stable/tests/freqai/test_freqai_interface.py
2022-09-23 10:30:52 +02:00

460 lines
18 KiB
Python

import platform
import shutil
from pathlib import Path
from unittest.mock import MagicMock
import pytest
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
def is_arm() -> bool:
machine = platform.machine()
return "arm" in machine or "aarch64" in machine
def is_mac() -> bool:
machine = platform.system()
return "Darwin" in machine
@pytest.mark.parametrize('model', [
'LightGBMRegressor',
'XGBoostRegressor',
'CatboostRegressor',
'ReinforcementLearner',
'ReinforcementLearner_multiproc',
'ReinforcementLearner_test_4ac'
])
def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model):
if is_arm() and model == 'CatboostRegressor':
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"})
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,
"verbose": 1
}})
freqai_conf["freqai"].update({"model_save_type": 'stable_baselines'})
freqai_conf["freqai"]["rl_config"] = {
"train_cycles": 1,
"thread_count": 2,
"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
}}
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)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
freqai.dd.pair_dict = MagicMock()
data_load_timerange = TimeRange.parse_timerange("20180125-20180130")
new_timerange = TimeRange.parse_timerange("20180127-20180130")
freqai.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path /
f"{freqai.dk.model_filename}_model.{model_save_ext}").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
shutil.rmtree(Path(freqai.dk.full_path))
@pytest.mark.parametrize('model', [
'LightGBMRegressorMultiTarget',
'XGBoostRegressorMultiTarget',
'CatboostRegressorMultiTarget',
])
def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model):
if is_arm() and model == 'CatboostRegressorMultiTarget':
pytest.skip("CatBoost is not supported on ARM")
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"strategy": "freqai_test_multimodel_strat"})
freqai_conf.update({"freqaimodel": model})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
freqai.dd.pair_dict = MagicMock()
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
assert len(freqai.dk.label_list) == 2
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
assert len(freqai.dk.data['training_features_list']) == 14
shutil.rmtree(Path(freqai.dk.full_path))
@pytest.mark.parametrize('model', [
'LightGBMClassifier',
'CatboostClassifier',
'XGBoostClassifier',
])
def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
if is_arm() and model == 'CatboostClassifier':
pytest.skip("CatBoost is not supported on ARM")
freqai_conf.update({"freqaimodel": model})
freqai_conf.update({"strategy": "freqai_test_classifier"})
freqai_conf.update({"timerange": "20180110-20180130"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
freqai.dd.pair_dict = MagicMock()
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.extract_data_and_train_model(new_timerange, "ADA/BTC",
strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").exists()
shutil.rmtree(Path(freqai.dk.full_path))
@pytest.mark.parametrize('model', [
'LightGBMRegressor',
'XGBoostRegressor',
'CatboostRegressor',
'ReinforcementLearner',
'ReinforcementLearner_multiproc',
'ReinforcementLearner_test_4ac'
])
def test_start_backtesting(mocker, freqai_conf):
freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
if is_arm() and model == 'CatboostRegressor':
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"})
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,
"verbose": 1
}})
freqai_conf["freqai"].update({"model_save_type": 'stable_baselines'})
freqai_conf["freqai"]["rl_config"] = {
"train_cycles": 1,
"thread_count": 2,
"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
}}
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)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = False
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
metadata = {"pair": "LTC/BTC"}
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
shutil.rmtree(Path(freqai.dk.full_path))
def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180120-20180124"})
freqai_conf.get("freqai", {}).update({"backtest_period_days": 0.5})
freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = False
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
metadata = {"pair": "LTC/BTC"}
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) == 9
shutil.rmtree(Path(freqai.dk.full_path))
def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
freqai_conf.update({"timerange": "20180120-20180130"})
freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = False
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
metadata = {"pair": "ADA/BTC"}
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
# without deleting the exiting folder structure, re-run
freqai_conf.update({"timerange": "20180120-20180130"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = False
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
freqai.start_backtesting(df, metadata, freqai.dk)
assert log_has_re(
"Found backtesting prediction file ",
caplog,
)
path = (freqai.dd.full_path / freqai.dk.backtest_predictions_folder)
prediction_files = [x for x in path.iterdir() if x.is_file()]
assert len(prediction_files) == 5
shutil.rmtree(Path(freqai.dk.full_path))
def test_follow_mode(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
metadata = {"pair": "ADA/BTC"}
freqai.dd.set_pair_dict_info(metadata)
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
# start the follower and ask it to predict on existing files
freqai_conf.get("freqai", {}).update({"follow_mode": "true"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.live)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m')
freqai.start_live(df, metadata, strategy, freqai.dk)
assert len(freqai.dk.return_dataframe.index) == 5702
shutil.rmtree(Path(freqai.dk.full_path))
def test_principal_component_analysis(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
{"princpial_component_analysis": "true"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
freqai.dd.pair_dict = MagicMock()
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_pca_object.pkl")
shutil.rmtree(Path(freqai.dk.full_path))
def test_plot_feature_importance(mocker, freqai_conf):
from freqtrade.freqai.utils import plot_feature_importance
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
{"princpial_component_analysis": "true"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
freqai.dd.pair_dict = MagicMock()
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
model = freqai.dd.load_data("ADA/BTC", freqai.dk)
plot_feature_importance(model, "ADA/BTC", freqai.dk)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}.html")
shutil.rmtree(Path(freqai.dk.full_path))
@pytest.mark.parametrize('timeframes,corr_pairs', [
(['5m'], ['ADA/BTC', 'DASH/BTC']),
(['5m'], ['ADA/BTC', 'DASH/BTC', 'ETH/USDT']),
(['5m', '15m'], ['ADA/BTC', 'DASH/BTC', 'ETH/USDT']),
])
def test_freqai_informative_pairs(mocker, freqai_conf, timeframes, corr_pairs):
freqai_conf['freqai']['feature_parameters'].update({
'include_timeframes': timeframes,
'include_corr_pairlist': corr_pairs,
})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
pairlists = PairListManager(exchange, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange, pairlists)
pairlist = strategy.dp.current_whitelist()
pairs_a = strategy.informative_pairs()
assert len(pairs_a) == 0
pairs_b = strategy.gather_informative_pairs()
# we expect unique pairs * timeframes
assert len(pairs_b) == len(set(pairlist + corr_pairs)) * len(timeframes)