Automated Trading: From Theory to the Real Market

The challenge that nobody accounts for

We all know that manual trading is an emotional game. You see Bitcoin drop 5% and panic paralyzes you. You see it rise 10% and you want to put everything in. That's where algorithmic trading comes in—you let the machine do what you can't: be cold and systematic.

But here comes the important part: it's not magic. It's pure mathematics with code.

What is algo trading really?

Essentially, computer programs that execute buys and sells based on rules you define. No emotions. No FOMO. No checking the chart at 3 in the morning.

The algorithm does a simple but brutal job:

• Monitor market data in real time
• Identify opportunities based on your criteria
• Execute operations in milliseconds
• It adjusts according to market conditions

The 4 steps you need to know

1. Define your strategy You start with something basic. For example: “Buy when Bitcoin falls by 5%, sell when it rises by 5%.” It sounds simple because it is. Then you complicate it.

2. Encode the algorithm You convert that logic into Python ( or JavaScript ). Historical data comes in, orders go out. The Binance or Gate APIs make the connection to the real market.

3. Backtest before launching This is crucial: test your strategy with historical data. Would it have worked in 2021? And in the crash of 2022? If it doesn't survive past crises, it won't survive future ones either.

4. Constant monitoring Once live, the algorithm needs monitoring. Markets change. Connections fail. The code has bugs. Keep detailed logs of each operation.

The strategies that work (sometimes)

VWAP (Volume Weighted Average Price) Divide large orders into smaller pieces, execute them over time, aim for the volume-weighted average price. When you want to enter without moving the market.

TWAP (Time Weighted Average Price) Similar to VWAP but without thinking about volume—just extends the execution over time. It's the “play it safe” method.

POV (Percentage of Volume) If the market trades 1M BTC in 1 hour, you execute 10% of that volume. It automatically adjusts to market activity.

The good they sell vs. the reality

✓ Speed: Milliseconds. Humans do not compete. ✓ No emotions: The algorithm does not get scared by red candles. ✓ Backtest possible: You see the past before risking real money.

✗ But…

• Requires serious technical skills (programming + finance)
• A bug = total liquidation in minutes
• Failed connectivity = lost orders
• Illiquid markets = brutal slippage
• Overfitting is your silent enemy

The Ignored Factor: The Reality of the Market

Your backtest saw 95% winning trades in 2023. You launch in 2024 and lose everything in 2 weeks. Why? Because:

1. The market is not stationary—it changes regime.
2. The correlations you saw do not repeat.
3. There are factors that you did not program (political events, spreads on exchanges)
4. Other algos also see what you see

So… is it worth it?

Yes, but with caution:

• Start small. Very small.
• Backtest at least 5 years of data
• Live test with minimum money before scaling
• Monitor daily
• Get ready to lose

Automated trading is not the solution to all your problems. It is a tool. Like any tool, it can build or destroy depending on how you use it.