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refactor backtesting to its own method as we use it also in hyperopt

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This commit is contained in:
Samuel Husso
2017-10-24 07:58:42 +03:00
parent 79c3e0583d
commit f43ba44b15
2 changed files with 25 additions and 19 deletions
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25
freqtrade/tests/test_hyperopt.py
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@@ -14,15 +14,9 @@ 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
from freqtrade.tests.test_backtesting import backtest, print_results
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
))
logging.disable(logging.DEBUG) # disable debug logs that slow backtesting a lot
@pytest.fixture
def pairs():
@@ -42,7 +36,7 @@ def conf():
}
def backtest(conf, pairs, mocker, buy_strategy):
def backtest2(conf, pairs, mocker, buy_strategy):
trades = []
mocker.patch.dict('freqtrade.main._CONF', conf)
for pair in pairs:
@@ -121,8 +115,18 @@ def buy_strategy_generator(params):
@pytest.mark.skipif(not os.environ.get('BACKTEST', False), reason="BACKTEST not set")
def test_hyperopt(conf, pairs, mocker):
# def optimizer(params):
# return backtest2(conf, pairs, mocker, buy_strategy_generator(params))
def optimizer(params):
return backtest(conf, pairs, mocker, buy_strategy_generator(params))
buy_strategy = buy_strategy_generator(params)
mocker.patch('freqtrade.analyze.populate_buy_trend', side_effect=buy_strategy)
results = backtest(conf, pairs, mocker)
print_results(results)
if len(results.index) < 800: # require at least 800 trades
return 100000 # return large number to "ignore" this result
return results.duration.mean() ** 3 / results.profit.sum() / results.profit.mean() # the smaller the better
space = {
'mfi': hp.choice('mfi', [
@@ -162,5 +166,4 @@ def test_hyperopt(conf, pairs, mocker):
{'type': 'faststoch10'}
]),
}
print('Best parameters {}'.format(fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=40)))