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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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7
config_examples/config_freqai-rl.example.json
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@ -73,16 +73,12 @@
"5m",
"30m"
],
"label_period_candles": 80,
"include_shifted_candles": 0,
"DI_threshold": 0,
"weight_factor": 0.9,
"principal_component_analysis": false,
"use_SVM_to_remove_outliers": false,
"svm_params": {"shuffle": true, "nu": 0.1},
"stratify_training_data": 0,
"indicator_max_period_candles": 10,
"indicator_periods_candles": [5]
"indicator_periods_candles": [5, 10]
},
"data_split_parameters": {
"test_size": 0.5,
@ -90,7 +86,6 @@
"shuffle": false
},
"model_training_parameters": {
"n_steps": 2048,
"ent_coef": 0.005,
"learning_rate": 0.000025,
"batch_size": 256,

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:

15
freqtrade/freqai/example_strats/ReinforcementLearningExample3ac.py
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@ -36,7 +36,7 @@ class ReinforcementLearningExample3ac(IStrategy):
stoploss = -0.05
use_exit_signal = True
startup_candle_count: int = 300
can_short = False
can_short = True
linear_roi_offset = DecimalParameter(
0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
@ -76,8 +76,11 @@ class ReinforcementLearningExample3ac(IStrategy):
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
informative[f"%-{coin}raw_volume"] = informative["volume"]
# Raw price currently necessary for RL models:
informative[f"%-{coin}raw_price"] = informative["close"]
# The following features are necessary for RL models
informative[f"%-{coin}raw_close"] = informative["close"]
informative[f"%-{coin}raw_open"] = informative["open"]
informative[f"%-{coin}raw_high"] = informative["high"]
informative[f"%-{coin}raw_low"] = informative["low"]
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
@ -101,9 +104,9 @@ class ReinforcementLearningExample3ac(IStrategy):
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
# For RL, this is not a target, it is simply a filler until actions come out
# of the model.
# for Base3ActionEnv, 2 is netural (hold)
df["&-action"] = 2
return df

12
freqtrade/freqai/example_strats/ReinforcementLearningExample5ac.py
View File

@ -76,8 +76,11 @@ class ReinforcementLearningExample5ac(IStrategy):
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
informative[f"%-{coin}raw_volume"] = informative["volume"]
# Raw price currently necessary for RL models:
informative[f"%-{coin}raw_price"] = informative["close"]
# The following features are necessary for RL models
informative[f"%-{coin}raw_close"] = informative["close"]
informative[f"%-{coin}raw_open"] = informative["open"]
informative[f"%-{coin}raw_high"] = informative["high"]
informative[f"%-{coin}raw_low"] = informative["low"]
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
@ -101,9 +104,8 @@ class ReinforcementLearningExample5ac(IStrategy):
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
# For RL, there are no direct targets to set. This is filler (neutral)
# until the agent sends an action.
df["&-action"] = 2
return df

18
freqtrade/freqai/prediction_models/ReinforcementLearningPPO.py
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@ -3,9 +3,8 @@ from typing import Any, Dict # , Tuple
import numpy as np
# import numpy.typing as npt
# import pandas as pd
import torch as th
# from pandas import DataFrame
from pandas import DataFrame
from stable_baselines3 import PPO
from stable_baselines3.common.callbacks import EvalCallback
from stable_baselines3.common.monitor import Monitor
@ -22,7 +21,8 @@ class ReinforcementLearningPPO(BaseReinforcementLearningModel):
User created Reinforcement Learning Model prediction model.
"""
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_params = self.freqai_info['model_training_parameters']
reward_params = self.freqai_info['model_reward_parameters']
@ -31,18 +31,12 @@ class ReinforcementLearningPPO(BaseReinforcementLearningModel):
eval_freq = agent_params.get("eval_cycles", 4) * len(test_df)
total_timesteps = agent_params["train_cycles"] * len(train_df)
# price data for model training and evaluation
price = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(train_df.index))
price_test = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(
len(test_df.index))
# environments
train_env = MyRLEnv(df=train_df, prices=price, window_size=self.CONV_WIDTH,
train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
reward_kwargs=reward_params)
eval = MyRLEnv(df=test_df, prices=price_test,
eval = MyRLEnv(df=test_df, prices=prices_test,
window_size=self.CONV_WIDTH, reward_kwargs=reward_params)
eval_env = Monitor(eval, ".")
eval_env.reset()
path = dk.data_path
eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
@ -63,7 +57,7 @@ class ReinforcementLearningPPO(BaseReinforcementLearningModel):
callback=eval_callback
)
best_model = PPO.load(dk.data_path / "best_model.zip")
best_model = PPO.load(dk.data_path / "best_model")
print('Training finished!')

16
freqtrade/freqai/prediction_models/ReinforcementLearningPPO_multiproc.py
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@ -16,6 +16,7 @@ from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Posi
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
import gym
from pandas import DataFrame
logger = logging.getLogger(__name__)
@ -47,7 +48,8 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
User created Reinforcement Learning Model prediction model.
"""
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_params = self.freqai_info['model_training_parameters']
reward_params = self.freqai_info['model_reward_parameters']
@ -57,18 +59,14 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
total_timesteps = agent_params["train_cycles"] * len(train_df)
learning_rate = agent_params["learning_rate"]
# price data for model training and evaluation
price = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(train_df.index))
price_test = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(
len(test_df.index))
env_id = "train_env"
th.set_num_threads(dk.thread_count)
num_cpu = int(dk.thread_count / 2)
train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, price, reward_params,
train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, prices_train, reward_params,
self.CONV_WIDTH) for i in range(num_cpu)])
eval_env_id = 'eval_env'
eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, price_test, reward_params,
eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, prices_test, reward_params,
self.CONV_WIDTH, monitor=True) for i in range(num_cpu)])
path = dk.data_path
@ -92,7 +90,7 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
callback=eval_callback
)
best_model = PPO.load(dk.data_path / "best_model.zip")
best_model = PPO.load(dk.data_path / "best_model")
print('Training finished!')
eval_env.close()

15
freqtrade/freqai/prediction_models/ReinforcementLearningTDQN.py
View File

@ -10,6 +10,7 @@ from freqtrade.freqai.RL.TDQNagent import TDQN
from stable_baselines3 import DQN
from stable_baselines3.common.buffers import ReplayBuffer
import numpy as np
from pandas import DataFrame
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
@ -21,7 +22,8 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
User created Reinforcement Learning Model prediction model.
"""
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_params = self.freqai_info['model_training_parameters']
reward_params = self.freqai_info['model_reward_parameters']
@ -30,15 +32,10 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
eval_freq = agent_params["eval_cycles"] * len(test_df)
total_timesteps = agent_params["train_cycles"] * len(train_df)
# price data for model training and evaluation
price = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(train_df.index))
price_test = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(
len(test_df.index))
# environments
train_env = MyRLEnv(df=train_df, prices=price, window_size=self.CONV_WIDTH,
train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
reward_kwargs=reward_params)
eval = MyRLEnv(df=test_df, prices=price_test,
eval = MyRLEnv(df=test_df, prices=prices_test,
window_size=self.CONV_WIDTH, reward_kwargs=reward_params)
eval_env = Monitor(eval, ".")
eval_env.reset()
@ -66,7 +63,7 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
callback=eval_callback
)
best_model = DQN.load(dk.data_path / "best_model.zip")
best_model = DQN.load(dk.data_path / "best_model")
print('Training finished!')

14
freqtrade/freqai/prediction_models/ReinforcementLearningTDQN_multiproc.py
View File

@ -15,7 +15,7 @@ from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcement
from freqtrade.freqai.RL.TDQNagent import TDQN
from stable_baselines3.common.buffers import ReplayBuffer
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from pandas import DataFrame
logger = logging.getLogger(__name__)
@ -47,7 +47,8 @@ class ReinforcementLearningTDQN_multiproc(BaseReinforcementLearningModel):
User created Reinforcement Learning Model prediction model.
"""
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_params = self.freqai_info['model_training_parameters']
reward_params = self.freqai_info['model_reward_parameters']
@ -57,18 +58,13 @@ class ReinforcementLearningTDQN_multiproc(BaseReinforcementLearningModel):
total_timesteps = agent_params["train_cycles"] * len(train_df)
learning_rate = agent_params["learning_rate"]
# price data for model training and evaluation
price = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(train_df.index))
price_test = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(
len(test_df.index))
env_id = "train_env"
num_cpu = int(dk.thread_count / 2)
train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, price, reward_params,
train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, prices_train, reward_params,
self.CONV_WIDTH) for i in range(num_cpu)])
eval_env_id = 'eval_env'
eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, price_test, reward_params,
eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, prices_test, reward_params,
self.CONV_WIDTH, monitor=True) for i in range(num_cpu)])
path = dk.data_path