What Is Algorithmic Trading?
Algorithmic trading uses computer programs to execute trades automatically based on pre-defined rules, removing human emotion and enabling precise order management.
What Is Algorithmic Trading?
Algorithmic trading ("algo trading") is the use of computer programs to execute trades automatically based on pre-defined rules. Where a human clicks "buy," an algo submits the order in milliseconds. Where a human panics, an algo follows its rules. The discipline is as much about execution as it is about strategy.
Quant vs. algo: what's the difference?
| Term | Focus |
|---|---|
| Quantitative trading | Designing the strategy — the math and signals |
| Algorithmic trading | Executing trades automatically — the code and order management |
| Automated trading | Often synonymous with algo trading |
| High-frequency trading (HFT) | A subset operating in microseconds |
A quant designs a strategy. An algo executes it. Many quants use algos — but they aren't the same thing.
What algos do
Signal execution — an algo monitors data and fires trades when conditions are met:
IF price > 50-day MA AND RSI < 70:
BUY 100 shares
SET stop = entry − 2×ATR
SET target = entry + 3×ATR
Order slicing — institutional orders are too big to submit at once. Execution algos break them up:
| Algo | How it works |
|---|---|
| TWAP | Slices order evenly across time |
| VWAP | Slices in proportion to historical volume |
| Implementation Shortfall | Balances urgency vs. market impact |
| Iceberg | Hides true order size by showing small pieces |
Risk management — algos can enforce rules a human might break under stress: auto-flatten on a daily loss limit, refuse to trade during news, cut leverage if volatility spikes.
Why algos are popular
- Speed. Millisecond execution.
- Discipline. No emotion, no revenge trading.
- Precision. Exact sizing, stops, timing.
- Scale. Monitor hundreds of instruments simultaneously.
- Backtestable. The same code that trades can be tested on history.
Why algos aren't a free lunch
- Garbage in, garbage out. A bad strategy automated loses faster.
- Technical failures. Bugs, API outages, stale data (Knight Capital lost $440M in 45 minutes).
- Over-optimization. Strategies tuned perfectly to history often fail live.
- Infrastructure costs. Co-location, data feeds, compute cost real money.
- Regulatory scrutiny. Many jurisdictions require algo registration and risk controls.
How beginners start
- Learn a language — Python is the default.
- Paper trade first — run in simulation for at least a month.
- Use broker APIs — Alpaca, Interactive Brokers, OANDA, Binance.
- Start tiny — even $1 per trade reveals real-world slippage.
- Log everything — every trade, signal, and error.
- Build risk controls first — build the "stop the algo" button before adding alpha.
Common mistakes
- Skipping paper trading. Live money reveals bugs paper never does.
- No kill switch. A runaway algo can destroy an account in minutes.
- Trusting the backtest. Backtests assume perfect fills; live markets don't.
Bottom line
Algorithmic trading is automation applied to trading. It can make a good strategy better (faster, more disciplined, more scalable) and a bad strategy worse (faster losses, larger blow-ups). The right way in is to master the strategy first, then automate it — never the other way around. Build the kill switch before you build the alpha.