What Goes Into an Algo

Building an algo isn’t just about writing a few lines of Python and hitting “Run.” A good trading system has multiple moving parts working together — strategy, risk, testing, and monitoring. Miss any one of these, and the whole setup can crumble.

1. Strategy Logic

This is the “idea engine.” It could be something simple like a moving average crossover or more advanced like VWAP deviations. The key is clarity:

• What conditions trigger a buy?
  
• What conditions trigger a sell?
  
• Is there a clear edge, or is it just random guessing dressed up as code?
  

2. Risk Controls

Without guardrails, even the smartest algo can burn through your account. That’s where risk management comes in:

• Stop-loss: Define how much you’re willing to lose per trade.
  
• Profit targets: Lock in gains before they evaporate.
  
• Position sizing: Don’t bet the house on one setup.
  

Think of risk controls as airbags in a car — you hope you never need them, but they’ll save you when things go wrong.

3. Backtesting

Backtesting is where you feed your algo historical data and see how it would have performed. Done right, it gives you confidence. Done wrong, it’s a trap.

• Always use enough data (years, not weeks).
  
• Watch out for “curve fitting” — tweaking parameters until past results look perfect. The market won’t repeat history exactly.
  
• Factor in slippage and transaction costs. Otherwise, your backtest is fantasyland.
  

4. Execution Layer

This is the bridge between your logic and the real market. Your algo talks to the broker’s API, sending orders instantly. Here, speed and reliability matter:

• Latency (order delays) can ruin scalping strategies.
  
• Connection drops or API downtime can leave you exposed.
  
• Always test in a paper-trading environment before going live.
  

5. Monitoring

Algos aren’t “set and forget.” You’ll need dashboards, alerts, or even basic logging to keep tabs on what’s happening.

• Did an order fail?
  
• Did your stop-loss trigger?
  
• Is the algo looping orders because of a bug?
  

Monitoring makes sure you catch these issues before they become disasters.

Takeaway:

An algo is not just code — it’s an ecosystem. Strategy gives direction, risk keeps you alive, backtesting tells you if the edge is real, execution connects you to the market, and monitoring keeps everything honest. Nail these five parts, and your algo won’t just run — it will survive.

In the next chapter, we’ll understand the most important ingredient of all: data. What is market data, and how do algos use it to make decisions?

Let’s go!