The Unexpected Path to Profitable Trading: A Story from Quantitative Trading by Ernest P. Chan

Quantitative Trading by Ernest P. Chan: Part 1 - Review.

When John first heard about “quantitative trading,” he imagined rooms filled with Wall Street geniuses, armed with PhDs in physics, coding high-frequency trading bots that made millions in seconds. It sounded like an exclusive club—one where only the elite could play.

But then he stumbled upon Quantitative Trading by Ernest P. Chan, and everything changed.

The book’s first chapter felt like a conversation with a wise mentor. Chan wasn’t talking to hedge fund managers; he was talking to people like John—curious, ambitious, but completely new to algorithmic trading. And the first thing he made clear? You don’t need to be a math prodigy or a programming expert to succeed. You just need a systematic approach, a bit of coding knowledge, and a willingness to learn.

What Exactly is Quantitative Trading?

Imagine if, instead of making trading decisions based on gut feelings or news headlines, you could rely on a well-tested, automated system that follows strict rules. That’s exactly what quantitative trading is.

It’s like playing chess against the market—not relying on hunches, but using a strategy that has been carefully tested against historical data. Traders write computer programs that analyze patterns, execute trades, and manage risk—all without emotions getting in the way.

For John, this was a game-changer. He had always assumed successful trading required hours of staring at charts, following financial news, and making quick, stressful decisions. But Chan’s book revealed a different reality:

• Computers handle the hard work. A well-designed trading algorithm can execute trades faster and more efficiently than a human ever could.
• You don’t need to predict the market perfectly. You just need a strategy that works more often than it fails.
• Backtesting makes everything measurable. Instead of guessing if a strategy will work, you test it on past data to see if it has made money before.

Who Can Become a Quant Trader?

At this point, John was still skeptical. “Sure, it works for hedge funds, but can a regular person really do this?”

Chan had an answer for that, too.

He shared the story of his own journey—how he went from working at top banks and hedge funds to trading from his own home, using simple strategies that high school students could understand. He also talked about other traders who had come from surprising backgrounds: an architect, a biochemist, even a former investment banker who had no programming experience.

The key takeaway? You don’t need a finance degree from Harvard to succeed—you just need a good strategy and the discipline to follow it.

Of course, learning to program helps. But even if you’re not a coder, you can use tools like Excel or hire freelance programmers to build your strategy for you.

Why Quant Trading is the Perfect Business

John had always dreamed of starting his own business, but most ideas required too much time, money, or marketing. Then he read about why quant trading is different from traditional businesses:

1. Scalability: Unlike a normal job where you trade time for money, a good trading strategy can be scaled up. Once you have a working system, you can apply it to larger amounts of capital without extra effort.
2. Time Freedom: A fully automated system can run on its own, meaning you don’t have to be glued to your screen all day. Some successful traders check their strategies only once a month!
3. No Customers Needed: Unlike other businesses that require sales and marketing, trading is completely self-contained. There’s no need to convince anyone to buy your product—you just let your algorithm work.

For John, this sounded too good to be true. Could he really trade successfully without quitting his job?

Chan assured him that many quant traders start part-time. Some strategies only require placing orders once in the morning and once at night. Others can be fully automated so that the trader just needs to monitor their performance occasionally.

John’s Next Steps

By the end of Chapter 1, John felt something new: confidence.

He realized that algorithmic trading wasn’t some mysterious art—it was a skill he could learn. He just needed to follow a structured path:

1. Find a strategy that suits his lifestyle (long-term trades for part-time traders, short-term trades for full-time traders).
2. Backtest the strategy on past data to see if it actually works.
3. Set up a trading account and start small, making gradual improvements over time.

What had once seemed like a world of hedge funds and supercomputers now felt within reach.

And just like that, John decided: he was going to become a quant trader.

(To be continued…)

Dam Van Vi - AI investment, Quantitative Finance, Blockchain