stable/tests/freqai/conftest.py

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from copy import deepcopy
from pathlib import Path
import pytest
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
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
from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
from tests.conftest import get_patched_exchange
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@pytest.fixture(scope="function")
def freqai_conf(default_conf, tmpdir):
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freqaiconf = deepcopy(default_conf)
freqaiconf.update(
{
"datadir": Path(default_conf["datadir"]),
"strategy": "freqai_test_strat",
"user_data_dir": Path(tmpdir),
"strategy-path": "freqtrade/tests/strategy/strats",
"freqaimodel": "LightGBMRegressor",
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"freqaimodel_path": "freqai/prediction_models",
"timerange": "20180110-20180115",
"freqai": {
"startup_candles": 10000,
"purge_old_models": True,
"train_period_days": 5,
"backtest_period_days": 2,
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"live_retrain_hours": 0,
"expiration_hours": 1,
"identifier": "uniqe-id100",
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"live_trained_timestamp": 0,
"feature_parameters": {
"include_timeframes": ["5m"],
"include_corr_pairlist": ["ADA/BTC", "DASH/BTC"],
"label_period_candles": 20,
"include_shifted_candles": 1,
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"DI_threshold": 0.9,
"weight_factor": 0.9,
"principal_component_analysis": False,
"use_SVM_to_remove_outliers": True,
"stratify_training_data": 0,
"indicator_max_period_candles": 10,
"indicator_periods_candles": [10],
},
"data_split_parameters": {"test_size": 0.33, "random_state": 1},
"model_training_parameters": {"n_estimators": 100},
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},
"config_files": [Path('config_examples', 'config_freqai.example.json')]
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}
)
freqaiconf['exchange'].update({'pair_whitelist': ['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC']})
return freqaiconf
def get_patched_data_kitchen(mocker, freqaiconf):
dk = FreqaiDataKitchen(freqaiconf)
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return dk
def get_patched_data_drawer(mocker, freqaiconf):
# dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock())
dd = FreqaiDataDrawer(freqaiconf)
return dd
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
def get_patched_freqaimodel(mocker, freqaiconf):
freqaimodel = FreqaiModelResolver.load_freqaimodel(freqaiconf)
return freqaimodel
def get_freqai_live_analyzed_dataframe(mocker, freqaiconf):
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
exchange = get_patched_exchange(mocker, freqaiconf)
strategy.dp = DataProvider(freqaiconf, exchange)
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180114")
freqai.dk.load_all_pair_histories(timerange)
strategy.analyze_pair('ADA/BTC', '5m')
return strategy.dp.get_analyzed_dataframe('ADA/BTC', '5m')
def get_freqai_analyzed_dataframe(mocker, freqaiconf):
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
exchange = get_patched_exchange(mocker, freqaiconf)
strategy.dp = DataProvider(freqaiconf, exchange)
strategy.freqai_info = freqaiconf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180114")
freqai.dk.load_all_pair_histories(timerange)
sub_timerange = TimeRange.parse_timerange("20180111-20180114")
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
return freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, 'LTC/BTC')
def get_ready_to_train(mocker, freqaiconf):
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
exchange = get_patched_exchange(mocker, freqaiconf)
strategy.dp = DataProvider(freqaiconf, exchange)
strategy.freqai_info = freqaiconf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180114")
freqai.dk.load_all_pair_histories(timerange)
sub_timerange = TimeRange.parse_timerange("20180111-20180114")
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
return corr_df, base_df, freqai, strategy