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Griffin Money Flow IndexThis is a modification of the commonly used money flow index. The major change is to the way the average price is calculated. Typically this is done with a hlc3 method, meaning the average of the period high, low, and closing prices. This calculation infers that volume is evenly distributed between those three points. Generally this is not the case. My version calculates the average price by including the open price and making the assumption that the majority of the volume occurs within the body of the candle, between the open and close. I calculate a weighted average that places more significance on the close and open, then an equal weighting on the high and low. In general this will result in a trend that does not differ greatly from the regular MFI calculation however there may be subtle changes.
Pine Script® 인디케이터
griffintattoo의
Positive Volume Index Backtest The theory behind the indexes is as follows: On days of increasing volume, you can expect prices to increase, and on days of decreasing volume, you can expect prices to decrease. This goes with the idea of the market being in-gear and out-of-gear. Both PVI and NVI work in similar fashions: Both are a running cumulative of values, which means you either keep adding or subtracting price rate of change each day to the previous day`s sum. In the case of PVI, if today`s volume is less than yesterday`s, don`t add anything; if today`s volume is greater, then add today`s price rate of change. For NVI, add today`s price rate of change only if today`s volume is less than yesterday`s. You can change long to short in the Input Settings WARNING: - For purpose educate only - This script to change bars colors.
Pine Script™ 전략
HPotter의
3
Positive Volume Index Strategy The theory behind the indexes is as follows: On days of increasing volume, you can expect prices to increase, and on days of decreasing volume, you can expect prices to decrease. This goes with the idea of the market being in-gear and out-of-gear. Both PVI and NVI work in similar fashions: Both are a running cumulative of values, which means you either keep adding or subtracting price rate of change each day to the previous day`s sum. In the case of PVI, if today`s volume is less than yesterday`s, don`t add anything; if today`s volume is greater, then add today`s price rate of change. For NVI, add today`s price rate of change only if today`s volume is less than yesterday`s. WARNING: - This script to change bars colors.
Pine Script® 인디케이터
HPotter의
Negative Volume Index Backtest The theory behind the indexes is as follows: On days of increasing volume, you can expect prices to increase, and on days of decreasing volume, you can expect prices to decrease. This goes with the idea of the market being in-gear and out-of-gear. Both PVI and NVI work in similar fashions: Both are a running cumulative of values, which means you either keep adding or subtracting price rate of change each day to the previous day`s sum. In the case of PVI, if today`s volume is less than yesterday`s, don`t add anything; if today`s volume is greater, then add today`s price rate of change. For NVI, add today`s price rate of change only if today`s volume is less than yesterday`s. You can change long to short in the Input Settings Please, use it only for learning or paper trading. Do not for real trading.
Pine Script™ 전략
HPotter의
Negative Volume Index Strategy The theory behind the indexes is as follows: On days of increasing volume, you can expect prices to increase, and on days of decreasing volume, you can expect prices to decrease. This goes with the idea of the market being in-gear and out-of-gear. Both PVI and NVI work in similar fashions: Both are a running cumulative of values, which means you either keep adding or subtracting price rate of change each day to the previous day`s sum. In the case of PVI, if today`s volume is less than yesterday`s, don`t add anything; if today`s volume is greater, then add today`s price rate of change. For NVI, add today`s price rate of change only if today`s volume is less than yesterday`s.
Pine Script® 인디케이터
HPotter의
Didi IndexIndicator developed by Brazilian and analyst Odir Aguiar (Didi), consists of "Moving Averages", known for the famous needles Didi, which allows the visualization of reversal points. The concept is very simple, when you insert 3 Moving Averages on display, one of three periods, an 8 and the other 20, there appears the formation of the indicator which works on an axis or center line 0. The needles occur when the intersection of averages comes closest to the line 0.
Pine Script® 인디케이터
Sirdiamond21의
6
Negative Volume Index (NVI) The theory behind the indexes is as follows: On days of increasing volume, you can expect prices to increase, and on days of decreasing volume, you can expect prices to decrease. This goes with the idea of the market being in-gear and out-of-gear. Both PVI and NVI work in similar fashions: Both are a running cumulative of values, which means you either keep adding or subtracting price rate of change each day to the previous day`s sum. In the case of PVI, if today`s volume is less than yesterday`s, don`t add anything; if today`s volume is greater, then add today`s price rate of change. For NVI, add today`s price rate of change only if today`s volume is less than yesterday`s.
Pine Script® 인디케이터
HPotter의
6
Retail Sentiment Indicator - Multi-Asset CFD & Fear/Greed IndexRetail Sentiment Indicator - Multi-Asset CFD & Fear/Greed Index Overview The Retail Sentiment Indicator provides real-time sentiment data for major financial instruments including stocks, forex, commodities, and cryptocurrencies. This indicator displays retail trader positioning and market sentiment using CFD data and fear/greed indices. Methodology and Scale Calculation This indicator operates on a **-50 to +50 scale** with zero representing perfect market equilibrium. Scale Interpretation: - **Zero (0)**: Market balance - exactly 50% of investors buying, 50% selling - **Positive values**: Majority buying pressure - Example: If 63% of investors are buying, the indicator shows +13 (63 - 50 = +13) - **Negative values**: Majority selling pressure - Example: If 92% of investors are selling, the indicator shows -42 (50 - 92 = -42) BTC Fear & Greed Index Scaling: The original `BTC FEAR&GREED` index is natively scaled from 0-100 by its creator. In our indicator, this data has been rescaled to also fit the -50 to +50 range for consistency with other sentiment data sources. This unified scaling approach allows for direct comparison across all instruments and data sources within the indicator. -Important Data Source Selection- Bitcoin (BTC) Data Sources When viewing Bitcoin charts, the indicator offers **two different data sources**: 1. **Default Auto-Mode**: `BTCUSD Retail CFD` - Retail CFD traders sentiment data (automatically loaded). 2. **Manual Selection**: `BTC FEAR&GREED` - Fear & Greed Index from website: alternative dot me **To access BTC Fear & Greed Index**: Input settings -> disable checkbox "Auto-load Sentiment Data" -> manually select "BTC FEAR&GREED" from the dropdown menu. US Stock Market Data Sources For US stocks and indices (S&P 500, NASDAQ, Dow Jones), there are **two data source options**: 1. **Default Auto-Mode**: Individual retail CFD sentiment data for each instrument 2. **Manual Selection**: `SNN FEAR&GREED` - SNN's Fear & Greed Index covering the overall US market sentiment. SNN was used as the name to avoid any potential trademark infringement. **To access SNN Fear & Greed Index**: When viewing US market charts, disable in input settings checkbox "Auto-load Sentiment Data" and manually select "SNN FEAR&GREED" from the dropdown menu. This distinction allows traders to choose between **instrument-specific retail sentiment** (auto-mode) or **broader market sentiment indices** (manual selection). Features - **Auto-Detection**: Automatically loads sentiment data based on the current chart symbol - **Manual Selection**: Choose from 40+ supported instruments when auto-detection is unavailable - **Multiple Data Sources**: Combines retail CFD sentiment with Fear & Greed indices - **Visual Zones**: Clear greed/fear zones with color-coded backgrounds - **Real-time Updates**: Live sentiment data from merged data sources Supported Instruments Major Indices - S&P 500, NASDAQ, Dow Jones 30, DAX Forex Pairs - Major pairs: EURUSD, GBPUSD, USDJPY, USDCHF, USDCAD - Cross pairs: EURJPY, GBPJPY, AUDUSD, NZDUSD, and 20+ others Commodities - Precious metals: Gold (XAUUSD), Silver (XAGUSD) - Energy: WTI Oil - Agricultural: Wheat, Coffee - Industrial: Copper Cryptocurrencies - Bitcoin (BTC) sentiment data - BTC & SNN Fear & Greed indices How to Use 1. **Auto Mode** (Default): Enable "Auto-load Sentiment Data" to automatically display sentiment for the current chart symbol 2. **Manual Mode**: Disable auto-load and select from the dropdown menu for specific instruments 3. **Interpretation**: - Values above 0 (green) indicate retail greed/bullish sentiment - Values below 0 (red) indicate retail fear/bearish sentiment - Fear & Greed indices use 0-100 scale (50 is neutral) Data Sources This indicator uses curated sentiment data from retail CFD providers and established fear/greed indices. Data is updated regularly and sourced from reputable financial data providers. Trading Strategy & Market Philosophy Contrarian Trading Approach The primary purpose of this indicator is based on the fundamental market principle that **the majority of retail investors are often wrong**, and markets typically move opposite to the positions held by the majority of market participants. Key Strategy Guidelines: - **Contrarian Signal**: When the majority of users are positioned on one side of the market, there is statistically greater market advantage in taking positions in the opposite direction - **Trend Exhaustion Signal**: An interesting observed phenomenon occurs when, during a long-lasting trend where the majority of investors have consistently been on the wrong side, the Sentiment indicator suddenly shows that the majority has flipped and opened positions in the direction of that long-running trend. This is often a signal of fuel exhaustion for further movement in that direction Interpretation Examples - High greed readings (majority bullish) → Consider bearish opportunities - High fear readings (majority bearish) → Consider bullish opportunities - Sudden sentiment flip during established trends → Potential trend reversal signal Technical Notes - Built with PineScript v6 - Dynamic symbol detection with fallback options - Optimized for performance with minimal resource usage - Color-coded visualization with customizable zones Data Sources & Expansion Acknowledgments We extend our gratitude to **TradingView** for enabling the use of custom data feeds based on GitHub repositories, making this comprehensive sentiment analysis possible. Data Expansion Opportunities As the operator of this indicator, I am **open to suggestions for new data sources** that could be integrated and published. If you have ideas for additional instruments or sentiment data: How to Submit Suggestions: 1. Send a **private message** with your proposal 2. Include: **instrument/data type**, **source**, and **brief description** 3. If technically feasible, we will work to import and publish the data Data Infrastructure Status Current Data Upload Process: Please note that sentiment data uploads may occasionally experience minor interruptions. However, this should not pose significant issues as sentiment data typically changes gradually rather than rapidly. Infrastructure Development: We are actively working on establishing permanent cloud-based infrastructure to ensure continuous, automated data collection and upload processes. This will provide more reliable and consistent data availability in the future. Disclaimer This indicator is for educational and informational purposes only. Sentiment data should be used as part of a comprehensive trading strategy and not as the sole basis for trading decisions. Past performance does not guarantee future results. The contrarian approach described is a market theory and may not always produce profitable results.
Pine Script® 인디케이터
OpenTrading_ai의
Fear and Greed Index [DunesIsland]The Fear and Greed Index is a sentiment indicator designed to measure the emotions driving the stock market, specifically investor fear and greed. Fear represents pessimism and caution, while greed reflects optimism and risk-taking. This indicator aggregates multiple market metrics to provide a comprehensive view of market sentiment, helping traders and investors gauge whether the market is overly fearful or excessively greedy.How It WorksThe Fear and Greed Index is calculated using four key market indicators, each capturing a different aspect of market sentiment: Market Momentum (30% weight) Measures how the S&P 500 (SPX) is performing relative to its 125-day simple moving average (SMA). A higher value indicates that the market is trading well above its moving average, signaling greed. Stock Price Strength (20% weight) Calculates the net number of stocks hitting 52-week highs minus those hitting 52-week lows on the NYSE. A greater number of net highs suggests strong market breadth and greed. Put/Call Options (30% weight) Uses the 5-day average of the put/call ratio. A lower ratio (more call options being bought) indicates greed, as investors are betting on rising prices. Market Volatility (20% weight) Utilizes the VIX index, which measures market volatility. Lower volatility is associated with greed, as investors are less fearful of large market swings. Each component is normalized using a z-score over a 252-day lookback period (approximately one trading year) and scaled to a range of 0 to 100. The final Fear and Greed Index is a weighted average of these four components, with the weights specified above.Key FeaturesIndex Range: The index value ranges from 0 to 100: 0–25: Extreme Fear (red) 25–50: Fear (orange) 50–75: Neutral (yellow) 75–100: Greed (green) Dynamic Plot Color: The plot line changes color based on the index value, visually indicating the current sentiment zone. Reference Lines: Horizontal lines are plotted at 0, 25, 50, 75, and 100 to represent the different sentiment levels: Extreme Fear, Fear, Neutral, Greed, and Extreme Greed. How to Interpret Low Values (0–25): Indicate extreme fear, which may suggest that the market is oversold and could be due for a rebound. High Values (75–100): Indicate greed, which may signal that the market is overbought and could be at risk of a correction. Neutral Range (25–75): Suggests a balanced market sentiment, neither overly fearful nor greedy. This indicator is a valuable tool for contrarian investors, as extreme readings often precede market reversals. However, it should be used in conjunction with other technical and fundamental analysis tools for a well-rounded view of the market.
Pine Script® 인디케이터
dunesisland의
Normalized Volume IndexIn the realm of technical analysis, volume is more than just a measure of market activity—it’s a window into trader psychology. Two classic indicators that harness this insight are the Positive Volume Index (PVI) and Negative Volume Index (NVI). Developed in the early 20th century by Paul L. Dysart and later refined by Norman G. Fosback in 1976, these tools aim to distinguish between the behavior of the so-called “smart money” and the broader market crowd. - Positive Volume Index (PVI) tracks price changes only on days when trading volume increases. It assumes that rising volume reflects the actions of less-informed retail traders—those who follow the herd. - Negative Volume Index (NVI), on the other hand, focuses on days when volume decreases, under the premise that institutional investors (the “smart money”) are more active when the market is quiet. This dichotomy allows traders to interpret market sentiment through the lens of volume behavior. For example, a rising NVI during a price uptrend may suggest that institutional investors are quietly accumulating positions—often a bullish signal. Traders use PVI and NVI to: - Confirm trends: If NVI is above its moving average, it often signals a strong underlying trend supported by smart money. - Spot reversals: Divergences between price and either index can hint at weakening momentum or upcoming reversals. - Gauge participation: PVI rising faster than price may indicate overenthusiastic retail buying—potentially a contrarian signal. These indicators are often paired with moving averages (e.g., 255-day EMA) to generate actionable signals. Fosback’s research suggested that when NVI is above its one-year EMA, there’s a high probability of a bull market. While PVI and NVI are cumulative indices, normalizing them—for example, by rebasing to 100 or converting to percentage changes—offers several benefits: - Comparability: Normalized indices can be compared across different assets or timeframes. - Clarity: It becomes easier to visualize relative strength or weakness. - Backtesting: Normalized values are more suitable for algorithmic strategies and statistical analysis. Normalization also helps when combining PVI/NVI with other indicators in multi-factor models, ensuring no single metric dominates due to scale differences In essence, PVI and NVI offer a nuanced view of market dynamics by separating the noise of volume surges from the quiet confidence of institutional moves. When normalized and interpreted correctly, they become powerful allies in a trader’s decision-making toolkit. How to use this (Educational material): For instance, on average, when the Negative Volume Index (NVI) remains above its midline, the market tends to trend positively, reflecting consistent institutional participation. However, when the NVI dips and stays below the midline, it often signals a negative trend, indicating that smart money is stepping away or reducing exposure. Another telling scenario occurs when the Positive Volume Index (PVI) drops below the NVI. While this might coincide with a brief price dip, institutions often interpret this as an opportunity to buy the dip, quietly accumulating positions while retail participants exit in panic. The result? A market recovery driven by smart money. Conversely, when the PVI consistently remains above the NVI, it may point to retail enthusiasm outpacing institutional support. This imbalance can flag a tired or overextended trend, where the smart money has already positioned itself defensively. When this pattern persists, there's a high likelihood that institutions will pull the plug, leading to a pronounced trend reversal.
Pine Script® 인디케이터
chriskokal1의
1
Systemic Credit Market Pressure IndexSystemic Credit Market Pressure Index (SCMPI): A Composite Indicator for Credit Cycle Analysis The Systemic Credit Market Pressure Index (SCMPI) represents a novel composite indicator designed to quantify systemic stress within credit markets through the integration of multiple macroeconomic variables. This indicator employs advanced statistical normalization techniques, adaptive threshold mechanisms, and intelligent visualization systems to provide real-time assessment of credit market conditions across expansion, neutral, and stress regimes. The methodology combines credit spread analysis, labor market indicators, consumer credit conditions, and household debt metrics into a unified framework for systemic risk assessment, featuring dynamic Bollinger Band-style thresholds and theme-adaptive visualization capabilities. ## 1. Introduction Credit cycles represent fundamental drivers of economic fluctuations, with their dynamics significantly influencing financial stability and macroeconomic outcomes (Bernanke, Gertler & Gilchrist, 1999). The identification and measurement of credit market stress has become increasingly critical following the 2008 financial crisis, which highlighted the need for comprehensive early warning systems (Adrian & Brunnermeier, 2016). Traditional single-variable approaches often fail to capture the multidimensional nature of credit market dynamics, necessitating the development of composite indicators that integrate multiple information sources. The SCMPI addresses this gap by constructing a weighted composite index that synthesizes four key dimensions of credit market conditions: corporate credit spreads, labor market stress, consumer credit accessibility, and household leverage ratios. This approach aligns with the theoretical framework established by Minsky (1986) regarding financial instability hypothesis and builds upon empirical work by Gilchrist & Zakrajšek (2012) on credit market sentiment. ## 2. Theoretical Framework ### 2.1 Credit Cycle Theory The theoretical foundation of the SCMPI rests on the credit cycle literature, which posits that credit availability fluctuates in predictable patterns that amplify business cycle dynamics (Kiyotaki & Moore, 1997). During expansion phases, credit becomes increasingly available as risk perceptions decline and collateral values rise. Conversely, stress phases are characterized by credit contraction, elevated risk premiums, and deteriorating borrower conditions. The indicator incorporates Kindleberger's (1978) framework of financial crises, which identifies key stages in credit cycles: displacement, boom, euphoria, profit-taking, and panic. By monitoring multiple variables simultaneously, the SCMPI aims to capture transitions between these phases before they become apparent in individual metrics. ### 2.2 Systemic Risk Measurement Systemic risk, defined as the risk of collapse of an entire financial system or entire market (Kaufman & Scott, 2003), requires measurement approaches that capture interconnectedness and spillover effects. The SCMPI follows the methodology established by Bisias et al. (2012) in constructing composite measures that aggregate individual risk indicators into system-wide assessments. The index employs the concept of "financial stress" as defined by Illing & Liu (2006), encompassing increased uncertainty about fundamental asset values, increased uncertainty about other investors' behavior, increased flight to quality, and increased flight to liquidity. ## 3. Methodology ### 3.1 Component Variables The SCMPI integrates four primary components, each representing distinct aspects of credit market conditions: #### 3.1.1 Credit Spreads (BAA-10Y Treasury) Corporate credit spreads serve as the primary indicator of credit market stress, reflecting risk premiums demanded by investors for corporate debt relative to risk-free government securities (Gilchrist & Zakrajšek, 2012). The BAA-10Y spread specifically captures investment-grade corporate credit conditions, providing insight into broad credit market sentiment. #### 3.1.2 Unemployment Rate Labor market conditions directly influence credit quality through their impact on borrower repayment capacity (Bernanke & Gertler, 1995). Rising unemployment typically precedes credit deterioration, making it a valuable leading indicator for credit stress. #### 3.1.3 Consumer Credit Rates Consumer credit accessibility reflects the transmission of monetary policy and credit market conditions to household borrowing (Mishkin, 1995). Elevated consumer credit rates indicate tightening credit conditions and reduced credit availability for households. #### 3.1.4 Household Debt Service Ratio Household leverage ratios capture the debt burden relative to income, providing insight into household financial stress and potential credit losses (Mian & Sufi, 2014). High debt service ratios indicate vulnerable household sectors that may contribute to credit market instability. ### 3.2 Statistical Methodology #### 3.2.1 Z-Score Normalization Each component variable undergoes robust z-score normalization to ensure comparability across different scales and units: Z_i,t = (X_i,t - μ_i) / σ_i Where X_i,t represents the value of variable i at time t, μ_i is the historical mean, and σ_i is the historical standard deviation. The normalization period employs a rolling 252-day window to capture annual cyclical patterns while maintaining sensitivity to regime changes. #### 3.2.2 Adaptive Smoothing To reduce noise while preserving signal quality, the indicator employs exponential moving average (EMA) smoothing with adaptive parameters: EMA_t = α × Z_t + (1-α) × EMA_{t-1} Where α = 2/(n+1) and n represents the smoothing period (default: 63 days). #### 3.2.3 Weighted Aggregation The composite index combines normalized components using theoretically motivated weights: SCMPI_t = w_1×Z_spread,t + w_2×Z_unemployment,t + w_3×Z_consumer,t + w_4×Z_debt,t Default weights reflect the relative importance of each component based on empirical literature: credit spreads (35%), unemployment (25%), consumer credit (25%), and household debt (15%). ### 3.3 Dynamic Threshold Mechanism Unlike static threshold approaches, the SCMPI employs adaptive Bollinger Band-style thresholds that automatically adjust to changing market volatility and conditions (Bollinger, 2001): Expansion Threshold = μ_SCMPI - k × σ_SCMPI Stress Threshold = μ_SCMPI + k × σ_SCMPI Neutral Line = μ_SCMPI Where μ_SCMPI and σ_SCMPI represent the rolling mean and standard deviation of the composite index calculated over a configurable period (default: 126 days), and k is the threshold multiplier (default: 1.0). This approach ensures that thresholds remain relevant across different market regimes and volatility environments, providing more robust regime classification than fixed thresholds. ### 3.4 Visualization and User Interface The SCMPI incorporates advanced visualization capabilities designed for professional trading environments: #### 3.4.1 Adaptive Theme System The indicator features an intelligent dual-theme system that automatically optimizes colors and transparency levels for both dark and bright chart backgrounds. This ensures optimal readability across different trading platforms and user preferences. #### 3.4.2 Customizable Visual Elements Users can customize all visual aspects including: - Color Schemes: Automatic theme adaptation with optional custom color overrides - Line Styles: Configurable widths for main index, trend lines, and threshold boundaries - Transparency Optimization: Automatic adjustment based on selected theme for optimal contrast - Dynamic Zones: Color-coded regime areas with adaptive transparency #### 3.4.3 Professional Data Table A comprehensive 13-row data table provides real-time component analysis including: - Composite index value and regime classification - Individual component z-scores with color-coded stress indicators - Trend direction and signal strength assessment - Dynamic threshold status and volatility metrics - Component weight distribution for transparency ## 4. Regime Classification The SCMPI classifies credit market conditions into three distinct regimes: ### 4.1 Expansion Regime (SCMPI < Expansion Threshold) Characterized by favorable credit conditions, low risk premiums, and accommodative lending standards. This regime typically corresponds to economic expansion phases with low default rates and increasing credit availability. ### 4.2 Neutral Regime (Expansion Threshold ≤ SCMPI ≤ Stress Threshold) Represents balanced credit market conditions with moderate risk premiums and stable lending standards. This regime indicates neither significant stress nor excessive exuberance in credit markets. ### 4.3 Stress Regime (SCMPI > Stress Threshold) Indicates elevated credit market stress with high risk premiums, tightening lending standards, and deteriorating borrower conditions. This regime often precedes or coincides with economic contractions and financial market volatility. ## 5. Technical Implementation and Features ### 5.1 Alert System The SCMPI includes a comprehensive alert framework with seven distinct conditions: - Regime Transitions: Expansion, Neutral, and Stress phase entries - Extreme Conditions: Values exceeding ±2.0 standard deviations - Trend Reversals: Directional changes in the underlying trend component ### 5.2 Performance Optimization The indicator employs several optimization techniques: - Efficient Calculations: Pre-computed statistical measures to minimize computational overhead - Memory Management: Optimized variable declarations for real-time performance - Error Handling: Robust data validation and fallback mechanisms for missing data ## 6. Empirical Validation ### 6.1 Historical Performance Backtesting analysis demonstrates the SCMPI's ability to identify major credit stress episodes, including: - The 2008 Financial Crisis - The 2020 COVID-19 pandemic market disruption - Various regional banking crises - European sovereign debt crisis (2010-2012) ### 6.2 Leading Indicator Properties The composite nature and dynamic threshold system of the SCMPI provides enhanced leading indicator properties, typically signaling regime changes 1-3 months before they become apparent in individual components or market indices. The adaptive threshold mechanism reduces false signals during high-volatility periods while maintaining sensitivity during regime transitions. ## 7. Applications and Limitations ### 7.1 Applications - Risk Management: Portfolio managers can use SCMPI signals to adjust credit exposure and risk positioning - Academic Research: Researchers can employ the index for credit cycle analysis and systemic risk studies - Trading Systems: The comprehensive alert system enables automated trading strategy implementation - Financial Education: The transparent methodology and visual design facilitate understanding of credit market dynamics ### 7.2 Limitations - Data Dependency: The indicator relies on timely and accurate macroeconomic data from FRED sources - Regime Persistence: Dynamic thresholds may exhibit brief lag during extremely rapid regime transitions - Model Risk: Component weights and parameters require periodic recalibration based on evolving market structures - Computational Requirements: Real-time calculations may require adequate processing power for optimal performance ## References Adrian, T. & Brunnermeier, M.K. (2016). CoVaR. *American Economic Review*, 106(7), 1705-1741. Bernanke, B. & Gertler, M. (1995). Inside the black box: the credit channel of monetary policy transmission. *Journal of Economic Perspectives*, 9(4), 27-48. Bernanke, B., Gertler, M. & Gilchrist, S. (1999). The financial accelerator in a quantitative business cycle framework. *Handbook of Macroeconomics*, 1, 1341-1393. Bisias, D., Flood, M., Lo, A.W. & Valavanis, S. (2012). A survey of systemic risk analytics. *Annual Review of Financial Economics*, 4(1), 255-296. Bollinger, J. (2001). *Bollinger on Bollinger Bands*. McGraw-Hill Education. Gilchrist, S. & Zakrajšek, E. (2012). Credit spreads and business cycle fluctuations. *American Economic Review*, 102(4), 1692-1720. Illing, M. & Liu, Y. (2006). Measuring financial stress in a developed country: An application to Canada. *Journal of Financial Stability*, 2(3), 243-265. Kaufman, G.G. & Scott, K.E. (2003). What is systemic risk, and do bank regulators retard or contribute to it? *The Independent Review*, 7(3), 371-391. Kindleberger, C.P. (1978). *Manias, Panics and Crashes: A History of Financial Crises*. Basic Books. Kiyotaki, N. & Moore, J. (1997). Credit cycles. *Journal of Political Economy*, 105(2), 211-248. Mian, A. & Sufi, A. (2014). What explains the 2007–2009 drop in employment? *Econometrica*, 82(6), 2197-2223. Minsky, H.P. (1986). *Stabilizing an Unstable Economy*. Yale University Press. Mishkin, F.S. (1995). Symposium on the monetary transmission mechanism. *Journal of Economic Perspectives*, 9(4), 3-10.
Pine Script® 인디케이터
EdgeTools의
PLN IndexThe "PLN Index" is a custom indicator developed for TradingView using Pine Script (version 6). It tracks the relative strength of the Polish Zloty (PLN) against a basket of four major currencies: the U.S. Dollar (USD), Swiss Franc (CHF), Euro (EUR), and British Pound (GBP), with each currency contributing an equal weight of 25%. Modeled after the Polish Zloty Index (PLN_I) concept, this indicator offers traders a tool to monitor PLN’s performance across various forex market conditions. How It Works The indicator fetches closing prices for the currency pairs USDPLN, CHFPLN, EURPLN, and GBPPLN from TradingView’s data provider (FX_IDC). These pairs represent the amount of PLN needed to purchase one unit of each respective foreign currency. To measure PLN’s strength, the script inverts these rates (e.g., PLNUSD = 1/USDPLN) and calculates the geometric mean of the resulting values using the formula geom_mean = (PLNUSD * PLNCHF * PLNEUR * PLNGBP)^(0.25). The result is then normalized to a base value of 100 at the first bar with complete data, allowing users to observe relative changes in PLN’s value over time. A rising index indicates PLN appreciation, while a falling index suggests depreciation against the basket. Key Features Data Inputs: Retrieves closing prices for USDPLN, CHFPLN, EURPLN, and GBPPLN on the selected timeframe. Calculation: Computes the geometric mean of the inverted exchange rates and normalizes it to 100 based on the first valid bar. Visualization: Plots the index as a blue line with a linewidth of 2 on a separate chart pane (non-overlay). Robust Normalization: Normalizes the index using the first bar where all data is available, improving reliability across different timeframes. Usage The PLN Index is useful for: Evaluating the Polish Zloty’s strength or weakness relative to a balanced currency basket. Identifying long-term trends or short-term shifts in PLN’s value for forex trading or economic analysis. Supporting technical analysis when paired with additional indicators, such as moving averages or oscillators. Limitations Data Dependency: The indicator relies on the availability of historical data for all four currency pairs. Missing data (e.g., on higher timeframes like D1 or W1) may prevent accurate plotting. Relative Normalization: Unlike the official PLN_I, which uses a fixed historical base date (e.g., January 2, 1984), this indicator normalizes to 100 at the first valid bar, making it a relative rather than absolute measure. Potential Data Gaps: On higher timeframes, inconsistencies or limited historical data from the FX_IDC provider may result in incomplete index values. Notes This version of the PLN Index includes an improved normalization method that sets the base value (100) at the first bar with valid data, enhancing its adaptability compared to earlier iterations. It performs best on timeframes up to H4, where data availability is generally consistent. For higher timeframes, users should verify data completeness to ensure reliable results.
Pine Script® 인디케이터
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Cognitive Echo IndexCognitive Echo Index – User Guide Overview The Cognitive Echo Index is a composite indicator that combines several technical aspects—including price deviation from a moving average, normalized volatility (via ATR), and volume variations—to create a single numeric value. The output is scaled between -100 and +100, offering insights into market momentum and potential trend reversals. How It Works Price Component: The indicator calculates the percentage change between the current price and its 14-period simple moving average (SMA). This helps identify how far the price deviates from its recent trend. Volatility Component: Using the Average True Range (ATR) over a 14-period, the script normalizes current ATR against its 14-period SMA. This shows relative volatility, highlighting unusual market activity. Volume Component: It computes the percentage change between the current volume and its 14-period SMA to detect spikes or drops in trading activity. Composite Calculation: The three components are summed together to produce the final index value, which is then clipped to remain between -100 and +100. Interpreting the Indicator Indicator Value Scale: Positive Values (0 to +100): Suggest that bullish forces are stronger. Higher values (e.g., above +50) could indicate a strong bullish sentiment. Negative Values (0 to -100): Indicate bearish pressures. Lower values (e.g., below -50) may signal strong bearish conditions. Zero Level: The midline indicates a balanced market condition with no dominant trend. Key Horizontal Levels: +50 Level: When the indicator crosses above +50, it can be interpreted as a strong bullish signal. -50 Level: When the indicator crosses below -50, it can be considered a strong bearish signal. Usage Tips Confirmation Tool: Use the Cognitive Echo Index as an additional confirmation tool in conjunction with other technical indicators or chart patterns to make more informed trading decisions. Parameter Adjustments: The script uses a 14-period setting for the moving average, ATR, and volume SMA by default. Depending on your trading timeframe or asset, consider tweaking these periods for better sensitivity. Trend Analysis: Watch how the indicator behaves during major price moves. A divergence between the index and the price trend (e.g., price rises while the index falls) may suggest a potential reversal or a false breakout. Risk Management: Always incorporate sound risk management practices. Use stop losses and position sizing rules, and consider the indicator as one part of your overall trading strategy. Customization Adjusting the Weights: Although the current version gives equal weight to all three components, advanced users can modify the script to apply different weights to the price, volatility, and volume components based on historical performance or specific market conditions. Adding Additional Inputs: Future versions could incorporate external sentiment data or other technical factors if accessible. For now, the indicator focuses on technical inputs available directly within TradingView. By following this guide, traders can integrate the Cognitive Echo Index into their TradingView platform to gain a unique perspective on market momentum and potential turning points. Enjoy testing and refining the indicator to better suit your trading style!
Pine Script® 인디케이터
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