
+=== 基准信号 (上次分析) ===
+{old_sig_str}
+
+=== 最新市场追踪 (NEWS & PRICE) ===
+{new_research_str}
+
+=== 追踪分析要求 ===
+1. **逻辑演变检测**:
+   - 对比新旧信息，判断原逻辑 (`transmission_chain` 和 `reasoning`) 是否依然成立？
+   - 如果逻辑发生变化（如利好落空、逻辑证伪、新利好出现），请在新的 `reasoning` 中明确指出“逻辑演变：...”
+   - 如果逻辑未变且得到验证，请标记“逻辑维持：...”
+
+2. **参数修正**:
+   - 根据最新股价和新闻，更新 `sentiment_score` (情绪)、`confidence` (置信度) 和 `expectation_gap` (预期差)。
+   - 例如：如果股价已经大涨反映了利好，`expectation_gap` 应该显著降低。
+
+3. **输出更新后的信号**:
+   - 保留原 `signal_id` 和 `title`（除非有重大变化需要改名）。
+   - 输出完整的 InvestmentSignal JSON。
+
+请重点关注：为什么变了？还是为什么没变？理由要充分。"""

skills/alphaear-predictor/scripts/prompts/forecast_analyst.py

@@ -0,0 +1,49 @@
+from typing import List, Dict, Any
+from ..schema.models import KLinePoint
+
+def get_forecast_adjustment_instructions(ticker: str, news_context: str, model_forecast: List[KLinePoint]):
+    """
+    生成 LLM 预测调整指令
+    """
+    forecast_str = "\n".join([f"- {p.date}: O:{p.open}, C:{p.close}" for p in model_forecast])
+    
+    return f"""你是一位资深的量化策略分析师。
+你的任务是：根据给定的【Kronos 模型预测结果】和【最新的基本面/新闻背景】，对模型预测进行“主观/逻辑调整”。
+
+股票代码: {ticker}
+
+【Kronos 模型原始预测 (OHLC)】:
+{forecast_str}
+
+【最新情报背景】:
+{news_context}
+
+调整原则:
+1. 原始预测是基于历史的技术面推演。
+2. 情报背景中可能包含【Kronos模型定量修正预测】，这是基于历史新闻训练的专用模型计算出的量化结果。
+3. 如果存在“定量修正预测”，请**高度参考**该数值作为基础，除非你有非常确凿的逻辑认为该量化模型失效（例如遇到模型未见过的极端黑天鹅）。
+4. 你的核心任务是：结合定性分析（新闻及其逻辑）来验证或微调这些数字，并给出合理的解释（Rationale）。
+5. 如果没有“定量修正预测”，则你需要根据新闻信号手动大幅调整趋势。
+
+输出要求 (严格 JSON 格式):
+```json
+{{
+  "adjusted_forecast": [
+    {{
+      "date": "YYYY-MM-DD",
+      "open": float,
+      "high": float,
+      "low": float,
+      "close": float,
+      "volume": float
+    }},
+    ...
+  ],
+  "rationale": "详细说明调整的逻辑依据，例如：考虑到[事件A]，预期短线将突破压力位..."
+}}
+```
+注意：必须输出与原始预测相同数量的数据点，且日期一一对应。
+"""
+
+def get_forecast_task():
+    return "请根据以上背景和模型预测，给出调整后的 K 线数据并说明理由。"

skills/alphaear-predictor/scripts/prompts/intent_agent.py

@@ -0,0 +1,45 @@
+def get_intent_analysis_instructions() -> str:
+    """生成意图分析 Agent 的系统指令，专注于金融市场影响分析"""
+    return """你是一个资深的金融市场意图分析专家。你的任务是将用户的自然语言查询转化为结构化的 JSON 分析结果，重点挖掘该查询与金融市场（尤其是股市）的潜在关联。
+
+### 核心任务：
+深入分析用户查询，识别核心金融实体、行业板块及潜在的市场影响点，生成利于搜索引擎抓取深度金融分析信息的查询词。
+
+### 输出格式（严格 JSON）：
+```json
+{
+  "keywords": ["实体/行业/事件"],
+  "search_queries": ["针对市场影响的搜索词1", "针对行业变动的搜索词2"],
+  "affected_sectors": ["相关板块1", "相关板块2"],
+  "is_market_moving": true/false,
+  "time_range": "recent/all/specific_date",
+  "intent_summary": "一句话描述其金融市场分析意图"
+}
+```
+
+### 字段说明：
+1. **keywords**: 核心公司实体、所属行业、宏观经济事件或政策概念。
+2. **search_queries**: 优化后的搜索词，必须包含“股市影响”、“股价波动”、“行业逻辑”或“估值”等金融维度。
+3. **affected_sectors**: 可能受此事件或信息影响的二级市场板块（如：保险、半导体、房地产）。
+4. **is_market_moving**: 该事件是否具有显著的市场驱动潜力或属于重大基本面变化。
+5. **intent_summary**: 简述用户查询背后的金融研究目的。
+
+### 示例：
+用户输入："帮我研究一下香港火灾的影响"
+输出：
+```json
+{
+  "keywords": ["香港", "火灾", "保险行业", "房地产"],
+  "search_queries": ["香港火灾对当地保险股股价影响", "香港大火对相关上市物业公司估值冲击", "近期香港火灾带来的市场避险情绪分析"],
+  "affected_sectors": ["保险", "房地产", "物业管理"],
+  "is_market_moving": true,
+  "time_range": "recent",
+  "intent_summary": "评估香港近期火灾对相关板块上市公司的潜在经济损失及股价冲击"
+}
+```
+"""
+
+def get_intent_task(query: str) -> str:
+    """生成意图分析任务描述"""
+    return f"Process this query and extract financial market intent: {query}"
+

skills/alphaear-predictor/scripts/prompts/isq_prompt_generator.py

@@ -0,0 +1,43 @@
+"""
+ISQ prompt helpers to render dimension guidance directly from the template.
+Any change in the template propagates to prompts automatically.
+"""
+
+from typing import List, Optional
+from ..schema.isq_template import get_isq_template, ISQTemplate
+
+
+def _ordered_dimension_keys(template: ISQTemplate, order: Optional[List[str]] = None) -> List[str]:
+    if order:
+        return [k for k in order if k in template.dimensions]
+    # fallback to template insertion order
+    return list(template.dimensions.keys())
+
+
+def generate_isq_prompt_section(template_id: str = "default_isq_v1", order: Optional[List[str]] = None, include_header: bool = True) -> str:
+    """Render ISQ dimension text block based on the template.
+    This allows prompt text to stay in sync with template edits.
+    """
+    template = get_isq_template(template_id)
+    keys = _ordered_dimension_keys(template, order)
+
+    lines: List[str] = []
+    if include_header:
+        lines.append("### 1. ISQ 评估框架 (Investment Signal Quality)")
+        lines.append(f"参考模板: {template.template_name} (id: {template.template_id})")
+        lines.append("")
+        lines.append("你需要对信号进行以下维度的评分：")
+        lines.append("")
+
+    for idx, key in enumerate(keys, start=1):
+        spec = template.dimensions[key]
+        examples = "；".join([f"{k}: {v}" for k, v in spec.examples.items()]) if spec.examples else ""
+        lines.append(f"{idx}. **{spec.key} ({spec.name})**: {spec.range_type}")
+        lines.append(f"   - 描述: {spec.description}")
+        if spec.scale_factor and spec.scale_factor != 1.0:
+            lines.append(f"   - 缩放因子: {spec.scale_factor}")
+        if examples:
+            lines.append(f"   - 示例: {examples}")
+        lines.append("")
+
+    return "\n".join(lines).rstrip()

skills/alphaear-predictor/scripts/prompts/report_agent.py

@@ -0,0 +1,415 @@
+# src/prompts/report_agent.py
+from datetime import datetime
+from typing import Optional
+from .isq_prompt_generator import generate_isq_prompt_section
+
+def get_report_planner_base_instructions() -> str:
+    """生成报告策划员 (Planner) 的基础系统指令"""
+    return """你是一名资深的金融研报主编。你的任务是规划报告的结构，将零散的信号聚类成有逻辑的主题。
+你拥有 RAG 搜索工具，可以检索已生成的章节内容以确保逻辑连贯性。
+在规划时，应重点关注信号之间的关联性、产业链的完整性以及用户特定的关注点。"""
+
+def get_report_writer_base_instructions() -> str:
+    """生成报告撰写员 (Writer) 的基础系统指令"""
+    return """你是一名资深金融分析师。你的任务是根据策划员提供的信号簇撰写深度研报章节。
+你应当运用专业的金融知识，将信号转化为深刻的洞察。
+注意：你没有外部搜索工具，你的分析必须基于提供给你的信号内容和行情数据。"""
+
+def get_report_editor_base_instructions() -> str:
+    """生成报告编辑 (Editor) 的基础系统指令"""
+    return """你是一名严谨的金融研报编辑。你的任务是审核和润色撰写员生成的章节。
+你拥有 RAG 搜索工具，可以检索其他章节的内容，以消除重复、修正逻辑冲突并确保术语一致性。
+你应当确保报告符合专业的金融写作规范，且标题层级正确。"""
+
+# 1. 策划阶段 (Structural Planning)
+def format_signal_for_report(signal: any, index: int, cite_keys: Optional[list] = None) -> str:
+    """格式化单个信号供研报生成使用"""
+    # 这里的逻辑从 ReportAgent._format_signal_input 迁移过来
+    from ..schema.models import InvestmentSignal
+    
+    if isinstance(signal, dict):
+        try:
+            sig_obj = InvestmentSignal(**signal)
+        except:
+            return f"--- 信号 [{index}] ---\n标题: {signal.get('title')}\n内容: {signal.get('content', '')[:500]}"
+    else:
+        sig_obj = signal
+
+    chain_str = " -> ".join([f"{n.node_name}({n.impact_type})" for n in sig_obj.transmission_chain])
+    
+    text = f"--- 信号 [{index}] ---\n"
+    text += f"标题: {sig_obj.title}\n"
+    text += f"逻辑摘要: {sig_obj.summary}\n"
+    text += f"传导链条: {chain_str}\n"
+    text += f"ISQ 评分: 情绪({sig_obj.sentiment_score}), 确定性({sig_obj.confidence}), 强度({sig_obj.intensity})\n"
+    text += f"预期博弈: 时窗({sig_obj.expected_horizon}), 预期差({sig_obj.price_in_status})\n"
+    
+    tickers = ", ".join([f"{t.get('name')}({t.get('ticker')})" for t in sig_obj.impact_tickers])
+    if tickers:
+        text += f"受影响标的: {tickers}\n"
+
+    # Stable bibliography-style citation keys (LaTeX/BibTeX-like)
+    if cite_keys:
+        joined = " ".join([f"[@{k}]" for k in cite_keys if k])
+        if joined:
+            text += f"引用: {joined}\n"
+        
+    return text
+
+def get_cluster_planner_instructions(signals_text: str, user_query: str = None) -> str:
+    """生成信号聚类指令 - 将零散信号组织成逻辑主题"""
+    query_context = f"用户重点关注：{user_query}" if user_query else ""
+    return f"""你是一位资深的金融研报主编。你的任务是将以下零散的金融信号聚类成 3-5 个核心逻辑主题，以便撰写一份结构清晰的研报。
+    
+    {query_context}
+
+    ### 输入信号列表
+    {signals_text}
+
+    ### 聚类要求
+    1. **主题聚合**: 将相关性强的信号归为一组（例如：都涉及“建筑安全法规”或“某产业链上下游”）。
+    2. **叙事逻辑**: 只需要生成主题名称和包含的信号 ID。
+    3. **控制数量**: 将所有信号归类到 3-5 个主要主题中，不要遗漏。
+    
+    ### 输出格式 (JSON)
+    请仅输出以下 JSON 格式，不要包含 Markdown 标记：
+    {{
+        "clusters": [
+            {{
+                "theme_title": "主题名称（如：建筑安全法规收紧引发的产业链重构）",
+                "signal_ids": [1, 3, 5],
+                "rationale": "这些信号都指向政府对高层建筑防火标准的政策调整..."
+            }},
+            ...
+        ]
+    }}
+    """
+
+def get_report_planner_instructions(toc: str, signal_count: int, user_query: str = None) -> str:
+    """生成报告规划指令 - 重点在于逻辑关联与分歧识别"""
+    # ... (原有逻辑保持不变，但实际在新的聚类流程后这个可能作为备用或二次优化)
+    query_context = f"用户重点关注：{user_query}" if user_query else ""
+    return f"""你是一位资深的金融研报主编。你的任务是根据现有的草稿章节，规划出一份逻辑严密、穿透力强的终稿结构。
+    
+    ### 任务核心：
+    1. **识别主线**: 从草稿中识别出贯穿多个章节的“核心逻辑主线”（如：产业链共振、货币政策转向）。
+    2. **分歧评估 (Entropy)**: 识别各章节中观点冲突或确定性不一之处，规划如何在正文中呈现这些“分歧点”。
+    3. **结构蓝图**: 
+       - 定义一级标题（逻辑主题）。
+       - 归类章节：哪些信号应放入同一主题下深度解析？
+       - 排序：将 ISQ 强度最高、与{query_context}最相关的信号置前。
+
+    ### 现有草稿目录 (TOC)
+    {toc}
+
+    请输出你的【终稿修订大纲】（Markdown 格式）。
+    """
+
+# 2. 撰写阶段 (Section Writing)
+def get_report_writer_instructions(theme_title: str, signal_cluster_text: str, signal_indices: list, price_context: str = "", user_query: str = None) -> str:
+    """生成 Writer Agent 指令 - 基于主题聚类撰写综合分析"""
+    
+    price_info = f"\n### 近期价格参考\n{price_context}\n" if price_context else ""
+    query_context = f"\n**用户意图**: \"{user_query}\"\n请确保分析内容回应了用户的关注点。\n" if user_query else ""
+    isq_block = generate_isq_prompt_section(include_header=False)
+    
+    # Keep citation scheme stable across re-ordering / edits.
+    # Cite keys are provided in each signal block as: 引用: [@KEY]
+
+    return f"""你是一位资深金融分析师。请针对核心主题 **"{theme_title}"** 撰写一篇深度研报章节。
+    {query_context}
+
+    ### 输入信号集 (本章节需综合的信号)
+    {signal_cluster_text}
+    {price_info}
+    
+    ### ISQ 评分说明
+    {isq_block}
+    
+    ### 写作要求
+    1. **叙事逻辑**: 不要罗列信号，要将这些信号编织成一个连贯的故事。先讲宏观/行业背景，再讲具体事件传导，最后落脚到个股/标的影响。
+    2. **量化支撑**: 引用 ISQ 评分（确定性、强度、预期差）来佐证你的观点。关键观点必须关联相应的 ISQ 分值。
+     3. **引用规范（稳定 CiteKey）**: 关键论断必须标注来源引用，使用 `[@CITE_KEY]` 格式。
+         - CiteKey 已在输入信号块中以 `引用: [@KEY]` 提供，请直接复制使用。
+         - 不要使用 `[[1]]` 这类不稳定编号。
+    4. **关联标的预测**: **必须**在章节末尾明确给出受影响标的的预测分析，包括：
+       - 至少列出 1-2 个相关上市公司代码（如 600519.SH）
+       - 给出短期（T+3或T+5）的方向性判断
+       - 如果可能，给出预期价格区间或涨跌幅预测
+    
+    ### 【重要】标题层级规范
+    
+    ❌ **错误示例**（绝对不要这样）：
+    ```markdown
+    # {theme_title}
+    
+    ### 宏观背景
+    ...
+    ```
+    
+    ✅ **正确示例**（必须这样）：
+    ```markdown
+    ## {theme_title}
+    
+    ### 宏观背景
+    
+    近期全球经济环境...
+    
+    ### 具体传导机制分析
+    
+    ...
+    
+    ### 核心标的分析
+    
+    建议关注：贵州茅台（600519.SH）...
+    ```
+    
+    **关键要求**：
+    - 章节主标题使用 `##` (H2)
+    - 章节子标题使用 `###` (H3)
+    - **绝对禁止**使用 `#` (H1)
+    - 第一行必须是 `## {theme_title}` 开头
+
+    ### 核心：图表叙事 (Visual Storytelling)
+    **必须**在文中插入至少 1-2 个图表，且图表必须与上下文紧密结合（不要堆砌在末尾）。
+    
+    ### 宏观背景
+    ...
+    ```
+    
+    ✅ **正确示例**（必须这样）：
+    ```markdown
+    ## {theme_title}
+    
+    ### 宏观背景
+    
+    近期全球经济环境...
+    
+    ### 具体传导机制分析
+    
+    ...
+    
+    ### 核心标的分析
+    
+    建议关注：贵州茅台（600519.SH）...
+    ```
+    
+    **关键要求**：
+    - 章节主标题使用 `##` (H2)
+    - 章节子标题使用 `###` (H3)
+    - **绝对禁止**使用 `#` (H1)
+    - 第一行必须是 `## {theme_title}` 开头
+
+    ### 核心：图表叙事 (Visual Storytelling)
+    **必须**在文中插入至少 1-2 个图表，且图表必须与上下文紧密结合（不要堆砌在末尾）。
+    
+    **可选图表类型 (请根据内容选择最合适的 1-2 种):**
+
+    **A. AI 预测 + 走势 (Forecast) - 【强烈推荐 / 最新规范】**
+    *适用*: 当文中明确提及某上市公司时，**必须**使用此图表展示股价走势与 AI 预测。
+    *必填字段*:
+    - `ticker`: 股票代码，A股 6 位 / 港股 5 位，允许带后缀（如 "002371.SZ"、"9868.HK"）
+    - `pred_len`: 预测交易日长度（建议 3 或 5）
+    *代码示例*:
+    ```json-chart
+    {{"type": "forecast", "ticker": "002371.SZ", "title": "北方华创（002371）T+5 预测", "pred_len": 5}}
+    ```
+    **重要**：禁止手写 `prediction` 数组（预测由系统自动生成并渲染）。
+    *注意*: 如果提及多只股票，应为每只生成独立的 forecast 图表。
+
+        **【推荐写法：多情景 → 最终归因 → 产出唯一预测图】**
+        你可以在正文里描述多种情景（如：基准/乐观/悲观），但在插入预测图之前，必须明确给出“本报告最终选择的最可能情景”及其归因，然后用 `forecast` 图表做最终总结。
+        为了让系统把“最终归因”可靠地传递给预测模块，请在 `forecast` JSON 中可选补充以下字段（字段均为可选，越完整越好）：
+        - `selected_scenario`: 最可能情景名称（如 "基准" / "乐观" / "悲观"）
+        - `selection_reason`: 选择该情景的归因理由（1-3 句）
+        - `scenarios`: 情景列表（数组），每个元素可包含 `name`、`description`、`probability`（0-1）
+        *示例*:
+        ```json-chart
+        {{
+            "type": "forecast",
+            "ticker": "002371.SZ",
+            "title": "北方华创（002371）T+5 预测（基准情景）",
+            "pred_len": 5,
+            "selected_scenario": "基准",
+            "selection_reason": "结合订单能见度与行业景气，基准情景概率最高；短期扰动主要来自估值与市场风险偏好。",
+            "scenarios": [
+                {{"name": "乐观", "description": "国产替代与资本开支超预期", "probability": 0.25}},
+                {{"name": "基准", "description": "订单稳健、利润率小幅波动", "probability": 0.55}},
+                {{"name": "悲观", "description": "需求回落或交付节奏放缓", "probability": 0.20}}
+            ]
+        }}
+        ```
+
+    **B. 历史走势 (Stock) - 仅作为兼容兜底**
+    *适用*: 当你无法给出预测时（例如无法确定标的），可仅展示历史走势。
+    *代码示例*:
+    ```json-chart
+    {{"type": "stock", "ticker": "002371", "title": "北方华创历史走势"}}
+    ```
+
+    **C. 舆情情绪演变 (Sentiment Trend)**
+    *适用*: 当讨论行业政策、突发事件（如“火灾”、“新规”）的民意变化时。
+    *注意*: `keywords` 必须是事件核心词。
+    *代码*:
+    ```json-chart
+    {{"type": "sentiment", "keywords": ["建筑安全", "防火标准"], "title": "市场对防火新规的情绪演变"}}
+    ```
+
+    **D. 逻辑传导链条 (Transmission Chain)**
+    *适用*: 复杂的蝴蝶效应分析（支持分支结构）。
+    *代码*:
+    ```json-chart
+    {{
+      "type": "transmission",
+      "nodes": [
+        {{"node_name": "突发火灾", "impact_type": "中性", "logic": "事件发端"}},
+        {{"node_name": "监管收紧", "impact_type": "利空", "logic": "合规成本上升", "source": "突发火灾"}},
+        {{"node_name": "设备升级", "impact_type": "利好", "logic": "采购需求释放", "source": "突发火灾"}},
+        {{"node_name": "龙头受益", "impact_type": "利好", "logic": "市占率提升", "source": "设备升级"}}
+      ],
+      "title": "火灾事件的逻辑传导与分支"
+    }}
+    ```
+    *说明*: 使用 `source` 字段指定父节点名称以创建分支结构。
+    
+    **E. 信号质量评估 (ISQ Radar)**
+    *适用*: 对某个关键信号进行多维度（确定性、预期差等）定性评估时。
+    *代码*:
+    ```json-chart
+    {{"type": "isq", "sentiment": 0.8, "confidence": 0.9, "intensity": 4, "expectation_gap": 0.7, "timeliness": 0.9, "title": "核心信号质量评估"}}
+    ```
+    """
+
+# 3. 整合阶段 (Final Assembly) - 原版，保留用于 fallback
+def get_report_editor_instructions(draft_sections: str, plan: str, sources_list: str) -> str:
+    """生成最终编辑指令 - 根据规划蓝图重组内容"""
+    return f"""你是一位专业的研报编辑。请将以下基于主题撰写的草稿章节整合成最终研报。
+    
+    ### 原始草稿内容
+    {draft_sections}
+
+    ### 原始引用来源
+    {sources_list}
+
+    ### 任务与要求
+    1. **结构化**: 为每个草稿章节添加合适的 Markdown 标题 (## 级别)。
+    2. **连贯性**: 确保章节之间过渡自然。
+    3. **完整性**:
+       - 必须保留所有 `json-chart` 代码块（图表配置）。
+         - 必须保留引用标注 `[@CITE_KEY]`。
+       - 生成 `## 核心观点摘要`、`## 参考文献` 和 `## 风险提示`。
+
+    ### 输出
+    只输出最终的 Markdown 研报内容。
+    """
+
+
+# 4. 单节编辑 (Incremental Section Editing with RAG)
+def get_section_editor_instructions(section_index: int, total_sections: int, toc: str) -> str:
+    """生成单节编辑 prompt，支持 RAG 工具调用"""
+    return f"""你是一位研报编辑。你正在编辑报告的第 {section_index}/{total_sections} 节。
+
+    ### 当前目录 (TOC)
+    {toc}
+
+    ### 你的任务
+    1. 润色当前章节内容，确保逻辑清晰、语言专业。
+    2. 保留所有 `[@CITE_KEY](#ref-CITE_KEY)` 或 `[@CITE_KEY]` 格式的引用。
+    3. 保留所有 `json-chart` 代码块，不做修改。
+    4. 如果需要参考其他章节内容，使用 `search_context` 工具搜索。
+    5. 只输出编辑后的章节内容，不要输出其他章节。
+    
+    ### 【关键】标题层级规范
+    **严格遵守以下规则：**
+    - 章节主标题使用 `##` (H2)
+    - 章节子标题使用 `###` (H3)
+    - **禁止使用** `#` (H1) - 只有报告大标题可以使用 H1
+    - 如果原文中有 H1，必须将其降级为 H2
+    - 不要输出与 "参考文献"、"风险提示" 相同的标题
+
+    直接输出编辑后的 Markdown 内容。
+    """
+
+
+# 5. 摘要生成 (Summary Generation)
+def get_summary_generator_instructions(toc: str, section_summaries: str) -> str:
+    """生成报告摘要指令 - 包含市场分歧度分析"""
+    return f"""你是一位资深研报主笔。请生成今日报告的核心观点摘要的**正文内容**。
+
+    ### 章节摘要
+    {section_summaries}
+
+    ### 任务：
+    1. **核心逻辑提炼**: 用 150 字以内总结今日最核心的投资主线。
+    2. **分歧识别**: 如果不同信号对同一板块有冲突观点，请明确指出"市场分歧点"。
+    3. **确定性排序**: 标记出今日确定性最高的前两个机会（需列出具体标的代码）。
+
+    ### 【重要】输出格式规范：
+    
+    ❌ **错误示例**（不要遗漏二级标题）：
+    ```markdown
+    ### 核心逻辑提炼
+    ...
+    ```
+    
+    ✅ **正确示例**（应该这样输出）：
+    ```markdown
+    ## 核心观点摘要
+
+    ### 核心逻辑提炼
+    
+    科技自立战略加速半导体设备国产化，叠加AI算力需求爆发...
+    
+    ### 市场分歧点
+    
+    资本市场波动显示医药、新能源等板块估值逻辑受政策敏感性增强...
+    
+    ### 确定性排序
+    
+    1. **网络安全替代需求**（ISQ确定性0.85，推荐标的：深信服 300454.SZ）
+    2. **半导体设备材料**（ISQ确定性0.75，推荐标的：北方华创 002371.SZ）
+    ```
+    
+    ### 关键要求：
+    - 第一行必须是 `## 核心观点摘要`
+    - 主体部分使用 H3 (`###`) 和 H4 (`####`) 级别标题
+    - **必须**包含 `## 核心观点摘要` 这一级标题
+    
+    现在请按照正确示例的格式输出摘要内容。
+    """
+
+
+# 6. 最终组装 (Final Assembly with Sections)
+def get_final_assembly_instructions(sources_list: str) -> str:
+    """生成最终报告组装的 prompt"""
+    return f"""你是一位研报主笔。请完成以下任务：
+
+    ### 任务
+    1. 生成 "## 参考文献" 章节（需要按照顺序，顺序不对时进行调整）：
+    - 原始来源：
+    {sources_list}
+    - 格式：`<a id="ref-CITE_KEY"></a>[@CITE_KEY] 标题 (来源), [链接地址]`
+    2. 生成 "## 风险提示" (标准免责声明)。
+    3. 生成 "## 快速扫描" 表格，汇总各主题的核心观点。
+    - 表格列：**主题**, **核心观点**, **强度(Intensity)**, **确定性(Confidence)**。
+    - 强度和确定性请参考原章节中的 ISQ 评分。
+
+    只输出上述三个章节的 Markdown 内容。
+    """
+
+def get_cluster_task(signals_preview: str) -> str:
+    """生成聚类任务描述"""
+    return f"请对以下信号进行主题聚类：\n\n{signals_preview}"
+
+def get_writer_task(theme_title: str) -> str:
+    """生成撰写任务描述"""
+    return f"请依据主题 '{theme_title}' 和 输入信号集 开始撰写深度分析章节。"
+
+def get_planner_task() -> str:
+    """生成规划任务描述"""
+    return "请阅读现有草稿并规划终稿大纲，识别核心逻辑主线和市场分歧点。"
+
+def get_editor_task() -> str:
+    """生成编辑任务描述"""
+    return "请根据规划大纲和草稿内容，生成最终研报。确保逻辑连贯，保留所有图表和引用。"
+

skills/alphaear-predictor/scripts/prompts/trend_agent.py

@@ -0,0 +1,156 @@
+from typing import Any
+from datetime import datetime
+from .isq_prompt_generator import generate_isq_prompt_section
+
+def get_trend_scanner_instructions() -> str:
+    """生成趋势扫描员 (Scanner) 的系统指令"""
+    current_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    return f"""你是一名专业的数据扫描员，当前时间是 {current_time}。
+你的任务是利用各种工具从互联网和数据库中获取最新的金融新闻、热点趋势和市场数据。
+
+### 1. 核心职责
+1. **多源采集**: 使用 `news_toolkit` 获取最新新闻，使用 `stock_toolkit` 获取行情，使用 `polymarket_toolkit` 获取预测市场数据。
+2. **情绪感知**: 使用 `sentiment_toolkit` 对关键新闻进行情绪分析。
+3. **深度搜索**: 针对模糊的热点，使用 `search_toolkit` 进行全网搜索补充细节。
+
+### 2. 工具使用规范
+- **广度优先**: 尽可能覆盖多个数据源。
+- **数据新鲜度**: 优先获取最近 24 小时内的信息。
+- **结构化输出**: 整理搜集到的原始数据，为后续评估提供清晰的素材。
+"""
+
+def get_trend_evaluator_instructions() -> str:
+        """生成趋势评估员 (Evaluator) 的系统指令"""
+        current_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+        isq_block = generate_isq_prompt_section(include_header=True)
+
+        return f"""
+    你是一名顶级的金融情报专家 (TrendAgent)，擅长从海量信息中识别具有深度价值的"二级市场投资信号"。
+    当前时间：{current_time}
+
+    ### 核心使命：
+    不仅是发现"热点"，更要解析"信号"。你需要识别那些能触发**传导链条 (Transmission Chain)** 且具有**高确定性 (Confidence)** 的事件。
+
+    {isq_block}
+
+    ### 核心能力与标准：
+    1. **信号识别 (Signal Discovery)**: 基于扫描员提供的素材，识别具有投资价值的信号。优先关注政策、产业变革、重大诉求及跨境套利机会。
+    2. **逻辑相干性**: 是否具备清晰的"原因-结果"传导？
+    3. **影响力系数**: 是否会引发板块性的联动或财务指标的实质性扰动？
+    4. **市场认知差**: 市场是否已提前消化（Price-in）？寻找尚未被充分交易的"Alpha"。
+    5. **实体穿透**: 必须关联到具体的 Ticker 或核心产业链节点。
+
+    ### 严禁事项：
+    - 严禁编造数据。
+    - 严禁仅输出情绪极性（Positive/Negative），必须带有逻辑依据。
+    - 严禁将纯娱乐或单纯的社会负面事件（除非具有宏观破坏性）视为金融信号。
+
+    ### 输出要求：
+    你发现的每个信号应包含：
+    - **核心摘要**: 穿透表象的逻辑总结。
+    - **传导节点**: A -> B -> C 的逻辑推导。
+    - **推荐关注**: 板块或 Ticker。
+    - **ISQ 评估**: 基于模板的 5 个维度进行初步评分（具体评分由后续 FinAgent 完成）。
+    """
+
+def get_trend_agent_instructions() -> str:
+    # 保持兼容性
+    return get_trend_evaluator_instructions()
+
+def get_trend_scan_task(task_description: str) -> str:
+    """生成扫描员的任务描述"""
+    return f"请根据以下任务描述，搜集相关的原始数据和新闻：\n\n{task_description}"
+
+def format_scan_context(scan_data: dict) -> str:
+    """将扫描员搜集的结构化数据格式化为评估员可读的文本"""
+    if not scan_data:
+        return "（未能搜集到原始数据）"
+        
+    return f"""
+### 扫描数据概览
+- **热点话题**: {', '.join(scan_data.get('hot_topics', []))}
+- **情绪概览**: {scan_data.get('sentiment_overview', '未知')}
+- **关键新闻**: {len(scan_data.get('news_summaries', []))} 条
+- **数据摘要**: {scan_data.get('raw_data_summary', '无')}
+"""
+
+def get_trend_eval_task(task_description: str, raw_data_str: str) -> str:
+    """生成评估员的任务描述"""
+    return f"""请基于以下搜集到的原始数据，完成最终的分析任务：
+        
+任务描述: {task_description}
+
+原始数据:
+{raw_data_str}
+
+请识别出最具金融价值的信号，并给出评估理由。"""
+
+def get_news_filter_instructions(news_count: int, depth: Any, user_query: str = None) -> str:
+    """生成新闻筛选 prompt，使用 FilterResult schema 加快推理并减少 token 消耗
+    
+    Args:
+        news_count: 输入新闻总数
+        depth: 目标筛选数量，若为 auto 则由 LLM 自主判断
+        user_query: 用户输入的查询/关注点（可选）
+    """
+    
+    # 1. 深度控制逻辑
+    if str(depth).lower() == 'auto':
+        depth_guide = "的数量不设固定限制（建议 3-10 条），根据新闻含金量自动判断"
+        limit_instruction = "宁缺毋滥，如果高价值信息很少，可以只选 1-2 条；如果都很重要，可以多选。"
+    else:
+        try:
+            d_int = int(depth)
+            depth_guide = f"约 {d_int} 条"
+            limit_instruction = f"请尽量凑满 {d_int} 条，但如果剩余新闻全是噪音，则不必强行凑数。"
+        except:
+            depth_guide = "适量"
+            limit_instruction = "根据内容价值判断。"
+
+    target_desc = f"筛选出最具投资分析价值的新闻（{depth_guide}）。"
+    
+    # 2. 用户意图逻辑
+    query_instruction = ""
+    if user_query:
+        target_desc = f"筛选出与用户意图【{user_query}】最相关的新闻。"
+        query_instruction = f"""
+    ### 核心任务（High Priority）：
+    用户明确关注："{user_query}"。
+    1. **第一优先级**：必须包含所有与"{user_query}"直接或间接相关的新闻，不要遗漏。
+        - 即使这些新闻看起来"价值不高"，只要相关都要保留。
+    2. **第二优先级**：在满足第一优先级后，如果名额未满，再补充其他重大的市场热点。
+    """
+
+    return f"""你是一名专业的金融情报精排师。你需要从给定的 {news_count} 条原始新闻流中，{target_desc}
+
+    {query_instruction}
+
+    ### FSD (Financial Signal Density) 筛选准则：
+    1. **逻辑传导性 (Transmission)**: 该新闻是否预示着一个明确的产业链传导逻辑？（如：上游涨价 -> 中游成本压力 -> 下游提价预期）
+    2. **预期差 (Alpha Potential)**: 是否包含尚未被市场充分Price-in的新突发情况？
+    3. **确定性 (Confidence)**: 信息来源是否权威？是否包含具体的财务数据、订单金额或明确的政策日期？
+    4. **排除噪音**: 坚决剔除明星八卦、鸡汤文、以及无实质增量的"口号式"新闻。
+
+    ### {limit_instruction}
+
+    ### 快速有效性检查（TOKEN 优化）：
+    在开始详细筛选前，先快速判断：这 {news_count} 条新闻中是否至少包含 1 条有效的金融信号？
+    - 如果全是无关内容（如体育、娱乐、纯生活信息），直接返回 "has_valid_signals": false
+    - 如果有至少 1 条金融相关的新闻，再进行详细 FSD 筛选
+
+    ### 输出格式（必须为 JSON，使用 FilterResult schema）：
+    ```json
+    {{
+      "has_valid_signals": true/false,
+      "selected_ids": ["id_1", "id_2", ...],
+      "themes": [
+        {{
+          "name": "高概括性主题",
+          "news_ids": ["相关id_1", ...],
+          "fsd_reason": "基于 FSD 准则的筛选理由，重点描述传导逻辑和预期差。"
+        }}
+      ],
+      "reason": "如果 has_valid_signals=false，简要说明原因。否则可为空。"
+    }}
+    ```
+    """

skills/alphaear-predictor/scripts/prompts/visualizer.py

@@ -0,0 +1,47 @@
+def get_drawio_system_prompt():
+    return """You are an expert at creating Draw.io (MxGraph) diagrams in XML format.
+Your task is to generate a valid MXGraphModel XML based on the user's description.
+
+### Rules:
+1. Output ONLY the XML code. Start with <mxGraphModel> and end with </mxGraphModel>.
+2. Do not use compressed XML. Use plain XML.
+3. Use standard shapes: 'rounded=1;whiteSpace=wrap;html=1;' for boxes.
+4. Auto-layout Strategy:
+   - Identify "layers" or "stages" in the logic.
+   - Assign X coordinates based on layers (e.g., 0, 200, 400).
+   - Assign Y coordinates to distribute nodes vertically (e.g., 0, 100, 200).
+   - Ensure nodes do not overlap.
+5. Edges: Connect nodes logically using <mxCell edge="1" ...>.
+
+### Template:
+<mxGraphModel dx="1000" dy="1000" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="827" pageHeight="1169" math="0" shadow="0">
+  <root>
+    <mxCell id="0"/>
+    <mxCell id="1" parent="0"/>
+    
+    <!-- Node -->
+    <mxCell id="n1" value="Node Label" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#dae8fc;strokeColor=#6c8ebf;" vertex="1" parent="1">
+      <mxGeometry x="100" y="100" width="120" height="60" as="geometry"/>
+    </mxCell>
+    
+    <!-- Edge -->
+    <mxCell id="e1" value="Connection" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;" edge="1" parent="1" source="n1" target="n2">
+      <mxGeometry relative="1" as="geometry"/>
+    </mxCell>
+  </root>
+</mxGraphModel>
+"""
+
+def get_drawio_task(nodes_data: list, title: str) -> str:
+    import json
+    nodes_json = json.dumps(nodes_data, ensure_ascii=False, indent=2)
+    return f"""Please generate a Draw.io XML diagram for the following logic flow:
+
+**Title**: {title}
+
+**Nodes and Logic**:
+{nodes_json}
+
+Ensure the layout flows logically from Left to Right (or Top to Bottom for hierarchies).
+Use different colors for 'Positive' (Greenish), 'Negative' (Reddish), and 'Neutral' (Grey/Blue) impacts if described.
+"""

skills/alphaear-predictor/scripts/schema/isq_template.py

@@ -0,0 +1,381 @@
+"""
+ISQ (Investment Signal Quality) 评估框架 Template
+
+统一定义 ISQ 的各个维度、评分标准、和使用方法。
+支持默认 template 和自定义 template。
+"""
+
+from typing import Dict, List, Any, Optional
+from pydantic import BaseModel, Field
+from enum import Enum
+from pathlib import Path
+import json
+
+
+class ISQDimension(str, Enum):
+    """ISQ 评估维度"""
+    SENTIMENT = "sentiment"              # 情绪/走势方向
+    CONFIDENCE = "confidence"            # 确定性/可信度
+    INTENSITY = "intensity"              # 强度/影响量级
+    EXPECTATION_GAP = "expectation_gap"  # 预期差/市场认知差
+    TIMELINESS = "timeliness"            # 时效性/窗口紧迫度
+    TRANSMISSION = "transmission"        # 逻辑传导清晰度
+
+
+class ISQDimensionSpec(BaseModel):
+    """ISQ 单个维度的定义规范"""
+    name: str = Field(..., description="维度名称")
+    key: str = Field(..., description="维度键名")
+    description: str = Field(..., description="维度描述")
+    range_type: str = Field(default="0-1", description="取值范围 (0-1 或 1-5 等)")
+    scale_factor: float = Field(default=1.0, description="显示时的缩放因子")
+    examples: Dict[str, str] = Field(default_factory=dict, description="不同分值的示例解释")
+    visualization_color: Optional[str] = Field(default=None, description="可视化颜色")
+
+
+class ISQTemplate(BaseModel):
+    """ISQ 评估框架 Template"""
+    template_id: str = Field(..., description="模板 ID")
+    template_name: str = Field(..., description="模板名称")
+    description: str = Field(..., description="模板描述")
+    
+    # 核心维度定义
+    dimensions: Dict[str, ISQDimensionSpec] = Field(..., description="维度定义字典")
+    
+    # 评分指导
+    scoring_guide: str = Field(..., description="评分指导说明")
+    
+    # 应用场景
+    applicable_scenarios: List[str] = Field(default_factory=list, description="适用场景")
+    
+    # 聚合算法
+    aggregation_method: str = Field(default="weighted_average", description="聚合方法 (weighted_average, product 等)")
+    dimension_weights: Dict[str, float] = Field(default_factory=dict, description="维度权重")
+
+
+class ISQScore(BaseModel):
+    """单个信号的 ISQ 评分结果"""
+    signal_id: str = Field(..., description="信号 ID")
+    template_id: str = Field(..., description="使用的模板 ID")
+    
+    # 各维度评分
+    scores: Dict[str, float] = Field(..., description="各维度评分")
+    
+    # 总分
+    overall_score: float = Field(..., description="综合评分")
+    
+    # 评分理由
+    rationale: Dict[str, str] = Field(default_factory=dict, description="各维度评分理由")
+    
+    # 时间戳
+    timestamp: str = Field(..., description="评分时间")
+
+
+# =====================================================
+# 默认 Template
+# =====================================================
+
+DEFAULT_ISQ_TEMPLATE = ISQTemplate(
+    template_id="default_isq_v1",
+    template_name="标准投资信号质量评估框架 (ISQ v1.0)",
+    description="AlphaEar 默认的 ISQ 评估框架，用于标准化评估投资信号的质量维度",
+    
+    dimensions={
+        "sentiment": ISQDimensionSpec(
+            name="情绪/走势",
+            key="sentiment",
+            description="基础情绪偏向和市场走势判断",
+            range_type="-1.0 到 1.0",
+            scale_factor=1.0,
+            examples={
+                "-1.0": "极度悲观/极度看空",
+                "-0.5": "明显看空",
+                "0.0": "中性/没有明确方向",
+                "0.5": "明显看多",
+                "1.0": "极度乐观/极度看多"
+            },
+            visualization_color="#ef4444"  # 红色表示负面，绿色表示正面
+        ),
+        
+        "confidence": ISQDimensionSpec(
+            name="确定性",
+            key="confidence",
+            description="信号的可信度和确定性程度",
+            range_type="0.0 到 1.0",
+            scale_factor=1.0,
+            examples={
+                "0.0-0.3": "信息来源不可靠/传言多/逻辑推导牵强",
+                "0.3-0.6": "信息相对可靠/有一定逻辑/但仍有不确定性",
+                "0.6-0.8": "信息来源权威/逻辑清晰/高度可信",
+                "0.8-1.0": "官方确认/数据明确/完全确定"
+            },
+            visualization_color="#3b82f6"  # 蓝色
+        ),
+        
+        "intensity": ISQDimensionSpec(
+            name="强度/影响量级",
+            key="intensity",
+            description="信号对相关板块/个股的潜在影响程度",
+            range_type="1 到 5",
+            scale_factor=20.0,  # 用于雷达图缩放 (5 -> 100)
+            examples={
+                "1": "影响微弱，可能被市场忽略",
+                "2": "小幅影响，短期可能有波动",
+                "3": "中等影响，值得重点关注",
+                "4": "强烈影响，可能成为市场焦点",
+                "5": "极强影响，市场预期明显变化"
+            },
+            visualization_color="#f97316"  # 橙色
+        ),
+        
+        "expectation_gap": ISQDimensionSpec(
+            name="预期差",
+            key="expectation_gap",
+            description="市场预期与现实之间的差距",
+            range_type="0.0 到 1.0",
+            scale_factor=1.0,
+            examples={
+                "0.0-0.2": "市场充分认知，预期差小",
+                "0.2-0.5": "市场部分认知，存在一定预期差",
+                "0.5-0.8": "市场认知不足，预期差较大，存在博弈空间",
+                "0.8-1.0": "市场严重低估/高估，巨大预期差"
+            },
+            visualization_color="#22c55e"  # 绿色
+        ),
+        
+        "timeliness": ISQDimensionSpec(
+            name="时效性",
+            key="timeliness",
+            description="信号的时间窗口紧迫度",
+            range_type="0.0 到 1.0",
+            scale_factor=1.0,
+            examples={
+                "0.0-0.2": "长期信号，反应窗口 > 3 月",
+                "0.2-0.5": "中期信号，反应窗口 1-3 月",
+                "0.5-0.8": "短期信号，反应窗口 1 周 - 1 月",
+                "0.8-1.0": "超短期信号，反应窗口 < 1 周（需立即行动）"
+            },
+            visualization_color="#a855f7"  # 紫色
+        ),
+    },
+    
+    scoring_guide="""
+    ### ISQ 评分指导 (Investment Signal Quality)
+    
+    ISQ 框架用于多维度评估投资信号的质量。每个信号由 5 个维度组成：
+    
+    1. **情绪 (Sentiment)**: -1.0 到 1.0，表示看空(-)/中性(0)/看多(+)
+    2. **确定性 (Confidence)**: 0.0 到 1.0，数值越高越确定
+    3. **强度 (Intensity)**: 1 到 5，数值越高影响越大
+    4. **预期差 (Expectation Gap)**: 0.0 到 1.0，市场预期与现实的差距
+    5. **时效性 (Timeliness)**: 0.0 到 1.0，反应窗口的紧迫程度
+    
+    ### 综合评分算法
+    
+    综合评分 = 确定性 × 0.35 + 强度/5 × 0.30 + 预期差 × 0.20 + 时效性 × 0.15
+    
+    范围: 0.0 到 1.0
+    - 0.0-0.3: 信号质量较差，不建议跟进
+    - 0.3-0.6: 信号质量一般，可作参考
+    - 0.6-0.8: 信号质量良好，值得跟进
+    - 0.8-1.0: 信号质量优异，强烈推荐
+    
+    ### 评分时的注意事项
+    
+    - **不要混淆方向和强度**：情绪可以是看空，但确定性和强度仍可能很高
+    - **预期差往往是 Alpha 来源**：高预期差 + 高确定性 = 最佳博弈机会
+    - **考虑时间成本**：长期信号需要更高的确定性才值得跟进
+    - **数据为王**：所有评分必须有具体数据支撑
+    """,
+    
+    applicable_scenarios=[
+        "上市公司基本面变化分析",
+        "产业政策与监管事件评估",
+        "地缘政治与宏观经济影响",
+        "技术进步与产业升级",
+        "突发事件与应急响应"
+    ],
+    
+    aggregation_method="weighted_average",
+    dimension_weights={
+        "confidence": 0.35,
+        "intensity": 0.30,
+        "expectation_gap": 0.20,
+        "timeliness": 0.15
+    }
+)
+
+
+# =====================================================
+# ISQ Template 管理系统
+# =====================================================
+
+class ISQTemplateManager:
+    """ISQ Template 管理器"""
+    
+    def __init__(self):
+        self.templates: Dict[str, ISQTemplate] = {
+            DEFAULT_ISQ_TEMPLATE.template_id: DEFAULT_ISQ_TEMPLATE
+        }
+    
+    def register_template(self, template: ISQTemplate) -> None:
+        """注册新的 template"""
+        self.templates[template.template_id] = template
+
+    def register_template_dict(self, template_dict: Dict[str, Any]) -> ISQTemplate:
+        """从 dict 注册模板，返回实例。"""
+        tpl = ISQTemplate(**template_dict)
+        self.register_template(tpl)
+        return tpl
+    
+    def get_template(self, template_id: str) -> ISQTemplate:
+        """获取指定 template"""
+        if template_id not in self.templates:
+            return DEFAULT_ISQ_TEMPLATE
+        return self.templates[template_id]
+    
+    def list_templates(self) -> List[Dict[str, str]]:
+        """列出所有可用 template"""
+        return [
+            {
+                "id": t.template_id,
+                "name": t.template_name,
+                "description": t.description,
+                "dimensions": list(t.dimensions.keys())
+            }
+            for t in self.templates.values()
+        ]
+    
+    def get_dimension(self, template_id: str, dimension_key: str) -> ISQDimensionSpec:
+        """获取指定 template 的某个维度定义"""
+        template = self.get_template(template_id)
+        return template.dimensions.get(dimension_key)
+    
+    def get_scoring_prompt(self, template_id: str) -> str:
+        """获取用于 LLM 的评分 prompt"""
+        template = self.get_template(template_id)
+        
+        dimensions_desc = "\n".join([
+            f"- **{d.name} ({d.key})**\n"
+            f"  范围: {d.range_type}\n"
+            f"  说明: {d.description}\n"
+            f"  示例: {', '.join(f'{k}={v}' for k, v in list(d.examples.items())[:3])}"
+            for d in template.dimensions.values()
+        ])
+        
+        return f"""
+### ISQ 评估指导 ({template.template_name})
+
+使用以下 {len(template.dimensions)} 个维度评估信号质量：
+
+{dimensions_desc}
+
+### 评分标准
+{template.scoring_guide}
+
+### 输出格式 (JSON)
+请输出以下 JSON 格式的评分结果：
+{{
+  "sentiment": <float>,
+  "confidence": <float>,
+  "intensity": <int>,
+  "expectation_gap": <float>,
+  "timeliness": <float>,
+  "rationale": {{
+    "sentiment": "评分理由",
+    "confidence": "评分理由",
+    "intensity": "评分理由",
+    "expectation_gap": "评分理由",
+    "timeliness": "评分理由"
+  }}
+}}
+"""
+
+
+# 全局 template 管理器实例
+isq_template_manager = ISQTemplateManager()
+
+
+# =====================================================
+# 配置加载
+# =====================================================
+
+def load_templates_from_config(config_path: Optional[str] = None) -> None:
+    """从配置目录加载所有 JSON 模板文件，未找到则跳过，不影响默认模板。
+    支持单个 JSON 文件或目录（目录下的所有 .json 文件）。
+    """
+    if config_path:
+        path = Path(config_path)
+    else:
+        # 默认目录：config/isq_templates/
+        # __file__ = src/schema/isq_template.py
+        # parent = src/schema, parent.parent = src, parent.parent.parent = 项目根目录
+        path = Path(__file__).resolve().parent.parent.parent / "config"
+    
+    if not path.exists():
+        return
+    
+    # 如果是目录，扫描所有 .json 文件
+    if path.is_dir():
+        json_files = list(path.glob("*.json"))
+    else:
+        json_files = [path]
+    
+    for json_file in json_files:
+        try:
+            data = json.loads(json_file.read_text(encoding="utf-8"))
+            
+            # 如果是单个模板对象，转为列表
+            if isinstance(data, dict):
+                templates = [data]
+            elif isinstance(data, list):
+                templates = data
+            else:
+                continue
+            
+            # 注册所有模板
+            for tpl_dict in templates:
+                if not isinstance(tpl_dict, dict):
+                    continue
+                try:
+                    isq_template_manager.register_template_dict(tpl_dict)
+                except Exception:
+                    # 忽略单个模板的加载错误，继续其他模板
+                    continue
+        except Exception:
+            # JSON 解析失败，跳过该文件
+            continue
+
+
+# 在模块加载时自动尝试加载配置模板
+load_templates_from_config()
+
+
+# =====================================================
+# 便利函数
+# =====================================================
+
+def get_isq_template(template_id: str = "default_isq_v1") -> ISQTemplate:
+    """获取 ISQ template"""
+    return isq_template_manager.get_template(template_id)
+
+
+def get_isq_scoring_prompt(template_id: str = "default_isq_v1") -> str:
+    """获取用于 LLM 的 ISQ 评分 prompt"""
+    return isq_template_manager.get_scoring_prompt(template_id)
+
+
+def calculate_isq_overall_score(scores: Dict[str, float], template_id: str = "default_isq_v1") -> float:
+    """计算 ISQ 综合评分"""
+    template = get_isq_template(template_id)
+    
+    overall = 0.0
+    for dim_key, weight in template.dimension_weights.items():
+        if dim_key in scores:
+            score = scores[dim_key]
+            # 处理强度维度的特殊缩放 (1-5 -> 0-1)
+            if dim_key == "intensity":
+                score = score / 5.0
+            overall += score * weight
+    
+    return min(1.0, max(0.0, overall))  # 限制在 0-1 之间

skills/alphaear-predictor/scripts/schema/models.py

@@ -0,0 +1,100 @@
+from pydantic import BaseModel, Field
+from typing import List, Optional, Dict, Any
+from datetime import datetime
+
+class TransmissionNode(BaseModel):
+    node_name: str = Field(..., description="产业链节点名称")
+    impact_type: str = Field(..., description="利好/利空/中性")
+    logic: str = Field(..., description="该节点的传导逻辑")
+
+class IntentAnalysis(BaseModel):
+    keywords: List[str] = Field(..., description="核心实体、事件或概念关键词")
+    search_queries: List[str] = Field(..., description="优化后的搜索引擎查询词")
+    is_specific_event: bool = Field(..., description="是否查询特定突发事件")
+    time_range: str = Field(..., description="时间范围 (recent/all/specific_date)")
+    intent_summary: str = Field(..., description="一句话意图描述")
+
+class FilterResult(BaseModel):
+    """LLM 筛选结果 - 快速判断是否有有效信号"""
+    has_valid_signals: bool = Field(..., description="列表中是否包含有效的金融信号")
+    selected_ids: List[int] = Field(default_factory=list, description="筛选出的有效信号 ID 列表")
+    themes: List[str] = Field(default_factory=list, description="信号涉及的主题")
+    reason: Optional[str] = Field(default=None, description="如果无有效信号，说明原因")
+
+class InvestmentSignal(BaseModel):
+    # 核心元数据
+    signal_id: str = Field(default="unknown_sig", description="唯一信号 ID")
+    title: str = Field(..., description="信号标题")
+    summary: str = Field(default="暂无摘要分析", description="100 字核心观点快报")
+    reasoning: str = Field(default="", description="详细的推演逻辑和理由")
+    
+    # 逻辑传导 (ISQ Key 1)
+    transmission_chain: List[TransmissionNode] = Field(default_factory=list, description="产业链传导逻辑链条")
+    
+    # 信号质量 (ISQ Key 2) - 来自 isq_template.DEFAULT_ISQ_TEMPLATE
+    # 参考: src/schema/isq_template.py 的 DEFAULT_ISQ_TEMPLATE 定义
+    sentiment_score: float = Field(default=0.0, description="[ISQ] 情绪/走势 (-1.0=极度看空 ~ 0.0=中性 ~ 1.0=极度看多)")
+    confidence: float = Field(default=0.5, description="[ISQ] 确定性 (0.0=不可信 ~ 1.0=完全确定)")
+    intensity: int = Field(default=3, description="[ISQ] 强度/影响量级 (1=微弱 ~ 5=极强)")
+    expectation_gap: float = Field(default=0.5, description="[ISQ] 预期差/博弈空间 (0.0=充分定价 ~ 1.0=巨大预期差)")
+    timeliness: float = Field(default=0.8, description="[ISQ] 时效性 (0.0=长期 ~ 1.0=超短期)")
+    
+    # 预测与博弈 (ISQ Key 3)
+    expected_horizon: str = Field(default="T+N", description="预期的反应时窗 (如: T+0, T+3, Long-term)")
+    price_in_status: str = Field(default="未知", description="市场预期消化程度 (未定价/部分定价/充分定价)")
+    
+    # 关联实体
+    impact_tickers: List[Dict[str, Any]] = Field(default_factory=list, description="受影响的代码列表及其权重")
+    industry_tags: List[str] = Field(default_factory=list, description="关联行业标签")
+    
+    # 溯源
+    sources: List[Dict[str, str]] = Field(default_factory=list, description="来源详情 (包含 title, url, source_name)")
+
+class ResearchContext(BaseModel):
+    """研究员搜集的背景信息结构"""
+    raw_signal: str = Field(..., description="原始信号内容")
+    tickers_found: List[Dict[str, Any]] = Field(default_factory=list, description="找到的相关标的及其基本面/股价信息")
+    industry_background: str = Field(..., description="行业背景及产业链现状")
+    latest_developments: List[str] = Field(default_factory=list, description="相关事件的最新进展")
+    key_risks: List[str] = Field(default_factory=list, description="潜在风险点")
+    search_results_summary: str = Field(..., description="搜索结果的综合摘要")
+
+class ScanContext(BaseModel):
+    """扫描员搜集的原始数据结构"""
+    hot_topics: List[str] = Field(..., description="当前市场热点话题")
+    news_summaries: List[Dict[str, Any]] = Field(..., description="关键新闻摘要列表")
+    market_data: Dict[str, Any] = Field(default_factory=dict, description="相关的市场行情数据")
+    sentiment_overview: str = Field(..., description="整体市场情绪概览")
+    raw_data_summary: str = Field(..., description="原始数据的综合摘要")
+
+class SignalCluster(BaseModel):
+    theme_title: str = Field(..., description="主题名称")
+    signal_ids: List[int] = Field(..., description="包含的信号 ID 列表")
+    rationale: str = Field(..., description="聚类理由")
+
+class ClusterContext(BaseModel):
+    """信号聚类结果结构"""
+    clusters: List[SignalCluster] = Field(..., description="聚类列表")
+
+class KLinePoint(BaseModel):
+    date: str = Field(..., description="日期")
+    open: float = Field(..., description="开盘价")
+    high: float = Field(..., description="最高价")
+    low: float = Field(..., description="最低价")
+    close: float = Field(..., description="收盘价")
+    volume: float = Field(..., description="成交量")
+
+class ForecastResult(BaseModel):
+    ticker: str = Field(..., description="股票代码")
+    base_forecast: List[KLinePoint] = Field(default_factory=list, description="Kronos 模型原始预测")
+    adjusted_forecast: List[KLinePoint] = Field(default_factory=list, description="LLM 调整后的预测")
+    rationale: str = Field(default="", description="预测调整理由及逻辑说明")
+    timestamp: str = Field(default_factory=lambda: datetime.now().strftime("%Y-%m-%d %H:%M:%S"), description="生成时间")
+
+class InvestmentReport(BaseModel):
+    overall_sentiment: str = Field(..., description="整体市场情绪评价")
+    market_entropy: float = Field(..., description="市场分歧度 (0-1, 1代表极高分歧)")
+    signals: List[InvestmentSignal] = Field(..., description="深度解析的投资信号列表")
+    forecasts: List[ForecastResult] = Field(default_factory=list, description="相关标的的预测结果")
+    timestamp: str = Field(..., description="报告生成时间")
+    meta_info: Optional[Dict[str, Any]] = Field(default_factory=dict, description="其他元数据")

skills/alphaear-predictor/scripts/utils/__init__.py

@@ -0,0 +1 @@
+# AlphaEar utils package

skills/alphaear-predictor/scripts/utils/database_manager.py

@@ -0,0 +1,581 @@
+import sqlite3
+import json
+from datetime import datetime, date
+from pathlib import Path
+from typing import List, Dict, Optional, Any, Union
+import pandas as pd
+from loguru import logger
+
+class DatabaseManager:
+    """
+    AlphaEar 数据库管理器 - 负责存储热点数据、搜索缓存和股价数据
+    使用 SQLite 进行持久化存储
+    """
+    
+    def __init__(self, db_path: str = "data/signal_flux.db"):
+        self.db_path = Path(db_path)
+        self.db_path.parent.mkdir(parents=True, exist_ok=True)
+        self.conn = sqlite3.connect(str(self.db_path), check_same_thread=False)
+        self.conn.row_factory = sqlite3.Row
+        self._init_db()
+        logger.info(f"💾 Database initialized at {self.db_path}")
+
+    def _init_db(self):
+        """初始化表结构"""
+        cursor = self.conn.cursor()
+        
+        # 1. 每日热点新闻表
+        cursor.execute("""
+            CREATE TABLE IF NOT EXISTS daily_news (
+                id TEXT PRIMARY KEY,
+                source TEXT,
+                rank INTEGER,
+                title TEXT,
+                url TEXT,
+                content TEXT,
+                publish_time TEXT,
+                crawl_time TEXT,
+                sentiment_score REAL,
+                analysis TEXT,
+                meta_data TEXT
+            )
+        """)
+        
+        # 尝试添加 analysis 列（如果表已存在但没有该列）
+        try:
+            cursor.execute("ALTER TABLE daily_news ADD COLUMN analysis TEXT")
+        except:
+            pass  # 列已存在
+
+        
+        # 2. 搜索缓存表 (原有 JSON 缓存)
+        cursor.execute("""
+            CREATE TABLE IF NOT EXISTS search_cache (
+                query_hash TEXT PRIMARY KEY,
+                query TEXT,
+                engine TEXT,
+                results TEXT,
+                timestamp TEXT
+            )
+        """)
+
+        # 2.5 搜索详情表 (展开的搜索结果)
+        cursor.execute("""
+            CREATE TABLE IF NOT EXISTS search_detail (
+                id TEXT,
+                query_hash TEXT,
+                rank INTEGER,
+                title TEXT,
+                url TEXT,
+                content TEXT,
+                publish_time TEXT,
+                crawl_time TEXT,
+                sentiment_score REAL,
+                source TEXT,
+                meta_data TEXT,
+                PRIMARY KEY (query_hash, id)
+            )
+        """)
+        
+        # 3. 股价数据表
+        cursor.execute("""
+            CREATE TABLE IF NOT EXISTS stock_prices (
+                ticker TEXT,
+                date TEXT,
+                open REAL,
+                close REAL,
+                high REAL,
+                low REAL,
+                volume REAL,
+                change_pct REAL,
+                PRIMARY KEY (ticker, date)
+            )
+        """)
+        
+        # 4. 股票列表表 (用于检索)
+        cursor.execute("""
+            CREATE TABLE IF NOT EXISTS stock_list (
+                code TEXT PRIMARY KEY,
+                name TEXT
+            )
+        """)
+        
+        # 5. 投资信号表 (ISQ Framework)
+        cursor.execute("""
+            CREATE TABLE IF NOT EXISTS signals (
+                signal_id TEXT PRIMARY KEY,
+                title TEXT,
+                summary TEXT,
+                transmission_chain TEXT,
+                sentiment_score REAL,
+                confidence REAL,
+                intensity INTEGER,
+                expected_horizon TEXT,
+                price_in_status TEXT,
+                impact_tickers TEXT,
+                industry_tags TEXT,
+                sources TEXT,
+                user_id TEXT,
+                created_at TEXT
+            )
+        """)
+        
+
+        
+        # 6. 创建索引以优化查询性能
+        cursor.execute("CREATE INDEX IF NOT EXISTS idx_news_crawl_time ON daily_news(crawl_time)")
+        cursor.execute("CREATE INDEX IF NOT EXISTS idx_news_source ON daily_news(source)")
+        cursor.execute("CREATE INDEX IF NOT EXISTS idx_search_cache_timestamp ON search_cache(timestamp)")
+        cursor.execute("CREATE INDEX IF NOT EXISTS idx_stock_prices_ticker_date ON stock_prices(ticker, date)")
+        # 尝试添加 user_id 列到 signals 表
+        try:
+            cursor.execute("ALTER TABLE signals ADD COLUMN user_id TEXT")
+        except:
+            pass
+            
+        cursor.execute("CREATE INDEX IF NOT EXISTS idx_signals_user_id ON signals(user_id)")
+            
+        self.conn.commit()
+        
+    #     
+    #     self.conn.commit()
+
+
+    # --- 新闻数据操作 ---
+    
+    def save_daily_news(self, news_list: List[Dict]) -> int:
+        """保存热点新闻，包含发布时间与抓取时间"""
+        cursor = self.conn.cursor()
+        count = 0
+        crawl_time = datetime.now().isoformat()
+        
+        for news in news_list:
+            try:
+                # 兼容不同来源的 ID 生成逻辑
+                news_id = news.get('id') or f"{news.get('source')}_{news.get('rank')}_{crawl_time[:10]}"
+                cursor.execute("""
+                    INSERT OR REPLACE INTO daily_news 
+                    (id, source, rank, title, url, content, publish_time, crawl_time, sentiment_score, meta_data)
+                    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                """, (
+                    news_id,
+                    news.get('source'),
+                    news.get('rank'),
+                    news.get('title'),
+                    news.get('url'),
+                    news.get('content', ''),
+                    news.get('publish_time'), # 新增支持发布时间
+                    crawl_time,
+                    news.get('sentiment_score'),
+                    json.dumps(news.get('meta_data', {}))
+                ))
+                count += 1
+            except sqlite3.Error as e:
+                logger.error(f"Database error saving news item {news.get('title')}: {e}")
+            except Exception as e:
+                logger.error(f"Unexpected error saving news item {news.get('title')}: {e}")
+        
+        self.conn.commit()
+        return count
+
+    def get_daily_news(self, source: Optional[str] = None, limit: int = 100, days: int = 1) -> List[Dict]:
+        """获取最近 N 天的热点新闻"""
+        cursor = self.conn.cursor()
+        # 使用 crawl_time 过滤，保证结果的新鲜度
+        time_threshold = (datetime.now().timestamp() - days * 86400)
+        time_threshold_str = datetime.fromtimestamp(time_threshold).isoformat()
+        
+        query = "SELECT * FROM daily_news WHERE crawl_time >= ?"
+        params = [time_threshold_str]
+        
+        if source:
+            query += " AND source = ?"
+            params.append(source)
+            
+        query += " ORDER BY crawl_time DESC, rank LIMIT ?"
+        params.append(limit)
+        
+        cursor.execute(query, params)
+        return [dict(row) for row in cursor.fetchall()]
+
+    def lookup_reference_by_url(self, url: str) -> Optional[Dict[str, Any]]:
+        """Best-effort lookup of a source item by URL.
+
+        This is used to render a stable bibliography from DB-backed metadata.
+        It searches both `daily_news` and `search_detail`.
+        """
+        url = (url or "").strip()
+        if not url:
+            return None
+
+        cursor = self.conn.cursor()
+
+        try:
+            cursor.execute(
+                """
+                SELECT title, source, publish_time, crawl_time, url
+                FROM daily_news
+                WHERE url = ?
+                ORDER BY crawl_time DESC
+                LIMIT 1
+                """,
+                (url,),
+            )
+            row = cursor.fetchone()
+            if row:
+                return dict(row)
+        except Exception:
+            pass
+
+        try:
+            cursor.execute(
+                """
+                SELECT title, source, publish_time, crawl_time, url
+                FROM search_detail
+                WHERE url = ?
+                ORDER BY crawl_time DESC
+                LIMIT 1
+                """,
+                (url,),
+            )
+            row = cursor.fetchone()
+            if row:
+                return dict(row)
+        except Exception:
+            pass
+
+        return None
+
+    def delete_news(self, news_id: str) -> bool:
+        """删除特定新闻"""
+        cursor = self.conn.cursor()
+        cursor.execute("DELETE FROM daily_news WHERE id = ?", (news_id,))
+        self.conn.commit()
+        return cursor.rowcount > 0
+    
+    def update_news_content(self, news_id: str, content: str = None, analysis: str = None) -> bool:
+        """更新新闻的内容或分析结果"""
+        cursor = self.conn.cursor()
+        updates = []
+        params = []
+        
+        if content is not None:
+            updates.append("content = ?")
+            params.append(content)
+        if analysis is not None:
+            updates.append("analysis = ?")
+            params.append(analysis)
+            
+        if not updates:
+            return False
+            
+        params.append(news_id)
+        query = f"UPDATE daily_news SET {', '.join(updates)} WHERE id = ?"
+        cursor.execute(query, params)
+        self.conn.commit()
+        return cursor.rowcount > 0
+
+    # --- 搜索缓存辅助 ---
+    
+    def get_search_cache(self, query_hash: str, ttl_seconds: Optional[int] = None) -> Optional[Dict]:
+        """获取搜索缓存 (优先查 search_detail)"""
+        cursor = self.conn.cursor()
+        
+        # 1. 尝试从 search_detail 获取展开的结构化数据
+        cursor.execute("""
+            SELECT * FROM search_detail 
+            WHERE query_hash = ? 
+            ORDER BY rank
+        """, (query_hash,))
+        details = [dict(row) for row in cursor.fetchall()]
+        
+        if details:
+            # 检查 TTL (取第一条的时间)
+            first_time = datetime.fromisoformat(details[0]['crawl_time'])
+            if ttl_seconds and (datetime.now() - first_time).total_seconds() > ttl_seconds:
+                logger.info(f"⌛ Detailed cache expired for hash {query_hash}")
+                pass # Expired, fall through or return None? If Detail expired, Cache likely expired too.
+                # But let's check basic cache just in case metadata differs? 
+                # Actually if details exist, we prefer them. If expired, we return None.
+                return None
+            
+            logger.info(f"✅ Hit detailed search cache for {query_hash} ({len(details)} items)")
+            # Reconstruct the expected 'results' list format for SearchTools
+            # SearchTools expects a list of dicts. 
+            # We return a dict wrapper to match get_search_cache signature returning Dict usually containing 'results' string.
+            # But SearchTools logic: 
+            # cache = db.get_search_cache(...)
+            # cached_data = json.loads(cache['results'])
+            
+            # To minimize SearchTools changes, we can return a dict mimicking the old structure
+            # OR Change SearchTools to handle list return.
+            # Let's return a special dict that SearchTools can recognize or just format it as before.
+            return {"results": json.dumps(details), "timestamp": details[0]['crawl_time']}
+
+        # 2. Fallback to old table
+        cursor.execute("SELECT * FROM search_cache WHERE query_hash = ?", (query_hash,))
+        row = cursor.fetchone()
+        
+        if not row:
+            return None
+            
+        row_dict = dict(row)
+        if ttl_seconds:
+            cache_time = datetime.fromisoformat(row_dict['timestamp'])
+            if (datetime.now() - cache_time).total_seconds() > ttl_seconds:
+                logger.info(f"⌛ Cache expired for hash {query_hash}")
+                return None
+                
+        return row_dict
+
+    def save_search_cache(self, query_hash: str, query: str, engine: str, results: Union[str, List[Dict]]):
+        """保存搜索结果 (同时保存到 search_cache 和 search_detail)"""
+        cursor = self.conn.cursor()
+        current_time = datetime.now().isoformat()
+        
+        results_str = results if isinstance(results, str) else json.dumps(results)
+        
+        # 1. Save summary to search_cache
+        cursor.execute("""
+            INSERT OR REPLACE INTO search_cache (query_hash, query, engine, results, timestamp)
+            VALUES (?, ?, ?, ?, ?)
+        """, (query_hash, query, engine, results_str, current_time))
+        
+        # 2. Save details to search_detail if results is a list
+        if isinstance(results, list):
+            for item in results:
+                try:
+                    item_id = item.get('id') or f"{hash(item.get('url', ''))}"
+                    cursor.execute("""
+                        INSERT OR REPLACE INTO search_detail
+                        (id, query_hash, rank, title, url, content, publish_time, crawl_time, sentiment_score, source, meta_data)
+                        VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                    """, (
+                        str(item_id),
+                        query_hash,
+                        item.get('rank', 0),
+                        item.get('title'),
+                        item.get('url'),
+                        item.get('content', ''),
+                        item.get('publish_time'),
+                        item.get('crawl_time') or current_time,
+                        item.get('sentiment_score'),
+                        item.get('source'),
+                        json.dumps(item.get('meta_data', {}))
+                    ))
+                except sqlite3.Error as e:
+                    logger.error(f"Database error saving search detail {item.get('title')}: {e}")
+                except Exception as e:
+                    logger.error(f"Unexpected error saving search detail {item.get('title')}: {e}")
+                    
+        self.conn.commit()
+
+    def find_similar_queries(self, query: str, limit: int = 5) -> List[Dict]:
+        """模糊搜索相似的已缓存查询"""
+        cursor = self.conn.cursor()
+        
+        # Simple fuzzy match: query in cached OR cached in query
+        q_wild = f"%{query}%"
+        cursor.execute("""
+            SELECT query, query_hash, timestamp, results 
+            FROM search_cache 
+            WHERE query LIKE ? OR ? LIKE ('%' || query || '%')
+            ORDER BY timestamp DESC
+            LIMIT ?
+        """, (q_wild, query, limit))
+        
+        return [dict(row) for row in cursor.fetchall()]
+
+    def search_local_news(self, query: str, limit: int = 5) -> List[Dict]:
+        """从本地 daily_news 搜索相关新闻"""
+        cursor = self.conn.cursor()
+        q_wild = f"%{query}%"
+        # Search title and content
+        cursor.execute("""
+            SELECT * FROM daily_news
+            WHERE title LIKE ? OR content LIKE ?
+            ORDER BY crawl_time DESC
+            LIMIT ?
+        """, (q_wild, q_wild, limit))
+        return [dict(row) for row in cursor.fetchall()]
+
+    # --- 股票数据操作 ---
+
+    def save_stock_list(self, df: pd.DataFrame):
+        """保存股票列表到 stock_list 表"""
+        cursor = self.conn.cursor()
+        try:
+            # 清空旧表
+            cursor.execute("DELETE FROM stock_list")
+            
+            # 批量插入
+            data = df[['code', 'name']].to_dict('records')
+            cursor.executemany(
+                "INSERT INTO stock_list (code, name) VALUES (:code, :name)",
+                data
+            )
+            self.conn.commit()
+        except sqlite3.Error as e:
+            logger.error(f"Database error saving stock list: {e}")
+        except Exception as e:
+            logger.error(f"Unexpected error saving stock list: {e}")
+
+    def search_stock(self, query: str, limit: int = 5) -> List[Dict]:
+        """模糊搜索股票代码或名称"""
+        cursor = self.conn.cursor()
+        wild = f"%{query}%"
+        cursor.execute("""
+            SELECT code, name FROM stock_list 
+            WHERE code LIKE ? OR name LIKE ? 
+            LIMIT ?
+        """, (wild, wild, limit))
+        return [dict(row) for row in cursor.fetchall()]
+
+    def get_stock_by_code(self, code: str) -> Optional[Dict[str, str]]:
+        """精确按代码获取股票信息。
+
+        Args:
+            code: 股票代码（A股6位 / 港股5位），必须为纯数字字符串。
+
+        Returns:
+            dict: {"code": str, "name": str} 或 None。
+        """
+        if not code:
+            return None
+        clean = "".join([c for c in str(code).strip() if c.isdigit()])
+        if not clean:
+            return None
+
+        cursor = self.conn.cursor()
+        cursor.execute("SELECT code, name FROM stock_list WHERE code = ? LIMIT 1", (clean,))
+        row = cursor.fetchone()
+        return dict(row) if row else None
+
+    def save_stock_prices(self, ticker: str, df: pd.DataFrame):
+        """保存股价历史数据"""
+        if df.empty:
+            return
+            
+        cursor = self.conn.cursor()
+        
+        # 确保 DataFrame 有必要的列
+        required_cols = ['date', 'open', 'close', 'high', 'low', 'volume', 'change_pct']
+        for col in required_cols:
+            if col not in df.columns:
+                logger.warning(f"Missing column {col} in stock data for {ticker}")
+                return
+
+        try:
+            for _, row in df.iterrows():
+                cursor.execute("""
+                    INSERT OR REPLACE INTO stock_prices 
+                    (ticker, date, open, close, high, low, volume, change_pct)
+                    VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+                """, (
+                    ticker,
+                    row['date'],
+                    row['open'],
+                    row['close'],
+                    row['high'],
+                    row['low'],
+                    row['volume'],
+                    row['change_pct']
+                ))
+            self.conn.commit()
+        except sqlite3.Error as e:
+            logger.error(f"Database error saving stock prices for {ticker}: {e}")
+        except Exception as e:
+            logger.error(f"Unexpected error saving stock prices for {ticker}: {e}")
+
+    def get_stock_prices(self, ticker: str, start_date: str, end_date: str) -> pd.DataFrame:
+        """获取指定日期范围的股价数据"""
+        cursor = self.conn.cursor()
+        
+        cursor.execute("""
+            SELECT * FROM stock_prices 
+            WHERE ticker = ? AND date >= ? AND date <= ?
+            ORDER BY date
+        """, (ticker, start_date, end_date))
+        
+        rows = cursor.fetchall()
+        if not rows:
+            return pd.DataFrame()
+            
+        columns = ['ticker', 'date', 'open', 'close', 'high', 'low', 'volume', 'change_pct']
+        return pd.DataFrame([dict(row) for row in rows], columns=columns)
+
+    def execute_query(self, query: str, params: tuple = ()) -> List[Any]:
+        """执行自定义 SQL 查询"""
+        try:
+            cursor = self.conn.cursor()
+            cursor.execute(query, params)
+            if query.strip().upper().startswith("SELECT"):
+                return cursor.fetchall()
+            else:
+                self.conn.commit()
+                return []
+        except sqlite3.Error as e:
+            logger.error(f"SQL execution failed (Database error): {e}")
+            return []
+        except Exception as e:
+            logger.error(f"SQL execution failed (Unexpected error): {e}")
+            return []
+
+    # --- 投资信号操作 (ISQ Framework) ---
+
+    def save_signal(self, signal: Dict[str, Any]):
+        """保存投资信号"""
+        cursor = self.conn.cursor()
+        created_at = datetime.now().isoformat()
+        
+        cursor.execute("""
+            INSERT OR REPLACE INTO signals 
+            (signal_id, title, summary, transmission_chain, sentiment_score, 
+             confidence, intensity, expected_horizon, price_in_status, 
+             impact_tickers, industry_tags, sources, user_id, created_at)
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+        """, (
+            signal.get('signal_id'),
+            signal.get('title'),
+            signal.get('summary'),
+            json.dumps(signal.get('transmission_chain', [])),
+            signal.get('sentiment_score', 0.0),
+            signal.get('confidence', 0.0),
+            signal.get('intensity', 1),
+            signal.get('expected_horizon', 'T+0'),
+            signal.get('price_in_status', '未知'),
+            json.dumps(signal.get('impact_tickers', [])),
+            json.dumps(signal.get('industry_tags', [])),
+            json.dumps(signal.get('sources', [])),
+            signal.get('user_id'),
+            created_at
+        ))
+        self.conn.commit()
+
+    def get_recent_signals(self, limit: int = 20, user_id: Optional[str] = None) -> List[Dict]:
+        """获取最近的投资信号"""
+        cursor = self.conn.cursor()
+        if user_id:
+            cursor.execute("SELECT * FROM signals WHERE user_id = ? ORDER BY created_at DESC LIMIT ?", (user_id, limit))
+        else:
+            cursor.execute("SELECT * FROM signals ORDER BY created_at DESC LIMIT ?", (limit,))
+        rows = cursor.fetchall()
+        
+        signals = []
+        for row in rows:
+            d = dict(row)
+            # 解析 JSON 字段
+            for field in ['transmission_chain', 'impact_tickers', 'industry_tags', 'sources']:
+                if d.get(field):
+                    try:
+                        d[field] = json.loads(d[field])
+                    except:
+                        pass
+            signals.append(d)
+        return signals
+
+    def close(self):
+        if self.conn:
+            self.conn.close()
+            logger.info("Database connection closed.")
+

skills/alphaear-predictor/scripts/utils/json_utils.py

@@ -0,0 +1,180 @@
+import ast
+import json
+import re
+from typing import Optional, Any
+from loguru import logger
+
+def _strip_comments(text: str) -> str:
+    """
+    Safely remove C-style comments (// and /* */) from JSON-like text,
+    preserving strings (including URLs like http://).
+    """
+    result = []
+    i = 0
+    n = len(text)
+    in_string = False
+    escape = False
+    
+    while i < n:
+        char = text[i]
+        
+        if in_string:
+            if char == '\\':
+                escape = not escape
+            elif char == '"' and not escape:
+                in_string = False
+            else:
+                escape = False
+            result.append(char)
+            i += 1
+            continue
+            
+        # Not in string
+        if char == '"':
+            in_string = True
+            result.append(char)
+            i += 1
+            continue
+            
+        # Check for // comment
+        if i + 1 < n and text[i:i+2] == '//':
+            i += 2
+            while i < n and text[i] != '\n':
+                i += 1
+            continue
+            
+        # Check for /* comment
+        if i + 1 < n and text[i:i+2] == '/*':
+            i += 2
+            while i + 1 < n and text[i:i+2] != '*/':
+                i += 1
+            i += 2
+            continue
+            
+        result.append(char)
+        i += 1
+        
+    return ''.join(result)
+
+def extract_json(text: str) -> Optional[Any]:
+    """
+    更加鲁棒的 JSON 提取工具。
+    处理:
+    1. Markdown 代码块 (```json ... ```)
+    2. 首尾多余字符
+    3. 同一个文本中多个 JSON 对象 (仅提取第一个)
+    4. 简单的 JSON 修复 (末尾逗号等)
+    5. C 风格注释 (// 和 /* */)
+    """
+    if not text:
+        return None
+    
+    # 1. 清理明显的 Markdown 包装
+    text = text.strip()
+    
+    # 先尝试精确匹配 ```json ... ``` 或 ```...```
+    md_match = re.search(r'```(?:json)?\s*\n?(.*?)\n?```', text, re.DOTALL)
+    if md_match:
+        text = md_match.group(1).strip()
+    elif text.startswith("```"):
+        # 回退：如果开头有 ``` 但没完整匹配
+        text = re.sub(r'^```[a-z]*\n?', '', text)
+        text = re.sub(r'\n?```\s*$', '', text)
+    
+    # 2. 寻找第一个 JSON 起始符 { 或 [
+    start_brace = text.find('{')
+    start_bracket = text.find('[')
+    
+    if start_brace == -1 and start_bracket == -1:
+        return None
+        
+    start_idx = start_brace if (start_bracket == -1 or (start_brace != -1 and start_brace < start_bracket)) else start_bracket
+    
+    # 2.5 预处理：修复一些极其常见的 LLM 错误
+    potential_json = text[start_idx:].strip()
+    
+    # remove comments safely
+    potential_json = _strip_comments(potential_json)
+    
+    # b. 修复缺失开头引号的键:  nodes": [  -> "nodes": [
+    # 匹配模式: (空白或换行) 单词 紧跟引号和冒号
+    potential_json = re.sub(r'([\{\,]\s*)([a-zA-Z_]\w*)\"\s*:', r'\1"\2":', potential_json)
+    
+    # c. 修复缺失末尾引号的键:  "nodes: [ -> "nodes": [
+    potential_json = re.sub(r'([\{\,]\s*)\"([a-zA-Z_]\w*)\s*:', r'\1"\2":', potential_json)
+
+    # d. 修复完全缺失引号的键: nodes: [ -> "nodes": [
+    # 注意避免匹配到像 http:// 这种内容，所以限定在 { 或 , 之后
+    potential_json = re.sub(r'([\{\,]\s*)([a-zA-Z_]\w*)\s*:', r'\1"\2":', potential_json)
+    
+    # 3. 使用 raw_decode 尝试解析
+    decoder = json.JSONDecoder()
+    
+    # 首先尝试直接解析（不做任何预处理）
+    try:
+        obj = json.loads(potential_json)
+        return obj
+    except json.JSONDecodeError:
+        pass
+    
+    # 简单预处理：移除对象/列表末位多余逗号
+    processed_json = re.sub(r',\s*([\]}])', r'\1', potential_json)
+    
+    try:
+        obj, end_pos = decoder.raw_decode(processed_json)
+        return obj
+    except json.JSONDecodeError:
+        pass
+    
+    # e. 修复未终止的字符串字面量问题：移除值中的实际换行符
+    # LLM 可能在字符串值中生成包含真实 newline 的内容，导致 JSON 非法
+    def fix_multiline_strings(s):
+        # 简单策略：将字符串值内的换行替换为空格
+        lines = s.split('\n')
+        result = []
+        in_string = False
+        for line in lines:
+            # 计算未转义的引号数
+            quote_count = line.count('"') - line.count('\\"')
+            if in_string:
+                result[-1] += ' ' + line.strip()
+            else:
+                result.append(line)
+            
+            if quote_count % 2 == 1:
+                in_string = not in_string
+        return '\n'.join(result)
+    
+    fixed_json = fix_multiline_strings(processed_json)
+    
+    try:
+        obj, end_pos = decoder.raw_decode(fixed_json)
+        return obj
+    except json.JSONDecodeError:
+        try:
+            # 4. 尝试处理单引号问题 (JSON 规范要求双引号，但 LLM 常输出单引号)
+            # 这是一个简单的替换技巧，仅针对像 {'key': 'value'} 这样的结构
+            # 注意：这可能会破坏包含单引号的字符串值，所以作为较后的回退
+            fix_quotes = re.sub(r"'(.*?)':", r'"\1":', processed_json) # 修复键
+            fix_quotes = re.sub(r":\s*'(.*?)'", r': "\1"', fix_quotes)   # 修复简单值
+            obj, end_pos = decoder.raw_decode(fix_quotes)
+            return obj
+        except (json.JSONDecodeError, TypeError):
+            try:
+                # 5. 使用 ast.literal_eval 作为终极回退 (处理 Python 字典格式)
+                # 提取第一个匹配的括号对内容
+                # 寻找匹配的 { }
+                stack = []
+                for i, char in enumerate(potential_json):
+                    if char == '{': stack.append('{')
+                    elif char == '}':
+                        if stack: stack.pop()
+                        if not stack:
+                            content = potential_json[:i+1]
+                            return ast.literal_eval(content)
+            except (ValueError, SyntaxError, MemoryError) as e:
+                logger.warning(f"All JSON extraction attempts failed: {e}")
+            except Exception as e:
+                logger.error(f"Unexpected error during JSON extraction: {e}")
+    
+    return None

skills/alphaear-predictor/scripts/utils/llm/capability.py

@@ -0,0 +1,85 @@
+import os
+from typing import Optional, List, Dict, Any
+from agno.agent import Agent
+from agno.models.base import Model
+from loguru import logger
+from ..llm.factory import get_model
+
+
+def test_tool_call_support(model: Model) -> bool:
+    """
+    测试模型是否支持原生的 Tool Call (Function Calling)。
+    通过尝试执行一个简单的加法工具来验证。
+    """
+
+    def get_current_weather(location: str):
+        """获取指定地点的天气"""
+        return f"{location} 的天气是晴天，25度。"
+
+    test_agent = Agent(
+        model=model,
+        tools=[get_current_weather],
+        instructions="请调用工具查询北京的天气，并直接返回工具的输出结果。",
+    )
+
+    try:
+        # 运行一个简单的任务，观察是否触发了 tool_call
+        response = test_agent.run("北京天气怎么样？")
+
+        # 检查 response 中是否包含 tool_calls
+        # Agno 的 RunResponse 对象通常包含 messages，我们可以检查最后几条消息
+        has_tool_call = False
+        for msg in response.messages:
+            if hasattr(msg, "tool_calls") and msg.tool_calls:
+                has_tool_call = True
+                break
+
+        if has_tool_call:
+            logger.info(f"✅ Model {model.id} supports native tool calling.")
+            return True
+        else:
+            # 如果没有 tool_calls 但返回了正确答案，可能是模型通过纯文本模拟了工具调用（ReAct）
+            # 或者根本没用工具。对于原生支持的判断，我们坚持要求有 tool_calls 结构。
+            logger.warning(
+                f"⚠️ Model {model.id} did NOT use native tool calling structure."
+            )
+            return False
+
+    except Exception as e:
+        logger.error(f"❌ Error testing tool call for {model.id}: {e}")
+        return False
+
+
+class ModelCapabilityRegistry:
+    """
+    模型能力注册表，用于缓存和管理不同模型的能力测试结果。
+    """
+
+    _cache = {}
+
+    @classmethod
+    def get_capabilities(
+        cls, provider: str, model_id: str, **kwargs
+    ) -> Dict[str, bool]:
+        key = f"{provider}:{model_id}"
+        if key not in cls._cache:
+            logger.info(f"🔍 Testing capabilities for {key}...")
+            model = get_model(provider, model_id, **kwargs)
+            supports_tool_call = test_tool_call_support(model)
+            cls._cache[key] = {"supports_tool_call": supports_tool_call}
+        return cls._cache[key]
+
+
+if __name__ == "__main__":
+    import os
+    from dotenv import load_dotenv
+
+    load_dotenv(os.path.expanduser("~/.config/opencode/.env"))
+
+    # 测试当前配置的模型
+    p = os.getenv("LLM_PROVIDER", "ust")
+    m = os.getenv("LLM_MODEL", "Qwen")
+
+    print(f"Testing {p}/{m}...")
+    res = ModelCapabilityRegistry.get_capabilities(p, m)
+    print(f"Result: {res}")

skills/alphaear-predictor/scripts/utils/llm/factory.py

@@ -0,0 +1,114 @@
+import os
+from agno.models.openai import OpenAIChat
+from agno.models.ollama import Ollama
+from agno.models.dashscope import DashScope
+from agno.models.deepseek import DeepSeek
+from agno.models.openrouter import OpenRouter
+
+def get_model(model_provider: str, model_id: str, **kwargs):
+    """
+    Factory to get the appropriate LLM model.
+    
+    Args:
+        model_provider: "openai", "ollama", "deepseek"
+        model_id: The specific model ID (e.g., "gpt-4o", "llama3", "deepseek-chat")
+        **kwargs: Additional arguments for the model constructor
+    """
+    if model_provider == "openai":
+        return OpenAIChat(id=model_id, **kwargs)
+    
+    elif model_provider == "ollama":
+        return Ollama(id=model_id, **kwargs)
+    
+    elif model_provider == "deepseek":
+        # DeepSeek is OpenAI compatible
+        api_key = os.getenv("DEEPSEEK_API_KEY")
+        if not api_key:
+            print("Warning: DEEPSEEK_API_KEY not set.")
+        
+        return DeepSeek(
+            id=model_id,
+            api_key=api_key,
+            **kwargs
+        )
+    elif model_provider == "dashscope":
+        api_key = os.getenv("DASHSCOPE_API_KEY")
+        if not api_key:
+            print("Warning: DASHSCOPE_API_KEY not set.")
+        
+        return DashScope(
+            id=model_id,
+            base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+            api_key=api_key,
+            **kwargs
+        )
+    elif model_provider == 'openrouter':
+        api_key = os.getenv("OPENROUTER_API_KEY")
+        if not api_key:
+            print('Warning: OPENROUTER_API_KEY not set.')
+        
+        return OpenRouter(
+            id=model_id,
+            api_key=api_key,
+            **kwargs
+        )
+
+    elif model_provider == 'zai':
+        api_key = os.getenv("ZAI_KEY_API")
+        if not api_key:
+            print('Warning: ZAI_KEY_API not set.')
+
+        # role_map to ensure compatibility.
+        default_role_map = {
+            "system": "system",
+            "user": "user",
+            "assistant": "assistant",
+            "tool": "tool",
+            "model": "assistant",
+        }
+
+        # Allow callers to override role_map via kwargs, otherwise use default
+        role_map = kwargs.pop("role_map", default_role_map)
+        
+        return OpenAIChat(
+            id=model_id,
+            base_url="https://api.z.ai/api/paas/v4",
+            api_key=api_key,
+            timeout=60,
+            role_map=role_map,
+            extra_body={"enable_thinking": False}, # TODO: one more setting for thinking
+            **kwargs
+        )
+    
+    elif model_provider == 'ust':
+        api_key = os.getenv("UST_KEY_API")
+        if not api_key:
+            print('Warning: UST_KEY_API not set.')
+        
+        # Some UST-compatible endpoints expect the standard OpenAI role names
+        # (e.g. "system", "user", "assistant") rather than Agno's default
+        # mapping which maps "system" -> "developer". Provide an explicit
+        # role_map to ensure compatibility.
+        default_role_map = {
+            "system": "system",
+            "user": "user",
+            "assistant": "assistant",
+            "tool": "tool",
+            "model": "assistant",
+        }
+
+        # Allow callers to override role_map via kwargs, otherwise use default
+        role_map = kwargs.pop("role_map", default_role_map)
+
+        return OpenAIChat(
+            id=model_id,
+            api_key=api_key,
+            base_url=os.getenv("UST_URL"),
+            role_map=role_map,
+            extra_body={"enable_thinking": False}, # TODO: one more setting for thinking
+            **kwargs
+        )
+    
+    else:
+        raise ValueError(f"Unknown model provider: {model_provider}")
+

skills/alphaear-predictor/scripts/utils/llm/router.py

@@ -0,0 +1,81 @@
+import os
+from typing import Optional, List, Dict, Any, Union
+from agno.models.base import Model
+from loguru import logger
+from dotenv import load_dotenv
+from ..llm.factory import get_model
+from ..llm.capability import ModelCapabilityRegistry
+
+# Load environment variables from universal .env
+load_dotenv(os.path.expanduser("~/.config/opencode/.env"))
+
+
+class ModelRouter:
+    """
+    模型路由管理器
+
+    功能：
+    1. 管理“推理/写作模型” (Reasoning Model) 和“工具调用模型” (Tool Model)。
+    2. 根据任务需求自动选择合适的模型。
+    """
+
+    def __init__(self):
+        # 默认从环境变量读取
+        self.reasoning_provider = os.getenv(
+            "REASONING_MODEL_PROVIDER", os.getenv("LLM_PROVIDER", "openai")
+        )
+        self.reasoning_id = os.getenv(
+            "REASONING_MODEL_ID", os.getenv("LLM_MODEL", "gpt-4o")
+        )
+        self.reasoning_host = os.getenv("REASONING_MODEL_HOST", os.getenv("LLM_HOST"))
+
+        self.tool_provider = os.getenv("TOOL_MODEL_PROVIDER", self.reasoning_provider)
+        self.tool_id = os.getenv("TOOL_MODEL_ID", self.reasoning_id)
+        self.tool_host = os.getenv("TOOL_MODEL_HOST", self.reasoning_host)
+
+        self._reasoning_model = None
+        self._tool_model = None
+
+        logger.info(
+            f"🤖 ModelRouter initialized: Reasoning={self.reasoning_id} ({self.reasoning_host or 'default'}), Tool={self.tool_id} ({self.tool_host or 'default'})"
+        )
+
+    def get_reasoning_model(self, **kwargs) -> Model:
+        if not self._reasoning_model:
+            # 优先使用路由配置的 host
+            if self.reasoning_host and "host" not in kwargs:
+                kwargs["host"] = self.reasoning_host
+            self._reasoning_model = get_model(
+                self.reasoning_provider, self.reasoning_id, **kwargs
+            )
+        return self._reasoning_model
+
+    def get_tool_model(self, **kwargs) -> Model:
+        if not self._tool_model:
+            # 优先使用路由配置的 host
+            if self.tool_host and "host" not in kwargs:
+                kwargs["host"] = self.tool_host
+
+            # 检查 tool_model 是否真的支持 tool call
+            caps = ModelCapabilityRegistry.get_capabilities(
+                self.tool_provider, self.tool_id, **kwargs
+            )
+            if not caps["supports_tool_call"]:
+                logger.warning(
+                    f"⚠️ Configured tool model {self.tool_id} might not support native tool calls! Consider using ReAct mode or a different model."
+                )
+
+            self._tool_model = get_model(self.tool_provider, self.tool_id, **kwargs)
+        return self._tool_model
+
+    def get_model_for_agent(self, has_tools: bool = False, **kwargs) -> Model:
+        """
+        根据 Agent 是否包含工具来返回合适的模型。
+        """
+        if has_tools:
+            return self.get_tool_model(**kwargs)
+        return self.get_reasoning_model(**kwargs)
+
+
+# 全局单例
+router = ModelRouter()

skills/alphaear-predictor/scripts/utils/logging_setup.py

@@ -0,0 +1,45 @@
+import os
+import sys
+from datetime import datetime
+from typing import Optional
+
+from loguru import logger
+
+
+def setup_file_logging(
+    run_id: str,
+    log_dir: str = "logs",
+    level: str = "INFO",
+    retention: str = "10 days",
+    rotation: str = "20 MB",
+) -> str:
+    """Configure Loguru to log to stderr + a per-run file.
+
+    Returns the log file path.
+    """
+    os.makedirs(log_dir, exist_ok=True)
+
+    # Remove default handler to avoid duplicate logs.
+    logger.remove()
+
+    # Console
+    logger.add(sys.stderr, level=level, backtrace=False, diagnose=False)
+
+    # File (safe for multi-thread via enqueue)
+    log_path = os.path.join(log_dir, f"signalflux_{run_id}.log")
+    logger.add(
+        log_path,
+        level=level,
+        rotation=rotation,
+        retention=retention,
+        enqueue=True,
+        backtrace=True,
+        diagnose=False,
+        encoding="utf-8",
+    )
+    return log_path
+
+
+def make_run_id(prefix: Optional[str] = None) -> str:
+    ts = datetime.now().strftime("%Y%m%d_%H%M%S")
+    return f"{prefix}_{ts}" if prefix else ts

skills/alphaear-predictor/scripts/utils/predictor/evaluation.py

@@ -0,0 +1,137 @@
+import os
+import sys
+import torch
+import pandas as pd
+import numpy as np
+import glob
+from loguru import logger
+from datetime import datetime, timedelta
+
+# Setup paths
+KRONOS_DIR = os.path.dirname(os.path.abspath(__file__))
+SRC_DIR = os.path.dirname(os.path.dirname(KRONOS_DIR))
+if SRC_DIR not in sys.path:
+    sys.path.insert(0, SRC_DIR)
+
+from ..kronos.auto_synthesis_training import AutoSynthesisTrainer
+from ..kronos.model import KronosPredictor
+from ..visualizer import VisualizerTools
+from ..schema.models import ForecastResult, KLinePoint
+
+class NewsModelEvaluator:
+    def __init__(self, model_path=None):
+        self.trainer = AutoSynthesisTrainer()
+        self.device = self.trainer.device
+        
+        if model_path is None:
+            # Try to find the latest model in exports/models
+            model_files = glob.glob(os.path.join(SRC_DIR, "exports/models/*.pt"))
+            if not model_files:
+                logger.warning("⚠️ No trained models found in exports/models/. Using base model (zero-init proj).")
+            else:
+                model_path = max(model_files, key=os.path.getctime)
+        
+        if model_path:
+            self.load_weights(model_path)
+
+    def load_weights(self, path):
+        logger.info(f"🔄 Loading model weights from {path}...")
+        checkpoint = torch.load(path, map_location=self.device)
+        self.trainer.model.news_proj.load_state_dict(checkpoint['news_proj_state_dict'])
+        logger.success("✅ News projection layer loaded.")
+
+    def evaluate_range(self, start_idx=100, end_idx=200, pred_len=5):
+        # 1. Fetch Tickers
+        res = self.trainer.db.execute_query("SELECT code FROM stock_list")
+        all_tickers = [row['code'] for row in res]
+        test_tickers = all_tickers[start_idx:end_idx]
+        
+        if not test_tickers:
+            logger.error(f"No tickers found in range {start_idx}-{end_idx}")
+            return
+
+        logger.info(f"🚀 Evaluating News Model on stocks {start_idx} to {end_idx}...")
+        
+        # 2. Discover Shocks
+        shocks = self.trainer.discover_shocks(test_tickers, pred_len=pred_len)
+        
+        # 3. Associate News & Predict
+        self.trainer.model.eval()
+        predictor = KronosPredictor(self.trainer.model, self.trainer.tokenizer, device=self.device)
+        
+        save_dir = os.path.join(SRC_DIR, "exports/evaluation_results")
+        os.makedirs(save_dir, exist_ok=True)
+
+        count = 0
+        for shock in shocks:
+            summary = self.trainer.find_reason_and_verify(shock)
+            if not summary:
+                continue
+            
+            logger.info(f"📈 Testing shock: {shock['ticker']} on {shock['date']}")
+            
+            # Embedding news
+            news_emb = self.trainer.embedder.encode(summary)
+            
+            # Prediction
+            h = shock['history']
+            t = shock['target']
+            actuals = t['close'].values[:pred_len]
+            
+            x_ts = pd.to_datetime(h['date'])
+            future_dates = pd.date_range(start=x_ts.iloc[-1] + timedelta(days=1), periods=pred_len, freq='B')
+            y_ts = pd.Series(future_dates)
+            
+            # A. Base Prediction (No news)
+            p_base = predictor.predict(h, x_ts, y_ts, pred_len=pred_len, news_emb=None, verbose=False)
+            
+            # B. News-Aware Prediction
+            p_news = predictor.predict(h, x_ts, y_ts, pred_len=pred_len, news_emb=news_emb, verbose=False)
+            
+            # Calculate Improvement
+            b_preds = p_base['close'].values[:len(actuals)]
+            n_preds = p_news['close'].values[:len(actuals)]
+            b_mae = np.mean(np.abs(b_preds - actuals))
+            n_mae = np.mean(np.abs(n_preds - actuals))
+            improvement = (b_mae - n_mae) / (b_mae + 1e-6) * 100
+
+            # C. Visualize
+            try:
+                def to_kp_list(preds_df):
+                    points = []
+                    for idx, row in preds_df.iterrows():
+                        points.append(KLinePoint(
+                            date=str(idx)[:10], open=row['open'], high=row['high'],
+                            low=row['low'], close=row['close'], volume=row.get('volume', 0)
+                        ))
+                    return points
+
+                forecast_obj = ForecastResult(
+                    ticker=shock['ticker'],
+                    base_forecast=to_kp_list(p_base),
+                    adjusted_forecast=to_kp_list(p_news),
+                    rationale=summary
+                )
+
+                chart = VisualizerTools.generate_stock_chart(
+                    df=h, ticker=shock['ticker'],
+                    title=f"Test Eval: {shock['ticker']} ({shock['date']}) Imp: {improvement:.1f}%",
+                    forecast=forecast_obj,
+                    ground_truth=t[['date', 'open', 'high', 'low', 'close', 'volume']]
+                )
+                
+                safe_date = shock['date'].replace("-", "")
+                filename = f"test_{shock['ticker']}_{safe_date}.html"
+                VisualizerTools.render_chart_to_file(chart, os.path.join(save_dir, filename))
+                
+                logger.success(f"📊 Result for {shock['ticker']} saved. Base MAE: {b_mae:.4f}, News MAE: {n_mae:.4f}")
+                count += 1
+            except Exception as e:
+                logger.error(f"Visualization failed: {e}")
+
+        logger.info(f"🏁 Finished evaluation. {count} cases visualized in {save_dir}")
+
+if __name__ == "__main__":
+    # If you have a specific model, pass the path here. Otherwise it picks the latest.
+    evaluator = NewsModelEvaluator()
+    evaluator.evaluate_range(start_idx=100, end_idx=200, pred_len=1)

skills/alphaear-predictor/scripts/utils/predictor/kline_generate.py

@@ -0,0 +1,196 @@
+# Ref: https://github.com/shiyu-coder/Kronos
+
+from model import Kronos, KronosTokenizer, KronosPredictor
+import pandas as pd
+import sqlite3
+import torch
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+from pandas.tseries.offsets import BusinessDay
+import numpy as np
+
+def get_device():
+    device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+    print(f"Using device: {device}")
+    return device
+
+def load_predictor():
+    tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base")
+    model = Kronos.from_pretrained("NeoQuasar/Kronos-base")
+    device = get_device()
+    tokenizer = tokenizer.to(device)
+    model = model.to(device)
+    return KronosPredictor(model, tokenizer, device=device, max_context=512)
+
+def load_data(ticker="002111", db_path="AlphaEar/data/signal_flux.db"):
+    with sqlite3.connect(db_path) as conn:
+        df = pd.read_sql_query(f"SELECT * FROM stock_prices WHERE ticker = '{ticker}'", conn)
+    df['date'] = pd.to_datetime(df['date'])
+    df = df.sort_values('date').reset_index(drop=True)
+    return df
+
+def plot_kline_matplotlib(ax, ax_vol, dates, df, label_suffix="", color_up='#ef4444', color_down='#22c55e', alpha=1.0, is_prediction=False):
+    """
+    绘制 K 线图和成交量
+    """
+    # X axis mapping to integers for consistent spacing
+    x = np.arange(len(dates))
+    
+    # K-line data
+    opens = df['open'].values
+    closes = df['close'].values
+    highs = df['high'].values
+    lows = df['low'].values
+    volumes = df['volume'].values
+    
+    # Width of the candlestick
+    width = 0.6
+    
+    for i in range(len(x)):
+        color = color_up if closes[i] >= opens[i] else color_down
+        linestyle = '--' if is_prediction else '-'
+        
+        # Wick
+        ax.vlines(x[i], lows[i], highs[i], color=color, linewidth=1, alpha=alpha, linestyle=linestyle)
+        
+        # Body
+        rect_bottom = min(opens[i], closes[i])
+        rect_height = abs(opens[i] - closes[i])
+        if rect_height == 0: rect_height = 0.001 # Visual hair
+        
+        ax.add_patch(plt.Rectangle((x[i] - width/2, rect_bottom), width, rect_height, 
+                                 edgecolor=color, facecolor=color if not is_prediction else 'none', 
+                                 alpha=alpha, linewidth=1, linestyle=linestyle))
+        
+        # Volume
+        ax_vol.bar(x[i], volumes[i], color=color, alpha=alpha * 0.5, width=width)
+
+def render_comparison_chart(history_df, actual_df, pred_df, title):
+    """
+    渲染组合图：历史 K 线 + 真值 K 线 + 预测 K 线
+    """
+    # Combine all dates for X axis
+    all_dates = pd.concat([history_df['date'], actual_df['date'] if actual_df is not None else pred_df.index.to_series()]).unique()
+    all_dates = sorted(all_dates)
+    date_to_idx = {date: i for i, date in enumerate(all_dates)}
+    
+    fig = plt.figure(figsize=(14, 8), facecolor='white')
+    gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], hspace=0.1)
+    ax_main = fig.add_subplot(gs[0])
+    ax_vol = fig.add_subplot(gs[1], sharex=ax_main)
+    
+    # 1. Plot History
+    hist_indices = [date_to_idx[d] for d in history_df['date']]
+    # We use a custom x for plotting to ensure continuity
+    plot_kline_matplotlib(ax_main, ax_vol, history_df['date'], history_df, alpha=0.8)
+    
+    offset = len(history_df)
+    
+    # 2. Plot Actual if exists
+    if actual_df is not None:
+        # Shift indices
+        actual_x = np.arange(len(actual_df)) + offset
+        # Plotting manually to handle offset
+        for i in range(len(actual_df)):
+            idx = actual_x[i]
+            row = actual_df.iloc[i]
+            color = '#ef4444' if row['close'] >= row['open'] else '#22c55e'
+            ax_main.vlines(idx, row['low'], row['high'], color=color, linewidth=1, alpha=0.9)
+            ax_main.add_patch(plt.Rectangle((idx - 0.3, min(row['open'], row['close'])), 0.6, abs(row['open']-row['close']), 
+                                         edgecolor=color, facecolor=color, alpha=0.9))
+            ax_vol.bar(idx, row['volume'], color=color, alpha=0.4)
+            
+    # 3. Plot Prediction
+    pred_x = np.arange(len(pred_df)) + offset
+    for i in range(len(pred_df)):
+        idx = pred_x[i]
+        row = pred_df.iloc[i]
+        color = '#ff8c00' # Orange for prediction to distinguish
+        ax_main.vlines(idx, row['low'], row['high'], color=color, linewidth=1.5, linestyle='--')
+        ax_main.add_patch(plt.Rectangle((idx - 0.3, min(row['open'], row['close'])), 0.6, abs(row['open']-row['close']), 
+                                     edgecolor=color, facecolor='none', linewidth=1.5, linestyle='--'))
+        # Plot secondary prediction line for close
+        if i == 0:
+            # Connect to history
+            ax_main.plot([offset-1, idx], [history_df['close'].iloc[-1], row['close']], color=color, linestyle='--', alpha=0.6)
+        elif i > 0:
+            ax_main.plot([idx-1, idx], [pred_df['close'].iloc[i-1], row['close']], color=color, linestyle='--', alpha=0.6)
+
+    # Styling
+    ax_main.set_title(title, fontsize=14, fontweight='bold')
+    ax_main.grid(True, linestyle=':', alpha=0.6)
+    ax_vol.grid(True, linestyle=':', alpha=0.6)
+    ax_vol.set_ylabel('Volume')
+    ax_main.set_ylabel('Price')
+    
+    # Set X ticks
+    step = max(1, len(all_dates) // 10)
+    ax_vol.set_xticks(np.arange(0, len(all_dates), step))
+    ax_vol.set_xticklabels([all_dates[i].strftime('%Y-%m-%d') for i in range(0, len(all_dates), step)], rotation=45)
+    
+    plt.tight_layout()
+    plt.show()
+    plt.close()
+
+def run_backtest(df, predictor, lookback, pred_len, start_index=0):
+    total_len = len(df)
+    history_start = start_index
+    history_end = start_index + lookback 
+    pred_start = history_end
+    
+    available_pred_len = total_len - pred_start
+    if available_pred_len <= 0: return
+    actual_pred_len = min(pred_len, available_pred_len)
+    pred_end = pred_start + actual_pred_len
+    
+    x_df = df.iloc[history_start : history_end].copy()
+    y_true_df = df.iloc[pred_start : pred_end].copy()
+    y_timestamp = y_true_df['date']
+    
+    print(f"Backtesting: {x_df['date'].iloc[0].date()} to {y_timestamp.iloc[-1].date()}")
+    
+    pred_df = predictor.predict(
+        df=x_df[['open', 'high', 'low', 'close', 'volume']],
+        x_timestamp=x_df['date'],
+        y_timestamp=y_timestamp,
+        pred_len=actual_pred_len,
+        T=1.0, top_p=0.9, sample_count=1
+    )
+    
+    render_comparison_chart(x_df, y_true_df, pred_df, f"Backtest: {TICKER} K-Line Comparison")
+
+def run_forecast(df, predictor, lookback, pred_len):
+    if len(df) < lookback: return
+    x_df = df.iloc[-lookback:].copy()
+    last_date = x_df['date'].iloc[-1]
+    future_dates = pd.date_range(start=last_date + BusinessDay(1), periods=pred_len, freq='B')
+    future_dates = pd.Series(future_dates)
+    
+    print(f"Forecasting: Starting from {future_dates.iloc[0].date()}")
+    
+    pred_df = predictor.predict(
+        df=x_df[['open', 'high', 'low', 'close', 'volume']],
+        x_timestamp=x_df['date'],
+        y_timestamp=future_dates,
+        pred_len=pred_len,
+        T=1.0, top_p=0.9, sample_count=1
+    )
+    
+    render_comparison_chart(x_df, None, pred_df, f"Forecast: {TICKER} Future K-Line")
+
+if __name__ == "__main__":
+    LOOKBACK = 20
+    PRED_LEN = 10
+    TICKER = '002111'
+    
+    pred_model = load_predictor()
+    stock_data = load_data(TICKER)
+    
+    total_rows = len(stock_data)
+    backtest_start = max(0, total_rows - LOOKBACK - PRED_LEN - 10) # Leave some space to see trend
+    
+    print("\n--- Running Backtest ---")
+    run_backtest(stock_data, pred_model, LOOKBACK, PRED_LEN, start_index=backtest_start)
+    
+    print("\n--- Running Forecast ---")
+    run_forecast(stock_data, pred_model, LOOKBACK, PRED_LEN)

skills/alphaear-predictor/scripts/utils/predictor/model/__init__.py

@@ -0,0 +1,16 @@
+from .kronos import KronosTokenizer, Kronos, KronosPredictor
+
+model_dict = {
+    'kronos_tokenizer': KronosTokenizer,
+    'kronos': Kronos,
+    'kronos_predictor': KronosPredictor
+}
+
+
+def get_model_class(model_name):
+    if model_name in model_dict:
+        return model_dict[model_name]
+    else:
+        print(f"Model {model_name} not found in model_dict")
+        raise NotImplementedError
+

skills/alphaear-predictor/scripts/utils/predictor/model/kronos.py

@@ -0,0 +1,676 @@
+import numpy as np
+import pandas as pd
+import torch
+from huggingface_hub import PyTorchModelHubMixin
+import sys
+
+from tqdm import trange
+
+sys.path.append("../")
+from model.module import *
+
+
+class KronosTokenizer(nn.Module, PyTorchModelHubMixin):
+    """
+    KronosTokenizer module for tokenizing input data using a hybrid quantization approach.
+
+    This tokenizer utilizes a combination of encoder and decoder Transformer blocks
+    along with the Binary Spherical Quantization (BSQuantizer) to compress and decompress input data.
+
+    Args:
+           d_in (int): Input dimension.
+           d_model (int): Model dimension.
+           n_heads (int): Number of attention heads.
+           ff_dim (int): Feed-forward dimension.
+           n_enc_layers (int): Number of encoder layers.
+           n_dec_layers (int): Number of decoder layers.
+           ffn_dropout_p (float): Dropout probability for feed-forward networks.
+           attn_dropout_p (float): Dropout probability for attention mechanisms.
+           resid_dropout_p (float): Dropout probability for residual connections.
+           s1_bits (int): Number of bits for the pre token in BSQuantizer.
+           s2_bits (int): Number of bits for the post token in BSQuantizer.
+           beta (float): Beta parameter for BSQuantizer.
+           gamma0 (float): Gamma0 parameter for BSQuantizer.
+           gamma (float): Gamma parameter for BSQuantizer.
+           zeta (float): Zeta parameter for BSQuantizer.
+           group_size (int): Group size parameter for BSQuantizer.
+
+    """
+
+    def __init__(self, d_in, d_model, n_heads, ff_dim, n_enc_layers, n_dec_layers, ffn_dropout_p, attn_dropout_p, resid_dropout_p, s1_bits, s2_bits, beta, gamma0, gamma, zeta, group_size):
+
+        super().__init__()
+        self.d_in = d_in
+        self.d_model = d_model
+        self.n_heads = n_heads
+        self.ff_dim = ff_dim
+        self.enc_layers = n_enc_layers
+        self.dec_layers = n_dec_layers
+        self.ffn_dropout_p = ffn_dropout_p
+        self.attn_dropout_p = attn_dropout_p
+        self.resid_dropout_p = resid_dropout_p
+
+        self.s1_bits = s1_bits
+        self.s2_bits = s2_bits
+        self.codebook_dim = s1_bits + s2_bits # Total dimension of the codebook after quantization
+        self.embed = nn.Linear(self.d_in, self.d_model)
+        self.head = nn.Linear(self.d_model, self.d_in)
+
+        # Encoder Transformer Blocks
+        self.encoder = nn.ModuleList([
+            TransformerBlock(self.d_model, self.n_heads, self.ff_dim, self.ffn_dropout_p, self.attn_dropout_p, self.resid_dropout_p)
+            for _ in range(self.enc_layers - 1)
+        ])
+        # Decoder Transformer Blocks
+        self.decoder = nn.ModuleList([
+            TransformerBlock(self.d_model, self.n_heads, self.ff_dim, self.ffn_dropout_p, self.attn_dropout_p, self.resid_dropout_p)
+            for _ in range(self.dec_layers - 1)
+        ])
+        self.quant_embed = nn.Linear(in_features=self.d_model, out_features=self.codebook_dim) # Linear layer before quantization
+        self.post_quant_embed_pre = nn.Linear(in_features=self.s1_bits, out_features=self.d_model) # Linear layer after quantization (pre part - s1 bits)
+        self.post_quant_embed = nn.Linear(in_features=self.codebook_dim, out_features=self.d_model) # Linear layer after quantization (full codebook)
+        self.tokenizer = BSQuantizer(self.s1_bits, self.s2_bits, beta, gamma0, gamma, zeta, group_size) # BSQuantizer module
+
+    def forward(self, x):
+        """
+        Forward pass of the KronosTokenizer.
+
+        Args:
+            x (torch.Tensor): Input tensor of shape (batch_size, seq_len, d_in).
+
+        Returns:
+            tuple: A tuple containing:
+                - tuple: (z_pre, z) - Reconstructed outputs from decoder with s1_bits and full codebook respectively,
+                         both of shape (batch_size, seq_len, d_in).
+                - torch.Tensor: bsq_loss - Loss from the BSQuantizer.
+                - torch.Tensor: quantized - Quantized representation from BSQuantizer.
+                - torch.Tensor: z_indices - Indices from the BSQuantizer.
+        """
+        z = self.embed(x)
+
+        for layer in self.encoder:
+            z = layer(z)
+
+        z = self.quant_embed(z) # (B, T, codebook)
+
+        bsq_loss, quantized, z_indices = self.tokenizer(z)
+
+        quantized_pre = quantized[:, :, :self.s1_bits] # Extract the first part of quantized representation (s1_bits)
+        z_pre = self.post_quant_embed_pre(quantized_pre)
+
+        z = self.post_quant_embed(quantized)
+
+        # Decoder layers (for pre part - s1 bits)
+        for layer in self.decoder:
+            z_pre = layer(z_pre)
+        z_pre = self.head(z_pre)
+
+        # Decoder layers (for full codebook)
+        for layer in self.decoder:
+            z = layer(z)
+        z = self.head(z)
+
+        return (z_pre, z), bsq_loss, quantized, z_indices
+
+    def indices_to_bits(self, x, half=False):
+        """
+        Converts indices to bit representations and scales them.
+
+        Args:
+            x (torch.Tensor): Indices tensor.
+            half (bool, optional): Whether to process only half of the codebook dimension. Defaults to False.
+
+        Returns:
+            torch.Tensor: Bit representation tensor.
+        """
+        if half:
+            x1 = x[0] # Assuming x is a tuple of indices if half is True
+            x2 = x[1]
+            mask = 2 ** torch.arange(self.codebook_dim//2, device=x1.device, dtype=torch.long) # Create a mask for bit extraction
+            x1 = (x1.unsqueeze(-1) & mask) != 0 # Extract bits for the first half
+            x2 = (x2.unsqueeze(-1) & mask) != 0 # Extract bits for the second half
+            x = torch.cat([x1, x2], dim=-1) # Concatenate the bit representations
+        else:
+            mask = 2 ** torch.arange(self.codebook_dim, device=x.device, dtype=torch.long) # Create a mask for bit extraction
+            x = (x.unsqueeze(-1) & mask) != 0 # Extract bits
+
+        x = x.float() * 2 - 1 # Convert boolean to bipolar (-1, 1)
+        q_scale = 1. / (self.codebook_dim ** 0.5) # Scaling factor
+        x = x * q_scale
+        return x
+
+    def encode(self, x, half=False):
+        """
+        Encodes the input data into quantized indices.
+
+        Args:
+            x (torch.Tensor): Input tensor of shape (batch_size, seq_len, d_in).
+            half (bool, optional): Whether to use half quantization in BSQuantizer. Defaults to False.
+
+        Returns:
+            torch.Tensor: Quantized indices from BSQuantizer.
+        """
+        z = self.embed(x)
+        for layer in self.encoder:
+            z = layer(z)
+        z = self.quant_embed(z)
+
+        bsq_loss, quantized, z_indices = self.tokenizer(z, half=half, collect_metrics=False)
+        return z_indices
+
+    def decode(self, x, half=False):
+        """
+        Decodes quantized indices back to the input data space.
+
+        Args:
+            x (torch.Tensor): Quantized indices tensor.
+            half (bool, optional): Whether the indices were generated with half quantization. Defaults to False.
+
+        Returns:
+            torch.Tensor: Reconstructed output tensor of shape (batch_size, seq_len, d_in).
+        """
+        quantized = self.indices_to_bits(x, half)
+        z = self.post_quant_embed(quantized)
+        for layer in self.decoder:
+            z = layer(z)
+        z = self.head(z)
+        return z
+
+
+class Kronos(nn.Module, PyTorchModelHubMixin):
+    """
+    Kronos Model.
+
+    Args:
+        s1_bits (int): Number of bits for pre tokens.
+        s2_bits (int): Number of bits for post tokens.
+        n_layers (int): Number of Transformer blocks.
+        d_model (int): Dimension of the model's embeddings and hidden states.
+        n_heads (int): Number of attention heads in the MultiheadAttention layers.
+        ff_dim (int): Dimension of the feedforward network in the Transformer blocks.
+        ffn_dropout_p (float): Dropout probability for the feedforward network.
+        attn_dropout_p (float): Dropout probability for the attention layers.
+        resid_dropout_p (float): Dropout probability for residual connections.
+        token_dropout_p (float): Dropout probability for token embeddings.
+        learn_te (bool): Whether to use learnable temporal embeddings.
+    """
+
+    def __init__(self, s1_bits, s2_bits, n_layers, d_model, n_heads, ff_dim, ffn_dropout_p, attn_dropout_p, resid_dropout_p, token_dropout_p, learn_te, news_dim=None):
+        super().__init__()
+        self.s1_bits = s1_bits
+        self.s2_bits = s2_bits
+        self.n_layers = n_layers
+        self.d_model = d_model
+        self.n_heads = n_heads
+        self.learn_te = learn_te
+        self.ff_dim = ff_dim
+        self.ffn_dropout_p = ffn_dropout_p
+        self.attn_dropout_p = attn_dropout_p
+        self.resid_dropout_p = resid_dropout_p
+        self.token_dropout_p = token_dropout_p
+        self.news_dim = news_dim
+
+        self.s1_vocab_size = 2 ** self.s1_bits
+        self.token_drop = nn.Dropout(self.token_dropout_p)
+        self.embedding = HierarchicalEmbedding(self.s1_bits, self.s2_bits, self.d_model)
+        self.time_emb = TemporalEmbedding(self.d_model, self.learn_te)
+        self.transformer = nn.ModuleList([
+            TransformerBlock(self.d_model, self.n_heads, self.ff_dim, self.ffn_dropout_p, self.attn_dropout_p, self.resid_dropout_p)
+            for _ in range(self.n_layers)
+        ])
+        self.norm = RMSNorm(self.d_model)
+        self.dep_layer = DependencyAwareLayer(self.d_model)
+        self.head = DualHead(self.s1_bits, self.s2_bits, self.d_model)
+
+        if self.news_dim is not None:
+            self.news_proj = nn.Linear(self.news_dim, self.d_model)
+        else:
+            self.news_proj = None
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+
+        if isinstance(module, nn.Linear):
+            nn.init.xavier_normal_(module.weight)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, mean=0, std=self.embedding.d_model ** -0.5)
+        elif isinstance(module, nn.LayerNorm):
+            nn.init.ones_(module.weight)
+            nn.init.zeros_(module.bias)
+        elif isinstance(module, RMSNorm):
+            nn.init.ones_(module.weight)
+
+    def forward(self, s1_ids, s2_ids, stamp=None, padding_mask=None, use_teacher_forcing=False, s1_targets=None, news_emb=None):
+        """
+        Args:
+            s1_ids (torch.Tensor): Input tensor of s1 token IDs. Shape: [batch_size, seq_len]
+            s2_ids (torch.Tensor): Input tensor of s2 token IDs. Shape: [batch_size, seq_len]
+            stamp (torch.Tensor, optional): Temporal stamp tensor. Shape: [batch_size, seq_len]. Defaults to None.
+            padding_mask (torch.Tensor, optional): Mask for padding tokens. Shape: [batch_size, seq_len]. Defaults to None.
+            use_teacher_forcing (bool, optional): Whether to use teacher forcing for s1 decoding. Defaults to False.
+            s1_targets (torch.Tensor, optional): Target s1 token IDs for teacher forcing. Shape: [batch_size, seq_len]. Defaults to None.
+            news_emb (torch.Tensor, optional): News embedding tensor. Shape: [batch_size, news_dim]. Defaults to None.
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor]:
+                - s1 logits: Logits for s1 token predictions. Shape: [batch_size, seq_len, s1_vocab_size]
+                - s2_logits: Logits for s2 token predictions, conditioned on s1. Shape: [batch_size, seq_len, s2_vocab_size]
+        """
+        x = self.embedding([s1_ids, s2_ids])
+        if stamp is not None:
+            time_embedding = self.time_emb(stamp)
+            x = x + time_embedding
+        x = self.token_drop(x)
+
+        for layer in self.transformer:
+            x = layer(x, key_padding_mask=padding_mask)
+
+        x = self.norm(x)
+
+        if news_emb is not None and self.news_proj is not None:
+            news_bias = self.news_proj(news_emb).unsqueeze(1) # [B, 1, d_model]
+            x = x + news_bias
+
+        s1_logits = self.head(x)
+
+        if use_teacher_forcing:
+            sibling_embed = self.embedding.emb_s1(s1_targets)
+        else:
+            s1_probs = F.softmax(s1_logits.detach(), dim=-1)
+            sample_s1_ids = torch.multinomial(s1_probs.view(-1, self.s1_vocab_size), 1).view(s1_ids.shape)
+            sibling_embed = self.embedding.emb_s1(sample_s1_ids)
+
+        x2 = self.dep_layer(x, sibling_embed, key_padding_mask=padding_mask) # Dependency Aware Layer: Condition on s1 embeddings
+        s2_logits = self.head.cond_forward(x2)
+        return s1_logits, s2_logits
+
+    def decode_s1(self, s1_ids, s2_ids, stamp=None, padding_mask=None, news_emb=None):
+        """
+        Decodes only the s1 tokens.
+
+        This method performs a forward pass to predict only s1 tokens. It returns the s1 logits
+        and the context representation from the Transformer, which can be used for subsequent s2 decoding.
+
+        Args:
+            s1_ids (torch.Tensor): Input tensor of s1 token IDs. Shape: [batch_size, seq_len]
+            s2_ids (torch.Tensor): Input tensor of s2 token IDs. Shape: [batch_size, seq_len]
+            stamp (torch.Tensor, optional): Temporal stamp tensor. Shape: [batch_size, seq_len]. Defaults to None.
+            padding_mask (torch.Tensor, optional): Mask for padding tokens. Shape: [batch_size, seq_len]. Defaults to None.
+            news_emb (torch.Tensor, optional): News embedding tensor. Shape: [batch_size, news_dim]. Defaults to None.
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor]:
+                - s1 logits: Logits for s1 token predictions. Shape: [batch_size, seq_len, s1_vocab_size]
+                - context: Context representation from the Transformer. Shape: [batch_size, seq_len, d_model]
+        """
+        x = self.embedding([s1_ids, s2_ids])
+        if stamp is not None:
+            time_embedding = self.time_emb(stamp)
+            x = x + time_embedding
+        x = self.token_drop(x)
+
+        for layer in self.transformer:
+            x = layer(x, key_padding_mask=padding_mask)
+
+        x = self.norm(x)
+
+        if news_emb is not None and self.news_proj is not None:
+            news_bias = self.news_proj(news_emb).unsqueeze(1) # [B, 1, d_model]
+            x = x + news_bias
+
+        s1_logits = self.head(x)
+        return s1_logits, x
+
+    def decode_s2(self, context, s1_ids, padding_mask=None):
+        """
+        Decodes the s2 tokens, conditioned on the context and s1 tokens.
+
+        This method decodes s2 tokens based on a pre-computed context representation (typically from `decode_s1`)
+        and the s1 token IDs. It uses the dependency-aware layer and the conditional s2 head to predict s2 tokens.
+
+        Args:
+            context (torch.Tensor): Context representation from the transformer (output of decode_s1).
+                                     Shape: [batch_size, seq_len, d_model]
+            s1_ids (torch.torch.Tensor): Input tensor of s1 token IDs. Shape: [batch_size, seq_len]
+            padding_mask (torch.Tensor, optional): Mask for padding tokens. Shape: [batch_size, seq_len]. Defaults to None.
+
+        Returns:
+            torch.Tensor: s2 logits. Shape: [batch_size, seq_len, s2_vocab_size]
+        """
+        sibling_embed = self.embedding.emb_s1(s1_ids)
+        x2 = self.dep_layer(context, sibling_embed, key_padding_mask=padding_mask)
+        return self.head.cond_forward(x2)
+
+
+def top_k_top_p_filtering(
+        logits,
+        top_k: int = 0,
+        top_p: float = 1.0,
+        filter_value: float = -float("Inf"),
+        min_tokens_to_keep: int = 1,
+):
+    """Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
+    Args:
+        logits: logits distribution shape (batch size, vocabulary size)
+        if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
+        if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
+            Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
+        Make sure we keep at least min_tokens_to_keep per batch example in the output
+    From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
+    """
+    if top_k > 0:
+        top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1))  # Safety check
+        # Remove all tokens with a probability less than the last token of the top-k
+        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+        logits[indices_to_remove] = filter_value
+        return logits
+
+    if top_p < 1.0:
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+        cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+
+        # Remove tokens with cumulative probability above the threshold (token with 0 are kept)
+        sorted_indices_to_remove = cumulative_probs > top_p
+        if min_tokens_to_keep > 1:
+            # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
+            sorted_indices_to_remove[..., :min_tokens_to_keep] = 0
+        # Shift the indices to the right to keep also the first token above the threshold
+        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+        sorted_indices_to_remove[..., 0] = 0
+
+        # scatter sorted tensors to original indexing
+        indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+        logits[indices_to_remove] = filter_value
+        return logits
+
+
+def sample_from_logits(logits, temperature=1.0, top_k=None, top_p=None, sample_logits=True):
+    logits = logits / temperature
+    if top_k is not None or top_p is not None:
+        if top_k > 0 or top_p < 1.0:
+            logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
+
+    probs = F.softmax(logits, dim=-1)
+
+    if not sample_logits:
+        _, x = top_k(probs, k=1, dim=-1)
+    else:
+        x = torch.multinomial(probs, num_samples=1)
+
+    return x
+
+
+def auto_regressive_inference(tokenizer, model, x, x_stamp, y_stamp, max_context, pred_len, clip=5, T=1.0, top_k=0, top_p=0.99, sample_count=5, verbose=False, news_emb=None):
+    with torch.no_grad():
+        x = torch.clip(x, -clip, clip)
+
+        device = x.device
+        x = x.unsqueeze(1).repeat(1, sample_count, 1, 1).reshape(-1, x.size(1), x.size(2)).to(device)
+        x_stamp = x_stamp.unsqueeze(1).repeat(1, sample_count, 1, 1).reshape(-1, x_stamp.size(1), x_stamp.size(2)).to(device)
+        y_stamp = y_stamp.unsqueeze(1).repeat(1, sample_count, 1, 1).reshape(-1, y_stamp.size(1), y_stamp.size(2)).to(device)
+
+        x_token = tokenizer.encode(x, half=True)
+        
+        initial_seq_len = x.size(1)
+        batch_size = x_token[0].size(0)
+        total_seq_len = initial_seq_len + pred_len
+        full_stamp = torch.cat([x_stamp, y_stamp], dim=1)
+
+        generated_pre = x_token[0].new_empty(batch_size, pred_len)
+        generated_post = x_token[1].new_empty(batch_size, pred_len)
+
+        pre_buffer = x_token[0].new_zeros(batch_size, max_context)
+        post_buffer = x_token[1].new_zeros(batch_size, max_context)
+        buffer_len = min(initial_seq_len, max_context)
+        if buffer_len > 0:
+            start_idx = max(0, initial_seq_len - max_context)
+            pre_buffer[:, :buffer_len] = x_token[0][:, start_idx:start_idx + buffer_len]
+            post_buffer[:, :buffer_len] = x_token[1][:, start_idx:start_idx + buffer_len]
+
+        if verbose:
+            ran = trange
+        else:
+            ran = range
+        for i in ran(pred_len):
+            current_seq_len = initial_seq_len + i
+            window_len = min(current_seq_len, max_context)
+
+            if current_seq_len <= max_context:
+                input_tokens = [
+                    pre_buffer[:, :window_len],
+                    post_buffer[:, :window_len]
+                ]
+            else:
+                input_tokens = [pre_buffer, post_buffer]
+
+            context_end = current_seq_len
+            context_start = max(0, context_end - max_context)
+            current_stamp = full_stamp[:, context_start:context_end, :].contiguous()
+
+            s1_logits, context = model.decode_s1(input_tokens[0], input_tokens[1], current_stamp, news_emb=news_emb)
+            s1_logits = s1_logits[:, -1, :]
+            sample_pre = sample_from_logits(s1_logits, temperature=T, top_k=top_k, top_p=top_p, sample_logits=True)
+
+            s2_logits = model.decode_s2(context, sample_pre)
+            s2_logits = s2_logits[:, -1, :]
+            sample_post = sample_from_logits(s2_logits, temperature=T, top_k=top_k, top_p=top_p, sample_logits=True)
+
+            generated_pre[:, i] = sample_pre.squeeze(-1)
+            generated_post[:, i] = sample_post.squeeze(-1)
+
+            if current_seq_len < max_context:
+                pre_buffer[:, current_seq_len] = sample_pre.squeeze(-1)
+                post_buffer[:, current_seq_len] = sample_post.squeeze(-1)
+            else:
+                pre_buffer.copy_(torch.roll(pre_buffer, shifts=-1, dims=1))
+                post_buffer.copy_(torch.roll(post_buffer, shifts=-1, dims=1))
+                pre_buffer[:, -1] = sample_pre.squeeze(-1)
+                post_buffer[:, -1] = sample_post.squeeze(-1)
+
+        full_pre = torch.cat([x_token[0], generated_pre], dim=1)
+        full_post = torch.cat([x_token[1], generated_post], dim=1)
+
+        context_start = max(0, total_seq_len - max_context)
+        input_tokens = [
+            full_pre[:, context_start:total_seq_len].contiguous(),
+            full_post[:, context_start:total_seq_len].contiguous()
+        ]
+        z = tokenizer.decode(input_tokens, half=True)
+        z = z.reshape(-1, sample_count, z.size(1), z.size(2))
+        preds = z.cpu().numpy()
+        preds = np.mean(preds, axis=1)
+
+        return preds
+
+
+def calc_time_stamps(x_timestamp):
+    time_df = pd.DataFrame()
+    time_df['minute'] = x_timestamp.dt.minute
+    time_df['hour'] = x_timestamp.dt.hour
+    time_df['weekday'] = x_timestamp.dt.weekday
+    time_df['day'] = x_timestamp.dt.day
+    time_df['month'] = x_timestamp.dt.month
+    return time_df
+
+
+class KronosPredictor:
+
+    def __init__(self, model, tokenizer, device="cuda:0", max_context=512, clip=5):
+        self.tokenizer = tokenizer
+        self.model = model
+        self.max_context = max_context
+        self.clip = clip
+        self.price_cols = ['open', 'high', 'low', 'close']
+        self.vol_col = 'volume'
+        self.amt_vol = 'amount'
+        self.time_cols = ['minute', 'hour', 'weekday', 'day', 'month']
+        self.device = device
+
+        self.tokenizer = self.tokenizer.to(self.device)
+        self.model = self.model.to(self.device)
+
+    def generate(self, x, x_stamp, y_stamp, pred_len, T, top_k, top_p, sample_count, verbose, news_emb=None):
+
+        x_tensor = torch.from_numpy(np.array(x).astype(np.float32)).to(self.device)
+        x_stamp_tensor = torch.from_numpy(np.array(x_stamp).astype(np.float32)).to(self.device)
+        y_stamp_tensor = torch.from_numpy(np.array(y_stamp).astype(np.float32)).to(self.device)
+
+        preds = auto_regressive_inference(self.tokenizer, self.model, x_tensor, x_stamp_tensor, y_stamp_tensor, self.max_context, pred_len,
+                                          self.clip, T, top_k, top_p, sample_count, verbose, news_emb=news_emb)
+        preds = preds[:, -pred_len:, :]
+        return preds
+
+    def predict(self, df, x_timestamp, y_timestamp, pred_len, T=1.0, top_k=0, top_p=0.9, sample_count=1, verbose=True, news_emb=None):
+
+        if not isinstance(df, pd.DataFrame):
+            raise ValueError("Input must be a pandas DataFrame.")
+
+        if not all(col in df.columns for col in self.price_cols):
+            raise ValueError(f"Price columns {self.price_cols} not found in DataFrame.")
+
+        df = df.copy()
+        if self.vol_col not in df.columns:
+            df[self.vol_col] = 0.0  # Fill missing volume with zeros
+            df[self.amt_vol] = 0.0  # Fill missing amount with zeros
+        if self.amt_vol not in df.columns and self.vol_col in df.columns:
+            df[self.amt_vol] = df[self.vol_col] * df[self.price_cols].mean(axis=1)
+
+        if df[self.price_cols + [self.vol_col, self.amt_vol]].isnull().values.any():
+            raise ValueError("Input DataFrame contains NaN values in price or volume columns.")
+
+        x_time_df = calc_time_stamps(x_timestamp)
+        y_time_df = calc_time_stamps(y_timestamp)
+
+        x = df[self.price_cols + [self.vol_col, self.amt_vol]].values.astype(np.float32)
+        x_stamp = x_time_df.values.astype(np.float32)
+        y_stamp = y_time_df.values.astype(np.float32)
+
+        x_mean, x_std = np.mean(x, axis=0), np.std(x, axis=0)
+
+        x = (x - x_mean) / (x_std + 1e-5)
+        x = np.clip(x, -self.clip, self.clip)
+
+        x = x[np.newaxis, :]
+        x_stamp = x_stamp[np.newaxis, :]
+        y_stamp = y_stamp[np.newaxis, :]
+
+        if news_emb is not None:
+            news_emb_tensor = torch.from_numpy(np.array(news_emb).astype(np.float32)).to(self.device)
+            # Ensure batch dimension for news_emb if only one sample
+            if news_emb_tensor.ndim == 1:
+                news_emb_tensor = news_emb_tensor.unsqueeze(0)
+        else:
+            news_emb_tensor = None
+
+        preds = self.generate(x, x_stamp, y_stamp, pred_len, T, top_k, top_p, sample_count, verbose, news_emb=news_emb_tensor)
+
+        preds = preds.squeeze(0)
+        preds = preds * (x_std + 1e-5) + x_mean
+
+        pred_df = pd.DataFrame(preds, columns=self.price_cols + [self.vol_col, self.amt_vol], index=y_timestamp)
+        return pred_df
+
+
+    def predict_batch(self, df_list, x_timestamp_list, y_timestamp_list, pred_len, T=1.0, top_k=0, top_p=0.9, sample_count=1, verbose=True):
+        """
+        Perform parallel (batch) prediction on multiple time series. All series must have the same historical length and prediction length (pred_len).
+
+        Args:
+            df_list (List[pd.DataFrame]): List of input DataFrames, each containing price columns and optional volume/amount columns.
+            x_timestamp_list (List[pd.DatetimeIndex or Series]): List of timestamps corresponding to historical data, length should match the number of rows in each DataFrame.
+            y_timestamp_list (List[pd.DatetimeIndex or Series]): List of future prediction timestamps, length should equal pred_len.
+            pred_len (int): Number of prediction steps.
+            T (float): Sampling temperature.
+            top_k (int): Top-k filtering threshold.
+            top_p (float): Top-p (nucleus sampling) threshold.
+            sample_count (int): Number of parallel samples per series, automatically averaged internally.
+            verbose (bool): Whether to display autoregressive progress.
+
+        Returns:
+            List[pd.DataFrame]: List of prediction results in the same order as input, each DataFrame contains
+                                `open, high, low, close, volume, amount` columns, indexed by corresponding `y_timestamp`.
+        """
+        # Basic validation
+        if not isinstance(df_list, (list, tuple)) or not isinstance(x_timestamp_list, (list, tuple)) or not isinstance(y_timestamp_list, (list, tuple)):
+            raise ValueError("df_list, x_timestamp_list, y_timestamp_list must be list or tuple types.")
+        if not (len(df_list) == len(x_timestamp_list) == len(y_timestamp_list)):
+            raise ValueError("df_list, x_timestamp_list, y_timestamp_list must have consistent lengths.")
+
+        num_series = len(df_list)
+
+        x_list = []
+        x_stamp_list = []
+        y_stamp_list = []
+        means = []
+        stds = []
+        seq_lens = []
+        y_lens = []
+
+        for i in range(num_series):
+            df = df_list[i]
+            if not isinstance(df, pd.DataFrame):
+                raise ValueError(f"Input at index {i} is not a pandas DataFrame.")
+            if not all(col in df.columns for col in self.price_cols):
+                raise ValueError(f"DataFrame at index {i} is missing price columns {self.price_cols}.")
+
+            df = df.copy()
+            if self.vol_col not in df.columns:
+                df[self.vol_col] = 0.0
+                df[self.amt_vol] = 0.0
+            if self.amt_vol not in df.columns and self.vol_col in df.columns:
+                df[self.amt_vol] = df[self.vol_col] * df[self.price_cols].mean(axis=1)
+
+            if df[self.price_cols + [self.vol_col, self.amt_vol]].isnull().values.any():
+                raise ValueError(f"DataFrame at index {i} contains NaN values in price or volume columns.")
+
+            x_timestamp = x_timestamp_list[i]
+            y_timestamp = y_timestamp_list[i]
+
+            x_time_df = calc_time_stamps(x_timestamp)
+            y_time_df = calc_time_stamps(y_timestamp)
+
+            x = df[self.price_cols + [self.vol_col, self.amt_vol]].values.astype(np.float32)
+            x_stamp = x_time_df.values.astype(np.float32)
+            y_stamp = y_time_df.values.astype(np.float32)
+
+            if x.shape[0] != x_stamp.shape[0]:
+                raise ValueError(f"Inconsistent lengths at index {i}: x has {x.shape[0]} vs x_stamp has {x_stamp.shape[0]}.")
+            if y_stamp.shape[0] != pred_len:
+                raise ValueError(f"y_timestamp length at index {i} should equal pred_len={pred_len}, got {y_stamp.shape[0]}.")
+
+            x_mean, x_std = np.mean(x, axis=0), np.std(x, axis=0)
+            x_norm = (x - x_mean) / (x_std + 1e-5)
+            x_norm = np.clip(x_norm, -self.clip, self.clip)
+
+            x_list.append(x_norm)
+            x_stamp_list.append(x_stamp)
+            y_stamp_list.append(y_stamp)
+            means.append(x_mean)
+            stds.append(x_std)
+
+            seq_lens.append(x_norm.shape[0])
+            y_lens.append(y_stamp.shape[0])
+
+        # Require all series to have consistent historical and prediction lengths for batch processing
+        if len(set(seq_lens)) != 1:
+            raise ValueError(f"Parallel prediction requires all series to have consistent historical lengths, got: {seq_lens}")
+        if len(set(y_lens)) != 1:
+            raise ValueError(f"Parallel prediction requires all series to have consistent prediction lengths, got: {y_lens}")
+
+        x_batch = np.stack(x_list, axis=0).astype(np.float32)           # (B, seq_len, feat)
+        x_stamp_batch = np.stack(x_stamp_list, axis=0).astype(np.float32) # (B, seq_len, time_feat)
+        y_stamp_batch = np.stack(y_stamp_list, axis=0).astype(np.float32) # (B, pred_len, time_feat)
+
+        preds = self.generate(x_batch, x_stamp_batch, y_stamp_batch, pred_len, T, top_k, top_p, sample_count, verbose)
+        # preds: (B, pred_len, feat)
+
+        pred_dfs = []
+        for i in range(num_series):
+            preds_i = preds[i] * (stds[i] + 1e-5) + means[i]
+            pred_df = pd.DataFrame(preds_i, columns=self.price_cols + [self.vol_col, self.amt_vol], index=y_timestamp_list[i])
+            pred_dfs.append(pred_df)
+
+        return pred_dfs

skills/alphaear-predictor/scripts/utils/predictor/model/module.py

@@ -0,0 +1,562 @@
+import math
+
+from einops import rearrange, reduce
+import torch
+import torch.nn as nn
+from torch.autograd import Function
+import torch.nn.functional as F
+
+
+class DifferentiableEntropyFunction(Function):
+    @staticmethod
+    def forward(ctx, zq, basis, K, eps):
+        zb = (zq + 1) / 2
+        zi = ((zb * basis).sum(-1)).to(torch.int64)
+        cnt = torch.scatter_reduce(torch.zeros(2 ** K, device=zq.device, dtype=zq.dtype),
+                                   0,
+                                   zi.flatten(),
+                                   torch.ones_like(zi.flatten()).to(zq.dtype),
+                                   'sum')
+        prob = (cnt + eps) / (cnt + eps).sum()
+        H = -(prob * torch.log(prob)).sum()
+        ctx.save_for_backward(zq, zi, prob)
+        ctx.K = K
+        return H
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        zq, zi, prob = ctx.saved_tensors
+        grad_array = -grad_output * (torch.log(prob) + 1) / zi.numel() / ctx.K
+        reord_grad = grad_array[zi.flatten()].reshape(zi.shape)
+        grad_input = reord_grad.unsqueeze(-1) * zq
+        return grad_input, None, None, None, None
+
+
+def codebook_entropy(zq, basis, K, eps=1e-4):
+    return DifferentiableEntropyFunction.apply(zq, basis, K, eps)
+
+
+class BinarySphericalQuantizer(nn.Module):
+    def __init__(self, embed_dim, beta, gamma0, gamma, zeta,
+                 input_format='bchw',
+                 soft_entropy=True, group_size=9,
+                 persample_entropy_compute='analytical',
+                 cb_entropy_compute='group',
+                 l2_norm=True,
+                 inv_temperature=1):
+        """
+        Paper link: https://arxiv.org/pdf/2406.07548.pdf
+        Here we use the official implementation of the BinarySphericalQuantizer.
+        """
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.beta = beta  # loss weight for commit loss
+        self.gamma0 = gamma0  # loss weight for entropy penalty
+        self.gamma = gamma  # loss weight for entropy penalty
+        self.zeta = zeta  # loss weight for entire entropy penalty
+        self.input_format = input_format
+        assert self.embed_dim % group_size == 0, "embed_dim must be divisible by group_size"
+        self.num_groups = self.embed_dim // group_size
+        self.group_size = group_size
+        assert persample_entropy_compute in ['group', 'analytical'], "persample_entropy_compute must be either 'group' or 'analytical'"
+        assert cb_entropy_compute in ['group', 'nce'], "cb_entropy_compute must be either 'group' or 'nce'"
+        self.persample_entropy_compute = persample_entropy_compute
+        self.cb_entropy_compute = cb_entropy_compute
+        self.l2_norm = l2_norm
+        self.inv_temperature = inv_temperature
+
+        self.register_buffer('basis', 2 ** torch.arange(embed_dim - 1, -1, -1))
+        self.register_buffer('group_basis', 2 ** torch.arange(group_size - 1, -1, -1))
+
+        self.num_dimensions = 2 ** embed_dim
+        self.bits_per_index = embed_dim
+
+        # we only need to keep the codebook portion up to the group size
+        # because we approximate the H loss with this subcode
+        group_codes = torch.arange(2 ** self.group_size)
+        group_codebook = self.indexes_to_codes(group_codes).float()[:, -group_size:]
+        self.register_buffer('group_codebook', group_codebook, persistent=False)
+
+        self.soft_entropy = soft_entropy  # soft_entropy: Sec 3.2 of https://arxiv.org/pdf/1911.05894.pdf
+
+    def quantize(self, z):
+        assert z.shape[-1] == self.embed_dim, f"Expected {self.embed_dim} dimensions, got {z.shape[-1]}"
+
+        zhat = torch.where(z > 0,
+                           torch.tensor(1, dtype=z.dtype, device=z.device),
+                           torch.tensor(-1, dtype=z.dtype, device=z.device))
+        return z + (zhat - z).detach()
+
+    def forward(self, z, collect_metrics=True):
+        # if self.input_format == 'bchw':
+        #     z = rearrange(z, 'b c h w -> b h w c')
+        zq = self.quantize(z)
+
+        q_scale = 1. / (self.embed_dim ** 0.5) if self.l2_norm else 1.
+
+        zq = zq * q_scale
+
+        if not collect_metrics:
+            return zq, zq.new_zeros(()), {}
+
+        indices = self.codes_to_indexes(zq.detach())
+        group_indices = self.codes_to_group_indexes(zq.detach())
+        if not self.training:
+            used_codes = torch.unique(indices, return_counts=False)
+        else:
+            used_codes = None
+
+        if self.soft_entropy:
+            persample_entropy, cb_entropy, avg_prob = self.soft_entropy_loss(z)
+            entropy_penalty = self.gamma0 * persample_entropy - self.gamma * cb_entropy
+        else:
+            zb_by_sample = ((zq + 1) / 2).reshape(z.shape[0], -1, z.shape[-1]).to(torch.float32)
+            persample_entropy = self.get_hard_per_sample_entropy(zb_by_sample)
+            cb_entropy = codebook_entropy(zq, self.basis, self.embed_dim)
+            entropy_penalty = self.gamma0 * persample_entropy - self.gamma * cb_entropy
+
+        # commit loss
+        commit_loss = self.beta * torch.mean(((zq.detach() - z) ** 2).sum(dim=-1))
+
+        # if self.input_format == 'bchw':
+        #     zq = rearrange(zq, 'b h w c -> b c h w')
+
+        return (
+            zq,
+            commit_loss + self.zeta * entropy_penalty / self.inv_temperature,
+            {"H": cb_entropy, "used_codes": used_codes, "indices": indices, "group_indices": group_indices,
+             "avg_prob": avg_prob}
+        )
+
+    def soft_entropy_loss(self, z):
+        # if we divide the code in subgroups of size group_size, the codebook will be of size 2 ** group_size
+        # the sub-code is the last group_size bits of the full code
+        group_code_book = self.group_codebook / (self.embed_dim ** 0.5 if self.l2_norm else 1)
+        divided_z = rearrange(z, '... (g c) -> ... g c', c=self.group_size)
+
+        # we calculate the distance between the divided_z and the codebook for each subgroup
+        distance = - 2 * torch.einsum('... g c, d c ->... g d', divided_z, group_code_book)
+        prob = (-distance * self.inv_temperature).softmax(dim=-1)
+        if self.persample_entropy_compute == 'analytical':
+            if self.l2_norm:
+                p = torch.sigmoid(-4 * z / (self.embed_dim ** 0.5) * self.inv_temperature)
+            else:
+                p = torch.sigmoid(-4 * z * self.inv_temperature)
+            prob = torch.stack([p, 1 - p], dim=-1)
+            per_sample_entropy = self.get_entropy(prob, dim=-1, normalize=False).sum(dim=-1).mean()
+        else:
+            per_sample_entropy = self.get_entropy(prob, dim=-1, normalize=False).sum(dim=-1).mean()
+
+        # macro average of the probability of each subgroup
+        avg_prob = reduce(prob, '... g d ->g d', 'mean')
+        codebook_entropy = self.get_entropy(avg_prob, dim=-1, normalize=False)
+
+        # the approximation of the entropy is the sum of the entropy of each subgroup
+        return per_sample_entropy, codebook_entropy.sum(), avg_prob
+
+    def get_hard_per_sample_entropy(self, zb_by_sample):
+        probs_per_dim = zb_by_sample.sum(1) / zb_by_sample.shape[1]
+        persample_entropy = - probs_per_dim * torch.log(probs_per_dim + 1e-8) - (1 - probs_per_dim) * torch.log(1 - probs_per_dim + 1e-8)
+        persample_entropy = persample_entropy.sum(-1)
+        return persample_entropy.mean()
+
+    def codes_to_indexes(self, zhat):
+        """Converts a `code` to an index in the codebook.
+        Args:
+            zhat: A tensor of shape (B, ..., C) containing the codes. must be in {-1, 1}
+        """
+        assert zhat.shape[-1] == self.embed_dim, f"Expected {self.embed_dim} dimensions, got {zhat.shape[-1]}"
+        return ((zhat + 1) / 2 * self.basis).sum(axis=-1).to(torch.int64)
+
+    def codes_to_group_indexes(self, zhat):
+        """Converts a `code` to a list of indexes (in groups) in the codebook.
+        Args:
+            zhat: A tensor of shape (B, ..., C) containing the codes. must be in {-1, 1}
+        """
+        zhat_in_group = rearrange(zhat, 'b ... (g c) -> b ... g c', c=self.group_size)
+        return ((zhat_in_group + 1) / 2 * self.group_basis).sum(axis=-1).to(torch.int64)
+
+    def indexes_to_codes(self, indices):
+        """Inverse of `indexes_to_codes`."""
+        indices = indices.unsqueeze(-1)
+        codes_non_centered = torch.remainder(
+            torch.floor_divide(indices, self.basis), 2
+        )
+        return codes_non_centered * 2 - 1
+
+    def group_indexes_to_codes(self, group_indices):
+        """Inverse of `group_indexes_to_codes`."""
+        group_indices = group_indices.unsqueeze(-1)
+        codes_non_centered = torch.remainder(
+            torch.floor_divide(group_indices, self.group_basis), 2
+        )
+        codes_non_centered = rearrange(codes_non_centered, 'b ... g c -> b ... (g c)')
+        return codes_non_centered * 2 - 1
+
+    def get_entropy(self, count, dim=-1, eps=1e-4, normalize=True):
+        if normalize:
+            probs = (count + eps) / (count + eps).sum(dim=dim, keepdim=True)
+        else:
+            probs = count
+        H = -(probs * torch.log(probs + 1e-8)).sum(dim=dim)
+        return H
+
+    def get_group_codebook_entry(self, group_indices):
+        z_q = self.group_indexes_to_codes(group_indices)
+        q_scale = 1. / (self.embed_dim ** 0.5) if self.l2_norm else 1.
+        z_q = z_q * q_scale
+        if self.input_format == 'bchw':
+            h, w = int(z_q.shape[1] ** 0.5)
+            assert h * w == z_q.shape[1], 'Invalid sequence length'
+            z_q = rearrange(z_q, 'b (h w) c -> b c h w', h=h)
+        return z_q
+
+    def get_codebook_entry(self, indices):
+        z_q = self.indexes_to_codes(indices)
+        q_scale = 1. / (self.embed_dim ** 0.5) if self.l2_norm else 1.
+        z_q = z_q * q_scale
+        if self.input_format == 'bchw':
+            h, w = int(z_q.shape[1] ** 0.5)
+            assert h * w == z_q.shape[1], 'Invalid sequence length'
+            z_q = rearrange(z_q, 'b (h w) c -> b c h w', h=h)
+        return z_q
+
+
+class BSQuantizer(nn.Module):
+
+    def __init__(self, s1_bits, s2_bits, beta, gamma0, gamma, zeta, group_size):
+        super().__init__()
+        self.codebook_dim = s1_bits + s2_bits
+        self.s1_bits = s1_bits
+        self.s2_bits = s2_bits
+        self.bsq = BinarySphericalQuantizer(self.codebook_dim, beta, gamma0, gamma, zeta, group_size=group_size)
+
+    def bits_to_indices(self, bits):
+        bits = (bits >= 0).to(torch.long)
+        indices = 2 ** torch.arange(
+            0,
+            bits.shape[-1],
+            1,
+            dtype=torch.long,
+            device=bits.device,
+        )
+        return (bits * indices).sum(-1)
+
+    def forward(self, z, half=False, collect_metrics=True):
+        z = F.normalize(z, dim=-1)
+        quantized, bsq_loss, metrics = self.bsq(z, collect_metrics=collect_metrics)
+        if half:
+            q_pre = quantized[:, :, :self.s1_bits]
+            q_post = quantized[:, :, self.s1_bits:]
+            z_indices = [self.bits_to_indices(q_pre), self.bits_to_indices(q_post)]
+        else:
+            z_indices = self.bits_to_indices(quantized)
+        return bsq_loss, quantized, z_indices
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-5):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight
+
+
+class FeedForward(nn.Module):
+    def __init__(self, d_model, ff_dim, ffn_dropout_p=0.0):
+        super().__init__()
+
+        self.w1 = nn.Linear(d_model, ff_dim, bias=False)
+        self.w3 = nn.Linear(d_model, ff_dim, bias=False)
+        self.w2 = nn.Linear(ff_dim, d_model, bias=False)
+        self.ffn_dropout = nn.Dropout(ffn_dropout_p)
+
+    def forward(self, x):
+        return self.ffn_dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
+
+
+class RotaryPositionalEmbedding(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq)
+        self.seq_len_cached = None
+        self.cos_cached = None
+        self.sin_cached = None
+
+    def _update_cos_sin_cache(self, x, seq_len):
+        if seq_len != self.seq_len_cached:
+            self.seq_len_cached = seq_len
+            t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq)
+            freqs = torch.einsum('i,j->ij', t, self.inv_freq)
+            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
+            self.cos_cached = emb.cos()[None, None, :, :]
+            self.sin_cached = emb.sin()[None, None, :, :]
+        return self.cos_cached, self.sin_cached
+
+    def forward(self, q, k):
+        cos, sin = self._update_cos_sin_cache(q, q.shape[-2])
+        return (
+            (q * cos) + (self._rotate_half(q) * sin),
+            (k * cos) + (self._rotate_half(k) * sin),
+        )
+
+    def _rotate_half(self, x):
+        x1, x2 = x.chunk(2, dim=-1)
+        return torch.cat((-x2, x1), dim=-1)
+
+
+class MultiHeadAttentionWithRoPE(nn.Module):
+    def __init__(self, d_model, n_heads, attn_dropout_p=0.0, resid_dropout_p=0.0):
+        super().__init__()
+        self.d_model = d_model
+        self.n_heads = n_heads
+        self.head_dim = d_model // n_heads
+
+        self.q_proj = nn.Linear(d_model, d_model)
+        self.k_proj = nn.Linear(d_model, d_model)
+        self.v_proj = nn.Linear(d_model, d_model)
+        self.out_proj = nn.Linear(d_model, d_model)
+        self.rotary = RotaryPositionalEmbedding(self.head_dim)
+        self.attn_dropout_p = attn_dropout_p
+        self.resid_dropout = nn.Dropout(resid_dropout_p)
+
+    def forward(self, x, key_padding_mask=None):
+        batch_size, seq_len, _ = x.shape
+
+        q = self.q_proj(x).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
+        k = self.k_proj(x).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
+        v = self.v_proj(x).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
+
+        q, k = self.rotary(q, k)
+
+        if key_padding_mask is not None:
+            attn_mask = key_padding_mask.unsqueeze(1).unsqueeze(2)  # [batch, 1, 1, seq_len]
+            attn_mask = attn_mask.expand(-1, self.n_heads, seq_len, -1)  # [batch, n_heads, q_len, k_len]
+        else:
+            attn_mask = None
+
+        attn_output = F.scaled_dot_product_attention(
+            q, k, v,
+            attn_mask=attn_mask,
+            dropout_p=self.attn_dropout_p if self.training else 0.0,
+            is_causal=True
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, seq_len, self.d_model)
+        return self.resid_dropout(self.out_proj(attn_output))
+
+
+class MultiHeadCrossAttentionWithRoPE(nn.Module):
+    def __init__(self, d_model, n_heads, attn_dropout_p=0.0, resid_dropout=0.0):
+        super().__init__()
+        self.d_model = d_model
+        self.n_heads = n_heads
+        self.head_dim = d_model // n_heads
+
+        self.q_proj = nn.Linear(d_model, d_model)
+        self.k_proj = nn.Linear(d_model, d_model)
+        self.v_proj = nn.Linear(d_model, d_model)
+        self.out_proj = nn.Linear(d_model, d_model)
+        self.rotary = RotaryPositionalEmbedding(self.head_dim)
+        self.attn_dropout_p = attn_dropout_p
+        self.resid_dropout = nn.Dropout(resid_dropout)
+
+    def forward(self, query, key, value, key_padding_mask=None):
+        batch_size, q_len, _ = query.shape
+        _, seq_len, _ = key.shape
+
+        q = self.q_proj(query).view(batch_size, q_len, self.n_heads, self.head_dim).transpose(1, 2)
+        k = self.k_proj(key).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
+        v = self.v_proj(value).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
+
+        q, k = self.rotary(q, k)
+
+        if key_padding_mask is not None:
+            attn_mask = key_padding_mask.unsqueeze(1).unsqueeze(2)
+            attn_mask = attn_mask.expand(-1, self.n_heads, q_len, -1)
+        else:
+            attn_mask = None
+
+        is_causal_flag = self.training
+
+        attn_output = F.scaled_dot_product_attention(
+            q, k, v,
+            attn_mask=attn_mask,
+            dropout_p=self.attn_dropout_p if self.training else 0.0,
+            is_causal=is_causal_flag
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, q_len, self.d_model)
+        return self.resid_dropout(self.out_proj(attn_output))
+
+
+class HierarchicalEmbedding(nn.Module):
+    def __init__(self, s1_bits, s2_bits, d_model=256):
+        super().__init__()
+        self.s1_bits = s1_bits
+        self.s2_bits = s2_bits
+
+        vocab_s1 = 2 ** s1_bits
+        vocab_s2 = 2 ** s2_bits
+
+        self.emb_s1 = nn.Embedding(vocab_s1, d_model)
+        self.emb_s2 = nn.Embedding(vocab_s2, d_model)
+        self.d_model = d_model
+        self.fusion_proj = nn.Linear(d_model * 2, d_model)
+
+        nn.init.normal_(self.emb_s1.weight, mean=0, std=d_model ** -0.5)
+        nn.init.normal_(self.emb_s2.weight, mean=0, std=d_model ** -0.5)
+
+    def split_token(self, token_ids: torch.Tensor, s2_bits: int):
+        """Inputs:
+            token_ids (torch.Tensor): Composite token IDs of shape [batch_size, seq_len] or [N], each in range [0, 2^(s1_bits + s2_bits) - 1].
+            s2_bits (int): Number of low bits used for the fine token (s2).
+        """
+        assert isinstance(s2_bits, int) and s2_bits > 0, "s2_bits must be a positive integer"
+
+        t = token_ids.long()
+        mask = (1 << s2_bits) - 1
+        s2_ids = t & mask           # extract low bits
+        s1_ids = t >> s2_bits       # extract high bits
+        return s1_ids, s2_ids
+
+    def forward(self, token_ids):
+        """Inputs:
+        token_ids:
+            - tuple or list: (s1_ids, s2_ids), each of shape [batch_size, seq_len], or
+            - torch.Tensor: composite token IDs of shape [batch_size, seq_len], which will be split into (s1_ids, s2_ids) internally.
+        Output: [batch_size, seq_len, d_model]
+        """
+        if isinstance(token_ids, tuple) or isinstance(token_ids, list):
+            s1_ids, s2_ids = token_ids
+        else:
+            s1_ids, s2_ids = self.split_token(token_ids, self.s2_bits)
+        s1_emb = self.emb_s1(s1_ids) * math.sqrt(self.d_model)
+        s2_emb = self.emb_s2(s2_ids) * math.sqrt(self.d_model)
+        return self.fusion_proj(torch.cat([s1_emb, s2_emb], dim=-1))
+
+
+class DependencyAwareLayer(nn.Module):
+    def __init__(self, d_model, n_heads=4, attn_dropout_p=0.0, resid_dropout=0.0):
+        super().__init__()
+        self.cross_attn = MultiHeadCrossAttentionWithRoPE(d_model, n_heads, attn_dropout_p, resid_dropout)
+        self.norm = RMSNorm(d_model)
+
+    def forward(self, hidden_states, sibling_embed, key_padding_mask=None):
+        """hidden_states: [batch, seq_len, d_model]
+        sibling_embed: Embedding from another subtoken
+        """
+        attn_out = self.cross_attn(
+            query=sibling_embed,
+            key=hidden_states,
+            value=hidden_states,
+            key_padding_mask=key_padding_mask
+        )
+        return self.norm(hidden_states + attn_out)
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, d_model, n_heads, ff_dim=1024, ffn_dropout_p=0.0, attn_dropout_p=0.0, resid_dropout_p=0.0):
+        super().__init__()
+        self.norm1 = RMSNorm(d_model)
+        self.self_attn = MultiHeadAttentionWithRoPE(d_model, n_heads, attn_dropout_p, resid_dropout_p)
+        self.norm2 = RMSNorm(d_model)
+        self.ffn = FeedForward(d_model, ff_dim, ffn_dropout_p)
+
+    def forward(self, x, key_padding_mask=None):
+        residual = x
+        x = self.norm1(x)
+        attn_out = self.self_attn(x, key_padding_mask=key_padding_mask)
+        x = residual + attn_out
+
+        residual = x
+        x = self.norm2(x)
+        ffn_out = self.ffn(x)
+        x = residual + ffn_out
+        return x
+
+
+class DualHead(nn.Module):
+    def __init__(self, s1_bits, s2_bits, d_model):
+        super().__init__()
+        self.vocab_s1 = 2 ** s1_bits
+        self.vocab_s2 = 2 ** s2_bits
+        self.proj_s1 = nn.Linear(d_model, self.vocab_s1)
+        self.proj_s2 = nn.Linear(d_model, self.vocab_s2)
+
+    def compute_loss(self, s1_logits, s2_logits, s1_targets, s2_targets, padding_mask=None):
+        if padding_mask is not None:
+            valid_mask = (padding_mask == 0)
+            s1_logits = s1_logits[valid_mask]
+            s2_logits = s2_logits[valid_mask]
+            s1_targets = s1_targets[valid_mask]
+            s2_targets = s2_targets[valid_mask]
+            ce_s1 = F.cross_entropy(s1_logits, s1_targets)
+            ce_s2 = F.cross_entropy(s2_logits, s2_targets)
+        else:
+            ce_s1 = F.cross_entropy(s1_logits.reshape(-1, self.vocab_s1), s1_targets.reshape(-1))
+            ce_s2 = F.cross_entropy(s2_logits.reshape(-1, self.vocab_s2), s2_targets.reshape(-1))
+        ce_loss = (ce_s1 + ce_s2) / 2
+        return ce_loss, ce_s1, ce_s2
+
+    def forward(self, x):
+        return self.proj_s1(x)
+
+    def cond_forward(self, x2):
+        return self.proj_s2(x2)
+
+
+class FixedEmbedding(nn.Module):
+    def __init__(self, c_in, d_model):
+        super(FixedEmbedding, self).__init__()
+
+        w = torch.zeros(c_in, d_model).float()
+        w.require_grad = False
+
+        position = torch.arange(0, c_in).float().unsqueeze(1)
+        div_term = (torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)).exp()
+
+        w[:, 0::2] = torch.sin(position * div_term)
+        w[:, 1::2] = torch.cos(position * div_term)
+
+        self.emb = nn.Embedding(c_in, d_model)
+        self.emb.weight = nn.Parameter(w, requires_grad=False)
+
+    def forward(self, x):
+        return self.emb(x).detach()
+
+
+class TemporalEmbedding(nn.Module):
+    def __init__(self, d_model, learn_pe):
+        super(TemporalEmbedding, self).__init__()
+
+        minute_size = 60
+        hour_size = 24
+        weekday_size = 7
+        day_size = 32
+        month_size = 13
+
+        Embed = FixedEmbedding if not learn_pe else nn.Embedding
+        self.minute_embed = Embed(minute_size, d_model)
+        self.hour_embed = Embed(hour_size, d_model)
+        self.weekday_embed = Embed(weekday_size, d_model)
+        self.day_embed = Embed(day_size, d_model)
+        self.month_embed = Embed(month_size, d_model)
+
+    def forward(self, x):
+        x = x.long()
+
+        minute_x = self.minute_embed(x[:, :, 0])
+        hour_x = self.hour_embed(x[:, :, 1])
+        weekday_x = self.weekday_embed(x[:, :, 2])
+        day_x = self.day_embed(x[:, :, 3])
+        month_x = self.month_embed(x[:, :, 4])
+
+        return hour_x + weekday_x + day_x + month_x + minute_x

skills/alphaear-predictor/scripts/utils/predictor/training.py

@@ -0,0 +1,539 @@
+import os
+import sys
+import time
+import torch
+import torch.nn as nn
+import pandas as pd
+import numpy as np
+import json
+import random
+from loguru import logger
+from datetime import datetime, timedelta
+from sentence_transformers import SentenceTransformer
+from dotenv import load_dotenv
+
+load_dotenv(os.path.expanduser("~/.config/opencode/.env"))
+
+# Setup paths
+KRONOS_DIR = os.path.dirname(os.path.abspath(__file__))
+SRC_DIR = os.path.dirname(os.path.dirname(KRONOS_DIR))
+if SRC_DIR not in sys.path:
+    sys.path.insert(0, SRC_DIR)
+
+from ..kronos.model import Kronos, KronosTokenizer, KronosPredictor
+from ..database_manager import DatabaseManager
+from ..stock_tools import StockTools
+from ..search_tools import SearchTools
+from ..llm.factory import get_model
+from ..visualizer import VisualizerTools
+from ..schema.models import ForecastResult, KLinePoint
+from agno.agent import Agent
+
+
+class AutoSynthesisTrainer:
+    def __init__(self, news_dim=384):
+        self.device = (
+            "cuda"
+            if torch.cuda.is_available()
+            else "mps"
+            if torch.backends.mps.is_available()
+            else "cpu"
+        )
+        self.db = DatabaseManager()
+        self.tools = StockTools(self.db)
+        self.searcher = SearchTools(self.db)
+        # Try loading from local cache first to avoid network timeouts
+        model_name = os.getenv(
+            "EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2"
+        )
+        try:
+            logger.info(f"🔄 Attempting to load {model_name} from local cache...")
+            self.embedder = SentenceTransformer(
+                model_name, device=self.device, local_files_only=True
+            )
+            logger.success("✅ Model loaded from local cache.")
+        except Exception:
+            logger.warning(
+                "⚠️ Local cache not found or incomplete. Attempting to download..."
+            )
+            self.embedder = SentenceTransformer(model_name, device=self.device)
+        self.news_dim = news_dim
+
+        # Try loading from local cache first to avoid network timeouts
+        try:
+            logger.info(
+                "🔄 Attempting to load Kronos and Tokenizer from local cache..."
+            )
+            self.tokenizer = KronosTokenizer.from_pretrained(
+                "NeoQuasar/Kronos-Tokenizer-base", local_files_only=True
+            ).to(self.device)
+            base_model = Kronos.from_pretrained(
+                "NeoQuasar/Kronos-base", local_files_only=True
+            )
+            logger.success("✅ Kronos and Tokenizer loaded from local cache.")
+        except Exception:
+            logger.warning(
+                "⚠️ Local Kronos/Tokenizer not found or incomplete. Attempting to download..."
+            )
+            self.tokenizer = KronosTokenizer.from_pretrained(
+                "NeoQuasar/Kronos-Tokenizer-base"
+            ).to(self.device)
+            base_model = Kronos.from_pretrained("NeoQuasar/Kronos-base")
+
+        self.model = Kronos(
+            base_model.s1_bits,
+            base_model.s2_bits,
+            base_model.n_layers,
+            base_model.d_model,
+            base_model.n_heads,
+            base_model.ff_dim,
+            base_model.ffn_dropout_p,
+            base_model.attn_dropout_p,
+            base_model.resid_dropout_p,
+            base_model.token_dropout_p,
+            base_model.learn_te,
+            news_dim=self.news_dim,
+        ).to(self.device)
+        self.model.load_state_dict(base_model.state_dict(), strict=False)
+
+        # LLM for causality verification
+        provider = os.getenv("LLM_PROVIDER", "ust")
+        model_id = os.getenv("LLM_MODEL", "Qwen")
+        self.llm_agent = Agent(model=get_model(provider, model_id))
+
+    def discover_shocks(
+        self, ticker_list, threshold=2.0, limit_per_stock=5, days=365, pred_len=5
+    ):
+        """1. Find days with significant price movements (Look back 1 year)"""
+        shocks = []
+        end_date = datetime.now().strftime("%Y-%m-%d")
+        start_date = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
+
+        for ticker in ticker_list:
+            df = self.tools.get_stock_price(
+                ticker, start_date=start_date, end_date=end_date
+            )
+            if df.empty or len(df) < 60:
+                continue
+
+            # Look for big moves
+            moves = df[df["change_pct"].abs() > threshold].copy()
+            if moves.empty:
+                continue
+
+            count = 0
+            for idx, row in moves.iterrows():
+                # Ensure we have history before this day AND enough future days for eval
+                date_idx = df.index.get_loc(idx)
+                if date_idx < 50 or date_idx + pred_len > len(df):
+                    continue
+
+                shocks.append(
+                    {
+                        "ticker": ticker,
+                        "date": row["date"],
+                        "change": row["change_pct"],
+                        "history": df.iloc[date_idx - 50 : date_idx],
+                        "target": df.iloc[
+                            date_idx : date_idx + pred_len
+                        ],  # Now capturing pred_len days
+                    }
+                )
+                count += 1
+                if count >= limit_per_stock:
+                    break
+
+        logger.info(
+            f"✨ Discovered {len(shocks)} potential price shocks over the last {days} days."
+        )
+        return shocks
+
+    def find_reason_and_verify(self, shock):
+        """2. Search for reasons and verify causality using LLM"""
+        ticker_info = self.db.get_stock_by_code(shock["ticker"])
+        name = ticker_info["name"] if ticker_info else shock["ticker"]
+        date_str = shock["date"]
+
+        # Try multiple query variations and engines
+        queries = [
+            f"{name} ({shock['ticker']}) {date_str} 为什么涨跌 原因",
+            f"{name} {date_str} 异动 原因",
+            f"{shock['ticker']} {date_str} 新闻",
+        ]
+
+        search_results = []
+        for query in queries:
+            logger.info(f"🔍 Searching for reason: {query}")
+            # Try alternate engines
+            for engine in ["baidu"]:
+                try:
+                    results = self.searcher.search_list(
+                        query, engine=engine, max_results=3, enrich=False
+                    )
+                    if results:
+                        search_results = results
+                        break
+                except Exception as e:
+                    logger.warning(f"Search failed for {query} on {engine}: {e}")
+
+            if search_results:
+                break
+            time.sleep(random.uniform(1.0, 2.0))
+
+        if not search_results:
+            logger.warning(
+                f"⚠️ No search results found for {name} on {date_str} after multiple attempts."
+            )
+            return None
+
+        context = "\n".join(
+            [f"- {r['title']}: {r.get('content', '')[:300]}" for r in search_results]
+        )
+
+        prompt = f"""
+        任务：判断以下新闻是否解释了该股票在 {date_str} 的 {shock["change"]:.2f}% 价格变动。
+        
+        股票：{name}
+        日期：{date_str}
+        变动：{shock["change"]:.2f}%
+        
+        搜索结果：
+        {context}
+        
+        要求：
+        1. 该新闻是否在该日期左右发生？
+        2. 该新闻是否能逻辑上解释这种大幅波动（如财报、利好政策、重组、大环境暴跌等）？
+        3. 如果是，请总结一段 100 字以内的“核心推动原因”。
+        4. 返回 JSON: {{"is_causal": true/false, "summary": "原因摘要"}}
+        """
+
+        try:
+            res = self.llm_agent.run(prompt)
+            data = json.loads(
+                res.content.replace("```json", "").replace("```", "").strip()
+            )
+            if data.get("is_causal"):
+                logger.success(
+                    f"✅ Verified cause for {name} on {date_str}: {data['summary']}"
+                )
+                return data["summary"]
+            else:
+                logger.warning(
+                    f"❌ Verified cause for {name} on {date_str}: {data['summary']}"
+                )
+                return None
+        except Exception as e:
+            logger.warning(f"Verification failed: {e}")
+        return None
+
+    def save_model(self, path=None):
+        """Save the news_proj weights"""
+        if path is None:
+            save_dir = os.path.join(SRC_DIR, "exports/models")
+            os.makedirs(save_dir, exist_ok=True)
+            path = os.path.join(
+                save_dir, f"kronos_news_v1_{datetime.now().strftime('%Y%m%d_%H%M')}.pt"
+            )
+
+        # We only really need to save the news_proj part as it's the only one we train
+        torch.save(
+            {
+                "news_proj_state_dict": self.model.news_proj.state_dict(),
+                "news_dim": self.news_dim,
+                "d_model": self.model.d_model,
+            },
+            path,
+        )
+        logger.success(f"💾 Model weights saved to {path}")
+        return path
+
+    def run_synthesis_and_train(self, tickers, pred_len=5):
+        # 1. Discovery
+        shocks = self.discover_shocks(tickers, pred_len=pred_len)
+        print(f"find {len(shocks)} shocks")
+
+        # 2. News Association & Verification
+        dataset = []
+        max_news_items = 200  # Limit to 200 news items per session to avoid search bans
+
+        logger.info(
+            f"🧬 Starting News Association for {len(shocks)} shocks (Max limit: {max_news_items})"
+        )
+
+        for i, shock in enumerate(shocks):
+            if len(dataset) >= max_news_items:
+                logger.info("Reached maximum news items limit for this session.")
+                break
+
+            summary = self.find_reason_and_verify(shock)
+            if summary:
+                # 3. Embedding news
+                emb = self.embedder.encode(summary)
+                dataset.append(
+                    {
+                        "history": shock["history"],
+                        "target": shock["target"],
+                        "news_emb": emb,
+                        "summary": summary,
+                    }
+                )
+
+            # Add delay after search with randomness to avoid being blocked
+            if i < len(shocks) - 1:
+                delay = random.uniform(2.0, 4.0)
+                time.sleep(delay)
+
+        if not dataset:
+            logger.error(
+                "❌ No verified news-price pairs found. Adjust threshold or check if news is available in that period."
+            )
+            return
+
+        # 4. Train/Val Split
+        random.seed(42)
+        random.shuffle(dataset)
+
+        if len(dataset) < 2:
+            train_set = dataset
+            val_set = []
+            logger.warning(
+                f"⚠️ Only {len(dataset)} sample(s) found. Training on all, skipping validation."
+            )
+        else:
+            split_idx = max(1, int(len(dataset) * 0.8))
+            if split_idx >= len(dataset):
+                split_idx = len(dataset) - 1
+
+            train_set = dataset[:split_idx]
+            val_set = dataset[split_idx:]
+            logger.info(
+                f"🏗️ Dataset Split: {len(train_set)} samples for training, {len(val_set)} for validation."
+            )
+
+        if not train_set:
+            logger.error("❌ No samples for training.")
+            return
+
+        # 5. Training (Few-shot)
+        optimizer = torch.optim.Adam(self.model.news_proj.parameters(), lr=1e-3)
+        criterion = nn.CrossEntropyLoss()
+        self.model.train()
+
+        loss_history = []
+        logger.info(f"🚀 Training for 30 epochs...")
+        for epoch in range(30):
+            total_loss = 0
+            for item in train_set:
+                optimizer.zero_grad()
+
+                # Prep Data
+                hist_df = item["history"]
+                # For training, we still focus on the immediate next point (teacher forcing)
+                target_df = item["target"].iloc[:1]
+
+                hist_raw = hist_df[
+                    ["open", "high", "low", "close", "volume"]
+                ].values.astype(np.float32)
+                hist_raw = np.column_stack([hist_raw, hist_raw[:, 3] * hist_raw[:, 4]])
+
+                mean, std = hist_raw.mean(axis=0), hist_raw.std(axis=0) + 1e-5
+                hist_norm = (
+                    torch.from_numpy((hist_raw - mean) / std)
+                    .unsqueeze(0)
+                    .to(self.device)
+                )
+
+                target_raw = target_df[
+                    ["open", "high", "low", "close", "volume"]
+                ].values.astype(np.float32)
+                target_raw = np.column_stack(
+                    [target_raw, target_raw[:, 3] * target_raw[:, 4]]
+                )
+                target_norm = (
+                    torch.from_numpy((target_raw - mean) / std)
+                    .unsqueeze(0)
+                    .to(self.device)
+                )
+
+                with torch.no_grad():
+                    z_indices = self.tokenizer.encode(hist_norm, half=True)
+                    t_indices = self.tokenizer.encode(target_norm, half=True)
+                    s1_ids, s2_ids = z_indices[0], z_indices[1]
+                    t_s1, t_s2 = t_indices[0], t_indices[1]
+
+                news_t = torch.from_numpy(item["news_emb"]).unsqueeze(0).to(self.device)
+                s1_logits, s2_logits = self.model(
+                    s1_ids,
+                    s2_ids,
+                    news_emb=news_t,
+                    use_teacher_forcing=True,
+                    s1_targets=t_s1,
+                )
+
+                loss = (
+                    criterion(s1_logits[:, -1, :], t_s1[:, 0])
+                    + criterion(s2_logits[:, -1, :], t_s2[:, 0])
+                ) / 2
+                loss.backward()
+                optimizer.step()
+                total_loss += loss.item()
+
+            avg_epoch_loss = total_loss / max(1, len(train_set))
+            loss_history.append(avg_epoch_loss)
+
+            if (epoch + 1) % 10 == 0:
+                logger.info(f"Epoch {epoch + 1} Loss: {avg_epoch_loss:.4f}")
+
+        # 5.1 Visualize Loss Curve
+        loss_chart = VisualizerTools.generate_loss_chart(loss_history)
+        VisualizerTools.render_chart_to_file(
+            loss_chart,
+            os.path.join(SRC_DIR, "exports/training_results/loss_curve.html"),
+        )
+
+        # 5.2 Save final model
+        self.save_model()
+
+        # 6. Final Evaluation on Validation Set
+        if not val_set:
+            logger.warning("⚠️ Validation set is empty. Skipping statistical analysis.")
+            return
+
+        logger.info(
+            f"🧪 Final Evaluation: Base vs News-Integrated ({pred_len}-day Window)"
+        )
+        self.model.eval()
+        predictor = KronosPredictor(self.model, self.tokenizer, device=self.device)
+
+        base_maes = []
+        news_maes = []
+
+        print("\n" + "=" * 90)
+        print(
+            f"{'Date':<12} | {'Ticker':<8} | {'Base MAE':<15} | {'News MAE':<15} | {'Improvement'}"
+        )
+        print("-" * 90)
+
+        for item in val_set:
+            h = item["history"]
+            t = item["target"]
+            actuals = t["close"].values[:pred_len]
+
+            x_ts = pd.to_datetime(h["date"])
+            # Future timestamps: handle business days if possible, or just simple offset
+            future_dates = pd.date_range(
+                start=x_ts.iloc[-1] + timedelta(days=1), periods=pred_len, freq="B"
+            )
+            y_ts = pd.Series(future_dates)
+
+            # A. Base Prediction
+            p_base = predictor.predict(
+                h, x_ts, y_ts, pred_len=pred_len, news_emb=None, verbose=False
+            )
+            b_preds = p_base["close"].values[: len(actuals)]
+
+            # B. News-Aware Prediction
+            p_news = predictor.predict(
+                h,
+                x_ts,
+                y_ts,
+                pred_len=pred_len,
+                news_emb=item["news_emb"],
+                verbose=False,
+            )
+            n_preds = p_news["close"].values[: len(actuals)]
+
+            # Calculate MAE over the window
+            b_mae = np.mean(np.abs(b_preds - actuals))
+            n_mae = np.mean(np.abs(n_preds - actuals))
+
+            base_maes.append(b_mae)
+            news_maes.append(n_mae)
+
+            improvement = (b_mae - n_mae) / (b_mae + 1e-6) * 100
+
+            date_str = str(t["date"].values[0])[:10]
+            ticker = h.iloc[-1]["ticker"] if "ticker" in h.columns else "Stock"
+            print(
+                f"{date_str:<12} | {ticker:<8} | {b_mae:<15.4f} | {n_mae:<15.4f} | {improvement:>+7.1f}%"
+            )
+
+            # C. Generate Visualization for this case
+            try:
+                # Helper to convert DF to KLinePoints
+                def to_kp_list(preds_df):
+                    points = []
+                    for idx, row in preds_df.iterrows():
+                        points.append(
+                            KLinePoint(
+                                date=str(idx)[:10],
+                                open=row["open"],
+                                high=row["high"],
+                                low=row["low"],
+                                close=row["close"],
+                                volume=row["volume"] if "volume" in row else 0,
+                            )
+                        )
+                    return points
+
+                forecast_obj = ForecastResult(
+                    ticker=ticker,
+                    base_forecast=to_kp_list(p_base),
+                    adjusted_forecast=to_kp_list(p_news),
+                    rationale=item["summary"],
+                )
+
+                # Ground truth for visualizer expects a DataFrame with 'date' and 'close'
+                gt_df = t[["date", "open", "high", "low", "close", "volume"]]
+
+                chart = VisualizerTools.generate_stock_chart(
+                    df=h,
+                    ticker=ticker,
+                    title=f"Training Eval: {ticker} ({date_str}) Improvement: {improvement:.1f}%",
+                    forecast=forecast_obj,
+                    ground_truth=gt_df,
+                )
+
+                safe_date = date_str.replace("-", "")
+                filename = f"eval_{ticker}_{safe_date}.html"
+                VisualizerTools.render_chart_to_file(
+                    chart, os.path.join(SRC_DIR, f"exports/training_results/{filename}")
+                )
+            except Exception as e:
+                logger.error(f"Failed to generate eval chart for {ticker}: {e}")
+
+        # Summary Statistics
+        avg_base_err = sum(base_maes) / max(1, len(base_maes))
+        avg_news_err = sum(news_maes) / max(1, len(news_maes))
+        overall_imp = (avg_base_err - avg_news_err) / (avg_base_err + 1e-6) * 100
+
+        print("-" * 90)
+        print(
+            f"{'AVERAGE':<12} | {'-':<8} | {avg_base_err:<15.4f} | {avg_news_err:<15.4f} | {overall_imp:>+7.1f}%"
+        )
+        print("=" * 90 + "\n")
+
+        logger.success(
+            f"🏁 Statistical Analysis Complete. Avg Error Reduction ({pred_len}-day): {overall_imp:.2f}%"
+        )
+        logger.info(
+            f"📊 Visualization results saved to: {os.path.join(SRC_DIR, 'exports/training_results/')}"
+        )
+
+
+if __name__ == "__main__":
+    trainer = AutoSynthesisTrainer()
+
+    logger.info("📂 Fetching all stock codes from database...")
+    res = trainer.db.execute_query("SELECT code FROM stock_list")
+    all_tickers = [row["code"] for row in res]
+
+    if not all_tickers:
+        logger.warning("⚠️ No tickers found in stock_list table. Trying to sync...")
+        trainer.tools._check_and_update_stock_list(force=True)
+        res = trainer.db.execute_query("SELECT code FROM stock_list")
+        all_tickers = [row["code"] for row in res]
+
+    logger.info(f"🚀 Starting training on potential stocks (1-year scan)...")
+    # 为了演示，我们扫描前 100 个股票，寻找最近一年的冲击点
+    trainer.run_synthesis_and_train(all_tickers[:100], pred_len=1)

skills/alphaear-predictor/scripts/utils/search_tools.py

@@ -0,0 +1,611 @@
+import os
+import hashlib
+import json
+import re
+import requests
+import time
+import threading
+from typing import List, Dict, Optional, Any
+from agno.tools.duckduckgo import DuckDuckGoTools
+from agno.tools.baidusearch import BaiduSearchTools
+from agno.agent import Agent
+from loguru import logger
+from datetime import datetime
+from .database_manager import DatabaseManager
+from .content_extractor import ContentExtractor
+from .llm.factory import get_model
+from .hybrid_search import LocalNewsSearch
+
+# 默认搜索缓存 TTL（秒），可通过环境变量覆盖
+DEFAULT_SEARCH_TTL = int(os.getenv("SEARCH_CACHE_TTL", "3600"))  # 默认 1 小时
+
+
+class JinaSearchEngine:
+    """Jina Search API 封装 - 使用 s.jina.ai 进行网络搜索"""
+    
+    JINA_SEARCH_URL = "https://s.jina.ai/"
+    
+    # 速率限制配置
+    _rate_limit_no_key = 10  # 无 key 时每分钟最大请求数
+    _rate_window = 60.0
+    _min_interval = 2.0
+    _request_times = []
+    _last_request_time = 0.0
+    _lock = threading.Lock()
+    
+    def __init__(self):
+        self.api_key = os.getenv("JINA_API_KEY", "").strip()
+        self.has_api_key = bool(self.api_key)
+        if self.has_api_key:
+            logger.info("✅ Jina Search API key configured")
+    
+    @classmethod
+    def _wait_for_rate_limit(cls, has_api_key: bool) -> None:
+        """等待以满足速率限制"""
+        if has_api_key:
+            time.sleep(0.3)
+            return
+        
+        with cls._lock:
+            current_time = time.time()
+            cls._request_times = [t for t in cls._request_times if current_time - t < cls._rate_window]
+            
+            if len(cls._request_times) >= cls._rate_limit_no_key:
+                oldest = cls._request_times[0]
+                wait_time = cls._rate_window - (current_time - oldest) + 1.0
+                if wait_time > 0:
+                    logger.warning(f"⏳ Jina Search rate limit, waiting {wait_time:.1f}s...")
+                    time.sleep(wait_time)
+                    current_time = time.time()
+                    cls._request_times = [t for t in cls._request_times if current_time - t < cls._rate_window]
+            
+            time_since_last = current_time - cls._last_request_time
+            if time_since_last < cls._min_interval:
+                time.sleep(cls._min_interval - time_since_last)
+            
+            cls._request_times.append(time.time())
+            cls._last_request_time = time.time()
+    
+    def search(self, query: str, max_results: int = 5) -> List[Dict]:
+        """
+        使用 Jina Search API 执行搜索
+        
+        Args:
+            query: 搜索关键词
+            max_results: 返回结果数量
+            
+        Returns:
+            搜索结果列表，每个结果包含 title, url, content
+        """
+        if not query:
+            return []
+        
+        logger.info(f"🔍 Jina Search: {query}")
+        
+        # 等待速率限制
+        self._wait_for_rate_limit(self.has_api_key)
+        
+        headers = {
+            "Accept": "application/json",
+            "X-Retain-Images": "none",
+        }
+        
+        if self.has_api_key:
+            headers["Authorization"] = f"Bearer {self.api_key}"
+        
+        try:
+            # Jina Search API: https://s.jina.ai/{query}
+            import urllib.parse
+            encoded_query = urllib.parse.quote(query)
+            url = f"{self.JINA_SEARCH_URL}{encoded_query}"
+            
+            response = requests.get(url, headers=headers, timeout=30)
+            
+            if response.status_code == 429:
+                logger.warning("⚠️ Jina Search rate limited (429), waiting 30s...")
+                time.sleep(30)
+                return self.search(query, max_results)
+            
+            if response.status_code != 200:
+                logger.warning(f"Jina Search failed (Status {response.status_code})")
+                return []
+            
+            # 解析响应
+            try:
+                data = response.json()
+            except json.JSONDecodeError:
+                # 如果返回纯文本，尝试解析
+                data = {"data": [{"title": "Search Result", "url": "", "content": response.text}]}
+            
+            results = []
+            
+            # Jina 返回格式可能是 {"data": [...]} 或直接是列表
+            items = data.get("data", []) if isinstance(data, dict) else data
+            if not isinstance(items, list):
+                items = [items] if items else []
+            
+            for i, item in enumerate(items[:max_results]):
+                if isinstance(item, dict):
+                    results.append({
+                        "title": item.get("title", f"Result {i+1}"),
+                        "url": item.get("url", ""),
+                        "href": item.get("url", ""),  # 兼容性
+                        "content": item.get("content", item.get("description", "")),
+                        "body": item.get("content", item.get("description", "")),  # 兼容性
+                    })
+                elif isinstance(item, str):
+                    results.append({
+                        "title": f"Result {i+1}",
+                        "url": "",
+                        "content": item
+                    })
+            
+            logger.info(f"✅ Jina Search returned {len(results)} results")
+            return results
+            
+        except requests.exceptions.Timeout:
+            logger.error("Jina Search timeout")
+            return []
+        except requests.exceptions.RequestException as e:
+            logger.error(f"Jina Search request error: {e}")
+            return []
+        except Exception as e:
+            logger.error(f"Jina Search unexpected error: {e}")
+            return []
+
+class SearchTools:
+    """扩展性搜索工具库 - 支持多引擎聚合与内容缓存"""
+    
+    def __init__(self, db: DatabaseManager):
+        self.db = db
+        
+        # 检查 Jina API Key 是否配置
+        jina_api_key = os.getenv("JINA_API_KEY", "").strip()
+        self._jina_enabled = bool(jina_api_key)
+        
+        self._engines = {
+            "ddg": DuckDuckGoTools(),
+            "baidu": BaiduSearchTools(),
+            "local": LocalNewsSearch(db)
+        }
+        
+        # 如果配置了 Jina API Key，添加 Jina 引擎
+        if self._jina_enabled:
+            self._engines["jina"] = JinaSearchEngine()
+            logger.info("🚀 Jina Search engine enabled (JINA_API_KEY configured)")
+        
+        # 确定默认搜索引擎
+        self._default_engine = "jina" if self._jina_enabled else "ddg"
+
+    def _generate_hash(self, query: str, engine: str, max_results: int) -> str:
+        return hashlib.md5(f"{engine}:{query}:{max_results}".encode()).hexdigest()
+
+    def search(self, query: str, engine: str = None, max_results: int = 5, ttl: Optional[int] = None) -> str:
+        """
+        使用指定搜索引擎执行网络搜索，结果会被缓存以提高效率。
+        
+        Args:
+            query: 搜索关键词，如 "英伟达财报" 或 "光伏行业政策"。
+            engine: 搜索引擎选择。可选值: 
+                    "jina" (Jina Search，需配置 JINA_API_KEY，LLM友好输出),
+                    "ddg" (DuckDuckGo，推荐英文/国际搜索), 
+                    "baidu" (百度，推荐中文/国内搜索),
+                    "local" (本地历史新闻搜索，基于向量+BM25)。
+                    默认: 若配置了 JINA_API_KEY 则使用 "jina"，否则 "ddg"。
+            max_results: 期望返回的结果数量，默认 5 条。
+            ttl: 缓存有效期（秒）。如果缓存超过此时间会重新搜索。
+                 默认使用环境变量 SEARCH_CACHE_TTL 或 3600 秒。
+                 设为 0 可强制刷新。
+        
+        Returns:
+            搜索结果的文本描述，包含标题、摘要和链接。
+        """
+        # 使用默认引擎（如果配置了 Jina 则优先使用 Jina）
+        if engine is None:
+            engine = self._default_engine
+        
+        if engine not in self._engines:
+            return f"Error: Unsupported engine '{engine}'. Available: {list(self._engines.keys())}"
+
+        query_hash = self._generate_hash(query, engine, max_results)
+        effective_ttl = ttl if ttl is not None else DEFAULT_SEARCH_TTL
+        
+        # 1. 尝试从缓存读取 (local 引擎不缓存，因为它本身就是查库)
+        if engine != "local":
+            cache = self.db.get_search_cache(query_hash, ttl_seconds=effective_ttl if effective_ttl > 0 else None)
+            if cache and effective_ttl != 0:
+                logger.info(f"ℹ️ Found search results in cache for: {query} ({engine})")
+                return cache['results']
+
+        # 2. 执行真实搜索
+        logger.info(f"📡 Searching {engine} for: {query}")
+        try:
+            tool = self._engines[engine]
+            if engine == "jina":
+                # Jina Search 返回 List[Dict]
+                jina_results = tool.search(query, max_results=max_results)
+                results = []
+                for r in jina_results:
+                    results.append({
+                        "title": r.get("title", ""),
+                        "href": r.get("url", ""),
+                        "body": r.get("content", "")
+                    })
+            elif engine == "ddg":
+                results = tool.duckduckgo_search(query, max_results=max_results)
+            elif engine == "baidu":
+                results = tool.baidu_search(query, max_results=max_results)
+            elif engine == "local":
+                # LocalNewsSearch 返回的是 List[Dict]
+                local_results = tool.search(query, top_n=max_results)
+                results = []
+                for r in local_results:
+                    results.append({
+                        "title": r.get("title"),
+                        "href": r.get("url", "local"),
+                        "body": r.get("content", "")
+                    })
+            else:
+                results = "Search not implemented for this engine."
+            
+            results_str = str(results)
+            if engine != "local":
+                self.db.save_search_cache(query_hash, query, engine, results_str)
+            return results_str
+            
+        except Exception as e:
+            # 搜索失败时的降级策略
+            if engine == "jina":
+                logger.warning(f"⚠️ Jina search failed, falling back to ddg: {query} ({e})")
+                try:
+                    return self.search(query, engine="ddg", max_results=max_results, ttl=ttl)
+                except Exception as e2:
+                    logger.error(f"❌ DDG fallback also failed for {query}: {e2}")
+            elif engine == "ddg":
+                logger.warning(f"⚠️ DDG search failed, falling back to baidu: {query} ({e})")
+                try:
+                    return self.search(query, engine="baidu", max_results=max_results, ttl=ttl)
+                except Exception as e2:
+                    logger.error(f"❌ Baidu fallback also failed for {query}: {e2}")
+
+            logger.error(f"❌ Search failed for {query}: {e}")
+            return f"Error occurred during search: {str(e)}"
+
+    def search_list(self, query: str, engine: str = None, max_results: int = 5, ttl: Optional[int] = None, enrich: bool = True) -> List[Dict]:
+        """
+        执行搜索并返回结构化列表 (List[Dict])。
+        Dict 包含: title, href (or url), body (or snippet)
+        
+        Args:
+            engine: 搜索引擎，默认使用配置的默认引擎（Jina 优先）
+            enrich: 是否抓取正文内容 (默认 True)
+        """
+        # 使用默认引擎
+        if engine is None:
+            engine = self._default_engine
+            
+        if engine not in self._engines:
+            logger.error(f"Unsupported engine {engine}")
+            return []
+            
+        # 不同的 hash 以区分是否 enrichment
+        enrich_suffix = ":enriched" if enrich else ""
+        query_hash = self._generate_hash(query, engine + enrich_suffix, max_results)
+        effective_ttl = ttl if ttl is not None else DEFAULT_SEARCH_TTL
+        
+        # 1. 尝试从缓存读取
+        cache = self.db.get_search_cache(query_hash, ttl_seconds=effective_ttl if effective_ttl > 0 else None)
+        if cache and effective_ttl != 0:
+            try:
+                cached_data = json.loads(cache['results'])
+                if isinstance(cached_data, list):
+                    logger.info(f"ℹ️ Found structured search cache for: {query}")
+                    return cached_data
+            except:
+                pass
+        
+        # 1.5 Smart Cache (Fuzzy + LLM)
+        if effective_ttl != 0:
+            try:
+                # 1. Similar cached queries
+                similar_queries = self.db.find_similar_queries(query, limit=3)
+                # Filter by TTL
+                valid_candidates = []
+                for q in similar_queries:
+                    if q['query'] == query: continue 
+                    q_time = datetime.fromisoformat(q['timestamp'])
+                    if effective_ttl and (datetime.now() - q_time).total_seconds() > effective_ttl:
+                        continue
+                    q['type'] = 'cached_search'
+                    valid_candidates.append(q)
+
+                # 2. Relevant local news (as search results)
+                local_news = self.db.search_local_news(query, limit=3)
+                if local_news:
+                    # Group local news as a single "candidate" source? Or individual?
+                    # Better to treat "Local News Database" as one candidate source that contains X items.
+                    # Or just add them to candidates list?
+                    # Let's package strictly relevant news as a "local_news_bundle"
+                    valid_candidates.append({
+                        'type': 'local_news',
+                        'query': 'Local Database News',
+                        'items': local_news,
+                        'timestamp': datetime.now().isoformat()
+                    })
+                
+                if valid_candidates:
+                    logger.info(f"🤔 Found {len(valid_candidates)} smart cache candidates (Queries/News). Asking LLM...")
+                    evaluation = self._evaluate_cache_relevance(query, valid_candidates)
+                    
+                    if evaluation and evaluation.get('reuse', False):
+                        idx = evaluation.get('index', -1)
+                        if 0 <= idx < len(valid_candidates):
+                            chosen = valid_candidates[idx]
+                            logger.info(f"🤖 LLM suggested reusing: '{chosen.get('query')}' ({chosen['type']})")
+                            
+                            if chosen['type'] == 'cached_search':
+                                # Load the chosen cache
+                                cache = self.db.get_search_cache(chosen['query_hash']) 
+                                if cache:
+                                    try:
+                                        cached_data = json.loads(cache['results'])
+                                        if isinstance(cached_data, list):
+                                            return cached_data
+                                    except:
+                                        pass
+                            elif chosen['type'] == 'local_news':
+                                # Convert local news items to search result format
+                                news_results = []
+                                for i, news in enumerate(chosen['items'], 1):
+                                    news_results.append({
+                                        "id": news.get('id'),
+                                        "rank": i,
+                                        "title": news.get('title'),
+                                        "url": news.get('url'),
+                                        "content": news.get('content'),
+                                        "original_snippet": news.get('content')[:200] if news.get('content') else '',
+                                        "source": f"Local News ({news.get('source')})",
+                                        "publish_time": news.get('publish_time'),
+                                        "crawl_time": news.get('crawl_time'),
+                                        "sentiment_score": news.get('sentiment_score', 0),
+                                        "meta_data": {"origin": "local_db"}
+                                    })
+                                return news_results
+
+            except Exception as e:
+                logger.warning(f"Smart cache check failed: {e}")
+        
+        # 2. 执行搜索
+        logger.info(f"📡 Searching {engine} (structured) for: {query}")
+        try:
+            tool = self._engines[engine]
+            results = []
+            if engine == "jina":
+                # Jina Search 直接返回结构化数据
+                jina_results = tool.search(query, max_results=max_results)
+                for r in jina_results:
+                    results.append({
+                        "title": r.get("title", ""),
+                        "url": r.get("url", ""),
+                        "href": r.get("url", ""),
+                        "body": r.get("content", ""),
+                        "content": r.get("content", ""),
+                        "source": "Jina Search"
+                    })
+            elif engine == "ddg":
+                results = tool.duckduckgo_search(query, max_results=max_results)
+            elif engine == "baidu":
+                results = tool.baidu_search(query, max_results=max_results)
+            elif engine == "local":
+                # LocalNewsSearch 返回的是 List[Dict]
+                local_results = tool.search(query, top_n=max_results)
+                results = []
+                for r in local_results:
+                    results.append({
+                        "title": r.get("title"),
+                        "url": r.get("url", "local"),
+                        "body": r.get("content", "")[:500],
+                        "source": f"Local ({r.get('source', 'db')})",
+                        "publish_time": r.get("publish_time")
+                    })
+            
+            # 处理字符串类型的 JSON 返回 (Baidu 常返 JSON 字符串)
+            if isinstance(results, str) and engine not in ["local", "jina"]:
+                try:
+                    results = json.loads(results)
+                except:
+                    pass
+            
+            # 转为统一格式
+            normalized_results = []
+            if isinstance(results, list):
+                
+                for i, r in enumerate(results, 1):
+                    title = r.get('title', '')
+                    url = r.get('href') or r.get('url') or r.get('link', '')
+                    content = r.get('body') or r.get('snippet') or r.get('abstract', '')
+                    
+                    if title and url:
+                        normalized_results.append({
+                            "id": self._generate_hash(url + query, "search_item", i),
+                            "rank": i,
+                            "title": title,
+                            "url": url,
+                            "content": content,
+                            "original_snippet": content, # 保留摘要
+                            "source": f"Search ({engine})",
+                            "publish_time": datetime.now().isoformat(), # 暂用当前时间
+                            "crawl_time": datetime.now().isoformat(),
+                            "meta_data": {"query": query, "engine": engine}
+                        })
+            
+            # Fallback if still string and failed to parse
+            elif isinstance(results, str) and results:
+                 normalized_results.append({"title": query, "url": "", "content": results, "source": engine})
+
+            # 3. 抓取正文 & 计算情绪 (Enrichment)
+            # 注意：如果使用 Jina Search，内容已经是 LLM 友好格式，可选择跳过 enrichment
+            skip_content_enrichment = (engine == "jina")
+            
+            if enrich and normalized_results:
+                logger.info(f"🕸️ Enriching {len(normalized_results)} search results with Jina & Sentiment...")
+                extractor = ContentExtractor()
+                
+                # Lazy load sentiment tool
+                if not hasattr(self, 'sentiment_tool') or self.sentiment_tool is None:
+                    from ..sentiment_tools import SentimentTools
+                    self.sentiment_tool = SentimentTools(self.db)
+                
+                for item in normalized_results:
+                    if item.get("url"):
+                        try:
+                            # 如果是 Jina Search，内容已经足够好，跳过额外抓取
+                            if skip_content_enrichment and item.get("content") and len(item.get("content", "")) > 100:
+                                full_content = item["content"]
+                            else:
+                                # Use Jina Reader to get full content
+                                full_content = extractor.extract_with_jina(item["url"], timeout=60)
+                            
+                            if full_content and len(full_content) > 100:
+                                item["content"] = full_content
+                                
+                                # Calculate sentiment
+                                # Use title + snippet of content for efficiency
+                                text_to_analyze = f"{item['title']} {full_content[:500]}"
+                                sent_result = self.sentiment_tool.analyze_sentiment(text_to_analyze)  # Using self.sentiment_tool
+                                score = sent_result.get('score', 0.0)
+                                item["sentiment_score"] = float(score)
+                                
+                                logger.info(f"  ✅ Enriched: {item['title'][:20]}... (Sentiment: {score:.2f})")
+                            else:
+                                # Fallback: Use snippet for sentiment
+                                logger.info(f"  ⚠️ Content short/failed for {item['url']}, using snippet for sentiment.")
+                                text_to_analyze = f"{item['title']} {item['content']}" # content is snippet here
+                                sent_result = self.sentiment_tool.analyze_sentiment(text_to_analyze)
+                                score = sent_result.get('score', 0.0)
+                                item["sentiment_score"] = float(score)
+
+                        except Exception as e:
+                             # Fallback: Use snippet for sentiment on error
+                            logger.warning(f"Failed to enrich {item['url']}: {e}. Using snippet.")
+                            text_to_analyze = f"{item['title']} {item['content']}"
+                            sent_result = self.sentiment_tool.analyze_sentiment(text_to_analyze)
+                            score = sent_result.get('score', 0.0)
+                            item["sentiment_score"] = float(score)
+            
+            # 缓存结果 list
+            if normalized_results:
+                # Pass list directly, DB manager will handle JSON dump for main cache and populate search_details
+                # Only cache if NOT from local news reuse (though this logic path is for fresh search)
+                self.db.save_search_cache(query_hash, query, engine, normalized_results)
+            
+            return normalized_results
+            
+        except Exception as e:
+            # 搜索失败时的降级策略
+            if engine == "jina":
+                logger.warning(f"⚠️ Jina search_list failed, falling back to ddg: {query} ({e})")
+                try:
+                    return self.search_list(query, engine="ddg", max_results=max_results, ttl=ttl, enrich=enrich)
+                except Exception as e2:
+                    logger.error(f"❌ DDG fallback (search_list) also failed for {query}: {e2}")
+            elif engine == "ddg":
+                logger.warning(f"⚠️ DDG search_list failed, falling back to baidu: {query} ({e})")
+                try:
+                    return self.search_list(query, engine="baidu", max_results=max_results, ttl=ttl, enrich=enrich)
+                except Exception as e2:
+                    logger.error(f"❌ Baidu fallback (search_list) also failed for {query}: {e2}")
+
+            logger.error(f"❌ Structured search failed for {query}: {e}")
+            return []
+
+    def _evaluate_cache_relevance(self, current_query: str, candidates: List[Dict]) -> Dict:
+        """
+        使用 LLM 评估缓存候选是否足以回答当前问题。
+        """
+        try:
+            # Prepare candidates text
+            candidates_desc = []
+            for i, c in enumerate(candidates):
+                if c['type'] == 'cached_search':
+                    # Preview cached results if available? 
+                    # Maybe just use the query string as a proxy for what's in there.
+                    # Or peek at 'results' snippet.
+                    preview = ""
+                    try:
+                         # Attempt to peek first result title from JSON string
+                         # Note: c.get('results') might be a stringified JSON list
+                         res_list = json.loads(c.get('results', '[]'))
+                         if res_list and isinstance(res_list, list) and len(res_list) > 0:
+                             first_item = res_list[0]
+                             if isinstance(first_item, dict) and 'title' in first_item:
+                                 preview = f" (Contains: {first_item.get('title', '')[:50]}...)"
+                    except:
+                        pass
+                    candidates_desc.append(f"[{i}] Old Search Query: '{c['query']}' {preview} (Time: {c['timestamp']})")
+                elif c['type'] == 'local_news':
+                     # List titles of local news
+                     titles = [item['title'] for item in c['items'][:3]]
+                     candidates_desc.append(f"[{i}] Local Database News: {', '.join(titles)}... (Time: {c['timestamp']})")
+
+            prompt = f"""
+            Task: Decide if existing information is sufficient for the new search query.
+            
+            New Query: "{current_query}"
+            
+            Available Information Candidates:
+            {chr(10).join(candidates_desc)}
+            
+            Instructions:
+            1. Analyze if any candidate provides ENOUGH up-to-date info for the "New Query".
+            2. If yes, choose the best one.
+            3. If the query implies needing LATEST real-time info and candidates are old, choose none.
+            4. Return strictly JSON: {{"reuse": true/false, "index": <candidate_index_int>, "reason": "short explanation"}}
+            """
+            # 初始化模型
+            provider = os.getenv("LLM_PROVIDER", "ust")
+            model_id = os.getenv("LLM_MODEL", "Qwen")
+            host = os.getenv("LLM_HOST")
+            if host:
+                model = get_model(provider, model_id, host=host)
+            else:
+                model = get_model(provider, model_id)
+                
+            agent = Agent(model=model, markdown=True)
+            
+            response = agent.run(prompt)
+            content = response.content
+            
+            # Parse JSON
+            json_match = re.search(r'```json\s*(.*?)\s*```', content, re.DOTALL)
+            if json_match:
+                return json.loads(json_match.group(1))
+            elif '{' in content:
+                 # Fallback for cases where LLM doesn't wrap in ```json
+                 return json.loads(content[content.find('{'):content.rfind('}')+1])
+            return {"reuse": False}
+            
+        except Exception as e:
+            logger.warning(f"LLM evaluation failed: {e}")
+            return {"reuse": False}
+
+    def aggregate_search(self, query: str, engines: Optional[List[str]] = None, max_results: int = 5) -> str:
+        """
+        使用多个搜索引擎同时搜索并聚合结果，获得更全面的信息覆盖。
+        
+        Args:
+            query: 搜索关键词。
+            engines: 要使用的搜索引擎列表。可选值: ["ddg", "baidu"]。
+                     默认同时使用 ddg 和 baidu。
+            max_results: 每个引擎期望返回的结果数量。
+        
+        Returns:
+            聚合后的搜索结果，按引擎分组显示。
+        """
+        engines = engines or ["ddg", "baidu"]
+        aggregated_results = []
+        for engine in engines:
+            res = self.search(query, engine=engine, max_results=max_results)
+            aggregated_results.append(f"--- Results from {engine.upper()} ---\n{res}")
+        
+        return "\n\n".join(aggregated_results)

skills/alphaear-predictor/scripts/utils/stock_tools.py

@@ -0,0 +1,257 @@
+from datetime import datetime, timedelta
+from typing import List, Dict, Optional
+import akshare as ak
+import pandas as pd
+import re
+import sqlite3
+from requests.exceptions import RequestException
+from loguru import logger
+from .database_manager import DatabaseManager
+import os
+from contextlib import contextmanager
+
+@contextmanager
+def temporary_no_proxy():
+    """Context manager to temporarily unset proxy environment variables."""
+    proxies = {k: os.environ.get(k) for k in ['http_proxy', 'https_proxy', 'HTTP_PROXY', 'HTTPS_PROXY']}
+    for k in proxies:
+        if k in os.environ:
+            del os.environ[k]
+    try:
+        yield
+    finally:
+        for k, v in proxies.items():
+            if v is not None:
+                os.environ[k] = v
+
+class StockTools:
+    """金融分析股票工具 - 结合高性能数据库缓存与增量更新"""
+    
+    def __init__(self, db: DatabaseManager, auto_update: bool = True):
+        """
+        初始化股票工具
+        
+        Args:
+            db: 数据库管理器
+            auto_update: 是否在列表为空时自动更新，默认 True
+        """
+        self.db = db
+        if auto_update:
+            self._check_and_update_stock_list()
+
+    def _check_and_update_stock_list(self, force: bool = False):
+        """检查并更新股票列表。仅在列表为空或 force=True 时从网络拉取。"""
+        # 直接查询表中记录数
+        cursor = self.db.conn.cursor()
+        cursor.execute("SELECT COUNT(*) FROM stock_list")
+        count = cursor.fetchone()[0]
+        
+        if count > 0 and not force:
+            logger.info(f"ℹ️ Stock list already cached ({count} stocks)")
+            return
+        
+        logger.info("📡 Updating A-share and HK-share stock list from akshare...")
+        
+        def fetch_data():
+            # A-share
+            df_a = ak.stock_zh_a_spot_em()
+            df_a = df_a[['代码', '名称']].copy()
+            df_a.columns = ['code', 'name']
+            
+            # HK-share
+            df_hk = ak.stock_hk_spot_em()
+            df_hk = df_hk[['代码', '名称']].copy()
+            df_hk.columns = ['code', 'name']
+            
+            # Combine
+            return pd.concat([df_a, df_hk], ignore_index=True)
+
+        try:
+            try:
+                df_combined = fetch_data()
+            except (RequestException, Exception) as e:
+                if "Proxy" in str(e) or "proxy" in str(e):
+                    logger.warning(f"⚠️ Proxy error detected: {e}. Retrying with proxy disabled...")
+                    with temporary_no_proxy():
+                        df_combined = fetch_data()
+                else:
+                    raise e
+            
+            self.db.save_stock_list(df_combined)
+            logger.info(f"✅ Cached {len(df_combined)} stocks (A-share + HK) to database.")
+            
+        except Exception as e:
+            logger.error(f"❌ Failed to sync stock list: {e}")
+
+
+    def search_ticker(self, query: str, limit: int = 5) -> List[Dict]:
+        """
+        模糊搜索 A 股股票代码或名称，支持常见缩写。
+        """
+        # 清洗后缀 (如 CATL.SZ -> CATL, 000001.SZ -> 000001)
+        clean_query = re.sub(r'\.(SZ|SH|HK|US)$', '', query, flags=re.IGNORECASE)
+        
+        # 常见缩写映射
+        aliases = {
+            "CATL": "宁德时代",
+            "BYD": "比亚迪",
+            "TSLA": "特斯拉",
+            "Moutai": "贵州茅台",
+            "Tencent": "腾讯",
+            "Alibaba": "阿里巴巴",
+            "Meituan": "美团",
+        }
+        
+        search_query = aliases.get(clean_query.upper(), clean_query)
+        
+        # Robustness: if regex-like ticker code is embedded in query (e.g. "300364 中文在线"), try to extract it
+        if not search_query.isdigit():
+             # Extract explicit 5-6 digit codes
+             match = re.search(r'\b(\d{5,6})\b', clean_query)
+             if match:
+                 search_query = match.group(1)
+
+        return self.db.search_stock(search_query, limit)
+
+    def get_stock_price(
+        self,
+        ticker: str,
+        start_date: Optional[str] = None,
+        end_date: Optional[str] = None,
+        force_sync: bool = False,
+    ) -> pd.DataFrame:
+        """
+        获取指定股票的历史价格数据。优先从本地缓存读取，缺失时自动从网络补齐。
+        
+        Args:
+            ticker: 股票代码，如 "600519"（贵州茅台）或 "000001"（平安银行）。
+            start_date: 开始日期，格式 "YYYY-MM-DD"。默认为 90 天前。
+            end_date: 结束日期，格式 "YYYY-MM-DD"。默认为今天。
+        
+        Returns:
+            包含 date, open, close, high, low, volume, change_pct 列的 DataFrame。
+        """
+        now = datetime.now()
+        if not end_date:
+            end_date = now.strftime('%Y-%m-%d')
+        if not start_date:
+            start_date = (now - timedelta(days=90)).strftime('%Y-%m-%d')
+
+        df_db = self.db.get_stock_prices(ticker, start_date, end_date)
+        
+        need_update = False
+        if df_db.empty:
+            need_update = True
+        else:
+            db_latest = pd.to_datetime(df_db['date'].max())
+            req_latest = pd.to_datetime(end_date)
+            if (req_latest - db_latest).days > 2:
+                need_update = True
+
+        if force_sync:
+            need_update = True
+
+        if need_update:
+            logger.info(f"📡 Data stale or missing for {ticker}, syncing from network...")
+            
+            # 清洗 ticker，确保只包含数字（Akshare A 股接口通常只需要数字代码）
+            clean_ticker = "".join(filter(str.isdigit, ticker))
+            if not clean_ticker:
+                # Non A/H numeric tickers are not supported by the current data source.
+                logger.warning(f"⚠️ Unsupported ticker format (A/H only): {ticker}")
+                return df_db
+
+            try:
+                s_fmt = start_date.replace("-", "")
+                e_fmt = end_date.replace("-", "")
+                
+                df_remote = None
+                
+                def fetch_data():
+                    if len(clean_ticker) == 5:
+                        # HK Stock
+                        return ak.stock_hk_hist(
+                            symbol=clean_ticker, period="daily",
+                            start_date=s_fmt, end_date=e_fmt,
+                            adjust="qfq"
+                        )
+                    else:
+                        # A-share Stock
+                        return ak.stock_zh_a_hist(
+                            symbol=clean_ticker, period="daily",
+                            start_date=s_fmt, end_date=e_fmt,
+                            adjust="qfq"
+                        )
+
+                try:
+                    df_remote = fetch_data()
+                except (RequestException, Exception) as e:
+                    if "Proxy" in str(e) or "proxy" in str(e):
+                        logger.warning(f"⚠️ Proxy error detected: {e}. Retrying with proxy disabled...")
+                        with temporary_no_proxy():
+                            df_remote = fetch_data()
+                    else:
+                        raise e
+                
+                if df_remote is not None and not df_remote.empty:
+                    df_remote = df_remote.rename(columns={
+                        '日期': 'date', '开盘': 'open', '收盘': 'close',
+                        '最高': 'high', '最低': 'low', '成交量': 'volume',
+                        '涨跌幅': 'change_pct'
+                    })
+                    # 确保日期格式正确
+                    df_remote['date'] = pd.to_datetime(df_remote['date']).dt.strftime('%Y-%m-%d')
+                    
+                    # 只有在获取到有意义的数据时才保存
+                    self.db.save_stock_prices(clean_ticker, df_remote) # 保存时使用清洗后的 clean_ticker
+                    
+                    # 重新查询数据库返回结果，保证一致性
+                    return self.db.get_stock_prices(clean_ticker, start_date, end_date)
+                else:
+                    logger.warning(f"⚠️ Akshare returned empty data for {clean_ticker}")
+                    
+            except KeyError as e:
+                # Akshare 有时在某些股票无数据时会抛出 KeyError
+                logger.warning(f"⚠️ Akshare data missing for {clean_ticker}: {e}")
+            except (RequestException, ConnectionError) as e:
+                logger.error(f"❌ Network error during Akshare sync for {clean_ticker}: {e}")
+            except sqlite3.Error as e:
+                logger.error(f"❌ Database error during Akshare sync for {clean_ticker}: {e}")
+            except Exception as e:
+                logger.error(f"❌ Unexpected error during Akshare sync for {clean_ticker}: {e}")
+        
+        return df_db
+
+
+def get_stock_analysis(ticker: str, db: DatabaseManager) -> str:
+    """
+    生成指定股票的分析摘要报告。
+    
+    Args:
+        ticker: 股票代码
+        db: 数据库管理器实例
+    
+    Returns:
+        Markdown 格式的分析报告，包含价格走势和关键指标。
+    """
+    tools = StockTools(db)
+    df = tools.get_stock_price(ticker)
+    
+    if df.empty:
+        return f"❌ 未能获取 {ticker} 的股价数据。"
+    
+    latest = df.iloc[-1]
+    change = ((latest['close'] - df.iloc[0]['close']) / df.iloc[0]['close']) * 100
+    
+    report = [
+        f"## 📊 {ticker} 分析报告",
+        f"- **查询时段**: {df.iloc[0]['date']} -> {latest['date']}",
+        f"- **当前价**: ¥{latest['close']:.2f}",
+        f"- **时段涨跌**: {change:+.2f}%",
+        f"- **最高/最低**: ¥{df['high'].max():.2f} / ¥{df['low'].min():.2f}",
+        "\n### 最近交易概览",
+        "```",
+        df.tail(5)[['date', 'close', 'change_pct', 'volume']].to_string(index=False),
+        "```"
+    ]
+    return "\n".join(report)

skills/alphaear-predictor/tests/test_predictor.py

@@ -0,0 +1,29 @@
+import sys
+import os
+import unittest
+
+# Add skill root to path
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+try:
+    from scripts.kronos_predictor import KronosPredictorUtility
+    from scripts.utils.database_manager import DatabaseManager
+except ImportError as e:
+    print(f"Import Error: {e}")
+    sys.exit(1)
+
+class TestPredictor(unittest.TestCase):
+    def test_init(self):
+        print("Testing KronosPredictorUtility Iteration...")
+        db = DatabaseManager(":memory:")
+        # Kronos might need model files, but init should pass if we don't call predict?
+        # Note: Kronos loads model in init. This might fail if model path is invalid.
+        # We wrap in try-except to catch model loading errors which are expected in this env
+        try:
+            tools = KronosPredictorUtility()
+            self.assertIsNotNone(tools)
+        except Exception as e:
+            print(f"Kronos Init failed (expected if no model): {e}")
+
+if __name__ == '__main__':
+    unittest.main()