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provide user directions, clean up strategy, remove unnecessary code.

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This commit is contained in:
robcaulk 2022-08-20 17:02:18 +02:00
parent b44bd0171c
commit 90c03178b1
2 changed files with 82 additions and 123 deletions
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1
config_examples/config_freqai.example.json
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@ -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]
},

184
freqtrade/templates/FreqaiHybridExampleStrategy.py
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@ -1,64 +1,72 @@
import logging
from datetime import datetime, timedelta
from functools import reduce
from typing import Optional
import numpy as np
import pandas as pd
import talib.abstract as ta
from freqtrade.exchange import timeframe_to_prev_date
from freqtrade.persistence import Trade
from freqtrade.strategy import (DecimalParameter, IntParameter, IStrategy,
merge_informative_pair)
from numpy.lib import math
from pandas import DataFrame
from technical import qtpylib
logger = logging.getLogger(__name__)
class FreqaiExampleHybridStrategy(IStrategy):
"""
Example classifier hybrid strategy showing how the user connects their own
IFreqaiModel to the strategy. Namely, the user uses:
self.freqai.start(dataframe, metadata)
Example of a hybrid FreqAI strat, designed to illustrate how a user may employ
FreqAI to bolster a typical Freqtrade strategy.
to make predictions on their data. populate_any_indicators() automatically
generates the variety of features indicated by the user in the
canonical freqtrade configuration file under config['freqai'].
Launching this strategy would be:
The underlying original supertrend strat is authored by @juankysoriano (Juan Carlos Soriano)
* github: https://github.com/juankysoriano/
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": 0.33,
"random_state": 1
},
"model_training_parameters": {
"n_estimators": 800
}
},
This strategy is not designed to be used live
"""
minimal_roi = {"0": 0.1, "30": 0.75, "60": 0.05, "120": 0.025, "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 = -0.1
use_exit_signal = True
startup_candle_count: int = 300
can_short = True
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)
buy_params = {
"buy_m1": 4,
"buy_m2": 7,
@ -92,6 +100,7 @@ class FreqaiExampleHybridStrategy(IStrategy):
sell_p2 = IntParameter(7, 21, default=10)
sell_p3 = IntParameter(7, 21, default=10)
# FreqAI required function, leave as is or add you 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"]
@ -105,16 +114,15 @@ class FreqaiExampleHybridStrategy(IStrategy):
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
):
"""
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + coin `
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
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
@ -135,34 +143,14 @@ class FreqaiExampleHybridStrategy(IStrategy):
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}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(informative), window=t, stds=2.2
)
informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
informative[f"%-{coin}bb_width-period_{t}"] = (
informative[f"{coin}bb_upperband-period_{t}"]
- informative[f"{coin}bb_lowerband-period_{t}"]
) / informative[f"{coin}bb_middleband-period_{t}"]
informative[f"%-{coin}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{coin}bb_lowerband-period_{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()
)
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
informative[f"%-{coin}raw_volume"] = informative["volume"]
informative[f"%-{coin}raw_price"] = informative["close"]
# 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):
@ -178,55 +166,21 @@ class FreqaiExampleHybridStrategy(IStrategy):
]
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
# User can set the "target" here (in present case it is the
# "up" or "down")
if set_generalized_indicators:
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
# Classifiers are typically set up with strings as targets:
df['&s-up_or_down'] = np.where( df["close"].shift(-50) >
df["close"], 'up', 'down')
# REGRESSOR Model: Can use single or multi traget
# user adds targets here by prepending them with &- (see convention below)
#df["&-s_close"] = (
# df["close"]
# .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
# .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
# .mean()
# / df["close"]
# - 1
#)
# If user wishes to use multiple targets, they can add more by
# appending more columns with '&'. User should keep in mind that multi targets
# requires a multioutput prediction model such as
# templates/CatboostPredictionMultiModel.py,
# df["&-s_range"] = (
# df["close"]
# .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
# .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
# .max()
# -
# df["close"]
# .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
# .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
# .min()
# )
# 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
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# All indicators must be populated by populate_any_indicators() for live functionality
# to work correctly.
# the model will return all labels created by user in `populate_any_indicators`
# (& appended targets), an indication of whether or not the prediction should be accepted,
# the target mean/std values for each of the labels created by user in
# `populate_any_indicators()` for each training period.
# User creates their own custom strat here. Present example is a supertrend
# based strategy.
for multiplier in self.buy_m1.range:
for period in self.buy_p1.range:
@ -270,6 +224,9 @@ class FreqaiExampleHybridStrategy(IStrategy):
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
# User now can use their custom strat creation in addition to their
# future prediction "up" or "down".
df.loc[
(df[f"supertrend_1_buy_{self.buy_m1.value}_{self.buy_p1.value}"] == "up") &
(df[f"supertrend_2_buy_{self.buy_m2.value}_{self.buy_p2.value}"] == "up") &
@ -308,8 +265,8 @@ class FreqaiExampleHybridStrategy(IStrategy):
return int(self.config["timeframe"][:-1])
def confirm_trade_entry(self, pair: str, order_type: str, amount: float,
rate: float, time_in_force: str, current_time, entry_tag, side: str,
**kwargs, ) -> bool:
rate: float, time_in_force: str, current_time, entry_tag, side: str,
**kwargs, ) -> bool:
df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = df.iloc[-1].squeeze()
@ -335,6 +292,7 @@ class FreqaiExampleHybridStrategy(IStrategy):
"""
def supertrend(self, dataframe: DataFrame, multiplier, period):
df = dataframe.copy()
last_row = dataframe.tail(1).index.item()
@ -354,17 +312,19 @@ class FreqaiExampleHybridStrategy(IStrategy):
# Compute final upper and lower bands
for i in range(period, last_row + 1):
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]
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]
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]
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]
# Set the Supertrend value
for i in range(period, last_row + 1):
ST[i] = FINAL_UB[i] if ST[i - 1] == FINAL_UB[i - 1] and CLOSE[i] <= FINAL_UB[i] else \
FINAL_LB[i] if ST[i - 1] == FINAL_UB[i - 1] and CLOSE[i] > FINAL_UB[i] else \
FINAL_LB[i] if ST[i - 1] == FINAL_UB[i - 1] and CLOSE[i] > FINAL_UB[i] else \
FINAL_LB[i] if ST[i - 1] == FINAL_LB[i - 1] and CLOSE[i] >= FINAL_LB[i] else \
FINAL_UB[i] if ST[i - 1] == FINAL_LB[i - 1] and CLOSE[i] < FINAL_LB[i] else 0.00
FINAL_UB[i] if ST[i - 1] == FINAL_LB[i - 1] and CLOSE[i] < FINAL_LB[i] else 0.00
df_ST = pd.DataFrame(ST, columns=[st])
df = pd.concat([df, df_ST],axis=1)
df = pd.concat([df, df_ST], axis=1)
# Mark the trend direction up/down
df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down', 'up'), np.NaN)
@ -372,6 +332,6 @@ class FreqaiExampleHybridStrategy(IStrategy):
df.fillna(0, inplace=True)
return DataFrame(index=df.index, data={
'ST' : df[st],
'STX' : df[stx]
'ST': df[st],
'STX': df[stx]
})