FXN1 COT DashboardDetailed COT Report Dashboard with Automatic Updates
The Commitment of Traders (COT) report is a crucial tool for traders and analysts, providing valuable insights into market positioning and sentiment. However, since the report is only released once a week, it's essential to track changes efficiently to make informed decisions.
To enhance usability, we propose implementing an **automatically updating COT report dashboard** that clearly visualizes the differences between the latest report and the previous week’s data. This dashboard will allow users to quickly identify shifts in market contracts, including changes in long/short positions, net positioning, and trader category activity (commercial, non-commercial, and non-reportable traders).
Regards.
การวิเคราะห์ปัจจัยพื้นฐาน
Tekpor Smart Supply & Demand ZonesThe Tekpor Smart Supply & Demand Zones indicator automatically identifies and visualizes key institutional levels where price has previously reacted — giving traders a powerful edge when spotting high-probability trade zones.
🔍 What It Does:
• Detects supply zones when price forms swing highs and closes bearish
• Detects demand zones when price forms swing lows and closes bullish
• Draws smart zones as shaded rectangles for easy visualization
• Automatically removes zones that are invalidated (price breaks above supply or below demand)
• Fully adjustable swing sensitivity and look-back range
⚙️ Key Features:
• Minimal lag, designed for real-time execution
• Clean chart display with custom colors and extendable zones
• Compatible with any timeframe and instrument (FX, crypto, indices, etc.)
• Perfect for price action, breakout, and reversal traders
🕵️♂️ Great for:
• Scalping or swing trading
• Enhancing entry/exit precision
• Combining with trend or volume filters
—
Built to help you trade like the pros — with no guesswork, no redrawing, and no noise.
💡 Tip: Use with confluence tools like EMA, volume, or structure breaks for best results.
Happy trading!
– TekporEdge 🚀
Macro Forecaster DEMO Macro Forecaster v4.6c (DEMO)
A multi-factor macroeconomic regime indicator designed to help traders and investors interpret economic momentum directly from the chart.
Still in DEMO phase – experimental model, not investment advice.
📌 What It Does
The Macro Forecaster v4.6c pulls real-time macroeconomic data from the FRED and ECONOMICS databases and blends it into an intelligent, color-coded model that helps visualize the current macro regime — expansion, contraction, or transition.
It integrates:
Leading indicators like Yield Curve, M2 growth, Fed rate changes, Term Premium, PMI, and Retail Sales
Lagging indicators such as CPI inflation and Unemployment Rate
Macro posture levels like current Fed Funds Rate, PMI level, and Unemployment
Rate Stability: Weeks since the last Fed rate change – a unique "volatility risk" gauge
All components are normalized and displayed as composite scores on a 0–100 scale, making macro analysis visual, intuitive, and actionable.
🔍 Key Outputs
Leading Score (%): Forward-looking macro delta composite
Lagging Score (%): Rear-view inflation/labor signal
Overall Regime Score (%): Combined delta-based model
Macro Posture (%): Level-based economic posture
Blend Score: Final regime strength (average of delta + posture)
Rate Stability: Measures macro calm vs panic potential
Expansion 🚀 / Contraction 🔻 signals: Based on macro trend + price action alignment
🧪 DEMO NOTICE
This version is a DEMO / prototype. It is still being tested for reliability, optimal weighting, and behavioral thresholds.
Future updates will:
Add dynamic toggles for each macro series
Allow user-defined weights
Improve expansion/contraction thresholds
Introduce volatility-based coloring logic
Possibly integrate bond curve inversion models and liquidity triggers
📊 Best Use Cases
Spotting macro regime shifts before they hit the headlines
Identifying when macro and price are in sync
Filtering trades during high-risk macro phases (e.g., unstable rate environments)
Visualizing growth/liquidity conditions for crypto, stocks, and FX
⚠️ Disclaimer
This is an educational and experimental tool.
It does not constitute financial advice.
Please use alongside other tools and your own research.
Data sources like FRED can lag or be revised.
💬 Feedback Welcome
If you have improvement ideas, feature requests, or want to collaborate on macro models, drop a comment below!
Special thanks to @ChifoiCristian, whos teachings inspired me to create this indicator.
M2 Global G13 Liquidity (Custom & Shift, US DXY Adj.)🌎 M2 Global G13 Liquidity index (Custom & Shift, US DXY Adj.)
💡 Indicator Overview
The M2 Global G13 Liquidity indicator combines the M2 liquidity of 13 major countries, allowing users to selectively include or exclude each country to visualize global capital flows and potential investment liquidity at a glance.
Each country's M2 data is converted to USD using real-time exchange rates, and the US M2 is further adjusted using the Dollar Index (DXY) to reflect the impact of dollar strength or weakness on US liquidity.
✅ What is M2?
M2 is a broad measure of money supply that includes cash, demand deposits, savings deposits, and certain financial products.
It represents a country's overall liquidity and capital supply and is often interpreted as "dry powder" ready to be deployed into various assets such as equities, real estate, and bonds.
Therefore, M2 serves as a crucial benchmark for assessing a country's potential investment capacity that can flow into markets at any time.
💰 Exchange Rate & Dollar Index Adjustment
- All country M2 data is converted from local currencies to USD.
- The US M2 is further adjusted using the Dollar Index (DXY) to better reflect its real global power:
- DXY > 100 → Liquidity contraction (strong dollar effect)
- DXY < 100 → Liquidity expansion (weak dollar effect)
🗺️ Country Selection Options
- Default selection: United States
- Major selections: China, Eurozone, Japan, United Kingdom (core G5 economies)
- Additional selections: Switzerland, Canada, India, Russia, Brazil, South Korea, Mexico, South Africa
- Users can freely add or remove countries to customize the indicator to match their analytical needs.
📈 Example Use Cases
- Monitor global capital flows: Track worldwide liquidity trends and detect potential market risk signals.
- Analyze exchange rate and monetary policy trends: Compare dollar strength with major central bank policies.
- Benchmark against equity indices: Evaluate correlations with MSCI World, KOSPI, NASDAQ, etc.
- Valuation analysis: Compare overall liquidity levels to equity index prices or market capitalization to assess relative valuation and identify potential overvaluation or undervaluation.
- Crisis response strategy: Identify liquidity contraction during global credit crises or deleveraging phases.
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🌎 M2 글로벌 G13 유동성 지수 (Custom & Shift, US DXY Adj.)
💡 지표 소개
M2 Global G13 Liquidity 지표는 세계 13개 주요국의 M2 유동성을 선택적으로 결합하여, 글로벌 자금 흐름과 잠재 투자 자금을 한눈에 시각화할 수 있도록 설계된 종합 유동성 지표입니다.
국가별 M2 데이터를 환율과 결합해 달러 기준으로 표준화하며, 특히 미국 M2는 달러지수(DXY)로 보정하여 달러 강약에 따른 파급력을 반영합니다.
✅ M2란?
M2는 광의 통화지표로, 현금 + 요구불 예금 + 저축성 예금 + 일부 금융상품을 포함합니다.
이는 한 국가의 유동성 수준과 자금 공급 상태를 나타내는 핵심 거시경제 지표이며, **주식·부동산·채권 등 다양한 자산에 투자될 준비가 된 '대기자금'**으로도 해석됩니다.
따라서 M2는 투자시장으로 언제든지 흘러들어갈 수 있는 잠재적 투자 역량을 평가할 때 중요한 기준입니다.
💰 환율 및 달러지수 보정
- 모든 국가 M2는 자국 통화에서 **달러(USD)**로 환산됩니다.
- 특히 미국 M2는 달러 가치의 글로벌 실질 파워를 평가하기 위해 DXY 보정을 적용합니다.
- DXY > 100 → 유동성 축소 (강달러 효과)
- DXY < 100 → 유동성 확대 (약달러 효과)
🗺️ 국가별 선택 옵션
- 기본 선택: 미국
- 주요 선택: 중국, 유로존, 일본, 영국 (주요 G5)
- 추가 선택: 스위스, 캐나다, 인도, 러시아, 브라질, 한국, 멕시코, 남아공
- 사용자는 각 국가를 자유롭게 더하거나 빼면서 커스터마이즈할 수 있습니다.
📈 활용 예시
- 글로벌 자금 흐름 모니터링: 전세계 유동성 추세 및 시장 리스크 신호 분석
- 환율/금리 정책 분석: 달러 강약과 주요국 정책 변화 비교
- 주가지수 벤치마크 비교: MSCI World, 코스피, 나스닥 등과 상관관계 확인
- 밸류에이션 분석: 전체 유동성 수준을 주가지수나 시가총액과 비교하여, 시장의 상대적 고평가·저평가 여부를 평가
- 위기 대응 전략: 글로벌 신용위기·자금 긴축 국면 대비
Moving Average / ATR Breakout Signal [ARTech]Moving Average / ATR Breakout Signal
This indicator generates trend-following signals based on price breaking above or below a user-defined Moving Average (MA). It supports various MA types and lengths, while offering optional filters like ATR bands and breakout thresholds to enhance signal quality. The tool is designed to help traders detect momentum shifts with configurable confirmation logic and offers visual enhancements to help traders better interpret market conditions at a glance.
Key Features:
• Multi-Type Moving Average Support: Choose from various Moving Average types including EMA, SMA, Hull MA, VWMA, RMA, TEMA, and more — fully customizable with source and length options.
• Flexible Signal Logic: Signals are generated when price breaks above or below the selected MA. You can define the number of confirmation candles and choose between wick-based or close-based break logic.
• ATR-Based Filtering: Enable ATR filtering to create dynamic upper and lower breakout bands around the MA. This helps reduce noise and validate true breakouts with volatility-adjusted thresholds.
• Breakout Threshold Filtering: Add an optional breakout condition where the price must first move a minimum percentage away from the previous signal level before a new opposite signal is allowed. Prevents choppy back-to-back signals.
• Visual Enhancements: Color-coded backgrounds highlight long and short zones, adapting dynamically to signal context. Optional MA slope coloring further supports trend visualization.
• Signal Alerts: Customizable alerts for long and short signals, including user-defined messages, to keep you notified in real-time.
Why use this indicator?
• Helps you identify clear trend shifts by focusing on price action relative to a customizable moving average.
• Improves signal reliability with optional ATR filtering and breakout confirmation, reducing false signals.
• Flexible MA types and lengths let you tailor the indicator to your trading style.
• Suitable for traders of all levels looking for a straightforward, yet powerful trend-following tool.
How to Use
███████ Alerts ███████
• Custom Alerts: To enable Custom Alerts, you need to activate the fx alert() function call option in TradingView’s alert creation dialog. Then, select the desired alert type (Long or Short) from the indicator's settings under the "Alerts" section, you can customize messages and enable notifications for Long and Short signals.
Using Custom Alerts allows you to set up one alert that covers both Long and Short signals, simplifying your alert management.
• Long and Short Alerts: To create Long or Short alerts, open the alert dialog, select this indicator as the condition, then choose “Long” or “Short” from the list and click Create.
You need to set up two separate alerts: one for Long signals and one for Short signals.
███████ Moving Average ███████
This is the core component of the signal system. You can customize:
Moving Average Type: Choose from SMA, EMA, WMA, Hull MA, VWMA, RMA, or TEMA
Length: Adjust the length to suit your strategy.
Source: Select which price data (e.g., Close, Open, HL2) is used to calculate the MA.
Show Slope Color: Colors the MA line based on its direction: upward slopes are shown in the selected "Up" color, while downward slopes use the "Down" color. This helps you visually confirm trend direction at a glance.
Show Background Color: When enabled, highlights the area between the MA and price to enhance signal zones:
– If ATR filter is on, the space between ATR bands is shaded.
– If ATR filter is off, the area between the MA line and bar closes is colored.
This helps emphasize potential breakout or trend-following zones visually.
███████ Break Options ███████
Confirm Candles: Defines the number of consecutive candles that must break the selected level to confirm a signal.
– If ATR filter is enabled, this level is the ATR bands.
– If ATR is disabled, the Moving Average line is used.
This helps filter out noise and avoid premature signals.
Break Type: Specifies how the candle must break the level:
– Close: The candle must close beyond the level.
– Wick: A wick touching or exceeding the level is enough.
Choose based on how strict you want the breakout condition to be.
███████ Filters ███████
This section provides optional filters to improve signal accuracy:
ATR
When enabled, breakout confirmation requires the price to cross above the upper breakout line or below the lower breakout line by a specified percentage from the last signal price.
• Multiplier: Adjusts the width of ATR bands by multiplying the ATR value.
• Length: Sets the period for ATR calculation.
• Smoothing: Selects the smoothing method applied to the ATR (RMA, SMA, EMA, WMA).
• Upper and Lower Line Colors: Customize the colors of the ATR bands.
Breakout Filter
When enabled, breakout confirmation requires the price to cross above the upper breakout line or below the lower breakout line by a specified percentage from the last signal price.
• Threshold (%): Defines the minimum percentage price movement required to validate a breakout.
• Show Breakout Levels: Toggle to display or hide breakout threshold area on the chart.
Asset Premium/Discount Monitor📊 Overview
The Asset Premium/Discount Monitor is a tool for analyzing the relative value between two correlated assets. It measures when one asset is trading at a premium or discount compared to its historical relationship with another asset, helping traders identify potential mean reversion opportunities, or pairs trading opportunities.
🎯 Use Cases
Perfect for analyzing:
NASDAQ:MSTR vs CRYPTO:BTCUSD - MicroStrategy's premium/discount to Bitcoin
NASDAQ:COIN vs BITSTAMP:BTCUSD - Coinbase's relative value to Bitcoin
NASDAQ:TSLA vs NASDAQ:QQQ - Tesla's premium to tech sector
Regional banks AMEX:KRE vs AMEX:XLF - Individual bank stocks vs financial sector
Any two correlated assets where relative value matters
Example of a trade: MSTR vs BTC - When indicator shows MSTR at 95% percentile (extreme premium): Short MSTR, Buy BTC. Then exit when the spread reverts to the mean, say 40-60% percentile.
🔧 How It Works
Core Calculation
Ratio Analysis: Calculates the price ratio between your asset and the correlated asset
Historical Baseline: Establishes the "normal" relationship using a 252-day moving average. You can change this.
Premium Measurement: Measures current deviation from historical average as a percentage
Statistical Context: Provides percentile rankings and standard deviation bands
The Math
Premium % = (Current Ratio / Historical Average Ratio - 1) × 100
🎨 Customization Options
Correlated Asset: Choose any symbol for comparison
Lookback Period: Adjust historical baseline (50-1000 days)
Smoothing: Reduce noise with moving average (1-50 days)
Visual Toggles: Show/hide bands and percentile lines
Color Themes: Customize premium/discount colors
📊 Interpretation Guide
Premium/Discount Reading
Positive %: Asset trading above historical relationship (premium)
Negative %: Asset trading below historical relationship (discount)
Near 0%: Asset at fair value relative to correlation
Percentile Ranking
90%+: Near recent highs - potential selling opportunity
10% and below: Near recent lows - potential buying opportunity
25-75%: Normal trading range
Signal Classifications
🔴 SELL PREMIUM: Asset expensive relative to recent range
🟡 Premium Rich: Moderately expensive, monitor for reversal
⚪ NEUTRAL: Fair value territory
🟡 Discount Opportunity: Moderately cheap, potential accumulation zone
🟢 BUY DISCOUNT: Asset cheap relative to recent range
🚨 Built-in Alerts
Extreme Premium Alert: Triggers when percentile > 95%
Extreme Discount Alert: Triggers when percentile < 5%
⚠️ Important Notes
Works best with highly correlated assets
Historical relationships can change - monitor correlation strength
Not investment advice - use as one factor in your analysis
Backtest thoroughly before implementing any strategy
🔄 Updates & Future Features
This indicator will be continuously improved based on user feedback. So... please give me your feedback!
Gold Reversal Sniper + TTM Squeeze FilterA reversal indciator for gold, layered with TTM squeeze to filter out excess signals
Korea M2 Liquidity Index💡 Korea M2 Liquidity Index
- This indicator visualizes Korea's M2 liquidity trends, designed to help both domestic and global investors easily understand the overall money supply situation in the Korean economy.
- In particular, by comparing it with the KOSPI index, investors can assess the equity market level relative to liquidity, allowing for a more precise valuation analysis to determine whether the Korean stock market is overvalued or undervalued.
✅ What is M2?
- M2 is a broad measure of money supply, which includes cash, demand deposits, savings deposits, and certain financial products.
- It serves as a crucial macroeconomic indicator that reflects the overall liquidity and capital supply in the Korean economy.
💰 KRW and USD display options
- KRW basis: Displays the total M2 amount in Korean won (in trillion units).
- USD basis: Converts the total M2 amount into US dollars using the KRW/USD exchange rate(KRW/USD) making it useful for global investors or those analyzing in USD terms.
📊 Display style and interpretation
- Users can freely choose to display Korea’s M2 and liquidity index and turn them on or off as needed.
- The index is simplified and displayed in trillion won units, allowing for an intuitive view of long-term trends and structural changes.
- The Offset (days) feature enables temporal adjustments, making it easier to compare this indicator with other economic or financial data series.
🌏 Example use cases
- Domestic policy analysis: Analyze the correlation between Bank of Korea's monetary policy changes (base rates, liquidity injections, etc.) and M2 growth.
- FX and global capital flow analysis: Understand the relationship between KRW/USD exchange rate fluctuations and changes in domestic liquidity.
- Leading indicator for asset markets: Use it as a forward-looking signal for stock, real estate, and bond markets.
- Comparison with KOSPI index: Identify gaps between liquidity and market levels to support strategic investment decisions and evaluate market capitalization levels more precisely.
copyright @invest_hedgeway
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💡 Korea M2 Liquidity Index
- 이 지표는 대한민국의 M2 유동성 흐름을 시각화하여, 국내 및 글로벌 투자자들이 한국 경제의 자금 공급 상태를 한눈에 파악할 수 있도록 설계되었습니다.
- 특히 코스피 지수와 비교 분석함으로써 유동성 대비 주가지수 수준을 평가하고, 한국 증시의 상대적 고평가·저평가 여부를 판단해 보다 정교한 밸류에이션 분석에 활용할 수 있습니다.
✅ M2란?
- M2는 광의통화 지표로, 현금 + 요구불 예금 + 저축성 예금 + 금융상품(일부) 등을 포함하는 총 유동성을 의미합니다. 이는 한국 경제의 자금 공급 상태를 나타내는 중요한 거시경제 지표로 활용됩니다.
💰 KRW 및 USD 표시 선택
- KRW(원화) 기준: 한국 원화 기준으로 M2 총액(조 단위)을 나타냅니다.
- USD 기준: M2 총액을 환율(KRW/USD) 기준으로 달러화 환산 후 표시하여, 글로벌 투자자나 달러화 기준 평가 시 활용 가능합니다.
📊 표시 방식과 해석
- 사용자는 한국의 M2와 유동성지수를 자유롭게 선택해 원하는 방식으로 켜거나 끌 수 있습니다.
- 지표는 조원(Trillion won) 단위로 단순화해 표시되며, 장기 흐름과 추세 변화를 시각적으로 확인할 수 있습니다.
- Offset (days) 기능을 통해 시리즈를 시차 조정할 수 있어, 다른 경제 지표와의 비교 분석에 유용합니다.
🌏 활용 예시
- 국내 정책 분석: 한국은행의 통화정책 변화(기준금리, 유동성 공급 등)와 M2 증가율 간 상관성 분석.
- 환율 및 글로벌 자금 흐름 분석: 원/달러 환율 변동과 유동성 간 상관관계 파악.
- 주식, 부동산, 채권 등 자산시장 선행 지표로서 활용.
- 코스피 지수와의 비교 분석: 시장 유동성과 지수의 괴리를 파악하여 전략적 투자 판단과 시가총액 수준에 대한 평가에 활용.
copyright @invest_hedgeway
Nasdaq Market Direction ProbabilitiesA table in the bottom-left corner showing bullish, bearish, and neutral probabilities for Nasdaq market direction, calculated from weighted indicators (moving averages, RSI, volume trend, futures change, and sentiment).
A label on the chart with a recommendation ("Long", "Short", or "Monitor") based on the highest probability.
A histogram of the bullish probability in a separate pane.
The probabilities update on each confirmed bar, using the chart’s timeframe (ideally 60 minutes).
Ticker Industry and Competitor LookupThe Ticker Industry and Competitor Lookup is a comprehensive indicator that provides instant access to industry classification data and competitive intelligence for any ticker symbol. Built using the advanced SIC_TICKER_DATA library, this tool delivers professional-grade sector analysis with enterprise-level performance. It's a simple yet great tool for competitor research, sector studies, portfolio diversification, and investment decision-making.
This indicator is a simple tool built on based on our SIC_TICKER_DATA library to demonstrate the use cases of the library. In this case, you enter a ticker and it displays the sector, SIC or Standard Industrial Classification which is a SEC identifier, and more importantly, the competitors that are listed to be in the exact same SIC by SEC.
There isn't much to say about the indicator itself but we strongly recommend checking out the SIC_TICKER_DATA library we just published to learn more about the types of indicators you can build using it.
Correlation Coefficient with MA & BB中文版介紹
相關係數、移動平均線與布林帶指標 (Correlation Coefficient with MA & BB)
這個 Pine Script 指標是一款強大的工具，旨在幫助交易者和投資者深入分析兩個市場標的之間的關係強度與方向，並結合移動平均線 (MA) 和布林帶 (BB) 來進一步洞察這種關係的趨勢和波動性。
無論您是想尋找配對交易機會、管理投資組合風險，還是僅僅想更好地理解市場動態，這個指標都能提供有價值的見解。
指標特色與功能：
動態相關係數計算：
您可以選擇任何您想比較的股票、商品或加密貨幣代號（例如，預設為 GOOG）。
指標會自動計算當前圖表（主數據源，預設為收盤價）與您指定標的之間的相關係數。
相關係數值介於 -1 (完美負相關) 至 1 (完美正相關) 之間，0 表示無線性關係。
視覺化呈現相關係數線，並標示 1、0、-1 參考水平線，同時填充完美相關區間，讓您一目了然。
特別之處：程式碼中包含了 ticker.modify，確保比較標的數據考慮了股息調整或延長交易時段，使相關性分析更加精準。
相關係數的移動平均線 (MA)：
為了平滑相關係數的短期波動，指標提供了多種移動平均線類型供您選擇，包括：SMA、EMA、WMA、SMMA。
您可以設定計算 MA 的週期長度（預設 20 週期）。
這條 MA 線有助於識別相關係數的長期趨勢，判斷兩者關係是趨於增強還是減弱。
相關係數的布林帶 (BB)：
將布林帶應用於相關係數，以衡量其波動性和相對高低水平。
中軌與您選擇的移動平均線保持一致。
上軌和下軌則根據相關係數的標準差和您設定的 Z 值（預設 2.0 倍標準差）動態調整。
布林帶可以幫助您識別相關係數何時處於極端水平，可能預示著未來會回歸均值。
如何運用這個指標？
配對交易策略：當兩個通常高度相關的資產，其相關係數短期內顯著偏離平均水平（例如，一個資產價格上漲而另一個原地踏步），您可能可以考慮利用此「失衡」進行配對交易。
投資組合多元化：了解不同資產之間的相關性，有助於構建更穩健的投資組合，避免過度集中於同向變動的資產，有效分散風險。
市場趨勢洞察：透過觀察相關係數的趨勢和波動，您可以更好地理解不同市場板塊或資產類別之間的聯動性，為您的宏觀經濟分析提供數據支持。
請注意，相關性不等於因果性。使用此指標時，請結合您的整體交易策略、宏觀經濟分析以及其他技術指標進行綜合判斷。
English Version Introduction
Correlation Coefficient with Moving Average & Bollinger Bands Indicator (Correlation Coefficient with MA & BB)
This Pine Script indicator is a powerful tool designed to help traders and investors deeply analyze the strength and direction of the relationship between two market instruments. It integrates Moving Averages (MA) and Bollinger Bands (BB) to further insight into the trend and volatility of this relationship.
Whether you're looking for pair trading opportunities, managing portfolio risk, or simply aiming to better understand market dynamics, this indicator can provide valuable insights.
Indicator Features & Functionality:
Dynamic Correlation Coefficient Calculation:
You can select any symbol you wish to compare (e.g., default is GOOG), be it stocks, commodities, or cryptocurrencies.
The indicator automatically calculates the correlation coefficient between the current chart (main data source, default is close price) and your specified symbol.
Correlation values range from -1 (perfect negative correlation) to 1 (perfect positive correlation), with 0 indicating no linear relationship.
It visually plots the correlation line, marks 1, 0, -1 reference levels, and fills the perfect correlation zone for clear visualization.
Special Feature: The code includes ticker.modify, ensuring that the comparative symbol's data accounts for dividend adjustments or extended trading hours, leading to more precise correlation analysis.
Moving Average (MA) for Correlation:
To smooth out short-term fluctuations in the correlation coefficient, the indicator offers multiple MA types for you to choose from: SMA, EMA, WMA, SMMA.
You can set the length of the MA period (default 20 periods).
This MA line helps identify the long-term trend of the correlation coefficient, indicating whether the relationship between the two instruments is strengthening or weakening.
Bollinger Bands (BB) for Correlation:
Bollinger Bands are applied to the correlation coefficient itself to gauge its volatility and relative high/low levels.
The middle band aligns with your chosen Moving Average.
The upper and lower bands dynamically adjust based on the correlation coefficient's standard deviation and your set Z-score (default 2.0 standard deviations).
Bollinger Bands can help you identify when the correlation coefficient is at extreme levels, potentially signaling a future reversion to the mean.
How to Utilize This Indicator:
Pair Trading Strategies: When two typically highly correlated assets show a significant short-term deviation from their average correlation (e.g., one asset's price rises while the other stagnates), you might consider exploiting this "imbalance" for pair trading.
Portfolio Diversification: Understanding the correlation between different assets helps build a more robust investment portfolio, preventing over-concentration in co-moving assets and effectively diversifying risk.
Market Trend Insight: By observing the trend and volatility of the correlation coefficient, you can better understand the联动 (interconnectedness) between different market sectors or asset classes, providing data support for your macroeconomic analysis.
Please note that correlation does not imply causation. When using this indicator, combine it with your overall trading strategy, macroeconomic analysis, and other technical indicators for comprehensive decision-making.
RISK ROTATION MATRIX ║ BullVision [3.0]🔍 Overview
The Risk Rotation Matrix is a comprehensive market regime detection system that analyzes global market conditions across four critical domains: Liquidity, Macroeconomic, Crypto/Commodities, and Risk/Volatility. Through proprietary algorithms and advanced statistical analysis, it transforms 20+ diverse market metrics into a unified framework for identifying regime transitions and risk rotations.
This institutional-grade system aims to solve a fundamental challenge: how to synthesize complex, multi-domain market data into clear, actionable trading intelligence. By combining proprietary liquidity calculations with sophisticated cross-asset analysis.
The Four-Domain Architecture
1. 💧 LIQUIDITY DOMAIN
Our liquidity analysis combines standard metrics with proprietary calculations:
Proprietary Components:
Custom Global Liquidity Index (GLI): Unique formula aggregating central bank assets, credit spreads, and FX dynamics through our weighted algorithm
Federal Reserve Balance Proxy: Advanced calculation incorporating reverse repos, TGA fluctuations, and QE/QT impacts
China Liquidity Proxy: First-of-its-kind metric combining PBOC operations with FX-adjusted aggregates
Global M2 Composite: Custom multi-currency M2 aggregation with proprietary FX normalization
2. 📈 MACRO DOMAIN
Sophisticated integration of global economic indicators:
S&P 500: Momentum and trend analysis with custom z-score normalization
China Blue Chips: Asian market sentiment with correlation filtering
MBA Purchase Index: Real estate market health indicator
Emerging Markets (EEMS): Risk appetite measurement
Global ETF (URTH): Worldwide equity exposure tracking
Each metric undergoes proprietary transformation to ensure comparability and regime-specific sensitivity.
3. 🪙 CRYPTO/COMMODITIES DOMAIN
Unique cross-asset analysis combining:
Total Crypto Market Cap: Liquidity flow indicator with custom smoothing
Bitcoin SOPR: On-chain profitability analysis with adaptive periods
MVRV Z-Score: Advanced implementation with multiple MA options
BTC/Silver Ratio: Novel commodity-crypto relationship metric
Our algorithms detect when crypto markets lead or lag traditional assets, providing crucial timing signals.
4. ⚡ RISK/VOLATILITY DOMAIN
Advanced volatility regime detection through:
MOVE Index: Bond volatility with inverse correlation analysis
VVIX/VIX Ratio: Volatility-of-volatility for regime extremes
SKEW Index: Tail risk measurement with custom normalization
Credit Stress Composite: Proprietary combination of credit spreads
USDT Dominance: Crypto flight-to-safety indicator
All risk metrics are inverted and normalized to align with the unified scoring system.
🧠 Advanced Integration Methodology
Multi-Stage Processing Pipeline
Data Collection: Real-time aggregation from 20+ sources
Normalization: Custom z-score variants accounting for regime-specific volatility
Domain Scoring: Proprietary weighting within each domain
Cross-Domain Synthesis: Advanced correlation matrix between domains
Regime Detection: State-transition model identifying four market phases
Signal Generation: Composite score with adaptive smoothing
🔁 Composite Smoothing & Signal Generation
The user can apply smoothing (ALMA, EMA, etc.) to highlight trends and reduce noise. Smoothing length, type, and parameters are fully customizable for different trading styles.
🎯 Color Feedback & Market Regimes
Visual dynamics (color gradients, labels, trails, and quadrant placement) offer an at-a-glance interpretation of the market’s evolving risk environment—without forecasting or forward-looking assumptions.
🎯 The Quadrant Visualization System
Our innovative visual framework transforms complex calculations into intuitive intelligence:
Dynamic Ehlers Loop: Shows current position and momentum
Trailing History: Visual path of regime transitions
Real-Time Animation: Immediate feedback on condition changes
Multi-Layer Information: Depth through color, size, and positioning
🚀 Practical Applications
Primary Use Cases
Multi-Asset Portfolio Management: Optimize allocation across asset classes based on regime
Risk Budgeting: Adjust exposure dynamically with regime changes
Tactical Trading: Time entries/exits using regime transitions
Hedging Strategies: Implement protection before risk-off phases
Specific Trading Scenarios
Domain Divergence: When liquidity improves but risk metrics deteriorate
Early Rotation Detection: Crypto/commodity signals often lead broader markets
Volatility Regime Trades: Position for mean reversion or trend following
Cross-Asset Arbitrage: Exploit temporary dislocations between domains
⚙️ How It Works
The Composite Score Engine
The system's intelligence emerges from how it combines domains:
Each domain produces a normalized score (-2 to +2 range)
Proprietary algorithms weight domains based on market conditions
Composite score indicates overall market regime
Smoothing options (ALMA, EMA, etc.) optimize for different timeframes
Regime Classification
🟢 Risk-On (Green): Positive composite + positive momentum
🟠 Weakening (Orange): Positive composite + negative momentum
🔵 Recovery (Blue): Negative composite + positive momentum
🔴 Risk-Off (Red): Negative composite + negative momentum
Signal Interpretation Framework
The indicator provides three levels of analysis:
Composite Score: Overall market regime (-2 to +2)
Domain Scores: Identify which factors drive regime
Individual Metrics: Granular analysis of specific components
🎨 Features & Functionality
Core Components
Risk Rotation Quadrant: Primary visual interface with Ehlers loop
Data Matrix Dashboard: Real-time display of all 20+ metrics
Domain Aggregation: Separate scores for each domain
Composite Calculation: Unified score with multiple smoothing options
Customization Options
Selective Metrics: Enable/disable individual components
Period Adjustment: Optimize lookback for each metric
Smoothing Selection: 10 different MA types including ALMA
Visual Configuration: Quadrant scale, colors, trails, effects
Advanced Settings
Pre-smoothing: Reduce noise before final calculation
Adaptive Periods: Automatic adjustment during volatility
Correlation Filters: Remove redundant signals
Regime Memory: Hysteresis to prevent whipsaws
📋 Implementation Guide
Setup Process
Add to chart (optimized for daily, works on all timeframes)
Review default settings for your market focus
Adjust domain weights based on trading style
Configure visual preferences
Optimization by Trading Style
Position Trading: Longer periods (60-150), heavy smoothing
Swing Trading: Medium periods (20-60), balanced smoothing
Active Trading: Shorter periods (10-40), minimal smoothing
Best Practices
Monitor domain divergences for early signals
Use extreme readings (-1.5/+1.5) for high-conviction trades
Combine with price action for confirmation
Adjust parameters during major events (FOMC, earnings)
💎 What Makes This Unique
Beyond Traditional Indicators
Multi-Domain Integration: Only system combining liquidity, macro, crypto, and volatility
Proprietary Calculations: Custom formulas for GLI, Fed, China, and M2 proxies
Adaptive Architecture: Dynamically adjusts to market regimes
Institutional Depth: 20+ integrated metrics vs typical 3-5
Technical Innovation
Statistical Normalization: Custom z-score variants for cross-asset comparison
Correlation Management: Prevents double-counting related signals
Regime Persistence: Algorithms to identify sustainable vs temporary shifts
Visual Intelligence: Information-dense display without overwhelming
🔢 Performance Characteristics
Strengths
Early regime detection (typically 1-3 weeks ahead)
Robust across different market environments
Clear visual feedback reduces interpretation errors
Comprehensive coverage prevents blind spots
Optimal Conditions
Most effective with 100+ bars of history
Best on daily timeframe (4H minimum recommended)
Requires liquid markets for accurate signals
Performance improves with more enabled components
⚠️ Risk Considerations & Limitations
Important Disclaimers
Probabilistic system, not predictive
Requires understanding of macro relationships
Signals should complement other analysis
Past regime behavior doesn't guarantee future patterns
Known Limitations
Black swan events may cause temporary distortions
Central bank interventions can override signals
Requires active management during regime transitions
Not suitable for pure technical traders
💎 Conclusion
The Risk Rotation Matrix represents a new paradigm in market regime analysis. By combining proprietary liquidity calculations with comprehensive multi-domain monitoring, it provides institutional-grade intelligence previously available only to large funds. The system's strength lies not just in its individual components, but in how it synthesizes diverse market information into clear, actionable trading signals.
⚠️ Access & Intellectual Property Notice
This invite-only indicator contains proprietary algorithms, custom calculations, and years of quantitative research. The mathematical formulations for our liquidity proxies, cross-domain correlation matrices, and regime detection algorithms represent significant intellectual property. Access is restricted to protect these innovations and maintain their effectiveness for serious traders who understand the value of comprehensive market regime analysis.
Smart Money Concepts [Modificado ALX]Smart Money Concepts MODIFICADO - Advanced Market Structure & Liquidity Analysis
Smart Money Concepts is a comprehensive Pine Script indicator designed to help traders identify and visualize key market structure and liquidity concepts utilized by "Smart Money" (large institutional operators). This advanced tool provides a clear view of order flow and areas of interest on the chart, enhancing your technical analysis and trading decisions.
Key Features:
Internal and Swing Market Structure: Automatically identifies "Break of Structure" (BOS) and "Change of Character" (CHoCH) in both internal and swing structure, providing a deep understanding of trend direction and shifts.
Strong and Weak Swing Points: Highlights strong and weak highs and lows, helping you recognize the strength or weakness of price levels.
Traditional and Volumetric Order Blocks:
Traditional Order Blocks: Detects and draws internal and swing order blocks, filtered by ATR or Cumulative Mean Range, to identify high-probability price reaction zones.
NEW - Volumetric Order Blocks: A dedicated section that identifies order blocks based on significant volume activity, offering an additional perspective on institutional participation at key levels. You can configure the volume filtering period to suit your trading style.
Order Block Mitigation: Highlights order blocks that have already been mitigated by price, allowing you to differentiate between active and past zones.
Equal Highs/Lows (EQH/EQL): Draws dotted lines to identify "Equal Highs" and "Equal Lows" zones, indicating potential liquidity areas and price targets.
Fair Value Gaps (FVG): Detects and visualizes Fair Value Gaps, price inefficiency zones that often act as magnets for future price revaluation.
Premium/Discount/Equilibrium Zones: Delimits price areas as Premium (expensive), Discount (cheap), and Equilibrium, facilitating decision-making relative to the asset's current value.
Previous Day/Week/Month Highs & Lows: Displays key price levels from previous periods for multi-timeframe liquidity analysis.
Buy/Sell Signals with Count (NEW): Generates clear "BUY" and "SELL" labels on the chart, with a counter that resets when an opposing signal appears, offering a straightforward visualization of order sequences.
Full Customization: Adjust colors, visibility of each component, and filtering parameters to adapt the indicator to your visual and strategic preferences.
Smart Money Concepts is an essential tool for traders seeking an edge by operating with Smart Money logic, providing a clear visual representation of the most important concepts in liquidity and market structure analysis.
Ralph Indicator - ZaraTrust Smart MoneyThe Ralph Indicator – ZaraTrust Smart Money is a powerful yet simple Smart Money Concepts (SMC) based tool designed for traders who want to trade like institutions. It auto-detects high-probability Buy/Sell zones, Support/Resistance levels, and Demand/Supply areas on the chart — giving you clear, visual, and actionable signals without the clutter.
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🔍 Key Features:
✅ Smart Money Structure
• Uses pivot-based logic to identify potential structure points
• Helps you understand market flow (e.g., BOS, CHoCH simplified logic)
✅ Automatic Support & Resistance
• Plots major levels based on significant highs and lows
• Helps catch key reversal or breakout zones
✅ Demand & Supply Zones
• Visually shows areas where price may react strongly
• Based on smart pivot detection from recent swings
✅ Buy/Sell Trade Signals
• Highlights buy when price breaks resistance (possible bullish shift)
• Highlights sell when price breaks support (possible bearish shift)
✅ Clean & Easy UI
• Toggle features on/off from settings panel
• Labels and shapes are plotted clearly on the chart for instant reading
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🛠️ Recommended Use:
• Use on 15min to 4H timeframe for intraday or swing trading
• Combine with price action (e.g., confirmation candles, liquidity grab)
• Works best when paired with institutional logic (OBs, FVG, liquidity)
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⚠️ Disclaimer:
This indicator is a tool, not a signal service.
It does not guarantee 98% accuracy, but it’s designed to highlight smart money zones and high-probability areas. Always do your own risk management and backtest before using on a live account.
10 EMA, 20 EMA & 50 SMAThis script plots three key moving averages on the price chart to help identify trends and potential trade opportunities:
10 EMA (Exponential Moving Average):
A fast-reacting average that captures short-term price momentum. Useful for spotting quick trend changes.
20 EMA (Exponential Moving Average):
A medium-term average that smooths out more noise while still being responsive to price changes.
50 SMA (Simple Moving Average):
A widely-used long-term trend indicator. It smooths price data over a longer period and is often used to define overall market direction.
VampFX Kill Zone🦇 VampFX Kill Zone Indicator
Built for Smart Money Traders by Vamp FX
This custom Kill Zone tool highlights the optimal institutional trading window — when volume, liquidity, and precision align.
🔹 What It Does:
• Shades the VampFX Kill Zone (default: 8:00 AM to 12:30 PM UTC-4 / New York)
• Designed for New York session scalping/sniping
• Helps isolate high-probability Smart Money setups (liquidity sweeps, FVGs, BOS entries)
🔧 Default Settings:
• Timezone: UTC -4 (New York)
• Session Start: 08:00
• Session End: 12:30
• Adjustable to fit your strategy or local session bias
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📈 Why Use It:
The VampFX Kill Zone reflects when algos run, liquidity gets manipulated, and clean entries occur.
Avoid noise — trade when the market actually moves.
“We don’t chase the market. We wait inside the zone… then strike with precision.”
— 🦇 VampFX Code
Advanced Risk Appetite Index ProThe Advanced Risk Appetite Index (RAI) represents a sophisticated institutional-grade measurement system for quantifying market risk sentiment through proprietary multi-factor fundamental analysis. This indicator synthesizes behavioral finance theory, market microstructure research, and macroeconomic indicators to provide real-time assessment of market participants' risk tolerance and investment appetite.
## Theoretical Foundation
### Academic Framework
The Risk Appetite Index is grounded in established financial theory, particularly the behavioral finance paradigm introduced by Kahneman and Tversky (1979) in their seminal work on prospect theory¹. The indicator incorporates insights from market microstructure theory (O'Hara, 1995)² and extends the risk-on/risk-off framework developed by Kumar and Lee (2006)³ through advanced statistical modeling techniques.
The theoretical foundation draws from multiple academic disciplines:
**Behavioral Finance**: The indicator recognizes that market participants exhibit systematic biases in risk perception, as documented by Shefrin and Statman (1985)⁴. These cognitive biases create measurable patterns in asset pricing and cross-asset relationships.
**Market Microstructure**: Following the work of Hasbrouck (1991)⁵, the model incorporates liquidity dynamics and market structure effects that influence risk sentiment transmission.
**Macroeconomic Theory**: The indicator integrates insights from monetary economics (Taylor, 1993)⁶ and international finance (Dornbusch, 1976)⁷ to capture policy impact on market sentiment.
### Methodological Approach
The Advanced Risk Appetite Index employs a proprietary multi-factor modeling approach that combines elements of:
1. **Advanced Factor Analysis**: Following established methodologies from Fama and French (1993)⁸, the system identifies fundamental factors that explain risk appetite variations.
2. **Regime-Adaptive Modeling**: Incorporating insights from Hamilton (1989)⁹ on regime-switching models to adapt to changing market conditions.
3. **Robust Statistical Framework**: Implementation of robust estimation methods (Huber, 1981)¹⁰ to ensure signal reliability and minimize noise impact.
## Technical Architecture
### Proprietary Multi-Factor Framework
The indicator processes information from multiple fundamental market dimensions through a sophisticated weighting and normalization system. The specific factor selection and weighting methodology represents proprietary intellectual property developed through extensive empirical research and optimization.
**Statistical Processing**: All inputs undergo robust statistical transformation using advanced normalization techniques based on Rousseeuw and Croux (1993)²⁰ to ensure consistent signal generation across different market environments.
**Dynamic Adaptation**: The system incorporates dynamic weighting adjustments based on market regime detection, drawing from the dynamic factor model literature (Stock and Watson, 2002)²¹.
**Quality Assurance**: Multi-layered quality assessment ensures signal reliability through proprietary filtering mechanisms that evaluate:
- Factor consensus requirements
- Signal persistence validation
- Data quality thresholds
- Regime-dependent adjustments
## Implementation and Usage
### Professional Visualization
The indicator provides institutional-grade visualization through:
**Multi-Theme Color Schemes**: Eight professional color themes optimized for different trading environments, following data visualization best practices (Tufte, 2001)²².
**Dynamic Background System**: Real-time visual feedback system that provides immediate market risk appetite assessment.
**Signal Quality Indicators**: Professional-grade visual representations of signal strength and reliability metrics.
### Analytics Dashboard
The comprehensive dashboard provides key institutional metrics including:
- Strategy position status and signal tracking
- Risk level assessment and market sentiment indicators
- Uncertainty measurements and volatility forecasting
- Trading signal quality and regime identification
- Performance analytics and model diagnostics
### Professional Alert System
Comprehensive alert framework covering:
- Entry and exit signal notifications
- Threshold breach warnings
- Market regime change alerts
- Signal quality degradation warnings
## Trading Applications
### Signal Generation Framework
The indicator generates professionally validated signals through proprietary algorithms:
**Long Entry Signals**: Generated when risk appetite conditions satisfy multiple proprietary criteria, indicating favorable risk asset exposure conditions.
**Position Management Signals**: Generated when risk appetite deteriorates below critical thresholds, suggesting defensive positioning requirements.
### Risk Management Integration
The indicator seamlessly integrates with institutional risk management frameworks through:
- Real-time regime identification and classification
- Advanced volatility forecasting capabilities
- Crisis detection and early warning systems
- Comprehensive uncertainty quantification
### Multi-Timeframe Applications
While optimized for daily analysis, the indicator supports various analytical timeframes for:
- Strategic asset allocation decisions
- Tactical portfolio rebalancing
- Risk management applications
## Empirical Validation
### Performance Characteristics
The indicator has undergone extensive empirical validation across multiple market environments, demonstrating:
- Consistent performance across different market regimes
- Robust signal generation during crisis periods
- Effective risk-adjusted return enhancement capabilities
### Statistical Validation
All model components and signal generation rules have been validated using:
- Comprehensive out-of-sample testing protocols
- Monte Carlo simulation analysis
- Cross-regime performance evaluation
- Statistical significance testing
## Model Specifications
### Market Applications and Target Instruments
**Primary Target Market**: The Advanced Risk Appetite Index is specifically optimized for S&P 500 Index (SPX) analysis, where it demonstrates peak performance characteristics. The model's proprietary factor weighting and signal generation algorithms have been calibrated primarily against SPX historical data, ensuring optimal sensitivity to US large-cap equity market dynamics.
**Secondary Market Applications**: While designed for SPX, the indicator demonstrates robust performance across other major equity indices, including:
- NASDAQ-100 (NDX) and related instruments
- Dow Jones Industrial Average (DJIA)
- Russell 2000 (RUT) for small-cap exposure
- International indices with sufficient liquidity and data availability
**Cross-Market Validation**: The model's fundamental approach to risk appetite measurement provides meaningful signals across different equity markets, though performance characteristics may vary based on market structure, liquidity, and regional economic factors.
### Data Requirements
The indicator requires access to institutional-grade market data across multiple asset classes and economic indicators. Specific data requirements and processing methodologies are proprietary.
### Computational Framework
The system utilizes advanced computational techniques including:
- Robust statistical estimation methods
- Dynamic factor modeling approaches
- Regime-switching algorithms
- Real-time signal processing capabilities
## Limitations and Risk Disclosure
### Model Limitations
**Data Dependency**: The indicator requires comprehensive market data and may experience performance variations during periods of limited data availability.
**Regime Sensitivity**: Performance characteristics may vary across different market regimes and structural breaks.
### Risk Warnings
**Past Performance Disclaimer**: Historical results do not guarantee future performance. All trading involves substantial risk of loss.
**Model Risk**: Quantitative models are subject to model risk and may fail to predict future market movements accurately.
**Market Risk**: The indicator does not eliminate market risk and must be used within comprehensive risk management frameworks.
## Professional Applications
### Target Users
The Advanced Risk Appetite Index is designed for:
- Institutional portfolio managers and investment professionals
- Risk management teams and quantitative analysts
- Professional traders and hedge fund managers
- Academic researchers and financial consultants
### Integration Capabilities
The indicator supports integration with:
- Portfolio optimization and management systems
- Risk management and monitoring platforms
- Automated trading and execution systems
- Research and analytics databases
## References
1. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.
2. O'Hara, M. (1995). Market Microstructure Theory. Cambridge, MA: Blackwell Publishers.
3. Kumar, A., & Lee, C. M. (2006). Retail Investor Sentiment and Return Comovements. Journal of Finance, 61(5), 2451-2486.
4. Shefrin, H., & Statman, M. (1985). The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence. Journal of Finance, 40(3), 777-790.
5. Hasbrouck, J. (1991). Measuring the Information Content of Stock Trades. Journal of Finance, 46(1), 179-207.
6. Taylor, J. B. (1993). Discretion versus Policy Rules in Practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214.
7. Dornbusch, R. (1976). Expectations and Exchange Rate Dynamics. Journal of Political Economy, 84(6), 1161-1176.
8. Fama, E. F., & French, K. R. (1993). Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics, 33(1), 3-56.
9. Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.
10. Huber, P. J. (1981). Robust Statistics. New York: John Wiley & Sons.
11. Breeden, D. T. (1979). An Intertemporal Asset Pricing Model with Stochastic Consumption and Investment Opportunities. Journal of Financial Economics, 7(3), 265-296.
12. Mishkin, F. S. (1990). What Does the Term Structure Tell Us About Future Inflation? Journal of Monetary Economics, 25(1), 77-95.
13. Estrella, A., & Hardouvelis, G. A. (1991). The Term Structure as a Predictor of Real Economic Activity. Journal of Finance, 46(2), 555-576.
14. Collin-Dufresne, P., Goldstein, R. S., & Martin, J. S. (2001). The Determinants of Credit Spread Changes. Journal of Finance, 56(6), 2177-2207.
15. Carr, P., & Wu, L. (2009). Variance Risk Premiums. Review of Financial Studies, 22(3), 1311-1341.
16. Engel, C. (1996). The Forward Discount Anomaly and the Risk Premium: A Survey of Recent Evidence. Journal of Empirical Finance, 3(2), 123-192.
17. Ranaldo, A., & Söderlind, P. (2010). Safe Haven Currencies. Review of Finance, 14(3), 385-407.
18. Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
19. Pástor, L., & Stambaugh, R. F. (2003). Liquidity Risk and Expected Stock Returns. Journal of Political Economy, 111(3), 642-685.
20. Rousseeuw, P. J., & Croux, C. (1993). Alternatives to the Median Absolute Deviation. Journal of the American Statistical Association, 88(424), 1273-1283.
21. Stock, J. H., & Watson, M. W. (2002). Dynamic Factor Models. Oxford Handbook of Econometrics, 1, 35-59.
22. Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd ed.). Cheshire, CT: Graphics Press.
Real 10Y Yield (DGS10 - T10YIE)The Real 10Y Yield (DGS10 – T10YIE) indicator computes the inflation-adjusted U.S. 10-year Treasury yield by subtracting the 10-year breakeven inflation rate (T10YIE) from the nominal 10-year Treasury yield (DGS10), both sourced directly from FRED. By filtering out inflation expectations, this script reveals the true, real borrowing cost over a 10-year horizon—one of the most reliable gauges of overall risk sentiment and capital–market health.
How It Works
Data Inputs
• DGS10 (Nominal 10-Year Treasury Yield)
• T10YIE (10-Year Breakeven Inflation Rate)
Both series are fetched on a daily timeframe via request.security from FRED.
Real Yield Calculation
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real10y = DGS10 – T10YIE
A positive value indicates that nominal yields exceed inflation expectations (real yields are positive), while a negative value signals deep-negative real rates.
Thresholds & Coloring
• Bullish Zone: Real yield < –0.1 %
• Bearish Zone: Real yield > +0.1 %
The background turns green when real yields drop below –0.1 %, reflecting an ultra-accommodative environment that historically aligns with risk-on rallies. It turns red when real yields exceed +0.1 %, indicating expensive real borrowing costs and a potential shift toward risk-off.
Alerts
• Deep-Negative Real Yields (Bullish): Triggers when real yield < –0.1 %
• High Real Yields (Bearish): Triggers when real yield > +0.1 %
Why It’s Powerful
Forward-Looking Sentiment Gauge
Real yields incorporate both market-implied inflation and nominal rates, making them a leading indicator for risk appetite, equity flows, and crypto demand.
Clear, Actionable Zones
The –0.1 % / +0.1 % thresholds cleanly delineate structurally bullish vs. bearish regimes, removing noise and false signals common in nominal-only yield studies.
Macro & Cross-Asset Confluence
Combine with equity indices, dollar strength (DXY), or credit spreads for a fully contextual macro view. When real yields break deeper negative alongside weakening dollar, it often precedes stretch in risk assets.
Automatic Alerts
Never miss regime shifts—alerts notify you the moment real yields breach key zones, so you can align your strategy with prevailing macro momentum.
How to Use
Add to a separate pane for unobstructed visibility.
Monitor breaks beneath –0.1 % for early “risk-on” signals in stocks, commodities, and crypto.
Watch for climbs above +0.1 % to hedge or rotate into defensive assets.
Combine with your existing trend-following or mean-reversion strategies to improve timing around major market turning points.
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Feel free to adjust the threshold lines to your preferred sensitivity (e.g., tighten to ±0.05 %), or overlay with moving averages to smooth out whipsaws. This script is ideal for macro traders, portfolio managers, and quantitative quants who demand a distilled, inflation-adjusted view of real rates.
National Financial Conditions Index (NFCI)This is one of the most important macro indicators in my trading arsenal due to its reliability across different market regimes. I'm excited to share this with the TradingView community because this Federal Reserve data is not only completely free but extraordinarily useful for portfolio management and risk assessment.
**Important Disclaimers**: Be aware that some NFCI components are updated only monthly but carry significant weighting in the composite index. Additionally, the Fed occasionally revises historical NFCI data, so historical backtests should be interpreted with some caution. Nevertheless, this remains a crucial leading indicator for financial stress conditions.
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## What is the National Financial Conditions Index?
The National Financial Conditions Index (NFCI) is a comprehensive measure of financial stress and liquidity conditions developed by the Federal Reserve Bank of Chicago. This indicator synthesizes over 100 financial market variables into a single, interpretable metric that captures the overall state of financial conditions in the United States (Brave & Butters, 2011).
**Key Principle**: When the NFCI is positive, financial conditions are tighter than average; when negative, conditions are looser than average. Values above +1.0 historically coincide with financial crises, while values below -1.0 often signal bubble-like conditions.
## Scientific Foundation & Research
The NFCI methodology is grounded in extensive academic research:
### Core Research Foundation
- **Brave, S., & Butters, R. A. (2011)**. "Monitoring financial stability: A financial conditions index approach." *Economic Perspectives*, 35(1), 22-43.
- **Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010)**. "Financial conditions indexes: A fresh look after the financial crisis." *US Monetary Policy Forum Report*, No. 23.
- **Kliesen, K. L., Owyang, M. T., & Vermann, E. K. (2012)**. "Disentangling diverse measures: A survey of financial stress indexes." *Federal Reserve Bank of St. Louis Review*, 94(5), 369-397.
### Methodological Validation
The NFCI employs Principal Component Analysis (PCA) to extract common factors from financial market data, following the methodology established by **English, W. B., Tsatsaronis, K., & Zoli, E. (2005)** in "Assessing the predictive power of measures of financial conditions for macroeconomic variables." The index has been validated through extensive academic research (Koop & Korobilis, 2014).
## NFCI Components Explained
This indicator provides access to all five official NFCI variants:
### 1. **Main NFCI**
The primary composite index incorporating all financial market sectors. This serves as the main signal for portfolio allocation decisions.
### 2. **Adjusted NFCI (ANFCI)**
Removes the influence of credit market disruptions to focus on non-credit financial stress. Particularly useful during banking crises when credit markets may be impaired but other financial conditions remain stable.
### 3. **Credit Sub-Index**
Isolates credit market conditions including corporate bond spreads, commercial paper rates, and bank lending standards. Important for assessing corporate financing stress.
### 4. **Leverage Sub-Index**
Measures systemic leverage through margin requirements, dealer financing, and institutional leverage metrics. Useful for identifying leverage-driven market stress.
### 5. **Risk Sub-Index**
Captures market-based risk measures including volatility, correlation, and tail risk indicators. Provides indication of risk appetite shifts.
## Practical Trading Applications
### Portfolio Allocation Framework
Based on the academic research, the NFCI can be used for portfolio positioning:
**Risk-On Positioning (NFCI declining):**
- Consider increasing equity exposure
- Reduce defensive positions
- Evaluate growth-oriented sectors
**Risk-Off Positioning (NFCI rising):**
- Consider reducing equity exposure
- Increase defensive positioning
- Favor large-cap, dividend-paying stocks
### Academic Validation
According to **Oet, M. V., Eiben, R., Bianco, T., Gramlich, D., & Ong, S. J. (2011)** in "The financial stress index: Identification of systemic risk conditions," financial conditions indices like the NFCI provide early warning capabilities for systemic risk conditions.
**Illing, M., & Liu, Y. (2006)** demonstrated in "Measuring financial stress in a developed country: An application to Canada" that composite financial stress measures can be useful for predicting economic downturns.
## Advanced Features of This Implementation
### Dynamic Background Coloring
- **Green backgrounds**: Risk-On conditions - potentially favorable for equity investment
- **Red backgrounds**: Risk-Off conditions - time for defensive positioning
- **Intensity varies**: Based on deviation from trend for nuanced risk assessment
### Professional Dashboard
Real-time analytics table showing:
- Current NFCI level and interpretation (TIGHT/LOOSE/NEUTRAL)
- Individual sub-index readings
- Change analysis
- Portfolio guidance (Risk On/Risk Off)
### Alert System
Professional-grade alerts for:
- Risk regime changes
- Extreme stress conditions (NFCI > 1.0)
- Bubble risk warnings (NFCI < -1.0)
- Major trend reversals
## Optimal Usage Guidelines
### Best Timeframes
- **Daily charts**: Recommended for intermediate-term positioning
- **Weekly charts**: Suitable for longer-term portfolio allocation
- **Intraday**: Less effective due to weekly update frequency
### Complementary Indicators
For enhanced analysis, combine NFCI signals with:
- **VIX levels**: Confirm stress readings
- **Credit spreads**: Validate credit sub-index signals
- **Moving averages**: Determine overall market trend context
- **Economic surprise indices**: Gauge fundamental backdrop
### Position Sizing Considerations
- **Extreme readings** (|NFCI| > 1.0): Consider higher conviction positioning
- **Moderate readings** (|NFCI| 0.3-1.0): Standard position sizing
- **Neutral readings** (|NFCI| < 0.3): Consider reduced conviction
## Important Limitations & Considerations
### Data Frequency Issues
**Critical Warning**: While the main NFCI updates weekly (typically Wednesdays), some underlying components update monthly. Corporate bond indices and commercial paper rates, which carry significant weight, may cause delayed reactions to current market conditions.
**Component Update Schedule:**
- **Weekly Updates**: Main NFCI composite, most equity volatility measures
- **Monthly Updates**: Corporate bond spreads, commercial paper rates
- **Quarterly Updates**: Banking sector surveys
- **Impact**: Significant portion of index weight may lag current conditions
### Historical Revisions
The Federal Reserve occasionally revises NFCI historical data as new information becomes available or methodologies are refined. This means backtesting results should be interpreted cautiously, and the indicator works best for forward-looking analysis rather than precise historical replication.
### Market Regime Dependency
The NFCI effectiveness may vary across different market regimes. During extended sideways markets or regime transitions, signals may be less reliable. Consider combining with trend-following indicators for optimal results.
**Bottom Line**: Use NFCI for medium-term portfolio positioning guidance. Trust the directional signals while remaining aware of data revision risks and update frequency limitations. This indicator is particularly valuable during periods of financial stress when reliable guidance is most needed.
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**Data Source**: Federal Reserve Bank of Chicago
**Update Frequency**: Weekly (typically Wednesdays)
**Historical Coverage**: 1973-present
**Cost**: Free (public Fed data)
*This indicator is for educational and analytical purposes. Always conduct your own research and risk assessment before making investment decisions.*
## References
Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. *Economic Perspectives*, 35(1), 22-43.
English, W. B., Tsatsaronis, K., & Zoli, E. (2005). Assessing the predictive power of measures of financial conditions for macroeconomic variables. *BIS Papers*, 22, 228-252.
Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010). Financial conditions indexes: A fresh look after the financial crisis. *US Monetary Policy Forum Report*, No. 23.
Illing, M., & Liu, Y. (2006). Measuring financial stress in a developed country: An application to Canada. *Bank of Canada Working Paper*, 2006-02.
Kliesen, K. L., Owyang, M. T., & Vermann, E. K. (2012). Disentangling diverse measures: A survey of financial stress indexes. *Federal Reserve Bank of St. Louis Review*, 94(5), 369-397.
Koop, G., & Korobilis, D. (2014). A new index of financial conditions. *European Economic Review*, 71, 101-116.
Oet, M. V., Eiben, R., Bianco, T., Gramlich, D., & Ong, S. J. (2011). The financial stress index: Identification of systemic risk conditions. *Federal Reserve Bank of Cleveland Working Paper*, 11-30.
QBCore Algø Pro EditionQBCore Algo Pro Edition is a smart-money-based indicator designed for precision trading .
This tool includes real-time CHoCH/BOS detection, internal & swing structure mapping, fair value gaps, premium/discount zones, and dynamic order block logic.
Crowding model ║ BullVision🔬 Overview
The Crypto Crowding Model Pro is a sophisticated analytical tool designed to visualize and quantify market conditions across multiple cryptocurrencies. By leveraging Relative Strength Index (RSI) and Z-score calculations, this indicator provides traders with an intuitive and detailed snapshot of current crypto market dynamics, highlighting areas of extreme momentum, crowded trades, and potential reversal points.
⚙️ Key Concepts
📊 RSI and Z-Score Analysis
RSI (Relative Strength Index) evaluates the momentum and strength of each cryptocurrency, identifying overbought or oversold conditions.
Z-Score Normalization measures each asset's current price deviation relative to its historical average, identifying statistically significant extremes.
🎯 Crowding Analytics
An integrated analytics panel provides real-time crowding metrics, quantifying market sentiment into four distinct categories:
🔥 FOMO (Fear of Missing Out): High momentum, potential exhaustion.
❄️ Fear: Low momentum, potential reversal or consolidation.
📈 Recovery: Moderate upward momentum after a downward trend.
💪 Strength: Stable bullish conditions with sustained momentum.
🖥️ Visual Scatter Plot
Assets are plotted on a dynamic scatter plot, positioning each cryptocurrency according to its RSI and Z-score.
Color coding, symbol shapes, and sizes help quickly identify main market segments (BTC, ETH, TOTAL, OTHERS) and individual asset conditions.
🧩 Quadrant Classification
Assets are categorized into four quadrants based on their momentum and deviation:
Overbought Extended: High RSI and positive Z-score.
Recovery Phase: Low RSI but positive Z-score.
Oversold Compressed: Low RSI and negative Z-score.
Strong Consolidation: High RSI but negative Z-score.
🔧 User Customization
🎨 Visual Settings
Bar Scale: Adjust the scatter plot visual scale.
Asset Visibility: Optionally display key market benchmarks (TOTAL, BTC, ETH, OTHERS).
Gradient Background: Enhances visual interpretation of asset clusters.
Crowding Analytics Panel: Toggle the analytics panel on/off.
📊 Indicator Parameters
RSI Length: Defines the calculation period for RSI.
Z-score Lookback: Historical lookback period for normalization.
Crowding Alert Threshold: Sets alert sensitivity for crowded market conditions.
🎯 Zone Settings
Quadrant Labels: Displays descriptive labels for each quadrant.
Danger Zones: Highlights extreme RSI levels indicative of heightened market risk.
📈 Visual Output
Dynamic Scatter Plot: Visualizes asset positioning clearly and intuitively.
Gradient and Grid: Professional gridlines and subtle gradient backgrounds assist visual assessment.
Danger Zone Highlights: Visually indicates RSI extremes to warn of potential market turning points.
Crowding Analytics Panel: Real-time summary of market sentiment and asset distribution.
🔍 Use Cases
This indicator is particularly beneficial for traders and analysts looking to:
Identify crowded trades and potential reversal points.
Quickly assess overall market sentiment and individual asset strength.
Integrate a robust momentum analysis into broader technical or fundamental strategies.
Enhance market timing and improve risk management decisions.
⚠️ Important Notes
This indicator does not provide explicit buy or sell signals.
It is intended solely for informational, analytical, and educational purposes.
Past performance and signals are not indicative of future market results.
Always combine with additional tools and analysis as part of comprehensive decision-making.
Buy sell Trend VolumeThis indicator analyzes the flow of volume and price changes to identify potential trends.
Understanding Volume Indicator: A Comprehensive Guide
Introduction. The volume indicator is a vital tool investors and traders use to understand the liquidity and market activity in trading.
Auto LevelsSimple auto level tracker that automatically detects and plots the high/low for the current week, day, and month, as well as the previous week/day/month.
Includes a built-in dashboard that shows how close or far price is from each level, along with directional guidance (above/below). The closest level to current price is automatically highlighted for quick awareness.
Everything is fully toggleable to only show the levels and info that is needed.
Ultra Supply & DemandUltra Supply and Demand fixed.
Order Block Detection: Identifies potential order blocks (demand/supply zones)
