So you want to build your own reinforcement learning trading system? Smart. Why pay for a black-box bot when you can create something custom, right? But here’s where most people get stuck: picking the right Python library to actually build the thing.
I’ve spent the last year working with every major financial RL library I could find, and let me tell you — the differences matter. FinRL gets all the hype, but is it actually the best choice? Let’s find out.
Grab your keyboard, because we’re about to dive into the real strengths and weaknesses of these libraries. No marketing fluff, just honest comparisons from someone who’s written thousands of lines of code with each one.
Here’s what beginners don’t realize: choosing the wrong library can cost you months of wasted time. You’ll either fight against limitations, struggle with terrible documentation, or discover halfway through your project that the library can’t do what you need.
Think of it like choosing a framework for web development. Sure, you could build anything with any tool if you’re stubborn enough. But using the right tool makes the difference between enjoying the process and wanting to throw your laptop out the window.
The right library should:
Let me show you which libraries deliver on these promises and which ones are all talk.
Let’s start with the elephant in the room: FinRL. This library gets mentioned in every discussion about financial RL, and for good reason — it’s specifically built for financial applications.
FinRL was created by researchers who actually understand both finance and RL. It’s not a general-purpose RL library with some finance examples tacked on — it’s built from the ground up for trading.
The library gives you:
I’ve used FinRL for three different trading projects, and honestly, it saved me countless hours of setup work.
Here’s what actually using FinRL looks like:
Getting started is genuinely straightforward. You can have a basic trading agent running in under 50 lines of code. The library handles downloading historical data, creating the environment, training the agent, and backtesting — all with relatively simple API calls.
The good stuff:
Nothing’s perfect, and FinRL has real limitations:
I’ve hit the customization wall a few times when trying to implement unusual strategies. FinRL makes common things easy but uncommon things harder.
This library is perfect if you:
If you’re a hedge fund building production systems that trade millions? You’ll probably outgrow FinRL. For everyone else? It’s genuinely excellent.
TensorTrade takes a completely different approach. Instead of giving you a pre-packaged solution, it provides building blocks to create exactly what you need.
TensorTrade is built around composability. Everything is a component that you can mix and match:
It’s like Lego blocks for trading systems. More flexible, but you need to assemble the pieces yourself.
I built a multi-asset portfolio manager with TensorTrade, and the flexibility was both a blessing and a curse.
The powerful parts:
The painful parts:
Here’s the honesty check. A basic trading agent in FinRL? Maybe 50 lines. The same thing in TensorTrade? Closer to 150 lines because you’re building more from scratch.
But that extra code buys you flexibility. When I needed to implement a custom reward function that considered portfolio diversity and risk metrics — easy in TensorTrade, painful in FinRL.
This library fits you if:
IMO, TensorTrade is the choice for advanced users who know exactly what they want and are willing to build it.
Here’s the thing nobody tells beginners: you don’t need a specialized financial library. You can build trading environments using Gym (or Gymnasium) and train agents with Stable-Baselines3.
Sometimes the best tool is the one you build yourself:
I’ve built two trading systems this way, and while it’s more work upfront, the control is worth it for serious projects.
Let’s be honest about what this approach requires:
You’ll need to handle:
That’s a lot. But you know what? You learn infinitely more doing it this way.
Creating a custom Gym environment took me about a week of solid work. But that environment does exactly what I need, no compromises.
The advantages I discovered:
The challenges I faced:
Go this route if you:
This isn’t a weekend project. But if you’re serious about financial RL, eventually you might end up here anyway.
RLlib from Ray is the library nobody talks about in financial RL circles, which is honestly a shame because it solves a real problem: scale.
RLlib is part of the Ray ecosystem, which means it’s built for distributed computing:
If you’re training on massive datasets or need serious computational power, RLlib is unmatched.
I used RLlib when I needed to train an agent on 10 years of tick data across 50 stocks. FinRL and TensorTrade would’ve taken forever. RLlib handled it beautifully.
What impressed me:
What frustrated me:
RLlib is like using a commercial jet to go to the grocery store. If you actually need a commercial jet, it’s perfect. If you don’t, it’s absurd overkill.
For financial RL specifically, you probably don’t need RLlib unless:
For personal projects or small-scale research? Stick with simpler tools.
Let me throw in a wildcard: QuantConnect Lean. This isn’t purely an RL library — it’s a complete algorithmic trading framework that happens to support RL.
Lean gives you everything:
It’s less about RL specifically and more about building complete trading systems where RL is one component.
I’ve used Lean for one project where I needed the full infrastructure, and it’s genuinely impressive:
The complete package:
The tradeoffs:
Lean makes sense if you’re building a complete algorithmic trading system where RL is one piece of a larger puzzle. If you just want to experiment with RL strategies? It’s probably too much.
Let me break this down in a way that’s actually useful:
For beginners: Start with FinRL. The learning curve is gentle, documentation is good, and you’ll be productive quickly.
For researchers: FinRL or TensorTrade depending on how standard your research is. Standard strategies? FinRL. Novel approaches? TensorTrade.
For production systems: Build custom with Gym + Stable-Baselines3, or use Lean if you need full infrastructure.
For scale: RLlib if you have serious compute resources and genuinely need distributed training.
For learning: Actually build custom with Gym + SB3. You’ll learn more struggling through the implementation than using any pre-built solution.
There’s no universal winner. The “best” library depends entirely on what you’re trying to accomplish.
Since you probably want to know: I use different tools for different projects.
Personal research projects: FinRL every time. I want to test ideas quickly, not build infrastructure.
Client work: Custom Gym environments with Stable-Baselines3. Clients pay for customization and performance, not convenience.
Large-scale experiments: RLlib when I have cloud credits to burn and genuinely need speed.
Learning: I force myself to build from scratch occasionally. Keeps my skills sharp and understanding deep.
The right tool for the job changes with the job. Anyone who tells you there’s one perfect library for everything is either lying or inexperienced.
Let me share some painful lessons:
Mistake #1: Starting with RLlib because it sounded impressive. I spent three weeks fighting complexity for a project that FinRL would’ve handled in three days.
Mistake #2: Using FinRL for a highly custom strategy, then hitting limitations and needing to rewrite everything in TensorTrade.
Mistake #3: Building everything custom for a simple project that didn’t need it. Wasted two weeks on infrastructure that added zero value.
Mistake #4: Not reading the documentation thoroughly. Every library has quirks you’ll discover the hard way if you don’t read first.
Mistake #5: Choosing based on GitHub stars instead of actual fit for my needs. Popularity doesn’t equal suitability.
FYI, matching your tool to your actual requirements matters way more than picking the “best” library in the abstract.
After working extensively with all these libraries, here’s my honest take:
FinRL is the best starting point for most people. It’s specifically designed for financial RL, has great documentation, and you’ll be productive immediately. The limitations only matter once you’re doing advanced work.
TensorTrade is brilliant for custom strategies but requires more expertise. Choose it when FinRL’s structure feels constraining.
Gym + Stable-Baselines3 is the power user route. Maximum flexibility and performance, but you build everything yourself.
RLlib is overkill unless you genuinely need distributed training at scale.
Lean is for complete trading systems where RL is one component among many.
Start with FinRL. Seriously. Most people asking “which library should I use?” should start with FinRL. Build a few projects. Hit some limitations. Then you’ll know exactly which direction to go next.
The “best” library is the one that lets you ship your project, not the one that sounds most impressive. Choose pragmatically, not aspirationally. Your future self will thank you. :)
Comments
Post a Comment