214 lines
10 KiB
Python
214 lines
10 KiB
Python
import os
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import sys
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import sqlite3
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import logging
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import configparser
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from datetime import datetime, timedelta
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import pandas as pd
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import numpy as np
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import requests
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from quant_strategy import QuantStateMachine
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from util import get_history_k, get_5min_k
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# =====================================================================
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# 初始化基础环境与日志
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# =====================================================================
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current_dir = os.path.dirname(os.path.abspath(__file__))
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log_filename = os.path.join(current_dir, "quant_daily.log")
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logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(levelname)s] %(message)s',
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handlers=[logging.FileHandler(log_filename, encoding='utf-8')])
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config = configparser.ConfigParser()
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config.read(os.path.join(current_dir, "config.ini"), encoding='utf-8')
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BOT_TOKEN = config.get("telegram", "bot_token")
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CHAT_ID = config.get("telegram", "chat_id")
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DB_FILE = os.path.join(current_dir, config.get("database", "db_name", fallback="watchlist.db"))
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PROXY_ENABLED = config.getint("proxy", "enabled", fallback=0)
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PROXY_URL = config.get("proxy", "url", fallback="")
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# =====================================================================
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# 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)
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# =====================================================================
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def fetch_data_from_adata(stock_code):
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# 🚨 注意：这里是模拟数据，实际请使用你的真实 adata 接口替换
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end_date = datetime.today().strftime('%Y%m%d')
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start_date = (datetime.today() - timedelta(days=100)).strftime('%Y%m%d')
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daily_df = get_history_k(str(stock_code), start_date=start_date, end_date=end_date)
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min_df = get_5min_k(stock_code, start_date='20260703', end_date='20260703')
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return daily_df, min_df
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# =====================================================================
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# 补全缺失的类 2: 针对 Telegram 消息推送优化的子类状态机
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# =====================================================================
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class TelegramQuantStateMachine(QuantStateMachine):
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def generate_telegram_report(self, daily_df, min_df=None):
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from quant_strategy import FeatureEngine, StateRuleBook
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engine = FeatureEngine(daily_df, min_df)
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f = engine.calculate_all_features()
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next_state, comment = StateRuleBook.evaluate_next_state(self.current_state, f)
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self.current_state = next_state
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conn = sqlite3.connect(DB_FILE)
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cursor = conn.cursor()
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cursor.execute("SELECT stock_name FROM watchlist WHERE is_active = 1 and stock_code = ?", (self.stock_code,))
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row = cursor.fetchone()
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conn.close()
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name = row[0] if row else "unknow"
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msg = f"📊 *【量化做T复盘报告】* \n"
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msg += f"🤖 股票代码: `{self.stock_code}`\n"
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msg += f"🤖 股票名称: `{name}`\n"
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msg += f"🕒 诊断时间: {datetime.now().strftime('%Y-%m-%d %H:%M')}\n"
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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"
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msg += f"🔍 状态: *{self.current_state}*\n"
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msg += f"📝 诊断: _{comment}_\n"
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msg += "-" * 30 + "\n"
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msg += f"💡 *[明日手动操作指南]*：\n"
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if self.current_state in ["STATE_1_OSCILLATION", "STATE_1_OSCILLATION_SQUEEZE"]:
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if self.current_state == "STATE_1_OSCILLATION_SQUEEZE":
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msg += "⚠️ *[变盘警告]*：弹簧已压紧，随时大突破，做T手速要快！\n"
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## 盘整阶段，三个指标会触发卖出提醒
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## kdj死叉，且cci在下降中，且收盘价在5日均线下
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## cci顶背离，且k线在d线之下，且收盘价在5日均线下
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## cci从100以上下降到100以下，且k线在d线之下，且收盘价在5日均线下
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if ((f['K'] < f['D'] and f['prev_K'] > f['prev_D']) ## kdj死叉
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and (f['cci'] < f['prev_cci']) ## cci在下降中
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and (f['close'] < f['ma5'])): ## 收盘价在5日均线下
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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*！"
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elif ((f['close'] > f['prev_close'] and f['cci'] < f['prev_cci']) ## cci顶背离
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and (f['K'] < f['D']) ## k线在d线之下
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and (f['close'] < f['ma5'])): ## 收盘价在5日均线下
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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*！"
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elif ((f['cci'] < 100 and f['prev_cci'] > 100) ## cci从100跌到100以下
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and (f['K'] < f['D']) ## k线在d线之下
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and (f['close'] < f['ma5'])): ## 收盘价在5日均线下
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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*！"
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## 盘整阶段，两个指标会触发买入提醒
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## kdj金叉，且cci连续两天上涨，且收盘价在5日均线上
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## cci连续两天上涨，且k线在d线上，且收盘价在5日均线上
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elif ((f['cci'] > f['prev_cci'] and f['prev_cci'] > f['prev_cci_2']) ## cci连续两天上涨
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and (f['K'] > f['D'] and f['prev_K'] <= f['prev_D']) ## kdj金叉
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and (f['close'] > f['ma5'])): ## 收盘价站上五日线
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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*！"
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elif ((f['cci'] > f['prev_cci'] and f['prev_cci'] > f['prev_cci_2'] and f['prev_cci_2'] < f['prev_cci_3'])
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and (f['K'] >= f['D'])
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and (f['close'] > f['ma5'])):
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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*！"
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else:
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msg += "⚪ *【建议观望】*：处于安全中枢内，未触及边界，明天*不要乱动*。"
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elif self.current_state == "STATE_3_MAIN_WAVE":
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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！"
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elif self.current_state == "STATE_4_WAVE_END":
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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。"
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elif self.current_state == "STATE_2_DOWN_BREAK":
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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。"
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elif self.current_state == "STATE_5_BOTTOMING":
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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*。"
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return msg
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# =====================================================================
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# 补全缺失的函数 3: 数据库状态加载与固化
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# =====================================================================
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def load_saved_state(stock_code):
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conn = sqlite3.connect(DB_FILE)
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cursor = conn.cursor()
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cursor.execute("SELECT current_state FROM stock_states WHERE stock_code = ?", (stock_code,))
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row = cursor.fetchone()
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conn.close()
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return row[0] if row else "STATE_1_OSCILLATION"
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def save_current_state(stock_code, state):
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conn = sqlite3.connect(DB_FILE)
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cursor = conn.cursor()
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cursor.execute("INSERT OR REPLACE INTO stock_states (stock_code, current_state, update_time) VALUES (?, ?, ?)",
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(stock_code, state, datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
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conn.commit()
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conn.close()
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# =====================================================================
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# 主运行入口
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# =====================================================================
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def main():
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logging.info("量化定时任务触发...")
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try:
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conn = sqlite3.connect(DB_FILE)
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df = pd.read_sql_query("SELECT stock_code FROM watchlist WHERE is_active = 1", conn)
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conn.close()
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watchlist = df['stock_code'].tolist()
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except Exception as e:
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logging.error(f"读取数据库自选列表失败: {e}")
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return
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if not watchlist:
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logging.warning("当前没有激活的关注股票。")
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return
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for stock_code in watchlist:
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try:
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daily_df, min_df = fetch_data_from_adata(stock_code)
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if daily_df.empty:
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logging.warning(f"[{stock_code}] 数据为空，跳过")
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continue
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saved_state = load_saved_state(stock_code)
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machine = TelegramQuantStateMachine(initial_state=saved_state)
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machine.stock_code = stock_code
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tg_report = machine.generate_telegram_report(daily_df, min_df)
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# 推送大字报到 TG
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url = f"https://api.telegram.org/bot{BOT_TOKEN}/sendMessage"
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proxies = None
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if PROXY_ENABLED and PROXY_URL:
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proxies = {
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"http": PROXY_URL,
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"https": PROXY_URL
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}
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# 将 proxies 字典作为参数传入 requests
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response = requests.post(
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url,
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json={"chat_id": CHAT_ID, "text": tg_report, "parse_mode": "Markdown"},
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proxies=proxies,
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timeout=60
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)
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save_current_state(stock_code, machine.current_state)
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except Exception as e:
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logging.error(f"[{stock_code}] 运行时异常: {e}", exc_info=True)
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def init_database_safely():
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conn = sqlite3.connect(DB_FILE)
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cursor = conn.cursor()
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cursor.execute("""
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CREATE TABLE IF NOT EXISTS watchlist (
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stock_code TEXT PRIMARY KEY,
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stock_name TEXT,
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is_active INTEGER DEFAULT 1
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)
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""")
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cursor.execute("""
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CREATE TABLE IF NOT EXISTS stock_states (
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stock_code TEXT PRIMARY KEY,
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current_state TEXT,
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update_time TEXT
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)
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""")
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conn.commit()
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conn.close()
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if __name__ == "__main__":
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init_database_safely() # 👈 核心：让 main.py 每次运行时也自己检查并建表
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main() |