Curious about Lean and QuantConnect from a real user’s perspective? Check our Lean platform review).

Introduction

In this post, we sit down with Alex, a developer-turned-algo-trader, to discuss his experience working with QuantConnect and its open source engine, Lean. He walks us through the learning curve, the strengths of the platform, and how he uses it in his daily trading workflows.

Q1: What got you interested in QuantConnect and Lean?

Alex:
I was initially exploring ways to backtest some intraday strategies in equities and crypto. Most platforms I tried felt either too basic or too closed-off. QuantConnect immediately stood out because of the Lean engine being open source. I loved the idea of being able to run serious quant research and backtesting in my own environment.

Q2: What was your first impression when setting up Lean locally?

Alex:
The setup was more involved than plug-and-play platforms, but once I followed the Docker instructions, I had it up and running. Having the full codebase open meant I could tweak how data was ingested or processed — a huge plus for a developer.

Q3: What do you use Lean for today?

Alex:
I mostly use it for strategy research and backtesting. I write my algorithms in Python, though Lean supports C# too. I’ve also used it for paper trading, and occasionally I deploy live strategies via QuantConnect Cloud when I need reliable infrastructure and brokerage integration.

Q4: What are your favorite features?

Alex:

  • Accurate historical data with lookahead bias protections
  • Modular architecture — I can plug in my own indicators or risk models
  • The cloud/local sync — I can iterate locally and deploy to QuantConnect’s cloud
  • Great support for multi-asset, multi-resolution strategies

Q5: What challenges did you face?

Alex:
There’s a learning curve, especially if you’ve never worked in a quant-style framework. You need to understand event-driven architecture, how Lean handles data slices, and the lifecycle of an algorithm. But once it clicks, it’s super powerful.

Also, debugging locally can be tricky compared to simpler platforms — but that’s where logging and tests come in.

Q6: Any advice for new developers getting started with Lean?

Alex:
Start small. Try to implement a simple moving average crossover strategy and understand how each part connects — from data loading to order placement. Use their documentation — it’s extensive and well-written.

And don’t be afraid to dig into the GitHub repo. That’s the beauty of open source.

Final Thoughts

Alex’s journey highlights what makes Lean + QuantConnect special: a bridge between serious quant infrastructure and developer flexibility. It’s not the simplest platform — but it’s one of the most capable.

Want to explore our review of Lean and how it compares to other tools? Head to:
QuantConnect & Lean – Full Review and Getting Started Guide