Herrick Payoff Index for Quandl DataUpdate to my previous Herrick Payoff Index script. This script pulls Quandl futures data with daily open interest. The prior version only used the weekly Commitment of Traders open interest data so could only be used on weekly bars. Note: Must use Quandl Symbol methodology in chart (i.e. enter symbol as QUANDL:CHRIS/CME_FC2, QUANDL:CME/FCX2016, ect.). Unfortunately, I haven't been able to program this to pull from the embedded futures data.
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UCS_S_Stochastic Pop and Drop StrategyMy Contribution to Jake Bernstein Educational Series, Initiated by Chris Moody.
The Stochastic Pop was developed by Jake Bernstein and modified by David Steckler. Bernstein's original Stochastic Pop is a trading strategy that identifies price pops when the Stochastic Oscillator surges above 80. Steckler modified this strategy by adding conditional filters using the Average Directional Index (ADX) and the weekly Stochastic Oscillator.
Modifications
1. Weekly Stochastic Oscillator for Trading Bias = 5* Daily Stochastic
2. Optional Volume Confirmation, Custom Average Volume Length
Future Plans
1. Adding Triggers for Entry, Stops and Target. - This will be release when we have ability to code the complete Strategy. Although it can be done with the current pinescript options, it would be far more easier if we have strategy ability.
Link for Educational Purpose
stockcharts.com
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Good Luck Trading
UCSgears
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.
Porsched Indicator🔧 Core Components:
1. Moving Averages with Clouds
EMA 25, 50, 75, and 150 with standard deviation bands
Visual clouds representing volatility around each EMA
Customizable colors for each average and its cloud
2. Dual Hull Bands
Two separate Hull bands with different periods (20 and 110)
Multiple variations: HMA, THMA, EHMA
Colored filling between Hull lines
Option to use higher timeframes for multi-timeframe analysis
3. Swing High/Low Detector
Identifies significant price reversal points
Configurable swing strength (default: 5 bars)
Solid lines for current swings and dotted for past ones
Alerts when swing levels are broken
4. Volume Analysis (PVSRA)
Vector Candles that change color based on volume:
Red/Green: Volume ≥ 200% of average or highest spread×volume
Blue/Violet: Volume ≥ 150% of average
Gray: Normal conditions
Vector Candle Zones (VCZ): Key areas based on volume candles
5. Daily & Weekly Levels
Previous day's high and low
Previous week's high and low
Stepline display with optional labels
6. UT Bot - Trailing Stop
Dynamic ATR-based stop loss
Bar coloring based on trend direction
Adjustable sensitivity via "Key Value"
7. Session Detector
Identifies session highs/lows (Sydney, Asia, Europe, etc.)
Visual boxes marking each trading session
⚙️ Customization Features:
Individual color schemes for all elements
Adjustable line thickness
Custom transparency settings
Flexible calculation periods
Multiple timeframe options
🎯 Trading Applications:
Trend Identification (EMAs + Hull)
Entry/Exit Points (Swings + Volume)
Risk Management (Trailing Stop)
Support/Resistance (VCZ + Highs/Lows)
Market Timing (Sessions + Volume)
💡 Key Benefits:
All-in-One Solution: Eliminates indicator clutter
Multi-Timeframe Analysis: Built-in higher timeframe data
Visual Clarity: Clean, organized display with color coding
Customizable Alerts: Swing break and trend change notifications
Professional Grade: Institutional-level volume analysis
This indicator is designed for traders who want a comprehensive market analysis tool without the complexity of managing multiple separate indicators, providing holistic market insight through different technical perspectives.
💸 Monetary Momentum Oscillator (MMO)Monetary Momentum Oscillator (MMO)
The Monetary Momentum Oscillator (MMO) measures the rate of change in the money supply (like M2, Fed Balance Sheet, or similar macro series) and applies a momentum-based RSI calculation to visualize liquidity acceleration and deceleration.
💡 Purpose:
MMO is designed for macro-level analysis — it identifies when monetary expansion is overheating (potential inflation or risk-on conditions) and when contraction is cooling off (liquidity tightening or deflationary stress).
📊 How It Works:
Calculates the percentage change of the selected data source over a chosen lookback period.
Applies an RSI transformation to visualize momentum extremes.
Overlays signal smoothing and highlights overheat/cooldown zones.
🔍 Interpretation:
Above 70 → Liquidity acceleration / overheating (potential inflationary impulse).
Below 30 → Liquidity deceleration / contraction (risk-off, tightening).
Crossovers → Momentum shifts that often precede macro trend reversals in risk assets.
⚙️ Best Used On:
Macroeconomic series such as M2SL, M2V, WALCL, or custom liquidity indexes.
Long-term charts (weekly or monthly) for detecting major monetary regime transitions.
🧩 Core Idea:
Liquidity is the real market engine — this oscillator quantifies its pulse.
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.
Advanced Speedometer Gauge [PhenLabs]Advanced Speedometer Gauge
Version: PineScript™v6
📌 Description
The Advanced Speedometer Gauge is a revolutionary multi-metric visualization tool that consolidates 13 distinct trading indicators into a single, intuitive speedometer display. Instead of cluttering your workspace with multiple oscillators and panels, this gauge provides a unified interface where you can switch between different metrics while maintaining consistent visual interpretation.
Built on PineScript™ v6, the indicator transforms complex technical calculations into an easy-to-read semi-circular gauge with color-coded zones and a precision needle indicator. Each of the 13 available metrics has been carefully normalized to a 0-100 scale, ensuring that whether you’re analyzing RSI, volume trends, or volatility extremes, the visual interpretation remains consistent and intuitive.
The gauge is designed for traders who value efficiency and clarity. By consolidating multiple analytical perspectives into one compact display, you can quickly assess market conditions without the visual noise of traditional multi-indicator setups. All metrics are non-overlapping, meaning each provides unique insights into different aspects of market behavior.
🚀 Points of Innovation
13 selectable metrics covering momentum, volume, volatility, trend, and statistical analysis, all accessible through a single dropdown menu
Universal 0-100 normalization system that standardizes different indicator scales for consistent visual interpretation across all metrics
Semi-circular gauge design with 21 arc segments providing smooth precision and clear visual feedback through color-coded zones
Non-redundant metric selection ensuring each indicator provides unique market insights without analytical overlap
Advanced metrics including MFI (volume-weighted momentum), CCI (statistical deviation), Volatility Rank (extended lookback), Trend Strength (ADX-style), Choppiness Index, Volume Trend, and Price Distance from MA
Flexible positioning system with 5 chart locations, 3 size options, and fully customizable color schemes for optimal workspace integration
🔧 Core Components
Metric Selection Engine: Dropdown interface allowing instant switching between 13 different technical indicators, each with independent parameter controls
Normalization System: All metrics converted to 0-100 scale using indicator-specific algorithms that preserve the statistical significance of each measurement
Semi-Circular Gauge: Visual display using 21 arc segments arranged in curved formation with two-row thickness for enhanced visibility
Color Zone System: Three distinct zones (0-40 green, 40-70 yellow, 70-100 red) providing instant visual feedback on metric extremes
Needle Indicator: Dynamic pointer that positions across the gauge arc based on precise current metric value
Table Implementation: Professional table structure ensuring consistent positioning and rendering across different chart configurations
🔥 Key Features
RSI (Relative Strength Index): Classic momentum oscillator measuring overbought/oversold conditions with adjustable period length (default 14)
Stochastic Oscillator: Compares closing price to price range over specified period with smoothing, ideal for identifying momentum shifts
MFI (Money Flow Index): Volume-weighted RSI that combines price movement with volume to measure buying and selling pressure intensity
CCI (Commodity Channel Index): Measures statistical deviation from average price, normalized from typical -200 to +200 range to 0-100 scale
Williams %R: Alternative overbought/oversold indicator using high-low range analysis, inverted to match 0-100 scale conventions
Volume %: Current volume relative to moving average expressed as percentage, capped at 100 for extreme spikes
Volume Trend: Cumulative directional volume flow showing whether volume is flowing into up moves or down moves over specified period
ATR Percentile: Current Average True Range position within historical range using specified lookback period (default 100 bars)
Volatility Rank: Close-to-close volatility measured against extended historical range (default 252 days), differs from ATR in calculation method
Momentum: Rate of change calculation showing price movement speed, centered at 50 and normalized to 0-100 range
Trend Strength: ADX-style calculation using directional movement to quantify trend intensity regardless of direction
Choppiness Index: Measures market choppiness versus trending behavior, where high values indicate ranging markets and low values indicate strong trends
Price Distance from MA: Measures current price over-extension from moving average using standard deviation calculations
🎨 Visualization
Semi-Circular Arc Display: Curved gauge spanning from 0 (left) to 100 (right) with smooth progression and two-row thickness for visibility
Color-Coded Zones: Green zone (0-40) for low/oversold conditions, yellow zone (40-70) for neutral readings, red zone (70-100) for high/overbought conditions
Needle Indicator: Downward-pointing triangle (▼) positioned precisely at current metric value along the gauge arc
Scale Markers: Vertical line markers at 0, 25, 50, 75, and 100 positions with corresponding numerical labels below
Title Display: Merged cell showing “𓄀 PhenLabs” branding plus currently selected metric name in monospace font
Large Value Display: Current metric value shown with two decimal precision in large text directly below title
Table Structure: Professional table with customizable background color, text color, and transparency for minimal chart obstruction
📖 Usage Guidelines
Metric Selection
Select Metric: Default: RSI | Options: RSI, Stochastic, Volume %, ATR Percentile, Momentum, MFI (Money Flow), CCI (Commodity Channel), Williams %R, Volatility Rank, Trend Strength, Choppiness Index, Volume Trend, Price Distance | Choose the technical indicator you want to display on the gauge based on your current analytical needs
RSI Settings
RSI Length: Default: 14 | Range: 1+ | Controls the lookback period for RSI calculation, shorter periods increase sensitivity to recent price changes
Stochastic Settings
Stochastic Length: Default: 14 | Range: 1+ | Lookback period for stochastic calculation comparing close to high-low range
Stochastic Smooth: Default: 3 | Range: 1+ | Smoothing period applied to raw stochastic value to reduce noise and false signals
Volume Settings
Volume MA Length: Default: 20 | Range: 1+ | Moving average period used to calculate average volume for comparison with current volume
Volume Trend Length: Default: 20 | Range: 5+ | Period for calculating cumulative directional volume flow trend
ATR and Volatility Settings
ATR Length: Default: 14 | Range: 1+ | Period for Average True Range calculation used in ATR Percentile metric
ATR Percentile Lookback: Default: 100 | Range: 20+ | Historical range used to determine current ATR position as percentile
Volatility Rank Lookback (Days): Default: 252 | Range: 50+ | Extended lookback period for Volatility Rank metric using close-to-close volatility
Momentum and Trend Settings
Momentum Length: Default: 10 | Range: 1+ | Lookback period for rate of change calculation in Momentum metric
Trend Strength Length: Default: 20 | Range: 5+ | Period for directional movement calculations in ADX-style Trend Strength metric
Advanced Metric Settings
MFI Length: Default: 14 | Range: 1+ | Lookback period for Money Flow Index calculation combining price and volume
CCI Length: Default: 20 | Range: 1+ | Period for Commodity Channel Index statistical deviation calculation
Williams %R Length: Default: 14 | Range: 1+ | Lookback period for Williams %R high-low range analysis
Choppiness Index Length: Default: 14 | Range: 5+ | Period for calculating market choppiness versus trending behavior
Price Distance MA Length: Default: 50 | Range: 10+ | Moving average period used for Price Distance standard deviation calculation
Visual Customization
Position: Default: Top Right | Options: Top Left, Top Right, Bottom Left, Bottom Right, Middle Right | Controls gauge placement on chart for optimal workspace organization
Size: Default: Normal | Options: Small, Normal, Large | Adjusts overall gauge dimensions and text size for different monitor resolutions and preferences
Low Zone Color (0-40): Default: Green (#00FF00) | Customize color for low/oversold zone of gauge arc
Medium Zone Color (40-70): Default: Yellow (#FFFF00) | Customize color for neutral/medium zone of gauge arc
High Zone Color (70-100): Default: Red (#FF0000) | Customize color for high/overbought zone of gauge arc
Background Color: Default: Semi-transparent dark gray | Customize gauge background for contrast and chart integration
Text Color: Default: White (#FFFFFF) | Customize all text elements including title, value, and scale labels
✅ Best Use Cases
Quick visual assessment of market conditions when you need instant feedback on whether an asset is in extreme territory across multiple analytical dimensions
Workspace organization for traders who monitor multiple indicators but want to reduce chart clutter and visual complexity
Metric comparison by switching between different indicators while maintaining consistent visual interpretation through the 0-100 normalization
Overbought/oversold identification using RSI, Stochastic, Williams %R, or MFI depending on whether you prefer price-only or volume-weighted analysis
Volume analysis through Volume %, Volume Trend, or MFI to confirm price movements with corresponding volume characteristics
Volatility monitoring using ATR Percentile or Volatility Rank to identify expansion/contraction cycles and adjust position sizing
Trend vs range identification by comparing Trend Strength (high values = trending) against Choppiness Index (high values = ranging)
Statistical over-extension detection using CCI or Price Distance to identify when price has deviated significantly from normal behavior
Multi-timeframe analysis by duplicating the gauge on different timeframe charts to compare metric readings across time horizons
Educational purposes for new traders learning to interpret technical indicators through consistent visual representation
⚠️ Limitations
The gauge displays only one metric at a time, requiring manual switching to compare different indicators rather than simultaneous multi-metric viewing
The 0-100 normalization, while providing consistency, may obscure the raw values and specific nuances of each underlying indicator
Table-based visualization cannot be exported or saved as an image separately from the full chart screenshot
Optimal parameter settings vary by asset type, timeframe, and market conditions, requiring user experimentation for best results
💡 What Makes This Unique
Unified Multi-Metric Interface: The only gauge-style indicator offering 13 distinct metrics through a single interface, eliminating the need for multiple oscillator panels
Non-Overlapping Analytics: Each metric provides genuinely unique insights—MFI combines volume with price, CCI measures statistical deviation, Volatility Rank uses extended lookback, Trend Strength quantifies directional movement, and Choppiness Index measures ranging behavior
Universal Normalization System: All metrics standardized to 0-100 scale using indicator-appropriate algorithms that preserve statistical meaning while enabling consistent visual interpretation
Professional Visual Design: Semi-circular gauge with 21 arc segments, precision needle positioning, color-coded zones, and clean table implementation that maintains clarity across all chart configurations
Extensive Customization: Independent parameter controls for each metric, five position options, three size presets, and full color customization for seamless workspace integration
🔬 How It Works
1. Metric Calculation Phase:
All 13 metrics are calculated simultaneously on every bar using their respective algorithms with user-defined parameters
Each metric applies its own specific calculation method—RSI uses average gains vs losses, Stochastic compares close to high-low range, MFI incorporates typical price and volume, CCI measures deviation from statistical mean, ATR calculates true range, directional indicators measure up/down movement, and statistical metrics analyze price relationships
2. Normalization Process:
Each calculated metric is converted to a standardized 0-100 scale using indicator-appropriate transformations
Some metrics are naturally 0-100 (RSI, Stochastic, MFI, Williams %R), while others require scaling—CCI transforms from ±200 range, Momentum centers around 50, Volume ratio caps at 2x for 100, ATR and Volatility Rank calculate percentile positions, and Price Distance scales by standard deviations
3. Gauge Rendering:
The selected metric’s normalized value determines the needle position across 21 arc segments spanning 0-100
Each arc segment receives its color based on position—segments 0-8 are green zone, segments 9-14 are yellow zone, segments 15-20 are red zone
The needle indicator (▼) appears in row 5 at the column corresponding to the current metric value, providing precise visual feedback
4. Table Construction:
The gauge uses TradingView’s table system with merged cells for title and value display, ensuring consistent positioning regardless of chart configuration
Rows are allocated as follows: Row 0 merged for title, Row 1 merged for large value display, Row 2 for spacing, Rows 3-4 for the semi-circular arc with curved shaping, Row 5 for needle indicator, Row 6 for scale markers, Row 7 for numerical labels at 0/25/50/75/100
All visual elements update on every bar when barstate.islast is true, ensuring real-time accuracy without performance impact
💡 Note:
This indicator is designed for visual analysis and market condition assessment, not as a standalone trading system. For best results, combine gauge readings with price action analysis, support and resistance levels, and broader market context. Parameter optimization is recommended based on your specific trading timeframe and asset class. The gauge works on all timeframes but may require different parameter settings for intraday versus daily/weekly analysis. Consider using multiple instances of the gauge set to different metrics for comprehensive market analysis without switching between settings.
VWAP Multi-Anchor (Día / 24h / Semana / Mes)With this you can have the VWAP and choose between weekly average, day session or even last 24 hours
Cumulative Volume Delta Z Score [BackQuant]Cumulative Volume Delta Z Score
The Cumulative Volume Delta Z Score indicator is a sophisticated tool that combines the cumulative volume delta (CVD) with Z-Score normalization to provide traders with a clearer view of market dynamics. By analyzing volume imbalances and standardizing them through a Z-Score, this tool helps identify significant price movements and market trends while filtering out noise.
Core Concept of Cumulative Volume Delta (CVD)
Cumulative Volume Delta (CVD) is a popular indicator that tracks the net difference between buying and selling volume over time. CVD helps traders understand whether buying or selling pressure is dominating the market. Positive CVD signals buying pressure, while negative CVD indicates selling pressure.
The addition of Z-Score normalization to CVD makes it easier to evaluate whether current volume imbalances are unusual compared to past behavior. Z-Score helps in detecting extreme conditions by showing how far the current CVD is from its historical mean in terms of standard deviations.
Key Features
Cumulative Volume Delta (CVD): Tracks the net buying vs. selling volume, allowing traders to gauge the overall market sentiment.
Z-Score Normalization: Converts CVD into a standardized value to highlight extreme movements in volume that are statistically significant.
Divergence Detection: The indicator can spot bullish and bearish divergences between price and CVD, which can signal potential trend reversals.
Pivot-Based Divergence: Identifies price and CVD pivots, highlighting divergence patterns that are crucial for predicting price changes.
Trend Analysis: Colors bars according to trend direction, providing a visual indication of bullish or bearish conditions based on Z-Score.
How It Works
Cumulative Volume Delta (CVD): The CVD is calculated by summing the difference between buying and selling volume for each bar. It represents the net buying or selling pressure, giving insights into market sentiment.
Z-Score Normalization: The Z-Score is applied to the CVD to normalize its values, making it easier to compare current conditions with historical averages. A Z-Score greater than 0 indicates a bullish market, while a Z-Score less than 0 signals a bearish market.
Divergence Detection: The indicator detects regular and hidden bullish and bearish divergences between price and CVD. These divergences often precede trend reversals, offering traders a potential entry point.
Pivot-Based Analysis: The indicator uses pivot highs and lows in both price and CVD to identify divergence patterns. A bullish divergence occurs when price makes a lower low, but CVD fails to follow, suggesting weakening selling pressure. Conversely, a bearish divergence happens when price makes a higher high, but CVD doesn't confirm the move, indicating potential selling pressure.
Trend Coloring: The bars are colored based on the trend direction. Green bars indicate an uptrend (CVD is positive), and red bars indicate a downtrend (CVD is negative). This provides an easy-to-read visualization of market conditions.
Standard Deviation Levels: The indicator plots ±1σ, ±2σ, and ±3σ levels to indicate the degree of deviation from the average CVD. These levels act as thresholds for identifying extreme buying or selling pressure.
Customization Options
Anchor Timeframe: The user can define an anchor timeframe to aggregate the CVD, which can be customized based on the trader’s needs (e.g., daily, weekly, custom lower timeframes).
Z-Score Period: The period for calculating the Z-Score can be adjusted, allowing traders to fine-tune the indicator's sensitivity.
Divergence Detection: The tool offers controls to enable or disable divergence detection, with the ability to adjust the lookback periods for pivot detection.
Trend Coloring and Visuals: Traders can choose whether to color bars based on trend direction, display standard deviation levels, or visualize the data as a histogram or line plot.
Display Options: The indicator also allows for various display options, including showing the Z-Score values and divergence signals, with customizable colors and line widths.
Alerts and Signals
The Cumulative Volume Delta Z Score comes with pre-configured alert conditions for:
Z-Score Crossovers: Alerts are triggered when the Z-Score crosses the 0 line, indicating a potential trend reversal.
Shifting Trend: Alerts for when the Z-Score shifts direction, signaling a change in market sentiment.
Divergence Detection: Alerts for both regular and hidden bullish and bearish divergences, offering potential reversal signals.
Extreme Imbalances: Alerts when the Z-Score reaches extreme positive or negative levels, indicating overbought or oversold market conditions.
Applications in Trading
Trend Identification: Use the Z-Score to confirm bullish or bearish trends based on cumulative volume data, filtering out noise and false signals.
Reversal Signals: Divergences between price and CVD can help identify potential trend reversals, making it a powerful tool for swing traders.
Volume-Based Confirmation: The Z-Score allows traders to confirm price movements with volume data, providing more reliable signals compared to price action alone.
Divergence Strategy: Use the divergence signals to identify potential points of entry, particularly when regular or hidden divergences appear.
Volatility and Market Sentiment: The Z-Score provides insights into market volatility by measuring the deviation of CVD from its historical mean, helping to predict price movement strength.
The Cumulative Volume Delta Z Score is a powerful tool that combines volume analysis with statistical normalization. By focusing on volume imbalances and applying Z-Score normalization, this indicator provides clear, reliable signals for trend identification and potential reversals. It is especially useful for filtering out market noise and ensuring that trades are based on significant price movements driven by substantial volume changes.
This indicator is perfect for traders looking to add volume-based analysis to their strategy, offering a more robust and accurate way to gauge market sentiment and trend strength.
ATH Levels v4# ATH Levels v4
A powerful indicator for tracking All-Time Highs (ATH) and setting customizable price levels based on percentage drops from the ATH. Perfect for cryptocurrency trading, DCA strategies, and risk management.
## Overview
ATH Levels v4 helps traders visualize key support levels calculated as percentage drops from the All-Time High within a configurable lookback period. The indicator also tracks the All-Time Low (ATL) since the last ATH, providing a complete picture of price range dynamics.
## Key Features
### Configurable Percentage Levels
- Define up to 8 custom price levels as percentage drops from ATH
- No longer limited to fixed 10% intervals
- Each level can be set anywhere from 0% to 100% drop
- Default levels: 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%
### ATL Tracking (NEW in v4)
- Automatically tracks the All-Time Low since the last ATH was reached
- Displays ATL price and percentage drop from ATH
- Resets when a new ATH is detected
- Can be toggled on/off
### Portfolio Management
- Allocate pot size percentages to each level
- Visualize dollar amounts for each level based on your total pot size
- Plan your DCA (Dollar Cost Averaging) strategy
- Only displays levels with allocated pot percentages
### Flexible Display Options
- Show/hide level lines
- Hide ATH level for zooming into lower levels
- Configurable lookback period (default 365 days)
- Adjustable right margin positioning for labels
- Color-coded labels with transparency gradient
## How to Use
### Basic Setup
1. Add the indicator to your chart
2. Set your total pot size in dollars
3. Configure the percentage drops for each level (where you want to buy/accumulate)
4. Allocate pot size percentages to each level
### Example DCA Strategy
```
Total Pot Size: $10,000
Level 3 (-30%): 10% pot = $1,000
Level 4 (-40%): 20% pot = $2,000
Level 5 (-50%): 25% pot = $2,500
Level 6 (-60%): 30% pot = $3,000
Level 7 (-70%): 10% pot = $1,000
Level 8 (-80%): 5% pot = $500
```
## Settings
### Display Options
- **Show level lines**: Toggle horizontal lines on/off
- **Hide ATH level**: Hide the ATH label for cleaner charts
- **Show ATL since last ATH**: Display/hide the All-Time Low indicator
- **Days to Lookback**: Period for calculating ATH (default: 365)
- **Margin from last bar**: Spacing between chart and labels (default: 10)
### Level Configuration
- **Level 1-8 % drop from ATH**: Set custom percentage drops (0-100%)
- **Level 1-8 pot %**: Allocate your portfolio percentage to each level (0-100%)
**Note**: Levels only display if they have a pot percentage allocated (>0%)
### Pot Size
- **Pot size**: Total amount in dollars available for the strategy
## Version History
### V4 (October 2025)
- Upgraded to PineScript v6
- Configurable percentage drops from ATH (no longer hardcoded)
- ATL tracking and display since last ATH
- Updated syntax and functions for v6 compatibility
### V3 (May 2020)
- Added option to hide ATH level for better chart zoom
### V2
- Hide/show level lines
- Configurable lookback period
- Configurable right margin
- Only shows levels with pot size % set
### V1
- Initial release with 8 fixed levels
## Use Cases
### Cryptocurrency Trading
- Plan accumulation zones during bear markets
- Set alerts at key percentage drops from ATH
- Track historical ATH and ATL ranges
### Risk Management
- Visualize potential support zones
- Plan position sizing at different levels
- Monitor distance from ATH in real-time
### DCA Strategies
- Automate dollar-cost averaging planning
- Allocate budget across multiple price levels
- Track execution of your DCA plan
## Technical Details
- **Version**: PineScript v6
- **Type**: Indicator
- **Overlay**: Yes
- **Default Timeframe**: Works on all timeframes
- **Calculations**: Based on closing prices within lookback period
## Credits
Original concept inspired by daytask. Enhanced and maintained by SilvesterScorpion.com
## License
This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
---
**Tip**: For best results, use on higher timeframes (4H, Daily, Weekly) to identify major support zones. Combine with volume analysis and other indicators for confirmation.
Octopus Indicator 🐙 Octopus Indicator - Technical Analysis Description
Overview
The Octopus Indicator is a comprehensive TradingView technical analysis tool that combines multiple trading methodologies into a single, powerful script. It provides a complete market analysis framework through seven integrated components.
🔧 Core Components:
1. Moving Averages with Clouds
EMA 25, 50, 75, and 150 with standard deviation bands
Visual clouds representing volatility around each EMA
Customizable colors for each average and its cloud
2. Dual Hull Bands
Two separate Hull bands with different periods (20 and 110)
Multiple variations: HMA, THMA, EHMA
Colored filling between Hull lines
Option to use higher timeframes for multi-timeframe analysis
3. Swing High/Low Detector
Identifies significant price reversal points
Configurable swing strength (default: 5 bars)
Solid lines for current swings and dotted for past ones
Alerts when swing levels are broken
4. Volume Analysis (PVSRA)
Vector Candles that change color based on volume:
Red/Green: Volume ≥ 200% of average or highest spread×volume
Blue/Violet: Volume ≥ 150% of average
Gray: Normal conditions
Vector Candle Zones (VCZ): Key areas based on volume candles
5. Daily & Weekly Levels
Previous day's high and low
Previous week's high and low
Stepline display with optional labels
6. UT Bot - Trailing Stop
Dynamic ATR-based stop loss
Bar coloring based on trend direction
Adjustable sensitivity via "Key Value"
7. Session Detector
Identifies session highs/lows (Sydney, Asia, Europe, etc.)
Visual boxes marking each trading session
⚙️ Customization Features:
Individual color schemes for all elements
Adjustable line thickness
Custom transparency settings
Flexible calculation periods
Multiple timeframe options
🎯 Trading Applications:
Trend Identification (EMAs + Hull)
Entry/Exit Points (Swings + Volume)
Risk Management (Trailing Stop)
Support/Resistance (VCZ + Highs/Lows)
Market Timing (Sessions + Volume)
💡 Key Benefits:
All-in-One Solution: Eliminates indicator clutter
Multi-Timeframe Analysis: Built-in higher timeframe data
Visual Clarity: Clean, organized display with color coding
Customizable Alerts: Swing break and trend change notifications
Professional Grade: Institutional-level volume analysis
This indicator is designed for traders who want a comprehensive market analysis tool without the complexity of managing multiple separate indicators, providing holistic market insight through different technical perspectives.
Enhanced Holt-Winters RSI [BOSWaves]Enhanced Holt-Winters RSI – Next-Level Momentum Smoothing & Signal Precision
Overview
The Enhanced Holt-Winters RSI transforms the classic Relative Strength Index into a robust, lag-minimized momentum oscillator through Holt-Winters triple exponential smoothing. By modeling the level, trend, and cyclical behavior of the RSI series, this indicator delivers smoother, more responsive signals that highlight overbought/oversold conditions, momentum shifts, and high-conviction trading setups without cluttering the chart with noise.
Unlike traditional RSI, which reacts to historical data and produces frequent whipsaws, the Enhanced Holt-Winters RSI filters transient price fluctuations, enabling traders to detect emerging momentum and potential reversal zones earlier.
Theoretical Foundation
The traditional RSI measures relative strength by comparing average gains and losses, but suffers from:
Lag in trend recognition : Signals often arrive after momentum has shifted.
Noise sensitivity : High-frequency price movements generate unreliable crossovers.
Limited insight into structural market shifts : Standard RSI cannot contextualize cyclical or momentum patterns.
The Enhanced Holt-Winters RSI addresses these limitations by applying triple exponential smoothing directly to the RSI series. This decomposes the series into:
Level (Lₜ) : Represents the smoothed central tendency of RSI.
Trend (Tₜ) : Captures rate-of-change in smoothed momentum.
Seasonal Component (Sₜ) : Models short-term cyclical deviations in momentum.
By incorporating these elements, the oscillator produces smoothed RSI values that react faster to emerging trends while suppressing erratic noise. Its internal forecast is mathematical, influencing the smoothed RSI output and signals, rather than being directly plotted.
How It Works
The Enhanced Holt-Winters RSI builds its signal framework through several layers:
1. Base RSI Calculation
Computes standard RSI over the selected period as the primary momentum input.
2. Triple Exponential Smoothing (Holt-Winters)
The RSI is smoothed recursively to extract underlying momentum structure:
Level, trend, and seasonal components are combined to produce a smoothed RSI.
This internal smoothing reduces lag and enhances signal reliability.
3. Momentum Analysis
Short-term momentum shifts are tracked via a moving average of the smoothed RSI, highlighting acceleration or deceleration in directional strength.
4. Volume Confirmation (Optional)
Buy/sell signals can be filtered through a configurable volume threshold, ensuring only high-conviction moves trigger alerts.
5. Visual Output
Colored Candles : Represent overbought (red), oversold (green), or neutral (yellow) conditions.
Oscillator Panel : Plots the smoothed RSI with dynamic color coding for immediate trend context.
Signals : Triangular markers indicate bullish or bearish setups, with stronger signals flagged in extreme zones.
Interpretation
The Enhanced Holt-Winters RSI provides a multi-dimensional perspective on price action:
Trend Strength : Smoothed RSI slope and color coding reflect the direction and momentum intensity.
Momentum Shifts : Rapid changes in the smoothed RSI indicate emerging strength or weakness.
Overbought/Oversold Zones : Highlight areas where price is stretched relative to recent momentum.
High-Conviction Signals : Combined with volume filtering, markers indicate optimal entries/exits.
Cycle Awareness : Smoothing reveals structural patterns, helping traders avoid reacting to noise.
By combining these elements, traders gain early insight into market structure and momentum without relying on raw, lag-prone RSI data.
Strategy Integration
The Enhanced Holt-Winters RSI can be applied across trading styles:
Trend Following
Enter when RSI is aligned with price momentum and color-coded signals confirm trend direction.
Strong slope in the smoothed RSI signals trend continuation.
Reversal Trading
Look for RSI extremes with momentum shifts and strong signal markers.
Compression in oscillator values often precedes reversal setups.
Breakout Detection
Oscillator flattening in neutral zones followed by directional expansion indicates potential breakout conditions.
Multi-Timeframe Confluence
Higher timeframes provide directional bias; lower timeframes refine entry timing using smoothed RSI dynamics.
Technical Implementation Details
Input Source : Close, open, high, low, or price.
Smoothing : Holt-Winters triple exponential smoothing applied to RSI.
Parameters :
Level (α) : Controls smoothing of RSI.
Trend (β) : Adjusts responsiveness to momentum changes.
Seasonal Length : Defines cycles for short-term adjustments.
Delta Smoothing : Reduces choppiness in smoothed RSI difference.
Outputs :
Smoothed RSI
Colored candles and oscillator panel
Buy/Sell signal markers (with optional strength filtering)
Volume Filtering : Optional threshold to confirm signals.
Optimal Application Parameters
Asset-Specific Guidance:
Forex : Use moderate smoothing (α, β) to capture medium-term momentum swings while filtering minor price noise. Works best when combined with volume or volatility filters.
Equities : Balance responsiveness and smoothness to identify sustained sector momentum or rotational shifts; ideal for capturing clean directional transitions.
Cryptocurrency : Increase smoothing parameters slightly to stabilize RSI during extreme volatility; optional volume confirmation can help filter false signals.
Futures/Indices : Lower smoothing sensitivity emphasizes macro momentum and structural trend durability over short-term fluctuations.
Timeframe Optimization:
Scalping (1-5m) : Use higher sensitivity (lower smoothing factors) to react quickly to micro-momentum reversals.
Intraday (15m-1h) : Balance smoothing and responsiveness for detecting short-term acceleration and exhaustion zones.
Swing (4h-Daily) : Apply moderate smoothing to reveal underlying directional persistence and cyclical reversals.
Position (Daily-Weekly) : Use stronger smoothing to isolate dominant momentum trends and filter temporary pullbacks.
Integration Guidelines
Combine with trend filters (EMAs, SuperSmoother MA, ATR-based tools) for confirmation.
Use volume and signal strength markers to filter low-conviction trades.
Slope, color, and signal alignment can guide entry, stop placement, and scaling.
Disclaimer
The Enhanced Holt-Winters RSI is a technical analysis tool, not a guaranteed profit system. Effectiveness depends on proper settings, market structure, and disciplined risk management. Always backtest before live trading.
Period Separator + Future Lines (Exchange-Time Synced)Monthly, Weekly, Daily,4hr and hr dividers and future separators (custom as wish, how many lines it should show in future)
Future separators corrected
First week of the yearA very simple indicator that marks a channel on the candlestick for the first week of the year.
The channel can serve as an entry/exit point with a medium and long term focus.
Note: This indicator should be observed exclusively on the weekly timeframe.
Opening Range Fibonacci Extensions (ATR Adjusted)this script displays daily, weekly, or monthly range extensions as a function of ATR in a Fibonacci retracement
Confluence Dashboard + Strategy [Daily + Weekly Adaptive]Removed duplicate strategy() declarations
Scoped getWeeklyBias() safely with correct request.security() usage
Ensured all variables are declared before use
Aligned background shading with bias logic
Streamlined signal tier logic to avoid overlap
Integrated strategy entries/exits cleanly
Confluence Zone BuilderWhat It Does
The Confluence Zone Builder is a technical analysis indicator that identifies high-probability price levels by detecting where multiple technical factors align (converge) at the same price area. These "confluence zones" represent levels where price is statistically more likely to react - either bouncing (support/resistance) or breaking through (breakout targets).
How It Works
1. Multi-Factor Analysis
The indicator calculates key technical levels from various sources:
Fibonacci Retracements (23.6%, 38.2%, 50%, 61.8%, 78.6%) - Support/resistance levels based on recent price swings
Fibonacci Extensions (127.2%, 141.4%, 161.8%, 200%, 261.8%) - Breakout targets beyond the current range (both bullish and bearish)
Pivot Points (Classic pivots: P, R1-R3, S1-S3) - Daily/weekly reference points traders watch
Moving Averages (EMA 20, 50 and SMA 100, 200) - Dynamic support/resistance that institutions track
VWAP - Volume-weighted average price, popular among institutional traders
Psychological Levels - Round numbers that attract orders
Previous Period Levels - Prior day/week high, low, and close
2. Proximity Clustering
When multiple factors fall within a defined proximity range (default 0.5%), they're grouped together into a single "confluence zone." This prevents cluttering the chart with dozens of individual lines.
3. Weighted Scoring System
Not all technical factors are equal. The indicator assigns importance weights:
Key Fibonacci levels (61.8%) and major MAs (200, 50) get higher weights (2.0-2.5x)
Pivot points and VWAP get medium weights (1.5x)
Minor factors get lower weights (1.0x)
The total score reflects both the number of factors and their importance.
4. Historical Validation
The indicator analyzes the last 50 bars (customizable) to track:
Touches: How many times price reached each zone
Rejections: Times price bounced off the zone (✅)
Breaks: Times price broke through the zone (❌)
Win Rate: Percentage of times the zone held (rejections ÷ touches)
5. Dynamic Adjustment
Zones aren't static - they adapt based on how price interacts with them:
Strengthens (+0.5 per rejection, +0.2 per touch): Zones that repeatedly hold become more important
Weakens (-0.8 per break): Zones that fail to hold lose credibility
Visual Indicators:
Thick solid lines = Strong zones (more rejections than breaks)
Dashed lines = Weak zones (more breaks than rejections)
Color-coded by score: Blue (low), Yellow (medium), Red (high)
What You Gain From Using It
For Support/Resistance Trading:
High-probability entries: Enter at zones with high confluence scores and strong historical win rates
Better risk management: Place stops beyond strong confluence zones that are likely to hold
Reduced false signals: Multi-factor confirmation reduces reliance on single indicators
For Breakout Trading:
Target identification: Fibonacci extensions provide profit targets beyond current ranges
Breakout confirmation: Weak zones (dashed lines, low win rates) are easier to break - ideal for breakout entries
False breakout avoidance: Strong zones (thick lines, high win rates) require more confirmation before entering
For Position Management:
Exit planning: Take profits at high-confluence zones ahead
Stop placement: Use strong zones as logical stop-loss levels
Trade filtering: Higher probability setups occur at stronger zones
Key Advantages:
Objective confluence detection - No manual line drawing needed
Data-driven validation - Historical performance shows which zones actually matter
Adaptive intelligence - Zones strengthen/weaken based on real price action
Clean visualization - Top zones only, with compact labels showing score and factors
Customizable - Adjust weights, components, and thresholds to your trading style
Bottom Line:
Instead of guessing which technical level matters most, this indicator does the heavy lifting - analyzing multiple factors, validating them historically, and highlighting only the zones where price is most likely to react. It's like having confluence analysis automated with statistical backing.
RSI Donchian Channel [DCAUT]█ RSI Donchian Channel
📊 ORIGINALITY & INNOVATION
The RSI Donchian Channel represents an important synthesis of two complementary analytical frameworks: momentum oscillators and breakout detection systems. This indicator addresses a common limitation in traditional RSI analysis by replacing fixed overbought/oversold thresholds with adaptive zones derived from historical RSI extremes.
Key Enhancement:
Traditional RSI analysis relies on static threshold levels (typically 30/70), which may not adequately reflect changing market volatility regimes. This indicator adapts the reference zones dynamically based on the actual RSI behavior over the lookback period, helping traders identify meaningful momentum extremes relative to recent price action rather than arbitrary fixed levels.
The implementation combines the proven momentum measurement capabilities of RSI with Donchian Channel's breakout detection methodology, creating a framework that identifies both momentum exhaustion points and potential continuation signals through the same analytical lens.
📐 MATHEMATICAL FOUNDATION
Core Calculation Process:
Step 1: RSI Calculation
The Relative Strength Index measures momentum by comparing the magnitude of recent gains to recent losses:
Calculate price changes between consecutive periods
Separate positive changes (gains) from negative changes (losses)
Apply selected smoothing method (RMA standard, also supports SMA, EMA, WMA) to both gain and loss series
Compute Relative Strength (RS) as the ratio of smoothed gains to smoothed losses
Transform RS into bounded 0-100 scale using the formula: RSI = 100 - (100 / (1 + RS))
Step 2: Donchian Channel Application
The Donchian Channel identifies the highest and lowest RSI values within the specified lookback period:
Upper Channel: Highest RSI value over the lookback period, represents the recent momentum peak
Lower Channel: Lowest RSI value over the lookback period, represents the recent momentum trough
Middle Channel (Basis): Average of upper and lower channels, serves as equilibrium reference
Channel Width Dynamics:
The distance between upper and lower channels reflects RSI volatility. Wide channels indicate high momentum variability, while narrow channels suggest momentum consolidation and potential breakout preparation. The indicator monitors channel width over a 100-period window to identify squeeze conditions that often precede significant momentum shifts.
📊 COMPREHENSIVE SIGNAL ANALYSIS
Primary Signal Categories:
Breakout Signals:
Upper Breakout: RSI crosses above the upper channel, indicates momentum reaching new relative highs and potential trend continuation, particularly significant when accompanied by price confirmation
Lower Breakout: RSI crosses below the lower channel, suggests momentum reaching new relative lows and potential trend exhaustion or reversal setup
Breakout strength is enhanced when the channel is narrow prior to the breakout, indicating a transition from consolidation to directional movement
Mean Reversion Signals:
Upper Touch Without Breakout: RSI reaches the upper channel but fails to break through, may indicate momentum exhaustion and potential reversal opportunity
Lower Touch Without Breakout: RSI reaches the lower channel without breakdown, suggests potential bounce as momentum reaches oversold extremes
Return to Basis: RSI moving back toward the middle channel after touching extremes signals momentum normalization
Trend Strength Assessment:
Sustained Upper Channel Riding: RSI consistently remains near or above the upper channel during strong uptrends, indicates persistent bullish momentum
Sustained Lower Channel Riding: RSI stays near or below the lower channel during strong downtrends, reflects persistent bearish pressure
Basis Line Position: RSI position relative to the middle channel helps identify the prevailing momentum bias
Channel Compression Patterns:
Squeeze Detection: Channel width narrowing to 100-period lows indicates momentum consolidation, often precedes significant directional moves
Expansion Phase: Channel widening after a squeeze confirms the initiation of a new momentum regime
Persistent Narrow Channels: Extended periods of tight channels suggest market indecision and accumulation/distribution phases
🎯 STRATEGIC APPLICATIONS
Trend Continuation Strategy:
This approach focuses on identifying and trading momentum breakouts that confirm established trends:
Identify the prevailing price trend using higher timeframe analysis or trend-following indicators
Wait for RSI to break above the upper channel in uptrends (or below the lower channel in downtrends)
Enter positions in the direction of the breakout when price action confirms the momentum shift
Place protective stops below the recent swing low (long positions) or above swing high (short positions)
Target profit levels based on prior swing extremes or use trailing stops to capture extended moves
Exit when RSI crosses back through the basis line in the opposite direction
Mean Reversion Strategy:
This method capitalizes on momentum extremes and subsequent corrections toward equilibrium:
Monitor for RSI reaching the upper or lower channel boundaries
Look for rejection signals (price reversal patterns, volume divergence) when RSI touches the channels
Enter counter-trend positions when RSI begins moving back toward the basis line
Use the basis line as the initial profit target for mean reversion trades
Implement tight stops beyond the channel extremes to limit risk on failed reversals
Scale out of positions as RSI approaches the basis line and closes the position when RSI crosses the basis
Breakout Preparation Strategy:
This approach positions traders ahead of potential volatility expansion from consolidation phases:
Identify squeeze conditions when channel width reaches 100-period lows
Monitor price action for consolidation patterns (triangles, rectangles, flags) during the squeeze
Prepare conditional orders for breakouts in both directions from the consolidation
Enter positions when RSI breaks out of the narrow channel with expanding width
Use the channel width expansion as a confirmation signal for the breakout's validity
Manage risk with stops just inside the opposite channel boundary
Multi-Timeframe Confluence Strategy:
Combining RSI Donchian Channel analysis across multiple timeframes can improve signal reliability:
Identify the primary trend direction using a higher timeframe RSI Donchian Channel (e.g., daily or weekly)
Use a lower timeframe (e.g., 4-hour or hourly) to time precise entry points
Enter long positions when both timeframes show RSI above their respective basis lines
Enter short positions when both timeframes show RSI below their respective basis lines
Avoid trades when timeframes provide conflicting signals (e.g., higher timeframe below basis, lower timeframe above)
Exit when the higher timeframe RSI crosses its basis line in the opposite direction
Risk Management Guidelines:
Effective risk management is essential for all RSI Donchian Channel strategies:
Position Sizing: Calculate position sizes based on the distance between entry point and stop loss, limiting risk to 1-2% of capital per trade
Stop Loss Placement: For breakout trades, place stops just inside the opposite channel boundary; for mean reversion trades, use stops beyond the channel extremes
Profit Targets: Use the basis line as a minimum target for mean reversion trades; for trend trades, target prior swing extremes or use trailing stops
Channel Width Context: Increase position sizes during narrow channels (lower volatility) and reduce sizes during wide channels (higher volatility)
Correlation Awareness: Monitor correlations between traded instruments to avoid over-concentration in similar setups
📋 DETAILED PARAMETER CONFIGURATION
RSI Source:
Defines the price data series used for RSI calculation:
Close (Default): Standard choice providing end-of-period momentum assessment, suitable for most trading styles and timeframes
High-Low Average (HL2): Reduces the impact of closing auction dynamics, useful for markets with significant end-of-day volatility
High-Low-Close Average (HLC3): Provides a more balanced view incorporating the entire period's range
Open-High-Low-Close Average (OHLC4): Offers the most comprehensive price representation, helpful for identifying overall period sentiment
Strategy Consideration: Use Close for end-of-period signals, HL2 or HLC3 for intraday volatility reduction, OHLC4 for capturing full period dynamics
RSI Length:
Controls the number of periods used for RSI calculation:
Short Periods (5-9): Highly responsive to recent price changes, produces more frequent signals with increased false signal risk, suitable for short-term trading and volatile markets
Standard Period (14): Widely accepted default balancing responsiveness with stability, appropriate for swing trading and intermediate-term analysis
Long Periods (21-28): Produces smoother RSI with fewer signals but more reliable trend identification, better for position trading and reducing noise in choppy markets
Optimization Approach: Test different lengths against historical data for your specific market and timeframe, consider using longer periods in ranging markets and shorter periods in trending markets
RSI MA Type:
Determines the smoothing method applied to price changes in RSI calculation:
RMA (Relative Moving Average - Default): Wilder's original smoothing method providing stable momentum measurement with gradual response to changes, maintains consistency with classical RSI interpretation
SMA (Simple Moving Average): Treats all periods equally, responds more quickly to changes than RMA but may produce more whipsaws in volatile conditions
EMA (Exponential Moving Average): Weights recent periods more heavily, increases responsiveness at the cost of potential noise, suitable for traders prioritizing early signal generation
WMA (Weighted Moving Average): Applies linear weighting favoring recent data, offers a middle ground between SMA and EMA responsiveness
Selection Guidance: Maintain RMA for consistency with traditional RSI analysis, use EMA or WMA for more responsive signals in fast-moving markets, apply SMA for maximum simplicity and transparency
DC Length:
Specifies the lookback period for Donchian Channel calculation on RSI values:
Short Periods (10-14): Creates tight channels that adapt quickly to changing momentum conditions, generates more frequent trading signals but increases sensitivity to short-term RSI fluctuations
Standard Period (20): Balances channel responsiveness with stability, aligns with traditional Bollinger Bands and moving average periods, suitable for most trading styles
Long Periods (30-50): Produces wider, more stable channels that better represent sustained momentum extremes, reduces signal frequency while improving reliability, appropriate for position traders and higher timeframes
Calibration Strategy: Match DC length to your trading timeframe (shorter for day trading, longer for swing trading), test channel width behavior during different market regimes, consider using adaptive periods that adjust to volatility conditions
Market Adaptation: Use shorter DC lengths in trending markets to capture momentum shifts earlier, apply longer periods in ranging markets to filter noise and focus on significant extremes
Parameter Combination Recommendations:
Scalping/Day Trading: RSI Length 5-9, DC Length 10-14, EMA or WMA smoothing for maximum responsiveness
Swing Trading: RSI Length 14, DC Length 20, RMA smoothing for balanced analysis (default configuration)
Position Trading: RSI Length 21-28, DC Length 30-50, RMA or SMA smoothing for stable signals
High Volatility Markets: Longer RSI periods (21+) with standard DC length (20) to reduce noise
Low Volatility Markets: Standard RSI length (14) with shorter DC length (10-14) to capture subtle momentum shifts
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Adaptive Threshold Mechanism:
Unlike traditional RSI analysis with fixed 30/70 thresholds, this indicator's Donchian Channel approach provides several improvements:
Context-Aware Extremes: Overbought/oversold levels adjust automatically based on recent momentum behavior rather than arbitrary fixed values
Volatility Adaptation: In low volatility periods, channels narrow to reflect tighter momentum ranges; in high volatility, channels widen appropriately
Market Regime Recognition: The indicator implicitly adapts to different market conditions without manual threshold adjustments
False Signal Reduction: Adaptive channels help reduce premature reversal signals that often occur with fixed thresholds during strong trends
Signal Quality Characteristics:
The indicator's dual-purpose design provides distinct advantages for different trading objectives:
Breakout Trading: Channel boundaries offer clear, objective breakout levels that update dynamically, eliminating the ambiguity of when momentum becomes "too high" or "too low"
Mean Reversion: The basis line provides a natural profit target for reversion trades, representing the midpoint of recent momentum extremes
Trend Strength: Persistent channel boundary riding offers an objective measure of trend strength without additional indicators
Consolidation Detection: Channel width analysis provides early warning of potential volatility expansion from compression phases
Comparative Analysis:
When compared to traditional RSI implementations and other momentum frameworks:
vs. Fixed Threshold RSI: Provides market-adaptive reference levels rather than static values, helping to reduce false signals during trending markets where RSI can remain "overbought" or "oversold" for extended periods
vs. RSI Bollinger Bands: Offers clearer breakout signals and more intuitive extreme identification through actual high/low boundaries rather than statistical standard deviations
vs. Stochastic Oscillator: Maintains RSI's momentum measurement advantages (unbounded calculation avoiding scale compression) while adding the breakout detection capabilities of Donchian Channels
vs. Standard Donchian Channels: Applies breakout methodology to momentum space rather than price, providing earlier signals of potential trend changes before price breakouts occur
Performance Characteristics:
The indicator exhibits specific behavioral patterns across different market conditions:
Trending Markets: Excels at identifying momentum continuation through channel breakouts, RSI tends to ride one channel boundary during strong trends, providing trend confirmation
Ranging Markets: Channel width narrows during consolidation, offering early preparation signals for potential breakout trading opportunities
High Volatility: Channels widen to reflect increased momentum variability, automatically adjusting signal sensitivity to match market conditions
Low Volatility: Channels contract, making the indicator more sensitive to subtle momentum shifts that may be significant in calm market environments
Transition Periods: Channel squeezes often precede major trend changes, offering advance warning of potential regime shifts
Limitations and Considerations:
Users should be aware of certain operational characteristics:
Lookback Dependency: Channel boundaries depend entirely on the lookback period, meaning the indicator has no predictive element beyond identifying current momentum relative to recent history
Lag Characteristics: As with all moving average-based indicators, RSI calculation introduces lag, and channel boundaries update only as new extremes occur within the lookback window
Range-Bound Sensitivity: In extremely tight ranges, channels may become very narrow, potentially generating excessive signals from minor momentum fluctuations
Trending Persistence: During very strong trends, RSI may remain at channel extremes for extended periods, requiring patience for mean reversion setups or commitment to trend-following approaches
No Absolute Levels: Unlike traditional RSI, this indicator provides no fixed reference points (like 50), making it less suitable for strategies that depend on absolute momentum readings
USAGE NOTES
This indicator is designed for technical analysis and educational purposes to help traders understand momentum dynamics and identify potential trading opportunities. The RSI Donchian Channel has limitations and should not be used as the sole basis for trading decisions.
Important considerations:
Performance varies significantly across different market conditions, timeframes, and instruments
Historical signal patterns do not guarantee future results, as market behavior continuously evolves
Effective use requires understanding of both RSI momentum principles and Donchian Channel breakout concepts
Risk management practices (stop losses, position sizing, diversification) are essential for any trading application
Consider combining with additional analytical tools such as volume analysis, price action patterns, or trend indicators for confirmation
Backtest thoroughly on your specific instruments and timeframes before live trading implementation
Be aware that optimization on historical data may lead to curve-fitting and poor forward performance
The indicator performs best when used as part of a comprehensive trading methodology that incorporates multiple forms of market analysis, sound risk management, and realistic expectations about win rates and drawdowns.
