automatically detect maximum required data based on user fed indicators (to avoid NaNs in dataset for rolling indicators), add new config parameter for backtesting to let users increase their startup_candles to accommodate high timeframe indicators, add docs to explain all. Add new feature for automatic indicator duplication according to user defined intervals (exhibited in example strat and configs now).
This commit is contained in:
robcaulk
@@ -85,55 +85,58 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
: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)
|
||||
# first loop is automatically duplicating indicators for time periods
|
||||
for t in np.arange(10, self.freqai_info["feature_parameters"]["indicator_max_period"],
|
||||
self.freqai_info["feature_parameters"]["indicator_interval"]):
|
||||
|
||||
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"]
|
||||
t = int(t)
|
||||
informative['%-' + coin + "rsi-period_" + str(t)] = ta.RSI(informative, timeperiod=t)
|
||||
informative['%-' + coin + "mfi-period_" + str(t)] = ta.MFI(informative, timeperiod=t)
|
||||
informative['%-' + coin + "adx-period_" + str(t)] = ta.ADX(informative, window=t)
|
||||
informative[coin + "20sma-period_" + str(t)] = ta.SMA(informative, timeperiod=t)
|
||||
informative[coin + "21ema-period_" + str(t)] = ta.EMA(informative, timeperiod=t)
|
||||
informative['%-' + coin + "close_over_20sma-period_" +
|
||||
str(t)] = (informative["close"] /
|
||||
informative[coin + "20sma-period_" + str(t)])
|
||||
|
||||
informative['%-' + coin + "mfi"] = ta.MFI(informative, timeperiod=25)
|
||||
informative['%-' + coin + "mfi-period_" + str(t)] = ta.MFI(informative, timeperiod=t)
|
||||
|
||||
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 + "ema21-period_" + str(t)] = ta.EMA(informative, timeperiod=t)
|
||||
informative[coin + "sma20-period_" + str(t)] = ta.SMA(informative, timeperiod=t)
|
||||
|
||||
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"]
|
||||
)
|
||||
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=t,
|
||||
stds=2.2)
|
||||
informative[coin + "bb_lowerband-period_" + str(t)] = bollinger["lower"]
|
||||
informative[coin + "bb_middleband-period_" + str(t)] = bollinger["mid"]
|
||||
informative[coin + "bb_upperband-period_" + str(t)] = bollinger["upper"]
|
||||
informative['%-' + coin + "bb_width-period_" + str(t)] = (
|
||||
informative[coin + "bb_upperband-period_" + str(t)] -
|
||||
informative[coin + "bb_lowerband-period_" + str(t)]
|
||||
) / informative[coin + "bb_middleband-period_" + str(t)]
|
||||
informative['%-' + coin + "close-bb_lower-period_" + str(t)] = (
|
||||
informative["close"] / informative[coin + "bb_lowerband-period_" + str(t)]
|
||||
)
|
||||
|
||||
informative['%-' + coin + "roc"] = ta.ROC(informative, timeperiod=3)
|
||||
informative['%-' + coin + "adx"] = ta.ADX(informative, window=14)
|
||||
informative['%-' + coin + "roc-period_" + str(t)] = ta.ROC(informative, timeperiod=t)
|
||||
informative['%-' + coin + "adx-period_" + str(t)] = ta.ADX(informative, window=t)
|
||||
|
||||
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()
|
||||
)
|
||||
macd = ta.MACD(informative, timeperiod=t)
|
||||
informative['%-' + coin + "macd-period_" + str(t)] = macd["macd"]
|
||||
|
||||
informative[coin + "pct-change"] = informative["close"].pct_change()
|
||||
informative['%-' + coin + "relative_volume-period_" + str(t)] = (
|
||||
informative["volume"] / informative["volume"].rolling(t).mean()
|
||||
)
|
||||
|
||||
informative['%-' + coin + "pct-change"] = informative["close"].pct_change()
|
||||
informative['%-' + coin + "raw_volume"] = informative["volume"]
|
||||
informative['%-' + coin + 'raw_price'] = informative['close']
|
||||
|
||||
# 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('%')]
|
||||
# This loop duplicates and shifts all indicators to add a sense of recency to data
|
||||
for n in range(self.freqai_info["feature_parameters"]["shift"] + 1):
|
||||
if n == 0:
|
||||
continue
|
||||
@@ -141,15 +144,12 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
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
|
||||
# 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 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
|
||||
@@ -314,10 +314,10 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
last_candle = df.iloc[-1].squeeze()
|
||||
|
||||
if side == 'long':
|
||||
if last_candle['close'] > (last_candle['close'] * (1 + 0.0025)):
|
||||
if rate > (last_candle['close'] * (1 + 0.0025)):
|
||||
return False
|
||||
else:
|
||||
if last_candle['close'] < (last_candle['close'] * (1 - 0.0025)):
|
||||
if rate < (last_candle['close'] * (1 - 0.0025)):
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
Reference in New Issue
Block a user