New Python quant framework

Quant Science is pitching a new Python-based algorithmic trading workflow to beginners with a free public workshop set for Thursday, April 30, at 10 a.m. Eastern. (learn.quantscience.io) Algorithmic trading means turning buy-and-sell rules into code, then testing those rules on old market data before risking real money. Quant Science’s workshop page says it will cover data pipelines, signal testing, backtesting, risk controls, automation, and live execution. (learn.quantscience.io) Backtesting is the core idea: you take a strategy, run it against historical prices, and measure how it would have behaved. Quant Science’s older public framework post lays out that process as strategy formation, preliminary analysis, backtesting, and live trading inside a six-step workflow. (quantscience.io) The company already has public GitHub repositories that show two common backtesting styles in Python: vector-based testing with VectorBT and event-based testing with Zipline Reloaded. Its GitHub organization lists both repositories, with `vectorbt_backtesting` updated on November 7, 2023, and `zipline_backtesting` updated on July 1, 2024. (github.com) The VectorBT example starts with Apple price data, builds a simple moving-average crossover strategy, compares it with buy-and-hold, and then sweeps through 99 moving-average window combinations to find the best result. That is the kind of multi-strategy comparison beginners usually have to assemble from separate notebooks and libraries. (github.com) The Zipline example uses a different style that simulates trades as events over time, closer to how orders and portfolio updates happen in a live system. The sample file includes environment setup instructions, Zipline imports, data-bundle ingestion, and a runnable backtest script. (github.com) Quant Science’s site says many beginners struggle not with one trading rule, but with the full research stack around it: data storage, testing, reporting, and automation. Its marketing now frames that stack as a faster path to “start algorithmic trading with Python in under 60 days.” (quantscience.io) That pitch lands at a moment when Python remains the default language for retail quant education because most of the core tooling is open source and notebook-friendly. Quant Science’s 2023 framework post explicitly pointed readers to a “Python Quant Scientist Stack” of free libraries and a downloadable framework. (quantscience.io) The workshop page also puts a commercial wrapper around the free entry point. It says the live session has 500 seats, includes instant bonus PDFs, and is tied to a broader program built around the same software stack and trading process. (learn.quantscience.io) For newcomers, the immediate date to watch is April 30. That session is where Quant Science says it will show how its Python workflow moves from research notebooks into automated trading operations. (learn.quantscience.io)