TEST HYPEROPTS
This commit is contained in:
Jérôme Delacotte
@@ -852,28 +852,28 @@ class FrictradeLearning(IStrategy):
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short_pair = self.getShortName(pair)
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# path=f"user_data/strategies/plots/{short_pair}/"
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self.model = joblib.load(f"{self.path}/{short_pair}_rf_model.pkl")
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# Préparer les features pour la prédiction
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features = dataframe[self.model_indicators].fillna(0)
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# Prédiction : probabilité que le prix monte
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# Affichage des colonnes intérressantes dans le model
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features_pruned, kept_features = self.prune_features(
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model=self.model,
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dataframe=dataframe,
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feature_columns=self.model_indicators,
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importance_threshold=0.005 # enlever features < % importance
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)
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probs = self.model.predict_proba(features)[:, 1]
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# Sauvegarder la probabilité pour l’analyse
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dataframe['ml_prob'] = probs
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if False and self.dp.runmode.value in ('backtest'):
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self.inspect_model(self.model)
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# self.model = joblib.load(f"{self.path}/{short_pair}_rf_model.pkl")
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#
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# # Préparer les features pour la prédiction
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# features = dataframe[self.model_indicators].fillna(0)
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#
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# # Prédiction : probabilité que le prix monte
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#
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# # Affichage des colonnes intérressantes dans le model
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# features_pruned, kept_features = self.prune_features(
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# model=self.model,
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# dataframe=dataframe,
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# feature_columns=self.model_indicators,
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# importance_threshold=0.005 # enlever features < % importance
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# )
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#
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# probs = self.model.predict_proba(features)[:, 1]
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#
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# # Sauvegarder la probabilité pour l’analyse
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# dataframe['ml_prob'] = probs
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#
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# if False and self.dp.runmode.value in ('backtest'):
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# self.inspect_model(self.model)
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#
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# absolute_min = dataframe['absolute_min'].min()
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@@ -910,6 +910,9 @@ class FrictradeLearning(IStrategy):
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horizon = 180
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self.calculateScores(dataframe, horizon)
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dataframe['cross_sma60'] = qtpylib.crossed_below(dataframe["sma12"], dataframe['sma60'])
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# val = 90000
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# steps = 12
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# [0.018, 0.022, 0.025, 0.028, 0.032, 0.035, 0.038, 0.042, 0.045, 0.048, 0.052, 0.055]
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@@ -1066,22 +1069,34 @@ class FrictradeLearning(IStrategy):
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# threshold = 0.4 #self.buy_threshold # ex: 0.80 or 1.10 depending on your model
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# 20% des signaux les plus forts
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threshold = np.percentile(dataframe["ml_prob"], 80)
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# threshold = np.percentile(dataframe["ml_prob"], 80)
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# Buy = prediction > threshold
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dataframe["buy"] = 0
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# dataframe.loc[
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# # (dataframe["ml_prob"].shift(1) < dataframe["ml_prob"])
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# (dataframe['sma60_deriv1'] > -0.0000)
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# & (dataframe['sma12_deriv1'] > 0)
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# & (dataframe['sma12'] < dataframe['sma60'])
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# # & (dataframe['rsi'] < 77)
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# # & (dataframe['heat_score_1h'] < 0.5)
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# # & (dataframe['sma180_deriv1'] > 0)
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# # & (dataframe['open'] < dataframe['max180'] * 0.997)
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# # & (dataframe['min180'].shift(3) == dataframe['min180'])
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# , ['enter_long', 'enter_tag']
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# ] = (1, f"future")
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dataframe.loc[
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(dataframe["ml_prob"].shift(1) < dataframe["ml_prob"])
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& (dataframe['sma24_deriv1'] > 0)
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& (dataframe['sma5_deriv1'] > 0)
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& (dataframe['sma5_deriv2'] > 0)
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& (dataframe['rsi'] < 77)
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& (dataframe['heat_score_1h'] < 0.5)
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# & (dataframe['sma180_deriv1'] > 0)
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# & (dataframe['open'] < dataframe['max180'] * 0.997)
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# & (dataframe['min180'].shift(3) == dataframe['min180'])
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# (dataframe["ml_prob"].shift(1) < dataframe["ml_prob"])
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(
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(dataframe['close'].shift(3) < dataframe['min180'].shift(3)) |
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(dataframe['close'].shift(4) < dataframe['min180'].shift(4)) |
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(dataframe['close'].shift(5) < dataframe['min180'].shift(5))
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)
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& (dataframe['hapercent'] > 0)
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, ['enter_long', 'enter_tag']
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] = (1, f"future")
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] = (1, f"min180")
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dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
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return dataframe
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@@ -1210,18 +1225,14 @@ class FrictradeLearning(IStrategy):
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return max(round(y), 1) # évite les valeurs négatives
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def adjust_stake_amount(self, pair: str, last_candle: DataFrame):
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if self.pairs[pair]['first_amount'] > 0:
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mises = self.pairs[pair]['mises']
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count = self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain']
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return mises[count] if count < len(mises) else self.pairs[pair]['first_amount']
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ath = max(self.pairs[pair]['last_max'], self.get_last_ath_before_candle(last_candle))
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full, mises, steps = self.calculateMises(pair, ath, last_candle['mid'])
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base_stake = mises[self.pairs[pair]['count_of_buys']] if self.pairs[pair]['count_of_buys'] < len(
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mises) else full / (steps * 2)
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return base_stake
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if (self.pairs[pair]['first_amount'] > 0):
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amount = min(self.wallets.get_available_stake_amount(), self.pairs[pair]['first_amount'])
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else:
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if last_candle['enter_tag'] in ['fall', 'bear', 'Force', 'Range-']:
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amount = self.wallets.get_available_stake_amount() / 5
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else:
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amount = self.wallets.get_available_stake_amount() / 3# / (2 * self.pairs[pair]['count_of_lost'] + 1)
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return min(amount, self.wallets.get_available_stake_amount())
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def calculateMises(self, pair, ath, val):
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# ath = max(self.pairs[pair]['last_max'], self.get_last_ath_before_candle(last_candle))
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@@ -1515,8 +1526,19 @@ class FrictradeLearning(IStrategy):
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# buys=count_of_buys,
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# stake=0
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# )
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if profit < 0.65: #5 or last_candle['rsi_1d'] < 30:
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return None
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if current_profit < - 0.02 and last_candle[f"close"] <= last_candle['sma60']:
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self.pairs[pair]['force_sell'] = True
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return 'sma60'
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if profit > 5 and \
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(baisse > 0.25 and last_candle[f"close"] <= last_candle['sma24']) \
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and last_candle['hapercent'] <0 :
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self.pairs[pair]['force_sell'] = True
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return 'B30'
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if profit > 0 and last_candle['cross_sma60']: #5 or last_candle['rsi_1d'] < 30:
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return 'Cross'
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if last_candle['max_rsi_24'] > 88 and last_candle['hapercent'] < 0\
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and last_candle['sma5_deriv2'] < -0.1:
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@@ -1535,44 +1557,48 @@ class FrictradeLearning(IStrategy):
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# if (minutes > 1440 and last_candle['sma60_deriv1'] > 0) :
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# return None
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# ----- 4) OFFSET : faut-il attendre de dépasser trailing_stop_positive_offset ? -----
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if current_trailing_only_offset_is_reached and max_profit > current_trailing_stop_positive_offset:
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# Max profit pas atteint ET perte < 2 * current_trailing_stop_positive
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if profit > limit: # 2 * current_trailing_stop_positive:
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print(
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f"{current_time} trailing non atteint trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} "
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f"max={round(max_profit, 4)} offset={round(current_trailing_stop_positive_offset, 4)} baisse={round(baisse,2)}")
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return None # ne pas activer le trailing encore
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else:
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print(
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f"{current_time} trailing atteint trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} "
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f"max={round(max_profit, 4)} offset={round(current_trailing_stop_positive_offset, 4)} baisse={round(baisse,2)}")
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else:
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# print(
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# f"1 - {current_time} trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} max={round(max_profit, 4)} "
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# f"limit={round(limit, 4)} offset={round(current_trailing_stop_positive_offset, 4)}"
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# f" baisse={round(baisse,2)} {round(last_candle['sma180_deriv1'], 4)} {round(last_candle['sma60_deriv1'], 4)} {round(last_candle['sma24_deriv1'], 4)}")
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# # ----- 4) OFFSET : faut-il attendre de dépasser trailing_stop_positive_offset ? -----
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# if current_trailing_only_offset_is_reached and max_profit > current_trailing_stop_positive_offset:
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# # Max profit pas atteint ET perte < 2 * current_trailing_stop_positive
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# if profit > limit: # 2 * current_trailing_stop_positive:
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# print(
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# f"{current_time} trailing non atteint trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} "
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# f"max={round(max_profit, 4)} offset={round(current_trailing_stop_positive_offset, 4)} baisse={round(baisse,2)}")
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# return None # ne pas activer le trailing encore
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# else:
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# print(
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# f"{current_time} trailing atteint trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} "
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# f"max={round(max_profit, 4)} offset={round(current_trailing_stop_positive_offset, 4)} baisse={round(baisse,2)}")
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# else:
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# # print(
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# # f"1 - {current_time} trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} max={round(max_profit, 4)} "
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# # f"limit={round(limit, 4)} offset={round(current_trailing_stop_positive_offset, 4)}"
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# # f" baisse={round(baisse,2)} {round(last_candle['sma180_deriv1'], 4)} {round(last_candle['sma60_deriv1'], 4)} {round(last_candle['sma24_deriv1'], 4)}")
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#
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# return None
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# # Sinon : trailing actif dès le début
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#
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# # ----- 6) Condition de vente -----
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# if 0 < profit <= trailing_stop: # and last_candle['mid'] < last_candle['sma5']: # and profit > current_trailing_stop_positive_offset:
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# self.pairs[pair]['force_buy'] = True
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# print(
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# f"{current_time} Condition de vente trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} max={round(max_profit, 4)} "
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# f"{round(limit, 4)} offset={round(current_trailing_stop_positive_offset, 4)} "
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# f"baisse={round(baisse,2)}")
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#
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# return f"stop_{count_of_buys}_{self.pairs[pair]['has_gain']}"
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return None
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# Sinon : trailing actif dès le début
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# ----- 6) Condition de vente -----
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if 0 < profit <= trailing_stop: # and last_candle['mid'] < last_candle['sma5']: # and profit > current_trailing_stop_positive_offset:
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self.pairs[pair]['force_buy'] = True
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print(
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f"{current_time} Condition de vente trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} max={round(max_profit, 4)} "
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f"{round(limit, 4)} offset={round(current_trailing_stop_positive_offset, 4)} "
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f"baisse={round(baisse,2)}")
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return f"stop_{count_of_buys}_{self.pairs[pair]['has_gain']}"
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# print(
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# f"2 - {current_time} trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} max={round(max_profit, 4)} "
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# f"{round(limit, 4)} offset={round(current_trailing_stop_positive_offset, 4)} "
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# f"baisse={round(baisse,2)} {round(last_candle['sma180_deriv1'], 4)} {round(last_candle['sma60_deriv1'], 4)} {round(last_candle['sma24_deriv1'], 4)}")
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return None
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print(
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f"2 - {current_time} trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} max={round(max_profit, 4)} "
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f"{round(limit, 4)} offset={round(current_trailing_stop_positive_offset, 4)} "
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f"baisse={round(baisse,2)} {round(last_candle['sma180_deriv1'], 4)} {round(last_candle['sma60_deriv1'], 4)} {round(last_candle['sma24_deriv1'], 4)}")
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return None
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def informative_pairs(self):
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# get access to all pairs available in whitelist.
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pairs = self.dp.current_whitelist()
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@@ -2440,6 +2466,21 @@ class FrictradeLearning(IStrategy):
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# d2s_col = f"{name}{suffixe}_deriv2_smooth"
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tendency_col = f"{name}{suffixe}_state"
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d1_col = f"{name}{suffixe}_deriv1"
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d2_col = f"{name}{suffixe}_deriv2"
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tendency_col = f"{name}{suffixe}_state"
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series = dataframe[f"{name}{suffixe}"]
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d1 = series.diff()
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d2 = d1.diff()
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pmin = int(ema_period / 3)
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cond_bas = (d1.rolling(pmin).mean() > d1.rolling(ema_period).mean())
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cond_haut = (d1.rolling(pmin).mean() < d1.rolling(ema_period).mean())
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dataframe[d1_col] = (dataframe[name] - dataframe[name].shift(3)) / dataframe[name].shift(3)
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dataframe[d2_col] = (dataframe[d1_col] - dataframe[d1_col].shift(1))
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factor1 = 100 * (ema_period / 5)
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factor2 = 10 * (ema_period / 5)
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@@ -2450,15 +2491,15 @@ class FrictradeLearning(IStrategy):
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dataframe[f"{name}{suffixe}_dist"] = (dataframe['close'] - dataframe[f"{name}{suffixe}"]) / dataframe[
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f"{name}{suffixe}"]
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# dérivée relative simple
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dataframe[d1_col] = (dataframe[name] - dataframe[name].shift(1)) / dataframe[name].shift(1)
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# lissage EMA
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dataframe[d1_col] = factor1 * dataframe[d1_col].ewm(span=ema_period, adjust=False).mean()
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# dataframe[d1_col] = dataframe[d1_col].rolling(window=ema_period, center=True).median()
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dataframe[d2_col] = dataframe[d1_col] - dataframe[d1_col].shift(1)
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dataframe[d2_col] = factor2 * dataframe[d2_col].ewm(span=ema_period, adjust=False).mean()
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# # dérivée relative simple
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# dataframe[d1_col] = (dataframe[name] - dataframe[name].shift(1)) / dataframe[name].shift(1)
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# # lissage EMA
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# dataframe[d1_col] = factor1 * dataframe[d1_col].ewm(span=ema_period, adjust=False).mean()
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#
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# # dataframe[d1_col] = dataframe[d1_col].rolling(window=ema_period, center=True).median()
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#
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# dataframe[d2_col] = dataframe[d1_col] - dataframe[d1_col].shift(1)
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# dataframe[d2_col] = factor2 * dataframe[d2_col].ewm(span=ema_period, adjust=False).mean()
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# epsilon adaptatif via rolling percentile
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p_low_d1 = dataframe[d1_col].rolling(window=window, min_periods=1).quantile(0.05)
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