Michael Egger
GitHub
commit
bab59fbacd
@ -39,9 +39,7 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
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dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
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dataframe['mfi'] = ta.MFI(dataframe)
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dataframe['cci'] = ta.CCI(dataframe)
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dataframe['rsi'] = ta.RSI(dataframe)
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dataframe['mom'] = ta.MOM(dataframe)
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dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
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dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
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dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
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@ -51,6 +49,9 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
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dataframe['macd'] = macd['macd']
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dataframe['macdsignal'] = macd['macdsignal']
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dataframe['macdhist'] = macd['macdhist']
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hilbert = ta.HT_SINE(dataframe)
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dataframe['htsine'] = hilbert['sine']
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dataframe['htleadsine'] = hilbert['leadsine']
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return dataframe
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@ -15,41 +15,44 @@ from freqtrade.vendor.qtpylib.indicators import crossed_above
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logging.disable(logging.DEBUG) # disable debug logs that slow backtesting a lot
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# set TARGET_TRADES to suit your number concurrent trades so its realistic to 20days of data
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TARGET_TRADES = 1200
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TARGET_TRADES = 1300
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TOTAL_TRIES = 4
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current_tries = 0
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def buy_strategy_generator(params):
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print(params)
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def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
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conditions = []
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# GUARDS AND TRENDS
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if params['uptrend_long_ema']['enabled']:
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conditions.append(dataframe['ema50'] > dataframe['ema100'])
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if params['uptrend_short_ema']['enabled']:
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conditions.append(dataframe['ema5'] > dataframe['ema10'])
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if params['mfi']['enabled']:
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conditions.append(dataframe['mfi'] < params['mfi']['value'])
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if params['fastd']['enabled']:
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conditions.append(dataframe['fastd'] < params['fastd']['value'])
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if params['adx']['enabled']:
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conditions.append(dataframe['adx'] > params['adx']['value'])
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if params['cci']['enabled']:
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conditions.append(dataframe['cci'] < params['cci']['value'])
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if params['rsi']['enabled']:
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conditions.append(dataframe['rsi'] < params['rsi']['value'])
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if params['over_sar']['enabled']:
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conditions.append(dataframe['close'] > dataframe['sar'])
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if params['green_candle']['enabled']:
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conditions.append(dataframe['close'] > dataframe['open'])
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if params['uptrend_sma']['enabled']:
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prevsma = dataframe['sma'].shift(1)
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conditions.append(dataframe['sma'] > prevsma)
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prev_fastd = dataframe['fastd'].shift(1)
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# TRIGGERS
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triggers = {
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'lower_bb': dataframe['tema'] <= dataframe['blower'],
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'faststoch10': (dataframe['fastd'] >= 10) & (prev_fastd < 10),
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'faststoch10': (crossed_above(dataframe['fastd'], 10.0)),
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'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)),
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'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])),
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'macd_cross_signal': (crossed_above(dataframe['macd'], dataframe['macdsignal'])),
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'sar_reversal': (crossed_above(dataframe['close'], dataframe['sar'])),
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'stochf_cross': (crossed_above(dataframe['fastk'], dataframe['fastd'])),
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'ht_sine': (crossed_above(dataframe['htleadsine'], dataframe['htsine'])),
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}
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conditions.append(triggers.get(params['trigger']['type']))
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@ -72,13 +75,16 @@ def test_hyperopt(backtest_conf, backdata, mocker):
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results = backtest(backtest_conf, backdata, mocker)
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result = format_results(results)
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print(result)
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total_profit = results.profit.sum() * 1000
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trade_count = len(results.index)
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trade_loss = 1 - 0.8 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5)
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profit_loss = exp(-total_profit**3 / 10**11)
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trade_loss = 1 - 0.4 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.2)
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profit_loss = max(0, 1 - total_profit / 15000) # max profit 15000
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global current_tries
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current_tries += 1
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print('{}/{}: {}'.format(current_tries, TOTAL_TRIES, result))
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return {
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'loss': trade_loss + profit_loss,
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@ -89,32 +95,36 @@ def test_hyperopt(backtest_conf, backdata, mocker):
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space = {
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'mfi': hp.choice('mfi', [
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{'enabled': False},
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{'enabled': True, 'value': hp.uniform('mfi-value', 5, 15)}
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{'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)}
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]),
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'fastd': hp.choice('fastd', [
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{'enabled': False},
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{'enabled': True, 'value': hp.uniform('fastd-value', 5, 40)}
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{'enabled': True, 'value': hp.quniform('fastd-value', 10, 50, 1)}
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]),
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'adx': hp.choice('adx', [
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{'enabled': False},
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{'enabled': True, 'value': hp.uniform('adx-value', 10, 30)}
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]),
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'cci': hp.choice('cci', [
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{'enabled': False},
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{'enabled': True, 'value': hp.uniform('cci-value', -150, -100)}
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{'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
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]),
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'rsi': hp.choice('rsi', [
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{'enabled': False},
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{'enabled': True, 'value': hp.uniform('rsi-value', 20, 30)}
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{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
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]),
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'uptrend_long_ema': hp.choice('uptrend_long_ema', [
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{'enabled': False},
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{'enabled': True}
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]),
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'uptrend_short_ema': hp.choice('uptrend_short_ema', [
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{'enabled': False},
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{'enabled': True}
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]),
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'over_sar': hp.choice('over_sar', [
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{'enabled': False},
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{'enabled': True}
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]),
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'green_candle': hp.choice('green_candle', [
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{'enabled': False},
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{'enabled': True}
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]),
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'uptrend_sma': hp.choice('uptrend_sma', [
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{'enabled': False},
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{'enabled': True}
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@ -125,10 +135,13 @@ def test_hyperopt(backtest_conf, backdata, mocker):
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{'type': 'ao_cross_zero'},
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{'type': 'ema5_cross_ema10'},
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{'type': 'macd_cross_signal'},
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{'type': 'sar_reversal'},
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{'type': 'stochf_cross'},
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{'type': 'ht_sine'},
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]),
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}
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trials = Trials()
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best = fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=4, trials=trials)
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best = fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=TOTAL_TRIES, trials=trials)
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print('\n\n\n\n==================== HYPEROPT BACKTESTING REPORT ==============================')
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print('Best parameters {}'.format(best))
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newlist = sorted(trials.results, key=itemgetter('loss'))
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