frypto 0.1.3

Crypto data feature engineering package.

Frypto: Feature Engineering for Financial Time Series (Crypto)

Frypto is a Python package designed to compute a set of financial time series features, including price-based, volume-based, volatility, momentum, trend, and statistical features.

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Table of Contents

• Why Frypto?
• Quick Start
• Usage
• Jupyter Notebooks
• Contributing
• License

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Why Frypto?

• In the G-Research competition, the top 3 winners demonstrated the critical role feature engineering plays in predicting real-world crypto market data.
• Frypto saves over 100 hours of manual feature creation and uses memory optimization tools to handle large datasets smoothly.

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Quick Start

Install with pip

pip install frypto

Running Frypto

Load your cryptocurrency data (or download data with yfinance as shown below).

from frypto import AllFeatures
import yfinance as yf

# Download BTC-USD data from Yahoo Finance (or use your dataset)
data = yf.download('BTC-USD', period='max', interval='1d')

# Initialize Frypto and compute features
allfeatures = AllFeatures(data)
features = allfeatures.compute()  # Returns a pd.DataFrame with the new features

# View the top rows of the generated features
print(features.head())  # New DataFrame with 41 features

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Usage

Frypto computes a range of financial time series features, including price changes, spreads, rolling statistics, momentum indicators, trend indicators, volatility measures, volume-based features, and lagged features.

• Price and Spread Features: Price_change, next_log_return, high_low_spread, close_open_spread
• Volatility Features: rolling_std, upper_band, lower_band, ATR
• Momentum Indicators: RSI, MACD, Signal_Line, SMA, EMA, ROC
• Statistical Features: Skew_20, Kurtosis_20, ZScore_20
• Volume-Based Features: volume_change, OBV
• Lagged and Rolling Features: lag_1, lag_2, lag_3, rolling_mean, rolling_max, rolling_min (for windows 5, 10, and 20)
• Trend Indicators: +DI, -DI, ADX, support_line, resistance_line, tenkan_sen, kijun_sen, senkou_span_a, senkou_span_b, chikou_span

import numpy as np
import pandas as pd
import yfinance as yf
from price import PriceFeatures
from volatility import VolatilityFeatures
from momentum import MomentumFeatures
from statistical_features import StatisticalFeatures
from volume import VolumeFeatures
from trend_features import TrendFeatures
from lag_rolling import LagRollingFeatures

# Download historical BTC-USD data
data = yf.download('BTC-USD', period='max', interval='1d')
close = data['Close'].values
high = data['High'].values
low = data['Low'].values
volume = data['Volume'].values
open_ = data['Open'].values

# Initialize and compute Price Features
price_features = PriceFeatures(close=close, high=high, low=low, open=open_)
price_df = price_features.compute()
print("Price Features:\n", price_df.head())

# Initialize and compute Volatility Features with a specified window
volatility_features = VolatilityFeatures(close=close, high=high, low=low)
volatility_df = volatility_features.compute(window=15)
print("Volatility Features:\n", volatility_df.head())

# Initialize and compute Momentum Features, with specified windows for RSI and MACD
momentum_features = MomentumFeatures(close=close)
momentum_df = momentum_features.compute(window=15, rsi_window=14, macd_windows=(12, 26, 9))
print("Momentum Features:\n", momentum_df.head())

# Initialize and compute Statistical Features with a specified rolling window
statistical_features = StatisticalFeatures(close=close)
statistical_df = statistical_features.compute(window=20)
print("Statistical Features:\n", statistical_df.head())

# Initialize and compute Volume Features
volume_features = VolumeFeatures(close=close, volume=volume)
volume_df = volume_features.compute()
print("Volume Features:\n", volume_df.head())

# Initialize and compute Trend Indicators with a specified window
trend_features = TrendFeatures(close=close, high=high, low=low)
trend_df = trend_features.compute(window=15)
print("Trend Features:\n", trend_df.head())

# Initialize and compute Lagged and Rolling Statistics with specified lags and windows
lag_rolling_features = LagRollingFeatures(close=close)
lag_rolling_df = lag_rolling_features.compute(lags=[1, 2, 3], windows=[5, 10, 20])
print("Lag and Rolling Features:\n", lag_rolling_df.head())

# Combine all computed features into a single DataFrame
all_features_df = pd.concat([
    price_df, 
    volatility_df, 
    momentum_df, 
    statistical_df, 
    volume_df, 
    trend_df, 
    lag_rolling_df
], axis=1)

print("All Computed Features:\n", all_features_df.head())

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Jupyter Notebooks

Explore the example notebooks to learn more about Frypto:

• Getting Started with Frypto: Step-by-step guide to setting up and using Frypto.
• Exploring Individual Feature Types: Detailed exploration of each feature type and its applications.

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Contributing

Clone the repo

git clone https://github.com/NotAbdelrahmanelsayed/frypto
cd frypto

Create the environment

Ensure virtualenv is installed:

pip install virtualenv

Then, create and activate a virtual environment:

Linux

virtualenv venv
source venv/bin/activate

Windows Powershell

virtualenv venv
.\venv\Scripts\Activate.ps1

Install the requirements

pip install -r requirements.txt

Run the tests

To run unit tests:

pytest

To test multiple Python versions (3.7, 3.8, 3.9, 3.10, 3.11, 3.12):

tox

Submit a pull request

If you'd like to contribute, please fork the repository and open a pull request to the main branch.

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License

This project is licensed under the MIT License.