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increase test coverage for RL and FreqAI

This commit is contained in:
robcaulk 2022-09-23 18:04:43 +02:00
parent 95121550ef
commit 9c361f4422
3 changed files with 61 additions and 146 deletions
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109
config_examples/config_freqai-rl.example.json
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@ -1,109 +0,0 @@
{
"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",
"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": {
"enabled": true,
"model_save_type": "stable_baselines",
"conv_width": 4,
"purge_old_models": true,
"limit_ram_usage": false,
"train_period_days": 5,
"backtest_period_days": 2,
"identifier": "unique-id",
"continual_learning": false,
"data_kitchen_thread_count": 2,
"feature_parameters": {
"include_corr_pairlist": [
"BTC/USDT",
"ETH/USDT"
],
"include_timeframes": [
"5m",
"30m"
],
"indicator_max_period_candles": 20,
"indicator_periods_candles": [14]
},
"data_split_parameters": {
"test_size": 0.5,
"random_state": 1,
"shuffle": false
},
"model_training_parameters": {
"learning_rate": 0.00025,
"gamma": 0.9,
"verbose": 1
},
"rl_config": {
"train_cycles": 6,
"thread_count": 4,
"max_trade_duration_candles": 300,
"model_type": "PPO",
"policy_type": "MlpPolicy",
"max_training_drawdown_pct": 0.5,
"model_reward_parameters": {
"rr": 1,
"profit_aim": 0.02,
"win_reward_factor": 2
}
}
},
"bot_name": "RL_test",
"force_entry_enable": true,
"initial_state": "running",
"internals": {
"process_throttle_secs": 5
}
}

19
freqtrade/freqai/data_drawer.py
View File

@ -602,22 +602,3 @@ class FreqaiDataDrawer:
)
return corr_dataframes, base_dataframes
# to be used if we want to send predictions directly to the follower instead of forcing
# follower to load models and inference
# def save_model_return_values_to_disk(self) -> None:
# with open(self.full_path / str('model_return_values.json'), "w") as fp:
# json.dump(self.model_return_values, fp, default=self.np_encoder)
# def load_model_return_values_from_disk(self, dk: FreqaiDataKitchen) -> FreqaiDataKitchen:
# exists = Path(self.full_path / str('model_return_values.json')).resolve().exists()
# if exists:
# with open(self.full_path / str('model_return_values.json'), "r") as fp:
# self.model_return_values = json.load(fp)
# elif not self.follow_mode:
# logger.info("Could not find existing datadrawer, starting from scratch")
# else:
# logger.warning(f'Follower could not find pair_dictionary at {self.full_path} '
# 'sending null values back to strategy')
# return exists, dk

79
tests/freqai/test_freqai_interface.py
View File

@ -4,13 +4,15 @@ from pathlib import Path
from unittest.mock import MagicMock
import pytest
from freqtrade.enums import RunMode
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.plugins.pairlistmanager import PairListManager
from tests.conftest import get_patched_exchange, log_has_re
from tests.freqai.conftest import get_patched_freqai_strategy
from freqtrade.persistence import Trade
from freqtrade.freqai.utils import download_all_data_for_training, get_required_data_timerange
def is_arm() -> bool:
@ -173,29 +175,34 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
shutil.rmtree(Path(freqai.dk.full_path))
@pytest.mark.parametrize('model', [
'LightGBMRegressor',
'XGBoostRegressor',
'CatboostRegressor',
'ReinforcementLearner'
])
def test_start_backtesting(mocker, freqai_conf, model):
@pytest.mark.parametrize(
"model, num_files, strat",
[
("LightGBMRegressor", 6, "freqai_test_strat"),
("XGBoostRegressor", 6, "freqai_test_strat"),
("CatboostRegressor", 6, "freqai_test_strat"),
("ReinforcementLearner", 7, "freqai_rl_test_strat"),
("XGBoostClassifier", 6, "freqai_test_classifier"),
("LightGBMClassifier", 6, "freqai_test_classifier"),
("CatboostClassifier", 6, "freqai_test_classifier")
],
)
def test_start_backtesting(mocker, freqai_conf, model, num_files, strat):
freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
if is_arm() and model == 'CatboostRegressor':
freqai_conf['runmode'] = RunMode.BACKTEST
Trade.use_db = False
if is_arm() and "Catboost" in model:
pytest.skip("CatBoost is not supported on ARM")
if is_mac():
pytest.skip("Reinforcement learning module not available on intel based Mac OS")
model_save_ext = 'joblib'
freqai_conf.update({"freqaimodel": model})
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"strategy": "freqai_test_strat"})
freqai_conf.update({"timerange": "20180120-20180130"})
freqai_conf.update({"strategy": strat})
if 'ReinforcementLearner' in model:
model_save_ext = 'zip'
freqai_conf.update({"strategy": "freqai_rl_test_strat"})
freqai_conf["freqai"].update({"model_training_parameters": {
"learning_rate": 0.00025,
"gamma": 0.9,
@ -217,8 +224,7 @@ def test_start_backtesting(mocker, freqai_conf, model):
if 'test_4ac' in model:
freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
@ -237,7 +243,7 @@ def test_start_backtesting(mocker, freqai_conf, model):
freqai.start_backtesting(df, metadata, freqai.dk)
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
assert len(model_folders) == 6
assert len(model_folders) == num_files
shutil.rmtree(Path(freqai.dk.full_path))
@ -455,3 +461,40 @@ def test_freqai_informative_pairs(mocker, freqai_conf, timeframes, corr_pairs):
pairs_b = strategy.gather_informative_pairs()
# we expect unique pairs * timeframes
assert len(pairs_b) == len(set(pairlist + corr_pairs)) * len(timeframes)
def test_start_set_train_queue(mocker, freqai_conf, caplog):
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
pairlist = PairListManager(exchange, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange, pairlist)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = False
freqai.train_queue = freqai._set_train_queue()
assert log_has_re(
"Set fresh train queue from whitelist.",
caplog,
)
def test_get_required_data_timerange(mocker, freqai_conf):
time_range = get_required_data_timerange(freqai_conf)
assert (time_range.stopts - time_range.startts) == 177300
def test_download_all_data_for_training(mocker, freqai_conf, caplog, tmpdir):
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
pairlist = PairListManager(exchange, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange, pairlist)
freqai_conf['pairs'] = freqai_conf['exchange']['pair_whitelist']
freqai_conf['datadir'] = Path(tmpdir)
download_all_data_for_training(strategy.dp, freqai_conf)
assert log_has_re(
"Downloading",
caplog,
)