adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

set cpu threads in config

Browse Source
This commit is contained in:
robcaulk 2022-08-15 18:01:15 +02:00
parent 57c488a6f1
commit bf7ceba958
9 changed files with 159 additions and 21 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
.gitignore vendored
View File

@ -113,3 +113,4 @@ target/
!config_examples/config_full.example.json
!config_examples/config_kraken.example.json
!config_examples/config_freqai.example.json
!config_examples/config_freqai-rl.example.json

110
config_examples/config_freqai-rl.example.json Normal file
View File

@ -0,0 +1,110 @@
{
"trading_mode": "futures",
"new_pairs_days": 30,
"margin_mode": "isolated",
"max_open_trades": 8,
"stake_currency": "USDT",
"stake_amount": 1000,
"tradable_balance_ratio": 1,
"fiat_display_currency": "USD",
"dry_run": true,
"timeframe": "5m",
"dataformat_ohlcv": "json",
"dry_run_wallet": 12000,
"cancel_open_orders_on_exit": true,
"unfilledtimeout": {
"entry": 10,
"exit": 30
},
"exchange": {
"name": "binance",
"key": "",
"secret": "",
"ccxt_config": {
"enableRateLimit": true
},
"ccxt_async_config": {
"enableRateLimit": true,
"rateLimit": 200
},
"pair_whitelist": [
"1INCH/USDT",
"AAVE/USDT"
],
"pair_blacklist": []
},
"entry_pricing": {
"price_side": "same",
"purge_old_models": true,
"use_order_book": true,
"order_book_top": 1,
"price_last_balance": 0.0,
"check_depth_of_market": {
"enabled": false,
"bids_to_ask_delta": 1
}
},
"exit_pricing": {
"price_side": "other",
"use_order_book": true,
"order_book_top": 1
},
"pairlists": [
{
"method": "StaticPairList"
}
],
"freqai": {
"model_save_type": "stable_baselines_ppo",
"conv_width": 10,
"follow_mode": false,
"purge_old_models": true,
"train_period_days": 10,
"backtest_period_days": 2,
"identifier": "unique-id",
"data_kitchen_thread_count": 4,
"feature_parameters": {
"include_corr_pairlist": [
"BTC/USDT",
"ETH/USDT"
],
"include_timeframes": [
"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]
},
"data_split_parameters": {
"test_size": 0.5,
"random_state": 1,
"shuffle": false
},
"model_training_parameters": {
"n_steps": 2048,
"ent_coef": 0.005,
"learning_rate": 0.000025,
"batch_size": 256,
"eval_cycles" : 5,
"train_cycles" : 15
},
"model_reward_parameters": {
"rr": 1,
"profit_aim": 0.01
}
},
"bot_name": "RL_test",
"force_entry_enable": true,
"initial_state": "running",
"internals": {
"process_throttle_secs": 5
}
}

19
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
View File

@ -56,7 +56,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
model = self.fit(data_dictionary, pair)
model = self.fit_rl(data_dictionary, pair, dk)
if pair not in self.dd.historic_predictions:
self.set_initial_historic_predictions(
@ -69,7 +69,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
return model
@abstractmethod
def fit(self, data_dictionary: Dict[str, Any], pair: str = ''):
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen):
"""
Agent customizations and abstract Reinforcement Learning customizations
go in here. Abstract method, so this function must be overridden by
@ -164,6 +164,21 @@ class BaseReinforcementLearningModel(IFreqaiModel):
for return_str in dk.data['extra_returns_per_train']:
hist_preds_df[return_str] = 0
# TODO take care of this appendage. Right now it needs to be called because FreqAI enforces it.
# But FreqaiRL needs more objects passed to fit() (like DK) and we dont want to go refactor
# all the other existing fit() functions to include dk argument. For now we instantiate and
# leave it.
def fit(self, data_dictionary: Dict[str, Any], pair: str = '') -> Any:
"""
Most regressors use the same function names and arguments e.g. user
can drop in LGBMRegressor in place of CatBoostRegressor and all data
management will be properly handled by Freqai.
:param data_dictionary: Dict = the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
return
class MyRLEnv(Base3ActionRLEnv):

7
freqtrade/freqai/data_drawer.py
View File

@ -471,11 +471,12 @@ class FreqaiDataDrawer:
elif model_type == 'keras':
from tensorflow import keras
model = keras.models.load_model(dk.data_path / f"{dk.model_filename}_model.h5")
elif model_type == 'stable_baselines':
elif model_type == 'stable_baselines_ppo':
from stable_baselines3.ppo.ppo import PPO
model = PPO.load(dk.data_path / f"{dk.model_filename}_model.zip")
elif model_type == 'stable_baselines_dqn':
from stable_baselines3 import DQN
#model = PPO.load(dk.data_path / f"{dk.model_filename}_model.zip")
model = DQN.load(dk.data_path / f"best_model.zip")
model = DQN.load(dk.data_path / f"{dk.model_filename}_model.zip")
if Path(dk.data_path / f"{dk.model_filename}_svm_model.joblib").is_file():
dk.svm_model = load(dk.data_path / f"{dk.model_filename}_svm_model.joblib")

2
freqtrade/freqai/prediction_models/CatboostClassifier.py
View File

@ -16,7 +16,7 @@ class CatboostClassifier(BaseClassifierModel):
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
def fit(self, data_dictionary: Dict[str, Any], pair: str = '') -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:

2
freqtrade/freqai/prediction_models/CatboostRegressor.py
View File

@ -17,7 +17,7 @@ class CatboostRegressor(BaseRegressionModel):
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
def fit(self, data_dictionary: Dict[str, Any], pair: str = '') -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold

13
freqtrade/freqai/prediction_models/ReinforcementLearningPPO.py
View File

@ -9,9 +9,9 @@ import torch as th
from stable_baselines3 import PPO
from stable_baselines3.common.callbacks import EvalCallback
from stable_baselines3.common.monitor import Monitor
# from stable_baselines3.common.vec_env import SubprocVecEnv
from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@ -22,7 +22,7 @@ class ReinforcementLearningPPO(BaseReinforcementLearningModel):
User created Reinforcement Learning Model prediction model.
"""
def fit(self, data_dictionary: Dict[str, Any], pair: str = ''):
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen):
agent_params = self.freqai_info['model_training_parameters']
reward_params = self.freqai_info['model_reward_parameters']
@ -44,7 +44,7 @@ class ReinforcementLearningPPO(BaseReinforcementLearningModel):
eval_env = Monitor(eval, ".")
eval_env.reset()
path = self.dk.data_path
path = dk.data_path
eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
log_path=f"{path}/ppo/logs/", eval_freq=int(eval_freq),
deterministic=True, render=False)
@ -54,7 +54,8 @@ class ReinforcementLearningPPO(BaseReinforcementLearningModel):
net_arch=[256, 256, 128])
model = PPO('MlpPolicy', train_env, policy_kwargs=policy_kwargs,
tensorboard_log=f"{path}/ppo/tensorboard/", learning_rate=0.00025, gamma=0.9, verbose=1
tensorboard_log=f"{path}/ppo/tensorboard/", learning_rate=0.00025,
gamma=0.9, verbose=1
)
model.learn(
@ -62,9 +63,11 @@ class ReinforcementLearningPPO(BaseReinforcementLearningModel):
callback=eval_callback
)
best_model = PPO.load(dk.data_path / "best_model.zip")
print('Training finished!')
return model
return best_model
class MyRLEnv(Base3ActionRLEnv):

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

@ -13,7 +13,9 @@ from stable_baselines3.common.vec_env import SubprocVecEnv
from stable_baselines3.common.utils import set_random_seed
from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
import gym
logger = logging.getLogger(__name__)
@ -42,7 +44,7 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
User created Reinforcement Learning Model prediction model.
"""
def fit(self, data_dictionary: Dict[str, Any], pair: str = ''):
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen):
agent_params = self.freqai_info['model_training_parameters']
reward_params = self.freqai_info['model_reward_parameters']
@ -58,16 +60,15 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
len(test_df.index))
env_id = "train_env"
train_num_cpu = 6
num_cpu = int(dk.thread_count / 2)
train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, price, reward_params,
self.CONV_WIDTH) for i in range(train_num_cpu)])
eval_num_cpu = 6
eval_env_id = 'eval_env'
eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, price_test, reward_params,
self.CONV_WIDTH) for i in range(eval_num_cpu)])
self.CONV_WIDTH) for i in range(num_cpu)])
path = self.dk.data_path
path = dk.data_path
eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
log_path=f"{path}/ppo/logs/", eval_freq=int(eval_freq),
deterministic=True, render=False)
@ -85,10 +86,12 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
callback=eval_callback
)
# TODO get callback working so the best model is saved. For now we save last model
# best_model = PPO.load(dk.data_path / "best_model.zip")
print('Training finished!')
eval_env.close()
return model
return model # best_model
class MyRLEnv(Base3ActionRLEnv):

11
freqtrade/freqai/prediction_models/ReinforcementLearningTDQN.py
View File

@ -7,9 +7,12 @@ from stable_baselines3.common.monitor import Monitor
from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
from freqtrade.freqai.RL.TDQNagent import TDQN
from stable_baselines3 import DQN
from stable_baselines3.common.buffers import ReplayBuffer
import numpy as np
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@ -18,7 +21,7 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
User created Reinforcement Learning Model prediction model.
"""
def fit(self, data_dictionary: Dict[str, Any], pair: str = ''):
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen):
agent_params = self.freqai_info['model_training_parameters']
reward_params = self.freqai_info['model_reward_parameters']
@ -40,7 +43,7 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
eval_env = Monitor(eval, ".")
eval_env.reset()
path = self.dk.data_path
path = dk.data_path
eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
log_path=f"{path}/tdqn/logs/", eval_freq=int(eval_freq),
deterministic=True, render=False)
@ -63,9 +66,11 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
callback=eval_callback
)
best_model = DQN.load(dk.data_path / "best_model.zip")
print('Training finished!')
return model
return best_model
class MyRLEnv(Base3ActionRLEnv):