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https://github.com/freqtrade/freqtrade.git
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increase test coverage for RL and FreqAI
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robcaulk
parent
95121550ef
commit
9c361f4422
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@ -1,109 +0,0 @@
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{
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"trading_mode": "futures",
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"new_pairs_days": 30,
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"margin_mode": "isolated",
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"max_open_trades": 8,
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"stake_currency": "USDT",
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"stake_amount": 1000,
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"tradable_balance_ratio": 1,
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"fiat_display_currency": "USD",
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"dry_run": true,
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"timeframe": "5m",
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"dataformat_ohlcv": "json",
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"dry_run_wallet": 12000,
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"cancel_open_orders_on_exit": true,
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"unfilledtimeout": {
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"entry": 10,
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"exit": 30
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},
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"exchange": {
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"name": "binance",
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"key": "",
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"secret": "",
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"ccxt_config": {
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"enableRateLimit": true
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},
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"ccxt_async_config": {
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"enableRateLimit": true,
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"rateLimit": 200
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},
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"pair_whitelist": [
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"1INCH/USDT",
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"AAVE/USDT"
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],
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"pair_blacklist": []
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},
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"entry_pricing": {
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"price_side": "same",
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"use_order_book": true,
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"order_book_top": 1,
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"price_last_balance": 0.0,
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"check_depth_of_market": {
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"enabled": false,
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"bids_to_ask_delta": 1
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}
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},
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"exit_pricing": {
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"price_side": "other",
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"use_order_book": true,
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"order_book_top": 1
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},
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"pairlists": [
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{
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"method": "StaticPairList"
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}
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],
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"freqai": {
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"enabled": true,
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"model_save_type": "stable_baselines",
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"conv_width": 4,
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"purge_old_models": true,
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"limit_ram_usage": false,
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"train_period_days": 5,
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"backtest_period_days": 2,
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"identifier": "unique-id",
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"continual_learning": false,
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"data_kitchen_thread_count": 2,
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"feature_parameters": {
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"include_corr_pairlist": [
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"BTC/USDT",
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"ETH/USDT"
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],
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"include_timeframes": [
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"5m",
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"30m"
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],
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"indicator_max_period_candles": 20,
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"indicator_periods_candles": [14]
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},
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"data_split_parameters": {
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"test_size": 0.5,
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"random_state": 1,
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"shuffle": false
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},
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"model_training_parameters": {
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"learning_rate": 0.00025,
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"gamma": 0.9,
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"verbose": 1
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},
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"rl_config": {
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"train_cycles": 6,
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"thread_count": 4,
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"max_trade_duration_candles": 300,
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"model_type": "PPO",
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"policy_type": "MlpPolicy",
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"max_training_drawdown_pct": 0.5,
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"model_reward_parameters": {
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"rr": 1,
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"profit_aim": 0.02,
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"win_reward_factor": 2
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}
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}
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},
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"bot_name": "RL_test",
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"force_entry_enable": true,
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"initial_state": "running",
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"internals": {
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"process_throttle_secs": 5
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}
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}
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@ -602,22 +602,3 @@ class FreqaiDataDrawer:
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)
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return corr_dataframes, base_dataframes
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# to be used if we want to send predictions directly to the follower instead of forcing
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# follower to load models and inference
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# def save_model_return_values_to_disk(self) -> None:
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# with open(self.full_path / str('model_return_values.json'), "w") as fp:
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# json.dump(self.model_return_values, fp, default=self.np_encoder)
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# def load_model_return_values_from_disk(self, dk: FreqaiDataKitchen) -> FreqaiDataKitchen:
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# exists = Path(self.full_path / str('model_return_values.json')).resolve().exists()
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# if exists:
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# with open(self.full_path / str('model_return_values.json'), "r") as fp:
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# self.model_return_values = json.load(fp)
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# elif not self.follow_mode:
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# logger.info("Could not find existing datadrawer, starting from scratch")
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# else:
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# logger.warning(f'Follower could not find pair_dictionary at {self.full_path} '
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# 'sending null values back to strategy')
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# return exists, dk
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@ -4,13 +4,15 @@ from pathlib import Path
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from unittest.mock import MagicMock
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import pytest
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from freqtrade.enums import RunMode
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from freqtrade.configuration import TimeRange
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from freqtrade.data.dataprovider import DataProvider
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.plugins.pairlistmanager import PairListManager
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from tests.conftest import get_patched_exchange, log_has_re
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from tests.freqai.conftest import get_patched_freqai_strategy
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from freqtrade.persistence import Trade
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from freqtrade.freqai.utils import download_all_data_for_training, get_required_data_timerange
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def is_arm() -> bool:
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@ -173,29 +175,34 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
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shutil.rmtree(Path(freqai.dk.full_path))
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@pytest.mark.parametrize('model', [
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'LightGBMRegressor',
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'XGBoostRegressor',
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'CatboostRegressor',
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'ReinforcementLearner'
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])
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def test_start_backtesting(mocker, freqai_conf, model):
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@pytest.mark.parametrize(
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"model, num_files, strat",
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[
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("LightGBMRegressor", 6, "freqai_test_strat"),
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("XGBoostRegressor", 6, "freqai_test_strat"),
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("CatboostRegressor", 6, "freqai_test_strat"),
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("ReinforcementLearner", 7, "freqai_rl_test_strat"),
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("XGBoostClassifier", 6, "freqai_test_classifier"),
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("LightGBMClassifier", 6, "freqai_test_classifier"),
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("CatboostClassifier", 6, "freqai_test_classifier")
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],
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)
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def test_start_backtesting(mocker, freqai_conf, model, num_files, strat):
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freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
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if is_arm() and model == 'CatboostRegressor':
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freqai_conf['runmode'] = RunMode.BACKTEST
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Trade.use_db = False
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if is_arm() and "Catboost" in model:
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pytest.skip("CatBoost is not supported on ARM")
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if is_mac():
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pytest.skip("Reinforcement learning module not available on intel based Mac OS")
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model_save_ext = 'joblib'
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freqai_conf.update({"freqaimodel": model})
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freqai_conf.update({"timerange": "20180110-20180130"})
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freqai_conf.update({"strategy": "freqai_test_strat"})
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freqai_conf.update({"timerange": "20180120-20180130"})
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freqai_conf.update({"strategy": strat})
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if 'ReinforcementLearner' in model:
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model_save_ext = 'zip'
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freqai_conf.update({"strategy": "freqai_rl_test_strat"})
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freqai_conf["freqai"].update({"model_training_parameters": {
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"learning_rate": 0.00025,
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"gamma": 0.9,
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@ -217,8 +224,7 @@ def test_start_backtesting(mocker, freqai_conf, model):
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if 'test_4ac' in model:
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freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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@ -237,7 +243,7 @@ def test_start_backtesting(mocker, freqai_conf, model):
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freqai.start_backtesting(df, metadata, freqai.dk)
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model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
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assert len(model_folders) == 6
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assert len(model_folders) == num_files
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shutil.rmtree(Path(freqai.dk.full_path))
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@ -455,3 +461,40 @@ def test_freqai_informative_pairs(mocker, freqai_conf, timeframes, corr_pairs):
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pairs_b = strategy.gather_informative_pairs()
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# we expect unique pairs * timeframes
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assert len(pairs_b) == len(set(pairlist + corr_pairs)) * len(timeframes)
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def test_start_set_train_queue(mocker, freqai_conf, caplog):
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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pairlist = PairListManager(exchange, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange, pairlist)
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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = False
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freqai.train_queue = freqai._set_train_queue()
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assert log_has_re(
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"Set fresh train queue from whitelist.",
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caplog,
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)
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def test_get_required_data_timerange(mocker, freqai_conf):
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time_range = get_required_data_timerange(freqai_conf)
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assert (time_range.stopts - time_range.startts) == 177300
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def test_download_all_data_for_training(mocker, freqai_conf, caplog, tmpdir):
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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pairlist = PairListManager(exchange, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange, pairlist)
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freqai_conf['pairs'] = freqai_conf['exchange']['pair_whitelist']
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freqai_conf['datadir'] = Path(tmpdir)
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download_all_data_for_training(strategy.dp, freqai_conf)
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assert log_has_re(
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"Downloading",
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caplog,
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)
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