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

.gitignore

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+# ==========================================
+# 🚨 核心核心：严禁上传的个人隐私与本地配置
+# ==========================================
+config.ini
+*.conf
+*.env
+.env
+
+# ==========================================
+# 📊 本地数据与状态文件（拒绝上传 SQLite 库和本地日志）
+# ==========================================
+*.db
+*.db-journal
+*.sqlite3
+*.log
+quant_daily.log
+bot_listener.log
+*.csv
+test.py
+
+# ==========================================
+# 🐍 Python 环境与虚拟环境（只传代码，不传依赖包）
+# ==========================================
+.venv/
+venv/
+ENV/
+env/
+dist/
+build/
+*.egg-info/
+__pycache__/
+*.py[cod]
+*$py.class
+
+# ==========================================
+# 💻 操作系统与 IDE 特有缓存
+# ==========================================
+.idea/
+.vscode/
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.swp
+.DS_Store
+Thumbs.db

bot_listener.py

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+import os
+import sys
+import sqlite3
+import time
+import logging
+import configparser
+import requests
+
+# =====================================================================
+# 0. 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, "bot_listener.log")
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s [%(levelname)s] %(message)s',
+    handlers=[logging.FileHandler(log_filename, encoding='utf-8'), logging.StreamHandler(sys.stdout)]
+)
+
+# 读取 config.ini
+config = configparser.ConfigParser()
+config_path = os.path.join(current_dir, "config.ini")
+if not os.path.exists(config_path):
+    logging.error("❌ 错误：找不到 config.ini 配置文件！")
+    sys.exit(1)
+
+config.read(config_path, encoding='utf-8')
+BOT_TOKEN = config.get("telegram", "bot_token")
+CHAT_ID = int(config.get("telegram", "chat_id"))
+DB_FILE = os.path.join(current_dir, config.get("database", "db_name", fallback="watchlist.db"))
+
+# 👇 核心核心：读入代理配置并构建成 requests 专用的字典
+PROXY_ENABLED = config.getint("proxy", "enabled", fallback=0)
+PROXY_URL = config.get("proxy", "url", fallback="")
+
+PROXIES = None
+if PROXY_ENABLED and PROXY_URL:
+    PROXIES = {
+        "http": PROXY_URL,
+        "https": PROXY_URL
+    }
+    logging.info(f"🚀 机器人监听服务已成功挂载代理: {PROXY_URL}")
+else:
+    logging.warning("⚠️ 机器人监听服务当前未启用代理，国内环境可能会连接超时。")
+
+
+# =====================================================================
+# 1. 数据库管理指令封装
+# =====================================================================
+def db_add_stock(code, name):
+    conn = sqlite3.connect(DB_FILE)
+    cursor = conn.cursor()
+    cursor.execute("INSERT OR REPLACE INTO watchlist (stock_code, stock_name, is_active) VALUES (?, ?, 1)",
+                   (code, name))
+    conn.commit()
+    conn.close()
+    return f"✅ 成功添加/激活股票：`{code}` ({name})"
+
+
+def db_remove_stock(code):
+    conn = sqlite3.connect(DB_FILE)
+    cursor = conn.cursor()
+    cursor.execute("UPDATE watchlist SET is_active = 0 WHERE stock_code = ?", (code,))
+    conn.commit()
+    conn.close()
+    return f"❌ 成功将股票 `{code}` 移出自选监控"
+
+
+def db_list_watchlist():
+    conn = sqlite3.connect(DB_FILE)
+    cursor = conn.cursor()
+    cursor.execute("SELECT stock_code, stock_name FROM watchlist WHERE is_active = 1")
+    rows = cursor.fetchall()
+    conn.close()
+    if not rows:
+        return "📋 当前量化关注列表为空。\n使用 `/add 代码 名称` 添加。"
+    msg = "📋 *当前量化系统关注列表*：\n"
+    for row in rows:
+        msg += f"🔹 `{row[0]}` | {row[1]}\n"
+    return msg
+
+
+# =====================================================================
+# 2. 消息处理与分发
+# =====================================================================
+def send_reply(text):
+    """发送回执消息（带代理）"""
+    url = f"https://api.telegram.org/bot{BOT_TOKEN}/sendMessage"
+    try:
+        requests.post(
+            url,
+            json={"chat_id": CHAT_ID, "text": text, "parse_mode": "Markdown"},
+            proxies=PROXIES,  # 👈 带上代理
+            timeout=10
+        )
+    except Exception as e:
+        logging.error(f"发送回执失败: {e}")
+
+
+def handle_message(message):
+    from_id = message.get("from", {}).get("id")
+    # 安全风控
+    if from_id != CHAT_ID:
+        logging.warning(f"拒绝了未授权用户 {from_id} 的指令")
+        return
+
+    text = message.get("text", "").strip()
+    if not text.startswith("/"):
+        return
+
+    parts = text.split()
+    cmd = parts[0].lower()
+
+    if cmd == "/list":
+        reply = db_list_watchlist()
+    elif cmd == "/add":
+        if len(parts) < 3:
+            reply = "⚠️ 格式错误！请输入: `/add 代码 名称`\n例如: `/add 300750.SZ 宁德时代`"
+        else:
+            reply = db_add_stock(parts[1].upper(), parts[2])
+    elif cmd == "/del":
+        if len(parts) < 2:
+            reply = "⚠️ 格式错误！请输入: `/del 代码`\n例如: `/del 300750.SZ`"
+        else:
+            reply = db_remove_stock(parts[1].upper())
+    elif cmd in ["/start", "/help"]:
+        reply = "🤖 *量化做T小助手指令集*：\n\n" \
+                "🔍 查看列表：/list\n" \
+                "➕ 增加关注：`/add 代码 名称`\n" \
+                "➖ 移除关注：`/del 代码`"
+    else:
+        reply = "❓ 未知指令，输入 /help 查看帮助。"
+
+    send_reply(reply)
+
+
+# =====================================================================
+# 3. 长轮询核心监听流
+# =====================================================================
+def main_loop():
+    logging.info("Telegram 监听守护进程进入主循环...")
+    offset = 0
+
+    while True:
+        try:
+            url = f"https://api.telegram.org/bot{BOT_TOKEN}/getUpdates"
+            params = {"offset": offset, "timeout": 20}
+
+            # 👇 核心核心：获取消息的 get 请求也必须带上代理
+            response = requests.get(
+                url,
+                params=params,
+                proxies=PROXIES,
+                timeout=25
+            ).json()
+
+            if response.get("ok") and response.get("result"):
+                for update in response["result"]:
+                    offset = update["update_id"] + 1
+                    if "message" in update:
+                        handle_message(update["message"])
+
+        except requests.exceptions.RequestException as e:
+            logging.error(f"网络连接异常（可能代理挂了），5秒后重试: {e}")
+            time.sleep(5)
+        except Exception as e:
+            logging.error(f"监听进程捕获未知错误: {e}", exc_info=True)
+            time.sleep(2)
+
+
+if __name__ == "__main__":
+    main_loop()

config.sample.ini

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+[telegram]
+# 你的 Telegram Bot Token (找 @BotFather 申请得到)
+bot_token =
+# 你的个人 Chat ID 或频道/群组 ID
+chat_id =
+
+[database]
+# SQLite 数据库文件名
+db_name = watchlist.db
+
+
+[proxy]
+# 🚨 开启代理开关：1 为启用，0 为关闭
+enabled = 0
+# 🚨 填入你 Linux 本地或局域网的代理软件端口（支持 http 或 socks5）
+# 比如你本地运行了 Clash/V2Ray，通常 http 端口是 7890 或 10809
+url = http://127.0.0.1:7890

main.py

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+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()

quant_strategy.py

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+import numpy as np
+import pandas as pd
+import talib
+
+
+# =====================================================================
+# 1. 指标引擎：未来所有新发掘的指标，全写在这里
+# =====================================================================
+class FeatureEngine:
+
+    def __init__(self, daily_df, min5_df):
+        """
+        daily_df: 日线数据 DataFrame
+        min5_df:  当天 5分钟 级别 K 线数据 DataFrame (需要包含 'close', 'volume')
+        """
+        self.df = daily_df.copy()
+        self.min5_df = min5_df.copy()
+        self._extract_basic_arrays()
+
+    def _extract_basic_arrays(self):
+        self.close = self.df["close"].astype(float).values
+        self.high = self.df["high"].astype(float).values
+        self.low = self.df["low"].astype(float).values
+        self.amount = self.df["amount"].astype(float).values
+
+    def calculate_all_features(self):
+        features = {}
+
+        # =====================================================================
+        # 1. 🔄 【核心修改】：用 5分钟 K 线精确估算今日分时均价
+        # =====================================================================
+        if not self.min5_df.empty:
+            # 计算每 5 分钟的成交金额（收盘价 * 成交量）
+            # 注意：如果你的 5分钟接口直接自带 'amount'(成交额) 列，请直接用 self.min5_df['amount']
+            m5_close = self.min5_df["close"].astype(float)
+            m5_volume = self.min5_df["volume"].astype(float)
+
+            total_amount = (m5_close * m5_volume).sum()
+            total_volume = m5_volume.sum()
+
+            if total_volume > 0:
+                # 算出截止到当前（14:48）的 A 股全天分时均价
+                vwap_today = total_amount / total_volume
+                # 当前最新价格（最后一根 5分钟线的收盘价，最接近实时现价）
+                current_price = m5_close.iloc[-1]
+
+                # 计算偏离度：(最新价 - 分时均价) / 分时均价
+                features["min_bias"] = (current_price - vwap_today) / vwap_today
+            else:
+                features["min_bias"] = 0.0
+        else:
+            features["min_bias"] = 0.0
+
+        # =====================================================================
+        # 2. 日线常规指标计算（保持你的经典布林带逻辑不变）
+        # =====================================================================
+        up, mid, low = talib.BBANDS(
+            self.close, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0
+        )
+        self.df["bb_up"], self.df["bb_mid"], self.df["bb_low"] = up, mid, low
+
+        self.df["percent_b"] = (self.df["close"] - low) / (up - low)
+        self.df["bandwidth"] = (up - low) / mid
+        self.df["amount_avg_20d"] = (
+            self.df["amount"].rolling(20).mean().shift(1)
+        )
+        self.df["price_max_60d"] = self.df["close"].rolling(60).max().shift(1)
+        self.df["low_min_10d"] = self.df["low"].rolling(10).min().shift(1)
+
+        latest = self.df.iloc[-1]
+        prev = self.df.iloc[-2]
+
+        # 如果 5分钟线数据拿到了，现价以 5分钟最新收盘价为准（更接近 14:48 真实盘面）
+        # 如果没拿到，退化使用日线昨日收盘（做测试用）
+        features["close"] = (
+            current_price if not self.min5_df.empty else latest["close"]
+        )
+
+        features["prev_close"] = prev["close"]
+        features["amount"] = latest["amount"]
+        features["amount_avg_20d"] = latest["amount_avg_20d"]
+        features["ma5"] = latest["ma5"] if "ma5" in latest else latest["close"]
+        features["ma10"] = latest["ma10"] if "ma10" in latest else latest["close"]  # 👈 核心：补上这一行！
+        features["ma20"] = latest["bb_mid"]
+        features["ma60"] = latest["ma60"] if "ma60" in latest else latest["close"]  # 👈 顺便把ma60也安全带上
+        features["percent_b"] = latest["percent_b"]
+        features["bandwidth"] = latest["bandwidth"]
+        features["is_price_60d_max"] = (
+            latest["close"] >= latest["price_max_60d"]
+        )
+        features["is_not_new_low_10d"] = latest["close"] > latest["low_min_10d"]
+
+        history_bw = self.df["bandwidth"].iloc[-250:]
+        features["bw_quantile"] = (history_bw < latest["bandwidth"]).mean()
+
+        return features
+
+# =====================================================================
+# 2. 状态规则书：这里只根据指标数据定义状态切换门槛
+# =====================================================================
+class StateRuleBook:
+
+    @staticmethod
+    def evaluate_next_state(current_state, f):
+        """f 传入的是 FeatureEngine 计算出来的最新特征字典"""
+
+        # 计算主升浪基础多头条件
+        is_ma_bull = (f["ma5"] > f["ma10"] > f["ma20"]) and (
+            f["ma5"] > f["prev_ma5"]
+        )
+
+        # 核心决策流：利用解耦后的字典 f 进行条件拆解
+        # 【判断是否从主升浪跌破】
+        if (
+            current_state == "STATE_3_MAIN_WAVE"
+            and f["close"] < f["ma10"]
+            and f["prev_close"] > f["prev_ma10"]
+        ):
+            return (
+                "STATE_4_WAVE_END",
+                "⚠️ 主升浪确认结束！清空做T仓，准备重新激活做T。",
+            )
+
+        # 【判断是否爆发主升浪】
+        if (
+            is_ma_bull
+            and f["is_price_60d_max"]
+            and (f["amount"] > f["amount_avg_20d"] * 1.8)
+        ):
+            return (
+                "STATE_3_MAIN_WAVE",
+                "🚀 主升浪开启 / 放量向上突破！做T脚本自动拉闸休眠，锁仓死拿！",
+            )
+
+        # 【判断是否向下破位】
+        if f["percent_b"] < 0.0:  # 跌破布林下轨
+            return "STATE_2_DOWN_BREAK", "❌ 向下破位！停止低吸做T，转为观望。"
+
+        # 【判断是否低量筑底】
+        is_low_volume = f["amount"] < (f["amount_avg_20d"] * 0.5)
+        is_ma_converge = abs(f["ma5"] - f["ma20"]) / f["ma20"] < 0.03
+        if f["is_not_new_low_10d"] and is_low_volume and is_ma_converge:
+            return (
+                "STATE_5_BOTTOMING",
+                "🌱 下跌结束，正在地量筑底。允许开始尝试轻仓做T。",
+            )
+
+        # 【判断是否处于变盘前夜】
+        if f["bw_quantile"] < 0.12:
+            return (
+                "STATE_1_OSCILLATION_SQUEEZE",
+                "🎚️ 变盘前夜：带宽极度压缩。保持做T，但防范单边突破。",
+            )
+
+        # 默认返回横盘震荡
+        return (
+            "STATE_1_OSCILLATION",
+            "☕ 正常横盘震荡期。激活做T脚本，正常执行高抛低吸。",
+        )
+
+
+# =====================================================================
+# 3. 策略状态机：调度核心
+# =====================================================================
+class QuantStateMachine:
+
+    def __init__(self, initial_state="STATE_1_OSCILLATION"):
+        self.current_state = initial_state
+
+    def run_daily_diagnostic(self, daily_df, min_df=None):
+        # 1. 扔给传感器计算指标
+        engine = FeatureEngine(daily_df, min_df)
+        features = engine.calculate_all_features()
+
+        # 2. 扔给规则书判定状态
+        next_state, comment = StateRuleBook.evaluate_next_state(
+            self.current_state, features
+        )
+
+        # 3. 更新并固化状态
+        self.current_state = next_state
+
+        # 4. 根据最终状态，分发当天的交易指令
+        self._execute_trading_action(features, comment)
+
+    def _execute_trading_action(self, f, comment):
+        print(f"\n[当前系统状态]: {self.current_state}")
+        print(f"[状态诊断提示]: {comment}")
+
+        # 具体的交易动作分发
+        if self.current_state in [
+            "STATE_1_OSCILLATION",
+            "STATE_1_OSCILLATION_SQUEEZE",
+        ]:
+            # 只有在震荡期，才读取 %B 或分时执行做T
+            if f["percent_b"] >= 1.0 or f["min_bias"] > 0.035:
+                print(">>> 💰 【执行动作】：尾盘高抛，卖出 20% 网格仓。")
+            elif f["percent_b"] <= 0.0 or f["min_bias"] < -0.035:
+                print(">>> 🛒 【执行动作】：尾盘低吸，接回 20% 网格仓。")
+            else:
+                print(">>> ☕ 【执行动作】：未触及极端做T边界，长线持股观望。")
+
+        elif self.current_state == "STATE_3_MAIN_WAVE":
+            print(">>> 🔒 【执行动作】：主升浪锁仓护航中，禁止任何人乱动做T筹码。")
+
+        elif self.current_state == "STATE_2_DOWN_BREAK":
+            print(">>> 🛑 【执行动作】：市场向下破位，做T有被套风险，禁止低吸！")
+
+        elif self.current_state == "STATE_5_BOTTOMING":
+            print(">>> 🔬 【执行动作】：地量筑底阶段，允许小仓位底部分批低吸。")

requirements.txt

@@ -0,0 +1,40 @@
+adata==2.9.5
+akracer==0.0.14
+akshare==1.18.64
+beautifulsoup4==4.15.0
+build==1.5.0
+certifi==2026.6.17
+cffi==2.0.0
+charset-normalizer==3.4.7
+curl_cffi==0.15.0
+decorator==5.3.1
+et_xmlfile==2.0.0
+html5lib==1.1
+idna==3.18
+jsonpath==0.82.2
+lxml==6.1.1
+markdown-it-py==4.2.0
+mdurl==0.1.2
+numpy==2.2.6
+openpyxl==3.1.5
+packaging==26.2
+pandas==2.3.3
+py-mini-racer==0.6.0
+pycparser==3.0
+Pygments==2.20.0
+pyproject_hooks==1.2.0
+python-dateutil==2.9.0.post0
+pytz==2026.2
+requests==2.34.2
+rich==15.0.0
+six==1.17.0
+soupsieve==2.8.4
+TA-Lib==0.7.0
+tabulate==0.10.0
+tomli==2.4.1
+tqdm==4.68.3
+typing_extensions==4.16.0
+tzdata==2026.2
+urllib3==2.7.0
+webencodings==0.5.1
+xlrd==2.0.2

util.py

@@ -0,0 +1,89 @@
+import requests
+import pandas as pd
+
+CN_CODE_TYPE = {
+    "600":'sh',
+    "601":'sh',
+    "603":'sh',
+    "605":'sh',
+    "000":'sz',
+    "300":'sz',
+    "688":'sh',
+    "002":'sz',
+    "50":"sh",
+    "51":"sh",
+    "52":"sh",
+    "16":"sz",
+    "15":"sz",
+    "18":"sz"
+    }
+
+
+
+def get_history_k(code, start_date='19700101', end_date=None):
+
+    if '.' in code:
+        code = code.split('.')[0]
+
+    stock_code = ''
+
+    if code[0:2] in CN_CODE_TYPE:
+        stock_code = code + '.' + CN_CODE_TYPE[code[0:2]]
+    elif code[0:3] in CN_CODE_TYPE:
+        stock_code = code + '.' + CN_CODE_TYPE[code[0:3]]
+    print(stock_code)
+    if stock_code != '':
+
+        url = "https://api.zhituapi.com/hs/history/%s/d/f?token=6E0E86BC-15AD-4275-8A95-B02D168D63C1&st=%s"%(stock_code, start_date)
+
+        if end_date is not None:
+            url += "&end_date=%s"%(end_date)
+
+        response = requests.get(url)
+
+        data = response.json()
+        print(data)
+        df = pd.DataFrame(data)
+
+        df['t'] = df['t'].apply(pd.to_datetime)
+        df.rename({'t': 'date', 'o': 'open', 'h': 'high', 'l': 'low', 'c': 'close', 'v': 'volume', 'a': 'amount'},
+                  axis='columns', inplace=True)
+        df = df[['date', 'open', 'high', 'low', 'close', 'volume', 'amount']].set_index('date')
+
+    else:
+        df = pd.DataFrame()
+
+
+    return df
+
+
+def get_5min_k(code, start_date='19700101', end_date=None):
+    stock_code = ''
+
+    if code[0:2] in CN_CODE_TYPE:
+        stock_code = code + '.' + CN_CODE_TYPE[code[0:2]]
+    elif code[0:3] in CN_CODE_TYPE:
+        stock_code = code + '.' + CN_CODE_TYPE[code[0:3]]
+
+    if stock_code != '':
+
+        url = "https://api.zhituapi.com/hs/history/%s/5/n?token=6E0E86BC-15AD-4275-8A95-B02D168D63C1&st=%s"%(stock_code, start_date)
+
+        if end_date is not None:
+            url += "&end_date=%s"%(end_date)
+
+        response = requests.get(url)
+
+        data = response.json()
+
+        df = pd.DataFrame(data)
+        df['t'] = df['t'].apply(pd.to_datetime)
+        df.rename({'t': 'date', 'o': 'open', 'h': 'high', 'l': 'low', 'c': 'close', 'v': 'volume', 'a': 'amount'},
+                  axis='columns', inplace=True)
+        df = df[['date','close', 'volume']].set_index('date')
+
+    else:
+        df = pd.DataFrame()
+
+
+    return df