mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-10 10:21:59 +00:00
248 lines
9.1 KiB
Python
248 lines
9.1 KiB
Python
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import shutil
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from pathlib import Path
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from unittest.mock import patch
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import pandas as pd
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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.exceptions import OperationalException
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from tests.conftest import get_patched_exchange
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from tests.freqai.conftest import get_patched_freqai_strategy
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def test_update_historic_data(mocker, freqai_conf):
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freqai_conf['runmode'] = 'backtest'
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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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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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timerange = TimeRange.parse_timerange("20180110-20180114")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
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dp_candles = len(strategy.dp.get_pair_dataframe("ADA/BTC", "5m"))
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candle_difference = dp_candles - historic_candles
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freqai.dk.pair = "ADA/BTC"
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freqai.dd.update_historic_data(strategy, freqai.dk)
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updated_historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
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assert updated_historic_candles - historic_candles == candle_difference
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shutil.rmtree(Path(freqai.dk.full_path))
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def test_load_all_pairs_histories(mocker, freqai_conf):
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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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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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timerange = TimeRange.parse_timerange("20180110-20180114")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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assert len(freqai.dd.historic_data.keys()) == len(
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freqai_conf.get("exchange", {}).get("pair_whitelist")
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)
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assert len(freqai.dd.historic_data["ADA/BTC"]) == len(
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freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
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)
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shutil.rmtree(Path(freqai.dk.full_path))
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def test_get_base_and_corr_dataframes(mocker, freqai_conf):
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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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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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timerange = TimeRange.parse_timerange("20180110-20180114")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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sub_timerange = TimeRange.parse_timerange("20180111-20180114")
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corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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num_tfs = len(
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freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
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)
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assert len(base_df.keys()) == num_tfs
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assert len(corr_df.keys()) == len(
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freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_corr_pairlist")
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)
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assert len(corr_df["ADA/BTC"].keys()) == num_tfs
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shutil.rmtree(Path(freqai.dk.full_path))
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def test_use_strategy_to_populate_indicators(mocker, freqai_conf):
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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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timerange = TimeRange.parse_timerange("20180110-20180114")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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sub_timerange = TimeRange.parse_timerange("20180111-20180114")
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corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, 'LTC/BTC')
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assert len(df.columns) == 33
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shutil.rmtree(Path(freqai.dk.full_path))
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def test_get_timerange_from_live_historic_predictions(mocker, freqai_conf):
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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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freqai = strategy.freqai
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freqai.live = False
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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freqai.dk.live = False
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timerange = TimeRange.parse_timerange("20180126-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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sub_timerange = TimeRange.parse_timerange("20180128-20180130")
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_, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "ADA/BTC", freqai.dk)
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base_df["5m"]["date_pred"] = base_df["5m"]["date"]
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freqai.dd.historic_predictions = {}
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freqai.dd.historic_predictions["ADA/USDT"] = base_df["5m"]
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freqai.dd.save_historic_predictions_to_disk()
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freqai.dd.save_global_metadata_to_disk({"start_dry_live_date": 1516406400})
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timerange = freqai.dd.get_timerange_from_live_historic_predictions()
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assert timerange.startts == 1516406400
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assert timerange.stopts == 1517356500
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def test_get_timerange_from_backtesting_live_df_pred_not_found(mocker, freqai_conf):
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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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freqai = strategy.freqai
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with pytest.raises(
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OperationalException,
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match=r'Historic predictions not found.*'
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):
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freqai.dd.get_timerange_from_live_historic_predictions()
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def test_set_initial_return_values(mocker, freqai_conf):
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"""
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Simple test of the set initial return values that ensures
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we are concatening and ffilling values properly.
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"""
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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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freqai = strategy.freqai
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freqai.live = False
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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# Setup
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pair = "BTC/USD"
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end_x = "2023-08-31"
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start_x_plus_1 = "2023-08-30"
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end_x_plus_5 = "2023-09-03"
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historic_data = {
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'date_pred': pd.date_range(end=end_x, periods=5),
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'value': range(1, 6)
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}
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new_data = {
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'date': pd.date_range(start=start_x_plus_1, end=end_x_plus_5),
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'value': range(6, 11)
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}
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freqai.dd.historic_predictions[pair] = pd.DataFrame(historic_data)
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new_pred_df = pd.DataFrame(new_data)
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dataframe = pd.DataFrame(new_data)
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# Action
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with patch('logging.Logger.warning') as mock_logger_warning:
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freqai.dd.set_initial_return_values(pair, new_pred_df, dataframe)
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# Assertions
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hist_pred_df = freqai.dd.historic_predictions[pair]
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model_return_df = freqai.dd.model_return_values[pair]
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assert hist_pred_df['date_pred'].iloc[-1] == pd.Timestamp(end_x_plus_5)
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assert 'date_pred' in hist_pred_df.columns
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assert hist_pred_df.shape[0] == 8
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# compare values in model_return_df with hist_pred_df
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assert (model_return_df["value"].values ==
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hist_pred_df.tail(len(dataframe))["value"].values).all()
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assert model_return_df.shape[0] == len(dataframe)
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# Ensure logger error is not called
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mock_logger_warning.assert_not_called()
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def test_set_initial_return_values_warning(mocker, freqai_conf):
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"""
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Simple test of set_initial_return_values that hits the warning
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associated with leaving a FreqAI bot offline so long that the
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exchange candles have no common date with the historic predictions
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"""
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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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freqai = strategy.freqai
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freqai.live = False
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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# Setup
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pair = "BTC/USD"
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end_x = "2023-08-31"
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start_x_plus_1 = "2023-09-01"
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end_x_plus_5 = "2023-09-05"
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historic_data = {
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'date_pred': pd.date_range(end=end_x, periods=5),
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'value': range(1, 6)
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}
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new_data = {
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'date': pd.date_range(start=start_x_plus_1, end=end_x_plus_5),
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'value': range(6, 11)
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}
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freqai.dd.historic_predictions[pair] = pd.DataFrame(historic_data)
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new_pred_df = pd.DataFrame(new_data)
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dataframe = pd.DataFrame(new_data)
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# Action
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with patch('logging.Logger.warning') as mock_logger_warning:
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freqai.dd.set_initial_return_values(pair, new_pred_df, dataframe)
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# Assertions
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hist_pred_df = freqai.dd.historic_predictions[pair]
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model_return_df = freqai.dd.model_return_values[pair]
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assert hist_pred_df['date_pred'].iloc[-1] == pd.Timestamp(end_x_plus_5)
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assert 'date_pred' in hist_pred_df.columns
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assert hist_pred_df.shape[0] == 10
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# compare values in model_return_df with hist_pred_df
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assert (model_return_df["value"].values == hist_pred_df.tail(
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len(dataframe))["value"].values).all()
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assert model_return_df.shape[0] == len(dataframe)
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# Ensure logger error is not called
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mock_logger_warning.assert_called()
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