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Fix date parsing, timezone, and type errors in Yahoo collector #2118

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Fix date parsing, timezone, and type errors in Yahoo collector #2118

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2 changes: 1 addition & 1 deletion scripts/data_collector/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -305,7 +305,7 @@ def _executor(self, file_path: Path):
df = self._normalize_obj.normalize(df)
if df is not None and not df.empty:
if self._end_date is not None:
_mask = pd.to_datetime(df[self._date_field_name]) <= pd.Timestamp(self._end_date)
_mask = pd.to_datetime(df[self._date_field_name], utc=True).dt.tz_convert(None) <= pd.Timestamp(self._end_date)
df = df[_mask]
df.to_csv(self._target_dir.joinpath(file_path.name), index=False)

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8 changes: 6 additions & 2 deletions scripts/data_collector/yahoo/collector.py
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Original file line number Diff line number Diff line change
Expand Up @@ -392,8 +392,8 @@ def normalize_yahoo(
columns = copy.deepcopy(YahooNormalize.COLUMNS)
df = df.copy()
df.set_index(date_field_name, inplace=True)
df.index = pd.to_datetime(df.index)
df.index = df.index.tz_localize(None)
df.index = pd.to_datetime(df.index, utc=True)
df.index = df.index.tz_convert(None)
df = df[~df.index.duplicated(keep="first")]
if calendar_list is not None:
df = df.reindex(
Expand Down Expand Up @@ -458,13 +458,16 @@ def adjusted_price(self, df: pd.DataFrame) -> pd.DataFrame:
df = df.copy()
df.set_index(self._date_field_name, inplace=True)
if "adjclose" in df:
df["adjclose"] = pd.to_numeric(df["adjclose"], errors="coerce")
df["close"] = pd.to_numeric(df["close"], errors="coerce")
df["factor"] = df["adjclose"] / df["close"]
df["factor"] = df["factor"].ffill()
else:
df["factor"] = 1
for _col in self.COLUMNS:
if _col not in df.columns:
continue
df[_col] = pd.to_numeric(df[_col], errors="coerce")
if _col == "volume":
df[_col] = df[_col] / df["factor"]
else:
Expand Down Expand Up @@ -500,6 +503,7 @@ def _manual_adj_data(self, df: pd.DataFrame) -> pd.DataFrame:
# NOTE: retain original adjclose, required for incremental updates
if _col in [self._symbol_field_name, "adjclose", "change"]:
continue
df[_col] = pd.to_numeric(df[_col], errors="coerce")
if _col == "volume":
df[_col] = df[_col] * _close
else:
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