Lab
Modular Tools to Develop, Test, and Validate Your Investment Ideas.
Data Curator
Open-source Python library that retrieves, validates and homogenizes financial data from multiple providers into clean, point-in-time datasets ready for research. Every provider's fields map to one readable naming scheme, so you can switch vendors without changing your code. Add your own calculated features with plain Python functions, configure from Excel or code, and output to CSV, Parquet or pandas DataFrames.
Spend less time integrating data. Focus on alpha.
Data Refinery
Turns curated data into research-grade features. It enriches Data Curator outputs with additional datasets such as historical sector classifications, macro indicators or alternative data, and builds cross-sectional, rolling, relative and interaction features across assets and time. Complex features become reusable building blocks you can scale to any universe.
Data Analyzer
Understand your data before it reaches your strategies. Run exploratory analysis, large-scale outlier detection and pattern discovery across broad universes of tickers and features to check stability, robustness and economic intuition. Interactive dashboards and systematic outlier reports flag anomalies at the feature, ticker and cross-sectional level, without modifying the underlying data.
Portfolio Construction
Turns features, alpha signals and risk factors into investable portfolios. Define universe rules, ranking and selection logic, position sizing, constraints and rebalancing timing (calendar-based, dynamic or hybrid). Allocate with ranking-based weighting, mean-variance optimization, Black-Litterman, Hierarchical Risk Parity and more. The output is a full history of portfolios, ready to run in the Backtest Engine.
Backtest Engine
High-performance backtesting framework for realistic, reproducible portfolio simulations. Rebalances are executed with configurable execution prices (VWAP or adjusted close), commissions and slippage. Results include 30+ performance and risk metrics against your benchmark, an interactive dashboard and multi-sheet Excel reports, and Backtest Suite runs multiple scenarios side by side.
Test more strategies. Trust your results.
Attribution Analysis
Explains where your returns came from. Decompose a portfolio's active return against its benchmark with Brinson-Fachler (allocation, selection and interaction effects by asset and date) and a Factor Model (the contribution of each style and industry factor, plus the stock-specific residual). Run either or both from an Excel workbook with no code, and explore the results in an interactive dashboard.
Know what worked, and why.
