Hazel nut BB Strategy, volume base- lite versionHazel nut BB Strategy, volume base — lite version
Having knowledge and information in financial markets is only useful when a trader operates with a well-defined trading strategy. Trading strategies assist in capital management, profit-taking, and reducing potential losses.
This strategy is built upon the core principle of supply and demand dynamics. Alongside this foundation, one of the widely used technical tools — the Bollinger Bands — is employed to structure a framework for profit management and risk control.
In this strategy, the interaction of these tools is explained in detail. A key point to note is that for calculating buy and sell volumes, a lower timeframe function is used. When applied with a tick-level resolution, this provides the most precise measurement of buyer/seller flows. However, this comes with a limitation of reduced historical depth. Users should be aware of this trade-off: if precise tick-level data is required, shorter timeframes should be considered to extend historical coverage .
The strategy offers multiple configuration options. Nevertheless, it should be treated strictly as a supportive tool rather than a standalone trading system. Decisions must integrate personal analysis and other instruments. For example, in highly volatile assets with narrow ranges, it is recommended to adjust profit-taking and stop-loss percentages to smaller values.
◉ Volume Settings
• Buyer and seller volume (up/down volume) are requested from a lower timeframe, with an option to override the automatic resolution.
• A global lookback period is applied to calculate moving averages and cumulative sums of buy/sell/delta volumes.
• Ratios of buyers/sellers to total volume are derived both on the current bar and across the lookback window.
◉ Bollinger Band
• Bands are computed using configurable moving averages (SMA, EMA, RMA, WMA, VWMA).
• Inputs allow control of length, standard deviation multiplier, and offset.
• The basis, upper, and lower bands are plotted, with a shaded background between them.
◉ Progress & Proximity
• Relative position of the price to the Bollinger basis is expressed as percentages (qPlus/qMinus).
• “Near band” conditions are triggered when price progress toward the upper or lower band exceeds a user-defined threshold (%).
• A signed score (sScore) represents how far the close has moved above or below the basis relative to band width.
◉ Info Table
• Optional compact table summarizing:
• - Upper/lower band margins
• - Buyer/seller volumes with moving averages
• - Delta and cumulative delta
• - Buyer/seller ratios per bar and across the window
• - Money flow values (buy/sell/delta × price) for bar-level and summed periods
• The table is neutral-colored and resizable for different chart layouts.
◉ Zone Event Gate
• Tracks entry into and exit from “near band” zones.
• Arming logic: a side is armed when price enters a band proximity zone.
• Trigger logic: on exit, a trade event is generated if cumulative buyer or seller volume dominates over a configurable window.
◉ Trading Logic
• Orders are placed only on zone-exit events, conditional on volume dominance.
• Position sizing is defined as a fixed percentage of strategy equity.
• Long entries occur when leaving the lower zone with buyer dominance; short entries occur when leaving the upper zone with seller dominance.
◉ Exit Rules
• Open positions are managed by a strict priority sequence:
• 1. Stop-loss (% of entry price)
• 2. Take-profit (% of entry price)
• 3. Opposite-side event (zone exit with dominance in the other direction)
• Stop-loss and take-profit levels are configurable
◉ Notes
• This lite version is intended to demonstrate the interaction of Bollinger Bands and volume-based dominance logic.
• It provides a framework to observe how price reacts at band boundaries under varying buy/sell pressure, and how zone exits can be systematically converted into entry/exit signals.
When configuring this strategy, it is essential to carefully review the settings within the Strategy Tester. Ensure that the chosen parameters and historical data options are correctly aligned with the intended use. Accurate back testing depends on applying proper configurations for historical reference. The figure below illustrates sample result and configuration type.
In den Scripts nach "GOLD" suchen
GC Checklist Signals (All TF, v6 • SR-safe • Clean blocks)GC (COMEX Gold) checklist strategy with a 3:1 reward-to-risk to your training bot. It enforces the following rules:
Heiken Ashi chart logic for color, wicks, and doji detection
100-EMA filter (only buys above / sells below)
Market structure: higher-low above EMA for buys; lower-high below EMA for sells (simple pivot check)
Clean pullback: at least 2 opposite-color candles; clean = no top wicks (buys) / no bottom wicks (sells)
Entry: on high-volume doji (body ≤ ~12% of range and volume ≥ last 1–3 candles), as soon as it closes
Stops: sell = above doji high; buy = below doji low
GC Checklist Signals (All Timeframes, v6)GC (COMEX Gold) checklist strategy with a 3:1 reward-to-risk to your training bot. It enforces your rules:
Heiken Ashi chart logic for color, wicks, and doji detection
100-EMA filter (only buys above / sells below)
Market structure: higher-low above EMA for buys; lower-high below EMA for sells (simple pivot check)
Clean pullback: at least 2 opposite-color candles; clean = no top wicks (buys) / no bottom wicks (sells)
Entry: on high-volume doji (body ≤ ~12% of range and volume ≥ last 1–3 candles), as soon as it closes
Stops: sell = above doji high; buy = below doji low
Bias + VWAP Pullback — v4 (PA + BOS/CHOCH)Simple idea: I identify the trend (bias) from the larger timeframe, and only trade pullbacks to the VWAP/EMA during liquidity (London/New York). When the trend is clear, gold moves strongly, and its pullbacks to the balance lines provide clear opportunities.
Timeframe and Sessions (Cairo Time)
Analysis: H1 to determine the trend.
Implementation: 5m (or 1m if professional).
Trading window:
London Opening: 10:00–12:30
New York Opening: 16:30–19:00
(avoid the rest of the day unless there is exceptional traffic).
Direction determination (BIAS)
On H1:
If the price is above the 200 EMA and the daily VWAP is bullish and the price is above it → uptrend (long-only).
If the price is below the 200 EMA and the daily VWAP is bearish and the price is below it → bearish trend (short-only).
Determine your levels: yesterday's high/low (PDH/PDL) + approximate Asia range (03:00–09:30).
Entry Rules (Setup A: Trend Continuation)
Asia range breakout towards Bias during liquidity window.
Wait for a withdrawal to:
Daily VWAP, or
EMA50 on 5m frame (best if both cross).
Confirmation: Confirmation low/high on 5m (HL buy/LH sell) + clear impulse candle (Body is greater than average of last 10 candles).
Entry:
Buy: When the price returns above VWAP/EMA50 with a confirmation candle close.
Sell: The exact opposite.
Stop Loss (SL): Below/above the last confirmation low/high or ATR(14, 5m) x 1.5 (largest).
Objectives:
TP1 = 1R (Close 50% and move the rest Break-even).
TP2 = 2.5R to 3R or at an important HTF level (PDH/PDL/Bid/Demand Zone).
Entry Rules (Setup B: Reversion to VWAP – “Mean Reversion”)
Use with extreme caution, once daily maximum:
Price deviation from VWAP by more than ~1.5 x ATR(14, 5m) with rejection candles appearing near PDH/PDL.
Reverse entry towards the return of VWAP.
SL small behind rejection top/bottom.
Main target: VWAP. (Don't get greedy — this scenario is for extended periods only.)
News Filtering and Risk Management
Avoid trading 15–30 minutes before/after strong US news (CPI, NFP, FOMC).
Maximum daily loss: 1.5–2% of account balance.
Risk per trade: 0.25–0.5% (if you are learning) or 0.5–1% (if you are experienced).
Do not exceed two consecutive losing trades per day.
Don't chase the market after the opportunity has passed — wait for the next pullback.
Smart Deal Management
After TP1: Move stop to entry point + trail the rest with EMA20 on 5m or ATR Trailing = ATR(14)×1.0.
If the price touches a strong daily level (PDH/PDL) and fails to break, consider taking additional profit.
If VWAP starts to flatten and breaks against the trend on H1, stop trading for the day.
Quick Checklist (Before Entry)
H1 trend is clear and consistent with 200EMA + VWAP.
Penetrating the Asia range towards Bias.
Clean pull to VWAP/EMA50 on 5m.
Confirmation candle and real push.
SL is logical (behind swing/ATR×1.5) and R :R ≥ 1:2.
No red news coming soon.
Example of "ready-made" settings
EMA: 20, 50, 200 on 5m, 200 only on H1.
VWAP: Daily (reset daily).
ATR: 14 on 5m.
Levels: PDH/PDL + Asia Band (03:00–09:30 Cairo).
Gold Notes
Gold is fast and sharp at the open; don't get in early — wait for the draw.
Fakeouts are common before news: it is best to call with the trend after the price returns above/below VWAP.
Don't expect 80% consistent wins every day — the advantage comes from discipline, filtering out bad days, and only withdrawing when you're on the right track.
تعتبر شركة الماسة الألمانية أحد المؤسسات العاملة بالمملكة العربية السعودية ولها تاريخ طويل من الخدمات الكثيرة والمتنوعة التى مازالت تقدمها للكثير من العملاء داخل جميع مدن وأحياء المملكة حيث نقدم أفضل ما لدينا من خلال مجموعة الشركات التالية والتي من خلالها ستتلقي كل ما تحتاج إلية في كل المجال المختلفة فنحن نعمل منذ عام 2015 ولنا سابقات اعمال فى مختلف المجالات الحيوية التى نخدم من خلالها عملائنا ونوفر لهم أرخص الأسعار وبأعلى جودة من الممكن توفرها فى المجالات التالية :-
خدمات تنظيف المنازل والفلل والشقق
خدمات عزل الخزانات تنظيف غسيل صيانة اصلاح
خدمات جلي البلاط والرخام والسيراميك
خدمات نقل العفش عمالة فلبينية مدربة
خدمات مكافحة الحشرات بجدة
كل هذة الخدمات وأكثر نوفرها لكل المتعاقدين بأفضل الطرق مع توفير خطط وبرامج متنوعة لأتمام العمل المسنود إلينا بأفضل وأحدث الطرق الحديثة والعصرية سواء فى شركات النظافة بجدة ومكة المكرمة أو شركات نقل العفش بجدة عمالة فلبينية وباقى الخدمات مثل جلي وتلميع الرخام بمكة وجدة ولا ننسي شركة مكافحة حشرات بجدة التى ساعدت آلاف المواطنين على تنظيف منازلهم من الحشرات بأفضل مبيدات حشرية.
Futures Forward Price [NeoButane]In futures markets, the theoretical value of a futures contract can be derived from its underlying price and cost of carry. By baking in the costs and potential yields, the theoretical forward price then be used in basis against futures prices in place of the underlying spot price.
Usage
The script creates plots on the main chart and a separate window pane. Both are meant to be used to visualize dislocations in the market.
By using a futures vs. forward basis instead of futures vs. spot basis, discounts in the market are clearer.
Last month, the gold futures market GCZ2025 traded >1% above forward price when tariffs were announced and fell back in line once the tariffs were verbally retracted.
View roll spreads over a back-adjusted continuous chart. I guess. I don't think spread traders only look at one chart. This is as educational for me as it is you.
Configuration
The underlying reference needs to be changed to match the futures contract you are using.
The Risk-Free Rate defaults to FRED:SOFR. I found the contract month matched 3-Month SOFR Futures to be the closest for forward price.
Risk-Free Rate: The interest rate source for forward price.
Constant Risk-Free Rate: a static interest rate that can be used in advance of future changes in risk-free rate.
Underlying Reference: spot or index price. Some examples include TVC:SPX, TVC:GOLD, CRYPTO:BTCUSD, TVC:USOIL.
Forward Price Compounding: determines which formula to use. They're similar and become closer as the contract matures.
Alternative Contract: enable and select a futures contract to use it on a chart different than the main.
Storage Cost and Yield: for use with commodities. I haven't found a proper use for them yet but enabling is simple if you are able to.
The following are meant to be used with the continuous formula as they are compounded. However the rate sources don't differ much for the purpose of futures prices.
3-Month CME SOFR Futures
3-Month ICEEUR SONIA Futures
3-Month Osaka TONA Futures
The other rate sources are either meant for futures contracts shorter than quarterly such as monthly crypto futures or were meant to help myself understand how different rates would align with futures prices, like inflation.
What this script does
It uses the cost of carry formula to output the forward price (red line). The underlying reference (green line) is plotted alongside and a futures-derived reference (blue line) can be displayed to see how it looks next to the real reference price.
The data pane displays either the nominal difference or percentage difference between the real futures price and the calculated forward price.
Further reading
www.investopedia.com
www.cmegroup.com
www.oxfordenergy.org
www-2.rotman.utoronto.ca
www.cmegroup.com
3-month rate futures
www.cmegroup.com
www.ice.com
www.bankofengland.co.uk
www.jpx.co.jp
Dynamic Swing Anchored VWAP STRAT (Zeiierman/PineIndicators)Dynamic Swing Anchored VWAP STRATEGY — Zeiierman × PineIndicators (Pine Script v6)
A pivot-to-pivot Anchored VWAP strategy that adapts to volatility, enters long on bullish structure, and closes on bearish structure. Built for TradingView in Pine Script v6.
Full credits to zeiierman.
Repainting notice: The original indicator logic is repainting. Swing labels (HH/HL/LH/LL) are finalized after enough bars have printed, so labels do not occur in real time. It is not possible to execute at historical label points. Treat results as educational and validate with Bar Replay and paper trading before considering any discretionary use.
Concept
The script identifies swing highs/lows over a user-defined lookback ( Swing Period ). When structure flips (most recent swing low is newer than the most recent swing high, or vice versa), a new regime begins.
At each confirmed pivot, a fresh Anchored VWAP segment is started and updated bar-by-bar using an EWMA-style decay on price×volume and volume.
Responsiveness is controlled by Adaptive Price Tracking (APT) . Optionally, APT auto-adjusts with an ATR ratio so that high volatility accelerates responsiveness and low volatility smooths it.
Longs are opened/held in bullish regimes and closed when the regime turns bearish. No short positions are taken by design.
How it works (under the hood)
Swing detection: Uses ta.highestbars / ta.lowestbars over prd to update swing highs (ph) and lows (pl), plus their bar indices (phL, plL).
Regime logic: If phL > plL → bullish regime; else → bearish regime. A change in this condition triggers a re-anchor of the VWAP at the newest pivot.
Adaptive VWAP math: APT is converted to an exponential decay factor ( alphaFromAPT ), then applied to running sums of price×volume and volume, producing the current VWAP estimate.
Rendering: Each pivot-anchored VWAP segment is drawn as a polyline and color-coded by regime. Optional structure labels (HH/HL/LH/LL) annotate the swing character.
Orders: On bullish flips, strategy.entry("L") opens/maintains a long; on bearish flips, strategy.close("L") exits.
Inputs & controls
Swing Period (prd) — Higher values identify larger, slower swings; lower values catch more frequent pivots but add noise.
Adaptive Price Tracking (APT) — Governs the VWAP’s “half-life.” Smaller APT → faster/closer to price; larger APT → smoother/stabler.
Adapt APT by ATR ratio — When enabled, APT scales with volatility so the VWAP speeds up in turbulent markets and slows down in quiet markets.
Volatility Bias — Tunes the strength of APT’s response to volatility (above 1 = stronger effect; below 1 = milder).
Style settings — Colors for swing labels and VWAP segments, plus line width for visibility.
Trade logic summary
Entry: Long when the swing structure turns bullish (latest swing low is more recent than the last swing high).
Exit: Close the long when structure turns bearish.
Position size: qty = strategy.equity / close × 5 (dynamic sizing; scales with account equity and instrument price). Consider reducing the multiplier for a more conservative profile.
Recommended workflow
Apply to instruments with reliable volume (equities, futures, crypto; FX tick volume can work but varies by broker).
Start on your preferred timeframe. Intraday often benefits from smaller APT (more reactive); higher timeframes may prefer larger APT (smoother).
Begin with defaults ( prd=50, APT=20 ); then toggle “Adapt by ATR” and vary Volatility Bias to observe how segments tighten/loosen.
Use Bar Replay to watch how pivots confirm and how the strategy re-anchors VWAP at those confirmations.
Layer your own risk rules (stops/targets, max position cap, session filters) before any discretionary use.
Practical tips
Context filter: Consider combining with a higher-timeframe bias (e.g., daily trend) and using this strategy as an entry timing layer.
First pivot preference: Some traders prefer only the first bullish pivot after a bearish regime (and vice versa) to reduce whipsaw in choppy ranges.
Deviations: You can add VWAP deviation bands to pre-plan partial exits or re-entries on mean-reversion pulls.
Sessions: Session-based filters (RTH vs. ETH) can materially change behavior on futures and equities.
Extending the script (ideas)
Add stops/targets (e.g., ATR stop below last swing low; partial profits at k×VWAP deviation).
Introduce mirrored short logic for two-sided testing.
Include alert conditions for regime flips or for price-VWAP interactions.
Incorporate HTF confirmation (e.g., only long when daily VWAP slope ≥ 0).
Throttle entries (e.g., once per regime flip) to avoid over-trading in ranges.
Known limitations
Repainting: Swing labels and pivot confirmations depend on future bars; historical labels can look “perfect.” Treat them as annotations, not executable signals.
Execution realism: Strategy includes commission and slippage fields, yet actual fills differ by venue/liquidity.
No guarantees: Past behavior does not imply future results. This publication is for research/education only and not financial advice.
Defaults (backtest environment)
Initial capital: 10,000
Commission value: 0.01
Slippage: 1
Overlay: true
Max bars back: 5000; Max labels/polylines set for deep swing histories
Quick checklist
Add to chart and verify that the instrument has volume.
Use defaults, then tune APT and Volatility Bias with/without ATR adaptation.
Observe how each pivot re-anchors VWAP and how regime flips drive entries/exits.
Paper trade across several symbols/timeframes before any discretionary decisions.
Attribution & license
Original indicator concept and logic: Zeiierman — please credit the author.
Strategy wrapper and publication: PineIndicators .
License: CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike). Respect the license when forking or publishing derivatives.
Confluence Engine Confluence Engine is a practical, non-repainting decision aid that scores market conditions from −100…+100 by combining six proven modules: Trend, Momentum, Volatility, Volume, Structure, and an HTF confirmation. It’s designed for crypto, forex, indices, and stocks, and it fires entries only on confirmed bar closes.
What’s inside
Trend: EMA 20/50/200 alignment plus a Supertrend/KAMA toggle (you choose the baseline).
Momentum: RSI + MACD with confirmed-pivot divergence detection.
Volatility: ATR% and Bollinger Band width vs its average to favor expansion over chop.
Volume: OBV-style cumulative flow slope + volume surge vs SMA×multiplier.
Market Structure: Confirmed pivots, BOS (break of structure) and CHOCH (change of character).
HTF Filter: Closed higher-timeframe context via request.security(..., barmerge.gaps_on, barmerge.lookahead_off).
Why it does not repaint
Signals are computed and plotted on closed bars only.
Pivots/divergences use confirmed pivot points (no forward look).
HTF series are fetched with lookahead_off and use the last closed HTF bar in realtime.
No future bar references are used for entries or alerts.
How to use (3 steps)
Pick a timeframe pair: use a 4–6× HTF multiplier (5m→30m, 15m→1h, 1h→4h, 4h→1D, 1D→1W).
Trade with the HTF: take longs only when the HTF filter is bullish; shorts only when bearish.
Prefer expansion: act when BB width > its average and ATR% is elevated; skip most signals in compression.
Suggested presets (start here)
Crypto (BTC/ETH): 15m→1h, 1h→4h. stLen=10, stMult=3.0, bbLen=20, surgeMul=1.8–2.2, thresholds +40 / −40 (intraday can try +35 / −35).
Forex majors: 15m→1h, 1h→4h. stLen=10–14, stMult=2.5–3.0, surgeMul=1.5–1.8, thresholds +35 / −35 (swing: +45 / −45).
US equities (liquid): 5m→30m/1h, 15m→1h/2h. stMult=3.0–3.5, surgeMul=1.6–2.0, thresholds +45 / −45 to reduce chop.
Indices (ES/NQ): 5m→30m, 15m→1h. Defaults are fine; start at +40 / −40.
Gold/Oil: 15m→1h, 1h→4h. Thresholds +35 / −35, surgeMul=1.6–1.9.
Inputs (plain English)
Use Supertrend (off = KAMA): choose the trend baseline.
EMA Fast/Mid/Slow: 20/50/200 by default for classic stack.
RSI/MACD + divergence pivots: momentum and exhaustion context.
ATR Length & BB Length: volatility regime detection.
Volume SMA & Surge Multiplier: defines “meaningful” volume spikes.
Pivot left/right & “Confirm BOS/CHOCH on Close”: structure strictness.
Enable HTF & Higher Timeframe: confirms the lower timeframe direction.
Thresholds (+long / −short): when the score crosses these, you get signals.
Signals & alerts (IDs preserved)
Entry shapes plot at bar close when the score crosses thresholds.
Alerts you can enable:
CONFLUENCE LONG — long entry signal
CONFLUENCE SHORT — short entry signal
BULLISH BIAS — score turned positive
BEARISH BIAS — score turned negative
Best practices
Focus on signals with HTF agreement and volatility expansion; require volume participation (surge or rising OBV slope) for higher quality.
Raise thresholds (+45/−45 or +50/−50) to reduce whipsaws in choppy sessions.
Lower thresholds (+35/−35) only if you also require volatility/volume filters.
Performance & scope
Works across crypto/FX/equities/indices; no broker data or special feeds required.
No repainting by design; signals/alerts are computed on closed bars.
As with any tool, results vary by regime; always combine with risk management.
Disclosure
This script is for educational purposes only and is not financial advice. Trading involves risk. Test on historical data and paper trade before using live.
Multi-AVWAP - Anchored - Gold -V1This script uses multi-day anchored VWAP.
What it does
This study plots multiple Anchored VWAP (AVWAP) lines from recent session starts (1, 2, 3, 4, 5, 10, 15, 20, 30, 90).
from the anchor forward. Each line shows a live label with the line’s current value and the current price for quick distance checks.
Best practices
Use on intraday timeframes for session-anchored lines.
Ensure the chart has enough history loaded for the longest lookback (e.g., 90 days).
For crypto or 24×7 markets, set session to a 24h window (e.g., 0000-2359) and turn off the exclude-ETH toggle if you want full-time anchoring.
Limitations
Different exchanges/markets use different RTH windows—pick the one that matches your venue.
Corporate actions/volume adjustments can make small discrepancies across platforms.
If no RTH exists on the exact calendar day (holidays), the 90d line anchors to the most recent available RTH open before that date.
ATAI Volume analysis with price action V 1.00ATAI Volume Analysis with Price Action
1. Introduction
1.1 Overview
ATAI Volume Analysis with Price Action is a composite indicator designed for TradingView. It combines per‑side volume data —that is, how much buying and selling occurs during each bar—with standard price‑structure elements such as swings, trend lines and support/resistance. By blending these elements the script aims to help a trader understand which side is in control, whether a breakout is genuine, when markets are potentially exhausted and where liquidity providers might be active.
The indicator is built around TradingView’s up/down volume feed accessed via the TradingView/ta/10 library. The following excerpt from the script illustrates how this feed is configured:
import TradingView/ta/10 as tvta
// Determine lower timeframe string based on user choice and chart resolution
string lower_tf_breakout = use_custom_tf_input ? custom_tf_input :
timeframe.isseconds ? "1S" :
timeframe.isintraday ? "1" :
timeframe.isdaily ? "5" : "60"
// Request up/down volume (both positive)
= tvta.requestUpAndDownVolume(lower_tf_breakout)
Lower‑timeframe selection. If you do not specify a custom lower timeframe, the script chooses a default based on your chart resolution: 1 second for second charts, 1 minute for intraday charts, 5 minutes for daily charts and 60 minutes for anything longer. Smaller intervals provide a more precise view of buyer and seller flow but cover fewer bars. Larger intervals cover more history at the cost of granularity.
Tick vs. time bars. Many trading platforms offer a tick / intrabar calculation mode that updates an indicator on every trade rather than only on bar close. Turning on one‑tick calculation will give the most accurate split between buy and sell volume on the current bar, but it typically reduces the amount of historical data available. For the highest fidelity in live trading you can enable this mode; for studying longer histories you might prefer to disable it. When volume data is completely unavailable (some instruments and crypto pairs), all modules that rely on it will remain silent and only the price‑structure backbone will operate.
Figure caption, Each panel shows the indicator’s info table for a different volume sampling interval. In the left chart, the parentheses “(5)” beside the buy‑volume figure denote that the script is aggregating volume over five‑minute bars; the center chart uses “(1)” for one‑minute bars; and the right chart uses “(1T)” for a one‑tick interval. These notations tell you which lower timeframe is driving the volume calculations. Shorter intervals such as 1 minute or 1 tick provide finer detail on buyer and seller flow, but they cover fewer bars; longer intervals like five‑minute bars smooth the data and give more history.
Figure caption, The values in parentheses inside the info table come directly from the Breakout — Settings. The first row shows the custom lower-timeframe used for volume calculations (e.g., “(1)”, “(5)”, or “(1T)”)
2. Price‑Structure Backbone
Even without volume, the indicator draws structural features that underpin all other modules. These features are always on and serve as the reference levels for subsequent calculations.
2.1 What it draws
• Pivots: Swing highs and lows are detected using the pivot_left_input and pivot_right_input settings. A pivot high is identified when the high recorded pivot_right_input bars ago exceeds the highs of the preceding pivot_left_input bars and is also higher than (or equal to) the highs of the subsequent pivot_right_input bars; pivot lows follow the inverse logic. The indicator retains only a fixed number of such pivot points per side, as defined by point_count_input, discarding the oldest ones when the limit is exceeded.
• Trend lines: For each side, the indicator connects the earliest stored pivot and the most recent pivot (oldest high to newest high, and oldest low to newest low). When a new pivot is added or an old one drops out of the lookback window, the line’s endpoints—and therefore its slope—are recalculated accordingly.
• Horizontal support/resistance: The highest high and lowest low within the lookback window defined by length_input are plotted as horizontal dashed lines. These serve as short‑term support and resistance levels.
• Ranked labels: If showPivotLabels is enabled the indicator prints labels such as “HH1”, “HH2”, “LL1” and “LL2” near each pivot. The ranking is determined by comparing the price of each stored pivot: HH1 is the highest high, HH2 is the second highest, and so on; LL1 is the lowest low, LL2 is the second lowest. In the case of equal prices the newer pivot gets the better rank. Labels are offset from price using ½ × ATR × label_atr_multiplier, with the ATR length defined by label_atr_len_input. A dotted connector links each label to the candle’s wick.
2.2 Key settings
• length_input: Window length for finding the highest and lowest values and for determining trend line endpoints. A larger value considers more history and will generate longer trend lines and S/R levels.
• pivot_left_input, pivot_right_input: Strictness of swing confirmation. Higher values require more bars on either side to form a pivot; lower values create more pivots but may include minor swings.
• point_count_input: How many pivots are kept in memory on each side. When new pivots exceed this number the oldest ones are discarded.
• label_atr_len_input and label_atr_multiplier: Determine how far pivot labels are offset from the bar using ATR. Increasing the multiplier moves labels further away from price.
• Styling inputs for trend lines, horizontal lines and labels (color, width and line style).
Figure caption, The chart illustrates how the indicator’s price‑structure backbone operates. In this daily example, the script scans for bars where the high (or low) pivot_right_input bars back is higher (or lower) than the preceding pivot_left_input bars and higher or lower than the subsequent pivot_right_input bars; only those bars are marked as pivots.
These pivot points are stored and ranked: the highest high is labelled “HH1”, the second‑highest “HH2”, and so on, while lows are marked “LL1”, “LL2”, etc. Each label is offset from the price by half of an ATR‑based distance to keep the chart clear, and a dotted connector links the label to the actual candle.
The red diagonal line connects the earliest and latest stored high pivots, and the green line does the same for low pivots; when a new pivot is added or an old one drops out of the lookback window, the end‑points and slopes adjust accordingly. Dashed horizontal lines mark the highest high and lowest low within the current lookback window, providing visual support and resistance levels. Together, these elements form the structural backbone that other modules reference, even when volume data is unavailable.
3. Breakout Module
3.1 Concept
This module confirms that a price break beyond a recent high or low is supported by a genuine shift in buying or selling pressure. It requires price to clear the highest high (“HH1”) or lowest low (“LL1”) and, simultaneously, that the winning side shows a significant volume spike, dominance and ranking. Only when all volume and price conditions pass is a breakout labelled.
3.2 Inputs
• lookback_break_input : This controls the number of bars used to compute moving averages and percentiles for volume. A larger value smooths the averages and percentiles but makes the indicator respond more slowly.
• vol_mult_input : The “spike” multiplier; the current buy or sell volume must be at least this multiple of its moving average over the lookback window to qualify as a breakout.
• rank_threshold_input (0–100) : Defines a volume percentile cutoff: the current buyer/seller volume must be in the top (100−threshold)%(100−threshold)% of all volumes within the lookback window. For example, if set to 80, the current volume must be in the top 20 % of the lookback distribution.
• ratio_threshold_input (0–1) : Specifies the minimum share of total volume that the buyer (for a bullish breakout) or seller (for bearish) must hold on the current bar; the code also requires that the cumulative buyer volume over the lookback window exceeds the seller volume (and vice versa for bearish cases).
• use_custom_tf_input / custom_tf_input : When enabled, these inputs override the automatic choice of lower timeframe for up/down volume; otherwise the script selects a sensible default based on the chart’s timeframe.
• Label appearance settings : Separate options control the ATR-based offset length, offset multiplier, label size and colors for bullish and bearish breakout labels, as well as the connector style and width.
3.3 Detection logic
1. Data preparation : Retrieve per‑side volume from the lower timeframe and take absolute values. Build rolling arrays of the last lookback_break_input values to compute simple moving averages (SMAs), cumulative sums and percentile ranks for buy and sell volume.
2. Volume spike: A spike is flagged when the current buy (or, in the bearish case, sell) volume is at least vol_mult_input times its SMA over the lookback window.
3. Dominance test: The buyer’s (or seller’s) share of total volume on the current bar must meet or exceed ratio_threshold_input. In addition, the cumulative sum of buyer volume over the window must exceed the cumulative sum of seller volume for a bullish breakout (and vice versa for bearish). A separate requirement checks the sign of delta: for bullish breakouts delta_breakout must be non‑negative; for bearish breakouts it must be non‑positive.
4. Percentile rank: The current volume must fall within the top (100 – rank_threshold_input) percent of the lookback distribution—ensuring that the spike is unusually large relative to recent history.
5. Price test: For a bullish signal, the closing price must close above the highest pivot (HH1); for a bearish signal, the close must be below the lowest pivot (LL1).
6. Labeling: When all conditions above are satisfied, the indicator prints “Breakout ↑” above the bar (bullish) or “Breakout ↓” below the bar (bearish). Labels are offset using half of an ATR‑based distance and linked to the candle with a dotted connector.
Figure caption, (Breakout ↑ example) , On this daily chart, price pushes above the red trendline and the highest prior pivot (HH1). The indicator recognizes this as a valid breakout because the buyer‑side volume on the lower timeframe spikes above its recent moving average and buyers dominate the volume statistics over the lookback period; when combined with a close above HH1, this satisfies the breakout conditions. The “Breakout ↑” label appears above the candle, and the info table highlights that up‑volume is elevated relative to its 11‑bar average, buyer share exceeds the dominance threshold and money‑flow metrics support the move.
Figure caption, In this daily example, price breaks below the lowest pivot (LL1) and the lower green trendline. The indicator identifies this as a bearish breakout because sell‑side volume is sharply elevated—about twice its 11‑bar average—and sellers dominate both the bar and the lookback window. With the close falling below LL1, the script triggers a Breakout ↓ label and marks the corresponding row in the info table, which shows strong down volume, negative delta and a seller share comfortably above the dominance threshold.
4. Market Phase Module (Volume Only)
4.1 Concept
Not all markets trend; many cycle between periods of accumulation (buying pressure building up), distribution (selling pressure dominating) and neutral behavior. This module classifies the current bar into one of these phases without using ATR , relying solely on buyer and seller volume statistics. It looks at net flows, ratio changes and an OBV‑like cumulative line with dual‑reference (1‑ and 2‑bar) trends. The result is displayed both as on‑chart labels and in a dedicated row of the info table.
4.2 Inputs
• phase_period_len: Number of bars over which to compute sums and ratios for phase detection.
• phase_ratio_thresh : Minimum buyer share (for accumulation) or minimum seller share (for distribution, derived as 1 − phase_ratio_thresh) of the total volume.
• strict_mode: When enabled, both the 1‑bar and 2‑bar changes in each statistic must agree on the direction (strict confirmation); when disabled, only one of the two references needs to agree (looser confirmation).
• Color customisation for info table cells and label styling for accumulation and distribution phases, including ATR length, multiplier, label size, colors and connector styles.
• show_phase_module: Toggles the entire phase detection subsystem.
• show_phase_labels: Controls whether on‑chart labels are drawn when accumulation or distribution is detected.
4.3 Detection logic
The module computes three families of statistics over the volume window defined by phase_period_len:
1. Net sum (buyers minus sellers): net_sum_phase = Σ(buy) − Σ(sell). A positive value indicates a predominance of buyers. The code also computes the differences between the current value and the values 1 and 2 bars ago (d_net_1, d_net_2) to derive up/down trends.
2. Buyer ratio: The instantaneous ratio TF_buy_breakout / TF_tot_breakout and the window ratio Σ(buy) / Σ(total). The current ratio must exceed phase_ratio_thresh for accumulation or fall below 1 − phase_ratio_thresh for distribution. The first and second differences of the window ratio (d_ratio_1, d_ratio_2) determine trend direction.
3. OBV‑like cumulative net flow: An on‑balance volume analogue obv_net_phase increments by TF_buy_breakout − TF_sell_breakout each bar. Its differences over the last 1 and 2 bars (d_obv_1, d_obv_2) provide trend clues.
The algorithm then combines these signals:
• For strict mode , accumulation requires: (a) current ratio ≥ threshold, (b) cumulative ratio ≥ threshold, (c) both ratio differences ≥ 0, (d) net sum differences ≥ 0, and (e) OBV differences ≥ 0. Distribution is the mirror case.
• For loose mode , it relaxes the directional tests: either the 1‑ or the 2‑bar difference needs to agree in each category.
If all conditions for accumulation are satisfied, the phase is labelled “Accumulation” ; if all conditions for distribution are satisfied, it’s labelled “Distribution” ; otherwise the phase is “Neutral” .
4.4 Outputs
• Info table row : Row 8 displays “Market Phase (Vol)” on the left and the detected phase (Accumulation, Distribution or Neutral) on the right. The text colour of both cells matches a user‑selectable palette (typically green for accumulation, red for distribution and grey for neutral).
• On‑chart labels : When show_phase_labels is enabled and a phase persists for at least one bar, the module prints a label above the bar ( “Accum” ) or below the bar ( “Dist” ) with a dashed or dotted connector. The label is offset using ATR based on phase_label_atr_len_input and phase_label_multiplier and is styled according to user preferences.
Figure caption, The chart displays a red “Dist” label above a particular bar, indicating that the accumulation/distribution module identified a distribution phase at that point. The detection is based on seller dominance: during that bar, the net buyer-minus-seller flow and the OBV‑style cumulative flow were trending down, and the buyer ratio had dropped below the preset threshold. These conditions satisfy the distribution criteria in strict mode. The label is placed above the bar using an ATR‑based offset and a dashed connector. By the time of the current bar in the screenshot, the phase indicator shows “Neutral” in the info table—signaling that neither accumulation nor distribution conditions are currently met—yet the historical “Dist” label remains to mark where the prior distribution phase began.
Figure caption, In this example the market phase module has signaled an Accumulation phase. Three bars before the current candle, the algorithm detected a shift toward buyers: up‑volume exceeded its moving average, down‑volume was below average, and the buyer share of total volume climbed above the threshold while the on‑balance net flow and cumulative ratios were trending upwards. The blue “Accum” label anchored below that bar marks the start of the phase; it remains on the chart because successive bars continue to satisfy the accumulation conditions. The info table confirms this: the “Market Phase (Vol)” row still reads Accumulation, and the ratio and sum rows show buyers dominating both on the current bar and across the lookback window.
5. OB/OS Spike Module
5.1 What overbought/oversold means here
In many markets, a rapid extension up or down is often followed by a period of consolidation or reversal. The indicator interprets overbought (OB) conditions as abnormally strong selling risk at or after a price rally and oversold (OS) conditions as unusually strong buying risk after a decline. Importantly, these are not direct trade signals; rather they flag areas where caution or contrarian setups may be appropriate.
5.2 Inputs
• minHits_obos (1–7): Minimum number of oscillators that must agree on an overbought or oversold condition for a label to print.
• syncWin_obos: Length of a small sliding window over which oscillator votes are smoothed by taking the maximum count observed. This helps filter out choppy signals.
• Volume spike criteria: kVolRatio_obos (ratio of current volume to its SMA) and zVolThr_obos (Z‑score threshold) across volLen_obos. Either threshold can trigger a spike.
• Oscillator toggles and periods: Each of RSI, Stochastic (K and D), Williams %R, CCI, MFI, DeMarker and Stochastic RSI can be independently enabled; their periods are adjustable.
• Label appearance: ATR‑based offset, size, colors for OB and OS labels, plus connector style and width.
5.3 Detection logic
1. Directional volume spikes: Volume spikes are computed separately for buyer and seller volumes. A sell volume spike (sellVolSpike) flags a potential OverBought bar, while a buy volume spike (buyVolSpike) flags a potential OverSold bar. A spike occurs when the respective volume exceeds kVolRatio_obos times its simple moving average over the window or when its Z‑score exceeds zVolThr_obos.
2. Oscillator votes: For each enabled oscillator, calculate its overbought and oversold state using standard thresholds (e.g., RSI ≥ 70 for OB and ≤ 30 for OS; Stochastic %K/%D ≥ 80 for OB and ≤ 20 for OS; etc.). Count how many oscillators vote for OB and how many vote for OS.
3. Minimum hits: Apply the smoothing window syncWin_obos to the vote counts using a maximum‑of‑last‑N approach. A candidate bar is only considered if the smoothed OB hit count ≥ minHits_obos (for OverBought) or the smoothed OS hit count ≥ minHits_obos (for OverSold).
4. Tie‑breaking: If both OverBought and OverSold spike conditions are present on the same bar, compare the smoothed hit counts: the side with the higher count is selected; ties default to OverBought.
5. Label printing: When conditions are met, the bar is labelled as “OverBought X/7” above the candle or “OverSold X/7” below it. “X” is the number of oscillators confirming, and the bracket lists the abbreviations of contributing oscillators. Labels are offset from price using half of an ATR‑scaled distance and can optionally include a dotted or dashed connector line.
Figure caption, In this chart the overbought/oversold module has flagged an OverSold signal. A sell‑off from the prior highs brought price down to the lower trend‑line, where the bar marked “OverSold 3/7 DeM” appears. This label indicates that on that bar the module detected a buy‑side volume spike and that at least three of the seven enabled oscillators—in this case including the DeMarker—were in oversold territory. The label is printed below the candle with a dotted connector, signaling that the market may be temporarily exhausted on the downside. After this oversold print, price begins to rebound towards the upper red trend‑line and higher pivot levels.
Figure caption, This example shows the overbought/oversold module in action. In the left‑hand panel you can see the OB/OS settings where each oscillator (RSI, Stochastic, Williams %R, CCI, MFI, DeMarker and Stochastic RSI) can be enabled or disabled, and the ATR length and label offset multiplier adjusted. On the chart itself, price has pushed up to the descending red trendline and triggered an “OverBought 3/7” label. That means the sell‑side volume spiked relative to its average and three out of the seven enabled oscillators were in overbought territory. The label is offset above the candle by half of an ATR and connected with a dashed line, signaling that upside momentum may be overextended and a pause or pullback could follow.
6. Buyer/Seller Trap Module
6.1 Concept
A bull trap occurs when price appears to break above resistance, attracting buyers, but fails to sustain the move and quickly reverses, leaving a long upper wick and trapping late entrants. A bear trap is the opposite: price breaks below support, lures in sellers, then snaps back, leaving a long lower wick and trapping shorts. This module detects such traps by looking for price structure sweeps, order‑flow mismatches and dominance reversals. It uses a scoring system to differentiate risk from confirmed traps.
6.2 Inputs
• trap_lookback_len: Window length used to rank extremes and detect sweeps.
• trap_wick_threshold: Minimum proportion of a bar’s range that must be wick (upper for bull traps, lower for bear traps) to qualify as a sweep.
• trap_score_risk: Minimum aggregated score required to flag a trap risk. (The code defines a trap_score_confirm input, but confirmation is actually based on price reversal rather than a separate score threshold.)
• trap_confirm_bars: Maximum number of bars allowed for price to reverse and confirm the trap. If price does not reverse in this window, the risk label will expire or remain unconfirmed.
• Label settings: ATR length and multiplier for offsetting, size, colours for risk and confirmed labels, and connector style and width. Separate settings exist for bull and bear traps.
• Toggle inputs: show_trap_module and show_trap_labels enable the module and control whether labels are drawn on the chart.
6.3 Scoring logic
The module assigns points to several conditions and sums them to determine whether a trap risk is present. For bull traps, the score is built from the following (bear traps mirror the logic with highs and lows swapped):
1. Sweep (2 points): Price trades above the high pivot (HH1) but fails to close above it and leaves a long upper wick at least trap_wick_threshold × range. For bear traps, price dips below the low pivot (LL1), fails to close below and leaves a long lower wick.
2. Close break (1 point): Price closes beyond HH1 or LL1 without leaving a long wick.
3. Candle/delta mismatch (2 points): The candle closes bullish yet the order flow delta is negative or the seller ratio exceeds 50%, indicating hidden supply. Conversely, a bearish close with positive delta or buyer dominance suggests hidden demand.
4. Dominance inversion (2 points): The current bar’s buyer volume has the highest rank in the lookback window while cumulative sums favor sellers, or vice versa.
5. Low‑volume break (1 point): Price crosses the pivot but total volume is below its moving average.
The total score for each side is compared to trap_score_risk. If the score is high enough, a “Bull Trap Risk” or “Bear Trap Risk” label is drawn, offset from the candle by half of an ATR‑scaled distance using a dashed outline. If, within trap_confirm_bars, price reverses beyond the opposite level—drops back below the high pivot for bull traps or rises above the low pivot for bear traps—the label is upgraded to a solid “Bull Trap” or “Bear Trap” . In this version of the code, there is no separate score threshold for confirmation: the variable trap_score_confirm is unused; confirmation depends solely on a successful price reversal within the specified number of bars.
Figure caption, In this example the trap module has flagged a Bear Trap Risk. Price initially breaks below the most recent low pivot (LL1), but the bar closes back above that level and leaves a long lower wick, suggesting a failed push lower. Combined with a mismatch between the candle direction and the order flow (buyers regain control) and a reversal in volume dominance, the aggregate score exceeds the risk threshold, so a dashed “Bear Trap Risk” label prints beneath the bar. The green and red trend lines mark the current low and high pivot trajectories, while the horizontal dashed lines show the highest and lowest values in the lookback window. If, within the next few bars, price closes decisively above the support, the risk label would upgrade to a solid “Bear Trap” label.
Figure caption, In this example the trap module has identified both ends of a price range. Near the highs, price briefly pushes above the descending red trendline and the recent pivot high, but fails to close there and leaves a noticeable upper wick. That combination of a sweep above resistance and order‑flow mismatch generates a Bull Trap Risk label with a dashed outline, warning that the upside break may not hold. At the opposite extreme, price later dips below the green trendline and the labelled low pivot, then quickly snaps back and closes higher. The long lower wick and subsequent price reversal upgrade the previous bear‑trap risk into a confirmed Bear Trap (solid label), indicating that sellers were caught on a false breakdown. Horizontal dashed lines mark the highest high and lowest low of the lookback window, while the red and green diagonals connect the earliest and latest pivot highs and lows to visualize the range.
7. Sharp Move Module
7.1 Concept
Markets sometimes display absorption or climax behavior—periods when one side steadily gains the upper hand before price breaks out with a sharp move. This module evaluates several order‑flow and volume conditions to anticipate such moves. Users can choose how many conditions must be met to flag a risk and how many (plus a price break) are required for confirmation.
7.2 Inputs
• sharp Lookback: Number of bars in the window used to compute moving averages, sums, percentile ranks and reference levels.
• sharpPercentile: Minimum percentile rank for the current side’s volume; the current buy (or sell) volume must be greater than or equal to this percentile of historical volumes over the lookback window.
• sharpVolMult: Multiplier used in the volume climax check. The current side’s volume must exceed this multiple of its average to count as a climax.
• sharpRatioThr: Minimum dominance ratio (current side’s volume relative to the opposite side) used in both the instant and cumulative dominance checks.
• sharpChurnThr: Maximum ratio of a bar’s range to its ATR for absorption/churn detection; lower values indicate more absorption (large volume in a small range).
• sharpScoreRisk: Minimum number of conditions that must be true to print a risk label.
• sharpScoreConfirm: Minimum number of conditions plus a price break required for confirmation.
• sharpCvdThr: Threshold for cumulative delta divergence versus price change (positive for bullish accumulation, negative for bearish distribution).
• Label settings: ATR length (sharpATRlen) and multiplier (sharpLabelMult) for positioning labels, label size, colors and connector styles for bullish and bearish sharp moves.
• Toggles: enableSharp activates the module; show_sharp_labels controls whether labels are drawn.
7.3 Conditions (six per side)
For each side, the indicator computes six boolean conditions and sums them to form a score:
1. Dominance (instant and cumulative):
– Instant dominance: current buy volume ≥ sharpRatioThr × current sell volume.
– Cumulative dominance: sum of buy volumes over the window ≥ sharpRatioThr × sum of sell volumes (and vice versa for bearish checks).
2. Accumulation/Distribution divergence: Over the lookback window, cumulative delta rises by at least sharpCvdThr while price fails to rise (bullish), or cumulative delta falls by at least sharpCvdThr while price fails to fall (bearish).
3. Volume climax: The current side’s volume is ≥ sharpVolMult × its average and the product of volume and bar range is the highest in the lookback window.
4. Absorption/Churn: The current side’s volume divided by the bar’s range equals the highest value in the window and the bar’s range divided by ATR ≤ sharpChurnThr (indicating large volume within a small range).
5. Percentile rank: The current side’s volume percentile rank is ≥ sharp Percentile.
6. Mirror logic for sellers: The above checks are repeated with buyer and seller roles swapped and the price break levels reversed.
Each condition that passes contributes one point to the corresponding side’s score (0 or 1). Risk and confirmation thresholds are then applied to these scores.
7.4 Scoring and labels
• Risk: If scoreBull ≥ sharpScoreRisk, a “Sharp ↑ Risk” label is drawn above the bar. If scoreBear ≥ sharpScoreRisk, a “Sharp ↓ Risk” label is drawn below the bar.
• Confirmation: A risk label is upgraded to “Sharp ↑” when scoreBull ≥ sharpScoreConfirm and the bar closes above the highest recent pivot (HH1); for bearish cases, confirmation requires scoreBear ≥ sharpScoreConfirm and a close below the lowest pivot (LL1).
• Label positioning: Labels are offset from the candle by ATR × sharpLabelMult (full ATR times multiplier), not half, and may include a dashed or dotted connector line if enabled.
Figure caption, In this chart both bullish and bearish sharp‑move setups have been flagged. Earlier in the range, a “Sharp ↓ Risk” label appears beneath a candle: the sell‑side score met the risk threshold, signaling that the combination of strong sell volume, dominance and absorption within a narrow range suggested a potential sharp decline. The price did not close below the lower pivot, so this label remains a “risk” and no confirmation occurred. Later, as the market recovered and volume shifted back to the buy side, a “Sharp ↑ Risk” label prints above a candle near the top of the channel. Here, buy‑side dominance, cumulative delta divergence and a volume climax aligned, but price has not yet closed above the upper pivot (HH1), so the alert is still a risk rather than a confirmed sharp‑up move.
Figure caption, In this chart a Sharp ↑ label is displayed above a candle, indicating that the sharp move module has confirmed a bullish breakout. Prior bars satisfied the risk threshold — showing buy‑side dominance, positive cumulative delta divergence, a volume climax and strong absorption in a narrow range — and this candle closes above the highest recent pivot, upgrading the earlier “Sharp ↑ Risk” alert to a full Sharp ↑ signal. The green label is offset from the candle with a dashed connector, while the red and green trend lines trace the high and low pivot trajectories and the dashed horizontals mark the highest and lowest values of the lookback window.
8. Market‑Maker / Spread‑Capture Module
8.1 Concept
Liquidity providers often “capture the spread” by buying and selling in almost equal amounts within a very narrow price range. These bars can signal temporary congestion before a move or reflect algorithmic activity. This module flags bars where both buyer and seller volumes are high, the price range is only a few ticks and the buy/sell split remains close to 50%. It helps traders spot potential liquidity pockets.
8.2 Inputs
• scalpLookback: Window length used to compute volume averages.
• scalpVolMult: Multiplier applied to each side’s average volume; both buy and sell volumes must exceed this multiple.
• scalpTickCount: Maximum allowed number of ticks in a bar’s range (calculated as (high − low) / minTick). A value of 1 or 2 captures ultra‑small bars; increasing it relaxes the range requirement.
• scalpDeltaRatio: Maximum deviation from a perfect 50/50 split. For example, 0.05 means the buyer share must be between 45% and 55%.
• Label settings: ATR length, multiplier, size, colors, connector style and width.
• Toggles : show_scalp_module and show_scalp_labels to enable the module and its labels.
8.3 Signal
When, on the current bar, both TF_buy_breakout and TF_sell_breakout exceed scalpVolMult times their respective averages and (high − low)/minTick ≤ scalpTickCount and the buyer share is within scalpDeltaRatio of 50%, the module prints a “Spread ↔” label above the bar. The label uses the same ATR offset logic as other modules and draws a connector if enabled.
Figure caption, In this chart the spread‑capture module has identified a potential liquidity pocket. Buyer and seller volumes both spiked above their recent averages, yet the candle’s range measured only a couple of ticks and the buy/sell split stayed close to 50 %. This combination met the module’s criteria, so it printed a grey “Spread ↔” label above the bar. The red and green trend lines link the earliest and latest high and low pivots, and the dashed horizontals mark the highest high and lowest low within the current lookback window.
9. Money Flow Module
9.1 Concept
To translate volume into a monetary measure, this module multiplies each side’s volume by the closing price. It tracks buying and selling system money default currency on a per-bar basis and sums them over a chosen period. The difference between buy and sell currencies (Δ$) shows net inflow or outflow.
9.2 Inputs
• mf_period_len_mf: Number of bars used for summing buy and sell dollars.
• Label appearance settings: ATR length, multiplier, size, colors for up/down labels, and connector style and width.
• Toggles: Use enableMoneyFlowLabel_mf and showMFLabels to control whether the module and its labels are displayed.
9.3 Calculations
• Per-bar money: Buy $ = TF_buy_breakout × close; Sell $ = TF_sell_breakout × close. Their difference is Δ$ = Buy $ − Sell $.
• Summations: Over mf_period_len_mf bars, compute Σ Buy $, Σ Sell $ and ΣΔ$ using math.sum().
• Info table entries: Rows 9–13 display these values as texts like “↑ USD 1234 (1M)” or “ΣΔ USD −5678 (14)”, with colors reflecting whether buyers or sellers dominate.
• Money flow status: If Δ$ is positive the bar is marked “Money flow in” ; if negative, “Money flow out” ; if zero, “Neutral”. The cumulative status is similarly derived from ΣΔ.Labels print at the bar that changes the sign of ΣΔ, offset using ATR × label multiplier and styled per user preferences.
Figure caption, The chart illustrates a steady rise toward the highest recent pivot (HH1) with price riding between a rising green trend‑line and a red trend‑line drawn through earlier pivot highs. A green Money flow in label appears above the bar near the top of the channel, signaling that net dollar flow turned positive on this bar: buy‑side dollar volume exceeded sell‑side dollar volume, pushing the cumulative sum ΣΔ$ above zero. In the info table, the “Money flow (bar)” and “Money flow Σ” rows both read In, confirming that the indicator’s money‑flow module has detected an inflow at both bar and aggregate levels, while other modules (pivots, trend lines and support/resistance) remain active to provide structural context.
In this example the Money Flow module signals a net outflow. Price has been trending downward: successive high pivots form a falling red trend‑line and the low pivots form a descending green support line. When the latest bar broke below the previous low pivot (LL1), both the bar‑level and cumulative net dollar flow turned negative—selling volume at the close exceeded buying volume and pushed the cumulative Δ$ below zero. The module reacts by printing a red “Money flow out” label beneath the candle; the info table confirms that the “Money flow (bar)” and “Money flow Σ” rows both show Out, indicating sustained dominance of sellers in this period.
10. Info Table
10.1 Purpose
When enabled, the Info Table appears in the lower right of your chart. It summarises key values computed by the indicator—such as buy and sell volume, delta, total volume, breakout status, market phase, and money flow—so you can see at a glance which side is dominant and which signals are active.
10.2 Symbols
• ↑ / ↓ — Up (↑) denotes buy volume or money; down (↓) denotes sell volume or money.
• MA — Moving average. In the table it shows the average value of a series over the lookback period.
• Σ (Sigma) — Cumulative sum over the chosen lookback period.
• Δ (Delta) — Difference between buy and sell values.
• B / S — Buyer and seller share of total volume, expressed as percentages.
• Ref. Price — Reference price for breakout calculations, based on the latest pivot.
• Status — Indicates whether a breakout condition is currently active (True) or has failed.
10.3 Row definitions
1. Up volume / MA up volume – Displays current buy volume on the lower timeframe and its moving average over the lookback period.
2. Down volume / MA down volume – Shows current sell volume and its moving average; sell values are formatted in red for clarity.
3. Δ / ΣΔ – Lists the difference between buy and sell volume for the current bar and the cumulative delta volume over the lookback period.
4. Σ / MA Σ (Vol/MA) – Total volume (buy + sell) for the bar, with the ratio of this volume to its moving average; the right cell shows the average total volume.
5. B/S ratio – Buy and sell share of the total volume: current bar percentages and the average percentages across the lookback period.
6. Buyer Rank / Seller Rank – Ranks the bar’s buy and sell volumes among the last (n) bars; lower rank numbers indicate higher relative volume.
7. Σ Buy / Σ Sell – Sum of buy and sell volumes over the lookback window, indicating which side has traded more.
8. Breakout UP / DOWN – Shows the breakout thresholds (Ref. Price) and whether the breakout condition is active (True) or has failed.
9. Market Phase (Vol) – Reports the current volume‑only phase: Accumulation, Distribution or Neutral.
10. Money Flow – The final rows display dollar amounts and status:
– ↑ USD / Σ↑ USD – Buy dollars for the current bar and the cumulative sum over the money‑flow period.
– ↓ USD / Σ↓ USD – Sell dollars and their cumulative sum.
– Δ USD / ΣΔ USD – Net dollar difference (buy minus sell) for the bar and cumulatively.
– Money flow (bar) – Indicates whether the bar’s net dollar flow is positive (In), negative (Out) or neutral.
– Money flow Σ – Shows whether the cumulative net dollar flow across the chosen period is positive, negative or neutral.
The chart above shows a sequence of different signals from the indicator. A Bull Trap Risk appears after price briefly pushes above resistance but fails to hold, then a green Accum label identifies an accumulation phase. An upward breakout follows, confirmed by a Money flow in print. Later, a Sharp ↓ Risk warns of a possible sharp downturn; after price dips below support but quickly recovers, a Bear Trap label marks a false breakdown. The highlighted info table in the center summarizes key metrics at that moment, including current and average buy/sell volumes, net delta, total volume versus its moving average, breakout status (up and down), market phase (volume), and bar‑level and cumulative money flow (In/Out).
11. Conclusion & Final Remarks
This indicator was developed as a holistic study of market structure and order flow. It brings together several well‑known concepts from technical analysis—breakouts, accumulation and distribution phases, overbought and oversold extremes, bull and bear traps, sharp directional moves, market‑maker spread bars and money flow—into a single Pine Script tool. Each module is based on widely recognized trading ideas and was implemented after consulting reference materials and example strategies, so you can see in real time how these concepts interact on your chart.
A distinctive feature of this indicator is its reliance on per‑side volume: instead of tallying only total volume, it separately measures buy and sell transactions on a lower time frame. This approach gives a clearer view of who is in control—buyers or sellers—and helps filter breakouts, detect phases of accumulation or distribution, recognize potential traps, anticipate sharp moves and gauge whether liquidity providers are active. The money‑flow module extends this analysis by converting volume into currency values and tracking net inflow or outflow across a chosen window.
Although comprehensive, this indicator is intended solely as a guide. It highlights conditions and statistics that many traders find useful, but it does not generate trading signals or guarantee results. Ultimately, you remain responsible for your positions. Use the information presented here to inform your analysis, combine it with other tools and risk‑management techniques, and always make your own decisions when trading.
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.
Stop Loss vs Take Profit Probability and EVThis stop loss and take profit calculator uses a Monte Carlo simulation to calculate the probability of hitting your Stop Loss or Take Profit levels across different time horizons (expressed in bars).
It provides data-driven insights to optimize your risk management and position sizing by showing Expected Value for each scenario.
As a quant, I love using statistical data to help my decisions and get better EV from my trades.
🔬 How It's Calculated
Monte Carlo Simulation: Runs 1,000-10,000 price simulations using a random walk model
Volatility Analysis: Combines ATR-based and Historical Volatility for accurate price movement modeling
Expected Value: Calculates profit/loss expectation using formula: (TP_Probability × Reward) - (SL_Probability × Risk)
Time Horizons: Tests multiple timeframes (1, 5, 10, 20, 50 bars) to find optimal holding periods
Risk/Reward Ratios: Automatically calculates and displays R:R ratios for quick assessment
💡 Use Cases
Position Sizing - Determine optimal risk per trade based on Expected Value
Time Horizon Optimization - Find the best holding period for your strategy
Stop Loss Placement - Validate SL levels using probability analysis
Take Profit Optimization - Set TP levels with statistical backing
Strategy Backtesting - Compare different R:R setups before entering trades
Risk Management - Avoid trades with negative Expected Value
Swing vs Day Trading - Choose timeframes with highest success probability
🎯 How to Use
Setup Trade: Enter your entry price, stop loss, and take profit levels
You can add or remove time horizons denominated in bars. Say you are looking at 1h candles, adding a 24-bar time horizon means you are looking into 24 hours
Choose Direction: Select Long or Short position
Review Table
Analyze Expected Value: Focus on positive EV scenarios (green background)
Optimize Timing: Select time horizons with best risk/reward profile
Adjust Parameters: Modify volatility calculation method and simulation count if needed
Examples
Here's how you can read the tables.
Example 1:
In this chart, we are analyzing the TP and SL probabilities as well as the EV (expected value) for a stock. I want to check what the likelihood is that my SL and TP get triggered over the next 5 days. The stock market is open for 6.5 hours per day, which is 13 bars in this 30-minute bar chart. 26 bars is 2 days, 39 bars is 3 days and so on.
Although this trade is more likely to trigger my SL than my TP, in some of the time horizons we have a positive expected value because of the risk/reward of our trade (i.e. distance of the SL and TP from the price) and the probability of hitting SL and TP.
Example 2:
In this example, we have applied the indicator to gold. Because the TP is much closer to the price, the probability of hitting the TP is much higher.
We can also observe that the expected Value in the shorter time frames is better than in the longer ones. This can give us some clues to set up our trade. If we know that the EV is positive, we can allocate more to that specific trade.
Enjoy, and please let me know your feedback! 😊🥂
Universal Stochastic Fusion (Simplified) — v6What this indicator is
This indicator is called Universal Stochastic Fusion (USF).
It’s a tool that helps traders see when the market might be too high (overbought) or too low (oversold), and when it might be a good time to buy or sell.
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How it works
Think of the market like a rubber band.
• If the band stretches too far up, it usually snaps back down.
• If it stretches too far down, it usually bounces back up.
The USF indicator measures this stretch using something called the Stochastic Oscillator (just a fancy way of saying it looks at where the current price sits compared to recent highs and lows).
It shows this on a scale from 0 to 100:
• Near 100 → market is stretched upward (too hot).
• Near 0 → market is stretched downward (too cold).
• Around 50 → normal, middle ground.
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What’s special about USF
1. Two views at once
o It lets you see the market’s stretch on your current chart and on another timeframe (like a daily view).
o This way, you can see the short-term and the bigger picture together.
2. Smart levels
o Instead of always using the same “too high/too low” levels (like 80 and 20), it can adjust these lines automatically depending on how wild or calm the market is.
3. Buy and Sell signals
o When the market looks too low and starts turning up, it can mark a BUY.
o When the market looks too high and starts turning down, it can mark a SELL.
4. Extra filter (optional)
o It can also use another tool (RSI) to double-check signals, so you don’t get as many false alerts.
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How this helps traders
• It helps traders avoid buying when prices are already too high.
• It helps them spot possible bottoms where prices may bounce back.
• It combines short-term and long-term signals so traders don’t get tricked by quick moves.
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Where it works
This indicator is universal — meaning it works on almost any market:
• Stocks (like Apple, Tesla, etc.)
• Forex (currencies like EUR/USD)
• Crypto (Bitcoin, Ethereum, etc.)
• Commodities (Gold, Oil, etc.)
• Futures and Indices (S&P 500, Nasdaq, etc.)
Because all these markets share the same pattern of prices going up and down too much and then pulling back, the USF can be applied everywhere.
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👉 In short:
The Universal Stochastic Fusion is like a heat meter for the market.
It tells you when prices might be too hot (good chance to sell) or too cold (good chance to buy), and it works in all markets and timeframes.
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Retail Sentiment Indicator - Multi-Asset CFD & Fear/Greed IndexRetail Sentiment Indicator - Multi-Asset CFD & Fear/Greed Index
Overview
The Retail Sentiment Indicator provides real-time sentiment data for major financial instruments including stocks, forex, commodities, and cryptocurrencies. This indicator displays retail trader positioning and market sentiment using CFD data and fear/greed indices.
Methodology and Scale Calculation
This indicator operates on a **-50 to +50 scale** with zero representing perfect market equilibrium.
Scale Interpretation:
- **Zero (0)**: Market balance - exactly 50% of investors buying, 50% selling
- **Positive values**: Majority buying pressure
- Example: If 63% of investors are buying, the indicator shows +13 (63 - 50 = +13)
- **Negative values**: Majority selling pressure
- Example: If 92% of investors are selling, the indicator shows -42 (50 - 92 = -42)
BTC Fear & Greed Index Scaling:
The original `BTC FEAR&GREED` index is natively scaled from 0-100 by its creator. In our indicator, this data has been rescaled to also fit the -50 to +50 range for consistency with other sentiment data sources.
This unified scaling approach allows for direct comparison across all instruments and data sources within the indicator.
-Important Data Source Selection-
Bitcoin (BTC) Data Sources
When viewing Bitcoin charts, the indicator offers **two different data sources**:
1. **Default Auto-Mode**: `BTCUSD Retail CFD` - Retail CFD traders sentiment data (automatically loaded).
2. **Manual Selection**: `BTC FEAR&GREED` - Fear & Greed Index from website: alternative dot me
**To access BTC Fear & Greed Index**: Input settings -> disable checkbox "Auto-load Sentiment Data" -> manually select "BTC FEAR&GREED" from the dropdown menu.
US Stock Market Data Sources
For US stocks and indices (S&P 500, NASDAQ, Dow Jones), there are **two data source options**:
1. **Default Auto-Mode**: Individual retail CFD sentiment data for each instrument
2. **Manual Selection**: `SNN FEAR&GREED` - SNN's Fear & Greed Index covering the overall US market sentiment. SNN was used as the name to avoid any potential trademark infringement.
**To access SNN Fear & Greed Index**: When viewing US market charts, disable in input settings checkbox "Auto-load Sentiment Data" and manually select "SNN FEAR&GREED" from the dropdown menu.
This distinction allows traders to choose between **instrument-specific retail sentiment** (auto-mode) or **broader market sentiment indices** (manual selection).
Features
- **Auto-Detection**: Automatically loads sentiment data based on the current chart symbol
- **Manual Selection**: Choose from 40+ supported instruments when auto-detection is unavailable
- **Multiple Data Sources**: Combines retail CFD sentiment with Fear & Greed indices
- **Visual Zones**: Clear greed/fear zones with color-coded backgrounds
- **Real-time Updates**: Live sentiment data from merged data sources
Supported Instruments
Major Indices
- S&P 500, NASDAQ, Dow Jones 30, DAX
Forex Pairs
- Major pairs: EURUSD, GBPUSD, USDJPY, USDCHF, USDCAD
- Cross pairs: EURJPY, GBPJPY, AUDUSD, NZDUSD, and 20+ others
Commodities
- Precious metals: Gold (XAUUSD), Silver (XAGUSD)
- Energy: WTI Oil
- Agricultural: Wheat, Coffee
- Industrial: Copper
Cryptocurrencies
- Bitcoin (BTC) sentiment data
- BTC & SNN Fear & Greed indices
How to Use
1. **Auto Mode** (Default): Enable "Auto-load Sentiment Data" to automatically display sentiment for the current chart symbol
2. **Manual Mode**: Disable auto-load and select from the dropdown menu for specific instruments
3. **Interpretation**:
- Values above 0 (green) indicate retail greed/bullish sentiment
- Values below 0 (red) indicate retail fear/bearish sentiment
- Fear & Greed indices use 0-100 scale (50 is neutral)
Data Sources
This indicator uses curated sentiment data from retail CFD providers and established fear/greed indices. Data is updated regularly and sourced from reputable financial data providers.
Trading Strategy & Market Philosophy
Contrarian Trading Approach
The primary purpose of this indicator is based on the fundamental market principle that **the majority of retail investors are often wrong**, and markets typically move opposite to the positions held by the majority of market participants.
Key Strategy Guidelines:
- **Contrarian Signal**: When the majority of users are positioned on one side of the market, there is statistically greater market advantage in taking positions in the opposite direction
- **Trend Exhaustion Signal**: An interesting observed phenomenon occurs when, during a long-lasting trend where the majority of investors have consistently been on the wrong side, the Sentiment indicator suddenly shows that the majority has flipped and opened positions in the direction of that long-running trend. This is often a signal of fuel exhaustion for further movement in that direction
Interpretation Examples
- High greed readings (majority bullish) → Consider bearish opportunities
- High fear readings (majority bearish) → Consider bullish opportunities
- Sudden sentiment flip during established trends → Potential trend reversal signal
Technical Notes
- Built with PineScript v6
- Dynamic symbol detection with fallback options
- Optimized for performance with minimal resource usage
- Color-coded visualization with customizable zones
Data Sources & Expansion
Acknowledgments
We extend our gratitude to **TradingView** for enabling the use of custom data feeds based on GitHub repositories, making this comprehensive sentiment analysis possible.
Data Expansion Opportunities
As the operator of this indicator, I am **open to suggestions for new data sources** that could be integrated and published. If you have ideas for additional instruments or sentiment data:
How to Submit Suggestions:
1. Send a **private message** with your proposal
2. Include: **instrument/data type**, **source**, and **brief description**
3. If technically feasible, we will work to import and publish the data
Data Infrastructure Status
Current Data Upload Process:
Please note that sentiment data uploads may occasionally experience minor interruptions. However, this should not pose significant issues as sentiment data typically changes gradually rather than rapidly.
Infrastructure Development:
We are actively working on establishing permanent cloud-based infrastructure to ensure continuous, automated data collection and upload processes. This will provide more reliable and consistent data availability in the future.
Disclaimer
This indicator is for educational and informational purposes only. Sentiment data should be used as part of a comprehensive trading strategy and not as the sole basis for trading decisions. Past performance does not guarantee future results. The contrarian approach described is a market theory and may not always produce profitable results.
🟥 Synthetic 10Y Real Yield (US10Y - Breakeven)This script calculates and plots a synthetic U.S. 10-Year Real Yield by subtracting the 10-Year Breakeven Inflation Rate (USGGBE10) from the nominal 10-Year Treasury Yield (US10Y).
Real yields are a core macro driver for gold, crypto, growth stocks, and bond pricing, and are closely monitored by institutional traders.
The script includes key reference lines:
0% = Below zero = deeply accommodative regime
1.5% = Common threshold used by macro desks to evaluate gold upside breakout conditions
📈 Use this to monitor macro shifts in real-time and front-run capital flows during major CPI, NFP, and Fed events.
Update Frequency: Daily (based on Treasury market data)
buy sell ultra systemWhat it is
EMA-POC Momentum System Ultra combines a proven trend stack (EMA 20/50/238), a price-of-control layer (POC via Bar-POC or VWAP alternative), and a momentum trigger (RSI) to surface higher-quality entries only when multiple, independent conditions align. This is not a cosmetic mashup; each component gates the others.
How components work together
Trend (EMA 20/50/238): Defines short/medium/long bias and filters counter-trend signals.
POC (Bar-POC or Alt-POC/VWAP): Locates the most-traded/weighted price area; a neutral band around POC helps avoid chop.
Control background: Above POC → buyers likely in control; below → sellers.
Momentum (RSI): Entry arrows print only when RSI confirms with trend and price location vs POC; optional “cross 50” requirement reduces noise.
Optional HTF trend: Confluence with a higher-timeframe EMA stack for stricter filtering.
Why it’s original/useful
Signals require confluence of (1) EMA trend stack, (2) POC location and neutral-zone filtering, (3) momentum confirmation, (4) optional slope and distance-to-POC checks, and (5) optional HTF trend. This reduces false positives compared with using any layer in isolation.
How to use
Markets/TFs: Built for XAUUSD (Gold) and US30. Works 1m–1h for intraday; 2h–4h for swing.
Entries:
Long: EMA stack bullish, price above POC, not in neutral band, RSI condition true → “Buy” arrow.
Short: Opposite conditions → “Sell” arrow.
Stops/Targets (suggested):
Initial stop beyond POC/neutral band or recent swing.
First target around 1R; trail with EMA20/50 or structure breaks.
Settings to tune:
POC Mode: Bar-POC (highest-volume bar’s close over lookback) or Alt-POC (VWAP).
Neutral Band %: 0.10–0.35 typical intraday.
Min distance from POC: 0.10–0.50% helps avoid low-RR entries right at POC.
RSI: Choose “cross 50” for stricter triggers or simple >/< 50 for more signals.
HTF trend: Turn on for extra confluence.
Alerts:
Buy Signal and Sell Signal (separate), or one Combined Buy/Sell alert.
Set to “Once per bar close” if you want only confirmed arrows.
Repainting / limitations
Shapes can move until bar close (standard Pine behavior) when using intrabar conditions; final confirmation at close. No system guarantees profitability—forward test and adapt to your market/instrument.
Clean chart
The published chart contains only this script so outputs are easy to identify.
Versions / updates
Use Publish → Update for minor changes; do not create new publications for small tweaks. If you fork to preserve older behavior, explain why and how your fork differs.
Changelog
v1.1 – Tuning for Gold/US30, neutral-band & distance filters, optional HTF trend, combined alert.
v1.0 – Initial public release (EMA stack + POC modes + RSI + alerts).
License & credits
Open-source for learning and improvement. Please credit on forks and explain modifications in your description.
SESSIONS Golden Team SESSIONS — Multi-Session Forex Box & Range Analysis
This indicator displays the major Forex market sessions — London, New York, Tokyo, Sydney, and Frankfurt — directly on the chart. Each session is shown as a customizable colored box with optional Fibonacci levels and opening range markers.
It also calculates and displays the average pip range of each session over a user-defined number of past days, allowing traders to analyze volatility patterns for each trading period.
Key Features:
Configurable session times and time zones
Individual on/off toggle for each session
Custom colors, box transparency, and border styles
Optional Opening Range and Fibonacci retracement levels for each session
Average pip range table for quick volatility reference
Works on any intraday timeframe
How It Works:
The script identifies the start and end times of each session based on user settings.
A box is drawn around the high/low of the session period.
At the end of each session, the pip range is recorded, and an average is calculated over the last N sessions (default: 20).
The results are displayed in a statistics table showing average pips and whether the session is currently active.
Suggested Use:
Identify high-volatility sessions for breakout trading
Filter trades to active trading hours
Study historical volatility to refine entry timing
EMA 21 & 78 - With Instrument Nameonly add EMA21 and 78, I saw the 21 and 78 period is the most favor period when trading with gold
MA Availability ETA (SMA100/EMA200)This tool helps traders understand when long-term moving averages become available on any chosen timeframe.
Many new symbols, pairs, or timeframes don’t have enough price history to immediately plot long moving averages like SMA(100) and EMA(200). This script calculates and displays:
✅ Bars Remaining – how many bars are still needed before each moving average can be plotted reliably.
✅ ETA Duration – an estimate of how long (in chart time units) it will take until each MA is available.
✅ Status Table & Label – compact visual summary on the chart and in a table at the top-right corner.
✅ Vertical Marker – a dotted line showing exactly where both SMA(100) & EMA(200) first appear together.
✅ Alerts – optional alerts notify you the moment SMA(100) or EMA(200) become available.
🔑 Features
Works on any timeframe and instrument.
Highlights SMA(100) and EMA(200) on the chart for reference.
Lets you choose whether EMA(200) should be considered ready immediately, or only after a full 200-bar history.
Useful for traders who rely on long-term MA signals (golden cross, dynamic support/resistance, trend confirmation) and want to know when these tools will be ready on fresh charts.
🎯 Use Cases
New listings / low-history assets → See when SMA100 & EMA200 become usable.
Backtesting or forward-testing → Anticipate when long-term signals will first appear.
Trend-following strategies → Prepare in advance for crossovers or key support/resistance confluence zones.
⚠️ Note: ETAs are based on chart resolution and assume continuous data; real-world session gaps, weekends, or illiquid trading can make availability slightly later.
👉 Add this to your chart and you’ll always know when the big moving averages arrive — a critical moment for many upside moves and long-term strategies.
ORB with Fib Levels - TradingbrockOpening Range (OR) Indicator Overview
This TradingView indicator analyzes and displays the Opening Range - a popular day trading concept that tracks price movement during the first 30-60 minutes of the trading session.
Core Functionality:
Opening Range Detection: By default, it monitors the 9:30-10:00 AM ET period and tracks the highest high and lowest low during this time frame, creating upper and lower boundaries.
Fibonacci Retracement Levels: Inside the opening range, it displays five key Fibonacci levels:
0.236 (23.6% - shallow retracement)
0.382 (38.2% - standard retracement)
0.500 (50% - halfway point)
0.618 (61.8% - golden ratio)
0.786 (78.6% - deep retracement)
Extension Levels: The indicator projects additional levels beyond the opening range:
1x extension above/below the range
2x extension levels that only appear when price breaks the first extension
Trading Applications:
Support & Resistance: The opening range high/low often act as key levels throughout the trading day
Breakout Trading: Many traders watch for price to break above or below the opening range
Mean Reversion: The Fibonacci levels within the range can serve as potential reversal points
Risk Management: Helps define clear levels for stop losses and profit targets
The indicator essentially gives traders a framework to understand how price is behaving relative to the early session's established range, which often sets the tone for the entire trading day.
XAU 0/5 GridThis indicator draws horizontal price grids for XAUUSD. It anchors the grid to a base price that ends with 0 or 5, then plots equally spaced levels every 5 price units above and below that base. It’s a clean way to eyeball fixed-interval structure for rough support/resistance zones and simple TP/SL planning.
How it works
Base (0/5):
base = floor(close / 5) × 5 → forces the base to always end with 0/5.
Grid levels:
level_i = base + i × 5, where i is any integer (positive/negative).
The script updates positions only when the base changes to avoid flicker and reduce chart load.
It uses a persistent line array to manage the line objects efficiently.
Usage
Add the indicator to an XAUUSD chart on any timeframe.
Configure in the panel:
Show Lines – toggle visibility
Lines each side – number of lines above/below the base
Line Color / Line Width – appearance
Use the grid as fixed reference levels (e.g., 3490, 3495, 3500, 3505, …) for planning TP/SL or observing grid breaks.
Highlights
Strict 0/5 anchoring keeps levels evenly spaced and easy to read on gold.
Auto-reanchors when price moves to a new 0/5 zone, maintaining a steady view.
Lightweight design: lines are created once and then updated, minimizing overhead.
Limitations
Visualization only — not a buy/sell signal.
Spacing is fixed at 5 price units, optimized for XAUUSD. If used on other symbols/brokers with different tick scales, adjust the logic accordingly.
Grid lines do not guarantee support/resistance; always combine with broader market context.
BTC Macro Composite Global liquidity Index -OffsetThis indicator is based on the thesis that Bitcoin price movements are heavily influenced by macro liquidity trends. It calculates a weighted composite index based on the following components:
• Global Liquidity (41%): Sum of central bank balance sheets (Fed , ECB , BoJ , and PBoC ), adjusted to USD.
• Investor Risk Appetite (22%): Derived from the Copper/Gold ratio, inverse VIX (as a risk-on signal), and the spread between High Yield and Investment Grade bonds (HY vs IG OAS).
• Gold Sensitivity (15–20%): Combines the XAUUSD price with BTC/Gold ratio to reflect the historical influence of gold on Bitcoin pricing.
Each component is normalized and then offset forward by 90 days to attempt predictive alignment with Bitcoin’s price.
The goal is to identify macro inflection points with high predictive value for BTC. It is not a trading signal generator but rather a macro trend context indicator.
❗ Important: This script should be used with caution. It does not account for geopolitical shocks, regulatory events, or internal BTC market structure (e.g., miner behavior, on-chain metrics).
💡 How to use:
• Use on the 1D timeframe.
• Look for divergences between BTC price and the macro index.
• Apply in confluence with other technical or fundamental frameworks.
🔍 Originality:
While similar components exist in macro dashboards, this script combines them uniquely using time-forward offsets and custom weighting specifically tailored for BTC behavior.
Greer Fair Value✅ Greer Fair Value
Greer Fair Value: Graham intrinsic value + Buffett-style DCF with auto EPS/FCF and auto growth (CAGR of FCF/share), defaulting to a simple GFV badge that color-codes opportunity at a glance.
📜 Full description
Greer Fair Value is inspired by the valuation frameworks of Benjamin Graham and Warren Buffett. It combines Graham’s rate-adjusted intrinsic value with a two-stage, per-share DCF. The script auto-populates EPS (TTM) and Free Cash Flow per share (FY/FQ/TTM) from request.financial(), and can auto-estimate the near-term growth rate (g₁) using the CAGR of FCF/share over a user-selected lookback (with sensible caps). All assumptions remain editable.
Default view: only the GFV badge is shown to keep charts clean.
Badge color logic:
Gold — both DCF and Graham fair values are above the current price
Green — exactly one of them is above the current price
Red — the current price is above both values
Show more detail (optional):
Toggle “Show Graham Lines” and/or “Show DCF Lines” to plot fair values (and optional MoS bands) over time.
Toggle “Show Dashboard” for a compact data table of assumptions and outputs.
Optional summary label can be enabled for a quick on-chart readout.
Inputs you can customize: EPS source/manual fallback, FCF/share source (FY/FQ/TTM), g₁ auto-CAGR lookback & caps, terminal growth gT, discount rate r, MoS levels, step-style plots, table position, and decimals.
Note: TradingView’s UI controls whether “Inputs/Values in Status Line” are shown. If you prefer a clean status line, open the indicator’s settings and uncheck those options, then Save as default.
Disclaimer: For educational/informational purposes only; not financial advice. Markets involve risk—do your own research.
Ai Golden Support and Resistance Adaptive Support & Resistance (ADR-scaled ABCD + Breakout/Retest Zones)
What it does
This indicator detects actionable support/resistance zones from swing structure and breakout events, then keeps each zone active until it’s invalidated by price. It adapts zone sensitivity using Average Daily Range (ADR) so the same rules scale across symbols and vol regimes.
Core Logic (high level)
Swing & ABCD pattern seed
Detects alternating pivots (high–low–high–low or low–high–low–high) using a user-selected lookback.
Validates basic AB–BC–CD proportions: BC must retrace a portion of AB; CD must extend BC within a set range.
From a valid sequence, sets a candidate level (top for bearish, bottom for bullish).
Breakout confirmation
A level becomes confirmed when price closes beyond it (crossover/crossunder).
On confirmation, the script draws a dotted reference line and records how many bars elapsed from the seed pivot to breakout. That count defines the lookback window used for local extremes.
Zone construction
Supply (bearish): builds a box around the most recent local range near the bearish seed;
Demand (bullish): builds a box around the most recent local range near the bullish seed.
Each zone’s height is derived from nearby extremes and the seed swing, so boxes reflect local structure rather than fixed pip widths.
Volatility normalization (ADR%)
ADR is computed from daily candles.
The Risk Profile input (“High/Medium/Low”) scales required move sizes using ADR%, and adjusts pivot sensitivity (fewer/more bars).
Higher risk → more sensitive (smaller ADR %, tighter pivot lookback).
Lower risk → stricter filters (larger ADR %, wider pivot lookback).
Explosive-move filter (streak logic)
Searches the seeded lookback for consecutive same-color candles (config via the risk profile).
Requires the cumulative % move of that streak to exceed an ADR-scaled threshold.
When found, the zone is tagged as originating from an “explosive” move (potentially higher reaction probability).
Zone persistence & invalidation
Zones persist and auto-extend to the right until invalidated.
Invalidation occurs when price closes through a rule-based threshold derived from the seed structure (stored per zone).
Once invalidated, the zone is marked inactive and stops updating.
Inputs & Controls
Risk Profile: High / Medium / Low (sets pivot lookback, streak length, and ADR% thresholds).
Labels & Visuals: Toggle labels and level lines; set line width.
Colors/Boxes: Supply (red), Demand (green); dotted breakout references.
No broker/session settings are required; the script adapts per symbol via ADR.
On-Chart Elements
Dotted breakout lines at confirmed levels (with measured bars-to-breakout).
Supply/Demand boxes that extend until invalidation.
Optional labels for clarity; minimal clutter by default.
How to Use
Context: Use higher-TF context for bias; apply zones on your trading TF.
Confluence: Combine zones with your own triggers (structure breaks, rejection wicks, momentum shifts).
Invalidation: If price closes beyond a zone’s invalidation threshold, treat that zone as inactive.
Sensitivity: If too many zones appear, switch to Medium/Low Risk (stricter ADR% & pivots); if too few, use High Risk.
Notes & Limitations
Logic is rule-based; there is no machine learning.
Daily ADR is computed from D timeframe, so intraday charts inherit daily volatility context.
Results vary by symbol and timeframe; validate settings per market.
This is an indicator (no orders or P/L).
