stable/freqtrade/tests/test_hyperopt.py

149 lines
5.3 KiB
Python

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