Quantitative trading (systematic / quant) uses explicit rules and often code to generate signals, size positions, and manage risk — instead of relying only on discretionary intuition.
In plain terms — You write (or buy) a system: «if A happens, buy; if B, exit». The computer executes. It is the industrial extension of systematic trading: explicit rules, data, backtesting.
| Component | Example |
|---|---|
| Signal | Moving average cross, momentum factor, cyclic model |
| Data | Historical prices, volume, fundamentals, alt data |
| Backtest | Simulation on past data — beware overfitting |
| Execution | Broker API, minimise slippage |
| Risk | stop-loss-and-take-profit, size, portfolio correlation |
Quant vs other approaches
| Approach | Key question |
|---|---|
| Cyclic | Where are we in the cycle? When is timing favourable? |
| Volumetric | What is supply/demand doing? |
| Quant | Does rule X have repeatable statistical edge? |
| Discretionary | Does the setup «look right» today? |
They are not mutually exclusive: a quant fund can use volatility filters; a cyclic or volumetric trader can automate screening with their own rules.
Limits to know
- Optimistic backtest ≠ future profit
- Regime change: rules that worked can stop
- Transaction costs and liquidity eat edge on high-turnover strategies