Algorithmic trading is why quants at Citadel get paid the big bucks. It is difficult, edges are small and can vanish randomly, but when done effectively and fulfilling a clear purpose in a trading process, it is a great asset.
And it is way easier now thanks to AI.
THE AI REVOLUTION IN ALGO TRADING
Before AI, you would have to pay a developer and it would be a nightmare to test ideas effectively. Now you can use an LLM with the power of a full-stack developer that can run your trade idea in under 5 minutes.
I recommend Claude as it is best for coding. The $100/month Pro Max plan is amazing if you're building something serious—unlimited usage so you can iterate all day. But for most people, the $20/month Pro plan is the best option for this kind of work with no risk of tokens running out. It's more than enough to build, test, and refine a simple algo strategy. But ultimately, for my algo strategy we want to keep things simple.
KEEP IT SIMPLE
Markets are complex, and often the more complex your algo is, the less edge it has.
- If your algo focuses solely on volatility and breakouts, you can build one that reliably finds those setups
- But if you chase pure alpha and high win rate, you tend to run into issues that compound with every added change
Focus on testing 3–4 solid setup ideas—such as mean reversion on oversold conditions, breakout continuation on volume spikes, or trend-following on moving average crosses. Keep the variables in each system as low as possible so that each one is actually making a measurable difference. If you can't explain what each variable does and why it's there, remove it. The goal is an identifiable, repeatable system—not a black box with 30 parameters that happened to backtest well.
Every additional parameter, filter, or condition you add to an algorithm increases the risk of overfitting to historical data. Simple systems with clear logic tend to be more robust in live trading.
Don't obsess over win rate. What actually matters is win rate combined with average win and average loss. A 40% win rate system that averages +5% on winners and -1.5% on losers will destroy a 75% win rate system that averages +1% on winners and -4% on losers. Always evaluate your algo on expectancy: (win rate × avg win) − (loss rate × avg loss). That's the number that tells you if your system actually makes money.
Where a durable edge can truly be found is mixing a systematic approach with a discretionary one. Our algorithms are good on their own—but combined with our tradable asset grading and macro overlay, we've found a winning strategy that has lasted. Pure algos break. Pure discretion is inconsistent. The combination of both is where alpha lives, and that's exactly what Vector Ridge delivers.
HOW TO GET STARTED
- Start with a clear trade idea — what exactly are you trying to capture?
- Implement simple versions — use Claude to code basic prototypes
- Test on historical data — before risking real capital
- Keep iterating simply — resist the urge to over-optimize
Data Resources
For data (which can be expensive), I recommend FirstRate Data as the cheapest high-quality option:
OR JUST USE OUR SIGNALS
OUR TRADING SIGNALS ARE OUR ALGORITHMS
You can skip the hassle entirely and just use our signals. We've done the hard work of building, testing, and refining—you get the output.
KEY TAKEAWAYS
- Algo trading is hard — edges are small and can vanish
- AI has changed the game — use Claude to code and test ideas quickly
- Simplicity wins — complex algos often have less edge
- Focus on one thing — volatility, breakouts, or momentum—not everything
- Use quality data — FirstRate Data for affordable historical data
- Or skip it entirely — our signals are our algorithms