Algorithmic vs. Quantitative Trading: Which Path Should You Take
I’ve always wondered why anyone would stick to traditional trading methods when algorithms and mathematical models could do all the heavy lifting.
I started questioning everything:
• Why do so many mentors still swear by discretionary trading when algorithms could handle all the heavy lifting?
• Do they really have solid proof of their “own” success, or is it just talk?
• Or are they keeping things complex and discretionary on purpose, to confuse people and keep them as members longer?
• Why deal with the stress of emotions and decisions when an algorithm can take care of it all?
• Imagine how much further ahead you could be if you stopped wasting time on manual trades and instead focused on market research and developing your own models.
When I first got into trading, I thought Algorithmic Trading and Quantitative Trading were basically the same thing. But as I dug deeper, I realized they’re two completely different worlds.
Algorithmic Trading: It’s simple – you set the rules and the algorithm executes the trades. No more sitting in front of the screen “controlling your emotions” and trying to manage every little detail. Instead, you let the algorithm handle it, based on the rules you’ve set. It frees up your time to focus on other things rather than staring at price charts all day.
But here’s the thing – it’s not perfect. You’ll still need to test the rules to make sure the data and results you’re getting aren’t overfitted or just random.
Quantitative Trading: A whole different level. It’s not just about executing trades; it’s about understanding the data and math behind market movements. You analyze historical price, economic, and political data, using math and machine learning to predict the future. But it can be complex – techniques like Deep Learning can turn it into a serious challenge.
The upside? This is the most reliable way to trade, and it’s exactly what over 80% of hedge funds do. They rely on quant models to minimize risk and to outperform the market.
So, which path should you choose?
Quantitative Trading can feel overwhelming at first, I recommend starting with the basics. Begin with Pine Script coding in TradingView—start building a foundation with simple strategies and indicators. As you grow more confident, start coding your own ideas into rules and refining your approach to eventually automated your trading strategy.
TradingView is a great tool for this, and I’d highly suggest grabbing the Premium plan. This will give you access to more data and features to make your learning journey smoother.
Dive into the Pine Script documentation, and begin bringing your ideas to life.
I promise, the more you focus on this, the better and more independent you’ll become in trading.
Every day, aim to get just 1% better.
To Your success,
Moein
I started questioning everything:
• Why do so many mentors still swear by discretionary trading when algorithms could handle all the heavy lifting?
• Do they really have solid proof of their “own” success, or is it just talk?
• Or are they keeping things complex and discretionary on purpose, to confuse people and keep them as members longer?
• Why deal with the stress of emotions and decisions when an algorithm can take care of it all?
• Imagine how much further ahead you could be if you stopped wasting time on manual trades and instead focused on market research and developing your own models.
When I first got into trading, I thought Algorithmic Trading and Quantitative Trading were basically the same thing. But as I dug deeper, I realized they’re two completely different worlds.
Algorithmic Trading: It’s simple – you set the rules and the algorithm executes the trades. No more sitting in front of the screen “controlling your emotions” and trying to manage every little detail. Instead, you let the algorithm handle it, based on the rules you’ve set. It frees up your time to focus on other things rather than staring at price charts all day.
But here’s the thing – it’s not perfect. You’ll still need to test the rules to make sure the data and results you’re getting aren’t overfitted or just random.
Quantitative Trading: A whole different level. It’s not just about executing trades; it’s about understanding the data and math behind market movements. You analyze historical price, economic, and political data, using math and machine learning to predict the future. But it can be complex – techniques like Deep Learning can turn it into a serious challenge.
The upside? This is the most reliable way to trade, and it’s exactly what over 80% of hedge funds do. They rely on quant models to minimize risk and to outperform the market.
So, which path should you choose?
Quantitative Trading can feel overwhelming at first, I recommend starting with the basics. Begin with Pine Script coding in TradingView—start building a foundation with simple strategies and indicators. As you grow more confident, start coding your own ideas into rules and refining your approach to eventually automated your trading strategy.
TradingView is a great tool for this, and I’d highly suggest grabbing the Premium plan. This will give you access to more data and features to make your learning journey smoother.
Dive into the Pine Script documentation, and begin bringing your ideas to life.
I promise, the more you focus on this, the better and more independent you’ll become in trading.
Every day, aim to get just 1% better.
To Your success,
Moein
Note
Here are my top 10 books, if you want to dive deep into the world of algo/quant trading:
1- Quantitative Trading: How to Build Your Own Algorithmic Trading Business by Ernest Chan.
2-Machine Trading: Deploying Computer Algorithms to Conquer the Markets by Ernest Chan
3-Finding Alphas: A Quantitative Approach to Building Trading Strategies by Igor Tulchinsky.
4-Advances in Financial Machine Learning by Marcos Lopez de Prado
5-Machine Learning for Asset Managers by Marcos Lopez de Prado
6-Advances in Active Portfolio Management by Richard Grinold, Ronald Kahn
7-151 Trading Strategies by Zura Kakushadze, Juan Andres Serur
8-Machine Learning for Asset Management by Emmanuel Jurczenko
9- Python for Algorithmic Trading: From Idea to Cloud Deployment by Yves Hilpisch
10- Machine Learning for Algorithmic Trading by Stefan Jansen
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