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