forked from lab/TPM
- Add investment_advice_v2.py (educational content) - Add market_benchmark.py (0050.TW/SPY real-time data) - Update main.py (CoT parameter support) - Update llm_service.py (V2 integration) - Update result_view.html (CoT toggle, marked.js) - Add render.yaml (deployment config) - Update .gitignore (protect .env, node_modules, logs)data-init-fixes
Eric0801
1 month ago
parent
1c16cb4321
commit
cbd580bb12
17 changed files with 1284 additions and 108 deletions
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""" |
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市場基準資料模組 |
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|
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從資料庫取得實際的市場基準資料（台股加權指數、S&P 500） |
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用於 Context Engineering 的市場環境背景 |
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""" |
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|
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import psycopg2 |
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import pandas as pd |
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import numpy as np |
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from datetime import datetime, timedelta |
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from typing import Dict, Any, Optional |
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import logging |
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logger = logging.getLogger(__name__) |
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|
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# 從 config 匯入資料庫設定 |
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try: |
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from config import SQL_CONFIG |
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except ImportError: |
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# Fallback 設定 |
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SQL_CONFIG = { |
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"database": "portfolio_platform", |
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"user": "postgres", |
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"host": "db", |
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"port": "5432", |
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"password": "thiispassword1qaz!QAZ" |
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} |
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|
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|
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class MarketBenchmark: |
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"""市場基準資料類別""" |
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def __init__(self): |
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"""初始化市場基準資料""" |
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self.cache = {} |
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self.cache_timeout = 3600 # 1小時快取 |
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self.cache_time = {} |
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|
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def _is_cache_valid(self, key: str) -> bool: |
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"""檢查快取是否有效""" |
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if key not in self.cache_time: |
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return False |
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return (datetime.now().timestamp() - self.cache_time[key]) < self.cache_timeout |
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|
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def get_market_context(self, tw: bool = True, force_refresh: bool = False) -> Dict[str, Any]: |
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""" |
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獲取市場環境背景（從資料庫計算實際數據） |
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|
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Args: |
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tw: True=台灣市場，False=美國市場 |
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force_refresh: 強制重新計算（不使用快取） |
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Returns: |
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市場環境背景字典 |
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""" |
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cache_key = f"market_{'tw' if tw else 'us'}" |
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# 檢查快取 |
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if not force_refresh and self._is_cache_valid(cache_key): |
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logger.info(f"Using cached market context for {'TW' if tw else 'US'}") |
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return self.cache[cache_key] |
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try: |
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if tw: |
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context = self._get_tw_market_context() |
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else: |
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context = self._get_us_market_context() |
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# 更新快取 |
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self.cache[cache_key] = context |
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self.cache_time[cache_key] = datetime.now().timestamp() |
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logger.info(f"Calculated market context for {'TW' if tw else 'US'}: {context}") |
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return context |
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except Exception as e: |
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logger.error(f"Error getting market context: {e}") |
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# Fallback 到靜態資料 |
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return self._get_fallback_context(tw) |
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def _get_tw_market_context(self) -> Dict[str, Any]: |
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"""取得台灣市場基準資料（從資料庫計算）""" |
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conn = psycopg2.connect(**SQL_CONFIG) |
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try: |
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# 取得 0050.TW 近期資料 |
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query = """ |
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SELECT date, price |
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FROM stock_price_tw |
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WHERE ticker = '0050.TW' |
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ORDER BY date DESC |
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LIMIT 1260 -- 約5年交易日 |
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""" |
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df = pd.read_sql(query, conn) |
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df = df.sort_values('date') |
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df['return'] = df['price'].pct_change() |
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# 計算各項指標 |
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latest_price = df['price'].iloc[-1] |
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year_start_idx = max(0, len(df) - 252) # 今年開始（約252交易日） |
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ytd_return = (latest_price / df['price'].iloc[year_start_idx]) - 1 |
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# 近5年年化報酬 |
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total_return = (latest_price / df['price'].iloc[0]) - 1 |
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years = len(df) / 252 |
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avg_5y_return = (1 + total_return) ** (1 / years) - 1 |
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# 年化波動率 |
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volatility = df['return'].std() * np.sqrt(252) |
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# 市場情緒判斷（基於近期趨勢） |
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recent_returns = df['return'].iloc[-63:].sum() # 最近3個月 |
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if recent_returns > 0.05: |
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sentiment = "bull" |
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elif recent_returns < -0.05: |
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sentiment = "bear" |
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else: |
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sentiment = "neutral" |
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return { |
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"market_name": "台灣加權指數（0050.TW）", |
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"ytd_return": float(ytd_return), |
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"avg_5y_return": float(avg_5y_return), |
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"current_price": float(latest_price), |
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"volatility": float(volatility), |
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"sentiment": sentiment, |
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"last_update": df['date'].iloc[-1].strftime("%Y-%m-%d"), |
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"data_points": len(df) |
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} |
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finally: |
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conn.close() |
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def _get_us_market_context(self) -> Dict[str, Any]: |
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"""取得美國市場基準資料（從資料庫計算）""" |
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conn = psycopg2.connect(**SQL_CONFIG) |
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try: |
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# 取得 SPY 近期資料 |
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query = """ |
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SELECT date, price |
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FROM stock_price |
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WHERE ticker = 'SPY' |
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ORDER BY date DESC |
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LIMIT 1260 -- 約5年交易日 |
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""" |
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df = pd.read_sql(query, conn) |
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df = df.sort_values('date') |
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df['return'] = df['price'].pct_change() |
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# 計算各項指標 |
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latest_price = df['price'].iloc[-1] |
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year_start_idx = max(0, len(df) - 252) |
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ytd_return = (latest_price / df['price'].iloc[year_start_idx]) - 1 |
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# 近5年年化報酬 |
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total_return = (latest_price / df['price'].iloc[0]) - 1 |
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years = len(df) / 252 |
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avg_5y_return = (1 + total_return) ** (1 / years) - 1 |
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# 年化波動率 |
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volatility = df['return'].std() * np.sqrt(252) |
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# 市場情緒判斷 |
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recent_returns = df['return'].iloc[-63:].sum() |
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if recent_returns > 0.05: |
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sentiment = "bull" |
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elif recent_returns < -0.05: |
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sentiment = "bear" |
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else: |
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sentiment = "neutral" |
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return { |
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"market_name": "S&P 500（SPY）", |
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"ytd_return": float(ytd_return), |
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"avg_5y_return": float(avg_5y_return), |
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"current_price": float(latest_price), |
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"volatility": float(volatility), |
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"sentiment": sentiment, |
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"last_update": df['date'].iloc[-1].strftime("%Y-%m-%d"), |
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"data_points": len(df) |
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} |
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finally: |
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conn.close() |
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def _get_fallback_context(self, tw: bool) -> Dict[str, Any]: |
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"""Fallback 靜態資料（資料庫查詢失敗時使用）""" |
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if tw: |
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return { |
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"market_name": "台灣加權指數", |
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"ytd_return": 0.18, |
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"avg_5y_return": 0.09, |
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"volatility": 0.15, |
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"sentiment": "neutral", |
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"last_update": "static", |
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"is_fallback": True |
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} |
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else: |
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return { |
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"market_name": "S&P 500", |
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"ytd_return": 0.22, |
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"avg_5y_return": 0.12, |
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"volatility": 0.14, |
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"sentiment": "bull", |
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"last_update": "static", |
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"is_fallback": True |
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} |
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# 單例模式 |
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_market_benchmark_instance = None |
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def get_market_benchmark() -> MarketBenchmark: |
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"""獲取市場基準實例（單例）""" |
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global _market_benchmark_instance |
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if _market_benchmark_instance is None: |
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_market_benchmark_instance = MarketBenchmark() |
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return _market_benchmark_instance |
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# 便利函數（向後兼容） |
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def get_market_context(tw: bool = True) -> Dict[str, Any]: |
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""" |
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獲取市場環境背景 |
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此函數與 prompts/investment_advice_v2.py 中的函數簽名相同 |
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可直接替換使用 |
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""" |
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benchmark = get_market_benchmark() |
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return benchmark.get_market_context(tw) |
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if __name__ == "__main__": |
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# 測試腳本 |
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import json |
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logging.basicConfig(level=logging.INFO) |
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print("="*80) |
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print("測試市場基準資料模組") |
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print("="*80) |
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# 測試台灣市場 |
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print("\n台灣市場基準：") |
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tw_context = get_market_context(tw=True) |
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print(json.dumps(tw_context, indent=2, ensure_ascii=False)) |
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# 測試美國市場 |
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print("\n美國市場基準：") |
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us_context = get_market_context(tw=False) |
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print(json.dumps(us_context, indent=2, ensure_ascii=False)) |
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print("\n" + "="*80) |
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print("測試完成！") |
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print("="*80) |
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@ -0,0 +1,46 @@ |
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services: |
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# Flask Web Service |
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- type: web |
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name: tpm-flask |
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env: docker |
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dockerfilePath: ./Dockerfile |
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plan: free |
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healthCheckPath: / |
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envVars: |
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- key: DATABASE_URL |
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fromDatabase: |
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name: tpm-db |
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property: connectionString |
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- key: REDIS_URL |
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fromService: |
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name: tpm-redis |
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type: redis |
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property: connectionString |
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- key: OPENROUTER_API_KEY |
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sync: false |
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- key: OPENROUTER_MODEL |
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value: google/gemini-2.0-flash-exp:free |
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- key: MOCK_LLM |
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value: false |
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- key: LLM_TIMEOUT |
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value: 60 |
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- key: LLM_MAX_TOKENS |
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value: 1500 |
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- key: LLM_TEMPERATURE |
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value: 0.6 |
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# PostgreSQL Database |
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- type: pserv |
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name: tpm-db |
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env: docker |
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plan: free |
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disk: |
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name: postgres-data |
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mountPath: /var/lib/postgresql/data |
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sizeGB: 1 |
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# Redis Cache |
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- type: redis |
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name: tpm-redis |
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plan: free |
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maxmemoryPolicy: allkeys-lru |
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