155 lines
5.3 KiB
Python
155 lines
5.3 KiB
Python
# pragma pylint: disable=missing-docstring
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import logging
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import os
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from functools import reduce
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from math import exp
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from operator import itemgetter
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import pytest
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from hyperopt import fmin, tpe, hp, Trials, STATUS_OK
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from pandas import DataFrame
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from freqtrade.tests.test_backtesting import backtest, format_results
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from freqtrade.vendor.qtpylib.indicators import crossed_above
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logging.disable(logging.DEBUG) # disable debug logs that slow backtesting a lot
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# set TARGET_TRADES to suit your number concurrent trades so its realistic to 20days of data
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TARGET_TRADES = 1200
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@pytest.fixture
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def pairs():
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return ['btc-neo', 'btc-eth', 'btc-omg', 'btc-edg', 'btc-pay',
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'btc-pivx', 'btc-qtum', 'btc-mtl', 'btc-etc', 'btc-ltc']
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@pytest.fixture
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def conf():
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return {
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"minimal_roi": {
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"40": 0.0,
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"30": 0.01,
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"20": 0.02,
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"0": 0.04
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},
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"stoploss": -0.05
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}
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def buy_strategy_generator(params):
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print(params)
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def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
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conditions = []
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# GUARDS AND TRENDS
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if params['uptrend_long_ema']['enabled']:
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conditions.append(dataframe['ema50'] > dataframe['ema100'])
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if params['mfi']['enabled']:
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conditions.append(dataframe['mfi'] < params['mfi']['value'])
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if params['fastd']['enabled']:
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conditions.append(dataframe['fastd'] < params['fastd']['value'])
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if params['adx']['enabled']:
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conditions.append(dataframe['adx'] > params['adx']['value'])
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if params['cci']['enabled']:
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conditions.append(dataframe['cci'] < params['cci']['value'])
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if params['rsi']['enabled']:
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conditions.append(dataframe['rsi'] < params['rsi']['value'])
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if params['over_sar']['enabled']:
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conditions.append(dataframe['close'] > dataframe['sar'])
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if params['uptrend_sma']['enabled']:
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prevsma = dataframe['sma'].shift(1)
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conditions.append(dataframe['sma'] > prevsma)
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prev_fastd = dataframe['fastd'].shift(1)
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# TRIGGERS
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triggers = {
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'lower_bb': dataframe['tema'] <= dataframe['blower'],
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'faststoch10': (dataframe['fastd'] >= 10) & (prev_fastd < 10),
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'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)),
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'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])),
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'macd_cross_signal': (crossed_above(dataframe['macd'], dataframe['macdsignal'])),
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}
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conditions.append(triggers.get(params['trigger']['type']))
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'buy'] = 1
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dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
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return dataframe
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return populate_buy_trend
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@pytest.mark.skipif(not os.environ.get('BACKTEST', False), reason="BACKTEST not set")
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def test_hyperopt(conf, pairs, mocker):
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mocked_buy_trend = mocker.patch('freqtrade.analyze.populate_buy_trend')
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def optimizer(params):
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mocked_buy_trend.side_effect = buy_strategy_generator(params)
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results = backtest(conf, pairs, mocker)
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result = format_results(results)
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print(result)
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total_profit = results.profit.sum() * 1000
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trade_count = len(results.index)
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trade_loss = 1 - 0.8 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5)
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profit_loss = exp(-total_profit**3 / 10**11)
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return {
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'loss': trade_loss + profit_loss,
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'status': STATUS_OK,
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'result': result
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}
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space = {
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'mfi': hp.choice('mfi', [
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{'enabled': False},
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{'enabled': True, 'value': hp.uniform('mfi-value', 5, 15)}
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]),
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'fastd': hp.choice('fastd', [
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{'enabled': False},
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{'enabled': True, 'value': hp.uniform('fastd-value', 5, 40)}
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]),
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'adx': hp.choice('adx', [
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{'enabled': False},
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{'enabled': True, 'value': hp.uniform('adx-value', 10, 30)}
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]),
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'cci': hp.choice('cci', [
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{'enabled': False},
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{'enabled': True, 'value': hp.uniform('cci-value', -150, -100)}
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]),
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'rsi': hp.choice('rsi', [
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{'enabled': False},
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{'enabled': True, 'value': hp.uniform('rsi-value', 20, 30)}
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]),
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'uptrend_long_ema': hp.choice('uptrend_long_ema', [
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{'enabled': False},
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{'enabled': True}
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]),
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'over_sar': hp.choice('over_sar', [
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{'enabled': False},
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{'enabled': True}
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]),
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'uptrend_sma': hp.choice('uptrend_sma', [
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{'enabled': False},
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{'enabled': True}
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]),
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'trigger': hp.choice('trigger', [
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{'type': 'lower_bb'},
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{'type': 'faststoch10'},
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{'type': 'ao_cross_zero'},
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{'type': 'ema5_cross_ema10'},
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{'type': 'macd_cross_signal'},
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]),
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}
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trials = Trials()
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best = fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=4, trials=trials)
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print('\n\n\n\n====================== HYPEROPT BACKTESTING REPORT ================================')
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print('Best parameters {}'.format(best))
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newlist = sorted(trials.results, key=itemgetter('loss'))
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print('Result: {}'.format(newlist[0]['result']))
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