Adaptive Trend Mapper-ATM (Arjo)Adaptive Trend Mapper (ATM) is a multi-factor trend, momentum, and compression-analysis tool designed to help traders visually map the strength and direction of market pressure.
Instead of simply combining existing indicators, ATM creates a new composite framework that blends momentum imbalance, directional strength, volatility contraction, and adaptive smoothing into a single, unified model.
Originality and usefulness
Adaptive Trend Mapper (ATM) does not replicate any one indicator.
It generates two custom indices— Bull Pressure Index and Bear Pressure Index —derived from a mathematical combination of RSI, inverse-RSI, and ADX. These indices behave differently from traditional oscillators:
They represent directional pressure on a 0–100 scale , not momentum.
They are designed to converge/diverge, forming a basis for the built-in Squeeze Detection Engine.
They can be optionally step-compressed , making the movement easier to read on fast or small charts.
The script also integrates a custom SuperSmoother trend model (not TradingView’s built-in function), which acts as an adaptive trend curve on the chart.
All calculations are combined intentionally—not as a mashup—to create a framework that allows traders to understand trend strength, compression phases, and micro-trend shifts in one place.
How the Indicator Works
1. Bull & Bear Pressure Indices:
These indices measure directional imbalance:
Bull Index = ADX strength weighted against inverse-RSI
Bear Index = ADX strength weighted against normal RSI
This produces two opposing pressure curves that rise or fall depending on whether buyers or sellers dominate.
You can optionally smooth these using:
SMA / EMA / WMA / RMA via the “Smoothing Settings” panel.
2. Squeeze & Compression Detection:
A squeeze is detected when:
ADX stays below a user-defined threshold
Bull–Bear Index difference shrinks
Average difference is falling (convergence)
This is a volatility-contraction model inspired by squeeze logic but applied to directional pressure, not Bollinger Bands/Keltner Channels .
3. Adaptive Trend Curve (SuperSmoother Engine)
The indicator applies a two-pole SuperSmoother filter to the price, then smooths it again using EMA.
The slope color flips between bullish and bearish and is displayed using:
A thin SuperSmoother curve
A thicker band for visual context
4. EMA-50 Trend Context:
An optional EMA-50 helps identify broad directional bias .
5. Step-Based Scaling
You can quantize the Bull/Bear indices using custom step intervals.
This makes the indicator easier to read on noisy intraday charts.
How to Use the Indicator
1. Trend Analysis
A rising Bull Index shows strengthening upward pressure
A rising Bear Index shows strengthening downward pressure
Wide divergence between the indices signals a strong trend
2. Compression / Squeeze Analysis
Yellow background = volatility compression + pressure convergence
Breakouts from this zone often precede directional expansion
3. Trendline Reading
SuperSmoother line color flip = micro trend shift
EMA-50 slope gives macro-trend direction
Perfect for combining trend and momentum maps on the same chart
4. Visual Interpretation
Cyan/teal → strong bullish pressure
Purple/red/orange → various levels of bearish control
Neutral/teal background → weak ADX
Yellow background → squeeze zone
Open-Source Notes
This script uses:
TradingView built-in RSI, ADX/DMI, and smoothing functions
A SuperSmoother implementation based on known DSP filter coefficients
All remaining logic, signal methods, composite indices, and compression model are original developments by ARJO .
The script is published open-source to comply with TradingView’s reuse policy.
Disclaimer
This tool is for educational and analytical purposes only.
It does not generate buy or sell signals.
Always use proper risk management.
Happy Trading (ARJO)
Momentum-analysis
Volatility Signal-to-Noise Ratio🙏🏻 this is VSNR: the most effective and simple volatility regime detector & automatic volatility threshold scaler that somehow no1 ever talks about.
This is simply an inverse of the coefficient of variation of absolute returns, but properly constructed taking into account temporal information, and made online via recursive math with algocomplexity O(1) both in expanding and moving windows modes.
How do the available alternatives differ (while some’re just worse)?
Mainstream quant stat tests like Durbin-Watson, Dickey-Fuller etc: default implementations are ALL not time aware. They measure different kinds of regime, which is less (if at all) relevant for actual trading context. Mix of different math, high algocomplexity.
The closest one is MMI by financialhacker, but his approach is also not time aware, and has a higher algocomplexity anyways. Best alternative to mine, but pls modify it to use a time-weighted median.
Fractal dimension & its derivatives by John Ehlers: again not time aware, very low info gain, relies on bar sizes (high and lows), which don’t always exist unlike changes between datapoints. But it’s a geometric tool in essence, so this is fundamental. Let it watch your back if you already use it.
Hurst exponent: much higher algocomplexity, mix of parametric and non-parametric math inside. An invention, not a math entity. Again, not time aware. Also measures different kinds of regime.
How to set it up:
Given my other tools, I choose length so that it will match the amount of data that your trading method or study uses multiplied by ~ 4-5. E.g if you use some kind of bands to trade volatility and you calculate them over moving window 64, put VSNR on 256.
However it depends mathematically on many things, so for your methods you may instead need multipliers of 1 or ~ 16.
Additionally if you wanna use all data to estimate SNR, put 0 into length input.
How to use for regime detection:
First we define:
MR bias: mean reversion bias meaning volatility shorts would work better, fading levels would work better
Momo bias: momentum bias meaning volatility longs would work better, trading breakouts of levels would work better.
The study plots 3 horizontal thresholds for VSNR, just check its location:
Above upper level: significant Momo bias
Above 1 : Momo bias
Below 1 : MR bias
Below lower level: significant MR bias
Take a look at the screenshots, 2 completely different volatility regimes are spotted by VSNR, while an ADF does not show different regime:
^^ CBOT:ZN1!
^^ INDEX:BTCUSD
How to use as automatic volatility threshold scaler
Copy the code from the script, and use VSNR as a multiplier for your volatility threshold.
E.g you use a regression channel and fade/push upper and lower thresholds which are RMSEs multiples. Inside the code, multiply RMSE by VSNR, now you’re adaptive.
^^ The same logic as when MM bots widen spreads with vola goes wild.
How it works:
Returns follow Laplace distro -> logically abs returns follow exponential distro , cuz laplace = double exponential.
Exponential distro has a natural coefficient of variation = 1 -> signal to noise ratio defined as mean/stdev = 1 as well. The same can be said for Student t distro with parameter v = 4. So 1 is our main threshold.
We can add additional thresholds by discovering SNRs of Student t with v = 3 and v = 5 (+- 1 from baseline v = 4). These have lighter & heavier tails each favoring mean reversion or momentum more. I computed the SNR values you see in the code with mpmath python module, with precision 256 decimals, so you can trust it I put it on my momma.
Then I use exponential smoothing with properly defined alphas (one matches cumulative WMA and another minimizes error with WMA in moving window mode) to estimate SNR of abs returns.
…
Lightweight huh?
∞
