simplify hybrid template
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smarmau
robcaulk
parent
6189aa817c
commit
005594c29c
@ -4,6 +4,7 @@ import numpy as np
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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.strategy import IntParameter, IStrategy, merge_informative_pair
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@ -48,7 +49,7 @@ class FreqaiExampleHybridStrategy(IStrategy):
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"indicator_periods_candles": [10, 20]
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},
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"data_split_parameters": {
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"test_size": 0.33,
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"test_size": 0,
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"random_state": 1
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},
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"model_training_parameters": {
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@ -56,49 +57,44 @@ class FreqaiExampleHybridStrategy(IStrategy):
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}
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},
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Thanks to @smarm and @jooopieeert for developing and sharing the strategy.
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Thanks to @smarmau and @johanvulgt for developing and sharing the strategy.
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"""
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minimal_roi = {"0": 0.1, "30": 0.75, "60": 0.05, "120": 0.025, "240": -1}
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minimal_roi = {
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"60": 0.01,
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"30": 0.02,
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"0": 0.04
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}
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plot_config = {
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'main_plot': {
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'tema': {},
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},
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'subplots': {
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"MACD": {
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'macd': {'color': 'blue'},
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'macdsignal': {'color': 'orange'},
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},
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"RSI": {
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'rsi': {'color': 'red'},
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},
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"Up_or_down": {
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'&s-up_or_down': {'color': 'green'},
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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 = -0.1
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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 = True
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buy_params = {
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"buy_m1": 4,
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"buy_m2": 7,
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"buy_m3": 1,
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"buy_p1": 8,
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"buy_p2": 9,
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"buy_p3": 8,
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}
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# Sell hyperspace params:
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sell_params = {
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"sell_m1": 1,
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"sell_m2": 3,
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"sell_m3": 6,
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"sell_p1": 16,
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"sell_p2": 18,
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"sell_p3": 18,
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}
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buy_m1 = IntParameter(1, 7, default=1)
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buy_m2 = IntParameter(1, 7, default=3)
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buy_m3 = IntParameter(1, 7, default=4)
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buy_p1 = IntParameter(7, 21, default=14)
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buy_p2 = IntParameter(7, 21, default=10)
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buy_p3 = IntParameter(7, 21, default=10)
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sell_m1 = IntParameter(1, 7, default=1)
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sell_m2 = IntParameter(1, 7, default=3)
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sell_m3 = IntParameter(1, 7, default=4)
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sell_p1 = IntParameter(7, 21, default=14)
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sell_p2 = IntParameter(7, 21, default=10)
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sell_p3 = IntParameter(7, 21, default=10)
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# Hyperoptable parameters
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buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
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sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)
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short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
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exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
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# FreqAI required function, leave as is or add additional informatives to existing structure.
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def informative_pairs(self):
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@ -143,7 +139,6 @@ class FreqaiExampleHybridStrategy(IStrategy):
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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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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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@ -183,150 +178,85 @@ class FreqaiExampleHybridStrategy(IStrategy):
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# User creates their own custom strat here. Present example is a supertrend
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# based strategy.
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for multiplier in self.buy_m1.range:
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for period in self.buy_p1.range:
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dataframe[f"supertrend_1_buy_{multiplier}_{period}"] = self.supertrend(
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dataframe, multiplier, period
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)["STX"]
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for multiplier in self.buy_m2.range:
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for period in self.buy_p2.range:
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dataframe[f"supertrend_2_buy_{multiplier}_{period}"] = self.supertrend(
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dataframe, multiplier, period
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)["STX"]
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for multiplier in self.buy_m3.range:
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for period in self.buy_p3.range:
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dataframe[f"supertrend_3_buy_{multiplier}_{period}"] = self.supertrend(
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dataframe, multiplier, period
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)["STX"]
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for multiplier in self.sell_m1.range:
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for period in self.sell_p1.range:
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dataframe[f"supertrend_1_sell_{multiplier}_{period}"] = self.supertrend(
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dataframe, multiplier, period
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)["STX"]
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for multiplier in self.sell_m2.range:
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for period in self.sell_p2.range:
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dataframe[f"supertrend_2_sell_{multiplier}_{period}"] = self.supertrend(
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dataframe, multiplier, period
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)["STX"]
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for multiplier in self.sell_m3.range:
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for period in self.sell_p3.range:
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dataframe[f"supertrend_3_sell_{multiplier}_{period}"] = self.supertrend(
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dataframe, multiplier, period
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)["STX"]
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dataframe = self.freqai.start(dataframe, metadata, self)
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# TA indicators to combine with the Freqai targets
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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# Bollinger Bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_upperband'] = bollinger['upper']
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dataframe["bb_percent"] = (
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(dataframe["close"] - dataframe["bb_lowerband"]) /
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
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)
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dataframe["bb_width"] = (
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
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)
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# TEMA - Triple Exponential Moving Average
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
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return dataframe
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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# User now can use their custom strat creation in addition to their
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# future prediction "up" or "down".
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df.loc[
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(
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# Signal: RSI crosses above 30
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(qtpylib.crossed_above(df['rsi'], self.buy_rsi.value)) &
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(df['tema'] <= df['bb_middleband']) & # Guard: tema below BB middle
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(df['tema'] > df['tema'].shift(1)) & # Guard: tema is raising
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(df['volume'] > 0) & # Make sure Volume is not 0
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(df['do_predict'] == 1) & # Make sure Freqai is confident in the prediction
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# Only enter trade if Freqai thinks the trend is in this direction
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(df['&s-up_or_down'] == 'up')
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),
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'enter_long'] = 1
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df.loc[
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(df[f"supertrend_1_buy_{self.buy_m1.value}_{self.buy_p1.value}"] == "up") &
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(df[f"supertrend_2_buy_{self.buy_m2.value}_{self.buy_p2.value}"] == "up") &
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(df[f"supertrend_3_buy_{self.buy_m3.value}_{self.buy_p3.value}"] == "up") &
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(df["do_predict"] == 1) &
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(df['&s-up_or_down'] == 'up'),
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"enter_long",
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] = 1
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df.loc[
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(df[f"supertrend_1_sell_{self.sell_m1.value}_{self.sell_p1.value}"] == "down") &
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(df[f"supertrend_2_sell_{self.sell_m2.value}_{self.sell_p2.value}"] == "down") &
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(df[f"supertrend_3_sell_{self.sell_m3.value}_{self.sell_p3.value}"] == "down") &
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(df["do_predict"] == 1) &
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(df['&s-up_or_down'] == 'down'),
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"enter_short",
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] = 1
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(
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# Signal: RSI crosses above 70
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(qtpylib.crossed_above(df['rsi'], self.short_rsi.value)) &
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(df['tema'] > df['bb_middleband']) & # Guard: tema above BB middle
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(df['tema'] < df['tema'].shift(1)) & # Guard: tema is falling
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(df['volume'] > 0) & # Make sure Volume is not 0
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(df['do_predict'] == 1) & # Make sure Freqai is confident in the prediction
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# Only enter trade if Freqai thinks the trend is in this direction
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(df['&s-up_or_down'] == 'down')
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),
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'enter_short'] = 1
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return df
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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df.loc[
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(df[f"supertrend_2_sell_{self.sell_m2.value}_{self.sell_p2.value}"] == "down"),
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"exit_long",
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] = 1
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(
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# Signal: RSI crosses above 70
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(qtpylib.crossed_above(df['rsi'], self.sell_rsi.value)) &
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(df['tema'] > df['bb_middleband']) & # Guard: tema above BB middle
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(df['tema'] < df['tema'].shift(1)) & # Guard: tema is falling
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(df['volume'] > 0) # Make sure Volume is not 0
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),
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'exit_long'] = 1
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df.loc[
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(df[f"supertrend_2_buy_{self.buy_m2.value}_{self.buy_p2.value}"] == "up"),
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"exit_short",
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] = 1
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(
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# Signal: RSI crosses above 30
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(qtpylib.crossed_above(df['rsi'], self.exit_short_rsi.value)) &
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# Guard: tema below BB middle
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(df['tema'] <= df['bb_middleband']) &
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(df['tema'] > df['tema'].shift(1)) & # Guard: tema is raising
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(df['volume'] > 0) # Make sure Volume is not 0
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),
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'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 confirm_trade_entry(self, pair: str, order_type: str, amount: float,
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rate: float, time_in_force: str, current_time, entry_tag, side: str,
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**kwargs, ) -> 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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"""
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Supertrend Indicator; adapted for freqtrade, optimized by the math genius.
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from: Perkmeister#2394
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"""
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def supertrend(self, dataframe: DataFrame, multiplier, period):
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df = dataframe.copy()
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last_row = dataframe.tail(1).index.item()
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df['TR'] = ta.TRANGE(df)
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df['ATR'] = ta.SMA(df['TR'], period)
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st = 'ST_' + str(period) + '_' + str(multiplier)
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stx = 'STX_' + str(period) + '_' + str(multiplier)
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# Compute basic upper and lower bands
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BASIC_UB = ((df['high'] + df['low']) / 2 + multiplier * df['ATR']).values
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BASIC_LB = ((df['high'] + df['low']) / 2 - multiplier * df['ATR']).values
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FINAL_UB = np.zeros(last_row + 1)
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FINAL_LB = np.zeros(last_row + 1)
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ST = np.zeros(last_row + 1)
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CLOSE = df['close'].values
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# Compute final upper and lower bands
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for i in range(period, last_row + 1):
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FINAL_UB[i] = (BASIC_UB[i] if BASIC_UB[i] < FINAL_UB[i - 1]
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or CLOSE[i - 1] > FINAL_UB[i - 1] else FINAL_UB[i - 1])
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FINAL_LB[i] = (BASIC_LB[i] if BASIC_LB[i] > FINAL_LB[i - 1]
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or CLOSE[i - 1] < FINAL_LB[i - 1] else FINAL_LB[i - 1])
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# Set the Supertrend value
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for i in range(period, last_row + 1):
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ST[i] = FINAL_UB[i] if ST[i - 1] == FINAL_UB[i - 1] and CLOSE[i] <= FINAL_UB[i] else \
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FINAL_LB[i] if ST[i - 1] == FINAL_UB[i - 1] and CLOSE[i] > FINAL_UB[i] else \
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FINAL_LB[i] if ST[i - 1] == FINAL_LB[i - 1] and CLOSE[i] >= FINAL_LB[i] else \
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FINAL_UB[i] if ST[i - 1] == FINAL_LB[i - 1] and CLOSE[i] < FINAL_LB[i] else 0.00
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df_ST = pd.DataFrame(ST, columns=[st])
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df = pd.concat([df, df_ST], axis=1)
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# Mark the trend direction up/down
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df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down', 'up'), np.NaN)
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df.fillna(0, inplace=True)
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return DataFrame(index=df.index, data={
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'ST': df[st],
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'STX': df[stx]
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})
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