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Large Quantitative Models that use physics, chemistry, and scientific data to produce quantitative results.

Model financial instruments with an inspectable quantitative library

Find quantitative trading ideas and examine the research behind them

Organize machine-learning experiments for quantitative investment research

A Windows text and code editor with CSV tools, search, and support for very large files.

Develop Python strategies with graphical backtesting and market gateways

Explore supplied datasets through classical and Bayesian statistical analyses in a graphical application.

Analyze text collections quantitatively

Turn financial text into structured events and sentiment data

Build event-driven market simulations with a shared strategy engine

Open-source desktop app for running AI models on your own machine or connecting to online models.

Compare allocations using volatility, downside losses and drawdown risk

Search public trading disclosures and compare the dates behind them

Examine stock-factor returns, predictive relationships and turnover

Turn existing open-text feedback into a reviewed topic structure, segment comparisons and shared reports.

Code and compare qualitative material with quantitative descriptors in a shared research project.

Serverless hosting for open language models through one API key.

Select market datasets by instrument, interval and observation type

Research and backtest quantitative strategies in notebooks and code

Operate a large-file delivery service with access periods

AI APIs for image, video, audio, 3D and LLM models

Run and fine-tune AI models with a cloud API

Inspect the product, model and market inputs behind a valuation

Store and retrieve large-scale data with Apache's distributed, column-oriented Hadoop database.