Quant finance is becoming the most common way to invest, yet most investors have no way to compete due to the massive barriers to entry.
The future of quant finance will be radically open-source; why should millions of engineers re-invent the same brokerage and data integrations over and over again?
These connectors should be done once and open-sourced. Like device drivers for your mouse or keyboard, you should simply plugin a brokerage/dataset, which will work to a standard interface.
Yet funds and vendors keep these locked up because they're a "competitive advantage" to prevent other funds/vendors from starting.
We're breaking that cycle.
QuantConnect has just open-sourced 15 brokerage integrations. It represents roughly seven years of work from a dozen engineers. The Interactive Brokers connector has executed $20B in notional volume across 200,000 live strategy deployments.
Our mission is to create a Linux for quantitative finance. You will never need to write another brokerage connection or data set integration because they'll all be open-source and free.
Let's make quant finance more transparent and accessible.
I went to the page linked and expected to easily find a Python quick start example, where can I find it?
By contrast, Blankly has an easy quick start right in the Readme on the main repo.
I'm Jared the founder of QuantConnect. Happy to answer any questions! We're really excited to be sharing this milestone and have some incredible projects in the works!
QuantConnect recently announced full python library support; and we have launched https://www.quantconnect.com/tutorials to help people write quantiative strategies in Python.
QuantConnect & LEAN gives you ability to do tick->daily resolutions; for equity, morning-star, future, option, forex and cfd trading - all with a fully open source project which includes samples of data to get you started.
The grunt work is still done in C# so its faster than other full python based backtesting engines. Edit: I'm the Founder @ QC.
1. Any chance of a Robinhood integration à la Quantopian?
2. How are the architectural revisions[0] coming along?
Also some totally unsolicited feedback:
If I'm being completely honest, I found it difficult to get going with QC. The documentation is decent, but there's not enough to avoid having to review LEAN source right off the bat. The examples also tend to mix helper classes with lower-level functionality, and that can create confusion.
The framework itself feels a bit over-reliant on OOP. Some aspects feel too tightly coupled, others too little. Obviously LEAN has been around for many years now, so architectural baggage is perfectly understandable.
A total rewrite I'm sure isn't feasible, though I'd suggest the following ethos in any case:
a) Design primitive user-accessible data structures with virtually no inbuilt functionality.
b) Build low-level components that operate using those data structures.
c) Build high-level components that compose low-level components.
d) Allow users to author their own components, and to compose components of any type however they see fit.
Pretty sure you're already on that track in a sense, so it's good to see things headed in the right direction. What keeps me from writing a custom framework is the data, the fact QC does a ton of grunt work, and that it's well-tested.
tl;dr Please break apart the monolithic QCAlgorithm class as much as you can! :)
When promoting your company, please say so, also on QuantConnect one cannot actually see the data, so there is no way to verify how good the data is...
Sorry SirL! I edited it within 10 sec to be explicit but you must've refreshed before I'd updated it =)
We provide FX/CFD data for free download; the other data is restricted by the exchanges sadly so we can't make it available. Instead we put the tools we used to make it into LEAN format into LEAN (/Toolbox) so you can purchase it and convert it yourself.
Thanks jaredbroad, just out of curiosity, what are the average yearly returns on your top 3 users, how much are you investing with them and how do you split the profits?
First of all, you're criticizing QuantConnect's data when you claim to get your data from ebay.
Second, yes you absolutely can verify QuantConnect's data. You can use it as much as you want within the context of their platform, you just can't download the data en masse from their platform and use it on your own. But if you have tick data (equities) or minute data (options) yourself, you can certainly verify it (which of course, you don't, because you think ebay is a good source for financial data).
I am going to continue griefing you in threads like this where you spread blatant misinformation, because at this point I'm convinced you have an ulterior motive or are in fact selling this data you keep talking about on ebay.
Unfortunately you cannot display QC tic data to be able to very it against you broker for example. As I said before if you have a good and cheap source, please share it with everyone...
Futures have millisecond timestamps (trades/quotes), equity trade ticks are rounded to the nearest second, cfd/forex are millisecond quote bars. For options we have minute resolution data =)
At QuantConnect we offer minute level options trades"es for your backtesting. Its 400TB of data :D. (I'm founder of QC). To backtest it we run it on 5GHZ water cooled machines! We play with really fun toys :D
Equities, FOREX, Futures, Options; tick, second, minute, hour and daily resolutions. Python, C# and F# backtesting. Dozens of models for improving the accuracy of your backtest.
Live trading on IB, Tradier, FXCM, Oanda and paper trading.
Local charting built in for desktop and backtesting.
Lots of tools provided for free data downloads to work with public free data libraries. lean.quantconnect.com
It is a little disingenuous to say that zipline doesn't support python, the short description from github says: "Zipline, a Pythonic Algorithmic Trading Library".
Zipline also supports equities at minute and daily frequencies.
There is no charting built into zipline itself but tearsheets and graphs can
be generated with pyfolio (a project by the same people as zipline).
Zipline also comes with the ability to pull pricing and splits data from quandl and yahoo.
I realize I can't win you over but I wanted to present a fair comparison for others ;)
QuantConnect (https://www.quantconnect.com) is incredibly diverse and supports live trading crypto-currency securities (along with equities and FOREX). We have a vibrant community discussing and sharing strategies including bitcoin algorithms.
The platform is C# and runs roughly 20x faster than our nearest competitor Quantopian. We also support high resolution intraday data unlike our competitor. Its all free to backtest and free live trading if you open a brokerage account with our special offer: https://www.quantconnect.com/tradier2
The future of quant finance will be radically open-source; why should millions of engineers re-invent the same brokerage and data integrations over and over again?
These connectors should be done once and open-sourced. Like device drivers for your mouse or keyboard, you should simply plugin a brokerage/dataset, which will work to a standard interface.
Yet funds and vendors keep these locked up because they're a "competitive advantage" to prevent other funds/vendors from starting.
We're breaking that cycle.
QuantConnect has just open-sourced 15 brokerage integrations. It represents roughly seven years of work from a dozen engineers. The Interactive Brokers connector has executed $20B in notional volume across 200,000 live strategy deployments.
Our mission is to create a Linux for quantitative finance. You will never need to write another brokerage connection or data set integration because they'll all be open-source and free.
Let's make quant finance more transparent and accessible.