58f9380ec4
- 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
473 lines
19 KiB
Python
473 lines
19 KiB
Python
import os
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from typing import Dict, List, Any, Optional
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import pandas as pd
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from loguru import logger
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from pyecharts.charts import Kline, Line, Bar, Grid, Radar, Graph
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from pyecharts import options as opts
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from pyecharts.globals import ThemeType
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from datetime import datetime, timedelta
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class VisualizerTools:
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"""可视化工具库 - 使用 Pyecharts 生成 HTML 图表"""
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@staticmethod
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def generate_stock_chart(
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df: pd.DataFrame,
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ticker: str,
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title: str = None,
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prediction: Optional[List[float]] = None,
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forecast: Optional[Any] = None, # ForecastResult instance
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ground_truth: Optional[pd.DataFrame] = None # For training visualization
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) -> Grid:
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"""
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生成股票 K 线图 + 成交量 + 预测趋势 (支持多状态 K 线)
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"""
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if df.empty:
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return None
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# 数据预处理
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df = df.sort_values('date')
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dates = [str(d)[:10] for d in df['date'].tolist()]
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k_data = df[['open', 'close', 'low', 'high']].values.tolist()
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volumes = df['volume'].tolist()
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if not title:
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title = f"{ticker} 股价走势与预测"
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legend_items = ["日K"]
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# 1. 处理传统的简单预测线 (Line)
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pred_line = None
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if prediction and not forecast:
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try:
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last_date_str = dates[-1]
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last_date = datetime.strptime(last_date_str, "%Y-%m-%d")
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pred_dates = []
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for i in range(1, len(prediction) + 1):
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pred_dates.append((last_date + timedelta(days=i)).strftime("%Y-%m-%d"))
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ext_dates = dates + pred_dates
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last_close = df.iloc[-1]['close']
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pred_values = [None] * (len(df) - 1) + [float(last_close)] + prediction
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pred_line = (
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Line()
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.add_xaxis(ext_dates)
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.add_yaxis(
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"AI预测趋势",
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pred_values,
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is_connect_nones=True,
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is_symbol_show=True,
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linestyle_opts=opts.LineStyleOpts(width=2, type_="dashed", color="#FF8C00"),
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label_opts=opts.LabelOpts(is_show=False)
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)
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)
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dates = ext_dates
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legend_items.append("AI预测趋势")
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except Exception as e:
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logger.error(f"Failed to process simple prediction: {e}")
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# 2. 处理复杂的 Kronos 预测 (Kline)
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base_kline = None
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adj_kline = None
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if forecast:
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try:
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# 获取预测数据点
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base_points = forecast.base_forecast # List[KLinePoint]
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adj_points = forecast.adjusted_forecast # List[KLinePoint]
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# 提取日期
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pred_dates = [str(p.date)[:10] for p in (adj_points or base_points)]
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# 检查日期是否已经包含在主 dates 中，如果没有则扩展
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if pred_dates and pred_dates[0] not in dates:
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dates = dates + pred_dates
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# 构建 Baseline 预测 K 线数据
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if base_points:
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# 前面填充 None
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base_k_data = [[None]*4] * len(df) + [[p.open, p.close, p.low, p.high] for p in base_points]
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base_kline = (
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Kline()
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.add_xaxis(dates)
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.add_yaxis(
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"模型原始预测",
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base_k_data,
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itemstyle_opts=opts.ItemStyleOpts(
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color="transparent",
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color0="transparent",
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border_color="#FF8C00", # 橙色
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border_color0="#FF8C00",
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opacity=0.6,
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border_type="dashed"
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),
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)
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)
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legend_items.append("模型原始预测")
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# 构建 Adjusted 调优 K 线数据
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if adj_points:
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adj_k_data = [[None]*4] * len(df) + [[p.open, p.close, p.low, p.high] for p in adj_points]
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adj_kline = (
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Kline()
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.add_xaxis(dates)
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.add_yaxis(
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"LLM调优预测",
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adj_k_data,
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itemstyle_opts=opts.ItemStyleOpts(
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color="#9333ea", # 紫色
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color0="#9333ea",
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border_color="#9333ea",
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border_color0="#9333ea",
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opacity=0.8
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),
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)
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)
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legend_items.append("LLM调优预测")
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except Exception as e:
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logger.error(f"Failed to process complex forecast: {e}")
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# 2.5 处理 Ground Truth (用于训练评估可视化)
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gt_line = None
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if ground_truth is not None and not ground_truth.empty:
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try:
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gt_dates = [str(d)[:10] for d in ground_truth['date'].tolist()]
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# 确保日期包含在 dates 中
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for d in gt_dates:
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if d not in dates:
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dates.append(d)
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dates = sorted(list(set(dates))) # Re-sort to maintain order
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gt_values = [None] * len(dates)
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for _, row in ground_truth.iterrows():
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d_str = str(row['date'])[:10]
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if d_str in dates:
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idx = dates.index(d_str)
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gt_values[idx] = float(row['close'])
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gt_line = (
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Line()
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.add_xaxis(dates)
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.add_yaxis(
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"真实走势 (GT)",
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gt_values,
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is_connect_nones=True,
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linestyle_opts=opts.LineStyleOpts(width=3, color="#2ecc71"), # 绿色粗线
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label_opts=opts.LabelOpts(is_show=False)
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)
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)
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legend_items.append("真实走势 (GT)")
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except Exception as e:
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logger.error(f"Failed to process ground truth: {e}")
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# 3. 主 K 线图
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# 为了展示预测，也需要对主 K 线数据进行填充
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main_k_data = k_data + [[None]*4] * (len(dates) - len(df))
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kline = (
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Kline()
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.add_xaxis(dates)
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.add_yaxis(
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"日K",
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main_k_data,
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itemstyle_opts=opts.ItemStyleOpts(
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color="#ef4444", # 跌
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color0="#22c55e", # 涨
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border_color="#ef4444",
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border_color0="#22c55e",
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),
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)
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.set_global_opts(
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title_opts=opts.TitleOpts(title=title, pos_left="center"),
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xaxis_opts=opts.AxisOpts(is_scale=True),
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yaxis_opts=opts.AxisOpts(
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is_scale=True,
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splitarea_opts=opts.SplitAreaOpts(
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is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
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),
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),
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legend_opts=opts.LegendOpts(is_show=True, pos_top="5%"),
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datazoom_opts=[opts.DataZoomOpts(type_="inside", range_start=50)],
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tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
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)
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)
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# Overlap all series
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if pred_line: kline.overlap(pred_line)
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if base_kline: kline.overlap(base_kline)
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if adj_kline: kline.overlap(adj_kline)
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if gt_line: kline.overlap(gt_line)
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# 4. 成交量柱状图
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# 同理扩展成交量数据
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ext_volumes = volumes + [0] * (len(dates) - len(df))
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bar = (
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Bar()
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.add_xaxis(dates)
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.add_yaxis(
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"成交量",
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ext_volumes,
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xaxis_index=1,
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yaxis_index=1,
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label_opts=opts.LabelOpts(is_show=False),
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itemstyle_opts=opts.ItemStyleOpts(color="#7fbe9e"),
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)
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.set_global_opts(
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xaxis_opts=opts.AxisOpts(
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type_="category",
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grid_index=1,
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axislabel_opts=opts.LabelOpts(is_show=False),
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),
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legend_opts=opts.LegendOpts(is_show=False),
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)
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)
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# 5. 组合 Grid
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grid_chart = Grid(init_opts=opts.InitOpts(width="100%", height="450px", theme=ThemeType.LIGHT))
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grid_chart.add(
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kline,
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grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="50%"),
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)
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grid_chart.add(
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bar,
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grid_opts=opts.GridOpts(
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pos_left="10%", pos_right="8%", pos_top="65%", height="20%"
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),
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)
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return grid_chart
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@staticmethod
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def generate_loss_chart(losses: List[float], title: str = "训练损失收敛曲线") -> Line:
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"""生成 Loss 下降曲线图"""
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line = (
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Line(init_opts=opts.InitOpts(width="100%", height="400px", theme=ThemeType.LIGHT))
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.add_xaxis(list(range(1, len(losses) + 1)))
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.add_yaxis(
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"Training Loss",
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losses,
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is_smooth=True,
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linestyle_opts=opts.LineStyleOpts(width=2, color="#3b82f6"),
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label_opts=opts.LabelOpts(is_show=False),
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markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="min", name="最小值")])
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)
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.set_global_opts(
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title_opts=opts.TitleOpts(title=title, pos_left="center"),
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xaxis_opts=opts.AxisOpts(name="Epoch", is_scale=True),
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yaxis_opts=opts.AxisOpts(name="Loss", is_scale=True),
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tooltip_opts=opts.TooltipOpts(trigger="axis"),
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)
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)
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return line
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@staticmethod
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def generate_sentiment_trend_chart(sentiment_history: List[Dict[str, Any]]) -> Line:
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"""
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生成舆情情绪趋势图
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:param sentiment_history: [{"date": "2024-01-01", "score": 0.8}, ...]
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"""
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dates = [item['date'] for item in sentiment_history]
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scores = [item['score'] for item in sentiment_history]
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line = (
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Line(init_opts=opts.InitOpts(width="100%", height="300px", theme=ThemeType.LIGHT))
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.add_xaxis(dates)
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.add_yaxis(
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"情绪指数",
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scores,
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is_smooth=True,
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markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(y=0, name="中性线")]),
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itemstyle_opts=opts.ItemStyleOpts(color="#5470c6"),
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areastyle_opts=opts.AreaStyleOpts(opacity=0.3, color="#5470c6")
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)
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.set_global_opts(
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title_opts=opts.TitleOpts(title="舆情情绪趋势", pos_left="center"),
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legend_opts=opts.LegendOpts(pos_top="8%"),
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yaxis_opts=opts.AxisOpts(min_=-1, max_=1, name="Sentiment"),
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tooltip_opts=opts.TooltipOpts(trigger="axis"),
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)
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)
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return line
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@staticmethod
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def generate_isq_radar_chart(sentiment: float, confidence: float, intensity: int,
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expectation_gap: float = 0.5, timeliness: float = 0.8,
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title: str = "信号质量 ISQ 评估") -> Radar:
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"""生成信号质量雷达图"""
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# 标准化数据 (0-100)
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# sentiment 强度: 绝对值越大强度越高
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sent_val = min(100, abs(sentiment) * 100)
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# confidence: 0 to 1 -> 0 to 100
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conf_val = confidence * 100
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# intensity: 1 to 5 -> 20 to 100
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int_val = intensity * 20
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# gap & time: 0 to 1 -> 0 to 100
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gap_val = expectation_gap * 100
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time_val = timeliness * 100
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schema = [
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opts.RadarIndicatorItem(name="情绪强度", max_=100),
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opts.RadarIndicatorItem(name="确定性", max_=100),
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opts.RadarIndicatorItem(name="影响力", max_=100),
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opts.RadarIndicatorItem(name="预期差", max_=100),
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opts.RadarIndicatorItem(name="时效性", max_=100),
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]
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radar = (
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Radar(init_opts=opts.InitOpts(width="100%", height="400px", theme=ThemeType.LIGHT))
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.add_schema(schema=schema)
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.add(
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"信号特征",
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[[sent_val, conf_val, int_val, gap_val, time_val]],
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color="#f97316",
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areastyle_opts=opts.AreaStyleOpts(opacity=0.3, color="#fb923c"),
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)
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.set_global_opts(
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title_opts=opts.TitleOpts(title=title, pos_left="center"),
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legend_opts=opts.LegendOpts(is_show=False),
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)
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)
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return radar
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@staticmethod
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def generate_transmission_graph(nodes_data: List[Dict[str, str]], title: str = "投资逻辑传导链条") -> Graph:
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"""生成逻辑传导拓扑图 (支持分支结构)"""
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nodes = []
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links = []
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# Helper for text wrapping
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def wrap_text(text, width=6):
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return '\n'.join([text[i:i+width] for i in range(0, len(text), width)])
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# Map original names to wrapped names to handle links
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name_map = {}
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for i, item in enumerate(nodes_data):
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# 节点样式
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color = "#ef4444" if "利空" in item.get("impact_type", "") else "#22c55e"
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if "中性" in item.get("impact_type", ""): color = "#6b7280"
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original_name = item.get("node_name", f"节点{i}")
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wrapped_name = wrap_text(original_name)
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name_map[original_name] = wrapped_name
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name_map[str(item.get("id", ""))] = wrapped_name # Map ID if present
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nodes.append({
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"name": wrapped_name,
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"symbolSize": 60 if i == 0 else 50,
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"value": item.get("logic", ""),
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"itemStyle": {"color": color},
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# Improve label readability
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"label": {"show": True, "formatter": "{b}"}
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})
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# Logic for Links
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source_key = item.get("source") or item.get("parent") or item.get("parent_id")
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if source_key:
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# Branching logic: Link from specified source
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# Source needs to be resolved to its (wrapped) name
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target_source_name = name_map.get(source_key)
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if not target_source_name and source_key in name_map.values():
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target_source_name = source_key # It was already a mapped name?
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# If we found the source in our map (meaning it appeared before this node)
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if target_source_name:
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links.append({"source": target_source_name, "target": wrapped_name})
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elif i > 0:
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# Fallback: Linear chain
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links.append({"source": nodes[i-1]["name"], "target": wrapped_name})
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graph = (
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Graph(init_opts=opts.InitOpts(width="100%", height="400px", theme=ThemeType.LIGHT))
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.add(
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"",
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nodes,
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links,
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repulsion=5000,
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layout="force",
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is_roam=True,
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is_draggable=True,
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symbol="circle",
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edge_symbol=['circle', 'arrow'], # Add arrows
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edge_symbol_size=[4, 10],
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linestyle_opts=opts.LineStyleOpts(width=2, curve=0.2, opacity=0.9),
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label_opts=opts.LabelOpts(is_show=True, position="inside", color="white", font_size=10),
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edge_label=opts.LabelOpts(is_show=False),
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)
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.set_global_opts(
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title_opts=opts.TitleOpts(title=title, pos_left="center"),
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tooltip_opts=opts.TooltipOpts(formatter="{b}: {c}")
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)
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)
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return graph
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@staticmethod
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def render_drawio_to_html(xml_content: str, filename: str, title: str = "Logic Diagram") -> str:
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"""
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将 Draw.io XML 渲染为包含 Viewer 的 HTML 文件
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"""
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import json
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# 构造配置字典
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config = {
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"highlight": "#0000ff",
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"nav": True,
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"resize": True,
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"toolbar": "zoom",
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"xml": xml_content
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}
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# 1. 转为 JSON 字符串 (自动处理内部的引号转义、换行符转义等)
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json_str = json.dumps(config)
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# 2. 转为 HTML 属性安全的字符串 (主要是转义单引号，因为我们在 HTML 中用单引号包裹)
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import html
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safe_json_str = html.escape(json_str, quote=True)
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html_template = f"""
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="UTF-8">
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<title>{title}</title>
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<style>
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body {{ font-family: sans-serif; padding: 20px; }}
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.mxgraph {{ border: 1px solid #ddd; background: #fff; }}
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</style>
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</head>
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<body>
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<h2>{title}</h2>
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<div class="mxgraph" style="max-width:100%;border:1px solid transparent;" data-mxgraph='{safe_json_str}'></div>
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<script type="text/javascript" src="https://viewer.diagrams.net/js/viewer-static.min.js"></script>
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</body>
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</html>
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"""
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try:
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os.makedirs(os.path.dirname(filename), exist_ok=True)
|
|
# Use 'w' mode with utf-8 encoding
|
|
with open(filename, 'w', encoding='utf-8') as f:
|
|
f.write(html_template)
|
|
logger.info(f"✅ Draw.io chart rendered to {filename}")
|
|
return filename
|
|
except Exception as e:
|
|
logger.error(f"Failed to render drawio chart: {e}")
|
|
return ""
|
|
|
|
@staticmethod
|
|
def render_chart_to_file(chart: Any, filename: str) -> str:
|
|
"""渲染并保存 HTML"""
|
|
try:
|
|
# 确保目录存在
|
|
os.makedirs(os.path.dirname(filename), exist_ok=True)
|
|
chart.render(filename)
|
|
logger.info(f"✅ Chart rendered to {filename}")
|
|
return filename
|
|
except Exception as e:
|
|
logger.error(f"Failed to render chart: {e}")
|
|
return ""
|