QuantRocket

Want a quick overview of QuantRocket? Check out this interactive demo of the user interface and core features: https://lnkd.in/eM6HX4UX QuantRocket is a Python-based platform for researching, backtesting, and trading quantitative strategies. It provides a JupyterLab environment, offers a suite of data integrations, and supports multiple backtesters: Zipline, the open-source backtester that originally powered Quantopian; Alphalens, an alpha factor analysis library; Moonshot, a vectorized backtester based on pandas; and MoonshotML, a walk-forward machine learning backtester. Built on Docker, QuantRocket can be deployed locally or to the cloud and has an open architecture that is flexible and extensible.

In Pipeline, use masks 🎭 when you want to compute ranks for a subset of stocks, such as the top 100 biggest gainers that aren’t penny stocks. Simply ANDing multiple filters (𝘁𝗼𝗽 𝟭𝟬𝟬 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗴𝗮𝗶𝗻𝗲𝗿𝘀 & 𝗻𝗼𝘁 𝗮 𝗽𝗲𝗻𝗻𝘆 𝘀𝘁𝗼𝗰𝗸) can yield unexpectedly few results because you are computing the intersection of stocks that pass BOTH filters (like a Venn diagram). With masks, only the subset of stocks you care about are eligible to be ranked. Learn more about masking in Lesson 8 of the Pipeline tutorial: https://lnkd.in/gcyX4v3p

To run quant strategies in multiple global markets, use a separate crontab (aka, scheduling file) for each time zone where you trade. ⏰ Scheduling each job in the time zone of the market it relates to is easier and less error-prone than scheduling everything in a single time zone. The single time zone approach gets especially complicated and requires editing your job schedules when the crontab time zone switches to daylight savings time and the market time zone doesn’t. Learn more about running multiple crontabs in QuantRocket: https://lnkd.in/e8qtr3dU

What plot should you look at first in a Pyfolio tear sheet? Most traders go straight to the equity curve to see how their strategy performed. Instead, start with a sanity check of the total holdings plot: does it show your strategy holding the number of positions you intended? The total holdings plot can quickly surface bugs in your code that an equity curve won’t reveal.

Borrow fee data can tell you if a rally has staying power. Short sellers are smart money, and borrow fees express their collective enthuiasm to short a stock. When meme stock GameStop rallied in May 2024 on no news other than influencer Roaring Kitty’s return to social media, borrow fees spiked, indicating short sellers’ skepticism of the rally. Sure enough, the rally was short-lived. In contrast, when Nvidia rallied in May 2023 on blowout earnings, borrow fees didn’t budge, indicating that short sellers respected the rally and wanted no part of shorting Nvidia. They were right again: the rally kept going. Learn more about QuantRocket’s borrow fee data, with global coverage and history back to 2018: https://lnkd.in/g2HfcVc8

If your backtesting software or data provider uses ticker symbols as its primary identifiers for stocks, that’s a dead giveaway for survivorship bias. When a company’s stock is delisted, often another company takes over the ticker symbol, especially if the ticker symbol is desirable (like GOLD in the example below). When this happens, the original company’s historical data becomes unreachable if you can only query by ticker symbol, since the ticker now returns the later company's data. Financial software providers who understand survivorship bias use unique, persistent identifiers to avoid this problem. QuantRocket’s identifiers are based on FIGI, an open symbology standard sponsored by Bloomberg. Learn more about survivorship bias: https://lnkd.in/d7n8Kiz

The Pipeline API is a powerful library for performing cross-sectional computations on the entire universe of US stocks. But what if you need to research just a few stocks? Using the `screen` argument to filter the stocks you want isn’t ideal because Pipeline will compute values for all stocks then apply the filter afterwards, resulting in unnecessary computational load. A better approach is to use the `initial_universe` argument (a QuantRocket-only enhancement to the Pipeline API), which drastically speeds up performance by applying the filter before computing values, avoiding excess computations. Learn more about when to use `initial_universe` and when to use `screen`: https://lnkd.in/dgB-eScY

Need to run multiple variants of a trading strategy, such as a large-cap variant and a mid-cap variant, or a long variant and a short variant? Don’t resort to copying and pasting the same code into multiple files, which is error-prone and hard to maintain. Moonshot and Zipline offer a better approach: use inheritance to create multiple variants of the same strategy without code duplication. Learn more: https://lnkd.in/eAGNrdCz  

The pipe character ( | ) allows you to chain two or more commands together, sending the output of one command as the input to the next command. Using Moonshot with pipes, you can send your trading strategy’s orders to the broker for execution or to the logs for troubleshooting or review. Pipes are one example of the many ways QuantRocket leverages standard Unix concepts to maximize flexibility. Learn more: https://lnkd.in/ekVqbS66

With Zipline's "dry run" option, you can write orders to a file instead of sending them to your broker. This lets you troubleshoot your strategy without placing orders, manually inspect orders before placing them, or generate orders for manual execution outside QuantRocket. Similar functionality is available for Moonshot. Learn more: https://lnkd.in/eTvNv7DB