Quantitative Trading

Quantitative trading, often called quant trading, uses mathematics, statistics and computer code to find patterns in market data and trade them by rules. Instead of reading a chart and deciding, a quant forms a hypothesis, tests it on historical data, measures whether the results are statistically meaningful and, if so, trades it systematically. Quant trading ranges from simple rule based systems run by individuals to large firms running thousands of models across global markets.

How quant trading works#

A quant strategy usually follows a research pipeline. See The Trading Research Process.

1. Idea: a hypothesis about why a pattern should exist, for example "stocks that rose most over the past year tend to keep outperforming".
2. Data: gather clean historical prices and any other inputs. See Historical Data for Backtesting.
3. Signal: turn the idea into a number for each market at each time, such as 12 month return.
4. Backtest: simulate trading the signal on history, with realistic costs. See Backtesting Methodology.
5. Validation: test on data not used in development and check robustness. See In-Sample vs Out-of-Sample Testing.
6. Portfolio and risk: decide position sizes and limits. See Portfolio Construction.
7. Execution: trade, often automatically. See Execution Algorithms vs Alpha Algorithms.
8. Monitoring: compare live results with expectations and watch for decay. See Signal and Alpha Decay.

Common types of quant strategies#

Skills quant traders use#

• Statistics and probability: to tell real effects from noise. See Probability for Traders and Statistical Significance in Trading.
• Programming: usually Python for research, sometimes C++ for speed. See Python for Trading.
• Data handling: cleaning, aligning and storing large datasets. See Cleaning Market Data.
• Market knowledge: understanding why an effect might exist and how trading costs work.
• Risk management: measuring and controlling portfolio risk.

The biggest pitfalls#

• Overfitting: finding patterns that existed only by chance in the data you tested. See Overfitting and Curve Fitting.
• Look ahead bias: using information that was not available at the time. See Look-Ahead Bias.
• Survivorship bias: testing only on markets or stocks that still exist. See Survivorship and Selection Bias.
• Ignoring costs: many signals vanish after realistic spreads and slippage.
• Crowding: when many firms trade the same signal, returns shrink and crashes can be sharper. See Factor Timing, Crowding and Crashes.

Quant trading for individuals#

Individuals can do quant trading with free or low cost data, Python and a broker API, especially at slower frequencies such as daily or weekly rebalancing. Competing at very high speeds is a different business that needs expensive infrastructure. See High-Frequency Trading. A structured route through the topics is in the Quant Trading Learning Path.

Frequently asked questions#

What is quantitative trading?#

Trading that uses data, statistics and code to find patterns, test them on history and trade them by fixed rules.

Do you need a maths degree to be a quant trader?#

Not to start. Solid statistics, programming and careful testing matter more than formal degrees for individual traders, though professional roles often require advanced study.

What programming language do quant traders use?#

Python is the most common for research. C++ and similar languages are used where speed matters, such as high frequency trading.

Next, start the core strategy families with Trend Following.