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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.strategy import CategoricalParameter, IStrategy, merge_informative_pair
logger = logging.getLogger(__name__)
class MultiTargetClassifierTestStrategy(IStrategy):
"""
Example 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'].
"""
minimal_roi = {"0": 0.1, "240": -1}
plot_config = {
"main_plot": {},
"subplots": {
"prediction": {"prediction": {"color": "blue"}},
"do_predict": {
"do_predict": {"color": "brown"},
},
},
}
process_only_new_candles = True
stoploss = -0.05
use_exit_signal = True
# this is the maximum period fed to talib (timeframe independent)
startup_candle_count: int = 40
can_short = False
std_dev_multiplier_buy = CategoricalParameter(
[0.75, 1, 1.25, 1.5, 1.75], default=1.25, space="buy", optimize=True)
std_dev_multiplier_sell = CategoricalParameter(
[0.75, 1, 1.25, 1.5, 1.75], space="sell", default=1.25, optimize=True)
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, timeperiod=t)
informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"%-{coin}ema-period_{t}"] = ta.EMA(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_long'] = np.where(
df["close"].shift(-100) > df["close"], 'up_long', 'down_long')
df['&s-up_or_down_medium'] = np.where(
df["close"].shift(-50) > df["close"], 'up_medium', 'down_medium')
df['&s-up_or_down_short'] = np.where(
df["close"].shift(-20) > df["close"], 'up_short', 'down_short')
# 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.
dataframe = self.freqai.start(dataframe, metadata, self)
for val in self.std_dev_multiplier_buy.range:
dataframe[f'target_roi_{val}'] = (
dataframe["up_long_mean"] + dataframe["up_long_std"] * val
)
for val in self.std_dev_multiplier_sell.range:
dataframe[f'sell_roi_{val}'] = (
dataframe["down_long_mean"] - dataframe["down_long_std"] * val
)
return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [
df["do_predict"] == 1,
df["up_long"] > df[f"target_roi_{self.std_dev_multiplier_buy.value}"],
]
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["down_long"] < df[f"sell_roi_{self.std_dev_multiplier_sell.value}"],
]
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["down_long"] < df[f"sell_roi_{self.std_dev_multiplier_sell.value}"] * 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["up_long"] > df[f"target_roi_{self.std_dev_multiplier_buy.value}"] * 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 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