Comments (3)
As a side note I would probably drop the calculation of mean_ret_quant_daily, it doesn't suit well with all possible data frequency passed to alphalens. Or, at lest, we should avoid mixing plots where the data is aggregated by day (e.g. cumulative plot by quantile, top minus bottom quantile mean return) with plots where it is not (e.g. factor weighted cumulative plot). This is confusing.
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To summarize, I believe the aggregation of data by day is a nice feature that would produce better plots for some scenarios (data frequency/data time span) but not for all the possible input of alphalens. This means the aggregation feature should be optional and the aggregation period (day, hour, week) configurable. Also, to be useful, the feature should be applied to all the plots consistently.
Currently the aggregation of data by time rule is not optional, it is not configurable and it is applied randomly to some plots only. This is confusing and I would completely remove the feature until properly implemented.
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The code doesn't exactly does what I thought and it's almost fine. Still it doesn't make sense to aggregate by day when calculating perf.mean_return_by_quantile for the reasons exaplained above.
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Related Issues (20)
- freq error: freq must be Day, BDay or CustomBusinessDay HOT 1
- How can I build my confidence that my strategy returns better HOT 1
- ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'' HOT 6
- cumulative_returns calculation no longer adjusts for periods > 1?
- When I use "bins" instead of "quantiles", it went wrong
- UnboundLocalError: local variable 'period_len' referenced before assignment HOT 1
- Problem:create_event_returns_tear_sheet
- Dropped 45.5% entries from factor data: 45.5% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions)
- Anyone can explain how the factors are bined? HOT 2
- Alphalens
- MissingDataError: exog contains inf or nans HOT 2
- importing alphalens 0.3.6 gets error "No module named 'pandas.util._decorators'" HOT 1
- AttributeError: 'Int64Index' object has no attribute 'tz' HOT 2
- Is alphalens still used? HOT 1
- got error: Error: No such keys(s): 'mode.use_inf_as_null'
- data = al.utils.get_clean_factor_and_forward_returns(cs_df.stack(), pricing, quantiles=5, bins=None, periods=[1])
- Unable to work with pyfolio integration
- invalid index to scalar variable @ mode(days_diffs).mode[0] HOT 11
- cant install alphalens from pip because of outdated configparser method call
- STOP USING WRITING READING
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