Algorithmic trading is trading executed by a computer program following a defined set of rules. At the institutional level it dominates markets. For retail traders, it's become accessible through platforms like TradingView. Here's what it actually involves and when it makes sense.
A trading algorithm is a set of if-then rules applied to market data. Example: "If the 9 EMA crosses above the 21 EMA on a 5-minute chart AND the RSI is above 50 AND we're in the New York session, then enter long with a 16-point stop." When conditions are met, the system executes the trade — no human required.
The main components:
Systematic (algo): Rules are fully defined before the trade. Execution is automatic. No discretion in the moment.
Discretionary: The trader makes real-time judgments based on experience, context and intuition. Rules may exist but are applied with judgment.
Most retail traders are discretionary. Most institutional trading is systematic. The two aren't mutually exclusive — many traders use systematic rules as a framework but apply discretion for certain high-conviction setups.
It removes emotional interference at the execution point. Fear and greed cause discretionary traders to move stops, exit early, skip valid setups and over-trade after losses. A system follows its rules regardless of recent performance.
Before trading a system live, you backtest it: apply the rules to historical data and see what the P&L would have been. Backtesting has serious limitations:
A robust system should work on out-of-sample data (data not used to build it) and across different market conditions.
It depends entirely on the quality of the rules. A good systematic strategy beats most discretionary retail traders because it eliminates emotional errors. A bad systematic strategy just loses mechanically and consistently. The edge must exist in the rules themselves — automation only removes execution variance, it doesn't create edge from nothing.
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