@DanKornas: AI in finance spans too many papers, tools, datasets, and trading frameworks to track one by one. Awesome AI in Finance…

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Awesome AI in Finance is an open-source curated resource list covering papers, tools, datasets, and trading frameworks for AI and quantitative finance, organized into categories like agents, research, strategies, data, and trading stacks.

AI in finance spans too many papers, tools, datasets, and trading frameworks to track one by one. Awesome AI in Finance is a curated resource list for builders and researchers exploring AI, machine learning, and quantitative finance. It helps you map the space and find starting points by organizing links across research, data, strategies, libraries, APIs, and trading systems. Key features: • Agents and LLMs – financial-analysis agents, simulation engines, benchmarks, and language-model resources • Research library – papers, courses, books, blogs, and practical code collections • Strategy map – time series, portfolio management, HFT, event-driven, crypto, technical analysis, and arbitrage • Data and research tools – traditional-market, crypto, news, and alternative-data sources alongside analytics libraries • Trading stack – backtesting and live-trading systems, TA libraries, exchange APIs, frameworks, and Gym environments It’s open-source (CC0 1.0 Universal license). Link in the reply
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AI in finance spans too many papers, tools, datasets, and trading frameworks to track one by one.

Awesome AI in Finance is a curated resource list for builders and researchers exploring AI, machine learning, and quantitative finance.

It helps you map the space and find starting points by organizing links across research, data, strategies, libraries, APIs, and trading systems.

Key features: • Agents and LLMs – financial-analysis agents, simulation engines, benchmarks, and language-model resources • Research library – papers, courses, books, blogs, and practical code collections • Strategy map – time series, portfolio management, HFT, event-driven, crypto, technical analysis, and arbitrage • Data and research tools – traditional-market, crypto, news, and alternative-data sources alongside analytics libraries • Trading stack – backtesting and live-trading systems, TA libraries, exchange APIs, frameworks, and Gym environments

It’s open-source (CC0 1.0 Universal license).

Link in the reply

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