Multiple Colored Moving AveragesMULTIPLE COLORED MOVING AVERAGES - USER GUIDE
DISCLAIMER
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Both the code and this documentation were created heavily using artificial intelligence. I'm lazy...
This indicator was inspired by repo32's "Moving Average Colored EMA/SMA" indicator. *
What is this indicator?
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This is a TradingView indicator that displays up to 4 different moving averages on your chart simultaneously. Each moving average can be customized with different calculation methods, colors, and filtering options.
Why would I use multiple moving averages?
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- See trend direction across different timeframes at once
- Identify support and resistance levels
- Spot crossover signals between fast and slow MAs
- Reduce false signals with filtering options
- Compare how different MA types react to price action
What moving average types are available?
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11 different types:
- SMA: Simple average, equal weight to all periods
- EMA: Exponential, more weight to recent prices
- WMA: Weighted, linear weighting toward recent data
- RMA: Running average, smooth like EMA
- DEMA: Double exponential, reduced lag
- TEMA: Triple exponential, even less lag
- HMA: Hull, fast and smooth combination
- VWMA: Volume weighted, includes volume data
- LSMA: Least squares, based on linear regression
- TMA: Triangular, double-smoothed
- ZLEMA: Zero lag exponential, compensated for lag
How do I set up the indicator?
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Each MA has these settings:
- Enable/Disable: Turn each MA on or off
- Type: Choose from the 11 calculation methods
- Length: Number of periods (21, 50, 100, 200 are common)
- Smoothing: 0-10 levels of extra smoothing
- Noise Filter: 0-5% to ignore small changes
- Colors: Bullish (rising) and bearish (falling) colors
- Line Width: 1-5 pixels thickness
What does the smoothing feature do?
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Smoothing applies extra calculations to make the moving average line smoother. Higher levels reduce noise but make the MA respond slower to price changes. Use higher smoothing in choppy markets, lower smoothing in trending markets.
What is the noise filter?
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The noise filter ignores small percentage changes in the moving average. For example, a 0.3% filter will ignore any MA movement smaller than 0.3%. This helps eliminate false signals from minor price fluctuations.
When should I use this indicator?
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- Trend analysis: See if market is going up, down, or sideways
- Entry timing: Look for price bounces off MA levels
- Exit signals: Watch for MA slope changes or crossovers
- Support/resistance: MAs often act as dynamic levels
- Multi-timeframe analysis: Use different lengths for different perspectives
What are some good settings to start with?
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Conservative approach:
- MA 1: EMA 21 (short-term trend)
- MA 2: SMA 50 (medium-term trend)
- MA 3: SMA 200 (long-term trend)
- Low noise filtering (0.1-0.3%)
Active trading:
- MA 1: HMA 9 (very responsive)
- MA 2: EMA 21 (short-term)
- MA 3: EMA 50 (medium-term)
- Minimal or no smoothing
How do I interpret the colors?
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Each MA changes color based on its direction:
- Bullish color: MA is rising (upward trend)
- Bearish color: MA is falling (downward trend)
- Gray: MA is flat or unchanged
What should I look for in crossovers?
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- Golden Cross: Fast MA crosses above slow MA (bullish signal)
- Death Cross: Fast MA crosses below slow MA (bearish signal)
- Multiple crossovers in same direction can confirm trend changes
- Wait for clear separation between MAs after crossover
How do I use MAs for support and resistance?
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- In uptrends: MAs often provide support when price pulls back
- In downtrends: MAs may act as resistance on rallies
- Multiple MAs create support/resistance zones
- Stronger levels where multiple MAs cluster together
Can I use this with other indicators?
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Yes, it works well with:
- Volume indicators for confirmation
- RSI or MACD for timing entries
- Bollinger Bands for volatility context
- Price action patterns for setup confirmation
What if I get too many signals?
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- Increase smoothing levels
- Raise noise filter percentages
- Use longer MA periods
- Focus on major crossovers only
- Wait for multiple MA confirmation
What if signals are too slow?
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- Reduce smoothing to 0
- Lower noise filter values
- Switch to faster MA types (HMA, ZLEMA, DEMA)
- Use shorter periods
- Focus on the fastest MA only
Which MA types work best in different markets?
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Trending markets: EMA, DEMA, TEMA (responsive to trends)
Choppy markets: SMA, TMA, HMA with smoothing (less whipsaws)
High volatility: Use higher smoothing and noise filtering
Low volatility: Use minimal filtering for better responsiveness
Do I need all the advanced features?
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No. Start with basic settings:
- Choose MA type and length
- Set colors you prefer
- Leave smoothing at 0
- Leave noise filter at 0
Add complexity only if needed to improve signal quality.
How do I know if my settings are working?
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- Backtest on historical data
- Paper trade the signals first
- Adjust based on market conditions
- Keep a trading journal to track performance
- Be willing to modify settings as markets change
Can I save different configurations?
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Yes, save different indicator templates in TradingView for:
- Different trading styles (scalping, swing trading)
- Different market conditions (trending, ranging)
- Different instruments (stocks, forex, crypto)
Buscar en scripts para "GOLD"
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.
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.
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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Asset in Every Fiat Currency📌 Description
Asset in Every Fiat Currency is an indicator that expresses the value of any chosen asset across a basket of global fiat currencies.
It uses exchange rates to calculate a weighted aggregate signal, allowing you to see how the asset behaves when priced simultaneously in the world’s most important currencies.
⚖️ Key Features
Choose any asset (default: Gold).
Weighted by major global currencies (USD, EUR, JPY, GBP, CNY, etc.).
Fully customizable weights through user inputs.
Automatic normalization factor for consistent scaling.
🛠️ Use Cases
Compare an asset’s performance beyond the USD lens.
Detect global strength/weakness of an asset in a diversified fiat basket.
Explore alternative ways of viewing asset pricing.
⚠️ Disclaimer: This script is for educational purposes only. It does not constitute financial advice. Always do your own research before making trading decisions.
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.
