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