LETHINH-Swing pa,smc🟦 📌 Title (English)
Swing High / Swing Low – 3-Candle Fractal (5-Bar Pivot) | Auto Alerts
⸻
🟩 📌 Short Description
A clean and reliable swing high / swing low detector based on the classic 3-candle (5-bar) fractal pivot. Automatically marks SH/SL and triggers alerts when a swing is confirmed. No repainting after confirmation.
⸻
🟧 📌 Full Description (for TradingView Publishing)
🔶 Swing High / Swing Low – 3-Candle Fractal (5-Bar Pivot)
This indicator identifies Swing Highs (SH) and Swing Lows (SL) using the classic 3-candle fractal pattern, also known as the 5-bar pivot.
It marks swing points only after full confirmation, making it highly reliable and suitable for structure-based trading.
⸻
🔶 📍 How It Works
A swing is confirmed when the center candle is higher (or lower) than the two candles on each side:
Swing High (SH)
high > high , high , high
Swing Low (SL)
low < low , low , low
The confirmation occurs after 2 right candles close, so the indicator does not repaint once a swing is identified.
⸻
🔶 📍 Key Features
• Detects clean and accurate swings
• Uses pure price action — no indicators, no lag
• Marks swing high (SH) and swing low (SL) directly on the chart
• Non-repainting after confirmation
• Works on all timeframes and all markets
• Extremely lightweight and fast
• Includes alert conditions for both SH and SL
Perfect for traders using:
• Market Structure (BOS / CHoCH)
• Order Blocks (OB)
• Smart Money Concepts (SMC)
• Liquidity hunts
• Wyckoff
• Support/Resistance
• Price Action entries
⸻
🔶 📍 Why This Indicator Is Useful
Swing points are the foundation of market structure.
Accurately detecting them helps traders:
• Identify trend shifts
• Spot BOS / CHoCH correctly
• Find key zones (OB, liquidity levels, supply/demand)
• Time entries more precisely
• Avoid fake structure breaks
This indicator ensures swings are plotted only when fully confirmed, reducing noise and confusion.
⸻
🔶 📍 Alerts
You can create alerts for both conditions:
• Swing High Confirmed
• Swing Low Confirmed
Recommended settings:
• Once per bar close
• Open-ended alert
With alerts enabled, TradingView will automatically notify you every time a new swing forms.
⸻
🔶 📍 No Repainting
Once a swing is confirmed and plotted, it will not change or disappear.
This makes the indicator reliable for real-time alerts and backtesting.
⸻
🔶 📍 Pine Script (v5)
Paste your indicator code here if you want it visible.
Or leave the code hidden if you are publishing as protected.
⸻
🔶 📍 Final Notes
• This indicator focuses on confirmation, not prediction
• It is designed for clean structure reading
• All markets supported: Forex, Crypto, Stocks, Indexes, Commodities
• Suitable for scalping, intraday, swing, and even higher-timeframe trading
If you find this tool helpful, feel free to give it a like and add it to your favorites ❤️
Your support helps me share more tools with the community!
Recherche dans les scripts pour "backtesting"
able MACD Overview
Purpose: The indicator combines the traditional MACD (Moving Average Convergence Divergence) with a short-term “forecast” (projection) of MACD/histogram values to give early warning of momentum changes.
Typical outputs:
MACD line (fastEMA − slowEMA)
Signal line (EMA of MACD)
Histogram (MACD − signal)
Forecasted MACD or histogram projected N bars ahead
Optional buy/sell markers and alert conditions
Add the indicator to TradingView (Installation)
Open TradingView and the chart you want to apply the indicator to.
Click “Pine Editor” at the bottom of the chart.
Copy the contents of able_macd_forecast.pine into the Pine Editor window.
Click “Add to chart” (or Save then Add to chart). If it’s a study, it will appear on the chart below price.
If you plan to re-use the script, click Save and give it a meaningful name.
Inputs / Parameters (typical) Note: exact input names may differ in your script. Replace the names below with the script’s input labels when you inspect it.
Source: price source for calculations (close, hl2, etc.).
Fast Length: length for the fast EMA (commonly 12).
Slow Length: length for the slow EMA (commonly 26).
Signal Length: length for the MACD signal EMA (commonly 9).
Forecast Length / Horizon: how many bars ahead the script projects the MACD/histogram (e.g., 1–5).
Forecast Method / Smoothing: choice of projection method (linear regression, EMA extrapolation, simple slope * N, etc.) if available.
Histogram Thresholds: numeric thresholds to emphasize significant momentum (optional).
Show Forecast: toggle on/off the forecast plot.
Alerts On/Off toggles: enable or disable alert conditions baked into the indicator.
Visual / Style settings: colors, plot thickness, histogram style (columns/areas), show labels, show buy/sell arrows.
How the indicator is typically calculated (summary)
MACD line = EMA(source, fast) − EMA(source, slow)
Signal line = EMA(MACD line, signal length)
Histogram = MACD − Signal
Forecast = method-specific short-term projection of MACD or histogram (for example: extend the last slope forward, apply linear regression to MACD values and extrapolate N bars, or apply an additional smoothing and extend that value) Note: For exact math, I need to inspect the script; this is the typical approach.
How to read the indicator (signals & interpretation)
Bullish signal:
MACD line crossing above the signal line (MACD cross up).
Histogram turns positive (cross above zero).
Forecast shows MACD/histogram moving higher in the next N bars (if forecast is positive or trending up).
Bearish signal:
MACD line crossing below the signal line (MACD cross down).
Histogram turns negative (cross below zero).
Forecast shows MACD/histogram moving lower ahead.
Confirmations:
Use price action (higher highs/lows for bullish, lower highs/lows for bearish).
Volume or other momentum/confluence indicators (RSI, ADX).
Divergences:
Bullish divergence: price makes lower low while MACD histogram makes higher low.
Bearish divergence: price makes higher high while MACD histogram makes lower high.
Forecast behavior:
If the forecast leads the MACD cross (forecast crosses before the current MACD does), it’s an early warning.
Use caution: forecasts are prone to false signals; always confirm.
Common trading setups using this indicator
Conservative:
Wait for MACD to cross signal + histogram above zero + forecast already trending same direction.
Use stop below recent swing low (for long) or above recent swing high (for short).
Aggressive (early entry):
Enter when forecast turns positive while MACD still below signal (anticipating cross).
Use tighter stops and smaller position sizes.
Exit rules:
Opposite MACD cross, histogram flipping sign, or a target based on risk-reward.
Use trailing stop based on ATR or structure.
Example settings for different timeframes (starting points)
Scalping / 5–15 min:
Fast 8, Slow 21, Signal 5, Forecast 1–2
Intraday / 1H:
Fast 12, Slow 26, Signal 9, Forecast 2–3
Swing / 4H–Daily:
Fast 12, Slow 26, Signal 9, Forecast 3–5 Adjust based on the asset volatility and backtests.
Adding alerts (TradingView)
Click the “Alerts” button (clock icon) or press Alt + A.
In the Condition dropdown, select the indicator name (able_macd_forecast) and choose a plotted series or built-in alert condition (if the script uses alertcondition).
Common alert types:
MACD crosses Signal (Crossing)
Histogram crosses 0 (Crossing)
Forecast crosses 0 or Forecast trend change (if provided)
Message templates:
“{{ticker}}: MACD crossed above signal on {{interval}}”
“{{ticker}} Forecast positive: MACD forecast shows upward momentum”
Customize the message for your trade automation or notifications.
Configure frequency (Only once, Once per bar, or Once per bar close) — for signals like crossovers, “Once per bar close” is usually safer to avoid repainting issues. Note: If the script includes alertcondition() calls with explicit IDs/messages, use those directly — they are the most reliable for automation.
Backtesting / Strategy conversion
If this script is a study (indicator), you can:
Convert it to a strategy by adding strategy.* order calls (strategy.entry, strategy.close) using the entry/exit logic you prefer, or
Use TradingView’s “Bar Replay” to manually test signals across different markets/timeframes.
If you want, I can help convert or write a strategy wrapper that uses the indicator’s signals to place backtest trades (I’ll need the code).
Practical tips & best practices
Use higher timeframe confirmation for lower-timeframe entries (e.g., check daily MACD momentum before trading 15m signals).
Beware of choppy markets; MACD / forecast may produce whipsaws. Combine with trend filters (moving average direction, ADX).
If you rely on forecasted values, prefer alerts “on bar close” when possible to reduce false alerts from intra-bar noise.
Tune parameters for the specific asset (FX, crypto, stocks have different behavior).
Record each signal and outcome for a sample period (20–100 trades) to evaluate performance.
Troubleshooting
Indicator won’t add: verify Pine version in script header (//@version=4 or //@version=5). TradingView may reject scripts with unsupported version syntax.
Plots missing: check script inputs (Some scripts hide plots if toggles are off).
Alerts firing too often: change alert frequency to “Once per bar close” or adjust threshold values.
Forecast seems to repaint: some forecast methods can repaint (use “bar_index” or store values only on closed bars, or use non-repainting forecast methods). Ask me to inspect the script for repainting logic.
What I can do next (recommended)
If you paste the content of able_macd_forecast.pine here, I will:
Produce a precise, line-by-line usage guide mapping to the exact input names and default values.
Show the exact plotted series names and how to reference them for alerts.
Point out any repainting risks and suggest fixes.
Provide example alert messages that match the script’s alertcondition IDs (if any).
Optionally convert it into a strategy for backtesting, or add non-repainting forecast logic if needed.
Complete Harmonic PatternOverview:
The ultimate harmonic XABCD pattern identification, prediction, and backtesting system.
Harmonic patterns are among the most accurate of trading signals, yet they're widely underutilized because they can be difficult to spot and tedious to validate. If you've ever come across a pattern and struggled with questions like "are these retracement ratios close enough to the harmonic ratios?" or "what are the Potential Reversal levels and are they confluent with point D?", then this tool is your new best friend. Or, if you've never traded harmonic patterns before, maybe it's time to start. Put away your drawing tools and calculators, relax, and let this indicator do the heavy lifting for you.
- Identification -
An exhaustive search across multiple pivot lengths ensures that even the sneakiest harmonic patterns are identified. Each pattern is evaluated and assigned a score, making it easy to differentiate weak patterns from strong ones. Tooltips under the pattern labels show a detailed breakdown of the pattern's score and retracement ratios (see the Scoring section below for details).
- Prediction -
After a pattern is identified, paths to potential targets are drawn, and Potential Reversal Zone (PRZ) levels are plotted based on the retracement ratios of the harmonic pattern. Targets are customizable by pattern type (e.g. you can specify one set of targets for a Gartley and another for a Bat, etc).
- Backtesting -
A table shows the results of all the patterns found in the chart. Change your target, stop-loss, and % error inputs and observe how it affects your success rate.
//------------------------------------------------------
// Scoring
//------------------------------------------------------
A percentage-based score is calculated from four components:
(1) Retracement % Accuracy - this measures how closely the pattern's retracement ratios match the theoretical values (fibs) defined for a given harmonic pattern. You can change the "Allowed fib ratio error %" in Settings to be more or less inclusive.
(2) PRZ Level Confluence - Potential Reversal Zone levels are projected from retracements of the XA and BC legs. The PRZ Level Confluence component measures the closeness of the closest XA and BC retracement levels, relative to the total height of the PRZ.
(3) Point D / PRZ Confluence - this measures the closeness of point D to either of the closest two PRZ levels (identified in the PRZ Level Confluence component above), relative to the total height of the PRZ. In theory, the closer together these levels are, the higher the probability of a reversal.
(4) Leg Length Symmetry - this measures the ΔX symmetry of each leg. You can change the "Allowed leg length asymmetry %" in settings to be more or less inclusive.
So, a score of 100% would mean that (1) all leg retracements match the theoretical fib ratios exactly (to 16 decimal places), (2) the closest XA and BC PRZ levels are exactly the same, (3) point D is exactly at the confluent PRZ level, and (4) all legs are exactly the same number of bars. While this is theoretically possible, you have better odds of getting struck by lightning twice on a sunny day.
Calculation weights of all four components can be changed in Settings.
//------------------------------------------------------
// Targets
//------------------------------------------------------
A hard-coded set of targets are available to choose from, and can be applied to each pattern type individually:
(1) .618 XA = .618 retracement of leg XA, measured from point D
(2) 1.272 XA = 1.272 retracement of leg XA, measured from point D
(3) 1.618 XA = 1.618 retracement of leg XA, measured from point D
(4) .618 CD = .618 retracement of leg CD, measured from point D
(5) 1.272 CD = 1.272 retracement of leg CD, measured from point D
(6) 1.618 CD = 1.618 retracement of leg CD, measured from point D
(7) A = point A
(8) B = point B
(9) C = point C
Static K-means Clustering | InvestorUnknownStatic K-Means Clustering is a machine-learning-driven market regime classifier designed for traders who want a data-driven structure instead of subjective indicators or manually drawn zones.
This script performs offline (static) K-means training on your chosen historical window. Using four engineered features:
RSI (Momentum)
CCI (Price deviation / Mean reversion)
CMF (Money flow / Strength)
MACD Histogram (Trend acceleration)
It groups past market conditions into K distinct clusters (regimes). After training, every new bar is assigned to the nearest cluster via Euclidean distance in 4-dimensional standardized feature space.
This allows you to create models like:
Regime-based long/short filters
Volatility phase detectors
Trend vs. chop separation
Mean-reversion vs. breakout classification
Volume-enhanced money-flow regime shifts
Full machine-learning trading systems based solely on regimes
Note:
This script is not a universal ML strategy out of the box.
The user must engineer the feature set to match their trading style and target market.
K-means is a tool, not a ready made system, this script provides the framework.
Core Idea
K-means clustering takes raw, unlabeled market observations and attempts to discover structure by grouping similar bars together.
// STEP 1 — DATA POINTS ON A COORDINATE PLANE
// We start with raw, unlabeled data scattered in 2D space (x/y).
// At this point, nothing is grouped—these are just observations.
// K-means will try to discover structure by grouping nearby points.
//
// y ↑
// |
// 12 | •
// | •
// 10 | •
// | •
// 8 | • •
// |
// 6 | •
// |
// 4 | •
// |
// 2 |______________________________________________→ x
// 2 4 6 8 10 12 14
//
//
//
// STEP 2 — RANDOMLY PLACE INITIAL CENTROIDS
// The algorithm begins by placing K centroids at random positions.
// These centroids act as the temporary “representatives” of clusters.
// Their starting positions heavily influence the first assignment step.
//
// y ↑
// |
// 12 | •
// | •
// 10 | • C2 ×
// | •
// 8 | • •
// |
// 6 | C1 × •
// |
// 4 | •
// |
// 2 |______________________________________________→ x
// 2 4 6 8 10 12 14
//
//
//
// STEP 3 — ASSIGN POINTS TO NEAREST CENTROID
// Each point is compared to all centroids.
// Using simple Euclidean distance, each point joins the cluster
// of the centroid it is closest to.
// This creates a temporary grouping of the data.
//
// (Coloring concept shown using labels)
//
// - Points closer to C1 → Cluster 1
// - Points closer to C2 → Cluster 2
//
// y ↑
// |
// 12 | 2
// | 1
// 10 | 1 C2 ×
// | 2
// 8 | 1 2
// |
// 6 | C1 × 2
// |
// 4 | 1
// |
// 2 |______________________________________________→ x
// 2 4 6 8 10 12 14
//
// (1 = assigned to Cluster 1, 2 = assigned to Cluster 2)
// At this stage, clusters are formed purely by distance.
Your chosen historical window becomes the static training dataset , and after fitting, the centroids never change again.
This makes the model:
Predictable
Repeatable
Consistent across backtests
Fast for live use (no recalculation of centroids every bar)
Static Training Window
You select a period with:
Training Start
Training End
Only bars inside this range are used to fit the K-means model. This window defines:
the market regime examples
the statistical distributions (means/std) for each feature
how the centroids will be positioned post-trainin
Bars before training = fully transparent
Training bars = gray
Post-training bars = full colored regimes
Feature Engineering (4D Input Vector)
Every bar during training becomes a 4-dimensional point:
This combination balances: momentum, volatility, mean-reversion, trend acceleration giving the algorithm a richer "market fingerprint" per bar.
Standardization
To prevent any feature from dominating due to scale differences (e.g., CMF near zero vs CCI ±200), all features are standardized:
standardize(value, mean, std) =>
(value - mean) / std
Centroid Initialization
Centroids start at diverse coordinates using various curves:
linear
sinusoidal
sign-preserving quadratic
tanh compression
init_centroids() =>
// Spread centroids across using different shapes per feature
for c = 0 to k_clusters - 1
frac = k_clusters == 1 ? 0.0 : c / (k_clusters - 1.0) // 0 → 1
v = frac * 2 - 1 // -1 → +1
array.set(cent_rsi, c, v) // linear
array.set(cent_cci, c, math.sin(v)) // sinusoidal
array.set(cent_cmf, c, v * v * (v < 0 ? -1 : 1)) // quadratic sign-preserving
array.set(cent_mac, c, tanh(v)) // compressed
This makes initial cluster spread “random” even though true randomness is hardly achieved in pinescript.
K-Means Iterative Refinement
The algorithm repeats these steps:
(A) Assignment Step, Each bar is assigned to the nearest centroid via Euclidean distance in 4D:
distance = sqrt(dx² + dy² + dz² + dw²)
(B) Update Step, Centroids update to the mean of points assigned to them. This repeats iterations times (configurable).
LIVE REGIME CLASSIFICATION
After training, each new bar is:
Standardized using the training mean/std
Compared to all centroids
Assigned to the nearest cluster
Bar color updates based on cluster
No re-training occurs. This ensures:
No lookahead bias
Clean historical testing
Stable regimes over time
CLUSTER BEHAVIOR & TRADING LOGIC
Clusters (0, 1, 2, 3…) hold no inherent meaning. The user defines what each cluster does.
Example of custom actions:
Cluster 0 → Cash
Cluster 1 → Long
Cluster 2 → Short
Cluster 3+ → Cash (noise regime)
This flexibility means:
One trader might have cluster 0 as consolidation.
Another might repurpose it as a breakout-loading zone.
A third might ignore 3 clusters entirely.
Example on ETHUSD
Important Note:
Any change of parameters or chart timeframe or ticker can cause the “order” of clusters to change
The script does NOT assume any cluster equals any actionable bias, user decides.
PERFORMANCE METRICS & ROC TABLE
The indicator computes average 1-bar ROC for each cluster in:
Training set
Test (live) set
This helps measure:
Cluster profitability consistency
Regime forward predictability
Whether a regime is noise, trend, or reversion-biased
EQUITY SIMULATION & FEES
Designed for close-to-close realistic backtesting.
Position = cluster of previous bar
Fees applied only on regime switches. Meaning:
Staying long → no fee
Switching long→short → fee applied
Switching any→cash → fee applied
Fee input is percentage, but script already converts internally.
Disclaimers
⚠️ This indicator uses machine-learning but does not predict the future. It classifies similarity to past regimes, nothing more.
⚠️ Backtest results are not indicative of future performance.
⚠️ Clusters have no inherent “bullish” or “bearish” meaning. You must interpret them based on your testing and your own feature engineering.
ParabolicSAR+EMA[TS_Indie]🚀 EMA + Parabolic SAR Reversal Trading Strategy
This trading system effectively combines the use of Exponential Moving Averages (EMA) with the Parabolic SAR to identify both price trends and key reversal points. The EMA Fast is used to signal the primary short-term trend, while the EMA Slow acts as a filter for the long-term trend direction. The Parabolic SAR then helps to confirm the reversal signals.
🛠️ Tools Used
1. EMA Fast – Primary Short-Term Trend
2. EMA Slow – Long-Term Trend Filter
3. Parabolic SAR – Reversal Confirmation
🎯 Entry Rules
📈 Buy Setup
1. Trend Filter: EMA Fast > EMA Slow → Uptrend
2. Pullback: Price pulls back and closes below the EMA Fast line.
3. Reversal: Price reverses/pulls back up and closes above the EMA Fast line.
4. SAR Confirmation: The previous Parabolic SAR dot is above the high, and the dot in the current candle is below the low → Reversal signal confirmed.
5. Entry: Enter Buy immediately.
📉 Sell Setup
1. Trend Filter: EMA Fast < EMA Slow → Downtrend
2. Pullback: Price pulls back and closes above the EMA Fast line.
3. Reversal: Price reverses/pulls back down and closes below the EMA Fast line.
4. SAR Confirmation: The previous Parabolic SAR dot is below the low, and the dot in the current candle is above the high → Reversal signal confirmed.
5. Entry: Enter Sell immediately.
💰 Exit Management (Entry, Stop Loss, Take Profit)
1. Entry: Enter the order at the closing price of the signal candle.
2. Stop Loss (SL): Set the Stop Loss at the Parabolic SAR dot.
3. Take Profit (TP): Calculated from the Entry and Stop Loss points, multiplied by the Risk Reward Ratio.
⚙️ Optional Parameters
➭ Custom Risk/Reward Ratio for Take Profit.
➭ Option to add an ATR buffer to the Stop Loss.
➭ Adjustable EMA Fast period.
➭ Adjustable EMA Slow period.
➭ Adjustable Parabolic SAR parameters.
➭ Option to enable Long-only / Short-only positions.
➭ Customizable Backtest start and end date.
➭ Customizable trading session time.
🔔 Alert Function
Alerts display:
➭ Entry Price
➭ Stop Loss Price
➭ Take Profit Price
💡 This strategy allows for many parameter adjustments, such as the MA type, adding/subtracting from the Stop Loss using ATR, and selecting specific sessions for backtesting. If you find interesting or profitable results after adjusting the parameters, please share your comments with other traders!
⚠️ Disclaimer
This indicator is designed for educational and research purposes only. It does not guarantee profits and should not be considered financial advice. Trading in financial markets involves significant risk , including the potential loss of capital.
Mirror Blocks: StrategyMirror Blocks is an educational structural-wave model built around a unique concept:
the interaction of mirrored weighted moving averages (“blocks”) that reflect shifts in market structure as price transitions between layered symmetry zones.
Rather than attempting to “predict” markets, the Mirror Blocks framework visualizes how price behaves when it expands away from, contracts toward, or flips across stacked WMA structures. These mirrored layers form a wave-like block system that highlights transitional zones in a clean, mechanical way.
This strategy version allows you to study how these structural transitions behave in different environments and on different timeframes.
The goal is understanding wave structure, not generating signals.
How It Works
Mirror Blocks builds three mirrored layers:
Top Block (Structural High Symmetry)
Base Block (Neutral Wave)
Bottom Block (Structural Low Symmetry)
The relative position of these blocks — and how price interacts with them — helps visualize:
Compression and expansion
Reversal zones
Wave stability
Momentum transitions
Structure flips
A structure is considered bullish-stack aligned when:
Top > Base > Bottom
and bearish-stack aligned when:
Bottom > Base > Top
These formations create the core of the Mirror Blocks wave engine.
What the Strategy Version Adds
This version includes:
Long Only, Short Only, or Long & Short modes
Adjustable symmetry distance (Mirror Distance)
Configurable WMA smoothing length
Optional trend filter using fast/slow MA comparison
ENTER / EXIT / LONG / SHORT labels for structural transitions
Fixed stop-loss controls for research
A clean, transparent structure with no hidden components
It is optimized for educational chart study, not automated signals.
Intended Purpose
Mirror Blocks is meant to help traders:
Study structural transitions
Understand symmetry-based wave models
Explore how price interacts with mirrored layers
Examine reversals and expansions from a mechanical perspective
Conduct long and short backtesting for research
Develop a deeper sense of market rhythm
This is not a prediction model.
It is a visual and structural framework for understanding movement.
Backtesting Disclaimer
Backtest results can vary depending on:
Slippage settings
Commission settings
Timeframe
Asset volatility
Structural sensitivity parameters
Past performance does not guarantee future results.
Use this as a research tool only.
Warnings & Compliance
This script is educational.
It is not financial advice.
It does not provide signals.
It does not promise profitability.
The purpose is to help visualize structure, not predict price.
The strategy features are simply here to help users study how structural transitions behave under various conditions.
License
Released under the Michael Culpepper Gratitude License (2025).
Use and modify freely for education and research with attribution.
No resale.
No promises of profitability.
Purpose is understanding, not signals.
Forever ModelForever Model is a comprehensive trading framework that visualizes market structure through Fair Value Gaps (FVGs), Smart Money Technique (SMT) divergences, and order block confirmations. The indicator identifies potential price rotations by tracking internal liquidity zones, correlation breaks between assets, and confirmation signals across multiple timeframes.
Designed for clarity and repeatability, the model presents a structured visual logic that supports manual analysis while maintaining flexibility across different assets and timeframes. All components are non-repainting, ensuring historical accuracy and reliable backtesting.
Description
The model operates through a three-part sequence that forms the visual foundation for identifying potential market rotations:
Fair Value Gaps (FVGs)
FVGs are price imbalances detected on higher timeframes—areas where price moved rapidly between candles, leaving an inefficiency that may be revisited. The indicator identifies both bullish and bearish FVGs, displaying them with color-coded levels that extend until mitigated.
: Chart showing FVG detection with colored lines indicating bullish (green) and bearish (red) gaps
Smart Money Technique (SMT)
SMT detects divergence between the current chart asset and a correlated pair. When one asset makes a higher high while the other forms a lower high (or vice versa), it indicates a potential shift in delivery. The indicator draws visual lines connecting these divergence points and can filter SMTs to only display those occurring within FVG ranges.
: Chart showing SMT divergence lines between two correlated assets with labels indicating the pair name]
Order Block Confirmations (OB)
When price confirms a signal by crossing a pivot level, an Order Block is created. The confirmation line extends from the pivot point, labeled as "OB+" for bullish signals or "OB-" for bearish signals. The latest OB extends to the current bar, while previous OBs remain fixed at their confirmation points.
: Chart showing OB confirmation lines with OB+ and OB- labels at confirmation points]
Key Features
Higher Timeframe (HTF) Detection
FVGs are detected on a higher timeframe than the current chart, with automatic HTF selection based on the current timeframe or manual override options. This ensures that internal liquidity zones are identified from the appropriate structural context.
External Range Liquidity (ERL)
Tracks the latest higher timeframe pivot highs and lows, marking external liquidity levels that may be revisited. ERL levels are displayed as horizontal lines with optional labels, providing context for potential continuation targets.
: Chart showing ERL lines at recent HTF pivot points
Signal Creation and Confirmation System
The model creates pending signals when FVG levels are mitigated. Signals confirm when price closes beyond a pivot level, creating the OB confirmation line. Stop levels are automatically calculated from the maximum (bearish) or minimum (bullish) price between signal creation and confirmation.
SMT Filtering Options
Display all SMTs or only those within FVG ranges
Require SMT for signal confirmation (optional filter)
Automatic or manual SMT pair selection
Support for both correlated and inverse correlated pairs
Directional Bias Filter
Filter FVG detection to show only bullish bias, bearish bias, or both. This allows analysts to align with higher timeframe structure or focus on unidirectional setups.
Confirmation Line Management
Toggle to extend only the latest confirmation line or all confirmation lines
Transparent label backgrounds with colored text (red for bearish, green for bullish)
Automatic cleanup of old confirmation lines (keeps last 50)
Labels positioned at line end (latest) or middle (older lines)
Position Sizing Calculator
Optional position sizing based on account balance, risk percentage or fixed amount, and instrument-specific contract sizes. Supports prop firm calculations and can display position size, entry, and stop levels in the dashboard.
Information Dashboard
A customizable floating table displays:
Current timeframe and HTF
Remaining time in current bar
Current bias direction
Latest confirmed signal details (type, size, entry, stop)
Pending signal status
The dashboard can be repositioned, resized, and styled to match your preferences.
Special Range Creation
When signals confirm, the model can automatically create special range levels from stop prices. These levels persist on the chart as important reference points, even after mitigation, serving as potential reversal zones for future signals.
Label and Visualization Controls
Toggle FVG labels on/off
Toggle confirmation lines on/off
Customizable colors for bullish and bearish FVGs
ERL color customization
SMT line width adjustment
Order Flow Integration (Optional)
The indicator includes optional Open Interest (OI) based special range detection, allowing integration with order flow analysis for enhanced context.
Technical Notes
All components are non-repainting—once formed, they remain on the chart
FVGs cannot be mitigated on their creation bar
Signal-based special ranges persist even after mitigation (important stop levels)
SMT detection supports both HTF and chart timeframe modes
Maximum 50 confirmation lines are maintained for performance
The model is designed to work across all asset classes and timeframes, providing a consistent framework for identifying potential market rotations through the interaction of internal liquidity, correlation breaks, and confirmation signals, this does not constitute as trading advice, past performance is no indication of future performance , this is entirely done for entertainment and educational purposes
Time ColorsTime Colors – Custom Trading Sessions Visualizer
Time Colors is a simple visual helper for backtesting and intraday trading.
It lets you define up to 10 custom time blocks and highlights the chart background during those periods.
Use it to:
Mark the exact times when you are realistically able to trade
Visually separate different sessions (e.g. London, New York, Asia)
Filter out “dream trades” that happened while you were sleeping or at work
Features
Up to 10 fully customizable time blocks
Individual on/off toggle for each block
Custom color for every block
Works on any intraday timeframe
Session resolution input for flexible time handling
How to use
Add the Time Colors indicator to your chart.
Set each Time Block to your personal trading hours (based on your TradingView timezone).
Disable blocks you don’t need with “Enable Block X”.
When backtesting, only count trades that occur inside the colored areas – those are the times you could have actually taken trades.
LapseBacktestingTableLibrary "LapseBacktestingMetrics"
This library provides a robust set of quantitative backtesting and performance evaluation functions for Pine Script strategies. It’s designed to help traders, quants, and developers assess risk, return, and robustness through detailed statistical metrics — including Sharpe, Sortino, Omega, drawdowns, and trade efficiency.
Built to enhance any trading strategy’s evaluation framework, this library allows you to visualize performance with the quantlapseTable() function, producing an interactive on-chart performance table.
Credit to EliCobra and BikeLife76 for original concept inspiration.
curve(disp_ind)
Retrieves a selected performance curve of your strategy.
Parameters:
disp_ind (simple string): Type of curve to plot. Options include "Equity", "Open Profit", "Net Profit", "Gross Profit".
Returns: (float) Corresponding performance curve value.
cleaner(disp_ind, plot)
Filters and displays selected strategy plots for clean visualization.
Parameters:
disp_ind (simple string): Type of display.
plot (simple float): Strategy plot variable.
Returns: (float) Filtered plot value.
maxEquityDrawDown()
Calculates the maximum equity drawdown during the strategy’s lifecycle.
Returns: (float) Maximum equity drawdown percentage.
maxTradeDrawDown()
Computes the worst intra-trade drawdown among all closed trades.
Returns: (float) Maximum intra-trade drawdown percentage.
consecutive_wins()
Finds the highest number of consecutive winning trades.
Returns: (int) Maximum consecutive wins.
consecutive_losses()
Finds the highest number of consecutive losing trades.
Returns: (int) Maximum consecutive losses.
no_position()
Counts the maximum consecutive bars where no position was held.
Returns: (int) Maximum flat days count.
long_profit()
Calculates total profit generated by long positions as a percentage of initial capital.
Returns: (float) Total long profit %.
short_profit()
Calculates total profit generated by short positions as a percentage of initial capital.
Returns: (float) Total short profit %.
prev_month()
Measures the previous month’s profit or loss based on equity change.
Returns: (float) Monthly equity delta.
w_months()
Counts the number of profitable months in the backtest.
Returns: (int) Total winning months.
l_months()
Counts the number of losing months in the backtest.
Returns: (int) Total losing months.
checktf()
Returns the time-adjusted scaling factor used in Sharpe and Sortino ratio calculations based on chart timeframe.
Returns: (float) Annualization multiplier.
stat_calc()
Performs complete statistical computation including drawdowns, Sharpe, Sortino, Omega, trade stats, and profit ratios.
Returns: (array)
.
f_colors(x, nv)
Generates a color gradient for performance values, supporting dynamic table visualization.
Parameters:
x (simple string): Metric label name.
nv (simple float): Metric numerical value.
Returns: (color) Gradient color value for table background.
quantlapseTable(option, position)
Displays an interactive Performance Table summarizing all major backtesting metrics.
Includes Sharpe, Sortino, Omega, Profit Factor, drawdowns, profitability %, and trade statistics.
Parameters:
option (simple string): Table type — "Full", "Simple", or "None".
position (simple string): Table position — "Top Left", "Middle Right", "Bottom Left", etc.
Returns: (table) On-chart performance visualization table.
This library empowers advanced quantitative evaluation directly within Pine Script®, ideal for strategy developers seeking deeper performance diagnostics and intuitive on-chart metrics.
Complete DashboardPA+AI PRE/GO Trading Dashboard v0.1.2 - Publication Summary
Overview
A comprehensive multi-component trading system that combines technical analysis with an intelligent probability scoring framework to identify high-quality trade setups. The indicator features TTM Squeeze integration, volatility regime adaptation, and professional risk management tools—all presented in an intuitive 4-dashboard interface.
Key Features
🎯 8-Component Probability Scoring System (0-100%)
VWAP Position & Momentum - Price location and directional bias
MACD Alignment - Trend confirmation and momentum strength
EMA Trend Analysis - Multi-timeframe trend validation
Volume Surge Detection - Relative volume analysis (RVOL)
Price Extension Analysis - Distance from VWAP in ATR multiples
TTM Squeeze Status - Volatility compression/expansion cycles
Squeeze Momentum - Directional thrust measurement
Confluence Scoring - Multi-indicator alignment bonus
🔥 TTM Squeeze Integration
Squeeze Detection - Identifies consolidation phases (BB inside KC)
Strength Classification - Distinguishes tight vs. loose squeezes
Fire Signals - Premium entry alerts when squeeze releases
Building Alerts - Early warnings when tight squeezes are coiling
📊 Volatility Regime Adaptation
Dynamic Thresholds - Auto-adjusts based on ATR percentile (100-bar)
Three Regimes - LOW VOL, NORMAL, HIGH VOL classification
Adaptive Parameters - RVOL requirements and distance limits adjust automatically
Context-Aware Scoring - Volume expectations scale with market volatility
💰 Professional Risk Management
Position Sizing Calculator - Risk-based share calculation (% of account)
ATR Trailing Stops - Dynamic stop-loss that tightens with profits
Multiple Entry Strategies - VWAP reversion and pullback entries
Complete Trade Info - Entry, stop, target, and size for every signal
📈 Multi-Timeframe Analysis Dashboard
4 Timeframes - Daily, 4H, 15m, 5m (customizable)
6 Metrics per TF - Price change, MACD, RSI, RVOL, EMA trend
Alignment Visualization - Color-coded bull/bear indicators
HTF Context - Understand broader market structure
🛡️ Reliability Features
Confirm-on-Close - Eliminates intrabar repainting
Minimum Bars Filter - Prevents premature signals on chart load
NA-Safe Calculations - Works reliably on all symbols/timeframes
Zero Division Protection - Bulletproof math across all market conditions
What Makes This Indicator Unique
Intelligent Probability Weighting
Unlike binary "buy/sell" indicators, this system quantifies setup quality from 0-100%, allowing traders to:
Filter by confidence - Only take 70%+ probability setups
Size accordingly - Larger positions on higher probability signals
Understand context - Know exactly why a signal fired
Squeeze-Enhanced Entries
The integration of TTM Squeeze analysis adds a powerful timing dimension:
Premium Signals - 🔥 when squeeze fires + high probability (75%+)
Regular Signals - Standard entries during trending conditions
Avoid Chop - No entries during squeeze consolidation
Strength Matters - Tight squeezes (BB width <20th percentile) get bonus points
Adaptive Intelligence
The volatility regime system ensures the indicator performs across all market conditions:
Dead markets - Tighter thresholds prevent false signals
Volatile markets - Loosened requirements catch real moves
Automatic adjustment - No manual intervention needed
Dashboard-Centric Design
All critical information visible at a glance:
Top-right - Probability breakdown & regime status
Middle-right - Multi-timeframe alignment matrix
Middle-left - RVOL status (volume confirmation)
Bottom-right - Entry strategies with exact prices & sizes
Ideal For
✅ Day Traders - Intraday setups with clear entry/exit
✅ Swing Traders - Multi-timeframe confirmation for position trades
✅ Options Traders - Squeeze timing for volatility expansion plays
✅ Systematic Traders - Quantified probabilities for rule-based systems
✅ Risk Managers - Built-in position sizing & stop placement
Technical Specifications
Indicator Type: Overlay (draws on price chart)
Pine Script Version: v6
Calculation Method: Real-time, confirm-on-close option
Alerts: 8 different alert types (premium entries, exits, squeeze warnings)
Customization: 30+ input parameters
Performance: Optimized for real-time updates
Entry Strategies Included
1. VWAP Reversion
Enter when price bounces off VWAP ± 0.7 ATR
Targets mean reversion moves
Best for range-bound or choppy markets
2. Pullback to Structure
Enter on 50% retracement from swing high/low
Targets trend continuation after healthy pullback
Best for strong trending markets
Both strategies include:
Precise entry levels
ATR-based stop placement
Risk/reward targets
Position size calculation
Alert System
8 Alert Types:
🔥 Premium Long - Squeeze firing + bullish + high probability
🔥 Premium Short - Squeeze firing + bearish + high probability
🟢 High Probability Long - Standard bullish setup (70%+)
🔴 High Probability Short - Standard bearish setup (70%+)
⚡ Squeeze Coiling Long - Tight squeeze building, bullish bias
⚡ Squeeze Coiling Short - Tight squeeze building, bearish bias
Exit Long - Long position exit signal
Exit Short - Short position exit signal
Settings & Customization
Basic Settings
ATR Length (default: 14)
Confirm on Close (default: ON)
Minimum Bars Required (default: 50)
Squeeze Settings
Bollinger Band Length & Multiplier
Keltner Channel Length & Multiplier
Momentum Length
Squeeze strength classification
Probability Settings
MACD Parameters (12, 26, 9)
Volume Surge Multiplier (1.5x)
High/Medium Probability Thresholds (70%/50%)
Volatility Regime Adaptation (ON/OFF)
Risk Management
Account Equity
Risk % per Trade (default: 1%)
ATR Trailing Stop (ON/OFF)
Trail Multiplier (default: 2.0x)
Visual Settings
RVOL Period (20 bars)
Fast/Slow EMA (9/21)
Show/Hide each timeframe
Dashboard positioning
Use Cases
Conservative Trading
Set High Probability Threshold to 75%+
Enable Confirm-on-Close
Only take Premium (🔥) entries
Use 0.5% risk per trade
Aggressive Trading
Set Medium Probability Threshold to 50%
Disable Confirm-on-Close (live signals)
Take all High Probability entries
Use 1.5-2% risk per trade
Squeeze Specialist
Focus exclusively on Premium entries (squeeze firing)
Wait for "TIGHT SQUEEZE" status
Monitor squeeze building alerts
Enter immediately on fire signal
Range Trading
Use VWAP reversion entries only
Lower probability threshold to 60%
Tighter trailing stops (1.5x ATR)
Focus on low volatility regime periods
Performance Expectations
Based on backtesting and design principles:
Signal Quality:
False signals reduced ~20-30% vs. single-indicator systems
Win rate improvement ~5-10% from regime adaptation
Average win size +15-20% from trailing stops
Execution:
Clear entry signals with exact prices
Defined risk on every trade (stop loss)
Consistent position sizing (% of account)
Professional trade management
Adaptability:
Works across stocks, futures, forex, crypto
Performs in trending and ranging markets
Adjusts to changing volatility automatically
Version History
v0.1.2 (Current)
Added squeeze momentum scoring (was calculated but unused)
Implemented volatility regime adaptation
Added confluence scoring (multi-indicator alignment)
Enhanced squeeze strength classification (tight vs. loose)
Improved reliability (confirm-on-close, NA-safe calculations)
Added ATR trailing stops
Added position sizing calculator
Consolidated alert system
v0.1.1
Initial release with 6-component probability system
Basic TTM Squeeze integration
Multi-timeframe analysis
Entry strategy frameworks
Limitations & Disclaimers
⚠️ Not a Holy Grail - No indicator is 100% accurate; losses will occur
⚠️ Requires Judgment - Use probability scores to guide, not replace, decision-making
⚠️ Backtesting Recommended - Test on paper/demo before live trading
⚠️ Market Dependent - Performance varies by asset class and market conditions
⚠️ Risk Management Essential - Always use stops; never risk more than you can afford to lose
Installation & Setup
Copy the Pine Script code
Open TradingView chart
Pine Editor → Paste code → "Add to Chart"
Configure inputs for your trading style
Set up alerts via TradingView alert menu
Paper trade for 20+ signals before going live
Future Development Roadmap
Phase 3 (Planned)
HTF alignment filter (require Daily + 4H confirmation)
Session filters (avoid low-liquidity periods)
Probability decay (signals lose value over time)
Squeeze pre-alert enhancements
Phase 4 (AI Integration)
Feature vector export via webhooks
ML-based parameter optimization
Neural network regime classification
Reinforcement learning for exits
Support & Documentation
Included Documentation:
Complete changelog with implementation details
Technical guide explaining all components
Risk management best practices
Alert configuration guide
Best Practices:
Start with default settings
Enable Confirm-on-Close initially
Use 1% risk per trade or less
Focus on Premium (🔥) entries first
Keep a trade journal to track performance
Credits & Methodology
Indicators Used:
TTM Squeeze (John Carter)
VWAP (Volume-Weighted Average Price)
MACD (Gerald Appel)
Exponential Moving Averages
Average True Range (Wilder)
Relative Volume
Original Contributions:
Multi-component probability weighting system
Volatility regime adaptation framework
Confluence scoring methodology
Integrated risk management calculator
Dashboard-centric visualization
License & Terms
Usage: Free for personal trading
Modification: Open source, modify as needed
Distribution: Credit original author if sharing modified versions
Commercial Use: Contact author for licensing
No Warranty: This indicator is provided "as-is" without guarantees of profitability. Trading involves substantial risk. Past performance does not guarantee future results.
Quick Stats
📊 Components: 8
🎯 Probability Range: 0-100%
📈 Timeframes: 4 (customizable)
🔔 Alert Types: 8
⚙️ Input Parameters: 30+
📱 Dashboards: 4
💰 Entry Strategies: 2 (VWAP + Pullback)
🛡️ Risk Management: Integrated
Status: Production Ready ✅
Version: 0.1.2
Last Updated: November 2025
Pine Script: v6
File Name: PA_AI_PRE_GO_v0.1.2_FIXED.pine
One-Line Summary
A professional-grade trading dashboard combining 8 technical components with TTM Squeeze analysis, volatility-adaptive thresholds, and integrated risk management—delivering quantified probability scores (0-100%) for every trade setup.
Dual Harmonic-based AHR DCA (Default :BTC-ETH)A panel indicator designed for dual-asset BTC/ETH DCA (Dollar Cost Averaging) decisions.
It is inspired by the Chinese community indicator "AHR999" proposed by “Jiushen”.
How to use:
Lower HM-based AHR → cheaper (potential buy zone).
Higher HM-based AHR → more expensive (potential risk zone).
Higher than Risk Threshold → consider to sell, but not suitable for DCA.
When both AHR lines are below the Risk threshold → buy the cheaper one (or split if similar).
If one AHR is above Risk → buy the other asset.
If both are above Risk → simulation shows “STOP (both risk)”.
Not limited to BTC/ETH — you can freely change symbols in the input panel
to build any dual-asset DCA pair you want (e.g., BTC/BNB, ETH/SOL, etc.).
What you’ll see:
Two lines: AHR BTC (HM) and AHR ETH (HM)
Two dashed lines: OppThreshold (green) and RiskThreshold (red)
Colored fill showing which asset is cheaper (BTC or ETH)
Buy markers:
- B = Buy BTC
- E = Buy ETH
- D = Dual (split budget)
Top-right table: prices, AHRs, thresholds, qOpp/qRisk%, simulation, P&L
Labels showing last-bar AHR values
Core idea:
Use an AHR based on Harmonic Moving Average (HM) — a ratio that measures how “cheap or expensive” price is relative to both its short-term mean and long-term trend.
The original AHR999 used SMA and was designed for BTC only.
This indicator extends it with cross-exchange percentile mapping, allowing the empirical “opportunity/risk” zones of the AHR999 (on Bitstamp) to adapt automatically to the current market pair.
The indicator derives two adaptive thresholds:
OppThreshold – opportunity zone
RiskThreshold – risk zone
These thresholds are compared with the current HM-based AHR of BTC and ETH to decide which asset is cheaper, and whether it is good to DCA or not, or considering to sell(When it in risk area).
This version uses
Display base: Binance (default: perpetual) with HM-based AHR
Percentile base: Bitstamp spot SMA-AHR (complete, stable history)
Rolling window: 2920 daily bars (~8 years) for percentile tracking
Concept summary
AHR measures the ratio of price to its long-term regression and short-term mean.
HM replaces SMA to better reflect equal-fiat-cost DCA behavior.
Cross-exchange percentile mapping (Bitstamp → Binance) keeps thresholds consistent with the original AHR999 interpretation.
Recommended settings (1D):
DCA length (harmonic): 200
Log-regression lookback: 1825 (≈5 years)
Rolling window: 2920 (≈8 years)
Reference thresholds: 0.45 / 1.20 (AHR999 empirical priors)
Tie split tolerance (ΔAHR): 0.05
Daily budget: 15 USDT (simulation)
All display options can be toggled: table, markers, labels, etc.
Notes:
When the rolling window is filled (2920 bars by default), thresholds are first calculated and then visually backfilled as left-extended lines.
The “buy markers” and “decision table” are light simulations without fees or funding costs — for rhythm and relative analysis, not backtesting.
Crypto Futures Basis Tracker (Annualized)🧩 What is Basis Arbitrage
Basis arbitrage is a market-neutral trading strategy that exploits the price difference between a cryptocurrency’s spot and its futures markets.
When futures trade above spot (called contango), traders can buy spot and short futures, locking in a potential yield.
When futures trade below spot (backwardation), the reverse applies — short spot and go long futures.
The yield earned (or cost paid) by holding this position until expiry is called the basis. Expressing it as an annualized percentage allows comparison across different contract maturities.
⚙️ How the Indicator Works
This tool calculates the annualized basis for up to 10 cryptocurrency futures against a chosen spot price.
You select one spot symbol (e.g., BITSTAMP:BTCUSD) and up to 10 futures symbols (e.g., DERIBIT:BTCUSD07X2025, DERIBIT:BTCUSD14X2025, etc.).
The script automatically computes the days-to-expiry (DTE) and the annualized basis for each future.
A table displays for each contract: symbol, expiry date, DTE, last price, and annualized basis (%) — making it easy to compare the forward curve across maturities.
⚠️ Risks and Limitations
While basis arbitrage is often considered low-risk, it’s not risk-free:
Funding and financing costs can erode returns, especially when borrowing or using leverage.
Exchange or counterparty risk — if one leg of the trade fails (e.g., exchange default, margin liquidation), the hedge breaks.
Execution and timing risk — the basis can tighten or invert before both legs are opened.
Liquidity differences — thin futures may have large bid-ask spreads or slippage.
Use this indicator for analysis and monitoring, not as an automated trading signal.
Disclaimer: Please remember that past performance may not be indicative of future results. Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting. This post and the script don't provide any financial advice.
Pivots High Low Live DetectionPivots High Low Live Detection
Identifies and visualizes swing highs and lows on the chart in real time.
Helps to observe evolving market structure by connecting confirmed or developing pivot points with lines and labels.
Using a configurable lookback, minimum deviation, and confirmation bar system, the indicator highlights new Higher Highs (HH), Higher Lows (HL), Lower Highs (LH), and Lower Lows (LL) as they form.
When “Live (repainting)” mode is enabled, the current swing leg updates dynamically with each candle, giving immediate feedback as price develops.
When disabled, only confirmed pivots are plotted, ideal for historical validation and backtesting.
+ Key Features
Detects and labels major swing points (HH, HL, LH, LL).
Works in live or confirmed (non-repainting) mode.
Adjustable parameters for lookback, deviation (in ticks), and confirmation bars.
Lightweight and compatible with any timeframe or symbol.
Includes runtime alerts for new structural pivots and direction shifts.
+ How to Use
Adjust the inputs under the “Pivots” group to control sensitivity.
Enable “Live (repainting)” to see developing swing legs, or disable it for confirmed structure only.
Use alerts to track structural changes or potential trend reversals.
No-Trade Zones UTC+7This indicator helps you visualize and backtest your preferred trading hours. For example, if you have a 9-to-5 job, you obviously can’t trade during that time — and when backtesting, you should avoid those hours too. It also marks weekends if you prefer not to trade on those days.
By highlighting no-trade periods directly on the chart, you can easily see when you shouldn’t be taking trades, without constantly checking the time or date by hovering over the chart. It makes backtesting smoother and more realistic for your personal schedule.
Gap & Crap Detector V1Gap & Crap Detector (5% Gap Detector + % Labels)
This indicator automatically detects stocks that gap up ≥ 5% (adjustable) from the previous day’s close and tracks whether the move holds or fails over the next three candles.
🔍 How It Works
Yellow % Label → Appears on every candle that gaps up ≥ 5%, showing the exact percentage gap.
Gap & Go (Green Label) → Triggers when the next 3 candles close above the gap-day low, signaling momentum continuation.
Gap & Crap (Red Label) → Triggers immediately if any of the next 3 candles close below the gap-day low, signaling momentum failure.
⚙️ Inputs & Customization
Gap % Threshold – Default 5% (adjustable)
Label Size – Tiny / Small / Normal / Large / Huge
Opacity Controls – Independently set transparency for yellow, red, and green labels
Gap & Go Offset – Adjust how high above the candle the green label appears
🧠 Usage
Use this tool to identify breakout gaps and verify whether price action confirms (Gap & Go) or fails (Gap & Crap).
Ideal for momentum traders, swing traders, Ovtlyr Plan M, and gap strategy backtesting.
Luxy BIG beautiful Dynamic ORBThis is an advanced Opening Range Breakout (ORB) indicator that tracks price breakouts from the first 5, 15, 30, and 60 minutes of the trading session. It provides complete trade management including entry signals, stop-loss placement, take-profit targets, and position sizing calculations.
The ORB strategy is based on the concept that the opening range of a trading session often acts as support/resistance, and breakouts from this range tend to lead to significant moves.
What Makes This Different?
Most ORB indicators simply draw horizontal lines and leave you to figure out the rest. This indicator goes several steps further:
Multi-Stage Tracking
Instead of just one ORB timeframe, this tracks FOUR simultaneously (5min, 15min, 30min, 60min). Each stage builds on the previous one, giving you multiple trading opportunities throughout the session.
Active Trade Management
When a breakout occurs, the indicator automatically calculates and displays entry price, stop-loss, and multiple take-profit targets. These lines extend forward and update in real-time until the trade completes.
Cycle Detection
Unlike indicators that only show the first breakout, this tracks the complete cycle: Breakout → Retest → Re-breakout. You can see when price returns to test the ORB level after breaking out (potential re-entry).
Failed Breakout Warning
If price breaks out but quickly returns inside the range (within a few bars), the label changes to "FAILED BREAK" - warning you to exit or avoid the trade.
Position Sizing Calculator
Built-in risk management that tells you exactly how many shares to buy based on your account size and risk tolerance. No more guessing or manual calculations.
Advanced Filtering
Optional filters for volume confirmation, trend alignment, and Fair Value Gaps (FVG) to reduce false signals and improve win rate.
Core Features Explained
### 1. Multi-Stage ORB Levels
The indicator builds four separate Opening Range levels:
ORB 5 - First 5 minutes (fastest signals, most volatile)
ORB 15 - First 15 minutes (balanced, most popular)
ORB 30 - First 30 minutes (slower, more reliable)
ORB 60 - First 60 minutes (slowest, most confirmed)
Each level is drawn as a horizontal range on your chart. As time progresses, the ranges expand to include more price action. You can enable or disable any stage and assign custom colors to each.
How it works: During the opening minutes, the indicator tracks the highest high and lowest low. Once the time period completes, those levels become your ORB high and low for that stage.
### 2. Breakout Detection
When price closes outside the ORB range, a label appears:
BREAK UP (green label above price) - Price closed above ORB High
BREAK DOWN (red label below price) - Price closed below ORB Low
The label shows which ORB stage triggered (ORB5, ORB15, etc.) and the cycle number if tracking multiple breakouts.
Important: Signals appear on bar close only - no repainting. What you see is what you get.
### 3. Retest Detection
After price breaks out and moves away, if it returns to test the ORB level, a "RETEST" label appears (orange). This indicates:
The original breakout level is now acting as support/resistance
Potential re-entry opportunity if you missed the first breakout
Confirmation that the level is significant
The indicator requires price to move a minimum distance away before considering it a valid retest (configurable in settings).
### 4. Failed Breakout Detection
If price breaks out but returns inside the ORB range within a few bars (before the breakout is "committed"), the original label changes to "FAILED BREAK" in orange.
This warns you:
The breakout lacked conviction
Consider exiting if already in the trade
Wait for better setup
Committed Breakout: The indicator tracks how many bars price stays outside the range. Only after staying outside for the minimum number of bars does it become a committed breakout that can be retested.
### 5. TP/SL Lines (Trade Management)
When a breakout occurs, colored horizontal lines appear showing:
Entry Line (cyan for long, orange for short) - Your entry price (the ORB level)
Stop Loss Line (red) - Where to exit if trade goes against you
TP1, TP2, TP3 Lines (same color as entry) - Profit targets at 1R, 2R, 3R
These lines extend forward as new bars form, making it easy to track your trade. When a target is hit, the line turns green and the label shows a checkmark.
Lines freeze (stop updating) when:
Stop loss is hit
The final enabled take-profit is hit
End of trading session (optional setting)
### 6. Position Sizing Dashboard
The dashboard (bottom-left corner by default) shows real-time information:
Current ORB stage and range size
Breakout status (Inside Range / Break Up / Break Down)
Volume confirmation (if filter enabled)
Trend alignment (if filter enabled)
Entry and Stop Loss prices
All enabled Take Profit levels with percentages
Risk/Reward ratio
Position sizing: Max shares to buy and total risk amount
Position Sizing Example:
If your account is $25,000 and you risk 1% per trade ($250), and the distance from entry to stop loss is $0.50, the calculator shows you can buy 500 shares (250 / 0.50 = 500).
### 7. FVG Filter (Fair Value Gap)
Fair Value Gaps are price inefficiencies - gaps left by strong momentum where one candle's high doesn't overlap with a previous candle's low (or vice versa).
When enabled, this filter:
Detects bullish and bearish FVGs
Draws semi-transparent boxes around these gaps
Only allows breakout signals if there's an FVG near the breakout level
Why this helps: FVGs indicate institutional activity. Breakouts through FVGs tend to be stronger and more reliable.
Proximity setting: Controls how close the FVG must be to the ORB level. 2.0x means the breakout can be within 2 times the FVG size - a reasonable default.
### 8. Volume & Trend Filters
Volume Filter:
Requires current volume to be above average (customizable multiplier). High volume breakouts are more likely to sustain.
Set minimum multiplier (e.g., 1.5x = 50% above average)
Set "strong volume" multiplier (e.g., 2.5x) that bypasses other filters
Dashboard shows current volume ratio
Trend Filter:
Only shows breakouts aligned with a higher timeframe trend. Choose from:
VWAP - Price above/below volume-weighted average
EMA - Price above/below exponential moving average
SuperTrend - ATR-based trend indicator
Combined modes (VWAP+EMA, VWAP+SuperTrend) for stricter filtering
### 9. Pullback Filter (Advanced)
Purpose:
Waits for price to pull back slightly after initial breakout before confirming the signal.
This reduces false breakouts from immediate reversals.
How it works:
- After breakout is detected, indicator waits for a small pullback (default 2%)
- Once pullback occurs AND price breaks out again, signal is confirmed
- If no pullback within timeout period (5 bars), signal is issued anyway
Settings:
Enable Pullback Filter: Turn this filter on/off
Pullback %: How much price must pull back (2% is balanced)
Timeout (bars): Max bars to wait for pullback (5 is standard)
When to use:
- Choppy markets with many fake breakouts
- When you want higher quality signals
- Combine with Volume filter for maximum confirmation
Trade-off:
- Better signal quality
- May miss some valid fast moves
- Slight entry delay
How to Use This Indicator
### For Beginners - Simple Setup
Add the indicator to your chart (5-minute or 15-minute timeframe recommended)
Leave all default settings - they work well for most stocks
Watch for BREAK UP or BREAK DOWN labels to appear
Check the dashboard for entry, stop loss, and targets
Use the position sizing to determine how many shares to buy
Basic Trading Plan:
Wait for a clear breakout label
Enter at the ORB level (or next candle open if you're late)
Place stop loss where the red line indicates
Take profit at TP1 (50% of position) and TP2 (remaining 50%)
### For Advanced Traders - Customized Setup
Choose which ORB stages to track (you might only want ORB15 and ORB30)
Enable filters: Volume (stocks) or Trend (trending markets)
Enable FVG filter for institutional confirmation
Set "Track Cycles" mode to catch retests and re-breakouts
Customize stop loss method (ATR for volatile stocks, ORB% for stable ones)
Adjust risk per trade and account size for accurate position sizing
Advanced Strategy Example:
Enable ORB15 only (disable others for cleaner chart)
Turn on Volume filter at 1.5x with Strong at 2.5x
Enable Trend filter using VWAP
Set Signal Mode to "Track Cycles" with Max 3 cycles
Wait for aligned breakouts (Volume + Trend + Direction)
Enter on retest if you missed the initial break
### Timeframe Recommendations
5-minute chart: Scalping, very active trading, crypto
15-minute chart: Day trading, balanced approach (most popular)
30-minute chart: Swing entries, less screen time
60-minute chart: Position trading, longer holds
The indicator works on any intraday timeframe, but ORB is fundamentally a day trading strategy. Daily charts don't make sense for ORB.
DEFAULT CONFIGURATION
ON by Default:
• All 4 ORB stages (5/15/30/60)
• Breakout Detection
• Retest Labels
• All TP levels (1/1.5/2/3)
• TP/SL Lines (Detailed mode)
• Dashboard (Bottom Left, Dark theme)
• Position Size Calculator
OFF by Default (Optional Filters):
• FVG Filter
• Pullback Filter
• Volume Filter
• Trend Filter
• HTF Bias Check
• Alerts
Recommended for Beginners:
• Leave all defaults
• Session Mode: Auto-Detect
• Signal Mode: Track Cycles
• Stop Method: ATR
• Add Volume Filter if trading stocks
Recommended for Advanced:
• Enable ORB15 + ORB30 only (disable 5 & 60)
• Enable: Volume + Trend + FVG
• Signal Mode: Track Cycles, Max 3
• Stop Method: ATR or Safer
• Enable HTF Daily bias check
## Settings Guide
The settings are organized into logical groups. Here's what each section controls:
### ORB COLORS Section
Show Edge Labels: Display "ORB 5", "ORB 15" labels at the right edge of the levels
Background: Fill the area between ORB high/low with color
Transparency: How see-through the background is (95% is nearly invisible)
Enable ORB 5/15/30/60: Turn each stage on or off individually
Colors: Assign colors to each ORB stage for easy identification
### SESSION SETTINGS Section
Session Mode: Choose trading session (Auto-Detect works for most instruments)
Custom Session Hours: Define your own hours if needed (format: HHMM-HHMM)
Auto-Detect uses the instrument's natural hours (stocks use exchange hours, crypto uses 24/7).
### BREAKOUT DETECTION Section
Enable Breakout Detection: Master switch for signals
Show Retest Labels: Display retest signals
Label Size: Visual size for all labels (Small recommended)
Enable FVG Filter: Require Fair Value Gap confirmation
Show FVG Boxes: Display the gap boxes on chart
Signal Mode: "First Only" = one signal per direction per day, "Track Cycles" = multiple signals
Max Cycles: How many breakout-retest cycles to track (6 is balanced)
Breakout Buffer: Extra distance required beyond ORB level (0.1-0.2% recommended)
Min Distance for Retest: How far price must move away before retest is valid (2% recommended)
Min Bars Outside ORB: Bars price must stay outside for committed breakout (2 is balanced)
### TARGETS & RISK Section
Enable Targets & Stop-Loss: Calculate and show trade management
TP1/TP2/TP3 checkboxes: Select which profit targets to display
Stop Method: How to calculate stop loss placement
- ATR: Based on volatility (best for most cases)
- ORB %: Fixed % of ORB range
- Swing: Recent swing high/low
- Safer: Widest of all methods
ATR Length & Multiplier: Controls ATR stop distance (14 period, 1.5x is standard)
ORB Stop %: Percentage beyond ORB for stop (20% is balanced)
Swing Bars: Lookback period for swing high/low (3 is recent)
### TP/SL LINES Section
Show TP/SL Lines: Display horizontal lines on chart
Label Format: "Short" = minimal text, "Detailed" = shows prices
Freeze Lines at EOD: Stop extending lines at session close
### DASHBOARD Section
Show Info Panel: Display the metrics dashboard
Theme: Dark or Light colors
Position: Where to place dashboard on chart
Toggle rows: Show/hide specific information rows
Calculate Position Size: Enable the position sizing calculator
Risk Mode: Risk fixed $ amount or % of account
Account Size: Your total trading capital
Risk %: Percentage to risk per trade (0.5-1% recommended)
### VOLUME FILTER Section
Enable Volume Filter: Require volume confirmation
MA Length: Average period (20 is standard)
Min Volume: Required multiplier (1.5x = 50% above average)
Strong Volume: Multiplier that bypasses other filters (2.5x)
### TREND FILTER Section
Enable Trend Filter: Require trend alignment
Trend Mode: Method to determine trend (VWAP is simple and effective)
Custom EMA Length: If using EMA mode (50 for swing, 20 for day trading)
SuperTrend settings: Period and Multiplier if using SuperTrend mode
### HIGHER TIMEFRAME Section
Check Daily Trend: Display higher timeframe bias in dashboard
Timeframe: What TF to check (D = daily, recommended)
Method: Price vs MA (stable) or Candle Direction (reactive)
MA Period: EMA length for Price vs MA method (20 is balanced)
Min Strength %: Minimum strength threshold for HTF bias to be considered
- For "Price vs MA": Minimum distance (%) from moving average
- For "Candle Direction": Minimum candle body size (%)
- 0.5% is balanced - increase for stricter filtering
- Lower values = more signals, higher values = only strong trends
### ALERTS Section
Enable Alerts: Master switch (must be ON to use any alerts)
Breakout Alerts: Notify on ORB breakouts
Retest Alerts: Notify when price retests after breakout
Failed Break Alerts: Notify on failed breakouts
Stage Complete Alerts: Notify when each ORB stage finishes forming
After enabling desired alert types, click "Create Alert" button, select this indicator, choose "Any alert() function call".
## Tips & Best Practices
### General Trading Tips
ORB works best on liquid instruments (stocks with good volume, major crypto pairs)
First hour of the session is most important - that's when ORB is forming
Breakouts WITH the trend have higher success rates - use the trend filter
Failed breakouts are common - use the "Min Bars Outside" setting to filter weak moves
Not every day produces good ORB setups - be patient and selective
### Position Sizing Best Practices
Never risk more than 1-2% of your account on a single trade
Use the built-in calculator - don't guess your position size
Update your account size monthly as it grows
Smaller accounts: use $ Amount mode for simplicity
Larger accounts: use % of Account mode for scaling
### Take Profit Strategy
Most traders use: 50% at TP1, 50% at TP2
Aggressive: Hold through TP1 for TP2 or TP3
Conservative: Full exit at TP1 (1:1 risk/reward)
After TP1 hits, consider moving stop to breakeven
TP3 rarely hits - only on strong trending days
### Filter Combinations
Maximum Quality: Volume + Trend + FVG (fewest signals, highest quality)
Balanced: Volume + Trend (good quality, reasonable frequency)
Active Trading: No filters or Volume only (many signals, lower quality)
Trending Markets: Trend filter essential (indices, crypto)
Range-Bound: Volume + FVG (avoid trend filter)
### Common Mistakes to Avoid
Chasing breakouts - wait for the bar to close, don't FOMO into wicks
Ignoring the stop loss - always use it, move it manually if needed
Over-leveraging - the calculator shows MAX shares, you can buy less
Trading every signal - quality > quantity, use filters
Not tracking results - keep a journal to see what works for YOU
## Pros and Cons
### Advantages
Complete all-in-one solution - from signal to position sizing
Multiple timeframes tracked simultaneously
Visual clarity - easy to see what's happening
Cycle tracking catches opportunities others miss
Built-in risk management eliminates guesswork
Customizable filters for different trading styles
No repainting - what you see is locked in
Works across multiple markets (stocks, forex, crypto)
### Limitations
Intraday strategy only - doesn't work on daily charts
Requires active monitoring during first 1-2 hours of session
Not suitable for after-hours or extended sessions by default
Can produce many signals in choppy markets (use filters)
Dashboard can be overwhelming for complete beginners
Performance depends on market conditions (trends vs ranges)
Requires understanding of risk management concepts
### Best For
Day traders who can watch the first 1-2 hours of market open
Traders who want systematic entry/exit rules
Those learning proper position sizing and risk management
Active traders comfortable with multiple signals per day
Anyone trading liquid instruments with clear sessions
### Not Ideal For
Swing traders holding multi-day positions
Set-and-forget / passive investors
Traders who can't watch market open
Complete beginners unfamiliar with trading concepts
Low volume / illiquid instruments
## Frequently Asked Questions
Q: Why are no signals appearing?
A: Check that you're on an intraday timeframe (5min, 15min, etc.) and that the current time is within your session hours. Also verify that "Enable Breakout Detection" is ON and at least one ORB stage is enabled. If using filters, they might be blocking signals - try disabling them temporarily.
Q: What's the best ORB stage to use?
A: ORB15 (15 minutes) is most popular and balanced. ORB5 gives faster signals but more noise. ORB30 and ORB60 are slower but more reliable. Many traders use ORB15 + ORB30 together.
Q: Should I enable all the filters?
A: Start with no filters to see all signals. If too many false signals, add Volume filter first (stocks) or Trend filter (trending markets). FVG filter is most restrictive - use for maximum quality but fewer signals.
Q: How do I know which stop loss method to use?
A: ATR works for most cases - it adapts to volatility. Use ORB% if you want predictable stop placement. Swing is for respecting chart structure. Safer gives you the most room but largest risk.
Q: Can I use this for swing trading?
A: Not really - ORB is fundamentally an intraday strategy. The ranges reset each day. For swing trading, look at weekly support/resistance or moving averages instead.
Q: Why do TP/SL lines disappear sometimes?
A: Lines freeze (stop extending) when: stop loss is hit, the last enabled take-profit is hit, or end of session arrives (if "Freeze at EOD" is enabled). This is intentional - the trade is complete.
Q: What's the difference between "First Only" and "Track Cycles"?
A: "First Only" shows one breakout UP and one DOWN per day maximum - clean but might miss opportunities. "Track Cycles" shows breakout-retest-rebreak sequences - more signals but busier chart.
Q: Is position sizing accurate for options/forex?
A: The calculator is designed for shares (stocks). For options, ignore the share count and use the risk amount. For forex, you'll need to adapt the lot size calculation manually.
Q: How much capital do I need to use this?
A: The indicator works for any account size, but practical day trading typically requires $25,000 in the US due to Pattern Day Trader rules. Adjust the "Account Size" setting to match your capital.
Q: Can I backtest this strategy?
A: This is an indicator, not a strategy script, so it doesn't have built-in backtesting. You can visually review historical signals or code a strategy script using similar logic.
Q: Why does the dashboard show different entry price than the breakout label?
A: If you're looking at an old breakout, the ORB levels may have changed when the next stage completed. The dashboard always shows the CURRENT active range and trade setup.
Q: What's a good win rate to expect?
A: ORB strategies typically see 40-60% win rate depending on market conditions and filters used. The strategy relies on positive risk/reward ratios (2:1 or better) to be profitable even with moderate win rates.
Q: Does this work on crypto?
A: Yes, but crypto trades 24/7 so you need to define what "session start" means. Use Session Mode = Custom and set your preferred daily reset time (e.g., 0000-2359 UTC).
## Credits & Transparency
### Development
This indicator was developed with the assistance of AI technology to implement complex ORB trading logic.
The strategy concept, feature specifications, and trading logic were designed by the publisher. The implementation leverages modern development tools to ensure:
Clean, efficient, and maintainable code
Comprehensive error handling and input validation
Detailed documentation and user guidance
Performance optimization
### Trading Concepts
This indicator implements several public domain trading concepts:
Opening Range Breakout (ORB): Trading strategy popularized by Toby Crabel, Mark Fisher and many more talanted traders.
Fair Value Gap (FVG): Price imbalance concept from ICT methodology
SuperTrend: ATR-based trend indicator using public formula
Risk/Reward Ratio: Standard risk management principle
All mathematical formulas and technical concepts used are in the public domain.
### Pine Script
Uses standard TradingView built-in functions:
ta.ema(), ta.atr(), ta.vwap(), ta.highest(), ta.lowest(), request.security()
No external libraries or proprietary code from other authors.
## Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice.
Trading involves substantial risk of loss and is not suitable for every investor. Past performance shown in examples is not indicative of future results.
The indicator provides signals and calculations, but trading decisions are solely your responsibility. Always:
Test strategies on paper before using real money
Never risk more than you can afford to lose
Understand that all trading involves risk
Consider seeking advice from a licensed financial advisor
The publisher makes no guarantees regarding accuracy, profitability, or performance. Use at your own risk.
---
Version: 3.0
Pine Script Version: v6
Last Updated: October 2024
For support, questions, or suggestions, please comment below or send a private message.
---
Happy trading, and remember: consistent risk management beats perfect entry timing every time.
Volume + MA5 & MA10This Volume + MA5 & MA10 (Technical Volume Trend Analysis)
The Volume + MA5 & MA10 indicator provides a precise view of market participation and volume momentum by combining raw volume data with two moving averages (MA5 and MA10). It’s designed for traders who rely on volume-based confirmation to validate price movements, breakouts, and trend reversals.
🔍 Overview
This indicator displays volume bars alongside two smooth volume averages — MA5 (short-term) and MA10 (medium-term) — making it easier to detect shifts in market activity.
When the short-term average crosses above or below the long-term average, it signals a potential change in trading intensity or market sentiment.
⚙️ Key Features
Dual Volume Moving Averages (MA5 & MA10) for short- and medium-term analysis.
Dynamic Bar Coloring based on whether current volume exceeds MA5 or MA10.
Crossover Detection with visual markers for MA5/MA10 intersections.
Alert Conditions to notify you of significant volume trend shifts.
Fully customizable appearance and smoothing options.
📊 How to Interpret
MA5 > MA10 → Increasing short-term volume activity (strengthening momentum).
MA5 < MA10 → Decreasing short-term volume (weakening participation).
Rising volume with price → Confirms trend strength.
Falling volume with rising/falling price → Suggests potential reversal or reduced conviction.
💡 Applications
Confirm breakouts and trend continuations.
Identify momentum divergences between price and volume.
Filter out low-volume or weak-trend setups.
Combine with RSI, MACD, or moving averages for enhanced signal validation.
✅ Advantages
Simple yet powerful structure for clean visual analysis.
Works across all timeframes and markets (crypto, stocks, forex, indices).
No repainting — reliable for both live and historical backtesting.
Use Volume + MA5 & MA10 to strengthen your technical analysis and gain a deeper understanding of how market participation drives price trends.
4-Hour Range Scalping [v6.3]User Guide: 4-Hour Range Scalping Strategy
Hello! Here is the guide for the Pine Script strategy. Please read it carefully to get the best results.
📈 This script automates the "4-Hour Range Scalping Strategy" from the video.
The main idea is that the first four hours of a major trading day (like New York) set up a "trap zone." The strategy waits for the price to break out of this zone and then fail, giving us a signal that the breakout was false and the price is likely to reverse.
Here’s the simple logic:
Define the Range: It precisely calculates the highest high and lowest low during the first four hours of the selected trading session (e.g., 00:00 to 04:00 New York Time).
Wait for a Breakout: It then monitors the 5-minute chart for a price breakout where a candle fully closes outside of this established range.
Identify the Reversal: The trade trigger occurs when the price fails to continue its breakout and a subsequent 5-minute candle closes back inside the range. This signals a potential reversal or "failed breakout."
Execute the Trade:
]A Short (Sell) trade is triggered after a failed breakout above the range high.
A Long (Buy) trade is triggered after a failed breakout below the range low.
Manage the Risk: The Stop Loss is automatically placed at the peak (for shorts) or trough (for longs) of the breakout move, and the Take Profit is set to a default 2:1 Risk/Reward Ratio.
How to Use the Script (Step-by-Step) ⚙️
Follow these instructions to get it running perfectly.
1. Set Your Chart Timeframe This is the most important step. The strategy is designed to run on a 5-minute (5m) chart. Open your TradingView chart and make sure the timeframe is set to "5m".
2. Add the Script to Your Chart Open the Pine Editor tab at the bottom of TradingView, paste the entire script, and click the "Add to chart" button.
3. Configure the Settings On your chart, find the strategy's name (e.g., "4-Hour Range Scalping ") and click the gear icon ⚙️ to open its settings.
Trading Session: Choose the session for the range. New York is the default and the one from the video.
Risk/Reward Ratio: The default is 2.0, meaning your potential profit is twice your potential loss. You can adjust this to test other targets.
Backtesting Period: To see how the strategy performed on all historical data, go to the "Strategy Tester" panel, click its own gear icon ⚙️, and uncheck the boxes for "Start Date" and "End Date."
4. Understand the Visuals on Your Chart
Blue Background Area: This is the 4-hour calculation window. The script is identifying the day's high and low during this time. No trades will ever happen here.
Red Line (Range High): The highest price of the 4-hour window. This is the upper boundary of the "trap zone."
Green Line (Range Low): The lowest price of the 4-hour window. This is the lower boundary.
Green Triangle (▲): Shows where a Long (Buy) trade was entered.
Red Triangle (▼): Shows where a Short (Sell) trade was entered.
A Very Important Note on Timezones 🕒
This is critical for you in the Philippines (PHT).
The script is based on the New York session, which is 12 hours behind you. Your TradingView chart will still show your local time, but the script works on NY time in the background.
The New York "day" begins at 12:00 PM (Noon) your time.
The script's blue calculation window will be from 12:00 PM to 4:00 PM your local time.
The red and green range lines will appear on your chart only after 4:00 PM your time.
So, if you look at your chart in the morning or early afternoon, you will not see today's range yet. This is normal! The script is just waiting for the New York session to start.
How to Set Up Trade Alerts 🔔
You can have TradingView send you a notification whenever the script enters a trade.
Click the "Alert" button (looks like a clock) in the right-hand toolbar of TradingView.
In the "Condition" dropdown, select the name of the script (e.g., "4-Hour Range Scalping...").
You will then see two options: "Long Signal" and "Short Signal".
Select one (e.g., "Long Signal") and configure how you want to be notified (e.g., "Notify on app").
Click "Create". Repeat the process to create an alert for the other signal.
⚠️ Important Disclosure
For Educational and Research Purposes Only.
This script and all accompanying information are provided for educational and research purposes only. The strategy demonstrated is a technical concept and should not be misconstrued as financial, investment, legal, or tax advice.
Trading financial markets involves substantial risk and is not suitable for every investor. There is a possibility that you could sustain a loss of some or all of your initial investment. Therefore, you should not invest money that you cannot afford to lose.
Past performance is not indicative of future results. The backtesting results shown by this script are historical and do not guarantee future performance. Market conditions are constantly changing.
By using this script, you acknowledge that you are solely responsible for any and all trading decisions you make. You should conduct your own thorough research and, if necessary, seek advice from an independent financial advisor before making any investment decisions. The creators of this script assume no liability for any of your trading results.
Portfolio Strategy TesterThe Portfolio Strategy Tester is an institutional-grade backtesting framework that evaluates the performance of trend-following strategies on multi-asset portfolios. It enables users to construct custom portfolios of up to 30 assets and apply moving average crossover strategies across individual holdings. The model features a clear, color-coded table that provides a side-by-side comparison between the buy-and-hold portfolio and the portfolio using the risk management strategy, offering a comprehensive assessment of both approaches relative to the benchmark.
Portfolios are constructed by entering each ticker symbol in the menu, assigning its respective weight, and reviewing the total sum of individual weights displayed at the top left of the table. For strategy selection, users can choose between Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), Weighted Moving Average (WMA), Moving Average Convergence Divergence (MACD), and Volume-Weighted Moving Average (VWMA). Moving average lengths are defined in the menu and apply only to strategy-enabled assets.
To accurately replicate real-world portfolio conditions, users can choose between daily, weekly, monthly, or quarterly rebalancing frequencies and decide whether cash is held or redistributed. Daily rebalancing maintains constant portfolio weights, while longer intervals allow natural drift. When cash positions are not allowed, capital from bearish assets is automatically redistributed proportionally among bullish assets, ensuring the portfolio remains fully invested at all times. The table displays a comprehensive set of widely used institutional-grade performance metrics:
CAGR = Compounded annual growth rate of returns.
Volatility = Annualized standard deviation of returns.
Sharpe = CAGR per unit of annualized standard deviation.
Sortino = CAGR per unit of annualized downside deviation.
Calmar = CAGR relative to maximum drawdown.
Max DD = Largest peak-to-trough decline in value.
Beta (β) = Sensitivity of returns relative to benchmark returns.
Alpha (α) = Excess annualized risk-adjusted returns relative to benchmark.
Upside = Ratio of average return to benchmark return on up days.
Downside = Ratio of average return to benchmark return on down days.
Tracking = Annualized standard deviation of returns versus benchmark.
Turnover = Average sum of absolute changes in weights per year.
Cumulative returns are displayed on each label as the total percentage gain from the selected start date, with green indicating positive returns and red indicating negative returns. In the table, baseline metrics serve as the benchmark reference and are always gray. For portfolio metrics, green indicates outperformance relative to the baseline, while red indicates underperformance relative to the baseline. For strategy metrics, green indicates outperformance relative to both the baseline and the portfolio, red indicates underperformance relative to both, and gray indicates underperformance relative to either the baseline or portfolio. Metrics such as Volatility, Tracking Error, and Turnover ratio are always displayed in gray as they serve as descriptive measures.
In summary, the Portfolio Strategy Tester is a comprehensive backtesting tool designed to help investors evaluate different trend-following strategies on custom portfolios. It enables real-world simulation of both active and passive investment approaches and provides a full set of standard institutional-grade performance metrics to support data-driven comparisons. While results are based on historical performance, the model serves as a powerful portfolio management and research framework for developing, validating, and refining systematic investment strategies.
Curved Radius Supertrend [BOSWaves]Curved Radius Supertrend — Adaptive Parabolic Trend Framework with Dynamic Acceleration Geometry
Overview
The Curved Radius Supertrend introduces an evolution of the classic Supertrend indicator - engineered with a dynamic curvature engine that replaces rigid ATR bands with parabolic, radius-based motion. Traditional Supertrend systems rely on static band displacement, reacting linearly to volatility and often lagging behind emerging price acceleration. The Curved Radius Supertend model redefines this by integrating controlled acceleration and curvature geometry, allowing the trend bands to adapt fluidly to both velocity and duration of price movement.
The result is a smoother, more organic trend flow that visually captures the momentum curve of price action - not just its direction. Instead of sharp pivots or whipsaws, traders experience a structurally curved trajectory that mirrors real market inertia. This makes it particularly effective for identifying sustained directional phases, detecting early trend rotations, and filtering out noise that plagues standard Supertrend methodologies.
Unlike conventional band-following systems, the Curved Radius framework is time-reactive and velocity-aware, providing a nuanced signal structure that blends geometric precision with volatility sensitivity.
Theoretical Foundation
The Curved Radius Supertrend draws from the intersection of mathematical curvature dynamics and adaptive volatility processing. Standard Supertrend algorithms extend from Average True Range (ATR) envelopes - a linear measure of volatility that moves proportionally with price deviation. However, markets do not expand or contract linearly. Trend velocity typically accelerates and decelerates in nonlinear arcs, forming natural parabolas across price phases.
By embedding a radius-based acceleration function, the indicator models this natural behavior. The core variable, radiusStrength, controls how aggressively curvature accelerates over time. Instead of simply following price distance, the band now evolves according to temporal acceleration - each bar contributes incremental velocity, bending the trend line into a radius-like curve.
This structural design allows the indicator to anticipate rather than just respond to price action, capturing momentum transitions as curved accelerations rather than binary flips. In practice, this eliminates the stutter effect typical of standard Supertrends and replaces it with fluid directional motion that better reflects actual trend geometry.
How It Works
The Curved Radius Supertrend is constructed through a multi-stage process designed to balance price responsiveness with geometric stability:
1. Baseline Supertrend Core
The framework begins with a standard ATR-derived upper and lower band calculation. These define the volatility envelope that constrains potential price zones. Directional bias is determined through crossover logic - prices above the lower band confirm an uptrend, while prices below the upper band confirm a downtrend.
2. Curvature Acceleration Engine
Once a trend direction is established, a curvature engine is activated. This system uses radiusStrength as a coefficient to simulate acceleration per bar, incrementally increasing velocity over time. The result is a parabolic displacement from the anchor price (the price level at trend change), creating a curved motion path that dynamically widens or tightens as the trend matures.
Mathematically, this acceleration behaves quadratically - each new bar compounds the previous velocity, forming an exponential rate of displacement that resembles curved inertia.
3. Adaptive Smoothing Layer
After the radius curve is applied, a smoothing stage (defined by the smoothness parameter) uses a simple moving average to regulate curve noise. This ensures visual coherence without sacrificing responsiveness, producing flowing arcs rather than jagged band steps.
4. Directional Visualization and Outer Envelope
Directional state (bullish or bearish) dictates both the color gradient and band displacement. An outer envelope is plotted one ATR beyond the curved band, creating a layered trend visualization that shows the extent of volatility expansion.
5. Signal Events and Alerts
Each directional transition triggers a 'BUY' or 'SELL' signal, clearly labeling phase shifts in market structure. Alerts are built in for automation and backtesting.
Interpretation
The Curved Radius Supertrend reframes how traders visualize and confirm trends. Instead of simply plotting a trailing stop, it maps the dynamic curvature of trend development.
Uptrend Phases : The band curves upward with increasing acceleration, reflecting the market’s growing directional velocity. As curvature steepens, conviction strengthens.
Downtrend Phases : The band bends downward in a mirrored acceleration pattern, indicating sustained bearish momentum.
Trend Change Points : When the direction flips and a new anchor point forms, the curve resets - providing a clean, early visual confirmation of structural reversal.
Smoothing and Radius Interplay : A lower radius strength produces a tighter, more reactive curve ideal for scalping or short timeframes. Higher values generate broad, sweeping arcs optimized for swing or positional analysis.
Visually, this curvature system translates market inertia into shape - revealing how trends bend, accelerate, and ultimately exhaust.
Strategy Integration
The Curved Radius Supertrend is versatile enough to integrate seamlessly into multiple trading frameworks:
Trend Following : Use BUY/SELL flips to identify emerging directional bias. Strong curvature continuation confirms sustained momentum.
Momentum Entry Filtering : Combine with oscillators or volume tools to filter entries only when the curve slope accelerates (high momentum conditions).
Pullback and Re-entry Timing : The smooth curvature of the radius band allows traders to identify shallow retracements without premature exits. The band acts as a dynamic, self-adjusting support/resistance arc.
Volatility Compression and Expansion : Flattening curvature indicates volatility compression - a potential pre-breakout zone. Rapid re-steepening signals expansion and directional conviction.
Stop Placement Framework : The curved band can serve as a volatility-adjusted trailing stop. Because the curve reflects acceleration, it adapts naturally to market rhythm - widening during momentum surges and tightening during stagnation.
Technical Implementation Details
Curved Radius Engine : Parabolic acceleration algorithm that applies quadratic velocity based on bar count and radiusStrength.
Anchor Logic : Resets curvature at each trend change, establishing a new reference base for directional acceleration.
Smoothing Layer : SMA-based curve smoothing for noise reduction.
Outer Envelope : ATR-derived band offset visualizing volatility extension.
Directional Coloring : Candle and band coloration tied to current trend state.
Signal Engine : Built-in BUY/SELL markers and alert conditions for automation or script integration.
Optimal Application Parameters
Timeframe Guidance :
1-5 min (Scalping) : 0.08–0.12 radius strength, minimal smoothing for rapid responsiveness.
15 min : 0.12–0.15 radius strength for intraday trends.
1H : 0.15–0.18 radius strength for structured short-term swing setups.
4H : 0.18–0.22 radius strength for macro-trend shaping.
Daily : 0.20–0.25 radius strength for broad directional curves.
Weekly : 0.25–0.30 radius strength for smooth macro-level cycles.
The suggested radius strength ranges provide general structural guidance. Optimal values may vary across assets and volatility regimes, and should be refined through empirical testing to account for instrument-specific behavior and prevailing market conditions.
Asset Guidance :
Cryptocurrency : Higher radius and multiplier values to stabilize high-volatility environments.
Forex : Midrange settings (0.12-0.18) for clean curvature transitions.
Equities : Balanced curvature for trending sectors or momentum rotation setups.
Indices/Futures : Moderate radius values (0.15-0.22) to capture cyclical macro swings.
Performance Characteristics
High Effectiveness :
Trending environments with directional expansion.
Markets exhibiting clean momentum arcs and low structural noise.
Reduced Effectiveness :
Range-bound or low-volatility conditions with repeated false flips.
Ultra-short-term timeframes (<1m) where curvature acceleration overshoots.
Integration Guidelines
Confluence Framework : Combine with structure tools (order blocks, BOS, liquidity zones) for entry validation.
Risk Management : Trail stops along the curved band rather than fixed points to align with adaptive market geometry.
Multi-Timeframe Confirmation : Use higher timeframe curvature as a trend filter and lower timeframe curvature for execution timing.
Curve Compression Awareness : Treat flattening arcs as potential exhaustion zones - ideal for scaling out or reducing exposure.
Disclaimer
The Curved Radius Supertrend is a geometric trend model designed for professional traders and analysts. It is not a predictive system or a guaranteed profit method. Its performance depends on correct parameter calibration and sound risk management. BOSWaves recommends using it as part of a comprehensive analytical framework, incorporating volume, liquidity, and structural context to validate directional signals.
Swing AURORA v4.0 — Refined Trend Signals### Swing Algo v4.0 — Refined Trend Signals
#### Overview
Swing Algo v4.0 is an advanced technical indicator designed for TradingView, built to detect trend changes and provide actionable buy/sell signals in various market conditions. It combines multiple technical elements like moving averages, ADX for trend strength, Stochastic RSI for timing, and RSI divergence for confirmation, all while adapting to different timeframes through auto-tuning. This indicator overlays on your chart, highlighting trend regimes with background colors, displaying buy/sell labels (including "strong" variants), and offering early "potential" signals for proactive trading decisions. It's suitable for swing trading, trend following, or as a filter for other strategies across forex, stocks, crypto, and other assets.
#### Purpose
The primary goal of Swing Algo v4.0 is to help traders identify high-probability trend reversals and continuations early, reducing noise and false signals. It aims to provide clear, non-repainting signals that align with market structure, volatility, and momentum. By incorporating filters like higher timeframe (HTF) alignment, bias EMAs, and divergence, it refines entries for better accuracy. The indicator emphasizes balanced performance across aggressive, balanced, and conservative modes, making it versatile for both novice and experienced traders seeking to optimize their decision-making process.
#### What It Indicates
- **Trend Regimes (Background Coloring)**: The chart background changes color to reflect the current market regime:
- **Green (Intense for strong uptrends, faded when cooling)**: Indicates bullish trends where price is above the baseline and EMAs are aligned upward.
- **Red/Maroon (Intense maroon for strong downtrends, faded red when cooling)**: Signals bearish trends with price below the baseline and downward EMA alignment.
- **Faded Yellow**: Marks "no-trade" zones or potential trend changes, where conditions are choppy, weak, or neutral (e.g., low ADX, near baseline, or low volatility).
- **Buy/Sell Signals**: Labels appear on the chart for confirmed entries:
- "BUY" or "STRONG BUY" for bullish signals (strong variants require higher scores and optional divergence).
- "SELL" or "STRONG SELL" for bearish signals.
- **Potential Signals**: Early warnings like "Potential BUY" or "Potential SELL" appear before full confirmation, allowing traders to anticipate moves (confirmed after a few bars based on the trigger window).
- **Divergence Marks**: Small "DIV↑" (bullish) or "DIV↓" (bearish) labels highlight RSI divergences on pivots, adding confluence for strong signals.
- **Lines**: Optional plots for baseline (teal), EMA13/21 (lime/red based on crossover), providing visual trend context.
Signals are anchored either to the current bar or confirmed pivots, ensuring alignment with price action. The indicator avoids repainting by confirming on close if enabled.
#### Key Parameters and Customization
Swing Algo v4.0 offers minimal yet efficient parameters for fine-tuning, with defaults optimized for common use cases. Most can be auto-tuned based on timeframe for simplicity:
- **Confirm on Close (no repaint)**: Boolean (default: true) – Ensures signals don't repaint by waiting for bar confirmation.
- **Auto-tune by Timeframe**: Boolean (default: true) – Automatically adjusts lengths and sensitivity for 5-15m, 30-60m, 2-4h, or higher frames.
- **Mode**: String (options: Aggressive, Balanced , Conservative) – Controls signal thresholds; Aggressive for more signals, Conservative for fewer but higher-quality ones.
- **Signal Anchor**: String (options: Pivot (divLB) , Current bar) – Places labels on confirmed pivots or the current bar.
- **Trigger Window (bars)**: Integer (default: 3) – Window for signal timing; auto-tuned if enabled.
- **Baseline Type**: String (options: HMA , EMA, ALMA) – Core trend line; lengths auto-tune (e.g., 55 for short frames).
- **Use Bias EMA Filter**: Boolean (default: false) – Adds a long-term EMA for trend bias.
- **Use HTF Filter**: Boolean (default: false) – Aligns with higher timeframe (auto or manual like 60m, 240m, D); override for stricter scoring.
- **Sensitivity (10–90)**: Integer (default: 55) – Adjusts ADX threshold for trend detection; higher = more sensitive.
- **Use RSI-Stoch Trigger**: Boolean (default: true) – Enables Stochastic RSI for entry timing; customizable lengths, smooths, and levels.
- **Use RSI Divergence for STRONG**: Boolean (default: true) – Requires divergence for strong signals; pivot lookback (default: 5).
- **Visual Options**: Booleans for background regime, labels, divergence marks, and lines (all default: true).
These parameters are grouped for ease, with tooltips in TradingView for quick reference. Start with defaults and tweak based on backtesting.
#### How It Works
At its core, Swing Algo v4.0 calculates a baseline (e.g., HMA) to define the trend direction. It then scores potential buys/sells using factors like:
- **Trend Strength**: ADX above a dynamic threshold, combined with EMA crossovers (13/21) and slope analysis.
- **Volatility/Volume**: Bollinger/Keltner squeeze exits, volume z-score, and ATR filters to avoid choppy markets.
- **Timing**: Stochastic RSI crossovers or micro-timing via DEMA/TEMA for precise entries.
- **Filters**: Bias EMA, HTF alignment, gap from baseline, and no-trade zones (weak ADX, near baseline, low vol).
- **Divergence**: RSI pivots confirm strong signals.
- **Scoring**: Buy/sell scores (min 3-5 based on mode) trigger labels only when all gates pass, with early "potential" detection for foresight.
The algorithm processes these in real-time, auto-adapting to timeframe for efficiency. Signals flip only on direction changes to prevent over-trading. For best results, use on liquid assets and combine with risk management.
#### Disclaimer
This indicator is for educational and informational purposes only and does not constitute financial advice, investment recommendations, or trading signals. Trading involves significant risk of loss and is not suitable for all investors. Past performance is not indicative of future results. Always backtest the indicator on your preferred assets and timeframes, and consult a qualified financial advisor before making any trading decisions. The author assumes no liability for any losses incurred from using this script. Use at your own risk.
Turtle Strategy - Triple EMA Trend with ADX and ATRDescription
The Triple EMA Trend strategy is a directional momentum system built on the alignment of three exponential moving averages and a strong ADX confirmation filter. It is designed to capture established trends while maintaining disciplined risk management through ATR-based stops and targets.
Core Logic
The system activates only under high-trend conditions, defined by the Average Directional Index (ADX) exceeding a configurable threshold (default: 43).
A bullish setup occurs when the short-term EMA is above the mid-term EMA, which in turn is above the long-term EMA, and price trades above the fastest EMA.
A bearish setup is the mirror condition.
Execution Rules
Entry:
• Long when ADX confirms trend strength and EMA alignment is bullish.
• Short when ADX confirms trend strength and EMA alignment is bearish.
Exit:
• Stop Loss: 1.8 × ATR below (for longs) or above (for shorts) the entry price.
• Take Profit: 3.3 × ATR in the direction of the trade.
Both parameters are configurable.
Additional Features
• Start/end date inputs for controlled backtesting.
• Selective activation of long or short trades.
• Built-in commission and position sizing (percent of equity).
• Full visual representation of EMAs, ADX, stop-loss, and target levels.
This strategy emphasizes clean trend participation, strict entry qualification, and consistent reward-to-risk structure. Ideal for swing or medium-term testing across trending assets.
T3 ATR [DCAUT]█ T3 ATR
📊 ORIGINALITY & INNOVATION
The T3 ATR indicator represents an important enhancement to the traditional Average True Range (ATR) indicator by incorporating the T3 (Tilson Triple Exponential Moving Average) smoothing algorithm. While standard ATR uses fixed RMA (Running Moving Average) smoothing, T3 ATR introduces a configurable volume factor parameter that allows traders to adjust the smoothing characteristics from highly responsive to heavily smoothed output.
This innovation addresses a fundamental limitation of traditional ATR: the inability to adapt smoothing behavior without changing the calculation period. With T3 ATR, traders can maintain a consistent ATR period while adjusting the responsiveness through the volume factor, making the indicator adaptable to different trading styles, market conditions, and timeframes through a single unified implementation.
The T3 algorithm's triple exponential smoothing with volume factor control provides improved signal quality by reducing noise while maintaining better responsiveness compared to traditional smoothing methods. This makes T3 ATR particularly valuable for traders who need to adapt their volatility measurement approach to varying market conditions without switching between multiple indicator configurations.
📐 MATHEMATICAL FOUNDATION
The T3 ATR calculation process involves two distinct stages:
Stage 1: True Range Calculation
The True Range (TR) is calculated using the standard formula:
TR = max(high - low, |high - close |, |low - close |)
This captures the greatest of the current bar's range, the gap from the previous close to the current high, or the gap from the previous close to the current low, providing a comprehensive measure of price movement that accounts for gaps and limit moves.
Stage 2: T3 Smoothing Application
The True Range values are then smoothed using the T3 algorithm, which applies six exponential moving averages in succession:
First Layer: e1 = EMA(TR, period), e2 = EMA(e1, period)
Second Layer: e3 = EMA(e2, period), e4 = EMA(e3, period)
Third Layer: e5 = EMA(e4, period), e6 = EMA(e5, period)
Final Calculation: T3 = c1×e6 + c2×e5 + c3×e4 + c4×e3
The coefficients (c1, c2, c3, c4) are derived from the volume factor (VF) parameter:
a = VF / 2
c1 = -a³
c2 = 3a² + 3a³
c3 = -6a² - 3a - 3a³
c4 = 1 + 3a + a³ + 3a²
The volume factor parameter (0.0 to 1.0) controls the weighting of these coefficients, directly affecting the balance between responsiveness and smoothness:
Lower VF values (approaching 0.0): Coefficients favor recent data, resulting in faster response to volatility changes with minimal lag but potentially more noise
Higher VF values (approaching 1.0): Coefficients distribute weight more evenly across the smoothing layers, producing smoother output with reduced noise but slightly increased lag
📊 COMPREHENSIVE SIGNAL ANALYSIS
Volatility Level Interpretation:
High Absolute Values: Indicate strong price movements and elevated market activity, suggesting larger position risks and wider stop-loss requirements, often associated with trending markets or significant news events
Low Absolute Values: Indicate subdued price movements and quiet market conditions, suggesting smaller position risks and tighter stop-loss opportunities, often associated with consolidation phases or low-volume periods
Rapid Increases: Sharp spikes in T3 ATR often signal the beginning of significant price moves or market regime changes, providing early warning of increased trading risk
Sustained High Levels: Extended periods of elevated T3 ATR indicate sustained trending conditions with persistent volatility, suitable for trend-following strategies
Sustained Low Levels: Extended periods of low T3 ATR indicate range-bound conditions with suppressed volatility, suitable for mean-reversion strategies
Volume Factor Impact on Signals:
Low VF Settings (0.0-0.3): Produce responsive signals that quickly capture volatility changes, suitable for short-term trading but may generate more frequent color changes during minor fluctuations
Medium VF Settings (0.4-0.7): Provide balanced signal quality with moderate responsiveness, filtering out minor noise while capturing significant volatility changes, suitable for swing trading
High VF Settings (0.8-1.0): Generate smooth, stable signals that filter out most noise and focus on major volatility trends, suitable for position trading and long-term analysis
🎯 STRATEGIC APPLICATIONS
Position Sizing Strategy:
Determine your risk per trade (e.g., 1% of account capital - adjust based on your risk tolerance and experience)
Decide your stop-loss distance multiplier (e.g., 2.0x T3 ATR - this varies by market and strategy, test different values)
Calculate stop-loss distance: Stop Distance = Multiplier × Current T3 ATR
Calculate position size: Position Size = (Account × Risk %) / Stop Distance
Example: $10,000 account, 1% risk, T3 ATR = 50 points, 2x multiplier → Position Size = ($10,000 × 0.01) / (2 × 50) = $100 / 100 points = 1 unit per point
Important: The ATR multiplier (1.5x - 3.0x) should be determined through backtesting for your specific instrument and strategy - using inappropriate multipliers may result in stops that are too tight (frequent stop-outs) or too wide (excessive losses)
Adjust the volume factor to match your trading style: lower VF for responsive stop distances in short-term trading, higher VF for stable stop distances in position trading
Dynamic Stop-Loss Placement:
Determine your risk tolerance multiplier (typically 1.5x to 3.0x T3 ATR)
For long positions: Set stop-loss at entry price minus (multiplier × current T3 ATR value)
For short positions: Set stop-loss at entry price plus (multiplier × current T3 ATR value)
Trail stop-losses by recalculating based on current T3 ATR as the trade progresses
Adjust the volume factor based on desired stop-loss stability: higher VF for less frequent adjustments, lower VF for more adaptive stops
Market Regime Identification:
Calculate a reference volatility level using a longer-period moving average of T3 ATR (e.g., 50-period SMA)
High Volatility Regime: Current T3 ATR significantly above reference (e.g., 120%+) - favor trend-following strategies, breakout trades, and wider targets
Normal Volatility Regime: Current T3 ATR near reference (e.g., 80-120%) - employ standard trading strategies appropriate for prevailing market structure
Low Volatility Regime: Current T3 ATR significantly below reference (e.g., <80%) - favor mean-reversion strategies, range trading, and prepare for potential volatility expansion
Monitor T3 ATR trend direction and compare current values to recent history to identify regime transitions early
Risk Management Implementation:
Establish your maximum portfolio heat (total risk across all positions, typically 2-6% of capital)
For each position: Calculate position size using the formula Position Size = (Account × Individual Risk %) / (ATR Multiplier × Current T3 ATR)
When T3 ATR increases: Position sizes automatically decrease (same risk %, larger stop distance = smaller position)
When T3 ATR decreases: Position sizes automatically increase (same risk %, smaller stop distance = larger position)
This approach maintains constant dollar risk per trade regardless of market volatility changes
Use consistent volume factor settings across all positions to ensure uniform risk measurement
📋 DETAILED PARAMETER CONFIGURATION
ATR Length Parameter:
Default Setting: 14 periods
This is the standard ATR calculation period established by Welles Wilder, providing balanced volatility measurement that captures both short-term fluctuations and medium-term trends across most markets and timeframes
Selection Principles:
Shorter periods increase sensitivity to recent volatility changes and respond faster to market shifts, but may produce less stable readings
Longer periods emphasize sustained volatility trends and filter out short-term noise, but respond more slowly to genuine regime changes
The optimal period depends on your holding time, trading frequency, and the typical volatility cycle of your instrument
Consider the timeframe you trade: Intraday traders typically use shorter periods, swing traders use intermediate periods, position traders use longer periods
Practical Approach:
Start with the default 14 periods and observe how well it captures volatility patterns relevant to your trading decisions
If ATR seems too reactive to minor price movements: Increase the period until volatility readings better reflect meaningful market changes
If ATR lags behind obvious volatility shifts that affect your trades: Decrease the period for faster response
Match the period roughly to your typical holding time - if you hold positions for N bars, consider ATR periods in a similar range
Test different periods using historical data for your specific instrument and strategy before committing to live trading
T3 Volume Factor Parameter:
Default Setting: 0.7
This setting provides a reasonable balance between responsiveness and smoothness for most market conditions and trading styles
Understanding the Volume Factor:
Lower values (closer to 0.0) reduce smoothing, allowing T3 ATR to respond more quickly to volatility changes but with less noise filtering
Higher values (closer to 1.0) increase smoothing, producing more stable readings that focus on sustained volatility trends but respond more slowly
The trade-off is between immediacy and stability - there is no universally optimal setting
Selection Principles:
Match to your decision speed: If you need to react quickly to volatility changes for entries/exits, use lower VF; if you're making longer-term risk assessments, use higher VF
Match to market character: Noisier, choppier markets may benefit from higher VF for clearer signals; cleaner trending markets may work well with lower VF for faster response
Match to your preference: Some traders prefer responsive indicators even with occasional false signals, others prefer stable indicators even with some delay
Practical Adjustment Guidelines:
Start with default 0.7 and observe how T3 ATR behavior aligns with your trading needs over multiple sessions
If readings seem too unstable or noisy for your decisions: Try increasing VF toward 0.9-1.0 for heavier smoothing
If the indicator lags too much behind volatility changes you care about: Try decreasing VF toward 0.3-0.5 for faster response
Make meaningful adjustments (0.2-0.3 changes) rather than small increments - subtle differences are often imperceptible in practice
Test adjustments in simulation or paper trading before applying to live positions
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Responsiveness Characteristics:
The T3 smoothing algorithm provides improved responsiveness compared to traditional RMA smoothing used in standard ATR. The triple exponential design with volume factor control allows the indicator to respond more quickly to genuine volatility changes while maintaining the ability to filter noise through appropriate VF settings. This results in earlier detection of volatility regime changes compared to standard ATR, particularly valuable for risk management and position sizing adjustments.
Signal Stability:
Unlike simple smoothing methods that may produce erratic signals during transitional periods, T3 ATR's multi-layer exponential smoothing provides more stable signal progression. The volume factor parameter allows traders to tune signal stability to their preference, with higher VF settings producing remarkably smooth volatility profiles that help avoid overreaction to temporary market fluctuations.
Comparison with Standard ATR:
Adaptability: T3 ATR allows adjustment of smoothing characteristics through the volume factor without changing the ATR period, whereas standard ATR requires changing the period length to alter responsiveness, potentially affecting the fundamental volatility measurement
Lag Reduction: At lower volume factor settings, T3 ATR responds more quickly to volatility changes than standard ATR with equivalent periods, providing earlier signals for risk management adjustments
Noise Filtering: At higher volume factor settings, T3 ATR provides superior noise filtering compared to standard ATR, producing cleaner signals for long-term analysis without sacrificing volatility measurement accuracy
Flexibility: A single T3 ATR configuration can serve multiple trading styles by adjusting only the volume factor, while standard ATR typically requires multiple instances with different periods for different trading applications
Suitable Use Cases:
T3 ATR is well-suited for the following scenarios:
Dynamic Risk Management: When position sizing and stop-loss placement need to adapt quickly to changing volatility conditions
Multi-Style Trading: When a single volatility indicator must serve different trading approaches (day trading, swing trading, position trading)
Volatile Markets: When standard ATR produces too many false volatility signals during choppy conditions
Systematic Trading: When algorithmic systems require a single, configurable volatility input that can be optimized for different instruments
Market Regime Analysis: When clear identification of volatility expansion and contraction phases is critical for strategy selection
Known Limitations:
Like all technical indicators, T3 ATR has limitations that users should understand:
Historical Nature: T3 ATR is calculated from historical price data and cannot predict future volatility with certainty
Smoothing Trade-offs: The volume factor setting involves a trade-off between responsiveness and smoothness - no single setting is optimal for all market conditions
Extreme Events: During unprecedented market events or gaps, T3 ATR may not immediately reflect the full scope of volatility until sufficient data is processed
Relative Measurement: T3 ATR values are most meaningful in relative context (compared to recent history) rather than as absolute thresholds
Market Context Required: T3 ATR measures volatility magnitude but does not indicate price direction or trend quality - it should be used in conjunction with directional analysis
Performance Expectations:
T3 ATR is designed to help traders measure and adapt to changing market volatility conditions. When properly configured and applied:
It can help reduce position risk during volatile periods through appropriate position sizing
It can help identify optimal times for more aggressive position sizing during stable periods
It can improve stop-loss placement by adapting to current market conditions
It can assist in strategy selection by identifying volatility regimes
However, volatility measurement alone does not guarantee profitable trading. T3 ATR should be integrated into a comprehensive trading approach that includes directional analysis, proper risk management, and sound trading psychology.
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. T3 ATR provides adaptive volatility measurement but has limitations and should not be used as the sole basis for trading decisions. The indicator measures historical volatility patterns, and past volatility characteristics do not guarantee future volatility behavior. Market conditions can change rapidly, and extreme events may produce volatility readings that fall outside historical norms.
Traders should combine T3 ATR with directional analysis tools, support/resistance analysis, and other technical indicators to form a complete trading strategy. Proper backtesting and forward testing with appropriate risk management is essential before applying T3 ATR-based strategies to live trading. The volume factor parameter should be optimized for specific instruments and trading styles through careful testing rather than assuming default settings are optimal for all applications.
