Qlib
github.comOrganize machine-learning experiments for quantitative investment research
Organize machine-learning experiments for quantitative investment research
Choose your own shares or use a managed investment portfolio
Track investments and examine account and portfolio performance
Develop Python strategies with graphical backtesting and market gateways
Build chart-based research and explain market decisions with weighted models
Coordinate schedules, tasks, resources, and project reporting across multiple views.
Study traded volume with footprint charts, profiles and heatmaps
A personal task timer with calendar logs, goal reports, and exports for reviewing where time goes.
Crypto purchases with quote controls and consolidated portfolio activity
Turn task-board data into delivery-flow charts and scheduled progress reports.
Link customer feedback and product strategy to prioritization, roadmaps, and delivery views.
Multi-asset wallet with portfolio sorting and fee controls
Choose how much of portfolio management to automate
Develop and compare trading strategies in a Windows backtesting workspace
Delegate an index portfolio or choose your own stocks and ETFs
Host a structured bug database with searchable reports, status histories, and charts.
Study CFD markets with chart, alert and simulated-account tools
View option chains and charts beside broader market context
Explore defined Bitcoin metrics and download the underlying chart data
Newsletter portfolio production with reusable templates, approvals and revenue operations
Analyze returns from imported investment histories
A computational canvas for connecting evidence, assumptions, data, and forecast charts.
Connect product strategy, intake, capacity scenarios, roadmaps, and portfolio delivery.