refactor backtesting to its own method as we use it also in hyperopt
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@ -21,6 +21,10 @@ def print_results(results):
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results.duration.mean() * 5
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results.duration.mean() * 5
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))
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))
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def print_pair_results(pair, results):
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print('For currency {}:'.format(pair))
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print_results(results[results.currency == pair])
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@pytest.fixture
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@pytest.fixture
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def pairs():
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def pairs():
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return ['btc-neo', 'btc-eth', 'btc-omg', 'btc-edg', 'btc-pay',
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return ['btc-neo', 'btc-eth', 'btc-omg', 'btc-edg', 'btc-pay',
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@ -38,9 +42,7 @@ def conf():
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"stoploss": -0.40
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"stoploss": -0.40
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}
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}
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def backtest(conf, pairs, mocker):
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@pytest.mark.skipif(not os.environ.get('BACKTEST', False), reason="BACKTEST not set")
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def test_backtest(conf, pairs, mocker):
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trades = []
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trades = []
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mocker.patch.dict('freqtrade.main._CONF', conf)
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mocker.patch.dict('freqtrade.main._CONF', conf)
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for pair in pairs:
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for pair in pairs:
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@ -64,14 +66,15 @@ def test_backtest(conf, pairs, mocker):
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trades.append((pair, current_profit, index2 - index))
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trades.append((pair, current_profit, index2 - index))
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break
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break
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labels = ['currency', 'profit', 'duration']
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labels = ['currency', 'profit', 'duration']
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results = DataFrame.from_records(trades, columns=labels)
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results = DataFrame.from_records(trades, columns=labels)
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return results
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@pytest.mark.skipif(not os.environ.get('BACKTEST', False), reason="BACKTEST not set")
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def test_backtest(conf, pairs, mocker, report=True):
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results = backtest(conf, pairs, mocker)
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print('====================== BACKTESTING REPORT ================================')
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print('====================== BACKTESTING REPORT ================================')
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[print_pair_results(pair, results) for pair in pairs]
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for pair in pairs:
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print('For currency {}:'.format(pair))
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print_results(results[results.currency == pair])
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print('TOTAL OVER ALL TRADES:')
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print('TOTAL OVER ALL TRADES:')
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print_results(results)
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print_results(results)
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@ -14,15 +14,9 @@ from freqtrade.analyze import analyze_ticker
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from freqtrade.main import should_sell
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from freqtrade.main import should_sell
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from freqtrade.persistence import Trade
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from freqtrade.persistence import Trade
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logging.disable(logging.DEBUG) # disable debug logs that slow backtesting a lot
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from freqtrade.tests.test_backtesting import backtest, print_results
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def print_results(results):
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logging.disable(logging.DEBUG) # disable debug logs that slow backtesting a lot
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print('Made {} buys. Average profit {:.2f}%. Total profit was {:.3f}. Average duration {:.1f} mins.'.format(
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len(results.index),
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results.profit.mean() * 100.0,
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results.profit.sum(),
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results.duration.mean() * 5
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))
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@pytest.fixture
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@pytest.fixture
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def pairs():
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def pairs():
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@ -42,7 +36,7 @@ def conf():
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}
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}
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def backtest(conf, pairs, mocker, buy_strategy):
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def backtest2(conf, pairs, mocker, buy_strategy):
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trades = []
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trades = []
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mocker.patch.dict('freqtrade.main._CONF', conf)
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mocker.patch.dict('freqtrade.main._CONF', conf)
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for pair in pairs:
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for pair in pairs:
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@ -121,8 +115,18 @@ def buy_strategy_generator(params):
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@pytest.mark.skipif(not os.environ.get('BACKTEST', False), reason="BACKTEST not set")
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@pytest.mark.skipif(not os.environ.get('BACKTEST', False), reason="BACKTEST not set")
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def test_hyperopt(conf, pairs, mocker):
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def test_hyperopt(conf, pairs, mocker):
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# def optimizer(params):
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# return backtest2(conf, pairs, mocker, buy_strategy_generator(params))
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def optimizer(params):
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def optimizer(params):
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return backtest(conf, pairs, mocker, buy_strategy_generator(params))
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buy_strategy = buy_strategy_generator(params)
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mocker.patch('freqtrade.analyze.populate_buy_trend', side_effect=buy_strategy)
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results = backtest(conf, pairs, mocker)
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print_results(results)
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if len(results.index) < 800: # require at least 800 trades
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return 100000 # return large number to "ignore" this result
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return results.duration.mean() ** 3 / results.profit.sum() / results.profit.mean() # the smaller the better
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space = {
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space = {
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'mfi': hp.choice('mfi', [
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'mfi': hp.choice('mfi', [
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@ -162,5 +166,4 @@ def test_hyperopt(conf, pairs, mocker):
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{'type': 'faststoch10'}
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{'type': 'faststoch10'}
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]),
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]),
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}
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}
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print('Best parameters {}'.format(fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=40)))
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print('Best parameters {}'.format(fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=40)))
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