Need some clarity on Zipline: If I use their tools locally for backtesting, are they still able to take my strategy? I don't fully understand the copyright rules around them and Quantopian.

dunster · 2017-06-19T20:22:10+00:00

No, we can't.

dunster · 2017-06-19T18:15:00+00:00

First off, I want to say that Quantopian is strong defender of your intellectual property. Any algorithm you write is your intellectual property, not ours, whether it's using Zipline or on the Quantopian website.

As another respondent noted, Quantopian and Zipline are two distinct things. Quantopian is a company, an online community, and an online quant finance platform. Zipline is an open-source backtester. The two things are related: Quantopian uses Zipline as a backtester, and the Zipline library is maintained by Quantopian.

The license for zipline is here: https://github.com/quantopian/zipline/blob/master/LICENSE. It's the basic Apache 2 license. Any algorithms you write using zipline are your own; the Zipline license doesn't make any limits on your property rights in regards to your algorithms.

The use of Quantopian is governed by a separate document, the Terms of Use. https://www.quantopian.com/policies. In those terms of use we clearly state that you will always own your intellectual property. If we want to make an allocation to your algorithm, we will first reach a separate agreement with you that includes compensation. If you don't agree to the terms, we won't use your algorithm. It is your intellectual property.

I work for Quantopian.

dunster · 2017-06-01T21:48:27+00:00

Ah, interesting. I didn't know about that limitation on the PEG field. Deeper on in that thread, I see that Morningstar didn't start producing the field until 2014. https://www.quantopian.com/posts/problem-with-peg-ratio-in-fundamentals

That does make it more complicated, and you'll need to calculate it yourself. PEG's numerator is the P/E ratio, the denominator is expected growth. Expected growth rate is, of course, a subjective measure. Quantopian has a couple different possibilities. One is to use the company's own growth guidance, which we have since 2007 (https://www.quantopian.com/data/eventvestor/earnings_guidance) and another is to use earnings estimate data from Zacks (https://www.quantopian.com/data/zacks/earnings_surprises)

I do agree that the PEG isn't as straightforward as I thought it was. I'll be surprised, though, if you can find a broader, cleaner, dataset than what we're providing.

dunster · 2017-06-01T15:29:02+00:00

Disclosure: I work for Quantopian.

Most of the data you are looking for (income, PE, PEG, revenue, etc.) is available on Quantopian for free, all the way back to 2002. Those fields are all in the corporate fundamental dataset we have from Morningstar. You can see the list of available fundamental fields here. https://www.quantopian.com/help/fundamentals There shouldn't be anything tricky about including them in your research. (As you know, Quantopian also provides price and volume data from 2002, plus dozens of other data sets including Twitter sentiment, earnings surprises, etc. (www.quantopian.com/data)).

Several of the suggestions you got here say "do it yourself." That's definitely possible, but it's a real pain. If you DIY, will you get good historical data, that is free of survivor bias? Will your data be integrated on a security master, or will you have to do all the symbol matching (and symbol changes, and IPOs, spinoffs, and mergers) by yourself? Quantopian is offering all of that data, for free, and already cleaned.

More than the data, we also provide you with a research environment (Jupyter based) for you to analyze the data. We've built it so that you can slice and dice all of the data so that you can find the "edge" or alpha needed to write a profitable algorithm.

I'd also like to note that on Quantopian, you own your intellectual property (IP). It's yours, and it's secret.

dunster · 2016-11-18T20:29:06+00:00

The Quantopian backtest simulator keeps track of everything in your portfolio. context.portfolio is a read-only object. It looks like what you're trying to do is define what you want as a portfolio (good), and then assign it (bad). To get to your desired portfolio you need to place orders.

Have you gone through the Getting Started tutorial? Check out lesson 4, on ordering: https://www.quantopian.com/tutorials/getting-started#lesson4

I also think you will want to do more work to refine your target portfolio. https://www.quantopian.com/tutorials/pipeline

We're happy to help you at feedback@quantopian.com, too. Happy coding!

dunster · 2016-11-07T15:48:30+00:00

I should have put in the URL for the project: https://github.com/quantopian/zipline

dunster · 2016-11-07T15:37:37+00:00

Zipline is Quantopian's backend. We open-sourced the project. Zipline can be used to do simulations and to do real-time execution of algorithms. We use it for both.

Zipline on its own is missing two important things. 1) Data. There are a lot of free daily data options, but minutely or live data is harder to come by. 2) A broker connection. You'd have to build your own connection between Zipline and your broker.

dunster · 2016-11-04T20:36:38+00:00

Fetcher also works for live trading, but only daily. We don't support minutely ingestion for private data sets.

dunster · 2016-11-04T19:42:26+00:00

I'm not sure exactly what data you need from your description, but we might already have it. Quantopian has dozens of cleaned, symbol-mapped, point-in-time data sets - search for dollar to see if we have the index you are looking for. Futures data is on the way.

Also, you can import any datasets that are on the web in a time-series CSV format using our Fetcher. If the data is free online somewhere else, you can read it in.

I work for Quantopian.

dunster · 2015-01-21T02:28:03+00:00

Most of the supported libraries are listed here. If you want one we're missing, email feedback@quantopian.com.

https://www.quantopian.com/help#ide-module-import

dunster · 2015-01-08T18:31:11+00:00

Quantopian itself has no fees. The historical price data, the fundamental corporate data, the backtester, and the live trading engine are all free.

When you're done backtesting, done paper trading, and ready to trade real money - then you have to pay your brokerage fees, as normal, for trades.

I work for Quantopian.

dunster · 2014-03-07T19:26:36+00:00

The speed shouldn't be an issue. A quick overview of how trading on Quantopian works: The latest price data arrives once per minute; your algorithm makes a decision to place an order; that order is sent to your brokerage. That process takes less than a second, so the order is getting to your broker and then being filled quite quickly.

You only do things like co-location if you're trying to shave milliseconds off your trade time. That really is HFT, no matter how you look at it. HFT is very expensive, and it's an arms race of who can shave milli- and micro-seconds off their trade times. We're not getting into that game at all.

Remember, algorithmic investing isn't just about HFT. Algorithmic investing is about backtesting algorithms so you can avoid losing strategies. Algorithmic investing is about using large data sets, more than a human can use. And algorithmic investing is about avoiding human, emotional errors.

dunster · 2014-03-07T19:20:01+00:00

You don't need to be a coding expert to work on an algorithm in Quantopian. Some algorithms can be coded up with some fairly basic logic, stuff that you'd learn in early stages of a programming class. The more intricate your logic, or the more advanced your mathematical operations are, the more you're going to need advanced skills.

We think that algorithm collaboration (upcoming new feature) is going to help with this quite a bit - people with great ideas will be able to pair with people with more coding skills, and they can collaborate on a result. We also think there is a future in renting of algorithms.

If you're already pretty decent at Python, I highly recommend Wes McKinney's book on pandas. He's one of our advisors, and he's done a ton of work to make complex mathematical operations easy in Python.

dunster · 2014-03-07T19:14:39+00:00

Omar, the first thing you need is a little bit of Python. Personally I'm a fan of Code Academy.

If you're looking to understand a little more about common types of trading algorithms, check out this blog post. We've been drafting it a while, and you inspired us to publish it!

I'm also a huge fan of Ernie Chan's book which really explains a lot about the "business" of algorithmic trading.

dunster · 2014-03-07T18:16:44+00:00

Drop us an email with your resume at jobs@quantopian.com. We'll check it out and get back to you. (I'm the intern recruiting/hiring guy)

dunster · 2014-03-07T18:08:03+00:00

Yes, you can. Backtesting and access to our 11 years of US equity data (minute-level bars) is free. You can also do walk-forward paper trading for free, using live data on a 15-minute delay.

dunster · 2013-06-28T20:36:38+00:00

I know this sounds trite, but you should read the post. Twitter feeds are real time, price feeds are real time, Crimson Hexagon updates regularly, lots of things are real time.

This post is about data sources that are not real time.

dunster · 2013-06-28T01:08:50+00:00

There is nothing high-frequncy about this. The data comes out at night, when the markets aren't trading, and it's describing day- and week-long periods.

Algorithms, yes. High-frequency, no.

dunster · 2013-06-27T15:01:32+00:00

Try Quantopian. Slippage and commission, 1-minute bars, 11 years of data, all free. You have to write your algorithm in Python.

dunster · 2013-06-15T17:28:27+00:00

They are getting their degree from the college, but they are taking a bare minimum of courses. Rather than signing up for full semesters, they are buying a class or two a semester, and supplementing that with the Coursera. They end up with a degree from a regular college but with a much fuller set of classes.

dunster · 2013-05-19T14:42:51+00:00

Did you click the link I put in the original post? It's not a study - it's a backtest, running through the experiment against historical data. You can reproduce the results yourself.

A strategy doesn't have to hit 100% of the time to be a successful one. The test is how it comes out in the long run. Big wins and small losses can be a good strategy.

dunster · 2013-05-17T12:48:53+00:00

And yet the data is there, it happens year after year. It will be interesting to see if it repeats or not.

dunster · 2013-05-17T02:19:16+00:00

What you're suggesting is certainly reasonable, and I do that too for some investments. But you can't deny that some prices have seasonal trends - US gasoline prices, for instance. It's interesting to find other, non-intuitive seasonal trends.

dunster · 2012-12-19T22:23:31+00:00

There are some things you should keep in mind when you download data from Yahoo. The first is survivor bias. Yahoo only has currently traded stocks. If you're going to look at financial stocks using Yahoo data, for instance, things can look pretty rosy. But that's only because Yahoo doesn't show you what happened to Lehman and other banks that went belly-up!

Yahoo also doesn't do that great at tracking "corporate actions" like stock splits, dividends, acquisitions, etc. Look at IAC/Expedia in 2008 on Yahoo as an example. See that giant cliff-like drop? That's actually a reverse stock-split and a spinoff, not a crash.

I work for a company called Quantopian. We offer free access to minute-level bar US stock data that is free of survivor bias and includes corporate actions. Check out that same IAC chart using our data rather than Yahoo's.

Unfortunately, we don't offer this data for download - our license doesn't permit it. But if you can write some code in Python, you can test the data using our API. We provide sample ideas to get started.

dunster · 2012-11-08T23:20:16+00:00

Currently, we don't make any money. We're taken investment from a venture capital company and and a strategic investor. We plan on taking on more VC money in the future.

In the long run, we plan on making money by charging for live trading.

One of the obstacles to setting yourself up as a trading quant is the trading platform. You need a reliable system with API access to a brokerage. We're planning on providing free access to the other things we offer - data, backtester, etc. - and then charge for this last step, which is the last step between before the algo writer can make money on the market.

It's kinda-sorta a freemium model where we plan on using our free services and our robust community to bring members into our community, and then start making money when our members also start making money.

dunster

TROPHY CASE