TinyCTA 0.7.24

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📈 TinyCTA

📦 A lightweight Python package for implementing Commodity Trading Advisor (CTA) strategies with a focus on trend-following signals and linear algebra operations.

📋 Overview

TinyCTA provides essential tools for quantitative finance and algorithmic trading, particularly for trend-following strategies. The package includes:

• Signal processing functions for creating oscillators and adjusting returns
• Linear algebra utilities that handle matrices with missing values
• Matrix shrinkage techniques commonly used in portfolio optimization

This package is designed to be the foundation for implementing CTA strategies in just a few lines of code, hence the name "TinyCTA".

🚀 Installation

Using pip

pip install tinycta

From source

Clone the repository and install using the provided Makefile:

git clone https://github.com/tschm/tinycta.git
cd tinycta
make install

This will install uv (a fast Python package installer) and create a virtual environment with all dependencies.

💻 Usage

Creating an oscillator

import pandas as pd
from src.tinycta.signal import osc

# Load price data
prices = pd.read_csv('prices.csv', index_col=0, parse_dates=True)

# Create an oscillator with default parameters
oscillator = prices.apply(osc)

# Create an oscillator with custom parameters
custom_oscillator = prices.apply(osc, fast=16, slow=64, scaling=False)

Adjusting returns for volatility

import pandas as pd
from src.tinycta.signal import returns_adjust

# Load price data
prices = pd.read_csv('prices.csv', index_col=0, parse_dates=True)

# Adjust returns for volatility
adjusted_returns = prices.apply(returns_adjust)

Linear algebra operations

import numpy as np
from src.tinycta.linalg import solve, shrink2id

# Create a matrix and right-hand side vector
matrix = np.array([[1.0, 0.5], [0.5, 1.0]])
rhs = np.array([1.0, 2.0])

# Solve the linear system
solution = solve(matrix, rhs)

# Apply shrinkage to the matrix
shrunk_matrix = shrink2id(matrix, lamb=0.5)

📚 API Reference

Signal Processing

• osc(prices, fast=32, slow=96, scaling=True): Creates an oscillator based on the difference between fast and slow moving averages
• returns_adjust(price, com=32, min_periods=300, clip=4.2): Adjusts log-returns by volatility and applies winsorization
• shrink2id(matrix, lamb=1.0): Performs shrinkage of a matrix towards the identity matrix

Linear Algebra

• valid(matrix): Constructs a valid subset of a matrix by filtering out rows/columns with NaN values
• a_norm(vector, matrix=None): Computes the matrix-norm of a vector with respect to a matrix
• inv_a_norm(vector, matrix=None): Computes the inverse matrix-norm of a vector
• solve(matrix, rhs): Solves a linear system of equations, handling matrices with NaN values

🛠️ Development

Setting up the development environment

make install

Running tests

make test

Code formatting and linting

make fmt

Cleaning up

make clean

📄 License

TinyCTA is licensed under the MIT License. See the LICENSE file for details.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.