import os import sys import sqlite3 import logging import configparser from datetime import datetime, timedelta import pandas as pd import numpy as np import requests from quant_strategy import QuantStateMachine from util import get_history_k, get_5min_k # ===================================================================== # 初始化基础环境与日志 # ===================================================================== current_dir = os.path.dirname(os.path.abspath(__file__)) log_filename = os.path.join(current_dir, "quant_daily.log") logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(levelname)s] %(message)s', handlers=[logging.FileHandler(log_filename, encoding='utf-8')]) config = configparser.ConfigParser() config.read(os.path.join(current_dir, "config.ini"), encoding='utf-8') BOT_TOKEN = config.get("telegram", "bot_token") CHAT_ID = config.get("telegram", "chat_id") DB_FILE = os.path.join(current_dir, config.get("database", "db_name", fallback="watchlist.db")) PROXY_ENABLED = config.getint("proxy", "enabled", fallback=0) PROXY_URL = config.get("proxy", "url", fallback="") # ===================================================================== # 补全缺失的函数 1: 真实/模拟数据接口 (请在此替换为你实际的 adata 调用代码) # ===================================================================== def fetch_data_from_adata(stock_code): # 🚨 注意：这里是模拟数据，实际请使用你的真实 adata 接口替换 end_date = datetime.today().strftime('%Y%m%d') start_date = (datetime.today() - timedelta(days=100)).strftime('%Y%m%d') daily_df = get_history_k(str(stock_code), start_date=start_date, end_date=end_date) min_df = get_5min_k(stock_code, start_date='20260703', end_date='20260703') return daily_df, min_df # ===================================================================== # 补全缺失的类 2: 针对 Telegram 消息推送优化的子类状态机 # ===================================================================== class TelegramQuantStateMachine(QuantStateMachine): def generate_telegram_report(self, daily_df, min_df=None): from quant_strategy import FeatureEngine, StateRuleBook engine = FeatureEngine(daily_df, min_df) f = engine.calculate_all_features() next_state, comment = StateRuleBook.evaluate_next_state(self.current_state, f) self.current_state = next_state conn = sqlite3.connect(DB_FILE) cursor = conn.cursor() cursor.execute("SELECT stock_name FROM watchlist WHERE is_active = 1 and stock_code = ?", (self.stock_code,)) row = cursor.fetchone() conn.close() name = row[0] if row else "unknow" msg = f"📊 *【量化做T复盘报告】* \n" msg += f"🤖 股票代码: `{self.stock_code}`\n" msg += f"🤖 股票名称: `{name}`\n" msg += f"🕒 诊断时间: {datetime.now().strftime('%Y-%m-%d %H:%M')}\n" msg += f"📈 因子: `%B`={f['percent_b']:.2f} | 分时偏离={f['min_bias']:.2%} | 带宽分位={f['bw_quantile']:.2%}\n" msg += f"🔍 状态: *{self.current_state}*\n" msg += f"📝 诊断: _{comment}_\n" msg += "-" * 30 + "\n" msg += f"💡 *[明日手动操作指南]*：\n" if self.current_state in ["STATE_1_OSCILLATION", "STATE_1_OSCILLATION_SQUEEZE"]: if self.current_state == "STATE_1_OSCILLATION_SQUEEZE": msg += "⚠️ *[变盘警告]*：弹簧已压紧，随时大突破，做T手速要快！\n" if f['percent_b'] >= 1.0 or f['min_bias'] > 0.035: msg += f"🟢 *【建议高抛】*：当前处于震荡高位(价格:{f['close']})，建议尾盘或明日开盘*手动卖出网格仓*！" # elif f['percent_b'] <= 0.0 or f['min_bias'] < -0.035: elif ((f['cci'] > f['prev_cci'] and f['prev_cci'] > f['prev_cci_2']) ## cci连续两天上涨 and (f['K'] > f['D'] and f['prev_K'] <= f['prev_D']) ## kdj金叉 and (f['close'] > f['ma5'])): ## 收盘价站上五日线 msg += f"🔴 *【建议低吸】*：当前处于震荡超跌区(价格:{f['close']}，J：{f['J']})，建议尾盘或明日开盘*手动买回筹码*！" else: msg += "⚪ *【建议观望】*：处于安全中枢内，未触及边界，明天*不要乱动*。" elif self.current_state == "STATE_3_MAIN_WAVE": msg += "🔥 *【强烈建议死守】*：科技股主升浪狂飙中！*禁止日内做T高抛*，锁仓死拿，享受主升浪最大利润！" elif self.current_state == "STATE_4_WAVE_END": msg += "⚠️ *【建议减仓】*：主升浪确认破位结束。建议手动*大举高抛/清空做T仓位*，落袋为安。" elif self.current_state == "STATE_2_DOWN_BREAK": msg += "🛑 *【严禁抄底】*：技术形态向下破位崩塌！明天*千万不要低吸接飞刀*，保持观望。" elif self.current_state == "STATE_5_BOTTOMING": msg += "🌱 *【建议潜伏】*：个股地量筑底阶段。不建议激进日内做T，但适合长线资金手动分批*定投埋伏*。" return msg # ===================================================================== # 补全缺失的函数 3: 数据库状态加载与固化 # ===================================================================== def load_saved_state(stock_code): conn = sqlite3.connect(DB_FILE) cursor = conn.cursor() cursor.execute("SELECT current_state FROM stock_states WHERE stock_code = ?", (stock_code,)) row = cursor.fetchone() conn.close() return row[0] if row else "STATE_1_OSCILLATION" def save_current_state(stock_code, state): conn = sqlite3.connect(DB_FILE) cursor = conn.cursor() cursor.execute("INSERT OR REPLACE INTO stock_states (stock_code, current_state, update_time) VALUES (?, ?, ?)", (stock_code, state, datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) conn.commit() conn.close() # ===================================================================== # 主运行入口 # ===================================================================== def main(): logging.info("量化定时任务触发...") try: conn = sqlite3.connect(DB_FILE) df = pd.read_sql_query("SELECT stock_code FROM watchlist WHERE is_active = 1", conn) conn.close() watchlist = df['stock_code'].tolist() except Exception as e: logging.error(f"读取数据库自选列表失败: {e}") return if not watchlist: logging.warning("当前没有激活的关注股票。") return for stock_code in watchlist: try: daily_df, min_df = fetch_data_from_adata(stock_code) if daily_df.empty: logging.warning(f"[{stock_code}] 数据为空，跳过") continue saved_state = load_saved_state(stock_code) machine = TelegramQuantStateMachine(initial_state=saved_state) machine.stock_code = stock_code tg_report = machine.generate_telegram_report(daily_df, min_df) # 推送大字报到 TG url = f"https://api.telegram.org/bot{BOT_TOKEN}/sendMessage" proxies = None if PROXY_ENABLED and PROXY_URL: proxies = { "http": PROXY_URL, "https": PROXY_URL } # 将 proxies 字典作为参数传入 requests response = requests.post( url, json={"chat_id": CHAT_ID, "text": tg_report, "parse_mode": "Markdown"}, proxies=proxies, timeout=60 ) save_current_state(stock_code, machine.current_state) except Exception as e: logging.error(f"[{stock_code}] 运行时异常: {e}", exc_info=True) def init_database_safely(): conn = sqlite3.connect(DB_FILE) cursor = conn.cursor() cursor.execute(""" CREATE TABLE IF NOT EXISTS watchlist ( stock_code TEXT PRIMARY KEY, stock_name TEXT, is_active INTEGER DEFAULT 1 ) """) cursor.execute(""" CREATE TABLE IF NOT EXISTS stock_states ( stock_code TEXT PRIMARY KEY, current_state TEXT, update_time TEXT ) """) conn.commit() conn.close() if __name__ == "__main__": init_database_safely() # 👈 核心：让 main.py 每次运行时也自己检查并建表 main()