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store result strings, display best result in summary. switch to a lot better objective algo

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This commit is contained in:
Janne Sinivirta 2017-10-28 15:22:15 +03:00
parent 08ca7a8166
commit 649781d823
1 changed files with 24 additions and 7 deletions
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31
freqtrade/tests/test_hyperopt.py
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@ -1,15 +1,17 @@
# 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
from hyperopt import fmin, tpe, hp, Trials, STATUS_OK
from freqtrade.analyze import analyze_ticker
from freqtrade.main import should_sell
@ -19,6 +21,10 @@ 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',
@ -85,12 +91,18 @@ def test_hyperopt(conf, pairs, mocker):
result = format_results(results)
print(result)
total_profit = results.profit.sum() * 1000
trade_count = len(results.index)
# set TARGET_TRADES 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
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', [
@ -131,4 +143,9 @@ def test_hyperopt(conf, pairs, mocker):
{'type': 'ao_cross_zero'}
]),
}
print('Best parameters {}'.format(fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=40)))
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']))