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Multi-Ticker TableMulti-Ticker Table A customizable TradingView indicator that displays a clean, organized table of up to 10 user-defined ticker symbols with their current daily price, daily dollar change, and daily percentage change. Key features include: Enable/disable individual tickers with custom symbols Customizable font sizes and colors for header and body rows Customizable table background colors for header and data rows Flexible table positioning anywhere on the chart (top/middle/bottom × left/center/right) Highlights positive changes in green and negative changes in red for quick visual analysis Hides chart candles to display the table as a standalone dashboard Ideal for traders who want a quick, at-a-glance summary of multiple markets or instruments without cluttering the chart.
Penunjuk Pine Script®
oleh d1re
Hann Window FIR Filter Ribbon [BigBeluga]🔵 OVERVIEW The Hann Window FIR Filter Ribbon is a trend-following visualization tool based on a family of FIR filters using the Hann window function. It plots a smooth and dynamic ribbon formed by six Hann filters of progressively increasing length. Gradient coloring and filled bands reveal trend direction and compression/expansion behavior. When short-term trend shifts occur (via filter crossover), it automatically anchors visual support/resistance zones at the nearest swing highs or lows. 🔵 CONCEPTS Hann FIR Filter: A finite impulse response filter that uses a Hann (cosine-based) window for weighting past price values, resulting in a non-lag, ultra-smooth output. hannFilter(length)=> var float hann = na // Final filter output float filt = 0 float coef = 0 for i = 1 to length weight = 1 - math.cos(2 * math.pi * i / (length + 1)) filt += price * weight coef += weight hann := coef != 0 ? filt / coef : na Ribbon Stack: The indicator plots 6 Hann FIR filters with increasing lengths, creating a smooth "ribbon" that adapts to price shifts and visually encodes volatility. Gradient Coloring: Line colors and fill opacity between layers are dynamically adjusted based on the distance between the filters, showing momentum expansion or contraction. Dynamic Swing Zones: When the shortest filter crosses its nearest neighbor, a swing high/low is located, and a triangle-style level is anchored and projected to the right. Self-Extending Levels: These dynamic levels persist and extend until invalidated or replaced by a new opposite trend break. 🔵 FEATURES Plots 6 Hann FIR filters with increasing lengths (controlled by Ribbon Size input). Automatically colors each filter and the fill between them with smooth gradient transitions. Detects trend shifts via filter crossover and anchors visual resistance (red) or support (green) zones. Support/resistance zones are triangle-style bands built around recent swing highs/lows. Levels auto-extend right and adapt in real time until invalidated by price action. Ribbon responds smoothly to price and shows contraction or expansion behavior clearly. No lag in crossover detection thanks to FIR architecture. Adjustable sensitivity via Length and Ribbon Size inputs. 🔵 HOW TO USE Use the ribbon gradient as a visual trend strength and smooth direction cue. Watch for crossover of shortest filters as early trend change signals. Monitor support/resistance zones as potential high-probability reaction points. Combine with other tools like momentum or volume to confirm trend breaks. Adjust ribbon thickness and length to suit your trading timeframe and volatility preference. 🔵 CONCLUSION Hann Window FIR Filter Ribbon blends digital signal processing with trading logic to deliver a visually refined, non-lagging trend tool. The adaptive ribbon offers insight into momentum compression and release, while swing-based levels give structure to potential reversals. Ideal for traders who seek smooth trend detection with intelligent, auto-adaptive zone plotting.
Penunjuk Pine Script®
oleh BigBeluga
2
Square-root Decay Volume ProfileThis indicator displays a custom price profile that mimics a volume profile using occurrence-based weighting rather than actual volume. It counts how often the selected price source (e.g., close) falls within each price bin over a lookback period. What makes it unique is the use of square-root time decay: more recent price occurrences are given greater importance, while older data is discounted proportionally to the inverse square root of its age. Each bin's relative weight is visualized as a horizontal bar aligned to the right edge of the chart, showing where price has "spent time" more recently. This allows traders to identify areas of interest, balance zones, and potential support/resistance levels based on decayed price density. Key Features: Square-root decay weighting favors recent price action Adjustable lookback period, bin count, and histogram width Works with any price source (close, hl2, etc.) Plots boxes directly on the chart for clear visualization This tool is especially useful for discretionary traders seeking a price-centric alternative to traditional volume profiles, with an added emphasis on recency.
Penunjuk Pine Script®
oleh Tradenometry
Linear Mean Reversion Strategy📘 Strategy Introduction: Linear Mean Reversion with Fixed Stop This strategy implements a simple yet powerful mean reversion model that assumes price tends to oscillate around a dynamic average over time. It identifies statistically significant deviations from the moving average using a z-score, and enters trades expecting a return to the mean. 🧠 Core Logic: A z-score is calculated by comparing the current price to its moving average, normalized by standard deviation, over a user-defined half-life window. Trades are entered when the z-score crosses a threshold (e.g., ±1), signaling overbought or oversold conditions. The strategy exits positions either when price reverts back near the mean (z-score close to 0), or if a fixed stop loss of 100 points is hit, whichever comes first. ⚙️ Key Features: Dynamic mean and volatility estimation using moving average and standard deviation Configurable z-score thresholds for entry and exit Position size scaling based on z-score magnitude Fixed stop loss to control risk and avoid prolonged drawdowns 🧪 Use Case: Ideal for range-bound markets or assets that exhibit stationary behavior around a mean, this strategy is especially useful on assets with mean-reverting characteristics like currency pairs, ETFs, or large-cap stocks. It is best suited for traders looking for short-term reversions rather than long-term trends.
Strategi Pine Script®
oleh XiaoboFu
Recession Warning Model [BackQuant]Recession Warning Model Overview The Recession Warning Model (RWM) is a Pine Script® indicator designed to estimate the probability of an economic recession by integrating multiple macroeconomic, market sentiment, and labor market indicators. It combines over a dozen data series into a transparent, adaptive, and actionable tool for traders, portfolio managers, and researchers. The model provides customizable complexity levels, display modes, and data processing options to accommodate various analytical requirements while ensuring robustness through dynamic weighting and regime-aware adjustments. Purpose The RWM fulfills the need for a concise yet comprehensive tool to monitor recession risk. Unlike approaches relying on a single metric, such as yield-curve inversion, or extensive economic reports, it consolidates multiple data sources into a single probability output. The model identifies active indicators, their confidence levels, and the current economic regime, enabling users to anticipate downturns and adjust strategies accordingly. Core Features - Indicator Families : Incorporates 13 indicators across five categories: Yield, Labor, Sentiment, Production, and Financial Stress. - Dynamic Weighting : Adjusts indicator weights based on recent predictive accuracy, constrained within user-defined boundaries. - Leading and Coincident Split : Separates early-warning (leading) and confirmatory (coincident) signals, with adjustable weighting (default 60/40 mix). - Economic Regime Sensitivity : Modulates output sensitivity based on market conditions (Expansion, Late-Cycle, Stress, Crisis), using a composite of VIX, yield-curve, financial conditions, and credit spreads. - Display Options : Supports four modes—Probability (0-100%), Binary (four risk bins), Lead/Coincident, and Ensemble (blended probability). - Confidence Intervals : Reflects model stability, widening during high volatility or conflicting signals. - Alerts : Configurable thresholds (Watch, Caution, Warning, Alert) with persistence filters to minimize false signals. - Data Export : Enables CSV output for probabilities, signals, and regimes, facilitating external analysis in Python or R. Model Complexity Levels Users can select from four tiers to balance simplicity and depth: 1. Essential : Focuses on three core indicators—yield-curve spread, jobless claims, and unemployment change—for minimalistic monitoring. 2. Standard : Expands to nine indicators, adding consumer confidence, PMI, VIX, S&P 500 trend, money supply vs. GDP, and the Sahm Rule. 3. Professional : Includes all 13 indicators, incorporating financial conditions, credit spreads, JOLTS vacancies, and wage growth. 4. Research : Unlocks all indicators plus experimental settings for advanced users. Key Indicators Below is a summary of the 13 indicators, their data sources, and economic significance: - Yield-Curve Spread : Difference between 10-year and 3-month Treasury yields. Negative spreads signal banking sector stress. - Jobless Claims : Four-week moving average of unemployment claims. Sustained increases indicate rising layoffs. - Unemployment Change : Three-month change in unemployment rate. Sharp rises often precede recessions. - Sahm Rule : Triggers when unemployment rises 0.5% above its 12-month low, a reliable recession indicator. - Consumer Confidence : University of Michigan survey. Declines reflect household pessimism, impacting spending. - PMI : Purchasing Managers’ Index. Values below 50 indicate manufacturing contraction. - VIX : CBOE Volatility Index. Elevated levels suggest market anticipation of economic distress. - S&P 500 Growth : Weekly moving average trend. Declines reduce wealth effects, curbing consumption. - M2 + GDP Trend : Monitors money supply and real GDP. Simultaneous declines signal credit contraction. - NFCI : Chicago Fed’s National Financial Conditions Index. Positive values indicate tighter conditions. - Credit Spreads : Proxy for corporate bond spreads using 10-year vs. 2-year Treasury yields. Widening spreads reflect stress. - JOLTS Vacancies : Job openings data. Significant drops precede hiring slowdowns. - Wage Growth : Year-over-year change in average hourly earnings. Late-cycle spikes often signal economic overheating. Data Processing - Rate of Change (ROC) : Optionally applied to capture momentum in data series (default: 21-bar period). - Z-Score Normalization : Standardizes indicators to a common scale (default: 252-bar lookback). - Smoothing : Applies a short moving average to final signals (default: 5-bar period) to reduce noise. - Binary Signals : Generated for each indicator (e.g., yield-curve inverted or PMI below 50) based on thresholds or Z-score deviations. Probability Calculation 1. Each indicator’s binary signal is weighted according to user settings or dynamic performance. 2. Weights are normalized to sum to 100% across active indicators. 3. Leading and coincident signals are aggregated separately (if split mode is enabled) and combined using the specified mix. 4. The probability is adjusted by a regime multiplier, amplifying risk during Stress or Crisis regimes. 5. Optional smoothing ensures stable outputs. Display and Visualization - Probability Mode : Plots a continuous 0-100% recession probability with color gradients and confidence bands. - Binary Mode : Categorizes risk into four levels (Minimal, Watch, Caution, Alert) for simplified dashboards. - Lead/Coincident Mode : Displays leading and coincident probabilities separately to track signal divergence. - Ensemble Mode : Averages traditional and split probabilities for a balanced view. - Regime Background : Color-coded overlays (green for Expansion, orange for Late-Cycle, amber for Stress, red for Crisis). - Analytics Table : Optional dashboard showing probability, confidence, regime, and top indicator statuses. Practical Applications - Asset Allocation : Adjust equity or bond exposures based on sustained probability increases. - Risk Management : Hedge portfolios with VIX futures or options during regime shifts to Stress or Crisis. - Sector Rotation : Shift toward defensive sectors when coincident signals rise above 50%. - Trading Filters : Disable short-term strategies during high-risk regimes. - Event Timing : Scale positions ahead of high-impact data releases when probability and VIX are elevated. Configuration Guidelines - Enable ROC and Z-score for consistent indicator comparison unless raw data is preferred. - Use dynamic weighting with at least one economic cycle of data for optimal performance. - Monitor stress composite scores above 80 alongside probabilities above 70 for critical risk signals. - Adjust adaptation speed (default: 0.1) to 0.2 during Crisis regimes for faster indicator prioritization. - Combine RWM with complementary tools (e.g., liquidity metrics) for intraday or short-term trading. Limitations - Macro indicators lag intraday market moves, making RWM better suited for strategic rather than tactical trading. - Historical data availability may constrain dynamic weighting on shorter timeframes. - Model accuracy depends on the quality and timeliness of economic data feeds. Final Note The Recession Warning Model provides a disciplined framework for monitoring economic downturn risks. By integrating diverse indicators with transparent weighting and regime-aware adjustments, it empowers users to make informed decisions in portfolio management, risk hedging, or macroeconomic research. Regular review of model outputs alongside market-specific tools ensures its effective application across varying market conditions.
Penunjuk Pine Script®
oleh BackQuant
3
Ghost Month HighlighterGhost Month and Trading: Understanding the Phenomenon Ghost Month (鬼月) is the seventh month of the lunar calendar in Chinese culture, typically falling between late July and September. During this period, it's believed that the gates of the afterlife open and spirits roam the earth. This deeply rooted cultural belief has significant implications for Asian markets, particularly in regions with large Chinese populations like Taiwan, Hong Kong, Singapore, and mainland China. Why Markets Often Decline or Stay Flat During Ghost Month: Reduced Business Activity : Many businesses avoid launching new products, signing major contracts, or making significant investments during this period, believing it brings bad luck. Property Market Slowdown : Real estate transactions drop significantly as people avoid moving homes or making large purchases. In some markets, property sales can decline by 20-30%. IPO and M&A Drought : Companies often delay IPOs and merger announcements until after Ghost Month, reducing market catalysts. Retail Spending Drops : Consumer spending on big-ticket items decreases, though spending on offerings and religious items increases. Self-Fulfilling Prophecy : Many traders and investors reduce positions or stay on the sidelines, creating lower volumes and increased volatility. This becomes a self-fulfilling prophecy where expectation of poor performance leads to actual underperformance. Tourism and Entertainment Impact : Travel and entertainment sectors see reduced activity as people avoid unnecessary trips and celebrations. Historical data shows that Asian equity markets often underperform during Ghost Month, with some studies indicating average returns can be 2-5% lower than other months. However, this also creates opportunities for contrarian investors who buy during the seasonal weakness. Inspired by @honey_xbt
Penunjuk Pine Script®
oleh ThatDudeFreak
FunctionADFLibrary "FunctionADF" Augmented Dickey-Fuller test (ADF), The ADF test is a statistical method used to assess whether a time series is stationary – meaning its statistical properties (like mean and variance) do not change over time. A time series with a unit root is considered non-stationary and often exhibits non-mean-reverting behavior, which is a key concept in technical analysis. Reference: - - rtmath.net - en.wikipedia.org adftest(data, n_lag, conf)   : Augmented Dickey-Fuller test for stationarity.   Parameters:      data (array) : Data series.      n_lag (int) : Maximum lag.      conf (string) : Confidence Probability level used to test for critical value, (`90%`, `95%`, `99%`).   Returns: `adf` The test statistic. \ `crit` Critical value for the test statistic at the 10 % levels. \ `nobs` Number of observations used for the ADF regression and calculation of the critical values.
Perpustakaan Pine Script®
oleh RicardoSantos
8