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improve price df handling to enable backtesting

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This commit is contained in:
robcaulk
2022-08-17 12:51:14 +02:00
parent 2080ff86ed
commit b90da46b1b
8 changed files with 77 additions and 59 deletions
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39
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
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@@ -10,8 +10,11 @@ from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.freqai_interface import IFreqaiModel
from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
from freqtrade.persistence import Trade
import torch.multiprocessing
import torch as th
logger = logging.getLogger(__name__)
th.set_num_threads(8)
torch.multiprocessing.set_sharing_strategy('file_system')
class BaseReinforcementLearningModel(IFreqaiModel):
@@ -46,6 +49,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
dk.fit_labels() # useless for now, but just satiating append methods
# normalize all data based on train_dataset only
prices_train, prices_test = self.build_ohlc_price_dataframes(dk.data_dictionary, pair, dk)
data_dictionary = dk.normalize_data(data_dictionary)
# optional additional data cleaning/analysis
@@ -56,7 +60,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
model = self.fit_rl(data_dictionary, pair, dk)
model = self.fit_rl(data_dictionary, pair, dk, prices_train, prices_test)
if pair not in self.dd.historic_predictions:
self.set_initial_historic_predictions(
@@ -69,7 +73,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
return model
@abstractmethod
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen):
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen,
prices_train: DataFrame, prices_test: DataFrame):
"""
Agent customizations and abstract Reinforcement Learning customizations
go in here. Abstract method, so this function must be overridden by
@@ -141,6 +146,34 @@ class BaseReinforcementLearningModel(IFreqaiModel):
return output
def build_ohlc_price_dataframes(self, data_dictionary: dict,
pair: str, dk: FreqaiDataKitchen) -> Tuple[DataFrame,
DataFrame]:
"""
Builds the train prices and test prices for the environment.
"""
coin = pair.split('/')[0]
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
# price data for model training and evaluation
tf = self.config['timeframe']
ohlc_list = [f'%-{coin}raw_open_{tf}', f'%-{coin}raw_low_{tf}',
f'%-{coin}raw_high_{tf}', f'%-{coin}raw_close_{tf}']
rename_dict = {f'%-{coin}raw_open_{tf}': 'open', f'%-{coin}raw_low_{tf}': 'low',
f'%-{coin}raw_high_{tf}': ' high', f'%-{coin}raw_close_{tf}': 'close'}
prices_train = train_df.filter(ohlc_list, axis=1)
prices_train.rename(columns=rename_dict, inplace=True)
prices_train.reset_index(drop=True)
prices_test = test_df.filter(ohlc_list, axis=1)
prices_test.rename(columns=rename_dict, inplace=True)
prices_test.reset_index(drop=True)
return prices_train, prices_test
def set_initial_historic_predictions(
self, df: DataFrame, model: Any, dk: FreqaiDataKitchen, pair: str
) -> None: