75083500446154a3ee3a394a175a5376ec53af35,QUANTAXIS/QAFetch/QATdx.py,,QA_fetch_get_stock_day,#Any#Any#Any#Any#Any#Any#,72

Before Change

            data["date_stamp"] = data["date"].apply(
                lambda x: QA_util_date_stamp(x))
            data["date"] = pd.to_datetime(data["date"])
            data.set_index("date", drop=False, inplace=True)
            data["date"] = data["date"].apply(lambda x: str(x)[0:10])
            data = data.drop(["year", "month", "day", "hour",
                            "minute", "datetime"], axis=1)
            data = pd.concat([data, info[["fenhong", "peigu", "peigujia",

After Change

            info = xdxr_data[xdxr_data["category"] == 1]

            
            data = pd.concat([data.assign(date=pd.to_datetime(data["datetime"].apply(lambda x: x[0:10]))).assign(code=str(code))\
                .assign(date_stamp=data["datetime"].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
                .set_index("date", drop=False, inplace=False)\
                .drop(["year", "month", "day", "hour",
                    "minute", "datetime"], axis=1), info[["fenhong", "peigu", "peigujia",
                                        "songzhuangu"]][data.index[0]:]], axis=1).fillna(0)
            data["preclose"] = (data["close"].shift(1) * 10 - data["fenhong"] + data["peigu"]
                                * data["peigujia"]) / (10 + data["peigu"] + data["songzhuangu"])
            data["adj"] = (data["preclose"].shift(-1) /
                        data["close"]).fillna(1)[::-1].cumprod()
            data["open"] = data["open"] * data["adj"]
            data["high"] = data["high"] * data["adj"]
            data["low"] = data["low"] * data["adj"]
            data["close"] = data["close"] * data["adj"]
            data["preclose"] = data["preclose"] * data["adj"]
            return data[start_date:end_date]
        elif if_fq in ["02", "hfq"]:
            xdxr_data = QA_fetch_get_stock_xdxr(code)
            info = xdxr_data[xdxr_data["category"] == 1]
            data["date"] = data["datetime"].apply(lambda x: x[0:10])

Instances