From 1c81ec601683205460ccf1cd86b2522249e0d255 Mon Sep 17 00:00:00 2001 From: MukavaValkku Date: Mon, 15 Aug 2022 14:20:57 +0300 Subject: [PATCH] 3ac and 5ac example strategies --- ....py => ReinforcementLearningExample3ac.py} | 2 +- .../ReinforcementLearningExample5ac.py | 147 ++++++++++++++++++ 2 files changed, 148 insertions(+), 1 deletion(-) rename freqtrade/freqai/example_strats/{ReinforcementLearningExample.py => ReinforcementLearningExample3ac.py} (99%) create mode 100644 freqtrade/freqai/example_strats/ReinforcementLearningExample5ac.py diff --git a/freqtrade/freqai/example_strats/ReinforcementLearningExample.py b/freqtrade/freqai/example_strats/ReinforcementLearningExample3ac.py similarity index 99% rename from freqtrade/freqai/example_strats/ReinforcementLearningExample.py rename to freqtrade/freqai/example_strats/ReinforcementLearningExample3ac.py index 1bafdbb80..8473fc6a9 100644 --- a/freqtrade/freqai/example_strats/ReinforcementLearningExample.py +++ b/freqtrade/freqai/example_strats/ReinforcementLearningExample3ac.py @@ -11,7 +11,7 @@ from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_ logger = logging.getLogger(__name__) -class ReinforcementLearningExample(IStrategy): +class RLExample3ac(IStrategy): """ Test strategy - used for testing freqAI functionalities. DO not use in production. diff --git a/freqtrade/freqai/example_strats/ReinforcementLearningExample5ac.py b/freqtrade/freqai/example_strats/ReinforcementLearningExample5ac.py new file mode 100644 index 000000000..1da9a8ab1 --- /dev/null +++ b/freqtrade/freqai/example_strats/ReinforcementLearningExample5ac.py @@ -0,0 +1,147 @@ +import logging +from functools import reduce + +import pandas as pd +import talib.abstract as ta +from pandas import DataFrame + +from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair + + +logger = logging.getLogger(__name__) + + +class RLExample5ac(IStrategy): + """ + Test strategy - used for testing freqAI functionalities. + DO not use in production. + """ + + minimal_roi = {"0": 0.1, "240": -1} + + plot_config = { + "main_plot": {}, + "subplots": { + "prediction": {"prediction": {"color": "blue"}}, + "target_roi": { + "target_roi": {"color": "brown"}, + }, + "do_predict": { + "do_predict": {"color": "brown"}, + }, + }, + } + + process_only_new_candles = True + stoploss = -0.05 + use_exit_signal = True + startup_candle_count: int = 300 + can_short = False + + linear_roi_offset = DecimalParameter( + 0.00, 0.02, default=0.005, space="sell", optimize=False, load=True + ) + max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True) + + def informative_pairs(self): + whitelist_pairs = self.dp.current_whitelist() + corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"] + informative_pairs = [] + for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]: + for pair in whitelist_pairs: + informative_pairs.append((pair, tf)) + for pair in corr_pairs: + if pair in whitelist_pairs: + continue # avoid duplication + informative_pairs.append((pair, tf)) + return informative_pairs + + def populate_any_indicators( + self, pair, df, tf, informative=None, set_generalized_indicators=False + ): + + coin = pair.split('/')[0] + + with self.freqai.lock: + if informative is None: + informative = self.dp.get_pair_dataframe(pair, tf) + + # first loop is automatically duplicating indicators for time periods + for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: + + t = int(t) + informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) + informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) + informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) + + informative[f"%-{coin}pct-change"] = informative["close"].pct_change() + informative[f"%-{coin}raw_volume"] = informative["volume"] + + # Raw price currently necessary for RL models: + informative[f"%-{coin}raw_price"] = informative["close"] + + indicators = [col for col in informative if col.startswith("%")] + # This loop duplicates and shifts all indicators to add a sense of recency to data + for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): + if n == 0: + continue + informative_shift = informative[indicators].shift(n) + informative_shift = informative_shift.add_suffix("_shift-" + str(n)) + informative = pd.concat((informative, informative_shift), axis=1) + + df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) + skip_columns = [ + (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] + ] + df = df.drop(columns=skip_columns) + + # Add generalized indicators here (because in live, it will call this + # function to populate indicators during training). Notice how we ensure not to + # add them multiple times + if set_generalized_indicators: + df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 + df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 + + # user adds targets here by prepending them with &- (see convention below) + # If user wishes to use multiple targets, a multioutput prediction model + # needs to be used such as templates/CatboostPredictionMultiModel.py + df["&-action"] = 2 + + return df + + def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + + self.freqai_info = self.config["freqai"] + + dataframe = self.freqai.start(dataframe, metadata, self) + + return dataframe + + def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: + + enter_long_conditions = [df["do_predict"] == 1, df["&-action"] == 1] + + if enter_long_conditions: + df.loc[ + reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"] + ] = (1, "long") + + enter_short_conditions = [df["do_predict"] == 1, df["&-action"] == 3] + + if enter_short_conditions: + df.loc[ + reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"] + ] = (1, "short") + + return df + + def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: + exit_long_conditions = [df["do_predict"] == 1, df["&-action"] == 2] + if exit_long_conditions: + df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1 + + exit_short_conditions = [df["do_predict"] == 1, df["&-action"] == 4] + if exit_short_conditions: + df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1 + + return df