Merge pull request #76 from gcarq/hyperopt
Use hyperopt to find optimal parameters for buy strategy
This commit is contained in:
Michael Egger
GitHub
commit
79c3e0583d
@ -24,21 +24,23 @@ def parse_ticker_dataframe(ticker: list, minimum_date: arrow.Arrow) -> DataFrame
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.drop('BV', 1) \
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.rename(columns={'C':'close', 'V':'volume', 'O':'open', 'H':'high', 'L':'low', 'T':'date'}) \
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.sort_values('date')
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return df[df['date'].map(arrow.get) > minimum_date]
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return df
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def populate_indicators(dataframe: DataFrame) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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"""
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dataframe['sar'] = ta.SAR(dataframe)
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dataframe['adx'] = ta.ADX(dataframe)
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stoch = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch['fastd']
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dataframe['fastk'] = stoch['fastk']
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dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
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dataframe['sma'] = ta.SMA(dataframe, timeperiod=30)
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dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
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dataframe['mfi'] = ta.MFI(dataframe)
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dataframe['cci'] = ta.CCI(dataframe)
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return dataframe
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166
freqtrade/tests/test_hyperopt.py
Normal file
166
freqtrade/tests/test_hyperopt.py
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@ -0,0 +1,166 @@
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# 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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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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"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 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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# set the value below to suit your number concurrent trades so its realistic to 20days of data
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TARGET_TRADES = 1200
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if results.profit.sum() == 0 or results.profit.mean() == 0:
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return 49999999999 # avoid division by zero, return huge value to discard result
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return abs(len(results.index) - 1200.1) / (results.profit.sum() ** 2) * results.duration.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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# GUARDS AND TRENDS
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if params['below_sma']['enabled']:
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conditions.append(dataframe['close'] < dataframe['sma'])
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if params['over_sma']['enabled']:
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conditions.append(dataframe['close'] > dataframe['sma'])
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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['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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}
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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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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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'cci': hp.choice('cci', [
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{'enabled': False},
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{'enabled': True, 'value': hp.uniform('cci-value', -200, -100)}
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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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'over_sma': hp.choice('over_sma', [
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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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]),
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}
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print('Best parameters {}'.format(fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=40)))
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@ -14,6 +14,9 @@ TA-Lib==0.4.10
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pytest==3.2.3
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pytest-mock==1.6.3
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pytest-cov==2.5.1
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hyperopt==0.1
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# do not upgrade networkx before this is fixed https://github.com/hyperopt/hyperopt/issues/325
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networkx==1.11
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# Required for plotting data
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#matplotlib==2.1.0
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