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move price assignment to feature_engineering_standard() to reduce un-requested feature additions in RL. Ensure old method of price assignment still works, add deprecation warning to help users migrate their strategies

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This commit is contained in:
robcaulk 2022-12-30 13:02:39 +01:00
parent 2e30bdb9b2
commit b2bab68fba
2 changed files with 28 additions and 22 deletions
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40
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
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@ -283,27 +283,33 @@ class BaseReinforcementLearningModel(IFreqaiModel):
# %-raw_volume_gen_shift-2_ETH/USDT_1h # %-raw_volume_gen_shift-2_ETH/USDT_1h
# price data for model training and evaluation # price data for model training and evaluation
tf = self.config['timeframe'] tf = self.config['timeframe']
ohlc_list = [f'%-raw_open_gen_{pair}_{tf}', f'%-raw_low_gen_{pair}_{tf}', rename_dict = {'%-raw_open': 'open', '%-raw_low': 'low',
f'%-raw_high_gen_{pair}_{tf}', f'%-raw_close_gen_{pair}_{tf}'] '%-raw_high': ' high', '%-raw_close': 'close'}
rename_dict = {f'%-raw_open_gen_{pair}_{tf}': 'open', rename_dict_old = {f'%-{pair}raw_open_{tf}': 'open', f'%-{pair}raw_low_{tf}': 'low',
f'%-raw_low_gen_{pair}_{tf}': 'low', f'%-{pair}raw_high_{tf}': ' high', f'%-{pair}raw_close_{tf}': 'close'}
f'%-raw_high_gen_{pair}_{tf}': ' high',
f'%-raw_close_gen_{pair}_{tf}': 'close'} prices_train = train_df.filter(rename_dict.keys(), axis=1)
prices_train_old = train_df.filter(rename_dict_old.keys(), axis=1)
if prices_train.empty or not prices_train_old.empty:
if not prices_train_old.empty:
prices_train = prices_train_old
rename_dict = rename_dict_old
logger.warning('Reinforcement learning module didnt find the correct raw prices '
'assigned in feature_engineering_standard(). '
'Please assign them with:\n'
'dataframe["%-raw_close"] = dataframe["close"]\n'
'dataframe["%-raw_open"] = dataframe["open"]\n'
'dataframe["%-raw_high"] = dataframe["high"]\n'
'dataframe["%-raw_low"] = dataframe["low"]\n'
'inside `feature_engineering_standard()')
elif prices_train.empty:
raise OperationalException("No prices found, please follow log warning "
"instructions to correct the strategy.")
prices_train = train_df.filter(ohlc_list, axis=1)
if prices_train.empty:
raise OperationalException('Reinforcement learning module didnt find the raw prices '
'assigned in feature_engineering_standard(). '
'Please assign them with:\n'
'dataframe["%-raw_close"] = dataframe["close"]\n'
'dataframe["%-raw_open"] = dataframe["open"]\n'
'dataframe["%-raw_high"] = dataframe["high"]\n'
'dataframe["%-raw_low"] = dataframe["low"]\n'
'inside `feature_engineering_expand_basic()`')
prices_train.rename(columns=rename_dict, inplace=True) prices_train.rename(columns=rename_dict, inplace=True)
prices_train.reset_index(drop=True) prices_train.reset_index(drop=True)
prices_test = test_df.filter(ohlc_list, axis=1) prices_test = test_df.filter(rename_dict.keys(), axis=1)
prices_test.rename(columns=rename_dict, inplace=True) prices_test.rename(columns=rename_dict, inplace=True)
prices_test.reset_index(drop=True) prices_test.reset_index(drop=True)

10
tests/strategy/strats/freqai_rl_test_strat.py
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@ -35,11 +35,6 @@ class freqai_rl_test_strat(IStrategy):
dataframe["%-pct-change"] = dataframe["close"].pct_change() dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"] dataframe["%-raw_volume"] = dataframe["volume"]
dataframe["%-raw_close"] = dataframe["close"]
dataframe["%-raw_open"] = dataframe["open"]
dataframe["%-raw_high"] = dataframe["high"]
dataframe["%-raw_low"] = dataframe["low"]
return dataframe return dataframe
def feature_engineering_standard(self, dataframe, **kwargs): def feature_engineering_standard(self, dataframe, **kwargs):
@ -47,6 +42,11 @@ class freqai_rl_test_strat(IStrategy):
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
dataframe["%-raw_close"] = dataframe["close"]
dataframe["%-raw_open"] = dataframe["open"]
dataframe["%-raw_high"] = dataframe["high"]
dataframe["%-raw_low"] = dataframe["low"]
return dataframe return dataframe
def set_freqai_targets(self, dataframe, **kwargs): def set_freqai_targets(self, dataframe, **kwargs):