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quantopian
zipline
Python2012–2024
quantopian / zipline

zipline

Zipline, a Pythonic Algorithmic Trading Library

Finance & CommerceLibraries & SDKs
Explore on GitHubRead Hacker News discussions
14Distinct people
18Mentions
0Explicit recommenders
20.2KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Zipline, a Pythonic Algorithmic Trading Library

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year18 dated mentions

Observed mentions: 2012-11-07 00:55:34 UTC to 2024-01-26 20:40:40 UTC.

024682012-01-01 00:00:00 UTC to 2013-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20122013-01-01 00:00:00 UTC to 2014-01-01 00:00:00 UTC (exclusive): 8 mentions; 1 submissions, 7 comments2014-01-01 00:00:00 UTC to 2015-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20182019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20122012-01-01 00:00:00 UTC2013-01-01 00:00:00 UTC101
20132013-01-01 00:00:00 UTC2014-01-01 00:00:00 UTC817
20142014-01-01 00:00:00 UTC2015-01-01 00:00:00 UTC202
20152015-01-01 00:00:00 UTC2016-01-01 00:00:00 UTC202
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC110
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC303
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC000
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC101

What Hacker News says

18 sources
westurner·Nov 28, 2017
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.
In Ask HN: Why would anyone share trading algorithms and compare by performance?
dsacco·Feb 10, 2017
Very cool. Funds have been capitalizing on Trump's tweets since at least December (but probably November) of 2016: https://www.bloomberg.com/view/articles/2016-12-07/flash-cra... I'd be interested in learning why TradeKing was used , as I haven't used it myself - was it for an especially solid API, or just because TradeKing is easy to get set up and doesn't require a minimum investment? If anyone wants to play around with this and has at least $10k to play with, Interactive Brokers will give you better fees. OP, you might be interested in Quantopian's Zipline algorithmic trading library, which is also in Python: https://github.com/quantopian/zipline Also, I feel like there should be a disclaimer that given how accessible this strategy is (both in required skill and resources) and how much attention the Trump tweets phenomenon has gotten,
Read the rest
any alpha from this has been almost certainly lost to other firms. This is a cool project, but it's probably not actually an effective strategy anymore.
In Trump2cash – A stock trading bot powered by Trump tweets
lewisl9029·Aug 20, 2015
On the topic of automated trading: I strongly encourage anyone interested in real-world algorithmic trading to take a look at Quantopian. https://www.quantopian.com/home It's not HFT by any measure, but it manages to make algorithmic trading extremely accessible by providing a platform where users can develop their algorithms, backtest them against 13 years of market data, live trade them with paper money, or live trade with real money by linking a brokerage account. They have also open-sourced the algorithmic trading engine used to run their platform: https://github.com/quantopian/zipline
In Designing and Building Stockfighter, Our Programming Game
jik·Sep 2, 2014
Quantopian (https://www.quantopian.com/about), Boston, MA Quantopian is hiring software and operations engineers to help us disrupt the world of finance. We've built the world's first web-based algorithmic trading platform, and we're growing our team to support our rapidly expanding user base and to build everything on our ambitious product roadmap. We're well-financed by some of the savviest investors in the business (Spark Capital, Khosla Ventures, GETCO, and various individuals). We work on interesting problems, such as: * Running arbitrary user code on our own servers, with all the associated security implications * Designing and building intuitive and powerful research and development tools and APIs for our users Designing data stores to model real-world financial data and optimizing them for high throughput when running trading simu
Read the rest
lations * Communicating with broker APIs to trade real money on our users' behalf * Metering and autoscaling our cloud infrastructure to respond to varying load * Designing and building an amazing user experience, both on desktop and mobile So far, we've built Quantopian with Python and Ruby on Rails (and a little Node.js) on AWS and Heroku. We depend heavily on Redis, and our Docker usage is rapidly growing. However, we are very pragmatic and our highest priority is shipping user-delighting features built with the most sensible technologies. Be sure to check out Zipline (https://github.com/quantopian/zipline), our open-source backtester. We have several other projects that we plan on open-sourcing as well. We've assembled a top-notch product and engineering team here in Boston, and we're growing fast. We're open to all levels of experience, from still-in-college to seasoned veteran. A financial background is not necessary at all (but is always nice to have). We're still small enough that you will have a material impact on our company's trajectory. Our small size and ambitious goals dictate our approach to talent acquisition and retention: we believe in hiring the best engineers and giving them hard problems to solve and the autonomy to solve them. Ideally, you: * have a solid background in computer science fundamentals * have good communication skills * thrive on designing, building, and shipping mind-blowing features that delight our users We provide: * All the standard stuff (fully paid health and dental for you and your family, 5% 401(k) matching, gym and phone reimbursement, pretax transit, weekly catered lunch). * Your choice of computer equipment * A beautiful new office (in October) in downtown Boston * Fun and intellectually stimulating work environment. We ship early and often. We have lives outside of work. We like each other.
In Ask HN: Who is hiring? (September 2014)
jbredeche·Mar 26, 2013
(disclaimer: I work for Quantopian) I can't speak for the author of the algo, but from the algo, it looks like the relevant lines for your question are 34 (order(stock,indicator * context.bet_amount)) and 35 (log.info("%f shares of %s sold." %(context.bet_amount * indicator,stock)). Our backtester (Zipline) will only order whole number of shares, obviously. If you pass it a fractional number, we take the floor: https://github.com/quantopian/zipline/blob/master/zipline/ge... The log line you're seeing should probably flip the sign of the number of shares before logging. order(-63, sid(6109)) means sell 63 shares of security 6109. The log line is simply logging the negative value instead of the positive one. Users can log anything they want in their backtest. As for the slow performance, apologies - being on HN has resulted in a lot of
Read the rest
people running this algo and while we're scaling up new servers, it's taking a bit of time to distribute load. thanks for using Quantopian! [Edit - added source link to Zipline's order method]
In Show HN: How I Used Machine Learning to Optimize My Trading Algorithm
twiecki·Mar 25, 2013
That's currently not implemented, but definitely something we would like include. What I'd really like to see is a client for a bitcoin exchange. See our contribution request page for more details: https://github.com/quantopian/zipline/wiki/Contribution-Requ... Finally, live trading is something which we are working on to enable on Quantopian (which uses zipline for backtesting your algorithms). The syntax is very similar so you could just convert your zipline strategy to Quantopian (which is free to use).
In Show HN: Optimized trading algorithms using IPython parallel and ec2
dunster·Mar 23, 2013
That's a very interesting system you've built. The the thing about backtesting a strategy is that it is very easy to make a mistake in your backtester. Look ahead bias is the most common mistake. Another challenge is the data. Are you testing against a history of stocks that includes bankruptcies? If not you have survivorship bias. I suggest you take a look at my website, www.quantopian.com. Look at our open-sourced backtester, https://github.com/quantopian/zipline. Between the two we can help you get past those two sources of error.
In Show HN: Bateman, a stock trading system I'm working on
fawce·Jan 24, 2013
Trust is essential (http://blog.quantopian.com/on-trust/). We see trust as something we earn over time with transparency and openness. We even open sourced our core intellectual property, the backtester itself: https://github.com/quantopian/zipline We also believe setting our own incentives to match our members' needs is key to sustainable trust. That's why Quantopian doesn't invest its own capital - we want to be purely focused on and motivated by serving quants. Being a successful quant takes many things - talent, mentorship, access to data, great systems, discipline. We started with backtesting because many aspiring quants end up skipping rigorous backtesting because of the time necessary to develop the test harness. We focused on community from the start to help connect new talent with mentors. If you're not ready to trust us with you
Read the rest
r algorithms, I hope you'll trust us with a bit of your time. Come share some of what you've learned over the years with our community.
In Quantopian Brings Algorithmic Trading To The Masses

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