Example Classifier strat
This commit is contained in:
Johan van der Vlugt
GitHub
parent
16af10a5bc
commit
b44bd0171c
377
freqtrade/templates/FreqaiHybridExampleStrategy.py
Normal file
377
freqtrade/templates/FreqaiHybridExampleStrategy.py
Normal file
@ -0,0 +1,377 @@
|
||||
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)
|
||||
|
||||
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'].
|
||||
|
||||
The underlying original supertrend strat is authored by @juankysoriano (Juan Carlos Soriano)
|
||||
* github: https://github.com/juankysoriano/
|
||||
|
||||
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,
|
||||
"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,
|
||||
}
|
||||
|
||||
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)
|
||||
|
||||
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
|
||||
|
||||
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.
|
||||
: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)
|
||||
|
||||
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"]
|
||||
|
||||
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)
|
||||
|
||||
# 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
|
||||
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()
|
||||
# )
|
||||
|
||||
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.
|
||||
|
||||
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:
|
||||
|
||||
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'),
|
||||
"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) &
|
||||
(df['&s-up_or_down'] == 'down'),
|
||||
"enter_short",
|
||||
] = 1
|
||||
|
||||
return df
|
||||
|
||||
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
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:
|
||||
|
||||
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
|
||||
|
||||
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):
|
||||
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]
|
||||
|
||||
# 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_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
|
||||
df_ST = pd.DataFrame(ST, columns=[st])
|
||||
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)
|
||||
|
||||
df.fillna(0, inplace=True)
|
||||
|
||||
return DataFrame(index=df.index, data={
|
||||
'ST' : df[st],
|
||||
'STX' : df[stx]
|
||||
})
|
||||
Loading…
Reference in New Issue
Block a user