From b8f78cb1878ef4fff90353d63305a0dc56be19dd Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 10 Jul 2018 21:08:44 +0200 Subject: [PATCH] Refactor tests, implement @creslinux's data --- .../tests/optimize/test_backtest_detail.py | 196 ++++++++++++++---- 1 file changed, 159 insertions(+), 37 deletions(-) diff --git a/freqtrade/tests/optimize/test_backtest_detail.py b/freqtrade/tests/optimize/test_backtest_detail.py index fb88fe2a7..5023f4b24 100644 --- a/freqtrade/tests/optimize/test_backtest_detail.py +++ b/freqtrade/tests/optimize/test_backtest_detail.py @@ -1,55 +1,177 @@ # pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument import logging from unittest.mock import MagicMock +from typing import NamedTuple -import pandas as pd +from pandas import DataFrame import pytest from arrow import get as getdate + from freqtrade.optimize.backtesting import Backtesting from freqtrade.tests.conftest import patch_exchange, log_has +class BTContainer(NamedTuple): + """ + NamedTuple Defining BacktestResults inputs. + """ + data: DataFrame + stop_loss: float + roi: float + trades: int + profit_perc: float + sl: float + + columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'buy', 'sell'] -data_profit = pd.DataFrame([[getdate('2018-07-08 18:00:00').datetime, - 0.0009910, 0.001011, 0.00098618, 0.001000, 47027.0, 1, 0], - [getdate('2018-07-08 19:00:00').datetime, - 0.001000, 0.001010, 0.0009900, 0.0009900, 87116.0, 0, 0], - [getdate('2018-07-08 20:00:00').datetime, - 0.0009900, 0.001011, 0.00091618, 0.0009900, 58539.0, 0, 0], - [getdate('2018-07-08 21:00:00').datetime, - 0.001000, 0.001011, 0.00098618, 0.001100, 37498.0, 0, 1], - [getdate('2018-07-08 22:00:00').datetime, - 0.001000, 0.001011, 0.00098618, 0.0009900, 59792.0, 0, 0]], - columns=columns) +data_profit = DataFrame([ + [getdate('2018-07-08 18:00:00').datetime, 0.0009910, + 0.001011, 0.00098618, 0.001000, 12345, 1, 0], + [getdate('2018-07-08 19:00:00').datetime, 0.001000, + 0.001010, 0.0009900, 0.0009900, 12345, 0, 0], + [getdate('2018-07-08 20:00:00').datetime, 0.0009900, + 0.001011, 0.00091618, 0.0009900, 12345, 0, 0], + [getdate('2018-07-08 21:00:00').datetime, 0.001000, + 0.001011, 0.00098618, 0.001100, 12345, 0, 1], + [getdate('2018-07-08 22:00:00').datetime, 0.001000, + 0.001011, 0.00098618, 0.0009900, 12345, 0, 0] +], columns=columns) -data_loss = pd.DataFrame([[getdate('2018-07-08 18:00:00').datetime, - 0.0009910, 0.001011, 0.00098618, 0.001000, 47027.0, 1, 0], - [getdate('2018-07-08 19:00:00').datetime, - 0.001000, 0.001010, 0.0009900, 0.001000, 87116.0, 0, 0], - [getdate('2018-07-08 20:00:00').datetime, - 0.001000, 0.001011, 0.0010618, 0.00091618, 58539.0, 0, 0], - [getdate('2018-07-08 21:00:00').datetime, - 0.001000, 0.001011, 0.00098618, 0.00091618, 37498.0, 0, 0], - [getdate('2018-07-08 22:00:00').datetime, - 0.001000, 0.001011, 0.00098618, 0.00091618, 59792.0, 0, 0]], - columns=columns) +tc_profit1 = BTContainer(data=data_profit, stop_loss=-0.01, roi=1, trades=1, + profit_perc=0.10557, sl=False) # should be stoploss - drops 8% +tc_profit2 = BTContainer(data=data_profit, stop_loss=-0.10, roi=1, + trades=1, profit_perc=0.10557, sl=True) -@pytest.mark.parametrize("data, stoploss, tradecount, profit_perc, sl", [ - (data_profit, -0.01, 1, 0.10557, False), # should be stoploss - drops 8% - # (data_profit, -0.10, 1, 0.10557, True), # win - (data_loss, -0.05, 1, -0.08839, True), # Stoploss ... - ]) -def test_backtest_results(default_conf, fee, mocker, caplog, - data, stoploss, tradecount, profit_perc, sl) -> None: +tc_loss0 = BTContainer(data=DataFrame([ + [getdate('2018-07-08 18:00:00').datetime, 0.0009910, + 0.001011, 0.00098618, 0.001000, 12345, 1, 0], + [getdate('2018-07-08 19:00:00').datetime, 0.001000, + 0.001010, 0.0009900, 0.001000, 12345, 0, 0], + [getdate('2018-07-08 20:00:00').datetime, 0.001000, + 0.001011, 0.0010618, 0.00091618, 12345, 0, 0], + [getdate('2018-07-08 21:00:00').datetime, 0.001000, + 0.001011, 0.00098618, 0.00091618, 12345, 0, 0], + [getdate('2018-07-08 22:00:00').datetime, 0.001000, + 0.001011, 0.00098618, 0.00091618, 12345, 0, 0] +], columns=columns), + stop_loss=-0.05, roi=1, trades=1, profit_perc=-0.08839, sl=True) + + +# Test 1 Minus 8% Close +# Candle Data for test 1 – close at -8% (9200) +# Test with Stop-loss at 1% +tc1 = BTContainer(data=DataFrame([ + [getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0], + [getdate('2018-06-10 09:00:00').datetime, 9975, 10025, 9925, 9950, 12345, 0, 0], + [getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9960, 9955, 12345, 0, 0], + [getdate('2018-06-10 11:00:00').datetime, 9955, 9975, 9955, 9990, 12345, 0, 0], + [getdate('2018-06-10 12:00:00').datetime, 9990, 9990, 9200, 9200, 12345, 0, 0] +], columns=columns), + stop_loss=-0.01, roi=1, trades=1, profit_perc=-0.07999, sl=True) + +# Test 2 Minus 4% Low, minus 1% close +# Candle Data for test 2 +# Test with Stop-Loss at 3% +tc2 = BTContainer(data=DataFrame([ + [getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0], + [getdate('2018-06-10 09:00:00').datetime, 9975, 10025, 9925, 9950, 12345, 0, 0], + [getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9600, 9925, 12345, 0, 0], + [getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9875, 9900, 12345, 0, 0], + [getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0] +], columns=columns), stop_loss=-0.03, roi=1, trades=1, profit_perc=-0.00999, sl=False) # + + +# Test 3 Candle drops 4%, Recovers 1%. +# Entry Criteria Met +# Candle drops 20% +# Candle Data for test 3 +# Test with Stop-Loss at 2% +tc3 = BTContainer(data=DataFrame([ + [getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0], + [getdate('2018-06-10 09:00:00').datetime, 9975, 10025, 9600, 9950, 12345, 0, 0], + [getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 1, 0], + [getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 8000, 8000, 12345, 0, 0], + [getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9950, 9900, 12345, 0, 0] +], columns=columns), stop_loss=-0.02, roi=1, trades=1, profit_perc=-0.19999, sl=True) # + + +# Test 4 Minus 3% / recovery +15% +# Candle Data for test 4 – Candle drops 3% Closed 15% up +# Test with Stop-loss at 2% ROI 6% + +tc4 = BTContainer(data=DataFrame([ + [getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0], + [getdate('2018-06-10 09:00:00').datetime, 9975, 11500, 9700, 11500, 12345, 0, 0], + [getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 0, 0], + [getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9875, 9900, 12345, 0, 0], + [getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0] +], columns=columns), + stop_loss=-0.02, roi=0.06, trades=1, profit_perc=-0.141, sl=True) + +# Test 5 / Drops 0.5% Closes +20% +# Candle Data for test 5 +# Set stop-loss at 1% ROI 3% +tc5 = BTContainer(data=DataFrame([ + [getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0], + [getdate('2018-06-10 09:00:00').datetime, 9975, 12000, 9950, 12000, 12345, 0, 0], + [getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 0, 0], + [getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9945, 9900, 12345, 0, 0], + [getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0] +], columns=columns), + stop_loss=-0.01, roi=0.03, trades=1, profit_perc=-0.177, sl=True) + +# Test 6 / Drops 3% / Recovers 6% Positive / Closes 1% positve +# Candle Data for test 6 +# Set stop-loss at 2% ROI at 5% +tc6 = BTContainer(data=DataFrame([ + [getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0], + [getdate('2018-06-10 09:00:00').datetime, 9975, 10600, 9700, 10100, 12345, 0, 0], + [getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 0, 0], + [getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9945, 9900, 12345, 0, 0], + [getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0] +], columns=columns), stop_loss=-0.02, roi=0.05, + trades=1, profit_perc=-0.025, sl=False) + +# Test 7 - 6% Positive / 1% Negative / Close 1% Positve +# Candle Data for test 7 +# Set stop-loss at 2% ROI at 3% + +tc7 = BTContainer(data=DataFrame([ + [getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0], + [getdate('2018-06-10 09:00:00').datetime, 9975, 10600, 9900, 10100, 12345, 0, 0], + [getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 0, 0], + [getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9945, 9900, 12345, 0, 0], + [getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0] +], columns=columns), stop_loss=-0.02, roi=0.03, + trades=1, profit_perc=-0.025, sl=False) + +TESTS = [ + # tc_profit1, + # tc_profit2, + tc_loss0, + tc1, + tc2, + tc3, + tc4, + tc5, + tc6, + tc7, +] + + +@pytest.mark.parametrize("data", TESTS) +def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None: """ run functional tests """ - default_conf["stoploss"] = stoploss + default_conf["stoploss"] = data.stop_loss + default_conf["minimal_roi"] = {"0": data.roi} mocker.patch('freqtrade.exchange.Exchange.get_fee', fee) - mocker.patch('freqtrade.analyze.Analyze.populate_sell_trend', MagicMock(return_value=data)) - mocker.patch('freqtrade.analyze.Analyze.populate_buy_trend', MagicMock(return_value=data)) + mocker.patch.multiple('freqtrade.analyze.Analyze', + populate_sell_trend=MagicMock(return_value=data.data), + populate_buy_trend=MagicMock(return_value=data.data)) patch_exchange(mocker) backtesting = Backtesting(default_conf) @@ -57,7 +179,7 @@ def test_backtest_results(default_conf, fee, mocker, caplog, pair = 'UNITTEST/BTC' # Dummy data as we mock the analyze functions - data_processed = {pair: pd.DataFrame()} + data_processed = {pair: DataFrame()} results = backtesting.backtest( { 'stake_amount': default_conf['stake_amount'], @@ -68,9 +190,9 @@ def test_backtest_results(default_conf, fee, mocker, caplog, ) print(results.T) - assert len(results) == tradecount - assert round(results["profit_percent"].sum(), 5) == profit_perc - if sl: + assert len(results) == data.trades + assert round(results["profit_percent"].sum(), 3) == round(data.profit_perc, 3) + if data.sl: assert log_has("Stop loss hit.", caplog.record_tuples) else: