pyfolio

The S&P500 is a backtesting reference benchmark. You can compare portfolio performance through drawdown periods with backtesting tools. Macroeconomic drawdowns are good times to have cash for acquisitions; instead of free government cheese. "Tear Sheets: Definition and Examples in Finance, Vs. Prospectus" https://www.investopedia.com/terms/t/tearsheets.asp : > While tear sheets date back to the old days when stockbrokers would rip individual pages out of the S&P summary book and send them to current or potential clients, most information is extracted online today. Therefore, any concise representation of a company's business fundamentals could be considered a tear sheet. From https://news.ycombinator.com/item?id=24428206#24429801 : > pyfolio.tears.create_interesting_times_tear_sheet measures algorithmic trading algorithm performance duri

(Sustainable) Index ETFs in the stocks.txt universe would likely be less sensitive to single performers' effects in unbalanced portfolios. > pyfolio.tears.create_interesting_times_tear_sheet measures algorithmic trading algorithm performance during "stress events" https://github.com/quantopian/pyfolio/blob/03568e0f328783a6a...

Catalyst, like Quantopian, is also built on top of Zipline; but for cryptocurrencies. https://enigmampc.github.io/catalyst/example-algos.html Zipline (backtesting and live trading of algorithms with initialize(context) and handle_data(context, data) functions; with the SPY S&P 500 ETF as a benchmark) https://github.com/quantopian/zipline Pyfolio (for objectively comparing the performance of trading strategies over time) https://github.com/quantopian/pyfolio ... "Community Algorithms Migrated to Quantopian 2" https://www.quantopian.com/posts/community-algorithms-migrat... - "Reply to minimum variance w/ contrast" seems to far outperform the S&P 500.