freqtrade_origin/freqtrade/templates/FreqaiExampleHybridStrategy.py

243 lines
9.1 KiB
Python

import logging
import numpy as np
import pandas as pd
import talib.abstract as ta
from pandas import DataFrame
from technical import qtpylib
from freqtrade.strategy import IntParameter, IStrategy, merge_informative_pair
logger = logging.getLogger(__name__)
class FreqaiExampleHybridStrategy(IStrategy):
"""
Example of a hybrid FreqAI strat, designed to illustrate how a user may employ
FreqAI to bolster a typical Freqtrade strategy.
Launching this strategy would be:
freqtrade trade --strategy FreqaiExampleHyridStrategy --strategy-path freqtrade/templates
--freqaimodel CatboostClassifier --config config_examples/config_freqai.example.json
or the user simply adds this to their config:
"freqai": {
"enabled": true,
"purge_old_models": true,
"train_period_days": 15,
"identifier": "uniqe-id",
"feature_parameters": {
"include_timeframes": [
"3m",
"15m",
"1h"
],
"include_corr_pairlist": [
"BTC/USDT",
"ETH/USDT"
],
"label_period_candles": 20,
"include_shifted_candles": 2,
"DI_threshold": 0.9,
"weight_factor": 0.9,
"principal_component_analysis": false,
"use_SVM_to_remove_outliers": true,
"indicator_periods_candles": [10, 20]
},
"data_split_parameters": {
"test_size": 0,
"random_state": 1
},
"model_training_parameters": {
"n_estimators": 800
}
},
Thanks to @smarmau and @johanvulgt for developing and sharing the strategy.
"""
minimal_roi = {
"60": 0.01,
"30": 0.02,
"0": 0.04
}
plot_config = {
'main_plot': {
'tema': {},
},
'subplots': {
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"RSI": {
'rsi': {'color': 'red'},
},
"Up_or_down": {
'&s-up_or_down': {'color': 'green'},
}
}
}
process_only_new_candles = True
stoploss = -0.05
use_exit_signal = True
startup_candle_count: int = 300
can_short = True
# Hyperoptable parameters
buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)
short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
# FreqAI required function, user can add or remove indicators, but general structure
# must stay the same.
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
"""
User feeds these indicators to FreqAI to train a classifier to decide
if the market will go up or down.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
"""
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"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
informative[f"%-{pair}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
)
# FreqAI needs the following lines in order to detect features and automatically
# expand upon them.
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)
# User can set the "target" here (in present case it is the
# "up" or "down")
if set_generalized_indicators:
# User "looks into the future" here to figure out if the future
# will be "up" or "down". This same column name is available to
# the user
df['&s-up_or_down'] = np.where(df["close"].shift(-50) >
df["close"], 'up', 'down')
return df
# flake8: noqa: C901
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# User creates their own custom strat here. Present example is a supertrend
# based strategy.
dataframe = self.freqai.start(dataframe, metadata, self)
# TA indicators to combine with the Freqai targets
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Bollinger Bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe["bb_percent"] = (
(dataframe["close"] - dataframe["bb_lowerband"]) /
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
)
dataframe["bb_width"] = (
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
)
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
df.loc[
(
# Signal: RSI crosses above 30
(qtpylib.crossed_above(df['rsi'], self.buy_rsi.value)) &
(df['tema'] <= df['bb_middleband']) & # Guard: tema below BB middle
(df['tema'] > df['tema'].shift(1)) & # Guard: tema is raising
(df['volume'] > 0) & # Make sure Volume is not 0
(df['do_predict'] == 1) & # Make sure Freqai is confident in the prediction
# Only enter trade if Freqai thinks the trend is in this direction
(df['&s-up_or_down'] == 'up')
),
'enter_long'] = 1
df.loc[
(
# Signal: RSI crosses above 70
(qtpylib.crossed_above(df['rsi'], self.short_rsi.value)) &
(df['tema'] > df['bb_middleband']) & # Guard: tema above BB middle
(df['tema'] < df['tema'].shift(1)) & # Guard: tema is falling
(df['volume'] > 0) & # Make sure Volume is not 0
(df['do_predict'] == 1) & # Make sure Freqai is confident in the prediction
# Only enter trade if Freqai thinks the trend is in this direction
(df['&s-up_or_down'] == 'down')
),
'enter_short'] = 1
return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
df.loc[
(
# Signal: RSI crosses above 70
(qtpylib.crossed_above(df['rsi'], self.sell_rsi.value)) &
(df['tema'] > df['bb_middleband']) & # Guard: tema above BB middle
(df['tema'] < df['tema'].shift(1)) & # Guard: tema is falling
(df['volume'] > 0) # Make sure Volume is not 0
),
'exit_long'] = 1
df.loc[
(
# Signal: RSI crosses above 30
(qtpylib.crossed_above(df['rsi'], self.exit_short_rsi.value)) &
# Guard: tema below BB middle
(df['tema'] <= df['bb_middleband']) &
(df['tema'] > df['tema'].shift(1)) & # Guard: tema is raising
(df['volume'] > 0) # Make sure Volume is not 0
),
'exit_short'] = 1
return df