feat: 首次提交，包含多因子状态机量化策略及 TG 机器人监听服务

2026-07-05 19:32:31 +08:00
commit 0ae7f38f20
7 changed files with 752 additions and 0 deletions
@@ -0,0 +1,178 @@
+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
+
+        msg = f"📊 *【量化做T复盘报告】* \n"
+        msg += f"🤖 股票代码: `{self.stock_code}`\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:
+                msg += f"🔴 *【建议低吸】*：当前处于震荡超跌区(价格:{f['close']})，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()