Alpaca Skills
Official Alpaca skills for backtesting, paper trading, and Broker API workflows
Provider official
157 Supports live execution
AI INVEST MAP · TOOLS & RESEARCH GUIDES
Discover tools for AI investment research
Tools for market data, investment research and strategy backtesting.
About this order
Recommendations prioritize relevant official financial Skills. Selected markets take priority over language preferences; language does not establish your location or eligibility.
AI Hedge Fund
Optional Jev provider for structured judgments in multi-agent US-equity research and backtests
64,138
TradingAgents
It arranges analyst, researcher, and risk roles into a collaborative research flow for examining market information, forming investment hypotheses, and seeing how different roles reason about a decision. It helps explore multi-agent workflows for financial analysis.
Who it suitsDevelopers exploring multi-agent financial research who can configure model and data services.
112,755
Lean
QuantConnect describes LEAN as a unified-API engine covering quantitative research, backtesting, and live trading.
22,145
Supports live execution
Eiten
It implements eigen, minimum-variance, maximum-Sharpe, and genetic-algorithm portfolio strategies.
3,323
Lumibot
Python runtime for backtesting, paper trading, and live broker execution
2,144
Supports live execution
Qlib
Build machine-learning-based quantitative research flows that bring data preparation, feature and model experiments, and strategy evaluation into one research platform. Start with the project examples, then adapt them to data you are authorized to use and your own hypotheses.
Who it suitsResearchers and developers comfortable with Python who want to run machine-learning quant experiments.
Coding required: Coding required
49,590
Freqtrade
Develop crypto strategies with tools for historical backtesting, result plots, money management, and optimization, with operation through a web interface or Telegram. You can first assess an idea against historical data, then review the documented options for simulation or live setup.
Who it suitsPython users who want to write and evaluate crypto strategies.
Coding required: Coding required
55,739
Supports live execution
OctoBot
It provides a visual interface and supports manual instances plus workflows such as backtesting, Telegram, and TradingView.
6,837
Supports live execution
AI-Scalpel-Trading-Bot
The repository describes itself as an implementation of Freqtrade for experimenting with different machine-learning methods.
464
Supports live execution
FinRL-X
AI-native quantitative research and trading infrastructure from the FinRL team
3,845
Supports live execution
FinRL
Study and research financial reinforcement learning through experiments involving environments, data, and algorithms. It is suited to reproducing classic tutorials or benchmarks and exploring how reinforcement-learning methods apply to financial tasks; distinguish this repository from newer projects.
Who it suitsResearchers with Python and machine-learning experience who want to study financial reinforcement learning.
Coding required: Coding required
16,782
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Guides
Information and source links, not investment advice. Inclusion and source checks do not guarantee security, compliance or returns. How information is reviewed