Search results

Find quantitative trading ideas and examine the research behind them

Model financial instruments with an inspectable quantitative library

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

Develop Python strategies with graphical backtesting and market gateways

Organize machine-learning experiments for quantitative investment research

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

Analyze text collections quantitatively

Build event-driven market simulations with a shared strategy engine

Search public trading disclosures and compare the dates behind them

Examine stock-factor returns, predictive relationships and turnover

Research and backtest quantitative strategies in notebooks and code

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.

Compare allocations using volatility, downside losses and drawdown risk

Turn financial text into structured events and sentiment data

Select market datasets by instrument, interval and observation type

Provision self-hosted development workspaces that developers can open through a browser IDE or desktop editor.

An IEC 61131-3 development system for programming industrial controllers and automation devices.

An unauthenticated HTTP API for querying World Bank country and development-indicator time series.

Open a Git repository in a browser IDE backed by a Kubernetes development workspace.

Jupyter-native AI agent that writes, runs, and debugs Python in notebooks.

A browser IDE that pairs a cloud Linux workspace with a code editor, terminal, and server tools.

Inspect the product, model and market inputs behind a valuation

Research trading decisions with reinforcement-learning experiments