Merge branch 'feat/freqai' of https://github.com/freqtrade/freqtrade into feat/freqai
This commit is contained in:
@@ -70,7 +70,9 @@ class FreqaiExampleStrategy(IStrategy):
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def bot_start(self):
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self.model = CustomModel(self.config)
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def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin=""):
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def populate_any_indicators(
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self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False
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):
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"""
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Function designed to automatically generate, name and merge features
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from user indicated timeframes in the configuration file. User controls the indicators
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@@ -120,9 +122,7 @@ class FreqaiExampleStrategy(IStrategy):
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informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
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)
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informative[f"%-{coin}roc-period_{t}"] = ta.ROC(
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informative, timeperiod=t
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)
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informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
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macd = ta.MACD(informative, timeperiod=t)
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informative[f"%-{coin}macd-period_{t}"] = macd["macd"]
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@@ -152,17 +152,17 @@ class FreqaiExampleStrategy(IStrategy):
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# Add generalized indicators here (because in live, it will call this
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# function to populate indicators during training). Notice how we ensure not to
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# add them multiple times
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if pair == self.freqai_info['corr_pairlist'][0] and tf == self.timeframe:
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if set_generalized_indicators:
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df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
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df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
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# user adds targets here by prepending them with &- (see convention below)
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# If user wishes to use multiple targets, a multioutput prediction model
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# needs to be used such as templates/CatboostPredictionMultiModel.py
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df['&-s_close'] = (
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df["&-s_close"] = (
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df["close"]
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.shift(-self.freqai_info['feature_parameters']["period"])
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.rolling(self.freqai_info['feature_parameters']["period"])
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.shift(-self.freqai_info["feature_parameters"]["period"])
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.rolling(self.freqai_info["feature_parameters"]["period"])
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.mean()
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/ df["close"]
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- 1
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@@ -174,15 +174,21 @@ class FreqaiExampleStrategy(IStrategy):
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self.freqai_info = self.config["freqai"]
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self.pair = metadata["pair"]
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sgi = True
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# the following loops are necessary for building the features
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# indicated by the user in the configuration file.
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# All indicators must be populated by populate_any_indicators() for live functionality
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# to work correctly.
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for tf in self.freqai_info["timeframes"]:
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dataframe = self.populate_any_indicators(
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metadata, self.pair, dataframe.copy(), tf, coin=self.pair.split("/")[0] + "-"
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metadata,
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self.pair,
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dataframe.copy(),
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tf,
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coin=self.pair.split("/")[0] + "-",
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set_generalized_indicators=sgi,
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)
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sgi = False
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for pair in self.freqai_info["corr_pairlist"]:
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if metadata["pair"] in pair:
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continue # do not include whitelisted pair twice if it is in corr_pairlist
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@@ -231,51 +237,55 @@ class FreqaiExampleStrategy(IStrategy):
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def get_ticker_indicator(self):
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return int(self.config["timeframe"][:-1])
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def custom_exit(self, pair: str, trade: Trade, current_time, current_rate,
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current_profit, **kwargs):
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def custom_exit(
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self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs
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):
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dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
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trade_date = timeframe_to_prev_date(self.config['timeframe'], trade.open_date_utc)
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trade_candle = dataframe.loc[(dataframe['date'] == trade_date)]
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trade_date = timeframe_to_prev_date(self.config["timeframe"], trade.open_date_utc)
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trade_candle = dataframe.loc[(dataframe["date"] == trade_date)]
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if trade_candle.empty:
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return None
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trade_candle = trade_candle.squeeze()
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follow_mode = self.config.get('freqai', {}).get('follow_mode', False)
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follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
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if not follow_mode:
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pair_dict = self.model.bridge.data_drawer.pair_dict
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pair_dict = self.model.bridge.dd.pair_dict
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else:
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pair_dict = self.model.bridge.data_drawer.follower_dict
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pair_dict = self.model.bridge.dd.follower_dict
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entry_tag = trade.enter_tag
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if ('prediction' + entry_tag not in pair_dict[pair] or
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pair_dict[pair]['prediction' + entry_tag] > 0):
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if (
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"prediction" + entry_tag not in pair_dict[pair]
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or pair_dict[pair]["prediction" + entry_tag] > 0
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):
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with self.model.bridge.lock:
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pair_dict[pair]['prediction' + entry_tag] = abs(trade_candle['&-s_close'])
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pair_dict[pair]["prediction" + entry_tag] = abs(trade_candle["&-s_close"])
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if not follow_mode:
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self.model.bridge.data_drawer.save_drawer_to_disk()
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self.model.bridge.dd.save_drawer_to_disk()
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else:
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self.model.bridge.data_drawer.save_follower_dict_to_disk()
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self.model.bridge.dd.save_follower_dict_to_disk()
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roi_price = pair_dict[pair]['prediction' + entry_tag]
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roi_price = pair_dict[pair]["prediction" + entry_tag]
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roi_time = self.max_roi_time_long.value
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roi_decay = roi_price * (1 - ((current_time - trade.open_date_utc).seconds) /
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(roi_time * 60))
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roi_decay = roi_price * (
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1 - ((current_time - trade.open_date_utc).seconds) / (roi_time * 60)
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)
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if roi_decay < 0:
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roi_decay = self.linear_roi_offset.value
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else:
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roi_decay += self.linear_roi_offset.value
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if current_profit > roi_decay:
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return 'roi_custom_win'
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return "roi_custom_win"
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if current_profit < -roi_decay:
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return 'roi_custom_loss'
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return "roi_custom_loss"
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def confirm_trade_exit(
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self,
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@@ -287,22 +297,22 @@ class FreqaiExampleStrategy(IStrategy):
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time_in_force: str,
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exit_reason: str,
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current_time,
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**kwargs
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**kwargs,
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) -> bool:
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entry_tag = trade.enter_tag
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follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
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if not follow_mode:
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pair_dict = self.model.bridge.data_drawer.pair_dict
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pair_dict = self.model.bridge.dd.pair_dict
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else:
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pair_dict = self.model.bridge.data_drawer.follower_dict
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pair_dict = self.model.bridge.dd.follower_dict
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with self.model.bridge.lock:
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pair_dict[pair]["prediction" + entry_tag] = 0
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if not follow_mode:
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self.model.bridge.data_drawer.save_drawer_to_disk()
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self.model.bridge.dd.save_drawer_to_disk()
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else:
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self.model.bridge.data_drawer.save_follower_dict_to_disk()
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self.model.bridge.dd.save_follower_dict_to_disk()
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return True
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@@ -316,7 +326,7 @@ class FreqaiExampleStrategy(IStrategy):
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current_time,
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entry_tag,
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side: str,
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**kwargs
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**kwargs,
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) -> bool:
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df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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