stable/freqtrade/tests/test_hyperopt.py
2017-10-22 17:15:57 +03:00

167 lines
5.9 KiB
Python

# pragma pylint: disable=missing-docstring
import json
import logging
import os
from functools import reduce
import pytest
import arrow
from pandas import DataFrame
from hyperopt import fmin, tpe, hp
from freqtrade.analyze import analyze_ticker
from freqtrade.main import should_sell
from freqtrade.persistence import Trade
logging.disable(logging.DEBUG) # disable debug logs that slow backtesting a lot
def print_results(results):
print('Made {} buys. Average profit {:.2f}%. Total profit was {:.3f}. Average duration {:.1f} mins.'.format(
len(results.index),
results.profit.mean() * 100.0,
results.profit.sum(),
results.duration.mean() * 5
))
@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 backtest(conf, pairs, mocker, buy_strategy):
trades = []
mocker.patch.dict('freqtrade.main._CONF', conf)
for pair in pairs:
with open('freqtrade/tests/testdata/'+pair+'.json') as data_file:
data = json.load(data_file)
mocker.patch('freqtrade.analyze.get_ticker_history', return_value=data)
mocker.patch('arrow.utcnow', return_value=arrow.get('2017-08-20T14:50:00'))
mocker.patch('freqtrade.analyze.populate_buy_trend', side_effect=buy_strategy)
ticker = analyze_ticker(pair)
# for each buy point
for index, row in ticker[ticker.buy == 1].iterrows():
trade = Trade(
open_rate=row['close'],
open_date=arrow.get(row['date']).datetime,
amount=1,
)
# calculate win/lose forwards from buy point
for index2, row2 in ticker[index:].iterrows():
if should_sell(trade, row2['close'], arrow.get(row2['date']).datetime):
current_profit = (row2['close'] - trade.open_rate) / trade.open_rate
trades.append((pair, current_profit, index2 - index))
break
labels = ['currency', 'profit', 'duration']
results = DataFrame.from_records(trades, columns=labels)
print_results(results)
# set the value below to suit your number concurrent trades so its realistic to 20days of data
TARGET_TRADES = 1200
if results.profit.sum() == 0 or results.profit.mean() == 0:
return 49999999999 # avoid division by zero, return huge value to discard result
return abs(len(results.index) - 1200.1) / (results.profit.sum() ** 2) * results.duration.mean() # the smaller the better
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'])
if params['cci']['enabled']:
conditions.append(dataframe['cci'] < params['cci']['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),
}
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):
return backtest(conf, pairs, mocker, buy_strategy_generator(params))
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)}
]),
'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}
]),
'uptrend_sma': hp.choice('uptrend_sma', [
{'enabled': False},
{'enabled': True}
]),
'trigger': hp.choice('trigger', [
{'type': 'lower_bb'},
{'type': 'faststoch10'}
]),
}
print('Best parameters {}'.format(fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=40)))