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auto populate features based on a prepended % in the strategy (remove feature assignment from config). Update doc/constants/example strategy to reflect change

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robcaulk
2022-05-17 18:15:03 +02:00
parent 8664e8f9a3
commit d1d451c27e
6 changed files with 80 additions and 69 deletions
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40
freqtrade/templates/FreqaiExampleStrategy.py
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@@ -62,8 +62,11 @@ class FreqaiExampleStrategy(IStrategy):
def populate_any_indicators(self, pair, df, tf, informative=None, coin=""):
"""
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User can add
additional features here, but must follow the naming convention.
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
@@ -74,49 +77,50 @@ class FreqaiExampleStrategy(IStrategy):
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 + "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 + "bmsb"] = np.where(
informative[coin + "20sma"].lt(informative[coin + "21ema"]), 1, 0
)
informative[coin + "close_over_20sma"] = informative["close"] / informative[coin + "20sma"]
informative['%-' + coin + "close_over_20sma"] = informative["close"] / informative[
coin + "20sma"]
informative[coin + "mfi"] = ta.MFI(informative, timeperiod=25)
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"]
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_width"] = (
informative[coin + "bb_upperband"] - informative[coin + "bb_lowerband"]
) / informative[coin + "bb_middleband"]
informative[coin + "close-bb_lower"] = (
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)
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 + "macd"] = macd["macd"]
informative[coin + "pct-change"] = informative["close"].pct_change()
informative[coin + "relative_volume"] = (
informative['%-' + coin + "relative_volume"] = (
informative["volume"] / informative["volume"].rolling(10).mean()
)
informative[coin + "pct-change"] = informative["close"].pct_change()
indicators = [col for col in informative if col.startswith(coin)]
indicators = [col for col in informative if col.startswith('%')]
for n in range(self.freqai_info["feature_parameters"]["shift"] + 1):
if n == 0:
@@ -154,7 +158,6 @@ class FreqaiExampleStrategy(IStrategy):
pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-"
)
print('dataframe_built')
# 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.
@@ -181,7 +184,6 @@ class FreqaiExampleStrategy(IStrategy):
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# sell_goal = eval('self.'+metadata['pair'].split("/")[0]+'_sell_goal.value')
sell_conditions = [
(dataframe["prediction"] < dataframe["sell_roi"]) & (dataframe["do_predict"] == 1)
]