stable/freqtrade/tests/test_hyperopt.py

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# pragma pylint: disable=missing-docstring
import json
import logging
import os
from functools import reduce
import pytest
import arrow
from pandas import DataFrame
import hyperopt.pyll.stochastic
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": {
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"40": 0.0,
"30": 0.01,
"20": 0.02,
"0": 0.04
},
"stoploss": -0.40
}
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)
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# 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'])
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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),
}
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)}
]),
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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'}
]),
}
# print(hyperopt.pyll.stochastic.sample(space))
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print('Best parameters {}'.format(fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=40)))