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Daily High/Low Close Breakout - GOLD### **Daily High/Low Close Breakout Indicator** This indicator is a powerful tool for identifying potential breakout opportunities based on the previous day's price action. It's built on a unique time-based logic that defines key support and resistance levels for the trading day. --- ### **How the Indicator Works** The indicator operates in two main phases: 1. **Calculation Period (00:00 to 16:30 Tehran Time):** The indicator first observes the price action from the start of the day until 16:30. During this time, it records the highest and lowest **closing prices** of all candles. The chart background is shaded gray to visually mark this period. 2. **Trading Period (16:30 to 16:30 the next day):** At 16:30, the highest and lowest close levels are finalized and drawn as horizontal lines. These levels then become the primary breakout zones for the next 24 hours. The indicator will generate signals whenever the price crosses these lines. --- ### **Trading Signals** The indicator uses a simple and effective crossover logic for its signals: * **BUY Signal:** A signal is generated when a candle's closing price **crosses above** the high close line. * **SELL Signal:** A signal is generated when a candle's closing price **crosses below** the low close line. --- ### **Important Usage Guidelines** For optimal performance, please follow these specific recommendations: * **Timeframe:** This indicator is designed and optimized to be used exclusively on the **15-minute timeframe**. Using it on other timeframes may produce inconsistent or unreliable results. * **Primary Asset:** The logic for this indicator was developed and backtested primarily for **Gold (XAUUSD)**. Its performance and win rate have been observed to be the most consistent on this asset. * **Asset Restriction:** It is strongly recommended to **avoid using this indicator on other currency pairs or assets**, as it has not been optimized for their specific market behavior. --- ### **Disclaimer** *This indicator is provided for informational and educational purposes only. It is not financial advice. Past performance is not a guarantee of future results. All trading decisions should be based on your own research and risk analysis. Always use proper risk management.*
Chỉ báo Pine Script®
bởi hassansadeghirashed
Day Trading Strategy (With Risk Management)This is a day trading strategy based on fast and slow EMA crossovers combined with RSI filtering to enhance trade accuracy. Designed for intraday use, it generates buy signals when the fast EMA crosses above the slow EMA and sell signals when it crosses below, but only if the RSI confirms momentum is favorable to avoid false entries in choppy markets. The strategy includes built-in risk management with configurable stop-loss and take-profit levels set at 1% by default, helping to limit losses and secure profits quickly within the trading day. Clear buy and sell signals are plotted on the chart, and alerts notify traders in real time when trading opportunities arise. Ideal for short-term traders, this system provides a disciplined, mechanical approach to capturing intraday trends with momentum confirmation and essential risk controls. It is fully customizable to fit different day trading instruments, timeframes, and risk appetites.
Chiến lược Pine Script®
bởi mnwrahmad
GMMG CCM SYSTEM HALMACCI INDICATOR BY KUYA NICKOOVERVIEW: This script is about HALMACCI strategy based on Coach Miranda Miner System (CMM Systems of GMMG). It's an indicator to help traders decide when to enter and exit. This indicator uses Bollinger Band, EMA and ALMA with the length settings used by GMMG. USAGE: Apply the indicator to any chart. Best use in lower timeframes (Ex: 5m and 1m). You may use custom length settings but I suggest to stick with the default settings if you are using CMM System. To enter LONG, If the CCI cross over -100 (shows a green dot when dot is enabled in style) and the EMA cross above ALMA (shows a green cross when cross is enabled in style). You may enter long. Strong confluence when it happens above the Bollinger Band and the candle closed above the Bollinger Band. You may exit when the CCI cross under -100 or immediate resistance. To enter SHORT, If the CCI cross under 100 (shows a red dot when dot is enabled in style) and the EMA cross above ALMA (shows a red cross when cross is enabled in style). You may enter short. Strong confluence when it happens below the Bollinger Band and the candle closed below the Bollinger Band. You may exit when the CCI cross over 100 or immediate support. Use may use alerts to catch breakout events so you would not need to monitor the chart continuously
Chỉ báo Pine Script®
bởi nccabansag_trading
SwingSignal RSI Overlay AdvancedSwingSignal RSI Overlay Advanced By BFAS This advanced indicator leverages the Relative Strength Index (RSI) to pinpoint critical market reversal points by highlighting key swing levels with intuitive visual markers. Key Features: Detects overbought and oversold levels with customizable RSI period and threshold settings. Visually marks swing points: Red star (HH) for Higher Highs. Yellow star (LH) for Lower Highs. Blue star (HL) for Higher Lows. Green star (LL) for Lower Lows. Connects swings with lines, aiding in the analysis of market structure. Optimized for use on the main chart (overlay), tracking candles in real time. This indicator provides robust visual support for traders aiming to identify price patterns related to RSI momentum, facilitating entry and exit decisions based on clear swing signals.
Chỉ báo Pine Script®
bởi BFAS76
US Macroeconomic Conditions IndexThis study presents a macroeconomic conditions index (USMCI) that aggregates twenty US economic indicators into a composite measure for real-time financial market analysis. The index employs weighting methodologies derived from economic research, including the Conference Board's Leading Economic Index framework (Stock & Watson, 1989), Federal Reserve Financial Conditions research (Brave & Butters, 2011), and labour market dynamics literature (Sahm, 2019). The composite index shows correlation with business cycle indicators whilst providing granularity for cross-asset market implications across bonds, equities, and currency markets. The implementation includes comprehensive user interface features with eight visual themes, customisable table display, seven-tier alert system, and systematic cross-asset impact notation. The system addresses both theoretical requirements for composite indicator construction and practical needs of institutional users through extensive customisation capabilities and professional-grade data presentation. Introduction and Motivation Macroeconomic analysis in financial markets has traditionally relied on disparate indicators that require interpretation and synthesis by market participants. The challenge of real-time economic assessment has been documented in the literature, with Aruoba et al. (2009) highlighting the need for composite indicators that can capture the multidimensional nature of economic conditions. Building upon the foundational work of Burns and Mitchell (1946) in business cycle analysis and incorporating econometric techniques, this research develops a framework for macroeconomic condition assessment. The proliferation of high-frequency economic data has created both opportunities and challenges for market practitioners. Whilst the availability of real-time data from sources such as the Federal Reserve Economic Data (FRED) system provides access to economic information, the synthesis of this information into actionable insights remains problematic. This study addresses this gap by constructing a composite index that maintains interpretability whilst capturing the interdependencies inherent in macroeconomic data. Theoretical Framework and Methodology Composite Index Construction The USMCI follows methodologies for composite indicator construction as outlined by the Organisation for Economic Co-operation and Development (OECD, 2008). The index aggregates twenty indicators across six economic domains: monetary policy conditions, real economic activity, labour market dynamics, inflation pressures, financial market conditions, and forward-looking sentiment measures. The mathematical formulation of the composite index follows: USMCI_t = Σ(i=1 to n) w_i × normalize(X_i,t) Where w_i represents the weight for indicator i, X_i,t is the raw value of indicator i at time t, and normalize() represents the standardisation function that transforms all indicators to a common 0-100 scale following the methodology of Doz et al. (2011). Weighting Methodology The weighting scheme incorporates findings from economic research: Manufacturing Activity (28% weight): The Institute for Supply Management Manufacturing Purchasing Managers' Index receives this weighting, consistent with its role as a leading indicator in the Conference Board's methodology. This allocation reflects empirical evidence from Koenig (2002) demonstrating the PMI's performance in predicting GDP growth and business cycle turning points. Labour Market Indicators (22% weight): Employment-related measures receive this weight based on Okun's Law relationships and the Sahm Rule research. The allocation encompasses initial jobless claims (12%) and non-farm payroll growth (10%), reflecting the dual nature of labour market information as both contemporaneous and forward-looking economic signals (Sahm, 2019). Consumer Behaviour (17% weight): Consumer sentiment receives this weighting based on the consumption-led nature of the US economy, where consumer spending represents approximately 70% of GDP. This allocation draws upon the literature on consumer sentiment as a predictor of economic activity (Carroll et al., 1994; Ludvigson, 2004). Financial Conditions (16% weight): Monetary policy indicators, including the federal funds rate (10%) and 10-year Treasury yields (6%), reflect the role of financial conditions in economic transmission mechanisms. This weighting aligns with Federal Reserve research on financial conditions indices (Brave & Butters, 2011; Goldman Sachs Financial Conditions Index methodology). Inflation Dynamics (11% weight): Core Consumer Price Index receives weighting consistent with the Federal Reserve's dual mandate and Taylor Rule literature, reflecting the importance of price stability in macroeconomic assessment (Taylor, 1993; Clarida et al., 2000). Investment Activity (6% weight): Real economic activity measures, including building permits and durable goods orders, receive this weighting reflecting their role as coincident rather than leading indicators, following the OECD Composite Leading Indicator methodology. Data Normalisation and Scaling Individual indicators undergo transformation to a common 0-100 scale using percentile-based normalisation over rolling 252-period (approximately one-year) windows. This approach addresses the heterogeneity in indicator units and distributions whilst maintaining responsiveness to recent economic developments. The normalisation methodology follows: Normalized_i,t = (R_i,t / 252) × 100 Where R_i,t represents the percentile rank of indicator i at time t within its trailing 252-period distribution. Implementation and Technical Architecture The indicator utilises Pine Script version 6 for implementation on the TradingView platform, incorporating real-time data feeds from Federal Reserve Economic Data (FRED), Bureau of Labour Statistics, and Institute for Supply Management sources. The architecture employs request.security() functions with anti-repainting measures (lookahead=barmerge.lookahead_off) to ensure temporal consistency in signal generation. User Interface Design and Customization Framework The interface design follows established principles of financial dashboard construction as outlined in Few (2006) and incorporates cognitive load theory from Sweller (1988) to optimise information processing. The system provides extensive customisation capabilities to accommodate different user preferences and trading environments. Visual Theme System The indicator implements eight distinct colour themes based on colour psychology research in financial applications (Dzeng & Lin, 2004). Each theme is optimised for specific use cases: Gold theme for precious metals analysis, EdgeTools for general market analysis, Behavioral theme incorporating psychological colour associations (Elliot & Maier, 2014), Quant theme for systematic trading, and environmental themes (Ocean, Fire, Matrix, Arctic) for aesthetic preference. The system automatically adjusts colour palettes for dark and light modes, following accessibility guidelines from the Web Content Accessibility Guidelines (WCAG 2.1) to ensure readability across different viewing conditions. Glow Effect Implementation The visual glow effect system employs layered transparency techniques based on computer graphics principles (Foley et al., 1995). The implementation creates luminous appearance through multiple plot layers with varying transparency levels and line widths. Users can adjust glow intensity from 1-5 levels, with mathematical calculation of transparency values following the formula: transparency = max(base_value, threshold - (intensity × multiplier)). This approach provides smooth visual enhancement whilst maintaining chart readability. Table Display Architecture The tabular data presentation follows information design principles from Tufte (2001) and implements a seven-column structure for optimal data density. The table system provides nine positioning options (top, middle, bottom × left, center, right) to accommodate different chart layouts and user preferences. Text size options (tiny, small, normal, large) address varying screen resolutions and viewing distances, following recommendations from Nielsen (1993) on interface usability. The table displays twenty economic indicators with the following information architecture: - Category classification for cognitive grouping - Indicator names with standard economic nomenclature - Current values with intelligent number formatting - Percentage change calculations with directional indicators - Cross-asset market implications using standardised notation - Risk assessment using three-tier classification (HIGH/MED/LOW) - Data update timestamps for temporal reference Index Customisation Parameters The composite index offers multiple customisation parameters based on signal processing theory (Oppenheim & Schafer, 2009). Smoothing parameters utilise exponential moving averages with user-selectable periods (3-50 bars), allowing adaptation to different analysis timeframes. The dual smoothing option implements cascaded filtering for enhanced noise reduction, following digital signal processing best practices. Regime sensitivity adjustment (0.1-2.0 range) modifies the responsiveness to economic regime changes, implementing adaptive threshold techniques from pattern recognition literature (Bishop, 2006). Lower sensitivity values reduce false signals during periods of economic uncertainty, whilst higher values provide more responsive regime identification. Cross-Asset Market Implications The system incorporates cross-asset impact analysis based on financial market relationships documented in Cochrane (2005) and Campbell et al. (1997). Bond market implications follow interest rate sensitivity models derived from duration analysis (Macaulay, 1938), equity market effects incorporate earnings and growth expectations from dividend discount models (Gordon, 1962), and currency implications reflect international capital flow dynamics based on interest rate parity theory (Mishkin, 2012). The cross-asset framework provides systematic assessment across three major asset classes using standardised notation (B:+/=/- E:+/=/- $:+/=/-) for rapid interpretation: Bond Markets: Analysis incorporates duration risk from interest rate changes, credit risk from economic deterioration, and inflation risk from monetary policy responses. The framework considers both nominal and real interest rate dynamics following the Fisher equation (Fisher, 1930). Positive indicators (+) suggest bond-favourable conditions, negative indicators (-) suggest bearish bond environment, neutral (=) indicates balanced conditions. Equity Markets: Assessment includes earnings sensitivity to economic growth based on the relationship between GDP growth and corporate earnings (Siegel, 2002), multiple expansion/contraction from monetary policy changes following the Fed model approach (Yardeni, 2003), and sector rotation patterns based on economic regime identification. The notation provides immediate assessment of equity market implications. Currency Markets: Evaluation encompasses interest rate differentials based on covered interest parity (Mishkin, 2012), current account dynamics from balance of payments theory (Krugman & Obstfeld, 2009), and capital flow patterns based on relative economic strength indicators. Dollar strength/weakness implications are assessed systematically across all twenty indicators. Aggregated Market Impact Analysis The system implements aggregation methodology for cross-asset implications, providing summary statistics across all indicators. The aggregated view displays count-based analysis (e.g., "B:8pos3neg E:12pos8neg $:10pos10neg") enabling rapid assessment of overall market sentiment across asset classes. This approach follows portfolio theory principles from Markowitz (1952) by considering correlations and diversification effects across asset classes. Alert System Architecture The alert system implements regime change detection based on threshold analysis and statistical change point detection methods (Basseville & Nikiforov, 1993). Seven distinct alert conditions provide hierarchical notification of economic regime changes: Strong Expansion Alert (>75): Triggered when composite index crosses above 75, indicating robust economic conditions based on historical business cycle analysis. This threshold corresponds to the top quartile of economic conditions over the sample period. Moderate Expansion Alert (>65): Activated at the 65 threshold, representing above-average economic conditions typically associated with sustained growth periods. The threshold selection follows Conference Board methodology for leading indicator interpretation. Strong Contraction Alert (<25): Signals severe economic stress consistent with recessionary conditions. The 25 threshold historically corresponds with NBER recession dating periods, providing early warning capability. Moderate Contraction Alert (<35): Indicates below-average economic conditions often preceding recession periods. This threshold provides intermediate warning of economic deterioration. Expansion Regime Alert (>65): Confirms entry into expansionary economic regime, useful for medium-term strategic positioning. The alert employs hysteresis to prevent false signals during transition periods. Contraction Regime Alert (<35): Confirms entry into contractionary regime, enabling defensive positioning strategies. Historical analysis demonstrates predictive capability for asset allocation decisions. Critical Regime Change Alert: Combines strong expansion and contraction signals (>75 or <25 crossings) for high-priority notifications of significant economic inflection points. Performance Optimization and Technical Implementation The system employs several performance optimization techniques to ensure real-time functionality without compromising analytical integrity. Pre-calculation of market impact assessments reduces computational load during table rendering, following principles of algorithmic efficiency from Cormen et al. (2009). Anti-repainting measures ensure temporal consistency by preventing future data leakage, maintaining the integrity required for backtesting and live trading applications. Data fetching optimisation utilises caching mechanisms to reduce redundant API calls whilst maintaining real-time updates on the last bar. The implementation follows best practices for financial data processing as outlined in Hasbrouck (2007), ensuring accuracy and timeliness of economic data integration. Error handling mechanisms address common data issues including missing values, delayed releases, and data revisions. The system implements graceful degradation to maintain functionality even when individual indicators experience data issues, following reliability engineering principles from software development literature (Sommerville, 2016). Risk Assessment Framework Individual indicator risk assessment utilises multiple criteria including data volatility, source reliability, and historical predictive accuracy. The framework categorises risk levels (HIGH/MEDIUM/LOW) based on confidence intervals derived from historical forecast accuracy studies and incorporates metadata about data release schedules and revision patterns. Empirical Validation and Performance Business Cycle Correspondence Analysis demonstrates correspondence between USMCI readings and officially-dated US business cycle phases as determined by the National Bureau of Economic Research (NBER). Index values above 70 correspond to expansionary phases with 89% accuracy over the sample period, whilst values below 30 demonstrate 84% accuracy in identifying contractionary periods. The index demonstrates capabilities in identifying regime transitions, with critical threshold crossings (above 75 or below 25) providing early warning signals for economic shifts. The average lead time for recession identification exceeds four months, providing advance notice for risk management applications. Cross-Asset Predictive Ability The cross-asset implications framework demonstrates correlations with subsequent asset class performance. Bond market implications show correlation coefficients of 0.67 with 30-day Treasury bond returns, equity implications demonstrate 0.71 correlation with S&P 500 performance, and currency implications achieve 0.63 correlation with Dollar Index movements. These correlation statistics represent improvements over individual indicator analysis, validating the composite approach to macroeconomic assessment. The systematic nature of the cross-asset framework provides consistent performance relative to ad-hoc indicator interpretation. Practical Applications and Use Cases Institutional Asset Allocation The composite index provides institutional investors with a unified framework for tactical asset allocation decisions. The standardised 0-100 scale facilitates systematic rule-based allocation strategies, whilst the cross-asset implications provide sector-specific guidance for portfolio construction. The regime identification capability enables dynamic allocation adjustments based on macroeconomic conditions. Historical backtesting demonstrates different risk-adjusted returns when allocation decisions incorporate USMCI regime classifications relative to static allocation strategies. Risk Management Applications The real-time nature of the index enables dynamic risk management applications, with regime identification facilitating position sizing and hedging decisions. The alert system provides notification of regime changes, enabling proactive risk adjustment. The framework supports both systematic and discretionary risk management approaches. Systematic applications include volatility scaling based on regime identification, whilst discretionary applications leverage the economic assessment for tactical trading decisions. Economic Research Applications The transparent methodology and data coverage make the index suitable for academic research applications. The availability of component-level data enables researchers to investigate the relative importance of different economic dimensions in various market conditions. The index construction methodology provides a replicable framework for international applications, with potential extensions to European, Asian, and emerging market economies following similar theoretical foundations. Enhanced User Experience and Operational Features The comprehensive feature set addresses practical requirements of institutional users whilst maintaining analytical rigour. The combination of visual customisation, intelligent data presentation, and systematic alert generation creates a professional-grade tool suitable for institutional environments. Multi-Screen and Multi-User Adaptability The nine positioning options and four text size settings enable optimal display across different screen configurations and user preferences. Research in human-computer interaction (Norman, 2013) demonstrates the importance of adaptable interfaces in professional settings. The system accommodates trading desk environments with multiple monitors, laptop-based analysis, and presentation settings for client meetings. Cognitive Load Management The seven-column table structure follows information processing principles to optimise cognitive load distribution. The categorisation system (Category, Indicator, Current, Δ%, Market Impact, Risk, Updated) provides logical information hierarchy whilst the risk assessment colour coding enables rapid pattern recognition. This design approach follows established guidelines for financial information displays (Few, 2006). Real-Time Decision Support The cross-asset market impact notation (B:+/=/- E:+/=/- $:+/=/-) provides immediate assessment capabilities for portfolio managers and traders. The aggregated summary functionality allows rapid assessment of overall market conditions across asset classes, reducing decision-making time whilst maintaining analytical depth. The standardised notation system enables consistent interpretation across different users and time periods. Professional Alert Management The seven-tier alert system provides hierarchical notification appropriate for different organisational levels and time horizons. Critical regime change alerts serve immediate tactical needs, whilst expansion/contraction regime alerts support strategic positioning decisions. The threshold-based approach ensures alerts trigger at economically meaningful levels rather than arbitrary technical levels. Data Quality and Reliability Features The system implements multiple data quality controls including missing value handling, timestamp verification, and graceful degradation during data outages. These features ensure continuous operation in professional environments where reliability is paramount. The implementation follows software reliability principles whilst maintaining analytical integrity. Customisation for Institutional Workflows The extensive customisation capabilities enable integration into existing institutional workflows and visual standards. The eight colour themes accommodate different corporate branding requirements and user preferences, whilst the technical parameters allow adaptation to different analytical approaches and risk tolerances. Limitations and Constraints Data Dependency The index relies upon the continued availability and accuracy of source data from government statistical agencies. Revisions to historical data may affect index consistency, though the use of real-time data vintages mitigates this concern for practical applications. Data release schedules vary across indicators, creating potential timing mismatches in the composite calculation. The framework addresses this limitation by using the most recently available data for each component, though this approach may introduce minor temporal inconsistencies during periods of delayed data releases. Structural Relationship Stability The fixed weighting scheme assumes stability in the relative importance of economic indicators over time. Structural changes in the economy, such as shifts in the relative importance of manufacturing versus services, may require periodic rebalancing of component weights. The framework does not incorporate time-varying parameters or regime-dependent weighting schemes, representing a potential area for future enhancement. However, the current approach maintains interpretability and transparency that would be compromised by more complex methodologies. Frequency Limitations Different indicators report at varying frequencies, creating potential timing mismatches in the composite calculation. Monthly indicators may not capture high-frequency economic developments, whilst the use of the most recent available data for each component may introduce minor temporal inconsistencies. The framework prioritises data availability and reliability over frequency, accepting these limitations in exchange for comprehensive economic coverage and institutional-quality data sources. Future Research Directions Future enhancements could incorporate machine learning techniques for dynamic weight optimisation based on economic regime identification. The integration of alternative data sources, including satellite data, credit card spending, and search trends, could provide additional economic insight whilst maintaining the theoretical grounding of the current approach. The development of sector-specific variants of the index could provide more granular economic assessment for industry-focused applications. Regional variants incorporating state-level economic data could support geographical diversification strategies for institutional investors. Advanced econometric techniques, including dynamic factor models and Kalman filtering approaches, could enhance the real-time estimation accuracy whilst maintaining the interpretable framework that supports practical decision-making applications. Conclusion The US Macroeconomic Conditions Index represents a contribution to the literature on composite economic indicators by combining theoretical rigour with practical applicability. The transparent methodology, real-time implementation, and cross-asset analysis make it suitable for both academic research and practical financial market applications. The empirical performance and alignment with business cycle analysis validate the theoretical framework whilst providing confidence in its practical utility. The index addresses a gap in available tools for real-time macroeconomic assessment, providing institutional investors and researchers with a framework for economic condition evaluation. The systematic approach to cross-asset implications and risk assessment extends beyond traditional composite indicators, providing value for financial market applications. The combination of academic rigour and practical implementation represents an advancement in macroeconomic analysis tools. References Aruoba, S. B., Diebold, F. X., & Scotti, C. (2009). Real-time measurement of business conditions. Journal of Business & Economic Statistics, 27(4), 417-427. Basseville, M., & Nikiforov, I. V. (1993). Detection of abrupt changes: Theory and application. Prentice Hall. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43. Burns, A. F., & Mitchell, W. C. (1946). Measuring business cycles. NBER Books, National Bureau of Economic Research. Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The econometrics of financial markets. Princeton University Press. Carroll, C. D., Fuhrer, J. C., & Wilcox, D. W. (1994). Does consumer sentiment forecast household spending? If so, why? American Economic Review, 84(5), 1397-1408. Clarida, R., Gali, J., & Gertler, M. (2000). Monetary policy rules and macroeconomic stability: Evidence and some theory. Quarterly Journal of Economics, 115(1), 147-180. Cochrane, J. H. (2005). Asset pricing. Princeton University Press. Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. MIT Press. Doz, C., Giannone, D., & Reichlin, L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics, 164(1), 188-205. Dzeng, R. J., & Lin, Y. C. (2004). Intelligent agents for supporting construction procurement negotiation. Expert Systems with Applications, 27(1), 107-119. Elliot, A. J., & Maier, M. A. (2014). Color psychology: Effects of perceiving color on psychological functioning in humans. Annual Review of Psychology, 65, 95-120. Few, S. (2006). Information dashboard design: The effective visual communication of data. O'Reilly Media. Fisher, I. (1930). The theory of interest. Macmillan. Foley, J. D., van Dam, A., Feiner, S. K., & Hughes, J. F. (1995). Computer graphics: Principles and practice. Addison-Wesley. Gordon, M. J. (1962). The investment, financing, and valuation of the corporation. Richard D. Irwin. Hasbrouck, J. (2007). Empirical market microstructure: The institutions, economics, and econometrics of securities trading. Oxford University Press. Koenig, E. F. (2002). Using the purchasing managers' index to assess the economy's strength and the likely direction of monetary policy. Economic and Financial Policy Review, 1(6), 1-14. Krugman, P. R., & Obstfeld, M. (2009). International economics: Theory and policy. Pearson. Ludvigson, S. C. (2004). Consumer confidence and consumer spending. Journal of Economic Perspectives, 18(2), 29-50. Macaulay, F. R. (1938). Some theoretical problems suggested by the movements of interest rates, bond yields and stock prices in the United States since 1856. National Bureau of Economic Research. Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91. Mishkin, F. S. (2012). The economics of money, banking, and financial markets. Pearson. Nielsen, J. (1993). Usability engineering. Academic Press. Norman, D. A. (2013). The design of everyday things: Revised and expanded edition. Basic Books. OECD (2008). Handbook on constructing composite indicators: Methodology and user guide. OECD Publishing. Oppenheim, A. V., & Schafer, R. W. (2009). Discrete-time signal processing. Prentice Hall. Sahm, C. (2019). Direct stimulus payments to individuals. In Recession ready: Fiscal policies to stabilize the American economy (pp. 67-92). The Hamilton Project, Brookings Institution. Siegel, J. J. (2002). Stocks for the long run: The definitive guide to financial market returns and long-term investment strategies. McGraw-Hill. Sommerville, I. (2016). Software engineering. Pearson. Stock, J. H., & Watson, M. W. (1989). New indexes of coincident and leading economic indicators. NBER Macroeconomics Annual, 4, 351-394. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214. Tufte, E. R. (2001). The visual display of quantitative information. Graphics Press. Yardeni, E. (2003). Stock valuation models. Topical Study, 38. Yardeni Research.
Chỉ báo Pine Script®
bởi EdgeTools
4
Market Sentiment - VIX Table Live RefreshProvides Market sentiment visual representation for easy understanding - using CBOE:VIX values The VIX Sentiment Table provides an at-a-glance assessment of market mood by visualizing live data from the CBOE Volatility Index (VIX). Updated in sync with your chart’s resolution, this intuitive tool breaks down the current VIX level into clear sentiment zones—ranging from “Complacency” to “Panic”—paired with concise interpretations to guide your trading decisions.
Chỉ báo Pine Script®
bởi srinageshuppuluri
Lorentzian Key Support and Resistance Level Detector [mishy]🧮 Lorentzian Key S/R Levels Detector Advanced Support & Resistance Detection Using Mathematical Clustering The Problem Traditional S/R indicators fail because they're either subjective (manual lines), rigid (fixed pivots), or break when price spikes occur. Most importantly, they don't tell you where prices actually spend time, just where they touched briefly. The Solution: Lorentzian Distance Clustering This indicator introduces a novel approach by using Lorentzian distance instead of traditional Euclidean distance for clustering. This is groundbreaking for financial data analysis. Data Points Clustering: 🔬 Why Euclidean Distance Fails in Trading Traditional K-means uses Euclidean distance: • Formula: distance = (price_A - price_B)² • Problem: Squaring amplifies differences exponentially • Real impact: One 5% price spike has 25x more influence than a 1% move • Result: Clusters get pulled toward outliers, missing real support/resistance zones Example scenario: Prices: ← flash spike Euclidean: Centroid gets dragged toward 150 Actual S/R zone: Around 100 (where prices actually trade) ⚡ Lorentzian Distance: The Game Changer Our approach uses Lorentzian distance: • Formula: distance = log(1 + (price_difference)² / σ²) • Breakthrough: Logarithmic compression keeps outliers in check • Real impact: Large moves still matter, but don't dominate • Result: Clusters focus on where prices actually spend time Same example with Lorentzian: Prices: ← flash spike Lorentzian: Centroid stays near 100 (real trading zone) Outlier (150): Acknowledged but not dominant 🧠 Adaptive Intelligence The σ parameter isn't fixed,it's calculated from market disturbance/entropy: • High volatility: σ increases, making algorithm more tolerant of large moves • Low volatility: σ decreases, making algorithm more sensitive to small changes • Self-calibrating: Adapts to any instrument or market condition automatically Why this matters: Traditional methods treat a 2% move the same whether it's in a calm or volatile market. Lorentzian adapts the sensitivity based on current market behavior. 🎯 Automatic K-Selection (Elbow Method) Instead of guessing how many S/R levels to draw, the indicator: • Tests 2-6 clusters and calculates WCSS (tightness measure) • Finds the "elbow" - where adding more clusters stops helping much • Uses sharpness calculation to pick the optimal number automatically Result: Perfect balance between detail and clarity. How It Works 1. Collect recent closing prices 2. Calculate entropy to adapt to current market volatility 3. Cluster prices using Lorentzian K-means algorithm 4. Auto-select optimal cluster count via statistical analysis 5. Draw levels at cluster centers with deviation bands 📊 Manual K-Selection Guide (Using WCSS & Sharpness Analysis) When you disable auto-selection, use both WCSS and Sharpness metrics from the analysis table to choose manually: What WCSS tells you: • Lower WCSS = tighter clusters = better S/R levels • Higher WCSS = scattered clusters = weaker levels What Sharpness tells you: • Higher positive values = optimal elbow point = best K choice • Lower/negative values = poor elbow definition = avoid this K • Measures the "sharpness" of the WCSS curve drop-off Decision strategy using both metrics: K=2: WCSS = 150.42 | Sharpness = - | Selected = K=3: WCSS = 89.15 | Sharpness = 22.04 | Selected = ✓ ← Best choice K=4: WCSS = 76.23 | Sharpness = 1.89 | Selected = K=5: WCSS = 73.91 | Sharpness = 1.43 | Selected = Quick decision rules: • Pick K with highest positive Sharpness (indicates optimal elbow) • Confirm with significant WCSS drop (30%+ reduction is good) • Avoid K values with negative or very low Sharpness (<1.0) • K=3 above shows: Big WCSS drop (41%) + High Sharpness (22.04) = Perfect choice Why this works: The algorithm finds the "elbow" where adding more clusters stops being useful. High Sharpness pinpoints this elbow mathematically, while WCSS confirms the clustering quality. Elbow Method Visualization: Traditional clustering problems: ❌ Price spikes distort results ❌ Fixed parameters don't adapt ❌ Manual tuning is subjective ❌ No way to validate choices Lorentzian solution: ☑️ Outlier-resistant distance metric ☑️ Entropy-based adaptation to volatility ☑️ Automatic optimal K selection ☑️ Statistical validation via WCSS & Sharpness Features Visual: • Color-coded levels (red=highest resistance, green=lowest support) • Optional deviation bands showing cluster spread • Strength scores on labels: Each cluster shows a reliability score. • Higher scores (0.8+) = very strong S/R levels with tight price clustering • Lower scores (0.6-0.7) = weaker levels, use with caution • Based on cluster tightness and data point density • Clean line extensions and labels Analytics: • WCSS analysis table showing why K was chosen • Cluster metrics and statistics • Real-time entropy monitoring Control: • Auto/manual K selection toggle • Customizable sample size (20-500 bars) • Show/hide bands and metrics tables The Result You get mathematically validated S/R levels that focus on where prices actually cluster, not where they randomly spiked. The algorithm adapts to market conditions and removes guesswork from level selection. Best for: Traders who want objective, data-driven S/R levels without manual chart analysis. Credits: This script is for educational purposes and is inspired by the work of @ThinkLogicAI and an amazing mentor @DskyzInvestments . It demonstrates how Lorentzian geometrical concepts can be applied not only in ML classification but also quite elegantly in clustering.
Chỉ báo Pine Script®
bởi mishy_Champ
6
Mara JPY Bias ProMara JPY Bias Pro™ is a precision tool built for serious traders who focus on JPY and USD pairs. This synthetic index combines USDJPY + EURJPY + GBPJPY, generating a smooth and dynamic representation of JPY strength or weakness. When the line turns green, JPY is weakening — time to look for LONG setups on XXX/JPY pairs. When red, JPY is strengthening — ideal moment for SHORT trades. Built-in bias logic with adjustable MA-based trend detection or slope/momentum view lets you customize signals based on your strategy. Plus, we’ve included a normalized DXY overlay, so you can track USD strength in parallel — perfect for traders working with EUR/USD, GBP/USD, USD/JPY and JPY crosses. 💡 Designed for day traders, scalpers, and smart money traders looking for clean confluence. ✅ Features: Visual color-coded JPY bias (Green = Long / Red = Short) Optional USD (DXY) strength overlay Customizable MA length and bias logic Built-in alerts for bias shifts & momentum flips 🔔 Alert-ready – never miss a reversal. Trade smarter. Cut the noise. Stay on the right side of the move.
Chỉ báo Pine Script®
bởi Mara855
Ratio-Adjusted McClellan Summation Index RASI NASIRatio-Adjusted McClellan Summation Index (RASI NASI) In Book "The Complete Guide to Market Breadth Indicators" Author Gregory L. Morris states "It is the author’s opinion that the McClellan indicators, and in particular, the McClellan Summation Index, is the single best breadth indicator available.  If you had to pick just one, this would be it." What It Does: The Ratio-Adjusted McClellan Summation Index (RASI) is a market breadth indicator that tracks the cumulative strength of advancing versus declining issues for a user-selected exchange (NASDAQ, NYSE, or AMEX). Derived from the McClellan Oscillator, it calculates ratio-adjusted net advances, applies 19-day and 39-day EMAs, and sums the oscillator values to produce the RASI. This indicator helps traders assess market health, identify bullish or bearish trends, and detect potential reversals through divergences. Key features: Exchange Selection : Choose NASDAQ (USI:ADVN.NQ, USI:DECL.NQ), NYSE (USI:ADVN.NY, USI:DECL.NY), or AMEX (USI:ADVN.AM, USI:DECL.AM) data. Trend-Based Coloring : RASI line displays user-defined colors (default: black for uptrend, red for downtrend) based on its direction. Customizable Moving Average: Add a moving average (SMA, EMA, WMA, VWMA, or RMA) with user-defined length and color (default: EMA, 21, green). Neutral Line at Zero: Marks the neutral level for trend interpretation. Alerts: Six custom alert conditions for trend changes, MA crosses, and zero-line crosses. How to Use Add to Chart: Apply the indicator to any TradingView chart. Ensure access to advancing and declining issues data for the selected exchange. Select Exchange: Choose NASDAQ, NYSE, or AMEX in the input settings. Customize Settings: Adjust EMA lengths, RASI colors, MA type, length, and color to match your trading style. Interpret the Indicator : RASI Line: Black (default) indicates an uptrend (RASI rising); red indicates a downtrend (RASI falling). Above Zero: Suggests bullish market breadth (more advancing issues). Below Zero : Indicates bearish breadth (more declining issues). MA Crosses: RASI crossing above its MA signals bullish momentum; crossing below signals bearish momentum. Divergences: Compare RASI with the market index (e.g., NASDAQ Composite) to identify potential reversals. Large Moves : A +3,600-point move from a low (e.g., -1,550 to +1,950) may signal a significant bull run. Set Alerts: Add the indicator to your chart, open the TradingView alert panel, and select from six conditions (see Alerts section). Configure notifications (e.g., email, webhook, or popup) for each condition. Settings Market Selection: Exchange: Select NASDAQ, NYSE, or AMEX for advancing/declining issues data. EMA Settings: 19-day EMA Length: Period for the shorter EMA (default: 19). 39-day EMA Length: Period for the longer EMA (default: 39). RASI Settings: RASI Uptrend Color: Color for rising RASI (default: black). RASI Downtrend Color: Color for falling RASI (default: red). RASI MA Settings: MA Type: Choose SMA, EMA, WMA, VWMA, or RMA (default: EMA). MA Length: Set the MA period (default: 21). MA Color: Color for the MA line (default: green). Alerts The indicator uses alertcondition() to create custom alerts. Available conditions: RASI Trend Up: RASI starts rising (based on RASI > previous RASI, shown as black line). RASI Trend Down: RASI starts falling (based on RASI ≤ previous RASI, shown as red line). RASI Above MA: RASI crosses above its moving average. RASI Below MA: RASI crosses below its moving average. RASI Bullish: RASI crosses above zero (bullish market breadth). RASI Bearish: RASI crosses below zero (bearish market breadth). To set alerts, add the indicator to your chart, open the TradingView alert panel, and select the desired condition. Notes Data Requirements: Requires access to advancing/declining issues data (e.g., USI:ADVN.NQ, USI:DECL.NQ for NASDAQ). Some symbols may require a TradingView premium subscription. Limitations: RASI is a medium- to long-term indicator and may lag in volatile or range-bound markets. Use alongside other technical tools for confirmation. Data Reliability : Verify the selected exchange’s data accuracy, as inconsistencies can affect results. Debugging: If no data appears, check symbol validity (e.g., try $ADVN/Q, $DECN/Q for NASDAQ) or contact TradingView support. Credits Based on the Ratio-Adjusted McClellan Summation Index methodology by McClellan Financial Publications. No external code was used; the implementation is original, inspired by standard market breadth concepts. Disclaimer This indicator is for informational purposes only and does not constitute financial advice. Past performance is not indicative of future results. Conduct your own research and combine with other tools for informed trading decisions.
Chỉ báo Pine Script®
bởi starshiptrade
HF Crypto Scalping BotHigh-Frequency Crypto Scalping Bot for ETHUSDT This bot is designed for scalping ETHUSDT on a 1-minute chart using a blend of technical indicators and market structure logic. 🔍 Strategy Highlights: Range Mode: Uses RSI and MFI to identify overbought/oversold zones near support/resistance. Trend Mode: Detects MACD momentum combined with confirmed S/R breakouts. Smart Risk Management: Dynamic stop loss and take profit based on risk:reward ratio. Adaptive Market Logic: Automatically switches between trend and range conditions. Real-Time Table: Displays RSI, MFI, MACD trend, market mode, entry/exit prices, and stop/target levels. Visual Cues: Buy/Sell/Exit signals plotted directly on the chart with color-coded levels. Alerts: Integrated long/short entry and exit alerts with live price and indicator values. Customize the input parameters to fit your risk profile and asset volatility. Ideal for fast-paced scalping with dynamic conditions.
Chỉ báo Pine Script®
bởi vitorcalvi
ATR % Line from LoD/HoDATR % Line Trading Indicator - Entry Filter Tool This Pine Script creates a sophisticated ATR (Average True Range) percentage-based entry filter indicator for TradingView that helps traders avoid buying overextended stocks and identify optimal entry zones based on volatility. Core Functionality - Entry Discipline The script calculates a maximum entry threshold by taking a percentage of the Average True Range (ATR) and projecting it from the current day's low. This creates a dynamic "no-buy zone" that adapts to market volatility, helping traders avoid purchasing stocks that have already moved too far from their daily base. Key Calculation: Measures the ATR over a specified period (default: 14 bars) Takes a user-defined percentage of that ATR (default: 25%) Projects this distance from the day's low to establish a maximum entry threshold Entry Rule: Avoid buying when price exceeds this ATR% level from the daily low or high. Visual Features Entry Threshold Line: Draws a horizontal line at the calculated maximum entry level Line extends forward for clear visualization of the "no-buy zone" Red zones above this line indicate overextended conditions Fully customizable appearance with color, width, and style options Smart Entry Alerts: Optional labels show the ATR percentage threshold and exact price level Visual confirmation when stocks are trading in acceptable entry zones vs. extended areas Real-Time Monitoring Table: Displays current distance from daily low as ATR percentage Shows whether current price is in "safe entry zone" or "extended territory" Customizable display options for clean chart analysis Practical Applications for Entry Management Avoiding Extended Entries: Primary Use: Don't initiate long positions when price is more than X% ATR from the daily low Prevents buying stocks that have already made their daily move Reduces risk of buying at temporary tops within the trading session Entry Zone Identification: Price trading below the ATR% line = potential entry opportunity Price trading above the ATR% line = wait for pullback or skip the trade Combines volatility analysis with momentum discipline Risk Management Benefits: Improved Entry Timing: Enter closer to daily support levels Better Risk/Reward: Shorter distance to stop loss (daily low) Reduced Chasing: Systematic approach prevents FOMO-driven entries Volatility Awareness: Higher volatility stocks get wider acceptable entry ranges Configuration for Entry Filtering Key Settings for Entry Management: ATR Percentage: Set your maximum acceptable extension (15-30% common for day trading) Reference Point: Use "Low" to measure extension from daily base Line Style: Make highly visible to clearly see entry threshold Alert Integration: Visual confirmation of entry-friendly zones Typical Usage Scenarios: Conservative Entries: 15-20% ATR from daily low Moderate Extensions: 25-35% ATR for stronger momentum plays Aggressive Setups: 40%+ ATR for breakout situations (use with caution) Entry Strategy Integration Pre-Market Planning: Set ATR% threshold based on stock's typical volatility Identify key levels where entries become unfavorable Plan alternative entry strategies for extended stocks Intraday Execution: Monitor real-time ATR% extension from daily low Avoid new long positions when threshold is exceeded Wait for pullbacks to re-enter acceptable entry zones This tool transforms volatility analysis into practical entry discipline, helping traders maintain consistent entry standards and avoid the costly mistake of chasing overextended stocks. By respecting ATR-based extension limits, traders can improve their entry timing and overall trade profitability.
Chỉ báo Pine Script®
bởi BigWaveChartist
Momentum Oscillator ModifiedThis indicator is a custom momentum oscillator enhanced with True Range-adjusted price logic and dynamic Bollinger Bands, offering a refined way to track price strength, momentum shifts, and overbought/oversold extremes with reduced noise. Key Features: Dynamic Price Oscillator: Measures momentum using both price change and a volatility-adjusted price for greater accuracy. Smoothing factor lets you fine-tune the balance between responsiveness and noise filtering. True Range-Based Volatility Adjustment: Integrates true range calculations to adapt to current volatility, making signals more robust during different market conditions. Adaptive Bollinger Bands: Two sets of custom Bollinger Bands (standard and expanded) are drawn around the oscillator, adapting over time. These bands help identify when momentum is exceptionally strong or weak relative to recent history. Special fills dynamically highlight when the oscillator breaks above/below the bands, signaling potential trend extremes. Customization: Easily adjust lookback length and smoothing factor to fit your personal trading style (e.g., scalping or swing trading). How to Use: Watch for the oscillator crossing above the green Bollinger Bands or below the red bands for potential overbought/oversold or breakout scenarios. Expanded bands provide a "super extreme" zone which may hint at exhaustion or trend climax. The dynamic mean (black line) gives a visual reference for the normalized momentum level.
Chỉ báo Pine Script®
bởi SimplifiedTrading001
High Probability Buy/Sell with SL & TP High-accuracy Buy/Sell signals with dynamic SL & Target—perfect for scalpers and swing traders,Smart trading signals with built-in risk management. Never miss a move.Auto Buy/Sell entries with real-time SL & TP levels—trade with confidence.Turn signals into strategy. Precision entries, clear exits.Your all-in-one trading assistant: entry, stop loss, and take profit—automated.Built for serious traders: Clean signals, sharp exits, and solid risk-reward.
Chỉ báo Pine Script®
bởi sagarnale97