Moving Average Signals : Support ResistanceThis indicator plots a Simple Moving Average (default 50-period, adjustable) and highlights potential bounce or rejection signals when price interacts with the SMA.
It is designed to identify moments when price tests the moving average from one side and then continues in the prior direction, signaling a possible continuation trade.
🔴 Red Triangle (Bearish Rejection)
A red triangle is plotted above the bar when:
Price has been trading below the SMA.
Price tests the SMA from below (the high touches or pierces the SMA but closes back below it).
Price then continues lower on the next bar.
This suggests the SMA acted as resistance and the downtrend may resume.
🟢 Green Triangle (Bullish Rejection)
A green triangle is plotted below the bar when:
Price has been trading above the SMA.
Price tests the SMA from above (the low touches or pierces the SMA but closes back above it).
Price then continues higher on the next bar.
This suggests the SMA acted as support and the uptrend may resume.
⚡ HOW TO USE IN TRADING
Trend Confirmation
Use this indicator in trending markets (not choppy ranges).
A rising SMA suggests bullish trend bias; a falling SMA suggests bearish trend bias.
Signal Entry
Green Triangle: Consider long entries when the SMA supports price and a bullish continuation is signaled.
Red Triangle: Consider short entries when the SMA rejects price and a bearish continuation is signaled.
Stop-Loss Placement
Place stops just beyond the SMA or the rejection candle’s high/low.
Example: For a red signal, stop above the SMA or rejection candle’s high.
Take-Profit Ideas
Target prior swing highs/lows or use risk/reward multiples (e.g., 2R, 3R).
You can also trail stops behind the SMA in a strong trend.
Filters for Higher Accuracy (optional)
Confirm signals with volume, momentum indicators (e.g., RSI, MACD), or higher-timeframe trend.
Avoid trading signals against strong higher-timeframe bias.
Search in scripts for "bias"
NY Anchored VWAP and Auto SMANY Anchored VWAP and Auto SMA
This script is a versatile trading indicator for the TradingView platform that combines two powerful components: a New York-anchored Volume-Weighted Average Price (VWAP) and a dynamic Simple Moving Average (SMA). Designed for traders who utilize VWAP for intraday trend analysis, this tool provides a clear visual representation of average price and volatility-adjusted moving averages, generating automated alerts for key crossover signals.
Indicator Components
1. NY Anchored VWAP
The VWAP is a crucial tool that represents the average price of a security adjusted for volume. This version is "anchored" to the start of the New York trading session, resetting at the beginning of each new session. This provides a clean, session-specific anchor point to gauge market sentiment and trend. The VWAP line changes color to reflect its slope:
Green: When the VWAP is trending upwards, indicating a bullish bias.
Red: When the VWAP is trending downwards, indicating a bearish bias.
2. Auto SMA
The Auto SMA is a moving average with a unique twist: its lookback period is not fixed. Instead, it dynamically adjusts based on market volatility. The script measures volatility using the Average True Range (ATR) and a Z-Score calculation.
When volatility is expanding, the SMA's length shortens, making it more sensitive to recent price changes.
When volatility is contracting, the SMA's length lengthens, smoothing out the price action to filter out noise.
This adaptive approach allows the SMA to react appropriately to different market conditions.
Suggested Trading Strategy
This indicator is particularly effective when used on a one-minute chart for identifying high-probability trade entries. The core of the strategy is to trade the crossover between the VWAP and the Auto SMA, with confirmation from a candle close.
The strategy works best when the entry signal aligns with the overall bias of the higher timeframe market structure. For example, if the daily or 4-hour chart is in an uptrend, you would look for bullish signals on the one-minute chart.
Bullish Entry Signal: A potential entry is signaled when the VWAP crosses above the Auto SMA, and is confirmed when the one-minute candle closes above both the VWAP and the SMA. This indicates a potential continuation of the bullish momentum.
Bearish Entry Signal: A potential entry is signaled when the VWAP crosses below the Auto SMA, and is confirmed when the one-minute candle closes below both the VWAP and the SMA. This indicates a potential continuation of the bearish momentum.
The built-in alerts for these crossovers allow you to receive notifications without having to constantly monitor the charts, ensuring you don't miss a potential setup.
Adaptive Valuation [BackQuant]Adaptive Valuation
What this is
A composite, zero-centered oscillator that standardizes several classic indicators and blends them into one “valuation” line. It computes RSI, CCI, Demarker, and the Price Zone Oscillator, converts each to a rolling z-score, then forms a weighted average. Optional smoothing, dynamic overbought and oversold bands, and an on-chart table make the inputs and the final score easy to inspect.
How it works
Components
• RSI with its own lookback.
• CCI with its own lookback.
• DM (Demarker) with its own lookback.
• PZO (Price Zone Oscillator) with its own lookback.
Standardization via z-score
Each component is transformed using a rolling z-score over lookback bars:
z = (value − mean) ÷ stdev , where the mean is an EMA and the stdev is rolling.
This puts all inputs on a comparable scale measured in standard deviations.
Weighted blend
The z-scores are combined with user weights w_rsi, w_cci, w_dm, w_pzo to produce a single valuation series. If desired, it is then smoothed with a selected moving average (SMA, EMA, WMA, HMA, RMA, DEMA, TEMA, LINREG, ALMA, T3). ALMA’s sigma input shapes its curve.
Dynamic thresholds (optional)
Two ways to set overbought and oversold:
• Static : fixed levels at ob_thres and os_thres .
• Dynamic : ±k·σ bands, where σ is the rolling standard deviation of the valuation over dynLen .
Bands can be centered at zero or around the valuation’s rolling mean ( centerZero ).
Visualization and UI
• Zero line at 0 with gradient fill that darkens as the valuation moves away from 0.
• Optional plotting of band lines and background highlights when OB or OS is active.
• Optional candle and background coloring driven by the valuation.
• Summary table showing each component’s current z-score, the final score, and a compact status.
How it can be used
• Bias filter : treat crosses above 0 as bullish bias and below 0 as bearish bias.
• Mean-reversion context : look for exhaustion when the valuation enters the OB or OS region, then watch for exits from those regions or a return toward 0.
• Signal confirmation : use the final score to confirm setups from structure or price action.
• Adaptive banding : with dynamic thresholds, OB and OS adjust to prevailing variability rather than relying on fixed lines.
• Component tuning : change weights to emphasize trend (raise DM, reduce RSI/CCI) or range behavior (raise RSI/CCI, reduce DM). PZO can help in swing environments.
Why z-score blending helps
Indicators often live on different scales. Z-scoring places them on a common, unitless axis, so a one-sigma move in RSI has comparable influence to a one-sigma move in CCI. This reduces scale bias and allows transparent weighting. It also facilitates regime-aware thresholds because the dynamic bands scale with recent dispersion.
Inputs to know
• Component lookbacks : rsilb, ccilb, dmlb, pzolb control each raw signal.
• Standardization window : lookback sets the z-score memory. Longer smooths, shorter reacts.
• Weights : w_rsi, w_cci, w_dm, w_pzo determine each component’s influence.
• Smoothing : maType, smoothP, sig govern optional post-blend smoothing.
• Dynamic bands : dyn_thres, dynLen, thres_k, centerZero configure the adaptive OB/OS logic.
• UI : toggle the plot, table, candle coloring, and threshold lines.
Reading the plot
• Above 0 : composite pressure is positive.
• Below 0 : composite pressure is negative.
• OB region : valuation above the chosen OB line. Risk of mean reversion rises and momentum continuation needs evidence.
• OS region : mirror logic on the downside.
• Band exits : leaving OB or OS can serve as a normalization cue.
Strengths
• Normalizes heterogeneous signals into one interpretable series.
• Adjustable component weights to match instrument behavior.
• Dynamic thresholds adapt to changing volatility and drift.
• Transparent diagnostics from the on-chart table.
• Flexible smoothing choices, including ALMA and T3.
Limitations and cautions
• Z-scores assume a reasonably stationary window. Sharp regime shifts can make recent bands unrepresentative.
• Highly correlated components can overweight the same effect. Consider adjusting weights to avoid double counting.
• More smoothing adds lag. Less smoothing adds noise.
• Dynamic bands recalibrate with dynLen ; if set too short, bands may swing excessively. If too long, bands can be slow to adapt.
Practical tuning tips
• Trending symbols: increase w_dm , use a modest smoother like EMA or T3, and use centerZero dynamic bands.
• Choppy symbols: increase w_rsi and w_cci , consider ALMA with a higher sigma , and widen bands with a larger thres_k .
• Multiday swing charts: lengthen lookback and dynLen to stabilize the scale.
• Lower timeframes: shorten component lookbacks slightly and reduce smoothing to keep signals timely.
Alerts
• Enter and exit of Overbought and Oversold, based on the active band choice.
• Bullish and bearish zero crosses.
Use alerts as prompts to review context rather than as stand-alone trade commands.
Final Remarks
We created this to show people a different way of making indicators & trading.
You can process normal indicators in multiple ways to enhance or change the signal, especially with this you can utilise machine learning to optimise the weights, then trade accordingly.
All of the different components were selected to give some sort of signal, its made out of simple components yet is effective. As long as the user calibrates it to their Trading/ investing style you can find good results. Do not use anything standalone, ensure you are backtesting and creating a proper system.
Volume Spikes + Daily VWAP SD BandsVolume Spikes + Daily VWAP SD Bands
This indicator combines volume spike detection to help traders identify potential absorption zones with daily VWAP and standard deviation bands , key price levels, continuation opportunities, and possible institutional bias.
Features:
Volume Spike Detection
Highlights candles with unusually high volume relative to a configurable SMA.
Optional filters:
Local highs/lows only (Only Use Valid Highs & Lows)
Candle shapes: Hammer / Shooter only
Candle color match: bullish spikes on green, bearish on red
Plots small circles above/below bars for bullish and bearish volume spikes.
Alerts available for both bullish and bearish spikes.
Interpretation: Volume spikes at local highs/lows can indicate absorption, where one side absorbs aggressive buying/selling pressure.
Daily VWAP
Calculates volume-weighted average price (VWAP) for the current day.
Optionally shows previous day’s VWAP for reference.
Plot lines are customizable with optional circles on lines for visual clarity.
Labels on the last bar show exact VWAP values.
Institutional Bias Insight: Price above both current and previous VWAPs may indicate bullish positioning; price below both VWAPs may indicate bearish positioning. Many professional traders consider this a clue to institutional bias, but it’s not guaranteed. Always confirm with volume, delta, or orderflow analysis.
Standard Deviation Bands
Optional x1 and x2 SD bands around the daily VWAP.
Visual fill between bands shows price volatility zones.
Can be used to identify potential support/resistance or absorption zones.
Use Case: Price bounces off first SD band may indicate continuation signals, especially when volume spikes occur at those levels.
Customizable Visuals
Colors for bullish and bearish volume spikes
VWAP and SD band colors and thickness
Optional circles and filled bands for better readability
Alerts
Bullish / Bearish Volume Spikes
Supports TradingView alert system for automated notifications
Advanced Use Cases:
Combine with Cumulative Delta or Orderflow tools to confirm true absorption zones.
Identify high-volume rejection candles signaling possible trend continuation.
Use VWAP positioning relative to price to assess potential institutional bias, keeping in mind it is probabilistic, not guaranteed.
Visualize intraday VWAP levels and volatility with SD bands for better trade timing.
Settings: Fully customizable, including volume multiplier, SMA length, session filter, candle shape, color options, and VWAP/SD display preferences.
Pivot Matrix & Multi-Timeframe Support-Resistance Analytics________________________________________
📘 Study Material for Pivot Matrix & Multi Timeframe Support-Resistance Analytics
(By aiTrendview — Educational Use Only)
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🎯 Introduction
The Pivot Matrix & Multi Timeframe Support-Resistance Analytics indicator is designed to help traders visualize pivot points, support/resistance levels, VWAP, and volume flow analytics all in one place. Rather than giving explicit buy/sell calls, the dashboard provides reference insights so a learner may understand how different technical levels interact in real time.
This document explains its functionality step by step with formulas and usage guides.
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1️⃣ Pivot System Logic
Pivot points are classic tools for mapping market support and resistance levels.
✦ How Calculated?
Using the Traditional Method:
• Pivot Point (PP):
PP=Highprev+Lowprev+Closeprev3PP = \frac{High_{prev} + Low_{prev} + Close_{prev}}{3}PP=3Highprev+Lowprev+Closeprev
• First Support/Resistance:
R1=2×PP−Lowprev,S1=2×PP−HighprevR1 = 2 \times PP - Low_{prev}, \quad S1 = 2 \times PP - High_{prev}R1=2×PP−Lowprev,S1=2×PP−Highprev
• Second Support/Resistance:
R2=PP+(Highprev−Lowprev),S2=PP−(Highprev−Lowprev)R2 = PP + (High_{prev} - Low_{prev}), \quad S2 = PP - (High_{prev} - Low_{prev})R2=PP+(Highprev−Lowprev),S2=PP−(Highprev−Lowprev)
• Third Levels:
R3=Highprev+2×(PP−Lowprev),S3=Lowprev−2×(Highprev−PP)R3 = High_{prev} + 2 \times (PP - Low_{prev}), \quad S3 = Low_{prev} - 2 \times (High_{prev} - PP)R3=Highprev+2×(PP−Lowprev),S3=Lowprev−2×(Highprev−PP)
• Similarly, R4/R5 and S4/S5 are extrapolated from extended range multipliers.
✦ How Used?
• Price above PP → bullish control bias.
• Price below PP → bearish control bias.
• R1–R5 levels act as resistances; S1–S5 act as supports.
Learners should watch how candles behave when approaching R/S zones to spot breakout vs. rejection conditions.
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2️⃣ Multi Timeframe Logic
The indicator allows using daily-based pivot values (via request.security). This ensures alignment with institutional daily levels, not just intraday recalculations.
✦ Teaching Value
Understanding MTF pivots shows how markets respect higher timeframe levels (daily > intraday, weekly > daily). This helps learners grasp nested support-resistance structures.
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3️⃣ VWAP (Volume Weighted Average Price)
Formula:
VWAPt=∑(Pricei×Volumei)∑(Volumei),Pricei=High+Low+Close3VWAP_t = \frac{\sum (Price_i \times Volume_i)}{\sum (Volume_i)}, \quad Price_i = \frac{High + Low + Close}{3}VWAPt=∑(Volumei)∑(Pricei×Volumei),Pricei=3High+Low+Close
Usage:
• VWAP is used as an institutional benchmark of fair value.
• Above VWAP = bullish flow.
• Below VWAP = bearish flow.
Learners should check whether price respects VWAP as a magnet or uses it as support/resistance.
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4️⃣ Volume Flow Analysis
The script classifies buy volume, sell volume, and neutral volume.
• Buy Volume = if close > open.
• Sell Volume = if close < open.
• Neutral Volume = if close = open.
For daily tracking:
Buy%=DayBuyVolDayTotalVol×100,Sell%=DaySellVolDayTotalVol×100Buy\% = \frac{DayBuyVol}{DayTotalVol} \times 100, \quad Sell\% = \frac{DaySellVol}{DayTotalVol} \times 100Buy%=DayTotalVolDayBuyVol×100,Sell%=DayTotalVolDaySellVol×100
Usage for Learners:
• Dominant Buy% → accumulation/ bullish pressure.
• Dominant Sell% → distribution/ bearish pressure.
• Balanced → sideways liquidity building.
This teaches observation of order flow bias rather than relying only on price.
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5️⃣ Dashboard Progress Bars & Colors
The script uses visual progress bars and dynamic colors for clarity. For example:
• VWAP Backgrounds: Green shades when price strongly above VWAP, Red when below.
• Volume Bars: More green blocks mean buying dominance, red means selling pressure.
This visual design turns concepts into easy-to-digest cues, useful for training.
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6️⃣ Market Status Summary
Finally, the dashboard synthesizes all data points:
• Price vs Pivot (above or below).
• Price vs VWAP (above or below).
• Volume Pressure (buy side vs sell side).
Status Rule:
• If all three align bullish → Status box turns green.
• If mixed → Neutral grey.
• If bearish dominance → weaker tone.
Why Important?
This teaches learners that market conditions should align in confluence across indicators before confidence arises.
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⚠️ Strict Disclaimer (aiTrendview)
The Pivot Matrix & Multi Timeframe Support-Resistance Analytics tool is developed by aiTrendview for strictly educational and research purposes.
❌ It does NOT provide buy/sell recommendations.
❌ It does NOT guarantee profits.
❌ Unauthorized use, copying, or redistribution of this code is prohibited.
⚠️ Trading Risk Warning:
• Trading involves high risk of financial loss.
• You may lose more than your capital.
• Past levels and indicators do not predict future outcomes.
This tool must be viewed as a visual education aid to practice technical analysis skills, not as trading advice.
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✅ Now you have a step by step study guide:
• Pivot calculations explained
• VWAP with logic
• Volume breakdown
• Visual analytics
• Status confluence logic
• Disclaimer for compliance
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⚠️ Warning:
• Trading financial markets involves substantial risk.
• You can lose more money than you invest.
• Past performance of indicators does not guarantee future results.
• This script must not be copied, resold, or republished without authorization from aiTrendview.
By using this material or the code, you agree to take full responsibility for your trading decisions and acknowledge that this is not financial advice.
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⚠️ Disclaimer and Warning (From aiTrendview)
This Dynamic Trading Dashboard is created strictly for educational and research purposes on the TradingView platform. It does not provide financial advice, buy/sell recommendations, or guaranteed returns. Any use of this tool in live trading is completely at the user’s own risk. Markets are inherently risky; losses can exceed initial investment.
The intellectual property of this script and its methodology belongs to aiTrendview. Unauthorized reproduction, modification, or redistribution of this code is strictly prohibited. By using this study material or the script, you acknowledge personal responsibility for any trading outcomes. Always consult professional financial advisors before making investment decisions.
Pure Price Zone Flow🔎 What this indicator is
It’s a price-action-based zone indicator. Unlike moving average systems, this one relies only on:
1. Swing Highs & Swing Lows → The highest and lowest points within a recent lookback period (like "mini support & resistance").
2. ATR (Average True Range) → A volatility measure that expands the zone, making it more adaptive to different market conditions.
3. Breakouts & Retests → When price breaks above a swing high (bullish) or below a swing low (bearish), the indicator marks it and highlights the new trend.
👉 The goal is to spot clean structure shifts and define clear trend zones where traders can position themselves.
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⚙️ How it is calculated
1. Swing High & Swing Low
o We look back len candles (default 20).
o Find the highest high (swingHigh) and the lowest low (swingLow) in that window.
o This forms the price range zone.
2. ATR Expansion
o We calculate ATR over the same len.
o Add/subtract it (multiplied by atrMult) to the zone edges to expand them.
o This ensures the zones breathe with volatility (tight in quiet markets, wide in choppy ones).
3. Mid-Zone
o Simply the average of swingHigh and swingLow.
o If price is above mid → bullish bias.
o If below mid → bearish bias.
o This gives us the trend color for candles.
4. Breakouts
o If the close crosses above swingHigh, we mark a bullish breakout with a label.
o If the close crosses below swingLow, we mark a bearish breakdown.
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📊 How it helps traders
This indicator helps by:
1. Identifying Structure Shifts
o Many traders watch swing highs/lows for breakouts or reversals.
o This automates the process and visually confirms when structure is broken.
2. Dynamic Zone Trading
o Instead of fixed support/resistance, the ATR expansion adapts to volatility.
o This avoids false signals in high-volatility conditions.
3. Trend Bias at a Glance
o Candle coloring instantly tells you whether price is in bullish or bearish territory relative to the mid-zone.
4. Breakout Confirmation
o The labels show when a breakout has occurred, so traders can react quickly (e.g., enter with trend, wait for retest, or avoid fading moves).
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🌍 Markets it works best in
• Crypto (Bitcoin, Ethereum, etc.): Very effective since crypto is breakout-driven and respects swing levels.
• Forex: Good for volatility-adaptive structure analysis, especially in trending pairs.
• Indices (SPX, NASDAQ, DAX, NIFTY): Useful for breakout trading during session opens or key news events.
• Commodities (Gold, Oil, Silver): Works well to define intraday ranges and breakout levels.
⚠️ Less useful in low-volatility, mean-reverting assets (like some penny stocks or sideways ranges), because breakouts may be rare or fake.
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💡 How it adds value
• Strips away unnecessary complexity (no lagging averages).
• Focuses directly on what price is doing structurally.
• Adaptive → works across different markets & timeframes.
• Easy visualization → zones, trend coloring, breakout markers.
• Helps traders trade with the flow of the market, instead of guessing tops/bottoms.
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👉 In short:
This indicator turns raw price action into clear, actionable zones.
It highlights when the market shifts from balance to breakout, so traders can align with momentum rather than fighting it.
Ray Dalio's All Weather Strategy - Portfolio CalculatorTHE ALL WEATHER STRATEGY INDICATOR: A GUIDE TO RAY DALIO'S LEGENDARY PORTFOLIO APPROACH
Introduction: The Genesis of Financial Resilience
In the sprawling corridors of Bridgewater Associates, the world's largest hedge fund managing over 150 billion dollars in assets, Ray Dalio conceived what would become one of the most influential investment strategies of the modern era. The All Weather Strategy, born from decades of market observation and rigorous backtesting, represents a paradigm shift from traditional portfolio construction methods that have dominated Wall Street since Harry Markowitz's seminal work on Modern Portfolio Theory in 1952.
Unlike conventional approaches that chase returns through market timing or stock picking, the All Weather Strategy embraces a fundamental truth that has humbled countless investors throughout history: nobody can consistently predict the future direction of markets. Instead of fighting this uncertainty, Dalio's approach harnesses it, creating a portfolio designed to perform reasonably well across all economic environments, hence the evocative name "All Weather."
The strategy emerged from Bridgewater's extensive research into economic cycles and asset class behavior, culminating in what Dalio describes as "the Holy Grail of investing" in his bestselling book "Principles" (Dalio, 2017). This Holy Grail isn't about achieving spectacular returns, but rather about achieving consistent, risk-adjusted returns that compound steadily over time, much like the tortoise defeating the hare in Aesop's timeless fable.
HISTORICAL DEVELOPMENT AND EVOLUTION
The All Weather Strategy's origins trace back to the tumultuous economic periods of the 1970s and 1980s, when traditional portfolio construction methods proved inadequate for navigating simultaneous inflation and recession. Raymond Thomas Dalio, born in 1949 in Queens, New York, founded Bridgewater Associates from his Manhattan apartment in 1975, initially focusing on currency and fixed-income consulting for corporate clients.
Dalio's early experiences during the 1970s stagflation period profoundly shaped his investment philosophy. Unlike many of his contemporaries who viewed inflation and deflation as opposing forces, Dalio recognized that both conditions could coexist with either economic growth or contraction, creating four distinct economic environments rather than the traditional two-factor models that dominated academic finance.
The conceptual breakthrough came in the late 1980s when Dalio began systematically analyzing asset class performance across different economic regimes. Working with a small team of researchers, Bridgewater developed sophisticated models that decomposed economic conditions into growth and inflation components, then mapped historical asset class returns against these regimes. This research revealed that traditional portfolio construction, heavily weighted toward stocks and bonds, left investors vulnerable to specific economic scenarios.
The formal All Weather Strategy emerged in 1996 when Bridgewater was approached by a wealthy family seeking a portfolio that could protect their wealth across various economic conditions without requiring active management or market timing. Unlike Bridgewater's flagship Pure Alpha fund, which relied on active trading and leverage, the All Weather approach needed to be completely passive and unleveraged while still providing adequate diversification.
Dalio and his team spent months developing and testing various allocation schemes, ultimately settling on the 30/40/15/7.5/7.5 framework that balances risk contributions rather than dollar amounts. This approach was revolutionary because it focused on risk budgeting—ensuring that no single asset class dominated the portfolio's risk profile—rather than the traditional approach of equal dollar allocations or market-cap weighting.
The strategy's first institutional implementation began in 1996 with a family office client, followed by gradual expansion to other wealthy families and eventually institutional investors. By 2005, Bridgewater was managing over $15 billion in All Weather assets, making it one of the largest systematic strategy implementations in institutional investing.
The 2008 financial crisis provided the ultimate test of the All Weather methodology. While the S&P 500 declined by 37% and many hedge funds suffered double-digit losses, the All Weather strategy generated positive returns, validating Dalio's risk-balancing approach. This performance during extreme market stress attracted significant institutional attention, leading to rapid asset growth in subsequent years.
The strategy's theoretical foundations evolved throughout the 2000s as Bridgewater's research team, led by co-chief investment officers Greg Jensen and Bob Prince, refined the economic framework and incorporated insights from behavioral economics and complexity theory. Their research, published in numerous institutional white papers, demonstrated that traditional portfolio optimization methods consistently underperformed simpler risk-balanced approaches across various time periods and market conditions.
Academic validation came through partnerships with leading business schools and collaboration with prominent economists. The strategy's risk parity principles influenced an entire generation of institutional investors, leading to the creation of numerous risk parity funds managing hundreds of billions in aggregate assets.
In recent years, the democratization of sophisticated financial tools has made All Weather-style investing accessible to individual investors through ETFs and systematic platforms. The availability of high-quality, low-cost ETFs covering each required asset class has eliminated many of the barriers that previously limited sophisticated portfolio construction to institutional investors.
The development of advanced portfolio management software and platforms like TradingView has further democratized access to institutional-quality analytics and implementation tools. The All Weather Strategy Indicator represents the culmination of this trend, providing individual investors with capabilities that previously required teams of portfolio managers and risk analysts.
Understanding the Four Economic Seasons
The All Weather Strategy's theoretical foundation rests on Dalio's observation that all economic environments can be characterized by two primary variables: economic growth and inflation. These variables create four distinct "economic seasons," each favoring different asset classes. Rising growth benefits stocks and commodities, while falling growth favors bonds. Rising inflation helps commodities and inflation-protected securities, while falling inflation benefits nominal bonds and stocks.
This framework, detailed extensively in Bridgewater's research papers from the 1990s, suggests that by holding assets that perform well in each economic season, an investor can create a portfolio that remains resilient regardless of which season unfolds. The elegance lies not in predicting which season will occur, but in being prepared for all of them simultaneously.
Academic research supports this multi-environment approach. Ang and Bekaert (2002) demonstrated that regime changes in economic conditions significantly impact asset returns, while Fama and French (2004) showed that different asset classes exhibit varying sensitivities to economic factors. The All Weather Strategy essentially operationalizes these academic insights into a practical investment framework.
The Original All Weather Allocation: Simplicity Masquerading as Sophistication
The core All Weather portfolio, as implemented by Bridgewater for institutional clients and later adapted for retail investors, maintains a deceptively simple static allocation: 30% stocks, 40% long-term bonds, 15% intermediate-term bonds, 7.5% commodities, and 7.5% Treasury Inflation-Protected Securities (TIPS). This allocation may appear arbitrary to the uninitiated, but each percentage reflects careful consideration of historical volatilities, correlations, and economic sensitivities.
The 30% stock allocation provides growth exposure while limiting the portfolio's overall volatility. Stocks historically deliver superior long-term returns but with significant volatility, as evidenced by the Standard & Poor's 500 Index's average annual return of approximately 10% since 1926, accompanied by standard deviation exceeding 15% (Ibbotson Associates, 2023). By limiting stock exposure to 30%, the portfolio captures much of the equity risk premium while avoiding excessive volatility.
The combined 55% allocation to bonds (40% long-term plus 15% intermediate-term) serves as the portfolio's stabilizing force. Long-term bonds provide substantial interest rate sensitivity, performing well during economic slowdowns when central banks reduce rates. Intermediate-term bonds offer a balance between interest rate sensitivity and reduced duration risk. This bond-heavy allocation reflects Dalio's insight that bonds typically exhibit lower volatility than stocks while providing essential diversification benefits.
The 7.5% commodities allocation addresses inflation protection, as commodity prices typically rise during inflationary periods. Historical analysis by Bodie and Rosansky (1980) demonstrated that commodities provide meaningful diversification benefits and inflation hedging capabilities, though with considerable volatility. The relatively small allocation reflects commodities' high volatility and mixed long-term returns.
Finally, the 7.5% TIPS allocation provides explicit inflation protection through government-backed securities whose principal and interest payments adjust with inflation. Introduced by the U.S. Treasury in 1997, TIPS have proven effective inflation hedges, though they underperform nominal bonds during deflationary periods (Campbell & Viceira, 2001).
Historical Performance: The Evidence Speaks
Analyzing the All Weather Strategy's historical performance reveals both its strengths and limitations. Using monthly return data from 1970 to 2023, spanning over five decades of varying economic conditions, the strategy has delivered compelling risk-adjusted returns while experiencing lower volatility than traditional stock-heavy portfolios.
During this period, the All Weather allocation generated an average annual return of approximately 8.2%, compared to 10.5% for the S&P 500 Index. However, the strategy's annual volatility measured just 9.1%, substantially lower than the S&P 500's 15.8% volatility. This translated to a Sharpe ratio of 0.67 for the All Weather Strategy versus 0.54 for the S&P 500, indicating superior risk-adjusted performance.
More impressively, the strategy's maximum drawdown over this period was 12.3%, occurring during the 2008 financial crisis, compared to the S&P 500's maximum drawdown of 50.9% during the same period. This drawdown mitigation proves crucial for long-term wealth building, as Stein and DeMuth (2003) demonstrated that avoiding large losses significantly impacts compound returns over time.
The strategy performed particularly well during periods of economic stress. During the 1970s stagflation, when stocks and bonds both struggled, the All Weather portfolio's commodity and TIPS allocations provided essential protection. Similarly, during the 2000-2002 dot-com crash and the 2008 financial crisis, the portfolio's bond-heavy allocation cushioned losses while maintaining positive returns in several years when stocks declined significantly.
However, the strategy underperformed during sustained bull markets, particularly the 1990s technology boom and the 2010s post-financial crisis recovery. This underperformance reflects the strategy's conservative nature and diversified approach, which sacrifices potential upside for downside protection. As Dalio frequently emphasizes, the All Weather Strategy prioritizes "not losing money" over "making a lot of money."
Implementing the All Weather Strategy: A Practical Guide
The All Weather Strategy Indicator transforms Dalio's institutional-grade approach into an accessible tool for individual investors. The indicator provides real-time portfolio tracking, rebalancing signals, and performance analytics, eliminating much of the complexity traditionally associated with implementing sophisticated allocation strategies.
To begin implementation, investors must first determine their investable capital. As detailed analysis reveals, the All Weather Strategy requires meaningful capital to implement effectively due to transaction costs, minimum investment requirements, and the need for precise allocations across five different asset classes.
For portfolios below $50,000, the strategy becomes challenging to implement efficiently. Transaction costs consume a disproportionate share of returns, while the inability to purchase fractional shares creates allocation drift. Consider an investor with $25,000 attempting to allocate 7.5% to commodities through the iPath Bloomberg Commodity Index ETF (DJP), currently trading around $25 per share. This allocation targets $1,875, enough for only 75 shares, creating immediate tracking error.
At $50,000, implementation becomes feasible but not optimal. The 30% stock allocation ($15,000) purchases approximately 37 shares of the SPDR S&P 500 ETF (SPY) at current prices around $400 per share. The 40% long-term bond allocation ($20,000) buys 200 shares of the iShares 20+ Year Treasury Bond ETF (TLT) at approximately $100 per share. While workable, these allocations leave significant cash drag and rebalancing challenges.
The optimal minimum for individual implementation appears to be $100,000. At this level, each allocation becomes substantial enough for precise implementation while keeping transaction costs below 0.4% annually. The $30,000 stock allocation, $40,000 long-term bond allocation, $15,000 intermediate-term bond allocation, $7,500 commodity allocation, and $7,500 TIPS allocation each provide sufficient size for effective management.
For investors with $250,000 or more, the strategy implementation approaches institutional quality. Allocation precision improves, transaction costs decline as a percentage of assets, and rebalancing becomes highly efficient. These larger portfolios can also consider adding complexity through international diversification or alternative implementations.
The indicator recommends quarterly rebalancing to balance transaction costs with allocation discipline. Monthly rebalancing increases costs without substantial benefits for most investors, while annual rebalancing allows excessive drift that can meaningfully impact performance. Quarterly rebalancing, typically on the first trading day of each quarter, provides an optimal balance.
Understanding the Indicator's Functionality
The All Weather Strategy Indicator operates as a comprehensive portfolio management system, providing multiple analytical layers that professional money managers typically reserve for institutional clients. This sophisticated tool transforms Ray Dalio's institutional-grade strategy into an accessible platform for individual investors, offering features that rival professional portfolio management software.
The indicator's core architecture consists of several interconnected modules that work seamlessly together to provide complete portfolio oversight. At its foundation lies a real-time portfolio simulation engine that tracks the exact value of each ETF position based on current market prices, eliminating the need for manual calculations or external spreadsheets.
DETAILED INDICATOR COMPONENTS AND FUNCTIONS
Portfolio Configuration Module
The portfolio setup begins with the Portfolio Configuration section, which establishes the fundamental parameters for strategy implementation. The Portfolio Capital input accepts values from $1,000 to $10,000,000, accommodating everyone from beginning investors to institutional clients. This input directly drives all subsequent calculations, determining exact share quantities and portfolio values throughout the implementation period.
The Portfolio Start Date function allows users to specify when they began implementing the All Weather Strategy, creating a clear demarcation point for performance tracking. This feature proves essential for investors who want to track their actual implementation against theoretical performance, providing realistic assessment of strategy effectiveness including timing differences and implementation costs.
Rebalancing Frequency settings offer two options: Monthly and Quarterly. While monthly rebalancing provides more precise allocation control, quarterly rebalancing typically proves more cost-effective for most investors due to reduced transaction costs. The indicator automatically detects the first trading day of each period, ensuring rebalancing occurs at optimal times regardless of weekends, holidays, or market closures.
The Rebalancing Threshold parameter, adjustable from 0.5% to 10%, determines when allocation drift triggers rebalancing recommendations. Conservative settings like 1-2% maintain tight allocation control but increase trading frequency, while wider thresholds like 3-5% reduce trading costs but allow greater allocation drift. This flexibility accommodates different risk tolerances and cost structures.
Visual Display System
The Show All Weather Calculator toggle controls the main dashboard visibility, allowing users to focus on chart visualization when detailed metrics aren't needed. When enabled, this comprehensive dashboard displays current portfolio value, individual ETF allocations, target versus actual weights, rebalancing status, and performance metrics in a professionally formatted table.
Economic Environment Display provides context about current market conditions based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated regime detection, this feature helps users understand which economic "season" currently prevails and which asset classes should theoretically benefit.
Rebalancing Signals illuminate when portfolio drift exceeds user-defined thresholds, highlighting specific ETFs that require adjustment. These signals use color coding to indicate urgency: green for balanced allocations, yellow for moderate drift, and red for significant deviations requiring immediate attention.
Advanced Label System
The rebalancing label system represents one of the indicator's most innovative features, providing three distinct detail levels to accommodate different user needs and experience levels. The "None" setting displays simple symbols marking portfolio start and rebalancing events without cluttering the chart with text. This minimal approach suits experienced investors who understand the implications of each symbol.
"Basic" label mode shows essential information including portfolio values at each rebalancing point, enabling quick assessment of strategy performance over time. These labels display "START $X" for portfolio initiation and "RBL $Y" for rebalancing events, providing clear performance tracking without overwhelming detail.
"Detailed" labels provide comprehensive trading instructions including exact buy and sell quantities for each ETF. These labels might display "RBL $125,000 BUY 15 SPY SELL 25 TLT BUY 8 IEF NO TRADES DJP SELL 12 SCHP" providing complete implementation guidance. This feature essentially transforms the indicator into a personal portfolio manager, eliminating guesswork about exact trades required.
Professional Color Themes
Eight professionally designed color themes adapt the indicator's appearance to different aesthetic preferences and market analysis styles. The "Gold" theme reflects traditional wealth management aesthetics, while "EdgeTools" provides modern professional appearance. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making, while "Quant" employs high-contrast combinations favored by quantitative analysts.
"Ocean," "Fire," "Matrix," and "Arctic" themes provide distinctive visual identities for traders who prefer unique chart aesthetics. Each theme automatically adjusts for dark or light mode optimization, ensuring optimal readability across different TradingView configurations.
Real-Time Portfolio Tracking
The portfolio simulation engine continuously tracks five separate ETF positions: SPY for stocks, TLT for long-term bonds, IEF for intermediate-term bonds, DJP for commodities, and SCHP for TIPS. Each position's value updates in real-time based on current market prices, providing instant feedback about portfolio performance and allocation drift.
Current share calculations determine exact holdings based on the most recent rebalancing, while target shares reflect optimal allocation based on current portfolio value. Trade calculations show precisely how many shares to buy or sell during rebalancing, eliminating manual calculations and potential errors.
Performance Analytics Suite
The indicator's performance measurement capabilities rival professional portfolio analysis software. Sharpe ratio calculations incorporate current risk-free rates obtained from Treasury yield data, providing accurate risk-adjusted performance assessment. Volatility measurements use rolling periods to capture changing market conditions while maintaining statistical significance.
Portfolio return calculations track both absolute and relative performance, comparing the All Weather implementation against individual asset classes and benchmark indices. These metrics update continuously, providing real-time assessment of strategy effectiveness and implementation quality.
Data Quality Monitoring
Sophisticated data quality checks ensure reliable indicator operation across different market conditions and potential data interruptions. The system monitors all five ETF price feeds plus economic data sources, providing quality scores that alert users to potential data issues that might affect calculations.
When data quality degrades, the indicator automatically switches to fallback values or alternative data sources, maintaining functionality during temporary market data interruptions. This robust design ensures consistent operation even during volatile market conditions when data feeds occasionally experience disruptions.
Risk Management and Behavioral Considerations
Despite its sophisticated design, the All Weather Strategy faces behavioral challenges that have derailed countless well-intentioned investment plans. The strategy's conservative nature means it will underperform growth stocks during bull markets, potentially by substantial margins. Maintaining discipline during these periods requires understanding that the strategy optimizes for risk-adjusted returns over absolute returns.
Behavioral finance research by Kahneman and Tversky (1979) demonstrates that investors feel losses approximately twice as intensely as equivalent gains. This loss aversion creates powerful psychological pressure to abandon defensive strategies during bull markets when aggressive portfolios appear more attractive. The All Weather Strategy's bond-heavy allocation will seem overly conservative when technology stocks double in value, as occurred repeatedly during the 2010s.
Conversely, the strategy's defensive characteristics provide psychological comfort during market stress. When stocks crash 30-50%, as they periodically do, the All Weather portfolio's modest losses feel manageable rather than catastrophic. This emotional stability enables investors to maintain their investment discipline when others capitulate, often at the worst possible times.
Rebalancing discipline presents another behavioral challenge. Selling winners to buy losers contradicts natural human tendencies but remains essential for the strategy's success. When stocks have outperformed bonds for several quarters, rebalancing requires selling high-performing stock positions to purchase seemingly stagnant bond positions. This action feels counterintuitive but captures the strategy's systematic approach to risk management.
Tax considerations add complexity for taxable accounts. Frequent rebalancing generates taxable events that can erode after-tax returns, particularly for high-income investors facing elevated capital gains rates. Tax-advantaged accounts like 401(k)s and IRAs provide ideal vehicles for All Weather implementation, eliminating tax friction from rebalancing activities.
Capital Requirements and Cost Analysis
Comprehensive cost analysis reveals the capital requirements for effective All Weather implementation. Annual expenses include management fees for each ETF, transaction costs from rebalancing, and bid-ask spreads from trading less liquid securities.
ETF expense ratios vary significantly across asset classes. The SPDR S&P 500 ETF charges 0.09% annually, while the iShares 20+ Year Treasury Bond ETF charges 0.20%. The iShares 7-10 Year Treasury Bond ETF charges 0.15%, the Schwab US TIPS ETF charges 0.05%, and the iPath Bloomberg Commodity Index ETF charges 0.75%. Weighted by the All Weather allocations, total expense ratios average approximately 0.19% annually.
Transaction costs depend heavily on broker selection and account size. Premium brokers like Interactive Brokers charge $1-2 per trade, resulting in $20-40 annually for quarterly rebalancing. Discount brokers may charge higher per-trade fees but offer commission-free ETF trading for selected funds. Zero-commission brokers eliminate explicit trading costs but often impose wider bid-ask spreads that function as hidden fees.
Bid-ask spreads represent the difference between buying and selling prices for each security. Highly liquid ETFs like SPY maintain spreads of 1-2 basis points, while less liquid commodity ETFs may exhibit spreads of 5-10 basis points. These costs accumulate through rebalancing activities, typically totaling 10-15 basis points annually.
For a $100,000 portfolio, total annual costs including expense ratios, transaction fees, and spreads typically range from 0.35% to 0.45%, or $350-450 annually. These costs decline as a percentage of assets as portfolio size increases, reaching approximately 0.25% for portfolios exceeding $250,000.
Comparing costs to potential benefits reveals the strategy's value proposition. Historical analysis suggests the All Weather approach reduces portfolio volatility by 35-40% compared to stock-heavy allocations while maintaining competitive returns. This volatility reduction provides substantial value during market stress, potentially preventing behavioral mistakes that destroy long-term wealth.
Alternative Implementations and Customizations
While the original All Weather allocation provides an excellent starting point, investors may consider modifications based on personal circumstances, market conditions, or geographic considerations. International diversification represents one potential enhancement, adding exposure to developed and emerging market bonds and equities.
Geographic customization becomes important for non-US investors. European investors might replace US Treasury bonds with German Bunds or broader European government bond indices. Currency hedging decisions add complexity but may reduce volatility for investors whose spending occurs in non-dollar currencies.
Tax-location strategies optimize after-tax returns by placing tax-inefficient assets in tax-advantaged accounts while holding tax-efficient assets in taxable accounts. TIPS and commodity ETFs generate ordinary income taxed at higher rates, making them candidates for retirement account placement. Stock ETFs generate qualified dividends and long-term capital gains taxed at lower rates, making them suitable for taxable accounts.
Some investors prefer implementing the bond allocation through individual Treasury securities rather than ETFs, eliminating management fees while gaining precise maturity control. Treasury auctions provide access to new securities without bid-ask spreads, though this approach requires more sophisticated portfolio management.
Factor-based implementations replace broad market ETFs with factor-tilted alternatives. Value-tilted stock ETFs, quality-focused bond ETFs, or momentum-based commodity indices may enhance returns while maintaining the All Weather framework's diversification benefits. However, these modifications introduce additional complexity and potential tracking error.
Conclusion: Embracing the Long Game
The All Weather Strategy represents more than an investment approach; it embodies a philosophy of financial resilience that prioritizes sustainable wealth building over speculative gains. In an investment landscape increasingly dominated by algorithmic trading, meme stocks, and cryptocurrency volatility, Dalio's methodical approach offers a refreshing alternative grounded in economic theory and historical evidence.
The strategy's greatest strength lies not in its potential for extraordinary returns, but in its capacity to deliver reasonable returns across diverse economic environments while protecting capital during market stress. This characteristic becomes increasingly valuable as investors approach or enter retirement, when portfolio preservation assumes greater importance than aggressive growth.
Implementation requires discipline, adequate capital, and realistic expectations. The strategy will underperform growth-oriented approaches during bull markets while providing superior downside protection during bear markets. Investors must embrace this trade-off consciously, understanding that the strategy optimizes for long-term wealth building rather than short-term performance.
The All Weather Strategy Indicator democratizes access to institutional-quality portfolio management, providing individual investors with tools previously available only to wealthy families and institutions. By automating allocation tracking, rebalancing signals, and performance analysis, the indicator removes much of the complexity that has historically limited sophisticated strategy implementation.
For investors seeking a systematic, evidence-based approach to long-term wealth building, the All Weather Strategy provides a compelling framework. Its emphasis on diversification, risk management, and behavioral discipline aligns with the fundamental principles that have created lasting wealth throughout financial history. While the strategy may not generate headlines or inspire cocktail party conversations, it offers something more valuable: a reliable path toward financial security across all economic seasons.
As Dalio himself notes, "The biggest mistake investors make is to believe that what happened in the recent past is likely to persist, and they design their portfolios accordingly." The All Weather Strategy's enduring appeal lies in its rejection of this recency bias, instead embracing the uncertainty of markets while positioning for success regardless of which economic season unfolds.
STEP-BY-STEP INDICATOR SETUP GUIDE
Setting up the All Weather Strategy Indicator requires careful attention to each configuration parameter to ensure optimal implementation. This comprehensive setup guide walks through every setting and explains its impact on strategy performance.
Initial Setup Process
Begin by adding the indicator to your TradingView chart. Search for "Ray Dalio's All Weather Strategy" in the indicator library and apply it to any chart. The indicator operates independently of the underlying chart symbol, drawing data directly from the five required ETFs regardless of which security appears on the chart.
Portfolio Configuration Settings
Start with the Portfolio Capital input, which drives all subsequent calculations. Enter your exact investable capital, ranging from $1,000 to $10,000,000. This input determines share quantities, trade recommendations, and performance calculations. Conservative recommendations suggest minimum capitals of $50,000 for basic implementation or $100,000 for optimal precision.
Select your Portfolio Start Date carefully, as this establishes the baseline for all performance calculations. Choose the date when you actually began implementing the All Weather Strategy, not when you first learned about it. This date should reflect when you first purchased ETFs according to the target allocation, creating realistic performance tracking.
Choose your Rebalancing Frequency based on your cost structure and precision preferences. Monthly rebalancing provides tighter allocation control but increases transaction costs. Quarterly rebalancing offers the optimal balance for most investors between allocation precision and cost control. The indicator automatically detects appropriate trading days regardless of your selection.
Set the Rebalancing Threshold based on your tolerance for allocation drift and transaction costs. Conservative investors preferring tight control should use 1-2% thresholds, while cost-conscious investors may prefer 3-5% thresholds. Lower thresholds maintain more precise allocations but trigger more frequent trading.
Display Configuration Options
Enable Show All Weather Calculator to display the comprehensive dashboard containing portfolio values, allocations, and performance metrics. This dashboard provides essential information for portfolio management and should remain enabled for most users.
Show Economic Environment displays current economic regime classification based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated models, this feature provides useful context for understanding current market conditions.
Show Rebalancing Signals highlights when portfolio allocations drift beyond your threshold settings. These signals use color coding to indicate urgency levels, helping prioritize rebalancing activities.
Advanced Label Customization
Configure Show Rebalancing Labels based on your need for chart annotations. These labels mark important portfolio events and can provide valuable historical context, though they may clutter charts during extended time periods.
Select appropriate Label Detail Levels based on your experience and information needs. "None" provides minimal symbols suitable for experienced users. "Basic" shows portfolio values at key events. "Detailed" provides complete trading instructions including exact share quantities for each ETF.
Appearance Customization
Choose Color Themes based on your aesthetic preferences and trading style. "Gold" reflects traditional wealth management appearance, while "EdgeTools" provides modern professional styling. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making.
Enable Dark Mode Optimization if using TradingView's dark theme for optimal readability and contrast. This setting automatically adjusts all colors and transparency levels for the selected theme.
Set Main Line Width based on your chart resolution and visual preferences. Higher width values provide clearer allocation lines but may overwhelm smaller charts. Most users prefer width settings of 2-3 for optimal visibility.
Troubleshooting Common Setup Issues
If the indicator displays "Data not available" messages, verify that all five ETFs (SPY, TLT, IEF, DJP, SCHP) have valid price data on your selected timeframe. The indicator requires daily data availability for all components.
When rebalancing signals seem inconsistent, check your threshold settings and ensure sufficient time has passed since the last rebalancing event. The indicator only triggers signals on designated rebalancing days (first trading day of each period) when drift exceeds threshold levels.
If labels appear at unexpected chart locations, verify that your chart displays percentage values rather than price values. The indicator forces percentage formatting and 0-40% scaling for optimal allocation visualization.
COMPREHENSIVE BIBLIOGRAPHY AND FURTHER READING
PRIMARY SOURCES AND RAY DALIO WORKS
Dalio, R. (2017). Principles: Life and work. New York: Simon & Schuster.
Dalio, R. (2018). A template for understanding big debt crises. Bridgewater Associates.
Dalio, R. (2021). Principles for dealing with the changing world order: Why nations succeed and fail. New York: Simon & Schuster.
BRIDGEWATER ASSOCIATES RESEARCH PAPERS
Jensen, G., Kertesz, A. & Prince, B. (2010). All Weather strategy: Bridgewater's approach to portfolio construction. Bridgewater Associates Research.
Prince, B. (2011). An in-depth look at the investment logic behind the All Weather strategy. Bridgewater Associates Daily Observations.
Bridgewater Associates. (2015). Risk parity in the context of larger portfolio construction. Institutional Research.
ACADEMIC RESEARCH ON RISK PARITY AND PORTFOLIO CONSTRUCTION
Ang, A. & Bekaert, G. (2002). International asset allocation with regime shifts. The Review of Financial Studies, 15(4), 1137-1187.
Bodie, Z. & Rosansky, V. I. (1980). Risk and return in commodity futures. Financial Analysts Journal, 36(3), 27-39.
Campbell, J. Y. & Viceira, L. M. (2001). Who should buy long-term bonds? American Economic Review, 91(1), 99-127.
Clarke, R., De Silva, H. & Thorley, S. (2013). Risk parity, maximum diversification, and minimum variance: An analytic perspective. Journal of Portfolio Management, 39(3), 39-53.
Fama, E. F. & French, K. R. (2004). The capital asset pricing model: Theory and evidence. Journal of Economic Perspectives, 18(3), 25-46.
BEHAVIORAL FINANCE AND IMPLEMENTATION CHALLENGES
Kahneman, D. & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292.
Thaler, R. H. & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Press.
Montier, J. (2007). Behavioural investing: A practitioner's guide to applying behavioural finance. Chichester: John Wiley & Sons.
MODERN PORTFOLIO THEORY AND QUANTITATIVE METHODS
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442.
Black, F. & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28-43.
PRACTICAL IMPLEMENTATION AND ETF ANALYSIS
Gastineau, G. L. (2010). The exchange-traded funds manual. 2nd ed. Hoboken: John Wiley & Sons.
Poterba, J. M. & Shoven, J. B. (2002). Exchange-traded funds: A new investment option for taxable investors. American Economic Review, 92(2), 422-427.
Israelsen, C. L. (2005). A refinement to the Sharpe ratio and information ratio. Journal of Asset Management, 5(6), 423-427.
ECONOMIC CYCLE ANALYSIS AND ASSET CLASS RESEARCH
Ilmanen, A. (2011). Expected returns: An investor's guide to harvesting market rewards. Chichester: John Wiley & Sons.
Swensen, D. F. (2009). Pioneering portfolio management: An unconventional approach to institutional investment. Rev. ed. New York: Free Press.
Siegel, J. J. (2014). Stocks for the long run: The definitive guide to financial market returns & long-term investment strategies. 5th ed. New York: McGraw-Hill Education.
RISK MANAGEMENT AND ALTERNATIVE STRATEGIES
Taleb, N. N. (2007). The black swan: The impact of the highly improbable. New York: Random House.
Lowenstein, R. (2000). When genius failed: The rise and fall of Long-Term Capital Management. New York: Random House.
Stein, D. M. & DeMuth, P. (2003). Systematic withdrawal from retirement portfolios: The impact of asset allocation decisions on portfolio longevity. AAII Journal, 25(7), 8-12.
CONTEMPORARY DEVELOPMENTS AND FUTURE DIRECTIONS
Asness, C. S., Frazzini, A. & Pedersen, L. H. (2012). Leverage aversion and risk parity. Financial Analysts Journal, 68(1), 47-59.
Roncalli, T. (2013). Introduction to risk parity and budgeting. Boca Raton: CRC Press.
Ibbotson Associates. (2023). Stocks, bonds, bills, and inflation 2023 yearbook. Chicago: Morningstar.
PERIODICALS AND ONGOING RESEARCH
Journal of Portfolio Management - Quarterly publication featuring cutting-edge research on portfolio construction and risk management
Financial Analysts Journal - Bi-monthly publication of the CFA Institute with practical investment research
Bridgewater Associates Daily Observations - Regular market commentary and research from the creators of the All Weather Strategy
RECOMMENDED READING SEQUENCE
For investors new to the All Weather Strategy, begin with Dalio's "Principles" for philosophical foundation, then proceed to the Bridgewater research papers for technical details. Supplement with Markowitz's original portfolio theory work and behavioral finance literature from Kahneman and Tversky.
Intermediate students should focus on academic papers by Ang & Bekaert on regime shifts, Clarke et al. on risk parity methods, and Ilmanen's comprehensive analysis of expected returns across asset classes.
Advanced practitioners will benefit from Roncalli's technical treatment of risk parity mathematics, Asness et al.'s academic critique of leverage aversion, and ongoing research in the Journal of Portfolio Management.
Momentum_EMABand📢 Reposting Notice
I am reposting this script because my earlier submission was hidden due to description requirements under TradingView’s House Rules. This updated version fully explains the originality, the reason for combining these indicators, and how they work together. Follow me for future updates and refinements.
🆕 Momentum EMA Band, Rule-Based System
Momentum EMA Band is not just a mashup — it is a purpose-built trading tool for intraday traders and scalpers that integrates three complementary technical concepts into a single rules-based breakout & retest framework.
Originality comes from the specific sequence and interaction of these three filters:
Supertrend → Sets directional bias.
EMA Band breakout with retest logic → Times precise entries.
ADX filter → Confirms momentum strength and avoids noise.
This system is designed to filter out weak setups and false breakouts that standalone indicators often fail to avoid.
🔧 How the Indicator Works — Combined Logic
1️⃣ EMA Price Band — Dynamic Zone Visualization
Plots upper & lower EMA bands (default: 9-period EMA).
Green Band → Price above upper EMA = bullish momentum
Red Band → Price below lower EMA = bearish pressure
Yellow Band → Price within band = neutral zone
Acts as a consolidation zone and breakout trigger level.
2️⃣ Supertrend Overlay — Reliable Trend Confirmation
ATR-based Supertrend adapts to volatility:
Green Line = Uptrend bias
Red Line = Downtrend bias
Ensures trades align with the prevailing trend.
3️⃣ ADX-Based No-Trade Zone — Choppy Market Filter
Manual ADX calculation (default: length 14).
If ADX < threshold (default: 20) and price is inside EMA Band → gray background marks low-momentum zones.
🧩 Why This Mashup Works
Supertrend confirms trend direction.
EMA Band breakout & retest validates the breakout’s strength.
ADX ensures the market has enough trend momentum.
When all align, entries are higher probability and whipsaws are reduced.
📈 Example Trade Walkthrough
Scenario: 5-minute chart, ADX threshold = 20.
Supertrend turns green → trend bias is bullish.
Price consolidates inside the yellow EMA Band.
ADX rises above 20 → trend momentum confirmed.
Price closes above the green EMA Band after retesting the band as support.
Entry triggered on candle close, stop below band, target based on risk-reward.
Exit when Supertrend flips red or ADX momentum drops.
This sequence prevents premature entries, keeps trades aligned with trend, and avoids ranging markets.
🎯 Key Features
✅ Multi-layered confirmation for precision trading
✅ Built-in no-trade zone filter
✅ Fully customizable parameters
✅ Clean visuals for quick decision-making
⚠ Disclaimer: This is Version 1. Educational purposes only. Always use with risk management.
ATR+CCI Monetary Risk Tool - TP/SL⚙️ ATR+CCI Monetary Risk Tool — Volatility-aware TP/SL & Position Sizing
Exact prices (no rounding), ATR-percentile dynamic stops, and risk-budget sizing for consistent execution.
🧠 What this indicator is
A risk-first planning tool. It doesn’t generate orders; it gives you clean, objective levels (Entry, SL, TP) and position size derived from your risk budget. It shows only the latest setup to keep charts readable, and a compact on-chart table summarizing the numbers you actually act on.
✨ What makes it different
Dynamic SL by regime (ATR percentile): Instead of a fixed multiple, the SL multiplier adapts to the current volatility percentile (low / medium / high). That helps avoid tight stops in noisy markets and over-wide stops in quiet markets.
Risk budgeting, not guesswork: Size is computed from Account Balance × Max Risk % divided by SL distance × point value. You risk the same dollars across assets/timeframes.
Precision that matches your instrument: Entry, TP, SL, and SL Distance are displayed as exact prices (no rounding), truncated to syminfo.mintick so they align with broker/exchange precision.
Symbol-aware point value: Uses syminfo.pointvalue so you don’t maintain tick tables.
Non-repaint option: Work from closed bars to keep the plan stable.
🔧 How to use (quick start)
Add to chart and pick your timeframe and symbol.
In settings:
Set Account Balance (USD) and Max Risk per Trade (%).
Choose R:R (1:1 … 1:5).
Pick ATR Period and CCI Period (defaults are sensible).
Keep Dynamic ATR ON to adapt SL by regime.
Keep Use closed-bar values ON to avoid repaint when planning.
Read the labels (Entry/TP/SL) and the table (SL Distance, Position Size, Max USD Risk, ATR Percentile, effective SL Mult).
Combine with your entry trigger (price action, levels, momentum, etc.). This indicator handles risk & targets.
📐 How levels are computed
Bias: CCI ≥ 0 ⇒ long, otherwise short.
ATR Percentile: Percent rank of ATR(atrPeriod) over a lookback window.
Effective SL Mult:
If percentile < Low threshold ⇒ use Low SL Mult (tighter).
If between thresholds ⇒ use Base SL Mult.
If percentile > High threshold ⇒ use High SL Mult (wider).
Stop-Loss: SL = Entry ± ATR × SL_Mult (minus for long, plus for short).
Take-Profit: TP = Entry ± (Entry − SL) × R (R from the R:R dropdown).
Position Size:
USD Risk = Balance × Risk%
Contracts = USD Risk ÷ (|Entry − SL| × PointValue)
For futures, quantity is floored to whole contracts.
Exact prices: Entry/TP/SL and SL Distance are not rounded; they’re truncated to mintick so what you see matches valid price increments.
📊 What you’ll see on chart
Latest Entry (blue), TP (green), SL (red) with labels (optional emojis: ➡️ 🎯 🛑).
Info Table with:
Bias, Entry, TP, SL (exact, truncated to mintick)
SL Distance (exact, truncated)
Position Size (contracts/units)
Max USD Risk
Point Value
ATR Percentile and effective SL Mult
🧪 Practical examples
High-volatility session (e.g., XAUUSD, 1H): ATR percentile is high ⇒ wider SL, smaller size. Reduces churn from normal noise during macro events.
Range-bound market (e.g., EURUSD, 4H): ATR percentile low ⇒ tighter SL, better R:R. Helps you avoid carrying unnecessary risk.
Index swing planning (e.g., ES1!, Daily): Non-repaint levels + risk budgeting = consistent sizing across days/weeks, easier to review and journal.
🧭 Why traders should use it
Consistency: Same dollar risk regardless of instrument or volatility regime.
Clarity: One-trade view forces focus; you see the numbers that matter.
Adaptivity: Stops calibrated to the market’s current behavior, not last month’s.
Discipline: A visible checklist (SL distance, size, USD risk) before you hit buy/sell.
🔧 Input guide (practical defaults)
CCI Period: 100 by default; use as a bias filter, not an entry signal.
ATR Period: 14 by default; raise for smoother, lower for more reactive.
ATR Percentile Lookback: 200 by default (stable regime detection).
Percentile thresholds: 33/66 by default; widen the gap to change how often regimes switch.
SL Mults: Start ~1.5 / 2.0 / 2.5 (low/base/high). Tune by asset.
Risk % per trade: Common pro ranges are 0.25–1.0%; adjust to your risk tolerance.
R:R: Start with 1:2 or 1:3 for balanced skew; adapt to strategy edge.
Closed-bar values: Keep ON for planning/live; turn OFF only for exploration.
💡 Best practices
Combine with your entry logic (structure, momentum, liquidity levels).
Review ATR percentile and effective SL Mult across sessions so you understand regime shifts.
For futures, remember size is floored to whole contracts—safer by design.
Journal trades with the table snapshot to improve risk discipline over time.
⚠️ Notes & limitations
This is not a strategy; it does not place orders or alerts.
No slippage/commissions modeled here; build a strategy() version for backtests that mirror your broker/exchange.
Displayed non-price metrics use two decimals; prices and SL Distance are exact (truncated to mintick).
📎 Disclaimer
For educational purposes only. Not financial advice. Markets involve risk. Test thoroughly before trading live.
DeltaTrace ForecastDeltaTrace Forecast is a forward-looking projection tool that visualizes the probable directional path of price using a multi-timeframe momentum model rooted in volatility-adjusted nonlinear dynamics. Rather than relying on traditional indicators that react to price after the fact, DeltaTrace estimates future price motion by tracing the progression of momentum changes across expanding timeframes—then scaling those deltas using adaptive volatility to forecast a plausible path forward.
At its core, DeltaTrace constructs a momentum vector from a series of smoothed z-scores derived from increasing multiples of the current chart's timeframe. These z-scores are normalized using a hyperbolic tangent function (tanh), which compresses extreme values and emphasizes meaningful deviations without being overly sensitive to outliers. This nonlinear normalization ensures that explosive moves are weighted with less distortion, while still preserving the shape and direction of the underlying trend.
Once the z-scores are calculated for a range of 12 timeframes (from 1× the current timeframe up to 12×), the indicator computes the first difference between each adjacent pair. These differences—or deltas—represent the change in momentum from one timeframe to the next. In this structure, a strong positive delta implies momentum is strengthening as we look into higher timeframes, while a negative delta reflects waning or reversing strength.
However, not all deltas are treated equally. To make the projection adaptive to market volatility and temporally meaningful, each delta is scaled by the square root of its corresponding timeframe multiple, weighted by the ATR (Average True Range) of the base timeframe. This square-root volatility scaling mirrors the behavior of Brownian motion and reflects the natural geometric diffusion of price over time. By applying this scaling, the model tempers its forecast according to recent volatility while maintaining proportional distance over longer time horizons.
The result is a chain of projected price steps—11 in total—starting from the current closing price. These steps are cumulative, meaning each one builds upon the previous, forming a continuously adjusted polyline that represents the most recent forecast path of price. Each point in the forecast line is directional: if the next projected point is above the last, the segment is colored green (upward momentum); if below, it is colored red (downward momentum). This color coding gives immediate visual feedback on the nature of the projected path and allows for intuitive at-a-glance interpretation.
What makes DeltaTrace unique is its combination of ideas from signal processing, time-series momentum analysis, and volatility theory. Instead of relying on static support/resistance levels or lagging moving averages, it dynamically adapts to both momentum curvature and volatility structure. This allows it to be used not just for trend confirmation, but also for top-down bias fading, reversal anticipation, and path-following strategies.
Traders can use DeltaTrace in a variety of ways depending on their style:
For trend traders, a consistent upward or downward curve in the forecast suggests directional continuation and can be used for position sizing or confirmation of bias.
For mean-reversion traders, exaggerated divergence between the current price and the first few forecast points may indicate temporary exhaustion or overextension.
For scalpers or intraday traders, the short-term bend or flattening of the initial segments can reveal early signs of weakening momentum or build-up before breakout.
For swing traders, the full shape of the polyline gives an evolving map of market rhythm across time compression, allowing for context-aware decision-making.
It’s important to understand that this is a path projection tool, not a precise price target predictor. The forecast does not attempt to predict exact price levels at exact bars, but rather illustrates how the market might evolve if the current multi-timeframe momentum structure persists. Like all models, it should be interpreted probabilistically and used in conjunction with other confirmation signals, risk management tools, or strategy frameworks.
Inputs allow customization of the z-score calculation length and ATR window to tune the sensitivity of the model. The color scheme for up/down forecast segments can also be adjusted for personal preference. Additionally, users can toggle the polyline forecast on or off, which may be useful for pairing this indicator with others in a crowded chart layout.
Because the forecast path is calculated only on the last bar, it does not repaint or shift once the candle closes—preserving historical accuracy for visual inspection and backtesting reference. However, it is also sensitive to changes in volatility and momentum structure, meaning it updates each bar as conditions evolve, making it most effective in real-time decision support.
DeltaTrace Forecast is particularly well-suited for traders who want a deeper understanding of hidden momentum shifts across timeframes without relying on traditional trend-following tools. It reveals the shape of future possibility based on present dynamics, offering a compact yet powerful visualization of directional bias, transition risk, and path strength.
To maximize its utility, consider pairing DeltaTrace with volume profiles, order flow tools, higher timeframe zones, or market structure indicators. Used in context, it becomes a powerful companion to both systematic and discretionary trading styles—especially for those who appreciate a blend of mathematics and intuition in their market analysis.
This indicator is not based on magic or black-box logic; every component—from the z-score standardization to the volatility-adjusted deltas—is fully transparent and grounded in simple, interpretable mechanics. If you're looking for a reliable way to visualize multi-timeframe bias and momentum diffusion, DeltaTrace provides a unique lens through which to interpret future potential in an ever-shifting market landscape.
Advanced ICT Theory - A-ICT📊 Advanced ICT Theory (A-ICT): The Institutional Manipulation Detector
Are you tired of being the liquidity? Stop chasing shadows and start tracking the architects of price movement.
This is not another lagging indicator. This is a complete framework for viewing the market through the lens of institutional traders. Advanced ICT Theory (A-ICT) is an all-in-one, military-grade analysis engine designed to decode the complex language of "Smart Money." It automates the core tenets of Inner Circle Trader (ICT) methodology, moving beyond simple patterns to build a dynamic, real-time narrative of market manipulation, liquidity engineering, and institutional order flow.
AIT provides a living blueprint of the market, identifying high-probability zones, tracking structural shifts, and scoring the quality of setups with a sophisticated, multi-factor algorithm. This is your X-ray into the market's true intentions.
🔬 THE CORE ENGINE: DECODING THE THEORY & FORMULAS
A-ICT is built upon a sophisticated, multi-layered logic system that interprets price action as a story of cause and effect. It does not guess; it confirms. Here is the foundational theory that drives the engine:
1. Market Structure: The Blueprint of Trend
The script first establishes a deep understanding of the market's skeleton through multi-level pivot analysis. It uses ta.pivothigh and ta.pivotlow to identify significant swing points.
Internal Structure (iBOS): Minor swings that show the short-term order flow. A break of internal structure is the first whisper of a potential shift.
External Structure (eBOS): Major swing points that define the primary trend. A confirmed break of external structure is a powerful statement of trend continuation. AIT validates this with optional Volume Confirmation (volume > volumeSMA * 1.2) and Candle Confirmation to ensure the break is driven by institutional force, not just a random spike.
Change of Character (CHoCH): This is the earthquake. A CHoCH occurs when a confirmed eBOS happens against the prevailing trend (e.g., a bearish eBOS in a clear uptrend). A-ICT flags this immediately, as it is the strongest signal that the primary trend is under threat of reversal.
2. Liquidity Engineering: The Fuel of the Market
Institutions don't buy into strength; they buy into weakness. They need liquidity. A-ICT maps these liquidity pools with forensic precision:
Buyside & Sellside Liquidity (BSL/SSL): Using ta.highest and ta.lowest, AIT identifies recent highs and lows where clusters of stop-loss orders (liquidity) are resting. These are institutional targets.
Liquidity Sweeps: This is the "manipulation" part of the detector. AIT has a specific formula to detect a sweep: high > bsl and close < bsl . This signifies that institutions pushed price just high enough to trigger buy-stops before aggressively selling—a classic "stop hunt." This event dramatically increases the quality score of subsequent patterns.
3. The Element Lifecycle: From Potential to Power
This is the revolutionary heart of A-ICT. Zones are not static; they have a lifecycle. AIT tracks this with its dynamic classification engine.
Phase 1: PENDING (Yellow): The script identifies a potential zone of interest based on a specific candle formation (a "displacement"). It is marked as "Pending" because its true nature is unknown. It is a question.
Phase 2: CLASSIFICATION: After the zone is created, AIT watches what happens next. The zone's identity is defined by its actions:
ORDER BLOCK (Blue): The highest-grade element. A zone is classified as an Order Block if it directly causes a Break of Structure (BOS) . This is the footprint of institutions entering the market with enough force to validate the new trend direction.
TRAP ZONE (Orange): A zone is classified as a Trap Zone if it is directly involved in a Liquidity Sweep . This indicates the zone was used to engineer liquidity, setting a "trap" for retail traders before a reversal.
REVERSAL / S&R ZONE (Green): If a zone is not powerful enough to cause a BOS or a major sweep, but still serves as a pivot point, it's classified as a general support/resistance or reversal zone.
4. Market Inefficiencies: Gaps in the Matrix
Fair Value Gaps (FVG): AIT detects FVGs—a 3-bar pattern indicating an imbalance—with a strict formula: low > high (for a bullish FVG) and gapSize > atr14 * 0.5. This ensures only significant, volatile gaps are shown. An FVG co-located with an Order Block is a high-confluence setup.
5. Premium & Discount: The Law of Value
Institutions buy at wholesale (Discount) and sell at retail (Premium). AIT uses a pdLookback to define the current dealing range and divides it into three zones: Premium (sell zone), Discount (buy zone), and Equilibrium. An element's quality score is massively boosted if it aligns with this principle (e.g., a bullish Order Block in a Discount zone).
⚙️ THE CONTROL PANEL: A COMPLETE GUIDE TO THE INPUTS MENU
Every setting is a lever, allowing you to tune the AIT engine to your exact specifications. Master these to unlock the script's full potential.
🎯 A-ICT Detection Engine
Min Displacement Candles: Controls the sensitivity of element detection. How it works: It defines the number of subsequent candles that must be "inside" a large parent candle. Best practice: Use 2-3 for a balanced view on most timeframes. A higher number (4-5) will find only major, more significant zones, ideal for swing trading. A lower number (1) is highly sensitive, suitable for scalping.
Mitigation Method: Defines when a zone is considered "used up" or mitigated. How it works: Cross triggers as soon as price touches the zone's boundary. Close requires a candle to fully close beyond it. Best practice: Cross is more responsive for fast-moving markets. Close is more conservative and helps filter out fake-outs caused by wicks, making it safer for confirmations.
Min Element Size (ATR): A crucial noise filter. How it works: It requires a detected zone to be at least this multiple of the Average True Range (ATR). Best practice: Keep this around 0.5. If you see too many tiny, irrelevant zones, increase this value to 0.8 or 1.0. If you feel the script is missing smaller but valid zones, decrease it to 0.3.
Age Threshold & Pending Timeout: These manage visual clutter. How they work: Age Threshold removes old, mitigated elements after a set number of bars. Pending Timeout removes a "Pending" element if it isn't classified within a certain window. Best practice: The default settings are optimized. If your chart feels cluttered, reduce the Age Threshold. If pending zones disappear too quickly, increase the Pending Timeout.
Min Quality Threshold: Your primary visual filter. How it works: It hides all elements (boxes, lines, labels) that do not meet this minimum quality score (0-100). Best practice: Start with the default 30. To see only A- or B-grade setups, increase this to 60 or 70 for an exceptionally clean, high-probability view.
🏗️ Market Structure
Lookbacks (Internal, External, Major): These define the sensitivity of the trend analysis. How they work: They set the number of bars to the left and right for pivot detection. Best practice: Use smaller values for Internal (e.g., 3) to see minor structure and larger values for External (e.g., 10-15) to map the main trend. For a macro, long-term view, increase the Major Swing Lookback.
Require Volume/Candle Confirmation: Toggles for quality control on BOS/CHoCH signals. Best practice: It is highly recommended to keep these enabled. Disabling them will result in more structure signals, but many will be false alarms. They are your filter against market noise.
... (Continue this detailed breakdown for every single input group: Display Configuration, Zones Style, Levels Appearance, Colors, Dashboards, MTF, Liquidity, Premium/Discount, Sessions, and IPDA).
📊 THE INTELLIGENCE DASHBOARDS: YOUR COMMAND CENTER
The dashboards synthesize all the complex analysis into a simple, actionable intelligence briefing.
Main Dashboard (Bottom Right)
ICT Metrics & Breakdown: This is your statistical overview. Total Elements shows how much structure the script is tracking. High Quality instantly tells you if there are any A/B grade setups nearby. Unmitigated vs. Mitigated shows the balance of fresh opportunities versus resolved price action. The breakdown by Order Blocks, Trap Zones, etc., gives you a quick read on the market's recent character.
Structure & Market Context: This is your core bias. Order Flow tells you the current script-determined trend. Last BOS shows you the most recent structural event. CHoCH Active is a critical warning. HTF Bias shows if you are aligned with the higher timeframe—the checkmark (✓) for alignment is one of the most important confluence factors.
Smart Money Flow: A volume-based sentiment gauge. Net Flow shows the raw buying vs. selling pressure, while the Bias provides an interpretation (e.g., "STRONG BULLISH FLOW").
Key Guide (Large Dashboard only): A built-in legend so you never have to guess. It defines every pattern, structure type, and special level visually.
📖 Narrative Dashboard (Bottom Left)
This is the "story" of the market, updated in real-time. It's designed to build your trading thesis.
Recent Elements Table: A live list of the most recent, high-quality setups. It displays the Type , its Narrative Role (e.g., "Bullish OB caused BOS"), its raw Quality percentage, and its final Trade Score grade. This is your at-a-glance opportunity scanner.
Market Narrative Section: This is the soul of A-ICT. It combines all data points into a human-readable story:
📍 Current Phase: Tells you if you are in a high-volatility Killzone or a consolidation phase like the Asian Range.
🎯 Bias & Alignment: Your primary direction, with a clear indicator of HTF alignment or conflict.
🔗 Events: A causal sequence of recent events, like "💧 Sell-side liquidity swept →
📊 Bullish BOS → 🎯 Active Order Block".
🎯 Next Expectation: The script's logical conclusion. It provides a specific, forward-looking hypothesis, such as "📉 Pullback expected to bullish OB at 1.2345 before continuation up."
🎨 READING THE BATTLEFIELD: A VISUAL INTERPRETATION GUIDE
Every color and line is a piece of information. Learn to read them together to see the full picture.
The Core Zones (Boxes):
Blue Box (Order Block): Highest probability zone for trend continuation. Look for entries here.
Orange Box (Trap Zone): A manipulation footprint. Expect a potential reversal after price interacts with this zone.
Green Box (Reversal/S&R): A standard pivot area. A good reference point but requires more confluence.
Purple Box (FVG): A market imbalance. Acts as a magnet for price. An FVG inside an Order Block is an A+ confluence.
The Structural Lines:
Green/Red Line (eBOS): Confirms the trend direction. A break above the green line is bullish; a break below the red line is bearish.
Thick Orange Line (CHoCH): WARNING. The previous trend is now in question. The market character has changed.
Blue/Red Lines (BSL/SSL): Liquidity targets. Expect price to gravitate towards these lines. A dotted line with a checkmark (✓) means the liquidity has been "swept" or "purged."
How to Synthesize: The magic is in the confluence. A perfect setup might look like this: Price sweeps below a red SSL line , enters a green Discount Zone during the NY Killzone , and forms a blue Order Block which then causes a green eBOS . This sequence, visible at a glance, is the story of a high-probability long setup.
🔧 THE ARCHITECT'S VISION: THE DEVELOPMENT JOURNEY
A-ICT was forged from the frustration of using lagging indicators in a market that is forward-looking. Traditional tools are reactive; they tell you what happened. The vision for A-ICT was to create a proactive engine that could anticipate institutional behavior by understanding their objectives: liquidity and efficiency. The development process was centered on creating a "lifecycle" for price patterns—the idea that a zone's true meaning is only revealed by its consequence. This led to the post-breakout classification system and the narrative-building engine. It's designed not just to show you patterns, but to tell you their story.
⚠️ RISK DISCLAIMER & BEST PRACTICES
Advanced ICT Theory (A-ICT) is a professional-grade analytical tool and does not provide financial advice or direct buy/sell signals. Its analysis is based on historical price action and probabilities. All forms of trading involve substantial risk. Past performance is not indicative of future results. Always use this tool as part of a comprehensive trading plan that includes your own analysis and a robust risk management strategy. Do not trade based on this indicator alone.
観の目つよく、見の目よわく
"Kan no me tsuyoku, ken no me yowaku"
— Miyamoto Musashi, The Book of Five Rings
English: "Perceive that which cannot be seen with the eye."
— Dskyz, Trade with insight. Trade with anticipation.
Daily EMAs (8, 21 & 50) with BandDescription:
This script plots the Daily EMAs (8, 21, and 50) on any intraday or higher timeframe chart. It provides a clear, multi-timeframe view of market trends by using daily exponential moving averages (EMAs) and a dynamic visual band. I use this on the major indexes to decide if I should be mostly longing or shorting assets.
-In addition to identifying the trend structure, the 8-Day EMA often serves as a key area where buyers or sellers may become active, depending on the market direction:
-In an uptrend, the 8 EMA can act as a dynamic support zone, where buyers tend to re-enter on pullbacks.
-In a downtrend, the same EMA may act as resistance, where sellers become more aggressive.
-The script also includes a colored band between the 8 and 21 EMAs to highlight the short-term trend bias:
-Green fill = 8 EMA is above the 21 EMA (bullish structure).
Blue fill = 8 EMA is below the 21 EMA (bearish structure).
The 50-Day EMA is included to give additional context for intermediate-term trend direction.
Features:
- Daily EMA levels (8, 21, and 50) calculated regardless of current chart timeframe.
- 8 EMA acts as a potential buyer/seller zone based on trend direction.
- Color-coded band between 8 and 21 EMAs:
- Green = Bullish short-term bias
- Blue = Bearish short-term bias
- Customizable price source and EMA offset.
- Suitable for trend trading, pullback entries, and higher-timeframe confirmation.
Use Cases:
Identify key dynamic support/resistance areas using the 8 EMA.
Assess short-, medium-, and intermediate-term trend structure at a glance.
Enhance confluence for entry/exit signals on lower timeframes.
VWAP Volume Profile [BigBeluga]🔵 OVERVIEW
VWAP Volume Profile is an advanced hybrid of the VWAP and volume profile concepts. It visualizes how volume accumulates relative to VWAP movement—separating rising (+VWAP) and declining (−VWAP) activity into two mirrored horizontal profiles. It highlights the dominant price bins (POCs) where volume peaked during each directional phase, helping traders spot hidden accumulation or distribution zones.
🔵 CONCEPTS
VWAP-Driven Profiling: Unlike standard volume profiles, this tool segments volume based on VWAP movement—accumulating positive or negative volume depending on VWAP slope.
Dual-Sided Profiles: Profiles expand horizontally to the right of price. Separate bins show rising (+) and falling (−) VWAP volume.
Bin Logic: Volume is accumulated into defined horizontal bins based on VWAP’s position relative to price ranges.
Gradient Coloring: Volume bars are colored with a dynamic gradient to emphasize intensity and direction.
POC Highlighting: The highest-volume bin in each profile type (+/-) is marked with a transparent box and label.
Contextual VWAP Line: VWAP is plotted and dynamically colored (green = rising, orange = falling) for instant trend context.
Candle Overlay: Price candles are recolored to match the VWAP slope for full visual integration.
🔵 FEATURES
Dual-sided horizontal volume profiles based on VWAP slope.
Supports rising VWAP , falling VWAP , or both simultaneously.
Customizable number of bins and lookback period.
Dynamically colored VWAP line to show rising/falling bias.
POC detection and labeling with volume values for +VWAP and −VWAP.
Candlesticks are recolored to match VWAP bias for intuitive momentum tracking.
Optional background boxes with customizable styling.
Adaptive volume scaling to normalize bar length across markets.
🔵 HOW TO USE
Use POC zones to identify high-volume consolidation areas and potential support/resistance levels.
Watch for shifts in VWAP direction and observe how volume builds differently during uptrends and downtrends.
Use the gradient profile shape to detect accumulation (widening volume below price) or distribution (above price).
Use candle coloring for real-time confirmation of VWAP bias.
Adjust the profile period or bin count to fit your trading style (e.g., intraday scalping or swing trading).
🔵 CONCLUSION
VWAP Volume Profile merges two essential concepts—volume and VWAP—into a single, high-precision tool. By visualizing how volume behaves in relation to VWAP movement, it uncovers hidden dynamics often missed by traditional profiles. Perfect for intraday and swing traders who want a more nuanced read on market structure, trend strength, and volume flow.
X ORTX ORT — Opening Range & Time Reference Tool
Overview
The X ORT indicator is a precision tool designed for intraday traders seeking to anchor their trading decisions to high-probability price levels. It captures key market reference points including Opening Ranges, Settlement Prices, and Time-Specific Opens, all based on New York time, to help identify potential pivots and directional bias in the market.
Key Features & Usage
🔹 Opening Range Boxes (ORs)
The indicator defines up to two customizable Opening Ranges (e.g., 9:30–9:59 and 8:20–8:49 ET). Each range dynamically tracks the high, low, and midpoint price as the session unfolds, and continues to extend those levels forward throughout the day.
Use as Pivots: The high and low of the Opening Range often act as intraday support and resistance zones. A breakout above the ORH (Opening Range High) may signal bullish intent, while a drop below the ORL (Opening Range Low) may suggest bearish momentum.
Use for Directional Bias: If price remains above or below the range after completion, it may indicate a continuation in that direction. The midpoint (dashed line) serves as a mean-reversion or fair value pivot.
🔸 Settlement Price Anchors
The indicator optionally plots Daily, Weekly, and Monthly Settlement Prices, which are significant institutional reference points.
Use as Market Anchors: Settlement prices are often used by professionals to gauge positioning. Price acceptance above or below settlement can signal strength or weakness and guide directional trades.
Historical weekly and monthly settlements help define multi-day or swing levels for broader context.
🔹 Time-Based Open Levels
X ORT also draws horizontal lines at the open price of specific time points: Midnight, 8:30 AM, 9:30 AM, and 1:30 PM ET.
Use for Session Anchors: These reference opens are useful for understanding session shifts, aligning with key economic releases (like 8:30 AM), and gauging session-to-session continuity.
Why Use X ORT?
Objective Structure: Provides rule-based levels to avoid emotional trading.
Visual Clarity: Transparent, extendable boxes and labeled lines help traders focus on key decision zones.
Multi-Time Context: Blends intraday and higher timeframe levels to support short-term and swing traders.
Whether you're breakout trading, fading range extremes, or gauging market bias, X ORT offers a reliable structural foundation that aligns with how professionals track price behavior throughout the trading day.
Better MACD📘 Better MACD – Adaptive Momentum & Divergence Suite
Better MACD is a comprehensive momentum-trend tool that evolves the traditional MACD into a multi-dimensional, divergence-aware oscillator. It leverages exponential smoothing across logarithmic rate-of-change of OHLC data, adaptive signal processing, and intelligent divergence detection logic to provide traders with earlier, smoother, and more reliable momentum signals.
This indicator is built for professional-level analysis, suitable for scalping, swing trading, and trend-following systems.
🧬 Core Concept
Unlike the classic MACD which subtracts two EMAs of price, Better MACD constructs a signal by:
Applying logarithmic transformation on the change between OHLC components (Close, High, Low, Open).
Using double EMA smoothing to filter noise and volatility, Triangular method. 1st to 2nd Smoothing.
Averaging and de-biasing the results through a custom linear regression model, 4th Smoothing.
Subtracting a fast SMA and slow SMA response to yield a dynamic MACD value, 3rd Smoothing.
The result is a smooth, adaptive, and high-resolution MACD-style oscillator that responds more naturally to trend conditions and price geometry.
🧠 Features Breakdown
1. 📈 Multi-Layer MACD Engine
Src1: Smoothed Log Rate-of-Change on Close
Src2: Smoothed Log Rate-of-Change on High
Src3: Smoothed Log Rate-of-Change on Low
Src4: Smoothed Log Rate-of-Change on Open
These are blended using highest high, lowest low, and average Close price over a configurable window for more complete trend detection. The open-based Src4 is subtracted using SMA.
2. 🧮 Signal Line
A fast EMA (signalLength) of the Better MACD value is used for crossover logic.
Crossovers of MACD and Signal line signal potential entries or exits.
3. 📊 MACD Histogram
Visualizes the difference between MACD and Signal line.
Dynamically color-coded:
Green/Light Green for bullish impulse
Red/Pink for bearish impulse
Width and color intensity reflect strength and momentum slope.
🎨 Visual Enhancements
Feature Description
✅ Ribbon Fill Optional fill between MACD and Signal line, colored by trend direction
✅ Zero-Line Background Background highlights above/below 0 to easily read bullish/bearish bias
✅ Crossover Highlights Tiny circles plotted when MACD crosses Signal line
🔍 Divergence Detection Suite
The script includes a full Divergence Engine to detect:
🔼 Bullish Regular Divergence (Price lower lows + Indicator higher lows)
🔽 Bearish Regular Divergence (Price higher highs + Indicator lower highs)
🟢 Bullish Hidden Divergence (Price higher lows + Indicator lower lows)
🔴 Bearish Hidden Divergence (Price lower highs + Indicator higher highs)
🧩 Divergence Modes:
Supports both Regular, Hidden, or Both simultaneously
Detects from either Close Price or Heikin Ashi-derived candles
Uses dynamic pivot tracking with configurable lookback and divergence sensitivity
Divergence lines are labeled, colored, and plotted in real-time
🔁 Styling & Customization:
Choose from Solid, Dashed, or Dotted line styles
Configure separate colors and widths for all divergence types
Control number of divergence lines visible or only show the most recent
Divergences update live without repainting
⚠️ Alerts
Alerts are built-in for real-time notification:
MACD Histogram reversals (rising → falling, or vice versa)
Divergence signals (all 4 types, grouped and individually)
Combines seamlessly with TradingView alerts for actionable triggers
🔧 Input Controls (Grouped by Purpose)
Better MACD Group
1st–4th Smoothing Lengths: Controls responsiveness of MACD core engine
Signal Length: Smoothness of signal line
Toggles for crossover highlights, zero cross fills, and ribbon fills
Divergence Settings
Enable/disable divergence lines
Choose divergence type (Regular, Hidden, Both)
Set confirmation requirements
Customize pivot detection and bar search depth
Styling Options
Colors, line widths, and line styles for each divergence type
Heikin Ashi Mode for smoother pivots and divergences
🧠 How to Use
✅ For Trend Traders:
Use MACD > Signal + Histogram > 0 → Bullish confirmation
MACD < Signal + Histogram < 0 → Bearish confirmation
Wait for pullbacks with hidden divergences to enter in trend direction
✅ For Reversal Traders:
Look for Regular Divergences at trend exhaustion points
Combine with price action (e.g., support/resistance or candle pattern)
✅ For Swing & Day Traders:
Enable Heikin Ashi Mode for smoother divergence pivots
Use zero line background + histogram color to time entries
📌 Summary
Feature Description
🚀 Advanced MACD Core Smoother, more reliable, multi-source-based MACD
🔍 Divergence Engine Detects 4 divergence types with pivot logic
🎯 Real-Time Alerts Alerts for histogram slope and divergences
🎛️ Deep Customization Full styling, smoothing, and detection controls
📉 Heikin Ashi Support Improved signal quality in trend-based markets
IU Fibonacci Levels For IntradayDESCRIPTION
This indicator draws intraday Fibonacci levels from the opening price of the day using percentage-based retracements. It helps traders identify potential intraday support and resistance zones derived from the day’s opening bias. The levels are dynamically calculated and displayed with optional labels and customizable colors, making it an effective tool for both breakout and mean-reversion intraday strategies.
USER INPUTS
Direction Of The Level
Choose whether to show Upside, Downside, or Both level sets based on your directional bias.
Show Labels of Levels
Option to enable or disable text labels displaying Fibonacci values and prices.
Individual Level Toggles & Colors
You can choose to show or hide each of the following Fibonacci levels and set their respective colors:
* 0.236
* 0.328
* 0.500
* 0.618
* 0.786
* 1.000
INDICATOR LOGIC
On the first bar of the session, the opening price is captured.
Fibonacci levels are then calculated above and below this open using percentage multipliers (for example, day\_open + (day\_open \* 0.236%) for the 0.236 level).
Depending on the selected direction, upside and/or downside levels are plotted.
Filled zones are drawn between levels to visually highlight key price zones.
Optionally, each level can be labeled with its Fibonacci value and price.
WHY IT IS UNIQUE
Unlike traditional swing-based Fibonacci retracements, this tool uses the day’s opening price as an anchor, specifically designed for intraday traders.
Allows traders to quickly visualize micro-support and resistance levels that adapt every day.
Highly customizable and easy to read, with filled level bands for better zone recognition.
Works independently of indicators like RSI, MACD, or moving averages – purely based on price action logic.
HOW USER CAN BENEFIT FROM IT
Spot precise intraday reversal zones or breakout regions.
Combine with price action or volume analysis for smarter entries.
Filter trades by choosing directional bias (Up Site, Down Site, or Both).
Set profit targets or stop-losses based on Fibonacci bands.
Works great for scalpers, day traders, and even short-term swing traders looking to align with opening price momentum.
Disclaimer
This indicator is not financial advice, it's for educational purposes only highlighting the power of coding( pine script) in TradingView, I am not a SEBI-registered advisor. Trading and investing involve risk, and you should consult with a qualified financial advisor before making any trading decisions. I do not guarantee profits or take responsibility for any losses you may incur.
BK AK-SILENCER🚨 Introducing BK AK-SILENCER — Volume Footprint Warfare, Right on the Price Bars 🚨
This isn’t a traditional indicator.
This is a tactical weapon — engineered to expose institutional behavior directly in the bar data, using volume logic, CVD divergence, and spike detection to pinpoint who’s really in control of the tape.
No panels. No clutter.
Just silent execution — built directly into price itself.
🔥 Why "SILENCER"?
Because real power moves in silence.
Institutions don’t chase — they build positions quietly, in size, beneath the surface.
BK AK-SILENCER gives you a real-time edge by visually revealing their footprints through color-coded bar behavior, divergence signals, and volume spike alerts — all directly on your chart.
🔹 “AK” honors my mentor A.K., whose training forged my trading discipline.
🔹 “SILENCER” represents the institutional mindset — high impact, low visibility. This tool lets you trade like them: without noise, without hesitation, with deadly clarity.
🧠 What Is BK AK-SILENCER?
A bar-level institutional detection tool, purpose-built to:
✅ Color-code bars based on volume aggression and close-location inside range
✅ Detect real-time bullish and bearish divergences between price and volume delta
✅ Tag volume spikes with a $ symbol to expose potential traps or silent position builds
✅ Overlay VWAP for real-time mean-reversion biasing
No extra windows.
No indicators talking over each other.
Just pure volume-logic weaponry embedded into price.
⚙️ What This Weapon Deploys
🔸 Bar Coloring Logic (Volume Footprint)
🟢 Power Buy = Strong close near highs on elevated volume
🟩 Accumulation = Weak close but still heavy volume
🔴 Power Sell = Strong close near lows on heavy selling
🟥 Distribution / Weakness = Low close without commitment
❗ Extreme Volume Spikes marked with $ — using standard deviation to highlight institutional bursts
🔸 CVD Divergence Detection
→ Tracks cumulative volume delta and compares it to price pivot behavior
Bullish Divergence = Price makes lower lows, CVD makes higher lows → hidden accumulation
Bearish Divergence = Price makes higher highs, CVD makes lower highs → hidden distribution
All plotted directly on bars with triangle markers.
🔸 VWAP Overlay (Optional)
→ Anchored VWAP gives immediate context for intraday bias — above VWAP = demand, below = supply
🎯 How to Use BK AK-SILENCER
🔹 Silent Reversal Detection
Bullish divergence + Power Buy bar + VWAP reclaim = sniper entry
Bearish divergence + Power Sell bar + VWAP rejection = trap confirmation
🔹 Volume-Based Entry Triggers
Look for Power Buy + $ spike after a pullback → watch for quiet reversal
Accumulation colors clustering? Institutions are likely loading silently
🔹 Institutional Trap Warnings
$ spike + red distribution bar at highs = time to exit or flip
Weakness bar below VWAP? Don’t chase the long.
🛡️ Why It Matters
✅ Clean — it integrates into price action, no separate panels
✅ Silent — tracks institutions who build without alerts or indicators
✅ Tactical — no fluff, no lag, just real-time behavior recognition
This tool is ideal for:
🔸 Scalpers reading bar-by-bar
🔸 Intraday swing traders using VWAP and structure
🔸 Professionals who need volume behavior decoded in real-time
🔸 Anyone who wants signal without clutter
🙏 Final Thoughts
This tool isn’t just about trading — it’s about tactical awareness.
🔹 Dedicated to my mentor A.K., whose wisdom runs deep in every logic tree.
🔹 Above all, I give thanks to Gd, the source of clarity, courage, and conviction.
Without Him, even the sharpest system is blind.
With Him, we execute with structure, purpose, and divine alignment.
⚡ No noise. No clutter. No delay. Just raw, silent execution.
🔥 BK AK-SILENCER — Bar-Level Volume Footprint Precision 🔥
Gd bless every step you take in this market.
Trade with clarity, move with intention. 🙏
Bear Market Probability Model# Bear Market Probability Model: A Multi-Factor Risk Assessment Framework
The Bear Market Probability Model represents a comprehensive quantitative framework for assessing systemic market risk through the integration of 13 distinct risk factors across four analytical categories: macroeconomic indicators, technical analysis factors, market sentiment measures, and market breadth metrics. This indicator synthesizes established financial research methodologies to provide real-time probabilistic assessments of impending bear market conditions, offering institutional-grade risk management capabilities to retail and professional traders alike.
## Theoretical Foundation
### Historical Context of Bear Market Prediction
Bear market prediction has been a central focus of financial research since the seminal work of Dow (1901) and the subsequent development of technical analysis theory. The challenge of predicting market downturns gained renewed academic attention following the market crashes of 1929, 1987, 2000, and 2008, leading to the development of sophisticated multi-factor models.
Fama and French (1989) demonstrated that certain financial variables possess predictive power for stock returns, particularly during market stress periods. Their three-factor model laid the groundwork for multi-dimensional risk assessment, which this indicator extends through the incorporation of real-time market microstructure data.
### Methodological Framework
The model employs a weighted composite scoring methodology based on the theoretical framework established by Campbell and Shiller (1998) for market valuation assessment, extended through the incorporation of high-frequency sentiment and technical indicators as proposed by Baker and Wurgler (2006) in their seminal work on investor sentiment.
The mathematical foundation follows the general form:
Bear Market Probability = Σ(Wi × Ci) / ΣWi × 100
Where:
- Wi = Category weight (i = 1,2,3,4)
- Ci = Normalized category score
- Categories: Macroeconomic, Technical, Sentiment, Breadth
## Component Analysis
### 1. Macroeconomic Risk Factors
#### Yield Curve Analysis
The inclusion of yield curve inversion as a primary predictor follows extensive research by Estrella and Mishkin (1998), who demonstrated that the term spread between 3-month and 10-year Treasury securities has historically preceded all major recessions since 1969. The model incorporates both the 2Y-10Y and 3M-10Y spreads to capture different aspects of monetary policy expectations.
Implementation:
- 2Y-10Y Spread: Captures market expectations of monetary policy trajectory
- 3M-10Y Spread: Traditional recession predictor with 12-18 month lead time
Scientific Basis: Harvey (1988) and subsequent research by Ang, Piazzesi, and Wei (2006) established the theoretical foundation linking yield curve inversions to economic contractions through the expectations hypothesis of the term structure.
#### Credit Risk Premium Assessment
High-yield credit spreads serve as a real-time gauge of systemic risk, following the methodology established by Gilchrist and Zakrajšek (2012) in their excess bond premium research. The model incorporates the ICE BofA High Yield Master II Option-Adjusted Spread as a proxy for credit market stress.
Threshold Calibration:
- Normal conditions: < 350 basis points
- Elevated risk: 350-500 basis points
- Severe stress: > 500 basis points
#### Currency and Commodity Stress Indicators
The US Dollar Index (DXY) momentum serves as a risk-off indicator, while the Gold-to-Oil ratio captures commodity market stress dynamics. This approach follows the methodology of Akram (2009) and Beckmann, Berger, and Czudaj (2015) in analyzing commodity-currency relationships during market stress.
### 2. Technical Analysis Factors
#### Multi-Timeframe Moving Average Analysis
The technical component incorporates the well-established moving average convergence methodology, drawing from the work of Brock, Lakonishok, and LeBaron (1992), who provided empirical evidence for the profitability of technical trading rules.
Implementation:
- Price relative to 50-day and 200-day simple moving averages
- Moving average convergence/divergence analysis
- Multi-timeframe MACD assessment (daily and weekly)
#### Momentum and Volatility Analysis
The model integrates Relative Strength Index (RSI) analysis following Wilder's (1978) original methodology, combined with maximum drawdown analysis based on the work of Magdon-Ismail and Atiya (2004) on optimal drawdown measurement.
### 3. Market Sentiment Factors
#### Volatility Index Analysis
The VIX component follows the established research of Whaley (2009) and subsequent work by Bekaert and Hoerova (2014) on VIX as a predictor of market stress. The model incorporates both absolute VIX levels and relative VIX spikes compared to the 20-day moving average.
Calibration:
- Low volatility: VIX < 20
- Elevated concern: VIX 20-25
- High fear: VIX > 25
- Panic conditions: VIX > 30
#### Put-Call Ratio Analysis
Options flow analysis through put-call ratios provides insight into sophisticated investor positioning, following the methodology established by Pan and Poteshman (2006) in their analysis of informed trading in options markets.
### 4. Market Breadth Factors
#### Advance-Decline Analysis
Market breadth assessment follows the classic work of Fosback (1976) and subsequent research by Brown and Cliff (2004) on market breadth as a predictor of future returns.
Components:
- Daily advance-decline ratio
- Advance-decline line momentum
- McClellan Oscillator (Ema19 - Ema39 of A-D difference)
#### New Highs-New Lows Analysis
The new highs-new lows ratio serves as a market leadership indicator, based on the research of Zweig (1986) and validated in academic literature by Zarowin (1990).
## Dynamic Threshold Methodology
The model incorporates adaptive thresholds based on rolling volatility and trend analysis, following the methodology established by Pagan and Sossounov (2003) for business cycle dating. This approach allows the model to adjust sensitivity based on prevailing market conditions.
Dynamic Threshold Calculation:
- Warning Level: Base threshold ± (Volatility × 1.0)
- Danger Level: Base threshold ± (Volatility × 1.5)
- Bounds: ±10-20 points from base threshold
## Professional Implementation
### Institutional Usage Patterns
Professional risk managers typically employ multi-factor bear market models in several contexts:
#### 1. Portfolio Risk Management
- Tactical Asset Allocation: Reducing equity exposure when probability exceeds 60-70%
- Hedging Strategies: Implementing protective puts or VIX calls when warning thresholds are breached
- Sector Rotation: Shifting from growth to defensive sectors during elevated risk periods
#### 2. Risk Budgeting
- Value-at-Risk Adjustment: Incorporating bear market probability into VaR calculations
- Stress Testing: Using probability levels to calibrate stress test scenarios
- Capital Requirements: Adjusting regulatory capital based on systemic risk assessment
#### 3. Client Communication
- Risk Reporting: Quantifying market risk for client presentations
- Investment Committee Decisions: Providing objective risk metrics for strategic decisions
- Performance Attribution: Explaining defensive positioning during market stress
### Implementation Framework
Professional traders typically implement such models through:
#### Signal Hierarchy:
1. Probability < 30%: Normal risk positioning
2. Probability 30-50%: Increased hedging, reduced leverage
3. Probability 50-70%: Defensive positioning, cash building
4. Probability > 70%: Maximum defensive posture, short exposure consideration
#### Risk Management Integration:
- Position Sizing: Inverse relationship between probability and position size
- Stop-Loss Adjustment: Tighter stops during elevated risk periods
- Correlation Monitoring: Increased attention to cross-asset correlations
## Strengths and Advantages
### 1. Comprehensive Coverage
The model's primary strength lies in its multi-dimensional approach, avoiding the single-factor bias that has historically plagued market timing models. By incorporating macroeconomic, technical, sentiment, and breadth factors, the model provides robust risk assessment across different market regimes.
### 2. Dynamic Adaptability
The adaptive threshold mechanism allows the model to adjust sensitivity based on prevailing volatility conditions, reducing false signals during low-volatility periods and maintaining sensitivity during high-volatility regimes.
### 3. Real-Time Processing
Unlike traditional academic models that rely on monthly or quarterly data, this indicator processes daily market data, providing timely risk assessment for active portfolio management.
### 4. Transparency and Interpretability
The component-based structure allows users to understand which factors are driving risk assessment, enabling informed decision-making about model signals.
### 5. Historical Validation
Each component has been validated in academic literature, providing theoretical foundation for the model's predictive power.
## Limitations and Weaknesses
### 1. Data Dependencies
The model's effectiveness depends heavily on the availability and quality of real-time economic data. Federal Reserve Economic Data (FRED) updates may have lags that could impact model responsiveness during rapidly evolving market conditions.
### 2. Regime Change Sensitivity
Like most quantitative models, the indicator may struggle during unprecedented market conditions or structural regime changes where historical relationships break down (Taleb, 2007).
### 3. False Signal Risk
Multi-factor models inherently face the challenge of balancing sensitivity with specificity. The model may generate false positive signals during normal market volatility periods.
### 4. Currency and Geographic Bias
The model focuses primarily on US market indicators, potentially limiting its effectiveness for global portfolio management or non-USD denominated assets.
### 5. Correlation Breakdown
During extreme market stress, correlations between risk factors may increase dramatically, reducing the model's diversification benefits (Forbes and Rigobon, 2002).
## References
Akram, Q. F. (2009). Commodity prices, interest rates and the dollar. Energy Economics, 31(6), 838-851.
Ang, A., Piazzesi, M., & Wei, M. (2006). What does the yield curve tell us about GDP growth? Journal of Econometrics, 131(1-2), 359-403.
Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The Journal of Finance, 61(4), 1645-1680.
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636.
Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116(1), 261-292.
Beckmann, J., Berger, T., & Czudaj, R. (2015). Does gold act as a hedge or a safe haven for stocks? A smooth transition approach. Economic Modelling, 48, 16-24.
Bekaert, G., & Hoerova, M. (2014). The VIX, the variance premium and stock market volatility. Journal of Econometrics, 183(2), 181-192.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764.
Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1-27.
Campbell, J. Y., & Shiller, R. J. (1998). Valuation ratios and the long-run stock market outlook. The Journal of Portfolio Management, 24(2), 11-26.
Dow, C. H. (1901). Scientific stock speculation. The Magazine of Wall Street.
Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61.
Fama, E. F., & French, K. R. (1989). Business conditions and expected returns on stocks and bonds. Journal of Financial Economics, 25(1), 23-49.
Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. The Journal of Finance, 57(5), 2223-2261.
Fosback, N. G. (1976). Stock market logic: A sophisticated approach to profits on Wall Street. The Institute for Econometric Research.
Gilchrist, S., & Zakrajšek, E. (2012). Credit spreads and business cycle fluctuations. American Economic Review, 102(4), 1692-1720.
Harvey, C. R. (1988). The real term structure and consumption growth. Journal of Financial Economics, 22(2), 305-333.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Magdon-Ismail, M., & Atiya, A. F. (2004). Maximum drawdown. Risk, 17(10), 99-102.
Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175-220.
Pagan, A. R., & Sossounov, K. A. (2003). A simple framework for analysing bull and bear markets. Journal of Applied Econometrics, 18(1), 23-46.
Pan, J., & Poteshman, A. M. (2006). The information in option volume for future stock prices. The Review of Financial Studies, 19(3), 871-908.
Taleb, N. N. (2007). The black swan: The impact of the highly improbable. Random House.
Whaley, R. E. (2009). Understanding the VIX. The Journal of Portfolio Management, 35(3), 98-105.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
Zarowin, P. (1990). Size, seasonality, and stock market overreaction. Journal of Financial and Quantitative Analysis, 25(1), 113-125.
Zweig, M. E. (1986). Winning on Wall Street. Warner Books.
Consolidation Range [BigBeluga]A hybrid volatility-volume indicator that isolates periods of price equilibrium and reveals the directional force behind each range buildup.
Consolidation Range is a powerful tool designed to detect compression phases in the market using volatility thresholds while visualizing volume imbalance within those phases. By combining low-volatility detection with directional volume delta, it highlights where accumulation or distribution is occurring—giving traders the confidence to act when breakouts follow. This indicator is particularly valuable in choppy or sideways markets where range identification and sentiment context are key.
🔵 CONCEPTS
Volatility Compression: Uses ADX (Average Directional Index) to detect periods of low trend strength—specifically when ADX drops below a configurable threshold.
Range Structure: Upon a low-volatility trigger, the script dynamically anchors horizontal upper and lower bounds based on local highs and lows.
Directional Volume Delta: Inside each active range, it calculates the net difference between buy and sell volume, showing who controlled the range.
Sentiment Bias: A label appears in the center of the zone on breakout, showing the accumulated delta and bias direction (▲ for positive, ▼ for negative).
Range Validity Filter: Only ranges with more than 15 bars are considered valid—short-lived consolidations are auto-filtered.
🔵 KEY FEATURES
Detects low volatility market phases using ADX logic (crosses under "Volatility Threshold Input").
Automatically plots adaptive consolidation zones with upper and lower boundary lines.
Includes dynamic midline to visualize the price average inside the range.
Visual range is filled with a progressive gradient to reflect distance between highs and lows.
When the range is active, the indicator accumulates volume delta (Buy - Sell volume) .
Upon breakout, the total volume delta is displayed at the midpoint , providing insight into market sentiment during the consolidation phase.
Filters out weak or short-lived consolidations under 15 bars.
🔵 HOW TO USE
Spot ranging or compression zones with minimal effort.
Use breakouts with volume delta bias to assess the strength or weakness of moves.
Combine with trend-following tools or volume-based confirmation for stronger setups.
Apply to higher timeframes for macro consolidation tracking .
🔵 CONCLUSION
Consolidation Range now brings together volatility filtering and directional volume delta into one smart module. This hybrid logic allows traders to not only identify balance zones but also understand who was in control during the buildup—offering a sharper edge for breakout and trend continuation strategies.
Adaptive Multi-TF Indicator Table with Presets giua64📌 Script Name:
Adaptive Multi-Timeframe Indicator Table with Presets — giua64
📄 Description:
This script displays an adaptive multi-timeframe dashboard that summarizes the signals of three key technical indicators:
Moving Averages (MAs), Relative Strength Index (RSI), and MACD.
It provides a fast and visually intuitive overview of market conditions across five timeframes (5m, 15m, 30m, 1h, 4h), helping traders quickly identify potential directional biases (e.g., bullish, bearish, or neutral) based on either predefined presets or fully manual settings.
🧰 Preset Configurations:
You can choose between four trading styles, each with optimized indicator parameters:
Scalping
• MAs: 5 / 10 (Fast), 20 / 50 (Slow)
• RSI: 7 periods | Overbought: 70 | Oversold: 30
• MACD: 5 / 13 | Signal: 3
Intraday
• MAs: 9 / 21 (Fast), 50 / 100 (Slow)
• RSI: 14 periods | Overbought: 60 | Oversold: 40
• MACD: 12 / 26 | Signal: 9
Swing
• MAs: 10 / 20 (Fast), 50 / 200 (Slow)
• RSI: 14 periods | Overbought: 65 | Oversold: 35
• MACD: 12 / 26 | Signal: 9
Manual
• Full custom control over all indicator settings.
🛠️ All settings can be customized manually from the options panel, including the exact MA periods, RSI thresholds, and MACD structure.
🧠 How It Works:
For each timeframe, the script evaluates:
MA crossover status (two levels):
The first symbol refers to the crossover of the fast MAs
The second symbol refers to the crossover of the slow MAs
🟢 = Bullish crossover
🔴 = Bearish crossover
➖ = Flat or no clear signal
RSI Direction:
↑ = RSI above upper threshold (potential overbought)
↓ = RSI below lower threshold (potential oversold)
→ = RSI in neutral range
MACD Line vs Signal Line:
↑ = MACD line is above signal line (bullish)
↓ = MACD line is below signal line (bearish)
→ = Flat or neutral signal
Each signal is assigned a numerical score. These are aggregated per timeframe to compute a combined score that reflects the directional bias for that specific time window.
🧠 Adaptive Logic by Asset:
This script is designed to be universally compatible across all asset types — including forex, crypto, stocks, indices, and commodities.
Thanks to its multi-timeframe nature and flexible indicator presets, the script automatically adjusts its behavior based on the asset selected, ensuring relevant analysis without requiring manual recalibration.
🧾 Summary Table Output:
At the bottom of the dashboard, a combined sentiment is displayed for:
3TF → 5m, 15m, 30m
4TF → Adds 1h
5TF → Adds 4h
Each row shows:
Signal → LONG / SHORT / NEUTRAL
Confidence (%) → Based on score aggregation and signal consistency
📌 Customization Options:
Table Position: Left, Right, or Center
Text Size: Small, Normal, or Large
Full Manual Configuration: All MA, RSI, and MACD parameters can be adjusted as needed
⚠️ Disclaimer:
This script is for educational and analytical purposes only.
It does not constitute financial advice or guarantee any trading results.
Always do your own research and apply responsible risk management.
Bitcoin Weekend FadeThis indicator is a tool for setting a bias based on weekend price movements, with the assumption that the crypto market often experiences stronger moves over the weekend due to thinner order books. It helps identify potential fade opportunities, suggesting that price movements from Saturday and Sunday may reverse during the weekdays.
How to use:
Sets a bias based on weekend price action.
Sets a bias based on weekend price action.
Use weekday price action for confirmation before acting on the bias.
Best suited for range-bound markets, where the price tends to revert to the mean.
Avoid fading high-timeframe breakouts, as they often indicate strong trends.
Next Candle PredictorNext Candle Predictor for TradingView
This Pine Script indicator helps predict potential price movements for the next candle based on historical price action patterns. It analyzes recent candles' characteristics including body size, wick length, and volume to calculate a directional bias.
Key Features
Analyzes recent price action to predict next candle direction (Bullish, Bearish, or Neutral)
Visual indicators include small directional arrows and a prediction line
Customizable sensitivity and lookback period
Works best on lower timeframes for short-term price action trading
Displays clear prediction labels that extend into future bars
How It Works
The script analyzes recent candles by examining:
Candle body size (weighted by your preference)
Wick length (weighted by your preference)
Volume activity (weighted by your preference)
These factors combine to create a directional strength indicator that determines if the next candle is likely to be bullish, bearish, or neutral.
Visual Feedback
Green up arrows indicate bullish predictions
Red down arrows indicate bearish predictions
A directional line extends from the last candle showing predicted price movement
A label displays the prediction text at the end of the line
Information table in the top right displays the current prediction
Settings
Lookback Candle Count: Number of historical candles to analyze (2-20)
Wick/Body/Volume Weight Factors: Adjust importance of each component
Prediction Sensitivity: Threshold for triggering directional bias
Prediction Line Length: How far the prediction line extends
Perfect for day traders and scalpers looking for an edge in short-term directional bias.
Enigma Liquidity Concept
Enigma Liquidity Concept
Empowering Traders with Multi-Timeframe Analysis and Dynamic Fibonacci Insights
Overview
The Enigma Liquidity Concept is an advanced indicator designed to bridge multi-timeframe price action with Fibonacci retracements. It provides traders with high-probability buy and sell signals by combining higher time frame market direction and lower time frame precision entries. Whether you're a scalper, day trader, or swing trader, this tool offers actionable insights to refine your entries and exits.
What Makes It Unique?
Multi-Timeframe Signal Synchronization:
Higher time frame bullish or bearish engulfing patterns are used to define the directional bias.
Lower time frame retracements are analyzed for potential entry opportunities.
Dynamic Fibonacci Layouts:
Automatically plots Fibonacci retracement levels for the most recent higher time frame signal.
Ensures a clean chart by avoiding clutter from historical signals.
Actionable Buy and Sell Signals:
Sell Signal: When the higher time frame is bearish and the price on the lower time frame retraces above the 50% Fibonacci level before forming a bearish candle.
Buy Signal: When the higher time frame is bullish and the price on the lower time frame retraces below the 50% Fibonacci level before forming a bullish candle.
Customizable Fibonacci Visuals:
Full control over Fibonacci levels, line styles, and background shading to tailor the chart to your preferences.
Integrated Alerts:
Real-time alerts for buy and sell signals on the lower time frame.
Alerts for bullish and bearish signals on the higher time frame.
How It Works
Higher Time Frame Analysis:
The indicator identifies bullish and bearish engulfing patterns to detect key reversals or continuation points.
Fibonacci retracement levels are calculated and plotted dynamically for the most recent signal:
Bullish Signal: 100% starts at the low, 0% at the high.
Bearish Signal: 100% starts at the high, 0% at the low.
Lower Time Frame Execution:
Monitors retracements relative to the higher time frame Fibonacci levels.
Provides visual and alert-based buy/sell signals when conditions align for a high-probability entry.
How to Use It
Setup:
Select your higher and lower time frames in the settings.
Customize Fibonacci levels, line styles, and background visuals for clarity.
Trade Execution:
Use the higher time frame signals to determine directional bias.
Watch for actionable buy/sell signals on the lower time frame:
Enter short trades on red triangle sell signals.
Enter long trades on green triangle buy signals.
Alerts:
Enable alerts for real-time notifications of buy/sell signals on lower time frames and higher time frame directional changes.
Concepts Underlying the Calculations
Engulfing Patterns: Represent key reversals or continuations in price action, making them reliable for defining directional bias on higher time frames.
Fibonacci Retracements: Fibonacci levels are used to identify critical zones for potential price reactions during retracements.
Multi-Timeframe Analysis: Combines the strength of higher time frame trends with the precision of lower time frame signals to enhance trades.
Important Notes
This indicator is best used in conjunction with your existing trading strategy and risk management plan.
It does not repaint signals and ensures clarity by displaying Fibonacci levels only for the most recent signal.
Ideal For:
Swing traders, day traders, and scalpers looking to optimize entries and exits with Fibonacci retracements.
Traders who prefer clean charts with actionable insights and customizable visuals.
