# The Math Behind AFIRMA ## Components of AFIRMA AFIRMA is a hybrid beast, combining two main components: - Autoregressive Moving Average (ARMA) - Finite Impulse Response (FIR) filter ### ARMA Component $ X_t = c + \epsilon_t + \sum_{i=1}^p \phi_i X_{t-i} + \sum_{j=1}^q \theta_j \epsilon_{t-j} $ Where: - $X_t$ is the time series value at time $t$
- $c$ is a constant
- $\phi_i$ are the parameters of the autoregressive term
- $\theta_j$ are the parameters of the moving average term
- $\epsilon_t$ is white noise
### FIR Component $ y[n] = \sum_{i=0}^{N-1} b_i \cdot x[n-i] $ Where: - $y[n]$ is the output signal - $x[n]$ is the input signal - $b_i$ are the filter coefficients - $N$ is the filter order ### AFIRMA: Putting It All Together AFIRMA combines these components and adds cubic spline fitting to the mix. The general form can be expressed as: $ AFIRMA_t = ARMA_t + FIR_t + CS_t $ Where: - $ARMA_t$ is the ARMA component at time $t$ - $FIR_t$ is the FIR component at time $t$ - $CS_t$ is the cubic spline fitting component at time $t$ ### Digital Filtering Process - The price data is passed through the digital filter to smooth out fluctuations. - The filter coefficients are optimized based on the specified parameters (Periods, Taps, Window). ### Cubic Spline Fitting For the most recent bars: - A cubic spline is fitted to the data points using the least squares method. - This ensures a smooth transition between the filtered data and the most recent price movements. ### Parameter Definitions The AFIRMA indicator allows for the adjustment of three main parameters: - **Periods**: Affects the overall smoothness of the indicator. - *Taps*: Influences the complexity of the digital filter. - *Window*: Determines the number of recent bars to which the cubic spline fitting is applied. ### Computational Process - Apply the ARMA model to the price data. - Pass the result through the FIR filter. - Apply cubic spline fitting to the most recent data points. - Combine the results to produce the final AFIRMA value.