QuantConnect has a diverse, skilled, global community of quants, computer scientists, engineers, students, and data scientists. We are proud to be able to gather so many great minds to work together on our platform and provide them with access to numerous traditional and alternative data sets to create the alpha-generating signals available in our Alpha Streams API.

Quants power our platform so that we can automate alpha discovery for today’s funds, so we want to show the people behind the scenes. To that end, we are launching the “Meet Our QuantCommunity” series, a new set of blogs that will spotlight members of our community and the tremendous value they create.

In this first installment, get to know Cheng Peng, a QuantConnect author who’s currently building strategies for our Alpha Streams marketplace. Cheng is a software engineer from Canada with a keen interest in quantitative trading, behavioral finance, and quantamental research. His research is focused on equity statistical arbitrage that is based on quantamental data analysis, and behavioral market dynamics.

In his current role, Cheng is well rounded with experience in multiple industries including digital asset management (Betterment), property casualty insurance (AIG), and mobile technology (BlackBerry). He discovered his passion for trading after searching for a sustainable way to manage his own savings, and is now looking to develop his talent further by licensing his trading algorithms to institutions.

Cheng first got into finance from retail trading. After reading, learning, and lots of re-learning, he eventually discovered algorithmic trading to be the most practical method to trade, for him personally. He has been programming for about eight years, but professionally for about four.

Cheng has been using QuantConnect for about two years. He notes that the Alpha Streams project gives him an opportunity to do multiple things:

1. Maximize his alpha strategies beyond his own capital;
2. Build an independent track record that can be recognized seriously; and
3. Gain fixed compensation at a monthly rate that offers more stability.

His investing strategy consists of mostly equity statistical arbitrage based on both fundamental and technical indicators. Cheng also works with selective ETF strategies, factor modeling, and cryptocurrencies. He uses the equities asset class to build algorithms.

When asked to talk about one of his alphas, Cheng says:

One of the most confusing concepts in fundamental investing is understanding the difference between value and growth investing. Both can be profitable, but it depends on what price you pay and what price you expect in the future. The key in both value and growth investing is managing the timeframe to rebalance and determining how to effectively capture the trading signals. I take this approach in my alpha by combining both fundamental and technical signals to manage efficient rebalancing periods. Performance data: dollar neutrality on both the long and short sides, expected Sharpe ratio of 1.

You can check out Cheng Peng’s profile on QuantConnect here. Stay tuned for our next installment!