Jared Broad, CEO and Founder of QuantConnect, Explains Automated Investing Based on Data

By Peter Page Peter Page has been verified by Muck Rack's editorial team
Published on November 21, 2022

Jared Broad founded QuantConnect in 2011 and serves as the CEO of the algorithmic trading platform.

QuantConnect empowers engineers, data-scientists and quants with an ecosystem of tools to design and trade quantitative trading strategies. The platform serves financial data, and clients write python or C# strategies to find alpha and execute on it.

QuantConnect serves 220,000 clients from all around the world, and trade billions in notional volume every month.

Jared Broad

Grit Daily: Please explain your thought process and inspiration behind the creation of QuantConnect?

There are millions of investors with a disciplined method of investing. Many seek to automate their strategy to avoid human bias, and test it on historical data to see how it has performed.

I’ve been personally trading and investing manually for many years, but really started to explore how to automate my processes, and test strategies in 2012. Back then there was very little information around how to do it, or tools to make the process easier so I started to build something for myself. It was quite the rabbit hole and took a couple of years of full-time effort to have the initial frameworks up and running quantitative trading strategies.

QuantConnect was born shortly after to make this journey easier for others. We make designing and trading a quantitative investment strategy simple.

Grit Daily: Besides funding a new venture, what was the most challenging aspect about starting QuantConnect?

Quantitative finance technology is much more challenging than the average technology stack of start ups. The level of data processing and modeling required feels more like solving an challenging math. This makes creating new things incredibly rewarding but building the product has taken a decade of work from 20 engineers.

In funding meetings I often compared starting QuantConnect to launching a rocket to the moon. Seeing the difficulty that lay ahead we started pitching for funding pre-product and received 150+ rejections in 2013. Facing that we just had to knuckle down and build it with minimal funding or support – bootstrapping for many years. Its taken a while but we’re now leading the open-source algorithmic trading space with the largest community and platform in the world.

Grit Daily: How is WeFunder helping to grow QuantConnect for its users?

Keeping the platform and ecosystem accessible to all investors is important to our mission. The majority of the WeFunder fundraise has been from existing members of the QuantConnect community. Raising from our base enables us to focus on serving their needs better.

Over the last two years we’ve seen phenomenal growth in the platform and community and the funding will enable us to continue improving the core platform, and reaching new clients for years to come.

Grit Daily: Can you tell us a little bit about the services that you provide and how this sets you apart from other platforms that offer similar services?

Quantitative investment is a method of investment that bases investment decisions on statistical and systematic processes. It seeks evidence of patterns in the markets and invests based on those patterns. Its far beyond the reach of most individuals or smaller firms, but its slowly taking over as the predominant method of making investment decisions. In the last 7 years its grown from 10% to 40% of funds identifying as quant funds.

With QuantConnect, investors for the first time have a level playing field to access to this incredibly powerful technology and can implement their ideas relatively easily. There is very little similar out there. Other investment platforms tend to focus purely on technical indicators, and have a very narrow focus. We provide a professional level quant stack that lets you harness many data sources, and trade algorithmically across many asset classes.

Grit Daily: What’s the biggest mistake Quant investors make when it comes to harnessing their data?

The most common mistake is overfitting. Once they’ve created the basics of a strategy they might re-analyze the algorithm over and over, tuning the parameters to improve its performance. This is a risky endeavor and often leads to making the algorithm look amazing on historical data. When the “hyper-tuned” algorithm is deployed live it fails as its too sensitive to the algorithm parameter values.

Grit Daily:  How is QuantConnect changing the world?

We are taking an open source approach to quantitative finance, open sourcing execution and data technology that will save people millions of hours of work. This will make the quant finance world orders of magnitude more efficient. We believe this quant “plumbing” should be open source and freely available, created once, and maintained by the global quant community.

Open-source is common in other areas of technology; but in finance this is a radical concept. Most large organizations keep their source code secret so there is no compounding growth through leveraging other projects. We’re excited to see what the world builds off all of the open-source work that we’re creating.

By Peter Page Peter Page has been verified by Muck Rack's editorial team

Journalist verified by Muck Rack verified

Peter Page is an Editor-at-Large at Grit Daily. He is available to record live, old-school style interviews via Zoom, and run them at Grit Daily and Apple News, or BlockTelegraph for a fee.Formerly at Entrepreneur.com, he began his journalism career as a newspaper reporter long before print journalism had even heard of the internet, much less realized it would demolish the industry. The years he worked as a police reporter are a big influence on his world view to this day. Page has some degree of expertise in environmental policy, the energy economy, ecosystem dynamics, the anthropology of urban gangs, the workings of civil and criminal courts, politics, the machinations of government, and the art of crystallizing thought in writing.

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