mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-14 20:23:57 +00:00
277 lines
9.4 KiB
Python
277 lines
9.4 KiB
Python
import platform
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import sys
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from copy import deepcopy
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from pathlib import Path
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from typing import Any
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from unittest.mock import MagicMock
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import pytest
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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_drawer import FreqaiDataDrawer
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.resolvers import StrategyResolver
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from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
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from tests.conftest import get_patched_exchange
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def is_py12() -> bool:
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return sys.version_info >= (3, 12)
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def is_mac() -> bool:
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machine = platform.system()
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return "Darwin" in machine
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def is_arm() -> bool:
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machine = platform.machine()
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return "arm" in machine or "aarch64" in machine
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@pytest.fixture(autouse=True)
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def patch_torch_initlogs(mocker) -> None:
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if is_mac():
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# Mock torch import completely
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import sys
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import types
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module_name = "torch"
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mocked_module = types.ModuleType(module_name)
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sys.modules[module_name] = mocked_module
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else:
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mocker.patch("torch._logging._init_logs")
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@pytest.fixture(scope="function")
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def freqai_conf(default_conf, tmp_path):
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freqaiconf = deepcopy(default_conf)
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freqaiconf.update(
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{
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"datadir": Path(default_conf["datadir"]),
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"runmode": "backtest",
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"strategy": "freqai_test_strat",
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"user_data_dir": tmp_path,
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"strategy-path": "freqtrade/tests/strategy/strats",
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"freqaimodel": "LightGBMRegressor",
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"freqaimodel_path": "freqai/prediction_models",
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"timerange": "20180110-20180115",
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"freqai": {
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"enabled": True,
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"purge_old_models": 2,
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"train_period_days": 2,
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"backtest_period_days": 10,
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"live_retrain_hours": 0,
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"expiration_hours": 1,
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"identifier": "unique-id100",
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"live_trained_timestamp": 0,
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"data_kitchen_thread_count": 2,
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"activate_tensorboard": False,
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"feature_parameters": {
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"include_timeframes": ["5m"],
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"include_corr_pairlist": ["ADA/BTC"],
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"label_period_candles": 20,
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"include_shifted_candles": 1,
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"DI_threshold": 0.9,
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"weight_factor": 0.9,
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"principal_component_analysis": False,
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"use_SVM_to_remove_outliers": True,
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"stratify_training_data": 0,
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"indicator_periods_candles": [10],
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"shuffle_after_split": False,
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"buffer_train_data_candles": 0,
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},
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"data_split_parameters": {"test_size": 0.33, "shuffle": False},
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"model_training_parameters": {"n_estimators": 100},
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},
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"config_files": [Path("config_examples", "config_freqai.example.json")],
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}
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)
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freqaiconf["exchange"].update({"pair_whitelist": ["ADA/BTC", "DASH/BTC", "ETH/BTC", "LTC/BTC"]})
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return freqaiconf
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def make_rl_config(conf):
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conf.update({"strategy": "freqai_rl_test_strat"})
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conf["freqai"].update(
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{"model_training_parameters": {"learning_rate": 0.00025, "gamma": 0.9, "verbose": 1}}
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)
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conf["freqai"]["rl_config"] = {
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"train_cycles": 1,
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"thread_count": 2,
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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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"net_arch": [32, 32],
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"model_reward_parameters": {"rr": 1, "profit_aim": 0.02, "win_reward_factor": 2},
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"drop_ohlc_from_features": False,
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}
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return conf
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def mock_pytorch_mlp_model_training_parameters() -> dict[str, Any]:
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return {
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"learning_rate": 3e-4,
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"trainer_kwargs": {
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"n_steps": None,
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"batch_size": 64,
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"n_epochs": 1,
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},
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"model_kwargs": {
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"hidden_dim": 32,
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"dropout_percent": 0.2,
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"n_layer": 1,
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},
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}
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def get_patched_data_kitchen(mocker, freqaiconf):
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dk = FreqaiDataKitchen(freqaiconf)
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return dk
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def get_patched_data_drawer(mocker, freqaiconf):
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# dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock())
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dd = FreqaiDataDrawer(freqaiconf)
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return dd
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def get_patched_freqai_strategy(mocker, freqaiconf):
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strategy = StrategyResolver.load_strategy(freqaiconf)
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strategy.ft_bot_start()
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return strategy
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def get_patched_freqaimodel(mocker, freqaiconf):
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freqaimodel = FreqaiModelResolver.load_freqaimodel(freqaiconf)
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return freqaimodel
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def make_unfiltered_dataframe(mocker, freqai_conf):
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freqai_conf.update({"timerange": "20180110-20180130"})
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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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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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freqai.dk.live = True
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freqai.dk.pair = "ADA/BTC"
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data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(data_load_timerange, freqai.dk)
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freqai.dd.pair_dict = MagicMock()
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new_timerange = TimeRange.parse_timerange("20180120-20180130")
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corr_dataframes, base_dataframes = freqai.dd.get_base_and_corr_dataframes(
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data_load_timerange, freqai.dk.pair, freqai.dk
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)
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unfiltered_dataframe = freqai.dk.use_strategy_to_populate_indicators(
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strategy, corr_dataframes, base_dataframes, freqai.dk.pair
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)
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for i in range(5):
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unfiltered_dataframe[f"constant_{i}"] = i
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unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe)
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return freqai, unfiltered_dataframe
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def make_data_dictionary(mocker, freqai_conf):
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freqai_conf.update({"timerange": "20180110-20180130"})
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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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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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freqai.dk.live = True
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freqai.dk.pair = "ADA/BTC"
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data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(data_load_timerange, freqai.dk)
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freqai.dd.pair_dict = MagicMock()
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new_timerange = TimeRange.parse_timerange("20180120-20180130")
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corr_dataframes, base_dataframes = freqai.dd.get_base_and_corr_dataframes(
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data_load_timerange, freqai.dk.pair, freqai.dk
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)
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unfiltered_dataframe = freqai.dk.use_strategy_to_populate_indicators(
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strategy, corr_dataframes, base_dataframes, freqai.dk.pair
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)
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unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe)
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freqai.dk.find_features(unfiltered_dataframe)
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features_filtered, labels_filtered = freqai.dk.filter_features(
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unfiltered_dataframe,
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freqai.dk.training_features_list,
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freqai.dk.label_list,
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training_filter=True,
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)
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data_dictionary = freqai.dk.make_train_test_datasets(features_filtered, labels_filtered)
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data_dictionary = freqai.dk.normalize_data(data_dictionary)
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return freqai
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def get_freqai_live_analyzed_dataframe(mocker, freqaiconf):
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strategy = get_patched_freqai_strategy(mocker, freqaiconf)
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exchange = get_patched_exchange(mocker, freqaiconf)
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strategy.dp = DataProvider(freqaiconf, exchange)
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freqai = strategy.freqai
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
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timerange = TimeRange.parse_timerange("20180110-20180114")
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freqai.dk.load_all_pair_histories(timerange)
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strategy.analyze_pair("ADA/BTC", "5m")
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return strategy.dp.get_analyzed_dataframe("ADA/BTC", "5m")
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def get_freqai_analyzed_dataframe(mocker, freqaiconf):
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strategy = get_patched_freqai_strategy(mocker, freqaiconf)
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exchange = get_patched_exchange(mocker, freqaiconf)
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strategy.dp = DataProvider(freqaiconf, exchange)
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strategy.freqai_info = freqaiconf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
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timerange = TimeRange.parse_timerange("20180110-20180114")
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freqai.dk.load_all_pair_histories(timerange)
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sub_timerange = TimeRange.parse_timerange("20180111-20180114")
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corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
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return freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
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def get_ready_to_train(mocker, freqaiconf):
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strategy = get_patched_freqai_strategy(mocker, freqaiconf)
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exchange = get_patched_exchange(mocker, freqaiconf)
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strategy.dp = DataProvider(freqaiconf, exchange)
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strategy.freqai_info = freqaiconf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
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timerange = TimeRange.parse_timerange("20180110-20180114")
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freqai.dk.load_all_pair_histories(timerange)
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sub_timerange = TimeRange.parse_timerange("20180111-20180114")
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corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
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return corr_df, base_df, freqai, strategy
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