Free 7‑day algo course

PyQuant is pitching a familiar dream in a tighter package. The company’s latest hook is a free seven-lesson email course on algorithmic trading with Python, aimed at people who know they should learn this stuff but have not yet built a working system. The landing page says the course is free, delivered by email, and designed to move readers from vague interest to a first backtested strategy with “real code” instead of generic programming exercises (pyquantnews.com). That promise lands because the gap it targets is real. Most beginner Python courses teach syntax first and markets later, if at all. PyQuant’s pitch is the reverse. It frames the hard part not as learning `for` loops, but learning how code fits into an actual trading workflow: generate an idea, test it on historical data, measure the result, and only then think about execution. The site says the free course covers exactly that sequence in seven short lessons (pyquantnews.com). The details matter, because they show what PyQuant thinks beginners are missing. Lesson one is about choosing between the “only 2 types of algorithmic trading that matter,” at least in the company’s framing. Lesson two uses ChatGPT to generate strategy ideas. Later lessons move into writing a strategy template against historical data, learning the common backtesting mistake that can make a bad strategy look profitable, generating a performance report with QuantStats, and seeing the flow from signal to order submission (pyquantnews.com). This is not a math-heavy quant curriculum. It is a workflow curriculum. That also makes the free course an on-ramp to a larger business. PyQuant’s homepage places the email course next to a paid “Getting Started With Python for Quant Finance” program, which it says includes 13 modules, 134 lessons, 40 code templates, a private community, and more than 1,740 students. The homepage also says the broader newsletter now has 37,000 subscribers, while the paid-course page describes PyQuant News as an operation that started in 2015 and has grown to more than 200,000 newsletter subscribers over time. Those numbers are not presented in a single audited place, but they show the shape of the funnel: free lessons first, deeper training after that (pyquantnews.com, pyquantnews.com). The person behind that funnel is Jason Strimpel, according to PyQuant’s own course pages. He describes a background that mixes trading, risk, data engineering, and Python, including time at a hedge fund, a bank, and large trading operations. The free course page says it was built by “a quant with 20 years” at places including JP Morgan, BP Trading, and his own hedge fund. The paid-course page expands that biography into a longer argument: that many aspiring quants do not need a Ph.D. to become useful, but they do need finance-specific code examples that connect directly to market work (pyquantnews.com, pyquantnews.com). That argument has spread beyond PyQuant’s own site. Interactive Brokers’ IBKR Campus lists PyQuant News as a contributor and describes its material as practical Python tutorials for backtesting, algorithmic trading, market data analysis, machine learning, options trading, and derivatives pricing. The same contributor page shows a steady stream of recent PyQuant bylines on beginner Python topics, AI in finance, and trading analytics, which helps explain why a free email course can attract hobbyists and junior quants without pretending to be a university program (interactivebrokers.com). The free course also sits inside a broader ecosystem of reusable examples. PyQuant maintains a public GitHub repository with notebooks on pairs trading, statistical arbitrage, backtesting, risk parity, implied volatility surfaces, and Interactive Brokers automation. That matters because it turns the course from pure marketing copy into something closer to a working library of examples. The through-line is plain: learn just enough Python to test an idea, then borrow enough structure to keep going (github.com).