autofcholv

A Python library for Automated extraction of relevant features from OHLCV time series data. Designed to help quantitative researchers and traders quickly generate a massive set of highly predictive features from basic pricing and volume data.

🚀 Key Features

• Automated Validation & Cleaning: Built-in verification processes that ensure input OHLCV records don't contain logical errors, missing records, or erroneous overlaps (such as incorrect High/Low bounds).
• Vectorized Execution: Under the hood, feature extraction heavily uses vectorized pandas and numpy functions alongside pandas-ta to ensure lightning-fast execution times, even when handling millions of rows.
• Broad Feature Sets: Automatically extracts everything from time signatures and candlestick geometries to advanced fractal mathematics like the Hurst Exponent.
• CLI Included: Ships with an easy-to-use Command Line Interface (autofcholv) for users who want to run extractions in a terminal.

📦 Installation

To install autofcholv into your environment, use pip.

It is recommended to use a virtual environment (venv).

pip install autofcholv

Or from source:

# Clone the repository
git clone https://github.com/tempusoneps/autofcholv.git
cd autofcholv

# Install via pip
pip install .

# For development mode
pip install -e .[dev]

Requirements

• Python >= 3.12
• pandas
• numpy
• pandas_ta
• python-dotenv

🛠️ Usage

Using the Command Line Interface (CLI)

After installing, the autofcholv command is immediately available in your terminal.

extract — Extract features from a CSV file

autofcholv extract input.csv --output my_features.csv

Options:

• input: Path to your input OHLCV CSV file. It must contain Date, Open, High, Low, Close, and Volume columns.
• --output, -o: The path where the output features CSV will be saved (default: output_features.csv).

generate-config — Generate a default configuration file

autofcholv generate-config --path .env

Options:

• --path, -p: Path to save the generated config file (default: .env).

This creates a .env file pre-filled with all default configuration values. Edit it to customise feature behaviour (e.g. ONE_DAY_BARS=49, SELECTED_TIME_FRAME=15m).

Global options

• --version, -v: Print library version.
• --help, -h: Show help message.

Configuration loading priority

autofcholv resolves configuration in the following order (first match wins):

1. Environment variables — all required keys must be present in the environment.
2. Config file — a JSON or YAML file passed explicitly via the API (load_config(path)).
3. Built-in defaults — sensible defaults are applied automatically if neither of the above is available.

Using the Python API

You can easily use autofcholv directly in Jupyter Notebooks or Python scripts:

import pandas as pd
from autofcholv import extract_features

# 1. Load your OHLCV data into a Pandas DataFrame
# It is important that index is a DatetimeIndex and columns are correctly named
df = pd.read_csv("historic_data.csv", index_col="Date", parse_dates=True)

# 2. Run the extraction pipeline
features_df = extract_features(df)

# 3. View the results
print(features_df.tail())

Other Resources

Please refer to the Pipeline Overview for a comprehensive list of all generated features.

Please refer to the Extracted Features Catalog for a comprehensive list of all generated features.