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chore: add pair_detail test
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@ -16,7 +16,7 @@ from freqtrade.commands.optimize_commands import setup_optimize_configuration, s
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from freqtrade.configuration import TimeRange
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from freqtrade.data import history
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from freqtrade.data.btanalysis import BT_DATA_COLUMNS, evaluate_result_multi
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from freqtrade.data.converter import clean_ohlcv_dataframe
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from freqtrade.data.converter import clean_ohlcv_dataframe, ohlcv_fill_up_missing_data
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from freqtrade.data.dataprovider import DataProvider
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from freqtrade.data.history import get_timerange
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from freqtrade.enums import CandleType, ExitType, RunMode
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@ -30,6 +30,7 @@ from freqtrade.util.datetime_helpers import dt_utc
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from tests.conftest import (
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CURRENT_TEST_STRATEGY,
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EXMS,
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generate_test_data,
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get_args,
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log_has,
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log_has_re,
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@ -1560,6 +1561,136 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
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assert len(evaluate_result_multi(results["results"], "5m", 1)) == 0
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@pytest.mark.parametrize("pair", ["ADA/USDT", "LTC/USDT"])
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@pytest.mark.parametrize("tres", [0, 20, 30])
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def test_backtest_multi_pair_detail(
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default_conf_usdt,
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fee,
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mocker,
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tres,
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pair,
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):
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"""
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literally the same as test_backtest_multi_pair - but with artificial data
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and detail timeframe.
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"""
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def _trend_alternate_hold(dataframe=None, metadata=None):
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"""
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Buy every xth candle - sell every other xth -2 (hold on to pairs a bit)
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"""
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if metadata["pair"] in ("ETH/USDT", "LTC/USDT"):
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multi = 20
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else:
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multi = 18
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dataframe["enter_long"] = np.where(dataframe.index % multi == 0, 1, 0)
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dataframe["exit_long"] = np.where((dataframe.index + multi - 2) % multi == 0, 1, 0)
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dataframe["enter_short"] = 0
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dataframe["exit_short"] = 0
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return dataframe
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default_conf_usdt["runmode"] = "backtest"
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default_conf_usdt["stoploss"] = -1.0
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default_conf_usdt["minimal_roi"] = {"0": 100}
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mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
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mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
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mocker.patch(f"{EXMS}.get_fee", fee)
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patch_exchange(mocker)
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raw_candles_1m = generate_test_data("1m", 2500, "2022-01-03 12:00:00+00:00")
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raw_candles = ohlcv_fill_up_missing_data(raw_candles_1m, "5m", "dummy")
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pairs = ["ADA/USDT", "DASH/USDT", "ETH/USDT", "LTC/USDT", "NXT/USDT"]
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data = {pair: raw_candles for pair in pairs}
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# Only use 500 lines to increase performance
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data = trim_dictlist(data, -500)
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# Remove data for one pair from the beginning of the data
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if tres > 0:
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data[pair] = data[pair][tres:].reset_index()
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default_conf_usdt["timeframe"] = "5m"
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default_conf_usdt["max_open_trades"] = 3
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backtesting = Backtesting(default_conf_usdt)
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vr_spy = mocker.spy(backtesting, "validate_row")
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backtesting._set_strategy(backtesting.strategylist[0])
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backtesting.strategy.bot_loop_start = MagicMock()
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backtesting.strategy.advise_entry = _trend_alternate_hold # Override
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backtesting.strategy.advise_exit = _trend_alternate_hold # Override
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processed = backtesting.strategy.advise_all_indicators(data)
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min_date, max_date = get_timerange(processed)
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backtest_conf = {
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"processed": deepcopy(processed),
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"start_date": min_date,
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"end_date": max_date,
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}
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results = backtesting.backtest(**backtest_conf)
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# bot_loop_start is called once per candle.
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assert backtesting.strategy.bot_loop_start.call_count == 499
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# Validated row once per candle and pair
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assert vr_spy.call_count == 2495
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# List of calls pair args - in batches of 5 (s)
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calls_per_candle = defaultdict(list)
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for call in vr_spy.call_args_list:
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calls_per_candle[call[0][3]].append(call[0][1])
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all_orients = [x for _, x in calls_per_candle.items()]
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distinct_calls = [list(x) for x in set(tuple(x) for x in all_orients)]
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# All calls must be made for the full pairlist
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assert all(len(x) == 5 for x in distinct_calls)
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# order varied - and is not always identical
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assert not all(
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x == ["ADA/USDT", "DASH/USDT", "ETH/USDT", "LTC/USDT", "NXT/USDT"] for x in distinct_calls
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)
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# But some calls should've kept the original ordering
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assert any(
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x == ["ADA/USDT", "DASH/USDT", "ETH/USDT", "LTC/USDT", "NXT/USDT"] for x in distinct_calls
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)
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assert (
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# Ordering can be different, but should be one of the following
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any(
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x == ["ETH/USDT", "ADA/USDT", "DASH/USDT", "LTC/USDT", "NXT/USDT"]
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for x in distinct_calls
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)
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or any(
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x == ["ETH/USDT", "LTC/USDT", "ADA/USDT", "DASH/USDT", "NXT/USDT"]
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for x in distinct_calls
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)
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)
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# Make sure we have parallel trades
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assert len(evaluate_result_multi(results["results"], "5m", 2)) > 0
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# make sure we don't have trades with more than configured max_open_trades
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assert len(evaluate_result_multi(results["results"], "5m", 3)) == 0
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# Cached data correctly removed amounts
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offset = 1 if tres == 0 else 0
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removed_candles = len(data[pair]) - offset
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assert len(backtesting.dataprovider.get_analyzed_dataframe(pair, "5m")[0]) == removed_candles
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assert (
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len(backtesting.dataprovider.get_analyzed_dataframe("NXT/USDT", "5m")[0])
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== len(data["NXT/USDT"]) - 1
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)
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backtesting.strategy.max_open_trades = 1
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backtesting.config.update({"max_open_trades": 1})
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backtest_conf = {
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"processed": deepcopy(processed),
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"start_date": min_date,
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"end_date": max_date,
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}
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results = backtesting.backtest(**backtest_conf)
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assert len(evaluate_result_multi(results["results"], "5m", 1)) == 0
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def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):
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patch_exchange(mocker)
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mocker.patch("freqtrade.optimize.backtesting.Backtesting.backtest")
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