@queen_nunaa: For quantitative researchers, the annual salaries offered by Wall Street hedge funds are very attractive, typically ranging from $200,000 to $650,000. Recently, a quantitative trader from Jane Street shared a 50-minute video on YouTube, thoroughly laying out a complete roadmap for quantitative learning, including practical tips related to Polymarket…
Summary
This article introduces a 50-minute video shared by a Jane Street quant trader on YouTube, covering a full roadmap for quantitative learning, including practical tips on Polymarket, which is a valuable reference for quant practitioners.
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@WEB3_furture: What did the world's most expensive financial teams open source on GitHub? How can ordinary people learn about quantitative trading? Directly getting hands-on is the fastest way. Top quantitative and high-frequency trading institutions like Jane Street, Goldman Sachs, J.P. Morgan, etc., have released representative financial/engineering tools to help ordinary quant...
This tweet introduces three financial/engineering tools open-sourced by top quantitative institutions such as Jane Street, Goldman Sachs, and J.P. Morgan: magic-trace (high-precision process tracing), gs-quant (Python package for derivatives pricing and risk management), and Perspective (real-time data visualization tool), helping quant enthusiasts gain institutional-level technical capabilities for free.
@0xMovez: Jane Street pays $750k/ year for quants who can answer how to use Stochastic Process and Markov Chains in quant trading…
Jane Street offers $750k/year for quants who can apply Stochastic Processes and Markov Chains in trading, and a free MIT lecture covers similar material.
@KKaWSB: https://x.com/KKaWSB/status/2073914011524219109
This article details the learning path for an ordinary person to become a quantitative trader, covering five stages: probability, statistics, linear algebra, calculus, and stochastic calculus. It also explains the industry's compensation structure, interview requirements, and the rapid growth of AI/ML positions.
@KKaWSB: Going the extra mile, folks — quantitative trading projects on GitHub have reached a whole new level. There are plenty of ready-to-use strategies you can practice with (here's a curated list). What Wall Street teams earn millions for is now given to you for free by these open-source projects, complete with tutorials.
A curated list of open-source quantitative trading projects on GitHub, including AI-powered platforms like Qlib and FinGPT, multi-agent frameworks, and backtesting tools, all with tutorials and ready-to-use strategies.
@_zheergen: Earlier, I met a teacher who explained the entire quantitative pipeline very thoroughly. I asked him if there was a single video that could truly string together "data scraping → machine learning prediction → portfolio optimization → strategy backtesting." He showed me a very clear full-stack framework: from environment setup, ARIMA and NeuralProph…
Introduces a full-stack hands-on Python quantitative trading video tutorial, covering data scraping, machine learning prediction, portfolio optimization, and strategy backtesting, and mentions the open-source quantitative trading execution system Kungfu.