3cb00ac929a447e8a5bec0d684ed408008d9cc33,QUANTAXIS/QAFetch/QATdx.py,,QA_fetch_get_stock_day,#Any#Any#Any#Any#Any#Any#,72

Before Change

            data["code"] = code
            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=False)
            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",
                                        "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).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[data["open"] != 0][start_date:end_date]

def QA_fetch_get_stock_min(code, start, end, level="1min", ip=best_ip, port=7709):

After Change

            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]


def QA_fetch_get_stock_min(code, start, end, level="1min", ip=best_ip, port=7709):
    api = TdxHq_API()

Instances