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Fix high-freq data (#702)

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* fix the collector.py yahoo 1min factor calculation

* fix HFSignalRecord
This commit is contained in:
Pengrong Zhu
2021-11-20 15:03:53 +08:00
committed by GitHub
parent 9265b66e09
commit d224ea447e
6 changed files with 112 additions and 22 deletions
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18
scripts/data_collector/yahoo/collector.py
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@@ -601,11 +601,19 @@ class YahooNormalize1min(YahooNormalize, ABC):
# - Close price adjusted for splits. Adjusted close price adjusted for both dividends and splits.
# - data_1d.adjclose: Adjusted close price adjusted for both dividends and splits.
# - data_1d.close: `data_1d.adjclose / (close for the first trading day that is not np.nan)`
df["date_tmp"] = df[self._date_field_name].apply(lambda x: pd.Timestamp(x).date())
df.set_index("date_tmp", inplace=True)
df.loc[:, "factor"] = data_1d["close"] / df["close"]
df.loc[:, "paused"] = data_1d["paused"]
df.reset_index("date_tmp", drop=True, inplace=True)
def _calc_factor(df_1d: pd.DataFrame):
try:
_date = pd.Timestamp(pd.Timestamp(df_1d[self._date_field_name].iloc[0]).date())
df_1d["factor"] = (
data_1d.loc[_date]["close"] / df_1d.loc[df_1d["close"].last_valid_index()]["close"]
)
df_1d["paused"] = data_1d.loc[_date]["paused"]
except Exception:
df_1d["factor"] = np.nan
df_1d["paused"] = np.nan
return df_1d
df = df.groupby([df[self._date_field_name].dt.date]).apply(_calc_factor)
if self.CONSISTENT_1d:
# the date sequence is consistent with 1d