mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-10 18:23:55 +00:00
248 lines
9.9 KiB
Python
248 lines
9.9 KiB
Python
import logging
|
|
from functools import reduce
|
|
|
|
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 FreqaiExampleStrategy(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 `f'%-{pair}`
|
|
(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
|
|
"""
|
|
|
|
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"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
|
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
|
|
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
|
|
informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
|
|
informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
|
|
|
|
bollinger = qtpylib.bollinger_bands(
|
|
qtpylib.typical_price(informative), window=t, stds=2.2
|
|
)
|
|
informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
|
|
informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
|
|
informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
|
|
|
|
informative[f"%-{pair}bb_width-period_{t}"] = (
|
|
informative[f"{pair}bb_upperband-period_{t}"]
|
|
- informative[f"{pair}bb_lowerband-period_{t}"]
|
|
) / informative[f"{pair}bb_middleband-period_{t}"]
|
|
informative[f"%-{pair}close-bb_lower-period_{t}"] = (
|
|
informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
|
|
)
|
|
|
|
informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
|
|
|
|
informative[f"%-{pair}relative_volume-period_{t}"] = (
|
|
informative["volume"] / informative["volume"].rolling(t).mean()
|
|
)
|
|
|
|
informative[f"%-{pair}pct-change"] = informative["close"].pct_change()
|
|
informative[f"%-{pair}raw_volume"] = informative["volume"]
|
|
informative[f"%-{pair}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
|
|
|
|
# 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
|
|
)
|
|
|
|
# Classifiers are typically set up with strings as targets:
|
|
# df['&s-up_or_down'] = np.where( df["close"].shift(-100) >
|
|
# df["close"], 'up', 'down')
|
|
|
|
# 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["&-s_close_mean"] + dataframe["&-s_close_std"] * val
|
|
)
|
|
for val in self.std_dev_multiplier_sell.range:
|
|
dataframe[f'sell_roi_{val}'] = (
|
|
dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * val
|
|
)
|
|
return dataframe
|
|
|
|
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
|
|
|
enter_long_conditions = [
|
|
df["do_predict"] == 1,
|
|
df["&-s_close"] > 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["&-s_close"] < 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["&-s_close"] < 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["&-s_close"] > 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
|