Quantitative Trading

Measures risk-adjusted return — the excess return earned per unit of volatility. A Sharpe Ratio above 1.0 is acceptable; above 2.0 is considered excellent. Formula: (Portfolio Return − Risk-Free Rate) / Standard Deviation of Returns.

A refinement of the Sharpe Ratio that penalizes only downside volatility (harmful risk) rather than total volatility. Preferred by quants because it does not penalize positive upside swings.

The peak-to-trough decline in portfolio value over a specified period. Quantifies the worst-case loss a strategy has historically produced. Critical for position sizing and risk management frameworks.

Annualized return divided by Maximum Drawdown. Tells quants how much return is generated per unit of peak-to-trough risk. Higher is better; a ratio above 1.0 is generally desirable.

Measures a security's price sensitivity relative to the overall market. A beta of 1.0 means the asset moves in line with the market; > 1.0 means higher volatility; < 1.0 means lower volatility. Used in factor models and hedging.

The excess return of a strategy above a benchmark after accounting for market risk (beta). A positive alpha is the "edge" the quant strategy delivers — the holy grail of systematic trading.

Alpha generated per unit of tracking error (deviation from the benchmark). Used by institutional quants to evaluate how consistently an active strategy outperforms its benchmark.

Measures whether a time series's past values predict its future values. Positive autocorrelation (momentum) suggests trend-following strategies. Negative autocorrelation (mean reversion) favors mean-reversion strategies.

The number of standard deviations a data point is from the mean. Widely used in pairs trading to identify statistically extreme divergences that are likely to revert to the mean.

Variance measures the average squared deviation from the mean return; standard deviation is its square root. These are the fundamental building blocks of nearly every risk model used in quantitative finance.

Begin with a market anomaly or behavioral bias — e.g., "stocks with earnings surprises outperform in the following week." This becomes a testable hypothesis that drives the research process.

Translate the hypothesis into a measurable factor (e.g., earnings surprise magnitude, momentum score, valuation ratio). Factors are the quantified signals that drive portfolio decisions.

Apply the strategy rules to historical data to evaluate performance. Key checks: total return, drawdowns, Sharpe Ratio, and consistency of edge over time. Beware of overfitting — the strategy must generalize to out-of-sample data.

Use t-tests, p-values, and bootstrap simulations to confirm that results are not due to random chance. A p-value below 0.05 and a t-statistic above 2.0 are common thresholds for accepting a signal as statistically valid.

Once validated, the strategy is deployed with automated execution rules. Ongoing monitoring tracks live performance vs. backtest expectations, flagging regime changes or signal decay.

Models the linear or nonlinear relationship between a dependent variable (e.g., stock return) and one or more independent variables (e.g., earnings growth, momentum score). Used to build factor models and quantify the predictive power of each variable.

Studies how a variable evolves over time, identifying patterns such as trends, seasonality, and cycles. Models like ARIMA, GARCH, and VAR are used to forecast future prices and model volatility — foundational in options pricing and risk management.

A dimensionality reduction technique that transforms a large set of correlated variables into a smaller set of uncorrelated principal components. In finance, used to identify the dominant risk factors driving a portfolio and to remove multicollinearity in factor models.