Liquid RSI - Marrying The Relative Strength Index And The VolumeIntroduction
I recently derived the calculation of the relative strength index, an indicator that aim to spot overbought and oversold assets, but what is an overbought/sold asset ? Can such things be estimated with price alone ?
This why i propose a modification of the relative strength using my recently proposed efficient calculation including volume information in order to spot overbought/sold asset.
Scaling A Liquid Market
The relative strength index detect an overbought/sold asset when higher/lower than a certain level, often 80/20. An overbought asset, or better say over evaluated, is more attractive to sell because prices are no longer attractive to buy, it has reached its value of interest for traders looking to go long, we can then expect the price to correct and start a trend of opposite direction, while an oversold asset is more attractive to buy based on the same logic.
The idea of talking about over bought and over sold without taking into account the volume can be a bit strange, since volume is directly related to the quantity of contracts traded, an higher volume can show sign of a more active market, which can describe the terms : overbought/sold a bit better. Many indicators used the rsi framework with volume, the money flow index for example, but it can be interesting to provide other alternatives.
The Indicator
The indicator is based on the average positive changes in price multiplied by positive changes in volume divided by the average absolute change in price multiplied by the absolute changes in volume. The average is based on the wilder moving average which is a simple exponential filter with smoothing constant 1/length .
The indicator will react according to the volume magnitude, higher volumes will make the indicator go over/under the overbought/sold threshold more easily, in the image above, the indicator is currently saying that the market is under evaluated, which is not the case for the RSI. Such situation allow us to take a position that we could't take if we base our judgement only on price change magnitude.
The indicator has a tendency to be over/under the thresholds a longer period of time if the volume is relatively high.
An interesting effect the indicator has it to ignore movements with moderate volume, the indicator is less prone to cross under a threshold and to go back to it, this is shown in the image above. Another observation we can make is that the proposed indicator is smoother than the rsi, this is certainly due to the fact that the volume underweight small price changes.
Conclusions
I proposed a modification of the relative strength index that also take into account volume information. The proposed indicator is also smoother. Regarding its ability to detect overbought and oversold market, it has indeed the capacity to do it, however the problem remain the same, what is the extent of the correction following an overbought/oversold market ? We can see that the correction can be minor, and thus be followed by a large movements correlated with the main trend.
With those oscillators we are interested into knowing the end of the "whole trend", and they fail to do this because they use past information. I still hope the indicator find some creative usages amongst the community.
Thanks for reading ! And remember to ask before using the script code, it pains me to see minor changes on scripts i can pass 3 hours on.
ค้นหาในสคริปต์สำหรับ "change"
Open Interest Bubbles [BackQuant]Open Interest Bubbles
A visual OI positioning overlay that aggregates futures open interest across major venues, normalizes it into a consistent “signal strength” scale, then plots extreme events as bubbles, labels, and optional horizontal levels directly on price.
What this is for
Open interest is one of the cleanest ways to track when positioning is building, unwinding, or aggressively shifting. The problem is raw OI is noisy, exchange-specific, and hard to compare across time. This script solves that by:
- Aggregating OI across multiple exchanges.
- Letting you choose what “OI signal” you care about (raw, delta, percent versions).
- Normalizing the signal so “big events” are easy to spot.
- Plotting those events as bubbles and levels at the exact price they occurred.
You end up with a clean, fast visual map of where large positioning changes occurred, and where those events may later matter as reaction points.
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Plotting types (what you can display)
Bubbles
This mode plots OI events as size-bucketed circles on the chart. Bigger bubbles represent stronger normalized events. You can tune:
- Bubble sizing by bucket (Tiny → Huge).
- Heatmap vs solid color styling.
- Signed vs unsigned coloring (positive/negative separation or magnitude-only).
Best use:
- Spotting “where something changed” at a glance.
- Identifying clusters of positioning events around key price zones.
- Seeing whether the market is repeatedly building/closing positions at similar levels.
Levels
Levels mode draws a horizontal line at the anchor price when an extreme OI event triggers. These act like “positioning memory” levels:
- They do not claim to be support/resistance by themselves.
- They highlight prices where the derivatives market clearly did something meaningful.
Best use:
- Marking potential reaction zones.
- Combining with your price action tools (structure, OBs, FVGs) to confirm whether an OI level aligns with a technical level.
- Building a “map” of where leverage likely entered or exited.
Modes available in the script:
- Off
- Bubbles
- Bubbles + Labels
- Labels Only
- Levels + Labels
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Aggregated Open Interest source (multi-exchange)
This indicator builds a single aggregated OI series by requesting OI data from multiple exchanges and summing it. You can toggle exchanges on/off:
- Binance, Bybit, OKX, Bitget, Kraken, HTX, Deribit
You can also choose OI units:
- COIN , OI in base units (native sizing)
- USD , converted for a dollar-value representation
Important note:
Not every symbol has OI data on every venue. If the script cannot build an aggregated series for the symbol, it will throw an error rather than quietly plotting garbage.
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OI Source, what the bubbles are measuring
You control what “signal” is normalized and plotted:
- Delta , change in aggregated OI from the prior bar.
Use when you want to highlight bursts of new positioning or sudden unwind events.
- Raw OI , the aggregated open interest level itself.
Use when you want to highlight absolute positioning build-up periods.
- Delta % , percent change in OI.
Use when you want moves normalized to the current OI regime, useful across different market eras.
- Raw OI % , percent change form of the raw series.
Use when you want relative changes rather than absolute size.
Practical guidance:
- Delta modes are best for “event detection”.
- Raw modes are better for “regime context” and whether positioning is structurally rising or fading.
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Normalization (the key to making it readable)
Because OI varies massively across assets and time, the script includes multiple normalization modes to convert your chosen OI source into a comparable “strength” value.
Options:
- ZScore , deviation from a rolling mean in standard deviation units.
- StdNorm , scaled by rolling standard deviation.
- AbsZScore , absolute value version for magnitude-only mapping.
- AbsStdNorm , absolute value version for magnitude-only mapping.
- None , plots raw values (advanced users only, often too noisy visually).
Why this matters:
Normalization makes a “1.5” or “3.0” threshold mean something across different assets and timeframes, instead of being stuck to raw OI units.
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Threshold system (when bubbles/levels trigger)
The plot is driven by two user thresholds:
- Base Threshold
Controls where “meaningful” events start. Raising this reduces noise and focuses on larger deviations.
- Extreme Threshold
Controls what qualifies as a top-tier event. Extreme events are what you typically want to convert into labels and levels.
You also control side filtering:
- Both , show positive and negative events.
- Positive Only , show only increases (or positive signal side depending on source).
- Negative Only , show only decreases (or negative signal side).
In practice:
- Use Base Threshold to tune chart cleanliness.
- Use Extreme Threshold to mark only the “big stuff” that tends to matter later.
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Anchor Source (where the bubble/level is placed)
The indicator places bubbles, labels, and levels at a price anchor you choose:
- HL2, Close, Open, High, Low, VWAP
This is important because “where you pin the event” changes how it reads:
- Close is clean and consistent for backtesting and candle-close logic.
- High/Low can better represent where the fight occurred intrabar.
- VWAP can be useful for “fair price” anchoring in active markets.
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Style system (theme, palette, signed logic)
This script is built to look good and stay readable on busy charts.
Themes
- BackQuant, Classic, Ice, Fire, Mono, Custom
Palette Mode
- Solid , one consistent color
- Heatmap , intensity increases with magnitude
- Single Color Adaptive , adapts to chart background for clarity
Side Coloring
- Signed , positive and negative events can use different ramps
- Unsigned , magnitude-only coloring
Negative theme handling:
- Auto (mirrors your chosen theme),
- Invert (flips the ramp),
- Custom (fully user-defined negative palette).
What this gives you:
- You can run a clean “mono” look for professional charts.
- Or a high-contrast heatmap for fast scanning.
- Or fully custom branding colors for BackQuant-style presentation.
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Labels (what’s inside the label)
When labels are enabled, the script can display:
- OI , the aggregated OI value
- OI + Norm , OI plus normalized strength
- Norm Only , just the normalized strength
- Src + Norm , the selected source value (Delta, Raw, %) plus normalized strength
You can also control:
- Left/Center/Right label alignment
- Number formatting style (Raw, Compact, Volume format)
Best practice:
- Use “Src + Norm” when you want both the raw event size and its rarity.
- Use “Norm Only” when you want a clean, minimal chart.
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Levels and object limits (performance and cleanliness)
Because this script draws objects, it includes a hard cleanup system:
- You set Max Levels / Labels to control chart clutter.
- The script deletes older lines/labels when the limit is exceeded.
This is critical if you trade lower timeframes, where OI events can trigger frequently.
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How to interpret the signals
What a large bubble usually means:
- A statistically large positioning change relative to recent history.
- This can represent fresh leverage entering, forced liquidations, or aggressive de-risking, depending on direction and context.
How to use levels:
- Treat them as “attention levels”, not automatic entries.
- Combine them with structure and liquidity tools:
- If price revisits an OI level and shows rejection, it often confirms that level mattered.
- If price slices through with no reaction, it often indicates the OI event was transitional, not defended.
Common setups:
- Clustered extreme bubbles near a breakout zone, then retest later.
- Extreme negative event at capitulation low, followed by structure flip.
- Extreme positive build into resistance, then unwind and mean reversion.
Also, please check out @NoveltyTrade for the OI Aggregation logic & pulling the data source!
Here is the original script:
Regime [CHE] Regime — Minimal HTF MACD histogram regime marker with a simple rising versus falling state.
Summary
Regime is a lightweight overlay that turns a higher-timeframe-style MACD histogram condition into a simple regime marker on your chart. It queries an imported core module to determine whether the histogram is rising and then paints a consistent marker color based on that boolean state. The output is intentionally minimal: no lines, no panels, no extra smoothing visuals, just a repeated marker that reflects the current regime. This makes it useful as a quick context filter for other signals rather than a standalone system.
Motivation: Why this design?
A common problem in discretionary and systematic workflows is clutter and over-interpretation. Many regime tools draw multiple plots, which can distract from price structure. This script reduces the regime idea to one stable question: is the MACD histogram rising under a given preset and smoothing length. The core logic is delegated to a shared module to keep the indicator thin and consistent across scripts that rely on the same definition.
What’s different vs. standard approaches?
Reference baseline: A standard MACD histogram plotted in a separate pane with manual interpretation.
Architecture differences:
Uses a shared library call for the regime decision, rather than re-implementing MACD logic locally.
Uses a single boolean output to drive marker color, rather than plotting histogram bars.
Uses fixed marker placement at the bottom of the chart for consistent visibility.
Practical effect:
You get a persistent “context layer” on price without dedicating a separate pane or reading histogram amplitude. The chart shows state, not magnitude.
How it works (technical)
1. The script imports `chervolino/CoreMACDHTF/2` and calls `core.is_hist_rising()` on each bar.
2. Inputs provide the source series, a preset string for MACD-style parameters, and a smoothing length used by the library function.
3. The library returns a boolean `rising` that represents whether the histogram is rising according to the library’s internal definition.
4. The script maps that boolean to a color: yellow when rising, blue otherwise.
5. A circle marker is plotted on every bar at the bottom of the chart, colored by the current regime state. Only the most recent five hundred bars are displayed to limit visual load.
Notes:
The exact internal calculation details of `core.is_hist_rising()` are not shown in this code. Any higher timeframe mechanics, security usage, or confirmation behavior are determined by the imported library. (Unknown)
Parameter Guide
Source — Selects the price series used by the library call — Default: close — Tips: Use close for consistency; alternate sources may shift regime changes.
Preset — Chooses parameter preset for the library’s MACD-style configuration — Default: 3,10,16 — Trade-offs: Faster presets tend to flip more often; slower presets tend to react later.
Smoothing Length — Controls smoothing used inside the library regime decision — Default: 21 — Bounds: minimum one — Trade-offs: Higher values typically reduce noise but can delay transitions. (Library behavior: Unknown)
Reading & Interpretation
Yellow markers indicate the library considers the histogram to be rising at that bar.
Blue markers indicate the library considers it not rising, which may include falling or flat conditions depending on the library definition. (Unknown)
Because markers repeat on every bar, focus on transitions from one color to the other as regime changes.
This tool is best read as context: it does not express strength, only direction of change as defined by the library.
Practical Workflows & Combinations
Trend following:
Use yellow as a condition to allow long-side entries and blue as a condition to allow short-side entries, then trigger entries with your primary setup such as structure breaks or pullback patterns. (Optional)
Exits and stops:
Consider tightening management after a color transition against your position direction, but do not treat a single flip as an exit signal without price-based confirmation. (Optional)
Multi-asset and multi-timeframe:
Keep `Source` consistent across assets.
Use the slower preset when instruments are noisy, and the faster preset when you need earlier context shifts. The best transferability depends on the imported library’s behavior. (Unknown)
Behavior, Constraints & Performance
Repaint and confirmation:
This script itself uses no forward-looking indexing and no explicit closed-bar gating. It evaluates on every bar update.
Any repaint or confirmation behavior may come from the imported library. If the library uses higher timeframe data, intrabar updates can change the state until the higher timeframe bar closes. (Unknown)
security and HTF:
Not visible here. The library name suggests HTF behavior, but the implementation is not shown. Treat this as potentially higher-timeframe-driven unless you confirm the library source. (Unknown)
Resources:
No loops, no arrays, no heavy objects. The plotting is one marker series with a five hundred bar display window.
Known limits:
This indicator does not convey histogram magnitude, divergence, or volatility context.
A binary regime can flip in choppy phases depending on preset and smoothing.
Sensible Defaults & Quick Tuning
Starting point:
Source: close
Preset: 3,10,16
Smoothing Length: 21
Tuning recipes:
Too many flips: choose the slower preset and increase smoothing length.
Too sluggish: choose the faster preset and reduce smoothing length.
Regime changes feel misaligned with your entries: keep the preset, switch the source back to close, and tune smoothing length in small steps.
What this indicator is—and isn’t
This is a minimal regime visualization and a context filter. It is not a complete trading system, not a risk model, and not a prediction engine. Use it together with price structure, execution rules, and position management. The regime definition depends on the imported library, so validate it against your market and timeframe before relying on it.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
MACD HTF Hardcoded
LibVPrfLibrary "LibVPrf"
This library provides an object-oriented framework for volume
profile analysis in Pine Script®. It is built around the `VProf`
User-Defined Type (UDT), which encapsulates all data, settings,
and statistical metrics for a single profile, enabling stateful
analysis with on-demand calculations.
Key Features:
1. **Object-Oriented Design (UDT):** The library is built around
the `VProf` UDT. This object encapsulates all profile data
and provides methods for its full lifecycle management,
including creation, cloning, clearing, and merging of profiles.
2. **Volume Allocation (`AllotMode`):** Offers two methods for
allocating a bar's volume:
- **Classic:** Assigns the entire bar's volume to the close
price bucket.
- **PDF:** Distributes volume across the bar's range using a
statistical price distribution model from the `LibBrSt` library.
3. **Buy/Sell Volume Splitting (`SplitMode`):** Provides methods
for classifying volume into buying and selling pressure:
- **Classic:** Classifies volume based on the bar's color (Close vs. Open).
- **Dynamic:** A specific model that analyzes candle structure
(body vs. wicks) and a short-term trend factor to
estimate the buy/sell share at each price level.
4. **Statistical Analysis (On-Demand):** Offers a suite of
statistical metrics calculated using a "Lazy Evaluation"
pattern (computed only when requested via `get...` methods):
- **Central Tendency:** Point of Control (POC), VWAP, and Median.
- **Dispersion:** Value Area (VA) and Population Standard Deviation.
- **Shape:** Skewness and Excess Kurtosis.
- **Delta:** Cumulative Volume Delta, including its
historical high/low watermarks.
5. **Structural Analysis:** Includes a parameter-free method
(`getSegments`) to decompose a profile into its fundamental
unimodal segments, allowing for modality detection (e.g.,
identifying bimodal profiles).
6. **Dynamic Profile Management:**
- **Auto-Fitting:** Profiles set to `dynamic = true` will
automatically expand their price range to fit new data.
- **Manipulation:** The resolution, price range, and Value Area
of a dynamic profile can be changed at any time. This
triggers a resampling process that uses a **linear
interpolation model** to re-bucket existing volume.
- **Assumption:** Non-dynamic profiles are fixed and will throw
a `runtime.error` if `addBar` is called with data
outside their initial range.
7. **Bucket-Level Access:** Provides getter methods for direct
iteration and analysis of the raw buy/sell volume and price
boundaries of each individual price bucket.
---
**DISCLAIMER**
This library is provided "AS IS" and for informational and
educational purposes only. It does not constitute financial,
investment, or trading advice.
The author assumes no liability for any errors, inaccuracies,
or omissions in the code. Using this library to build
trading indicators or strategies is entirely at your own risk.
As a developer using this library, you are solely responsible
for the rigorous testing, validation, and performance of any
scripts you create based on these functions. The author shall
not be held liable for any financial losses incurred directly
or indirectly from the use of this library or any scripts
derived from it.
create(buckets, rangeUp, rangeLo, dynamic, valueArea, allot, estimator, cdfSteps, split, trendLen)
Construct a new `VProf` object with fixed bucket count & range.
Parameters:
buckets (int) : series int number of price buckets ≥ 1
rangeUp (float) : series float upper price bound (absolute)
rangeLo (float) : series float lower price bound (absolute)
dynamic (bool) : series bool Flag for dynamic adaption of profile ranges
valueArea (int) : series int Percentage of total volume to include in the Value Area (1..100)
allot (series AllotMode) : series AllotMode Allocation mode `classic` or `pdf` (default `classic`)
estimator (series PriceEst enum from AustrianTradingMachine/LibBrSt/1) : series LibBrSt.PriceEst PDF model when `model == PDF`. (deflault = 'uniform')
cdfSteps (int) : series int even #sub-intervals for Simpson rule (default 20)
split (series SplitMode) : series SplitMode Buy/Sell determination (default `classic`)
trendLen (int) : series int Look‑back bars for trend factor (default 3)
Returns: VProf freshly initialised profile
method clone(self)
Create a deep copy of the volume profile.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object to copy
Returns: VProf A new, independent copy of the profile
method clear(self)
Reset all bucket tallies while keeping configuration intact.
Namespace types: VProf
Parameters:
self (VProf) : VProf profile object
Returns: VProf cleared profile (chaining)
method merge(self, srcABuy, srcASell, srcRangeUp, srcRangeLo, srcCvd, srcCvdHi, srcCvdLo)
Merges volume data from a source profile into the current profile.
If resizing is needed, it performs a high-fidelity re-bucketing of existing
volume using a linear interpolation model inferred from neighboring buckets,
preventing aliasing artifacts and ensuring accurate volume preservation.
Namespace types: VProf
Parameters:
self (VProf) : VProf The target profile object to merge into.
srcABuy (array) : array The source profile's buy volume bucket array.
srcASell (array) : array The source profile's sell volume bucket array.
srcRangeUp (float) : series float The upper price bound of the source profile.
srcRangeLo (float) : series float The lower price bound of the source profile.
srcCvd (float) : series float The final Cumulative Volume Delta (CVD) value of the source profile.
srcCvdHi (float) : series float The historical high-water mark of the CVD from the source profile.
srcCvdLo (float) : series float The historical low-water mark of the CVD from the source profile.
Returns: VProf `self` (chaining), now containing the merged data.
method addBar(self, offset)
Add current bar’s volume to the profile (call once per realtime bar).
classic mode: allocates all volume to the close bucket and classifies
by `close >= open`. PDF mode: distributes volume across buckets by the
estimator’s CDF mass. For `split = dynamic`, the buy/sell share per
price is computed via context-driven piecewise s(u).
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object
offset (int) : series int To offset the calculated bar
Returns: VProf `self` (method chaining)
method setBuckets(self, buckets)
Sets the number of buckets for the volume profile.
Behavior depends on the `isDynamic` flag.
- If `dynamic = true`: Works on filled profiles by re-bucketing to a new resolution.
- If `dynamic = false`: Only works on empty profiles to prevent accidental changes.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object
buckets (int) : series int The new number of buckets
Returns: VProf `self` (chaining)
method setRanges(self, rangeUp, rangeLo)
Sets the price range for the volume profile.
Behavior depends on the `dynamic` flag.
- If `dynamic = true`: Works on filled profiles by re-bucketing existing volume.
- If `dynamic = false`: Only works on empty profiles to prevent accidental changes.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object
rangeUp (float) : series float The new upper price bound
rangeLo (float) : series float The new lower price bound
Returns: VProf `self` (chaining)
method setValueArea(self, valueArea)
Set the percentage of volume for the Value Area. If the value
changes, the profile is finalized again.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object
valueArea (int) : series int The new Value Area percentage (0..100)
Returns: VProf `self` (chaining)
method getBktBuyVol(self, idx)
Get Buy volume of a bucket.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object
idx (int) : series int Bucket index
Returns: series float Buy volume ≥ 0
method getBktSellVol(self, idx)
Get Sell volume of a bucket.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object
idx (int) : series int Bucket index
Returns: series float Sell volume ≥ 0
method getBktBnds(self, idx)
Get Bounds of a bucket.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object
idx (int) : series int Bucket index
Returns:
up series float The upper price bound of the bucket.
lo series float The lower price bound of the bucket.
method getPoc(self)
Get POC information.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object
Returns:
pocIndex series int The index of the Point of Control (POC) bucket.
pocPrice. series float The mid-price of the Point of Control (POC) bucket.
method getVA(self)
Get Value Area (VA) information.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object
Returns:
vaUpIndex series int The index of the upper bound bucket of the Value Area.
vaUpPrice series float The upper price bound of the Value Area.
vaLoIndex series int The index of the lower bound bucket of the Value Area.
vaLoPrice series float The lower price bound of the Value Area.
method getMedian(self)
Get the profile's median price and its bucket index. Calculates the value on-demand if stale.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object.
Returns:
medianIndex series int The index of the bucket containing the Median.
medianPrice series float The Median price of the profile.
method getVwap(self)
Get the profile's VWAP and its bucket index. Calculates the value on-demand if stale.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object.
Returns:
vwapIndex series int The index of the bucket containing the VWAP.
vwapPrice series float The Volume Weighted Average Price of the profile.
method getStdDev(self)
Get the profile's volume-weighted standard deviation. Calculates the value on-demand if stale.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object.
Returns: series float The Standard deviation of the profile.
method getSkewness(self)
Get the profile's skewness. Calculates the value on-demand if stale.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object.
Returns: series float The Skewness of the profile.
method getKurtosis(self)
Get the profile's excess kurtosis. Calculates the value on-demand if stale.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object.
Returns: series float The Kurtosis of the profile.
method getSegments(self)
Get the profile's fundamental unimodal segments. Calculates on-demand if stale.
Uses a parameter-free, pivot-based recursive algorithm.
Namespace types: VProf
Parameters:
self (VProf) : VProf The profile object.
Returns: matrix A 2-column matrix where each row is an pair.
method getCvd(self)
Cumulative Volume Delta (CVD) like metric over all buckets.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object.
Returns:
cvd series float The final Cumulative Volume Delta (Total Buy Vol - Total Sell Vol).
cvdHi series float The running high-water mark of the CVD as volume was added.
cvdLo series float The running low-water mark of the CVD as volume was added.
VProf
VProf Bucketed Buy/Sell volume profile plus meta information.
Fields:
buckets (series int) : int Number of price buckets (granularity ≥1)
rangeUp (series float) : float Upper price range (absolute)
rangeLo (series float) : float Lower price range (absolute)
dynamic (series bool) : bool Flag for dynamic adaption of profile ranges
valueArea (series int) : int Percentage of total volume to include in the Value Area (1..100)
allot (series AllotMode) : AllotMode Allocation mode `classic` or `pdf`
estimator (series PriceEst enum from AustrianTradingMachine/LibBrSt/1) : LibBrSt.PriceEst Price density model when `model == PDF`
cdfSteps (series int) : int Simpson integration resolution (even ≥2)
split (series SplitMode) : SplitMode Buy/Sell split strategy per bar
trendLen (series int) : int Look‑back length for trend factor (≥1)
maxBkt (series int) : int User-defined number of buckets (unclamped)
aBuy (array) : array Buy volume per bucket
aSell (array) : array Sell volume per bucket
cvd (series float) : float Final Cumulative Volume Delta (Total Buy Vol - Total Sell Vol).
cvdHi (series float) : float Running high-water mark of the CVD as volume was added.
cvdLo (series float) : float Running low-water mark of the CVD as volume was added.
poc (series int) : int Index of max‑volume bucket (POC). Is `na` until calculated.
vaUp (series int) : int Index of upper Value‑Area bound. Is `na` until calculated.
vaLo (series int) : int Index of lower value‑Area bound. Is `na` until calculated.
median (series float) : float Median price of the volume distribution. Is `na` until calculated.
vwap (series float) : float Profile VWAP (Volume Weighted Average Price). Is `na` until calculated.
stdDev (series float) : float Standard Deviation of volume around the VWAP. Is `na` until calculated.
skewness (series float) : float Skewness of the volume distribution. Is `na` until calculated.
kurtosis (series float) : float Excess Kurtosis of the volume distribution. Is `na` until calculated.
segments (matrix) : matrix A 2-column matrix where each row is an pair. Is `na` until calculated.
Experimental Supertrend [CHE]Experimental Supertrend — Combines EMA crossovers for trend regime detection with an adaptive ATR-based hull that selects the narrowest band to contain recent highs and lows, minimizing false breaks in varying volatility.
Summary
This indicator overlays a dynamic supertrend boundary around a midline derived from dual EMAs, using EMA crossovers to switch between bullish and bearish regimes. The hull adapts by evaluating multiple ATR periods and selecting the tightest one that fully encloses price action over a specified window, which helps in creating more stable trend lines that hug price without excessive gaps or breaches. Fills between the midline and hull provide visual cues for trend strength, darkening temporarily after regime changes to highlight transitions. Alerts trigger on crossovers, and markers label entry points, making it suitable for trend-following setups where standard supertrends might whipsaw. Overall, it offers robustness through auto-adjustment, reducing sensitivity to noise while maintaining responsiveness to genuine shifts.
Motivation: Why this design?
Standard supertrend indicators often flip prematurely in choppy markets due to fixed multipliers that do not account for localized volatility patterns, leading to frequent false signals and eroded confidence in trends. This design addresses that by incorporating an EMA-based regime filter for directional bias and an auto-adaptive hull that dynamically tunes the band width based on recent price containment needs. By prioritizing the narrowest effective enclosure, it avoids over-wide bands in calm periods that cause lag or under-wide ones in volatility spikes that invite breaks, providing a more consistent trailing reference without manual tweaking.
What’s different vs. standard approaches?
- Reference baseline: Diverges from the classic ATR-multiplier supertrend, which uses a single fixed period and constant factor applied to close or high/low deviations.
- Architecture differences:
- Auto-selection from candidate ATR lengths to find the optimal period for current conditions.
- Dynamic multiplier clamped between floor and cap values, adjusted by padding to ensure reliable containment.
- Regime-gated rendering, where hull position flips based on EMA relative positioning.
- Post-transition visual fading to emphasize change points without altering core logic.
- Practical effect: Charts show tighter, more reactive bands that rarely breach during trends, reducing visual clutter from flips; the adaptive nature means less intervention across assets, as the hull self-adjusts to volatility clusters rather than applying a one-size-fits-all scale.
How it works (technical)
The indicator first computes two EMAs from close prices using lengths derived from a preset pair or manual inputs, establishing a midline as their average. This midline serves as the central reference for the hull. True range values are then smoothed into multiple ATR candidates using exponential weighting over the specified lengths. For each candidate, deviations of recent highs and lows from the midline are ratioed against the ATR to determine a required multiplier that would enclose all extremes in the containment window—the highest ratio plus padding sets the base, clamped to user-defined bounds. Among valid candidates (those with sufficient history), the one yielding the narrowest overall band width is selected. The hull boundaries are then offset from the midline by this multiplier times the chosen ATR, and further smoothed with a fixed EMA to reduce jitter. Regime direction from EMA comparison gates which boundary acts as support or resistance, with initialization seeding arrays on the first bar to handle state persistence. No higher timeframe data is used, so all logic runs on the chart's native bars without lookahead.
Parameter Guide
EMA Pair — Selects preset lengths for fast and slow EMAs, influencing regime sensitivity and midline stability. Default: "21/55". Trade-offs/Tips: Faster pairs like "9/21" increase cross frequency for scalping but raise false signals; slower like "50/200" smooths for swings, potentially missing early turns. Use Manual for fine control.
Manual Fast — Sets fast EMA length when Manual mode is active; shorter values make regime switches quicker. Default: 21. Trade-offs/Tips: Lower than 10 risks over-reactivity; pair with slow at least double for clear separation.
Manual Slow — Sets slow EMA length when Manual mode is active; longer values anchor the midline more firmly. Default: 55. Trade-offs/Tips: Above 100 adds lag in trends; balance with fast to avoid perpetual neutrality.
ATR Lengths (comma-separated) — Defines candidate periods for ATR smoothing; more options allow finer auto-selection. Default: "7,10,14,21,28,35". Trade-offs/Tips: Fewer candidates speed computation but may miss optimal fits; keep under 10 for efficiency.
Containment Window — Number of recent bars the hull must fully enclose highs/lows of; larger windows favor stability. Default: 50. Trade-offs/Tips: Shorter (under 20) adapts faster to breaks but increases breach risk; longer smooths but delays response.
Min Multiplier Floor — Lowest allowed multiplier for hull width; prevents overly tight bands in low volatility. Default: 0.5. Trade-offs/Tips: Raise to 0.75 for conservative enclosures; too low allows pinches that flip easily.
Max Multiplier Cap — Highest allowed multiplier; caps expansion in spikes to avoid wide, lagging bands. Default: 1.0. Trade-offs/Tips: Lower to 0.75 tightens overall; higher permits more room but risks detachment from price.
Padding (+) — Adds buffer to the auto-multiplier for safer containment without exact touches. Default: 0.05. Trade-offs/Tips: Increase to 0.10 in gappy markets; minimal values hug closer but may still breach on outliers.
Fill Between (Mid ↔ Supertrend) — Toggles shaded area between midline and active hull for trend visualization. Default: true. Trade-offs/Tips: Disable for cleaner charts; pairs well with transparency tweaks.
Base Fill Transparency (0..100) — Sets default opacity of fills; higher values make them subtler. Default: 80. Trade-offs/Tips: Under 50 overwhelms price action; adjust with darken boost for emphasis.
Darken on Trend Change — Enables temporary opacity increase after regime shifts to spotlight transitions. Default: true. Trade-offs/Tips: Off for steady visuals; on aids spotting reversals in real-time.
Darken Fade Bars — Duration in bars for the darken effect to ramp back to base; longer prolongs highlight. Default: 8. Trade-offs/Tips: Shorter (4-6) for fast-paced charts; longer holds attention on changes.
Darken Boost at Change (Δ transp) — Intensity of opacity reduction at crossover; higher values make shifts more prominent. Default: 50. Trade-offs/Tips: Cap at 70 to avoid blackout; tune down if fades obscure details.
Show Supertrend Line — Displays the active hull boundary as a line. Default: true. Trade-offs/Tips: Hide for fill-only views; linewidth fixed at 3 for visibility.
Show EMA Cross Markers — Places circles and labels at crossover points for entry cues. Default: true. Trade-offs/Tips: Disable in clutter; labels show "Buy"/"Sell" at absolute positions.
Alert: EMA Cross Up (Long) — Triggers notification on bullish crossover. Default: true. Trade-offs/Tips: Pair with filters; once-per-bar frequency.
Alert: EMA Cross Down (Short) — Triggers notification on bearish crossover. Default: true. Trade-offs/Tips: Use for exits; ensure broker integration.
Show Debug — Reveals internal diagnostics like selected ATR details (if implemented). Default: false. Trade-offs/Tips: Enable for troubleshooting selections; minimal overhead.
Reading & Interpretation
Bullish regime shows a green line below price as support, with upward fill from midline; bearish uses red line above as resistance, downward fill. Crossovers flip the active boundary, marked by tiny green/red circles and "Buy"/"Sell" labels at the hull level. Fills start at base transparency but darken sharply at changes, fading over the specified bars to signal fresh momentum. If the hull rarely breaches during trends, containment is effective; frequent touches without flips indicate tight adaptation. Debug mode (when enabled) overlays text or plots for selected length and multiplier, helping verify auto-choices.
Practical Workflows & Combinations
- Trend following: Enter long on green "Buy" label above prior low structure; confirm with higher high. Trail stops along the green hull line, tightening as fills stabilize post-fade.
- Exits/Stops: Conservative exit on opposite crossover or hull breach; aggressive hold until fade completes if volume supports. Use darken boost as a volatility cue—high delta suggests waiting for confirmation.
- Multi-asset/Multi-TF: Defaults suit forex/stocks on 15m-4h; for crypto, widen containment to 75 for gaps. Layer on volume oscillator for cross filters; avoid on low-liquidity assets where ATR candidates skew.
Behavior, Constraints & Performance
Closed-bar logic ensures signals confirm at bar end, with live bars updating hull adaptively but no repaints since no future data or security calls are used. Arrays persist ATR states across bars, initialized once with candidates parsed from string. Small fixed loops (over 6 lengths max, inner up to 50) run per bar, capped by max_bars_back=500 for history needs. Resources stay low with 500 labels/lines limits, but dense charts may hit on markers. Known limits include initial lag until containment history builds (50+ bars), potential wide bands on gaps, and suboptimal selections if candidates omit ideal lengths.
Sensible Defaults & Quick Tuning
Start with "21/55" pair, 50-window, 0.5-1.0 multipliers, and 80% transparency for balanced responsiveness on daily charts. For too many flips, raise min floor to 0.75 or add lengths like "42"; for sluggishness, shorten window to 30 or pick faster pair. In high-vol environments, boost padding to 0.10; for smoother visuals, extend fade bars to 12.
What this indicator is—and isn’t
This is a visualization and signal layer for trend regime and adaptive boundaries, aiding entry/exit timing in directional markets. It is not a standalone system—pair with price structure, risk sizing, and broader context. Not predictive of turns, just reactive to containment and crosses.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Happy trading
Chervolino
Adaptive Pivot Zones█ OVERVIEW
The "Adaptive Pivot Zones" indicator is a versatile tool designed to identify and visualize key pivot levels directly on the price chart. By detecting pivot highs and lows, the indicator calculates dynamic support and resistance zones based on user-defined levels (default: 0.382, 0.5, 0.618). These zones adapt to market volatility, providing traders with clear visual cues for potential reversal or continuation points. The indicator offers extensive customization options, such as adjusting colors, smoothing lines, and setting fill transparency, making it highly adaptable to various trading styles.
█ CONCEPTS
The "Adaptive Pivot Zones" indicator simplifies the identification of significant price levels by plotting three dynamic pivot lines, which can be smoothed to reduce market noise. The indicator dynamically changes the colors of the lines and fill zones based on price action, using bullish, bearish, or neutral colors to reflect market sentiment.
█ CALCULATIONS
The indicator relies on the following calculations:
- Pivot Detection: Pivot highs (ta.pivothigh) and pivot lows (ta.pivotlow) are identified using a user-defined pivot length (default: 10). Pivots represent significant price peaks and troughs. Higher pivot length values produce more stable levels but introduce a delay equal to the set value. For more aggressive strategies, the pivot length can be reduced.
- Pivot Levels: When both a pivot high and low are detected, the range between them is calculated (rng = drHigh - drLow). Three pivot levels are computed as:
Line 1: drLow + rng * pivotLevel1
Line 2: drLow + rng * pivotLevel2
Line 3: drLow + rng * pivotLevel3
- Smoothing: Pivot lines can be smoothed using a simple moving average (SMA) with a user-defined smoothing length (default: 1) to reduce noise and improve readability.
- Color Logic: Lines and fill zones are colored based on the price position relative to the pivot zones:
If the price is below the lowest pivot line, a bearish color is used (default: red).
If the price is above the highest pivot line, a bullish color is used (default: green).
If the price is within the pivot zones and the neutral color option is enabled, a neutral color is used (default: gray); otherwise, the previous color is retained.
- Fill Zones: The areas between pivot lines are filled with a user-defined transparency level (default: 80) to visually highlight support and resistance zones.
█ INDICATOR FEATURES
- Dynamic Pivot Lines: Three adaptive pivot lines (default levels: 0.382, 0.5, 0.618) are plotted on the price chart, adjusting to market volatility.
- Smoothing: User-defined smoothing length (default: 1) for pivot lines to reduce noise and enhance signal clarity.
- Dynamic Coloring: Lines and fill zones change color based on price action (bullish, bearish, or neutral when the price moves within the zone), reflecting market sentiment.
- Fill Zones: Transparent fills between pivot lines to visually highlight support and resistance zones.
- Customization: Options to adjust pivot length, pivot levels, smoothing, colors, transparency, and enable/disable neutral color logic.
█ HOW TO SET UP THE INDICATOR
- Add the "Adaptive Pivot Zones" indicator to your TradingView chart.
- Configure parameters in the settings, such as pivot length, pivot levels, smoothing length, and colors, to align with your trading strategy. Without smoothing, lines behave like levels; with smoothing, they act like bands. All three levels can be set to the same value to obtain a single level or a line behaving like a moving average derived from pivots.
- Enable or disable the neutral color option (for prices moving within the zone) and adjust fill transparency for optimal visualization.
- Adjust line thickness and style in the "Style" section to improve chart readability.
Example of bands – lines behave like support/resistance zones.
Example of a moving average derived from pivots – line behaves like a pivot-based MA.
█ HOW TO USE
Add the indicator to your chart, adjust the settings, and observe price interactions with the pivot lines and zones to identify potential trading opportunities. Key signals include:
- Price Interaction with Pivot Lines: When the price approaches or crosses a pivot line, it may indicate a potential support or resistance level. A bounce from a pivot line could signal a reversal, while a breakout might suggest trend continuation.
- Zone-Based Signals and Trend Line Usage: Price movement within or outside the filled zones can indicate market sentiment. Price below the lowest pivot line suggests bearish momentum, price above the highest pivot line suggests bullish momentum, and price within the zones may indicate consolidation. With higher pivot length values, the indicator can be used as a trend line, particularly during clear market movements.
- Color Changes: Shifts in line and fill colors (bullish, bearish, or neutral) provide visual cues about changing market conditions.
- Confirmation with Other Tools: Combine the indicator with tools like RSI or Bollinger Bands to validate signals and improve trade accuracy. For example, a buy signal from RSI in the oversold zone combined with a bounce from the lowest pivot line may indicate a strong entry point.
Multiple Colored Moving AveragesMULTIPLE COLORED MOVING AVERAGES - USER GUIDE
DISCLAIMER
----------
Both the code and this documentation were created heavily using artificial intelligence. I'm lazy...
This indicator was inspired by repo32's "Moving Average Colored EMA/SMA" indicator. *
What is this indicator?
-----------------------
This is a TradingView indicator that displays up to 4 different moving averages on your chart simultaneously. Each moving average can be customized with different calculation methods, colors, and filtering options.
Why would I use multiple moving averages?
-----------------------------------------
- See trend direction across different timeframes at once
- Identify support and resistance levels
- Spot crossover signals between fast and slow MAs
- Reduce false signals with filtering options
- Compare how different MA types react to price action
What moving average types are available?
----------------------------------------
11 different types:
- SMA: Simple average, equal weight to all periods
- EMA: Exponential, more weight to recent prices
- WMA: Weighted, linear weighting toward recent data
- RMA: Running average, smooth like EMA
- DEMA: Double exponential, reduced lag
- TEMA: Triple exponential, even less lag
- HMA: Hull, fast and smooth combination
- VWMA: Volume weighted, includes volume data
- LSMA: Least squares, based on linear regression
- TMA: Triangular, double-smoothed
- ZLEMA: Zero lag exponential, compensated for lag
How do I set up the indicator?
------------------------------
Each MA has these settings:
- Enable/Disable: Turn each MA on or off
- Type: Choose from the 11 calculation methods
- Length: Number of periods (21, 50, 100, 200 are common)
- Smoothing: 0-10 levels of extra smoothing
- Noise Filter: 0-5% to ignore small changes
- Colors: Bullish (rising) and bearish (falling) colors
- Line Width: 1-5 pixels thickness
What does the smoothing feature do?
-----------------------------------
Smoothing applies extra calculations to make the moving average line smoother. Higher levels reduce noise but make the MA respond slower to price changes. Use higher smoothing in choppy markets, lower smoothing in trending markets.
What is the noise filter?
--------------------------
The noise filter ignores small percentage changes in the moving average. For example, a 0.3% filter will ignore any MA movement smaller than 0.3%. This helps eliminate false signals from minor price fluctuations.
When should I use this indicator?
---------------------------------
- Trend analysis: See if market is going up, down, or sideways
- Entry timing: Look for price bounces off MA levels
- Exit signals: Watch for MA slope changes or crossovers
- Support/resistance: MAs often act as dynamic levels
- Multi-timeframe analysis: Use different lengths for different perspectives
What are some good settings to start with?
-------------------------------------------
Conservative approach:
- MA 1: EMA 21 (short-term trend)
- MA 2: SMA 50 (medium-term trend)
- MA 3: SMA 200 (long-term trend)
- Low noise filtering (0.1-0.3%)
Active trading:
- MA 1: HMA 9 (very responsive)
- MA 2: EMA 21 (short-term)
- MA 3: EMA 50 (medium-term)
- Minimal or no smoothing
How do I interpret the colors?
------------------------------
Each MA changes color based on its direction:
- Bullish color: MA is rising (upward trend)
- Bearish color: MA is falling (downward trend)
- Gray: MA is flat or unchanged
What should I look for in crossovers?
-------------------------------------
- Golden Cross: Fast MA crosses above slow MA (bullish signal)
- Death Cross: Fast MA crosses below slow MA (bearish signal)
- Multiple crossovers in same direction can confirm trend changes
- Wait for clear separation between MAs after crossover
How do I use MAs for support and resistance?
---------------------------------------------
- In uptrends: MAs often provide support when price pulls back
- In downtrends: MAs may act as resistance on rallies
- Multiple MAs create support/resistance zones
- Stronger levels where multiple MAs cluster together
Can I use this with other indicators?
-------------------------------------
Yes, it works well with:
- Volume indicators for confirmation
- RSI or MACD for timing entries
- Bollinger Bands for volatility context
- Price action patterns for setup confirmation
What if I get too many signals?
-------------------------------
- Increase smoothing levels
- Raise noise filter percentages
- Use longer MA periods
- Focus on major crossovers only
- Wait for multiple MA confirmation
What if signals are too slow?
-----------------------------
- Reduce smoothing to 0
- Lower noise filter values
- Switch to faster MA types (HMA, ZLEMA, DEMA)
- Use shorter periods
- Focus on the fastest MA only
Which MA types work best in different markets?
----------------------------------------------
Trending markets: EMA, DEMA, TEMA (responsive to trends)
Choppy markets: SMA, TMA, HMA with smoothing (less whipsaws)
High volatility: Use higher smoothing and noise filtering
Low volatility: Use minimal filtering for better responsiveness
Do I need all the advanced features?
------------------------------------
No. Start with basic settings:
- Choose MA type and length
- Set colors you prefer
- Leave smoothing at 0
- Leave noise filter at 0
Add complexity only if needed to improve signal quality.
How do I know if my settings are working?
-----------------------------------------
- Backtest on historical data
- Paper trade the signals first
- Adjust based on market conditions
- Keep a trading journal to track performance
- Be willing to modify settings as markets change
Can I save different configurations?
------------------------------------
Yes, save different indicator templates in TradingView for:
- Different trading styles (scalping, swing trading)
- Different market conditions (trending, ranging)
- Different instruments (stocks, forex, crypto)
Tzotchev Trend Measure [EdgeTools]Are you still measuring trend strength with moving averages? Here is a better variant at scientific level:
Tzotchev Trend Measure: A Statistical Approach to Trend Following
The Tzotchev Trend Measure represents a sophisticated advancement in quantitative trend analysis, moving beyond traditional moving average-based indicators toward a statistically rigorous framework for measuring trend strength. This indicator implements the methodology developed by Tzotchev et al. (2015) in their seminal J.P. Morgan research paper "Designing robust trend-following system: Behind the scenes of trend-following," which introduced a probabilistic approach to trend measurement that has since become a cornerstone of institutional trading strategies.
Mathematical Foundation and Statistical Theory
The core innovation of the Tzotchev Trend Measure lies in its transformation of price momentum into a probability-based metric through the application of statistical hypothesis testing principles. The indicator employs the fundamental formula ST = 2 × Φ(√T × r̄T / σ̂T) - 1, where ST represents the trend strength score bounded between -1 and +1, Φ(x) denotes the normal cumulative distribution function, T represents the lookback period in trading days, r̄T is the average logarithmic return over the specified period, and σ̂T represents the estimated daily return volatility.
This formulation transforms what is essentially a t-statistic into a probabilistic trend measure, testing the null hypothesis that the mean return equals zero against the alternative hypothesis of non-zero mean return. The use of logarithmic returns rather than simple returns provides several statistical advantages, including symmetry properties where log(P₁/P₀) = -log(P₀/P₁), additivity characteristics that allow for proper compounding analysis, and improved validity of normal distribution assumptions that underpin the statistical framework.
The implementation utilizes the Abramowitz and Stegun (1964) approximation for the normal cumulative distribution function, achieving accuracy within ±1.5 × 10⁻⁷ for all input values. This approximation employs Horner's method for polynomial evaluation to ensure numerical stability, particularly important when processing large datasets or extreme market conditions.
Comparative Analysis with Traditional Trend Measurement Methods
The Tzotchev Trend Measure demonstrates significant theoretical and empirical advantages over conventional trend analysis techniques. Traditional moving average-based systems, including simple moving averages (SMA), exponential moving averages (EMA), and their derivatives such as MACD, suffer from several fundamental limitations that the Tzotchev methodology addresses systematically.
Moving average systems exhibit inherent lag bias, as documented by Kaufman (2013) in "Trading Systems and Methods," where he demonstrates that moving averages inevitably lag price movements by approximately half their period length. This lag creates delayed signal generation that reduces profitability in trending markets and increases false signal frequency during consolidation periods. In contrast, the Tzotchev measure eliminates lag bias by directly analyzing the statistical properties of return distributions rather than smoothing price levels.
The volatility normalization inherent in the Tzotchev formula addresses a critical weakness in traditional momentum indicators. As shown by Bollinger (2001) in "Bollinger on Bollinger Bands," momentum oscillators like RSI and Stochastic fail to account for changing volatility regimes, leading to inconsistent signal interpretation across different market conditions. The Tzotchev measure's incorporation of return volatility in the denominator ensures that trend strength assessments remain consistent regardless of the underlying volatility environment.
Empirical studies by Hurst, Ooi, and Pedersen (2013) in "Demystifying Managed Futures" demonstrate that traditional trend-following indicators suffer from significant drawdowns during whipsaw markets, with Sharpe ratios frequently below 0.5 during challenging periods. The authors attribute these poor performance characteristics to the binary nature of most trend signals and their inability to quantify signal confidence. The Tzotchev measure addresses this limitation by providing continuous probability-based outputs that allow for more sophisticated risk management and position sizing strategies.
The statistical foundation of the Tzotchev approach provides superior robustness compared to technical indicators that lack theoretical grounding. Fama and French (1988) in "Permanent and Temporary Components of Stock Prices" established that price movements contain both permanent and temporary components, with traditional moving averages unable to distinguish between these elements effectively. The Tzotchev methodology's hypothesis testing framework specifically tests for the presence of permanent trend components while filtering out temporary noise, providing a more theoretically sound approach to trend identification.
Research by Moskowitz, Ooi, and Pedersen (2012) in "Time Series Momentum in the Cross Section of Asset Returns" found that traditional momentum indicators exhibit significant variation in effectiveness across asset classes and time periods. Their study of multiple asset classes over decades revealed that simple price-based momentum measures often fail to capture persistent trends in fixed income and commodity markets. The Tzotchev measure's normalization by volatility and its probabilistic interpretation provide consistent performance across diverse asset classes, as demonstrated in the original J.P. Morgan research.
Comparative performance studies conducted by AQR Capital Management (Asness, Moskowitz, and Pedersen, 2013) in "Value and Momentum Everywhere" show that volatility-adjusted momentum measures significantly outperform traditional price momentum across international equity, bond, commodity, and currency markets. The study documents Sharpe ratio improvements of 0.2 to 0.4 when incorporating volatility normalization, consistent with the theoretical advantages of the Tzotchev approach.
The regime detection capabilities of the Tzotchev measure provide additional advantages over binary trend classification systems. Research by Ang and Bekaert (2002) in "Regime Switches in Interest Rates" demonstrates that financial markets exhibit distinct regime characteristics that traditional indicators fail to capture adequately. The Tzotchev measure's five-tier classification system (Strong Bull, Weak Bull, Neutral, Weak Bear, Strong Bear) provides more nuanced market state identification than simple trend/no-trend binary systems.
Statistical testing by Jegadeesh and Titman (2001) in "Profitability of Momentum Strategies" revealed that traditional momentum indicators suffer from significant parameter instability, with optimal lookback periods varying substantially across market conditions and asset classes. The Tzotchev measure's statistical framework provides more stable parameter selection through its grounding in hypothesis testing theory, reducing the need for frequent parameter optimization that can lead to overfitting.
Advanced Noise Filtering and Market Regime Detection
A significant enhancement over the original Tzotchev methodology is the incorporation of a multi-factor noise filtering system designed to reduce false signals during sideways market conditions. The filtering mechanism employs four distinct approaches: adaptive thresholding based on current market regime strength, volatility-based filtering utilizing ATR percentile analysis, trend strength confirmation through momentum alignment, and a comprehensive multi-factor approach that combines all methodologies.
The adaptive filtering system analyzes market microstructure through price change relative to average true range, calculates volatility percentiles over rolling windows, and assesses trend alignment across multiple timeframes using exponential moving averages of varying periods. This approach addresses one of the primary limitations identified in traditional trend-following systems, namely their tendency to generate excessive false signals during periods of low volatility or sideways price action.
The regime detection component classifies market conditions into five distinct categories: Strong Bull (ST > 0.3), Weak Bull (0.1 < ST ≤ 0.3), Neutral (-0.1 ≤ ST ≤ 0.1), Weak Bear (-0.3 ≤ ST < -0.1), and Strong Bear (ST < -0.3). This classification system provides traders with clear, quantitative definitions of market regimes that can inform position sizing, risk management, and strategy selection decisions.
Professional Implementation and Trading Applications
The indicator incorporates three distinct trading profiles designed to accommodate different investment approaches and risk tolerances. The Conservative profile employs longer lookback periods (63 days), higher signal thresholds (0.2), and reduced filter sensitivity (0.5) to minimize false signals and focus on major trend changes. The Balanced profile utilizes standard academic parameters with moderate settings across all dimensions. The Aggressive profile implements shorter lookback periods (14 days), lower signal thresholds (-0.1), and increased filter sensitivity (1.5) to capture shorter-term trend movements.
Signal generation occurs through threshold crossover analysis, where long signals are generated when the trend measure crosses above the specified threshold and short signals when it crosses below. The implementation includes sophisticated signal confirmation mechanisms that consider trend alignment across multiple timeframes and momentum strength percentiles to reduce the likelihood of false breakouts.
The alert system provides real-time notifications for trend threshold crossovers, strong regime changes, and signal generation events, with configurable frequency controls to prevent notification spam. Alert messages are standardized to ensure consistency across different market conditions and timeframes.
Performance Optimization and Computational Efficiency
The implementation incorporates several performance optimization features designed to handle large datasets efficiently. The maximum bars back parameter allows users to control historical calculation depth, with default settings optimized for most trading applications while providing flexibility for extended historical analysis. The system includes automatic performance monitoring that generates warnings when computational limits are approached.
Error handling mechanisms protect against division by zero conditions, infinite values, and other numerical instabilities that can occur during extreme market conditions. The finite value checking system ensures data integrity throughout the calculation process, with fallback mechanisms that maintain indicator functionality even when encountering corrupted or missing price data.
Timeframe validation provides warnings when the indicator is applied to unsuitable timeframes, as the Tzotchev methodology was specifically designed for daily and higher timeframe analysis. This validation helps prevent misapplication of the indicator in contexts where its statistical assumptions may not hold.
Visual Design and User Interface
The indicator features eight professional color schemes designed for different trading environments and user preferences. The EdgeTools theme provides an institutional blue and steel color palette suitable for professional trading environments. The Gold theme offers warm colors optimized for commodities trading. The Behavioral theme incorporates psychology-based color contrasts that align with behavioral finance principles. The Quant theme provides neutral colors suitable for analytical applications.
Additional specialized themes include Ocean, Fire, Matrix, and Arctic variations, each optimized for specific visual preferences and trading contexts. All color schemes include automatic dark and light mode optimization to ensure optimal readability across different chart backgrounds and trading platforms.
The information table provides real-time display of key metrics including current trend measure value, market regime classification, signal strength, Z-score, average returns, volatility measures, filter threshold levels, and filter effectiveness percentages. This comprehensive dashboard allows traders to monitor all relevant indicator components simultaneously.
Theoretical Implications and Research Context
The Tzotchev Trend Measure addresses several theoretical limitations inherent in traditional technical analysis approaches. Unlike moving average-based systems that rely on price level comparisons, this methodology grounds trend analysis in statistical hypothesis testing, providing a more robust theoretical foundation for trading decisions.
The probabilistic interpretation of trend strength offers significant advantages over binary trend classification systems. Rather than simply indicating whether a trend exists, the measure quantifies the statistical confidence level associated with the trend assessment, allowing for more nuanced risk management and position sizing decisions.
The incorporation of volatility normalization addresses the well-documented problem of volatility clustering in financial time series, ensuring that trend strength assessments remain consistent across different market volatility regimes. This normalization is particularly important for portfolio management applications where consistent risk metrics across different assets and time periods are essential.
Practical Applications and Trading Strategy Integration
The Tzotchev Trend Measure can be effectively integrated into various trading strategies and portfolio management frameworks. For trend-following strategies, the indicator provides clear entry and exit signals with quantified confidence levels. For mean reversion strategies, extreme readings can signal potential turning points. For portfolio allocation, the regime classification system can inform dynamic asset allocation decisions.
The indicator's statistical foundation makes it particularly suitable for quantitative trading strategies where systematic, rules-based approaches are preferred over discretionary decision-making. The standardized output range facilitates easy integration with position sizing algorithms and risk management systems.
Risk management applications benefit from the indicator's ability to quantify trend strength and provide early warning signals of potential trend changes. The multi-timeframe analysis capability allows for the construction of robust risk management frameworks that consider both short-term tactical and long-term strategic market conditions.
Implementation Guide and Parameter Configuration
The practical application of the Tzotchev Trend Measure requires careful parameter configuration to optimize performance for specific trading objectives and market conditions. This section provides comprehensive guidance for parameter selection and indicator customization.
Core Calculation Parameters
The Lookback Period parameter controls the statistical window used for trend calculation and represents the most critical setting for the indicator. Default values range from 14 to 63 trading days, with shorter periods (14-21 days) providing more sensitive trend detection suitable for short-term trading strategies, while longer periods (42-63 days) offer more stable trend identification appropriate for position trading and long-term investment strategies. The parameter directly influences the statistical significance of trend measurements, with longer periods requiring stronger underlying trends to generate significant signals but providing greater reliability in trend identification.
The Price Source parameter determines which price series is used for return calculations. The default close price provides standard trend analysis, while alternative selections such as high-low midpoint ((high + low) / 2) can reduce noise in volatile markets, and volume-weighted average price (VWAP) offers superior trend identification in institutional trading environments where volume concentration matters significantly.
The Signal Threshold parameter establishes the minimum trend strength required for signal generation, with values ranging from -0.5 to 0.5. Conservative threshold settings (0.2 to 0.3) reduce false signals but may miss early trend opportunities, while aggressive settings (-0.1 to 0.1) provide earlier signal generation at the cost of increased false positive rates. The optimal threshold depends on the trader's risk tolerance and the volatility characteristics of the traded instrument.
Trading Profile Configuration
The Trading Profile system provides pre-configured parameter sets optimized for different trading approaches. The Conservative profile employs a 63-day lookback period with a 0.2 signal threshold and 0.5 noise sensitivity, designed for long-term position traders seeking high-probability trend signals with minimal false positives. The Balanced profile uses a 21-day lookback with 0.05 signal threshold and 1.0 noise sensitivity, suitable for swing traders requiring moderate signal frequency with acceptable noise levels. The Aggressive profile implements a 14-day lookback with -0.1 signal threshold and 1.5 noise sensitivity, optimized for day traders and scalpers requiring frequent signal generation despite higher noise levels.
Advanced Noise Filtering System
The noise filtering mechanism addresses the challenge of false signals during sideways market conditions through four distinct methodologies. The Adaptive filter adjusts thresholds based on current trend strength, increasing sensitivity during strong trending periods while raising thresholds during consolidation phases. The Volatility-based filter utilizes Average True Range (ATR) percentile analysis to suppress signals during abnormally volatile conditions that typically generate false trend indications.
The Trend Strength filter requires alignment between multiple momentum indicators before confirming signals, reducing the probability of false breakouts from consolidation patterns. The Multi-factor approach combines all filtering methodologies using weighted scoring to provide the most robust noise reduction while maintaining signal responsiveness during genuine trend initiations.
The Noise Sensitivity parameter controls the aggressiveness of the filtering system, with lower values (0.5-1.0) providing conservative filtering suitable for volatile instruments, while higher values (1.5-2.0) allow more signals through but may increase false positive rates during choppy market conditions.
Visual Customization and Display Options
The Color Scheme parameter offers eight professional visualization options designed for different analytical preferences and market conditions. The EdgeTools scheme provides high contrast visualization optimized for trend strength differentiation, while the Gold scheme offers warm tones suitable for commodity analysis. The Behavioral scheme uses psychological color associations to enhance decision-making speed, and the Quant scheme provides neutral colors appropriate for quantitative analysis environments.
The Ocean, Fire, Matrix, and Arctic schemes offer additional aesthetic options while maintaining analytical functionality. Each scheme includes optimized colors for both light and dark chart backgrounds, ensuring visibility across different trading platform configurations.
The Show Glow Effects parameter enhances plot visibility through multiple layered lines with progressive transparency, particularly useful when analyzing multiple timeframes simultaneously or when working with dense price data that might obscure trend signals.
Performance Optimization Settings
The Maximum Bars Back parameter controls the historical data depth available for calculations, with values ranging from 5,000 to 50,000 bars. Higher values enable analysis of longer-term trend patterns but may impact indicator loading speed on slower systems or when applied to multiple instruments simultaneously. The optimal setting depends on the intended analysis timeframe and available computational resources.
The Calculate on Every Tick parameter determines whether the indicator updates with every price change or only at bar close. Real-time calculation provides immediate signal updates suitable for scalping and day trading strategies, while bar-close calculation reduces computational overhead and eliminates signal flickering during bar formation, preferred for swing trading and position management applications.
Alert System Configuration
The Alert Frequency parameter controls notification generation, with options for all signals, bar close only, or once per bar. High-frequency trading strategies benefit from all signals mode, while position traders typically prefer bar close alerts to avoid premature position entries based on intrabar fluctuations.
The alert system generates four distinct notification types: Long Signal alerts when the trend measure crosses above the positive signal threshold, Short Signal alerts for negative threshold crossings, Bull Regime alerts when entering strong bullish conditions, and Bear Regime alerts for strong bearish regime identification.
Table Display and Information Management
The information table provides real-time statistical metrics including current trend value, regime classification, signal status, and filter effectiveness measurements. The table position can be customized for optimal screen real estate utilization, and individual metrics can be toggled based on analytical requirements.
The Language parameter supports both English and German display options for international users, while maintaining consistent calculation methodology regardless of display language selection.
Risk Management Integration
Effective risk management integration requires coordination between the trend measure signals and position sizing algorithms. Strong trend readings (above 0.5 or below -0.5) support larger position sizes due to higher probability of trend continuation, while neutral readings (between -0.2 and 0.2) suggest reduced position sizes or range-trading strategies.
The regime classification system provides additional risk management context, with Strong Bull and Strong Bear regimes supporting trend-following strategies, while Neutral regimes indicate potential for mean reversion approaches. The filter effectiveness metric helps traders assess current market conditions and adjust strategy parameters accordingly.
Timeframe Considerations and Multi-Timeframe Analysis
The indicator's effectiveness varies across different timeframes, with higher timeframes (daily, weekly) providing more reliable trend identification but slower signal generation, while lower timeframes (hourly, 15-minute) offer faster signals with increased noise levels. Multi-timeframe analysis combining trend alignment across multiple periods significantly improves signal quality and reduces false positive rates.
For optimal results, traders should consider trend alignment between the primary trading timeframe and at least one higher timeframe before entering positions. Divergences between timeframes often signal potential trend reversals or consolidation periods requiring strategy adjustment.
Conclusion
The Tzotchev Trend Measure represents a significant advancement in technical analysis methodology, combining rigorous statistical foundations with practical trading applications. Its implementation of the J.P. Morgan research methodology provides institutional-quality trend analysis capabilities previously available only to sophisticated quantitative trading firms.
The comprehensive parameter configuration options enable customization for diverse trading styles and market conditions, while the advanced noise filtering and regime detection capabilities provide superior signal quality compared to traditional trend-following indicators. Proper parameter selection and understanding of the indicator's statistical foundation are essential for achieving optimal trading results and effective risk management.
References
Abramowitz, M. and Stegun, I.A. (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Washington: National Bureau of Standards.
Ang, A. and Bekaert, G. (2002). Regime Switches in Interest Rates. Journal of Business and Economic Statistics, 20(2), 163-182.
Asness, C.S., Moskowitz, T.J., and Pedersen, L.H. (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929-985.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Fama, E.F. and French, K.R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273.
Hurst, B., Ooi, Y.H., and Pedersen, L.H. (2013). Demystifying Managed Futures. Journal of Investment Management, 11(3), 42-58.
Jegadeesh, N. and Titman, S. (2001). Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. Journal of Finance, 56(2), 699-720.
Kaufman, P.J. (2013). Trading Systems and Methods. 5th Edition. Hoboken: John Wiley & Sons.
Moskowitz, T.J., Ooi, Y.H., and Pedersen, L.H. (2012). Time Series Momentum. Journal of Financial Economics, 104(2), 228-250.
Tzotchev, D., Lo, A.W., and Hasanhodzic, J. (2015). Designing robust trend-following system: Behind the scenes of trend-following. J.P. Morgan Quantitative Research, Asset Management Division.
Savitzky-Golay Hampel Filter | AlphaNattSavitzky-Golay Hampel Filter | AlphaNatt
A revolutionary indicator combining NASA's satellite data processing algorithms with robust statistical outlier detection to create the most scientifically advanced trend filter available on TradingView.
"This is the same mathematics that processes signals from the Hubble Space Telescope and analyzes data from the Large Hadron Collider - now applied to financial markets."
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🚀 SCIENTIFIC PEDIGREE
Savitzky-Golay Filter Applications:
NASA: Satellite telemetry and space probe data processing
CERN: Particle physics data analysis at the LHC
Pharmaceutical: Chromatography and spectroscopy analysis
Astronomy: Processing signals from radio telescopes
Medical: ECG and EEG signal processing
Hampel Filter Usage:
Aerospace: Cleaning sensor data from aircraft and spacecraft
Manufacturing: Quality control in precision engineering
Seismology: Earthquake detection and analysis
Robotics: Sensor fusion and noise reduction
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🧬 THE MATHEMATICS
1. Savitzky-Golay Filter
The SG filter performs local polynomial regression on data points:
Fits a polynomial of degree n to a sliding window of data
Evaluates the polynomial at the center point
Preserves higher moments (peaks, valleys) unlike moving averages
Maintains derivative information for true momentum analysis
Originally published in Analytical Chemistry (1964)
Mathematical Properties:
Optimal smoothing in the least-squares sense
Preserves statistical moments up to polynomial order
Exact derivative calculation without additional lag
Superior frequency response vs traditional filters
2. Hampel Filter
A robust outlier detector based on Median Absolute Deviation (MAD):
Identifies outliers using robust statistics
Replaces spurious values with polynomial-fitted estimates
Resistant to up to 50% contaminated data
MAD is 1.4826 times more robust than standard deviation
Outlier Detection Formula:
|x - median| > k × 1.4826 × MAD
Where k is the threshold parameter (typically 3 for 99.7% confidence)
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💎 WHY THIS IS SUPERIOR
vs Moving Averages:
Preserves peaks and valleys (critical for catching tops/bottoms)
No lag penalty for smoothness
Maintains derivative information
Polynomial fitting > simple averaging
vs Other Filters:
Outlier immunity (Hampel component)
Scientifically optimal smoothing
Preserves higher-order features
Used in billion-dollar research projects
Unique Advantages:
Feature Preservation: Maintains market structure while smoothing
Spike Immunity: Ignores false breakouts and stop hunts
Derivative Accuracy: True momentum without additional indicators
Scientific Validation: 60+ years of academic research
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⚙️ PARAMETER OPTIMIZATION
1. Polynomial Order (2-5)
2 (Quadratic): Maximum smoothing, gentle curves
3 (Cubic): Balanced smoothing and responsiveness (recommended)
4-5 (Higher): More responsive, preserves more features
2. Window Size (7-51)
Must be odd number
Larger = smoother but more lag
Formula: 2×(desired smoothing period) + 1
Default 21 = analyzes 10 bars each side
3. Hampel Threshold (1.0-5.0)
1.0: Aggressive outlier removal (68% confidence)
2.0: Moderate outlier removal (95% confidence)
3.0: Conservative outlier removal (99.7% confidence) (default)
4.0+: Only extreme outliers removed
4. Final Smoothing (1-7)
Additional WMA smoothing after filtering
1 = No additional smoothing
3-5 = Recommended for most timeframes
7 = Ultra-smooth for position trading
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📊 TRADING STRATEGIES
Signal Recognition:
Cyan Line: Bullish trend with positive derivative
Pink Line: Bearish trend with negative derivative
Color Change: Trend reversal with polynomial confirmation
1. Trend Following Strategy
Enter when price crosses above cyan filter
Exit when filter turns pink
Use filter as dynamic stop loss
Best in trending markets
2. Mean Reversion Strategy
Enter long when price touches filter from below in uptrend
Enter short when price touches filter from above in downtrend
Exit at opposite band or filter color change
Excellent for range-bound markets
3. Derivative Strategy (Advanced)
The SG filter preserves derivative information
Acceleration = second derivative > 0
Enter on positive first derivative + positive acceleration
Exit on negative second derivative (momentum slowing)
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📈 PERFORMANCE CHARACTERISTICS
Strengths:
Outlier Immunity: Ignores stop hunts and flash crashes
Feature Preservation: Catches tops/bottoms better than MAs
Smooth Output: Reduces whipsaws significantly
Scientific Basis: Not curve-fitted or optimized to markets
Considerations:
Slight lag in extreme volatility (all filters have this)
Requires odd window sizes (mathematical requirement)
More complex than simple moving averages
Best with liquid instruments
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🔬 SCIENTIFIC BACKGROUND
Savitzky-Golay Publication:
"Smoothing and Differentiation of Data by Simplified Least Squares Procedures"
- Abraham Savitzky & Marcel Golay
- Analytical Chemistry, Vol. 36, No. 8, 1964
Hampel Filter Origin:
"Robust Statistics: The Approach Based on Influence Functions"
- Frank Hampel et al., 1986
- Princeton University Press
These techniques have been validated in thousands of scientific papers and are standard tools in:
NASA's Jet Propulsion Laboratory
European Space Agency
CERN (Large Hadron Collider)
MIT Lincoln Laboratory
Max Planck Institutes
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💡 ADVANCED TIPS
News Trading: Lower Hampel threshold before major events to catch spikes
Scalping: Use Order=2 for maximum smoothness, Window=11 for responsiveness
Position Trading: Increase Window to 31+ for long-term trends
Combine with Volume: Strong trends need volume confirmation
Multiple Timeframes: Use daily for trend, hourly for entry
Watch the Derivative: Filter color changes when first derivative changes sign
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⚠️ IMPORTANT NOTICES
Not financial advice - educational purposes only
Past performance does not guarantee future results
Always use proper risk management
Test settings on your specific instrument and timeframe
No indicator is perfect - part of complete trading system
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🏆 CONCLUSION
The Savitzky-Golay Hampel Filter represents the pinnacle of scientific signal processing applied to financial markets. By combining polynomial regression with robust outlier detection, traders gain access to the same mathematical tools that:
Guide spacecraft to other planets
Detect gravitational waves from black holes
Analyze particle collisions at near light-speed
Process signals from deep space
This isn't just another indicator - it's rocket science for trading .
"When NASA needs to separate signal from noise in billion-dollar missions, they use these exact algorithms. Now you can too."
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Developed by AlphaNatt
Version: 1.0
Release: 2025
Pine Script: v6
"Where Space Technology Meets Market Analysis"
Not financial advice. Always DYOR
Yelober - Market Internal direction+ Key levelsYelober – Market Internals + Key Levels is a focused intraday trading tool that helps you spot high-probability price direction by anchoring decisions to structure that matters: yesterday’s RTH High/Low, today’s pre-market High/Low, and a fast Value Area/POC from the prior session. Paired with a compact market internals dashboard (NYSE/NASDAQ UVOL vs. DVOL ratios, VOLD slopes, TICK/TICKQ momentum, and optional VIX trend), it gives you a real-time read on breadth so you can choose which direction to trade, when to enter (breaks, retests, or fades at PMH/PML/VAH/VAL/POC), and how to plan exits as internals confirm or deteriorate. On top of these intraday decision benefits, it also allows traders—in a very subtle but powerful way—to keep an eye on the VIX and immediately recognize significant spikes or sharp decreases that should be factored in before entering a trade, or used as a quick signal to modify an existing position. In short: clear levels for the chart, live internals for the context, and a smarter, rules-based path to execution.
# Yelober – Market Internals + Key Levels
*A TradingView indicator for session key levels + real‑time market internals (NYSE/NASDAQ TICK, UVOL/DVOL/VOLD, and VIX).*
**Script name in Pine:** `Yelober - Market Internal direction+ Key levels` (Pine v6)
---
## 1) What this indicator does
**Purpose:** Help intraday traders quickly find high‑probability reaction zones and read market internals momentum without switching charts. It overlays yesterday/today’s **automatic price levels** on your active chart and shows a **market breadth table** that summarizes NYSE/NASDAQ buying pressure and TICK direction, with an optional VIX trend read.
### Key features at a glance
* **Automatic Price Levels (overlay on chart)**
* Yesterday’s High/Low of Day (**yHoD**, **yLoD**)
* Extended Hours High/Low (**yEHH**, **yEHL**) across yesterday AH + today pre‑market
* Today’s Pre‑Market High/Low (**PMH**, **PML**)
* Yesterday’s **Value Area High/Low** (**VAH/VAL**) and **Point of Control (POC)** computed from a volume profile of yesterday’s **regular session**
* Smart de‑duplication:
* Shows **only the higher** of (yEHH vs PMH) and **only the lower** of (yEHL vs PML) to avoid redundant bands
* **Market Breadth Table (on‑chart table)**
* **NYSE ratio** = UVOL/DVOL (signed) with **VOLD slope** from session open
* **NASDAQ ratio** = UVOLQ/DVOLQ (signed) with **VOLDQ slope** from session open
* **TICK** and **TICKQ**: live cumulative ratio and short‑term slope
* **VIX** (optional): current value + slope over a configurable lookback/timeframe
* Color‑coded trends with sensible thresholds and optional normalization
---
## 2) How to use it (trader workflow)
1. **Mark your reaction zones**
* Watch **yHoD/yLoD**, **PMH/PML**, and **VAH/VAL/POC** for first touches, break/retest, and failure tests.
* Expect increased responsiveness when multiple levels cluster (e.g., PMH ≈ VAH ≈ daily pivot).
2. **Read the breadth panel for context**
* **NYSE/NASDAQ ratio** (>1 = more up‑volume than down‑volume; <−1 = down‑dominant). Strong green across both favors long setups; red favors short setups.
* **VOLD slopes** (NYSE & NASDAQ): positive and accelerating → broadening participation; negative → persistent pressure.
* **TICK/TICKQ**: cumulative ratio and **slope arrows** (↗ / ↘ / →). Use the slope to gauge **near‑term thrust or fade**.
* **VIX slope**: rising VIX (red) often coincides with risk‑off; falling VIX (green) with risk‑on.
3. **Confluence = higher confidence**
* Example: Price reclaims **PMH** while **NYSE/NASDAQ ratios** print green and **TICK slopes** point ↗ — consider break‑and‑go; if VIX slope is ↘, that adds risk‑on confidence.
* Example: Price rejects **VAH** while **VOLD slopes** roll negative and VIX ↗ — consider fade/reversal.
4. **Risk management**
* Place stops just beyond key levels tested; if breadth flips, tighten or exit.
> **Timeframes:** Works best on 1–15m charts for intraday. Value Area is computed from **yesterday’s RTH**; choose a smaller calculation timeframe (e.g., 5–15m) for stable profiles.
---
## 3) Inputs & settings (what each option controls)
### Global Style
* **Enable all automatic price levels**: master toggle for yHoD/yLoD, yEHH/yEHL, PMH/PML, VAH/VAL/POC.
* **Line style/width**: applies to all drawn levels.
* **Label size/style** and **label color linking**: use the same color as the line or override with a global label color.
* **Maximum bars lookback**: how far the script scans to build yesterday metrics (performance‑sensitive).
### Value Area / Volume Profile
* **Enable Value Area calculations** *(on by default)*: computes yesterday’s **POC**, **VAH**, **VAL** from a simplified intraday volume profile built from yesterday’s **regular session bars**.
* **Max Volume Profile Points** *(default 50)*: lower values = faster; higher = more precise.
* **Value Area Calculation Timeframe** *(default 15)*: the security timeframe used when collecting yesterday’s highs/lows/volumes.
### Individual Level Toggles & Colors
* **yHoD / yLoD** (yesterday high/low)
* **yEHH / yEHL** (yesterday AH + today pre‑market extremes)
* **PMH / PML** (today pre‑market extremes)
* **VAH / VAL / POC** (yesterday RTH value area + point of control)
### Market Breadth Panel
* **Show NYSE / NASDAQ / VIX**: choose which series to display in the table.
* **Table Position / Size / Background Color**: UI placement and legibility.
* **Slope Averaging Periods** *(default 5)*: number of recent TICK/TICKQ ratio points used in slope calculation.
* **Candles for Rate** *(default 10)* & **Normalize Rate**: VIX slope calculation as % change between `now` and `n` candles ago; normalize divides by `n`.
* **VIX Timeframe**: optionally compute VIX on a higher TF (e.g., 15, 30, 60) for a smoother regime read.
* **Volume Normalization** (NYSE & NASDAQ): display VOLD slopes scaled to `tens/thousands/millions/10th millions` for readable magnitudes; color thresholds adapt to your choice.
---
## 4) Data sources & definitions
* **UVOL/VOLD (NYSE)** and **UVOLQ/DVOLQ/VOLDQ (NASDAQ)** via `request.security()`
* **Ratio** = `UVOL/DVOL` (signed; negative when down‑volume dominates)
* **VOLD slope** ≈ `(VOLD_now − VOLD_open) / bars_since_open`, then normalized per your setting
* **TICK/TICKQ**: cumulative sum of prints this session with **positives vs negatives ratio**, plus a simple linear regression **slope** of the last `N` ratio values
* **VIX**: value and slope across a user‑selected timeframe and lookback
* **Sessions (EST/EDT)**
* **Regular:** 09:30–16:00
* **Pre‑Market:** 04:00–09:30
* **After Hours:** 16:00–20:00
* **Extended‑hours extremes** combine **yesterday AH** + **today PM**
> **Note:** All session checks are done with TradingView’s `time(…,"America/New_York")` context. If your broker’s RTH differs (e.g., futures), adjust expectations accordingly.
---
## 5) How the algorithms work (plain English)
### A) Key Levels
* **Yesterday’s RTH High/Low**: scans yesterday’s bars within 09:30–16:00 and records the extremes + bar indices.
* **Extended Hours**: scans yesterday AH and today PM to get **yEHH/yEHL**. Script shows **either yEHH or PMH** (whichever is **higher**) and **either yEHL or PML** (whichever is **lower**) to avoid duplicate bands stacked together.
* **Value Area & POC (RTH only)**
* Build a coarse volume profile with `Max Volume Profile Points` buckets across the price range formed by yesterday’s RTH bars.
* Distribute each bar’s volume uniformly across the buckets it spans (fast approximation to keep Pine within execution limits).
* **POC** = bucket with max volume. **VA** expands from POC outward until **70%** of cumulative volume is enclosed → yields **VAH/VAL**.
### B) Market Breadth Table
* **NYSE/NASDAQ Ratio**: signed UVOL/DVOL with basic coloring.
* **VOLD Slopes**: from session open to current, normalized to human‑readable units; colors flip green/red based on thresholds that map to your normalization setting (e.g., ±2M for NYSE, ±3.5×10M for NASDAQ).
* **TICK/TICKQ Slope**: linear regression over the last `N` ratio points → **↗ / → / ↘** with the rounded slope value.
* **VIX Slope**: % change between now and `n` candles ago (optionally divided by `n`). Red when rising beyond threshold; green when falling.
---
## 6) Recommended presets
* **Stocks (liquid, intraday)**
* Value Area **ON**, `Max Volume Points` = **40–60**, **Timeframe** = **5–15**
* Breadth: show **NYSE & NASDAQ & VIX**, `Slope periods` = **5–8**, `Candles for rate` = **10–20**, **Normalize VIX** = **ON**
* **Index futures / very high‑volume symbols**
* If you see Pine timeouts, set `Max Volume Points` = **20–40** or temporarily **disable Value Area**.
* Keep breadth panel **ON** (it’s light). Consider **VIX timeframe = 15/30** for regime clarity.
---
## 7) Tips, edge cases & performance
* **Performance:** The volume profile is capped (`maxBarsToProcess ≤ 500` and bucketed) to keep it responsive. If you experience slowdowns, reduce `Max Volume Points`, `Maximum bars lookback`, or disable Value Area.
* **Redundant lines:** The script **intentionally suppresses** PMH/PML when yEHH/yEHL are more extreme, and vice‑versa.
* **Label visibility:** Use `Label style = none` if you only want clean lines and read values from the right‑end labels.
* **Futures/RTH differences:** Value Area is from **yesterday’s RTH** only; for 24h instruments the RTH period may not reflect overnight structure.
* **Session transitions:** PMH/PML tracking stops as soon as RTH starts; values persist as static levels for the session.
---
## 8) Known limitations
* Uses public TradingView symbols: `UVOL`, `VOLD`, `UVOLQ`, `DVOLQ`, `VOLDQ`, `TICK`, `TICKQ`, `VIX`. If your data plan or region limits any symbol, the corresponding table rows may show `na`.
* The VA/POC approximation assumes uniform distribution of each bar’s volume across its high–low. That’s fast but not a tick‑level profile.
* Works best on US equities with standard NY session; alternative sessions may need code changes.
---
## 9) Troubleshooting
* **“Script is too slow / timed out”** → Lower `Max Volume Points`, lower `Maximum bars lookback`, or toggle **OFF** `Enable Value Area calculations` for that instrument.
* **Missing breadth values** → Ensure the symbols above load on your account; try reloading chart or switching timeframes once.
* **Overlapping labels** → Set `Label style = none` or reduce label size.
---
## 10) Version / license / contribution
* **Version:** Initial public release (Pine v6).
* **Author:** © yelober
* **License:** Free for community use and enhancement. Please keep author credit.
* **Contributing:** Open PRs/ideas: presets, alert conditions, multi‑day VA composites, optional mid‑value (`(VAH+VAL)/2`), session filter for futures, and alertable state machine for breadth regime transitions.
---
## 11) Quick start (TL;DR)
1. Add the indicator and **keep default settings**.
2. Trade **reactions** at yHoD/yLoD/PMH/PML/VAH/VAL/POC.
3. Use the **breadth table**: look for **green ratios + ↗ slopes** (risk‑on) or **red ratios + ↘ slopes** (risk‑off). Check **VIX** slope for confirmation.
4. Manage risk around levels; when breadth flips against you, tighten or exit.
---
### Changelog (public)
* **v1.0:** First community release with automatic RTH levels, VA/POC approximation, breadth dashboard (NYSE/NASDAQ/TICK/TICKQ/VIX) with normalization and adaptive color thresholds.
Quant Signals: Market Sentiment Monitor HUDWavelets & Scale Spectrum
This indicator is ideal for traders who adapt their strategy to market conditions — such as swing traders, intraday traders, and system developers.
Trend-followers can use it to confirm trending conditions before entering.
Mean-reversion traders can spot choppy markets where reversals are more likely.
Risk managers can monitor volatility shifts and regime changes to adjust position size or pause trading.
It works best as a market context filter — telling you the “weather” before you decide on the trade.
Wavelets are like tiny “measuring rulers” for price changes. Instead of looking at the whole chart at once, a wavelet looks at differences in price over a specific time scale — for example, 2 bars, 4 bars, 8 bars, and so on.
The scale spectrum is what you get when you measure volatility at several of these scales and then plot them against scale size.
If the spectrum forms a straight line on a log–log chart, it means price changes follow a consistent pattern across time scales (a power-law relationship).
The slope of that line gives the Hurst exponent (H) — telling you whether moves tend to persist (trend) or reverse (mean-revert).
The height of the line gives you the volatility (σ) — the average size of moves.
This approach works like a microscope, revealing whether the market’s behaviour is consistent across short-term and long-term horizons, and when that behaviour changes.
This tool applies a wavelet-based scale-spectrum analysis to price data to estimate three key market state measures inside a rolling window:
Hurst exponent (H) — measures persistence in price moves:
H > ~0.55 → market is trending (moves tend to continue).
H < ~0.45 → market is choppy/mean-reverting (moves tend to reverse).
Values near 0.5 indicate a neutral, random-walk-like regime.
Volatility (σ) — the average size of price swings at your chart’s timeframe, optionally annualized. Rising volatility means larger price moves, falling volatility means smaller moves.
Fit residual — how well the observed multi-scale volatility fits a clean power-law line. Low residual = stable behaviour; high residual = structural change (possible regime shift).
Info TableOverview
The Info Table V1 is a versatile TradingView indicator tailored for intraday futures traders, particularly those focusing on MESM2 (Micro E-mini S&P 500 futures) on 1-minute charts. It presents essential market insights through two customizable tables: the Main Table for predictive and macro metrics, and the New Metrics Table for momentum and volatility indicators. Designed for high-activity sessions like 9:30 AM–11:00 AM CDT, this tool helps traders assess price alignment, sentiment, and risk in real-time. Metrics update dynamically (except weekly COT data), with optional alerts for key conditions like volatility spikes or momentum shifts.
This indicator builds on foundational concepts like linear regression for predictions and adapts open-source elements for enhanced functionality. Gradient code is adapted from TradingView's Color Library. QQE logic is adapted from LuxAlgo's QQE Weighted Oscillator, licensed under CC BY-NC-SA 4.0. The script is released under the Mozilla Public License 2.0.
Key Features
Two Customizable Tables: Positioned independently (e.g., top-right for Main, bottom-right for New Metrics) with toggle options to show/hide for a clutter-free chart.
Gradient Coloring: User-defined high/low colors (default green/red) for quick visual interpretation of extremes, such as overbought/oversold or high volatility.
Arrows for Directional Bias: In the New Metrics Table, up (↑) or down (↓) arrows appear in value cells based on metric thresholds (top/bottom 25% of range), indicating bullish/high or bearish/low conditions.
Consensus Highlighting: The New Metrics Table's title cells ("Metric" and "Value") turn green if all arrows are ↑ (strong bullish consensus), red if all are ↓ (strong bearish consensus), or gray otherwise.
Predicted Price Plot: Optional line (default blue) overlaying the ML-predicted price for visual comparison with actual price action.
Alerts: Notifications for high/low Frahm Volatility (≥8 or ≤3) and QQE Bias crosses (bullish/bearish momentum shifts).
Main Table Metrics
This table focuses on predictive, positional, and macro insights:
ML-Predicted Price: A linear regression forecast using normalized price, volume, and RSI over a customizable lookback (default 500 bars). Gradient scales from low (red) to high (green) relative to the current price ± threshold (default 100 points).
Deviation %: Percentage difference between current price and predicted price. Gradient highlights extremes (±0.5% default threshold), signaling potential overextensions.
VWAP Deviation %: Percentage difference from Volume Weighted Average Price (VWAP). Gradient indicates if price is above (green) or below (red) fair value (±0.5% default).
FRED UNRATE % Change: Percentage change in U.S. unemployment rate (via FRED data). Cell turns red for increases (economic weakness), green for decreases (strength), gray if zero or disabled.
Open Interest: Total open MESM2 futures contracts. Gradient scales from low (red) to high (green) up to a hardcoded 300,000 threshold, reflecting market participation.
COT Commercial Long/Short: Weekly Commitment of Traders data for commercial positions. Long cell green if longs > shorts (bullish institutional sentiment); Short cell red if shorts > longs (bearish); gray otherwise.
New Metrics Table Metrics
This table emphasizes technical momentum and volatility, with arrows for quick bias assessment:
QQE Bias: Smoothed RSI vs. trailing stop (default length 14, factor 4.236, smooth 5). Green for bullish (RSI > stop, ↑ arrow), red for bearish (RSI < stop, ↓ arrow), gray for neutral.
RSI: Relative Strength Index (default period 14). Gradient from oversold (red, <30 + threshold offset, ↓ arrow if ≤40) to overbought (green, >70 - offset, ↑ arrow if ≥60).
ATR Volatility: Score (1–20) based on Average True Range (default period 14, lookback 50). High scores (green, ↑ if ≥15) signal swings; low (red, ↓ if ≤5) indicate calm.
ADX Trend: Average Directional Index (default period 14). Gradient from weak (red, ↓ if ≤0.25×25 threshold) to strong trends (green, ↑ if ≥0.75×25).
Volume Momentum: Score (1–20) comparing current to historical volume (lookback 50). High (green, ↑ if ≥15) suggests pressure; low (red, ↓ if ≤5) implies weakness.
Frahm Volatility: Score (1–20) from true range over a window (default 24 hours, multiplier 9). Dynamic gradient (green/red/yellow); ↑ if ≥7.5, ↓ if ≤2.5.
Frahm Avg Candle (Ticks): Average candle size in ticks over the window. Blue gradient (or dynamic green/red/yellow); ↑ if ≥0.75 percentile, ↓ if ≤0.25.
Arrows trigger on metric-specific logic (e.g., RSI ≥60 for ↑), providing directional cues without strict color ties.
Customization Options
Adapt the indicator to your strategy:
ML Inputs: Lookback (10–5000 bars) and RSI period (2+) for prediction sensitivity—shorter for volatility, longer for trends.
Timeframes: Individual per metric (e.g., 1H for QQE Bias to match higher frames; blank for chart timeframe).
Thresholds: Adjust gradients and arrows (e.g., Deviation 0.1–5%, ADX 0–100, RSI overbought/oversold).
QQE Settings: Length, factor, and smooth for fine-tuned momentum.
Data Toggles: Enable/disable FRED, Open Interest, COT for focus (e.g., disable macro for pure intraday).
Frahm Options: Window hours (1+), scale multiplier (1–10), dynamic colors for avg candle.
Plot/Table: Line color, positions, gradients, and visibility.
Ideal Use Case
Perfect for MESM2 scalpers and trend traders. Use the Main Table for entry confirmation via predicted deviations and institutional positioning. Leverage the New Metrics Table arrows for short-term signals—enter bullish on green consensus (all ↑), avoid chop on low volatility. Set alerts to catch shifts without constant monitoring.
Why It's Valuable
Info Table V1 consolidates diverse metrics into actionable visuals, answering critical questions: Is price mispriced? Is momentum aligning? Is volatility manageable? With real-time updates, consensus highlights, and extensive customization, it enhances precision in fast markets, reducing guesswork for confident trades.
Note: Optimized for futures; some metrics (OI, COT) unavailable on non-futures symbols. Test on demo accounts. No financial advice—use at your own risk.
The provided script reuses open-source elements from TradingView's Color Library and LuxAlgo's QQE Weighted Oscillator, as noted in the script comments and description. Credits are appropriately given in both the description and code comments, satisfying the requirement for attribution.
Regarding significant improvements and proportion:
The QQE logic comprises approximately 15 lines of code in a script exceeding 400 lines, representing a small proportion (<5%).
Adaptations include integration with multi-timeframe support via request.security, user-customizable inputs for length, factor, and smooth, and application within a broader table-based indicator for momentum bias display (with color gradients, arrows, and alerts). This extends the original QQE beyond standalone oscillator use, incorporating it as one of seven metrics in the New Metrics Table for confluence analysis (e.g., consensus highlighting when all metrics align). These are functional enhancements, not mere stylistic or variable changes.
The Color Library usage is via official import (import TradingView/Color/1 as Color), leveraging built-in gradient functions without copying code, and applied to enhance visual interpretation across multiple metrics.
The script complies with the rules: reused code is minimal, significantly improved through integration and expansion, and properly credited. It qualifies for open-source publication under the Mozilla Public License 2.0, as stated.
Alpha - Combined BreakoutThis Pine Script indicator, "Alpha - Combined Breakout," is a combination between Smart Money Breakout Signals and UT Bot Alert, The UT Bot Alert indicator was initially developer by Yo_adriiiiaan
The idea of original code belongs HPotter.
This Indicator helps you identify potential trading opportunities by combining two distinct strategies: Smart Money Breakout and a modified UT Bot (likely a variation of the Ultimate Trend Bot). It provides visual signals, draws lines for potential take profit (TP) and stop loss (SL) levels, and includes a dashboard to track performance metrics.
Tutorial:
Understanding and Using the "Alpha - Combined Breakout" Indicator
This indicator is designed for traders looking for confirmation of market direction and potential entry/exit points by blending structural analysis with a trend-following oscillator.
How it Works (General Concept)
The indicator combines two main components:
Smart Money Breakout: This part identifies significant breaks in market structure, which "smart money" traders often use to gauge shifts in supply and demand. It looks for higher highs/lows or lower highs/lows and flags when these structural points are broken.
UT Bot: This is a trend-following component that generates buy and sell signals based on price action relative to an Average True Range (ATR) based trailing stop.
You can choose to use these signals independently or combined to generate trading alerts and visual cues on your chart. The dashboard provides a quick overview of how well the signals are performing based on your chosen settings and display mode.
Parameters and What They Do
Let's break down each input parameter:
1. Smart Money Inputs
These settings control how the indicator identifies market structure and breakouts.
swingSize (Market Structure Time-Horizon):
What it does: This integer value defines the number of candles used to identify significant "swing" (pivot) points—highs and lows.
Effect: A larger swingSize creates a smoother market structure, focusing on longer-term trends. This means signals might appear less frequently and with some delay but could be more reliable for higher timeframes or broader market movements. A smaller swingSize will pick up more minor market structure changes, leading to more frequent but potentially noisier signals, suitable for lower timeframes or scalping.
Analogy: Think of it like a zoom level on your market structure map. Higher values zoom out, showing only major mountain ranges. Lower values zoom in, showing every hill and bump.
bosConfType (BOS Confirmation Type):
What it does: This string input determines how a Break of Structure (BOS) is confirmed. You have two options:
'Candle Close': A breakout is confirmed only if a candle's closing price surpasses the previous swing high (for bullish) or swing low (for bearish).
'Wicks': A breakout is confirmed if any part of the candle (including its wick) surpasses the previous swing high or low.
Effect: 'Candle Close' provides stronger, more conservative confirmation, as it implies sustained price movement beyond the structure. 'Wicks' provides earlier, more aggressive signals, as it captures momentary breaches of the structure.
Analogy: Imagine a wall. 'Candle Close' means the whole person must get over the wall. 'Wicks' means even a finger touching over the top counts as a breach.
choch (Show CHoCH):
What it does: A boolean (true/false) input to enable or disable the display of "Change of Character" (CHoCH) labels. CHoCH indicates the first structural break against the current dominant trend.
Effect: When true, it helps identify early signs of a potential trend reversal, as it marks where the market's "character" (its tendency to make higher highs/lows or lower lows/highs) first changes.
BULL (Bullish Color) & BEAR (Bearish Color):
What they do: These color inputs allow you to customize the visual appearance of bullish and bearish signals and lines drawn by the Smart Money component.
Effect: Purely cosmetic, helps with visual identification on the chart.
sm_tp_sl_multiplier (SM TP/SL Multiplier (ATR)):
What it does: A float value that acts as a multiplier for the Average True Range (ATR) to calculate the Take Profit (TP) and Stop Loss (SL) levels specifically when you're in "Smart Money Only" mode. It uses the ATR calculated by the UT Bot's nLoss_ut as its base.
Effect: A higher multiplier creates wider TP/SL levels, potentially leading to fewer trades but larger wins/losses. A lower multiplier creates tighter TP/SL levels, potentially leading to more frequent but smaller wins/losses.
2. UT Bot Alerts Inputs
These parameters control the behavior and sensitivity of the UT Bot component.
a_ut (UT Key Value (Sensitivity)):
What it does: This integer value adjusts the sensitivity of the UT Bot.
Effect: A higher value makes the UT Bot less sensitive to price fluctuations, resulting in fewer and potentially more reliable signals. A lower value makes it more sensitive, generating more signals, which can include more false signals.
Analogy: Like a noise filter. Higher values filter out more noise, keeping only strong signals.
c_ut (UT ATR Period):
What it does: This integer sets the look-back period for the Average True Range (ATR) calculation used by the UT Bot. ATR measures market volatility.
Effect: This period directly influences the calculation of the nLoss_ut (which is a_ut * xATR_ut), thus defining the distance of the trailing stop loss and take profit levels. A longer period makes the ATR smoother and less reactive to sudden price spikes. A shorter period makes it more responsive.
h_ut (UT Signals from Heikin Ashi Candles):
What it does: A boolean (true/false) input to determine if the UT Bot calculations should use standard candlestick data or Heikin Ashi candlestick data.
Effect: Heikin Ashi candles smooth out price action, often making trends clearer and reducing noise. Using them for UT Bot signals can lead to smoother, potentially delayed signals that stay with a trend longer. Standard candles are more reactive to raw price changes.
3. Line Drawing Control Buttons
These crucial boolean inputs determine which type of signals will trigger the drawing of TP/SL/Entry lines and flags on your chart. They act as a priority system.
drawLinesUtOnly (Draw Lines: UT Only):
What it does: If checked (true), lines and flags will only be drawn when the UT Bot generates a buy/sell signal.
Effect: Isolates UT Bot signals for visual analysis.
drawLinesSmartMoneyOnly (Draw Lines: Smart Money Only):
What it does: If checked (true), lines and flags will only be drawn when the Smart Money Breakout logic generates a bullish/bearish breakout.
Effect: Overrides drawLinesUtOnly if both are checked. Isolates Smart Money signals.
drawLinesCombined (Draw Lines: UT & Smart Money (Combined)):
What it does: If checked (true), lines and flags will only be drawn when both a UT Bot signal AND a Smart Money Breakout signal occur on the same bar.
Effect: Overrides both drawLinesUtOnly and drawLinesSmartMoneyOnly if checked. Provides the strictest entry criteria for line drawing, looking for strong confluence.
Dashboard Metrics Explained
The dashboard provides performance statistics based on the lines drawing control button selected. For example, if "Draw Lines: UT Only" is active, the dashboard will show stats only for UT Bot signals.
Total Signals: The total number of buy or sell signals generated by the selected drawing mode.
TP1 Win Rate: The percentage of signals where the price reached Take Profit 1 (TP1) before hitting the Stop Loss.
TP2 Win Rate: The percentage of signals where the price reached Take Profit 2 (TP2) before hitting the Stop Loss.
TP3 Win Rate: The percentage of signals where the price reached Take Profit 3 (TP3) before hitting the Stop Loss. (Note: TP1, TP2, TP3 are in order of distance from entry, with TP3 being furthest.)
SL before any TP rate: This crucial metric shows the number of times the Stop Loss was hit / the percentage of total signals where the stop loss was triggered before any of the three Take Profit levels were reached. This gives you a clear picture of how often a trade resulted in a loss without ever moving into profit target territory.
Short Tutorial: How to Use the Indicator
Add to Chart: Open your TradingView chart, go to "Indicators," search for "Alpha - Combined Breakout," and add it to your chart.
Access Settings: Once added, click the gear icon next to the indicator name on your chart to open its settings.
Choose Your Signal Mode:
For UT Bot only: Uncheck "Draw Lines: Smart Money Only" and "Draw Lines: UT & Smart Money (Combined)". Ensure "Draw Lines: UT Only" is checked.
For Smart Money only: Uncheck "Draw Lines: UT Only" and "Draw Lines: UT & Smart Money (Combined)". Ensure "Draw Lines: Smart Money Only" is checked.
For Combined Signals: Check "Draw Lines: UT & Smart Money (Combined)". This will override the other two.
Adjust Parameters:
Start with default settings. Observe how the signals appear on your chosen asset and timeframe.
Refine Smart Money: If you see too many "noisy" market structure breaks, increase swingSize. If you want earlier breakouts, try "Wicks" for bosConfType.
Refine UT Bot: Adjust a_ut (Sensitivity) to get more or fewer UT Bot signals. Change c_ut (ATR Period) if you want larger or smaller TP/SL distances. Experiment with h_ut to see if Heikin Ashi smoothing suits your trading style.
Adjust TP/SL Multiplier: If using "Smart Money Only" mode, fine-tune sm_tp_sl_multiplier to set appropriate risk/reward levels.
Interpret Signals & Lines:
Buy/Sell Flags: These indicate the presence of a signal based on your selected drawing mode.
Entry Line (Blue Solid): This is where the signal was generated (usually the close price of the signal candle).
SL Line (Red/Green Solid): Your calculated stop loss level.
TP Lines (Dashed): Your three calculated take profit levels (TP1, TP2, TP3, where TP3 is the furthest target).
Smart Money Lines (BOS/CHoCH): These lines indicate horizontal levels where market structure breaks occurred. CHoCH labels might appear at the first structural break against the prior trend.
Monitor Dashboard: Pay attention to the dashboard in the top right corner. This dynamically updates to show the win rates for each TP and, crucially, the "SL before any TP rate." Use these statistics to evaluate the effectiveness of the indicator's signals under your current settings and chosen mode.
*
Set Alerts (Optional): You can set up alerts for any of the specific signals (UT Bot Long/Short, Smart Money Bullish/Bearish, or the "Line Draw" combined signals) to notify you when they occur, even if you're not actively watching the chart.
By following this tutorial, you'll be able to effectively use and customize the "Alpha - Combined Breakout" indicator to suit your trading strategy.
Timeframe Resistance Evaluation And Detection - CoffeeKillerTREAD - Timeframe Resistance Evaluation And Detection Guide
🔔 Important Technical Limitation 🔔
**This indicator does NOT fetch true higher timeframe data.** Instead, it simulates higher timeframe levels by aggregating data from your current chart timeframe. This means:
- Results will vary depending on what chart timeframe you're viewing
- Levels may not match actual higher timeframe candle highs/lows
- You might miss important wicks or gaps that occurred between chart timeframe bars
- **Always verify levels against actual higher timeframe charts before trading**
Welcome traders! This guide will walk you through the TREAD (Timeframe Resistance Evaluation And Detection) indicator, a multi-timeframe analysis tool developed by CoffeeKiller that identifies support and resistance confluence across different time periods.(I am 50+ year old trader and always thought I was bad a teaching and explaining so you get a AI guide. I personally use this on the 5 minute chart with the default settings, but to each there own and if you can improve the trend detection methods please DM me. I would like to see the code. Thanks)
Core Components
1. Dual Timeframe Level Tracking
- Short Timeframe Levels: Tracks opening price extremes within shorter periods
- Long Timeframe Levels: Tracks actual high/low extremes within longer periods
- Dynamic Reset Mechanism: Levels reset at the start of each new timeframe period
- Momentum Detection: Identifies when levels change mid-period, indicating active price movement
2. Visual Zone System
- High Zones: Areas between long timeframe highs and short timeframe highs
- Low Zones: Areas between long timeframe lows and short timeframe lows
- Fill Coloring: Dynamic colors based on whether levels are static or actively changing
- Momentum Highlighting: Special colors when levels break during active periods
3. Customizable Display Options
- Multiple Plot Styles: Line, circles, or cross markers
- Flexible Timeframe Selection: Wide range of short and long timeframe combinations
- Color Customization: Separate colors for each level type and momentum state
- Toggle Controls: Show/hide different elements based on trading preference
Main Features
Timeframe Settings
- Short Timeframe Options: 15m, 30m, 1h, 2h, 4h
- Long Timeframe Options: 1h, 2h, 4h, 8h, 12h, 1D, 1W
- Recommended Combinations:
- Scalping: 15m/1h or 30m/2h
- Day Trading: 30m/4h or 1h/4h
- Swing Trading: 4h/1D or 1D/1W
Display Configuration
- Level Visibility: Toggle short/long timeframe levels independently
- Fill Zone Control: Enable/disable colored zones between levels
- Momentum Fills: Special highlighting for actively changing levels
- Line Customization: Width, style, and color options for all elements
Color System
- Short TF High: Default red for resistance levels
- Short TF Low: Default green for support levels
- Long TF High: Transparent red for broader resistance context
- Long TF Low: Transparent green for broader support context
- Momentum Colors: Brighter colors when levels are actively changing
Technical Implementation Details
How Level Tracking Works
The indicator uses a custom tracking function that:
1. Detects Timeframe Periods: Uses `time()` function to identify when new periods begin
2. Tracks Extremes: Monitors highest/lowest values within each period
3. Resets on New Periods: Clears tracking when timeframe periods change
4. Updates Mid-Period: Continues tracking if new extremes are reached
The Timeframe Limitation Explained
`pinescript
// What the indicator does:
short_tf_start = ta.change(time(short_timeframe)) != 0 // Detects 30m period start
= track_highest(open, short_tf_start) // BUT uses chart TF opens!
// What true multi-timeframe would be:
// short_tf_high = request.security(syminfo.tickerid, short_timeframe, high)
`
This means:
- On a 5m chart with 30m/4h settings: Tracks 5m bar opens during 30m and 4h windows
- On a 1m chart with same settings: Tracks 1m bar opens during 30m and 4h windows
- Results will be different between chart timeframes
- May miss important price action that occurred between your chart's bars
Visual Elements
1. Level Lines
- Short TF High: Upper resistance line from shorter timeframe analysis
- Short TF Low: Lower support line from shorter timeframe analysis
- Long TF High: Broader resistance context from longer timeframe
- Long TF Low: Broader support context from longer timeframe
2. Zone Fills
- High Zone: Area between long TF high and short TF high (potential resistance cluster)
- Low Zone: Area between long TF low and short TF low (potential support cluster)
- Regular Fill: Standard transparency when levels are static
- Momentum Fill: Enhanced visibility when levels are actively changing
3. Dynamic Coloring
- Static Periods: Normal colors when levels haven't changed recently
- Active Periods: Momentum colors when levels are being tested/broken
- Confluence Zones: Different intensities based on timeframe alignment
Trading Applications
1. Support/Resistance Trading
- Entry Points: Trade bounces from zone boundaries
- Confluence Areas: Focus on areas where short and long TF levels cluster
- Zone Breaks: Enter on confirmed breaks through entire zones
- Multiple Timeframe Confirmation: Stronger signals when both timeframes align
2. Range Trading
- Zone Boundaries: Use fill zones as range extremes
- Mean Reversion: Trade back toward opposite zone when price reaches extremes
- Breakout Preparation: Watch for momentum color changes indicating potential breakouts
- Risk Management: Place stops outside the opposite zone
3. Trend Following
- Direction Bias: Trade in direction of zone breaks
- Pullback Entries: Enter on pullbacks to broken zones (now support/resistance)
- Momentum Confirmation: Use momentum coloring to confirm trend strength
- Multiple Timeframe Alignment: Strongest trends when both timeframes agree
4. Scalping Applications
- Quick Bounces: Trade rapid moves between zone boundaries
- Momentum Signals: Enter when momentum colors appear
- Short-Term Targets: Use opposite zone as profit target
- Tight Stops: Place stops just outside current zone
Optimization Guide
1. Timeframe Selection
For Different Trading Styles:
- Scalping: 15m/1h - Quick levels, frequent updates
- Day Trading: 30m/4h - Balanced view, good for intraday moves
- Swing Trading: 4h/1D - Longer-term perspective, fewer false signals
- Position Trading: 1D/1W - Major structural levels
2. Chart Timeframe Considerations
**Important**: Your chart timeframe affects results
- Lower Chart TF: More granular level tracking, but may be noisy
- Higher Chart TF: Smoother levels, but may miss important price action
- Recommended: Use chart timeframe 2-4x smaller than short indicator timeframe
3. Display Settings
- Busy Charts: Disable fills, show only key levels
- Clean Analysis: Enable all fills and momentum coloring
- Multi-Monitor Setup: Use different color schemes for easy identification
- Mobile Trading: Increase line width for visibility
Best Practices
1. Level Verification
- Always Cross-Check: Verify levels against actual higher timeframe charts
- Multiple Timeframes: Check 2-3 different chart timeframes for consistency
- Price Action Confirmation: Wait for candlestick confirmation at levels
- Volume Analysis: Combine with volume for stronger confirmation
2. Risk Management
- Stop Placement: Use zones rather than exact prices for stops
- Position Sizing: Reduce size when zones are narrow (higher risk)
- Multiple Targets: Scale out at different zone boundaries
- False Break Protection: Allow for minor zone penetrations
3. Signal Quality Assessment
- Momentum Colors: Higher probability when momentum coloring appears
- Zone Width: Wider zones often provide stronger support/resistance
- Historical Testing: Backtest on your preferred timeframe combinations
- Market Conditions: Adjust sensitivity based on volatility
Advanced Features
1. Momentum Detection System
The indicator tracks when levels change mid-period:
`pinescript
short_high_changed = short_high != short_high and not short_tf_start
`
This identifies:
- Active level testing
- Potential breakout situations
- Increased market volatility
- Trend acceleration points
2. Dynamic Color System
Complex conditional logic determines fill colors:
- Static Zones: Regular transparency for stable levels
- Active Zones: Enhanced colors for changing levels
- Mixed States: Different combinations based on user preferences
- Custom Overrides: User can prioritize certain color schemes
3. Zone Interaction Analysis
- Convergence: When short and long TF levels approach each other
- Divergence: When timeframes show conflicting levels
- Alignment: When both timeframes agree on direction
- Transition: When one timeframe changes while other remains static
Common Issues and Solutions
1. Inconsistent Levels
Problem: Levels look different on various chart timeframes
Solution: Always verify against actual higher timeframe charts
2. Missing Price Action
Problem: Important wicks or gaps not reflected in levels
Solution: Use chart timeframe closer to indicator's short timeframe setting
3. Too Many Signals
Problem: Excessive level changes and momentum alerts
Solution: Increase timeframe settings or reduce chart timeframe granularity
4. Lagging Signals
Problem: Levels seem to update too slowly
Solution: Decrease chart timeframe or use more sensitive timeframe combinations
Recommended Setups
Conservative Approach
- Timeframes: 4h/1D
- Chart: 1h
- Display: Show fills only, no momentum coloring
- Use: Swing trading, position management
Aggressive Approach
- Timeframes: 15m/1h
- Chart: 5m
- Display: All features enabled, momentum highlighting
- Use: Scalping, quick reversal trades
Balanced Approach
- Timeframes: 30m/4h
- Chart: 15m
- Display: Selective fills, momentum on key levels
- Use: Day trading, multi-session analysis
Final Notes
**Remember**: This indicator provides a synthetic view of multi-timeframe levels, not true higher timeframe data. While useful for identifying potential confluence areas, always verify important levels by checking actual higher timeframe charts.
**Best Results When**:
- Combined with actual multi-timeframe analysis
- Used for confluence confirmation rather than primary signals
- Applied with proper risk management
- Verified against price action and volume
**DISCLAIMER**: This indicator and its signals are intended solely for educational and informational purposes. The timeframe limitation means results may not reflect true higher timeframe levels. Always conduct your own analysis and verify levels independently before making trading decisions. Trading involves significant risk of loss.
Parabolic SAR Deviation [BigBeluga]Parabolic SAR + Deviation is an enhanced Parabolic SAR indicator designed to detect trends while incorporating deviation levels and trend change markers for added depth in analyzing price movements.
🔵 Key Features:
> Parabolic SAR with Optimized Settings:
Built on the classic Parabolic SAR, this version uses predefined default settings to enhance its ability to detect and confirm trends.
Clear trend direction is indicated by smooth trend lines, allowing traders to easily visualize market movements.
Trend Change Markers:
When a trend change occurs based on the SAR, the indicator plots a triangle at the trend change point.
The triangle is accompanied by the price value of the trend change, allowing traders to identify key reversal points instantly.
> Deviation Levels:
Four deviation levels are automatically plotted when a trend change occurs (up or down).
Uptrend: Deviation levels are positioned above the entry point.
Downtrend: Deviation levels are positioned below the entry point.
Levels are labeled with numbers 1 to 4, representing increasing degrees of deviation.
> Dynamic Level Updates:
When the price crosses a deviation level, the level becomes dashed and its label changes to display the volume at the breakout point.
This volume information helps traders assess the strength of the breakout and the potential for trend continuation or reversal.
> Volume Analysis at Breakpoints:
The volume displayed at crossed deviation levels provides insight into the strength of the price movement.
High volume at a breakout may indicate strong momentum, while low volume could signal potential exhaustion or a false breakout.
🔵 Usage:
Identify Trends: Use the trend change triangles and smooth SAR trend lines to confirm whether the market is trending up or down.
Analyze Deviation Levels: Monitor deviation levels **1–4** to identify potential breakout points and assess the degree of price deviation from the entry point.
Observe Trend Change Points: Utilize the triangles and price labels to quickly spot significant trend changes.
Volume Insights: Evaluate the volume displayed at crossed levels to determine the strength of the breakout and assess the likelihood of trend continuation or reversal.
Risk Management: Use deviation levels as potential stop-loss or take-profit zones, depending on the strength of the trend and volume conditions.
Parabolic SAR + Deviation is an essential tool for traders seeking a straightforward yet powerful method to identify trends, analyze price deviations, and gain insights into volume dynamics at critical breakout and trend change levels.
MainFX session indicatorScript Title: MainFX Session Indicator with Customizable Lines
Overview:
This script is designed to help traders visually identify key market sessions on their TradingView charts. It marks both the opening and closing of major sessions (Frankfurt, London, New York, Sydney, and Tokyo) by drawing lines and labels on the chart. The indicator is highly customizable, allowing you to define specific session times, choose your preferred time zone, and adjust the visual appearance of all lines.
Key Features:
Custom Session Times:
Each session’s start and end times are defined by user inputs in a simple HHMM-HHMM format. This means you can adjust the sessions to match the exact market hours you follow, making the indicator flexible for different trading strategies and markets.
Time Zone Flexibility:
The "Chart/Local Time Zone" input lets you override the default time zone of your chart. By setting a specific time zone (e.g., "Africa/Lagos" or "Africa/Accra"), the script calculates session start and end events relative to that zone. This ensures that, regardless of where you are trading from, the session markers accurately reflect the intended market hours and adjust automatically for Daylight Saving Time if applicable.
Open Range Levels (ORH/ORL):
When a session opens or closes, the script draws horizontal lines at the high and low of the candle immediately before the event. These levels act as the Open Range High (ORH) and Open Range Low (ORL) markers. They serve as key reference points for traders to gauge price levels established just before a session change.
Customizable Visuals:
Every visual element is customizable. You can adjust the color, width, and style (defaulting to a dotted line) of both the ORH/ORL lines and the combined session lines that label open and close events. This allows you to tailor the indicator to match your charting style and ensure that the lines stand out clearly.
Session Event Detection:
The script utilizes helper functions to check each bar on the chart. It compares the current bar’s session status with that of the previous bar to determine whether a session has just started or ended. When such a transition is detected, it triggers the drawing of the appropriate lines and labels.
Optimized for Intraday Trading:
Since the script’s functionality is based on minute-level bar changes, it is best used on 1-minute or lower timeframes. This ensures precision in marking the exact moments when sessions transition, which is critical for intraday trading strategies.
How It Works:
Session Timing:
The script calculates the session periods using the time() function with the user-defined session strings and time zone. This makes it independent of the chart’s inherent time settings.
Event Triggering:
When the current bar transitions into or out of a session (i.e., the session status changes between bars), the script detects this change. It then draws horizontal lines at the previous candle’s high and low (marking ORH and ORL) and adds session labels for clarity.
Visual Customization:
Users can easily change the appearance of the drawn lines and session labels via the script’s input options, ensuring that the indicators are both aesthetically pleasing and functionally clear.
Usage:
For Traders:
Use this indicator to keep track of critical market sessions and to spot participants in the session.
Customization:
Adjust session times and the time zone to suit your local market or the specific market you are analyzing.
Visual Clarity:
Customize line styles to ensure that your chart remains clear and that the session markers are easy to interpret even during overlapping sessions.
BuyPrice TrackerDescription:
This custom indicator is designed to help traders and investors track the performance of multiple stocks relative to their specified buy prices. It offers a visual representation of price changes and percentage gains or losses, making it easier to monitor your portfolio at a glance.
Key Features:
1. Multiple Stock Tracking: Monitor up to 15 different stocks simultaneously.
2. Custom Buy Prices: Set individual buy prices for each stock you're tracking.
3. Dynamic Price Line: A yellow dotted line appears on the chart to mark your buy price.
4. Real-time Updates: The indicator updates with each new price bar, providing current information.
5. Percentage Change Display: Clearly shows the percentage change from your buy price to the current price.
6. Color-Coded Labels: Green for positive changes, red for negative, allowing for quick visual assessment.
7. Customizable Label Position: Choose between top-right or top-left corner of the chart for the information label.
8. Adjustable Text Size: Customize the size of the text in the label for better visibility.
9. Buy Price Scale Label: Displays your buy price directly on the price scale for easy reference.
How It Works:
Enter your stock symbols and corresponding buy prices in the indicator settings.
The indicator will display a yellow dotted line at your buy price for the current chart.
A label will appear showing the stock symbol, your buy price, and the current percentage change.
The label updates in real-time as prices change, allowing you to monitor performance instantly.
Benefits:
Effortlessly track multiple positions in your portfolio.
Quickly identify which stocks are performing well and which might need attention.
Make informed decisions about holding, selling, or buying more based on clear visual cues.
Customize the display to suit your trading style and chart setup.
This indicator is perfect for active traders managing multiple positions, long-term investors tracking their portfolio performance, or anyone looking to keep a close eye on specific price levels in the market.
Note: This indicator works best when applied to the specific stock charts you're tracking. Make sure to input the correct stock symbols and buy prices in the indicator settings for accurate results.
Crypto Candle Low Leverage TrackerCrypto Candle Low Leverage Tracker
The Candle Low Leverage Indicator is a powerful tool for long position traders seeking to manage risk effectively when using leverage. By evaluating the current candle's low price, this indicator helps traders make more informed decisions about potential entry points, stop losses, and leverage levels. The indicator matches the low of the candle to the leverage needed for liquidation, giving you a clear view of how leverage impacts your position.
This indicator provides two critical insights:
% from Candle Low: Tracks how much the price has moved from the low of the current candle. For long position traders, this percentage is crucial for understanding how far the price has come off the low and deciding whether it’s safe to enter a position or if further price action is needed.
Leverage Needed: Estimates the leverage required to reach the candle's low as the liquidation price. Long traders can use this information to adjust leverage to a safer level, ensuring they don’t overexpose themselves to liquidation risks by matching leverage to the candle’s low.
Key Features:
Customizable table positioning (top, middle, bottom).
Toggle options to show/hide % from Candle Low and Leverage Needed.
Visual indicators with color changes: green for positive change, red for negative change, and blue for leverage requirements.
Ideal for long traders, this tool helps evaluate market conditions, manage risks, and calculate the best leverage to use in long trades, ensuring that leverage aligns with the candle’s low to prevent unnecessary liquidations.
Auto-Length Moving Average + Trend Signals (Zeiierman)█ Overview
The Auto-Length Moving Average + Trend Signals (Zeiierman) is an easy-to-use indicator designed to help traders dynamically adjust their moving average length based on market conditions. This tool adapts in real-time, expanding and contracting the moving average based on trend strength and momentum shifts.
The indicator smooths out price fluctuations by modifying its length while ensuring responsiveness to new trends. In addition to its adaptive length algorithm, it incorporates trend confirmation signals, helping traders identify potential trend reversals and continuations with greater confidence.
This indicator suits scalpers, swing traders, and trend-following investors who want a self-adjusting moving average that adapts to volatility, momentum, and price action dynamics.
█ How It Works
⚪ Dynamic Moving Average Length
The core feature of this indicator is its ability to automatically adjust the length of the moving average based on trend persistence and market conditions:
Expands in strong trends to reduce noise.
Contracts in choppy or reversing markets for faster reaction.
This allows for a more accurate moving average that aligns with current price dynamics.
⚪ Trend Confirmation & Signals
The indicator includes built-in trend detection logic, classifying trends based on market structure. It evaluates trend strength based on consecutive bars and smooths out transitions between bullish, bearish, and neutral conditions.
Uptrend: Price is persistently above the adjusted moving average.
Downtrend: Price remains below the adjusted moving average.
Neutral: Price fluctuates around the moving average, indicating possible consolidation.
⚪ Adaptive Trend Smoothing
A smoothing factor is applied to enhance trend readability while minimizing excessive lag. This balances reactivity with stability, making it easier to follow longer-term trends while avoiding false signals.
█ How to Use
⚪ Trend Identification
Bullish Trend: The indicator confirms an uptrend when the price consistently stays above the dynamically adjusted moving average.
Bearish Trend: A downtrend is recognized when the price remains below the moving average.
⚪ Trade Entry & Exit
Enter long when the dynamic moving average is green and a trend signal occurs. Exit when the price crosses below the dynamic moving average.
Enter short when the dynamic moving average is red and a trend signal occurs. Exit when the price crosses above the dynamic moving average.
█ Slope-Based Reset
This mode resets the trend counter when the moving average slope changes direction.
⚪ Interpretation & Insights
Best for trend-following traders who want to filter out noise and only reset when a clear shift in momentum occurs.
Higher slope length (N): More stable trends, fewer resets.
Lower slope length (N): More reactive to small price swings, frequent resets.
Useful in swing trading to track significant trend reversals.
█ RSI-Based Reset
The counter resets when the Relative Strength Index (RSI) crosses predefined overbought or oversold levels.
⚪ Interpretation & Insights
Best for reversal traders who look for extreme overbought/oversold conditions.
High RSI threshold (e.g., 80/20): Fewer resets, only extreme conditions trigger adjustments.
Lower RSI threshold (e.g., 60/40): More frequent resets, detecting smaller corrections.
Great for detecting exhaustion in trends before potential reversals.
█ Volume-Based Reset
A reset occurs when current volume significantly exceeds its moving average, signaling a shift in market participation.
⚪ Interpretation & Insights
Best for traders who follow institutional activity (high volume often means large players are active).
Higher volume SMA length: More stable trends, only resets on massive volume spikes.
Lower volume SMA length: More reactive to short-term volume shifts.
Useful in identifying breakout conditions and trend acceleration points.
█ Bollinger Band-Based Reset
A reset occurs when price closes above the upper Bollinger Band or below the lower Bollinger Band, signaling potential overextension.
⚪ Interpretation & Insights
Best for traders looking for volatility-based trend shifts.
Higher Bollinger Band multiplier (k = 2.5+): Captures only major price extremes.
Lower Bollinger Band multiplier (k = 1.5): Resets on moderate volatility changes.
Useful for detecting overextensions in strong trends before potential retracements.
█ MACD-Based Reset
A reset occurs when the MACD line crosses the signal line, indicating a momentum shift.
⚪ Interpretation & Insights
Best for momentum traders looking for trend continuation vs. exhaustion signals.
Longer MACD lengths (260, 120, 90): Captures major trend shifts.
Shorter MACD lengths (10, 5, 3): Reacts quickly to momentum changes.
Useful for detecting strong divergences and market shifts.
█ Stochastic-Based Reset
A reset occurs when Stochastic %K crosses overbought or oversold levels.
⚪ Interpretation & Insights
Best for short-term traders looking for fast momentum shifts.
Longer Stochastic length: Filters out false signals.
Shorter Stochastic length: Captures quick intraday shifts.
█ CCI-Based Reset
A reset occurs when the Commodity Channel Index (CCI) crosses predefined overbought or oversold levels. The CCI measures the price deviation from its statistical mean, making it a useful tool for detecting overextensions in price action.
⚪ Interpretation & Insights
Best for cycle traders who aim to identify overextended price deviations in trending or ranging markets.
Higher CCI threshold (e.g., ±200): Detects extreme overbought/oversold conditions before reversals.
Lower CCI threshold (e.g., ±10): More sensitive to trend shifts, useful for early signal detection.
Ideal for detecting momentum shifts before price reverts to its mean or continues trending strongly.
█ Momentum-Based Reset
A reset occurs when Momentum (Rate of Change) crosses zero, indicating a potential shift in price direction.
⚪ Interpretation & Insights
Best for trend-following traders who want to track acceleration vs. deceleration.
Higher momentum length: Captures longer-term shifts.
Lower momentum length: More responsive to short-term trend changes.
█ How to Interpret the Trend Strength Table
The Trend Strength Table provides valuable insights into the current market conditions by tracking how the dynamic moving average is adjusting based on trend persistence. Each metric in the table plays a role in understanding the strength, longevity, and stability of a trend.
⚪ Counter Value
Represents the current length of trend persistence before a reset occurs.
The higher the counter, the longer the current trend has been in place without resetting.
When this value reaches the Counter Break Threshold, the moving average resets and contracts to become more reactive.
Example:
A low counter value (e.g., 10) suggests a recent trend reset, meaning the market might be changing directions frequently.
A high counter value (e.g., 495) means the trend has been ongoing for a long time, indicating strong trend persistence.
⚪ Trend Strength
Measures how strong the current trend is based on the trend confirmation logic.
Higher values indicate stronger trends, while lower values suggest weaker trends or consolidations.
This value is dynamic and updates based on price action.
Example:
Trend Strength of 760 → Indicates a high-confidence trend.
Trend Strength of 50 → Suggests weak price action, possibly a choppy market.
⚪ Highest Trend Score
Tracks the strongest trend score recorded during the session.
Helps traders identify the most dominant trend observed in the timeframe.
This metric is useful for analyzing historical trend strength and comparing it with current conditions.
Example:
Highest Trend Score = 760 → Suggests that at some point, there was a strong trend in play.
If the current trend strength is much lower than this value, it could indicate trend exhaustion.
⚪ Average Trend Score
This is a rolling average of trend strength across the session.
Provides a bigger picture of how the trend strength fluctuates over time.
If the average trend score is high, the market has had persistent trends.
If it's low, the market may have been choppy or sideways.
Example:
Average Trend Score of 147 vs. Current Trend Strength of 760 → Indicates that the current trend is significantly stronger than the historical average, meaning a breakout might be occurring.
Average Trend Score of 700+ → Suggests a strong trending market overall.
█ Settings
⚪ Dynamic MA Controls
Base MA Length – Sets the starting length of the moving average before dynamic adjustments.
Max Dynamic Length – Defines the upper limit for how much the moving average can expand.
Trend Confirmation Length – The number of bars required to validate an uptrend or downtrend.
⚪ Reset & Adaptive Conditions
Reset Condition Type – Choose what triggers the moving average reset (Slope, RSI, Volume, MACD, etc.).
Trend Smoothing Factor – Adjusts how smoothly the moving average responds to price changes.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Triple Trend Indicator [BigBeluga]Triple Trend Indicator is a versatile trend-following tool designed to help traders identify trend strength and potential pullback levels using a three-band system. Each band represents a varying degree of price deviation from the mean, providing progressively stronger trend signals.
🔵 Key Features:
Three Adaptive Bands:
The indicator dynamically calculates three bands (1, 2, and 3) based on moving averages (SMA, EMA, WMA) and ATR multipliers.
Bands are positioned below the price in an uptrend and above the price in a downtrend, offering clear trend direction visualization.
Signal System:
Signals are generated when price interacts with the bands:
Signal 1: Triggered when the price touches Band 1, indicating a minor pullback within the trend.
Signal 2: Triggered at Band 2, showing a stronger price deviation and trend confirmation.
Signal 3: Triggered at Band 3, representing the most significant price deviation and strongest trend signal.
The further the price deviates from the mean, the stronger the trend signal, with Signal 3 being the most robust.
Color-Coded Bands:
Bands dynamically change color based on the trend direction:
Green bands signify an uptrend.
Brown bands signify a downtrend.
Dynamic Trend Line Changes:
Dashed lines highlight trend changes, helping traders visualize key turning points in the market.
🔵 Usage:
Use the bands to identify trend direction and strength.
Monitor the signal system to assess the level of price deviation and potential pullback strength.
Combine Signal 1, 2, and 3 to confirm trend momentum:
Signal 1 suggests a weaker pullback and continuation.
Signal 2 indicates a stronger trend confirmation.
Signal 3 highlights the strongest momentum and potential exhaustion points.
Utilize the color-coded bands for an intuitive understanding of current market conditions.
The Triple Trend Indicator is an ideal tool for trend traders looking for structured signals and dynamic support and resistance levels to optimize entries and exits.
CHAKRA RISS ENGULFING CANDLESTICK STRATEGYChakra RISS Engulfing Candlestick Strategy
Type: Technical Indicator & Strategy
Platform: TradingView
Script Version: Pine Script v6
Overview:
The Chakra RISS Engulfing Candlestick Strategy combines a momentum-based approach using the Relative Strength Index (RSI) with Engulfing Candlestick Patterns to generate buy and sell signals. The strategy filters trades based on price movement relative to a 50-period Simple Moving Average (SMA), making it a trend-following strategy.
The indicator uses color-coded bars to visually represent market conditions, helping traders easily identify bullish and bearish trends. The strategy is designed to be dynamic, adapting to changing market conditions and filtering out noise using key technical indicators.
How It Works:
RSI-Based Color Conditions:
Green Bars: When the RSI crosses above a specified UpLevel (default: 50), indicating a bullish momentum and signaling potential buy conditions.
Red Bars: When the RSI crosses below a specified DownLevel (default: 50), indicating a bearish momentum and signaling potential sell conditions.
Buy Signal:
Triggered when the following conditions are met:
RSI crosses from below the UpLevel (default: 50) to above it, signaling increasing bullish momentum.
The close price is above the 50-period Simple Moving Average (SMA), confirming an uptrend.
The Buy Signal is plotted below the bar with a green arrow and a "BUY" label.
Sell Signal:
Triggered when the following conditions are met:
RSI crosses from above the DownLevel (default: 50) to below it, signaling increasing bearish momentum.
The close price is below the 50-period Simple Moving Average (SMA), confirming a downtrend.
The Sell Signal is plotted above the bar with a red arrow and a "SELL" label.
Stop Loss and Take Profit:
For long trades (buy signals), the stop loss is placed below the previous bar's low, and the take profit is set at 3% above the entry price.
For short trades (sell signals), the stop loss is placed above the previous bar's high, and the take profit is set at 3% below the entry price.
Dynamic Bar Coloring:
The bar colors change dynamically based on RSI levels:
Green Bars: Indicating a potential uptrend (bullish).
Red Bars: Indicating a potential downtrend (bearish).
These visual cues help traders quickly identify market trends and potential reversals.
Trend Filtering:
The 50-period Simple Moving Average (SMA) is used to filter trades based on the overall market trend:
Buy signals are only considered when the price is above the moving average, indicating an uptrend.
Sell signals are only considered when the price is below the moving average, indicating a downtrend.
Alerting System:
Alerts can be set for both buy and sell signals. These alerts notify traders in real-time when potential trades are generated, allowing them to act promptly.
Alerts can be configured to send notifications through email, SMS, or a webhook for integration with other services like IFTTT or Zapier.
Key Features:
RSI and Moving Average-Based Signals: Combines RSI with a moving average for more accurate trade signals.
Stop Loss and Take Profit: Dynamic risk management with custom stop loss and take profit levels based on previous high and low prices.
Buy and Sell Alerts: Provides real-time alerts when a buy or sell signal is triggered.
Trend Confirmation: Uses the 50-period Simple Moving Average to filter signals and confirm the direction of the trend.
Visual Bar Color Changes: Makes it easy to identify bullish or bearish trends with color-coded bars.
Usage:
This strategy is suitable for traders who prefer a trend-following approach and want to combine momentum indicators (RSI) with price action (Engulfing Candlestick patterns). It is particularly useful in volatile markets where quick identification of trend changes can lead to profitable trades.
Best Used For: Day trading, swing trading, and trend-following strategies.
Timeframes: Works well on various timeframes, from 1-minute charts for scalping to daily charts for swing trading.
Markets: Can be applied to any market with sufficient liquidity (stocks, forex, crypto, etc.).
Settings:
UpLevel: The RSI level above which the market is considered bullish (default: 50).
DownLevel: The RSI level below which the market is considered bearish (default: 50).
SMA Length: The period of the Simple Moving Average used to filter trades (default: 50).
Risk Management: Customizable stop loss and take profit settings based on price action (default: 3% above/below the entry price).
Multi-Timeframe Volatility ATR - [by Oberlunar]This script (for now in beta release) is specifically designed for scalping or traders operating on lower timeframes (if you are in a timeframe of one minute wait one minute to collect statistics). Its primary purpose is to provide detailed insights into market volatility by calculating the ATR (Average True Range) and its percentage changes, allowing traders to quickly identify shifts in market conditions.
The ATR is calculated across six user-defined timeframes, which can include very short intervals such as 5 or 15 seconds. This setup enables real-time monitoring of volatility, which is critical for scalping strategies. The script collects a rolling history of the last five ATR values for each timeframe. These historical values are used to calculate percentage changes by comparing the current ATR with the oldest value in the history, offering a clear view of how volatility is evolving over time.
Percentage changes are displayed dynamically in a table, with color-coded feedback to indicate the direction of the change: green for increases, red for decreases, and gray for stability or insufficient data. This visual representation makes it easy to spot whether market volatility is rising or falling at a glance.
By progressively collecting data, the script becomes increasingly effective as more ATR values are accumulated. This functionality is especially useful for traders on lower timeframes, where rapid changes in volatility can signal breakout opportunities or shifts in market dynamics.
Soon I will update personalized ATR parameters, and lookback strategies for statistics.
FRAMA Channel [BigBeluga]This is a trend-following indicator that utilizes the Fractal Adaptive Moving Average (FRAMA) to create a dynamic channel around the price. The FRAMA Channel helps identify uptrends, downtrends, and ranging markets by examining the relationship between the price and the channel's boundaries. It also marks trend changes with arrows, optionally displaying either price values or average volume at these key points.
🔵 IDEA
The core idea behind the FRAMA Channel indicator is to use the fractal nature of markets to adapt to different market conditions. By creating a channel around the FRAMA line, it not only tracks price trends but also adapts its sensitivity based on market volatility. When the price crosses the upper or lower bands of the channel, it signals a potential shift in trend direction. If the price remains within the channel and crosses over the upper or lower bands without a breakout, the market is likely in a ranging phase with low momentum. This adaptive approach makes the FRAMA Channel effective in both trending and ranging market environments.
🔵 KEY FEATURES & USAGE
◉ Dynamic FRAMA Channel with Trend Signals:
The FRAMA Channel uses a fractal-based moving average to create an adaptive channel around the price. When the price crosses above the upper band, it signals an uptrend and plots an upward arrow with the price (or average volume) value. Conversely, when the price crosses below the lower band, it signals a downtrend and marks the point with a downward arrow. This dynamic adaptation to market conditions helps traders identify key trend shifts effectively.
◉ Ranging Market Detection:
If the price remains within the channel, and only the high crosses the upper band or the low crosses the lower band, the indicator identifies a ranging market with low momentum. In this case, the channel turns gray, signaling a neutral trend. This is particularly useful for avoiding false signals during periods of market consolidation.
◉ Color-Coded Candles and Channel Bands:
Candles and channel bands are color-coded to reflect the current trend direction. Green indicates an upward trend, blue shows a downward trend, and gray signals a neutral or ranging market. This visual representation makes it easy to identify the market condition at a glance, helping traders make informed decisions quickly.
◉ Customizable Display of Price or Average Volume:
On trend change signals, the indicator allows users to choose whether to display the price at the point of trend change or the average volume of 10 bars. This flexibility enables traders to focus on the information that is most relevant to their strategy, whether it's the exact price entery or the volume context of the market shift. Displaying the average volume allows to see the strength of the trend change.
Price Data:
Average Volume of points:
🔵 CUSTOMIZATION
Length & Bands Distance: Adjust the length for the FRAMA calculation to control the sensitivity of the channel. A shorter length makes the channel more reactive to price changes, while a longer length smooths it out. The Bands Distance setting determines how far the bands are from the FRAMA line, helping to define the breakout and ranging conditions.
Signals Data: Choose between displaying the price or the average volume on trend change arrows. This allows traders to focus on either the exact price level of trend change or the market volume context.
Color Settings: Customize the colors for upward momentum, downward momentum, and neutral states to suit your charting preferences. You can also toggle whether to color the candles based on the momentum for a clearer visual of the trend direction.
The FRAMA Channel indicator adapts to market conditions, providing a versatile tool for identifying trends and ranging markets with clear visual cues.
