[Suggestion] Feature helper / ideas

[cryptocoinserver]

Candle information (might help the model learning candlestick patterns):

• hl2 Price
• hlc3 Price
• ohlc4 Price

def upper_shadow(df):
    return df['High'] - np.maximum(df['Close'], df['Open'])

def lower_shadow(df):
    return np.minimum(df['Close'], df['Open']) - df['Low']
    
def real_body(df):
     # maybe np.abs()
    return df['Close'] - df['Open']

def full_length(df):
     # maybe np.abs()
    return df['High'] - df['Low']

Seasonality information (See this great video, where this was recommended - https://www.youtube.com/watch?v=c0gpgCyjTM8): For example...

• day of week
• day of month
• month
• year
• week of year
• hour

Volatility Features:

• Realized
• Parkinson
• Garman-Klass
• Roger-Satchell
• Garman-Klass-Yang-Zhang
• Yang-Zhang

Source: https://www.kaggle.com/yamqwe/crypto-prediction-volatility-features#Feature-Engineering-%F0%9F%94%AC - this kaggle competition is a good source for more ideas.

TSfresh https://tsfresh.readthedocs.io/en/latest/text/list_of_features.html Great tool to easily add (and filter) a lot more valuable features.

[rayrui312]

Thanks for your valuable suggestions. We will try to add support for these features in the future version.

[YangletLiu]

@cryptocoinserver Hi, thanks. Would you be able to help us add some of them?

[cryptocoinserver]

Sadly, my time is pretty limited currently due to university and other project(s). If I'm not mistaken, you can just use the Volatility Features code from the kaggle notebook I linked: "This Notebook has been released under the Apache 2.0 open source license.".

And the seasonality stuff is easy, as pandas has those functions built in: https://pandas.pydata.org/docs/reference/api/pandas.Series.dt.dayofweek.html?highlight=dayofweek#pandas.Series.dt.dayofweek

I saw some people asking how to help on Slack. Maybe you could add some tasks to https://github.com/AI4Finance-Foundation/FinRL-Meta/projects for (those) contributors to work through?

[cryptocoinserver]

Might be an interesting feature to test: https://www.kaggle.com/wptouxx/g-research-cointegration-analysis Cointegration tests for all asset pairs and obtaining their p-values.