RTTADocumentation Built by Goblin Reactor ↗

RSI

Incremental, causal technical analysis documentation

Summary

RSI computes RTTA's incremental Relative Strength Index over one scalar price stream. It stores the previous value plus running positive and negative move state.

Update API

value = rtta.RSI(window=14, fillna=True).update(value)

The first sample initializes prev. With fillna=True, the first returned value is 50.0; with fillna=False, warmup returns NaN.

Theory Of Operation

RSI compares recent upward movement against recent downward movement and maps that ratio into a bounded oscillator. The RTTA C++ implementation updates the upward state on positive moves and the downward state on negative moves. During warmup it uses the current sample count as the averaging denominator; after warmup it uses Wilder-style smoothing with window.

Recurrence

Let \(x_t\) be the current value, \(x_{t-1}\) the prior value, and \(n\) be window.

\[g_t = \max(x_t - x_{t-1}, 0), \qquad \ell_t = \max(x_{t-1} - x_t, 0)\]

During warmup, RTTA updates only the side that moved:

\[G_t = \begin{cases} \frac{(t-1)G_{t-1}+g_t}{t}, & g_t > 0 \\ G_{t-1}, & g_t = 0 \end{cases}\]
\[L_t = \begin{cases} \frac{(t-1)L_{t-1}+\ell_t}{t}, & \ell_t > 0 \\ L_{t-1}, & \ell_t = 0 \end{cases}\]

After warmup, the same directional update uses \(n\):

\[G_t = \begin{cases} \frac{(n-1)G_{t-1}+g_t}{n}, & g_t > 0 \\ G_{t-1}, & g_t = 0 \end{cases} \qquad L_t = \begin{cases} \frac{(n-1)L_{t-1}+\ell_t}{n}, & \ell_t > 0 \\ L_{t-1}, & \ell_t = 0 \end{cases}\]

The output is:

\[RS_t = \frac{G_t/n}{L_t/n}, \qquad RSI_t = 100 - \frac{100}{1 + RS_t}\]

If the downside state is zero, RTTA returns 100.0 once it is past the initial sample path.

Implementation Notes

The recurrence is implemented in src/rtta/indicator.cpp in class RSI. The directional state-update behavior above follows the current C++ code exactly.

Reference