Quantopian is a crowd-sourced quantitative investment firm that provides the world's first browser-based algorithmic trading platform. On our platform, you can write code to execute your investment strategies, instead of relying on manual trades and emotional urges.
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 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:
Want suggestions how to write a robust trading strategy? Read our tips and suggestions.
Only strategies with 6 months or more out-of-sample trading (paper or real-money) on the Quantopian platform will be considered for an allocation. If selected, your algorithm will invest the capital and you will earn a share of the returns 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 community and backtester including price, volume, and corporate fundamentals data for all 8000+ US equities from 2002 to present is free for everyone to use. There are some third-party datasets that are free as well as some that are available by monthly subscription.
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.
We currently support Interactive Brokers (IB) and Robinhood for live trading in our open beta. If you have another broker you'd like us to add, let us know by sending us a feedback email.
Navigate to the Robinhood website and click 'Get Started' to begin the process of registering for a new account.
Navigate to the Interactive Brokers website and select your account type. Most people open an Individuals account to get started. If you have questions about the application process or creating an account, contact IB's Account Services Group at (312) 542-6856.
Interactive Brokers LLC is not affiliated with and does not endorse or recommend Quantopian, Inc. Interactive Brokers provides execution and clearing services to customers who integrate their Interactive Brokers account with their Quantopian account. For more information regarding Interactive Brokers LLC, please visit www.interactivebrokers.com.
Only one live trading algorithm can be run per real money account. To launch a second live trading algorithm, you will need to create a separate account.
The algorithm will synchronize all existing positions from your account into the Quantopian live trading dashboard. You will see the price update for all existing positions and be able to monitor your entire portfolio of US equities.
Paper trading is not currently available through Robinhood. Instead, we recommend that you test your algorithm through Quantopian's paper trading simulation
Interactive Brokers allows one free demo (paper trading) account per real money account. A demo account simulates the real market under no risk to the trader.
To set up an IB paper trading account go to Interactive brokers and navigate to: Account Management > Manage Account > Settings > Paper Trading.
Once the paper trading account is created, you can enter your credentials in your Quantopian trading profile and deploy to the market to test your strategy.
Adding a second user to your IB real money account is straightforward and can be done at no cost. Simply log in to Interactive Brokers and navigate to Manage Account > Access Rights > Users > Add User
If you have any trouble with the user addition process, you can reach IB Pro Services at (203) 618-7791 ext. 2
IB only lets you login from one location at a time. Quantopian needs to be logged in to your IB account in order to trade. That means you can’t log in with your IB account from another location while a Quantopian algorithm is trading. When you login to IB through Quantopian you stay logged in continuously, day after day, saving you the trouble of a daily login process.
Live Trading IB Accounts
If you have an algorithm deployed on Quantopian and decide to simultaneously login to WebTrader or TWS with the same account, then IB will log you out. This will terminate the Quantopian login to IB, and Quantopian will not be able to trade.
To manage this, IB permits you to create a second login for your IB account. You can use the second login if you want to login to TWS or WebTrader to monitor your account. Adding a second user to your IB real money account is straightforward and can be done at no cost.
Simply log in to Interactive Brokers and navigate to Manage Account > Access Rights > Users > Add 2nd User. For more detailed instructions, click here.
If you have any trouble with the user addition process, you can reach IB Pro Services at (203) 618-7791 ext. 2
Paper Trading IB Accounts
Unlike real money accounts, IB does not allow you to add additional users to demo accounts. During trading hours, you cannot monitor your IB demo account with TWS or WebTrader without interfering with your Quantopian algorithm.
If you attempt to login via TWS or WebTrader during market hours with the account configured in Quantopian, Quantopian will not be able to place trades. Quantopian will attempt to reconnect, which will log you out of TWS/WebTrader. If you need to log in to your paper account, you need to stop your algorithm first. To stop your algorithm, go to My Algorithms page, select your algo and press the 'Stop Algorithm' button.
If you login to WebTrader or TWS after market hours you will not interrupt any trading activity and Quantopian will automatically reconnect to your IB account in the early morning hours before market open.
Margin is handled by the broker. Here is an overview of the Interactive Brokers margin requirements.
The open beta only supports IB accounts with USD for the base currency. If you want to use an additional currency, you can add it as the funding currency to your IB account.
If you have an institutional IB account, you can create a separate base currency for each sub-account.
We currently provide minute-level price 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.
Minute-level bar data consists of the high, low, open, close, and volume for each minute that a stock is traded.
The price data includes all companies that were traded, including companies that have subsequently gone out of business. 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 third-party 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.
At this time, no. The only data we have is US stocks and ETFs.
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.
Our Quantopian documentation goes into great detail about what functions are available. Between the functions and the examples, you should have enough to get started.
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.
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.
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.
In minutely mode, 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.)
You can choose to run at minute or daily level of granularity. The only difference is that in daily mode, the backtest runs one event for every day of available data. Daily level data is simply the aggregation of the minute level data.
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.
You can control how this is applied within your algorithm. Learn more about this in our Quantopian documentation.
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.
We adjust our data to take into account corporate actions. We use 'adjusted close prices.' That means that the effect of all stock splits and merger activity are applied to price and volume data. As an example: The stock you're looking at is trading at $100, and has a 2:1 split. The new price is $50, and all past price data is retroactively updated 2:1. In effect, that means you can ignore stock splits unless the stock you are looking at will split in the near future - as soon as it does, that will be applied retroactively to all data.
As an example, you can see this backtest using
Dividends are applied differently. On market close of the dividend ex-date, the value of the stock is marked down by the amount of the dividend. On the dividen's pay date your portfolio cash is increased by the amount of the dividend.
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.
Every Thursday at 5PM we will 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.
As with your algorithms, we will never look at your notebooks without your permission. We have also architected the system in order 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. Your notebooks are not currently encrypted on the server where they are stored, and that is something we plan to rectify.
We take privacy and security extremely seriously. We will never access your algorithm unless we are helping you troubleshoot issues.
From a technical perspective, we have a number of security features in place. We never 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.
All trading algorithms in our database are protected by 256-bit, salted AES block-chaining encryption.
We never save the password of your brokerage account.
Quantopian is an investment management firm. We manage money with the best strategies, and pay the individual authors a share of the returns.
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, including our LinkedIn profiles, social media accounts, and code repositories on our About Us page.
We continue to improve the backtester, improve the community, improve the IDE, and add new data sources. Feedback is welcome and appreciated!
We launched our open beta for live trading and it is available now! If you have any questions about getting started please send us feedback.
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.
Quantopian uses Gravatar for profile photos. Go to Gravatar and create an account using the same email address as your Quantopian email address. Upload a profile image there and you'll then see it in Quantopian.