Puell Multiple Variants [OperationHeadLessChicken]Overview
This script contains three different, but related indicators to visualise Bitcoin miner revenue.
The classical Puell Multiple : historically, it has been good at signaling Bitcoin cycle tops and bottoms, but due to the diminishing rewards miners get after each halving, it is not clear how you determine overvalued and undervalued territories on it. Here is how the other two modified versions come into play:
Halving-Corrected Puell Multiple : The idea is to multiply the miner revenue after each halving with a correction factor, so overvalued levels are made comparable by a horizontal line across cycles. After experimentation, this correction factor turned out to be around 1.63. This brings cycle tops close to each other, but we lose the ability to see undervalued territories as a horizontal region. The third variant aims to fix this:
Miner Revenue Relative Strength Index (Miner Revenue RSI) : It uses RSI to map miner revenue into the 0-100 range, making it easy to visualise over/undervalued territories. With correct parameter settings, it eliminates the diminishing nature of the original Puell Multiple, and shows both over- and undervalued revenues correctly.
Example usage
The goal is to determine cycle tops and bottoms. I recommend using it on high timeframes, like monthly or weekly . Lower than that, you will see a lot of noise, but it could still be used. Here I use monthly as the example.
The classical Puell Multiple is included for reference. It is calculated as Miner Revenue divided by the 365-day Moving Average of the Miner Revenue . As you can see in the picture below, it has been good at signaling tops at 1,3,5,7.
The problems:
- I have to switch the Puell Multiple to a logarithmic scale
- Still, I cannot use a horizontal oversold territory
- 5 didn't touch the trendline, despite being a cycle top
- 9 touched the trendline despite not being a cycle top
Halving-Corrected Puell Multiple (yellow): Multiplies the Puell Multiple by 1.63 (a number determined via experimentation) after each halving. In the picture below, you can see how the Classical (white) and Corrected (yellow) Puell Multiples compare:
Advantages:
- Now you can set a constant overvalued level (12.49 in my case)
- 1,3,7 are signaled correctly as cycle tops
- 9 is correctly not signaled as a cycle top
Caveats:
- Now you don't have bottom signals anymore
- 5 is still not signaled as cycle top
Let's see if we can further improve this:
Miner Revenue RSI (blue):
On the monthly, you can see that an RSI period of 6, an overvalued threshold of 90, and an undervalued threshold of 35 have given historically pretty good signals.
Advantages:
- Uses two simple and clear horizontal levels for undervalued and overvalued levels
- Signaling 1,3,5,7 correctly as cycle tops
- Correctly does not signal 9 as a cycle top
- Signaling 4,6,8 correctly as cycle bottoms
Caveats:
- Misses two as a cycle bottom, although it was a long time ago when the Bitcoin market was much less mature
- In the past, gave some early overvalued signals
Usage
Using the example above, you can apply these indicators to any timeframe you like and tweak their parameters to obtain signals for overvalued/undervalued BTC prices
You can show or hide any of the three indicators individually
Set overvalued/undervalued thresholds for each => the background will highlight in green (undervalued) or red (overvalued)
Set special parameters for the given indicators: correction factor for the Corrected Puell and RSI period for Revenue RSI
Show or hide halving events on the indicator panel
All parameters and colours are adjustable
In den Scripts nach "weekly" suchen
Multi-TF Trend Dashboard (12H / D / W)Trend Alignment Dashboard (12H/D/W, 200 EMA)
Quickly see trend direction across 12H, Daily, and Weekly charts. Includes 12H 200 EMA for major trend confirmation. Perfect for spotting strong multi-timeframe alignment at a glance.
AUTOMATIC ANALYSIS MODULE🧭 Overview
“Automatic Analysis Module” is a professional, multi-indicator system that interprets market conditions in real time using TSI, RSI, and ATR metrics.
It automatically detects trend reversals, volatility compressions, and momentum exhaustion, helping traders identify high-probability setups without manual analysis.
⚙️ Core Logic
The script continuously evaluates:
TSI (True Strength Index) → trend direction, strength, and early reversal zones.
RSI (Relative Strength Index) → momentum extremes and technical divergences.
ATR (Average True Range) → volatility expansion or compression phases.
Multi-timeframe ATR comparison → detects whether the weekly structure supports or contradicts the local move.
The system combines these signals to produce an automatic interpretation displayed directly on the chart.
📊 Interpretation Table
At every new bar close, the indicator updates a compact dashboard (bottom right corner) showing:
🔵 Main interpretation → trend, reversal, exhaustion, or trap scenario.
🟢 Micro ATR context → volatility check and flow analysis (stable / expanding / contracting).
Each condition is expressed in plain English for quick decision-making — ideal for professional traders who manage multiple charts.
📈 How to Use
1️⃣ Load the indicator on your preferred asset and timeframe (recommended: Daily or 4H).
2️⃣ Watch the blue line message for the main trend interpretation.
3️⃣ Use the green line message as a volatility gauge before entering.
4️⃣ Confirm entries with your own strategy or price structure.
Typical examples:
“Possible bullish reversal” → early accumulation signal.
“Compression phase → wait for breakout” → avoid premature trades.
“Confirmed uptrend” → trend continuation zone.
⚡ Key Features
Real-time auto-interpretation of TSI/RSI/ATR signals.
Detects both bull/bear traps and trend exhaustion zones.
Highlights volatility transitions before breakouts occur.
Works across all assets and timeframes.
No repainting — stable on historical data.
✅ Ideal For
Swing traders, position traders, and institutional analysts who want automated context recognition instead of manual indicator reading.
RSI Value Table – match builtin🧭 Overview
“RSI Value Table – match builtin” displays the exact RSI value (identical to TradingView’s built-in RSI) for any selected timeframe — directly on your chart.
It’s designed for professional traders who need quick RSI confirmation without switching panels or opening multiple indicators.
⚙️ Core Logic
Reads RSI from any timeframe using request.security() with gaps_off and lookahead_off — ensuring a perfect match with the native RSI.
Optional EMA smoothing (non-standard) for visual stability.
Color-coded cell:
🟩 Green → RSI > 50 (bullish momentum)
🟥 Red → RSI < 50 (bearish momentum)
🟨 Yellow → Neutral zone around 50
Adjustable table position: top/bottom, left/right corners.
⚡ Alerts
Built-in alert conditions trigger automatically:
RSI > 50 → bullish momentum confirmation.
RSI < 50 → bearish momentum confirmation.
📈 How to Use
Select your preferred RSI timeframe (e.g., Daily, Weekly, 4H).
Watch the color-coded cell:
Green → trade long bias only.
Red → short bias only.
Ideal as a confirmation module for multi-timeframe systems or smart signal engines.
Cyclical Phases of the Market🧭 Overview
“Cyclical Phases of the Market” automatically detects major market cycles by connecting swing lows and measuring the average number of bars between them.
Once it learns the rhythm of past cycles, it projects the next expected cycle (in time and price) using a dashed orange line and a forecast label.
In simple terms:
The indicator shows where the next potential low is statistically expected to occur, based on the timing and depth of previous cycles.
⚙️ Core Logic – Step by Step
1️⃣ Pivot Detection
The script uses the built-in ta.pivotlow() and ta.pivothigh() functions to find local turning points:
pivotLow marks a local swing low, defined by pivotLeft and pivotRight bars on each side.
Only confirmed lows are used to define the major cycle points.
Each new pivot low is stored in two arrays:
cycleLows → price level of the low
cycleBars → bar index where the low occurred
2️⃣ Cycle Identification and Drawing
Every time two consecutive swing lows are found, the indicator:
Calculates the number of bars between them (cycle length).
If that distance is greater than or equal to minCycleBars, it draws a teal line connecting the two lows — visually representing one complete cycle.
These teal lines form the historical cycle structure of the market.
3️⃣ Average Cycle Length
Once there are at least three completed cycles, the script calculates the average duration (mean number of bars between lows).
This value — avgCycleLength — represents the dominant periodicity or cycle rhythm of the market.
4️⃣ Forecasting the Next Cycle
When a valid average cycle length exists, the model projects the next expected cycle:
Time projection:
Adds avgCycleLength to the last cycle’s ending bar index to find where the next low should occur.
Price projection:
Estimates the vertical amplitude by taking the difference between the last two cycle lows (priceDiff).
Adds this same difference to the last low price to forecast the next probable low level.
The result is drawn as an orange dashed line extending into the future, representing the Next Expected Cycle.
5️⃣ Forecast Label
An orange label 🔮 appears at the projected future point showing:
Text:
🔮 Upcoming Cycle Forecast
Price:
The label marks the probable area and timing of the next cyclical low.
(Note: the date/time calculation currently multiplies bar count by 7 days, so it’s designed mainly for daily charts. On other timeframes, that conversion can be adapted.)
📊 How to Read It on the Chart
Visual Element Meaning Interpretation
Teal lines Completed historical cycles (low to low) Show actual periodic rhythm of the market
Orange dashed line Projection of the next expected cycle Anticipated path toward the next cyclical low
Orange label 🔮 Upcoming Cycle Forecast Displays expected price and bar location
Average cycle length Internal variable (bars between lows) Represents the dominant cycle period
📈 Interpretation
When teal segments show consistent spacing, the market is following a stable rhythm → cycles are predictable.
When cycle spacing shortens, the market is accelerating (volatility rising).
When it widens, the market is slowing down or entering accumulation.
The orange dashed line represents the next expected low zone:
If the market drops near this line → cyclical pattern confirmed.
If the market breaks well below → cycle amplitude has increased (trend weakening).
If the market rises above and delays → a new longer cycle may be forming.
🧠 Practical Use
Combine with oscillators (e.g., RSI or TSI) to confirm momentum alignment near projected lows.
Use in conjunction with volume to identify accumulation or exhaustion near the expected turning point.
Compare across timeframes: weekly cycles confirm long-term rhythm; daily cycles refine short-term entries.
⚡ Summary
Aspect Description
Purpose Detect and forecast recurring market cycles
Cycle basis Low-to-Low pivot analysis
Visuals Teal historical cycles + Orange forecast line
Forecast Next expected low (price and time)
Ideal timeframe Daily
Main outputs Average cycle length, next projected cycle, visual cycle map
Tradytics Levels with EMA CloudThis indicator has tradytics price chart levels where you can put in the input code seen below.
The code has positive gamma (green lines), negative gamma (Red lines) and white dotted line are the darkpool levels.
This is Amazon's 5 minute from Sep30th to October 20th Gammas and weekly Darkpool levels. Just copy and paste code below in the input code and the chart would show the levels.
212.8*1*neutral 220.07*1*neutral 216.038*1*neutral 215.57*1*neutral 219.988*1*neutral 217.401*1*neutral 217.351*1*neutral 212.815*1*neutral 212.75*1*neutral 212.4*1*neutral 215*0*negative 222.5*0*positive 217.5*0*positive 220*0*positive
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.
Index of Civilization DevelopmentIndex of Civilization Development Indicator
This Pine Script (version 6) creates a custom technical indicator for TradingView, titled Index of Civilization Development. It generates a composite index by averaging normalized stock market performances from a selection of global country indices. The normalization is relative to each index's 100-period simple moving average (SMA), scaled to a percentage (100% baseline). This allows for a comparable "development" or performance metric across diverse markets, potentially highlighting trends in global economic or "civilizational" progress based on equity markets.The indicator plots as a single line in a separate pane (non-overlay) and is designed to handle up to 40 symbols to respect TradingView's request.security() call limits.Key FeaturesComposite Index Calculation: Fetches the previous bar's close (close ) and its 100-period SMA for each selected symbol.
Normalizes each: (close / SMA(100)) * 100.
Averages the valid normalizations (ignores invalid/NA data) to produce a single "Index (%)" value.
Symbol Selection Modes:Top N Countries: Selects from a predefined list of the top 50 global stock indices (by market cap/importance, e.g., SPX for USA, SHCOMP for China). Options: Top 5, 15, 25, or 50.
Democratic Countries: ~38 symbols from democracies (e.g., SPX, NI225, NIFTY; based on democracy indices ≥6/10, including flawed/parliamentary systems).
Dictatorships: ~12 symbols from authoritarian/hybrid regimes (e.g., SHCOMP, TASI, IMOEX; scores <6/10).
Customization:Line color (default: blue).
Line width (1-5, default: 2).
Line style: Solid line (default), Stepline, or Circles.
Data Handling:Uses request.security() with lookahead enabled for real-time accuracy, gaps off, and invalid symbol ignoring.
Runs calculations on every bar, with max_bars_back=2000 for historical depth.
Arrays are populated only on the first bar (barstate.isfirst) for efficiency.
Predefined Symbol Lists (Examples)Top 50: SPX (USA), SHCOMP (China), NI225 (Japan), ..., BAX (Bahrain).
Democratic: Focuses on free-market democracies like USA, Japan, UK, Canada, EU nations, Australia, etc.
Dictatorships: Authoritarian markets like China, Saudi Arabia, Russia, Turkey, etc.
Usage TipsAdd to any chart (e.g., daily/weekly timeframe) to view the composite line.
Ideal for macro analysis: Compare democratic vs. authoritarian performance, or track "top world" equity health.
Potential Limitations: Relies on TradingView's symbol availability; some exotic indices (e.g., KWSEIDX) may fail if not supported. The 40-symbol cap prevents errors.
Interpretation: Values >100 indicate above-trend performance; <100 suggest underperformance relative to recent averages.
This script blends financial data with geopolitical categorization for a unique "civilization index" perspective on global markets. For modifications, ensure symbol tickers match TradingView's format.
Dammu AI ADVANCED PRO1. Indicator Overview
Name: Dammu
Type: Overlay indicator (draws on price chart)
Purpose: Combines SuperTrend, SMA/EMA trends, Swing/Structure analysis, Order Blocks, Fair Value Gaps, High/Low levels, TP/SL labels, and alerts.
Pine Script Version: v5
2. SuperTrend Module
Computes SuperTrend line using ATR and sensitivity.
Signals:
Bullish: Price crosses above SuperTrend.
Bearish: Price crosses below SuperTrend.
Plots buy/sell labels 🚀🐻 based on SMA comparison and SuperTrend cross.
3. SMA/EMA Trend Components
SMA8 & SMA9: Used for additional trend confirmation.
EMA lines: Multiple EMAs with different multipliers for trend detection.
Trend Cloud: Uses Hull MA for trend smoothing.
4. Risk Management
TP/SL Levels: Automatic calculation of stop-loss and take-profit (TP1, TP2, TP3).
Configurable ATR-based risk percentage.
Lines and labels drawn for visual TP/SL.
5. Chart Features
Smooth Range Filter: Filters noise for trend detection.
Colored Trend Cloud: Upward trend = cyan, downward = red.
Sideways Market: ADX filter to color bars purple if trend is weak/sideways.
Bar Colors: Green/red based on SuperTrend signals.
6. Swing & Structure Analysis
Detects Swing Highs/Lows, labels as HH, LH, LL, HL.
Detects CHoCH (Change of Character) or BOS (Break of Structure).
Can show internal or swing structures with configurable label size and color.
7. Order Blocks (Smart Money Concepts)
Detects Internal Order Blocks (iOB) and Swing Order Blocks (OB).
Stores top/bottom/left/time/type in arrays.
Colors and shows boxes based on bullish/bearish type.
Automatically deletes OB if price breaks the block.
8. Fair Value Gaps (FVG)
Identifies gaps between candles as potential trading zones.
Configurable bullish/bearish colors and extension bars.
9. EQH/EQL (Equal Highs/Lows)
Detects equal highs/lows using a threshold.
Plots dotted lines and labels EQH/EQL.
10. High/Low Levels MTF
Optional plotting of previous daily, weekly, monthly highs/lows.
11. Premium/Discount Zones
Plots Premium, Discount, and Equilibrium Zones.
Colors: Premium = red, Discount = green, Equilibrium = gray.
12. Alerts
Buy/Sell alerts for:
SuperTrend crossover
BOS/CHoCH (swing/internal)
EQH/EQL triggers
13. Miscellaneous
Configurable visuals: line style, label size, transparency.
Adjustable volatility filters, ATR lengths, smoothing constants.
Integrated risk & reward visualization.
✅ In short:
This is an all-in-one Smart Money + Trend indicator with SuperTrend signals, swing/structure detection, order blocks, FVGs, EQH/EQL, TP/SL visualization, and optional alerts. It’s designed for both trend-following and order-block-based trading.
If you want, I can make a super-short 1-paragraph version that summarizes it even faster for quick reference.
Quadruple AlphaTrendKivancOzbilgi's 'Alpha Trend' indicator has been developed as 'Quadruple Alpha Trend'.
It has been extended to AlphaTrend1,2,3,4, and each line allows users to freely choose colors.
Each of the AT1 to 2 and AT3 to 4 was again color-transformed at the crossing point, respectively.
We believe that the value of AT can compensate a lot for all the shortcomings of a regular moving average.
It can show the support and resistance of the low and high points at each horizontal section and
pressed neck point at the same time
Draw a horizontal line type.
These advantages make it easy to visually break through and collapse support and resistance on the monthly, weekly, and daily charts
It makes it possible to distinguish. I think it's an excellent indicator design by Kivanc Ozbilgi.
The most similar indicator to this one is the "UT BOT", which is close to the moving average in terms of support and resistance
Because it gives a euphemism, the value of "Alpha Trend" as an index that includes horizontal support and resistance
Very highly appreciated. If you have any issues or need to develop further, please leave a note.
Niv Deal + Previ D W M + OPR + Asian🧭 Indicator Description (English)
Name: Niveaux Dealers + Previous D/W/M Auto + OPR + Asian Session
Platform: TradingView (Pine Script v6)
Type: Multi-module visual indicator for market structure and session ranges
🧩 Overview
This indicator combines three complementary modules to help traders visualize key market levels, opening ranges, and session dynamics — all in one comprehensive tool.
It is designed primarily for index and futures trading (e.g. NQ, ES, DAX), but can be applied to any market or timeframe.
MODULE 1 — Dealers Levels + Previous High/Low (Auto)
This first module automatically extracts and plots custom Dealer Levels and Previous Period Levels.
It can parse manually entered price levels (from a single text input) such as daily max/min, control levels, put supports, and call resistances — then draw horizontal lines and labels on the chart.
Features:
One text input for all dealer levels (easy copy-paste format).
Automatic parsing of prices from text (ignores irrelevant characters).
Groups of levels:
Maxima (Max 1D / Event / Extreme)
Minima (Min 1D / Event / Extreme)
Buyer/Seller Controls
Put Supports and Call Resistances
Independent color, style, and width for each line.
Transparent rectangular labels positioned perfectly on the levels.
Previous Daily, Weekly, and Monthly High/Low levels added automatically.
Optional summary table showing all levels and values in real time.
MODULE 2 — OPR (Opening Price Range)
The second module highlights the Opening Price Range, defined by the first 15 minutes (or any chosen period) of the trading session.
Features:
Fully configurable start and end time (local chart timezone).
Displays:
High, Low, and Midline (median)
Optional rectangle between high/low
Optional labels on each line
Independent color, line style, and thickness.
Works perfectly with non-standard sessions (e.g. 13:30–22:00 UTC for U.S. futures).
Uses local chart time instead of exchange time for intuitive control.
MODULE 3 — Asian Session Range
The third module draws the Asian trading session range, automatically detecting price action between configurable hours (default 17:00 → 01:00).
Features:
Adjustable start and end time (supports overnight sessions).
Plots Asian High, Asian Low, and Asian Middle (mid-range line).
Highlights the Asian box area with semi-transparent color.
Optional labels at the end of each level.
Fully synchronized with the chart’s local timezone (same logic as OPR).
Simple toggle to enable or disable the entire Asian module.
⚙️ Customization & Display
Each module can be toggled independently.
Colors, line styles (solid, dashed, dotted), and thickness are customizable.
Label visibility and extensions (left/right) can be adjusted.
The indicator is lightweight and optimized for real-time performance.
💡 Use Case
Traders can use this multi-module setup to:
Identify dealer reaction zones and institutional levels.
Track previous highs/lows for potential liquidity sweeps.
Monitor session ranges (Opening and Asian) for volatility shifts.
Combine all three perspectives (Dealer, Session, Historical) into one unified view.
Would you like me to rewrite this description in TradingView publication form
BTC Flow Dashboard : Spot Premium + OI + Funding + Cycle SignalsSpot Premium vs Perpetual Basket (%):
Tracks how aggressively perps are trading relative to spot, a leading indicator of speculative activity and leverage buildup.
Aggregated Open Interest Z-Score:
A normalized view of OI expansion/contraction across major exchanges (Binance, BitMEX, Bybit, Kraken, etc.), highlighting when leverage enters overheated zones.
Composite Funding Rate Analysis:
Calculates a TWAP-smoothed funding composite across major venues, with optional APR scaling, showing where perpetual markets are paying for long or short exposure.
Confluence Signal Engine:
Dynamically flags bullish or bearish market conditions based on premium behavior and leverage environment — including over-leverage warnings that often precede volatility spikes.
Extreme Cycle Tops & Bottoms (Experimental):
Optional signal module that highlights historically significant extremes (e.g., 2020 bottom or 2021 top) based on statistical Z-score thresholds across the three core metrics.
Notes & Tips
Works best on weekly or monthly timeframes for macro cycle analysis.
Daily and 3D views provide short-term leverage context but may produce more frequent signals.
The Extreme Signal Engine is experimental — not a trading signal on its own, but a contextual tool to support macro decision-making.
OPEX VIXEX datesUpdated ohlocracy's OPEX script till 2030
These dates are for standard equity, index, and ETF options expiration managed by OCC, with monthly expirations usually on the third Friday and weekly expirations on other Fridays, except holidays which cause adjustments to Thursdays or nearby trading days.
Quarterly options expiration dates in the US stock market are on the last trading day of the quarter, usually the last business day of March, June, September, and December.
These dates are the last trading day of each quarter, accounting for weekends and holidays when the market is closed. When the last calendar day falls on a weekend, the expiration is set to the last prior trading day.
The VIX monthly expiration is on the Wednesday prior to the stock market monthly opex (third Friday). When holidays affect these days, the expiration shifts to the business day before.
EMA Trend RecognitionEMA Trend Recognition — “Double-Vision Trend Glasses” 👓⚡
In short:
Your chart gets two voices — the Major trend (EMA50 vs EMA200) for the big picture, and the Minor trend (EMA9 vs EMA20) for the short-term mood.
When both sing the same tune, you get a STRONG signal.
When they argue, it’s a WEAK one. Simple. Clean. Effective.
🧭 What this indicator does
Major Trend (Long-Term):
EMA50 above EMA200 → Bullish.
EMA50 below EMA200 → Bearish.
This tells you where the market really wants to go.
Minor Trend (Short-Term):
EMA9 above EMA20 → Bullish.
EMA9 below EMA20 → Bearish.
This shows you what the market feels like right now.
Trend Combinations (The Magic):
🟢 STRONG BUY: Major ↑ + Minor ↑ → full alignment, go with the flow.
🔴 STRONG SELL: Major ↓ + Minor ↓ → both down, no mercy.
🟡 WEAK BUY: Major ↑, Minor ↓ → pullback zone? early dip? maybe.
🟠 WEAK SELL: Major ↓, Minor ↑ → short-term bounce inside a downtrend.
🎨 Background Colors & Info Panel
Bright Green: STRONG BUY
Bright Red: STRONG SELL
Faded Green/Red: WEAK signals (trend disagreement)
Bottom Info Table:
Major Trend: “BULLISH ↑” or “BEARISH ↓”
Minor Trend: same logic, faster tempo
Signal: shows STRONG/WEAK/NEUTRAL status
Price: latest close price (because yes, we all check that)
🔔 Alerts (so you don’t stare all day)
MAJOR TREND CHANGE: “Now Bullish!” or “Now Bearish!”
MINOR TREND CHANGE: quicker reversals
STRONG BUY/SELL: when both trends line up perfectly
(Alerts trigger only on bar close — no disco flicker alerts.)
🧠 Visuals — Simple but Smart
EMA 200 & 50: thick lines = your market highway
EMA 20 & 9: thin lines = your turn signals
Muted colors, so your eyes survive long trading sessions
🚀 Why it’s useful
Trend Trading: Filter out noise, ride the momentum.
Pullback Entries: WEAK signals often mark “turning back in” moments.
System Building: Use “STRONG” as a market bias filter, “MINOR” flips as entry triggers.
⚙️ Pro Tips
Timeframes: EMAs are fixed, but meaning scales with TF.
On 1H or 4H, they often reflect daily/weekly momentum.
Context: Combine with structure (HH/HL/LH/LL), zones (OB/FVG), or volume.
Risk Management: Signal ≠ free money. Always define SL/TP and RR.
⚠️ Disclaimer
No financial advice, no crystal ball.
This indicator helps you see — but you still decide when to act.
Backtest and paper-trade before going live.
Short Pitch (for the top “Summary” line on TradingView):
“Two EMA pairs, one clear trend compass — Major shows direction, Minor sets the rhythm. When both agree, it’s STRONG. When they argue, it’s WEAK. Clean, fast, and easy to read.” ✅
Feel free to commend and if u have inspirations to add something, let me know, cheers :D
HTF Fibonacci on intraday ChartThis indicator plots Higher Timeframe (HTF) Fibonacci retracement levels directly on your intraday chart, allowing you to visualize how the current price action reacts to key retracement zones derived from the higher timeframe trend.
Concept
Fibonacci retracement levels are powerful tools used to identify potential support and resistance zones within a price trend.
However, these levels are often calculated on a higher timeframe (like Daily or Weekly), while most traders execute entries on lower timeframes (like 15m, 30m, or 1H).
This indicator bridges that gap — it projects the higher timeframe’s Fibonacci levels onto your current intraday chart, helping you see where institutional reactions or swing pivots might occur in real time.
How It Works
Select the Higher Timeframe (HTF)
You can choose which higher timeframe the Fibonacci structure is derived from — default is Daily.
Define the Lookback Period
The script looks back over the chosen number of bars on the higher timeframe to find the highest high and lowest low — the base for Fibonacci calculations.
Plots Key Fibonacci Levels Automatically:
0% (Low)
23.6%
38.2%
50.0%
61.8%
78.6%
100% (High)
Dynamic Labels
Each Fibonacci level is labelled on the latest bar, updating in real time as new data forms on the higher timeframe.
Best Used For
Intraday traders who want to align lower-timeframe entries with higher-timeframe structure.
Swing traders confirming price reactions around major Fibonacci retracement zones.
Contextual analysis for pullback entries, breakout confirmations, or retests of key levels.
Recommended Settings
Higher Timeframe: Daily (for intraday analysis)
Lookback: 50 bars (adjust based on volatility)
Combine with MACD, RSI, CPR, or Pivots for confluence.
License & Credits
Created and published for educational and analytical purposes.
Inspired by standard Fibonacci analysis practices.
ATR Anchored Range %b by TradeSeekersAll time highs got you spooked to enter with no levels in sight?
Stuck in a multi-week range and wondering where the heck the pivots are!?
Wondering if you're longing the top or shorting the potential bottom and about to get smoked, sending you back to burger flipping?!
Fret not trading friends!
I've been crafting the ultimate map for scalpers, slingers, swingers, swindlers, swashbucklers -and traders too.
Why should I care about this, what's an ATR!?
Nearly any trader that's entered the markets has heard of ATR, perhaps even taken a stab at trying to calculate the flux capacity of a weekly ATR on a lower timeframe. Continually calculating things manually sucks!
Ok, so you haven't heard of ATR? It's the average true range... what's the true range!? It's simply the low subtracted from the high (high - low) of any given candle.
How is ATR useful?
The theory is simple, if the ATRs on the daily timeframe for a stock are 5, then traders may have a reasonable expectation that any day in the near future the stock will mostly move +/- 5 pts. This +/- 5 can be used as a possible daily high and low for traders to use.
But ATR changes as time passes, with every billionaire X post, viral cat meme, fed announcement or government shutdown the market makes it's move. This means without this tool, traders need to run the standard lame (sorry) ATR indicator and then hand draw a bunch of important levels (barf).
I'm convinced and ready to join the ATR army, what do I do?
Glad to have you aboard sailor, slap this indicator on your layout - it'll initially display a bottom panel, say nice things to it.
Usage
The lower panel provides a %b plot representative of the current price relative to the timeframe and period ATR. (Defaults to 1D timeframe and 20 - 20 trading days in a month yo)
This %b plot is a map for price against the key ATR based levels and resets each time the timeframe change occurs.
Keep reading! (maybe grab a snack, you're doing great)
If you want to see what the indicator sees, how it maths the math, open the settings and check the "overlay" option... it's amazing, I know.
Main base of operations
This will be the gray area between first red and green lines, imagine this is a future candle for the timeframe anchored. The red would represent the candle high (red means stop/overbought), and the green would represent the candle low (green means go/oversold).
Regardless of the timeframe anchored, this area always represents the area the ATR indicates will be the building area of the current candle being formed. Traders should expect most of the trading to occur within this area.
The mid line
Don't diddle in the middle, this by default is the open price and it's the ultimate bias filter for bull or bear riders.
Extension areas
Beyond the gray area is the extension zone, this provides a whole ATR from the mid line to the extension.
Assembling a trade plan
There are just a couple of key concepts to master in order to become the ultimate ATR samurai warrior, capable of slicing through even the messiest liquidity.
Above the midline and holding, but still within the gray area? Could be a great long entry with targets to upper levels. The same holds true for below open and holding while still being within the lower gray area.
As price makes it's ascension or decline towards the ends of the initial gray ATR range, consider managing trades here. If it's suspected, due to a strong hold of the midline, that the range low or high is the midline, then continue to manage trades towards the extension zones.
Timeframes and periods oh my
The tooltips already provide some hints, but not everyone goes around clicking and hovering everything in sight (maybe I'm the only one that does that?).
There's a thoughtful approach to the default values, I like to consider the big market participants with my day trades, swings trades and beyond.
By default I've chosen the daily timeframe and a period of 20, one for each trading day of the calendar month.
It's no large leap to consider alternatives, what about 1W timeframe and a period of 4 (1 month) or 52 (1 year)?
The possibilities are nearly infinite, comment on any particular favorite combos.
An Italian Special Bonus!!!
...sorry, it's not pizza....
First, did you know the famous Italian Fibonacci's real name was actually Leonardo? I'm not sure how I feel about that. Fun fact, my ancestors are Italian.
Alright, you may have guessed that the special bonus is the mythical Fibonacci inspired "Golden Pocket", maybe it's a foreshadowing of your pockets - one can only hope.
Use this feature to show the commonly referenced Fibonacci levels within each major ATR range. I've seen some totally mathematical epic-ness with these hence the addition.
Once key ATR levels have been hit look for reversals back to golden pockets (you tricksy hobbits) for potential entry back towards the prior hit ATR level.
The %b turns gold if you have the feature enabled and of course the overlay displays them also, how fun!
Final thoughts
I hope you have as much fun using this indicator as I do, it has brought much joy to my trading experience. If you don't have fun with it, well I hope you had fun reading about it at least.
100% human crafted and darn proud of it
- SyntaxGeek
Intrinsic Value AnalyzerThe Intrinsic Value Analyzer is an all-in-one valuation tool that automatically calculates the fair value of a stock using industry-standard valuation techniques. It estimates intrinsic value through Discounted Cash Flow (DCF), Enterprise Value to Revenue (EV/REV), Enterprise Value to EBITDA (EV/EBITDA), and Price to Earnings (P/EPS). The model features adjustable parameters and a built-in alert system that notifies investors in real time when valuation multiples reach predefined thresholds. It also includes a comprehensive, color-coded table that compares the company’s historical average growth rates, valuation multiples, and financial ratios with the most recent values, helping investors quickly assess how current values align with historical averages.
The model calculates the historical Compounded Annual Growth Rates (CAGR) and average valuation multiples over the selected Lookback Period. It then projects Revenue, Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), Earnings per Share (EPS), and Free Cash Flow (FCF) for the selected Forecast Period and discounts their future values back to the present using the Weighted Average Cost of Capital (WACC) or the Cost of Equity. By default, the model automatically applies the historical averages displayed in the table as the growth forecasts and target multiples. These assumptions can be modified in the menu by entering custom REV-G, EBITDA-G, EPS-G, and FCF-G growth forecasts, as well as EV/REV, EV/EBITDA, and P/EPS target multiples. When new input values are entered, the model recalculates the fair value in real time, allowing users to see how changes in these assumptions affect the company’s fair value.
DCF = (Sum of (FCF × (1 + FCF-G) ^ t ÷ (1 + WACC) ^ t) for each year t until Forecast Period + ((FCF × (1 + FCF-G) ^ Forecast Period × (1 + LT Growth)) ÷ ((WACC - LT Growth) × (1 + WACC) ^ Forecast Period)) + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding
EV/REV = ((Revenue × (1 + REV-G) ^ Forecast Period × EV/REV Target) ÷ (1 + WACC) ^ Forecast Period + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding
EV/EBITDA = ((EBITDA × (1 + EBITDA-G) ^ Forecast Period × EV/EBITDA Target) ÷ (1 + WACC) ^ Forecast Period + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding
P/EPS = (EPS × (1 + EPS-G) ^ Forecast Period × P/EPS Target) ÷ (1 + Cost of Equity) ^ Forecast Period
The discounted one-year average analyst price target (1Y PT) is also displayed alongside the valuation labels to provide an overview of consensus estimates. For the DCF model, the terminal long-term FCF growth rate (LT Growth) is based on the selected country to reflect expected long-term nominal GDP growth and can be modified in the menu. For metrics involving FCF, users can choose between reported FCF, calculated as Cash From Operations (CFO) - Capital Expenditures (CAPEX), or standardized FCF, calculated as Earnings Before Interest and Taxes (EBIT) × (1 - Average Tax Rate) + Depreciation and Amortization - Change in Net Working Capital - CAPEX. Historical average values displayed in the left column of the table are based on Fiscal Year (FY) data, while the latest values in the right column use the most recent Trailing Twelve Month (TTM) or Fiscal Quarter (FQ) data. The indicator displays color-coded price labels for each fair value estimate, showing the percentage upside or downside from the current price. Green indicates undervaluation, while red indicates overvaluation. The table follows a separate color logic:
REV-G, EBITDA-G, EPS-G, FCF-G = Green indicates positive annual growth when the CAGR is positive. Red indicates negative annual growth when the CAGR is negative.
EV/REV = Green indicates undervaluation when EV/REV ÷ REV-G is below 1. Red indicates overvaluation when EV/REV ÷ REV-G is above 2. Gray indicates fair value.
EV/EBITDA = Green indicates undervaluation when EV/EBITDA ÷ EBITDA-G is below 1. Red indicates overvaluation when EV/EBITDA ÷ EBITDA-G is above 2. Gray indicates fair value.
P/EPS = Green indicates undervaluation when P/EPS ÷ EPS-G is below 1. Red indicates overvaluation when P/EPS ÷ EPS-G is above 2. Gray indicates fair value.
EBITDA% = Green indicates profitable operations when the EBITDA margin is positive. Red indicates unprofitable operations when the EBITDA margin is negative.
FCF% = Green indicates strong cash conversion when FCF/EBITDA > 50%. Red indicates unsustainable FCF when FCF/EBITDA is negative. Gray indicates normal cash conversion.
ROIC = Green indicates value creation when ROIC > WACC. Red indicates value destruction when ROIC is negative. Gray indicates positive but insufficient returns.
ND/EBITDA = Green indicates low leverage when ND/EBITDA is below 1. Red indicates high leverage when ND/EBITDA is above 3. Gray indicates moderate leverage.
YIELD = Green indicates positive shareholder return when Shareholder Yield > 1%. Red indicates negative shareholder return when Shareholder Yield < -1%.
The Return on Invested Capital (ROIC) is calculated as EBIT × (1 - Average Tax Rate) ÷ (Average Debt + Average Equity - Average Cash). Shareholder Yield (YIELD) is calculated as the CAGR of Dividend Yield - Change in Shares Outstanding. The Weighted Average Cost of Capital (WACC) is displayed at the top left of the table and is derived from the current Market Cap (MC), Debt, Cost of Equity, and Cost of Debt. The Cost of Equity is calculated using the Equity Beta, Index Return, and Risk-Free Rate, which are based on the selected country. The Equity Beta (β) is calculated as the 5-year Blume-adjusted beta between the weekly logarithmic returns of the underlying stock and the selected country’s stock market index. For accurate calculations, it is recommended to use the stock ticker listed on the primary exchange corresponding to the company’s main index.
Cost of Debt = (Interest Expense on Debt ÷ Average Debt) × (1 - Average Tax Rate)
Cost of Equity = Risk-Free Rate + Equity Beta (β) × (Index Return - Risk-Free Rate)
WACC = (MC ÷ (MC + Debt)) × Cost of Equity + (Debt ÷ (MC + Debt)) × Cost of Debt
This indicator works best for operationally stable and profitable companies that are primarily valued based on fundamentals rather than speculative growth, such as those in the industrial, consumer, technology, and healthcare sectors. It is less suitable for early-stage, unprofitable, or highly cyclical companies, including energy, real estate, and financial institutions, as these often have irregular cash flows or distorted balance sheets. It is also worth noting that TradingView’s financial data provider, FactSet, standardizes financial data from official company filings to align with a consistent accounting framework. While this improves comparability across companies, industries, and countries, it may also result in differences from officially reported figures.
In summary, the Intrinsic Value Analyzer is a comprehensive valuation tool designed to help long-term investors estimate a company’s fair value while comparing historical averages with the latest values. Fair value estimates are driven by growth forecasts, target multiples, and discount rates, and should always be interpreted within the context of the underlying assumptions. By default, the model applies historical averages and current discount rates, which may not accurately reflect future conditions. Investors are therefore encouraged to adjust inputs in the menu to better understand how changes in these key assumptions influence the company’s fair value.
Seasonality Heatmap [QuantAlgo]🟢 Overview
The Seasonality Heatmap analyzes years of historical data to reveal which months and weekdays have consistently produced gains or losses, displaying results through color-coded tables with statistical metrics like consistency scores (1-10 rating) and positive occurrence rates. By calculating average returns for each calendar month and day-of-week combination, it identifies recognizable seasonal patterns (such as which months or weekdays tend to rally versus decline) and synthesizes this into actionable buy low/sell high timing possibilities for strategic entries and exits. This helps traders and investors spot high-probability seasonal windows where assets have historically shown strength or weakness, enabling them to align positions with recurring bull and bear market patterns.
🟢 How It Works
1. Monthly Heatmap
How % Return is Calculated:
The indicator fetches monthly closing prices (or Open/High/Low based on user selection) and calculates the percentage change from the previous month:
(Current Month Price - Previous Month Price) / Previous Month Price × 100
Each cell in the heatmap represents one month's return in a specific year, creating a multi-year historical view
Colors indicate performance intensity: greener/brighter shades for higher positive returns, redder/brighter shades for larger negative returns
What Averages Mean:
The "Avg %" row displays the arithmetic mean of all historical returns for each calendar month (e.g., averaging all Januaries together, all Februaries together, etc.)
This metric identifies historically recurring patterns by showing which months have tended to rise or fall on average
Positive averages indicate months that have typically trended upward; negative averages indicate historically weaker months
Example: If April shows +18.56% average, it means April has averaged a 18.56% gain across all years analyzed
What Months Up % Mean:
Shows the percentage of historical occurrences where that month had a positive return (closed higher than the previous month)
Calculated as:
(Number of Months with Positive Returns / Total Months) × 100
Values above 50% indicate the month has been positive more often than negative; below 50% indicates more frequent negative months
Example: If October shows "64%", then 64% of all historical Octobers had positive returns
What Consistency Score Means:
A 1-10 rating that measures how predictable and stable a month's returns have been
Calculated using the coefficient of variation (standard deviation / mean) - lower variation = higher consistency
High scores (8-10, green): The month has shown relatively stable behavior with similar outcomes year-to-year
Medium scores (5-7, gray): Moderate consistency with some variability
Low scores (1-4, red): High variability with unpredictable behavior across different years
Example: A consistency score of 8/10 indicates the month has exhibited recognizable patterns with relatively low deviation
What Best Means:
Shows the highest percentage return achieved for that specific month, along with the year it occurred
Reveals the maximum observed upside and identifies outlier years with exceptional performance
Useful for understanding the range of possible outcomes beyond the average
Example: "Best: 2016: +131.90%" means the strongest January in the dataset was in 2016 with an 131.90% gain
What Worst Means:
Shows the most negative percentage return for that specific month, along with the year it occurred
Reveals maximum observed downside and helps understand the range of historical outcomes
Important for risk assessment even in months with positive averages
Example: "Worst: 2022: -26.86%" means the weakest January in the dataset was in 2022 with a 26.86% loss
2. Day-of-Week Heatmap
How % Return is Calculated:
Calculates the percentage change from the previous day's close to the current day's price (based on user's price source selection)
Returns are aggregated by day of the week within each calendar month (e.g., all Mondays in January, all Tuesdays in January, etc.)
Each cell shows the average performance for that specific day-month combination across all historical data
Formula:
(Current Day Price - Previous Day Close) / Previous Day Close × 100
What Averages Mean:
The "Avg %" row at the bottom aggregates all months together to show the overall average return for each weekday
Identifies broad weekly patterns across the entire dataset
Calculated by summing all daily returns for that weekday across all months and dividing by total observations
Example: If Monday shows +0.04%, Mondays have averaged a 0.04% change across all months in the dataset
What Days Up % Mean:
Shows the percentage of historical occurrences where that weekday had a positive return
Calculated as:
(Number of Positive Days / Total Days Observed) × 100
Values above 50% indicate the day has been positive more often than negative; below 50% indicates more frequent negative days
Example: If Fridays show "54%", then 54% of all Fridays in the dataset had positive returns
What Consistency Score Means:
A 1-10 rating measuring how stable that weekday's performance has been across different months
Based on the coefficient of variation of daily returns for that weekday across all 12 months
High scores (8-10, green): The weekday has shown relatively consistent behavior month-to-month
Medium scores (5-7, gray): Moderate consistency with some month-to-month variation
Low scores (1-4, red): High variability across months, with behavior differing significantly by calendar month
Example: A consistency score of 7/10 for Wednesdays means they have performed with moderate consistency throughout the year
What Best Means:
Shows which calendar month had the strongest average performance for that specific weekday
Identifies favorable day-month combinations based on historical data
Format shows the month abbreviation and the average return achieved
Example: "Best: Oct: +0.20%" means Mondays averaged +0.20% during October months in the dataset
What Worst Means:
Shows which calendar month had the weakest average performance for that specific weekday
Identifies historically challenging day-month combinations
Useful for understanding which month-weekday pairings have shown weaker performance
Example: "Worst: Sep: -0.35%" means Tuesdays averaged -0.35% during September months in the dataset
3. Optimal Timing Table/Summary Table
→ Best Month to BUY: Identifies the month with the lowest average return (most negative or least positive historically), representing periods where prices have historically been relatively lower
Based on the observation that buying during historically weaker months may position for subsequent recovery
Shows the month name, its average return, and color-coded performance
Example: If May shows -0.86% as "Best Month to BUY", it means May has historically averaged -0.86% in the analyzed period
→ Best Month to SELL: Identifies the month with the highest average return (most positive historically), representing periods where prices have historically been relatively higher
Based on historical strength patterns in that month
Example: If July shows +1.42% as "Best Month to SELL", it means July has historically averaged +1.42% gains
→ 2nd Best Month to BUY: The second-lowest performing month based on average returns
Provides an alternative timing option based on historical patterns
Offers flexibility for staged entries or when the primary month doesn't align with strategy
Example: Identifies the next-most favorable historical buying period
→ 2nd Best Month to SELL: The second-highest performing month based on average returns
Provides an alternative exit timing based on historical data
Useful for staged profit-taking or multiple exit opportunities
Identifies the secondary historical strength period
Note: The same logic applies to "Best Day to BUY/SELL" and "2nd Best Day to BUY/SELL" rows, which identify weekdays based on average daily performance across all months. Days with lowest averages are marked as buying opportunities (historically weaker days), while days with highest averages are marked for selling (historically stronger days).
🟢 Examples
Example 1: NVIDIA NASDAQ:NVDA - Strong May Pattern with High Consistency
Analyzing NVIDIA from 2015 onwards, the Monthly Heatmap reveals May averaging +15.84% with 82% of months being positive and a consistency score of 8/10 (green). December shows -1.69% average with only 40% of months positive and a low 1/10 consistency score (red). The Optimal Timing table identifies December as "Best Month to BUY" and May as "Best Month to SELL." A trader recognizes this high-probability May strength pattern and considers entering positions in late December when prices have historically been weaker, then taking profits in May when the seasonal tailwind typically peaks. The high consistency score in May (8/10) provides additional confidence that this pattern has been relatively stable year-over-year.
Example 2: Crypto Market Cap CRYPTOCAP:TOTALES - October Rally Pattern
An investor examining total crypto market capitalization notices September averaging -2.42% with 45% of months positive and 5/10 consistency, while October shows a dramatic shift with +16.69% average, 90% of months positive, and an exceptional 9/10 consistency score (blue). The Day-of-Week heatmap reveals Mondays averaging +0.40% with 54% positive days and 9/10 consistency (blue), while Thursdays show only +0.08% with 1/10 consistency (yellow). The investor uses this multi-layered analysis to develop a strategy: enter crypto positions on Thursdays during late September (combining the historically weak month with the less consistent weekday), then hold through October's historically strong period, considering exits on Mondays when intraweek strength has been most consistent.
Example 3: Solana BINANCE:SOLUSDT - Extreme January Seasonality
A cryptocurrency trader analyzing Solana observes an extraordinary January pattern: +59.57% average return with 60% of months positive and 8/10 consistency (teal), while May shows -9.75% average with only 33% of months positive and 6/10 consistency. August also displays strength at +59.50% average with 7/10 consistency. The Optimal Timing table confirms May as "Best Month to BUY" and January as "Best Month to SELL." The Day-of-Week data shows Sundays averaging +0.77% with 8/10 consistency (teal). The trader develops a seasonal rotation strategy: accumulate SOL positions during May weakness, hold through the historically strong January period (which has shown this extreme pattern with reasonable consistency), and specifically target Sunday exits when the weekday data shows the most recognizable strength pattern.
X Feigenbaumplots forward “projection zones” derived from a user-defined Feigenbaum Deterministic Range (FDR). Starting from two anchor prices (p01a, p01b) that define the initial condition, the tool computes successive expansion zones above and below that range using fixed scale factors. Each zone is rendered as a shaded box with optional edge outlines, an auto-midline, and an optional label—giving you an at-a-glance map of where price may propagate next.
This indicator is a visual framework, not a signal generator. It’s meant to be combined with your existing structure/flow reads (order flow, VWAPs, ORs, HTF levels, etc.) to plan scenarios, targets, and invalidation.
Key ideas (context)
Initial condition → expansions: You define a deterministic base range (FDR) from which the script projects outward “echoes.”
Bidirectional mapping: Zones are drawn symmetrically as +1, +2, +3, +4 (above) and −1, −2, −3, −4 (below) to reflect potential propagation in either direction.
Diminishing confidence with distance: Farther zones are for scenario planning/targets; nearer zones are more actionable for risk placement and management.
How the levels are built
Feigenbaum Deterministic Range (FDR):
Inputs p01a and p01b define the initial range (FDR = p01a − p01b).
Category “F Range” draws that base box.
Projection Zones:
The script computes zone pairs by offsetting from the initial range using fixed multipliers of FDR. In code, these are the pre-set coefficients:
±1: 0.6714 and 1.5029
±2: 2.5699 and 3.6692
±3: 6.1398 and 8.3384
±4: 13.2796 and 17.6768
Each zone is two prices (a, b) forming a band; the same logic mirrors below the range for the negative side.
Rendering & midlines:
Each enabled category draws a filled box from the anchor bar to the right edge (current bar + extend_len).
Optional outlines (solid/dashed/dotted) for top/bottom/left/right edges.
Optional midline (always dashed) bisects each zone for quick reference.
Anchoring & timeframe logic
Anchor refresh: interval1 sets an HTF “clock” (e.g., Daily). On each new HTF bar, all categories re-anchor at that bar’s index so new projections start cleanly with the fresh session/period.
Extend control: extend_len nudges the right boundary beyond the latest bar for label/edge clarity.
Inputs & styling
Settings group:
Anchor 1 Timeframe (e.g., D) defines the refresh cadence.
Label toggles: show/hide, size, text color, and background.
Feigenbaum DR group:
Enable the base F range, set p01a/p01b, choose fill/line colors, outline style, and the mid toggle.
Ranger Factors groups (Zones ±1…±4):
Each zone can be enabled/disabled, inherits its computed prices, and has independent fill/line color, outline style, and mid toggle.
Practical usage
Scenario mapping: Use +/−1 zones for near-term impulse tracking and intraday targets; treat +/−3 and +/−4 as stretch objectives or “if trend persists” waypoints.
Confluence first: Prioritize trades when a Feigenbaum zone aligns with a known liquidity pool, session level (e.g., OR, ETH/RTH AVWAP), HTF pivot, or key option-derived levels.
Risk & invalidation: The base FDR and nearest zone edges provide clean invalidation references and partial-take structures.
Notes & limitations
The coefficients are fixed in this version (you can expose them as inputs if you want to calibrate per market).
Projections are descriptive, not predictive; treat farther zones as lower-confidence context.
Because anchors reset on the selected HTF, choose interval1 consistent with your playbook (e.g., Daily for RTH framing, Weekly for swing maps).
Output summary
Boxes: FDR (base), Zones +1/−1, +2/−2, +3/−3, +4/−4
Edges: Optional top/bottom/left/right per zone (styleable)
Midlines: Optional dashed mid per zone
Labels: Optional, style-controlled, positioned just beyond the right edge
Volume Cluster Heatmap [BackQuant]Volume Cluster Heatmap
A visualization tool that maps traded volume across price levels over a chosen lookback period. It highlights where the market builds balance through heavy participation and where it moves efficiently through low-volume zones. By combining a heatmap, volume profile, and high/low volume node detection, this indicator reveals structural areas of support, resistance, and liquidity that drive price behavior.
What Are Volume Clusters?
A volume cluster is a horizontal aggregation of traded volume at specific price levels, showing where market participants concentrated their buying and selling.
High Volume Nodes (HVN) : Price levels with significant trading activity; often act as support or resistance.
Low Volume Nodes (LVN) : Price levels with little trading activity; price moves quickly through these areas, reflecting low liquidity.
Volume clusters help identify key structural zones, reveal potential reversals, and gauge market efficiency by highlighting where the market is balanced versus areas of thin liquidity.
By creating heatmaps, profiles, and highlighting high and low volume nodes (HVNs and LVNs), it allows traders to see where the market builds balance and where it moves efficiently through thin liquidity zones.
Example: Bitcoin breaking away from the high-volume zone near 118k and moving cleanly through the low-volume pocket around 113k–115k, illustrating how markets seek efficiency:
Core Features
Visual Analysis Components:
Heatmap Display : Displays volume intensity as colored boxes, lines, or a combination for a dynamic view of market participation.
Volume Profile Overlay : Shows cumulative volume per price level along the right-hand side of the chart.
HVN & LVN Labels : Marks high and low volume nodes with color-coded lines and labels.
Customizable Colors & Transparency : Adjust high and low volume colors and minimum transparency for clear differentiation.
Session Reset & Timeframe Control : Dynamically resets clusters at the start of new sessions or chosen timeframes (intraday, daily, weekly).
Alerts
HVN / LVN Alerts : Notify when price reaches a significant high or low volume node.
High Volume Zone Alerts : Trigger when price enters the top X% of cumulative volume, signaling key areas of market interest.
How It Works
Each bar’s volume is distributed proportionally across the horizontal price levels it touches. Over the lookback period, this builds a cumulative volume profile, identifying price levels with the most and least trading activity. The highest cumulative volume levels become HVNs, while the lowest are LVNs. A side volume profile shows aggregated volume per level, and a heatmap overlay visually reinforces market structure.
Applications for Traders
Identify strong support and resistance at HVNs.
Detect areas of low liquidity where price may move quickly (LVNs).
Determine market balance zones where price may consolidate.
Filter noise: because volume clusters aggregate activity into levels, minor fluctuations and irrelevant micro-moves are removed, simplifying analysis and improving strategy development.
Combine with other indicators such as VWAP, Supertrend, or CVD for higher-probability entries and exits.
Use volume clusters to anticipate price reactions to breaking points in thin liquidity zones.
Advanced Display Options
Heatmap Styles : Boxes, lines, or both. Boxes provide a traditional heatmap, lines are better for high granularity data.
Line Mode Example : Simplified line visualization for easier reading at high level counts:
Profile Width & Offset : Adjust spacing and placement of the volume profile for clarity alongside price.
Transparency Control : Lower transparency for more opaque visualization of high-volume zones.
Best Practices for Usage
Reduce the number of levels when using line mode to avoid clutter.
Use HVN and LVN markers in conjunction with volume profiles to plan entries and exits.
Apply session resets to monitor intraday vs. multi-day volume accumulation.
Combine with other technical indicators to confirm high-probability trading signals.
Watch price interactions with LVNs for potential rapid movements and with HVNs for possible support/resistance or reversals.
Technical Notes
Each bar contributes volume proportionally to the price levels it spans, creating a dynamic and accurate representation of traded interest.
Volume profiles are scaled and offset for visual clarity alongside live price.
Alerts are fully integrated for HVN/LVN interaction and high-volume zone entries.
Optimized to handle large lookback windows and numerous price levels efficiently without performance degradation.
This indicator is ideal for understanding market structure, detecting key liquidity areas, and filtering out noise to model price more accurately in high-frequency or algorithmic strategies.
ATR Adaptive (auto timeframe)This indicator automatically adjusts the Average True Range (ATR) period based on the current chart timeframe, helping traders define dynamic Stop Loss (SL) and Take Profit (TP) levels that adapt to market volatility.
The ATR measures the average range of price movement over a defined number of bars. By using adaptive periods, the indicator ensures that volatility is interpreted consistently across different timeframes — from 1-minute charts to daily or weekly charts.
It plots two main levels on the chart:
🔴 Low – ATR × Multiplier → Suggested Stop Loss (below the candle’s low)
🟢 High + ATR × Multiplier → Suggested Take Profit or trailing level (above the candle’s high)
Optional additional lines show ATR-based TP levels calculated from the current close.
💡 How to use
Select your desired ATR multiplier (e.g., 1.3× for SL, 1.0× for TP).
The script automatically detects the chart timeframe and uses an appropriate ATR length (e.g., ATR(30) on M5, ATR(21) on H1, ATR(14) on Daily).
Use the plotted levels to:
Set Stop Loss just below the red ATR band (for long trades).
Set Take Profit near or slightly below the green ATR band (for short trades, reverse logic).
⚙️ Why it helps
Maintains consistent volatility-based risk across multiple timeframes.
Avoids arbitrary fixed SL/TP values.
Makes the trading strategy more responsive in high-volatility markets and more conservative when volatility contracts.
Particularly useful for intraday and swing trading, where volatility varies significantly between sessions.
