Rhoa Documentation

Rhoa is a Python package providing pandas DataFrame extension accessors for technical analysis and machine learning in financial markets.

Getting Started

User Guide

Examples

API Reference

Additional Resources

Overview

Rhoa streamlines technical analysis and machine learning workflows by extending pandas with specialized accessors for:

  • 📊 Technical Indicators - 13 professional-grade indicators

  • 🤖 ML Target Generation - Optimized binary targets with auto/manual modes

  • 📈 Visualization - Model evaluation with confusion matrices and price charts

  • 🔗 Pandas Integration - Seamless integration with existing workflows

Quick Example

import pandas as pd
import rhoa

# Load your price data
df = pd.read_csv('stock_prices.csv')

# Calculate technical indicators
df['SMA_20'] = df.rhoa.indicators.sma(window_size=20)
df['RSI_14'] = df.rhoa.indicators.rsi(window_size=14)

# Generate ML targets
from rhoa.targets import generate_target_combinations
targets, meta = generate_target_combinations(df, mode='auto')

# Visualize predictions
fig = df.rhoa.plots.signal(y_pred=predictions, y_true=targets['Target_7'])

Key Features

Pandas-Native Interface

All functionality accessible via pandas accessors. No need to learn a new API.

Intelligent Target Generation

Automatic optimization finds the best lookback period and threshold for your data.

Production-Ready

Type hints, comprehensive tests, and proper error handling throughout.

Well-Documented

Every function has detailed docstrings with examples and best practices.

Installation

Install via pip:

pip install rhoa

For detailed installation instructions, see the Installation guide.

Getting Help

Project Information

  • Version: 0.1.7

  • License: GNU General Public License v3.0

  • Author: nainajnahO

  • Python: 3.9+

  • Status: Pre-Alpha

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