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Fix polygon get historical prices #679

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merged 2 commits into from
Jan 10, 2025
Merged

Fix polygon get historical prices #679

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1faa9b9
missing_dates: re-enabled code that had been removed. Updated unit te…
davidlatte Jan 8, 2025
1ff9eaa
Merge remote-tracking branch 'origin/fix-polygon-get-historical-price…
davidlatte Jan 8, 2025
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11 changes: 11 additions & 0 deletions lumibot/tools/polygon_helper.py
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Expand Up @@ -411,6 +411,17 @@ def get_missing_dates(df_all, asset, start, end):
dates = pd.Series(df_all.index.date).unique()
missing_dates = sorted(set(trading_dates) - set(dates))

# Find any dates with nan values in the df_all DataFrame. This happens for some infrequently traded assets, but
# it is difficult to know if the data is actually missing or if it is just infrequent trading, query for it again.
missing_dates += df_all[df_all.isnull().all(axis=1)].index.date.tolist()
Comment on lines +414 to +416
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Over-querying for legitimate no-trade days category Functionality

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What is the issue?

The code treats all days with null values as missing data that needs to be re-queried, but for infrequently traded assets, these null values might legitimately represent days with no trading activity.

Why this matters

This could lead to unnecessary API calls to Polygon for dates that genuinely had no trading activity, potentially hitting rate limits and slowing down the data retrieval process without adding any valuable data.

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# make sure the dates are unique
missing_dates = list(set(missing_dates))
missing_dates.sort()

# finally, filter out any dates that are not in start/end range (inclusive)
missing_dates = [d for d in missing_dates if start.date() <= d <= end.date()]

return missing_dates


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7 changes: 1 addition & 6 deletions tests/test_polygon_helper.py
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Expand Up @@ -480,10 +480,5 @@ def test_polygon_missing_day_caching(self, mocker, tmpdir, timespan, force_cache
assert mock_polyclient.create().get_aggs.call_count == 3
assert expected_cachefile.exists()
assert len(df) == 7
df = ph.get_price_data_from_polygon(api_key, asset, start_date, end_date, timespan, force_cache_update=force_cache_update)
assert len(df) == 7
if force_cache_update:
assert mock_polyclient.create().get_aggs.call_count == 2 * 3
else:
assert mock_polyclient.create().get_aggs.call_count == 3

expected_cachefile.unlink()
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