Coinbase to Binance premium indicator/strategy
1) Offers bar/ma chart of premium
2) Offers different trading strats based on premium(ma cross, premium value cross, smoothed premium value cross)
supersmoother code credited to someone else from tradingview
스크립트에서 "N+credit最新动态"에 대해 찾기
Momentum Acceleration by DGTItalian physicist Galileo Galilei is usually credited with being the first to measure speed by considering the distance covered and the time it takes. Galileo defined speed as the distance covered during a period of time. In equation form, that is v = Δd / Δt where v is speed, Δd is change in distance, and Δt is change in time. The Greek symbol for delta, a triangle (Δ), means change.
Is the speed getting faster or slower?
Acceleration will be the answer, acceleration is defined as the rate of change of speed over a set period of time, meaning something is getting faster or slower. Mathematically expressed, acceleration denoted as a is a = Δv / Δt , where Δv is the change in speed and Δt is the change in time.
How to apply in trading
Lets think about Momentum, Rate of Return, Rate of Change all are calculated in almost same approach with Speed
Momentum measures change in price over a specified time period,
Rate of Change measures percent change in price over a specified time period,
Rate of Return measures the net gain or loss over a specified time period,
And Speed measures change in distance over a specified time period
So we may state that measuring the change in distance is also measuring the change in price over a specified time period which is length, hence
speed can be calculated as (source – source )/length and acceleration becomes (speed – speed )/length
In this study acceleration is used as signal line and result plotted as arrows demonstrating bull or bear direction where direction changes can be considered as trading setups
Just a little fun, since we deal with speed the short name of the study is named after famous cartoon character Speedy Gonzales
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
Disclaimer: The script is for informational and educational purposes only. Use of the script does not constitutes professional and/or financial advice. You alone the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
JSun - Guppy Multiple Moving AverAgeThe Guppy Multiple Moving Average (GMMA) is a technical indicator that identifies changing trends, breakouts, and trading opportunities in the price of an asset by combining two groups of moving averages (MA) with different time periods. There is a short-term group of MAs, and a long-term group of MA. Both contain six MAs, for a total of 12. The term gets its name from Daryl Guppy, an Australian trader who is credited with its development.
Key Takeaways:
1. The Gruppy Multiple Moving Average (GMMA) is applied as an overlay on the price chart of an asset.
2. The short-term MAs are typically set at 3, 5, 8, 10, 12, and 15 periods. The longer-term MAs are typically set at 30, 35, 40, 45, 50, and 60.
3. When the short-term group of averages moves above the longer-term group, it indicates a price uptrend in the asset could be emerging.
4. When the short-term group falls below the longer-term group of MAs, a price downtrend in the asset could be starting.
5. When there is lots of separation between the MAs, this helps confirm the price trend in the current direction.
6. If both groups become compressed with each other, or crisscross, it indicates the price has paused and a price trend reversal is possible.
7. Traders often trade in the direction the longer-term MA group is moving, and use the short-term group for trade signals to enter or exit.
strategy of RSI with trendlines and S/RBefore I go through this chart I want to acknowledge the great programmers who spent much of their time and effort to assist many TV users and traders.
Thank you to LunaOwl for the RSI American lines her script made me realize the need to have trendlines, supports, and resistance on RSI charts.
Also, a copy of Lij_MC code from was taken which had been credited to Duyck. Thank you Duyck.
The BB was copied from morpheus747
As I researched different strategies one strategy seemed to assist the trader for entry and exits. It was the combination of Support and resistance on the RSI. In addition, diagonal lines (Recently introduced in pine script V4) assists in the direction and reversals that may occur. What is supplied is only a graphical representation and no trade entry or exit points are selected.
On the chart you can use;
• RSI line or bar;
• Bollinger High / Low support line;
• Diagonal trend lines. A primary and a secondary group of trendlines; and
• Trendline candle highlighter.
I am hoping people with great skills could assist to develop this to the next level.
I hope this graphical strategy may help until further development. Enjoy.
Derivative Oscillator Cu [ID: AC-P]The "AC-P" version of the Derivative Oscillator is my personal customized version of Constance Brown's Derivative Oscillator (using Everget's implementation of it as the base), with the the following modifications and additions:
VWAP Indication - option to show whether the price input option is above or below the Daily VWAP (red triangles = price input is below vwap, green triangles = price input is above vwap)
Bullish and Bearish phases from shayankm's Waddah Attar Explosion V2 () is included as indication dots (bullish = blue dots, bearish = yellow dots) below/above the Derivative Oscillator histogram
Coral Trend from Lazybear () is included as indication dots (red/green dots below/above the Derivative Oscillator histogram
Input source options for vwap, Waddah components (MACD, Bollinger Upper/Lower)
Centerline option for Coral trend, and Horizontal center option for the Derivative Oscillator with circle indication (optional - provided as option for flexibility in use with overlaying with other indicators)
This indicator is a hybrid, with a combination of leading indicators and lagging trending indicators combined into one. Specifically, a few of the other indicators I use are lacking in the momentum and trend department, and this is one of the indicators I use to address that:
VWAP provides trend information on lower timeframes from a high timeframe interval (D)
Coral Trend provides additional confirmation to VWAP trend wise, and is adjustable
Waddah Attar Explosion provides a third level of confirmation for trending moves, taking into account shorter and longer timeframes (FastEMA and SlowEMA parameters).
Script base for the Derivative Oscillator is credited to Everget () and LazyBear ().
Source attribution to Constance Brown for the Derivative Oscillator formula/indicator:
// Brown, Constance.
// Reference 1: “The Derivative Oscillator: a New Approach to an Old Problem,” Journal of Technical Analysis (Winter-Spring 1994) 45–61.
// Reference 2: Technical Analysis for the Trading Professional. New York, NY: McGraw-Hill, 1999.
Information on the Derivative Oscillator:
www.investopedia.com
Bitcoin Stock to Flow Multiplethis study plots the price of btc over the Stock to Flow Model value
idea credited to: 100trillionUSD
my data is a bit off compared to the original source but overall it seems correct
Stock to Flowthis study gives the option to plot the stock to flow
OR the number of blocks per month. (you must edit the code by deleting the //)
it should be used only on the monthly timeframe
idea credited to:
medium.com
Multi Timeframe 3 ADX Lines with LabelsGuy this is not a new indicator this is the indicator which I have developed from some previous versions of indicator so no credit to me all credit to people who have developed multi time frame this ads I have used three lines three time frames so you can analyze the long term trend of EDX then midterm Trend and lower time from time not get confused that what time it is please use it and enjoy
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.
ArraysAssorted🟩 OVERVIEW
This library provides utility methods for working with arrays in Pine Script. The first method finds extreme values (highest/lowest) within a rolling lookback window and returns both the value and its position. I might extend the library for other ad-hoc methods I use to work with arrays.
🟩 HOW TO USE
Pine Script libraries contain reusable code for importing into indicators. You do not need to copy any code out of here. Just import the library and call the method you want.
For example, for version 1 of this library, import it like this:
import SimpleCryptoLife/ArraysAssorted/1
See the EXAMPLE USAGE sections within the library for examples of calling the methods.
You do not need permission to use Pine libraries in your open-source scripts.
However, you do need explicit permission to reuse code from a Pine Script library’s functions in a public protected or invite-only publication .
In any case, credit the author in your description. It is also good form to credit in open-source comments.
For more information on libraries and incorporating them into your scripts, see the Libraries section of the Pine Script User Manual.
🟩 METHOD 1: m_getHighestLowestFloat()
Finds the highest or lowest float value from an array. Simple enough. It also returns the index of the value as an offset from the end of the array.
• It works with rolling lookback windows, so you can find extremes within the last N elements
• It includes an offset parameter to skip recent elements if needed
• It handles edge cases like empty arrays and invalid ranges gracefully
• It can find either the first or last occurrence of the extreme value
We also export two enums whose sole purpose is to look pretty as method arguments.
method m_getHighestLowestFloat(_self, _highestLowest, _lookbackBars, _offset, _firstLastType)
Namespace types: array
This method finds the highest or lowest value in a float array within a rolling lookback window, and returns the value along with the offset (number of elements back from the end of the array) of its first or last occurrence.
Parameters:
_self (array) : The array of float values to search for extremes.
_highestLowest (HighestLowest) : Whether to search for the highest or lowest value. Use the enum value HighestLowest.highest or HighestLowest.lowest.
_lookbackBars (int) : The number of array elements to include in the rolling lookback window. Must be positive. Note: Array elements only correspond to bars if the consuming script always adds exactly one element on consecutive bars.
_offset (int) : The number of array elements back from the end of the array to start the lookback window. A value of zero means no offset. The _offset parameter offsets both the beginning and end of the range.
_firstLastType (FirstLast) : Whether to return the offset of the first (lowest index) or last (highest index) occurrence of the extreme value. Use FirstLast.first or FirstLast.last.
Returns: (tuple) A tuple containing the highest or lowest value and its offset -- the number of elements back from the end of the array. If not found, returns . NOTE: The _offsetFromEndOfArray value is not affected by the _offset parameter. In other words, it is not the offset from the end of the range but from the end of the array. This number may or may not have any relation to the number of *bars* back, depending on how the array is populated. The calling code needs to figure that out.
EXPORTED ENUMS
HighestLowest
Whether to return the highest value or lowest value in the range.
• highest : Find the highest value in the specified range
• lowest : Find the lowest value in the specified range
FirstLast
Whether to return the first (lowest index) or last (highest index) occurrence of the extreme value.
• first : Return the offset of the first occurrence of the extreme value
• last : Return the offset of the last occurrence of the extreme value
Bloomberg Financial Conditions Index (Proxy)The Bloomberg Financial Conditions Index (BFCI): A Proxy Implementation
Financial conditions indices (FCIs) have become essential tools for economists, policymakers, and market participants seeking to quantify and monitor the overall state of financial markets. Among these measures, the Bloomberg Financial Conditions Index (BFCI) has emerged as a particularly influential metric. Originally developed by Bloomberg L.P., the BFCI provides a comprehensive assessment of stress or ease in financial markets by aggregating various market-based indicators into a single, standardized value (Hatzius et al., 2010).
The original Bloomberg Financial Conditions Index synthesizes approximately 50 different financial market variables, including money market indicators, bond market spreads, equity market valuations, and volatility measures. These variables are normalized using a Z-score methodology, weighted according to their relative importance to overall financial conditions, and then aggregated to produce a composite index (Carlson et al., 2014). The resulting measure is centered around zero, with positive values indicating accommodative financial conditions and negative values representing tighter conditions relative to historical norms.
As Angelopoulou et al. (2014) note, financial conditions indices like the BFCI serve as forward-looking indicators that can signal potential economic developments before they manifest in traditional macroeconomic data. Research by Adrian et al. (2019) demonstrates that deteriorating financial conditions, as measured by indices such as the BFCI, often precede economic downturns by several months, making these indices valuable tools for predicting changes in economic activity.
Proxy Implementation Approach
The implementation presented in this Pine Script indicator represents a proxy of the original Bloomberg Financial Conditions Index, attempting to capture its essential features while acknowledging several significant constraints. Most critically, while the original BFCI incorporates approximately 50 financial variables, this proxy version utilizes only six key market components due to data accessibility limitations within the TradingView platform.
These components include:
Equity market performance (using SPY as a proxy for S&P 500)
Bond market yields (using TLT as a proxy for 20+ year Treasury yields)
Credit spreads (using the ratio between LQD and HYG as a proxy for investment-grade to high-yield spreads)
Market volatility (using VIX directly)
Short-term liquidity conditions (using SHY relative to equity prices as a proxy)
Each component is transformed into a Z-score based on log returns, weighted according to approximated importance (with weights derived from literature on financial conditions indices by Brave and Butters, 2011), and aggregated into a composite measure.
Differences from the Original BFCI
The methodology employed in this proxy differs from the original BFCI in several important ways. First, the variable selection is necessarily limited compared to Bloomberg's comprehensive approach. Second, the proxy relies on ETFs and publicly available indices rather than direct market rates and spreads used in the original. Third, the weighting scheme, while informed by academic literature, is simplified compared to Bloomberg's proprietary methodology, which may employ more sophisticated statistical techniques such as principal component analysis (Kliesen et al., 2012).
These differences mean that while the proxy BFCI captures the general direction and magnitude of financial conditions, it may not perfectly replicate the precision or sensitivity of the original index. As Aramonte et al. (2013) suggest, simplified proxies of financial conditions indices typically capture broad movements in financial conditions but may miss nuanced shifts in specific market segments that more comprehensive indices detect.
Practical Applications and Limitations
Despite these limitations, research by Arregui et al. (2018) indicates that even simplified financial conditions indices constructed from a limited set of variables can provide valuable signals about market stress and future economic activity. The proxy BFCI implemented here still offers significant insight into the relative ease or tightness of financial conditions, particularly during periods of market stress when correlations among financial variables tend to increase (Rey, 2015).
In practical applications, users should interpret this proxy BFCI as a directional indicator rather than an exact replication of Bloomberg's proprietary index. When the index moves substantially into negative territory, it suggests deteriorating financial conditions that may precede economic weakness. Conversely, strongly positive readings indicate unusually accommodative financial conditions that might support economic expansion but potentially also signal excessive risk-taking behavior in markets (López-Salido et al., 2017).
The visual implementation employs a color gradient system that enhances interpretation, with blue representing neutral conditions, green indicating accommodative conditions, and red signaling tightening conditions—a design choice informed by research on optimal data visualization in financial contexts (Few, 2009).
References
Adrian, T., Boyarchenko, N. and Giannone, D. (2019) 'Vulnerable Growth', American Economic Review, 109(4), pp. 1263-1289.
Angelopoulou, E., Balfoussia, H. and Gibson, H. (2014) 'Building a financial conditions index for the euro area and selected euro area countries: what does it tell us about the crisis?', Economic Modelling, 38, pp. 392-403.
Aramonte, S., Rosen, S. and Schindler, J. (2013) 'Assessing and Combining Financial Conditions Indexes', Finance and Economics Discussion Series, Federal Reserve Board, Washington, D.C.
Arregui, N., Elekdag, S., Gelos, G., Lafarguette, R. and Seneviratne, D. (2018) 'Can Countries Manage Their Financial Conditions Amid Globalization?', IMF Working Paper No. 18/15.
Brave, S. and Butters, R. (2011) 'Monitoring financial stability: A financial conditions index approach', Economic Perspectives, Federal Reserve Bank of Chicago, 35(1), pp. 22-43.
Carlson, M., Lewis, K. and Nelson, W. (2014) 'Using policy intervention to identify financial stress', International Journal of Finance & Economics, 19(1), pp. 59-72.
Few, S. (2009) Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press, Oakland, CA.
Hatzius, J., Hooper, P., Mishkin, F., Schoenholtz, K. and Watson, M. (2010) 'Financial Conditions Indexes: A Fresh Look after the Financial Crisis', NBER Working Paper No. 16150.
Kliesen, K., Owyang, M. and Vermann, E. (2012) 'Disentangling Diverse Measures: A Survey of Financial Stress Indexes', Federal Reserve Bank of St. Louis Review, 94(5), pp. 369-397.
López-Salido, D., Stein, J. and Zakrajšek, E. (2017) 'Credit-Market Sentiment and the Business Cycle', The Quarterly Journal of Economics, 132(3), pp. 1373-1426.
Rey, H. (2015) 'Dilemma not Trilemma: The Global Financial Cycle and Monetary Policy Independence', NBER Working Paper No. 21162.
Parabolic RSI Strategy [ChartPrime × PineIndicators]This strategy combines the strengths of the Relative Strength Index (RSI) with a Parabolic SAR logic applied directly to RSI values.
Full credit to ChartPrime for the original concept and indicator, licensed under the MPL 2.0.
It provides clear momentum-based trade signals using an innovative method that tracks RSI trend reversals via a customized Parabolic SAR, enhancing traditional oscillator strategies with dynamic trend confirmation.
How It Works
The system overlays a Parabolic SAR on the RSI, detecting trend shifts in RSI itself rather than on price, offering early reversal insight with visual and algorithmic clarity.
Core Components
1. RSI-Based Trend Detection
Calculates RSI using a customizable length (default: 14).
Uses upper and lower thresholds (default: 70/30) for overbought/oversold zones.
2. Parabolic SAR Applied to RSI
A custom Parabolic SAR function tracks momentum within the RSI, not price.
This allows the system to capture RSI trend reversals more responsively.
Configurable SAR parameters: Start, Increment, and Maximum acceleration.
3. Signal Generation
Long Entry: Triggered when the SAR flips below the RSI line.
Short Entry: Triggered when the SAR flips above the RSI line.
Optional RSI filter ensures that:
Long entries only occur above a minimum RSI (e.g. 50).
Short entries only occur below a maximum RSI.
Built-in logic prevents new positions from being opened against trend without prior exit.
Trade Modes & Controls
Choose from:
Long Only
Short Only
Long & Short
Optional setting to reverse positions on opposite signal (instead of waiting for a flat close).
Visual Features
1. RSI Plotting with Thresholds
RSI is displayed in a dedicated pane with overbought/oversold fill zones.
Custom horizontal lines mark threshold boundaries.
2. Parabolic SAR Overlay on RSI
SAR dots color-coded for trend direction.
Visible only when enabled by user input.
3. Entry & Exit Markers
Diamonds: Mark entry points (above for shorts, below for longs).
Crosses: Mark exit points.
Strategy Strengths
Provides early momentum reversal entries without relying on price candles.
Combines oscillator and trend logic without repainting.
Works well in both trending and mean-reverting markets.
Easy to configure with fine-tuned filter options.
Recommended Use Cases
Intraday or swing traders who want to catch RSI-based reversals early.
Traders seeking smoother signals than price-based Parabolic SAR entries.
Users of RSI looking to reduce false positives via trend tracking.
Customization Options
RSI Length and Thresholds.
SAR Start, Increment, and Maximum values.
Trade Direction Mode (Long, Short, Both).
Optional RSI filter and reverse-on-signal settings.
SAR dot color customization.
Conclusion
The Parabolic RSI Strategy is an innovative, non-repainting momentum strategy that enhances RSI-based systems with trend-confirming logic using Parabolic SAR. By applying SAR logic to RSI values, this strategy offers early, visualized, and filtered entries and exits that adapt to market dynamics.
Credit to ChartPrime for the original methodology, published under MPL-2.0.
Dual-Phase Trend Regime Strategy [Zeiierman X PineIndicators]This strategy is based on the Dual-Phase Trend Regime Indicator by Zeiierman.
Full credit for the original concept and logic goes to Zeiierman.
This non-repainting strategy dynamically switches between fast and slow oscillators based on market volatility, providing adaptive entries and exits with high clarity and reliability.
Core Concepts
1. Adaptive Dual Oscillator Logic
The system uses two oscillators:
Fast Oscillator: Activated in high-volatility phases for quick reaction.
Slow Oscillator: Used during low-volatility phases to reduce noise.
The system automatically selects the appropriate oscillator depending on the market's volatility regime.
2. Volatility Regime Detection
Volatility is calculated using the standard deviation of returns. A median-split algorithm clusters volatility into:
Low Volatility Cluster
High Volatility Cluster
The current volatility is then compared to these clusters to determine whether the regime is low or high volatility.
3. Trend Regime Identification
Based on the active oscillator:
Bullish Trend: Oscillator > 0.5
Bearish Trend: Oscillator < 0.5
Neutral Trend: Oscillator = 0.5
The strategy reacts to changes in this trend regime.
4. Signal Source Options
You can choose between:
Regime Shift (Arrows): Trade based on oscillator value changes (from bullish to bearish and vice versa).
Oscillator Cross: Trade based on crossovers between the fast and slow oscillators.
Trade Logic
Trade Direction Options
Long Only
Short Only
Long & Short
Entry Conditions
Long Entry: Triggered on bullish regime shift or fast crossing above slow.
Short Entry: Triggered on bearish regime shift or fast crossing below slow.
Exit Conditions
Long Exit: Triggered on bearish shift or fast crossing below slow.
Short Exit: Triggered on bullish shift or fast crossing above slow.
The strategy closes opposing positions before opening new ones.
Visual Features
Oscillator Bands: Plots fast and slow oscillators, colored by trend.
Background Highlight: Indicates current trend regime.
Signal Markers: Triangle shapes show bullish/bearish shifts.
Dashboard Table: Displays live trend status ("Bullish", "Bearish", "Neutral") in the chart’s corner.
Inputs & Customization
Oscillator Periods – Fast and slow lengths.
Refit Interval – How often volatility clusters update.
Volatility Lookback & Smoothing
Color Settings – Choose your own bullish/bearish colors.
Signal Mode – Regime shift or oscillator crossover.
Trade Direction Mode
Use Cases
Swing Trading: Take entries based on adaptive regime shifts.
Trend Following: Follow the active trend using filtered oscillator logic.
Volatility-Responsive Systems: Adjust your trade behavior depending on market volatility.
Clean Exit Management: Automatically closes positions on opposite signal.
Conclusion
The Dual-Phase Trend Regime Strategy is a smart, adaptive, non-repainting system that:
Automatically switches between fast and slow trend logic.
Responds dynamically to changes in volatility.
Provides clean and visual entry/exit signals.
Supports both momentum and reversal trading logic.
This strategy is ideal for traders seeking a volatility-aware, trend-sensitive tool across any market or timeframe.
Full credit to Zeiierman.
OverUnder Yield Spread🗺️ OverUnder is a structural regime visualizer , engineered to diagnose the shape, tone, and trajectory of the yield curve. Rather than signaling trades directly, it informs traders of the world they’re operating in. Yield curve steepening or flattening, normalizing or inverting — each regime reflects a macro pressure zone that impacts duration demand, liquidity conditions, and systemic risk appetite. OverUnder abstracts that complexity into a color-coded compression map, helping traders orient themselves before making risk decisions. Whether you’re in bonds, currencies, crypto, or equities, the regime matters — and OverUnder makes it visible.
🧠 Core Logic
Built to show the slope and intent of a selected rate pair, the OverUnder Yield Spread defaults to 🇺🇸US10Y-US2Y, but can just as easily compare global sovereign curves or even dislocated monetary systems. This value is continuously monitored and passed through a debounce filter to determine whether the curve is:
• Inverted, or
• Steepening
If the curve is flattening below zero: the world is bracing for contraction. Policy lags. Risk appetite deteriorates. Duration gets bid, but only as protection. Stocks and speculative assets suffer, regardless of positioning.
📍 Curve Regimes in Bull and Bear Contexts
• Flattening occurs when the short and long ends compress . In a bull regime, flattening may reflect long-end demand or fading growth expectations. In a bear regime, flattening often precedes or confirms central bank tightening.
• Steepening indicates expanding spread . In a bull context, this may signal healthy risk appetite or early expansion. In a bear or crisis context, it may reflect aggressive front-end cuts and dislocation between short- and long-term expectations.
• If the curve is steepening above zero: the world is rotating into early expansion. Risk assets behave constructively. Bond traders position for normalization. Equities and crypto begin trending higher on rising forward expectations.
🖐️ Dynamically Colored Spread Line Reflects 1 of 4 Regime States
• 🟢 Normal / Steepening — early expansion or reflation
• 🔵 Normal / Flattening — late-cycle or neutral slowdown
• 🟠 Inverted / Steepening — policy reversal or soft landing attempt
• 🔴 Inverted / Flattening — hard contraction, credit stress, policy lag
🍋 The Lemon Label
At every bar, an anchored label floats directly on the spread line. It displays the active regime (in plain English) and the precise spread in percent (or basis points, depending on resolution). Colored lemon yellow, neither green nor red, the label is always legible — a design choice to de-emphasize bias and center the data .
🎨 Fill Zones
These bands offer spatial, persistent views of macro compression or inversion depth.
• Blue fill appears above the zero line in normal (non-inverted) conditions
• Red fill appears below the zero line during inversion
🧪 Sample Reading: 1W chart of TLT
OverUnder reveals a multi-year arc of structural inversion and regime transition. From mid-2021 through late 2023, the spread remains decisively inverted, signaling persistent flattening and credit stress as bond prices trended sharply lower. This prolonged inversion aligns with a high-volatility phase in TLT, marked by lower highs and an accelerating downtrend, confirming policy lag and macro tightening conditions.
As of early 2025, the spread has crossed back above the zero baseline into a “Normal / Steepening” regime (annotated at +0.56%), suggesting a macro inflection point. Price action remains subdued, but the shift in yield structure may foreshadow a change in trend context — particularly if follow-through in steepening persists.
🎭 Different Traders Respond Differently:
• Bond traders monitor slope change to anticipate policy pivots or recession signals.
• Equity traders use regime shifts to time rotations, from growth into defense, or from contraction into reflation.
• Currency traders interpret curve steepening as yield compression or divergence depending on region.
• Crypto traders treat inversion as a liquidity vacuum — and steepening as an early-phase risk unlock.
🛡️ Can It Compare Different Bond Markets?
Yes — with caveats. The indicator can be used to compare distinct sovereign yield instruments, for example:
• 🇫🇷FR10Y vs 🇩🇪DE10Y - France vs Germany
• 🇯🇵JP10Y vs 🇺🇸US10Y - BoJ vs Fed policy curves
However:
🙈 This no longer visualizes the domestic yield curve, but rather the differential between rate expectations across regions
🙉 The interpretation of “inversion” changes — it reflects spread compression across nations , not within a domestic yield structure
🙊 Color regimes should then be viewed as relative rate positioning , not absolute curve health
🙋🏻 Example: OverUnder compares French vs German 10Y yields
1. 🇫🇷 Change the long-duration ticker to FR10Y
2. 🇩🇪 Set the short-duration ticker to DE10Y
3. 🤔 Interpret the result as: “How much higher is France’s long-term borrowing cost vs Germany’s?”
You’ll see steepening when the spread rises (France decoupling), flattening when the spread compresses (convergence), and inversions when Germany yields rise above France’s — historically rare and meaningful.
🧐 Suggested Use
OverUnder is not a signal engine — it’s a context map. Its value comes from situating any trade idea within the prevailing yield regime. Use it before entries, not after them.
• On the 1W timeframe, OverUnder excels as a macro overlay. Yield regime shifts unfold over quarters, not days. Weekly structure smooths out rate volatility and reveals the true curvature of policy response and liquidity pressure. Use this view to orient your portfolio, define directional bias, or confirm long-duration trend turns in assets like TLT, SPX, or BTC.
• On the 1D timeframe, the indicator becomes tactically useful — especially when aligning breakout setups or trend continuations with steepening or flattening transitions. Daily views can also identify early-stage regime cracks that may not yet be visible on the weekly.
• Avoid sub-daily use unless you’re anchoring a thesis already built on higher timeframe structure. The yield curve is a macro construct — it doesn’t oscillate cleanly at intraday speeds. Shorter views may offer clarity during event-driven spikes (like FOMC reactions), but they do not replace weekly context.
Ultimately, OverUnder helps you decide: What kind of world am I trading in? Use it to confirm macro context, avoid fighting the curve, and lean into trades aligned with the broader pressure regime.
Uptrick X PineIndicators: Z-Score Flow StrategyThis strategy is based on the Z-Score Flow Indicator developed by Uptrick. Full credit for the original concept and logic goes to Uptrick.
The Z-Score Flow Strategy combines statistical mean-reversion logic with trend filtering, RSI confirmation, and multi-mode trade execution, offering a flexible and structured approach to trading both reversals and trend continuations.
Core Concepts Behind Z-Score Flow
1. Z-Score Mean Reversion Logic
The Z-score measures how far current price deviates from its statistical mean, in standard deviations.
A high positive Z-score (e.g. > 2) suggests price is overbought and may revert downward.
A low negative Z-score (e.g. < -2) suggests price is oversold and may revert upward.
The strategy uses Z-score thresholds to trigger signals when price deviates far enough from its mean.
2. Trend Filtering with EMA
To prevent counter-trend entries, the strategy includes a trend filter based on a 50-period EMA:
Only allows long entries if price is below EMA (mean-reversion in downtrends).
Only allows short entries if price is above EMA (mean-reversion in uptrends).
3. RSI Confirmation and Lockout System
An RSI smoothing mechanism helps confirm signals and avoid whipsaws:
RSI must be below 30 and rising to allow buys.
RSI must be above 70 and falling to allow sells.
Once a signal occurs, it is "locked out" until RSI re-enters the neutral zone (30–70).
This avoids multiple signals in overextended zones and reduces overtrading.
Entry Signal Logic
A buy or sell is triggered when:
Z-score crosses below (buy) or above (sell) the threshold.
RSI smoothed condition is met (oversold and rising / overbought and falling).
The trend condition (EMA filter) aligns.
A cooldown period has passed since the last opposite trade.
This layered approach helps ensure signal quality and timing precision.
Trade Modes
The strategy includes three distinct trade modes to adapt to various market behaviors:
1. Standard Mode
Trades are opened using the Z-score + RSI + trend filter logic.
Each signal must pass all layered conditions.
2. Zero Cross Mode
Trades are based on the Z-score crossing zero.
This mode is useful in trend continuation setups, rather than mean reversion.
3. Trend Reversal Mode
Trades occur when the mean slope direction changes, i.e., basis line changes color.
Helps capture early trend shifts with less lag.
Each mode can be customized for long-only, short-only, or long & short execution.
Visual Components
1. Z-Score Mean Line
The basis (mean) line is colored based on slope direction.
Green = bullish slope, Purple = bearish slope, Gray = flat.
A wide shadow band underneath reflects current trend momentum.
2. Gradient Fill to Price
A gradient zone between price and the mean reflects:
Price above mean = bearish zone with purple overlay.
Price below mean = bullish zone with teal overlay.
This visual aid quickly reveals market positioning relative to equilibrium.
3. Signal Markers
"𝓤𝓹" labels appear for buy signals.
"𝓓𝓸𝔀𝓷" labels appear for sell signals.
These are colored and positioned according to trend context.
Customization Options
Z-Score Period & Thresholds: Define sensitivity to price deviations.
EMA Trend Filter Length: Filter entries with long-term bias.
RSI & Smoothing Periods: Fine-tune RSI confirmation conditions.
Cooldown Period: Prevent signal spam and enforce timing gaps.
Slope Index: Adjust how far back to compare mean slope.
Visual Settings: Toggle mean lines, gradients, and more.
Use Cases & Strategy Strengths
1. Mean-Reversion Trading
Ideal for catching pullbacks in trending markets or fading overextended price moves.
2. Trend Continuation or Reversal
With multiple trade modes, traders can choose between fading price extremes or trading slope momentum.
3. Signal Clarity and Risk Control
The combination of Z-score, RSI, EMA trend, and cooldown logic provides high-confidence signals with built-in filters.
Conclusion
The Z-Score Flow Strategy by Uptrick X PineIndicators is a versatile and structured trading system that:
Fuses statistical deviation (Z-score) with technical filters.
Provides both mean-reversion and trend-based entry logic.
Uses visual overlays and signal labels for clarity.
Prevents noise-driven trades via cooldown and lockout systems.
This strategy is well-suited for traders seeking a data-driven, multi-condition entry framework that can adapt to various market types.
Full credit for the original concept and indicator goes to Uptrick.
Triangular Hull Moving Average [BigBeluga X PineIndicators]This strategy is based on the original Triangular Hull Moving Average (THMA) + Volatility indicator by BigBeluga. Full credit for the concept and design goes to BigBeluga.
The strategy blends smoothed trend-following logic using a Triangular Hull Moving Average with dynamic volatility overlays, providing actionable trade signals with responsive visual feedback. It's designed for traders who want a non-lagging trend filter while also monitoring market volatility in real time.
How the Strategy Works
1. Triangular Hull Moving Average (THMA) Core
At its core, the strategy uses a Triangular Hull Moving Average (THMA) — a variation of the traditional Hull Moving Average with triple-smoothing logic:
It combines multiple weighted moving averages (WMAs) to create a faster and smoother trend line.
This reduces lag without compromising trend accuracy.
The THMA reacts more responsively to price movements than classic MAs.
THMA Formula:
thma(_src, _length) =>
ta.wma(ta.wma(_src,_length / 3) * 3 - ta.wma(_src, _length / 2) - ta.wma(_src, _length), _length)
This logic filters out short-term noise while still being sensitive to genuine trend shifts.
2. Volatility-Enhanced Candle Plotting
An optional volatility mode overlays the chart with custom candles that incorporate volatility bands:
Wicks expand and contract dynamically based on market volatility.
The volatility value is computed using a HMA of high-low range over a user-defined length.
The candle bodies reflect THMA values, while the wicks reflect the current volatility spread.
This feature allows traders to visually gauge the strength of price moves and anticipate possible breakouts or slowdowns.
3. Trend Reversal Signal Detection
The strategy identifies trend reversals when the THMA line crosses over/under its own past value:
A bullish signal is triggered when THMA crosses above its value from two bars ago.
A bearish signal is triggered when THMA crosses below its value from two bars ago.
These shifts are marked on the chart with triangle-shaped signals for clear visibility.
This logic helps detect momentum shifts early and enables reactive trade entries.
Trade Entry & Exit Logic
Trade Modes Supported
Users can choose between:
Only Long – Enters long trades only.
Only Short – Enters short trades only.
Long & Short – Enables both directions.
Entry Conditions
Long Entry:
Triggered when a bullish crossover is detected.
Active only if the strategy mode allows long trades.
Short Entry:
Triggered when a bearish crossover is detected.
Active only if the strategy mode allows short trades.
Exit Conditions
In Only Long mode, the strategy closes long positions when a bearish signal appears.
In Only Short mode, the strategy closes short positions when a bullish signal appears.
In Long & Short mode, the strategy does not auto-close positions — instead, it opens new positions on each confirmed signal.
Dashboard Visualization
In the bottom-right corner of the chart, a live dashboard displays:
The current trend direction (🢁 for bullish, 🢃 for bearish).
The current volatility level as a percentage.
This helps traders quickly assess market status and adjust their decisions accordingly.
Customization Options
THMA Length: Adjust how smooth or reactive the trend detection should be.
Volatility Toggle & Length: Enable or disable volatility visualization and set sensitivity.
Color Settings: Choose colors for up/down trend visualization.
Trade Direction Mode: Limit the strategy to long, short, or both types of trades.
Use Cases & Strategy Strengths
1. Trend Following
Use the THMA-based candles and triangle signals to enter with momentum. The indicator adapts quickly, reducing lag and improving trade timing.
2. Volatility Monitoring
Visualize the strength of the trend with volatility wicks. Use expanding bands to confirm breakouts and contracting ones to detect weakening moves.
3. Signal Confirmation
Combine this tool with other indicators or use the trend shift triangles as confirmations for manual entries.
Conclusion
The THMA + Volatility Strategy is a non-repainting trend-following system that integrates:
Triangular Hull MA for advanced trend detection.
Real-time volatility visualization.
Clear entry signals based on trend reversals.
Configurable trade direction settings.
It is ideal for traders who:
Prefer smoothed price analysis.
Want to follow trends with precision.
Value visual volatility feedback for breakout detection.
Full credit for the original concept and indicator goes to BigBeluga.
Rally Base Drop SND Pivots Strategy [LuxAlgo X PineIndicators]This strategy is based on the Rally Base Drop (RBD) SND Pivots indicator developed by LuxAlgo. Full credit for the concept and original indicator goes to LuxAlgo.
The Rally Base Drop SND Pivots Strategy is a non-repainting supply and demand trading system that detects pivot points based on Rally, Base, and Drop (RBD) candles. This strategy automatically identifies key market structure levels, allowing traders to:
Identify pivot-based supply and demand (SND) zones.
Use fixed criteria for trend continuation or reversals.
Filter out market noise by requiring structured price formations.
Enter trades based on breakouts of key SND pivot levels.
How the Rally Base Drop SND Pivots Strategy Works
1. Pivot Point Detection Using RBD Candles
The strategy follows a rigid market structure methodology, where pivots are detected only when:
A Rally (R) consists of multiple consecutive bullish candles.
A Drop (D) consists of multiple consecutive bearish candles.
A Base (B) is identified as a transition between Rallies and Drops, acting as a pivot point.
The pivot level is confirmed when the formation is complete.
Unlike traditional fractal-based pivots, RBD Pivots enforce stricter structural rules, ensuring that each pivot:
Has a well-defined bullish or bearish price movement.
Reduces false signals caused by single-bar fluctuations.
Provides clear supply and demand levels based on structured price movements.
These pivot levels are drawn on the chart using color-coded boxes:
Green zones represent bullish pivot levels (Rally Base formations).
Red zones represent bearish pivot levels (Drop Base formations).
Once a pivot is confirmed, the high or low of the base candle is used as the reference level for future trades.
2. Trade Entry Conditions
The strategy allows traders to select from three trading modes:
Long Only – Only takes long trades when bullish pivot breakouts occur.
Short Only – Only takes short trades when bearish pivot breakouts occur.
Long & Short – Trades in both directions based on pivot breakouts.
Trade entry signals are triggered when price breaks through a confirmed pivot level:
Long Entry:
A bullish pivot level is formed.
Price breaks above the bullish pivot level.
The strategy enters a long position.
Short Entry:
A bearish pivot level is formed.
Price breaks below the bearish pivot level.
The strategy enters a short position.
The strategy includes an optional mode to reverse long and short conditions, allowing traders to experiment with contrarian entries.
3. Exit Conditions Using ATR-Based Risk Management
This strategy uses the Average True Range (ATR) to calculate dynamic stop-loss and take-profit levels:
Stop-Loss (SL): Placed 1 ATR below entry for long trades and 1 ATR above entry for short trades.
Take-Profit (TP): Set using a Risk-Reward Ratio (RR) multiplier (default = 6x ATR).
When a trade is opened:
The entry price is recorded.
ATR is calculated at the time of entry to determine stop-loss and take-profit levels.
Trades exit automatically when either SL or TP is reached.
If reverse conditions mode is enabled, stop-loss and take-profit placements are flipped.
Visualization & Dynamic Support/Resistance Levels
1. Pivot Boxes for Market Structure
Each pivot is marked with a colored box:
Green boxes indicate bullish demand zones.
Red boxes indicate bearish supply zones.
These boxes remain on the chart to act as dynamic support and resistance levels, helping traders identify key price reaction zones.
2. Horizontal Entry, Stop-Loss, and Take-Profit Lines
When a trade is active, the strategy plots:
White line → Entry price.
Red line → Stop-loss level.
Green line → Take-profit level.
Labels display the exact entry, SL, and TP values, updating dynamically as price moves.
Customization Options
This strategy offers multiple adjustable settings to optimize performance for different market conditions:
Trade Mode Selection → Choose between Long Only, Short Only, or Long & Short.
Pivot Length → Defines the number of required Rally & Drop candles for a pivot.
ATR Exit Multiplier → Adjusts stop-loss distance based on ATR.
Risk-Reward Ratio (RR) → Modifies take-profit level relative to risk.
Historical Lookback → Limits how far back pivot zones are displayed.
Color Settings → Customize pivot box colors for bullish and bearish setups.
Considerations & Limitations
Pivot Breakouts Do Not Guarantee Reversals. Some pivot breaks may lead to continuation moves instead of trend reversals.
Not Optimized for Low Volatility Conditions. This strategy works best in trending markets with strong momentum.
ATR-Based Stop-Loss & Take-Profit May Require Optimization. Different assets may require different ATR multipliers and RR settings.
Market Noise May Still Influence Pivots. While this method filters some noise, fake breakouts can still occur.
Conclusion
The Rally Base Drop SND Pivots Strategy is a non-repainting supply and demand system that combines:
Pivot-based market structure analysis (using Rally, Base, and Drop candles).
Breakout-based trade entries at confirmed SND levels.
ATR-based dynamic risk management for stop-loss and take-profit calculation.
This strategy helps traders:
Identify high-probability supply and demand levels.
Trade based on structured market pivots.
Use a systematic approach to price action analysis.
Automatically manage risk with ATR-based exits.
The strict pivot detection rules and built-in breakout validation make this strategy ideal for traders looking to:
Trade based on market structure.
Use defined support & resistance levels.
Reduce noise compared to traditional fractals.
Implement a structured supply & demand trading model.
This strategy is fully customizable, allowing traders to adjust parameters to fit their market and trading style.
Full credit for the original concept and indicator goes to LuxAlgo.
Liquidity Sweep Filter Strategy [AlgoAlpha X PineIndicators]This strategy is based on the Liquidity Sweep Filter developed by AlgoAlpha. Full credit for the concept and original indicator goes to AlgoAlpha.
The Liquidity Sweep Filter Strategy is a non-repainting trading system designed to identify liquidity sweeps, trend shifts, and high-impact price levels. It incorporates volume-based liquidation analysis, trend confirmation, and dynamic support/resistance detection to optimize trade entries and exits.
This strategy helps traders:
Detect liquidity sweeps where major market participants trigger stop losses and liquidations.
Identify trend shifts using a volatility-based moving average system.
Analyze volume distribution with a built-in volume profile visualization.
Filter noise by differentiating between major and minor liquidity sweeps.
How the Liquidity Sweep Filter Strategy Works
1. Trend Detection Using Volatility-Based Filtering
The strategy applies a volatility-adjusted moving average system to determine trend direction:
A central trend line is calculated using an EMA smoothed over a user-defined length.
Upper and lower deviation bands are created based on the average price deviation over multiple periods.
If price closes above the upper band, the strategy signals an uptrend.
If price closes below the lower band, the strategy signals a downtrend.
This approach ensures that trend shifts are confirmed only when price significantly moves beyond normal market fluctuations.
2. Liquidity Sweep Detection
Liquidity sweeps occur when price temporarily breaks key levels, triggering stop-loss liquidations or margin call events. The strategy tracks swing highs and lows, marking potential liquidity grabs:
Bearish Liquidity Sweeps – Price breaks a recent high, then reverses downward.
Bullish Liquidity Sweeps – Price breaks a recent low, then reverses upward.
Volume Integration – The strategy analyzes trading volume at each sweep to differentiate between major and minor sweeps.
Key levels where liquidity sweeps occur are plotted as color-coded horizontal lines:
Red lines indicate bearish liquidity sweeps.
Green lines indicate bullish liquidity sweeps.
Labels are displayed at each sweep, showing the volume of liquidated positions at that level.
3. Volume Profile Analysis
The strategy includes an optional volume profile visualization, displaying how trading volume is distributed across different price levels.
Features of the volume profile:
Point of Control (POC) – The price level with the highest traded volume is marked as a key area of interest.
Bounding Box – The profile is enclosed within a transparent box, helping traders visualize the price range of high trading activity.
Customizable Resolution & Scale – Traders can adjust the granularity of the profile to match their preferred time frame.
The volume profile helps identify zones of strong support and resistance, making it easier to anticipate price reactions at key levels.
Trade Entry & Exit Conditions
The strategy allows traders to configure trade direction:
Long Only – Only takes long trades.
Short Only – Only takes short trades.
Long & Short – Trades in both directions.
Entry Conditions
Long Entry:
A bullish trend shift is confirmed.
A bullish liquidity sweep occurs (price sweeps below a key level and reverses).
The trade direction setting allows long trades.
Short Entry:
A bearish trend shift is confirmed.
A bearish liquidity sweep occurs (price sweeps above a key level and reverses).
The trade direction setting allows short trades.
Exit Conditions
Closing a Long Position:
A bearish trend shift occurs.
The position is liquidated at a predefined liquidity sweep level.
Closing a Short Position:
A bullish trend shift occurs.
The position is liquidated at a predefined liquidity sweep level.
Customization Options
The strategy offers multiple adjustable settings:
Trade Mode: Choose between Long Only, Short Only, or Long & Short.
Trend Calculation Length & Multiplier: Adjust how trend signals are calculated.
Liquidity Sweep Sensitivity: Customize how aggressively the strategy identifies sweeps.
Volume Profile Display: Enable or disable the volume profile visualization.
Bounding Box & Scaling: Control the size and position of the volume profile.
Color Customization: Adjust colors for bullish and bearish signals.
Considerations & Limitations
Liquidity sweeps do not always result in reversals. Some price sweeps may continue in the same direction.
Works best in volatile markets. In low-volatility environments, liquidity sweeps may be less reliable.
Trend confirmation adds a slight delay. The strategy ensures valid signals, but this may result in slightly later entries.
Large volume imbalances may distort the volume profile. Adjusting the scale settings can help improve visualization.
Conclusion
The Liquidity Sweep Filter Strategy is a volume-integrated trading system that combines liquidity sweeps, trend analysis, and volume profile data to optimize trade execution.
By identifying key price levels where liquidations occur, this strategy provides valuable insight into market behavior, helping traders make better-informed trading decisions.
Key use cases for this strategy:
Liquidity-Based Trading – Capturing moves triggered by stop hunts and liquidations.
Volume Analysis – Using volume profile data to confirm high-activity price zones.
Trend Following – Entering trades based on confirmed trend shifts.
Support & Resistance Trading – Using liquidity sweep levels as dynamic price zones.
This strategy is fully customizable, allowing traders to adapt it to different market conditions, timeframes, and risk preferences.
Full credit for the original concept and indicator goes to AlgoAlpha.
Market Trend Levels Non-Repainting [BigBeluga X PineIndicators]This strategy is based on the Market Trend Levels Detector developed by BigBeluga. Full credit for the concept and original indicator goes to BigBeluga.
The Market Trend Levels Detector Strategy is a non-repainting trend-following strategy that identifies market trend shifts using two Exponential Moving Averages (EMA). It also detects key price levels and allows traders to apply multiple filters to refine trade entries and exits.
This strategy is designed for trend trading and enables traders to:
Identify trend direction based on EMA crossovers.
Detect significant market levels using labeled trend lines.
Use multiple filter conditions to improve trade accuracy.
Avoid false signals through non-repainting calculations.
How the Market Trend Levels Detector Strategy Works
1. Core Trend Detection Using EMA Crossovers
The strategy detects trend shifts using two EMAs:
Fast EMA (default: 12 periods) – Reacts quickly to price movements.
Slow EMA (default: 25 periods) – Provides a smoother trend confirmation.
A bullish crossover (Fast EMA crosses above Slow EMA) signals an uptrend , while a bearish crossover (Fast EMA crosses below Slow EMA) signals a downtrend .
2. Market Level Detection & Visualization
Each time an EMA crossover occurs, a trend level line is drawn:
Bullish crossover → A green line is drawn at the low of the crossover candle.
Bearish crossover → A purple line is drawn at the high of the crossover candle.
Lines can be extended to act as support and resistance zones for future price action.
Additionally, a small label (●) appears at each crossover to mark the event on the chart.
3. Trade Entry & Exit Conditions
The strategy allows users to choose between three trading modes:
Long Only – Only enters long trades.
Short Only – Only enters short trades.
Long & Short – Trades in both directions.
Entry Conditions
Long Entry:
A bullish EMA crossover occurs.
The trade direction setting allows long trades.
Filter conditions (if enabled) confirm a valid long signal.
Short Entry:
A bearish EMA crossover occurs.
The trade direction setting allows short trades.
Filter conditions (if enabled) confirm a valid short signal.
Exit Conditions
Long Exit:
A bearish EMA crossover occurs.
Exit filters (if enabled) indicate an invalid long position.
Short Exit:
A bullish EMA crossover occurs.
Exit filters (if enabled) indicate an invalid short position.
Additional Trade Filters
To improve trade accuracy, the strategy allows traders to apply up to 7 additional filters:
RSI Filter: Only trades when RSI confirms a valid trend.
MACD Filter: Ensures MACD histogram supports the trade direction.
Stochastic Filter: Requires %K line to be above/below threshold values.
Bollinger Bands Filter: Confirms price position relative to the middle BB line.
ADX Filter: Ensures the trend strength is above a set threshold.
CCI Filter: Requires CCI to indicate momentum in the right direction.
Williams %R Filter: Ensures price momentum supports the trade.
Filters can be enabled or disabled individually based on trader preference.
Dynamic Level Extension Feature
The strategy provides an optional feature to extend trend lines until price interacts with them again:
Bullish support lines extend until price revisits them.
Bearish resistance lines extend until price revisits them.
If price breaks a line, the line turns into a dotted style , indicating it has been breached.
This helps traders identify key levels where trend shifts previously occurred, providing useful support and resistance insights.
Customization Options
The strategy includes several adjustable settings :
Trade Direction: Choose between Long Only, Short Only, or Long & Short.
Trend Lengths: Adjust the Fast & Slow EMA lengths.
Market Level Extension: Decide whether to extend support/resistance lines.
Filters for Trade Confirmation: Enable/disable individual filters.
Color Settings: Customize line colors for bullish and bearish trend shifts.
Maximum Displayed Lines: Limit the number of drawn support/resistance lines.
Considerations & Limitations
Trend Lag: As with any EMA-based strategy, signals may be slightly delayed compared to price action.
Sideways Markets: This strategy works best in trending conditions; frequent crossovers in sideways markets can produce false signals.
Filter Usage: Enabling multiple filters may reduce trade frequency, but can also improve trade quality.
Line Overlap: If many crossovers occur in a short period, the chart may become cluttered with multiple trend levels. Adjusting the "Display Last" setting can help.
Conclusion
The Market Trend Levels Detector Strategy is a non-repainting trend-following system that combines EMA crossovers, market level detection, and customizable filters to improve trade accuracy.
By identifying trend shifts and key price levels, this strategy can be used for:
Trend Confirmation – Using EMA crossovers and filters to confirm trend direction.
Support & Resistance Trading – Identifying dynamic levels where price reacts.
Momentum-Based Trading – Combining EMA crossovers with additional momentum filters.
This strategy is fully customizable and can be adapted to different trading styles, timeframes, and market conditions.
Full credit for the original concept and indicator goes to BigBeluga.
Gradient Trend Filter STRATEGY [ChartPrime/PineIndicators]This strategy is based on the Gradient Trend Filter indicator developed by ChartPrime. Full credit for the concept and indicator goes to ChartPrime.
The Gradient Trend Filter Strategy is designed to execute trades based on the trend analysis and filtering system provided by the Gradient Trend Filter indicator. It integrates a noise-filtered trend detection system with a color-gradient visualization, helping traders identify trend strength, momentum shifts, and potential reversals.
How the Gradient Trend Filter Strategy Works
1. Noise Filtering for Smoother Trends
To reduce false signals caused by market noise, the strategy applies a three-stage smoothing function to the source price. This function ensures that trend shifts are detected more accurately, minimizing unnecessary trade entries and exits.
The filter is based on an Exponential Moving Average (EMA)-style smoothing technique.
It processes price data in three successive passes, refining the trend signal before generating trade entries.
This filtering technique helps eliminate minor fluctuations and highlights the true underlying trend.
2. Multi-Layered Trend Bands & Color-Based Trend Visualization
The Gradient Trend Filter constructs multiple trend bands around the filtered trend line, acting as dynamic support and resistance zones.
The mid-line changes color based on the trend direction:
Green for uptrends
Red for downtrends
A gradient cloud is formed around the trend line, dynamically shifting colors to provide early warning signals of trend reversals.
The outer bands function as potential support and resistance, helping traders determine stop-loss and take-profit zones.
Visualization elements used in this strategy:
Trend Filter Line → Changes color between green (bullish) and red (bearish).
Trend Cloud → Dynamically adjusts color based on trend strength.
Orange Markers → Appear when a trend shift is confirmed.
Trade Entry & Exit Conditions
This strategy automatically enters trades based on confirmed trend shifts detected by the Gradient Trend Filter.
1. Trade Entry Rules
Long Entry:
A bullish trend shift is detected (trend direction changes to green).
The filtered trend value crosses above zero, confirming upward momentum.
The strategy enters a long position.
Short Entry:
A bearish trend shift is detected (trend direction changes to red).
The filtered trend value crosses below zero, confirming downward momentum.
The strategy enters a short position.
2. Trade Exit Rules
Closing a Long Position:
If a bearish trend shift occurs, the strategy closes the long position.
Closing a Short Position:
If a bullish trend shift occurs, the strategy closes the short position.
The trend shift markers (orange diamonds) act as a confirmation signal, reinforcing the validity of trade entries and exits.
Customization Options
This strategy allows traders to adjust key parameters for flexibility in different market conditions:
Trade Direction: Choose between Long Only, Short Only, or Long & Short .
Trend Length: Modify the length of the smoothing function to adapt to different timeframes.
Line Width & Colors: Customize the visual appearance of trend lines and cloud colors.
Performance Table: Enable or disable the equity performance table that tracks historical trade results.
Performance Tracking & Reporting
A built-in performance table is included to monitor monthly and yearly trading performance.
The table calculates monthly percentage returns, displaying them in a structured format.
Color-coded values highlight profitable months (blue) and losing months (red).
Tracks yearly cumulative performance to assess long-term strategy effectiveness.
Traders can use this feature to evaluate historical performance trends and optimize their strategy settings accordingly.
How to Use This Strategy
Identify Trend Strength & Reversals:
Use the trend line and cloud color changes to assess trend strength and detect potential reversals.
Monitor Momentum Shifts:
Pay attention to gradient cloud color shifts, as they often appear before the trend line changes color.
This can indicate early momentum weakening or strengthening.
Act on Trend Shift Markers:
Use orange diamonds as confirmation signals for trend shifts and trade entry/exit points.
Utilize Cloud Bands as Support/Resistance:
The outer bands of the cloud serve as dynamic support and resistance, helping with stop-loss and take-profit placement.
Considerations & Limitations
Trend Lag: Since the strategy applies a smoothing function, entries may be slightly delayed compared to raw price action.
Volatile Market Conditions: In high-volatility markets, trend shifts may occur more frequently, leading to higher trade frequency.
Optimized for Trend Trading: This strategy is best suited for trending markets and may produce false signals in sideways (ranging) conditions.
Conclusion
The Gradient Trend Filter Strategy is a trend-following system based on the Gradient Trend Filter indicator by ChartPrime. It integrates noise filtering, trend visualization, and gradient-based color shifts to help traders identify strong market trends and potential reversals.
By combining trend filtering with a multi-layered cloud system, the strategy provides clear trade signals while minimizing noise. Traders can use this strategy for long-term trend trading, momentum shifts, and support/resistance-based decision-making.
This strategy is a fully automated system that allows traders to execute long, short, or both directions, with customizable settings to adapt to different market conditions.
Credit for the original concept and indicator goes to ChartPrime.
M2 Global Liquidity Index - 10 Week Lead
M2 Global Liquidity Index - Forward Projection (10 Weeks)
This indicator provides a 10-week forward projection of the M2 Global Liquidity Index, offering traders insight into potential future market conditions based on global money supply trends.
What This Indicator Shows
The M2 Global Liquidity Index aggregates M2 money stock data from five major economies:
- China (CNY)
- United States (USD)
- European Union (EUR)
- Japan (JPY)
- Great Britain (GBP)
All values are converted to USD and presented as a unified global liquidity metric, providing a comprehensive view of worldwide monetary conditions.
Forward Projection Feature
This adaptation displays the indicator 10 weeks ahead of the current price, allowing you to visualize potential future liquidity conditions that might influence market behavior. The projection maintains data integrity while providing an advanced view of the liquidity landscape.
Trading Applications
- Anticipate potential market reactions to changing global liquidity conditions
- Identify divergences between projected liquidity and current price action
- Develop longer-term strategic positions based on forward liquidity projections
- Enhance your macro-economic analysis toolkit
Credit
This indicator is an adaptation of the original "M2 Global Liquidity Index" created by Mik3Christ3ns3n. Full credit for the original concept and implementation goes to the original author. This version simply adds a 10-week forward projection to the existing calculations.
Disclaimer
This indicator is for informational purposes only and should be used as one of many tools in your analysis. Past performance and projections are not guarantees of future results.
Business Cycle Indicators (Normalized)This script aggregates and normalizes several key economic indicators to provide a comprehensive view of the business cycle and overall market conditions. By combining these indicators into a single, normalized average line, the script helps identify overarching trends and shifts in the economy, aiding in more informed trading and investment decisions.
Included Indicators:
Inverted National Financial Conditions Index (NFCI):
Symbol: FRED:NFCI
Measures financial stress in the markets. An inverted NFCI aligns higher values with positive financial conditions.
Inverted Net Percentage of Banks Tightening Lending Standards (DRTSCIS):
Symbol: FRED:DRTSCIS
Reflects changes in bank lending practices. Inverting this indicator means higher values indicate easing lending standards, which is generally positive for economic growth.
HYG Close Price (iShares High Yield Corporate Bond ETF):
Symbol: AMEX:HYG
Represents the performance of high-yield corporate bonds, providing insight into credit market conditions.
Inverted High-Yield Credit Spread (BAMLH0A0HYM2):
Symbol: FRED:BAMLH0A0HYM2
Measures the spread between high-yield bonds and risk-free securities. A narrower (inverted) spread indicates better market conditions.
Manufacturing/Non-Manufacturing New Orders Ratio:
Symbols: ECONOMICS:USMNO (Manufacturing), ECONOMICS:USNMNO (Non-Manufacturing)
Compares manufacturing to non-manufacturing new orders to gauge shifts in economic activity.
US PMI (Purchasing Managers' Index):
Symbol: ECONOMICS:USBCOI
An indicator of the economic health of the manufacturing sector.
10-Year Inflation Breakeven (T10YIE):
Symbol: FRED:T10YIE
Represents market expectations of inflation over the next ten years.
Inverted 10-Year Real Yield (DFII10):
Symbol: FRED:DFII10
Reflects the real yield on 10-year Treasury Inflation-Protected Securities (TIPS). Inverted to align higher values with positive economic sentiment.
Copper/Gold Ratio:
Symbols: CAPITALCOM:COPPER (Copper), TVC:GOLD (Gold)
Compares the prices of copper and gold, often used as a barometer for global economic activity.
Features:
Normalized Indicators: Each indicator is normalized to a 0-100 scale to facilitate direct comparison, regardless of their original units or scales.
Normalized Average Line: Calculates and plots the average of all available normalized indicators, providing a single line that represents the combined economic signals.
Customizable Display:
Show Individual Indicators: Option to display individual normalized indicators for detailed analysis.
Show Normalized Average Line: Option to display the normalized average line for a consolidated view.
Dynamic Labeling: Displays the latest value of the normalized average directly on the chart for quick reference.
How to Use:
Adding the Script:
Apply the script to a chart in TradingView using a timeframe that aligns with the frequency of the economic data (daily or weekly recommended).
Customization:
Show Normalized Average Line: Enabled by default to display the combined indicator.
Show Individual Indicators: Enable this option in the script settings to display all individual normalized indicators.
Interpretation:
Normalized Scale (0-100): Higher values generally indicate stronger economic conditions, while lower values may suggest weakening conditions.
Trend Analysis: Use the normalized average line to identify trends and potential turning points in the business cycle.
Notes:
Data Availability: Ensure you have access to all the data sources used in the script. Some data feeds may require specific TradingView subscriptions.
Indicator Limitations: Economic indicators are subject to revisions and may not reflect real-time market conditions.
No Investment Advice: This script is a tool for analysis and should not be considered as financial advice. Always conduct your own research before making investment decisions.
CE_ZLSMA_5MIN_CANDLECHART-- Overview
The "CE_ZLSMA_5MIN_CANDLECHART" strategy, developed by DailyPanda, is a comprehensive trading strategy designed for analyzing trading on 5-minute candlestick charts.
It aims to use some indicators calculated from a Hekin Ashi chart, while running it on a normal candlestick chart, making sure that no price distortion affects the strategy results .
It also brings a feature to show, on the candlestick chart, where the entries would take place on the HA chart, to also be able to study the effect that the price distortion would make on your backtest.
-- Credit
The code in this script is based on open-source indicators originally written by veryfid and everget, I've made significant changes and additions to the scripts but all credit for the idea goes to them, I just built on top of it:
-- Key Features
It incorporate already built indicators (ZLSMA) and CandelierExit (CE)
-- Zero Lag Least Squares Moving Average (ZLSMA) - by veryfid
The ZLSMA is used to detect trends with minimal lag, improving the accuracy of entry and exit signals.
It incorporates a double-smoothed linear regression to minimize lag and enhance trend-following capabilities.
Buy signals are generated when the price closes above the ZLSMA together with the CE signal.
It is calculated based on the HA candlestick pattern.
-- Chandelier Exit (CE) - by everget
The Chandelier Exit indicator is used to dynamically manage stop-loss levels based on the Average True Range (ATR).
It ensures that stop-loss levels are adaptive to market volatility, protecting profits and limiting losses.
The ATR period and multiplier can be customized to fit different trading styles and risk tolerances.
It is calculated based on the HA candlestick pattern.
-- Heikin Ashi Candles
The strategy leverages Heikin Ashi candlesticks to be able identify trends more clearly and leverage this to stay on winning trades longer.
Traders can choose to display Heikin Ashi candlesticks and order fills on the chart for better visualization.
-- Risk Management
The strategy includes multiple risk management options to protect traders' capital.
Maximum intraday loss limit based on a percentage of equity.
Maximum stop-loss in points to filter out entries with excessive risk.
Daily profit target to stop trading once the goal is achieved.
Options to use fixed contract sizes or dynamically adjust based on a percentage of equity.
These features help traders manage risk and ensure sustainable trading practices.
Moving Averages
Several moving averages (EMA 9, EMA 20, EMA 50, EMA 72, EMA 200, SMA 200, and SMA 500) are plotted to provide additional context and trend confirmation.
A "Zone of Value" is highlighted between the EMA 200 and SMA 200 to identify potential support and resistance areas.
-- Customizable Inputs
The strategy includes various customizable inputs, allowing traders to tailor it to their specific needs.
Start and stop trading times.
Risk management parameters (e.g., maximum stop-loss, daily drawdown limit, and daily profit target).
Display options for Heikin Ashi candles and moving averages.
ZLSMA length and offset.
-- Usage
-- Setting Up the Strategy
Configure the start year for the strategy and the trading hours using the input fields. The first candle of each day will be filled black for easy identification, while candles that are outside the allowed time range will be filled purple.
Customize the risk management parameters to match your risk tolerance and trading style.
Enable or disable the display of Heikin Ashi candlesticks and moving averages as desired.
-- Interpreting Signals
Buy signals are indicated by a "Buy" label when the Heikin Ashi close price is above the ZLSMA and the Chandelier Exit indicates a long position.
The strategy will automatically enter a long position with a stop-loss level determined the swing low.
Positions are closed when the close price falls below the ZLSMA.
-- Risk Management
The strategy monitors the maximum intraday loss and stops trading if the loss limit is reached.
If enabled, also stops trading once the daily profit target is achieved, helping to lock in gains.
You have the option to filter operations based on a maximum accepted stop-loss level, based on your risk tolerance.
You can also operate with a fixed amount of contracts or dynamically adjust it based on your allowed risk per trade, ensuring optimal protection of capital.
-- Visual Aids
The strategy plots various moving averages to provide additional trend context.
The "Zone of Value" between the EMA 200 and SMA 200 highlights potential support and resistance areas.
Heikin Ashi candlesticks and order fills can be displayed to enhance the difference this strategy would take if you were to backtest it on a Heikin Ashi chart.
-- Table of results
This strategy also breaks down the results on a monthly basis for better understanding of your capital development along the way.
-- Conclusion
The "CE_ZLSMA_5MIN_CANDLECHART" strategy is a tool for intraday traders looking to understand and leaverage the Heikin Ashi chart while still using the normal candle chart. Traders can customize the strategy to fit their specific needs, making it a versatile addition to any trading toolkit.
