Institutional-grade quantitative backtesting and stock screening for the modern algorithmic investor.
EquitiesLab is a sophisticated quantitative research and backtesting platform that empowers investors to build, test, and optimize complex trading strategies without requiring deep programming knowledge. By 2026, it has positioned itself as the premier bridge between high-level retail investing and institutional-grade data science. The platform's core architecture utilizes a proprietary 'Visual Query Language' that translates human-readable logic into high-performance financial queries. It distinguishes itself from competitors by providing high-fidelity, point-in-time data that eliminates common pitfalls such as survivorship bias and look-ahead bias. The 2026 market position emphasizes its 'Active Investing AI' which assists users in refining factor-based models through recursive variable optimization. Its technical stack is optimized for low-latency processing of decades of fundamental and technical data, making it an essential tool for investors focused on value, momentum, and multi-factor quantitative strategies. EquitiesLab's unique selling proposition lies in its ability to visualize strategy performance through heatmaps and tree-maps, allowing users to identify alpha sources that standard spreadsheets would obscure.
A proprietary block-based syntax that translates logical operators into SQL-like queries against financial databases.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Includes data for companies that have gone bankrupt or been acquired to ensure historical accuracy.
Generates 3D and color-coded visualizations of factor performance across different market caps and sectors.
Ensures that the backtester only uses information that was actually available on the date being tested.
Allows users to rank stocks based on weighted scores across hundreds of fundamental metrics.
Runs thousands of simulations with randomized variables to test strategy robustness.
An AI-driven engine that tests incremental changes to variables to find the mathematical 'sweet spot'.
Identifying stocks trading below their intrinsic value that others missed.
Registry Updated:2/7/2026
Ensuring a dividend strategy survives market crashes.
Capturing rapid growth while minimizing drawdowns.