import logging
from typing import Any, Dict, Tuple
import numpy as np
import numpy.typing as npt
import pandas as pd
from pandas import DataFrame
from abc import abstractmethod
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
logger = logging.getLogger(__name__)
class BaseReinforcementLearningModel(IFreqaiModel):
"""
User created Reinforcement Learning Model prediction model.
"""
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
) -> Any:
"""
Filter the training data and train a model to it. Train makes heavy use of the datakitchen
for storing, saving, loading, and analyzing the data.
:param unfiltered_dataframe: Full dataframe for the current training period
:param metadata: pair metadata from strategy.
:returns:
:model: Trained model which can be used to inference (self.predict)
"""
logger.info("--------------------Starting training " f"{pair} --------------------")
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features(
unfiltered_dataframe,
dk.training_features_list,
dk.label_list,
training_filter=True,
)
data_dictionary: Dict[str, Any] = dk.make_train_test_datasets(
features_filtered, labels_filtered)
dk.fit_labels() # useless for now, but just satiating append methods
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)
# optional additional data cleaning/analysis
self.data_cleaning_train(dk)
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
model = self.fit(data_dictionary, pair)
if pair not in self.dd.historic_predictions:
self.set_initial_historic_predictions(
data_dictionary['train_features'], model, dk, pair)
self.dd.save_historic_predictions_to_disk()
logger.info(f"--------------------done training {pair}--------------------")
return model
@abstractmethod
def fit(self, data_dictionary: Dict[str, Any], pair: str = ''):
"""
Agent customizations and abstract Reinforcement Learning customizations
go in here. Abstract method, so this function must be overridden by
user class.
"""
return
def get_state_info(self, pair):
open_trades = Trade.get_trades(trade_filter=Trade.is_open.is_(True))
market_side = 0.5
current_profit = 0
for trade in open_trades:
if trade.pair == pair:
current_value = trade.open_trade_value
openrate = trade.open_rate
if 'long' in trade.enter_tag:
market_side = 1
else:
market_side = 0
current_profit = current_value / openrate - 1
total_profit = 0
closed_trades = Trade.get_trades(
trade_filter=[Trade.is_open.is_(False), Trade.pair == pair])
for trade in closed_trades:
total_profit += trade.close_profit
return market_side, current_profit, total_profit
def predict(
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (PCA and DI index)
"""
dk.find_features(unfiltered_dataframe)
filtered_dataframe, _ = dk.filter_features(
unfiltered_dataframe, dk.training_features_list, training_filter=False
)
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe
# optional additional data cleaning/analysis
self.data_cleaning_predict(dk, filtered_dataframe)
pred_df = self.rl_model_predict(dk.data_dictionary["prediction_features"], dk, self.model)
pred_df.fillna(0, inplace=True)
return (pred_df, dk.do_predict)
def rl_model_predict(self, dataframe: DataFrame,
dk: FreqaiDataKitchen, model: Any) -> DataFrame:
output = pd.DataFrame(np.full((len(dataframe), 1), 2), columns=dk.label_list)
def _predict(window):
observations = dataframe.iloc[window.index]
res, _ = model.predict(observations, deterministic=True)
return res
output = output.rolling(window=self.CONV_WIDTH).apply(_predict)
return output
def set_initial_historic_predictions(
self, df: DataFrame, model: Any, dk: FreqaiDataKitchen, pair: str
) -> None:
pred_df = self.rl_model_predict(df, dk, model)
pred_df.fillna(0, inplace=True)
self.dd.historic_predictions[pair] = pred_df
hist_preds_df = self.dd.historic_predictions[pair]
for label in hist_preds_df.columns:
if hist_preds_df[label].dtype == object:
continue
hist_preds_df[f'{label}_mean'] = 0
hist_preds_df[f'{label}_std'] = 0
hist_preds_df['do_predict'] = 0
if self.freqai_info['feature_parameters'].get('DI_threshold', 0) > 0:
hist_preds_df['DI_values'] = 0
for return_str in dk.data['extra_returns_per_train']:
hist_preds_df[return_str] = 0
class MyRLEnv(Base3ActionRLEnv):
def step(self, action):
self._done = False
self._current_tick += 1
if self._current_tick == self._end_tick:
self._done = True
self.update_portfolio_log_returns(action)
self._update_profit(action)
step_reward = self._calculate_reward(action)
self.total_reward += step_reward
trade_type = None
if self.is_tradesignal(action): # exclude 3 case not trade
# Update position
"""
Action: Neutral, position: Long -> Close Long
Action: Neutral, position: Short -> Close Short
Action: Long, position: Neutral -> Open Long
Action: Long, position: Short -> Close Short and Open Long
Action: Short, position: Neutral -> Open Short
Action: Short, position: Long -> Close Long and Open Short
"""
if action == Actions.Neutral.value:
self._position = Positions.Neutral
trade_type = "neutral"
elif action == Actions.Long.value:
self._position = Positions.Long
trade_type = "long"
elif action == Actions.Short.value:
self._position = Positions.Short
trade_type = "short"
else:
print("case not defined")
# Update last trade tick
self._last_trade_tick = self._current_tick
if trade_type is not None:
self.trade_history.append(
{'price': self.current_price(), 'index': self._current_tick,
'type': trade_type})
if self._total_profit < 0.2:
self._done = True
self._position_history.append(self._position)
observation = self._get_observation()
info = dict(
tick=self._current_tick,
total_reward=self.total_reward,
total_profit=self._total_profit,
position=self._position.value
)
self._update_history(info)
return observation, step_reward, self._done, info