A significant portion of the world's major exchanges' turnover today goes through algorithms. Automation solves the trader's main problem — the human factor: a robot is not afraid, doesn't get greedy, and doesn't get tired. But it also creates new problems that beginners usually underestimate.

What is algorithmic trading

Algorithmic trading is the execution of a formalized strategy by a program. Entry rules, exit rules, position size, and risk management are described in code, and the system monitors the market and places orders itself. A human designs, tests, and controls; the machine trades.

Types of algorithms

  • Trend-following systems — buying on breakouts, selling on trend breaks; the simplest and most common class.
  • Mean reversion — betting that a deviated price will return to average; works in sideways markets, destructive in trends.
  • Arbitrage — profiting from price discrepancies of the same asset on different platforms or related instruments; requires speed and capital.
  • Market making — simultaneous placement of buy and sell orders for the spread.
  • High-frequency trading (HFT) — thousands of trades per second on infrastructure costing millions; inaccessible to retail traders.

How a trading system is created

  1. Formalizing the idea. "Buy on pullbacks in a trend" becomes precise conditions: EMA 50 above EMA 200, price touched EMA 20, RSI below 40.
  2. Backtesting — running on historical data over several years accounting for commissions and slippage.
  3. Forward testing — checking on data the system hasn't "seen" during development.
  4. Paper trading in real time for 1–3 months.
  5. Launch with minimal volume and continuous monitoring.

The main trap: overfitting

Any strategy can be "fitted" to history by tweaking parameters until results on past data become perfect. Such a system brilliantly describes the past and fails in the future. A sign of overfitting is an overly perfect return curve in backtesting. Protection: simple rules, few parameters, mandatory testing on unseen data.

What automation doesn't solve

  • Market regime changes. A trend-following robot loses in sideways markets, a mean-reversion one loses in trends. Deciding when to turn systems on and off remains a human responsibility.
  • Technical failures. Connection loss, code error, frozen terminal — with an open position. You need emergency stops at the exchange and alerts.
  • Black swans. Events that didn't occur in history don't appear in backtests either.

Ready-made robots and "signals"

The market is flooded with ready-made robots promising "30% monthly returns." Logic suggests: if someone had such a system, they would scale it themselves rather than sell it for a hundred dollars. A typical commercial robot is either a system overfitted to history or a martingale doubling positions after losses — a strategy with attractive statistics and guaranteed ruin at the right moment. Be even more cautious with "robots" that need you to hand them your money for management.

Algorithmic trading on local markets

Automation requires liquidity and reliable programmatic access to trading. This exists on developed markets, forex, and crypto. Local stocks with rare transactions are poorly suited for classical algorithms — here automation is useful only for monitoring and placing pending orders for positional strategies.

Where to start

Not by buying a robot, but by formalizing your own profitable strategy: if you can't describe the rules in words so another person could execute them, there's nothing to automate. Then backtest on any available platform where strategies are built without programming, and only then code.

Conclusion: a robot is an executor, not a source of profit. It amplifies the discipline of a good strategy and accelerates the failure of a bad one. Algorithmic trading is a logical next step for a trader with a proven system, but not a shortcut for a beginner.