Software
Collection of Reusable Code to Develop and Test Your Investment Ideas.
Data Curator
A flexible open-source Python library for retrieving, validating, and homogenizing financial data from multiple providers.
Build custom feature calculations with simple Python functions and generate clean, point-in-time datasets ready for backtesting and research. Tag homogenization and automatic data validation dramatically reduce the time required to onboard new data vendors.
Run standalone, configure via Excel, or embed into larger systems. Output to CSV, Parquet, or seamlessly to Pandas DataFrames, PyArrow tables, or custom ETL pipelines.
Spend less time integrating data. Focus on alpha.
Feature Foundry
Transforms curated financial datasets into signal-ready, enriched feature libraries. It combines exploratory data analysis and feature engineering into a unified process that prepares the foundation for quantitative research and portfolio construction.
Its overarching goal is to help researchers detect outliers, discover meaningful data patterns, and weave multiple data sources into coherent, interpretable features that can later drive alpha generation and investment universe definitions.
Portfolio Construction
Provides a unified framework for transforming investment signals into structured, risk-aware portfolios. It bridges the gap between research and implementation by offering a consistent interface to design, test, and compare allocation methodologies, from classical optimization frameworks to practical, rule-based heuristics.
This library enables researchers and portfolio managers to move seamlessly from alpha generation to capital allocation, ensuring transparency, flexibility, and reproducibility throughout the universe filtering and investment process.
Backtest Engine
Designed to provide a transparent and reproducible environment for testing portfolio strategies over historical data. It supports custom rebalancing logic, dynamic weight allocation, execution price configuration (e.g., VWAP, adjusted close), and commission modeling.
The engine separates concerns between data input, order execution, portfolio valuation, and performance tracking, allowing users to plug in their own decision-making models while maintaining control over capital management and trading assumptions.
Attribution Analysis
Allows analysts and researchers to decompose portfolio returns into meaningful sources of value added. It supports return attribution across sectors, industries, factors (e.g., valuation, quality), or custom-defined groupings.
Compatible with both absolute and relative performance contexts, the tool provides granular insights into allocation effects, selection effects, and timing contributions. With flexible input formats and support for cross-sectional and time-series views, it integrates smoothly into the broader research and portfolio evaluation process.
