Quantopian inspires talented people everywhere to write investment algorithms. Select authors may license their algorithms to us and get paid based on performance.

Using Quantopian

1. Learn about data science for quantitative finance from our tutorials and lectures.
2. Analyze our dozens of data sets in a Quantopian Notebook, and look for a trading strategy.
3. Write an algorithm in Python using our Interactive Development Environment (IDE). You can clone an algorithm from our community to get an started, or code your own customized algorithm.
4. Enter it in the contest or see if you qualify for an allocation

Our goal is to make algorithmic trading available to everyone - regardless of your location, background, or experience. We provide the tools, data, and capital - all you need to think about is the strategy. And if you find an idea that is successful, we want you to share in the trading profits on a capital allocation.

Quantopian also hosts a community where members can ask for help, share ideas, discuss code, and share data. Members can learn from each other and work collaboratively.

There are more answers about Quantopian in the About Quantopian section below.

Quantopian is providing capital allocations for our community's top-performing algorithms. The author of the strategy shares in the net profits on the algorithm.

The first step to be selected for an allocation is to create a track record for your algorithm. Here's how:

• Develop your own strategy using our state-of-the-art algorithm development environment.
• Enter your algorithm in the contest, and see how it performs.

Want suggestions how to write a robust trading strategy? Read our lectures.

Only strategies with 6 months or more out-of-sample time will be considered for an allocation. If selected, your algorithm will invest the capital and you will earn a share of the net profits from your algorithm. The evaluation is based on the results of your strategy’s track record and backtest. As always, Quantopian will respect and protect your intellectual property. You are free to decline our allocation.

Yes! Quantopian's platform and data, including price, volume, and corporate fundamentals data for all 8000+ US equities and futures from 2002 to present is free for everyone to use. We also provide a collection of partner datasets. Some of these datasets are free and some are subscription based with free samples.

You own your algorithms. Everything you write is yours. Quantopian does not own your algorithms; you do.

Your algorithms are kept secret. We are committed to protecting your intellectual property and keeping it safe. Ideas are some of the most valuable assets anyone has. We take this responsibility to our members extremely seriously

If you choose to do so, you can share your algorithm so that others can see it. If you choose to share an algorithm, it is still owned by you, but it is obviously no longer a secret. A shared algorithm can be used and adapted by others.

Certain Quantopian employees can see your algorithm in very specific situations, such as when providing technical support or resolving a site stability issue. Our Terms of Use goes into greater detail about these exceptions.

The Getting Started Tutorial walks you through the basics of writing an algorithm on Quantopian. The tutorial is made up of 11 short lessons with plenty of examples in both written and video form. Check it out!

No.

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian.

In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.

We currently provide minute-level price, volume, and fundamental data of all US stocks from January 2002 through the previous trading day for backtesting. The previous day's data is uploaded every night. We also have price and volume data for 72 US futures, going as far back as January 2002.

Minute-level bar data consists of the high, low, open, close, and volume for each minute that a stock or futures contract is traded.

The price data includes all companies or futures that were traded, including companies that have subsequently stopped trading. This is very important in order to avoid survivor bias. Without this complete data set, your algorithms would be be blind to the possibility of bankruptcy and the resulting losses.

Fundamental data from Morningstar is available, free of charge, for over 5,000 companies. This data set includes over 600 metrics for use in Quantopian's backtester, as a point-in-time database.

We also offer a collection of partner datasets such as VIX, news sentiment, earnings calendars, and more. Some of these sets are completely free to use, and some have free samples with paid monthly subscriptions for the full set.

For paper trading and real-money trading, we get a realtime feed of trades from Nanex's NxCore product. Those trades are bundled into one-minute bars and fed to the trading algorithms. Paper trading data is provided on a 15-minute delay. Real-money trading is processed without delay. Note that live trading and paper trading are not currently available to algorithms that use futures data.

No, you don't need to buy data to be considered for an allocation.

All of the partner data sets on Quantopian include a free sample. You can write your algorithm using the free data, and your algorithm will be considered for an allocation.

At this time we support US equity trading and US futures trading. We have data for US stocks, ETFs, and US futures. There are 72 US futures that can be traded on Quantopian.

We plan on adding more data sources in the future. Please give us feedback and let us know what data you are looking for.

We also plan on making it easy for our members to add and share their own data sources. We'd like to see what data ideas the community can come up with.

Quantopian uses the last traded price as the close price for the security. Depending on the data source, others may use end-of-day (EOD) prices. For example, Yahoo is an EOD datasource. Yahoo and other EOD data providers get their price and volume data from the official exchange record. Quantopian's data is generated by the actual trades, regardless of what exchange the trade as made on. The EOD sources rarely exactly match data derived from intraday data. For instance, the official close for a NYSE stock is the last trade of the day for the stock on NYSE. But if the stock also trades on Chicago, Pacific or another regional exchange, the last trade on one of those exchanges could be our close.

If you haven't seen it yet, the Getting Started Tutorial walks you through the basics of writing an equity-trading algorithm on Quantopian. The tutorial is made up of 11 short lessons with plenty of examples in both written and video form. Check it out!

If you want to learn how to write a strategy that trades futures, the Getting Started with Futures Tutorial teaches you how to analyze futures data in Research and then walks you through implementing and backtesting an algorithm that trades on that data.

For more tutorials, see the Tutorials page.

The API documentation is a good reference for everything else not covered in the tutorials.

Once you've got the hang of the API, the Lecture Series teaches more advanced statistical content related to Quant Finance. Vetted by professors from top Universities, the Lecture Series is a good next step.

We have developed a list of 13 tips to write robust, intelligent algorithms for live trading. Keep these in mind as you're coding your strategy and looking for a capital allocation.

We don't have any Python resources ourselves, though we're thinking about ways to help our community be better Python programmers. Until then, you should take a look at Python tutorial, Dive into Python, and Learn Python.

Our best advice is to go to the community and see what they've done, and innovate on top of someone else's idea. If you're still stuck, write a new post in the forums - you might find someone with an idea who would like to collaborate with you. Obviously, don’t use a third party’s proprietary algorithms or materials without getting any necessary consents first.

Only specific, whitelisted Python modules can be imported. If you need a module that isn't on this list, please let us know.

Unfortunately, no, you can't. We're only supporting Python and we have no plans to support another language at this time. The good news is that Python is reasonably simple and very powerful. You won't have much difficulty porting your work into Python. If you get stuck, ask the community for help. You'll find them very happy to help.

You can use any IDE that you'd like, but in the end you need to paste the code into our IDE to run a backtest. Give our IDE a try, though, and we think you'll like it. There are many smart auto-complete features that make writing a backtest easier. We've done a lot of work to put in smart error messages that will help you debug. And you can't beat the feedback loop. Press the build button (or just control-B on your keyboard) and you'll see a quick backtest run immediately - you can't get that in your IDE.

If your heart is set on using your own development environment, take a look at Zipline. Zipline is our backtester, and we have open sourced the code. If you want, you can connect Zipline to a data source and use your own development environment.

Unfortunately, we cannot do that. We license the data from a third-party vendor. Our license permits you to use the data, but it does not permit us to give you a copy of it.

Our backtester runs an event loop (handle_data()) once per historical minute. Every run of the loop loads the data for that minute and executes your algorithm. If the algorithm calls for a buy or a sell, that order is placed. If there are any open orders, the backtester attempts to fill them. (Note, an order that is placed in one event loop is not filled in that loop; it is filled in the next loop. That prevents any look-ahead bias from leaking into your algorithm.)

For a walkthrough of how to write an algorithm in the bactester, check out the Getting Started Tutorial.

This is a very complex area, and there is no 'right' answer. It's impossible to say for sure what impact an order would have had in some alternate history where your algorithm was running.

Our backtester makes its best effort to calculate the realistic impact of your orders on the execution price you receive. When you place an order for a trade, your order affects the market. Your buy order drives prices up, and your sell order drives prices down; this is generally referred to as the 'price impact' of your trade. The size of the price impact is driven by how large your order is compared to the current trading volume. The backtester also evaluates if your order is simply too big: you can't trade more than market's volume, and generally you can't expect to trade more than a quarter of the volume in any minute. All of these concepts are wrapped into the backtester's slippage method. In addition, you can control how this is applied within your algorithm.

Learn more about how slippage works when trading futures in lesson 9 of the Getting Started Tutorial.

Learn more about how slippage works when trading futures in lesson 11 of the Getting Started with Futures Tutorial.

When you run a backtest your algorithm is compared to a benchmark. The default benchmark is the price return of SPY, an ETF designed to match the S&P; 500. You can also customize the benchmark in your algorithm.

When you 'build' your code in the IDE, that runs a backtest. A full backtest is essentially the same, but it permits you to see a lot more detail - deeper risk metrics, position information, transactions, and more.

Yes, you can! Quantopian's backtester is released as an open source project called Zipline. If that's where your passion lies, you can clone our code and modify it as you see fit. If you make a change that you think others would like, we'd be delighted to have you pass the change back to us.

When your algorithm calls for historical price or volume data, it is adjusted for splits, mergers, and dividends as of the current simulation date. In other words, if your algorithm asks for a historical window of prices, and there is a split in the middle of that window, the first part of that window will be adjusted for the split. This adustment is done so that your algorithm can do meaningful calculations using the values in the window.

For a more detailed explanation with an example, see this page.

Dividends are also applied point-in-time. On market close of the dividend ex-date, the value of the stock is marked down by the amount of the dividend. On the dividend's pay date your portfolio cash is increased by the amount of the dividend. When you look at a historical window of pricing data, it will be adjusted for the dividends.

Quantopian's goal is to provide everything you need to be a successful algorithm writer. Writing an algorithm takes more than just data, an IDE and a backtester. Before they get to the code, algorithm writers spend 90% of their time researching their ideas. The Quantopian Research platform was built to enable that research process by providing you with a great analytical tool and our huge, high-quality data sets.

In the software development process, beta is a phase where software is still incomplete. It can have bugs, or known limitations, but still has enough functionality to be useful to users. We want to get research out to our community so that they can start using it, and help us understand what to build next. However, the software isn't done.

There are lots of things we want to do to continue to develop this tool. We want to continue to improve the sharing and collaboration functionality, we want to enable better backtesting from research which will also facilitate parameter optimization. We want to make it easier to get backtest IDs so that you can more easily evaluate your backtests, and we want to allow you to evaluate your live trading results as well.

We also know we need to make the workflow between research and the IDE better. This includes sharing data and code between the two tools. We are committed to continuing to improve this tool and over time you should see a deeper and more robust research environment emerge.

Research is for exploring the data in the Quantopian platform and for researching algo ideas. The IDE has been optimized for building algorithms. While you can build algorithms in research using Zipline, it's different enough from how you need to write algorithms in the IDE that it tends to be frustrating. We recommend that when you're ready to start backtesting an algorithm, you move from research into the IDE. We are working on making the workflow between the two environments better, and hope to offer an easier transition between them soon.

About twice a week, we conduct maintenance on the research environment. This is to continue to allow for improvements to the system. Most of the time, research will not be down for more than a few minutes. During the maintenance window, you will see a maintenance page when you try to access the research platform. In addition, any files you have previously uploaded to the data folder will need to be re-uploaded and all of your kernels will have been restarted. This means that any information stored in memory will be lost and your code will need to be re-executed.

Except as described in our terms of use, we will not look at your notebooks without your permission. Your notebooks are encrypted on the server where they are stored. We have also architected the system in an effort to protect your notebooks from other users and to ensure that only you can see your work. With that said, security is always an on going effort and we have more to do.

We take privacy and security very seriously. Except as described in our terms of use, we will not access your algorithm without your permission.

From a technical perspective, we have a number of security features in place. We do not store your password in plaintext in our database. We require SSL encryption for the Quantopian application. We require secure websocket communication for all backtest data.

Trading algorithms in our database are protected by 256-bit, salted AES block-chaining encryption.

All features and data can be used for free. We only require credit card information for subscriptions to specific data sets from our partners. In these cases, we do not store your credit card information, relying on Stripe to process the payments.

Quantopian is an investment management firm. We manage money with the best strategies, and pay the individual authors a share of the returns.

Quantopian gets revenue from our investment management business. We have also received funding from Andreessen Horowitz, Bessemer Venture Partners, Khosla Ventures, Spark Capital, and angel investors.

Quantopian was founded by John 'Fawce' Fawcett and Jean Bredeche. Fawce previously founded Tamale, which was acquired by Advent Software. Jean has worked at several startups including Tamale, where he met Fawce, and HubSpot.

You can read more on our About Us page.

We continue to improve the education, improve the community, improve the platform, and add new data sources. Feedback is welcome and appreciated!

No, we don't offer that service. In the past, Quantopian had broker integrations, but that feature was discontinued.

No, it's not high-frequency trading. High-frequency traders are trying to shave milliseconds off order times and microseconds off of trade times. Quantopian is aimed at much more low-frequency trading. Our trading is on the minute level at the fastest, and most algorithms we test have position hold times for several days.

Not yet. All of our support is done through email to [email protected] or through questions in our forums. When we get closer to a paid product offering, we'll add paid support of some type.

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