334 lines
13 KiB
Python
334 lines
13 KiB
Python
import logging
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from functools import reduce
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import pandas as pd
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import talib.abstract as ta
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from pandas import DataFrame
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from technical import qtpylib
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from freqtrade.exchange import timeframe_to_prev_date
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from freqtrade.persistence import Trade
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from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
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logger = logging.getLogger(__name__)
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class FreqaiExampleStrategy(IStrategy):
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"""
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Example strategy showing how the user connects their own
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IFreqaiModel to the strategy. Namely, the user uses:
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self.freqai.start(dataframe, metadata)
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to make predictions on their data. populate_any_indicators() automatically
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generates the variety of features indicated by the user in the
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canonical freqtrade configuration file under config['freqai'].
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"""
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minimal_roi = {"0": 0.1, "240": -1}
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plot_config = {
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"main_plot": {},
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"subplots": {
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"prediction": {"prediction": {"color": "blue"}},
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"target_roi": {
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"target_roi": {"color": "brown"},
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},
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"do_predict": {
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"do_predict": {"color": "brown"},
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},
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},
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}
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process_only_new_candles = True
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stoploss = -0.05
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use_exit_signal = True
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startup_candle_count: int = 300
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can_short = False
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linear_roi_offset = DecimalParameter(
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0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
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)
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max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
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def informative_pairs(self):
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whitelist_pairs = self.dp.current_whitelist()
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corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
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informative_pairs = []
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for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
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for pair in whitelist_pairs:
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informative_pairs.append((pair, tf))
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for pair in corr_pairs:
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if pair in whitelist_pairs:
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continue # avoid duplication
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informative_pairs.append((pair, tf))
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return informative_pairs
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def populate_any_indicators(
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self, pair, df, tf, informative=None, 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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passed to the training/prediction by prepending indicators with `'%-' + coin `
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(see convention below). I.e. user should not prepend any supporting metrics
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(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
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model.
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:param pair: pair to be used as informative
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:param df: strategy dataframe which will receive merges from informatives
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:param tf: timeframe of the dataframe which will modify the feature names
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:param informative: the dataframe associated with the informative pair
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"""
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coin = pair.split('/')[0]
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with self.freqai.lock:
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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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# first loop is automatically duplicating indicators for time periods
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for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
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t = int(t)
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informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
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informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
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informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
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informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
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informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
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informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
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bollinger = qtpylib.bollinger_bands(
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qtpylib.typical_price(informative), window=t, stds=2.2
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)
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informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
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informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
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informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
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informative[f"%-{coin}bb_width-period_{t}"] = (
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informative[f"{coin}bb_upperband-period_{t}"]
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- informative[f"{coin}bb_lowerband-period_{t}"]
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) / informative[f"{coin}bb_middleband-period_{t}"]
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informative[f"%-{coin}close-bb_lower-period_{t}"] = (
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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(informative, timeperiod=t)
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informative[f"%-{coin}relative_volume-period_{t}"] = (
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informative["volume"] / informative["volume"].rolling(t).mean()
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)
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informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
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informative[f"%-{coin}raw_volume"] = informative["volume"]
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informative[f"%-{coin}raw_price"] = informative["close"]
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indicators = [col for col in informative if col.startswith("%")]
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# This loop duplicates and shifts all indicators to add a sense of recency to data
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for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
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if n == 0:
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continue
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informative_shift = informative[indicators].shift(n)
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informative_shift = informative_shift.add_suffix("_shift-" + str(n))
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informative = pd.concat((informative, informative_shift), axis=1)
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df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
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skip_columns = [
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(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
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]
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df = df.drop(columns=skip_columns)
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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 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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df["&-s_close"] = (
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df["close"]
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.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
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.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
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.mean()
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/ df["close"]
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- 1
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)
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# Classifiers are typically set up with strings as targets:
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# df['&s-up_or_down'] = np.where( df["close"].shift(-100) >
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# df["close"], 'up', 'down')
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# If user wishes to use multiple targets, they can add more by
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# appending more columns with '&'. User should keep in mind that multi targets
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# requires a multioutput prediction model such as
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# templates/CatboostPredictionMultiModel.py,
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# df["&-s_range"] = (
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# df["close"]
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# .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
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# .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
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# .max()
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# -
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# df["close"]
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# .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
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# .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
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# .min()
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# )
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return df
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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self.freqai_info = self.config["freqai"]
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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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# the model will return all labels created by user in `populate_any_indicators`
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# (& appended targets), an indication of whether or not the prediction should be accepted,
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# the target mean/std values for each of the labels created by user in
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# `populate_any_indicators()` for each training period.
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dataframe = self.freqai.start(dataframe, metadata, self)
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dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
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dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
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return dataframe
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
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if enter_long_conditions:
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df.loc[
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reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
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] = (1, "long")
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enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"]]
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if enter_short_conditions:
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df.loc[
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reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
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] = (1, "short")
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return df
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25]
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if exit_long_conditions:
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df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
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exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"] * 0.25]
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if exit_short_conditions:
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df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
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return df
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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(
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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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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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if not follow_mode:
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pair_dict = self.freqai.dd.pair_dict
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else:
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pair_dict = self.freqai.dd.follower_dict
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entry_tag = trade.enter_tag
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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.freqai.lock:
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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.freqai.dd.save_drawer_to_disk()
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else:
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self.freqai.dd.save_follower_dict_to_disk()
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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 * (
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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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if current_profit < -roi_decay:
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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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pair: str,
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trade: Trade,
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order_type: str,
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amount: float,
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rate: float,
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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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) -> 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.freqai.dd.pair_dict
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else:
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pair_dict = self.freqai.dd.follower_dict
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with self.freqai.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.freqai.dd.save_drawer_to_disk()
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else:
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self.freqai.dd.save_follower_dict_to_disk()
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return True
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def confirm_trade_entry(
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self,
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pair: str,
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order_type: str,
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amount: float,
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rate: float,
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time_in_force: str,
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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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) -> bool:
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df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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last_candle = df.iloc[-1].squeeze()
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if side == "long":
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if rate > (last_candle["close"] * (1 + 0.0025)):
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return False
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else:
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if rate < (last_candle["close"] * (1 - 0.0025)):
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return False
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return True
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