Quantitative Analysis & Backtesting Definitions

Alpha Generation refers to the process of creating returns that exceed a market benchmark through trading strategies, analysis, or innovative techniques. Successful alpha generation indicates a trader’s ability to achieve superior performance.

Annual Return % is the percentage gain or loss an investment generates over a year. It’s a key metric for evaluating the profitability of a trading system over time and comparing different strategies.

Average Bars Held is the average number of time units (bars) a trade is held before being closed. This metric helps traders assess trade duration and optimize strategy performance.

Average Profit/Loss measures the average monetary gain or loss per trade over a specified period. It helps traders evaluate profitability and efficiency of their trading systems.

Average Profit/Loss % represents the average percentage change in value per trade. It provides insight into a strategy’s risk-reward ratio and overall profitability.

Backtest Bias is the tendency for historical testing to produce overly optimistic results due to errors like data snooping, selection bias, or overfitting. It highlights the importance of objective testing procedures.

Backtesting is the process of testing a trading strategy using historical data to assess its profitability, robustness, and risk. Effective backtesting helps validate and refine trading systems before deploying them live.

CAR/MaxDD is the ratio of Compound Annual Return (CAR) to Maximum Drawdown. It measures the risk-adjusted performance of a trading strategy, with higher values indicating better performance.

Curve Fitting is the process of over-optimizing a trading system to fit historical data too closely, resulting in poor real-world performance. It occurs when models are excessively tailored to past data.

Data Integrity refers to the accuracy, consistency, and reliability of historical data used for backtesting and analysis. Poor data quality can lead to incorrect conclusions and faulty trading systems.

Data Mining Bias occurs when testing multiple strategies and only reporting the best-performing results. It results in misleading conclusions and unrealistic expectations about future performance.

Exposure % measures the proportion of capital actively committed to trades at any given time. It helps traders manage risk by assessing how much of their portfolio is at risk.

Forward Testing is the process of testing a trading system using live or unseen data after backtesting. It helps validate the robustness of a strategy before committing real capital.

Historical Data refers to past price, volume, and other market information used for analysis and backtesting. Reliable historical data is essential for developing and validating trading systems.

In-Sample Data is historical data used to develop, train, and optimize a trading strategy. It is later tested with out-of-sample data to assess robustness and avoid overfitting.

The K-Ratio measures the consistency of a trading system’s growth over time. A higher K-Ratio indicates a smoother, more reliable equity curve and more consistent performance.

Maximum system % drawdown is the largest percentage decline in a trading system’s equity during a specific period. It’s a key measure of risk and potential system failure.

Maximum system drawdown is the largest absolute loss experienced by a trading system from peak to trough. It reflects the worst-case loss scenario and impacts a trader’s confidence in the system.

Maximum trade % drawdown measures the largest percentage loss experienced on a single trade. It helps assess risk management and determine acceptable position sizes.

Maximum trade drawdown is the largest dollar loss experienced on a single trade from peak to trough. It’s a key metric for evaluating the risk of individual trades.

Mean is the average of a data set, while Median is the middle value when the data is ordered. Comparing mean and median helps traders assess the distribution and identify skewed data.

Model Fitting is the process of adjusting a trading model’s parameters to best fit historical data. It’s a critical step in strategy development but may result in overfitting if overdone.

Model Validation is the process of testing a trading model to ensure it performs as expected on new, unseen data. It confirms robustness and generalizability of the strategy.

Monte Carlo Simulation uses random sampling to evaluate the probability of different outcomes in trading strategies. It helps estimate potential drawdowns and returns under various market conditions.

Net Risk Adjusted Return % measures a trading strategy’s returns adjusted for risk, often using metrics like Sharpe Ratio or Sortino Ratio. It provides a clearer picture of performance efficiency.

Optimization is the process of adjusting parameters to improve a trading system’s performance. Effective optimization balances maximizing returns and minimizing risk without overfitting.

Out-of-Sample Data refers to data not used during strategy development. It is reserved for testing to ensure a trading model’s robustness and avoid overfitting.

Out-of-Sample Testing evaluates a trading system’s performance on data not previously used for model development. It confirms whether the strategy is likely to perform well in live markets.

Overfitting occurs when a trading model is excessively tailored to historical data, resulting in poor performance on new data. Avoiding overfitting is critical to developing robust systems.

Parameter Optimization is the process of fine-tuning a trading system’s variables to achieve the best performance. Careful optimization avoids overfitting and enhances robustness.

The Payoff Ratio is the ratio of average profit per winning trade to average loss per losing trade. A higher payoff ratio indicates a more profitable trading system.

Probability Distribution is a statistical function describing all possible outcomes and their likelihoods. It helps traders evaluate the risk and potential reward of various strategies.

Profit Factor is the ratio of gross profits to gross losses in a trading system. A profit factor above 1.5 indicates a profitable strategy, while a value below 1 suggests a losing system.

Quantitative Analysis involves using mathematical models, statistics, and computational techniques to analyze financial markets and develop trading strategies.

R-Squared measures how well a trading model’s predictions fit actual data. A higher value indicates a better fit but may also suggest overfitting if excessively high.

RAR/MaxDD is the ratio of Risk-Adjusted Return to Maximum Drawdown. It helps traders evaluate performance efficiency by comparing returns to potential drawdowns.

Random Walk Theory suggests that stock prices move randomly and cannot be accurately predicted. It challenges the validity of technical analysis and market timing. 

Recovery Factor measures how effectively a trading system recovers from drawdowns. A higher ratio indicates a quicker recovery, demonstrating robustness and efficiency.

Regression Analysis is a statistical technique used to identify relationships between variables. Traders use it to identify trends, optimize models, and forecast future price movements.

Risk Adjusted Return % measures profitability relative to the risk taken. Common metrics include Sharpe Ratio and Sortino Ratio, providing insight into a system’s efficiency.

The Risk-Reward Ratio compares the potential profit of a trade to the potential loss. A ratio of 1:3 means the potential reward is three times the potential risk. Traders aim for high ratios to ensure profitability despite occasional losses.

Robustness Testing involves evaluating a trading system’s performance across various market conditions, time periods, and asset classes. It helps identify strategies that perform consistently and are not overly sensitive to specific conditions.

The Sharpe Ratio measures the risk-adjusted return of an investment by comparing excess returns over a risk-free rate to the standard deviation of those returns. Higher values indicate better performance relative to risk taken.

Sharpe Ratio Calculation is performed by dividing the difference between the portfolio’s return and the risk-free rate by the standard deviation of the portfolio’s excess return. It’s commonly used to compare trading systems.

Sharpe Ratio of Trades measures the consistency of individual trade returns within a trading system. A high value indicates a well-performing system with less variability in returns.

Signal Filtering involves applying techniques to eliminate noise from trading signals, enhancing the accuracy and reliability of entry and exit points. Filters can include moving averages, volatility measures, or statistical methods.

Signal Generation is the process of creating buy or sell signals based on predefined criteria or algorithms. Effective signal generation minimizes subjective decision-making and improves trading consistency.

Signal-to-Noise Ratio measures the strength of a trading signal relative to market noise. A higher ratio indicates a clearer signal, which is more likely to produce profitable trades.

Skewness measures the asymmetry of a distribution, while Kurtosis measures the sharpness of its peak. High skewness or kurtosis indicates non-normal distributions, which can affect the reliability of statistical models.

The Sortino Ratio measures risk-adjusted returns, focusing only on downside risk instead of total volatility. It provides a more accurate assessment of performance for strategies aiming to avoid large losses

Standard Error measures the precision of a statistical estimate by evaluating the variability of sample data. It helps traders assess the reliability of performance metrics and predictive models.

Statistical Significance indicates whether a trading system’s results are likely due to skill rather than chance. Common methods include hypothesis testing and confidence intervals.

Systematic Trading relies on pre-established rules and algorithms to make trading decisions, minimizing emotional bias and improving consistency across various market conditions.

The Ulcer Index measures downside risk by calculating the depth and duration of drawdowns. Unlike standard deviation, it focuses only on negative price movements, making it useful for assessing risk.

The Ulcer Performance Index evaluates the return of an investment relative to its risk, as measured by the Ulcer Index. Higher values indicate better risk-adjusted performance.

Walk-Forward Testing is a validation method where a trading system is continuously optimized and tested on new data. It helps ensure that a strategy is robust and not overfit to past data.