QuanTAlib/lib/numerics/_index.md

# Numerics

> "Price is raw signal. Transform exposes hidden structure. Derivative reveals momentum. Normalization enables comparison."

Basic mathematical transforms and utility functions for time series. These building blocks convert raw price data into forms suitable for analysis, comparison, and downstream indicator consumption.

| Indicator | Full Name | Description |
| :--- | :--- | :--- |
| [ACCEL](lib/numerics/accel/Accel.md) | Acceleration | Momentum change; second derivative of price. |
| [CHANGE](lib/numerics/change/Change.md) | Percentage Change | Relative price movement over lookback period. |
| [EXPTRANS](lib/numerics/exptrans/Exptrans.md) | Exponential Transform | e^x transform for log-space conversion reversal. |
| [HIGHEST](lib/numerics/highest/Highest.md) | Rolling Maximum | Maximum value over lookback window. |
| [JERK](lib/numerics/jerk/Jerk.md) | Jerk | Rate of acceleration; third derivative of price. |
| [LINEARTRANS](lib/numerics/lineartrans/Lineartrans.md) | Linear Transform | y = ax + b scaling transformation. |
| [LOGTRANS](lib/numerics/logtrans/Logtrans.md) | Logarithmic Transform | Natural log for percentage-based analysis. |
| [LOWEST](lib/numerics/lowest/Lowest.md) | Rolling Minimum | Minimum value over lookback window. |
| [MIDPOINT](lib/numerics/midpoint/Midpoint.md) | Midrange | (Highest + Lowest) / 2 over lookback window. |
| [NORMALIZE](lib/numerics/normalize/Normalize.md) | Min-Max Normalization | Scale to [0,1] range using rolling min/max. |
| [RELU](lib/numerics/relu/Relu.md) | Rectified Linear Unit | max(0, x); neural network activation function. |
| [SIGMOID](lib/numerics/sigmoid/Sigmoid.md) | Logistic Function | 1/(1+e^-x); bounded [0,1] transform. |
| [SLOPE](lib/numerics/slope/Slope.md) | Rate of Change | First derivative; velocity of price movement. |
| [SQRTTRANS](lib/numerics/sqrttrans/Sqrttrans.md) | Square Root Transform | Variance-stabilizing transformation. |
| STANDARDIZE | Z-Score Normalization | (x - mean) / stddev; zero-mean unit-variance transform. |
| BETADIST | Beta Distribution | Continuous probability distribution defined on interval [0, 1]. |
| BINOMDIST | Binomial Distribution | Discrete probability distribution of successes in n independent trials. |
| CWT | Continuous Wavelet Transform | Analyzes time series data across different frequency scales continuously. |
| DWT | Discrete Wavelet Transform | Analyzes time series data across different frequency scales at discrete intervals. |
| EXPDIST | Exponential Distribution | Continuous probability distribution describing time between events. |
| FDIST | F-Distribution | Continuous probability distribution ratio of two chi-squared distributions. |
| FFT | Fast Fourier Transform | Efficient algorithm for computing the discrete Fourier transform and its inverse. |
| GAMMADIST | Gamma Distribution | Continuous probability distribution generalizing exponential and chi-squared. |
| IFFT | Inverse Fast Fourier Transform | Efficient algorithm for computing the inverse discrete Fourier transform. |
| LOGNORMDIST | Log-normal Distribution | Continuous probability distribution of a variable whose log is normally distributed. |
| NORMDIST | Normal Distribution | Gaussian bell-shaped probability distribution. |
| POISSONDIST | Poisson Distribution | Discrete probability distribution expressing events in fixed time interval. |
| TDIST | Student's t-Distribution | Continuous probability distribution when estimating mean of normally distributed population. |
| WEIBULLDIST | Weibull Distribution | Continuous probability distribution useful in reliability and survival analysis. |