I’ve been tinkering with n8n for a while now, mostly for automation projects that have nothing to do with trading. But recently, I decided to see how far I could push it into the world of options trading. Instead of going down the rabbit hole of writing a custom backtesting engine in Python, I thought, why not just stitch something together in n8n?
Turns out, it works surprisingly well.
The idea is pretty simple. I wanted a workflow that could:
- Pull in market data (options chains, underlying price, volume, etc.).
- Run some calculations based on my own rules.
- Spit out signals—basically a “buy put,” “buy call,” or “stay out” recommendation.
- Store the results so I can track how the logic performs over time.
n8n makes this surprisingly easy because each step in the workflow can be modular. For example, I can connect to APIs like Yahoo Finance, Alpha Vantage, or even a brokerage API. Once the data lands in n8n, I can push it through a Function node and run whatever logic I want in JavaScript. This is where I handle things like implied volatility comparisons, volume thresholds, or simple technical indicators.
The real magic is that I can iterate quickly. If I want to tweak the criteria say, change the delta range of the options I’m looking at or filter for contracts with tighter bid-ask spreads, I don’t need to rewrite a big chunk of code. I just update the logic inside a node and rerun the workflow.
Right now, the algorithm is more of a decision-support tool than an execution engine. I’m not placing trades automatically (yet). Instead, I get a neat little daily output of potential setups that match my rules. This keeps me in control while still saving me hours of manual scanning.
The biggest lesson so far is that n8n isn’t just for no-code enthusiasts or simple automation. With a bit of creativity, you can use it as a lightweight algorithmic trading framework. It won’t replace something like QuantConnect or a fully-fledged Python library, but for personal projects—and especially for testing ideas—it’s more than enough.
Next on my list is to add some risk management logic. Right now, the algorithm just gives me trade ideas. The plan is to extend it with portfolio tracking, stop-loss logic, and maybe even some automatic hedging triggers. That’s when it will really start to feel like a proper trading assistant.
For now, though, it’s been a fun experiment. And the best part is, it’s mine. I built it, I understand every part of it, and I can evolve it at my own pace.
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