Shared-filter combinations of the stochastic oscillator, MACD, and RSI

What the 2014 notes described

A 2014 product note described an add-on that plotted ten standard technical indicators, including the stochastic oscillator, MACD, and relative strength index, with automatic real-time adjustment intended to produce lower-lag, smoother values.

The same note described a second add-on that automatically adjusted fifteen standard technical indicators, including the stochastic oscillator, MACD, and relative strength index, so that price gaps such as overnight gaps were reflected without redrawing.

Both add-ons were described as usable for real-time analysis, historical backtesting, and walk-forward optimization. The lower-lag add-on was described as adding four velocity indexes alongside the enhanced standard indicators.

The three readings

The stochastic oscillator is a bounded momentum reading that places the latest close inside a lookback range of highs and lows and is treated here as a forecast output for out-of-sample comparison.

MACD is a moving-average convergence-divergence construction from price averages, treated here as a chart-scale signal rather than a standalone forecast model.

The relative strength index is a bounded oscillator that scales average up-moves against average down-moves over a defined lookback and is treated here as a forecast output for out-of-sample comparison.

One family, one inherited adjustment

Low-lag smoothing is a real-time recalculation that reduces indicator lag while aiming to keep the plot smoother than the textbook formula. Gap-aware adjustment is a real-time recalculation that folds overnight or other price gaps into the indicator without redrawing earlier bars.

The stochastic oscillator, MACD, and relative strength index were listed inside the same enhanced-indicator families, so any combined reading of the three would inherit whichever lag or gap adjustment that family applied.

Name the transform, then test the bundle

Editorial guidance is to name the shared transformation first, then compare that bundled construction with an explicit textbook baseline in a backtest and walk-forward check.

Walk-forward optimization is a rolling out-of-sample protocol that re-estimates parameters on successive windows instead of a single in-sample fit. Both add-ons were described as usable for that protocol, as well as for real-time analysis and historical backtesting.