freqtrade_origin/tests/freqai/conftest.py

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
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.resolvers import StrategyResolver
from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
from tests.conftest import get_patched_exchange


@pytest.fixture(scope="function")
def freqai_conf(default_conf, tmpdir):
    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",
            "freqaimodel_path": "freqai/prediction_models",
            "timerange": "20180110-20180115",
            "freqai": {
                "enabled": True,
                "startup_candles": 10000,
                "purge_old_models": True,
                "train_period_days": 5,
                "backtest_period_days": 2,
                "live_retrain_hours": 0,
                "expiration_hours": 1,
                "identifier": "uniqe-id100",
                "live_trained_timestamp": 0,
                "feature_parameters": {
                    "include_timeframes": ["5m"],
                    "include_corr_pairlist": ["ADA/BTC", "DASH/BTC"],
                    "label_period_candles": 20,
                    "include_shifted_candles": 1,
                    "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},
            },
            "config_files": [Path('config_examples', 'config_freqai.example.json')]
        }
    )
    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)
    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):
    strategy = StrategyResolver.load_strategy(freqaiconf)
    strategy.ft_bot_start()

    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