provide user directions, clean up strategy, remove unnecessary code.
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robcaulk
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@ -75,7 +75,6 @@
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"weight_factor": 0.9,
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"principal_component_analysis": false,
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"use_SVM_to_remove_outliers": true,
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"stratify_training_data": 0,
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"indicator_max_period_candles": 20,
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"indicator_periods_candles": [10, 20]
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},
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@ -1,64 +1,72 @@
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import logging
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from datetime import datetime, timedelta
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from functools import reduce
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from typing import Optional
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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 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,
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merge_informative_pair)
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from numpy.lib import math
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from pandas import DataFrame
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from technical import qtpylib
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logger = logging.getLogger(__name__)
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class FreqaiExampleHybridStrategy(IStrategy):
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"""
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Example classifier hybrid 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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Example of a hybrid FreqAI strat, designed to illustrate how a user may employ
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FreqAI to bolster a typical Freqtrade strategy.
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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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Launching this strategy would be:
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The underlying original supertrend strat is authored by @juankysoriano (Juan Carlos Soriano)
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* github: https://github.com/juankysoriano/
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freqtrade trade --strategy FreqaiExampleHyridStrategy --strategy-path freqtrade/templates
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--freqaimodel CatboostClassifier --config config_examples/config_freqai.example.json
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or the user simply adds this to their config:
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"freqai": {
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"enabled": true,
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"purge_old_models": true,
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"train_period_days": 15,
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"identifier": "uniqe-id",
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"feature_parameters": {
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"include_timeframes": [
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"3m",
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"15m",
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"1h"
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],
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"include_corr_pairlist": [
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"BTC/USDT",
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"ETH/USDT"
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],
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"label_period_candles": 20,
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"include_shifted_candles": 2,
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"DI_threshold": 0.9,
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"weight_factor": 0.9,
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"principal_component_analysis": false,
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"use_SVM_to_remove_outliers": true,
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"indicator_max_period_candles": 20,
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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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"random_state": 1
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},
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"model_training_parameters": {
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"n_estimators": 800
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}
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},
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This strategy is not designed to be used live
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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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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 = -0.1
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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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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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buy_params = {
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"buy_m1": 4,
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"buy_m2": 7,
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@ -92,6 +100,7 @@ class FreqaiExampleHybridStrategy(IStrategy):
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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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# FreqAI required function, leave as is or add you additional informatives to existing structure.
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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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@ -105,16 +114,15 @@ class FreqaiExampleHybridStrategy(IStrategy):
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informative_pairs.append((pair, tf))
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return informative_pairs
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# FreqAI required function, user can add or remove indicators, but general structure
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# must stay the same.
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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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User feeds these indicators to FreqAI to train a classifier to decide
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if the market will go up or down.
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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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@ -135,34 +143,14 @@ 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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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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# FreqAI needs the following lines in order to detect features and automatically
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# expand upon them.
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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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@ -178,55 +166,21 @@ class FreqaiExampleHybridStrategy(IStrategy):
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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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# User can set the "target" here (in present case it is the
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# "up" or "down")
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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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# Classifiers are typically set up with strings as targets:
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# User "looks into the future" here to figure out if the future
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# will be "up" or "down". This same column name is available to
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# the user
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df['&s-up_or_down'] = np.where(df["close"].shift(-50) >
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df["close"], 'up', 'down')
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# REGRESSOR Model: Can use single or multi traget
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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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# 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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# 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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# 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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@ -270,6 +224,9 @@ class FreqaiExampleHybridStrategy(IStrategy):
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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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(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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@ -335,6 +292,7 @@ class FreqaiExampleHybridStrategy(IStrategy):
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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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@ -354,8 +312,10 @@ class FreqaiExampleHybridStrategy(IStrategy):
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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] 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] or CLOSE[i - 1] < FINAL_LB[i - 1] else FINAL_LB[i - 1]
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FINAL_UB[i] = BASIC_UB[i] if BASIC_UB[i] < FINAL_UB[i -
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1] 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 -
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1] 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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