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).
インジケーターとストラテジー
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.
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.
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.
Image Plotter [theUltimator5]Image Plotter is a visual alerting tool that drops fun, high-contrast ASCII (braille) art (e.g., Rocket, Cat “hang in there”, Babe Ruth, etc.) directly on your price chart when a technical trigger fires. It’s designed for quick, glanceable callouts without cluttering your chart with lines or sub-indicators.
If there are any specific images you would like to be able to add to your plot, please comment with the image you want to see and if it is reasonable, I will add it.
How it works
On each bar close, the script evaluates your selected Trigger Source. When the condition is true, it places a label that contains the selected ASCII art at a configurable offset above or below the candle.
You can choose to only keep the most recent art on the chart, or accumulate every trigger as a historical breadcrumb trail.
Positioning uses either the bar’s high (for above-candle placements) or low (for below-candle placements), then applies your vertical % offset and horizontal bar shift.
Inputs & Controls
Trigger Source
Select which condition will fire the ASCII placement:
RSI Oversold / Overbought — Triggers on cross through the threshold (under/over).
MACD Bullish Cross / Bearish Cross — MACD line crossing the Signal line.
BB Lower Touch / BB Upper Touch — Price crossing below the lower band / above the upper band.
Stochastic Oversold / Overbought — %K crossing through your thresholds.
Volume Spike — Current volume > (Volume MA × Spike Multiplier).
Price Cross MA — Close crossing above the chosen moving average (bullish only).
Custom Condition — Optional user condition (see “Custom Condition” below).
Plot Mode
Latest Only — The indicator deletes the previous label and keeps only the newest trigger on chart.
Every Trigger — Leaves all triggered labels on the chart (historical markers).
Note: TradingView caps the number of labels per script; this indicator sets max_labels_count=500. Heavy triggering can still hit limits.
Practical usage tips
Choose “Latest Only” for cleanliness if your trigger is frequent. Use “Every Trigger” when you want a visual audit trail.
Tune vertical offset by symbol — low-priced tickers may need a smaller %; volatile names may need more spacing.
Quick start
Add the indicator to any chart (any timeframe).
Pick a Trigger Source (e.g., RSI Oversold) and set thresholds/lengths.
Choose ASCII Image, Position Above/Below, Offsets, and Plot Mode.
(Optional) Enable Custom Condition and select your Custom Plot Source.
Create an Alert on “ASCII Trigger Alert” using Once Per Bar Close.
Have a variant you’d like (e.g., bearish MA cross, multi-alert pack by trigger, or time-window filters)? Tell me what workflow you want and I’ll tailor the script/description to match.
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
Metallic Retracement LevelsThere's something that's always bothered me about how traders use Fibonacci retracements. Everyone treats the golden ratio like it's the only game in town, but mathematically speaking, it's completely arbitrary. The golden ratio is just the first member of an infinite family of metallic means, and there's no particular reason why 1.618 should be special for markets when we have the silver ratio at 2.414, the bronze ratio at 3.303, and literally every other metallic mean extending to infinity. We just picked one and decided it was magical.
The metallic means are a sequence of mathematical constants that generalize the golden ratio. They're defined by the equation x² = kx + 1, where k is any positive integer. When k equals 1, you get the golden ratio. When k equals 2, you get the silver ratio. When k equals 3, you get bronze, and so on forever. Each metallic mean generates its own set of ratios through successive powers, just like how the golden ratio gives you 0.618, 0.382, 0.236 and so forth. The silver ratio produces a completely different set of retracement levels, as does bronze, as does any arbitrary metallic number you want to choose.
This indicator calculates these metallic means using the standard alpha and beta formulas. For any metallic number k, alpha equals (k + sqrt(k² + 4)) / 2, and we generate retracement ratios by raising alpha to various negative powers. The script algorithmically generates these levels instead of hardcoding them, which is how it should have been done from the start. It's genuinely silly that most fib tools just hardcode the ratios when the math to generate them is straightforward. Even worse, traditional fib retracements use 0.5 as a level, which isn't even a fibonacci ratio. It's just thrown in there because it seems like it should be important.
The indicator works by first detecting swing points using the Sylvain Zig-Zag . The zig-zag identifies significant price swings by combining percentage change with ATR adjustments, filtering out noise and connecting major pivot points. This is what drives the retracement levels. Once a new swing is confirmed, the script calculates the range between the last two pivot points and generates metallic retracement levels from the most recent swing low or high.
You can adjust which metallic number to use (golden, silver, bronze, or any positive integer), control how many power ratios to display above and below the 1.0 level, and set how many complete retracement cycles you want drawn. The levels extend from the swing point and show you where price might react based on whichever metallic mean you've selected. The zig-zag settings let you tune the sensitivity of swing detection through ATR period, ATR multiplier, percentage reversal, and additional absolute or tick-based reversal values.
What this really demonstrates is that retracement analysis is more flexible than most traders realize. There's no mathematical law that says markets must respect the golden ratio over any other metallic mean. They're all valid mathematical constructs with the same kind of recursive properties. By making this tool, I wanted to highlight that using fibonacci retracements involves an arbitrary choice, and maybe that choice should be more deliberate or at least tested against alternatives. You can experiment with different metallic numbers and see which ones seem to work better for your particular market or timeframe, or just use this to understand that the standard fib levels everyone uses aren't as fundamental as they appear.
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.
MACD-V (Volatility-Normalized MACD)MACD-V with histogram and strength signals. It normalizes volatility across all assets and time frames.
Volatility Channel Oscillator█ OVERVIEW
"Volatility Channel Oscillator" is a technical indicator that analyzes price volatility relative to dynamic price channels, displaying an oscillator, its moving average, and signals based on crossovers and divergences. The indicator offers customizable overbought and oversold levels, gradient visualization, and divergence detection, supported by alerts for key signals.
█ CONCEPTS
The VCO indicator creates dynamic price channels based on a moving average of the price (calculated as the arithmetic mean of the high and low prices: (high + low) / 2) and market volatility (measured as the average candle range and body size). These channels are not displayed on the chart but are used to calculate the oscillator value, which reflects the position of the closing price relative to the channel width, scaled to a range from -100 to +100, with the zero line as the central point. A moving average of the oscillator (SMA) smooths its values, enabling signals based on crossovers with the zero line or overbought/oversold levels. The indicator also detects divergences between price and the oscillator, which may indicate potential trend reversals. VCO is useful for identifying market momentum, reversal points, and trend confirmation, especially when combined with other technical analysis tools.
█ FEATURES
- Volatility Channels: Calculates invisible chart boundaries based on a simple moving average (SMA) of the price (high + low) / 2 and volatility (average candle range and body). The length parameter (default 30) sets the SMA length, and scale (default 200%) adjusts the channel width.
- Oscillator: Determines the oscillator value in the range of -100 to +100, indicating the closing price's position relative to the volatility channel. Displayed with dynamic coloring (green for positive values, red for negative).
- Oscillator Moving Average: A simple moving average (SMA) of the oscillator values, smoothing its movements. The signalLength parameter (default 20) defines the SMA length. Displayed in yellow with an optional gradient.
- Overbought/Oversold Levels: Configurable thresholds for the oscillator (overbought, default 50; oversold, default -50) and its moving average (maOverbought, default 30; maOversold, default -30), shown as horizontal lines with optional gradients. Band colors change dynamically (red for overbought, green for oversold, gray for neutral) based on the moving average's position relative to maOverbought/maOversold, reinforcing other signals.
- Divergences: Detects bullish (price forms a lower low, oscillator a higher low) and bearish (price forms a higher high, oscillator a lower high) divergences using pivots (pivotLength, default 2). Divergences are displayed with a delay equal to the pivot length; larger lengths increase reliability but delay signals. Use as additional confirmation.
Signals:
- Overbought/Oversold Crossovers: Green triangles (buy) when the oscillator crosses above the oversold level, red triangles (sell) when it crosses below the overbought level.
- Zero Line Crossovers: Buy/sell signals when the oscillator crosses the zero line upward (buy) or downward (sell).
- Moving Average Crossovers: Buy/sell signals when the oscillator's moving average crosses the zero line or the maOverbought/maOversold levels. Dynamic band color changes (red/green) at these crossovers reinforce other signals.
- Visualization: Gradient lines for the oscillator, its moving average, overbought/oversold levels, and zero line, with adjustable transparency. Gradient fill between the oscillator and zero line.
Divergence Labels: "Bull" (bullish) and "Bear" (bearish) labels with customizable color and transparency.
- Alerts: Built-in alerts for divergences, overbought/oversold crossovers, and zero line crossovers by the oscillator and its moving average.
█ HOW TO USE
Add to Chart: Apply the indicator via Pine Editor or the Indicators menu on TradingView.
Configure Settings:
- Channel and Oscillator Settings: Adjust the channel SMA length (length, default 30) and channel scaling (scale, default 200%). Increase scale for high-volatility markets.
- Threshold Levels: Set oscillator overbought (overbought, default 50) and oversold (oversold, default -50) levels, and moving average thresholds (maOverbought, default 30; maOversold, default -30).
- Divergence Settings: Enable/disable divergence detection (calculateDivergence) and set pivot length (pivotLength, default 2). Larger values increase reliability but delay signals.
- Signal Settings: Choose signal types (signalType): overbought/oversold, zero line, moving average, or all.
- Styling: Customize colors for the oscillator, moving average, horizontal levels, and divergence labels. Adjust gradient and fill transparency.
Interpreting Signals:
- Buy Signals: Green triangles below the bar when the oscillator or its moving average crosses above the oversold level or zero line.
- Sell Signals: Red triangles above the bar when the oscillator or its moving average crosses below the overbought level or zero line.
- Moving Average Signals: Green/red triangles when the moving average crosses maOverbought/maOversold levels, indicating potential reversals or trend continuation. Dynamic band color changes (red for overbought, green for oversold) at these crossovers reinforce other signals.
- Divergences: "Bull" (bullish) and "Bear" (bearish) labels indicate potential trend reversals with a delay based on pivot length. Use as confirmation.
- Overbought/Oversold Levels: Monitor price reactions in these zones as potential reversal points. Dynamic band color changes based on the moving average reinforce signals.
Signal Confirmation: Use VCO with other tools, such as pivot levels (for key turning points) or Fibonacci levels (for support/resistance zones).
█ APPLICATIONS
- Trend Trading: Zero line crossovers by the oscillator or its moving average identify momentum in uptrends or downtrends.
- Range Trading: Overbought/oversold levels help identify entry/exit points in sideways markets.
- Divergences: Use bullish/bearish divergences as additional confirmation of reversals, especially near key price levels.
- Trend Identification: To analyze trends over a longer perspective, increase the moving average length (signalLength) for more stable signals.
█ NOTES
- Test the indicator across different timeframes and markets to optimize parameters, such as length and scale, for your trading style.
- In strong trends, overbought/oversold levels may persist, requiring additional signal verification.
- Divergences are more reliable on higher timeframes (H4, D1), where market noise is reduced, but their delay requires caution.
- In low-liquidity markets, signals may be less effective, so use on high-liquidity assets is recommended.
Volume Cluster Heatmap [BackQuant]Volume Cluster Heatmap
A visualization tool that maps traded volume across price levels over a chosen lookback period. It highlights where the market builds balance through heavy participation and where it moves efficiently through low-volume zones. By combining a heatmap, volume profile, and high/low volume node detection, this indicator reveals structural areas of support, resistance, and liquidity that drive price behavior.
What Are Volume Clusters?
A volume cluster is a horizontal aggregation of traded volume at specific price levels, showing where market participants concentrated their buying and selling.
High Volume Nodes (HVN) : Price levels with significant trading activity; often act as support or resistance.
Low Volume Nodes (LVN) : Price levels with little trading activity; price moves quickly through these areas, reflecting low liquidity.
Volume clusters help identify key structural zones, reveal potential reversals, and gauge market efficiency by highlighting where the market is balanced versus areas of thin liquidity.
By creating heatmaps, profiles, and highlighting high and low volume nodes (HVNs and LVNs), it allows traders to see where the market builds balance and where it moves efficiently through thin liquidity zones.
Example: Bitcoin breaking away from the high-volume zone near 118k and moving cleanly through the low-volume pocket around 113k–115k, illustrating how markets seek efficiency:
Core Features
Visual Analysis Components:
Heatmap Display : Displays volume intensity as colored boxes, lines, or a combination for a dynamic view of market participation.
Volume Profile Overlay : Shows cumulative volume per price level along the right-hand side of the chart.
HVN & LVN Labels : Marks high and low volume nodes with color-coded lines and labels.
Customizable Colors & Transparency : Adjust high and low volume colors and minimum transparency for clear differentiation.
Session Reset & Timeframe Control : Dynamically resets clusters at the start of new sessions or chosen timeframes (intraday, daily, weekly).
Alerts
HVN / LVN Alerts : Notify when price reaches a significant high or low volume node.
High Volume Zone Alerts : Trigger when price enters the top X% of cumulative volume, signaling key areas of market interest.
How It Works
Each bar’s volume is distributed proportionally across the horizontal price levels it touches. Over the lookback period, this builds a cumulative volume profile, identifying price levels with the most and least trading activity. The highest cumulative volume levels become HVNs, while the lowest are LVNs. A side volume profile shows aggregated volume per level, and a heatmap overlay visually reinforces market structure.
Applications for Traders
Identify strong support and resistance at HVNs.
Detect areas of low liquidity where price may move quickly (LVNs).
Determine market balance zones where price may consolidate.
Filter noise: because volume clusters aggregate activity into levels, minor fluctuations and irrelevant micro-moves are removed, simplifying analysis and improving strategy development.
Combine with other indicators such as VWAP, Supertrend, or CVD for higher-probability entries and exits.
Use volume clusters to anticipate price reactions to breaking points in thin liquidity zones.
Advanced Display Options
Heatmap Styles : Boxes, lines, or both. Boxes provide a traditional heatmap, lines are better for high granularity data.
Line Mode Example : Simplified line visualization for easier reading at high level counts:
Profile Width & Offset : Adjust spacing and placement of the volume profile for clarity alongside price.
Transparency Control : Lower transparency for more opaque visualization of high-volume zones.
Best Practices for Usage
Reduce the number of levels when using line mode to avoid clutter.
Use HVN and LVN markers in conjunction with volume profiles to plan entries and exits.
Apply session resets to monitor intraday vs. multi-day volume accumulation.
Combine with other technical indicators to confirm high-probability trading signals.
Watch price interactions with LVNs for potential rapid movements and with HVNs for possible support/resistance or reversals.
Technical Notes
Each bar contributes volume proportionally to the price levels it spans, creating a dynamic and accurate representation of traded interest.
Volume profiles are scaled and offset for visual clarity alongside live price.
Alerts are fully integrated for HVN/LVN interaction and high-volume zone entries.
Optimized to handle large lookback windows and numerous price levels efficiently without performance degradation.
This indicator is ideal for understanding market structure, detecting key liquidity areas, and filtering out noise to model price more accurately in high-frequency or algorithmic strategies.
ATR Anchored Range %b by TradeSeekersAll time highs got you spooked to enter with no levels in sight?
Stuck in a multi-week range and wondering where the heck the pivots are!?
Wondering if you're longing the top or shorting the potential bottom and about to get smoked, sending you back to burger flipping?!
Fret not trading friends!
I've been crafting the ultimate map for scalpers, slingers, swingers, swindlers, swashbucklers -and traders too.
Why should I care about this, what's an ATR!?
Nearly any trader that's entered the markets has heard of ATR, perhaps even taken a stab at trying to calculate the flux capacity of a weekly ATR on a lower timeframe. Continually calculating things manually sucks!
Ok, so you haven't heard of ATR? It's the average true range... what's the true range!? It's simply the low subtracted from the high (high - low) of any given candle.
How is ATR useful?
The theory is simple, if the ATRs on the daily timeframe for a stock are 5, then traders may have a reasonable expectation that any day in the near future the stock will mostly move +/- 5 pts. This +/- 5 can be used as a possible daily high and low for traders to use.
But ATR changes as time passes, with every billionaire X post, viral cat meme, fed announcement or government shutdown the market makes it's move. This means without this tool, traders need to run the standard lame (sorry) ATR indicator and then hand draw a bunch of important levels (barf).
I'm convinced and ready to join the ATR army, what do I do?
Glad to have you aboard sailor, slap this indicator on your layout - it'll initially display a bottom panel, say nice things to it.
Usage
The lower panel provides a %b plot representative of the current price relative to the timeframe and period ATR. (Defaults to 1D timeframe and 20 - 20 trading days in a month yo)
This %b plot is a map for price against the key ATR based levels and resets each time the timeframe change occurs.
Keep reading! (maybe grab a snack, you're doing great)
If you want to see what the indicator sees, how it maths the math, open the settings and check the "overlay" option... it's amazing, I know.
Main base of operations
This will be the gray area between first red and green lines, imagine this is a future candle for the timeframe anchored. The red would represent the candle high (red means stop/overbought), and the green would represent the candle low (green means go/oversold).
Regardless of the timeframe anchored, this area always represents the area the ATR indicates will be the building area of the current candle being formed. Traders should expect most of the trading to occur within this area.
The mid line
Don't diddle in the middle, this by default is the open price and it's the ultimate bias filter for bull or bear riders.
Extension areas
Beyond the gray area is the extension zone, this provides a whole ATR from the mid line to the extension.
Assembling a trade plan
There are just a couple of key concepts to master in order to become the ultimate ATR samurai warrior, capable of slicing through even the messiest liquidity.
Above the midline and holding, but still within the gray area? Could be a great long entry with targets to upper levels. The same holds true for below open and holding while still being within the lower gray area.
As price makes it's ascension or decline towards the ends of the initial gray ATR range, consider managing trades here. If it's suspected, due to a strong hold of the midline, that the range low or high is the midline, then continue to manage trades towards the extension zones.
Timeframes and periods oh my
The tooltips already provide some hints, but not everyone goes around clicking and hovering everything in sight (maybe I'm the only one that does that?).
There's a thoughtful approach to the default values, I like to consider the big market participants with my day trades, swings trades and beyond.
By default I've chosen the daily timeframe and a period of 20, one for each trading day of the calendar month.
It's no large leap to consider alternatives, what about 1W timeframe and a period of 4 (1 month) or 52 (1 year)?
The possibilities are nearly infinite, comment on any particular favorite combos.
An Italian Special Bonus!!!
...sorry, it's not pizza....
First, did you know the famous Italian Fibonacci's real name was actually Leonardo? I'm not sure how I feel about that. Fun fact, my ancestors are Italian.
Alright, you may have guessed that the special bonus is the mythical Fibonacci inspired "Golden Pocket", maybe it's a foreshadowing of your pockets - one can only hope.
Use this feature to show the commonly referenced Fibonacci levels within each major ATR range. I've seen some totally mathematical epic-ness with these hence the addition.
Once key ATR levels have been hit look for reversals back to golden pockets (you tricksy hobbits) for potential entry back towards the prior hit ATR level.
The %b turns gold if you have the feature enabled and of course the overlay displays them also, how fun!
Final thoughts
I hope you have as much fun using this indicator as I do, it has brought much joy to my trading experience. If you don't have fun with it, well I hope you had fun reading about it at least.
100% human crafted and darn proud of it
- SyntaxGeek
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
MACD-V with RSI Gradient## Overview
MACD-V is a volatility-adjusted momentum indicator that normalizes MACD using ATR. This version adds a dynamic RSI-based background gradient to highlight momentum zones visually.
## Features
- **MACD-V Line**: EMA-based momentum normalized by ATR
- **Signal Line**: EMA of MACD-V
- **Histogram**: Color-coded based on slope and polarity
- **RSI Gradient Background**: Shading from bright green (RSI > 75) to bright red (RSI < 30), with intermediate tones for momentum context
## Use Case
Designed for 30-minute oil futures charts, this indicator helps identify:
- Trend strength and reversals
- Momentum zones using RSI shading
- Pullback opportunities and exhaustion zones
## Inputs
- Fast EMA (default: 12)
- Slow EMA (default: 26)
- Signal EMA (default: 9)
- ATR Length (default: 26)
## Notes
- RSI shading is purely visual—no alerts are wired in yet
- Histogram renders behind MACD-V and Signal lines for clarity
- Colors are tuned for dark charts
## Credits
Developed by Mark (SylvaRocks), optimized for tactical clarity and scalping precision.
Diwali Lights Pro — 7-Diyas Signal Matrix [KedArc Quant]🎯 Overview
“Diwali Lights Pro — 7-Diyas Signal Matrix” is a precision-built trend-sentiment indicator that blends the glow of seven technical “diyas” — each representing a different momentum or strength dimension — into one intuitive signal matrix. It was designed to celebrate light, discipline, and clarity in trading — helping traders filter noise, identify strong trend shifts, and take trades with conviction. Each diya is powered by a proven indicator component: RSI, Stochastic, EMA trend strength, and momentum slopes.Together, they light up your chart with buy/sell signals only when technical confluence aligns — like the diyas of Diwali shining in harmony.
💡 Core Concept
The indicator computes a composite score (–9 to +9) by evaluating seven key parameters:
| # | Diya | Logic | Interpretation |
| 1 | RSI | Overbought / Oversold | Short-term momentum exhaustion |
| 2 | Stochastic | Direction & zones | Confirmation of RSI |
| 3 | Price vs EMA20 | Position of price | Near-term trend bias |
| 4 | EMA20 Slope | Short-term momentum | Strength confirmation |
| 5 | EMA50 Slope | Mid-term trend | Trend stability |
| 6 | EMA100 Slope | Medium-term sentiment | Institutional bias |
| 7 | EMA200 Slope | Long-term sentiment | Market direction baseline |
The total of these 7 diyas creates a signal matrix that dynamically adapts to trend conditions.
⚙️ Inputs & Configuration
| RSI Length | 14 | Standard RSI window |
| Stochastic Length | 14 | Measures momentum oscillation |
| EMA Periods | 20, 50, 100, 200 | Multi-layer trend structure |
| Overbought / Oversold Zones | 70 / 30 | Configurable thresholds |
| Show Buy/Sell Labels | ✅ | Toggle signal markers |
| Show Banner | ✅ | Festive Diwali header with fireworks |
| Twinkle Interval | 10 bars | Animation timing |
| Fireworks Count | 18 | Visual celebration intensity |
| Background Opacity | 100% | Style preference |
🧭 Entry & Exit Logic
# ✅ Buy Signal (🪔)
A Buy triggers when:
* The total diya score crosses above zero,
* And at least four of seven components turn bullish.
This indicates that short-term oscillators, price action, and moving averages are all turning in unison — a strong entry zone after a pullback.
# 🔥 Sell Signal (🔥)
A Sell triggers when:
* The total diya score crosses below zero,
* And multiple slopes or price conditions flip bearish.
This flags weakening momentum and possible trend exhaustion.
# 💬 Suggested Usage
* Works beautifully on 5-min to 1-hour charts.
* Best when used with trend confirmation tools (volume, price structure).
* Avoid entering trades when signals flip rapidly within narrow ranges (sideways zones).
🧪 Mathematical Formulae
1. RSI Bucket (p₁):
p₁ =
2 if RSI < Very Oversold
1 if RSI < Oversold
0 if neutral
-1 if RSI > Overbought
-2 if RSI > Very Overbought
2. Stochastic Bucket (p₂): Similar to RSI bucketing.
3. Price vs EMA20 (p₃):
p₃ = sign(close - EMA20)
4–7. Slope Sign (EMA20, 50, 100, 200):
p₄₋₇ = sign(EMA - EMA )
Total Score = Σ(p₁…p₇)
→ Crossover(total_score, 0) → Buy Signal
→ Crossunder(total_score, 0) → Sell Signal
📊 Why It’s Not Just a Mash-Up
Diwali Lights Pro uses:
* A unified scoring engine with weighted logic rather than conflicting triggers.
* Each component (diya) contributes equally, creating a normalized sentiment index.
* Smart signal filtering prevents repetitive false flips by enforcing trend alignment across multiple time frames.
* A dynamic, responsive structure optimized for clarity and minimal repainting.
🎆 Unique Add-Ons
* Top-Right Diwali Banner: Festive “Happy Diwali” with animated fireworks 🎇 and diyas 🪔.
* Signal Filtering: Reduces noise in volatile ranges.
* EMA Cloud Context: Visual clarity of multi-layer trend zones.
* Optional Light Mode: Change fireworks opacity for a subtle or bright effect.
📘 FAQ
Q1: Does this repaint?
No — it uses confirmed values (RSI, Stochastic, EMA slopes). Signals appear only after the bar closes.
Q2: Which timeframes work best?
Between 5m and 1h, depending on your strategy.
Use higher EMAs for swing setups.
Q3: Can I use it with alerts?
Yes, both Buy and Sell triggers come with built-in `alertcondition()` for instant notifications.
Q4: Can it be combined with other indicators?
Absolutely — it pairs well with volume profiles, volatility bands, or order-flow systems.
🪔 Glossary
| Diya | Candle or light — here, each diya = one technical indicator |
| EMA | Exponential Moving Average — measures smoothed trend bias |
| RSI | Relative Strength Index — momentum overbought/oversold oscillator |
| Stochastic | Momentum oscillator measuring closing levels relative to highs/lows |
| Slope Sign | Direction of EMA movement — rising or falling |
| Signal Matrix | The combined system of all seven diyas generating a unified score |
🧭 Final Note
> *Diwali Lights Pro* is not just a trading tool — it’s a visual celebration of confluence and discipline.
> When the diyas align, trends shine. Use it to trade in harmony with light, not against it. 🌟
⚠️ Disclaimer
This script is provided for educational purposes only.
Past performance does not guarantee future results.
Trading involves risk, and users should exercise caution and use proper risk management when applying this strategy.
☸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.
Dobrusky Volume PulseWhat it does & who it’s for
Volume Pulse is a lightweight, customizable volume profile overlay that shows traders how volume is distributed across price levels over a chosen lookback window. Unlike standard profiles, it also maps cumulative buy/sell pressure at each level, so you see not just where volume clustered, but which side dominated.
Core ideas
Cumulative volume by price: Builds a horizontal profile of traded volume at each level, based on user-defined depth and resolution.
Directional pressure mapping: At every price level, the script accumulates bullish vs. bearish volume based on candle closes vs. opens, providing a directional read on whether buyers or sellers had the upper hand.
POC: Automatically highlights the Point of Control (POC) — the level with the most activity.
Customizable presentation: Adjustable profile resolution, bar width, offset, colors, and whether to show cumulative, directional, or both.
How the components work together
The profile provides the “where,” while the buy/sell mapping adds the “who.” By combining these, traders can see whether a high-volume node was buyer-driven absorption or seller-driven distribution — a distinction classic profiles don’t reveal. This directional overlay reduces the guesswork of interpreting raw volume clusters.
How to use
Apply the overlay to your chart.
Watch the POC and areas of significant increase or decrease in volume (and pressure) as natural magnets or rejection areas.
When trading intraday, I've found that higher timeframe volume levels act as strong magnets. In the chart, you can see the volume levels I've drawn on the SPY daily chart. These levels are targets I use when trading the 5-minute chart.
Pay attention to color dominance at those zones — green-heavy nodes suggest buyer control; red-heavy nodes suggest seller control.
Combine with time-based volume tools and price-action for a more comprehensive trade plan.
Settings overview
Lookback depth: Number of bars used for profile calculation.
Profile resolution: Number of horizontal bars to split volume across price.
Bar style: Width, offset, and multiplier for scaling.
Toggle layers: Choose cumulative, directional, or both.
POC display: Optional highlight of the most traded level.
Limitations & best practices
This is a contextual overlay, not a trade-signal system.
Works best on liquid instruments (indices, futures, major stocks, liquid crypto) where volume distribution is meaningful.
Directional mapping uses candle body bias (close vs. open), not raw order flow. For full tape analysis, pair with actual order flow data.
Originality justification
Dual profile: combines cumulative volume-by-price and buyer/seller pressure per bin (close vs. open) — not a standard VP clone.
From-scratch binning + POC in a single pass for speed; no reused libraries.
Flexible display (cumulative / directional / both) with independent resolution, width, and offset for intraday or HTF use.
Clear visuals (optional POC, balanced node coloring) and open-source code so traders can audit and extend.
Kalman Exponentialy Weighted Moving Average | MisinkoMasterThe Kalman Exponentialy Weighted Moving Average is a technical analysis tool providing users with more responsive and smoother signals, providing crystal-clear signals and giving investors valuable insights on market trends, however it could be used in many cases.
A deeper dive into the indicator:
When going through my creation of strategies, I had stumbled on an indicator called "EWMA", which worked decently, but it was far too simple in my opinion so I decided to combine the EMA & WMA, but with a little more complexity, and it has worked .
I began by learning how both MAs work, I already knew how WMA works, but EMA I did not.
After learning both I found out they were quite simple in principle and that there was a way to combine them in such way that you would get really good signals, however it was way too noisy.
While it could avoid major dumps that were not avoided by most indicators, it would lose that edge because of being too noisy.
After testing out many conditions, combinations & more, the best working one was this one:
WMA > KEWMA = long
WMA < KEWMA = short
I will explain this later, but this gave fast signals, and while it still was noisy it was better then before.
To smooth it out, I started testing price filters => Gaussian Filter and many more were tested out, but they either slowed it down to the point it was no longer of much use, or did not smooth it at all.
After testing the Kalman filter on this thing, I was shocked.
It was just right and made the indicator a lot better, smoothed it and kept most of the responsivness it had.
Now to the big question: "How is it calculated?"
Now first it needs to calculate the Kalman source, which smooths the source which will be used.
After that, we calculate the Weighted Moving Average for " n " period on the Kalman source.
Now that we have our WMA values, we need to calculate " a ".
a is calculated in the following formula:
a = 2/(1+ n )
where n is the user defined length
Now for the last part:
KEWMA = WMAyesterday * (1-a) + WMAtoday * a
This creates a very accurate and reactive indicator, that can prove useful in many uses, beyond those I will and did talk about.
For the trend logic as mentioned before:
Long = WMA > KEWMA
Short = WMA < KEWMA
This worked best, but you might find better ways of using it.
I think that is all I have to say about it, I left it open source so you can all code it in your strategies and play around with it.
Enjoy Gs!
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
Herd Flow Oscillator — Volume Distribution Herd Flow Oscillator — Scientific Volume Distribution (herd-accurate rev)
A composite order-flow oscillator designed to surface true herding behavior — not just random bursts of buying or selling.
It’s built to detect when market participants start acting together, showing persistent, one-sided activity that statistically breaks away from normal market randomness.
Unlike traditional volume or momentum indicators, this tool doesn’t just look for “who’s buying” or “who’s selling.”
It tries to quantify crowd behavior by blending multiple statistical tests that describe how collective sentiment and coordination unfold in price and volume dynamics.
What it shows
The Herd Flow Oscillator works as a multi-layer detector of crowd-driven flow in the market. It examines how signed volume (buy vs. sell pressure) evolves, how persistent it is, and whether those actions are unusually coordinated compared to random expectations.
HerdFlow Composite (z) — the main signal line, showing how statistically extreme the current herding pressure is.
When this crosses above or below your set thresholds, it suggests a high probability of collective buying or selling.
You can optionally reveal component panels for deeper insight into why herding is detected:
DVI (Directional Volume Imbalance): Measures the ratio of bullish vs. bearish volume.
If it’s strongly positive, more volume is hitting the ask (buying); if negative, more is hitting the bid (selling).
LSV-style Herd Index : Inspired by academic finance measures of “herding.”
It compares how often volume is buying vs. selling versus what would happen by random chance.
If the result is significantly above chance, it means traders are collectively biased in one direction.
O rder-Flow Persistence (ρ 1..K): Averages autocorrelation of signed volume over several lags.
In simpler terms: checks if buying/selling pressure tends to continue in the same direction across bars.
Positive persistence = ongoing coordination, not just isolated trades.
Runs-Test Herding (−Z) : Statistical test that checks how often trade direction flips.
When there are fewer direction changes than expected, it means trades are clustering — a hallmark of herd behavior.
Skew (signed volume): Measures whether signed volume is heavily tilted to one side.
A positive skew means more aggressive buying bursts; a negative skew means more intense selling bursts.
CVD Slope (z): Looks at the slope of the Cumulative Volume Delta — essentially how quickly buy/sell pressure is accelerating.
It’s a short-term flow acceleration measure.
Shapes & background
▲ “BH” at the bottom = Bull Herding; ▼ “BH-” at the top = Bear Herding.
These markers appear when all conditions align to confirm a herding regime.
Persistence and clustering both confirm coordinated downside flow.
Core Windows
Primary Window (N) — the main sample length for herding calculations.
It’s like the "memory span" for detecting coordinated behavior. A longer N means smoother, more reliable signals.
Short Window (Nshort) — used for short-term measurements like imbalance and slope.
Smaller values react faster but can be noisy; larger values are steadier but slower.
Long Window (Nlong) — used for z-score normalization (statistical scaling).
This helps the indicator understand what’s “normal” behavior over a longer horizon, so it can spot when things deviate too far.
Autocorr lags (acLags) — how many steps to check when measuring persistence.
Higher values (e.g., 3–5) look further back to see if trends are truly continuing.
Calculation Options
Price Proxy for Tick Rule — defines how to decide if a trade is “buy” or “sell.”
hlc3 (average of high, low, and close) works as a neutral, smooth price proxy.
Use ATR for scaling — keeps signals comparable across assets and timeframes by dividing by volatility (ATR).
Prevents high-volatility periods from dominating the signal.
Median Filter (bars) — smooths out erratic data spikes without heavily lagging the response.
Odd values like 3 or 5 work best.
Signal Thresholds
Composite z-threshold — determines how extreme behavior must be before it counts as “herding.”
Higher values = fewer, more confident signals.
Imbalance threshold — the minimum directional volume imbalance to trigger interest.
Plotting
Show component panels — useful for analysts and developers who want to inspect the math behind signals.
Fill strong herding zones — purely visual aid to highlight key periods of coordinated trading.
How to use it (practical tips)
Understand the purpose: This is not just a “buy/sell” tool.
It’s a behavioral detector that identifies when traders or algorithms start acting in the same direction.
Timeframe flexibility:
15m–1h: reveals short-term crowd shifts.
4h–1D: better for swing-trade context and institutional positioning.
Combine with structure or trend:
When HerdFlow confirms a bullish regime during a breakout or retest, it adds confidence.
Conversely, a bearish cluster at resistance may hint at a crowd-driven rejection.
Threshold tuning:
To make it more selective, increase zThr and imbThr.
To make it more sensitive, lower those thresholds but expand your primary window N for smoother results.
Cross-market consistency:
Keep “Use ATR for scaling” enabled to maintain consistency across different instruments or timeframes.
Denoising:
A small median filter (3–5 bars) removes flicker from volume spikes but still preserves the essential crowd patterns.
Reading the components (why signals fire)
Each sub-metric describes a unique “dimension” of crowd behavior:
DVI: how imbalanced buying vs selling is.
Herd Index: how biased that imbalance is compared to random expectation.
Persistence (ρ): how continuous those flows are.
Runs-Test: how clumped together trades are — clustering means the crowd’s acting in sync.
Skew: how lopsided the volume distribution is — sudden surges of one-sided aggression.
CVD Slope: how strongly accelerating the current directional flow is.
When all of these line up, you’re seeing evidence that market participants are collectively moving in the same direction — i.e., true herding.
Z-Score Momentum | MisinkoMasterThe Z-Score Momentum is a new trend analysis indicator designed to catch reversals, and shifts in trends by comparing the "positive" and "negative" momentum by using the Z-Score.
This approach helps traders and investors get unique insight into the market of not just Crypto, but any market.
A deeper dive into the indicator
First, I want to cover the "Why?", as I believe it will ease of the part of the calculation to make it easier to understand, as by then you will understand how it fits the puzzle.
I had an attempt to create a momentum oscillator that would catch reversals and provide high tier accuracy while maintaining the main part => the speed.
I thought back to many concepts, divergences between averages?
- Did not work
Maybe a MACD rework?
- Did not work with what I tried :(
So I thought about statistics, Standard Deviation, Z-Score, Sharpe/Sortino/Omega ratio...
Wait, was that the Z-Score? I only tried the For Loop version of it :O
So on my way back from school I formulated a concept (originaly not like this but to that later) that would attempt to use the Z-Score as an accurate momentum oscillator.
Many ideas were falling out of the blue, but not many worked.
After almost giving up on this, and going to go back to developing my strategies, I tried one last thing:
What if we use divergences in the average, formulated like a Z-score?
Surprise-surprise, it worked!
Now to explain what I have been so passionately yapping about, and to connect the pieces of the puzzle once and for all:
The indicator compares the "strength" of the bullish/bearish factors (could be said differently, but this is my "speach bubble", and I think this describes it the best)
What could we use for the "bullish/bearish" factors?
How about high & low?
I mean, these are by definitions the highest and lowest points in price, which I decided to interpret as: The highest the bull & bear "factors" achieved that bar.
The problem here is comparison, I mean high will ALWAYS > low, unless the asset decided to unplug itself and stop moving, but otherwise that would be unfair.
Now if I use my Z-score, it will get higher while low is going up, which is the opposite of what I want, the bearish "factor" is weaker while we go up!
So I sat on my ret*rded a*s for 25 minutes, completly ignoring the fact the number "-1" exists.
Surprise surprise, multiplying the Z-Score of the low by -1 did what I wanted!
Now it reversed itself (magically). Now while the low keeps going down, the bear factor increases, and while it goes up the bear factor lowers.
This was btw still too noisy, so instead of the classic formula:
a = current value
b = average value
c = standard deviation of a
Z = (a-b)/c
I used:
a = average value over n/2 period
b = average value over n period
c = standard deviation of a
Z = (a-b)/c
And then compared the Z-Score of High to the Z-Score of Low by basic subtraction, which gives us final result and shows us the strength of trend, the direction of the trend, and possibly more, which I may have not found.
As always, this script is open source, so make sure to play around with it, you may uncover the treasure that I did not :)
Enjoy Gs!
Keltner Trend (Wick-Based, Ratcheting Lower Band)Keltner trend with ratcheting lower band stop loss.
We Buy / We Sell - #TheStrat SignalsWe Buy / We Sell - #TheStrat SignalsDescription
This indicator is inspired by the #TheStrat methodology from Rob Smith, designed to identify high-probability "We Buy" (bullish) and "We Sell" (bearish) signals for trading stocks, ETFs, or futures like AMEX:SPY or $VSAT. It combines price action reversal patterns, higher timeframe continuity (HTFC), and optional broadening formation (BF) breaks to time entries with market momentum. Key Features: We Buy Signals: Triggered on a 2d-2u reversal (bearish to bullish candle) when the higher timeframe (HTF) is bullish (green) and optionally at a BF bottom (pivot low break). Labeled as "We Buy" at the candle’s low with a green triangle.
We Sell Signals: Triggered on a 2u-2d reversal (bullish to bearish candle) when the HTF is bearish (red) and optionally at a BF top (pivot high break). Labeled as "We Sell" at the candle’s high with a red triangle.
Candle Numbering: Displays #TheStrat candle types (1=Inside, 2u=Up, 2d=Down, 3=Outside) for context.
Debug Labels: Enabled by default, showing why signals don’t fire (e.g., "No HTFC Buy" if HTF isn’t bullish).
Partial Signals: Optional faint circles for 2d-2u or 2u-2d reversals (without HTFC/BF), disabled by default.
HTFC Background: Green (HTF bullish) or red (HTF bearish) background for timeframe alignment.
How It Works
Based on #TheStrat, the indicator seeks evidence of aggressive buying ("We Buy") or selling ("We Sell") by analyzing: Reversal Patterns: 2d-2u (We Buy): A bearish directional candle (2d) followed by a bullish directional candle (2u), signaling a potential bullish reversal.
2u-2d (We Sell): A bullish directional candle (2u) followed by a bearish directional candle (2d), signaling a potential bearish reversal.
Higher Timeframe Continuity (HTFC): We Buy requires the HTF (e.g., 1H or Daily) to close above its open (bullish).
We Sell requires the HTF to close below its open (bearish).
Broadening Formation (BF): Optional pivot high/low breaks approximate BF extremes (tops for We Sell, bottoms for We Buy).
Can be disabled (use_bf=false) for more frequent signals.
How to Use Setup: Apply to a 5min chart of a liquid asset (e.g., AMEX:SPY , NASDAQ:VSAT ) for intraday trading, or higher timeframes for swing trading.
Ensure sufficient chart history (TradingView > Chart Settings > Max Bars > 1000+).
Settings: Higher Timeframe (htf): Default "60" (1H). Try "15" (15min) for faster signals or "D" (Daily) for swing trades.
Pivot Lookback Length (pivot_len): Default 3. Lower to 1 for more signals, higher for stricter BF breaks.
Require Broadening Formation (use_bf): Default true. Set to false to skip BF checks, increasing signal frequency.
Show We Buy/We Sell Labels: Default true. Shows "We Buy" or "We Sell" on signal candles.
Show Candle Numbers: Default true. Displays 1/2u/2d/3 for #TheStrat context.
Show Debug Labels: Default true. Shows "No HTFC Buy", "No BF Buy", etc., to diagnose missing signals.
Show Partial Signals: Default false. Enable to show faint circles for 2d-2u/2u-2d reversals without HTFC/BF.
Trading: We Buy: Enter long on a green "We Buy" label (with triangle). Set stops below the signal candle’s low. Target BF highs or resistance.
We Sell: Enter short on a red "We Sell" label (with triangle). Set stops above the signal candle’s high. Target BF lows or support.
Use debug labels to understand why signals don’t fire (e.g., "No HTFC Buy" means HTF isn’t bullish).
Partial signals (faint circles) indicate reversals without full conditions, useful for discretionary setups.
Alerts: Right-click the indicator > "Add Alert" on we_buy or we_sell for real-time notifications.
Tips Best Assets: Use on liquid tickers like AMEX:SPY , NASDAQ:QQQ , or NASDAQ:VSAT , as seen in @AlexsOptions
’ examples.
Volatility: Signals are more frequent in trending or volatile markets. Check historical periods (e.g., September 2025) for testing.
Risk Management: Always use stops (e.g., 1-2% risk per trade) and validate signals with market context (e.g., sector/index alignment).
Learning #TheStrat: Study Rob Smith’s #TheStrat for deeper understanding of candle types and FTFC.
Troubleshooting No Signals? Check debug labels (e.g., "No HTFC Buy" means HTF isn’t bullish). Adjust htf (e.g., "15" or "D").
Set use_bf=false or lower pivot_len to 1 for more signals.
Ensure reversals (2d-2u or 2u-2d) are present (check candle numbers).
Test on volatile periods or liquid tickers.
No Partial Signals? Enable show_partial in settings to see faint circles for 2d-2u/2u-2d reversals.
Confirm reversal patterns exist (e.g., "2d" → "2u" in candle numbers).
