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mirror of https://github.com/microsoft/qlib.git synced 2026-07-14 08:16:54 +08:00

fix CI error

This commit is contained in:
Linlang
2024-03-05 17:24:03 +08:00
parent 6ea921bd84
commit 8cf7bb3aaf
12 changed files with 24 additions and 35 deletions

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@@ -324,7 +324,6 @@ class TRAModel(Model):
class LSTM(nn.Module): class LSTM(nn.Module):
"""LSTM Model """LSTM Model
Args: Args:
@@ -414,7 +413,6 @@ class PositionalEncoding(nn.Module):
class Transformer(nn.Module): class Transformer(nn.Module):
"""Transformer Model """Transformer Model
Args: Args:
@@ -475,7 +473,6 @@ class Transformer(nn.Module):
class TRA(nn.Module): class TRA(nn.Module):
"""Temporal Routing Adaptor (TRA) """Temporal Routing Adaptor (TRA)
TRA takes historical prediction errors & latent representation as inputs, TRA takes historical prediction errors & latent representation as inputs,

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@@ -162,13 +162,15 @@ def create_account_instance(
init_cash=init_cash, init_cash=init_cash,
position_dict=position_dict, position_dict=position_dict,
pos_type=pos_type, pos_type=pos_type,
benchmark_config={} benchmark_config=(
if benchmark is None {}
else { if benchmark is None
"benchmark": benchmark, else {
"start_time": start_time, "benchmark": benchmark,
"end_time": end_time, "start_time": start_time,
}, "end_time": end_time,
}
),
) )

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@@ -622,9 +622,11 @@ class Indicator:
print( print(
"[Indicator({}) {}]: FFR: {}, PA: {}, POS: {}".format( "[Indicator({}) {}]: FFR: {}, PA: {}, POS: {}".format(
freq, freq,
trade_start_time (
if isinstance(trade_start_time, str) trade_start_time
else trade_start_time.strftime("%Y-%m-%d %H:%M:%S"), if isinstance(trade_start_time, str)
else trade_start_time.strftime("%Y-%m-%d %H:%M:%S")
),
fulfill_rate, fulfill_rate,
price_advantage, price_advantage,
positive_rate, positive_rate,

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@@ -3,6 +3,7 @@ Here is a batch of evaluation functions.
The interface should be redesigned carefully in the future. The interface should be redesigned carefully in the future.
""" """
import pandas as pd import pandas as pd
from typing import Tuple from typing import Tuple
from qlib import get_module_logger from qlib import get_module_logger

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@@ -511,7 +511,6 @@ class TRAModel(Model):
class RNN(nn.Module): class RNN(nn.Module):
"""RNN Model """RNN Model
Args: Args:
@@ -601,7 +600,6 @@ class PositionalEncoding(nn.Module):
class Transformer(nn.Module): class Transformer(nn.Module):
"""Transformer Model """Transformer Model
Args: Args:
@@ -649,7 +647,6 @@ class Transformer(nn.Module):
class TRA(nn.Module): class TRA(nn.Module):
"""Temporal Routing Adaptor (TRA) """Temporal Routing Adaptor (TRA)
TRA takes historical prediction errors & latent representation as inputs, TRA takes historical prediction errors & latent representation as inputs,

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@@ -373,7 +373,6 @@ class WeightStrategyBase(BaseSignalStrategy):
class EnhancedIndexingStrategy(WeightStrategyBase): class EnhancedIndexingStrategy(WeightStrategyBase):
"""Enhanced Indexing Strategy """Enhanced Indexing Strategy
Enhanced indexing combines the arts of active management and passive management, Enhanced indexing combines the arts of active management and passive management,

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@@ -71,15 +71,11 @@ def fetch_df_by_index(
if fetch_orig: if fetch_orig:
for slc in idx_slc: for slc in idx_slc:
if slc != slice(None, None): if slc != slice(None, None):
return df.loc[ return df.loc[pd.IndexSlice[idx_slc],] # noqa: E231
pd.IndexSlice[idx_slc],
] # noqa: E231
else: # pylint: disable=W0120 else: # pylint: disable=W0120
return df return df
else: else:
return df.loc[ return df.loc[pd.IndexSlice[idx_slc],] # noqa: E231
pd.IndexSlice[idx_slc],
] # noqa: E231
def fetch_df_by_col(df: pd.DataFrame, col_set: Union[str, List[str]]) -> pd.DataFrame: def fetch_df_by_col(df: pd.DataFrame, col_set: Union[str, List[str]]) -> pd.DataFrame:

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@@ -30,7 +30,6 @@ class Ensemble:
class SingleKeyEnsemble(Ensemble): class SingleKeyEnsemble(Ensemble):
""" """
Extract the object if there is only one key and value in the dict. Make the result more readable. Extract the object if there is only one key and value in the dict. Make the result more readable.
{Only key: Only value} -> Only value {Only key: Only value} -> Only value
@@ -64,7 +63,6 @@ class SingleKeyEnsemble(Ensemble):
class RollingEnsemble(Ensemble): class RollingEnsemble(Ensemble):
"""Merge a dict of rolling dataframe like `prediction` or `IC` into an ensemble. """Merge a dict of rolling dataframe like `prediction` or `IC` into an ensemble.
NOTE: The values of dict must be pd.DataFrame, and have the index "datetime". NOTE: The values of dict must be pd.DataFrame, and have the index "datetime".

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@@ -247,9 +247,7 @@ class ShrinkCovEstimator(RiskModel):
v1 = y.T.dot(z) / t - cov_mkt[:, None] * S v1 = y.T.dot(z) / t - cov_mkt[:, None] * S
roff1 = np.sum(v1 * cov_mkt[:, None].T) / var_mkt - np.sum(np.diag(v1) * cov_mkt) / var_mkt roff1 = np.sum(v1 * cov_mkt[:, None].T) / var_mkt - np.sum(np.diag(v1) * cov_mkt) / var_mkt
v3 = z.T.dot(z) / t - var_mkt * S v3 = z.T.dot(z) / t - var_mkt * S
roff3 = ( roff3 = np.sum(v3 * np.outer(cov_mkt, cov_mkt)) / var_mkt**2 - np.sum(np.diag(v3) * cov_mkt**2) / var_mkt**2
np.sum(v3 * np.outer(cov_mkt, cov_mkt)) / var_mkt**2 - np.sum(np.diag(v3) * cov_mkt**2) / var_mkt**2
)
roff = 2 * roff1 - roff3 roff = 2 * roff1 - roff3
rho = rdiag + roff rho = rdiag + roff

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@@ -90,7 +90,6 @@ class OnlineStrategy:
class RollingStrategy(OnlineStrategy): class RollingStrategy(OnlineStrategy):
""" """
This example strategy always uses the latest rolling model sas online models. This example strategy always uses the latest rolling model sas online models.
""" """

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@@ -146,9 +146,7 @@ class DumpDataBase:
return ( return (
self._include_fields self._include_fields
if self._include_fields if self._include_fields
else set(df_columns) - set(self._exclude_fields) else set(df_columns) - set(self._exclude_fields) if self._exclude_fields else df_columns
if self._exclude_fields
else df_columns
) )
@staticmethod @staticmethod

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@@ -132,9 +132,11 @@ class DumpPitData:
return ( return (
set(self._include_fields) set(self._include_fields)
if self._include_fields if self._include_fields
else set(df[self.field_column_name]) - set(self._exclude_fields) else (
if self._exclude_fields set(df[self.field_column_name]) - set(self._exclude_fields)
else set(df[self.field_column_name]) if self._exclude_fields
else set(df[self.field_column_name])
)
) )
def get_filenames(self, symbol, field, interval): def get_filenames(self, symbol, field, interval):