BRbra-ket·May 17, 2018
Quantlib api as a hobby: https://quantra.io
Backtester by author of quantstart: https://github.com/mhallsmoore/qstrader
In Ask HN: Anyone designs financial engineering tools as a hobby?mhallsmoore
qstrader
Python2016–2018
mhallsmoore / qstrader
qstrader
QuantStart.com - QSTrader backtesting simulation engine.
3Distinct people
3Mentions
0Explicit recommenders
3.5KGitHub stars
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What it is
QuantStart.com - QSTrader backtesting simulation engine.
GitHub
Where it is used
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What people are saying
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.
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Mentions over time
HN mentions by month3 dated mentions
Observed mentions: 2016-07-25 21:57:04 UTC to 2018-05-17 11:49:53 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Jul 2016 | 2016-07-01 00:00:00 UTC | 2016-08-01 00:00:00 UTC | 1 | 0 | 1 |
| Aug 2016 | 2016-08-01 00:00:00 UTC | 2016-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2016 | 2016-09-01 00:00:00 UTC | 2016-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2016 | 2016-10-01 00:00:00 UTC | 2016-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2016 | 2016-11-01 00:00:00 UTC | 2016-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2016 | 2016-12-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2017 | 2017-01-01 00:00:00 UTC | 2017-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2017 | 2017-02-01 00:00:00 UTC | 2017-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2017 | 2017-03-01 00:00:00 UTC | 2017-04-01 00:00:00 UTC | 1 | 0 | 1 |
| Apr 2017 | 2017-04-01 00:00:00 UTC | 2017-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2017 | 2017-05-01 00:00:00 UTC | 2017-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2017 | 2017-06-01 00:00:00 UTC | 2017-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2017 | 2017-07-01 00:00:00 UTC | 2017-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2017 | 2017-08-01 00:00:00 UTC | 2017-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2017 | 2017-09-01 00:00:00 UTC | 2017-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2017 | 2017-10-01 00:00:00 UTC | 2017-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2017 | 2017-11-01 00:00:00 UTC | 2017-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2017 | 2017-12-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2018 | 2018-01-01 00:00:00 UTC | 2018-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2018 | 2018-02-01 00:00:00 UTC | 2018-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2018 | 2018-03-01 00:00:00 UTC | 2018-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2018 | 2018-04-01 00:00:00 UTC | 2018-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2018 | 2018-05-01 00:00:00 UTC | 2018-06-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
3 sourcesI don't think any retailer can trade on the scale and speed that would require C/C++ over Python. With lower capital and significantly higher transaction costs, retail traders (mostly) need to have holding periods for at least hours, so speed isn't really an issue. It's more about finding longer term anomalies to deploy maybe at most a few dozen positions, as opposed to reacting to billions of quotes over any trading day.
I think Python is a great tool for finance, particularly with libraries like Pandas, Numpy, etc. Most of the work in those libraries is C/Fortan/etc anyway. Great community. For more 'academic' style work/exploration, I think R might have an edge, but I personally find it more difficult to write and maintain high quality code in R.
I'd like to just throw a quick plug in for an open source project I'm working on [1]. It's
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an event-driven trading library written in Python. We're pretty close to having live trade execution ready in an alpha state.
The motivation is that you can put together a pretty simply strategy in ~40 lines of code. The codebase is designed to be pretty flexible & modular. Mike from http://quantstart.com has some great resources & many of his recent articles use the same library.
[1]: https://github.com/mhallsmoore/qstrader
Disclaimer(s): QuantStart.com founder here, background as a quant dev at a small fund.
I should probably nuance my statement that it is easy to find trading strategies by saying that it is easy to find new trading /ideas/. There are a huge number of freely available trading ideas on forums, pre-print servers (arXiv, SSRN), blogs etc. The trick is knowing how to implement them properly, accounting for any transaction costs and adjusting the parameters of the model. This is often where the stated performance falls down. It takes a lot of time to carry out this sort of research.
Long-term profitable strategies are tricky to find, due to the ever-present spectre of "alpha decay". This is where your strategy's edge is "arb'd out" - everyone else knows what you're doing and so there's no tradeable edge anymore. Hence it is necessary to have a p
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ortfolio of strategies and gradually phase out the ones that aren't doing well, and bring in new ones over time.
That being said there are a large number of trend following funds (known as Commodity Trading Advisors, or CTAs, in the industry) that all broadly do the same thing (follow "trends" in the commodity futures markets) and have great years every now and then. There are some well-known "retail" quant traders who do well by trend following, but it does require quite a bit of capital to trade in futures.
The philosophy that I do try to emphasise is to always be learning and researching new ideas. Also, as you mention, I'm pretty keen on discussing the math(s)/statistics aspect because once you have a solid math capability, it is easier to see where potential edges might exist and how to really assess whether it is a true "edge" or just a statistical anomaly.
I believe someone else in a grandchild comment below said that there are many areas that bigger quant funds won't touch because of institutional incentives. If you have $10bn assets under management (AUM), then you're not going to care about investing $100-200k, even if the returns are good, because it won't move the needle on your monthly reports.
The trick is to niche down into markets that you can spend a lot of time researching to find a distinct edge, that won't likely be touched by bigger funds. One area that is becoming interesting recently, due to the prevalence of satellite data/AI/deep learning-esque VC-backed startups, is building commodity supply/demand models. A good example is forecasting oil supply/demand by analysing large quantities of storage tank heights in global refineries [1].
Also, a small related-to-Zipline plug: I've recently started a free Python-based MIT-licensed open-source backtester [2], predominantly as a learning tool for programming and quant trading. There's about 4-5 of us working on it at the moment and it's in an early alpha stage, but we're always looking for people willing to help.
[1] - https://orbitalinsight.com/solutions/
[2] - https://github.com/mhallsmoore/qstrader/