RPT Position Sizer🎯 Purpose
This indicator is a position sizing and stop-loss calculator designed to help traders instantly determine:
How many shares/contracts to buy,
How much risk (₹) they are taking per trade,
How much capital will be deployed, and
The precise stop-loss price level based on user-defined parameters.
It displays all key values in a compact on-chart table (bottom-left corner) for quick trade planning.
💡 Use Case
Perfect for discretionary swing traders, systematic position traders, and risk managers who want instant visual feedback of trade sizing metrics directly on the chart — eliminating manual calculations and improving discipline.
⚙️ Key Features
Dynamic Inputs
Trading Capital (₹) — total available capital for trading.
RPT % — risk-per-trade as a percentage of total capital.
SL % — stop-loss distance in percent below CMP (Current Market Price).
CMP Source — can be linked to close, hl2, etc.
Rounding Style — round position size to Nearest, Floor, or Ceil.
Decimals Show — control number formatting precision in the table.
Core Calculations
SL Points: CMP × SL%
SL Price: CMP − SL Points
Risk Amount (₹): Capital × RPT%
Position Size: Risk ÷ SL Points
Capital Used: Position Size × CMP
Clean On-Chart Table Display
Displays:
Trading Capital
RPT %
Risk Amount (₹)
Position Size (shares/contracts)
Capital Required (₹)
Stop-Loss % & SL Price
The table uses a minimalistic white-on-black design with clear labeling and rupee formatting for quick reference.
Data Window Integration
Plots hidden values (Position Size, Risk Amount, SL Points, Capital Used) for use in TradingView’s Data Window—ideal for strategy testing and exporting values.
Educational
8/13/200 EMA Crossover ScreenerHold your Options Longer
Entry (Long):
-Daily Chart
-8 EMA crosses above 200 and 13 EMA
- Price remains above 200 EMA
-Break of Structure
EXIT/STOP:
-Stop under recent swing low
-Exit when prices crosses below 13 EMA
Why:
Keeps you trading with trend, filters noise, avoids fights with the market.
Run stock Screener with filters: Price > 200 EMA, 8 EMA > 13 EMA, 8 EMA > 200 EMA, Volume > 500,000.
Check for Buy Signal.
Visually confirm a recent swing high (a peak from the last 30 days) and verify if the price broke above it with strong volume.
Use Pine Script, modify it to create an alert of the stock that you screened.
REQH/L [TakingProphets]OVERVIEW
This indicator identifies and maintains liquidity reference levels derived from swing highs and swing lows, then flags Relative Equal Highs (REQH) and Relative Equal Lows (REQL) when two active levels are within a user-defined distance.
It is intended for educational study of liquidity behavior and market structure. It does not predict price, provide signals, or recommend trades.
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PURPOSE AND SCOPE
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• Provide a consistent, rule-based way to mark possible equal-high/equal-low liquidity pools.
• Help users journal, review, and study how price interacts with those pools.
• Keep charts clear by automatically managing lines/labels and optionally fading traded-through levels.
This is an indicator, not a strategy. No entries, exits, or performance claims are made.
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CONCEPTS AND DEFINITIONS
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• Swing High / Swing Low: local extrema used to seed candidate liquidity levels.
• Buyside Liquidity (BSL): swing highs (potential buy-side stops).
• Sellside Liquidity (SSL): swing lows (potential sell-side stops).
• Relative Equal Highs (REQH): two unswept highs within a small price distance.
• Relative Equal Lows (REQL): two unswept lows within a small price distance.
• Traded-Through: a level is considered taken once price trades past it (high > level for BSL, low < level for SSL).
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HOW IT WORKS (ALGORITHMIC FLOW)
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Swing Detection
• Uses built-in pivot functions with a fixed swingStrength = 1.
• On a confirmed pivot high, a BSL level is created; on a pivot low, an SSL level is created.
• Each level stores: price, bar index, line handle, label handle, and status flags.
REQH / REQL Identification
• A constant REQ_THRESHOLD = 2.0 is used to test proximity between active levels of the same side.
• For BSL (highs): when two highs are within threshold, the higher level is kept and flagged REQH; the other is removed.
• For SSL (lows): when two lows are within threshold, the lower level is kept and flagged REQL; the other is removed.
• When a level is flagged, its line is revealed in side color and its label updates to “REQH” or “REQL”.
Traded-Through Handling
• If price trades through an active level (high > BSL price, or low < SSL price), two behaviors are possible:
– If Keep Traded-Through Levels = OFF: the level is deleted.
– If ON: the level is marked traded, its color is faded (opacity ≈ 75), and the line’s extension is frozen at the trade-through bar.
Line/Label Maintenance
• Lines are created initially invisible (fully transparent). Flagging reveals the line in color.
• Labels can be shown/hidden; placement can be Left (at level start, with left offset) or Right (at current bar, with right offset).
• All active lines extend to the right as bars progress.
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KEY INPUTS
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• Buyside Level Color (default #089981)
• Sellside Level Color (default #E91E63)
• Line Style (Solid / Dashed / Dotted) and Width
• Show Labels (on/off), Label Placement (Left/Right)
• Keep Traded-Through Levels (on/off), Traded Opacity (~75)
• REQ Threshold (fixed in code at 2.0 by default; represents the max distance between two levels to be considered “relative equal”)
Note: In this version, swingStrength is fixed to 1 inside the script. If you want a user control here, I can expose it as an input.
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PRACTICAL USAGE
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• Identify potential equal-high/equal-low zones using objective proximity logic.
• Observe if those zones attract price or are traded through during your session study.
• Journal how often flagged REQH/REQL zones remain intact versus get swept.
• Combine with your own analysis and risk framework; this script is informational only.
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VISUAL BEHAVIOR AND STYLE
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• Flagged levels are plotted in side color (buyside/sellside).
• Right-placement keeps labels aligned near the most recent bar for clarity; Left-placement anchors labels near the origin index.
• When keep-traded-levels is enabled, faded color indicates the level has been traded through, while preserving the historical reference.
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LIMITATIONS AND TECHNICAL NOTES
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• Timeframe and symbol volatility will influence the usefulness of a fixed REQ threshold. For very high-priced or low-priced instruments, consider adjusting the threshold in code to suit your market’s tick/point value.
• Using swingStrength = 1 introduces more sensitivity; users who prefer fewer, stronger pivots may wish to expose this as an input and increase it.
• No look-ahead is used; pivots are confirmed using standard pivot confirmation.
• Arrays and line/label objects are bounded by max_lines_count = 500; extremely long sessions or dense markets may require reducing visual retention.
• The script does not compute performance, signals, or recommendations.
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ORIGINALITY AND VALUE
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• Implements a simple, explicit REQ proximity engine that only reveals and labels lines after they qualify as REQH/REQL, keeping charts clean.
• Provides deterministic deletion or fading behavior once levels are traded through, preserving historical context when desired.
• Uses a clear line/label management model with consistent right-extension and optional label offsets to avoid overlap.
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TERMS AND DISCLAIMER
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This indicator is provided solely for educational and informational purposes.
It does not constitute financial advice, trading signals, or a recommendation to buy or sell any instrument.
Past behavior of price structures does not guarantee future results.
Users are fully responsible for their own decisions and outcomes.
This description is self-contained and does not solicit purchases or external contact.
VWAP + Multi-Condition RSI Signals + FibonacciPlatform / System
Platform: TradingView
Language: Pine Script® v6
Purpose: This script is an overlay indicator for technical analysis on charts. It combines multiple tools: VWAP, RSI signals, and Fibonacci levels.
1️⃣ VWAP (Volume Weighted Average Price)
What it does:
Plots the VWAP line on the chart, which is a weighted average price based on volume.
Can be anchored to different periods: Session, Week, Month, Quarter, Year, Decade, Century, or corporate events like Earnings, Dividends, Splits.
Optionally plots bands above and below VWAP based on standard deviation or a percentage.
Supports up to 3 bands with customizable multipliers.
Will not display if the timeframe is daily or higher and the hideonDWM option is enabled.
Visual on chart: A main VWAP line with optional shaded bands.
2️⃣ RSI (Relative Strength Index) Signals
What it does:
Calculates RSI with a configurable period.
Identifies overbought and oversold zones using user-defined levels.
Generates buy/sell signals based on:
RSI crossing above oversold → Buy
RSI crossing below overbought → Sell
Detects strong signals using divergences:
Bullish divergence: Price makes lower low, RSI makes higher low → Strong Buy
Bearish divergence: Price makes higher high, RSI makes lower high → Strong Sell
Optional momentum signals when RSI crosses 50 after recent overbought/oversold conditions.
Visual on chart:
Triangles for buy/sell
Different color triangles/circles for strong and momentum signals
Background shading in RSI overbought/oversold zones
Alerts: The script can trigger alerts when any of these signals occur.
3️⃣ Fibonacci Levels
What it does:
Calculates Fibonacci retracement and extension levels based on the highest high and lowest low over a configurable lookback period.
Plots standard Fibonacci levels: 0.146, 0.236, 0.382, 0.5, 0.618, 0.786, 1.0
Plots extension levels: 1.272, 1.618, 2.0, 2.618
Helps identify potential support/resistance zones.
Visual on chart: Horizontal lines at each Fibonacci level, shaded with different transparencies.
Summary
This script is essentially a multi-tool trading indicator that combines:
VWAP with dynamic bands for trend analysis and price positioning
RSI signals with divergences for entry/exit points
Fibonacci retracement and extension levels for support/resistance
It is interactive and visual, providing both chart overlays and alert functionality for active trading strategies.
This code is provided for training and educational purposes only. It is not financial advice and should not be used for live trading without proper testing and professional guidance.
CUBE's V17CUBE’s V15.1 — Sparkles ⚡ + Cubes 🟨 + Smart/LC 🟫 + Golden ✨ (multi-signal scalper & trend helper)
CUBE’s V15.1 is a multi-module toolkit for intraday momentum and quick-scalp decision making. It blends a trend engine, VWAP/EMA50 band logic, CRT + Volume pair detection, weighted divergence, OBV-MACD regime flips, and “Sparkles” presets—then fuses them into readable Cube labels and higher-conviction Golden combos.
What it prints (signal taxonomy)
🟨 Cube ++ Incoming — pre-signal when price enters VWAP/EMA50 “yellow” bands with trend alignment.
🟨 Cube’s Buy ++ / Sell ++ — the “plus-plus” confirmations after CRT context; gated to avoid spam.
🟫 Last Chance → 🟫 Last Chance ++ — RSI + divergence-weighted follow-through (waits for a tiny UT flip).
🟪 Smart Cube — post-Cube, waits for KC(1.2) location + OBV presence + divergence stack (more selective).
✨ Golden (Sparkles + Cube) — objective confluence labels that require Sparkles (preset wins) plus a Cube event inside a short window. Comes in Golden-2 (2+ sparkles in 3 bars) and Golden-1 (tight 0–1 bar proximity).
Each label automatically shows “(Quick Scalp)” when price is inside the careful bands, so you know when to downshift risk.
The engines (under the hood)
Trend: Pivot-Point SuperTrend (PPST 2/10/3) drives bullish/bearish context (invertible).
Bands: 5-minute VWAP + EMA50 zones with symbol-aware tolerances (majors/ETFs/crypto/megacaps tuned).
CRT + Volume Spike Pair: detects recent hammer/shooter + volume conditions and uses them to gate higher tiers.
Weighted Divergence: RSI / Stoch (weighted) / CCI / MOM / OBV (weighted) / CMF / MFI (and more) with CRT-recency gates to keep it relevant.
UT micro-flips: tiny ATR trail crosses used to “arm” Last Chance ++ entries.
OBV-MACD regime: structural flips for the Super7 and Smart Cube filters.
Super7 Sparkles: five presets (4/8/15/24/40 bars) that score 9 modules; you can show compact ✨ icons or 9/9 text.
Quick start (60 seconds)
Add to a 1–5m chart of your instrument.
Leave defaults on; optionally toggle “Sparkle Settings” (the presets are already on).
Watch for:
✨ Golden Buy/Sell → higher-quality scalp setups.
🟨 Cube’s Buy/Sell ++ → momentum continuation outside the yellow bands.
🟪 Smart Cube → selective continuation after a Cube with KC/OBV/div confluence.
Use the built-in alerts (see list below) to automate.
Inputs & customization highlights
Invert Trend Logic — flips bull/bear interpretation (useful in range regimes).
Reference TF label anchoring — place labels using a reference timeframe; optional “(tf)” tag.
Careful (Quick-Scalp) palettes — swap label colors when inside bands; hide/show quick-scalp labels per mode.
Duplicate filters — suppress Cube repeats within a window.
Session tools — optional 6:00 PM 5m “reset” box (purple) and 9:30 AM 1m NY Open box (yellow).
Backgrounds — optional ST(10,1) 0.5–0.7 ATR ribbons for context.
Presets — five Sparkle presets with per-side alternation and wipe logic.
Alerts (names as they appear in TradingView)
⬜ Cube’s Buy / Sell (or 🟨 Incoming if trend is inverted)
🟨 CUBE’S BUY ++ / CUBE’S SELL ++ (or “Cube’s … ++” if inverted)
🟫 Buy Last Chance / 🟫 Incoming (Sell Last Chance)
🟫 Cube’s Last Chance Buy ++ / Cube’s Last Chance Sell
🟪 Smart Cube’s Buy / Smart Cube’s Sell
🔔 ALL Cube Alerts (one catch-all)
✨ G✨lden Buy/Sell (Sparkles+Cube) and G✨lden-1/2 variants
Tip: Set close-bar alerts for most signals; if you want early heads-up, allow “once per bar” but expect more noise.
Reading the labels
“(Quick Scalp)” suffix = price inside the VWAP/EMA50 careful bands; tighten targets/size.
Some labels include indicator names + a weighted count (e.g., “Hist RSI MOM 3”) to hint at divergence depth.
Star ⭐ near a label means a CRT+VOL pair was detected within the recent window.
Golden text shows the most recent cube subtype (“Cube ++”, “Smart Cube”, etc.) that satisfied the window rule.
Recommended markets & timeframes
Built-in tuning for: NQ/ES/RTY/YM, GC/CL, XAU/XAG, FX majors, BTC/ETH/SOL, SPY/QQQ/IWM/DIA, and mega-caps (AAPL, MSFT, NVDA, etc.).
Best experience on 1m–5m for intraday. Works on higher TFs but is designed around the 5-minute VWAP/EMA50 backbone.
Best practices
Confluence over single prints: Use ✨ Golden or 🟪 Smart Cube + trend + structure.
Location matters: Prefer signals near session boxes, prior day H/L, and liquidity pools.
Risk first: Size down in (Quick Scalp) zones and during lunch hours/illiquid sessions.
Avoid double-counting: The script already suppresses blatant duplicates—don’t force extra alerts.
Repainting & transparency
Core signals evaluate on confirmed bars; major request.security calls use lookahead_off.
The 1m open/6pm boxes use alignment tricks for placement; they don’t feed signal logic.
As with any multi-TF logic, real-time bars can update intra-bar—use “on close” alerts for strict confirmation.
Disclaimer
This script is for educational purposes. It’s not financial advice and does not guarantee results. Markets carry risk—always test on replay/paper first, know your instrument’s tick/fee structure, and use hard stops.
Squeeze Momentum IndicatorThis indicator identifies periods of low market volatility—commonly referred to as a "squeeze"—by comparing Bollinger Bands and Keltner Channels. When volatility compresses, price often prepares for a directional breakout. The histogram visualizes momentum strength and direction once the squeeze ends.
**How it works:**
- **Squeeze detection**: A squeeze is active when Bollinger Bands are fully contained within Keltner Channels. This appears as black crosses on the zero line.
- **Volatility expansion**: When Bollinger Bands move outside Keltner Channels, volatility is increasing. This state is marked with blue crosses.
- **Momentum histogram**: The core signal is a linear regression of price relative to a dynamic baseline (average of the highest high, lowest low, and SMA over the lookback period).
- **Aqua**: Positive momentum that is accelerating.
- **Bright blue**: Positive momentum that is decelerating.
- **Yellow**: Negative momentum that is accelerating downward.
- **Orange**: Negative momentum that is decelerating (potential reversal zone).
**Usage notes:**
Traders often monitor the transition from squeeze (black) to expansion (blue) combined with a strong histogram move away from zero as a potential entry signal. Color changes in the histogram help assess momentum shifts before price makes large moves.
This script is designed for educational and analytical purposes. It does not constitute investment advice. Always test strategies in a simulated environment before applying them to live trading.
Intrinsic Value AnalyzerThe Intrinsic Value Analyzer is an all-in-one valuation tool that automatically calculates the fair value of a stock using industry-standard valuation techniques. It estimates intrinsic value through Discounted Cash Flow (DCF), Enterprise Value to Revenue (EV/REV), Enterprise Value to EBITDA (EV/EBITDA), and Price to Earnings (P/EPS). The model features adjustable parameters and a built-in alert system that notifies investors in real time when valuation multiples reach predefined thresholds. It also includes a comprehensive, color-coded table that compares the company’s historical average growth rates, valuation multiples, and financial ratios with the most recent values, helping investors quickly assess how current values align with historical averages.
The model calculates the historical Compounded Annual Growth Rates (CAGR) and average valuation multiples over the selected Lookback Period. It then projects Revenue, Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), Earnings per Share (EPS), and Free Cash Flow (FCF) for the selected Forecast Period and discounts their future values back to the present using the Weighted Average Cost of Capital (WACC) or the Cost of Equity. By default, the model automatically applies the historical averages displayed in the table as the growth forecasts and target multiples. These assumptions can be modified in the menu by entering custom REV-G, EBITDA-G, EPS-G, and FCF-G growth forecasts, as well as EV/REV, EV/EBITDA, and P/EPS target multiples. When new input values are entered, the model recalculates the fair value in real time, allowing users to see how changes in these assumptions affect the company’s fair value.
DCF = (Sum of (FCF × (1 + FCF-G) ^ t ÷ (1 + WACC) ^ t) for each year t until Forecast Period + ((FCF × (1 + FCF-G) ^ Forecast Period × (1 + LT Growth)) ÷ ((WACC - LT Growth) × (1 + WACC) ^ Forecast Period)) + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding
EV/REV = ((Revenue × (1 + REV-G) ^ Forecast Period × EV/REV Target) ÷ (1 + WACC) ^ Forecast Period + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding
EV/EBITDA = ((EBITDA × (1 + EBITDA-G) ^ Forecast Period × EV/EBITDA Target) ÷ (1 + WACC) ^ Forecast Period + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding
P/EPS = (EPS × (1 + EPS-G) ^ Forecast Period × P/EPS Target) ÷ (1 + Cost of Equity) ^ Forecast Period
The discounted one-year average analyst price target (1Y PT) is also displayed alongside the valuation labels to provide an overview of consensus estimates. For the DCF model, the terminal long-term FCF growth rate (LT Growth) is based on the selected country to reflect expected long-term nominal GDP growth and can be modified in the menu. For metrics involving FCF, users can choose between reported FCF, calculated as Cash From Operations (CFO) - Capital Expenditures (CAPEX), or standardized FCF, calculated as Earnings Before Interest and Taxes (EBIT) × (1 - Average Tax Rate) + Depreciation and Amortization - Change in Net Working Capital - CAPEX. Historical average values displayed in the left column of the table are based on Fiscal Year (FY) data, while the latest values in the right column use the most recent Trailing Twelve Month (TTM) or Fiscal Quarter (FQ) data. The indicator displays color-coded price labels for each fair value estimate, showing the percentage upside or downside from the current price. Green indicates undervaluation, while red indicates overvaluation. The table follows a separate color logic:
REV-G, EBITDA-G, EPS-G, FCF-G = Green indicates positive annual growth when the CAGR is positive. Red indicates negative annual growth when the CAGR is negative.
EV/REV = Green indicates undervaluation when EV/REV ÷ REV-G is below 1. Red indicates overvaluation when EV/REV ÷ REV-G is above 2. Gray indicates fair value.
EV/EBITDA = Green indicates undervaluation when EV/EBITDA ÷ EBITDA-G is below 1. Red indicates overvaluation when EV/EBITDA ÷ EBITDA-G is above 2. Gray indicates fair value.
P/EPS = Green indicates undervaluation when P/EPS ÷ EPS-G is below 1. Red indicates overvaluation when P/EPS ÷ EPS-G is above 2. Gray indicates fair value.
EBITDA% = Green indicates profitable operations when the EBITDA margin is positive. Red indicates unprofitable operations when the EBITDA margin is negative.
FCF% = Green indicates strong cash conversion when FCF/EBITDA > 50%. Red indicates unsustainable FCF when FCF/EBITDA is negative. Gray indicates normal cash conversion.
ROIC = Green indicates value creation when ROIC > WACC. Red indicates value destruction when ROIC is negative. Gray indicates positive but insufficient returns.
ND/EBITDA = Green indicates low leverage when ND/EBITDA is below 1. Red indicates high leverage when ND/EBITDA is above 3. Gray indicates moderate leverage.
YIELD = Green indicates positive shareholder return when Shareholder Yield > 1%. Red indicates negative shareholder return when Shareholder Yield < -1%.
The Return on Invested Capital (ROIC) is calculated as EBIT × (1 - Average Tax Rate) ÷ (Average Debt + Average Equity - Average Cash). Shareholder Yield (YIELD) is calculated as the CAGR of Dividend Yield - Change in Shares Outstanding. The Weighted Average Cost of Capital (WACC) is displayed at the top left of the table and is derived from the current Market Cap (MC), Debt, Cost of Equity, and Cost of Debt. The Cost of Equity is calculated using the Equity Beta, Index Return, and Risk-Free Rate, which are based on the selected country. The Equity Beta (β) is calculated as the 5-year Blume-adjusted beta between the weekly logarithmic returns of the underlying stock and the selected country’s stock market index. For accurate calculations, it is recommended to use the stock ticker listed on the primary exchange corresponding to the company’s main index.
Cost of Debt = (Interest Expense on Debt ÷ Average Debt) × (1 - Average Tax Rate)
Cost of Equity = Risk-Free Rate + Equity Beta (β) × (Index Return - Risk-Free Rate)
WACC = (MC ÷ (MC + Debt)) × Cost of Equity + (Debt ÷ (MC + Debt)) × Cost of Debt
This indicator works best for operationally stable and profitable companies that are primarily valued based on fundamentals rather than speculative growth, such as those in the industrial, consumer, technology, and healthcare sectors. It is less suitable for early-stage, unprofitable, or highly cyclical companies, including energy, real estate, and financial institutions, as these often have irregular cash flows or distorted balance sheets. It is also worth noting that TradingView’s financial data provider, FactSet, standardizes financial data from official company filings to align with a consistent accounting framework. While this improves comparability across companies, industries, and countries, it may also result in differences from officially reported figures.
In summary, the Intrinsic Value Analyzer is a comprehensive valuation tool designed to help long-term investors estimate a company’s fair value while comparing historical averages with the latest values. Fair value estimates are driven by growth forecasts, target multiples, and discount rates, and should always be interpreted within the context of the underlying assumptions. By default, the model applies historical averages and current discount rates, which may not accurately reflect future conditions. Investors are therefore encouraged to adjust inputs in the menu to better understand how changes in these key assumptions influence the company’s fair value.
Seasonality Heatmap [QuantAlgo]🟢 Overview
The Seasonality Heatmap analyzes years of historical data to reveal which months and weekdays have consistently produced gains or losses, displaying results through color-coded tables with statistical metrics like consistency scores (1-10 rating) and positive occurrence rates. By calculating average returns for each calendar month and day-of-week combination, it identifies recognizable seasonal patterns (such as which months or weekdays tend to rally versus decline) and synthesizes this into actionable buy low/sell high timing possibilities for strategic entries and exits. This helps traders and investors spot high-probability seasonal windows where assets have historically shown strength or weakness, enabling them to align positions with recurring bull and bear market patterns.
🟢 How It Works
1. Monthly Heatmap
How % Return is Calculated:
The indicator fetches monthly closing prices (or Open/High/Low based on user selection) and calculates the percentage change from the previous month:
(Current Month Price - Previous Month Price) / Previous Month Price × 100
Each cell in the heatmap represents one month's return in a specific year, creating a multi-year historical view
Colors indicate performance intensity: greener/brighter shades for higher positive returns, redder/brighter shades for larger negative returns
What Averages Mean:
The "Avg %" row displays the arithmetic mean of all historical returns for each calendar month (e.g., averaging all Januaries together, all Februaries together, etc.)
This metric identifies historically recurring patterns by showing which months have tended to rise or fall on average
Positive averages indicate months that have typically trended upward; negative averages indicate historically weaker months
Example: If April shows +18.56% average, it means April has averaged a 18.56% gain across all years analyzed
What Months Up % Mean:
Shows the percentage of historical occurrences where that month had a positive return (closed higher than the previous month)
Calculated as:
(Number of Months with Positive Returns / Total Months) × 100
Values above 50% indicate the month has been positive more often than negative; below 50% indicates more frequent negative months
Example: If October shows "64%", then 64% of all historical Octobers had positive returns
What Consistency Score Means:
A 1-10 rating that measures how predictable and stable a month's returns have been
Calculated using the coefficient of variation (standard deviation / mean) - lower variation = higher consistency
High scores (8-10, green): The month has shown relatively stable behavior with similar outcomes year-to-year
Medium scores (5-7, gray): Moderate consistency with some variability
Low scores (1-4, red): High variability with unpredictable behavior across different years
Example: A consistency score of 8/10 indicates the month has exhibited recognizable patterns with relatively low deviation
What Best Means:
Shows the highest percentage return achieved for that specific month, along with the year it occurred
Reveals the maximum observed upside and identifies outlier years with exceptional performance
Useful for understanding the range of possible outcomes beyond the average
Example: "Best: 2016: +131.90%" means the strongest January in the dataset was in 2016 with an 131.90% gain
What Worst Means:
Shows the most negative percentage return for that specific month, along with the year it occurred
Reveals maximum observed downside and helps understand the range of historical outcomes
Important for risk assessment even in months with positive averages
Example: "Worst: 2022: -26.86%" means the weakest January in the dataset was in 2022 with a 26.86% loss
2. Day-of-Week Heatmap
How % Return is Calculated:
Calculates the percentage change from the previous day's close to the current day's price (based on user's price source selection)
Returns are aggregated by day of the week within each calendar month (e.g., all Mondays in January, all Tuesdays in January, etc.)
Each cell shows the average performance for that specific day-month combination across all historical data
Formula:
(Current Day Price - Previous Day Close) / Previous Day Close × 100
What Averages Mean:
The "Avg %" row at the bottom aggregates all months together to show the overall average return for each weekday
Identifies broad weekly patterns across the entire dataset
Calculated by summing all daily returns for that weekday across all months and dividing by total observations
Example: If Monday shows +0.04%, Mondays have averaged a 0.04% change across all months in the dataset
What Days Up % Mean:
Shows the percentage of historical occurrences where that weekday had a positive return
Calculated as:
(Number of Positive Days / Total Days Observed) × 100
Values above 50% indicate the day has been positive more often than negative; below 50% indicates more frequent negative days
Example: If Fridays show "54%", then 54% of all Fridays in the dataset had positive returns
What Consistency Score Means:
A 1-10 rating measuring how stable that weekday's performance has been across different months
Based on the coefficient of variation of daily returns for that weekday across all 12 months
High scores (8-10, green): The weekday has shown relatively consistent behavior month-to-month
Medium scores (5-7, gray): Moderate consistency with some month-to-month variation
Low scores (1-4, red): High variability across months, with behavior differing significantly by calendar month
Example: A consistency score of 7/10 for Wednesdays means they have performed with moderate consistency throughout the year
What Best Means:
Shows which calendar month had the strongest average performance for that specific weekday
Identifies favorable day-month combinations based on historical data
Format shows the month abbreviation and the average return achieved
Example: "Best: Oct: +0.20%" means Mondays averaged +0.20% during October months in the dataset
What Worst Means:
Shows which calendar month had the weakest average performance for that specific weekday
Identifies historically challenging day-month combinations
Useful for understanding which month-weekday pairings have shown weaker performance
Example: "Worst: Sep: -0.35%" means Tuesdays averaged -0.35% during September months in the dataset
3. Optimal Timing Table/Summary Table
→ Best Month to BUY: Identifies the month with the lowest average return (most negative or least positive historically), representing periods where prices have historically been relatively lower
Based on the observation that buying during historically weaker months may position for subsequent recovery
Shows the month name, its average return, and color-coded performance
Example: If May shows -0.86% as "Best Month to BUY", it means May has historically averaged -0.86% in the analyzed period
→ Best Month to SELL: Identifies the month with the highest average return (most positive historically), representing periods where prices have historically been relatively higher
Based on historical strength patterns in that month
Example: If July shows +1.42% as "Best Month to SELL", it means July has historically averaged +1.42% gains
→ 2nd Best Month to BUY: The second-lowest performing month based on average returns
Provides an alternative timing option based on historical patterns
Offers flexibility for staged entries or when the primary month doesn't align with strategy
Example: Identifies the next-most favorable historical buying period
→ 2nd Best Month to SELL: The second-highest performing month based on average returns
Provides an alternative exit timing based on historical data
Useful for staged profit-taking or multiple exit opportunities
Identifies the secondary historical strength period
Note: The same logic applies to "Best Day to BUY/SELL" and "2nd Best Day to BUY/SELL" rows, which identify weekdays based on average daily performance across all months. Days with lowest averages are marked as buying opportunities (historically weaker days), while days with highest averages are marked for selling (historically stronger days).
🟢 Examples
Example 1: NVIDIA NASDAQ:NVDA - Strong May Pattern with High Consistency
Analyzing NVIDIA from 2015 onwards, the Monthly Heatmap reveals May averaging +15.84% with 82% of months being positive and a consistency score of 8/10 (green). December shows -1.69% average with only 40% of months positive and a low 1/10 consistency score (red). The Optimal Timing table identifies December as "Best Month to BUY" and May as "Best Month to SELL." A trader recognizes this high-probability May strength pattern and considers entering positions in late December when prices have historically been weaker, then taking profits in May when the seasonal tailwind typically peaks. The high consistency score in May (8/10) provides additional confidence that this pattern has been relatively stable year-over-year.
Example 2: Crypto Market Cap CRYPTOCAP:TOTALES - October Rally Pattern
An investor examining total crypto market capitalization notices September averaging -2.42% with 45% of months positive and 5/10 consistency, while October shows a dramatic shift with +16.69% average, 90% of months positive, and an exceptional 9/10 consistency score (blue). The Day-of-Week heatmap reveals Mondays averaging +0.40% with 54% positive days and 9/10 consistency (blue), while Thursdays show only +0.08% with 1/10 consistency (yellow). The investor uses this multi-layered analysis to develop a strategy: enter crypto positions on Thursdays during late September (combining the historically weak month with the less consistent weekday), then hold through October's historically strong period, considering exits on Mondays when intraweek strength has been most consistent.
Example 3: Solana BINANCE:SOLUSDT - Extreme January Seasonality
A cryptocurrency trader analyzing Solana observes an extraordinary January pattern: +59.57% average return with 60% of months positive and 8/10 consistency (teal), while May shows -9.75% average with only 33% of months positive and 6/10 consistency. August also displays strength at +59.50% average with 7/10 consistency. The Optimal Timing table confirms May as "Best Month to BUY" and January as "Best Month to SELL." The Day-of-Week data shows Sundays averaging +0.77% with 8/10 consistency (teal). The trader develops a seasonal rotation strategy: accumulate SOL positions during May weakness, hold through the historically strong January period (which has shown this extreme pattern with reasonable consistency), and specifically target Sunday exits when the weekday data shows the most recognizable strength pattern.
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.
Lorentzian Harmonic Flow - Temporal Market Dynamic Lorentzian Harmonic Flow - Temporal Market Dynamic (⚡LHF)
By: DskyzInvestments
What this is
LHF Pro is a research‑grade analytical instrument that models market time as a compressible medium , extracts directional flow in curved time using heavy‑tailed kernels, and consults a history‑based memory bank for context before synthesizing a final, bounded probabilistic score . It is not a mashup; each subsystem is mathematically coupled to a single clock (time dilation via gamma) and a single lens (Lorentzian heavy‑tailed weighting). This script is dense in logic (and therefore heavy) because it prioritizes rigor, interpretability, and visual clarity.
Intended use
Education and research. This tool expresses state recognition and regime context—not guarantees. It does not place orders. It is fully functional as published and contains no placeholders. Nothing herein is financial advice.
Why this is original and useful
Curved time: Markets do not move at a constant pace. LHF Pro computes a Lorentz‑style gamma (γ) from relative speed so its analytical windows contract when the tape accelerates and relax when it slows.
Heavy‑tailed lens: Lorentzian kernels weight information with fat tails to respect rare but consequential extremes (unlike Gaussian decay).
Memory of regimes: A K‑nearest‑neighbors engine works in a multi‑feature space using Lorentz kernels per dimension and exponential age fade , returning a memory bias (directional expectation) and assurance (confidence mass).
One ecosystem: Squeeze, TCI, flow, acceleration, and memory live on the same clock and blend into a single final_score —visualized and documented on the dashboard.
Cognitive map: A 2D heat map projects memory resonance by age and flow regime, making “where the past is speaking” visible.
Shadow portfolio metaphor: Neighbor outcomes act like tiny hypothetical positions whose weighted average forms an educational pressure gauge (no execution, purely didactic).
Mathematical framework (full transparency)
1) Returns, volatility, and speed‑of‑market
Log return: rₜ = ln(closeₜ / closeₜ₋₁)
Realized vol: rv = stdev(r, vol_len); vol‑of‑vol: burst = |rv − rv |
Speed‑of‑market (analog to c): c = c_multiplier × (EMA(rv) + 0.5 × EMA(burst) + ε)
2) Trend velocity and Lorentz gamma (time dilation)
Trend velocity: v = |close − close | / (vel_len × ATR)
Relative speed: v_rel = v / c
Gamma: γ = 1 / √(1 − v_rel²), stabilized by caps (e.g., ≤10)
Interpretation: γ > 1 compresses market time → use shorter effective windows.
3) Adaptive temporal scale
Adaptive length: L = base_len / γ^power (bounded for safety)
Harmonic horizons: Lₛ = L × short_ratio, Lₘ = L × mid_ratio, Lₗ = L × long_ratio
4) Lorentzian smoothing and Harmonic Flow
Kernel weight per lag i: wᵢ = 1 / (1 + (d/γ)²), d = i/L
Horizon baselines: lw_h = Σ wᵢ·price / Σ wᵢ
Z‑deviation: z_h = (close − lw_h)/ATR
Harmonic Flow (HFL): HFL = (w_short·zₛ + w_mid·zₘ + w_long·zₗ) / (w_short + w_mid + w_long)
5) Flow kinematics
Velocity: HFL_vel = HFL − HFL
Acceleration (curvature): HFL_acc = HFL − 2·HFL + HFL
6) Squeeze and temporal compression
Bollinger width vs Keltner width using L
Squeeze: BB_width < KC_width × squeeze_mult
Temporal Compression Index: TCI = base_len / L; TCI > 1 ⇒ compressed time
7) Entropy (regime complexity)
Shannon‑inspired proxy on |log returns| with numerical safeguards and smoothing. Higher entropy → more chaotic regime.
8) Memory bank and Lorentzian k‑NN
Feature vector (5D):
Outcomes stored: forward returns at H5, H13, H34
Per‑dimension similarity: k(Δ) = 1 / (1 + Δ²), weighted by user’s feature weights
Age fading: weight_age = mem_fade^age_bars
Neighbor score: sᵢ = similarityᵢ × weight_ageᵢ
Memory bias: mem_bias = Σ sᵢ·outcomeᵢ / Σ sᵢ
Assurance: mem_assurance = Σ sᵢ (confidence mass)
Normalization: mem_bias normalized by ATR and clamped into band
Shadow portfolio metaphor: neighbors behave like micro‑positions; their weighted net forward return becomes a continuous, adaptive expectation.
9) Blended score and breakout proxy
Blend factor: α_mem = 0.45 + 0.15 × (γ − 1)
Final score: final_score = (1−α_mem)·tanh(HFL / (flow_thr·1.5)) + α_mem·tanh(mem_bias_norm)
Breakout probability (bounded): energy = cap(TCI−1) + |HFL_acc|×k + cap(γ−1)×k + cap(mem_assurance)×k; breakout_prob = sigmoid(energy). Caps avoid runaway “100%” readings.
Inputs — every control, purpose, mechanics, and tuning
🔮 Lorentz Core
Auto‑Adapt (Vol/Entropy): On = L responds to γ and entropy (breathes with regime), Off = static testing.
Base Length: Calm‑market anchor horizon. Lower (21–28) for fast tapes; higher (55–89+) for slow.
Velocity Window (vel_len): Bars used in v. Shorter = more reactive γ; longer = steadier.
Volatility Window (vol_len): Bars used for rv/burst (c). Shorter = more sensitive c.
Speed‑of‑Market Multiplier (c_multiplier): Raises/lowers c. Lower values → easier γ spikes (more adaptation). Aim for strong trends to peak around γ ≈ 2–4.
Gamma Compression Power: Exponent of γ in L. <1 softens; >1 amplifies adaptation swings.
Max Kernel Span: Upper bound on smoothing loop (quality vs CPU).
🎼 Harmonic Flow
Short/Mid/Long Horizon Ratios: Partition L into fast/medium/slow views. Smaller short_ratio → faster reaction; larger long_ratio → sturdier bias.
Weights (w_short/w_mid/w_long): Governs HFL blend. Higher w_short → nimble; higher w_long → stable.
📈 Signals
Squeeze Strictness: Threshold for BB1 = compressed (coiled spring); <1 = dilated.
v/c: Relative speed; near 1 denotes extreme pacing. Diagnostic only.
Entropy: Regime complexity; high entropy suggests caution, smaller size, or waiting for order to return.
HFL: Curved‑time directional flow; sign and magnitude are the instantaneous bias.
HFL_acc: Curvature; spikes often accompany regime ignition post‑squeeze.
Mem Bias: Directional expectation from historical analogs (ATR‑normalized, bounded). Aligns or conflicts with HFL.
Assurance: Confidence mass from neighbors; higher → more reliable memory bias.
Squeeze: ON/RELEASE/OFF from BB
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
Volume v4 (Dollar Value) by Koenigsegg📊 Volume v3 (Dollar Value) by Koenigsegg
🎯 Purpose:
Volume v3 (Dollar Value) by Koenigsegg transforms traditional raw-unit volume into dollar-denominated volume, revealing how much money actually flows through each candle.
Instead of measuring how many coins or contracts were traded, this version calculates the total traded value = volume × average price (hlc3), allowing traders to visually assess capital intensity and market participation within each move.
⚙️ Core Features
- Converts raw volume into USD-based traded value for each candle.
- Color-coded bars show bullish (green/teal) vs. bearish (red) activity.
- Built-in SMA and SMMA overlays highlight sustained shifts in value flow.
- Designed for visual clarity to support momentum, exhaustion, and divergence studies.
📖 How to Read It
Rising Dollar Volume — indicates growing market participation and strong capital flow, often aligning with impulsive waves in trend direction.
Falling Dollar Volume — signals waning interest or reduced participation, potentially hinting at correction or exhaustion phases.
Comparing Legs — when price makes new highs/lows but dollar volume weakens, it can reveal divergences between price movement and actual capital commitment.
SMA / SMMA Lines — use them to identify longer-term accumulation or depletion of market activity, separating short bursts from sustained inflows or outflows.
The goal is to visualize the strength of market moves in terms of capital energy, not just tick activity. This distinction helps traders interpret whether a trend is being driven by genuine money flow or low-liquidity drift.
⚠️ Disclaimer
This script is provided for research and educational purposes only.
It does not constitute financial advice, investment recommendations, or trading signals.
Always conduct your own analysis and manage your own risk when trading live markets.
The author accepts no liability for financial losses incurred from use of this tool.
🧠 Credits
Developed and published by Koenigsegg.
Written in Pine Script® v6, fully compliant with TradingView’s House Rules for Pine Scripts.
Licensed under the Mozilla Public License 2.0.
Khosro XAUUSD Strategy [TradingFinder] Trading Room Hunter Setup🔵 Introduction
The Trading Room Hunter (TRH) strategy is an analytical model based on the Smart Money Concept, developed by Khosro, an Iranian international trader based in Dubai. This approach is built upon a deep understanding of liquidity engineering, market structure shifts, and institutional order flow. Its core objective is to identify the so-called TRH Zone, the area where market liquidity gets trapped and institutional investors begin accumulating positions. Unlike traditional indicator-based methods, the TRH Zone focuses purely on price behavior and supply & demand dynamics to pinpoint the most precise reversal zones in the market.
Within Smart Money logic, every impulsive move in price results from the displacement or absorption of liquidity in a specific range. In the TRH model, the last pivot preceding the impulsive move (Origin Pivot) is defined as the Distal Line, and the Break Candle, which disrupts the market structure, forms the Proximal Line. The area between these two points defines the Trading Room Hunter Zone, a reaction zone where price, after creating a displacement or Break of Structure (BoS), often returns to fill an imbalance and provide a precision entry opportunity.
In essence, the TRH Zone is the region where smart money seeks re-entry after a liquidity sweep and a confirmed CHoCH or BoS. It frequently lies between supply/demand boundaries and fair value gaps (FVGs), forming one of the strongest decision-making frameworks within modern price-action theory. Due to its structural accuracy, the TRH setup can also function as a Set & Forget Setup, where the trader defines the zone, places a limit order, and lets the market naturally react, eliminating emotional decision-making and allowing for automated execution aligned with institutional logic.
🔵 How to Use
In the TRH strategy, entries are taken based on price returning to the area between the last impulsive pivot and the break candle. This range (the TRH Zone) represents the region where liquidity from the previous move remains concentrated. Before continuing its main direction, price often revisits this zone to fill imbalances or mitigate unfilled orders. The logic is simple: every explosive move originates from a point where large orders were executed, and TRH precisely highlights that institutional footprint.
🟣 Bullish Setup
When the market breaks a structural high after a strong bearish leg, liquidity shifts from sellers to buyers. The last bearish candle before the breakout marks the origin of the bullish move, and the zone between that candle and the break candle becomes the smart-money entry area. As price revisits this zone and signs of exhaustion in selling pressure appear, that’s the optimal point for a long position. Stop-loss is placed slightly below the origin pivot, and targets are set at the next supply zone or upper liquidity pool.
🟣 Bearish Setup
Conversely, when the market breaks a structural low after a sharp bullish leg, liquidity transitions from buyers to sellers. The last bullish candle before the drop is identified as the origin pivot, while the bearish break candle defines the lower boundary of the zone. The range between these two points forms the TRH Supply Zone, where late buyers are trapped and fresh institutional selling begins. As price retraces into this zone, short entries can be placed near the upper boundary, with stops above the pivot and targets toward the next liquidity pool below.
Because of its structural precision and clearly defined reaction behavior, TRH is one of the most effective Set & Forget setups in Smart Money trading. Simply mark the zone, place your order, and let the market do the rest.
🔵Setting
🟣 Spike Filter | Movement
Minimum Spike Bars : Defines the minimum number of consecutive candles required for a valid spike.
Movement Power : Enables or disables the momentum-based spike filter.
Movement Power Level : Sets the strength threshold; higher values filter out weaker moves and only detect strong spikes.
Pivot Period : Defines the lookback range used to detect swing highs and swing lows in market structure. A higher value smooths out smaller fluctuations and focuses on major pivots, while a lower value increases sensitivity and identifies minor turning points more frequently.
🟣 Position Management
Stop-Loss Threshold : Enables or disables the stop-loss threshold feature.
Stop-Loss Threshold Value : Defines the value of the stop-loss threshold for risk management.
Risk-Reward Ratio : Sets the desired risk-to-reward ratio (e.g., 1:1 or 1:2).
Wide Zone Filter : Filters out zones that exceed a defined width threshold, preventing detection of overly broad TRH areas.
🟣 Display Settings
Display Mode : Chooses between Setup (showing setups) or Signal (showing trade signals).
Show Entry Levels : Displays entry levels on the chart (buy/sell zones) when enabled
Only Display the Last Position : Displays only the most recent position on the chart when enabled.
Setup Width Drawing : Adjusts the visual width of the setup drawings on the chart for better visibility.
🔵 Conclusion
The TRH strategy is a precise structural model of liquidity flow that identifies zones where smart money is most likely to enter and where price is most likely to react. By combining the Origin Pivot and Break Candle, TRH isolates the key areas that drive institutional order flow. Without relying on indicators, it focuses purely on price structure, making it highly effective for both reactive entries and Set & Forget setups.
Ultimately, TRH creates a balance between market structure and liquidity flow, enabling traders to identify institutional decision zones on the chart with minimal risk and maximum clarity
Directional Indicator Crossovers v1[JopAlgo]Directional Indicator Crossovers v1 — the classic DMI, made clearer and easier to act on
We'd like to introduce you to a more relaxed, streamlined version of DI. While it may not seem like it at first glance, we've taken the D+/D- method as a starting point and developed our own version of this indicator: two lines, a smooth green/red field indicating who's in control, and clear crossover alerts for a flip. We deliberately chose the step line representation because it closely matches the candlestick patterns on the chart. Designed to help you react faster—without clutter.
What you’ll see
+DI (green) and −DI (red) using classic Wilder smoothing.
A soft control zone between the lines: green when +DI dominates, red when −DI dominates.
Crossover alerts (no labels, no background flooding)—just the turning points.
Why this helps
Instant bias: the shaded field tells you who’s in control without reading values.
Cleaner execution: minimal visuals keep focus on the handoff (+DI↔−DI) and your price levels.
Actionable by design: built-in alerts fire right at the flip to route into your workflow.
How to read it
Bias: Green zone → buyers lead. Red zone → sellers lead.
Trigger: Consider entries on the DI crossover that aligns with your higher-timeframe context (trend, S/R, OB).
Patience in chop: If flips are frequent in tight ranges, wait for sustained zone dominance or confirm on a higher TF.
Exit/flip: Opposite crossover or a clear loss of dominance.
Settings that matter
DI Length (default 14): Higher = calmer, fewer flips. Lower = faster, more signals.
Visuals: Keep the control zone on for quick reads; hide crossover marks if you prefer pure lines.
Alerts: Enable bullish and bearish DI cross alerts; connect to notifications or webhooks as needed.
Starter presets
Intraday (15m–1H): DI Length 12–14 for quicker handoffs.
Swing (4H–1D): DI Length 14–20 for cleaner signals.
Choppy assets: Nudge length higher to dampen noise.
Where it shines (and limits)
Best: Liquid markets (crypto majors, indices, large caps) where handoffs matter.
Works elsewhere: Still useful on slower pairs; extend length for stability.
Limit: Frequent flips in low-range sessions—pair with HTF bias or structure.
Alerts included
Bullish DI Crossover: +DI crosses above −DI.
Bearish DI Crossover: −DI crosses above +DI.
Attribution & License
Built on the Directional Movement Index concept by J. Welles Wilder Jr. (1978).
Independent Pine v6 implementation (not derived from TradingView’s built-in source).
Released as Open Source (MPL-2.0)—please keep the license header intact.
Disclaimer
For educational purposes only; not financial advice. Trading involves risk. Test first, use clear levels, and manage risk. This project is independent and not affiliated with or endorsed by TradingView.
India VIX Based Nifty/BankNifty Range Calculator (Auto Fetch)VIX-Based Expected Daily Range (Auto Volatility Forecast)
Created by: Harshiv Symposium
📖 Purpose
This indicator automatically fetches the India VIX value and calculates the expected daily price range for major Indian indices such as Nifty and BankNifty.
It helps traders understand how much the market is likely to move today based on current volatility conditions.
Designed for educational and analytical awareness, not for signals or profit-making systems.
⚙️ Core Logic
Expected Daily Move (Range) = (India VIX × Current Index Price) ÷ Multiplier
- Multiplier for Nifty: 1000
- Multiplier for BankNifty: 700
This calculation projects the 1-standard-deviation (≈ 68% probability) and 2-standard-deviation (≈ 95% probability) movement zones for the day.
📊 Example
If India VIX = 15 and Nifty = 25,000:
Expected Move ≈ (15 × 25,000) ÷ 1000 = 375 points
Hence,
- 68% Range: 24,625 – 25,375
- 95% Range: 24,250 – 25,750
This gives traders a realistic idea of daily volatility boundaries.
🧭 Key Features
✅ Auto-Fetch India VIX
No need for manual input — automatically pulls live data from NSE:INDIAVIX.
✅ Dynamic Range Visualization
Plots upper/lower boundaries for 1σ and 2σ probability zones with shaded expected-move area.
✅ Dashboard Panel
Displays:
- Current VIX
- Expected Move (in points and %)
- Upper and Lower Ranges
✅ Smart Alerts
Alerts when price crosses upper or lower volatility range — potential breakout signal.
🎯 How It Helps
Intraday Traders:
Know the likely daily movement (e.g., ±220 pts on Nifty) and plan realistic targets or stops.
Options Traders:
Quickly assess whether it’s a seller-friendly (low VIX, small range) or buyer-friendly (high VIX, large range) session.
Risk Managers:
Use volatility context for stop-loss width and position sizing.
Breakout Traders:
If price breaks beyond the 2σ range → indicates potential volatility expansion.
💡 Interpretation Guide
Condition Market Behavior Strategy Insight
VIX ↓ ( < 14 ) Calm / Range-bound Option Selling Edge
VIX ↑ ( > 20 ) Volatile Sessions Option Buying Edge
Price within Range Stable Market Mean Reversion Setups
Price breaks Range Volatility Expansion Breakout Trades
⚠️ Disclaimer
This indicator is for educational and awareness purposes only.
It does not generate buy/sell signals or guarantee returns.
Always apply your own analysis and risk management.
SSMT [TakingProphets]SSMT (Sequential SMT) — multi-cycle intermarket divergence with quarter-based timing
Purpose
Informational overlay that detects intermarket SMT divergences between the chart symbol and a user-selected correlated symbol. It does not generate buy/sell signals and is not financial advice. Use it to structure analysis and alerts, not to automate trades.
What it does
Scans for SMT on five coordinated cycles: Micro, 90-Minute, Daily (Q1–Q4), Weekly, Monthly.
Draws anchored lines and labels where divergences occur and keeps them after the period ends so you can use historical SMTs as context.
Offers per-cycle alerts (high-side/bearish, low-side/bullish).
Optional session/quarter boxes for timing context.
Time base uses America/New_York to align with common session conventions (with a 17:00–18:00 ET pause guard for CME instruments).
Why these modules belong together (more than a mashup)
All cycles share a single time-partitioning framework (quarters/sessions → day → week → month). That common clock means:
Comparability: divergences on Micro/90m/D/W/M are directly comparable because they’re computed with the same boundaries for both instruments.
Sequencing: higher-cycle context can gate lower-cycle events (e.g., a Daily Q3 divergence framing how you treat a Micro divergence).
Persistence: drawings retain the cycle identity (e.g., , ) so prior signals remain interpretable as the market progresses.
This is a coherent engine—not separate indicators pasted together—because detection, labeling, alerts, and persistence are all driven by the same quarter/period state machine.
How it works (high-level mechanics)
Time partitioning
Daily quarters (ET):
Q1: 18:00–00:00
Q2: 00:00–06:00
Q3: 06:00–12:00
Q4: 12:00–18:00
90-Minute cycle: four 90-minute blocks inside the active session.
Micro cycle: finer 20–22 minute blocks inside the session for granular timing.
Weekly/Monthly: tracked by calendar periods (Mon–Fri, and calendar month).
Pause guard: 17:00–18:00 ET to avoid false transitions during CME’s daily maintenance window.
State tracking (per cycle)
Tracks previous vs. current highs/lows for the chart symbol and the correlated symbol (fetched at the same timeframe).
Maintains cycle IDs (e.g., year*100 + weekofyear for weekly) so drawings remain tied to the originating period.
Divergence condition (SMT)
High-side (bearish): one instrument makes a higher high vs. its previous period while the other does not.
Low-side (bullish): one instrument makes a lower low vs. its previous period while the other does not.
When detected, the script plots a labeled span/line (e.g., SSMT w/ES) and records it for persistence.
Alerts
Two per cycle: High-side (bearish) and Low-side (bullish).
Fire on the bar where the condition first becomes true.
Inputs & customization
Correlated symbol (default can be an index future).
Cycle toggles: Micro, 90m, Daily (Q1–Q4), Weekly, Monthly.
Styling: line color/width, label text/size.
Session/quarter boxes: on/off.
Alerts: per-cycle SMT events on/off.
How to use
Add the indicator to your chart (e.g., NQ, ES) and select a correlated symbol.
Turn on the cycles you want to monitor; optionally enable quarter/session boxes.
Interpret SMTs by side:
High-side (bearish): chart makes HH, correlated does not.
Low-side (bullish): chart makes LL, correlated does not.
Set alerts for the cycles that matter to your workflow.
Combine with your higher-timeframe narrative and risk rules.
Repainting, timing, and limitations
Uses higher-timeframe data without look-ahead; values can update intrabar until the period closes.
SMTs may form and resolve within a period; conservative users may wait for period close.
Assumes America/New_York timing; very thin markets may yield fewer or noisier signals.
SMT quality depends on the benchmark you select; correlations vary across regimes.
Educational tool only. No performance claims; not a signal generator.
Originality & scope (for protected/invite-only publications)
A multi-cycle SMT engine built on a shared quarter/period state machine (Micro → 90m → Daily Q1–Q4 → Weekly → Monthly).
Quarter-aware persistence keeps divergence drawings tied to their source cycle for durable context.
CME pause handling and stable calendar IDs make detections consistent across sessions and rollovers.
Implements SMT through extremum sequencing and cross-instrument comparison rather than wrapping generic divergence indicators.
CRT [TakingProphets]CRT (Candle Range Theory) — HTF context overlay with alerts
Purpose
Informational overlay to structure higher-timeframe (HTF) context. It does not generate buy/sell signals and is not financial advice. Use it to organize analysis and alerts—not to automate trades.
What it does
Projects HTF candles (1m → 1M) on any lower timeframe so the big picture stays on the chart.
Detects CRT transitions on the HTF (bullish/bearish “failed continuation” pattern).
Evaluates SMT divergence vs. a user-selected correlated instrument on the same HTF (historical & real-time).
Extends live HTF Open/High/Low/Close as developing reference levels.
Concepts (what it looks for)
Candle Range Theory (CRT) — a 3-bar HTF pattern where candle 2 fails to continue candle 1’s move:
Bearish CRT: candle 2 trades above candle 1’s high but closes back inside candle 1’s range and does not break its low.
Bullish CRT: candle 2 trades below candle 1’s low but closes back inside candle 1’s range and does not break its high.
SMT divergence (intermarket) — compares HTF swing extremes between the chart symbol and a correlated symbol:
Bearish SMT: one makes a higher high while the other does not.
Bullish SMT: one makes a lower low while the other does not.
Checked in two modes: historical (between the two last closed HTF bars) and real-time (last closed vs. current forming HTF bar).
How the elements work together (integration, not a mashup)
All modules share one HTF time base, so annotations describe the same segment of price action. The overlay produces an explicit context state by sequencing the modules in this order:
HTF Projection → Structural Frame
The last three HTF candles are drawn (bodies+wicks). This creates the “canvas” the rest of the logic references (ranges, highs/lows, and time boundaries).
CRT Test → Directional Bias Candidate
The script evaluates the 3-bar CRT conditions on those exact HTF candles (not lower-TF approximations).
If conditions are forming on the current HTF bar, status is CRT Forming.
If they complete on the close, status becomes CRT Confirmed (Bullish/Bearish).
SMT Check → Confirmation/Stress-Test on the Same HTF
Using the same HTF window, the tool compares swing progress with the correlated symbol.
Historical SMT comments on whether the prior HTF segment’s push had intermarket agreement.
Real-time SMT comments on the current forming push.
This lets you confirm a CRT bias (e.g., Bearish CRT + Bearish SMT) or challenge it (e.g., Bullish CRT but Bearish SMT).
Live HTF OHLC → Actionable Reference Levels
The current HTF Open/High/Low/Close are extended as levels. These are the decision rails you’ll typically use to judge follow-through, failure, mitigation, or targets in the same CRT/SMT context.
Resulting context states (what you’ll see in alerts/labels):
Neutral (no CRT; SMT may still inform context).
CRT Forming (monitor): HTF push is underway; watch real-time SMT into HTF High/Low/Close projections.
CRT Confirmed (bias): HTF failure pattern locked; use projections as reference for continuation/invalidations.
CRT + SMT Aligned (confluence): CRT direction agrees with SMT; strongest context.
CRT vs. SMT Mixed (caution): bias exists but intermarket is disagreeing; treat levels as potential fade zones.
Why this is not a mashup
Every module is computed and plotted in the same HTF coordinate system, so signals are about one thing: the current HTF segment.
CRT provides the bias hypothesis, SMT provides a cross-market test of that hypothesis in the same window, and live OHLC projections supply the exact levels used to act on or fade that hypothesis.
Alerts are tied to state transitions (e.g., CRT forming → confirmed; SMT flip), not to unrelated features.
Mechanics (high-level)
HTF Projection: pulls HTF OHLC/time for the last three HTF bars and renders body boxes + wicks; optional time labels adapt to intraday vs D/W/M.
CRT Labels: when the three-bar conditions are met, prints BULLISH CRT or BEARISH CRT on the HTF stack.
SMT Lines: draws labeled diagonals across the relevant HTF pair for historical and real-time checks using your correlated symbol.
Live Levels: extends the current HTF Open/High/Low/Close horizontally; anchors are deterministic (Open = first bar, High/Low = first occurrence, Close = current bar).
Inputs & customization
HTF timeframe: 1m–1M.
Display: candle width/opacity, borders/wicks, time labels (12h/24h).
SMT: enable/disable, correlated symbol, line style/width, optional labels.
Projections: enable/disable, left extension (bars), per-level styling and price labels.
Alerts: switches for CRT, SMT-historical, SMT-real-time.
Alerts (workflow prompts)
Bullish/Bearish CRT detected on the selected HTF.
Bullish/Bearish SMT (historical) between the two last closed HTF bars.
Bullish/Bearish SMT (real-time) between the last closed and current forming HTF bar.
Suggested text includes the HTF and current context state so you know if CRT and SMT are aligned or mixed.
Example use
Bearish scenario: A Bearish CRT confirms on the 4H; soon after, real-time SMT (bearish) appears while price probes the projected 4H High. Context = CRT + SMT Aligned → treat the projected Open/Close as near-term objectives.
Mixed scenario: A Bullish CRT forms on 1H, but historical SMT (bearish) printed in the prior segment. Context = Mixed → continue to monitor real-time SMT and projected Low for possible invalidation.
Notes & limitations
HTF values are provisional until the HTF bar closes; labels/lines can update while forming.
SMT depends on the correlated symbol you select; relationships vary by market/regime.
Session gaps/illiquid hours can distort extremes and time labels.
Educational tool: no performance claims, no entry/exit signals.
Originality & scope (for protected/invite-only publications)
A unified HTF projection → CRT test → SMT check → live level pipeline that yields explicit context states instead of separate, unrelated overlays.
Formal CRT detection performed on actual HTF bars (not lower-TF approximations).
Dual-mode SMT tied to the same HTF windows (historical + real-time), plotted as labeled span lines.
Deterministic OHLC projection (first-occurrence anchoring) to align decisions with the identified context.
Attribution: CRT/SMT concepts inspired by ICT. Design, implementation, and alert framework by TakingProphets.
Prophet Model [TakingProphets]The Prophet Model — context pipeline (HTF PDA → Sweep → CISD → EPE) with dynamic risk
Purpose
Informational overlay for organizing institutional context in real time. It does not issue buy/sell signals and is not financial advice. Use it to structure analysis and checklist-driven execution—not to automate decisions.
What it does (modules at a glance)
Projects HTF PD Arrays (FVGs) onto your current chart and maintains only the nearest active array.
Validates directional bias using Candle Range Theory (CRT) on the same HTF.
Tracks Liquidity Sweeps (BSL/SSL) on HTF-aware pivots.
Confirms Change in State of Delivery (CISD) via displacement after a sweep.
Optionally refines entries with EPE when a local (internal) imbalance forms right after CISD.
Derives dynamic TP/BE/SL from measured displacement and recent extremes (not fixed distances).
Keeps a rules checklist (PDA tap → CRT → Sweep → CISD) and a relationships table (common HTF↔LTF pairings) to enforce process.
How it works (integration, not a mashup)
The modules are sequenced on one HTF time base so each step gates the next:
HTF PD Arrays (context zone). The model identifies valid HTF FVGs, filters tiny/weekend gaps, removes arrays that are invalidated by clean trades-through, and persists only the nearest PDA. This focuses attention on the institutional zone most likely to matter now.
CRT (directional gating). CRT on the same HTF establishes a provisional bias. No entries are implied; CRT simply permits or forbids the following steps. If CRT disagrees with the PDA context, the checklist remains incomplete.
Liquidity Sweep (event). The model tracks HTF-aware BSL/SSL pivots. A sweep only “counts” if it occurs in relation to the active PDA (tap/engagement). This prevents generic swing-high/low tags from triggering downstream logic.
CISD (confirmation). After a qualified sweep, the tool looks for displacement through the sequence open (the open of the impulsive leg beginning at or immediately after the sweep). Crossing that threshold confirms CISD, which marks a structural delivery shift consistent with the CRT bias.
EPE (refinement, optional). Immediately following CISD, the model scans for a fresh internal imbalance. If found quickly, it promotes that price area as the Easiest Point of Entry (EPE) and relabels the reference. If not, the CISD level remains primary.
Dynamic risk levels. TP/BE/SL are derived from the measured displacement around the CISD leg (e.g., BE ≈ 1× leg, TP ≈ 2.25× stretch; SL aligned to nearby structural extremes rather than a fixed pip offset). Levels update with structure and can display prices.
By chaining PDA → CRT → Sweep → CISD → (EPE) → Risk on a single HTF backbone, the tool creates a coherent workflow where later signals simply do not appear without earlier context. That’s why this is not a bundle of independent features: each module’s output is another module’s input.
Concepts & operational rules (high level)
HTF PD Arrays (FVGs)
Uses a standard three-candle gap definition on the chosen HTF, with filters for weekend/tiny gaps.
Inverse mitigation: if price trades cleanly through an array, the box is removed and internal state resets.
Nearest-PDA persistence: when multiple arrays exist, only the closest remains visible to reduce clutter.
Optional right-extension draws lingering influence X bars forward.
Candle Range Theory (CRT)
Bullish CRT: candle 2 wicks below candle 1’s low but closes back inside candle 1’s range, without taking its high.
Bearish CRT: candle 2 wicks above candle 1’s high but closes back inside candle 1’s range, without taking its low.
Role: bias validation paired to CISD when alignments match the active PDA.
Liquidity Sweeps (BSL/SSL)
Tracks candidate HTF pivots as buy-/sell-side liquidity.
A sweep registers when price takes a tracked pivot in the vicinity of the active PDA.
CISD (Change in State of Delivery)
Finds the sequence open for the impulsive leg that begins at/after the sweep.
Bearish path (after BSL sweep): CISD when close < sequence-open.
Bullish path (after SSL sweep): CISD when close > sequence-open.
On confirmation, the model plots a CISD line, checks the box in the Strategy Checklist, and triggers risk calc.
EPE (Easiest Point of Entry)
Within a short window after CISD, scans for a local imbalance; if present, promotes that level as EPE.
If no imbalance forms, CISD remains the operative reference.
Dynamic TP / BE / SL
Built from the measured leg around CISD (not fixed pip steps).
Approximate geometry: BE ≈ 1× leg, TP ≈ 2.25× leg; SL respects nearby structural extremes.
Labels and price markers are optional.
Architecture notes
Maps the current chart to a higher timeframe (e.g., 15s→M5, M1→M15, M5→H1, M15→H4, H1→D, H4→W, D→M).
Retrieves HTF OHLC/time with no lookahead so structures update intrabar until the HTF bar closes.
Periodic cleanup clears obsolete lines/labels/boxes to keep charts responsive.
Inputs (summary)
FVGs/PD Arrays: show/hide, colors, borders, label size, right-extension, nearest-only toggle.
CRT: enable/disable, label style.
Sweeps/CISD/EPE: enable/disable, line/label styles, EPE window.
Risk Levels (TP/BE/SL): enable each, price labels on/off, colors.
Tables/Checklist: strategy checklist on/off; relationships table (common HTF↔LTF pairings); text sizes and header colors.
Alerts (optional)
You may add alertconditions aligned with these events in your own workspace:
HTF PDA tap (bullish/bearish box)
CRT detected (bullish/bearish)
CISD confirmed (bullish/bearish)
EPE set/updated
Example messages:
“Prophet: CISD confirmed on {{ticker}} / {{interval}}”
“Prophet: EPE refined at {{close}} ({{time}})”
Notes & limitations
HTF values are provisional until the HTF bar closes; labels/levels can update while forming.
CISD/EPE are live conditions; they can form and later invalidate within the same HTF bar.
Liquidity relationships vary by market/regime; thin sessions and large gaps can affect clarity.
Educational tool only. No performance claims; no trade signals.
Originality & scope (for protected/invite-only publications)
A single HTF-synchronized engine sequences PDA → CRT → Sweep → CISD → (EPE) and withholds later steps unless prerequisites are met.
Nearest-PDA persistence and inverse-mitigation enforce focus on the most relevant institutional zone.
Displacement-based risk math ties TP/BE/SL to structure instead of static offsets.
Checklist + relationships table promote consistent, rules-first behavior and reduce discretionary drift.
Attribution: Concepts inspired by ICT (PD arrays/FVGs, CRT, sweeps, displacement, refined entries). Design, integration logic, and risk framework by TakingProphets.
HTF Candles [TakingProphets]HTF Candles — higher-timeframe structure, SMT divergence, and live OHLC projections
Purpose
Informational overlay to keep higher-timeframe (HTF) context visible on a lower-timeframe chart. It does not generate buy/sell signals and is not financial advice. Use it to structure analysis and alerts, not to automate trading.
What it does
HTF candle visualization (up to 10 candles, optional right-side offset) with bodies, wicks, and time labels.
SMT divergence checks on the chosen HTF—both historical (last two completed HTF bars) and real-time (last closed vs. current forming bar) vs. a user-selected correlated symbol (default can be an index future).
Live HTF OHLC projections: forward-extending Open / High / Low / Close from the current HTF bar with optional price labels and styling.
HTF close timer (optional) to show when the active HTF candle ends.
Why these modules belong together (more than a mashup)
This overlay uses one HTF time base to align three lenses of the same context:
Candle projection provides the structural frame (ranges and bodies of true HTF bars).
SMT divergence provides intermarket confirmation/invalidations on that same HTF, so the divergence you see is directly comparable to the projected candles.
Live OHLC projections turn the current HTF bar’s evolving state into concrete reference levels for intraday decisions.
Because all three share the same HTF clock and data source, alerts and drawings change together when the HTF state actually changes. The intent is a coherent workflow tool where each module gates the others (structure → confirmation → actionable references), rather than separate indicators merely co-plotted.
How it works (high-level)
Timeframe mapping & data
You choose an HTF (1m–1M). The script retrieves HTF OHLC/time without look-ahead. Objects update intrabar until the HTF bar closes.
Candle rendering
Up to 10 recent HTF candles are drawn as body boxes with wicks.
A horizontal offset/spacing option places the stack right of the current price for clarity.
Visuals (colors, transparency, borders, wick width, label size/format 12h/24h) are configurable.
SMT divergence (historical & real-time)
Compares HTF highs/lows of your chart vs. a correlated symbol using the same HTF.
Bearish SMT (high-side): one makes a higher high while the other does not.
Bullish SMT (low-side): one makes a lower low while the other does not.
Historical mode compares HTF → HTF ; real-time mode compares HTF → HTF as the current HTF bar forms.
Optional lines/labels mark where the divergence is detected.
Live OHLC projections
Extends the current HTF Open / High / Low / Close forward as horizontal lines.
Anchors: Open = first bar of the HTF period; High/Low = first occurrence of each extreme inside the period; Close = current bar.
Each level has independent toggles for price labels, style, and width.
Alerts (workflow prompts)
Bullish SMT, Bearish SMT, Bullish Real-time SMT, Bearish Real-time SMT.
Fire on the bar where the condition first becomes true.
Inputs & customization
Timeframe: select HTF (1m–1M).
Display: number of candles (1–10), right-offset, candle width, transparency, time labels on/off (12h/24h), label size, HTF close timer on/off.
Visuals: bullish/bearish body colors, border color, wick color.
SMT: enable/disable, correlated symbol, line style/width, labels on/off, alerts on/off.
Projections: enable/disable, per-level toggles (Open/High/Low/Close), color/style/width, optional price labels.
Notes & limitations
HTF values are provisional until the HTF bar closes; lines/labels can update during formation.
SMT usefulness depends on the correlated symbol you select; relationships vary by market/regime.
Session gaps/low liquidity can affect extremes and time labels.
Educational tool only. No performance claims and no trade signals.
Originality & scope (for protected/invite-only publications)
A single HTF-synchronized engine: candle projection, dual-mode SMT, and live OHLC projections all computed from the same HTF series to ensure consistent timing and interpretation.
Real-time SMT explicitly ties the developing HTF bar to the prior closed bar, reducing ambiguity vs. generic divergence checks.
Projection anchoring (first-occurrence rules for H/L, period start for Open, current bar for Close) provides deterministic, reproducible reference levels.
ZLSMA_CEThis indicator combines the power of Chandelier Exit and Zero Lag LSMA (ZLSMA) to provide cleaner trend reversals and early entry alerts.
The Chandelier Exit acts as a dynamic stop-loss and trend tracker based on ATR, while ZLSMA smooths price movement with minimal lag — helping traders identify trend continuation or reversals more accurately.
When combined, this system provides visual and alert-based Buy/Sell signals that can be used for both swing and intraday strategies.
ZarzaZarza All-in-One Indicator for God’s Kingdom
“But remember the Lord your God, for it is He who gives you the power to get wealth, that He may establish His covenant.” — Deuteronomy 8:18
The Zarza All-in-One Indicator is more than a trading tool — it’s a divinely inspired system designed to help Kingdom traders operate with clarity, discipline, and spiritual alignment in the markets.
Built to detect momentum shifts, liquidity zones, reversals, and smart-money movements, this indicator brings together the best of technical precision and prophetic purpose.
This isn’t just about charts — it’s about stewardship.
Every trade is an act of faith and discernment, partnering with Heaven’s wisdom to prepare for the great wealth transfer that will fund God’s Kingdom projects and reach souls across the nations.
Moon Phases Long/Short StrategyThis is an experiment of Moon Phases, likely buy when full moon and sell when new moon with few changes, like it would buy a day ahead or sometimes sell a day post these events, with Stop loss and take profits, 50% profitable so sounds good to me
Long only good for bitcoin gold, both modes(L+S) better for stocks and alt coins
Rupeebees Active Option Levels V4This indicator helps you understand the nature of Active options in relationship each other and helps you to predict market trend .
