mirror of
https://github.com/freqtrade/freqtrade.git
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130 lines
4.5 KiB
Python
130 lines
4.5 KiB
Python
# pragma pylint: disable=missing-docstring
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import json
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import logging
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import os
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from functools import reduce
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import pytest
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import arrow
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from pandas import DataFrame
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import hyperopt.pyll.stochastic
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from hyperopt import fmin, tpe, hp
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from freqtrade.analyze import analyze_ticker
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from freqtrade.main import should_sell
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from freqtrade.persistence import Trade
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logging.disable(logging.DEBUG) # disable debug logs that slow backtesting a lot
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def print_results(results):
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print('Made {} buys. Average profit {:.2f}%. Total profit was {:.3f}. Average duration {:.1f} mins.'.format(
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len(results.index),
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results.profit.mean() * 100.0,
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results.profit.sum(),
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results.duration.mean() * 5
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))
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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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"50": 0.0,
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"40": 0.01,
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"30": 0.02,
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"0": 0.045
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},
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"stoploss": -0.40
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}
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def backtest(conf, pairs, mocker, buy_strategy):
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trades = []
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mocker.patch.dict('freqtrade.main._CONF', conf)
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for pair in pairs:
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with open('freqtrade/tests/testdata/'+pair+'.json') as data_file:
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data = json.load(data_file)
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mocker.patch('freqtrade.analyze.get_ticker_history', return_value=data)
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mocker.patch('arrow.utcnow', return_value=arrow.get('2017-08-20T14:50:00'))
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mocker.patch('freqtrade.analyze.populate_buy_trend', side_effect=buy_strategy)
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ticker = analyze_ticker(pair)
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# for each buy point
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for index, row in ticker[ticker.buy == 1].iterrows():
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trade = Trade(
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open_rate=row['close'],
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open_date=arrow.get(row['date']).datetime,
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amount=1,
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)
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# calculate win/lose forwards from buy point
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for index2, row2 in ticker[index:].iterrows():
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if should_sell(trade, row2['close'], arrow.get(row2['date']).datetime):
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current_profit = (row2['close'] - trade.open_rate) / trade.open_rate
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trades.append((pair, current_profit, index2 - index))
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break
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labels = ['currency', 'profit', 'duration']
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results = DataFrame.from_records(trades, columns=labels)
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print_results(results)
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if len(results.index) < 800:
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return 100000 # return large number to "ignore" this result
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return results.duration.mean() * results.duration.mean() / results.profit.sum() / results.profit.mean() # the smaller the better
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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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if params['below_sma']['enabled']:
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conditions.append(dataframe['close'] < dataframe['sma'])
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conditions.append(dataframe['tema'] <= dataframe['blower'])
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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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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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def optimizer(params):
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return backtest(conf, pairs, mocker, buy_strategy_generator(params))
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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', 2, 40)}
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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', 2, 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', 2, 40)}
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]),
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'below_sma': hp.choice('below_sma', [
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{'enabled': False},
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{'enabled': True}
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]),
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}
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# print(hyperopt.pyll.stochastic.sample(space))
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print('Best parameters {}'.format(fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=10)))
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