Aibuyzone.com Trade Signals — FREE (≤10m)Aibuyzone.com Trade Signals — FREE (≤10m)
What it is:
An educational tool that visualizes basic trend and momentum conditions using common indicators (EMA, RSI, MACD). It also draws optional swing-based TP/SL guide levels and simple auto-Fibonacci lines.
Timeframe limit (important):
This free version is intentionally limited to 10 minutes and under. If you add it on higher timeframes, the script displays a notice and does not run. Please keep the chart on a 10m (or lower) timeframe when using or publishing examples.
How it works (high level)
Trend filter: Fast/slow EMAs help classify context (bullish/bearish/neutral).
Momentum check: RSI and MACD alignment is used to form long/short signal labels on bar close.
Guide levels: When a signal appears, the script estimates a swing-based stop level and two risk-to-reward guide targets (TP1/TP2). These are visual references only.
Optional Auto-Fib: Draws common Fibonacci retracement lines over a recent swing range for context.
Note: Signals are calculated on confirmed bars. Real-time updates during a forming bar can differ from the final bar-close result, depending on your TradingView “recalculate” settings.
Inputs (summary)
Trend: Fast EMA, Slow EMA
Momentum: RSI length/levels; MACD fast/slow/signal
Exits: Swing lookback; TP1/TP2 risk-reward ratios
Auto Fibonacci (optional): Lookback, reversal mode, line width, label size, which levels to show
Style: Long/short/neutral colors, text color, label sizes, floating info-box offsets
Suggested usage
Add the indicator to a ≤10m chart (e.g., 1m, 3m, 5m, 10m).
Wait for bar close to evaluate a new label.
Use the trend/momentum context plus your own analysis (price action, S/R, volume, higher-TF context) to make decisions.
Treat TP/SL/Auto-Fib as visual references only. Always apply independent risk management.
Best practices for publishing an idea with this script
Use a clean chart: only this script visible (unless clearly explained).
Make sure symbol, timeframe (≤10m), and script name are visible on the screenshot.
Explain why the chart is clear (e.g., which inputs are on, what the labels mean).
Avoid performance claims, promises, or language implying certainty.
Disclaimers
Educational only. This script does not provide financial, investment, or trading advice and does not execute trades.
Past behavior of indicators does not imply future results. Markets involve risk, including potential loss of capital.
You are solely responsible for any trades/decisions made using this tool.
Notes & limitations
Designed for intraday use; it intentionally does not run on timeframes above 10 minutes.
Signals can be fewer or more frequent depending on market volatility and chosen inputs.
Combining with additional confirmation methods is recommended.
Indicadores e estratégias
Multi-Condition Alert Builder⚡ Multi-Condition Alert Builder — Modular Alert Framework
The Multi-Condition Alert Builder is a powerful, code-free alert engine for TradingView. It allows traders to build complex multi-condition Buy/Sell alerts using simple dropdown menus — no Pine Script experience required.
Combine up to five separate conditions per side and trigger alerts based on your own custom logic.
🧠 How It Works
Each “Buy” and “Sell” side includes up to five configurable slots, where you can define:
Two data sources (indicators, price, or custom inputs)
A comparison or crossover condition
A static value (optional)
Once your slots are defined, the script combines these individual conditions according to your chosen mode:
Any – triggers when any enabled condition is true
All – same bar – triggers only when all enabled conditions occur on the same bar
All – within bars – allows conditions to complete within a user-defined lookback window
This gives traders fine-grained control to design powerful, adaptive alert logic directly in the chart — no coding required.
⚙️ Key Features
🧩 Up to 5 Buy and 5 Sell Slots – Fully customizable condition slots
🧠 Combine Logic Modes – Any / All / Within Bars flexibility
🔔 Custom Alerts – Generates separate Buy, Sell, or combined alert events
⏱️ Close-Bar Confirmation Option – Avoids premature signals on open candles
💡 Visual Signals – Plots arrows on chart for clear alert visualization
🔄 Indicator-Agnostic – Works with any sources or indicators available in your chart
🧮 Combine Logic Modes Explained
Mode Description
Any Triggers an alert if any active condition is met
All – same bar Requires all active slots to confirm on the same candle
All – within bars Conditions may complete within a set lookback window
🧭 Example Use Cases
Combine RSI, MACD, and MA crossovers for precision entries
Create alert triggers for momentum confluence setups
Build “stacked signal” logic (e.g., RSI < 30 and MACD crossover within 3 bars)
Quickly prototype and test multi-factor alert conditions
🧠 Usage Tip
Once your conditions are set, simply add TradingView alerts tied to:
“BUY↟” for long signals
“SELL↡” for short signals
“ANY ALERT” to trigger on either event
The Alert Builder becomes especially powerful when combined with your favorite custom indicators — enabling smart, automated alerts without extra coding.
⚡ In Short
Build. Combine. Alert.
The Multi-Condition Alert Builder gives you total flexibility to design complex alert logic — visually, intuitively, and efficiently — right on your chart.
SMC by ASHY-JAYASHY-JAY "Smart Money" refers to funds under the control of institutional investors, central banks, funds, market makers, and other financial entities. Ordinary people recognize investments made by those who have a deep understanding of market performance and possess information typically inaccessible to regular investors as "Smart Money".
Consequently, when market movements often diverge from expectations, traders identify the footprints of smart money. For example, when a classic pattern forms in the market, traders take short positions. However, the market might move upward instead. They attribute this contradiction to smart money and seek to capitalize on such inconsistencies in their trades.
The "Smart Money Concept" (SMC) is one of the primary styles of technical analysis that falls under the subset of "Price Action". Price action encompasses various subcategories, with one of the most significant being "Supply and Demand", in which SMC is categorized.
The SMC method aims to identify trading opportunities by emphasizing the impact of large traders (Smart Money) on the market, offering specific patterns, techniques, and trading strategies.
🟣Key Terms of Smart Money Concept (SMC)
• Market Structure (Trend)
• Change of Character (ChoCh)
• Break of Structure (BoS)
• Order Blocks (Supply and Demand)
• Imbalance (IMB)
• Inefficiency (IFC)
• Fair Value Gap (FVG)
• Liquidity
• Premium and Discount
Bollinger Band Width Oscillator %🧠 Bollinger Band Width Oscillator %
The Bollinger Band Width Oscillator % is a volatility-focused tool that measures the relative width of Bollinger Bands and transforms it into an oscillator format. It helps traders visualize volatility expansions and contractions directly in an indicator pane — a powerful way to anticipate breakout or consolidation phases.
🔍 How It Works
Band Width %: Calculates the percentage distance between the upper and lower Bollinger Bands relative to the basis (SMA).
Smoothed Output: The raw bandwidth is smoothed using a moving average for cleaner, more stable signals.
Dynamic Volatility Zones: The script automatically computes average, high, and low volatility thresholds — each dynamically adapting to market conditions.
User-Adjustable Multipliers: Control how sensitive your high/low zones are with the High Zone Multiplier and Low Zone Multiplier inputs.
⚙️ Key Features
📊 Oscillator Format – Easy-to-read visualization of volatility compression and expansion.
🔥 High/Low Volatility Detection – Automatic labeling and color-coded alerts for shifts in volatility.
🧩 Dynamic Thresholds – Zones adjust automatically with market activity for adaptive sensitivity.
🧠 Hysteresis Logic – Prevents rapid signal flipping, improving clarity and reliability.
🎨 Custom Visuals – Adjustable smoothing and background highlights for quick interpretation.
📈 Trading Applications
Identify Breakouts: Rising bandwidth often precedes price breakouts.
Spot Consolidations: Low bandwidth indicates tightening volatility and potential range trades.
Volatility Regime Analysis: Understand market rhythm and adapt strategies accordingly.
⚡ Inputs
Parameter Description
Band Length Period for Bollinger Band calculation
Band Multiplier Standard deviation multiplier for the bands
Source Price source (default: close)
Smoothing Period for smoothing the oscillator line
High Zone Multiplier Adjusts the high-volatility threshold
Low Zone Multiplier Adjusts the low-volatility threshold
Highlight Volatility Zones Optional background color overlay
🧊 Usage Tip
Combine this indicator with momentum tools or price action analysis to confirm trade setups. Watch for transitions from low to high volatility zones — these often signal the beginning of major market moves.
Previous Period High/Low LevelsThis indicator plots the previous day, week, and month high and low levels to highlight key liquidity levels.
Perfect for traders using market structure, liquidity, or SMC concepts.
Features:
Auto-plots PDH/PDL, PWH/PWL, and PMH/PML
Adjustable line styles, widths, and label sizes
Toggle price display on or off
Accurate UTC offset handling
Rolling Performance Metrics TableRolling Performance Metrics Table
A clean, customizable table overlay that displays rolling performance metrics across multiple time periods. Perfect for quickly assessing price momentum and performance trends at a glance.
FEATURES:
- Displays performance across 5 time periods: 1 Week, 3 Month, 6 Month, 1 Year, and 2 Year
- Shows historical price at the start of each period
- Calculates both absolute price change and percentage change
- Color-coded results: Green for positive performance, Red for negative performance
- Fully transparent design with no background or borders - text floats cleanly over your chart
- Customizable table position (9 placement options)
DISPLAY COLUMNS:
1. Period - The lookback timeframe
2. Price - The historical price at the start of the period
3. Change (Value) - Absolute price change from the period start
4. Change (%) - Percentage return over the period
CUSTOMIZATION:
- Adjust the number of bars for each period (default: 1 Week = 5 bars, 3 Month = 63 bars, 6 Month = 126 bars, 1 Year = 252 bars, 2 Year = 504 bars)
- Choose from 9 table positions: Top, Middle, Bottom combined with Left, Center, Right
- Default position: Middle Left
USAGE:
Perfect for traders who want to quickly assess momentum across multiple timeframes. The transparent overlay design ensures minimal obstruction of chart analysis while providing critical performance data at a glance.
NOTE:
- The table only appears on the last bar of your chart
- Customize bar counts in settings to match your specific timeframe needs (e.g., daily vs hourly charts)
- "N/A" appears when historical data is insufficient for the selected period
Reactive Curvature Smoother Moving Average IndicatorSummary in one paragraph
RCS MA is a reactive curvature smoother for any liquid instrument on intraday through swing timeframes. It helps you act only when context strengthens by adapting its window length with a normalized path energy score and by smoothing with robust residual weights over a quadratic fit, then optionally blending a capped one step forecast. Add it to a clean chart and watch the single colored line. Shapes can shift while a bar forms and settle on close. For conservative use, judge on bar close.
Scope and intent
• Markets: major FX pairs, index futures, large cap equities, liquid crypto
• Timeframes: one minute to daily
• Purpose: reduce lag in trends while resisting chop and outliers
• Limits: indicator only, no orders
Originality and usefulness
• Novelty: adaptive window selection by minimizing normalized path energy with directionality bias, plus Huber weighted residuals and curvature aware penalty, finished with a mintick capped forecast blend
• Failure modes addressed: whipsaws from fixed length MAs and outlier spikes that pull means
• Testable: Inputs expose all components and optional diagnostics show chosen length, directionality, and energy
• Portable yardstick: forecast cap uses mintick to stay symbol aware
Method overview in plain language
Base measures
• Range span of the tested window and a path energy defined as the sum of squared price increments, normalized by span
Components
Adaptive window chooser: scans L between Min and Max using an energy over trend score and picks the lowest score
Robust smoother: fits a quadratic to the last L bars, computes residuals, applies Huber weights and an exponential residual penalty scaled down when curvature is high
Forecast blend: projects one step ahead from the quadratic, caps displacement by a multiple of mintick, blends by user weight
Fusion rule
• Final line equals robust mean plus optional capped forecast blend
Signal rule
• Visual bias only: color turns lime when close is above the line, red otherwise
What you will see on the chart
• One colored line that tightens in trends and relaxes in chop
• Optional debug overlays for core value, chosen L, directionality, and energy
• Optional last bar label with L, directionality, and energy
• Reminder: drawings can move intrabar and settle on close
Inputs with guidance
Setup
• Source: price series to smooth
Logic
• Min window l_min. Typical 5 to 21. Higher increases stability, adds lag
• Max window l_max. Typical 40 to 128. Higher reduces noise, adds lag ceiling
• Length step grid_step. Typical 1 to 8. Smaller is finer and heavier
• Trend bias trend_bias. Typical 0.50 to 0.80. Higher favors trend persistence
• Residual penalty lambda_base. Typical 0.8 to 2.0. Higher downweights large residuals more
• Huber threshold huber_k. Typical 1.5 to 3.0. Higher admits more outliers
• Curvature guard curv_guard. Typical 0.3 to 1.0. Higher reduces influence when curve is tight
• Forecast blend lead_blend. 0 disables. Typical 0.10 to 0.40
• Forecast cap lead_limit. Typical 1 to 5 minticks
• Show chosen L and metrics show_debug. Diagnostics toggle
Optional: enable diagnostics to see length, direction, and energy
Realism and responsible publication
• No performance claims. Past results never guarantee future outcomes
• Shapes can move while bars are open and settle on close
• Use on standard candles for analysis and combine with your own risk process
Honest limitations and failure modes
• Very quiet regimes can reduce energy contrast, length selection may hover near the bounds
• Gap heavy symbols can disrupt quadratic fit on the window edges
• Excessive forecast blend may look anticipatory; use low values and the cap
MarketMonkey-Indicator-Set-1 - GMMA open 🧠 MarketMonkey-Indicator-Set-1 — GMMA Open
GMMA (Guppy Multiple Moving Average) Toolkit for Trend Clarity & Timing
The MarketMonkey GMMA Open indicators brings a clean, high-performance visual of trend strength and direction using multiple exponential moving averages (EMAs) across short- and long-term time frames.
Designed for traders who want to see momentum shifts and market transitions as they happen, this version overlays directly on the price chart for quick and confident reads.
🔍 How It Works
* Short-term EMAs (3–15) track trader sentiment and momentum.
* Long-term EMAs (30–60) show investor trend commitment.
* The indicator dynamically colors the long-term EMAs:
* 🔵 Blue : Upward momentum
* 🔴 Red : Downward momentum
When the short-term group expands above the long-term group, it signals strength and potential continuation. Tightening or compression may warn of pauses or reversals.
💡 Features
* 12 adjustable EMA periods (customize your GMMA spacing)
* Automatic color shifts for trend clarity
* Live price flag for easy reference
* Compact ticker/date display in the top-right corner
* Minimalist, overlay-based design — no clutter, just clarity
📈 Best Used For
* Spotting early trend changes
* Confirming continuation or breakout setups
* Identifying compression zones before reversals
* Overlaying on ASX, S&P, FX, Gold, or Crypto charts
🔔 Part of the MarketMonkey Indicator Set series — tools built for real-world trend recognition and momentum trading.
Trappin Previous Timeframe LevelsTrappin Previous Timeframe Levels (Trappin PTL)
Overview
Trappin PTL is a comprehensive multi-timeframe support and resistance indicator that displays key price levels from multiple timeframes on a single chart. This indicator helps traders identify critical price zones where reversals or breakouts are likely to occur, making it ideal for both intraday and swing trading strategies.
💡 Origin Story
I got tired of manually drawing these lines that I learned from watching Wallstreet Trapper on Trappin Tuesdays YouTube live streams. After repeatedly marking the same previous timeframe levels on every chart, I decided to automate the process. Hope it helps you as much as it helps me!
Key Features
📊 Multiple Timeframe Levels
The indicator tracks and displays high/low levels from:
Previous Hour (PHH/PHL) - Purple lines
Previous Day (PDH/PDL) - Green lines
Previous Week (PWH/PWL) - Yellow lines
Previous Month (PMH/PML) - Blue lines
All-Time High (ATH) - Red line
52-Week High - Orange line
🎨 Fully Customizable
Colors - Change the color of each timeframe independently
Line Styles - Choose between Solid, Dashed, or Dotted lines
Line Widths - Adjust thickness from 1-4 pixels
All settings organized in intuitive groups for easy access
📍 Smart Line Extension
Lines extend back to show when the level was established
Lines project forward to show current relevance
Historical context helps identify key support/resistance zones
🏷️ Clear Price Labels
Each level displays its exact price value (no currency symbols)
Labels positioned horizontally to avoid overlap
Adaptive text color for visibility on any chart theme (dark or light mode)
Why "Trappin"?
The name is a tribute to Wallstreet Trapper and his Trappin Tuesdays YouTube live streams, where I learned the importance of marking previous timeframe levels. The name also reflects the indicator's purpose: identifying price levels where traders often get "trapped" - whether it's bulls getting trapped below resistance or bears getting trapped above support. These levels represent zones where significant order flow and liquidity exist, making them prime areas for reversals or breakouts.
Credits
Created by resoh
Inspired by Wallstreet Trapper and Trappin Tuesdays YouTube live streams
This indicator is provided for educational and informational purposes. Always practice proper risk management and conduct your own analysis before making trading decisions.
Version History
v1.0 - Initial Release
Multi-timeframe high/low levels
All-time high tracking
52-week high tracking
Fully customizable colors, styles, and widths
Adaptive labels with price display
Smart line extension showing historical context
st 47Усредненный Ишимоку (Custom: 9/48/96) [V6]st47 — Volume in Clouds
This indicator is a custom Ichimoku Cloud modification that dynamically reacts to market volume.
The color intensity of the Kumo (cloud) changes depending on the current trading volume — brighter clouds indicate stronger activity, while dimmer ones reflect low participation.
Key Features:
• Based on the Ichimoku Cloud system (8/48/96 settings)
• Volume-sensitive cloud visualization
• Works on any timeframe and pair
• Supports multi-ticker averaging (BTCUSDT, BTCUSDT.P, etc.)
• Displays additional volume histogram below the chart
Purpose:
Helps visualize both trend structure and the strength behind it by combining Ichimoku logic with real-time volume dynamics.
J.P. Morgan Efficiente 5 IndexJ.P. MORGAN EFFICIENTE 5 INDEX REPLICATION
Walk into any retail trading forum and you'll find the same scene playing out thousands of times a day: traders huddled over their screens, drawing trendlines on candlestick charts, hunting for the perfect entry signal, convinced that the next RSI crossover will unlock the path to financial freedom. Meanwhile, in the towers of lower Manhattan and the City of London, portfolio managers are doing something entirely different. They're not drawing lines. They're not hunting patterns. They're building fortresses of diversification, wielding mathematical frameworks that have survived decades of market chaos, and most importantly, they're thinking in portfolios while retail thinks in positions.
This divide is not just philosophical. It's structural, mathematical, and ultimately, profitable. The uncomfortable truth that retail traders must confront is this: while you're obsessing over whether the 50-day moving average will cross the 200-day, institutional investors are solving quadratic optimization problems across thirteen asset classes, rebalancing monthly according to Markowitz's Nobel Prize-winning framework, and targeting precise volatility levels that allow them to sleep at night regardless of what the VIX does tomorrow. The game you're playing and the game they're playing share the same field, but the rules are entirely different.
The question, then, is not whether retail traders can access institutional strategies. The question is whether they're willing to fundamentally change how they think about markets. Are you ready to stop painting lines and start building portfolios?
THE INSTITUTIONAL FRAMEWORK: HOW THE PROFESSIONALS ACTUALLY THINK
When Harry Markowitz published "Portfolio Selection" in The Journal of Finance in 1952, he fundamentally altered how sophisticated investors approach markets. His insight was deceptively simple: returns alone mean nothing. Risk-adjusted returns mean everything. For this revelation, he would eventually receive the Nobel Prize in Economics in 1990, and his framework would become the foundation upon which trillions of dollars are managed today (Markowitz, 1952).
Modern Portfolio Theory, as it came to be known, introduced a revolutionary concept: through diversification across imperfectly correlated assets, an investor could reduce portfolio risk without sacrificing expected returns. This wasn't about finding the single best asset. It was about constructing the optimal combination of assets. The mathematics are elegant in their logic: if two assets don't move in perfect lockstep, combining them creates a portfolio whose volatility is lower than the weighted average of the individual volatilities. This "free lunch" of diversification became the bedrock of institutional investment management (Elton et al., 2014).
But here's where retail traders miss the point entirely: this isn't about having ten different stocks instead of one. It's about systematic, mathematically rigorous allocation across asset classes with fundamentally different risk drivers. When equity markets crash, high-quality government bonds often rally. When inflation surges, commodities may provide protection even as stocks and bonds both suffer. When emerging markets are in vogue, developed markets may lag. The professional investor doesn't predict which scenario will unfold. Instead, they position for all of them simultaneously, with weights determined not by gut feeling but by quantitative optimization.
This is what J.P. Morgan Asset Management embedded into their Efficiente Index series. These are not actively managed funds where a portfolio manager makes discretionary calls. They are rules-based, systematic strategies that execute the Markowitz framework in real-time, rebalancing monthly to maintain optimal risk-adjusted positioning across global equities, fixed income, commodities, and defensive assets (J.P. Morgan Asset Management, 2016).
THE EFFICIENTE 5 STRATEGY: DECONSTRUCTING INSTITUTIONAL METHODOLOGY
The Efficiente 5 Index, specifically, targets a 5% annualized volatility. Let that sink in for a moment. While retail traders routinely accept 20%, 30%, or even 50% annual volatility in pursuit of returns, institutional allocators have determined that 5% volatility provides an optimal balance between growth potential and capital preservation. This isn't timidity. It's mathematics. At higher volatility levels, the compounding drag from large drawdowns becomes mathematically punishing. A 50% loss requires a 100% gain just to break even. The institutional solution: constrain volatility at the portfolio level, allowing the power of compounding to work unimpeded (Damodaran, 2008).
The strategy operates across thirteen exchange-traded funds spanning five distinct asset classes: developed equity markets (SPY, IWM, EFA), fixed income across the risk spectrum (TLT, LQD, HYG), emerging markets (EEM, EMB), alternatives (IYR, GSG, GLD), and defensive positioning (TIP, BIL). These aren't arbitrary choices. Each ETF represents a distinct factor exposure, and together they provide access to the primary drivers of global asset returns (Fama and French, 1993).
The methodology, as detailed in replication research by Jungle Rock (2025), follows a precise monthly cadence. At the end of each month, the strategy recalculates expected returns and volatilities for all thirteen assets using a 126-day rolling window. This six-month lookback balances responsiveness to changing market conditions against the noise of short-term fluctuations. The optimization engine then solves for the portfolio weights that maximize expected return subject to the 5% volatility target, with additional constraints to prevent excessive concentration.
These constraints are critical and reveal institutional wisdom that retail traders typically ignore. No single ETF can exceed 20% of the portfolio, except for TIP and BIL which can reach 50% given their defensive nature. At the asset class level, developed equities are capped at 50%, bonds at 50%, emerging markets at 25%, and alternatives at 25%. These aren't arbitrary limits. They're guardrails preventing the optimization from becoming too aggressive during periods when recent performance might suggest concentrating heavily in a single area that's been hot (Jorion, 1992).
After optimization, there's one final step that appears almost trivial but carries profound implications: weights are rounded to the nearest 5%. In a world of fractional shares and algorithmic execution, why round to 5%? The answer reveals institutional practicality over mathematical purity. A portfolio weight of 13.7% and 15.0% are functionally similar in their risk contribution, but the latter is vastly easier to communicate, to monitor, and to execute at scale. When you're managing billions, parsimony matters.
WHY THIS MATTERS FOR RETAIL: THE GAP BETWEEN APPROACH AND EXECUTION
Here's the uncomfortable reality: most retail traders are playing a different game entirely, and they don't even realize it. When a retail trader says "I'm bullish on tech," they buy QQQ and that's their entire technology exposure. When they say "I need some diversification," they buy ten different stocks, often in correlated sectors. This isn't diversification in the Markowitzian sense. It's concentration with extra steps.
The institutional approach represented by the Efficiente 5 is fundamentally different in several ways. First, it's systematic. Emotions don't drive the allocation. The mathematics do. When equities have rallied hard and now represent 55% of the portfolio despite a 50% cap, the system sells equities and buys bonds or alternatives, regardless of how bullish the headlines feel. This forced contrarianism is what retail traders know they should do but rarely execute (Kahneman and Tversky, 1979).
Second, it's forward-looking in its inputs but backward-looking in its process. The strategy doesn't try to predict the next crisis or the next boom. It simply measures what volatility and returns have been recently, assumes the immediate future resembles the immediate past more than it resembles some forecast, and positions accordingly. This humility regarding prediction is perhaps the most institutional characteristic of all.
Third, and most critically, it treats the portfolio as a single organism. Retail traders typically view their holdings as separate positions, each requiring individual management. The institutional approach recognizes that what matters is not whether Position A made money, but whether the portfolio as a whole achieved its risk-adjusted return target. A position can lose money and still be a valuable contributor if it reduced portfolio volatility or provided diversification during stress periods.
THE MATHEMATICAL FOUNDATION: MEAN-VARIANCE OPTIMIZATION IN PRACTICE
At its core, the Efficiente 5 strategy solves a constrained optimization problem each month. In technical terms, this is a quadratic programming problem: maximize expected portfolio return subject to a volatility constraint and position limits. The objective function is straightforward: maximize the weighted sum of expected returns. The constraint is that the weighted sum of variances and covariances must not exceed the volatility target squared (Markowitz, 1959).
The challenge, and this is crucial for understanding the Pine Script implementation, is that solving this problem properly requires calculating a covariance matrix. This 13x13 matrix captures not just the volatility of each asset but the correlation between every pair of assets. Two assets might each have 15% volatility, but if they're negatively correlated, combining them reduces portfolio risk. If they're positively correlated, it doesn't. The covariance matrix encodes these relationships.
True mean-variance optimization requires matrix algebra and quadratic programming solvers. Pine Script, by design, lacks these capabilities. The language doesn't support matrix operations, and certainly doesn't include a QP solver. This creates a fundamental challenge: how do you implement an institutional strategy in a language not designed for institutional mathematics?
The solution implemented here uses a pragmatic approximation. Instead of solving the full covariance problem, the indicator calculates a Sharpe-like ratio for each asset (return divided by volatility) and uses these ratios to determine initial weights. It then applies the individual and asset-class constraints, renormalizes, and produces the final portfolio. This isn't mathematically equivalent to true mean-variance optimization, but it captures the essential spirit: weight assets according to their risk-adjusted return potential, subject to diversification constraints.
For retail implementation, this approximation is likely sufficient. The difference between a theoretically optimal portfolio and a very good approximation is typically modest, and the discipline of systematic rebalancing across asset classes matters far more than the precise weights. Perfect is the enemy of good, and a good approximation executed consistently will outperform a perfect solution that never gets implemented (Arnott et al., 2013).
RETURNS, RISKS, AND THE POWER OF COMPOUNDING
The Efficiente 5 Index has, historically, delivered on its promise of 5% volatility with respectable returns. While past performance never guarantees future results, the framework reveals why low-volatility strategies can be surprisingly powerful. Consider two portfolios: Portfolio A averages 12% returns with 20% volatility, while Portfolio B averages 8% returns with 5% volatility. Which performs better over time?
The arithmetic return favors Portfolio A, but compound returns tell a different story. Portfolio A will experience occasional 20-30% drawdowns. Portfolio B rarely draws down more than 10%. Over a twenty-year horizon, the geometric return (what you actually experience) for Portfolio B may match or exceed Portfolio A, simply because it never gives back massive gains. This is the power of volatility management that retail traders chronically underestimate (Bernstein, 1996).
Moreover, low volatility enables behavioral advantages. When your portfolio draws down 35%, as it might with a high-volatility approach, the psychological pressure to sell at the worst possible time becomes overwhelming. When your maximum drawdown is 12%, as might occur with the Efficiente 5 approach, staying the course is far easier. Behavioral finance research has consistently shown that investor returns lag fund returns primarily due to poor timing decisions driven by emotional responses to volatility (Dalbar, 2020).
The indicator displays not just target and actual portfolio weights, but also tracks total return, portfolio value, and realized volatility. This isn't just data. It's feedback. Retail traders can see, in real-time, whether their actual portfolio volatility matches their target, whether their risk-adjusted returns are improving, and whether their allocation discipline is holding. This transparency transforms abstract concepts into concrete metrics.
WHAT RETAIL TRADERS MUST LEARN: THE MINDSET SHIFT
The path from retail to institutional thinking requires three fundamental shifts. First, stop thinking in positions and start thinking in portfolios. Your question should never be "Should I buy this stock?" but rather "How does this position change my portfolio's expected return and volatility?" If you can't answer that question quantitatively, you're not ready to make the trade.
Second, embrace systematic rebalancing even when it feels wrong. Perhaps especially when it feels wrong. The Efficiente 5 strategy rebalances monthly regardless of market conditions. If equities have surged and now exceed their target weight, the strategy sells equities and buys bonds or alternatives. Every retail trader knows this is what you "should" do, but almost none actually do it. The institutional edge isn't in having better information. It's in having better discipline (Swensen, 2009).
Third, accept that volatility is not your friend. The retail mythology that "higher risk equals higher returns" is true on average across assets, but it's not true for implementation. A 15% return with 30% volatility will compound more slowly than a 12% return with 10% volatility due to the mathematics of return distributions. Institutions figured this out decades ago. Retail is still learning.
The Efficiente 5 replication indicator provides a bridge. It won't solve the problem of prediction no indicator can. But it solves the problem of allocation, which is arguably more important. By implementing institutional methodology in an accessible format, it allows retail traders to see what professional portfolio construction actually looks like, not in theory but in executable code. The the colorful lines that retail traders love to draw, don't disappear. They simply become less central to the process. The portfolio becomes central instead.
IMPLEMENTATION CONSIDERATIONS AND PRACTICAL REALITY
Running this indicator on TradingView provides a dynamic view of how institutional allocation would evolve over time. The labels on each asset class line show current weights, updated continuously as prices change and rebalancing occurs. The dashboard displays the full allocation across all thirteen ETFs, showing both target weights (what the optimization suggests) and actual weights (what the portfolio currently holds after price movements).
Several key insights emerge from watching this process unfold. First, the strategy is not static. Weights change monthly as the optimization recalibrates to recent volatility and returns. What worked last month may not be optimal this month. Second, the strategy is not market-timing. It doesn't try to predict whether stocks will rise or fall. It simply measures recent behavior and positions accordingly. If volatility has risen, the strategy shifts toward defensive assets. If correlations have changed, the diversification benefits adjust.
Third, and perhaps most importantly for retail traders, the strategy demonstrates that sophistication and complexity are not synonyms. The Efficiente 5 methodology is sophisticated in its framework but simple in its execution. There are no exotic derivatives, no complex market-timing rules, no predictions of future scenarios. Just systematic optimization, monthly rebalancing, and discipline. This simplicity is a feature, not a bug.
The indicator also highlights limitations that retail traders must understand. The Pine Script implementation uses an approximation of true mean-variance optimization, as discussed earlier. Transaction costs are not modeled. Slippage is ignored. Tax implications are not considered. These simplifications mean the indicator is educational and analytical, not a fully operational trading system. For actual implementation, traders would need to account for these real-world factors.
Moreover, the strategy requires access to all thirteen ETFs and sufficient capital to hold meaningful positions in each. With 5% as the rounding increment, practical implementation probably requires at least $10,000 to avoid having positions that are too small to matter. The strategy is also explicitly designed for a 5% volatility target, which may be too conservative for younger investors with long time horizons or too aggressive for retirees living off their portfolio. The framework is adaptable, but adaptation requires understanding the trade-offs.
CAN RETAIL TRULY COMPETE WITH INSTITUTIONS?
The honest answer is nuanced. Retail traders will never have the same resources as institutions. They won't have Bloomberg terminals, proprietary research, or armies of analysts. But in portfolio construction, the resource gap matters less than the mindset gap. The mathematics of Markowitz are available to everyone. ETFs provide liquid, low-cost access to institutional-quality building blocks. Computing power is essentially free. The barriers are not technological or financial. They're conceptual.
If a retail trader understands why portfolios matter more than positions, why systematic discipline beats discretionary emotion, and why volatility management enables compounding, they can build portfolios that rival institutional allocation in their elegance and effectiveness. Not in their scale, not in their execution costs, but in their conceptual soundness. The Efficiente 5 framework proves this is possible.
What retail traders must recognize is that competing with institutions doesn't mean day-trading better than their algorithms. It means portfolio-building better than their average client. And that's achievable because most institutional clients, despite having access to the best managers, still make emotional decisions, chase performance, and abandon strategies at the worst possible times. The retail edge isn't in outsmarting professionals. It's in out-disciplining amateurs who happen to have more money.
The J.P. Morgan Efficiente 5 Index Replication indicator serves as both a tool and a teacher. As a tool, it provides a systematic framework for multi-asset allocation based on proven institutional methodology. As a teacher, it demonstrates daily what portfolio thinking actually looks like in practice. The colorful lines remain on the chart, but they're no longer the focus. The portfolio is the focus. The risk-adjusted return is the focus. The systematic discipline is the focus.
Stop painting lines. Start building portfolios. The institutions have been doing it for seventy years. It's time retail caught up.
REFERENCES
Arnott, R. D., Hsu, J., & Moore, P. (2013). Fundamental Indexation. Financial Analysts Journal, 61(2), 83-99.
Bernstein, W. J. (1996). The Intelligent Asset Allocator. New York: McGraw-Hill.
Dalbar, Inc. (2020). Quantitative Analysis of Investor Behavior. Boston: Dalbar.
Damodaran, A. (2008). Strategic Risk Taking: A Framework for Risk Management. Upper Saddle River: Pearson Education.
Elton, E. J., Gruber, M. J., Brown, S. J., & Goetzmann, W. N. (2014). Modern Portfolio Theory and Investment Analysis (9th ed.). Hoboken: John Wiley & Sons.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Jorion, P. (1992). Portfolio optimization in practice. Financial Analysts Journal, 48(1), 68-74.
J.P. Morgan Asset Management. (2016). Guide to the Markets. New York: J.P. Morgan.
Jungle Rock. (2025). Institutional Asset Allocation meets the Efficient Frontier: Replicating the JPMorgan Efficiente 5 Strategy. Working Paper.
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.
Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
Markowitz, H. (1959). Portfolio Selection: Efficient Diversification of Investments. New York: John Wiley & Sons.
Swensen, D. F. (2009). Pioneering Portfolio Management: An Unconventional Approach to Institutional Investment. New York: Free Press.
Market Opens + Killzones — New York, Tokyo & London (SMC/ICT)Market Opens + Killzones — New York, London & Tokyo (SMC/ICT) — TradingATH
Precision. Timing. Liquidity.
This refined overlay defines the world’s three dominant trading sessions — New York , London & Tokyo — plus their critical overlap. Each Opening and Killzone is plotted with full-height visual blocks and precise time anchoring, giving you an immediate understanding of when and where true price delivery begins.
Designed for ICT and SMC Traders , it provides a disciplined structure to navigate intraday volatility — aligning executions with the moments institutional liquidity enters the market.
What You’ll See
New York Killzone (08:30 – 10:30 NY) → Gray full-height Block
London Killzone (07:00 – 10:00 London) → Dark-gray Block
Tokyo Killzone (09:00 – 11:00 Tokyo) → Black Block
London–New York Overlap (13:30 – 16:00 London) → Blue Block
Session Opening Lines : Precise vertical markers with optional labels and customizable color, style, and width.
Every Block extends from chart top to bottom — forming crystal-clear time partitions that highlight where volatility and liquidity converge.
Features
True global time synchronization — automatic daylight-saving adjustment; no manual offset needed.
Full-height killzones — visually structured blocks that scale seamlessly across any timeframe.
Configurable session openings — control color, line width, label visibility, and transparency.
Daily auto-reset — clean, non-repainting visuals with no overlap or drift.
Lightweight performance — optimized rendering with zero lag, even on lower timeframes.
Perfect For
Intraday and Scalping Traders timing executions around session volatility.
ICT / Smart Money Concepts practitioners focusing on liquidity windows.
Traders seeking precise, time-based market context for entries and exits.
Recommended Settings
Line Width: 3–4 px for optimal visibility.
Block Transparency: 60 – 75 % for clean chart integration.
Focus: London + New York sessions for highest liquidity.
In Short
Simple. Accurate. Powerful.
Market Opens + Killzones — New York, London & Tokyo (SMC/ICT) delivers a clean, professional mapping of institutional trading hours — allowing you to trade exactly when the market moves with purpose.
Created by: TradingATH
BankNifty Etharia Aggresive Buyer / SellerOverview
Professional intraday trading strategy for BankNifty Futures that identifies high-probability setups by combining multiple technical indicators. Works in BOTH directions - LONG and SHORT.
Best Timeframe: 5-Minute Chart
Key Features:
✅ Multi-Confluence Entry System - All indicators must align for signal
✅ Bidirectional Trading - Captures both uptrends and downtrends
✅ Advanced Risk Management - Daily loss limits, consecutive loss protection
✅ Smart Exit System - Partial profit taking + trailing stops
✅ Session-Based Trading - Avoids opening and closing volatility
Entry Logic:
LONG Signals:
Price above Kernel Regression (trend confirmation)
Price above VWAP with positive slope (momentum)
Cumulative Volume Delta bullish (buying pressure)
Volume spike or increasing volume (strength confirmation)
Strong bullish candle with 60%+ body ratio
RSI filter to avoid overbought entries
SHORT Signals:
Price below Kernel Regression (downtrend confirmation)
Price below VWAP with negative slope (bearish momentum)
CVD bearish (selling pressure dominates)
High volume confirmation
Strong bearish candle pattern
RSI filter to avoid oversold entries
Exit Management:
🎯 Target 1: 1.5 R:R (50% position exit)
🎯 Target 2: 2.5 R:R (full exit)
🛡️ Stop Loss Options: ATR-based, Swing-based, or Fixed
🟡 Trailing Stop: Activates after 1.2 R:R, trails at 0.8 R:R
⏰ Time-Based Exit: Closes all positions 5 mins before session end
Risk Controls:
Maximum trades per day (default: 5)
Consecutive loss limit (default: 2)
Daily loss limit: 2.5% of capital
Daily profit target: 5% (stops trading when reached)
Position sizing based on account risk percentage
Recommended Settings:
Asset: BankNifty Futures (NSE:BANKNIFTY1!)
Timeframe: 5-minute
Initial Capital: ₹1,00,000
Risk per trade: 1%
Commission: 0.05%
Slippage: 5 points
Performance Expectations:
Win Rate: 55-65%
Profit Factor: 1.5-2.0
Average Trades/Day: 3-8
Risk:Reward: 1:1.8 average
Customizable Parameters:
Trading direction (Long Only / Short Only / Both)
Indicator lengths and thresholds
Stop loss type and targets
Risk management limits
Trading session hours
Best For:
Intraday traders seeking systematic, rule-based entries with strong confluence, proper risk management, and the ability to profit from both bullish and bearish market conditions.
DAMMU AUTOMATICAL AI ENRTY AND TARGET AND EXITMain Components
Supertrend System –
Detects market trend direction (Buy/Sell zones).
→ Green = Uptrend (Buy)
→ Red = Downtrend (Sell)
SMA Filter –
Uses 50 & 200 moving averages to confirm overall trend.
→ Price above both → Bullish
→ Price below both → Bearish
Buy/Sell Signals –
Generated when Supertrend flips direction and SMA confirms.
→ Triangle up = Buy
→ Triangle down = Sell
Take Profit / Stop Loss Levels –
Automatically calculated after Buy/Sell entry.
→ TP1, TP2, SL shown on chart
ADX (Sideways Zone Filter) –
If ADX < 25 → Market sideways → Avoid trades
Shows “No Trade Zone” area
Smart Money Concepts (SMC) Tools –
🔹 Market structure (HH, HL, LH, LL)
🔹 Order blocks (OB)
🔹 Equal highs/lows
🔹 Fair Value Gaps (FVG)
🔹 Premium & Discount zones
Helps find institutional entry points
Visual Display –
Color-coded background (trend zones)
Labels for buy/sell/structure
Optional FVG and order block boxes
Risk Management –
Input-based position sizing, SL & TP management
(to calculate profit levels and minimize loss)
DM Price ActionHere’s a tight, rules-based playbook for trading with your DM Price Action (FVG + S/R + Order Blocks + VWAP + Auto PDH/PDL/PMH/PML). It’s educational, not financial advice—tune to your market & risk.
Core ideas (what each tool does for you)
VWAP → intraday trend/mean.
PDH/PDL → yesterday’s extremes; magnet & reversal/continuation levels.
PMH/PML → premarket extremes; first liquidity tests after the open.
FVG → imbalance zones for continuation entries.
Order Blocks (OBs) → origin of impulses; mitigation/breaks = structure shifts.
S/R → target rails and break alerts.
Setups (long/short mirror)
1) Bias + Pullback (FVG/OB) at Key Level
Bias (need 2+ conditions):
Price above VWAP (bulls) / below VWAP (bears)
Price above PDH/PMH (bulls) or below PDL/PML (bears)
Most recent Swing OB bias in your direction (script updates via crosses)
Entry (bullish example):
Wait for a Bullish FVG to form after we reclaim PMH or PDH.
Prefer FVG overlapping a Bullish OB or sitting just above Support.
Enter on retrace into FVG midline or first bullish reversal candle inside.
Stop: a few ticks below OB low (or FVG bottom, whichever is wider).
Targets:
T1: nearest Resistance or PDH/PMH if not yet tested.
T2: next HTF S/R or fixed 2R–3R.
Manage: to BE at 1R, trail under swing lows or VWAP on trend days.
Bearish mirror: below VWAP, below PDL/PML, Bearish FVG into Bearish OB / Resistance; stop above OB high.
2) Range Break & Retest at PDH/PDL (with OB confirmation)
Context: Price consolidates under PDH (or over PDL).
Trigger: Clean break of PDH/PDL with an OB breakout alert in the break direction.
Entry: On retest of PDH/PDL from the other side, look for a small FVG forming with the move → enter on the pullback.
Stop: beyond the retest wick or the OB edge.
Targets: next S/R, opposing day extreme (e.g., from PDH to PMH/HTF level) or 2R/3R.
3) Premarket Sweep Reversal (open-specific)
Setup: At/near the cash open, price sweeps PMH/PML (wick through) but closes back inside, then a counter-direction OB forms.
Entry: On first FVG in the reversal direction that overlaps that new OB.
Stop: beyond the sweep extreme (PMH/PML).
Targets: VWAP first, then PD midline levels/SR.
Confluence checklist (score ≥3 before clicking)
+1 Above/below VWAP in trade direction
+1 Trading from a PDH/PDL/PMH/PML reaction (reclaim or rejection)
+1 FVG overlaps an OB
+1 Entry at S/R (use the script’s lines)
+1 Fresh zone (recently formed OB/FVG)
+1 Higher-TF structure aligned (e.g., 1H trend)
Take the trade only if score ≥3; size up only at ≥4.
Execution framework (simple & repeatable)
Timeframes: 1H (bias) → 5–15m (execution).
Risk per trade: 0.25–1.0% of account (fixed).
Position size: Size = Risk $ / Stop distance.
Management:
Scale ½ at T1 (nearest SR/PD level), move stop to BE at 1R.
Let runner to T2 (2R–3R) or next PD level.
If VWAP flips against you and closes 2 bars opposite, exit remainder.
Using the inputs (what to tweak)
Order Blocks:
Scalping mode for intraday speed; Day Trade for cleaner swings.
Hide Internal OBs if noise is high; keep Swing OBs for structure.
FVG:
Keep Auto Threshold = ON.
If noisy, plot higher TF FVG (e.g., 15m FVG on 5m chart).
PDH/PDL/PMH/PML:
If chart is cluttered, keep “Show lines only on last bar” ON and labels ON.
Session markets (futures/US equities): use default 0400–0930 premarket; FX/crypto can disable PM lines if irrelevant.
Alerts to set (so you only act on confluence)
Create alerts for:
Bullish/Bearish FVG (execution zones)
Swing/Internal OB Breakout (structure shift)
Support/Resistance Broken (targets/continuation)
(Optional) Crossing PDH/PDL: use TV “Price crossing” with the plotted PDH/PDL values or visually monitor the labels
Workflow: Wait for ≥2 alerts to line up (e.g., Swing OB Breakout + Bullish FVG near PDH), then open the chart and execute the rule set.
Example trade (bullish)
Price reclaims PDH, holds above VWAP.
Bullish FVG prints overlapping a Bullish Internal OB just above PDH.
Limit at FVG midline, stop below OB low.
T1 = next Resistance; T2 = 2R. Move to BE at 1R; trail under new swing lows.
Fib OscillatorWhat is Fib Oscillator and How to Use it?
🔶 1. Conceptual Overview
The Fib Oscillator is a Fibonacci-based relative position oscillator.
Instead of measuring momentum (like RSI or MACD), it measures where price currently sits between the recent swing high and swing low, expressed as a percentage within the Fibonacci range.
In other words:
It answers: “Where is price right now within its most recent dynamic range?”
It visualizes retracement and extension zones numerically, providing continuous feedback between 0% and 100% (and beyond if extended).
🔶 2. What the Script Does
The indicator:
Automatically detects recent high and low levels using an adaptive lookback window, which depends on ATR volatility.
Calculates the current price’s position between those levels as a percentage (0–100).
Plots that percentage as an oscillator — showing visually whether price is near the top, middle, or bottom of its recent range.
Overlays Fibonacci retracement levels (23.6%, 38.2%, 50%, 61.8%, 78.6%) as reference zones.
Generates alerts when the oscillator crosses key Fib thresholds — which can signal retracement completion, breakout potential, or pullback exhaustion.
🔶 3. Technical Flow Breakdown
(a) Inputs
Input Description Default Notes
atrLength ATR period used for volatility estimation 14 Used to dynamically tune lookback sensitivity
minLookback Minimum lookback window (candles) 20 Ensures stability even in low volatility
maxLookback Maximum lookback window 100 Limits over-expansion during high volatility
isInverse Inverts chart orientation false Useful for inverse markets (e.g. shorts or inverse BTC view)
(b) Volatility-Adaptive Lookback
Instead of using a fixed lookback, it calculates:
lookback
=
SMA(ATR,10)
/
SMA(Close,10)
×
500
lookback=SMA(ATR,10)/SMA(Close,10)×500
Then it clamps this between minLookback and maxLookback.
This makes the oscillator:
More reactive during high volatility (shorter lookback)
More stable during calm markets (longer lookback)
Essentially, it self-adjusts to market rhythm — you don’t have to constantly tweak lookback manually.
(c) High-Low Reference Points
It takes the highest and lowest points within the dynamic lookback window.
If isInverse = true, it flips the candle logic (useful if viewing inverse instruments like stablecoin pairs or when analyzing bearish setups invertedly).
(d) Oscillator Core
The main oscillator line:
osc
=
(
close
−
low
)
(
high
−
low
)
×
100
osc=
(high−low)
(close−low)
×100
0% = Price is at the lookback low.
100% = Price is at the lookback high.
50% = Midpoint (balanced).
Between Fibonacci percentages (23.6%, 38.2%, 61.8%, etc.), the oscillator indicates retracement stages.
(e) Fibonacci Levels as Reference
It overlays horizontal reference lines at:
0%, 23.6%, 38.2%, 50%, 61.8%, 78.6%, 100%
These act as support/resistance bands in oscillator space.
You can read it similar to how traders use Fibonacci retracements on charts, but compressed into a single line oscillator.
(f) Alerts
The script includes built-in alert conditions for crossovers at each major Fibonacci level.
You can set TradingView alerts such as:
“Oscillator crossed above 61.8%” → possible bullish continuation or breakout.
“Oscillator crossed below 38.2%” → possible pullback or correction starting.
This allows automated monitoring of fib retracement completions without manually drawing fib levels.
🔶 4. How to Use It
🔸 Visual Interpretation
Oscillator Value Zone Market Context
0–23.6% Deep Retracement Potential exhaustion of a down-move / early reversal
23.6–38.2% Shallow retracement zone Possible continuation phase
38.2–50% Mid retracement Neutral or indecisive structure
50–61.8% Key pivot region Common trend resumption zone
61.8–78.6% Late retracement Often “last pullback” area
78.6–100% Near high range Possible overextension / profit-taking
>100% Range breakout New leg formation / expansion
🔸 Practical Application Steps
Load the indicator on your chart (set overlay = false, so it’s below the main price chart).
Observe oscillator position relative to fib bands:
Use it to determine retracement depth.
Combine with structure tools:
Trend lines, swing points, or HTF market structure.
Use crossovers for timing:
Crossing above 61.8% in an uptrend often confirms breakout continuation.
Crossing below 38.2% in a downtrend signals renewed downside momentum.
For range markets, oscillator swings between 23.6% and 78.6% can define accumulation/distribution boundaries.
🔶 5. When to Use It
During Retracements: To gauge how deep the pullback has gone.
During Range Markets: To identify relative overbought/oversold positions.
Before Breakouts: Crossovers of 61.8% or 78.6% often precede impulsive moves.
In Multi-Timeframe Contexts:
LTF (15M–1H): Detect intraday retracement exhaustion.
HTF (4H–1D): Confirm major range expansions or key reversal zones.
🔶 6. Ideal Companion Indicators
The Fib Oscillator works best when contextualized with structure, volatility, and trend bias indicators.
Below are optimal pairings:
Companion Indicator Purpose Integration Insight
Market Structure MTF Tool Identify active trend direction Use Fib Oscillator only in trend direction for cleaner signals
EMA Ribbon / Supertrend Trend confirmation Align oscillator crossovers with EMA bias
ATR Bands / Volatility Envelope Validate breakout strength If oscillator >78.6% & ATR rising → valid breakout
Volume Oscillator Confirm retracement strength Volume contraction + oscillator under 38.2% → potential reversal
HTF Fib Retracement Tool Combine LTF oscillator with HTF fib confluence Powerful multi-timeframe setups
RSI or Stochastic Measure momentum relative to position RSI divergence while oscillator near 78.6% → exhaustion clue
🔶 7. Understanding the Settings
Setting Function Practical Impact
ATR Period (14) Controls volatility sampling Higher = smoother lookback adaptation
Min Lookback (20) Smallest window allowed Lower = more reactive but noisier
Max Lookback (100) Largest window allowed Higher = smoother but slower to react
Inverse Candle Chart Flips oscillator vertically Useful when analyzing bearish or inverse scenarios (e.g. short-side fib mapping)
Recommended Configs:
For scalping/intraday: ATR 10–14, lookback 20–50
For swing/position trading: ATR 14–21, lookback 50–100
🔶 8. Example Trade Logic (Practical Use)
Scenario: Uptrend on 4H chart
Oscillator drops to below 38.2% → retracement zone
Price consolidates → oscillator stabilizes
Oscillator crosses above 50% → pullback ending
Entry: Long when oscillator crosses above 61.8%
Exit: Near 78.6–100% zone or upon divergence with RSI
For Short Bias (Inverse Setup):
Enable isInverse = true to visually flip the oscillator (so lows become highs).
Use the same thresholds inversely.
🔶 9. Strengths & Limitations
✅ Strengths
Dynamic, self-adapting to volatility
Quantifies Fib retracement as a continuous function
Compact oscillator view (no clutter on chart)
Works well across all timeframes
Compatible with both trending and ranging markets
⚠️ Limitations
Doesn’t define trend direction — must be used with structure filters
Can whipsaw during choppy consolidations
The “lookback auto-adjust” may lag in sudden volatility shifts
Shouldn’t be used standalone for entries without structural confluence
🔶 10. Summary
The “Fib Oscillator” is a dynamic Fibonacci-relative positioning tool that merges retracement theory with adaptive volatility logic.
It gives traders an intuitive, quantified view of where price sits within its recent fib range, allowing anticipation of pullbacks, reversals, or breakout momentum.
Think of it as a "Fibonacci RSI", but instead of momentum strength, it shows positional depth — the vibrational location of price within its natural swing cycle.
[Kpt-Ahab] Assistant: Risk & DCA PlannerScript Description – Assistant: Risk & DCA Planner
The Risk & DCA Planner is a technical assistant for position and risk management.
It automatically calculates, based on volatility (ATR%), swing structure, and your settings:
Stop-Loss (SL) and corresponding Take-Profit targets (TPs) in R-multiples
DCA (Dollar-Cost-Averaging) levels — both price and amount
A market suitability check (based on volatility & volume)
Plus a clear table and summary label displayed on the chart
The script helps you plan risk, scaling, and profit targets consistently and quantitatively.
Core Logic
Risk Profile
Three modes: Low, Normal, High.
These define how reactive the script behaves internally:
Low → conservative, longer lookbacks, tighter analysis
Normal → balanced
High → aggressive, faster reaction, wider stops
Stop-Loss (SL)
Automatically calculated from ATR% and recent swing structure, limited by minimum and maximum thresholds.
The SL percentage defines the R-unit, which all TPs and DCA levels are based on.
Take-Profits (TPs)
Up to six targets, each a multiple of the defined risk (e.g., 1R, 2R, 3R).
Prices are automatically adjusted depending on long or short direction.
DCA Strategy
Optional. Adds scaling levels evenly between Entry and SL or in multiples of the ATR.
Each DCA allocation grows geometrically until the maximum position size is reached.
Suitability Check
Evaluates whether the market is within an appropriate ATR% range and has sufficient volume.
The table displays “OK” or “Caution” depending on volatility and historical consistency.
Visualization
Lines for SL, TPs, and DCA levels
A table with all parameters, prices, and risk data
A chart label summarizing key info (profile, direction, SL%, TPs, DCA, etc.)
HV Spike Strategy (HVP + OR Breakout + Reversal + TP/SL Modes)Here is a script that I tried to make it simple, although it has several parameters, I will try to explain, here we go:
Logic: Open Range Breakout: otherwise knows as First Candle Rule, usually used for the first candle in the opening of a market session, in my strategy there is an option to use it even for Crypto that operate 24/7, how to do that? Simply by detecting Volatility from the HVP (Historical Volatility Percentile). Then the ORB logic kicks in and the first candle with high volatility gives the ranges for the trades. The proper HVP Activation Threshold has to be selected for each currency pair/index/crypto in order to have maximum profit.
Enter a trade: when the price goes 100% above/below the First Candle Rule Range. That way it is filtering fake breakouts. Also if the price reverses back into the range the strategy takes the opposite trade.
Exit a trade: SL/TP By percentage or ATR, selection in the input menu.
My intention is to avoid using lagging indicators or guessing of Price Action, purely Bull/Bear indication by the first candle.
I hope you find this helpful! Wishing all successful Trades!
Average Daily Session Range PRO [Capitalize Labs]Average Daily Session Range PRO
The Average Daily Session Range PRO (ADSR PRO) is a professional-grade analytical tool designed to quantify and visualize the probabilistic range behavior of intraday sessions.
It calculates directional range statistics using historical session data to show how far price typically moves up or down from the session open.
This helps traders understand session volatility profiles, range asymmetry, and probabilistic extensions relative to prior performance.
Key Features
Asymmetric Range Modeling: Separately tracks average upside and downside excursions from each session open, revealing directional bias and volatility imbalance.
Probability Engine Modes: Choose between Rolling Window (fixed-length lookback) and Exponential Decay (weighted historical memory) to control how recent or historic data influences probabilities.
Session-Aware Statistics: Calculates values independently for each defined session, allowing region-specific insights (e.g., Tokyo, London, New York).
Dynamic Range Table: Displays key metrics such as average up/down ticks, expected range extensions, and percentage probabilities.
Adaptive Display: Works across timeframes and instruments, automatically aligning with user-defined session start and end times.
Visual Clarity: Includes clean range markers and labels optimized for both backtesting and live-chart analysis.
Intended Use
ADSR PRO is a statistical reference indicator.
It does not generate buy/sell signals or predictive forecasts.
Its purpose is to help users observe historical session behavior and volatility tendencies to support their own discretionary analysis.
Credits
Developed by Capitalize Labs, specialists in quantitative and discretionary market research tools.
Risk Warning
This material is educational research only and does not constitute financial advice, investment recommendation, or a solicitation to buy or sell any instrument.
Foreign exchange and CFDs are complex, leveraged products that carry a high risk of rapid losses; leverage amplifies both gains and losses, and you should not trade with funds you cannot afford to lose.
Market conditions can change without notice, and news or illiquidity may cause gaps and slippage; stop-loss orders are not guaranteed.
The analysis presented does not take into account your objectives, financial situation, or risk tolerance.
Before acting, assess suitability in light of your circumstances and consider seeking advice from a licensed professional.
Past performance and back-tested or hypothetical scenarios are not reliable indicators of future results, and no outcome or level mentioned here is assured.
You are solely responsible for all trading decisions, including position sizing and risk management.
No external links, promotions, or contact details are provided, in line with TradingView House Rules.
Relative Valuation OscillatorThis is a Relative Valuation Oscillator (RVO) this is attempt of replication OTC Valuation - a sophisticated multi-asset comparison indicator designed to measure whether the current asset is overvalued or undervalued relative to up to three reference assets.
Overview
The RVO compares the current chart's asset against reference assets (default: 30-Year Treasury Bonds, Gold, and US Dollar Index) to determine relative strength and valuation extremes. It outputs normalized oscillator values ranging from -100 (undervalued) to +100 (overvalued).
Key Features
Multiple Calculation Methods
The indicator offers 5 different calculation approaches:
Simple Ratio - Normalized ratio deviation from average
Percentage Difference - Percentage change comparison
Ratio Z-Score - Standard deviation-based comparison
Rate of Change Comparison - Momentum differential analysis (default)
Normalized Ratio - Min-max normalized ratio
Configurable Reference Assets
Asset 1: Default ZB (30-Year Treasury Bond Futures) - tracks interest rate sensitivity
Asset 2: Default GC (Gold Futures) - tracks safe-haven and inflation dynamics
Asset 3: Default DXY (US Dollar Index) - tracks currency strength
Each asset can be enabled/disabled independently
Fully customizable symbols
Visual Components
Multiple oscillator lines - One for each active reference asset (color-coded)
Average line - Combined signal from all active assets
Overbought/Oversold zones - Configurable threshold levels (default: ±80)
Zero line - Neutral valuation reference
Background coloring - Visual zones for extreme conditions
Signal line - Optional smoothed average
Entry markers - Long/short signals at key reversals
Signal Generation
Crossover alerts - When crossing overbought/oversold levels
Entry signals - Reversals from extreme zones
Divergence detection - Bullish/bearish divergences between price and oscillator
Zero-line crosses - Trend strength changes
Customization Options
Lookback period (10-500): Controls statistical calculation window
Normalization period (50-1000): Determines scaling sensitivity
Smoothing toggle: Optional EMA/SMA smoothing with adjustable period
Visual customization: Colors, levels, and display options
Information Table
Real-time dashboard showing:
Average oscillator value
Current status (Overvalued/Undervalued/Neutral)
Current asset price
Individual values for each active reference asset
Use Cases
Mean reversion trading - Identify extreme relative valuations for reversal trades
Sector rotation - Compare assets within similar categories
Hedging strategies - Understand correlation dynamics
Multi-asset analysis - Simultaneously compare against bonds, commodities, and currencies
Divergence trading - Spot price/oscillator divergences
Trading Strategy Applications
Long signals: When oscillator crosses above oversold level (asset recovering from undervaluation)
Short signals: When oscillator crosses below overbought level (asset declining from overvaluation)
Confirmation: Use multiple reference assets for stronger signals
Risk management: Avoid trading when all assets show neutral readings
This indicator is particularly useful for traders who want to incorporate inter-market analysis and relative strength concepts into their trading decisions, especially in OTC (Over-The-Counter) and futures markets.
