feat: Multi-user system with PostgreSQL - WIP temporary save
This commit is contained in:
TIANHE
@@ -1,220 +1,249 @@
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"""
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自动反思与验证服务
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用于记录分析预测，并在未来自动验证结果，实现闭环学习
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Auto-reflection and verification service (PostgreSQL).
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Records analysis predictions and auto-verifies results in the future
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to achieve closed-loop learning for AI agents.
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"""
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import sqlite3
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import os
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import json
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from datetime import datetime, timedelta
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from typing import List, Dict, Any, Optional
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from app.utils.logger import get_logger
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from app.utils.db import get_db_connection
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from .memory import AgentMemory
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from .tools import AgentTools
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logger = get_logger(__name__)
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class ReflectionService:
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"""反思服务：管理分析记录的存储和验证"""
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"""Reflection service: manages storage and verification of analysis records."""
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def __init__(self, db_path: Optional[str] = None):
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if db_path is None:
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# 默认数据库路径
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db_dir = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'data', 'memory')
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os.makedirs(db_dir, exist_ok=True)
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db_path = os.path.join(db_dir, 'reflection_records.db')
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self.db_path = db_path
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self.tools = AgentTools()
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self._init_database()
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def _init_database(self):
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"""初始化数据库表"""
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try:
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conn = sqlite3.connect(self.db_path)
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cursor = conn.cursor()
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# 创建分析记录表
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS analysis_records (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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market TEXT NOT NULL,
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symbol TEXT NOT NULL,
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initial_price REAL,
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decision TEXT,
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confidence INTEGER,
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reasoning TEXT,
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analysis_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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target_check_date TIMESTAMP,
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status TEXT DEFAULT 'PENDING', -- PENDING, COMPLETED, FAILED
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final_price REAL,
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actual_return REAL,
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check_result TEXT
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)
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''')
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# 创建索引
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cursor.execute('''
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CREATE INDEX IF NOT EXISTS idx_status_date ON analysis_records(status, target_check_date)
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''')
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conn.commit()
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conn.close()
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except Exception as e:
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logger.error(f"初始化反思数据库失败: {e}")
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def record_analysis(self, market: str, symbol: str, price: float,
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decision: str, confidence: int, reasoning: str,
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check_days: int = 7):
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"""
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记录一次分析，以便未来验证
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Initialize reflection service.
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Args:
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market: 市场
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symbol: 代码
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price: 当前价格
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decision: 决策 (BUY/SELL/HOLD)
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confidence: 置信度
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reasoning: 理由
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check_days: 几天后验证 (默认7天)
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db_path: Deprecated parameter, kept for backward compatibility
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"""
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self.tools = AgentTools()
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def record_analysis(
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self,
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market: str,
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symbol: str,
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price: float,
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decision: str,
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confidence: int,
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reasoning: str,
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check_days: int = 7
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):
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"""
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Record an analysis for future verification.
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Args:
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market: Market type
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symbol: Symbol code
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price: Current price
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decision: Decision (BUY/SELL/HOLD)
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confidence: Confidence level (0-100)
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reasoning: Reasoning text
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check_days: Days until verification (default 7)
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"""
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try:
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conn = sqlite3.connect(self.db_path)
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cursor = conn.cursor()
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target_date = datetime.now() + timedelta(days=check_days)
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cursor.execute('''
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INSERT INTO analysis_records
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(market, symbol, initial_price, decision, confidence, reasoning, target_check_date)
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VALUES (?, ?, ?, ?, ?, ?, ?)
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''', (market, symbol, price, decision, confidence, reasoning, target_date))
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conn.commit()
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conn.close()
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with get_db_connection() as conn:
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cur = conn.cursor()
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cur.execute(
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"""
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INSERT INTO qd_reflection_records
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(market, symbol, initial_price, decision, confidence, reasoning, target_check_date)
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VALUES (?, ?, ?, ?, ?, ?, ?)
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""",
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(market, symbol, price, decision, confidence, reasoning, target_date)
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)
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conn.commit()
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cur.close()
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logger.info(f"Recorded analysis for reflection: {market}:{symbol}, will verify after {check_days} day(s)")
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except Exception as e:
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logger.error(f"记录分析失败: {e}")
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logger.error(f"Failed to record analysis: {e}")
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def run_verification_cycle(self):
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"""
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执行验证周期：检查到期的记录，验证结果，并写入记忆
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Execute verification cycle: check due records, verify results, and write to memory.
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"""
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logger.info("开始执行自动反思验证周期...")
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logger.info("Starting auto-reflection verification cycle...")
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try:
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conn = sqlite3.connect(self.db_path)
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cursor = conn.cursor()
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# 1. 查找所有已到期且未处理的记录
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cursor.execute('''
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SELECT id, market, symbol, initial_price, decision, confidence, reasoning, analysis_date
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FROM analysis_records
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WHERE status = 'PENDING' AND target_check_date <= CURRENT_TIMESTAMP
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''')
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records = cursor.fetchall()
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if not records:
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logger.info("没有需要验证的记录")
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conn.close()
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return
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logger.info(f"发现 {len(records)} 条待验证记录")
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# 初始化记忆系统（用于写入验证结果）
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trader_memory = AgentMemory('trader_agent')
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for record in records:
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record_id, market, symbol, initial_price, decision, confidence, reasoning, analysis_date = record
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with get_db_connection() as conn:
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cur = conn.cursor()
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try:
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# 2. 获取当前最新价格
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current_price_data = self.tools.get_current_price(market, symbol)
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current_price = current_price_data.get('price')
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# 1. Find all due and pending records
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cur.execute(
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"""
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SELECT id, market, symbol, initial_price, decision, confidence, reasoning, analysis_date
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FROM qd_reflection_records
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WHERE status = 'PENDING' AND target_check_date <= NOW()
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"""
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)
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records = cur.fetchall() or []
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if not records:
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logger.info("No records to verify")
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cur.close()
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return
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logger.info(f"Found {len(records)} records to verify")
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# Initialize memory system for writing verification results
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trader_memory = AgentMemory('trader_agent')
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for record in records:
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record_id = record['id']
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market = record['market']
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symbol = record['symbol']
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initial_price = record['initial_price']
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decision = record['decision']
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confidence = record['confidence']
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reasoning = record['reasoning']
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analysis_date = record['analysis_date']
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if not current_price:
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logger.warning(f"无法获取 {market}:{symbol} 的当前价格，跳过")
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continue
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try:
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# 2. Get current price
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current_price_data = self.tools.get_current_price(market, symbol)
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current_price = current_price_data.get('price')
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# 3. 计算收益和结果
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if not initial_price or initial_price == 0:
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actual_return = 0.0
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else:
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actual_return = (current_price - initial_price) / initial_price * 100
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# 评估结果
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result_desc = ""
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is_good_prediction = False
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if decision == "BUY":
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if actual_return > 2.0:
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result_desc = "Correct: price rose after BUY"
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is_good_prediction = True
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elif actual_return < -2.0:
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result_desc = "Wrong: price fell after BUY"
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if not current_price:
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logger.warning(f"Cannot get current price for {market}:{symbol}, skipping")
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continue
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# 3. Calculate return and result
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if not initial_price or initial_price == 0:
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actual_return = 0.0
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else:
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result_desc = "Neutral: limited price movement"
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elif decision == "SELL":
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if actual_return < -2.0:
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result_desc = "Correct: price fell after SELL"
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is_good_prediction = True
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elif actual_return > 2.0:
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result_desc = "Wrong: price rose after SELL"
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else:
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result_desc = "Neutral: limited price movement"
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else: # HOLD
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if -2.0 <= actual_return <= 2.0:
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result_desc = "Correct: limited movement during HOLD"
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is_good_prediction = True
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else:
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result_desc = f"Deviated: large movement during HOLD ({actual_return:.2f}%)"
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actual_return = (current_price - initial_price) / initial_price * 100
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# Evaluate result
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result_desc = ""
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is_good_prediction = False
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if decision == "BUY":
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if actual_return > 2.0:
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result_desc = "Correct: price rose after BUY"
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is_good_prediction = True
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elif actual_return < -2.0:
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result_desc = "Wrong: price fell after BUY"
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else:
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result_desc = "Neutral: limited price movement"
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elif decision == "SELL":
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if actual_return < -2.0:
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result_desc = "Correct: price fell after SELL"
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is_good_prediction = True
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elif actual_return > 2.0:
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result_desc = "Wrong: price rose after SELL"
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else:
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result_desc = "Neutral: limited price movement"
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else: # HOLD
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if -2.0 <= actual_return <= 2.0:
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result_desc = "Correct: limited movement during HOLD"
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is_good_prediction = True
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else:
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result_desc = f"Deviated: large movement during HOLD ({actual_return:.2f}%)"
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# 4. 写入记忆系统 (Let the agent learn)
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memory_situation = f"{market}:{symbol} auto-verified (analysis_date: {analysis_date})"
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memory_recommendation = f"Decision: {decision} (confidence {confidence}), reasoning: {(reasoning or '')[:120]}"
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memory_result = f"Verification: {result_desc}; return={actual_return:.2f}% (initial {initial_price} -> final {current_price})"
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trader_memory.add_memory(
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memory_situation,
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memory_recommendation,
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memory_result,
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actual_return,
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metadata={
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"market": market,
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"symbol": symbol,
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"timeframe": "1D",
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"features": {
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"source": "auto_verify",
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"decision": decision,
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"confidence": confidence,
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"initial_price": initial_price,
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"final_price": current_price,
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"analysis_date": str(analysis_date),
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"result_desc": result_desc,
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"is_good_prediction": bool(is_good_prediction),
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},
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}
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)
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# 5. 更新记录状态
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cursor.execute('''
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UPDATE analysis_records
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SET status = 'COMPLETED', final_price = ?, actual_return = ?, check_result = ?
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WHERE id = ?
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''', (current_price, actual_return, result_desc, record_id))
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conn.commit()
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logger.info(f"验证完成 {market}:{symbol}: {result_desc}")
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except Exception as inner_e:
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logger.error(f"处理记录 {record_id} 失败: {inner_e}")
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# 标记为失败，避免重复处理
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# cursor.execute("UPDATE analysis_records SET status = 'FAILED' WHERE id = ?", (record_id,))
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# conn.commit()
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conn.close()
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logger.info("反思验证周期结束")
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# 4. Write to memory system (agent learning)
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memory_situation = f"{market}:{symbol} auto-verified (analysis_date: {analysis_date})"
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memory_recommendation = f"Decision: {decision} (confidence {confidence}), reasoning: {(reasoning or '')[:120]}"
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memory_result = f"Verification: {result_desc}; return={actual_return:.2f}% (initial {initial_price} -> final {current_price})"
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trader_memory.add_memory(
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memory_situation,
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memory_recommendation,
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memory_result,
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actual_return,
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metadata={
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"market": market,
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"symbol": symbol,
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"timeframe": "1D",
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"features": {
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"source": "auto_verify",
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"decision": decision,
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"confidence": confidence,
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"initial_price": initial_price,
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"final_price": current_price,
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"analysis_date": str(analysis_date),
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"result_desc": result_desc,
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"is_good_prediction": bool(is_good_prediction),
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},
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}
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)
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# 5. Update record status
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cur.execute(
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"""
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UPDATE qd_reflection_records
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SET status = 'COMPLETED', final_price = ?, actual_return = ?, check_result = ?
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WHERE id = ?
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""",
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(current_price, actual_return, result_desc, record_id)
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)
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conn.commit()
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logger.info(f"Verification completed {market}:{symbol}: {result_desc}")
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except Exception as inner_e:
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logger.error(f"Failed to process record {record_id}: {inner_e}")
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# Optionally mark as failed to avoid repeated processing
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# cur.execute("UPDATE qd_reflection_records SET status = 'FAILED' WHERE id = ?", (record_id,))
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# conn.commit()
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cur.close()
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logger.info("Reflection verification cycle completed")
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except Exception as e:
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logger.error(f"执行验证周期失败: {e}")
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logger.error(f"Failed to execute verification cycle: {e}")
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def get_pending_count(self) -> int:
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"""Get count of pending verification records."""
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try:
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with get_db_connection() as conn:
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cur = conn.cursor()
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cur.execute("SELECT COUNT(*) as cnt FROM qd_reflection_records WHERE status = 'PENDING'")
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count = cur.fetchone()['cnt']
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cur.close()
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return count
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except Exception as e:
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logger.error(f"Failed to get pending count: {e}")
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return 0
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def get_statistics(self) -> Dict[str, Any]:
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"""Get reflection statistics."""
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try:
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with get_db_connection() as conn:
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cur = conn.cursor()
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cur.execute("SELECT COUNT(*) as cnt FROM qd_reflection_records")
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total = cur.fetchone()['cnt']
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cur.execute("SELECT COUNT(*) as cnt FROM qd_reflection_records WHERE status = 'PENDING'")
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pending = cur.fetchone()['cnt']
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cur.execute("SELECT COUNT(*) as cnt FROM qd_reflection_records WHERE status = 'COMPLETED'")
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completed = cur.fetchone()['cnt']
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cur.execute(
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"SELECT AVG(actual_return) as avg_ret FROM qd_reflection_records WHERE status = 'COMPLETED' AND actual_return IS NOT NULL"
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)
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avg_return = cur.fetchone()['avg_ret'] or 0
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cur.close()
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return {
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'total_records': total,
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'pending_records': pending,
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'completed_records': completed,
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'average_return': round(avg_return, 2)
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}
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except Exception as e:
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logger.error(f"Failed to get statistics: {e}")
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return {}
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