Algo trading: how algorithmic trading works and how to start
Algo trading (algorithmic trading) is using software to place trades from predefined rules or models, without clicking buy and sell yourself. A strategy watches market data, decides when to trade and how much, and sends orders through an exchange API. Good algo trading is mostly risk control and testing: backtest, then paper trade on live data, then go live small with hard limits.
What is algo trading?
An algorithm is a set of steps. In trading, it is the logic that turns market data into orders: when to enter, how much, when to exit. The software runs it without hesitation or fatigue, which is its strength, and without judgement, which is its weakness. It will repeat a mistake a thousand times a minute if nothing stops it.
Common algo trading strategies
- Trend following and momentum. Buy what is rising, sell what is falling, on the idea that moves persist for a while.
- Mean reversion. Bet that a price stretched far from its average will come back.
- Market making. Quote both a buy and a sell price and earn the spread, while managing the inventory risk.
- Arbitrage. Trade the same or linked assets priced differently on two venues. The gaps are small and competition is fierce.
- Probability trading. On prediction markets like Polymarket and Kalshi, buy when your forecast of an event is higher than the market price. See the odds calculator.
From idea to live: backtest, paper, live
- Backtest on historical data, with realistic fees and fills. Keep some data aside that the strategy never saw while you tuned it.
- Paper trade on live market data with simulated fills. This catches what backtests miss: data delays, fills that never happen, API errors.
- Go live small with hard limits, and scale only when live results match paper.
Decide the bar for moving up a stage before you see the results, and write it down. Moving the bar after the fact is how most people fool themselves.
Why most algo trading bots fail
- Overfitting: a strategy that fits past noise, not a real pattern.
- Fees and slippage: an edge of 0.1% per trade vanishes after a 0.1% fee.
- Leverage: one bad move takes the account to its liquidation price.
- Oversizing: betting too much per trade. Use a position size calculator.
- No off switch: a bug keeps trading while you sleep.
Risk controls every algo needs
- A daily loss cap and a maximum drawdown from peak, enforced before every order.
- Per-position and total exposure limits.
- A kill switch that stops all new orders at once.
- An API key that can trade but not withdraw.
- A log of every decision, so you can see what happened and why.
Algo trading on your own account with AI Trading Fleet
AI Trading Fleet runs algo trading strategies on your own Binance or Bybit, prop-firm, Polymarket or Kalshi account. You set the limits, every order is checked against them, and every strategy goes through backtest and paper trading against a published bar before it touches real money.
Questions
Is algo trading legal?
Automated trading through an exchange’s official API is allowed on most major exchanges, but rules differ by country and by venue. Check your exchange’s terms and your local rules. This is not legal advice.
How much money do I need to start algo trading?
You can paper trade with none. For live trading, start with an amount you can afford to lose entirely, and remember that fees weigh more heavily on small accounts.
Can algo trading lose money?
Yes. Algorithms can lose money quickly, especially with leverage or a bug. Hard loss limits and a kill switch cap how much.
What is the best programming language for algo trading?
Python is the most common for research and most retail bots. Latency-sensitive trading uses C++ or Rust. For most strategies, testing discipline matters far more than the language.
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Trading involves risk of loss. Nothing here is investment advice. Read the risk disclosure.