stable/freqtrade/templates/FreqaiHybridExampleStrategy.py

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import logging
from typing import Optional
import numpy as np
import pandas as pd
import talib.abstract as ta
from freqtrade.strategy import (DecimalParameter, IntParameter, IStrategy,
merge_informative_pair)
from pandas import DataFrame
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": 0.33,
"random_state": 1
},
"model_training_parameters": {
"n_estimators": 800
}
},
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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}
process_only_new_candles = True
stoploss = -0.1
use_exit_signal = True
startup_candle_count: int = 300
can_short = True
buy_params = {
"buy_m1": 4,
"buy_m2": 7,
"buy_m3": 1,
"buy_p1": 8,
"buy_p2": 9,
"buy_p3": 8,
}
# Sell hyperspace params:
sell_params = {
"sell_m1": 1,
"sell_m2": 3,
"sell_m3": 6,
"sell_p1": 16,
"sell_p2": 18,
"sell_p3": 18,
}
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buy_m1 = IntParameter(1, 7, default=1)
buy_m2 = IntParameter(1, 7, default=3)
buy_m3 = IntParameter(1, 7, default=4)
buy_p1 = IntParameter(7, 21, default=14)
buy_p2 = IntParameter(7, 21, default=10)
buy_p3 = IntParameter(7, 21, default=10)
sell_m1 = IntParameter(1, 7, default=1)
sell_m2 = IntParameter(1, 7, default=3)
sell_m3 = IntParameter(1, 7, default=4)
sell_p1 = IntParameter(7, 21, default=14)
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.
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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.
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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.
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: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}mfi-period_{t}"] = ta.MFI(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.
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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")
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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')
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return df
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# User creates their own custom strat here. Present example is a supertrend
# based strategy.
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for multiplier in self.buy_m1.range:
for period in self.buy_p1.range:
dataframe[f"supertrend_1_buy_{multiplier}_{period}"] = self.supertrend(
dataframe, multiplier, period
)["STX"]
for multiplier in self.buy_m2.range:
for period in self.buy_p2.range:
dataframe[f"supertrend_2_buy_{multiplier}_{period}"] = self.supertrend(
dataframe, multiplier, period
)["STX"]
for multiplier in self.buy_m3.range:
for period in self.buy_p3.range:
dataframe[f"supertrend_3_buy_{multiplier}_{period}"] = self.supertrend(
dataframe, multiplier, period
)["STX"]
for multiplier in self.sell_m1.range:
for period in self.sell_p1.range:
dataframe[f"supertrend_1_sell_{multiplier}_{period}"] = self.supertrend(
dataframe, multiplier, period
)["STX"]
for multiplier in self.sell_m2.range:
for period in self.sell_p2.range:
dataframe[f"supertrend_2_sell_{multiplier}_{period}"] = self.supertrend(
dataframe, multiplier, period
)["STX"]
for multiplier in self.sell_m3.range:
for period in self.sell_p3.range:
dataframe[f"supertrend_3_sell_{multiplier}_{period}"] = self.supertrend(
dataframe, multiplier, period
)["STX"]
dataframe = self.freqai.start(dataframe, metadata, self)
return dataframe
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".
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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") &
(df[f"supertrend_3_buy_{self.buy_m3.value}_{self.buy_p3.value}"] == "up") &
(df["do_predict"] == 1) &
(df['&s-up_or_down'] == 'up'),
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"enter_long",
] = 1
df.loc[
(df[f"supertrend_1_sell_{self.sell_m1.value}_{self.sell_p1.value}"] == "down") &
(df[f"supertrend_2_sell_{self.sell_m2.value}_{self.sell_p2.value}"] == "down") &
(df[f"supertrend_3_sell_{self.sell_m3.value}_{self.sell_p3.value}"] == "down") &
(df["do_predict"] == 1) &
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(df['&s-up_or_down'] == 'down'),
"enter_short",
] = 1
return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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df.loc[
(df[f"supertrend_2_sell_{self.sell_m2.value}_{self.sell_p2.value}"] == "down"),
"exit_long",
] = 1
df.loc[
(df[f"supertrend_2_buy_{self.buy_m2.value}_{self.buy_p2.value}"] == "up"),
"exit_short",
] = 1
return df
def get_ticker_indicator(self):
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:
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df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = df.iloc[-1].squeeze()
if side == "long":
if rate > (last_candle["close"] * (1 + 0.0025)):
return False
else:
if rate < (last_candle["close"] * (1 - 0.0025)):
return False
return True
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def leverage(self, pair: str, current_time: datetime, current_rate: float,
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
**kwargs) -> float:
return 1
"""
Supertrend Indicator; adapted for freqtrade, optimized by the math genius.
from: Perkmeister#2394
"""
def supertrend(self, dataframe: DataFrame, multiplier, period):
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df = dataframe.copy()
last_row = dataframe.tail(1).index.item()
df['TR'] = ta.TRANGE(df)
df['ATR'] = ta.SMA(df['TR'], period)
st = 'ST_' + str(period) + '_' + str(multiplier)
stx = 'STX_' + str(period) + '_' + str(multiplier)
# Compute basic upper and lower bands
BASIC_UB = ((df['high'] + df['low']) / 2 + multiplier * df['ATR']).values
BASIC_LB = ((df['high'] + df['low']) / 2 - multiplier * df['ATR']).values
FINAL_UB = np.zeros(last_row + 1)
FINAL_LB = np.zeros(last_row + 1)
ST = np.zeros(last_row + 1)
CLOSE = df['close'].values
# 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]
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# 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 \
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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
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df_ST = pd.DataFrame(ST, columns=[st])
df = pd.concat([df, df_ST], axis=1)
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# Mark the trend direction up/down
df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down', 'up'), np.NaN)
df.fillna(0, inplace=True)
return DataFrame(index=df.index, data={
'ST': df[st],
'STX': df[stx]
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})