mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-10 02:12:01 +00:00
Merge pull request #7260 from JohanVlugt/develop
Example FreqAI hybrid strategy
This commit is contained in:
Matthias
GitHub
commit
8be8a12cc4
|
|
@ -75,7 +75,6 @@
|
|||
"weight_factor": 0.9,
|
||||
"principal_component_analysis": false,
|
||||
"use_SVM_to_remove_outliers": true,
|
||||
"stratify_training_data": 0,
|
||||
"indicator_max_period_candles": 20,
|
||||
"indicator_periods_candles": [10, 20]
|
||||
},
|
||||
|
|
|
|||
259
freqtrade/templates/FreqaiHybridExampleStrategy.py
Normal file
259
freqtrade/templates/FreqaiHybridExampleStrategy.py
Normal file
|
|
@ -0,0 +1,259 @@
|
|||
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_max_period_candles": 20,
|
||||
"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, leave as is or add additional informatives to existing structure.
|
||||
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
|
||||
|
||||
# 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
|
||||
"""
|
||||
|
||||
coin = pair.split('/')[0]
|
||||
|
||||
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}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
|
||||
informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
|
||||
informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
|
||||
informative[f"%-{coin}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
|
||||
Loading…
Reference in New Issue
Block a user