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.
Indicadores y estrategias
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
ICT Anchored Market Structures with Validation [LuxAlgo]The ICT Anchored Market Structures with Validation indicator is an advanced iteration of the original Pure-Price-Action-Structures tool, designed for price action traders.
It systematically tracks and validates key price action structures, distinguishing between true structural shifts/breaks and short-term sweeps to enhance trend and reversal analysis. The indicator automatically highlights structural points, confirms breakouts, identifies sweeps, and provides clear visual cues for short-term, intermediate-term, and long-term market structures.
A distinctive feature of this indicator is its exclusive reliance on price patterns. It does not depend on any user-defined input, ensuring that its analysis remains robust, objective, and uninfluenced by user bias, making it an effective tool for understanding market dynamics.
🔶 USAGE
Market structure is a cornerstone of price action analysis. This script automatically detects real-time market structures across short-term, intermediate-term, and long-term levels, simplifying trend analysis for traders. It assists in identifying both trend reversals and continuations with greater clarity.
Market structure shifts and breaks help traders identify changes in trend direction. A shift signals a potential reversal, often occurring when a swing high or low is breached, suggesting a transition in trend. A break, on the other hand, confirms the continuation of an established trend, reinforcing the current direction. Recognizing these shifts and breaks allows traders to anticipate price movement with greater accuracy.
It’s important to note that while a CHoCH may signal a potential trend reversal and a BoS suggests a continuation of the prevailing trend, neither guarantees a complete reversal or continuation. In some cases, CHoCH and BoS levels may act as liquidity zones or areas of consolidation rather than indicating a clear shift or continuation in market direction. The indicator’s validation component helps confirm whether the detected CHoCH and BoS are true breakouts or merely liquidity sweeps.
🔶 DETAILS
🔹 Market Structures
Market structures are derived from price action analysis, focusing on identifying key levels and patterns in the market. Swing point detection, a fundamental concept in ICT trading methodologies and teachings, plays a central role in this approach.
Swing points are automatically identified based exclusively on market movements, without requiring any user-defined input.
🔹 Utilizing Swing Points
Swing points are not identified in real-time as they form. Short-term swing points may appear with a delay of up to one bar, while the identification of intermediate and long-term swing points is entirely dependent on subsequent market movements. Importantly, this detection process is not influenced by any user-defined input, relying solely on pure price action. As a result, swing points are generally not intended for real-time trading scenarios.
Instead, traders often analyze historical swing points to understand market trends and identify potential entry and exit opportunities. By examining swing highs and lows, traders can:
Recognize Trends: Swing highs and lows provide insight into trend direction. Higher swing highs and higher swing lows signify an uptrend, while lower swing highs and lower swing lows indicate a downtrend.
Identify Support and Resistance Levels: Swing highs often act as resistance levels, referred to as Buyside Liquidity Levels in ICT terminology, while swing lows function as support levels, also known as Sellside Liquidity Levels. Traders can leverage these levels to plan their trade entries and exits.
Spot Reversal Patterns: Swing points can form key reversal patterns, such as double tops or bottoms, head and shoulders, and triangles. Recognizing these patterns can indicate potential trend reversals, enabling traders to adjust their strategies effectively.
Set Stop Loss and Take Profit Levels: In ICT teachings, swing levels represent price points with expected clusters of buy or sell orders. Traders can target these liquidity levels/pools for position accumulation or distribution, using swing points to define stop loss and take profit levels in their trades.
Overall, swing points provide valuable information about market dynamics and can assist traders in making more informed trading decisions.
🔹 Logic of Validation
The validation process in this script determines whether a detected market structure shift or break represents a confirmed breakout or a sweep.
The breakout is confirmed when the close price is significantly outside the deviation range of the last detected structural price. This deviation range is defined by the 17-period Average True Range (ATR), which creates a buffer around the detected market structure shift or break.
A sweep occurs when the price breaches the structural level within the deviation range but does not confirm a breakout. In this case, the label is updated to 'SWEEP.'
A visual box is created to represent the price range where the breakout or sweep occurs. If the validation process continues, the box is updated. This box visually highlights the price range involved in a sweep, helping traders identify liquidity events on the chart.
🔶 SETTINGS
The settings for Short-Term, Intermediate-Term, and Long-Term Structures are organized into groups, allowing users to customize swing points, market structures, and visual styles for each.
🔹 Structures
Swings and Size: Enables or disables the display of swing highs and lows, assigns icons to represent the structures, and adjusts the size of the icons.
Market Structures: Toggles the visibility of market structure lines.
Market Structure Validation: Enable or disable validation to distinguish true breakouts from liquidity sweeps.
Market Structure Labels: Displays or hides labels indicating the type of market structure.
Line Style and Width: Allows customization of the style and width of the lines representing market structures.
Swing and Line Colors: Provides options to adjust the colors of swing icons, market structure lines, and labels for better visualization.
🔶 RELATED SCRIPTS
Pure-Price-Action-Structures.
Market-Structures-(Intrabar).
TT ToniTrading Adjustable Price Fee Band [%]Simple but perfectly functional indicator with Trading fee bands.
Crypto Trading is with fees and very small trades often don't make sense due to the fees we need to pay. With this band you can visualize your fees before entering a trade and take smarter decisions for tight daytrading and scalping.
You type in the fee for just one trade, the Taker Fee for a Market Order. The bands show the fees in % times 2, so what you will pay for opening and closing the trade in reality. The band therefore shows the real break-even point, with included payed fees.
It additionally helps taking trading decisions or not with very small trades (Scalping).
You can smooth the bands if you want and you can addtionally show the true datapoints if you prefer smoothend bands. I recommend no bigger smoothing than 2, if you don't want to show the datapoints. Additionally you can fill the band, and of course adjust transperency, colour and all the general TradingView stuff.
Fee Overview in the current market for the indicator input field:
BingX with 10% fee reduction code = 0,045 %
BingX: Normal = 0,050 %
Bitget, ByBit, BitUnix, Blofin, Phemex: Normal = 0,060 %
Bitget, ByBit, BitUnix, Blofin, Phemex: with 20% fee reduction code = 0,048 %
Have fun Trading, Happy Profits!
Greetings
ToniTrading
Volume Rate of Change (VROC)# Volume Rate of Change (VROC)
**What it is:** VROC measures the rate of change in trading volume over a specified period, typically expressed as a percentage. Formula: `((Current Volume - Volume n periods ago) / Volume n periods ago) × 100`
## **Obvious Uses**
**1. Confirming Price Trends**
- Rising VROC with rising prices = strong bullish trend
- Rising VROC with falling prices = strong bearish trend
- Validates that price movements have conviction behind them
**2. Spotting Divergences**
- Price makes new highs but VROC doesn't = weakening momentum
- Price makes new lows but VROC doesn't = potential reversal
**3. Identifying Breakouts**
- Sudden VROC spikes often accompany legitimate breakouts from consolidation patterns
- Helps distinguish real breakouts from false ones
**4. Overbought/Oversold Conditions**
- Extreme VROC readings (very high or very low) suggest exhaustion
- Mean reversion opportunities when volume extremes occur
---
## **Non-Obvious Uses**
**1. Smart Money vs. Dumb Money Detection**
- Declining VROC during price rallies may indicate retail FOMO while institutions distribute
- Rising VROC during selloffs with price stability suggests institutional accumulation
**2. News Impact Measurement**
- Compare VROC before/after earnings or announcements
- Low VROC on "significant" news = market doesn't care (fade the move)
- High VROC = genuine market reaction (respect the move)
**3. Market Regime Changes**
- Persistent shifts in average VROC levels can signal transitions between bull/bear markets
- Declining baseline VROC over months = waning market participation/topping process
**4. Intraday Liquidity Profiling**
- VROC patterns across trading sessions identify best execution times
- Avoid trading when VROC is abnormally low (wider spreads, poor fills)
**5. Sector Rotation Analysis**
- Compare VROC across sector ETFs to identify where capital is flowing
- Rising VROC in defensive sectors + falling VROC in cyclicals = risk-off rotation
**6. Options Expiration Effects**
- VROC typically drops significantly post-options expiration
- Helps avoid false signals from mechanically-driven volume changes
**7. Algorithmic Activity Detection**
- Unusual VROC patterns (regular spikes at specific times) may indicate algo programs
- Can front-run or avoid periods of heavy algorithmic interference
**8. Liquidity Crisis Early Warning**
- Sharp, sustained VROC decline across multiple assets = liquidity withdrawal
- Can precede market stress events before price volatility emerges
**9. Cryptocurrency Wash Trading Detection**
- Comparing VROC across exchanges for same asset
- Discrepancies suggest artificial volume on certain platforms
**10. Pair Trading Optimization**
- Use relative VROC between correlated pairs
- Enter when VROC divergence is extreme, exit when it normalizes
The key to advanced VROC usage is context: combining it with price action, market structure, and other indicators rather than using it in isolation.
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.
RSI VWAP v1 [JopAlgo]RSI VWAP v1.1 made stronger by volume-aware!
We know there's nothing new and the original RSI already does an excellent job. We're just working on small, practical improvements – here's our take: The same basic idea, clearer display, and a single, specially developed rolling line: a VWAP of the RSI that incorporates volume (participation) into the calculation.
Do you prefer the pure classic?
You can still use Wilder or Cutler engines –
but the star here is the VW-RSI + rolling line.
This RSI also offers the possibility of illustrating a possible
POC (Point of Control - or the HAL or VAL) level.
However, the indicator does NOT plot any of these levels itself.
We have included an illustration in the chart for this!
We hope this version makes your decision-making easier.
What you’ll see
The RSI line with a 50 midline and optional bands: either static 70/30 or adaptive μ±k·σ of the Rolling Line.
One smoothing concept only: the Rolling Line (light blue) = VWAP of RSI.
Shadow shading between RSI and the Rolling Line (green when RSI > line, red when RSI < line).
A lighter tint only on the parts of that shadow that sit above the upper band or below the lower band (quick overbought/oversold context).
Simple divergence lines drawn from RSI pivots (green for regular bullish, red for regular bearish). No labels, no buy/sell text—kept deliberately clean.
What’s new, and why it helps
VW-RSI engine (default):
RSI can be computed from volume-weighted up/down moves, so momentum reflects how much traded when price moved—not just the direction.
Rolling Line (VWAP of RSI) with pure VWAP adaptation:
Low volume: blends toward a faster VWAP so early, thin starts aren’t missed.
Volume spikes: blends toward a slower VWAP so a single heavy bar doesn’t whip the curve.
You can reveal the Base Rolling (pre-adaptation) line to see exactly how much adaptation is happening.
Adaptive bands (optional):
Instead of fixed 70/30, use mean ± k·stdev of the Rolling Line over a lookback. Levels breathe with the market—useful in strong trends where static bounds stay pinned.
Minimal, readable panel:
One smoothing, one story. The shadow tells you who’s in control; the lighter highlight shows stretch beyond your lines.
How to read it (fast)
Bias: RSI above 50 (and a rising Rolling Line) → bullish bias; below 50 → bearish bias.
Trigger: RSI crossing the Rolling Line with the bias (e.g., above 50 and crossing up).
Stretch: Near/above the upper band, avoid chasing; near/below the lower band, avoid panic—prefer a cross back through the line.
Divergence lines: Use as context, not as standalone signals. They often help you wait for the next cross or avoid late entries into exhaustion.
Settings that actually matter
RSI Engine: VW-RSI (default), Wilder, or Cutler.
Rolling Line Length: the VWAP length on RSI (higher = calmer, lower = earlier).
Adaptive behavior (pure VWAP):
Speed-up on Low Volume → blends toward fast VWAP (factor of your length).
Dampen Spikes (volume z-score) → blends toward slow VWAP.
Fast/Slow Factors → how far those fast/slow variants sit from the base length.
Bands: choose Static 70/30 or Adaptive μ±k·σ (set the lookback and k).
Visuals: show/hide Base Rolling (ref), main shadow, and highlight beyond bands.
Signal gating: optional “ignore first bars” per day/session if you dislike open noise.
Starter presets
Scalp (1–5m): RSI 9–12, Rolling 12–18, FastFactor ~0.5, SlowFactor ~2.0, Adaptive on.
Intraday (15m–1H): RSI 10–14, Rolling 18–26, Bands k = 1.0–1.4.
Swing (4H–1D): RSI 14–20, Rolling 26–40, Bands k = 1.2–1.8, Adaptive on.
Where it shines (and limits)
Best: liquid markets where volume structure matters (majors, indices, large caps).
Works elsewhere: even with imperfect volume, the shadow + bands remain useful.
Limits: very thin/illiquid assets reduce the benefit of volume-weighting—lengthen settings if needed.
Attribution & License
Based on the concept and baseline implementation of the “Relative Strength Index” by TradingView (Pine v6 built-in).
Released as Open-source (MPL-2.0). Please keep the license header and attribution intact.
Disclaimer
For educational purposes only; not financial advice. Markets carry risk. Test first, use clear levels, and manage risk. This project is independent and not affiliated with or endorsed by TradingView.
Anchored VWAP Polyline [CHE] Anchored VWAP Polyline — Anchored VWAP drawn as a polyline from a user-defined bar count with last-bar updates and optional labels
Summary
This indicator renders an anchored Volume-Weighted Average Price as a continuous polyline starting from a user-selected anchor point a specified number of bars back. It accumulates price multiplied by volume only from the anchor forward and resets cleanly when the anchor moves. Drawing is object-based (polyline and labels) and updated on the most recent bar only, which reduces flicker and avoids excessive redraws. Optional labels mark the anchor and, conditionally, a delta label when the current close is below the historical close at the anchor offset.
Motivation: Why this design?
Anchored VWAP is often used to track fair value after a specific event such as a swing, breakout, or session start. Traditional plot-based lines can repaint during live updates or incur overhead when frequently redrawn. This implementation focuses on explicit state management, last-bar rendering, and object recycling so the line stays stable while remaining responsive when the anchor changes. The design emphasizes deterministic updates and simple session gating from the anchor.
What’s different vs. standard approaches?
Baseline: Classic VWAP lines plotted from session open or full history.
Architecture differences:
Anchor defined by a fixed bar offset rather than session or day boundaries.
Object-centric drawing via `polyline` with an array of `chart.point` objects.
Last-bar update pattern with deletion and replacement of the polyline to apply all points cleanly.
Conditional labels: an anchor marker and an optional delta label only when the current close is below the historical close at the offset.
Practical effect: You get a visually continuous anchored VWAP that resets when the anchor shifts and remains clean on chart refreshes. The labels act as lightweight diagnostics without clutter.
How it works (technical)
The anchor index is computed as the latest bar index minus the user-defined bar count.
A session flag turns true from the anchor forward; prior bars are excluded.
Two persistent accumulators track the running sum of price multiplied by volume and the running sum of volume; they reset when the session flag turns from false to true.
The anchored VWAP is the running sum divided by the running volume whenever both are valid and the volume is not zero.
Points are appended to an array only when the anchored VWAP is valid. On the most recent bar, any existing polyline is deleted and replaced with a new one built from the point array.
Labels are refreshed on the most recent bar:
A yellow warning label appears when there are not enough bars to compute the reference values.
The anchor label marks the anchor bar.
The delta label appears only when the current close is below the close at the anchor offset; otherwise it is suppressed.
No higher-timeframe requests are used; repaint is limited to normal live-bar behavior.
Parameter Guide
Bars back — Sets the anchor offset in bars; default two hundred thirty-three; minimum one. Larger values extend the anchored period and increase stability but respond more slowly to regime changes.
Labels — Toggles all labels; default enabled. Disable to keep the chart clean when using multiple instances.
Reading & Interpretation
The polyline represents the anchored VWAP from the chosen anchor to the current bar. Price above the line suggests strength relative to the anchored baseline; price below suggests weakness.
The anchor label shows where the accumulation starts.
The delta label appears only when today’s close is below the historical close at the offset; it provides a quick context for negative drift relative to that reference.
A yellow message at the current bar indicates the chart does not have enough history to compute the reference comparison yet.
Practical Workflows & Combinations
Trend following: Anchor after a breakout bar or a swing confirmation. Use the anchored VWAP as dynamic support or resistance; look for clean retests and holds for continuation.
Mean reversion: Anchor at a local extreme and watch for approaches back toward the line; require structure confirmation to avoid early entries.
Session or event studies: Re-set the anchor around earnings, macro releases, or session opens by adjusting the bar offset.
Combinations: Pair with structure tools such as swing highs and lows, or with volatility measures to filter chop. The labels can be disabled when combining multiple instances to maintain chart clarity.
Behavior, Constraints & Performance
Repaint and confirmation: The line is updated on the most recent bar only; historical values do not rely on future bars. Normal live-bar movement applies until the bar closes.
No higher timeframe: There is no `security` call; repaint paths related to higher-timeframe lookahead do not apply here.
Resources: Uses one polyline object that is rebuilt on the most recent bar, plus two labels when conditions are met. `max_bars_back` is two thousand. Arrays store points from the anchor forward; extremely long anchors or very long charts increase memory usage.
Known limits: With very thin volume, the VWAP can be unavailable for some bars. Very large anchors reduce responsiveness. Labels use ATR for vertical placement; extreme gaps can place them close to extremes.
Sensible Defaults & Quick Tuning
Starting point: Bars back two hundred thirty-three with Labels enabled works well on many assets and timeframes.
Too noisy around the line: Increase Bars back to extend the accumulation window.
Too sluggish after regime changes: Decrease Bars back to focus on a shorter anchored period.
Chart clutter with multiple instances: Disable Labels while keeping the polyline visible.
What this indicator is—and isn’t
This is a visualization of an anchored VWAP with optional diagnostics. It is not a full trading system and does not include entries, exits, or position management. Use it alongside clear market structure, risk controls, and a plan for trade management. It does not predict future prices.
Inputs with defaults
Bars back: two hundred thirty-three bars, minimum one.
Labels: enabled or disabled toggle, default enabled.
Pine version: v6
Overlay: true
Primary outputs: one polyline, optional labels (anchor, conditional delta, and a warning when insufficient bars).
Metrics and functions: volume, ATR for label offset, object drawing via polyline and chart points, last-bar update pattern.
Special techniques: session gating from the anchor, persistent state, object recycling, explicit guards against unavailable values and zero volume.
Compatibility and assets: Designed for standard candlestick or bar charts across liquid assets and common timeframes.
Diagnostics: Yellow warning label when history is insufficient.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
TrendShield Pro | DinkanWorldSmart Trailing Trend System Powered by EMA + ATR
TrendShield Pro is a powerful trend detection and trailing stop indicator designed for traders who rely on pure price movement and volatility tracking.
It dynamically adapts to market conditions using a combination of EMA (Exponential Moving Average) and ATR (Average True Range) to identify the active trend and place a visual trailing stop line.
🔍 How It Works
TrendShield Pro combines trend direction and volatility to create a self-adjusting trailing system:
EMA (Exponential Moving Average):
Smooths price fluctuations and identifies the overall market bias.
ATR (Average True Range):
Measures volatility to determine how far the trailing stop should follow the trend.
Dynamic Bands:
Two invisible thresholds are formed — up and down — around the EMA using the ATR and your chosen Factor value.
Trailing Logic:
When the EMA is rising, the Trailing Stop (TUp) locks in higher lows.
When the EMA is falling, the Trailing Stop (TDown) locks in lower highs.
The indicator switches trend automatically based on price crossing these trailing levels.
🧭 Visuals & Features
Green Trailing Line (Demand Trend): Indicates an active bullish trend.
Red Trailing Line (Supply Trend): Indicates an active bearish trend.
Arrow Signals:
🟢 Up Arrow → Bullish Trend Reversal
🔴 Down Arrow → Bearish Trend Reversal
Diamond Markers: Show points where the trailing line shifts, marking dynamic volatility changes.
⚙️ Inputs
Input Description
EMA Period Length of the Exponential Moving Average
ATR Period Period used for Average True Range calculation
Factor Multiplier for ATR-based volatility expansion
USDT.D Precision USDT.D Candles: overlays candle OHLC for CRYPTOCAP:USDT.D using request.security, renders real candles via plotcandle() on the main chart (overlay=true). Precision configurable (3–4 dp), optional last-value readout. Hide the base symbol to view only the candles.
Bitcoin Gold Fair Value Model | FREEBitcoin Gold Fair Value Model | FREE
This script presents a quantitative model that explores the historical relationship between Bitcoin (BTCUSD) and Gold (TVC:GOLD).
It estimates Bitcoin’s fair value projection based on the price of gold, using a rolling regression model calculated over a user-defined lookback period (default: 1000 days).
📘 How It Works
The model fits a simple linear regression of Bitcoin’s daily close price versus Gold’s daily close price.
From this relationship, it computes a projected Bitcoin price based on today’s gold value, plotted forward by a chosen number of days (default: 65).
Confidence ranges (±1 standard deviation and 95% interval) help visualize the uncertainty around the projection.
A statistical panel displays the projected price, range estimates, and R² value, indicating the strength of correlation between the two assets.
⚙️ Features
Rolling regression using historical BTC and Gold data.
Forward fair-value projection line (customizable projection period).
1σ (standard deviation) and 95% confidence bands.
On-chart statistical summary with current model values.
Real-time updates when new daily data becomes available.
📊 How to Use
Recommended for use on the daily timeframe with the INDEX:BTCUSD symbol.
The model provides a statistical estimate of Bitcoin’s price relative to gold trends, not a trading signal.
The R² value can be used to assess the current strength of correlation - higher R² suggests a more stable relationship, while lower values indicate weaker or changing dynamics.
⚠️ Important Notes
This indicator is intended for educational and analytical purposes only.
It does not predict prices or provide financial advice.
Relationships between assets can and do change over time.
Always perform your own research and use additional tools for confirmation.
[Fune]-Trend Technology🌊 - Trend Technology
“Flow with the trend — read every wave.”
🎯 Concept
Micro EMA (White) – Short-term pulse
Mid EMA (Aqua) – Medium-term direction
Macro EMA (Orange) – Long-term flow
Mid- to long-term references:
100 EMA = Yellow (trend balance)
300 EMA = Blue (structural anchor)
In addition, the PLR (Periodic Linear Regression) reveals the cyclical rhythm of the market trend — a recurring regression curve that reflects the underlying heartbeat of price movement.
📊 Trend Logic Summary
Condition Color Meaning Action
Mid > Macro 🟢 Green background Bullish trend Look for long opportunities
Mid < Macro 🔴 Red background Bearish trend Look for short opportunities
PLR slope > 0 📈 Upward bias Confirms bullish momentum
PLR slope < 0 📉 Downward bias Confirms bearish momentum
Micro EMA (White) dominant ⚪ White background Neutral / Resting phase Stand aside and wait
🧭 Trading Guidance
🟢 Long Setup: Green background + PLR slope upward + price above 100/300 EMA
🔴 Short Setup: Red background + PLR slope downward + price below 100/300 EMA
⚪ No Trade: White background, EMAs converging, or PLR slope flattening
⚓ Philosophy of
“ (The Boat) is a vessel sailing across the ocean of the market.
The EMAs are its sails, the PLR its compass.
The trader holds the helm, while the divine wind guides the waves.
Only those who move with the current — not against it —
will one day reach the state of ‘mindless clarity.’”
SFC Bollinger Band and Bandit StrategySFC Bollinger Band and Bandit Strategy
概述 (Overview)
SFC 布林通道與海盜策略 (SFC Bollinger Band and Bandit Strategy) 是一個基於 Pine Script™ v6 的技術分析指標，結合布林通道 (Bollinger Bands)、移動平均線 (Moving Averages) 以及布林海盜 (Bollinger Bandit) 交易策略，旨在為交易者提供多時間框架的趨勢分析與進出場訊號。該腳本支援風險管理功能，並提供視覺化圖表與交易訊號提示，適用於多種金融市場。
This script, written in Pine Script™ v6, combines Bollinger Bands, Moving Averages, and the Bollinger Bandit strategy to provide traders with multi-timeframe trend analysis and entry/exit signals. It includes risk management features and visualizes data through charts and trading signals, suitable for various financial markets.
Triple Supertrend (7,3) + (7,2) + (10,4)it gives 3 kind of supertrend collectively together and also give signals when all supertrend change colors in a single candle
☸Gap Detector [NHP]🔶This is a Pine Script code for a “Gap Detector” study in TradingView. The script scans for gaps in the price chart and labels them as either ‘🟢Bull gap’ or ‘🔴Bear gap’. Here’s a brief explanation of the code:
🔶Length and Width are user inputs that define the number of bars to look back and the width of the lines drawn, respectively.
➡️Gap_start and gap_end are variables that store the start and end of a gap.
➡️Gap_bull and gap_bear are boolean variables that indicate whether a bull or bear gap has been detected.
🔶Inf_gap and sup_gap are variables that store the lower and upper bounds of a gap.
The script then iterates over the specified length of bars. If a gap is detected (a high price that is lower than the previous bar’s low price for a bull gap, or a low price that is higher than the previous bar’s high price for a bear gap), it calculates the size of the gap and draws lines and labels on the chart if the gap is larger than 5 pips. ( pips meaning percentage in point)
🔶All content provided is for informational & educational purposes only. Past performance does not guarantee future results.
Fish OrbThis indicator marks and tracks the first 15-minute range of the New York session open (default 9:30–9:45 AM ET) — a critical volatility period for futures like NQ (Nasdaq).
It helps you visually anchor intraday price action to that initial opening range.
Core Functionality
1. Opening Range Calculation
It measures the High, Low, and Midpoint of the first 15 minutes after the NY market opens (default 09:30–09:45 ET).
You can change the window or timezone in the inputs.
2. Visual Overlays
During the 15-minute window:
A teal shaded box highlights the open range period.
Live white lines mark the current High and Low.
A red line marks the midpoint (mid-range).
These update in real-time as each bar forms.
3. Post-Window Behavior
When the 15-minute window ends:
The High, Low, and Midpoint are locked in.
The indicator draws persistent horizontal lines for those values.
4. Historical Days
You can keep today + a set number of previous days (configurable via “Previous Days to Keep”).
Older days automatically delete to keep charts clean.
5. Line Extension Control
Each day’s lines extend to the right after they form.
You can toggle “Stop Lines at Next NY Open”:
ON: Yesterday’s lines stop exactly at the next NY session open (09:30 ET).
OFF: Lines extend indefinitely across the chart.
Trend Flow Trend Flow — by Volume Hub
A clean momentum-based trend map built around EMA 21, EMA 50, and EMA 200.
TrendFlow helps you instantly see whether price is flowing with the trend or fighting against it.
When price trades above the short-term EMAs, momentum is bullish — when it falls below, the flow reverses.
🟢 How to use
Buy bias: when price is above EMA 21 & EMA 50 and both are aligned above the EMA 200.
The green zone between 21 & 50 acts as a dynamic support channel — ideal for pullback entries.
Sell bias: when price is below EMA 21 & EMA 50 and both are under the EMA 200.
The red zone highlights a resistance channel — look for rejection or continuation setups.
Neutral zone: when EMAs are tangled or flat — stay patient until structure expands again.
⚙️ Features
Soft, low-opacity EMA 21 & 50 for clear channel view
Dynamic EMA 200 color shift (green = bullish / red = bearish / gray = neutral)
Automatic color fill between EMA 21 & 50 for instant trend-strength feedback
🎯 Purpose
Designed for traders who prefer clean price structure and disciplined trend confirmation.
Use TrendFlow as your core directional filter — pair it with your own entry logic, liquidity zones, or volume confirmations.
📈 Created by: Volume Hub
Lorentzian Harmonic Flow - Temporal Market Dynamic Lorentzian Harmonic Flow - Temporal Market Dynamic (⚡LHF)
By: DskyzInvestments
What this is
LHF Pro is a research‑grade analytical instrument that models market time as a compressible medium , extracts directional flow in curved time using heavy‑tailed kernels, and consults a history‑based memory bank for context before synthesizing a final, bounded probabilistic score . It is not a mashup; each subsystem is mathematically coupled to a single clock (time dilation via gamma) and a single lens (Lorentzian heavy‑tailed weighting). This script is dense in logic (and therefore heavy) because it prioritizes rigor, interpretability, and visual clarity.
Intended use
Education and research. This tool expresses state recognition and regime context—not guarantees. It does not place orders. It is fully functional as published and contains no placeholders. Nothing herein is financial advice.
Why this is original and useful
Curved time: Markets do not move at a constant pace. LHF Pro computes a Lorentz‑style gamma (γ) from relative speed so its analytical windows contract when the tape accelerates and relax when it slows.
Heavy‑tailed lens: Lorentzian kernels weight information with fat tails to respect rare but consequential extremes (unlike Gaussian decay).
Memory of regimes: A K‑nearest‑neighbors engine works in a multi‑feature space using Lorentz kernels per dimension and exponential age fade , returning a memory bias (directional expectation) and assurance (confidence mass).
One ecosystem: Squeeze, TCI, flow, acceleration, and memory live on the same clock and blend into a single final_score —visualized and documented on the dashboard.
Cognitive map: A 2D heat map projects memory resonance by age and flow regime, making “where the past is speaking” visible.
Shadow portfolio metaphor: Neighbor outcomes act like tiny hypothetical positions whose weighted average forms an educational pressure gauge (no execution, purely didactic).
Mathematical framework (full transparency)
1) Returns, volatility, and speed‑of‑market
Log return: rₜ = ln(closeₜ / closeₜ₋₁)
Realized vol: rv = stdev(r, vol_len); vol‑of‑vol: burst = |rv − rv |
Speed‑of‑market (analog to c): c = c_multiplier × (EMA(rv) + 0.5 × EMA(burst) + ε)
2) Trend velocity and Lorentz gamma (time dilation)
Trend velocity: v = |close − close | / (vel_len × ATR)
Relative speed: v_rel = v / c
Gamma: γ = 1 / √(1 − v_rel²), stabilized by caps (e.g., ≤10)
Interpretation: γ > 1 compresses market time → use shorter effective windows.
3) Adaptive temporal scale
Adaptive length: L = base_len / γ^power (bounded for safety)
Harmonic horizons: Lₛ = L × short_ratio, Lₘ = L × mid_ratio, Lₗ = L × long_ratio
4) Lorentzian smoothing and Harmonic Flow
Kernel weight per lag i: wᵢ = 1 / (1 + (d/γ)²), d = i/L
Horizon baselines: lw_h = Σ wᵢ·price / Σ wᵢ
Z‑deviation: z_h = (close − lw_h)/ATR
Harmonic Flow (HFL): HFL = (w_short·zₛ + w_mid·zₘ + w_long·zₗ) / (w_short + w_mid + w_long)
5) Flow kinematics
Velocity: HFL_vel = HFL − HFL
Acceleration (curvature): HFL_acc = HFL − 2·HFL + HFL
6) Squeeze and temporal compression
Bollinger width vs Keltner width using L
Squeeze: BB_width < KC_width × squeeze_mult
Temporal Compression Index: TCI = base_len / L; TCI > 1 ⇒ compressed time
7) Entropy (regime complexity)
Shannon‑inspired proxy on |log returns| with numerical safeguards and smoothing. Higher entropy → more chaotic regime.
8) Memory bank and Lorentzian k‑NN
Feature vector (5D):
Outcomes stored: forward returns at H5, H13, H34
Per‑dimension similarity: k(Δ) = 1 / (1 + Δ²), weighted by user’s feature weights
Age fading: weight_age = mem_fade^age_bars
Neighbor score: sᵢ = similarityᵢ × weight_ageᵢ
Memory bias: mem_bias = Σ sᵢ·outcomeᵢ / Σ sᵢ
Assurance: mem_assurance = Σ sᵢ (confidence mass)
Normalization: mem_bias normalized by ATR and clamped into band
Shadow portfolio metaphor: neighbors behave like micro‑positions; their weighted net forward return becomes a continuous, adaptive expectation.
9) Blended score and breakout proxy
Blend factor: α_mem = 0.45 + 0.15 × (γ − 1)
Final score: final_score = (1−α_mem)·tanh(HFL / (flow_thr·1.5)) + α_mem·tanh(mem_bias_norm)
Breakout probability (bounded): energy = cap(TCI−1) + |HFL_acc|×k + cap(γ−1)×k + cap(mem_assurance)×k; breakout_prob = sigmoid(energy). Caps avoid runaway “100%” readings.
Inputs — every control, purpose, mechanics, and tuning
🔮 Lorentz Core
Auto‑Adapt (Vol/Entropy): On = L responds to γ and entropy (breathes with regime), Off = static testing.
Base Length: Calm‑market anchor horizon. Lower (21–28) for fast tapes; higher (55–89+) for slow.
Velocity Window (vel_len): Bars used in v. Shorter = more reactive γ; longer = steadier.
Volatility Window (vol_len): Bars used for rv/burst (c). Shorter = more sensitive c.
Speed‑of‑Market Multiplier (c_multiplier): Raises/lowers c. Lower values → easier γ spikes (more adaptation). Aim for strong trends to peak around γ ≈ 2–4.
Gamma Compression Power: Exponent of γ in L. <1 softens; >1 amplifies adaptation swings.
Max Kernel Span: Upper bound on smoothing loop (quality vs CPU).
🎼 Harmonic Flow
Short/Mid/Long Horizon Ratios: Partition L into fast/medium/slow views. Smaller short_ratio → faster reaction; larger long_ratio → sturdier bias.
Weights (w_short/w_mid/w_long): Governs HFL blend. Higher w_short → nimble; higher w_long → stable.
📈 Signals
Squeeze Strictness: Threshold for BB1 = compressed (coiled spring); <1 = dilated.
v/c: Relative speed; near 1 denotes extreme pacing. Diagnostic only.
Entropy: Regime complexity; high entropy suggests caution, smaller size, or waiting for order to return.
HFL: Curved‑time directional flow; sign and magnitude are the instantaneous bias.
HFL_acc: Curvature; spikes often accompany regime ignition post‑squeeze.
Mem Bias: Directional expectation from historical analogs (ATR‑normalized, bounded). Aligns or conflicts with HFL.
Assurance: Confidence mass from neighbors; higher → more reliable memory bias.
Squeeze: ON/RELEASE/OFF from BB
Squeeze Momentum with ADX Filter and Multi-Cycle WavesTitle:
Squeeze Momentum with ADX Filter and Multi-Cycle Waves
Description:
This indicator integrates three well-established technical analysis methodologies into a single oscillator to help traders assess volatility compression, trend strength, and cyclical momentum alignment:
Squeeze Momentum (TTM-style) – Based on Bollinger Bands and Keltner Channels, it identifies periods of low volatility ("the squeeze") followed by directional breakouts. The histogram reflects momentum using linear regression relative to a dynamic centerline. Positive values indicate upward momentum; negative values indicate downward momentum.
ADX with DI+/DI- (Welles Wilder, 1978) – The Average Directional Index is dynamically scaled to match the visual range of the Squeeze histogram. A user-defined Key Level (default: 32) serves as a reference threshold: when ADX rises above this level, it suggests a strong trend is present. DI+ (green) and DI- (red) show directional bias.
Multi-Cycle Waves (55/144/233) – Inspired by adaptive cycle analysis and MACD-style oscillators, these smoothed momentum waves help identify confluence across multiple timeframes. They are optional and appear as shaded areas when enabled.
Key Features:
The Squeeze Momentum Line appears as black/gray crosses at the zero level, indicating momentum polarity without visual clutter.
The Key Level is shown as a thick gray horizontal line, representing the ADX threshold in the scaled oscillator space.
ADX is plotted with increased line width (3) for better visibility.
All components are dynamically scaled to share the same vertical axis, enabling direct visual comparison.
Attribution:
Bollinger Bands: John Bollinger
Keltner Channels: Chester Keltner
Squeeze concept popularized by Linda Raschke and John Carter
ADX/DI system: J. Welles Wilder Jr.
Multi-cycle wave logic: inspired by John Ehlers’ work on market cycles
Integration, scaling logic, and visualization: © Carlos Mauricio Vizcarra (2025)
This script is published under the Mozilla Public License v2.0. It is open-source, non-promotional, and designed for educational and analytical use only. No investment advice is provided.
Session Opens by TradeSeekersIt doesn't get much simpler than this indicator for futures traders wanting to track four key session open prices.
Sessions
1. ETH open - extended hours starts
2. Midnight open - new calendar day starts
3. CME open - Chicago exchange opens, data releases
4. RTH open - regular trading hours, volume cometh
Usage
All four of these prices / areas are important for futures traders to pay attention to.
RTH opens far below ETH sometimes will retrace, CME and RTH together can act as a powerful range.
Midnight open sometimes has little importance for the day, but then again it's provided beautiful bounces. Again each level I find to be impactful nearly every session, so I like to keep them close by in an understated manner.
Timezone
If you're not EST, adjust the timezone string accordingly (refer to TradingView docs for string formats).
Proximity Detection
Also, I added proximity detection that aims to keep level collisions from occurring. If a particular session open isn't shown it may be due to being exactly the same price as another open or it's too close to another open.
The proximity sensitivity can be adjusted in settings. The on chart appearance doesn't impact the alerting capability.
Aesthetics
I don't like boring charts so I added a fun "glow" effect, I went with a palette that reminded me of clear sky colors at those times of day (if you're EST).
Alerting
Alerting can be done with just a single alert, first open the indicator config and uncheck any session opens you don't want to be alerted on (why!?), and then use the standard alert menus in TradingView to set the alert on "Any alert() function call".
Why does this beautiful indicator exist?
While there are a handful of indicators that plot open prices with some overlap to this one, I didn't see any that alerted automatically without much fuss.
🏆 GoldTradePro AutoCycle v5.0To trigger my alerts, this script is brilliant, a sensational indicator. Ask me for more if you're interested.
