Trend Patterns_Trend Model此腳本根據《超級績效：金融怪傑交易之道》中的【趨勢樣板】進行撰寫
當時股價高於一百五十天(三十週)與兩百天(四十週)移動平均。
一百五十天移動平均高於兩百天移動平均。
兩百天移動平均至少有一個月期間處於上升狀態(多數情況最好有四、五個月以上)。
五十天移動平均同時高於一百五十天與兩百天移動平均。
當時股價高於五十天移動平均。
當時股價較五十二週低點至少高出30%(很多最佳候選股在突破橫向整理而展開大規模漲勢之前,股價已經較五十二週低點高出100%、300%或更多。)
目前股價距離五十二週高點不超過25%(愈接近愈好)。
相對強度評等(relative strength ranking,根據《投資人經濟日報》 的資料)不低於70,最好是80多或90多,而且較佳候選股總是如此。
This script is based on the 【Trend Patterns】 in 《Trade Like a Stock Market Wizard》.
The current stock price is above both the 150-day (30-week) and the 200-day (40-week) moving average price lines.
The 150-day moving average is above the 200-day moving average.
The 200-day moving average line is trending up for at least 1 month (preferably 4-5 months minimum in most cases).
The 50-day (10-week) moving average is above both the 150-day and 200-day moving averages.
The current stock price is trading above the 50-day moving average.
The current stock price is at least 30 percent above its 52-week low. (Many of the best selections will be 100 percent, 300 percent, or greater above their 52-week low before they emerge from a solid consolidation period and mount a large scale advance.)
The current stock price is within at least 25 percent of its 52-week high (the closer to a new high the better).
The relative strength ranking (as reported in Investor's Business Daily) is no less than 70, and preferably in the 80s or 90s, which will generally be the case with the better selections.
圖表形態
Sessions Candle Colors1. Candle Display Mode
Choose how your candles are rendered:
Normal – Standard bullish/bearish candles with theme-based colors.
Normal – Single – Candles displayed in a single neutral tone.
Session – Candles colored by active trading sessions.
Session – Single – Session-based candles in a single tone.
None – Disables custom candles (useful if you prefer chart elements only).
2. Theme: Normal Candles
Includes a curated set of themes for standard candles.
Default: Light – BW
Available Themes:
Dark – Prime
Dark – Violet
Dark – Ice
Dark – Bronze
Dark – BW
Light – BW
Light – ICT (Inner Circle Trader)
Light – S&F (Set and Forget)
3. Theme: Session Candles
Custom palettes for session-based modes:
Light – AnandaDivine
Light – WealthFRX
Note: “Light” and “Dark” indicate which chart background the theme is optimized for.
4. Hide Gaps
Enables a custom gapless mode by forcing each candle’s open to match the previous close.
This option helps maintain visual continuity on charts with irregular price feeds.
Tip: For best results, disable TradingView’s built-in candles under chart settings before enabling this indicator.
REMS Synergy OverlayThis 3rd generation REMS indicator builds upon the foundations assessing the relationships between RSI, EMAs, MACDs, and Stochastic RSI across multiple timeframes. Designed to help traders identify less frequent, but high probability entries across 2 time frames. Uses 3 levels of confluence indicators for both long and short moves.
Confluence Level 1 (Highest Conviction):
Evaluates selected criteria across both timeframes. All selected criteria must be in confluence to trigger signal.
Confluence Level 2 (Moderate Conviction):
Selected criteria can be selected by each timeframe individually. All selected criteria must be in confluence to trigger signal.
Confluence Level 3 (Lower/supportive confluence):
Of the selected criteria, this level can evaluate a set number of conditions that must be met. Number of conditions is user-defined.
Includes VWAP and 4 EMAs as optional visual representations.
Includes 'Enhanced Candles' than can colour code candlesticks for better visual identification. (off by default)
Originally designed with 5 minute and 2 minute timeframes in mind, and pairs well with REMS First Strike and/or REMS Snap Shot indicators.
Values coded below:
RSI
-Primary: Length = 14, Smoothing = 20 (via SMA)
-Secondary: Length = 7, Smoothing = 20 (via SMA)
Stochastic RSI
Primary:
-RSI Length = 14
-Stochastic Length = 8
-%K = 3, %D = 3
Secondary:
-RSI Length = 7
-Stochastic Length = 7
-%K = 3, %D = 2
MACD - applied to both timeframes
-Fast = 12, Slow = 26, Signal = 9
TFPV — FULL Radial Kernel MA (Short/Long, Time Folding, Colored)TFPV is a pair of adaptive moving averages built with a radial kernel (Gaussian/Laplacian/Cauchy) on a joint metric of time, price, and volume. It can “fold” time along the market’s dominant cycle so that bars separated by entire cycles still contribute as if they were near each other—helpful for cyclical or range-bound markets. The short/long lines auto-color by regime and include cross alerts.
What it does
Radial-kernel averaging: Weights past bars by their distance from the current bar in a 3-axis space:
Time (αₜ): linear distance or cycle-aware phase distance
Price (αₚ): normalized by robust price scale
Volume (αᵥ): normalized by (log) volume scale
Time folding: Choose Linear (standard) or Circular using:
Homodyne (Hilbert) dominant period, or
ACF (autocorrelation) dominant period
This compresses distances for bars that are one or more full cycles apart, improving smoothing without lagging trends.
Adaptive scales: Price/volume bandwidths use Robust MAD, Stdev, or ATR. Optional Super Smoother center reduces noise before measuring distances.
Visual regime coloring: Short above Long → teal (bullish). Short below Long → orange (bearish). Optional fill highlights the spread.
How to read it
Trend filter: Trade in the direction of the color (teal bullish, orange bearish).
Crossovers: Short crossing above Long often marks early trend continuation after pullbacks; crossing below can warn of weakening momentum.
Spread width: A widening gap suggests strengthening trend; a shrinking gap hints at consolidation or a possible regime change.
Key settings
Lengths
Short/Long window: Lookback for each radial MA. Short reacts faster; Long stabilizes the regime.
Kernel & Metric
Kernel: Gaussian, Laplacian, or Cauchy (default). Cauchy is heavier-tailed (keeps more outliers), Gaussian is tighter.
Axis weights (αₜ, αₚ, αᵥ): Importance of time/price/volume distances. Increase a weight to make that axis matter more.
Ignore weights below: Hard cutoff for tiny kernel weights to speed up/clean contributions.
Time Folding
Topology: Linear (standard MA behavior) or Circular (Homodyne/ACF) (cycle-aware).
Cycle floor/ceil: Bounds for the dominant period search.
σₜ mode: Auto sets time bandwidth from the detected period (or length in Linear mode) × multiplier; Manual fixes σₜ in bars.
Price/Volume Scaling
Price scale: Robust MAD (outlier-resistant), Stdev, or ATR (trend-aware).
σₚ/σᵥ multipliers: Bandwidths for price/volume axes. Larger values = looser matching (smoother, more lag).
Use log(volume): Stabilizes volume’s scale across regimes; recommended.
Kernel Center
Price center: Raw (close) or Super Smoother to reduce noise before measuring price distance.
Plotting
Plot source: Show/hide the input source.
Fill between lines: Visual emphasis of the short/long spread.
Tips
Start with defaults: Cauchy, Circular (Homodyne), Robust MAD, log-volume on.
For choppy/cyclical symbols, Circular time folding often reduces false flips.
If signals feel too twitchy, either increase Short/Long lengths or raise σₚ/σᵥ multipliers (looser kernel).
For strong trends with regime shifts, try ATR price scaling.
Friday & Monday HighlighterFriday & Monday Institutional Range Marker — Know Where Big Firms Set the Trap!
🧠 Description
This indicator automatically highlights Friday and Monday sessions on your chart — days when institutional players and algorithmic firms (like Citadel, Jane Street, or Tower Research) quietly shape the upcoming week’s price structure.
🔍 Why Friday & Monday matter
Friday : Large institutions often book profits or hedge into the weekend. Their final-hour moves reveal the next week’s bias.
Monday : Big players rebuild positions, absorbing liquidity left behind by retail traders.
Together, these two days define the range traps and breakout zones that often control price action until midweek.
> In short, the Friday–Monday high and low often act as invisible walls — guiding scalpers, option sellers, and swing traders alike.
🧩 What this tool does
✅ Highlights Friday (red) and Monday (green) sessions
✅ Adds optional day labels above bars
✅ Works across all timeframes (best on 15min to 1hr charts)
✅ Helps you visually identify where institutions likely built their positions
Use it to quickly spot:
* Range boundaries that trap traders
* Gap zones likely to get filled
* High–low sweeps before reversals
⚙️ Recommended Use
1. Mark Friday’s high–low → Watch for liquidity sweeps on Monday.
2. When Monday holds above Friday’s high , breakout continuation is likely.
3. When Monday fails below Friday’s low , expect a reversal or trap.
4. Combine this with OI shifts, IV crush, and FII–DII flow data for confirmation.
⚠️ Disclaimer
This indicator is for **educational and analytical purposes only**.
It does **not constitute financial advice** or a trading signal.
Markets are dynamic — always perform your own research before trading or investing.
3D Candles (Zeiierman)█ Overview
3D Candles (Zeiierman) is a unique 3D take on classic candlesticks, offering a fresh, high-clarity way to visualize price action directly on your chart. Visualizing price in alternative ways can help traders interpret the same data differently and potentially gain a new perspective.
█ How It Works
⚪ 3D Body Construction
For each bar, the script computes the candle body (open/close bounds), then projects a top face offset by a depth amount. The depth is proportional to that candle’s high–low range, so it looks consistent across symbols with different prices/precisions.
rng = math.max(1e-10, high - low ) // candle range
depthMag = rng * depthPct * factorMag // % of range, shaped by tilt amount
depth = depthMag * factorSign // direction from dev (up/down)
depthPct → how “thick” the 3D effect is, as a % of each candle’s own range.
factorMag → scales the effect based on your tilt input (dev), with a smooth curve so small tilts still show.
factorSign → applies the direction of the tilt (up or down).
⚪ Tilt & Perspective
Tilt is controlled by dev and translated into a gentle perspective factor:
slope = (4.0 * math.abs(dev)) / width
factorMag = math.pow(math.min(1.0, slope), 0.5) // sqrt softens response
factorSign = dev == 0 ? 0.0 : math.sign(dev) // direction (up/down)
Larger dev → stronger 3D presence (up to a cap).
The square-root curve makes small dev values noticeable without overdoing it.
█ How to Use
Traders can use 3D Candles just like regular candlesticks. The difference is the 3D visualization, which can broaden your view and help you notice price behavior from a fresh perspective.
⚪ Quick setup (dual-view):
Split your TradingView layout into two synchronized charts.
Right pane: keep your standard candlestick or bar chart for live execution.
Left pane: add 3D Candles (Zeiierman) to compare the same symbol/timeframe.
Observe differences: the 3D rendering can make expansion/contraction and body emphasis easier to spot at a glance.
█ Go Full 3D
Take the experience further by pairing 3D Candles (Zeiierman) with Volume Profile 3D (Zeiierman) , a perfect complement that shows where activity is concentrated, while your 3D candles show how the price unfolded.
█ Settings
Candles — How many 3D candles to draw. Higher values draw more shapes and may impact performance on slower machines.
Block Width (bars) — Visual thickness of each 3D candle along the x-axis. Larger values look chunkier but can overlap more.
Up/Down — Controls the tilt and strength of the 3D top face.
3D depth (% of range) — Thickness of the 3D effect as a percentage of each candle’s own high–low range. Larger values exaggerate the depth.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
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.
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).
智能资金概念-NEWSmart Fund Concept-NEW
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Seasonality Forecast 4H A seasonality indicator shows recurring patterns in data that occur at the same time each year, such as retail sales peaking during the holidays or demand for ice cream rising in the summer. These indicators are used in fields like business, economics, and finance to identify predictable, time-based fluctuations, allowing for better forecasting and strategic planning, like adjusting inventory or staffing levels. In trading, a seasonality indicator can show historical patterns, like an asset's tendency to rise or fall in a specific month, to provide additional context for decision-making.
Seasonality reasoning basically seasonality works most stably on the daily frame with the input parameter being trading day 254 or calendar day 365, ..
Use seasonal effects such as sell in May, buy Christmas season, or exploit factors such as sell on Friday, ... to track the price movement.
The lower the time frame, the more parameters need to be calculated and the more complicated. I have tried to code the version with 1 hour, 15 minutes and 4 hours time frames
On the statistical language R and Python, Pine script
Tradingview uses the exclusive and unique Pine language. There is a parameter limit, just need to change the number of forecast days or calculate shorter or only calculate the basic end time value, we seasonality still works
but the overall results are easily noisy and related to controlling the number of orders per week/month and risk management.
The 4-hour frame version works well because we exploit the seasonal factor according to the 4-hour trading session as a trading session
Every 4 hours we have an input value that corresponds to the Asian, European, and American trading sessions
4 hours - half a morning Asian session.4 hours - half an afternoon Asian session, 4 hours - half a morning European session, 4 hours - half an afternoon European session, similar to the US and repeat the cycle.
Input Parameter Declaration
Tradingview does not exist declaration form day_of_year = dayofyear(time) Pine Script v5:
Instead of using dayofyear, we manually calculate the number of days in a year from the time components.
// Extract year, month, day, hour
year_now = year(time)
month_now = month(time)
day_now = dayofmonth(time)
hour_now = hour(time)
// Precomputed cumulative days per month (non-leap year)
days_before_month = array.from(0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334)
// Calculate day-of-year
day_of_year = array.get(days_before_month, month_now - 1) + day_now
Input parameter customization window
Lookback period years default is 10, max - the number of historical bars we have, should only be 5 years, 10 years, 15 years, 20 years, 30 years.
Future project bar default is 180 bars - 1 month. We can adjust arbitrarily 6*24*254 - day/month/year
smoothingLength Smooth the data (1 = no smoothing)
offsetBars Move the forecast line left/right to check the past
How to use
Combine seasonality with Supply Demand, Footprint volume profile to find long-term trends or potential reversal points
day_of_year := day_of_year + ((is_leap and month_now > 2) ? 1 : 0)
// Compute bin index
binIndex = (day_of_year * sessionsPerDay) + math.floor(hour_now / 4)
binIndex := binIndex % binsPerYear // Keep within array bounds
The above is the manual code to replace day of year
iFVG Ultimate+ | DodgysDDOVERVIEW
iFVG Ultimate+ | DodgysDD is a professional-grade visualization framework that automates the identification and management of Inversion Fair Value Gaps (IFVGs)
It is designed for analysts and educators studying institutional price behavior, liquidity dynamics, and displacement-based imbalances.
This indicator does not provide trading signals or forecasts.
All logic serves educational and analytical purposes only.
A Fair Value Gap (FVG) appears when strong directional displacement prevents candle bodies from overlapping.When a liquidity sweep occurs and price later closes through that gap, the imbalance is considered inverted. This often marks a shift in order-flow.
iFVG Ultimate+ tracks these transitions using a rule-based sequence:
Liquidity Sweep – Price sweeps a previous swing high or low.
Displacement – Body-to-body gap forms as price accelerates away.
Inversion – Full candle body closes through the gap after raid.
Validation and Tracking – Confirmed inversions are stored and managed until completion or invalidation.
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PURPOSE AND SCOPE
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The framework serves as a research tool to document and analyze IFVG behavior within liquidity and session contexts.
It is commonly used to:
-Record and journal IFVG formations for back-testing and model study.
-Assess how often gaps complete or invalidate after sweeps.
-Evaluate session-based patterns (London, Asia, New York).
-Overlay HTF PD Arrays to observe inter-timeframe delivery.
-Receive custom alerts to your phone
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LOGIC STRUCTURE
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iFVG Ultimate+ runs a five-stage validation process to ensure sequential, non-repainting behavior.
Liquidity Framework:
• Detects swing highs and lows on aligned timeframes (automatic or manual selection).
• Logs session highs/lows for Asia (20:00–00:00 NY) and London (02:00–05:00 NY).
• Includes data wicks around 08:30 NY for event reference.
FVG Detection and Displacement Filter:
• Identifies body-based imbalances using ATR-scaled sensitivity modes (Sensitive / Normal / Strict).
• Supports “Single” or “Series” modes to merge adjacent gaps.
• Excludes weak displacements using minimum ATR thresholds.
Inversion Validation:
• Confirms only when a complete candle body closes through a qualifying FVG within a user-defined window (6 or 15 bars).
• Duplicate detections are ignored; mitigation states are recorded.
HTF Context Integration:
• Maps higher-time-frame PD Arrays and tracks their delivery status.
• Labels active zones (e.g. “H4 PDA”) and updates on HTF close.
Model Lifecycle and Limits:
• Plots the inversion line and derives educational limit levels: Break-Even and Stop-Loss.
• Tracks until opposing liquidity is swept (model complete) or an invalidation event occurs.
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COMPONENTS AND VISUALS
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-IFVG Line — Marks confirmed inversion at close.
-Break-Even / Stop-Loss Lines — Calculated retrospectively for journal grading.
-Session High/Low Markers — London and Asia reference levels.
-Data Wicks — 8:30 NY “DATA.H/L” labels for event volatility.
-SMTs — Compares current symbol to correlated instrument for divergence confirmation.
-Checklist Panel — Tracks liquidity, momentum, HTF delivery, and SMT conditions.
-Setup Grade Display — Computes qualitative score (A+ to C) based on met conditions.
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INPUT CATEGORIES
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General — Detection mode, ATR strictness, bias filter, long/short window.
Liquidity — Automatic or manual timeframe alignment, session visuals.
FVG — Color themes, label sizes, inversion color change, HTF inclusion.
Entry / Limits — Enable or hide Entry, Break-Even, and Stop-Loss levels.
Alerts — Individual toggles for IFVG formation, session sweeps, multi-TF inversions, and invalidations.
Display — Info Box, relationship table, and grade styling.
All alerts output plain text messages only and do not execute orders.
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ALERT FRAMEWORK
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When enabled, alerts may notify for:
-Potential inversion detected.
-Confirmed IFVG formation.
-Liquidity sweeps (high/low or session).
-Multi-time-frame inversion.
-Invalidation or close warning.
-Alerts serve as educational markers only, not trade triggers.
The user will have the ability to create custom messages for each of these alert events.
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USAGE GUIDELINES
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iFVG Ultimate+ is suited for review and documentation of displacement-based price behavior.
Recommended educational workflows:
-Annotate IFVG events and review delivery into PD Arrays.
-Analyze frequency by session or timeframe.
-Assess how often IFVGs complete versus invalidate.
-Teach ICT-style liquidity mechanics in mentorship or training contexts.
-The indicator works across forex, futures, and crypto markets.
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OPERATIONAL NOTES AND LIMITATIONS
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-HTF calculations finalize on bar close (no look-ahead).
-ATR filter strength affects small-gap visibility.
-Session windows use New York time.
-Break-Even and Stop-Loss lines are visual aids only.
-Performance depends on chart density and bar count.
-No strategy module or backtest engine is included.
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ORIGINALITY AND PROTECTION
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iFVG Ultimate+ | DodgysDD integrates multiple independent systems into a single engine:
-PD Array context alignment with liquidity tracking.
-Dynamic session detection and macro data integration.
-Sequential IFVG validation pipeline with grade assignment.
-Multi-time-frame SMT confirmation module.
-Structured alerts and mitigation tracking.
The logic is entirely original, written in Pine v6, and protected as invite-only to preserve methodology integrity.
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ATTRIBUTION
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Core concepts such as Fair Value Gaps, Liquidity Sweeps, PD Arrays, and SMT Divergence are publicly taught within ICT-style market education. This implementation was designed and engineered by TakingProphets as iFVG Ultimate+ | DodgysDD, authored for TradingView publication by TakingProphets.
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TERMS AND DISCLAIMER
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This indicator is for educational and informational use only. It does not provide financial advice or predictive output. Historical patterns do not guarantee future results. All users remain responsible for their own decisions.Use of this script implies agreement with TradingView’s Vendor Requirements and Terms of Use.
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ACCESS INSTRUCTIONS
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Access is managed through TradingView’s invite-only framework. Users request access via private message to TakingProphets or access link
WRSignalsTimeframe ADX Smoothing DI Length ADX Threshold
1–5 min 1–2 7–10 20–25
15m–1h 2–3 10–14 25–30
4h–1d 3–5 14–20 20–25
Candlestick Body Ratio with MAHow It Works (Brief Overview):
It computes the ratio of the candle’s body size to its total range (high–low).
• A ratio close to 1 means a strong, decisive candle.
• A ratio near 0 means a weak or indecisive candle (like a doji).
• Visual Output:
• Plots the body ratio as an orange line. (Black Histogram)
• Optionally marks strong-bodied candles with a green triangle above the bar. (Orange Diamond)
Why It’s Useful:
Helps identify momentum candles with conviction. Filters out weak signals in breakout or reversal strategies. Can be combined with divergence or volume tools for confluence.
Think Like A Market Maker: ThePipAssassin
NSR FVG High Time FramesIndicator Name : NSR FVG High Time Frames
Short Title : NSR FVGHTF
Description :The NSR FVG High Time Frames indicator identifies and visualizes Fair Value Gaps (FVGs) on higher timeframes (4-hour, Daily, and Weekly) directly on your chart. FVGs are price gaps formed between the high and low of non-consecutive candles, often indicating areas of market inefficiency that price may revisit. This indicator is designed for traders who incorporate multi-timeframe analysis into their strategies, providing a clear visual representation of bullish and bearish FVGs with customizable settings.
Unique Feature :Unlike traditional FVG indicators that mark a gap as closed when the current candle’s close crosses the gap’s boundaries, NSR FVG High Time Frames employs a distinctive closure logic. It allows an additional candle to determine whether the price re-enters the gap or continues beyond it. This approach provides a more nuanced assessment of gap closure, potentially reducing false signals by giving the market an extra candle to confirm its direction. This feature makes the indicator particularly suitable for traders seeking to validate FVG interactions with greater precision.
Key Features :
Multi-Timeframe Support : Detects FVGs on 4-hour, Daily, and Weekly timeframes, with options to enable or disable each timeframe.
Customizable Appearance : Users can adjust the visual style (Line, Dotted, Dashed) and colors for bullish and bearish FVGs, as well as enable/disable extension of FVG boxes to the right.
Flexible Lookback : Configurable lookback periods for entry (up to 10,000 candles) and FVG detection (up to 70 FVGs), allowing users to tailor the indicator to their trading style.
Minimum FVG Size : Set a minimum gap size (in ticks) to filter out insignificant FVGs, ensuring only meaningful gaps are displayed.
Closed FVG Removal : Option to automatically remove closed FVGs from the chart for a cleaner view.
Alert Integration : Generates alerts for new FVGs and changes in their status (e.g., verified, partial, closed), enabling traders to set up custom notifications.
How to Use :
Add to Chart : Apply the indicator to any chart. It works best on lower timeframes (e.g., 1H, 4H) to visualize higher-timeframe FVGs.
Configure Settings : Adjust the inputs in the settings panel:
Enable/disable 4-hour, Daily, or Weekly FVGs based on your analysis needs.
Set the lookback periods and minimum FVG size to match your trading strategy.
Customize colors and line styles for better chart readability.
Interpret FVGs :
Bullish FVGs (green boxes): Represent gaps where price may act as support, potentially attracting price back to the gap.
Bearish FVGs (red boxes): Represent gaps where price may act as resistance.
Boxes are drawn between the relevant high and low of the candles forming the FVG, with text labels indicating the timeframe (e.g., "4H", "D", "Weekly").
Monitor Closure : Watch for price interaction with FVGs. The indicator considers an FVG closed only after an additional candle confirms the price has moved beyond the gap or failed to re-enter it, unlike standard FVG indicators.
Set Alerts : Use the alert feature to receive notifications when new FVGs form or their status changes (e.g., "partial" or "closed").
Settings :
Entry Lookback (candles) : Number of candles to look back for FVG detection (default: 10,000).
Number of FVG to Lookback : Maximum number of FVGs to display (default: 70).
Minimum FVG Size : Minimum gap size in ticks (default: 5).
Remove Closed : Toggle to remove closed FVGs from the chart (default: true).
Show/Extend 4Hour/Daily/Weekly : Enable/disable FVGs for each timeframe and choose whether to extend boxes to the right.
Color and Style Options : Customize fill and border colors, and select line styles (Line, Dotted, Dashed) for each timeframe.
Use Cases :
Swing Trading : Identify potential support/resistance zones on higher timeframes for entry or exit points.
Price Action Analysis : Use FVGs to confirm market inefficiencies or reversal zones.
Multi-Timeframe Strategies : Combine with lower-timeframe indicators to align entries with higher-timeframe FVGs.
Notes :
The indicator is optimized for lower timeframes to display higher-timeframe FVGs. Avoid using it on Weekly or Monthly charts for Daily/Weekly FVGs to prevent overlap issues.
The unique closure logic may delay FVG closure signals compared to other indicators, which can help filter out premature closures but requires patience for confirmation.
Performance may vary on very low timeframes with large lookback periods due to the number of FVGs processed.
Disclaimer :This indicator is for informational purposes only and does not constitute financial advice. Always conduct your own analysis and test the indicator thoroughly before using it in live trading.
NFTs vs SOL - Momentum Divergence RadarSee when NFT activity (proxy volumes) leads or lags SOL momentum
About:
A TradingView indicator that normalizes SOL momentum and a composite NFT proxy volume into comparable z-scores, then plots their difference (divergence). You get zero-line cross signals (“lead/lag”) and a rolling correlation table to judge regime quality.
What it does (concept):
SOL side: Rate of Change of SOL (ROC(len_mom)) → standardized over len_z → smoothed (smooth) → sol_z.
NFT side: Weighted sum of proxy volumes (BLUR, LOOKS, TNSR, MAGIC, APE, optional ME & PENGU) → log() to tame skew → standardized over len_z → smoothed → v_z.
Divergence: div = v_z - sol_z.
div > 0 → NFT activity is stronger than SOL momentum (possible lead).
div < 0 → NFT activity weaker than SOL momentum (possible lag).
On-chart elements
Orange histogram: div (NFT volume Z − SOL momentum Z).
Purple line: v_z (NFT composite activity).
Blue line: sol_z (SOL momentum).
Green/Red triangles: Zero-line crosses of div → lead (up) / lag (down).
Top-right table: Rolling Pearson correlation (len_z) + list of active proxies (non-blank & weight ≠ 0).
How to use (quick start):
Add to chart on your timeframe (30m–1D are common).
Fill proxy symbols with exchange prefixes (e.g., KUCOIN:BLURUSDT, GATEIO:LOOKS_USDT, MEXC:TNSR_USDT, OKX:MAGIC-USDT). Leave ME/PENGU blank until you have the exact tickers.
Set weights to reflect relevance/liquidity (start at 1; set 0 to exclude).
Trade the crosses with context:
Lead (↑0): NFTs heating up vs SOL → look for long setups if price structure agrees.
Lag (↓0): NFTs cooling vs SOL → reduce risk/hedge or look for shorts in downtrends.
Consult correlation:
High (+0.5~1): Both move together; rely more on cross timing than magnitude.
Low/negative: Decoupling regime; divergence magnitude gains importance.
Settings (what they mean):
SOL Symbol (sol_tkr) – Default BINANCE:SOLUSDT.
Proxy symbols (*_sym) – Enter exchange:pair strings; blank = ignored.
Weights (w_*) – Linear weights before log; emphasize reliable/liquid proxies.
Momentum Length (len_mom, 14) – ROC lookback for SOL. Bigger = smoother/slower.
Z-Score Length (len_z, 50) – Window for mean/stdev and correlation. Bigger = more stable normalization.
Smoothing (smooth, 5) – SMA applied to both z-scores to cut noise.
Preset ideas:
Swing (1D/4H): len_mom=14, len_z=100, smooth=7; BLUR=1, LOOKS=1, TNSR=1, MAGIC=0.5, APE=0.5.
Active (2H/1H): len_mom=10, len_z=50, smooth=5; same weights; alert on lead.
Reading the signals (playbook)
Fresh Lead (div crosses ↑0): Accumulation/participation rising; enter on pullbacks or structure breaks; confirm with volume/HTF trend.
Persistent Positive div: Momentum follow-through likely; trail stops below swing structure.
Fresh Lag (div crosses ↓0): Early cooling; take profits, tighten stops, or rotate.
Extreme bars (|div| > ~2): Outlier conditions; expect mean-reversion or breakout continuation—use price action to decide.
Re-cross whipsaws: Filter with higher smooth or require bar close confirmation.
Practical notes & tips:
Symbol validity: Always use exchange prefixes. If a token is unavailable on your TV plan/exchange, leave it blank (script treats it as zero volume).
Liquidity bias: Thin alts can distort the composite; set their weights low or 0.
Timeframe consistency: All request.security() calls run at the chart TF—no lower-TF aggregation.
Risk management: Treat crosses as context, not standalone buy/sell. Combine with S/R, trend filters, ATR stops, and volume profile.
Alerts (recommended):
Lead Cross Up: div crosses above 0.
Lag Cross Down: div crosses below 0.
Extreme Divergence: div > +2 or div < −2 (user threshold).
(Add alertcondition() lines if you want hard alerts.)
Troubleshooting
“Invalid symbol” popup: The input contains an unsupported/typo ticker. Use exact EXCHANGE:SYMBOL (e.g., OKX:MAGIC-USDT, not MAGICUSDT). Leave fields blank if unsure.
Flat/NaN early bars: Not enough history for len_z—normal; resolves as data accumulates.
No proxies listed in table: Ensure at least one proxy is non-blank and weight ≠ 0.
Limitations:
Proxy selection matters; if the set doesn’t reflect current NFT flow, signals degrade.
Z-scoring assumes local stationarity; regime shifts compress/expand readings.
Exchange symbol formats vary (- vs _); match TradingView exactly.
Changelog:
v1.2 (2025-10-14): Stability pass, proxy inputs default to blank to avoid symbol errors; refined table; kept v5 compatibility logic.
ILM Checklist [Nix]ILM Checklist and Ratings Indicator!
This is a checklist type guide for those that trade the ILM model and are having trouble rating setups on their own.
You can double click on the checklist to open its settings where you can select all the confluences you see on the chart while a setup is forming.
Then the checklist will give you a mechanical estimate of what rating would Nix give it.
Obviously discretion is important as an A+ mechanical setup if still an F setup if you are executing it during a news event.
I have also added a dark / light mode theme toggle to suit your chart.
LEGEND IsoPulse Fusion • Universal Volume Trend Buy Sell RadarLEGEND IsoPulse Fusion • Universal Volume Trend Buy Sell Radar
One line summary
LEGEND IsoPulse Fusion reads intent from price and volume together, learns which features matter most on your symbol, blends them into a single signed Fusion line in a stable unit range, and emits clear Buy Sell Close events with a structure gate and a liquidity safety gate so you act only when the tape is favorable.
What this script is and why it exists
Many traders keep separate windows for trend, volume, volatility, and regime filters. The result can feel fragmented. This script merges two complementary engines into one consistent view that is easy to read and simple to act on.
LEGEND Tensor estimates directional quality from five causally computed features that are normalized for stationarity. The features are Flow, Tail Pressure with Volume Mix, Path Curvature, Streak Persistence, and Entropy Order.
IsoPulse transforms raw volume into two decaying reservoirs for buy effort and sell effort using body location and wick geometry, then measures price travel per unit volume for efficiency, and detects volume bursts with a recency memory.
Both engines are mapped into the same unit range and fused by a regime aware mixer. When the tape is orderly the mixer leans toward trend features. When the tape is messy but a true push appears in volume efficiency with bursts the mixer allows IsoPulse to speak louder. The outcome is a single Fusion line that lives in a familiar range with calm behavior in quiet periods and expressive pushes when energy concentrates.
What makes it original and useful
Two reservoir volume split . The script assigns a portion of the bar volume to up effort and down effort using body location and wick geometry together. Effort decays through time using a forgetting factor so memory is present without becoming sticky.
Efficiency of move . Price travel per unit volume is often more informative than raw volume or raw range. The script normalizes both sides and centers the efficiency so it becomes signed fuel when multiplied by flow skew.
Burst detection with recency memory . Percent rank of volume highlights bursts. An exponential memory of how recently bursts clustered converts isolated blips into useful context.
Causal adaptive weighting . The LEGEND features do not receive static weights. The script learns, causally, which features have correlated with future returns on your symbol over a rolling window. Only positive contributions are allowed and weights are normalized for interpretability.
Regime aware fusion . Entropy based order and persistence create a mixer that blends IsoPulse with LEGEND. You see a single line rather than two competing panels, which reduces decision conflict.
How to read the screen in seconds
Fusion area . The pane fills above and below zero with a soft gradient. Deeper fill means stronger conviction. The white Fusion line sits on top for precise crossings.
Entry guides and exit guides . Two entry guides draw symmetrically at the active fused entry level. Two exit guides sit inside at a fraction of the entry. Think of them as an adaptive envelope.
Letters . B prints once when the script flips from flat to long. S prints once when the script flips from flat to short. C prints when a held position ends on the appropriate side. T prints when the structure gate first opens. A prints when the liquidity safety flag first appears.
Price bar paint . Bars tint green while long and red while short on the chart to mirror your virtual position.
HUD . A compact dashboard in the corner shows Fusion, IsoPulse, LEGEND, active entry and exit levels, regime status, current virtual position, and the vacuum z value with its avoid threshold.
What signals actually mean
Buy . A Buy prints when the Fusion line crosses above the active entry level while gates are open and the previous state was flat.
Sell . A Sell prints when the Fusion line crosses below the negative entry level while gates are open and the previous state was flat.
Close . A Close prints when Fusion cools back inside the exit envelope or when an opposite cross would occur or when a gate forces a stop, and the previous state was a hold.
Gates . The Trend gate requires sufficient entropy order or significant persistence. The Avoid gate uses a liquidity vacuum z score. Gates exist to protect you from weak tape and poor liquidity.
Inputs and practical tuning
Every input has a tooltip in the script. This section provides a concise reference that you can keep in mind while you work.
Setup
Core window . Controls statistics across features. Scalping often prefers the thirties or low fifties. Intraday often prefers the fifties to eighties. Swing often prefers the eighties to low hundreds. Smaller responds faster with more noise. Larger is calmer.
Smoothing . Short EMA on noisy features. A small value catches micro shifts. A larger value reduces whipsaw.
Fusion and thresholds
Weight lookback . Sample size for weight learning. Use at least five times the horizon. Larger is slower and more confident. Smaller is nimble and more reactive.
Weight horizon . How far ahead return is measured to assess feature value. Smaller favors quick reversion impulses. Larger favors continuation.
Adaptive thresholds . Entry and exit levels from rolling percentiles of the absolute LEGEND score. This self scales across assets and timeframes.
Entry percentile . Eighty selects the top quintile of pushes. Lower to seventy five for more signals. Raise for cleanliness.
Exit percentile . Mid fifties keeps trades honest without overstaying. Sixty holds longer with wider give back.
Order threshold . Minimum structure to trade. Zero point fifteen is a reasonable start. Lower to trade more. Raise to filter chop.
Avoid if Vac z . Liquidity safety level. One point two five is a good default on liquid markets. Thin markets may prefer a slightly higher setting to avoid permanent avoid mode.
IsoPulse
Iso forgetting per bar . Memory for the two reservoirs. Values near zero point nine eight to zero point nine nine five work across many symbols.
Wick weight in effort split . Balance between body location and wick geometry. Values near zero point three to zero point six capture useful behavior.
Efficiency window . Travel per volume window. Lower for snappy symbols. Higher for stability.
Burst percent rank window . Window for percent rank of volume. Around one hundred to three hundred covers most use cases.
Burst recency half life . How long burst clusters matter. Lower for quick fades. Higher for cluster memory.
IsoPulse gain . Pre compression gain before the atan mapping. Tune until the Fusion line lives inside a calm band most of the time with expressive spikes on true pushes.
Continuation and Reversal guides . Visual rails for IsoPulse that help you sense continuation or exhaustion zones. They do not force events.
Entry sensitivity and exit fraction
Entry sensitivity . Loose multiplies the fused entry level by a smaller factor which prints more trades. Strict multiplies by a larger factor which selects fewer and cleaner trades. Balanced is neutral.
Exit fraction . Exit level relative to the entry level in fused unit space. Values around one half to two thirds fit most symbols.
Visuals and UX
Columns and line . Use both to see context and precise crossings. If you present a very clean chart you can turn columns off and keep the line.
HUD . Keep it on while you learn the script. It teaches you how the gates and thresholds respond to your market.
Letters . B S C T A are informative and compact. For screenshots you can toggle them off.
Debug triggers . Show raw crosses even when gates block entries. This is useful when you tune the gates. Turn them off for normal use.
Quick start recipes
Scalping one to five minutes
Core window in the thirties to low fifties.
Horizon around five to eight.
Entry percentile around seventy five.
Exit fraction around zero point five five.
Order threshold around zero point one zero.
Avoid level around one point three zero.
Tune IsoPulse gain until normal Fusion sits inside a calm band and true squeezes push outside.
Intraday five to thirty minutes
Core window around fifty to eighty.
Horizon around ten to twelve.
Entry percentile around eighty.
Exit fraction around zero point five five to zero point six zero.
Order threshold around zero point one five.
Avoid level around one point two five.
Swing one hour to daily
Core window around eighty to one hundred twenty.
Horizon around twelve to twenty.
Entry percentile around eighty to eighty five.
Exit fraction around zero point six zero to zero point seven zero.
Order threshold around zero point two zero.
Avoid level around one point two zero.
How to connect signals to your risk plan
This is an indicator. You remain in control of orders and risk.
Stops . A simple choice is an ATR multiple measured on your chart timeframe. Intraday often prefers one point two five to one point five ATR. Swing often prefers one point five to two ATR. Adjust to symbol behavior and personal risk tolerance.
Exits . The script already prints a Close when Fusion cools inside the exit envelope. If you prefer targets you can mirror the entry envelope distance and convert that to points or percent in your own plan.
Position size . Fixed fractional or fixed risk per trade remains a sound baseline. One percent or less per trade is a common starting point for testing.
Sessions and news . Even with self scaling, some traders prefer to skip the first minutes after an open or scheduled news. Gate with your own session logic if needed.
Limitations and honest notes
No look ahead . The script is causal. The adaptive learner uses a shifted correlation, crosses are evaluated without peeking into the future, and no lookahead security calls are used. If you enable intrabar calculations a letter may appear then disappear before the close if the condition fails. This is normal for any cross based logic in real time.
No performance promises . Markets change. This is a decision aid, not a prediction machine. It will not win every sequence and it cannot guarantee statistical outcomes.
No dependence on other indicators . The chart should remain clean. You can add personal tools in private use but publications should keep the example chart readable.
Standard candles only for public signals . Non standard chart types can change event timing and produce unrealistic sequences. Use regular candles for demonstrations and publications.
Internal logic walkthrough
LEGEND feature block
Flow . Current return normalized by ATR then smoothed by a short EMA. This gives directional intent scaled to recent volatility.
Tail pressure with volume mix . The relative sizes of upper and lower wicks inside the high to low range produce a tail asymmetry. A volume based mix can emphasize wick information when volume is meaningful.
Path curvature . Second difference of close normalized by ATR and smoothed. This captures changes in impulse shape that can precede pushes or fades.
Streak persistence . Up and down close streaks are counted and netted. The result is normalized for the window length to keep behavior stable across symbols.
Entropy order . Shannon entropy of the probability of an up close. Lower entropy means more order. The value is oriented by Flow to preserve sign.
Causal weights . Each feature becomes a z score. A shifted correlation against future returns over the horizon produces a positive weight per feature. Weights are normalized so they sum to one for clarity. The result is angle mapped into a compact unit.
IsoPulse block
Effort split . The script estimates up effort and down effort per bar using both body location and wick geometry. Effort is integrated through time into two reservoirs using a forgetting factor.
Skew . The reservoir difference over the sum yields a stable skew in a known range. A short EMA smooths it.
Efficiency . Move size divided by average volume produces travel per unit volume. Normalization and centering around zero produce a symmetric measure.
Bursts and recency . Percent rank of volume highlights bursts. An exponential function of bars since last burst adds the notion of cluster memory.
IsoPulse unit . Skew multiplied by centered efficiency then scaled by the burst factor produces the raw IsoPulse that is angle mapped into the unit range.
Fusion and events
Regime factor . Entropy order and streak persistence form a mixer. Low structure favors IsoPulse. Higher structure favors LEGEND. The blend is convex so it remains interpretable.
Blended guides . Entry and exit guides are blended in the same way as the line so they stay consistent when regimes change. The envelope does not jump unexpectedly.
Virtual position . The script maintains state. Buy and Sell require a cross while flat and gates open. Close requires an exit or force condition while holding. Letters print once at the state change.
Disclosures
This script and description are educational. They do not constitute investment advice. Markets involve risk. You are responsible for your own decisions and for compliance with local rules. The logic is causal and does not look ahead. Signals on non standard chart types can be misleading and are not recommended for publication. When you test a strategy wrapper, use realistic commission and slippage, moderate risk per trade, and enough trades to form a meaningful sample, then document those assumptions if you share results.
Closing thoughts
Clarity builds confidence. The Fusion line gives a single view of intent. The letters communicate action without clutter. The HUD confirms context at a glance. The gates protect you from weak tape and poor liquidity. Tune it to your instrument, observe it across regimes, and use it as a consistent lens rather than a prediction oracle. The goal is not to trade every wiggle. The goal is to pick your spots with a calm process and to stand aside when the tape is not inviting.
ORB Multi-Range (5,10,15 min plain)Simple, accurate ORB lines for 5, 10, and 15-minute opening ranges — no clutter, just clean breakout levels.
Billionaire Gold ClubBillionaire Gold Club — Long-Term Gold Trend Follower
Overview
The Billionaire Gold Club indicator is designed for traders who follow the long-term bullish bias of Gold (XAU/USD).
It focuses only on BUY opportunities and encourages patience during market pullbacks.
The goal is to trade with the main trend, not against it.
Instructions
1. The script automatically plots 7MA (fast) and 200MA (slow).
2. When 7MA crosses above 200MA, a BUY signal appears.
3. When 7MA crosses below 200MA, a Standby signal appears — do not sell, just wait for the next BUY.
Usage Rules
• Recommended timeframe: 15-minute or higher.
• If used below 15 minutes, treat it as day trading — close trades within the same day.
• Focus on long-term holding and small lot sizes to protect your capital.
Signal Guide
🟢 BUY → Enter the trend direction.
🟠 Standby → Pause new entries and wait patiently.
Alerts
Set alerts to "Once per bar close":
• BUY Signal → Golden Cross confirmed.
• Standby Signal → Death Cross confirmed.
Philosophy
"Obey the rules, and your probability of success increases."
This system rewards patience, discipline, and long-term trend following.
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Billionaire Gold Clubは、ゴールド（XAU/USD）の長期上昇トレンドに沿って取引するためのインジケーターです。
基本的にBUYのみを狙い、デッドクロス時はStandby（待機）状態として次のBUYを待ちます。
推奨時間軸：15分足以上。
15分未満で使用する場合はデイトレードとして同日中にクローズすることを推奨します。
ロットを小さく保ち、長期保有で安定した運用を目指してください。
SMA 20/50/200 Strategy with TP/SLHere is a TradingView Pine Script indicator for the 20/50/200 SMA strategy. It generates Buy/Sell Signals and calculates Take Profit and Stop Loss prices for each signal.
Features:
Triggers only on the cross over/cross under bar (not continuous).
Buy: 20 SMA crosses above 50 SMA and is above 200 SMA.
Sell: 20 SMA crosses below 50 SMA and is below 200 SMA.
Plots take profit and stop loss.
Alerts repeat every cross over/cross under occurrence.
Tweezer & Kangaroo Zones [WavesUnchained]Tweezer & Kangaroo Zones
Pattern Recognition with Supply/Demand Zones
Indicator that detects tweezer and kangaroo tail (pin bar) reversal patterns and creates supply and demand zones. Includes volume validation, trend context, and confluence scoring.
What You See on Your Chart
Pattern Labels:
"T" (Red) - Tweezer Top detected above price → Bearish reversal signal
"T" (Green) - Tweezer Bottom detected below price → Bullish reversal signal
"K" (Red) - Kangaroo Bear (Pin Bar rejection from top) → Bearish signal
"K" (Green) - Kangaroo Bull (Pin Bar rejection from bottom) → Bullish signal
Label Colors Indicate Pattern Strength:
Dark Green/Red - Strong pattern (score ≥8.0)
Medium Green/Red - Good pattern (score ≥6.0)
Light Green/Red - Valid pattern (score <6.0)
Zone Boxes:
Red Boxes - Supply Zones (resistance, potential short areas)
Green Boxes - Demand Zones (support, potential long areas)
White Border - Active zone (fresh, not tested yet)
Gray Border - Inactive zone (expired or invalidated)
Pattern Detection
Tweezer Patterns (Classic Double-Top/Bottom):
Flexible Lookback - Detects patterns up to 3 bars apart (not just consecutive)
Precision Matching - 0.2% level tolerance for high-quality signals
Wick Similarity Check - Both candles must show similar rejection wicks
Volume Validation - Second candle requires elevated volume (0.8x average)
Pattern Strength Score - 0-1 quality rating based on level match + wick similarity
Optional Trend Context - Can require trend alignment (default: OFF for more signals)
Kangaroo Tail / Pin Bar Patterns:
No Pivot Delay - Instant detection without waiting for pivot confirmation
Body Position Check - Body must be at candle extremes (30% tolerance)
Volume Spike - Rejection must occur with volume (0.9x average)
Rejection Strength - Scores based on wick length (0.5-0.9 of range)
Optional Trend Context - Bearish in uptrends, Bullish in downtrends (default: OFF)
Zone Management
Auto-Created Zones - Every valid pattern creates a supply/demand zone
Overlap Prevention - Zones too close together (50% overlap) are not duplicated
Lifetime Control - Zones expire after 400 bars (configurable)
Smart Invalidation - Zones invalidate when price closes through them
Styling Options - Choose between Solid, Dashed, or Dotted borders
Border Width - 2px width for better visibility
Confluence Scoring System
Multi-factor confluence scoring (0-10 scale) with configurable weights:
Regime (EMA+HTF) - Trend alignment across timeframes (Weight: 2.0)
HTF Stack - Multi-timeframe trend confluence (Weight: 3.0)
Structure - Higher lows / Lower highs confirmation (Weight: 1.0)
Relative Volume - Volume surge validation (Weight: 1.0)
Chop Advantage - Favorable market conditions (Weight: 1.0)
Zone Thinness - Tight zones = better R/R (Weight: 1.0)
Supertrend - Trend indicator alignment (Weight: 1.0)
MOST - Moving Stop alignment (Weight: 1.0)
Pattern Strength - Quality of detected pattern (Weight: 1.5)
Zone Retest Signals
Signals generated when zones are retested:
BUY Signal - Price retests demand zone from above (score ≥4.5)
SELL Signal - Price retests supply zone from below (score ≥5.5)
Normalized Score - Displayed as 0-10 for easy interpretation
Optional Trend Gate - Require trend alignment for signals (default: OFF)
Alert Ready - Built-in alertconditions for automation
Additional Features
Auto-Threshold Tuning - Adapts to ATR and Choppiness automatically
Session Profiles - Different settings for RTH vs ETH sessions
Organized Settings - 15+ input groups for easy configuration
Optional Panels - HTF Stack overview and performance metrics (default: OFF)
Data Exports - Hidden plots for strategy/library integration
RTA Health Monitoring - Built-in performance tracking
Setup & Configuration
Quick Start:
1. Apply indicator to any timeframe
2. Patterns and zones appear automatically
3. Adjust pattern detection sensitivity if needed
4. Configure zone styling (Solid/Dashed/Dotted)
5. Set up alerts for zone retests
Key Settings to Adjust:
Pattern Detection:
• Min RelVolume: Lower = more signals (0.8 Tweezer, 0.9 Kangaroo)
• Require trend context: Enable for stricter, higher-quality patterns
• Check wick similarity: Ensures proper rejection structure
Zone Management:
• Zone lifetime: How long zones remain active (default: 400 bars)
• Invalidate on close-through: Remove zones when price breaks through
• Max overlap: Prevent duplicate zones (default: 50%)
Scoring:
• Min Score BUY/SELL: Higher = fewer but better signals (default: 4.5/5.5)
• Component weights: Customize what factors matter most
• Signals require trend gate: OFF = more signals, ON = higher quality
Visual Customization
Zone Colors - Light red/green with 85% transparency (non-intrusive)
Border Styles - Solid, Dashed, or Dotted
Label Intensity - Darker greens for better readability
Clean Charts - All panels OFF by default
Understanding the Zones
Supply Zones (Red):
Created from bearish patterns (Tweezer Tops, Kangaroo Bears). Price made a high attempt to push higher, but was rejected. These become resistance areas where sellers may step in again.
Demand Zones (Green):
Created from bullish patterns (Tweezer Bottoms, Kangaroo Bulls). Price made a low with strong rejection. These become support areas where buyers may step in again.
Zone Quality Indicators:
• White border = Fresh zone, not tested yet
• Gray border = Zone expired or invalidated
• Thin zones (tight range) = Better risk/reward ratio
• Thick zones = Less precise, wider stop required
Trading Applications
Reversal Trading - Enter at pattern detection with tight stops
Zone Retest Trading - Wait for retests of established zones
Trend Confluence - Trade only when patterns align with trend
Risk Management - Use zone boundaries for stop placement
Target Setting - Opposite zones become profit targets
Pro Tips
Best signals occur when pattern + zone retest + trend all align
Lower timeframes = more signals but more noise
Higher timeframes = fewer but more reliable signals
Start with default settings, adjust based on your market
Combine with other analysis (structure, key levels, etc.)
Use alerts to avoid staring at charts all day
Important Notes
Not all patterns will lead to successful trades
Use proper risk management and position sizing
Patterns work best in trending or range-bound markets
Very choppy conditions may produce lower-quality signals
Always confirm with your own analysis before trading
Technical Specifications
• Pine Script v6
• RTA-Core integration
• RTA Core Library integration
• Maximum 200 boxes, 500 labels
• Auto-tuning based on ATR and Choppiness
• Session-aware threshold adjustments
• Memory-optimized zone management
What's Included
Tweezer Top/Bottom detection
Kangaroo Tail / Pin Bar detection
Automatic supply/demand zone creation
Volume validation system
Pattern strength scoring
Zone retest signals
Multi-factor confluence scoring
Optional HTF Stack panel
Optional performance metrics
Session profile support
Auto-threshold tuning
Alert conditions
Data exports for strategies
Author Waves Unchained
Version 1.0
Status Public Indicator
Summary
Reversal pattern detection with zone management, volume validation, and confluence scoring for tweezer and kangaroo tail patterns.
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Disclaimer: This indicator is for educational and informational purposes only. Trading involves risk. Past performance does not guarantee future results. Always practice proper risk management.
PD Break Behavior AnalysisThe PD Break Behavior Analysis indicator tracks and classifies daily price action relative to the previous day's high (PDH) and low (PDL). It evaluates how often price:
Breaks only the PDH (single upper breakout)
Breaks only the PDL (single lower breakdown)
Breaks both PDH and PDL (double breakout)
Remains inside the previous day’s range (no break)
Gaps and stays entirely above the previous day’s high (strong bullish gap)
The indicator maintains rolling counts for the past:
50 trading days
100 trading days
300 trading days
These statistics are displayed in a clear on-chart table, providing insight into market behavior over multiple timeframes.
