stable/freqtrade/tests/test_hyperopt.py

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# pragma pylint: disable=missing-docstring
from operator import itemgetter
import json
import logging
import os
from functools import reduce
from math import exp
import pytest
import arrow
from pandas import DataFrame
from qtpylib.indicators import crossed_above
from hyperopt import fmin, tpe, hp, Trials, STATUS_OK
from freqtrade.analyze import analyze_ticker
from freqtrade.main import should_sell
from freqtrade.persistence import Trade
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": {
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"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['below_sma']['enabled']:
conditions.append(dataframe['close'] < dataframe['sma'])
if params['over_sma']['enabled']:
conditions.append(dataframe['close'] > dataframe['sma'])
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'])
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if params['cci']['enabled']:
conditions.append(dataframe['cci'] < params['cci']['value'])
if params['over_sar']['enabled']:
conditions.append(dataframe['close'] > dataframe['sar'])
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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)),
}
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', 2, 40)}
]),
'fastd': hp.choice('fastd', [
{'enabled': False},
{'enabled': True, 'value': hp.uniform('fastd-value', 2, 40)}
]),
'adx': hp.choice('adx', [
{'enabled': False},
{'enabled': True, 'value': hp.uniform('adx-value', 2, 40)}
]),
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'cci': hp.choice('cci', [
{'enabled': False},
{'enabled': True, 'value': hp.uniform('cci-value', -200, -100)}
]),
'below_sma': hp.choice('below_sma', [
{'enabled': False},
{'enabled': True}
]),
'over_sma': hp.choice('over_sma', [
{'enabled': False},
{'enabled': True}
]),
'over_sar': hp.choice('over_sar', [
{'enabled': False},
{'enabled': True}
]),
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'uptrend_sma': hp.choice('uptrend_sma', [
{'enabled': False},
{'enabled': True}
]),
'trigger': hp.choice('trigger', [
{'type': 'lower_bb'},
{'type': 'faststoch10'},
{'type': 'ao_cross_zero'}
]),
}
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']))