361‑page strategy compendium

A 361-page trading manual that maps 151 hedge-fund-style strategies is circulating again online, giving Python traders a single document to test ideas against historical data. (papers.ssrn.com; x.com) The document is *151 Trading Strategies* by Zura Kakushadze and Juan Andrés Serur, posted on Social Science Research Network in September 2018 and listed there as 361 pages. SSRN says the paper was written on August 17, 2018 and revised on September 13, 2019. (papers.ssrn.com) Its abstract says the book describes more than 150 strategies across stocks, options, fixed income, futures, exchange-traded funds, commodities, foreign exchange, real estate, distressed assets, cryptocurrencies, global macro and tax arbitrage. The same abstract says it includes more than 550 formulas, source code for out-of-sample backtesting, about 2,000 references, and more than 900 glossary and math definitions. (papers.ssrn.com) Backtesting is the basic exercise behind the post: a trader writes rules, runs them on past market data, and checks how the rules would have behaved before risking real money. Python has become a common language for that work because it plugs into data tools and dedicated backtesting libraries. (backtrader.com; vectorbt.dev; kernc.github.io) The renewed attention also fits a bigger retail-quant market that now sells courses, templates and research workflows to individual traders. Quant Science’s website pitches algorithmic trading with Python, offers a “5-Day Algo Trading Course,” and advertises a “Hedge Fund in a Box” app for students. (quantscience.io) The paper itself is not a code library or a live trading system. It is a catalog: SSRN describes it as “descriptive and pedagogical,” and the authors frame it as a reference that explains how a wide range of strategies are structured. (papers.ssrn.com) That matters for anyone treating the PDF as a shortcut to profits. Backtesting tools such as Backtrader, VectorBT and Backtesting.py all warn in different ways that historical testing is an evaluation method, not proof that a strategy will hold up in future markets. (backtrader.com; vectorbt.dev; kernc.github.io) The compendium’s appeal is scale: instead of starting with one moving-average crossover, a reader gets a menu of options, volatility, macro and arbitrage ideas to compare in the same notebook. The harder part starts after the download, when those ideas have to survive data cleaning, transaction costs, and out-of-sample tests. (papers.ssrn.com; vectorbt.dev; zipline.ml4trading.io)