stable/freqtrade/templates/FreqaiExampleStrategy.py

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import logging
from functools import reduce
import numpy as np
import pandas as pd
import talib.abstract as ta
from pandas import DataFrame
from technical import qtpylib
from freqtrade.exchange import timeframe_to_prev_date
from freqtrade.freqai.strategy_bridge import CustomModel
from freqtrade.persistence import Trade
from freqtrade.strategy import DecimalParameter, IntParameter, merge_informative_pair
from freqtrade.strategy.interface import IStrategy
logger = logging.getLogger(__name__)
class FreqaiExampleStrategy(IStrategy):
"""
Example strategy showing how the user connects their own
IFreqaiModel to the strategy. Namely, the user uses:
self.model = CustomModel(self.config)
self.model.bridge.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'].
"""
minimal_roi = {"0": 0.01, "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 = False
stoploss = -0.05
use_sell_signal = True
startup_candle_count: int = 300
can_short = False
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)
def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist()
corr_pairs = self.config["freqai"]["corr_pairlist"]
informative_pairs = []
for tf in self.config["freqai"]["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 bot_start(self):
self.model = CustomModel(self.config)
def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin=""):
"""
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.
:params:
:pair: pair to be used as informative
:df: strategy dataframe which will receive merges from informatives
:tf: timeframe of the dataframe which will modify the feature names
:informative: the dataframe associated with the informative pair
:coin: the name of the coin which will modify the feature names.
"""
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
informative['%-' + coin + "rsi"] = ta.RSI(informative, timeperiod=14)
informative['%-' + coin + "mfi"] = ta.MFI(informative, timeperiod=25)
informative['%-' + coin + "adx"] = ta.ADX(informative, window=20)
informative[coin + "20sma"] = ta.SMA(informative, timeperiod=20)
informative[coin + "21ema"] = ta.EMA(informative, timeperiod=21)
informative['%-' + coin + "bmsb"] = np.where(
informative[coin + "20sma"].lt(informative[coin + "21ema"]), 1, 0
)
informative['%-' + coin + "close_over_20sma"] = informative["close"] / informative[
coin + "20sma"]
informative['%-' + coin + "mfi"] = ta.MFI(informative, timeperiod=25)
informative[coin + "ema21"] = ta.EMA(informative, timeperiod=21)
informative[coin + "sma20"] = ta.SMA(informative, timeperiod=20)
stoch = ta.STOCHRSI(informative, 15, 20, 2, 2)
informative['%-' + coin + "srsi-fk"] = stoch["fastk"]
informative['%-' + coin + "srsi-fd"] = stoch["fastd"]
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=14, stds=2.2)
informative[coin + "bb_lowerband"] = bollinger["lower"]
informative[coin + "bb_middleband"] = bollinger["mid"]
informative[coin + "bb_upperband"] = bollinger["upper"]
informative['%-' + coin + "bb_width"] = (
informative[coin + "bb_upperband"] - informative[coin + "bb_lowerband"]
) / informative[coin + "bb_middleband"]
informative['%-' + coin + "close-bb_lower"] = (
informative["close"] / informative[coin + "bb_lowerband"]
)
informative['%-' + coin + "roc"] = ta.ROC(informative, timeperiod=3)
informative['%-' + coin + "adx"] = ta.ADX(informative, window=14)
macd = ta.MACD(informative)
informative['%-' + coin + "macd"] = macd["macd"]
informative[coin + "pct-change"] = informative["close"].pct_change()
informative['%-' + coin + "relative_volume"] = (
informative["volume"] / informative["volume"].rolling(10).mean()
)
informative[coin + "pct-change"] = informative["close"].pct_change()
# The following code automatically adds features according to the `shift` parameter passed
# in the config. Do not remove
indicators = [col for col in informative if col.startswith('%')]
for n in range(self.freqai_info["feature_parameters"]["shift"] + 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)
# The following code safely merges into the base timeframe.
# Do not remove.
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 (not associated to any individual coin or timeframe) 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 pair == metadata['pair'] and tf == self.timeframe:
df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7
df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25
return df
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self.freqai_info = self.config["freqai"]
self.pair = metadata['pair']
# the following loops are necessary for building the features
# indicated by the user in the configuration file.
# All indicators must be populated by populate_any_indicators() for live functionality
# to work correctly.
for tf in self.freqai_info["timeframes"]:
dataframe = self.populate_any_indicators(metadata, self.pair, dataframe.copy(), tf,
coin=self.pair.split("/")[0] + "-")
for pair in self.freqai_info["corr_pairlist"]:
if metadata['pair'] in pair:
continue # do not include whitelisted pair twice if it is in corr_pairlist
dataframe = self.populate_any_indicators(
metadata, pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-"
)
# the model will return 4 values, its prediction, an indication of whether or not the
# prediction should be accepted, the target mean/std values from the labels used during
# each training period.
(
dataframe["prediction"],
dataframe["do_predict"],
dataframe["target_mean"],
dataframe["target_std"],
) = self.model.bridge.start(dataframe, metadata, self)
dataframe["target_roi"] = dataframe["target_mean"] + dataframe["target_std"]
dataframe["sell_roi"] = dataframe["target_mean"] - dataframe["target_std"]
return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [
df['do_predict'] == 1,
df['prediction'] > df["target_roi"]
]
if enter_long_conditions:
df.loc[reduce(lambda x, y: x & y,
enter_long_conditions), ["enter_long", "enter_tag"]] = (1, 'long')
enter_short_conditions = [
df['do_predict'] == 1,
df['prediction'] < df["sell_roi"]
]
if enter_short_conditions:
df.loc[reduce(lambda x, y: x & y,
enter_short_conditions), ["enter_short", "enter_tag"]] = (1, 'short')
return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
exit_long_conditions = [
df['do_predict'] == 1,
df['prediction'] < df['sell_roi'] * 0.25
]
if exit_long_conditions:
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
exit_short_conditions = [
df['do_predict'] == 1,
df['prediction'] > df['target_roi'] * 0.25
]
if exit_short_conditions:
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
return df
def get_ticker_indicator(self):
return int(self.config["timeframe"][:-1])
def custom_exit(self, pair: str, trade: Trade, current_time, current_rate,
current_profit, **kwargs):
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
trade_date = timeframe_to_prev_date(self.config['timeframe'], trade.open_date_utc)
trade_candle = dataframe.loc[(dataframe['date'] == trade_date)]
if trade_candle.empty:
return None
trade_candle = trade_candle.squeeze()
pair_dict = self.model.bridge.data_drawer.pair_dict
entry_tag = trade.enter_tag
if 'prediction' + entry_tag not in pair_dict[pair]:
with self.model.bridge.lock:
self.model.bridge.data_drawer.pair_dict[pair][
'prediction' + entry_tag] = abs(trade_candle['prediction'])
self.model.bridge.data_drawer.save_drawer_to_disk()
else:
if pair_dict[pair]['prediction' + entry_tag] > 0:
2022-05-29 14:26:34 +00:00
roi_price = abs(trade_candle['prediction'])
else:
with self.model.bridge.lock:
self.model.bridge.data_drawer.pair_dict[pair][
'prediction' + entry_tag] = abs(trade_candle['prediction'])
self.model.bridge.data_drawer.save_drawer_to_disk()
roi_price = abs(trade_candle['prediction'])
roi_time = self.max_roi_time_long.value
roi_decay = roi_price * (1 - ((current_time - trade.open_date_utc).seconds) /
(roi_time * 60))
if roi_decay < 0:
roi_decay = self.linear_roi_offset.value
else:
roi_decay += self.linear_roi_offset.value
if current_profit > roi_price:
return 'roi_custom_win'
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, exit_reason: str,
current_time, **kwargs) -> bool:
entry_tag = trade.enter_tag
with self.model.bridge.lock:
self.model.bridge.data_drawer.pair_dict[pair]['prediction' + entry_tag] = 0
self.model.bridge.data_drawer.save_drawer_to_disk()
return True