计算指数的周线cci

requirements.txt

@@ -21,3 +21,4 @@ tqdm>=4.65.0
 python-dateutil>=2.8.0
 schedule
 akshare
+TA-Lib

signal_calc.py

@@ -0,0 +1,101 @@
+import pandas as pd
+from db_config import DatabaseManager, DatabaseConfig
+from loguru import logger
+from datetime import datetime
+import akshare as ak
+from index_downloader import get_all_stock_index
+import schedule
+import time
+import traceback
+from dingtalk import DingTalkBot
+import talib as ta
+
+db_config = DatabaseConfig(env="daily")
+logger.info(f"数据库连接: {db_config.connection_string}")
+
+# 如果只是测试连接
+db_manager = DatabaseManager(db_config)
+
+
+def get_all_index_code() -> list:
+    """
+    获取所有指数代码
+    :return:
+    """
+    sql = "SELECT distinct code FROM public.index_kline;"
+    res = db_manager.execute_query(sql)
+    code_list = [dict(item)["code"] for item in res]
+    return code_list
+
+
+def get_index_recent_date(code: str, limit: int = None) -> pd.DataFrame:
+    """
+    获取指数的近期数据
+    :param code:
+    :return:
+    """
+    limit_clause = f" LIMIT {limit}" if limit else ""
+    sql = f"SELECT date, code, open, high, low, close, volume  FROM public.index_kline WHERE code = '{code}' order by date desc {limit_clause};"
+    raw_data_list = db_manager.execute_query(sql)
+    data_list = [dict(item) for item in raw_data_list]
+
+    for i, data in enumerate(data_list):
+        data_list[i]["date"] = data["date"].strftime("%Y-%m-%d")
+        data_list[i]["volume"] = int(data["volume"])
+        data_list[i]["close"] = float(data["close"])
+        data_list[i]["open"] = float(data["open"])
+        data_list[i]["high"] = float(data["high"])
+        data_list[i]["low"] = float(data["low"])
+    df = pd.DataFrame(data_list)
+    return df
+
+
+def main():
+    webhook = "https://oapi.dingtalk.com/robot/send?access_token=fb70c1561d8beba94b4f11568f4bb15e3ae07ccbdc8ac19676434a9d1cd17546"  # 填写你的webhook
+    secret = "SEC1ae7cd2f1a6f9da3611af37da3e7d954c1e8533fc073c6c8cc5e5af3b6e5926b"  # 填写你的加签token（如果有），否则留空
+    dingtalk = DingTalkBot(webhook, secret)
+    # code_list = get_all_index_code()
+    code_df = get_all_stock_index()
+    code_df = code_df.drop_duplicates(subset=["代码"])
+    code_list = code_df.to_dict(orient="records")
+    signal_list = []
+    for i, code_info in enumerate(code_list):
+        code = code_info["代码"]
+        df = get_index_recent_date(code, 100)
+        if len(df) < 100:
+            continue
+        # 将 'date' 列转换为 datetime 类型，并设置为索引
+        df["date"] = pd.to_datetime(df["date"])
+        df = df.sort_values("date")
+        df.set_index("date", inplace=True)
+
+        # 按周重采样（以每周最后一天为sample），open为第一个、close为最后一个、high/low为最大/最小、volume为总和、code取第一个即可
+        df_weekly = pd.DataFrame(
+            {
+                "code": df["code"].resample("W").first(),
+                "open": df["open"].resample("W").first(),
+                "high": df["high"].resample("W").max(),
+                "low": df["low"].resample("W").min(),
+                "close": df["close"].resample("W").last(),
+                "volume": df["volume"].resample("W").sum(),
+            }
+        )
+
+        # 计算CCI指标（以典型的20周期为例，如果有更具体周期可以调整）
+        df_weekly["cci"] = ta.CCI(
+            high=df_weekly["high"],
+            low=df_weekly["low"],
+            close=df_weekly["close"],
+            timeperiod=14,
+        )
+        df_weekly = df_weekly.tail(1)
+        cci = df_weekly["cci"].values[0]
+        logger.info(f"{i}/{len(code_list)}: {code} cci: {cci}")
+        if cci < -100:
+            signal_list.append({"code": code, "name": code_info["名称"], "cci": cci})
+    signal_df = pd.DataFrame(signal_list)
+    dingtalk.send_markdown(f"CCI信号", signal_df.to_markdown())
+
+
+if __name__ == "__main__":
+    main()