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
Zyklen
DM ziggy lines..Use on chart
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Understand What ZigZag Shows
The ZigZag connects swing points:
Up move (Bull leg) → identifies higher swing lows (potential start of uptrend)
Down move (Bear leg) → identifies lower swing highs (potential start of downtrend)
It doesn’t repaint here (since you used a non-repainting logic), so signals are confirmed once the line appears.
Entry Logic (Trend-Following Style)
You can trade in the direction of the last confirmed ZigZag leg:
Long entry idea
Wait for the ZigZag to plot a bullish leg (green line in your script).
Confirm the higher low (price doesn’t break below the last swing low).
Enter when a candle closes above the recent swing high or with bullish momentum (e.g., a bullish engulfing candle).
📉 Short entry idea
Wait for the ZigZag to plot a bearish leg (red line).
Confirm the lower high (price doesn’t break above the last swing high).
Enter when a candle closes below the recent swing low or with bearish momentum.
. Exit or Stop-Loss
Stop-loss: Place just beyond the most recent swing point (ZigZag high/low).
Take-profit: Use risk/reward (e.g. 1:2 ratio) or next opposite ZigZag point.
Example:
Long trade after green ZigZag up-leg → stop below last swing low → exit near next swing high (red ZigZag reversal).
Optional Enhancements
You can combine the ZigZag logic with:
RSI or MACD → to confirm momentum.
Moving averages → to confirm overall trend.
Volume spikes → to confirm strong swing reversals.
Crypto Cycle Radar (TOTAL / TOTAL2 / TOTAL3 / BTC.D / USDT.D)Crypto Cycle Radar (TOTAL / TOTAL2 / TOTAL3 / BTC.D / USDT.D)
Outside Candle Session Breakout [CHE]Outside Candle Session Breakout
Session - anchored HTF levels for clear market-structure and precise breakout context
Summary
This indicator is a relevant market-structure tool. It anchors the session to the first higher-timeframe bar, then activates only when the second bar forms an outside condition. Price frequently reacts around these anchors, which provides precise breakout context and a clear overview on both lower and higher timeframes. Robustness comes from close-based validation, an adaptive volatility and tick buffer, first-touch enforcement, optional retest, one-signal-per-session, cooldown, and an optional trend filter.
Pine version: v6. Overlay: true.
Motivation: Why this design?
Short-term breakout tools often trigger during noise, duplicate within the same session, or drift when volatility shifts. The core idea is to gate signals behind a meaningful structure event: a first-bar anchor and a subsequent outside bar on the session timeframe. This narrows attention to structurally important breaks while adaptive buffering and debouncing reduce false or mid-run triggers.
What’s different vs. standard approaches?
Baseline: Simple high-low breaks or fixed buffers without session context.
Architecture: Session-anchored first-bar high/low; outside-bar gate; close-based confirmation with an adaptive ATR and tick buffer; first-touch enforcement; optional retest window; one-signal-per-session and cooldown; optional EMA trend and slope filter; higher-timeframe aggregation with lookahead disabled; themeable visuals and a range fill between levels.
Practical effect: Cleaner timing at structurally relevant levels, fewer redundant or late triggers, and better multi-timeframe situational awareness.
How it works (technical)
The chart timeframe is mapped to an analysis timeframe and a session timeframe.
The first session bar defines the anchor high and low. The setup becomes active only after the next bar forms an outside range relative to that first bar.
While active, the script tracks these anchors and checks for a breakout beyond a buffered threshold, using closing prices or wicks by preference.
The buffer scales with volatility and is limited by a minimum tick floor. First-touch enforcement avoids mid-run confirmations.
Optional retest requires a pullback to the raw anchor followed by a new close beyond the buffered level within a user window.
Optional trend gating uses an EMA on the analysis timeframe, including an optional slope requirement and price-location check.
Higher-timeframe data is requested with lookahead disabled. Values can update during a forming higher-timeframe bar; waiting and confirmation mitigate timing shifts.
Parameter Guide
Enable Long / Enable Short — Direction toggles. Default: true / true. Reduces unwanted side.
Wait Candles — Minimum bars after outside confirmation before entries. Default: five. More waiting increases stability.
Close-based Breakout — Confirm on candle close beyond buffer. Default: true. For wick sensitivity, disable.
ATR Buffer — Enables adaptive volatility buffer. Default: true.
ATR Multiplier — Buffer scaling. Default: zero point two. Increase to reduce noise.
Ticks Buffer — Minimum buffer in ticks. Default: two. Protects in quiet markets.
Cooldown Bars — Blocks new signals after a trigger. Default: three.
One Signal per Session — Prevents duplicates within a session. Default: true.
Require Retest — Pullback to raw anchor before confirming. Default: false.
Retest Window — Bars allowed for retest completion. Default: five.
HTF Trend Filter — EMA-based gating. Default: false.
EMA Length — EMA period. Default: two hundred.
Slope — Require EMA slope direction. Default: true.
Price Above/Below EMA — Require price location relative to EMA. Default: true.
Show Levels / Highlight Session / Show Signals — Visual controls. Default: true.
Color Theme — “Blue-Green” (default), “Monochrome”, “Earth Tones”, “Classic”, “Dark”.
Time Period Box — Visibility, size, position, and colors for the info box. (Optional)
Reading & Interpretation
The two level lines represent the session’s first-bar high and low. The filled band illustrates the active session range.
“OUT” marks that the outside condition is confirmed and the setup is live.
“LONG” or “SHORT” appears only when the breakout clears buffer, debounce, and optional gates.
Background tint indicates sessions where the setup is valid.
Alerts fire on confirmed long or short breakout events.
Practical Workflows & Combinations
Trend-following: Keep close-based validation, ATR buffer near the default, one-signal-per-session enabled; add EMA trend and slope for directional bias.
Retest confirmation: Enable retest with a short window to prioritize cleaner continuation after a pullback.
Lower-timeframe scalping: Reduce waiting and cooldown slightly; keep a small tick buffer to filter micro-whips.
Swing and position context: Increase ATR multiplier and waiting; maintain once-per-session to limit duplicates.
Timeframe Tiers and Trader Profiles
The script adapts its internal mapping based on the chart timeframe:
Under fifteen minutes → Analysis: one minute; Session: sixty minutes. Useful for scalpers and high-frequency intraday reads.
Between fifteen and under sixty minutes → Analysis: fifteen minutes; Session: one day. Suits day traders who need intraday alignment to the daily session.
Between sixty minutes and under one day → Analysis: sixty minutes; Session: one week. Serves intraday-to-swing transitions and end-of-day planning.
Between one day and under one week → Analysis: two hundred forty minutes; Session: two weeks. Fits swing traders who monitor multi-day structure.
Between one week and under thirty days → Analysis: one day; Session: three months. Supports position traders seeking quarterly context.
Thirty days and above → Analysis: one day; Session: twelve months. Provides a broad annual anchor for macro context.
These tiers are designed to keep anchors meaningful across regimes while preserving responsiveness appropriate to the trader profile.
Behavior, Constraints & Performance
Signals can be validated on closed bars through close-based logic; enabling this reduces intrabar flicker.
Higher-timeframe values may evolve during a forming bar; waiting parameters and the outside-bar gate reduce, but do not remove, this effect.
Resource footprint is light; the script uses standard indicators and a single higher-timeframe request per stream.
Known limits: rare setups during very quiet periods, sensitivity to gaps, and reduced reliability on illiquid symbols.
Sensible Defaults & Quick Tuning
Start with close-based validation on, ATR buffer on with a multiplier near zero point two, tick buffer two, cooldown three, once-per-session on.
Too many flips: increase the ATR multiplier and cooldown; consider enabling the EMA filter and slope.
Too sluggish: reduce the ATR multiplier and waiting; disable retest.
Choppy conditions: keep close-based validation, increase tick buffer, shorten the retest window.
What this indicator is—and isn’t
This is a visualization and signal layer for session-anchored breakouts with stability gates. It is not a complete trading system, risk framework, or predictive engine. Combine it with structured analysis, position sizing, and disciplined risk controls.
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
Multi-Resolution RSI with Machine LearningMulti-Resolution RSI
Developed by imaclone.x.
Last Updated: August 21st 2025
A single indicator that fuses my ML-RSI.ai pipeline with a classic multi-timeframe RSI. One script, dual-resolution oscillators if desired, plus a machine-learning similarity engine and modular signal-processing layers.
What it does
* Primary RSI augmented with KNN similarity engine (K, lookback, weighting). Feature embeddings include RSI magnitude, RSI momentum, volatility surface, regression slope, and price momentum vectors.
* Adaptive smoothing stack: Kalman filter recursion, Double EMA cascades, or ALMA convolution.
* Multi-resolution control for the primary oscillator timeframe.
* Optional *second* RSI projected from any timeframe for hierarchical confluence.
* Advanced visuals: upper/lower thresholds, midline, background regime highlighting, crossovers, and B/S event labels.
* Color architectures: None, Trend-Following (50-line bifurcation), or Impulse (band-breach). Optional bar tinting for full-chart context.
Inputs (groups)
* Timeframe Settings: primary + secondary RSI TF/lengths.
* Levels & Visuals: thresholds, highlights, cross events, B/S markers.
* RSI Base: smoothing toggle, MA class, ALMA sigma.
* KNN Machine Learning: enable, K neighbors, historical window, feature dimensionality, ML weighting.
* Advanced Filtering: method + intensity.
* Coloring: None, Trend-Following, Impulse.
Signals
* B flag when ML-RSI crosses upward through the lower threshold.
* S flag when ML-RSI crosses downward through the upper threshold.
* Secondary RSI = higher-timeframe confirmation, not standalone trigger.
Usage notes
* Raise ML weight + feature dimensionality for deeper similarity recognition; lower them for classic oscillator behavior.
* Kalman recursion delivers adaptive, low-lag smoothing; Double EMA and ALMA yield stronger dampening.
* Typical config: intraday primary RSI + higher-TF secondary RSI for regime anchoring.
Changelog
* v6 merge: Unified CM-style MTF RSI framework with my KNN-enhanced kernel and filter stack. One composite indicator replaces multiple scripts.
Credits
* MTF band logic inspired by earlier open-source frameworks.
* ML kernel and implementation by imaclone.x.
Disclaimer
For research and algorithmic experimentation only. No signals guaranteed.
And please kindly, for the love of God, DYOFR.
BBBDXY-9 - Intraday/ SwingWhat it is
A chart-grade USD basket that outputs full O/H/L/C candles. It combines nine FX pairs using weighted log-returns and geometric aggregation, then rescales to 100 at an anchor. You can build from 1m, 1H, 4H, or D and analyze it like a normal chart (drawings, structure, bar replay, and a clean Source output for other tools).
Why another USD gauge?
A legacy EUR-heavy USD measure can, at times, behave close to an EURUSD proxy and under-represent moves versus other majors and Asia EM. A diversified basket helps capture USD dynamics beyond EUR-centric effects. This study is independent and does not rely on third-party brands.
Basket (defaults are editable in Inputs)
Pairs used: EURUSD (inverted), USDJPY, USDCAD, GBPUSD (inverted), USDMXN, USDCNH, USDCHF, AUDUSD (inverted), USDSGD.
Inverted means the pair is flipped internally so rising values reflect USD strength. Weights default to a diversified mix and can be customized.
How it works (high level)
Sample each component on the chosen base timeframe.
Compute log-returns versus the anchor, multiply by weights, sum, and exponentiate (geometric combine).
Re-scale so the index equals 100 at the anchor.
Optional: plot an arithmetic comparison line.
Base-TF and aggregation rules
Build from timeframe: 1m, 1H, 4H, D.
If chart TF is greater than base TF, aggregate up from the base TF (no down-mixing).
If chart TF equals base TF, show native base candles.
If chart TF is lower than base TF, sample the base TF (step-like).
For intraday precision and exact wicks, choose 1m as base and view higher TFs by aggregation.
For longer history with fewer requests, choose 1H, 4H, or D as base.
Inputs (overview)
Build-from TF (1m, 1H, 4H, D) and a feed prefix if your broker symbols require one.
Per-pair weights (editable).
Anchor date and time (chart timezone) plus optional auto-rebase to first available bar if the exact anchor is missing.
Arithmetic comparison line (optional).
Source output selector (Open, High, Low, Close) for downstream tools.
Custom candle colors.
How to use
For granular intraday structure and wick accuracy, set base TF to 1m and view higher TFs by aggregation.
For swing and weekly context, set base TF to 1H, 4H, or D.
Keep default weights unless you have a specific reason to alter the basket.
Rising index suggests USD strength versus the basket; falling index suggests USD weakness.
Notes and limitations
Depends on availability and quality of the underlying FX symbols on your data feed.
Indicator only; no financial advice, no alerts, no orders.
No external open-source code reused.
Past behavior does not guarantee future results.
Figure (publication image)
Top: a legacy EUR-heavy USD index on 1H. The horizontal line marks a local swing high; price stalls and rolls over beneath it.
Bottom: this diversified USD basket on 1H. The horizontal line marks the same calendar window; the advance extends differently into that zone before rolling later.
This side-by-side illustrates that a EUR-centric gauge can diverge from a diversified USD basket. Similar divergences appear at other points; the example is illustrative and not a signal.
- riseofatrader
Moyennes Mobiles Pertinentes ema21vert ma50 bleue ma200 rougeUtilisez sur un même script un indicateur avec plusieurs moyennes mobiles servant de supports
HTF Order Blocks [TradeWithRon]HTF Order Blocks is a clean, multi-timeframe order-flow tool that maps bullish/bearish order blocks and optional breaker blocks from higher timeframes onto your current chart. It’s built for clarity and speed: minimal clutter, configurable labels, and optional Fibonacci extensions for quick projection work.
What it does
Identifies Order Blocks (OBs) using swing structure (configurable lookback).
Marks Breaker Blocks when an OB is breached and market structure flips.
Projects HTF zones from up to two higher timeframes (HTF1 & HTF2), with separate color themes.
Clean visuals: open lines, and smart label placement.
Optional Fibonacci extensions from each OB for quick confluence checks.
Alert-ready: receive alerts on creation of Bullish/Bearish OBs and Breakers.
Key Features
Multi-Timeframe Mapping
Enable HTF1 and/or HTF2 and project their OBs on your active chart.
Uses request.security() with lookahead_off to avoid future-bar leakage.
Fibonacci Add-On (optional)
How it works (logic overview)
Swing detection: Finds recent pivots using a lookback window.
OB creation:
Bullish OB: Created after price closes above a prior swing high, then scans the upswing for the most meaningful base candle range (high/low/open/close).
Bearish OB: Created after price closes below a prior swing low, with symmetric logic.
Breaker state:
A bullish OB turns breaker if price later closes below its base;
A bearish OB turns breaker if price closes above its base.
Cleanup: Breakers are removed if price subsequently recovers past the opposing OB boundary.
HTF OBs are detected on their native timeframe and projected to the active chart; confirmation occurs on the HTF bar’s close.
First 30M Candle Rule Ref / 5M EntriesThis strategy highlights and reacts to two key 30-minute candles on the Romanian market schedule — at 10:30 AM and 15:30 or 16:30 (depending on daylight saving time).
It is designed to run on a 5-minute chart, using the 30-minute candles as reference points for potential entries.
When each 30-minute candle closes, the script:
Colors the background during that specific 30-minute period (green for the morning session, red for the afternoon session)
Sends an alert confirming the candle’s closure
Places a symbolic long trade after the 10:30 candle closes and a symbolic short trade after the afternoon candle closes (for backtesting purposes)
This setup allows traders to test or automate strategies that rely on market reactions following key time-based candles, without plotting any extra lines on the chart.
EMA 21 Big Candle SetupKey Features:
EMA 21 plotted in yellow
Detects big candles that are 1.5x larger than the average candle size (using ATR)
Bullish Setup: Green triangle when a big bullish candle crosses above EMA 21
Bearish Setup: Red triangle when a big bearish candle crosses below EMA 21
Visual feedback: Big candles are highlighted with transparent colors
Info box: Shows current EMA value, candle size, and whether it's a big candle
Anchored VWAP Close-Above Alert (Daily)This indicator is a daily anchored VWAP (Volume-Weighted Average Price) tracker that alerts you when price closes above a key pivot line you’ve defined — either dynamically (anchored VWAP from a date like 9/5) or statically (a fixed level like 126.65).
Here’s how each piece works:
1. The anchor date
You give it a date — say September 5, 2025. The script starts calculating VWAP from that day forward. That line represents the average price buyers and sellers have transacted at since that anchor. It’s a kind of “fair-value line” for that period.
2. The logic
Every bar after that date, it adds up (price × volume) and divides by total volume — giving a running VWAP from that starting point. You can instead lock it to a flat number (like 126.65) if you don’t want it recalculating.
3. The alert trigger
Once per day — when the daily candle closes — it checks:
“Did price close above my watched line?”
If yes, it fires an alert (and can draw a label) to tell you that the day’s candle reclaimed that level.
4. The why
That kind of move — a daily close reclaiming a major anchored VWAP — often signals a shift in sentiment or trend strength. Traders watch it to confirm rebounds or invalidations of downside moves.
So in your context:
You anchored to 9/5 (the downside pivot).
You want to know if price closes back above that VWAP (~126.65).
If it does, the alert lights up — confirming the “reclaim” you mentioned in your note.
It’s basically your guard dog for the daily close reclaim.
黄金专用LPPL特征检测（Log-Periodic Power Law Singularity）专门用于黄金走势的LPPL检测，在技术分析中，LPPL 奇点指的是对数周期幂律奇异性（Log-Periodic Power Law Singularity），它是对数周期幂律模型（LPPL）中的一个关键概念。以下是关于它的详细介绍：
提出者及背景：LPPL 模型是由研究市场泡沫的先驱者、物理学家迪迪埃・索尔内特（Didier Sornette）等人提出的。该模型结合了理性预期泡沫的经济理论、投资者的模仿和羊群行为的行为金融学以及分岔和相变的数学统计物理学，用于检测金融市场中的泡沫和预测市场转折点。
模型原理：LPPL 模型假设当市场出现泡沫时，资产价格会呈现出一种特殊的波动模式，这种模式由正反馈机制驱动。在泡沫形成过程中，投资者的模仿和跟风行为导致市场参与者的一致性和协同性急剧上升，价格出现 “快于指数” 的增长，同时伴随着加速的对数周期振荡。而 LPPL 奇点就是价格增长和振荡达到极限的那个有限时间点，在这个点之前，价格增长越来越快，振荡频率也越来越高，当到达奇点时，泡沫破裂，市场往往会出现急剧的反转和崩盘。
数学表达：LPPL 模型的数学公式较为复杂，其原始形式提出了一个由 3 个线性参数和 4 个非线性参数组成的函数。通过将这个函数与对数价格时间序列进行拟合，可以估计出模型的参数，进而确定奇点的时间位置等信息。
在金融市场中的应用：LPPL 模型及其中的奇点概念主要用于检测金融市场中的泡沫和预测市场的崩溃点。例如，在 2008 年石油价格泡沫和 2009 年上海股市泡沫等事件中，该模型都被用于分析和预测市场的转折点。不过，该模型也存在一定的局限性，比如对奇点具体点位的预测误差较大，而且市场情况复杂多变，可能会有强大的外力干扰等因素影响模型的准确性。
The LPPL model was proposed by physicist Didier Sornette, a pioneer in the study of market bubbles, and others. The model combines the economic theory of rational expectations bubbles, behavioral finance on investor imitation and herding behavior, and the mathematical statistical physics of bifurcations and phase transitions to detect bubbles in financial markets and predict market turning points.
Model Principle: The LPPL model posits that when a market bubble forms, asset prices exhibit a distinctive pattern of fluctuation driven by a positive feedback mechanism. During the bubble's formation, investors' imitation and bandwagon-following behavior lead to a sharp increase in consistency and coordination among market participants, resulting in "faster-than-exponential" price growth accompanied by accelerating logarithmic-periodic oscillations. The LPPL singularity is the finite point in time where price growth and oscillation reach their limits. Prior to this point, prices grow increasingly faster, and the frequency of oscillations increases. When the singularity is reached, the bubble bursts, and the market often experiences a sharp reversal and crash.
Timebender – 369 PivotsTimebender – 369 Pivots is a clean visual study that marks swing highs and lows with numeric “369-sequence” digits derived from time.
Each digit is automatically color-coded into Accumulation (1 – 3), Manipulation (4 – 6), and Distribution (7 – 9) phases, helping traders identify rhythm and symmetry in market structure.
Labels float above or below bars for clear visibility and never overlap price, allowing smooth zoom and multi-timeframe use.
This base model focuses on clarity, precision, and efficient plotting — no toggles, no clutter — a stable foundation for future Timebender builds.
Gold and Bitcoin: The Evolution of Value!The Eternal Luster of Gold
In the dawn of time, when the earth was young and rivers whispered secrets to the stones, a wanderer named Elara found a gleam in the silt of a sun-kissed stream. It was pure gold, radiant like a captured star fallen from the heavens. She held it in her palm, feeling its warmth pulse like a heartbeat, and in that moment, humanity’s soul awakened to the allure of eternity.
As seasons turned to centuries, gold wove itself into the story of empires. In ancient Egypt, pharaohs crowned themselves with its glow, believing it to be the flesh of gods. It built pyramids that reached for the sky and tombs that guarded kings forever. Across the sands in Mesopotamia, merchants traded it for spices and silks, its weight a promise of power and trust.
Translation moment: Gold became the first universal symbol of value. People trusted it more than words or promises because it did not rust, fade, or vanish.
The Greeks saw in gold not only wealth but wisdom, the symbol of the sun’s eternal fire. Alexander the Great carried it across the continent, forging an empire of golden threads. Rome rose on its back, minting coins whose clink echoed through history.
Through the ages, gold endured the rush of California’s dreamers, the halls of Versailles, and the quiet vaults of modern fortunes. It has been both a curse and a blessing, the fuel of wars and the gift of love, whispering of beauty’s fragility and the human desire for something that lasts beyond the grave. In its shine, we see ourselves fragile yet forever chasing light.
The Digital Dawn of Bitcoin
Centuries later, under the glow of computer screens, a visionary named Satoshi dreamed of a new gold born not from the earth but from the ether of ideas. Bitcoin appeared in 2009 amid a world weary of banks and broken trust.
Like gold’s ancient gleam, Bitcoin was mined not with picks but with puzzles solved by machines. It promised freedom, a currency without kings, flowing from person to person, unbound by borders or empires.
Translation moment: Bitcoin works like digital gold. Instead of digging the ground, miners use computers to solve problems and unlock new coins. No one controls it, and that is what makes it powerful.
Through doubt and frenzy, it rose as a beacon for those seeking sovereignty in a digital world. Its volatility became its soul, a reminder that true value is built on belief. Bitcoin speaks to ingenuity and rebellion, a star of code guiding us toward a future where wealth is weightless yet profoundly honest.
Gold’s Cycles: Echoes of War and Crisis
In the early 20th century, gold was held under fixed prices until the Great Depression of 1929 shattered these illusions. The 1934 dollar devaluation lifted it from 20.67 to 35, restoring faith amid despair. When World War II erupted in 1939, gold’s role as a refuge was muted by controls, yet it quietly held its place as the world’s silent guardian.
The 1970s awakened its wild spirit. The Nixon Shock of 1971 freed gold from 35, sparking a bull run during the 1973 Oil Crisis. The 1979 Iranian Revolution led to a 1980 peak of 850, a leap of more than 2,000 percent, as investors sought safety from the chaos.
Translation moment: When fear rises, people rush to gold. Every major war or economic crisis has sent gold upward because it feels safe when paper money loses trust.
The 1987 stock crash caused brief dips, but the 1990 Gulf War reignited its glow. Around 2000, after the Dot-com Bust, gold found new life, climbing from $ 270 to over $1,900 during the 2008 Financial Crisis. It dipped to 1050 in 2015, then surged again past 2000 during the 2020 pandemic.
The 2022 Ukraine War added another chapter with prices climbing above 2700 by 2025. Across a century of crises, gold has risen whenever fear tested humanity’s resolve, teaching patience and fortitude through its quiet endurance.
Bitcoin’s Cycles: Echoes of Innovation and Crisis
Born from the ashes of the 2008 Financial Crisis, Bitcoin began its story at mere cents. It traded below $1 until 2011, when it reached $30 before crashing by 90 percent following the MTGOX collapse.
In 2013, it soared to 1242 only to fall again to 200 in 2015 as regulations tightened. The 2017 bull run lifted it to nearly 20000 before another long winter brought it to 3200 in 2018. Each fall taught resilience, each rise renewed belief.
During the 2020 pandemic, it fell below 5000 before rallying to 69000 in 2021. The Ukraine War and the FTX collapse of 2022 brought it down to 16000, but also proved its role in humanitarian aid. By 2024, the halving and ETF approvals helped it break 100000, marking Bitcoin’s rise as digital gold.
Translation moment: Bitcoin’s rhythm follows four-year halving cycles when mining rewards are cut in half. This keeps supply limited, which often triggers new bull runs as demand returns.
Every four years, it's halving cycles 2012, 2016, 2020, 2024, fueling new waves of adoption and correction. Bitcoin grows strongest in times of uncertainty, echoing humanity’s drive to evolve beyond limits.
The Harmony of Gold and Bitcoin Modern Parallels
In today’s markets, gold’s ancient glow meets Bitcoin’s electric pulse. As of October 17, 2025, their correlation stands near 0.85, close to its historic high of 0.9. Both rise as guardians against inflation and the erosion of trust in the dollar.
Gold trades near 4310 per ounce a record high while Bitcoin hovers around 104700 showing brief fractures in their unity. Gold offers the comfort of touch while Bitcoin provides the thrill of code. Together, they reflect fear and hope, the twin emotions that drive every market.
Translation moment: A correlation of 0.85 means they often move in the same direction. When fear or inflation rises, both gold and Bitcoin tend to rise in tandem.
Analysts warn of bubbles in stocks, gold, and crypto, yet optimism remains for Bitcoin’s growth through 2026, while gold holds its defensive strength.
Gold carries risks of storage cost and theft, but steadiness in chaos. Bitcoin carries volatility and regulatory challenges, but it also offers unmatched innovation and reach. One is the anchor, the other the dream, and both reward those who hold conviction through uncertainty.
Epilogue: The Timeless Balance
Gold and Bitcoin form a bridge between the ancient and the future. Gold, the earth’s eternal treasure, stands as a symbol of stability and truth. Bitcoin, the digital heir, shines with the spark of innovation and freedom.
Experts view gold as the ultimate inflation hedge, forged in fire and tested over centuries. They see Bitcoin as its digital counterpart, scarce by code and limitless in reach.
Gold’s weight grounds us in reality while Bitcoin’s light expands our imagination. In 2025, as gold surpasses $4,346 and Bitcoin hovers near $105,000, the wise investor sees not rivals but reflections.
Translation moment: Gold reminds us to protect what we have. Bitcoin reminds us to dream of what could be. Together, they balance caution and courage, the two forces every generation must master.
One whispers of legacy, the other of evolution, yet together they tell humanity’s oldest story, our unending quest to preserve value against time and to chase the light that never fades.
🙏 I ask (Allah) for guidance and success. 🤲
USCBBS-WDTGAL-RRPONTSYDThis is the U.S. Financial Market Net Liquidity.
The calculation method is to subtract the U.S. Treasury General Account balance (WDTGAL) and then the Overnight Reverse Repo balance (RRPONTSYD) from the Federal Reserve's balance sheet total (USCBBS).
Gold Market Cap vs BTC Market Cap Ratio
What the script calculates
Gold market cap: XAUUSD spot price × total above-ground stock (converted to troy ounces) in trillions USD.
BTC market cap: Live data from TradingView's CRYPTOCAP:BTC symbol, which provides Bitcoin's circulating market cap (already in USD, converted to trillions here).
Ratio: Gold market cap ÷ BTC market cap (e.g., 1.0 means gold market cap equals BTC; 2.0 means gold is twice BTC's market cap)
Timebender - Fractal CloseTimebender – Fractal Close displays which higher-timeframe candles (Daily, Weekly, Monthly) are scheduled to close within the next 24 hours — helping traders anticipate potential volatility and liquidity shifts around key session or higher-TF closes.
It automatically scans:
• Daily: 1D → 11D
• Weekly: 1W → 3W
• Monthly: 1M → 12M
The detected timeframes are shown in a compact on-chart table that can be positioned anywhere (top, middle, bottom — left, center, or right). You can also customize text color, background, and font size for visual clarity.
Use it to align intraday setups with higher-timeframe structure, or to prepare for major session transitions as multiple fractal closes converge.
Timebender - 90 Minute KillzonesTimebender – 90 Minute Killzones
This indicator divides each trading day into sixteen 90-minute blocks based on New York Time.
Each zone is color-coded by session:
🔴 Asian
🟢 London
🔵 New York AM
🟣 New York PM
It helps visualize recurring intraday rhythms and session overlaps without adding signals or bias.
Includes an optional Daily Close Line (18:00 NYT) to mark the end of the trading day, now zoom-safe and toggleable.
Built for structure, clarity, and visual balance — nothing more, nothing less.
Real Relative Strength Breakout & BreakdownReal Relative Strength Breakout & Breakdown Indicator
What It Does
Identifies high-probability trading setups by combining:
Technical Breakouts/Breakdowns - Price breaking support/resistance zones
Real Relative Strength (RRS) - Volatility-adjusted performance vs benchmark (SPY)
Key Insight: The strongest signals occur when price action contradicts market direction—breakouts during market weakness or breakdowns during market strength show exceptional buying/selling pressure.
Real Relative Strength (RRS) Calculation
RRS measures outperformance/underperformance on a volatility-adjusted basis:
Power Index = (Benchmark Price Move) / (Benchmark ATR)
RRS = (Stock Price Move - Power Index × Stock ATR) / Stock ATR
RRS (smoothed) = 3-period SMA of RRS
Interpretation:
RRS > 0 = Relative Strength (outperforming)
RRS < 0 = Relative Weakness (underperforming)
Signal Types
🟢 Large Green Triangle (Premium Long)
Condition: Breakout + RRS > 0
Meaning: Stock breaking resistance WHILE outperforming benchmark
Best when: Market is weak but stock breaks out anyway = exceptional strength
Use: High-conviction long entries
🔵 Small Blue Triangle (Standard Breakout)
Condition: Breakout + RRS ≤ 0
Meaning: Breaking resistance but underperforming benchmark
Typical: "Rising tide lifts all boats" scenario during market rally
Use: Lower conviction—may just be following market
🟠 Large Orange Triangle (Premium Short)
Condition: Breakdown + RRS < 0
Meaning: Stock breaking support WHILE underperforming benchmark
Best when: Market is strong but stock breaks down anyway = severe weakness
Use: High-conviction short entries
🔴 Small Red Triangle (Standard Breakdown)
Condition: Breakdown + RRS ≥ 0
Meaning: Breaking support but outperforming benchmark
Typical: Stock falling less than market during selloff
Use: Lower conviction—may recover when market does
Why Large Triangles Matter
Large signals show divergence = genuine institutional flow:
Stock breaking out while market falls → Aggressive buying despite headwinds
Stock breaking down while market rallies → Aggressive selling despite tailwinds
These setups reveal where real conviction lies, not just momentum-following behavior.
Quick Settings
RRS: 12-period lookback, 3-bar smoothing, vs SPY
Breakouts: 5-period pivots, 200-bar lookback, 3% zone width, 2 minimum tests
Gann Dynamic Levels [SmartFoxy]# 🌌 Gann Dynamic Levels
Gann Dynamic Levels is a dynamic Gann-based framework that calculates proportional and exponential levels using customizable methods — including planetary ratios.
Perfect for traders focused on cycles , ratios , and harmonic structures .
Inspired by the geometric and harmonic principles of W.D. Gann , this multifunctional tool automatically plots time–price projection levels based on user-defined anchor points.
It combines multiple calculation techniques to capture both linear and exponentia l market symmetries.
The indicator adapts dynamically to price movement, helping traders identify potential reversal zones , time clusters , and harmonic expansions derived from proportional and planetary relationships.
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## ⚙️ Core Features
Five Calculation Methods — Linear, ratio-based, geometric, and exponential spacing for multi-perspective analysis.
Planetary Scaling Mode — Optional mode based on astronomical distances (Titius–Bode Law), adding an astronomical dimension to level spacing.
Adaptive Offset Control — Shifts all projected levels left or right proportionally without changing their internal spacing.
Automatic Label Management — Dynamically updates or reuses labels for better clarity and improved chart performance.
Custom Styling — Full control over colors, widths, label positions, and line styles for each method.
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## 🌐 Purpose
Designed for traders who combine Gann theory , harmonic ratios , and cyclical timing to visualize equilibrium zones and future market symmetry.
Whether used for short-term timing or long-term structural projections, Gann Dynamic Levels provides an adaptive, geometry-based framework for interpreting market behavior.
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## 📘 How to Use
When first applied, the indicator prompts you to place two points on the chart — for example, at the start and end of a significant price range.
The indicator calculates the number of bars between these two points, known as Delta .
Delta serves as the base unit for all calculations in Methods #1–#5 .
The computed results are displayed in Table 1 , which can be toggled using the parameter “📱 Show Gann Levels Table”.
You can reset or reposition the initial points in two ways:
Drag the existing points to new positions on the chart.
Hover over the indicator name, click ⦁⦁⦁ (More) → select “ Reset Points ”, then set new reference points.
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## ⚙️ Method Logic
Classic – Evenly spaced levels based on the base Delta value. Ideal for identifying key support and resistance zones.
Coefficient (Coeff) – Scales Delta by fractional or whole-number coefficients for proportional level spacing.
Rounded – Rounds each calculated level to the nearest significant price value to align with major zones.
Subtractive – Generates levels by subtracting multiples of Delta from a reference point, emphasizing retracement-type structures.
Exponential – Applies an exponential growth model (10a = 4 + 3×2ⁿ) to project dynamic, non-linear level expansion.
Planetary – Uses the average distances of planets from the Sun (in Astronomical Units, AU ) as ratio multipliers to create harmonic projections.
Planetary distances can be customized in the user settings.
Data for Method #6 (Planetary) is displayed in Table 2 , toggled via “ 🪐 Show Planetary Table. ”
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## ➡️ Additional Feature
Offset – Shifts all Gann levels horizontally (left or right) without changing their spacing.
Useful for visually aligning levels with key market structures.
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### 🧭 Summary
A multi-method Gann framework combining geometric, harmonic, and planetary ratios for dynamic level projection and cycle analysis.
Session times for London (UTC 07:00–16:00 UTC)Session times for London (UTC 07:00–16:00 UTC). Shows the trading hours for the London Session Mon-Fri
Timebender - Sum of TimeTimebender – Sum of Time
A minimalist numerological clock that decodes the vibration of the moment.
It calculates and displays the digital sum of the current date and time, assigning colors based on the 1–3 (Accumulation), 4–6 (Manipulation), and 7–9 (Distribution) cycle.
Clean, efficient, and fully synchronized with your chart’s timezone.
Simplified Percentile ClusteringSimplified Percentile Clustering (SPC) is a clustering system for trend regime analysis.
Instead of relying on heavy iterative algorithms such as k-means, SPC takes a deterministic approach: it uses percentiles and running averages to form cluster centers directly from the data, producing smooth, interpretable market state segmentation that updates live with every bar.
Most clustering algorithms are designed for offline datasets, they require recomputation, multiple iterations, and fixed sample sizes.
SPC borrows from both statistical normalization and distance-based clustering theory , but simplifies them. Percentiles ensure that cluster centers are resistant to outliers , while the running mean provides a stable mid-point reference.
Unlike iterative methods, SPC’s centers evolve smoothly with time, ideal for charts that must update in real time without sudden reclassification noise.
SPC provides a simple yet powerful clustering heuristic that:
Runs continuously in a charting environment,
Remains interpretable and reproducible,
And allows traders to see how close the current market state is to transitioning between regimes.
Clustering by Percentiles
Traditional clustering methods find centers through iteration. SPC defines them deterministically using three simple statistics within a moving window:
Lower percentile (p_low) → captures the lower basin of feature values.
Upper percentile (p_high) → captures the upper basin.
Mean (mid) → represents the central tendency.
From these, SPC computes stable “centers”:
// K = 2 → two regimes (e.g., bullish / bearish)
=
// K = 3 → adds a neutral zone
=
These centers move gradually with the market, forming live regime boundaries without ever needing convergence steps.
Two clusters capture directional bias; three clusters add a neutral ‘range’ state.
Multi-Feature Fusion
While SPC can cluster a single feature such as RSI, CCI, Fisher Transform, DMI, Z-Score, or the price-to-MA ratio (MAR), its real strength lies in feature fusion. Each feature adds a unique lens to the clustering system. By toggling features on or off, traders can test how each dimension contributes to the regime structure.
In “Clusters” mode, SPC measures how far the current bar is from each cluster center across all enabled features, averages these distances, and assigns the bar to the nearest combined center. This effectively creates a multi-dimensional regime map , where each feature contributes equally to defining the overall market state.
The fusion distance is computed as:
dist := (rsi_d * on_off(use_rsi) + cci_d * on_off(use_cci) + fis_d * on_off(use_fis) + dmi_d * on_off(use_dmi) + zsc_d * on_off(use_zsc) + mar_d * on_off(use_mar)) / (on_off(use_rsi) + on_off(use_cci) + on_off(use_fis) + on_off(use_dmi) + on_off(use_zsc) + on_off(use_mar))
Because each feature can be standardized (Z-Score), the distances remain comparable across different scales.
Fusion mode combines multiple standardized features into a single smooth regime signal.
Visualizing Proximity - The Transition Gradient
Most indicators show binary or discrete conditions (e.g., bullish/bearish). SPC goes further, it quantifies how close the current value is to flipping into the next cluster.
It measures the distances to the two nearest cluster centers and interpolates between them:
rel_pos = min_dist / (min_dist + second_min_dist)
real_clust = cluster_val + (second_val - cluster_val) * rel_pos
This real_clust output forms a continuous line that moves smoothly between clusters:
Near 0.0 → firmly within the current regime
Around 0.5 → balanced between clusters (transition zone)
Near 1.0 → about to flip into the next regime
Smooth interpolation reveals when the market is close to a regime change.
How to Tune the Parameters
SPC includes intuitive parameters to adapt sensitivity and stability:
K Clusters (2–3): Defines the number of regimes. K = 2 for trend/range distinction, K = 3 for trend/neutral transitions.
Lookback: Determines the number of past bars used for percentile and mean calculations. Higher = smoother, more stable clusters. Lower = faster reaction to new trends.
Lower / Upper Percentiles: Define what counts as “low” and “high” states. Adjust to widen or tighten cluster ranges.
Shorter lookbacks react quickly to shifts; longer lookbacks smooth the clusters.
Visual Interpretation
In “Clusters” mode, SPC plots:
A colored histogram for each cluster (red, orange, green depending on K)
Horizontal guide lines separating cluster levels
Smooth proximity transitions between states
Each bar’s color also changes based on its assigned cluster, allowing quick recognition of when the market transitions between regimes.
Cluster bands visualize regime structure and transitions at a glance.
Practical Applications
Identify market regimes (bullish, neutral, bearish) in real time
Detect early transition phases before a trend flip occurs
Fuse multiple indicators into a single consistent signal
Engineer interpretable features for machine-learning research
Build adaptive filters or hybrid signals based on cluster proximity
Final Notes
Simplified Percentile Clustering (SPC) provides a balance between mathematical rigor and visual intuition. It replaces complex iterative algorithms with a clear, deterministic logic that any trader can understand, and yet retains the multidimensional insight of a fusion-based clustering system.
Use SPC to study how different indicators align, how regimes evolve, and how transitions emerge in real time. It’s not about predicting; it’s about seeing the structure of the market unfold.
Disclaimer
This indicator is intended for educational and analytical use.
It does not generate buy or sell signals.
Historical regime transitions are not indicative of future performance.
Always validate insights with independent analysis before making trading decisions.
