Adaptive Valuation [BackQuant]

10 saat önce

Adaptive Valuation [BackQuant]

What this is
A composite, zero-centered oscillator that standardizes several classic indicators and blends them into one “valuation” line. It computes RSI, CCI, Demarker, and the Price Zone Oscillator, converts each to a rolling z-score, then forms a weighted average. Optional smoothing, dynamic overbought and oversold bands, and an on-chart table make the inputs and the final score easy to inspect.

How it works
Components
• RSI with its own lookback.
• CCI with its own lookback.
• DM (Demarker) with its own lookback.
• PZO (Price Zone Oscillator) with its own lookback.

Standardization via z-score
Each component is transformed using a rolling z-score over lookback bars:

z = (value − mean) ÷ stdev

, where the mean is an EMA and the stdev is rolling.
This puts all inputs on a comparable scale measured in standard deviations.

Weighted blend
The z-scores are combined with user weights w_rsi, w_cci, w_dm, w_pzo to produce a single valuation series. If desired, it is then smoothed with a selected moving average (SMA, EMA, WMA, HMA, RMA, DEMA, TEMA, LINREG, ALMA, T3). ALMA’s sigma input shapes its curve.

Dynamic thresholds (optional)
Two ways to set overbought and oversold:
• Static: fixed levels at ob_thres and os_thres.
• Dynamic: ±k·σ bands, where σ is the rolling standard deviation of the valuation over dynLen.
Bands can be centered at zero or around the valuation’s rolling mean (centerZero).

Visualization and UI
• Zero line at 0 with gradient fill that darkens as the valuation moves away from 0.
• Optional plotting of band lines and background highlights when OB or OS is active.
• Optional candle and background coloring driven by the valuation.
• Summary table showing each component’s current z-score, the final score, and a compact status.

How it can be used
• Bias filter: treat crosses above 0 as bullish bias and below 0 as bearish bias.
• Mean-reversion context: look for exhaustion when the valuation enters the OB or OS region, then watch for exits from those regions or a return toward 0.
• Signal confirmation: use the final score to confirm setups from structure or price action.
• Adaptive banding: with dynamic thresholds, OB and OS adjust to prevailing variability rather than relying on fixed lines.
• Component tuning: change weights to emphasize trend (raise DM, reduce RSI/CCI) or range behavior (raise RSI/CCI, reduce DM). PZO can help in swing environments.

Why z-score blending helps
Indicators often live on different scales. Z-scoring places them on a common, unitless axis, so a one-sigma move in RSI has comparable influence to a one-sigma move in CCI. This reduces scale bias and allows transparent weighting. It also facilitates regime-aware thresholds because the dynamic bands scale with recent dispersion.

Inputs to know
• Component lookbacks: rsilb, ccilb, dmlb, pzolb control each raw signal.
• Standardization window: lookback sets the z-score memory. Longer smooths, shorter reacts.
• Weights: w_rsi, w_cci, w_dm, w_pzo determine each component’s influence.
• Smoothing: maType, smoothP, sig govern optional post-blend smoothing.
• Dynamic bands: dyn_thres, dynLen, thres_k, centerZero configure the adaptive OB/OS logic.
• UI: toggle the plot, table, candle coloring, and threshold lines.

Reading the plot
• Above 0: composite pressure is positive.
• Below 0: composite pressure is negative.
• OB region: valuation above the chosen OB line. Risk of mean reversion rises and momentum continuation needs evidence.
• OS region: mirror logic on the downside.
• Band exits: leaving OB or OS can serve as a normalization cue.

Strengths
• Normalizes heterogeneous signals into one interpretable series.
• Adjustable component weights to match instrument behavior.
• Dynamic thresholds adapt to changing volatility and drift.
• Transparent diagnostics from the on-chart table.
• Flexible smoothing choices, including ALMA and T3.

Limitations and cautions
• Z-scores assume a reasonably stationary window. Sharp regime shifts can make recent bands unrepresentative.
• Highly correlated components can overweight the same effect. Consider adjusting weights to avoid double counting.
• More smoothing adds lag. Less smoothing adds noise.
• Dynamic bands recalibrate with dynLen; if set too short, bands may swing excessively. If too long, bands can be slow to adapt.

Practical tuning tips
• Trending symbols: increase w_dm, use a modest smoother like EMA or T3, and use centerZero dynamic bands.
• Choppy symbols: increase w_rsi and w_cci, consider ALMA with a higher sigma, and widen bands with a larger thres_k.
• Multiday swing charts: lengthen lookback and dynLen to stabilize the scale.
• Lower timeframes: shorten component lookbacks slightly and reduce smoothing to keep signals timely.

Alerts
• Enter and exit of Overbought and Oversold, based on the active band choice.
• Bullish and bearish zero crosses.
Use alerts as prompts to review context rather than as stand-alone trade commands.

Final Remarks
We created this to show people a different way of making indicators & trading.

You can process normal indicators in multiple ways to enhance or change the signal, especially with this you can utilise machine learning to optimise the weights, then trade accordingly.

All of the different components were selected to give some sort of signal, its made out of simple components yet is effective. As long as the user calibrates it to their Trading/ investing style you can find good results. Do not use anything standalone, ensure you are backtesting and creating a proper system.

10 saat önce

Sürüm Notları

shorttitle = "Valuation [BackQuant]"

Chart fix too.