freqtrade_origin/freqtrade/freqai/example_strats/ReinforcementLearningExample3ac.py

148 lines
5.5 KiB
Python

import logging
from functools import reduce
import pandas as pd
import talib.abstract as ta
from pandas import DataFrame
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
logger = logging.getLogger(__name__)
class ReinforcementLearningExample3ac(IStrategy):
"""
Test strategy - used for testing freqAI functionalities.
DO not use in production.
"""
minimal_roi = {"0": 0.1, "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 = -0.05
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)
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
):
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}pct-change"] = informative["close"].pct_change()
informative[f"%-{coin}raw_volume"] = informative["volume"]
# The following features are necessary for RL models
informative[f"%-{coin}raw_close"] = informative["close"]
informative[f"%-{coin}raw_open"] = informative["open"]
informative[f"%-{coin}raw_high"] = informative["high"]
informative[f"%-{coin}raw_low"] = informative["low"]
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
# For RL, this is not a target, it is simply a filler until actions come out
# of the model.
# for Base3ActionEnv, 2 is netural (hold)
df["&-action"] = 2
return df
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe = self.freqai.start(dataframe, metadata, self)
return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [df["do_predict"] == 1, df["&-action"] == 1]
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["&-action"] == 2]
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["&-action"] == 2]
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["&-action"] == 1]
if exit_short_conditions:
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
return df