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Inertia

Incremental, causal technical analysis documentation

Summary

Inertia is RTTA's streaming implementation of Donald Dorsey's inertia indicator: a linear regression of the Relative Volatility Index (RVI). It smooths volatility-direction into a slower trend-of-volatility feature.

Update API

value = rtta.Inertia(
    std_window=10, smooth_window=14, reg_window=20, fillna=True
).update(close)

The update(...) call consumes one close. advance(...) updates state without returning a Python value. Scalar batch(...) returns a NumPy array.

Theory Of Operation

Dorsey's Relative Volatility Index is an RSI-style oscillator applied to the rolling standard deviation of close (up-vol vs down-vol). Inertia then applies a rolling linear regression to that RVI series so the output tracks the local trend of relative volatility rather than the raw RVI tick. High inertia means volatility has been persistently biased to the upside of the RVI construction; low inertia means the opposite.

Recurrence

Let \(c_t\) be close. First form Dorsey RVI as in RelativeVolatilityIndex with parameters std_window and smooth_window:

\[\sigma_t = \operatorname{StdDev}_{n_\sigma}(c_t)\]
\[u_t = \begin{cases} \sigma_t & c_t > c_{t-1} \\ \tfrac12\sigma_t & c_t = c_{t-1} \\ 0 & c_t < c_{t-1} \end{cases} \qquad d_t = \begin{cases} \sigma_t & c_t < c_{t-1} \\ \tfrac12\sigma_t & c_t = c_{t-1} \\ 0 & c_t > c_{t-1} \end{cases}\]
\[U_t = \operatorname{EMA}_{n_s}(u_t),\quad D_t = \operatorname{EMA}_{n_s}(d_t),\quad \operatorname{RVI}_t = 100\cdot\frac{U_t}{U_t+D_t}\]

Then inertia is the fitted linear-regression value of RVI over reg_window:

\[I_t = \operatorname{LinReg}_{n_r}(\operatorname{RVI})_t.\]

Implementation Notes

The recurrence is implemented in src/rtta/indicator.cpp in class Inertia, composing RelativeVolatilityIndex with LinearRegressionCore.

Reference