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AVGO Advanced Day Trading Strategy📈 Overview The AVGO Advanced Day Trading Strategy is a comprehensive, multi-timeframe trading system designed for active day traders seeking consistent performance with robust risk management. Originally optimized for AVGO (Broadcom), this strategy adapts well to other liquid stocks and can be customized for various trading styles. 🎯 Key Features Multiple Entry Methods EMA Crossover: Classic trend-following signals using fast (9) and medium (16) EMAs MACD + RSI Confluence: Momentum-based entries combining MACD crossovers with RSI positioning Price Momentum: Consecutive price action patterns with EMA and RSI confirmation Hybrid System: Advanced multi-trigger approach combining all methodologies Advanced Technical Arsenal When enabled, the strategy analyzes 8+ additional indicators for confluence: Volume Price Trend (VPT): Measures volume-weighted price momentum On-Balance Volume (OBV): Tracks cumulative volume flow Accumulation/Distribution Line: Identifies institutional money flow Williams %R: Momentum oscillator for entry timing Rate of Change Suite: Multi-timeframe momentum analysis (5, 14, 18 periods) Commodity Channel Index (CCI): Cyclical turning points Average Directional Index (ADX): Trend strength measurement Parabolic SAR: Dynamic support/resistance levels 🛡️ Risk Management System Position Sizing Risk-based position sizing (default 1% per trade) Maximum position limits (default 25% of equity) Daily loss limits with automatic position closure Multiple Profit Targets Target 1: 1.5% gain (50% position exit) Target 2: 2.5% gain (30% position exit) Target 3: 3.6% gain (20% position exit) Configurable exit percentages and target levels Stop Loss Protection ATR-based or percentage-based stop losses Optional trailing stops Dynamic stop adjustment based on market volatility 📊 Technical Specifications Primary Indicators EMAs: 9 (Fast), 16 (Medium), 50 (Long) VWAP: Volume-weighted average price filter RSI: 6-period momentum oscillator MACD: 8/13/5 configuration for faster signals Volume Confirmation Volume filter requiring 1.6x average volume 19-period volume moving average baseline Optional volume confirmation bypass Market Structure Analysis Bollinger Bands (20-period, 2.0 multiplier) Squeeze detection for breakout opportunities Fractal and pivot point analysis ⏰ Trading Hours & Filters Time Management Configurable trading hours (default: 9:30 AM - 3:30 PM EST) Weekend and holiday filtering Session-based trade management Market Condition Filters Trend alignment requirements VWAP positioning filters Volatility-based entry conditions 📱 Visual Features Information Dashboard Real-time display of: Current entry method and signals Bullish/bearish signal counts RSI and MACD status Trend direction and strength Position status and P&L Volume and time filter status Chart Visualization EMA plots with customizable colors Entry signal markers Target and stop level lines Background color coding for trends Optional Bollinger Bands and SAR display 🔔 Alert System Entry Alerts Customizable alerts for long and short entries Method-specific alert messages Signal confluence notifications Advanced Alerts Strong confluence threshold alerts Custom alert messages with signal counts Risk management alerts ⚙️ Customization Options Strategy Parameters Enable/disable long or short trades Adjustable risk parameters Multiple entry method selection Advanced indicator on/off toggle Visual Customization Color schemes for all indicators Dashboard position and size options Show/hide various chart elements Background color preferences 📋 Default Settings Initial Capital: $100,000 Commission: 0.1% Default Position Size: 10% of equity Risk Per Trade: 1.0% RSI Length: 6 periods MACD: 8/13/5 configuration Stop Loss: 1.1% or ATR-based 🎯 Best Use Cases Day Trading: Designed for intraday opportunities Swing Trading: Adaptable for longer-term positions Momentum Trading: Excellent for trending markets Risk-Conscious Trading: Built-in risk management protocols ⚠️ Important Notes Paper Trading Recommended: Test thoroughly before live trading Market Conditions: Performance varies with market volatility Customization: Adjust parameters based on your risk tolerance Educational Purpose: Use as a learning tool and customize for your needs 🏆 Performance Features Detailed performance metrics Trade-by-trade analysis capability Customizable risk/reward ratios Comprehensive backtesting support This strategy is for educational purposes. Past performance does not guarantee future results. Always practice proper risk management and consider your financial situation before trading.
Pine Script®策略
由r0b3rtcc提供
6
Simple Turnover (Enhanced v2)📊 Simple Turnover (Enhanced) 🔹 Overview The Simple Turnover Indicator calculates a stock’s turnover by combining both price and volume, and then compares it against quarterly highs. This helps traders quickly gauge whether market participation in a move is strong enough to confirm a breakout, or weak and likely to be false. Unlike volume alone, turnover considers both traded volume and price level, giving a truer reflection of capital flow in/out of a stock. ________________________________________ 🔹 Formulae Used 1. Average Price (SMA) AvgPrice=SMA(Close,n) 2. Average Volume (SMA) AvgVol=SMA(Volume,n) 3. Turnover (Raw) Turnover raw=AvgPrice × AvgVol 4. Unit Adjustment • If Millions → Turnover = Turnover raw × 10^−6 • If Crores → Turnover = Turnover raw × 10^−7 • If Raw → Turnover = Turnover raw 5. Quarterly High Turnover (qHigh) Within each calendar quarter (Jan–Mar, Apr–Jun, Jul–Sep, Oct–Dec), we track the maximum turnover seen: qHigh=max (Turnover within current quarter) ________________________________________ 🔹 Visualization • Bars → Color follows price candle: o Green if Close ≥ Open o Red if Close < Open • Blue Line → Rolling Quarterly High Turnover (qHigh) ________________________________________ 🔹 Strategy Use Case The Simple Turnover Indicator is most effective for confirming true vs false breakouts. • A true breakout should be supported by increasing turnover, showing real capital backing the move. • A false breakout often occurs with weak or declining turnover, suggesting lack of conviction. 📌 Example Strategy (3H timeframe): 1. Identify a demand zone using your preferred supply-demand indicator. 2. From this demand zone, monitor turnover bars. 3. A potential long entry is validated when: o The current turnover bar is at least 20% higher than the previous one or two bars. o Example setting: SMA length = 5 (i.e., turnover = 5-bar average close × 5-bar average volume). 4. This confirms strong participation in the move, increasing probability of a sustained breakout. ________________________________________ 🔹 Disclaimer ⚠️ This indicator/strategy does not guarantee 100% accurate results. It is intended to improve the probability of identifying true breakouts. The actual success of the strategy will depend on price action, market momentum, and prevailing market conditions. Always use this as a supporting tool along with broader trading analysis and risk management.
Pine Script®指標
由IndianCamel提供
2
ALGO IMB FVG // by hlva //@version=6 indicator("ALGO IMB BY HLVA", "ALGO IMB - BULL/BEAR", overlay = true, max_lines_count = 500, max_boxes_count = 500) //------------------------------------------------------------------------------ //Settings //-----------------------------------------------------------------------------{ thresholdPer = input.float(0, "Threshold %", minval = 0, maxval = 100, step = .1, inline = 'threshold') auto = input(false, "Auto", inline = 'threshold') showLast = input.int(0, 'Unmitigated Levels', minval = 0) mitigationLevels = input.bool(false, 'Mitigation Levels') tf = input.timeframe('', "Timeframe") //Style extend = input.int(20, 'Extend', minval = 0, inline = 'extend', group = 'Style') dynamic = input(false, 'Dynamic', inline = 'extend', group = 'Style') bullCss = input.color(color.new(#089981, 70), "Bullish FVG", group = 'Style') bearCss = input.color(color.new(#f23645, 70), "Bearish FVG", group = 'Style') //Dashboard showDash = input(false, 'Show Dashboard', group = 'Dashboard') dashLoc = input.string('Top Right', 'Location', options = , group = 'Dashboard') textSize = input.string('Small', 'Size' , options = , group = 'Dashboard') //-----------------------------------------------------------------------------} //UDT's //-----------------------------------------------------------------------------{ type fvg float max float min bool isbull int t = time //-----------------------------------------------------------------------------} //Methods/Functions //-----------------------------------------------------------------------------{ n = bar_index method tosolid(color id)=> color.rgb(color.r(id),color.g(id),color.b(id)) detect()=> var new_fvg = fvg.new(na, na, na, na) threshold = auto ? ta.cum((high - low) / low) / bar_index : thresholdPer / 100 bull_fvg = low > high and close > high and (low - high ) / high > threshold bear_fvg = high < low and close < low and (low - high) / high > threshold if bull_fvg new_fvg := fvg.new(low, high , true) else if bear_fvg new_fvg := fvg.new(low , high, false) //-----------------------------------------------------------------------------} //FVG's detection/display //-----------------------------------------------------------------------------{ var float max_bull_fvg = na, var float min_bull_fvg = na, var bull_count = 0, var bull_mitigated = 0 var float max_bear_fvg = na, var float min_bear_fvg = na, var bear_count = 0, var bear_mitigated = 0 var t = 0 var fvg_records = array.new(0) var fvg_areas = array.new(0) = request.security(syminfo.tickerid, tf, detect()) //Bull FVG's if bull_fvg and new_fvg.t != t if dynamic max_bull_fvg := new_fvg.max min_bull_fvg := new_fvg.min //Populate FVG array if not dynamic fvg_areas.unshift(box.new(n-2, new_fvg.max, n+extend, new_fvg.min, na, bgcolor = bullCss)) fvg_records.unshift(new_fvg) bull_count += 1 t := new_fvg.t else if dynamic max_bull_fvg := math.max(math.min(close, max_bull_fvg), min_bull_fvg) //Bear FVG's if bear_fvg and new_fvg.t != t if dynamic max_bear_fvg := new_fvg.max min_bear_fvg := new_fvg.min //Populate FVG array if not dynamic fvg_areas.unshift(box.new(n-2, new_fvg.max, n+extend, new_fvg.min, na, bgcolor = bearCss)) fvg_records.unshift(new_fvg) bear_count += 1 t := new_fvg.t else if dynamic min_bear_fvg := math.min(math.max(close, min_bear_fvg), max_bear_fvg) //-----------------------------------------------------------------------------} //Unmitigated/Mitigated lines //-----------------------------------------------------------------------------{ //Test for mitigation if fvg_records.size() > 0 for i = fvg_records.size()-1 to 0 get = fvg_records.get(i) if get.isbull if close < get.min //Display line if mitigated if mitigationLevels line.new(get.t , get.min , time , get.min , xloc.bar_time , color = bullCss , style = line.style_dashed) //Delete box if not dynamic area = fvg_areas.remove(i) area.delete() fvg_records.remove(i) bull_mitigated += 1 else if close > get.max //Display line if mitigated if mitigationLevels line.new(get.t , get.max , time , get.max , xloc.bar_time , color = bearCss , style = line.style_dashed) //Delete box if not dynamic area = fvg_areas.remove(i) area.delete() fvg_records.remove(i) bear_mitigated += 1 //Unmitigated lines var unmitigated = array.new(0) //Remove umitigated lines if barstate.islast and showLast > 0 and fvg_records.size() > 0 if unmitigated.size() > 0 for element in unmitigated element.delete() unmitigated.clear() for i = 0 to math.min(showLast-1, fvg_records.size()-1) get = fvg_records.get(i) unmitigated.push(line.new(get.t , get.isbull ? get.min : get.max , time , get.isbull ? get.min : get.max , xloc.bar_time , color = get.isbull ? bullCss : bearCss)) //-----------------------------------------------------------------------------} //Dashboard //-----------------------------------------------------------------------------{ var table_position = dashLoc == 'Bottom Left' ? position.bottom_left : dashLoc == 'Top Right' ? position.top_right : position.bottom_right var table_size = textSize == 'Tiny' ? size.tiny : textSize == 'Small' ? size.small : size.normal var tb = table.new(table_position, 3, 3 , bgcolor = #1e222d , border_color = #373a46 , border_width = 1 , frame_color = #373a46 , frame_width = 1) if showDash if barstate.isfirst tb.cell(1, 0, 'Bullish', text_color = bullCss.tosolid(), text_size = table_size) tb.cell(2, 0, 'Bearish', text_color = bearCss.tosolid(), text_size = table_size) tb.cell(0, 1, 'Count', text_size = table_size, text_color = color.white) tb.cell(0, 2, 'Mitigated', text_size = table_size, text_color = color.white) if barstate.islast tb.cell(1, 1, str.tostring(bull_count), text_color = bullCss.tosolid(), text_size = table_size) tb.cell(2, 1, str.tostring(bear_count), text_color = bearCss.tosolid(), text_size = table_size) tb.cell(1, 2, str.tostring(bull_mitigated / bull_count * 100, format.percent), text_color = bullCss.tosolid(), text_size = table_size) tb.cell(2, 2, str.tostring(bear_mitigated / bear_count * 100, format.percent), text_color = bearCss.tosolid(), text_size = table_size) //-----------------------------------------------------------------------------} //Plots //-----------------------------------------------------------------------------{ //Dynamic Bull FVG max_bull_plot = plot(max_bull_fvg, color = na) min_bull_plot = plot(min_bull_fvg, color = na) fill(max_bull_plot, min_bull_plot, color = bullCss) //Dynamic Bear FVG max_bear_plot = plot(max_bear_fvg, color = na) min_bear_plot = plot(min_bear_fvg, color = na) fill(max_bear_plot, min_bear_plot, color = bearCss) //-----------------------------------------------------------------------------} //Alerts //-----------------------------------------------------------------------------{ alertcondition(bull_count > bull_count , 'Bullish FVG', 'Bullish FVG detected') alertcondition(bear_count > bear_count , 'Bearish FVG', 'Bearish FVG detected') alertcondition(bull_mitigated > bull_mitigated , 'Bullish FVG Mitigation', 'Bullish FVG mitigated') alertcondition(bear_mitigated > bear_mitigated , 'Bearish FVG Mitigation', 'Bearish FVG mitigated') //-----------------------------------------------------------------------------}
Pine Script®指標
由hlva提供
Mongoose Unified Volatility Index (UVI) The Mongoose Unified Volatility Index (UVI) combines multiple volatility measures into a single normalized framework, helping traders track the full volatility cycle at a glance. Methodology UVI blends the following components: Bollinger Band Width% ATR% (Average True Range) Historical Volatility (close-to-close) Parkinson Volatility (high-low log range) Donchian Width% TR% (True Range percent) Each input is normalized into a 0–100 scale and weighted. A smoothed EMA acts as a trend filter. Adaptive percentiles define Quiet / Neutral / Active regimes, making UVI responsive across assets and timeframes. Features Composite Line (UVI) with dynamic coloring Green = volatility expanding above EMA Red = volatility decaying below EMA EMA Baseline (white) for context Regime Shading (Quiet, Neutral, Active) based on adaptive percentiles Expansion Signals (Exp Up / Exp Dn) when volatility crosses EMA around squeeze conditions Compact Stats Table (top-right) showing UVI, Percentile, Squeeze state, and Regime How to Use Quiet → Exp Up: Prime breakout setups. Market energy igniting. Active → Exp Dn: Trend exhaustion. Manage risk or fade extremes. Neutral Regime: Mid-volatility, expect chop and tactical swings. Gradient Fill: Quick bias check — green favors trend trades, red favors patience. UVI is best used as a volatility state detector to time entries/exits around compressions and expansions, rather than a standalone buy/sell tool.
Pine Script®指標
由TheRealMongoose提供
Multi-Period VWAP - SHERLOCKThe Multi-Timeframe VWAP Suite is a comprehensive and highly customizable indicator designed for traders who rely on Volume-Weighted Average Price (VWAP) across multiple timeframes and periods. This tool provides a complete suite of VWAP calculations, including daily, weekly, monthly, quarterly, yearly, and custom VWAPs, allowing traders to analyze price action and volume trends with precision. Whether you're a day trader, swing trader, or long-term investor, this indicator offers unparalleled flexibility and depth for your trading strategy. Multi-Timeframe VWAPs: Daily, Weekly, Monthly, Quarterly, and Yearly VWAPs: Track VWAP across various timeframes to identify key support and resistance levels.
Pine Script®指標
由NovaLiquidity提供
Jaxon007 Trend Cloud StrategyThe Jaxon007 Trend Cloud Strategy is a trend-following trading system that combines custom moving averages with ATR-based volatility zones to generate high-quality entries and exits. It is designed for traders who want to visualize trend strength and participate in retests during strong directional moves. 🔍 How It Works: Trend Direction is determined by comparing a fast and slow moving average. The selected moving average is adjusted using the Average True Range (ATR) to create dynamic volatility bands. When price crosses above the adjusted band in an uptrend, it triggers a long entry. When price crosses below the band in a downtrend, it triggers a short entry. Exits occur when the trend reverses or when price crosses the band in the opposite direction. ⚙ Customizable Parameters: MA Length – base moving average period. MA Type – choose from SMA, EMA, SMMA (RMA), WMA, or VWMA. ATR Length – fixed at 200 (can be made adjustable if needed). Cloud Colors – customizable for uptrend and downtrend visuals. Visual Features: Trend Cloud formed between the base MA and ATR-adjusted band. Buy/Sell Labels appear at valid entry points. Clear color-coded lines for quick trend recognition.
Pine Script®策略
由Jaxon007提供
RMA EMA Crossover | MisinkoMasterThe RMA EMA Crossover (REMAC) is a trend-following overlay indicator designed to detect shifts in market momentum using the interaction between a smoothed RMA (Relative Moving Average) and its EMA (Exponential Moving Average) counterpart. This combination provides fast, adaptive signals while reducing noise, making it suitable for a wide range of markets and timeframes. 🔎 Methodology RMA Calculation The Relative Moving Average (RMA) is calculated over the user-defined length. RMA is a type of smoothed moving average that reacts more gradually than a standard EMA, providing a stable baseline. EMA of RMA An Exponential Moving Average (EMA) is then applied to the RMA, creating a dual-layer moving average system. This combination amplifies trend signals while reducing false crossovers. Trend Detection (Crossover Logic) Bullish Signal (Trend Up) → When RMA crosses above EMA. Bearish Signal (Trend Down) → When EMA crosses above RMA. This simple crossover system identifies the direction of momentum shifts efficiently. 📈 Visualization RMA and EMA are plotted directly on the chart. Colors adapt dynamically to the current trend: Cyan / Green hues → RMA above EMA (bullish momentum). Magenta / Red hues → EMA above RMA (bearish momentum). Filled areas between the two lines highlight zones of trend alignment or divergence, making it easier to spot reversals at a glance. ⚡ Features Adjustable length parameter for RMA and EMA. Overlay format allows for direct integration with price charts. Visual trend scoring via color and fill for rapid assessment. Works well across all asset classes: crypto, forex, stocks, indices. ✅ Use Cases Trend Following → Stay on the right side of the market by following momentum shifts. Reversal Detection → Crossovers highlight early trend changes. Filter for Trading Systems → Use as a confirmation overlay for other indicators or strategies. Visual Market Insight → Filled zones provide immediate context for trend strength.
Pine Script®指標
由MisinkoMaster提供
Denys_MVT (Sessions Boxes)Denys_MVT (Sessions Boxes) This indicator highlights the main trading sessions — Asia, Frankfurt, London, and New York — directly on the chart. It helps traders visually separate market activity during different times of the day and quickly understand which session is currently active. 🔹 How it works You can choose between Box Mode (draws a box around the session’s high and low) or Fill Mode (background color for the session). Each session has its own customizable time range and color. Labels can be placed automatically at the beginning of each session. The script uses the time() function with your selected UTC offset to precisely map session times. 🔹 Features Displays Asia, Frankfurt, London, and New York sessions. Option to toggle between boxes and background shading. Adjustable transparency and session colors. Session labels for easier visual reference. Works on any symbol and timeframe. 🔹 How to use Add the indicator to your chart. Set your local UTC offset in the settings (default: UTC+2). Enable/disable sessions, change colors, or switch between Box/Fill mode. Use the session highlights to better understand when volatility typically increases and how different sessions interact.
Pine Script®指標
由Denys_MVT提供
MSFusion- MultiScoreFusionThis Pine Script strategy, MSFusion - MultiScoreFusion, combines Ichimoku components and Hull Moving Average (HMA) signals to generate a composite score for each bar. It evaluates several conditions—such as price crossing above HMA55, Tenkan and Kijun lines, and price position relative to the Ichimoku cloud—and assigns scores to each. The script displays a label with the total score and a tooltip listing the contributing conditions when a strong bullish signal is detected. This approach helps traders quickly assess market momentum and trend strength using multiple technical criteria.
Pine Script®指標
由anesboz3提供
Small Business Economic Conditions - Statistical Analysis ModelThe Small Business Economic Conditions Statistical Analysis Model (SBO-SAM) represents an econometric approach to measuring and analyzing the economic health of small business enterprises through multi-dimensional factor analysis and statistical methodologies. This indicator synthesizes eight fundamental economic components into a composite index that provides real-time assessment of small business operating conditions with statistical rigor. The model employs Z-score standardization, variance-weighted aggregation, higher-order moment analysis, and regime-switching detection to deliver comprehensive insights into small business economic conditions with statistical confidence intervals and multi-language accessibility. 1. Introduction and Theoretical Foundation The development of quantitative models for assessing small business economic conditions has gained significant importance in contemporary financial analysis, particularly given the critical role small enterprises play in economic development and employment generation. Small businesses, typically defined as enterprises with fewer than 500 employees according to the U.S. Small Business Administration, constitute approximately 99.9% of all businesses in the United States and employ nearly half of the private workforce (U.S. Small Business Administration, 2024). The theoretical framework underlying the SBO-SAM model draws extensively from established academic research in small business economics and quantitative finance. The foundational understanding of key drivers affecting small business performance builds upon the seminal work of Dunkelberg and Wade (2023) in their analysis of small business economic trends through the National Federation of Independent Business (NFIB) Small Business Economic Trends survey. Their research established the critical importance of optimism, hiring plans, capital expenditure intentions, and credit availability as primary determinants of small business performance. The model incorporates insights from Federal Reserve Board research, particularly the Senior Loan Officer Opinion Survey (Federal Reserve Board, 2024), which demonstrates the critical importance of credit market conditions in small business operations. This research consistently shows that small businesses face disproportionate challenges during periods of credit tightening, as they typically lack access to capital markets and rely heavily on bank financing. The statistical methodology employed in this model follows the econometric principles established by Hamilton (1989) in his work on regime-switching models and time series analysis. Hamilton's framework provides the theoretical foundation for identifying different economic regimes and understanding how economic relationships may vary across different market conditions. The variance-weighted aggregation technique draws from modern portfolio theory as developed by Markowitz (1952) and later refined by Sharpe (1964), applying these concepts to economic indicator construction rather than traditional asset allocation. Additional theoretical support comes from the work of Engle and Granger (1987) on cointegration analysis, which provides the statistical framework for combining multiple time series while maintaining long-term equilibrium relationships. The model also incorporates insights from behavioral economics research by Kahneman and Tversky (1979) on prospect theory, recognizing that small business decision-making may exhibit systematic biases that affect economic outcomes. 2. Model Architecture and Component Structure The SBO-SAM model employs eight orthogonalized economic factors that collectively capture the multifaceted nature of small business operating conditions. Each component is normalized using Z-score standardization with a rolling 252-day window, representing approximately one business year of trading data. This approach ensures statistical consistency across different market regimes and economic cycles, following the methodology established by Tsay (2010) in his treatment of financial time series analysis. 2.1 Small Cap Relative Performance Component The first component measures the performance of the Russell 2000 index relative to the S&P 500, capturing the market-based assessment of small business equity valuations. This component reflects investor sentiment toward smaller enterprises and provides a forward-looking perspective on small business prospects. The theoretical justification for this component stems from the efficient market hypothesis as formulated by Fama (1970), which suggests that stock prices incorporate all available information about future prospects. The calculation employs a 20-day rate of change with exponential smoothing to reduce noise while preserving signal integrity. The mathematical formulation is: Small_Cap_Performance = (Russell_2000_t / S&P_500_t) / (Russell_2000_{t-20} / S&P_500_{t-20}) - 1 This relative performance measure eliminates market-wide effects and isolates the specific performance differential between small and large capitalization stocks, providing a pure measure of small business market sentiment. 2.2 Credit Market Conditions Component Credit Market Conditions constitute the second component, incorporating commercial lending volumes and credit spread dynamics. This factor recognizes that small businesses are particularly sensitive to credit availability and borrowing costs, as established in numerous Federal Reserve studies (Bernanke and Gertler, 1995). Small businesses typically face higher borrowing costs and more stringent lending standards compared to larger enterprises, making credit conditions a critical determinant of their operating environment. The model calculates credit spreads using high-yield bond ETFs relative to Treasury securities, providing a market-based measure of credit risk premiums that directly affect small business borrowing costs. The component also incorporates commercial and industrial loan growth data from the Federal Reserve's H.8 statistical release, which provides direct evidence of lending activity to businesses. The mathematical specification combines these elements as: Credit_Conditions = α₁ × (HYG_t / TLT_t) + α₂ × C&I_Loan_Growth_t where HYG represents high-yield corporate bond ETF prices, TLT represents long-term Treasury ETF prices, and C&I_Loan_Growth represents the rate of change in commercial and industrial loans outstanding. 2.3 Labor Market Dynamics Component The Labor Market Dynamics component captures employment cost pressures and labor availability metrics through the relationship between job openings and unemployment claims. This factor acknowledges that labor market tightness significantly impacts small business operations, as these enterprises typically have less flexibility in wage negotiations and face greater challenges in attracting and retaining talent during periods of low unemployment. The theoretical foundation for this component draws from search and matching theory as developed by Mortensen and Pissarides (1994), which explains how labor market frictions affect employment dynamics. Small businesses often face higher search costs and longer hiring processes, making them particularly sensitive to labor market conditions. The component is calculated as: Labor_Tightness = Job_Openings_t / (Unemployment_Claims_t × 52) This ratio provides a measure of labor market tightness, with higher values indicating greater difficulty in finding workers and potential wage pressures. 2.4 Consumer Demand Strength Component Consumer Demand Strength represents the fourth component, combining consumer sentiment data with retail sales growth rates. Small businesses are disproportionately affected by consumer spending patterns, making this component crucial for assessing their operating environment. The theoretical justification comes from the permanent income hypothesis developed by Friedman (1957), which explains how consumer spending responds to both current conditions and future expectations. The model weights consumer confidence and actual spending data to provide both forward-looking sentiment and contemporaneous demand indicators. The specification is: Demand_Strength = β₁ × Consumer_Sentiment_t + β₂ × Retail_Sales_Growth_t where β₁ and β₂ are determined through principal component analysis to maximize the explanatory power of the combined measure. 2.5 Input Cost Pressures Component Input Cost Pressures form the fifth component, utilizing producer price index data to capture inflationary pressures on small business operations. This component is inversely weighted, recognizing that rising input costs negatively impact small business profitability and operating conditions. Small businesses typically have limited pricing power and face challenges in passing through cost increases to customers, making them particularly vulnerable to input cost inflation. The theoretical foundation draws from cost-push inflation theory as described by Gordon (1988), which explains how supply-side price pressures affect business operations. The model employs a 90-day rate of change to capture medium-term cost trends while filtering out short-term volatility: Cost_Pressure = -1 × (PPI_t / PPI_{t-90} - 1) The negative weighting reflects the inverse relationship between input costs and business conditions. 2.6 Monetary Policy Impact Component Monetary Policy Impact represents the sixth component, incorporating federal funds rates and yield curve dynamics. Small businesses are particularly sensitive to interest rate changes due to their higher reliance on variable-rate financing and limited access to capital markets. The theoretical foundation comes from monetary transmission mechanism theory as developed by Bernanke and Blinder (1992), which explains how monetary policy affects different segments of the economy. The model calculates the absolute deviation of federal funds rates from a neutral 2% level, recognizing that both extremely low and high rates can create operational challenges for small enterprises. The yield curve component captures the shape of the term structure, which affects both borrowing costs and economic expectations: Monetary_Impact = γ₁ × |Fed_Funds_Rate_t - 2.0| + γ₂ × (10Y_Yield_t - 2Y_Yield_t) 2.7 Currency Valuation Effects Component Currency Valuation Effects constitute the seventh component, measuring the impact of US Dollar strength on small business competitiveness. A stronger dollar can benefit businesses with significant import components while disadvantaging exporters. The model employs Dollar Index volatility as a proxy for currency-related uncertainty that affects small business planning and operations. The theoretical foundation draws from international trade theory and the work of Krugman (1987) on exchange rate effects on different business segments. Small businesses often lack hedging capabilities, making them more vulnerable to currency fluctuations: Currency_Impact = -1 × DXY_Volatility_t 2.8 Regional Banking Health Component The eighth and final component, Regional Banking Health, assesses the relative performance of regional banks compared to large financial institutions. Regional banks traditionally serve as primary lenders to small businesses, making their health a critical factor in small business credit availability and overall operating conditions. This component draws from the literature on relationship banking as developed by Boot (2000), which demonstrates the importance of bank-borrower relationships, particularly for small enterprises. The calculation compares regional bank performance to large financial institutions: Banking_Health = (Regional_Banks_Index_t / Large_Banks_Index_t) - 1 3. Statistical Methodology and Advanced Analytics The model employs statistical techniques to ensure robustness and reliability. Z-score normalization is applied to each component using rolling 252-day windows, providing standardized measures that remain consistent across different time periods and market conditions. This approach follows the methodology established by Engle and Granger (1987) in their cointegration analysis framework. 3.1 Variance-Weighted Aggregation The composite index calculation utilizes variance-weighted aggregation, where component weights are determined by the inverse of their historical variance. This approach, derived from modern portfolio theory, ensures that more stable components receive higher weights while reducing the impact of highly volatile factors. The mathematical formulation follows the principle that optimal weights are inversely proportional to variance, maximizing the signal-to-noise ratio of the composite indicator. The weight for component i is calculated as: w_i = (1/σᵢ²) / Σⱼ(1/σⱼ²) where σᵢ² represents the variance of component i over the lookback period. 3.2 Higher-Order Moment Analysis Higher-order moment analysis extends beyond traditional mean and variance calculations to include skewness and kurtosis measurements. Skewness provides insight into the asymmetry of the sentiment distribution, while kurtosis measures the tail behavior and potential for extreme events. These metrics offer valuable information about the underlying distribution characteristics and potential regime changes. Skewness is calculated as: Skewness = E / σ³ Kurtosis is calculated as: Kurtosis = E / σ⁴ - 3 where μ represents the mean and σ represents the standard deviation of the distribution. 3.3 Regime-Switching Detection The model incorporates regime-switching detection capabilities based on the Hamilton (1989) framework. This allows for identification of different economic regimes characterized by distinct statistical properties. The regime classification employs percentile-based thresholds: - Regime 3 (Very High): Percentile rank > 80 - Regime 2 (High): Percentile rank 60-80 - Regime 1 (Moderate High): Percentile rank 50-60 - Regime 0 (Neutral): Percentile rank 40-50 - Regime -1 (Moderate Low): Percentile rank 30-40 - Regime -2 (Low): Percentile rank 20-30 - Regime -3 (Very Low): Percentile rank < 20 3.4 Information Theory Applications The model incorporates information theory concepts, specifically Shannon entropy measurement, to assess the information content of the sentiment distribution. Shannon entropy, as developed by Shannon (1948), provides a measure of the uncertainty or information content in a probability distribution: H(X) = -Σᵢ p(xᵢ) log₂ p(xᵢ) Higher entropy values indicate greater unpredictability and information content in the sentiment series. 3.5 Long-Term Memory Analysis The Hurst exponent calculation provides insight into the long-term memory characteristics of the sentiment series. Originally developed by Hurst (1951) for analyzing Nile River flow patterns, this measure has found extensive application in financial time series analysis. The Hurst exponent H is calculated using the rescaled range statistic: H = log(R/S) / log(T) where R/S represents the rescaled range and T represents the time period. Values of H > 0.5 indicate long-term positive autocorrelation (persistence), while H < 0.5 indicates mean-reverting behavior. 3.6 Structural Break Detection The model employs Chow test approximation for structural break detection, based on the methodology developed by Chow (1960). This technique identifies potential structural changes in the underlying relationships by comparing the stability of regression parameters across different time periods: Chow_Statistic = (RSS_restricted - RSS_unrestricted) / RSS_unrestricted × (n-2k)/k where RSS represents residual sum of squares, n represents sample size, and k represents the number of parameters. 4. Implementation Parameters and Configuration 4.1 Language Selection Parameters The model provides comprehensive multi-language support across five languages: English, German (Deutsch), Spanish (Español), French (Français), and Japanese (日本語). This feature enhances accessibility for international users and ensures cultural appropriateness in terminology usage. The language selection affects all internal displays, statistical classifications, and alert messages while maintaining consistency in underlying calculations. 4.2 Model Configuration Parameters Calculation Method: Users can select from four aggregation methodologies: - Equal-Weighted: All components receive identical weights - Variance-Weighted: Components weighted inversely to their historical variance - Principal Component: Weights determined through principal component analysis - Dynamic: Adaptive weighting based on recent performance Sector Specification: The model allows for sector-specific calibration: - General: Broad-based small business assessment - Retail: Emphasis on consumer demand and seasonal factors - Manufacturing: Enhanced weighting of input costs and currency effects - Services: Focus on labor market dynamics and consumer demand - Construction: Emphasis on credit conditions and monetary policy Lookback Period: Statistical analysis window ranging from 126 to 504 trading days, with 252 days (one business year) as the optimal default based on academic research. Smoothing Period: Exponential moving average period from 1 to 21 days, with 5 days providing optimal noise reduction while preserving signal integrity. 4.3 Statistical Threshold Parameters Upper Statistical Boundary: Configurable threshold between 60-80 (default 70) representing the upper significance level for regime classification. Lower Statistical Boundary: Configurable threshold between 20-40 (default 30) representing the lower significance level for regime classification. Statistical Significance Level (α): Alpha level for statistical tests, configurable between 0.01-0.10 with 0.05 as the standard academic default. 4.4 Display and Visualization Parameters Color Theme Selection: Eight professional color schemes optimized for different user preferences and accessibility requirements: - Gold: Traditional financial industry colors - EdgeTools: Professional blue-gray scheme - Behavioral: Psychology-based color mapping - Quant: Value-based quantitative color scheme - Ocean: Blue-green maritime theme - Fire: Warm red-orange theme - Matrix: Green-black technology theme - Arctic: Cool blue-white theme Dark Mode Optimization: Automatic color adjustment for dark chart backgrounds, ensuring optimal readability across different viewing conditions. Line Width Configuration: Main index line thickness adjustable from 1-5 pixels for optimal visibility. Background Intensity: Transparency control for statistical regime backgrounds, adjustable from 90-99% for subtle visual enhancement without distraction. 4.5 Alert System Configuration Alert Frequency Options: Three frequency settings to match different trading styles: - Once Per Bar: Single alert per bar formation - Once Per Bar Close: Alert only on confirmed bar close - All: Continuous alerts for real-time monitoring Statistical Extreme Alerts: Notifications when the index reaches 99% confidence levels (Z-score > 2.576 or < -2.576). Regime Transition Alerts: Notifications when statistical boundaries are crossed, indicating potential regime changes. 5. Practical Application and Interpretation Guidelines 5.1 Index Interpretation Framework The SBO-SAM index operates on a 0-100 scale with statistical normalization ensuring consistent interpretation across different time periods and market conditions. Values above 70 indicate statistically elevated small business conditions, suggesting favorable operating environment with potential for expansion and growth. Values below 30 indicate statistically reduced conditions, suggesting challenging operating environment with potential constraints on business activity. The median reference line at 50 represents the long-term equilibrium level, with deviations providing insight into cyclical conditions relative to historical norms. The statistical confidence bands at 95% levels (approximately ±2 standard deviations) help identify when conditions reach statistically significant extremes. 5.2 Regime Classification System The model employs a seven-level regime classification system based on percentile rankings: Very High Regime (P80+): Exceptional small business conditions, typically associated with strong economic growth, easy credit availability, and favorable regulatory environment. Historical analysis suggests these periods often precede economic peaks and may warrant caution regarding sustainability. High Regime (P60-80): Above-average conditions supporting business expansion and investment. These periods typically feature moderate growth, stable credit conditions, and positive consumer sentiment. Moderate High Regime (P50-60): Slightly above-normal conditions with mixed signals. Careful monitoring of individual components helps identify emerging trends. Neutral Regime (P40-50): Balanced conditions near long-term equilibrium. These periods often represent transition phases between different economic cycles. Moderate Low Regime (P30-40): Slightly below-normal conditions with emerging headwinds. Early warning signals may appear in credit conditions or consumer demand. Low Regime (P20-30): Below-average conditions suggesting challenging operating environment. Businesses may face constraints on growth and expansion. Very Low Regime (P0-20): Severely constrained conditions, typically associated with economic recessions or financial crises. These periods often present opportunities for contrarian positioning. 5.3 Component Analysis and Diagnostics Individual component analysis provides valuable diagnostic information about the underlying drivers of overall conditions. Divergences between components can signal emerging trends or structural changes in the economy. Credit-Labor Divergence: When credit conditions improve while labor markets tighten, this may indicate early-stage economic acceleration with potential wage pressures. Demand-Cost Divergence: Strong consumer demand coupled with rising input costs suggests inflationary pressures that may constrain small business margins. Market-Fundamental Divergence: Disconnection between small-cap equity performance and fundamental conditions may indicate market inefficiencies or changing investor sentiment. 5.4 Temporal Analysis and Trend Identification The model provides multiple temporal perspectives through momentum analysis, rate of change calculations, and trend decomposition. The 20-day momentum indicator helps identify short-term directional changes, while the Hodrick-Prescott filter approximation separates cyclical components from long-term trends. Acceleration analysis through second-order momentum calculations provides early warning signals for potential trend reversals. Positive acceleration during declining conditions may indicate approaching inflection points, while negative acceleration during improving conditions may suggest momentum loss. 5.5 Statistical Confidence and Uncertainty Quantification The model provides comprehensive uncertainty quantification through confidence intervals, volatility measures, and regime stability analysis. The 95% confidence bands help users understand the statistical significance of current readings and identify when conditions reach historically extreme levels. Volatility analysis provides insight into the stability of current conditions, with higher volatility indicating greater uncertainty and potential for rapid changes. The regime stability measure, calculated as the inverse of volatility, helps assess the sustainability of current conditions. 6. Risk Management and Limitations 6.1 Model Limitations and Assumptions The SBO-SAM model operates under several important assumptions that users must understand for proper interpretation. The model assumes that historical relationships between economic variables remain stable over time, though the regime-switching framework helps accommodate some structural changes. The 252-day lookback period provides reasonable statistical power while maintaining sensitivity to changing conditions, but may not capture longer-term structural shifts. The model's reliance on publicly available economic data introduces inherent lags in some components, particularly those based on government statistics. Users should consider these timing differences when interpreting real-time conditions. Additionally, the model's focus on quantitative factors may not fully capture qualitative factors such as regulatory changes, geopolitical events, or technological disruptions that could significantly impact small business conditions. The model's timeframe restrictions ensure statistical validity by preventing application to intraday periods where the underlying economic relationships may be distorted by market microstructure effects, trading noise, and temporal misalignment with the fundamental data sources. Users must utilize daily or longer timeframes to ensure the model's statistical foundations remain valid and interpretable. 6.2 Data Quality and Reliability Considerations The model's accuracy depends heavily on the quality and availability of underlying economic data. Market-based components such as equity indices and bond prices provide real-time information but may be subject to short-term volatility unrelated to fundamental conditions. Economic statistics provide more stable fundamental information but may be subject to revisions and reporting delays. Users should be aware that extreme market conditions may temporarily distort some components, particularly those based on financial market data. The model's statistical normalization helps mitigate these effects, but users should exercise additional caution during periods of market stress or unusual volatility. 6.3 Interpretation Caveats and Best Practices The SBO-SAM model provides statistical analysis and should not be interpreted as investment advice or predictive forecasting. The model's output represents an assessment of current conditions based on historical relationships and may not accurately predict future outcomes. Users should combine the model's insights with other analytical tools and fundamental analysis for comprehensive decision-making. The model's regime classifications are based on historical percentile rankings and may not fully capture the unique characteristics of current economic conditions. Users should consider the broader economic context and potential structural changes when interpreting regime classifications. 7. Academic References and Bibliography Bernanke, B. S., & Blinder, A. S. (1992). The Federal Funds Rate and the Channels of Monetary Transmission. American Economic Review, 82(4), 901-921. Bernanke, B. S., & Gertler, M. (1995). Inside the Black Box: The Credit Channel of Monetary Policy Transmission. Journal of Economic Perspectives, 9(4), 27-48. Boot, A. W. A. (2000). Relationship Banking: What Do We Know? Journal of Financial Intermediation, 9(1), 7-25. Chow, G. C. (1960). Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica, 28(3), 591-605. Dunkelberg, W. C., & Wade, H. (2023). NFIB Small Business Economic Trends. National Federation of Independent Business Research Foundation, Washington, D.C. Engle, R. F., & Granger, C. W. J. (1987). Co-integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276. Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25(2), 383-417. Federal Reserve Board. (2024). Senior Loan Officer Opinion Survey on Bank Lending Practices. Board of Governors of the Federal Reserve System, Washington, D.C. Friedman, M. (1957). A Theory of the Consumption Function. Princeton University Press, Princeton, NJ. Gordon, R. J. (1988). The Role of Wages in the Inflation Process. American Economic Review, 78(2), 276-283. Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384. Hurst, H. E. (1951). Long-term Storage Capacity of Reservoirs. Transactions of the American Society of Civil Engineers, 116(1), 770-799. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291. Krugman, P. (1987). Pricing to Market When the Exchange Rate Changes. In S. W. Arndt & J. D. Richardson (Eds.), Real-Financial Linkages among Open Economies (pp. 49-70). MIT Press, Cambridge, MA. Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77-91. Mortensen, D. T., & Pissarides, C. A. (1994). Job Creation and Job Destruction in the Theory of Unemployment. Review of Economic Studies, 61(3), 397-415. Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423. Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. Journal of Finance, 19(3), 425-442. Tsay, R. S. (2010). Analysis of Financial Time Series (3rd ed.). John Wiley & Sons, Hoboken, NJ. U.S. Small Business Administration. (2024). Small Business Profile. Office of Advocacy, Washington, D.C. 8. Technical Implementation Notes The SBO-SAM model is implemented in Pine Script version 6 for the TradingView platform, ensuring compatibility with modern charting and analysis tools. The implementation follows best practices for financial indicator development, including proper error handling, data validation, and performance optimization. The model includes comprehensive timeframe validation to ensure statistical accuracy and reliability. The indicator operates exclusively on daily (1D) timeframes or higher, including weekly (1W), monthly (1M), and longer periods. This restriction ensures that the statistical analysis maintains appropriate temporal resolution for the underlying economic data sources, which are primarily reported on daily or longer intervals. When users attempt to apply the model to intraday timeframes (such as 1-minute, 5-minute, 15-minute, 30-minute, 1-hour, 2-hour, 4-hour, 6-hour, 8-hour, or 12-hour charts), the system displays a comprehensive error message in the user's selected language and prevents execution. This safeguard protects users from potentially misleading results that could occur when applying daily-based economic analysis to shorter timeframes where the underlying data relationships may not hold. The model's statistical calculations are performed using vectorized operations where possible to ensure computational efficiency. The multi-language support system employs Unicode character encoding to ensure proper display of international characters across different platforms and devices. The alert system utilizes TradingView's native alert functionality, providing users with flexible notification options including email, SMS, and webhook integrations. The alert messages include comprehensive statistical information to support informed decision-making. The model's visualization system employs professional color schemes designed for optimal readability across different chart backgrounds and display devices. The system includes dynamic color transitions based on momentum and volatility, professional glow effects for enhanced line visibility, and transparency controls that allow users to customize the visual intensity to match their preferences and analytical requirements. The clean confidence band implementation provides clear statistical boundaries without visual distractions, maintaining focus on the analytical content.
Pine Script®指標
由EdgeTools提供
Momentum Moving Averages | MisinkoMasterThe Momentum Moving Averages (MMA) indicator blends multiple moving averages into a single momentum-scoring framework, helping traders identify whether market conditions are favoring upside momentum or downside momentum. By comparing faster, more adaptive moving averages (DEMA, TEMA, ALMA, HMA) against a baseline EMA, the MMA produces a cumulative score that reflects the prevailing strength and direction of the trend. 🔎 Methodology Moving Averages Used EMA (Exponential Moving Average) → Baseline reference. DEMA (Double Exponential Moving Average) → Reacts faster than EMA. TEMA (Triple Exponential Moving Average) → Even faster, reduces lag further. ALMA (Arnaud Legoux Moving Average) → Smooth but adaptive, with adjustable σ and offset. HMA (Hull Moving Average) → Very responsive, reduces lag, ideal for momentum shifts. Scoring System Each comparison is made against the EMA baseline: If another MA is above EMA → +1 point. If another MA is below EMA → -1 point. The total score reflects overall momentum: Positive score → Bullish bias. Negative score → Bearish bias. Trend Logic Bullish Signal → When the score crosses above 0.1. Bearish Signal → When the score crosses below -0.1. Neutral or sideways trends are identified when the score remains between thresholds. 📈 Visualization All five moving averages are plotted on the chart. Colors adapt to the current score: Cyan (Bullish bias) → Positive momentum. Magenta (Bearish bias) → Negative momentum. Overlapping fills between MAs highlight zones of convergence/divergence, making momentum shifts visually clear. ⚡ Features Adjustable length parameter for all MAs. Adjustable ALMA parameters (sigma and offset). Cumulative momentum score system to filter false signals. Works across all markets (crypto, forex, stocks, indices). Overlay design for direct chart integration. ✅ Use Cases Trend Confirmation → Ensure alignment with market momentum. Momentum Shifts → Spot when faster MAs consistently outperform the baseline EMA. Entry & Exit Filter → Avoid trades when the score is neutral or indecisive. Divergence Visualizer → Filled zones make it easier to see when MAs begin separating or converging. Low History Required → Unlike most For Loops, this script does not require that much history, making it less lagging and more responsive ⚠️ Limitations Works best in trending conditions; performance decreases in sideways/choppy ranges. Sensitivity of signals depends on chosen length and ALMA settings. Should not be used as a standalone buy/sell system—combine with volume, structure, or higher timeframe analysis.
Pine Script®指標
由MisinkoMaster提供
Scenario Screener — Consolidation → Bullish SetupThe script combines multiple indicators to filter out false signals and only highlight strong conditions: Consolidation Check Uses ATR % of price → filters out stocks in tight ranges. Uses Choppiness Index → confirms sideways/non-trending behavior. Momentum Shift (Bullish Bias) MACD Histogram > 0 → bullish momentum starting. RSI between 55–70 → strength without being overbought. Stochastic %K & %D > 70 → confirms strong momentum. Volume & Accumulation Chaikin Money Flow (CMF > 0) → buying pressure. Chaikin Oscillator > 0 (debug only) → accumulation phase. Trend Direction +DI > -DI (from DMI) → buyers stronger than sellers. ADX between 18–40 → healthy trend strength (not too weak, not overheated). Breakout Filter (Optional) If enabled, requires price to cross above 20 SMA before signal confirmation. 📈 Outputs ✅ Green label (“MATCH”) below the bar when all bullish conditions align. ✅ Background highlight (light green) when signal appears. ✅ Info Table (top-right) summarizing key values: Signal = True/False MACD, CMF, Chaikin values
Pine Script®指標
由vaibhavchotaliya1990提供
DynamoSent DynamoSent Pro+ — Professional Listing (Preview) — Adaptive Macro Sentiment (v6) — Export, Adaptive Lookback, Confidence, Boxes, Heatmap + Dynamic OB/OS Preview / Experimental build. I’m actively refining this tool—your feedback is gold. If you spot edge cases, want new presets, or have market-specific ideas, please comment or DM me on TradingView. ⸻ What it is DynamoSent Pro+ is an adaptive, non-repainting macro sentiment engine that compresses VIX, DXY and a price-based activity proxy (e.g., SPX/sector ETF/your symbol) into a 0–100 sentiment line. It scales context by volatility (ATR%) and can self-calibrate with rolling quantile OB/OS. On top of that, it adds confidence scoring, a plain-English Context Coach, MTF agreement, exportable sentiment for other indicators, and a clean Light/Dark UI. Why it’s different • Adaptive lookback tracks regime changes: when volatility rises, we lengthen context; when it falls, we shorten—less whipsaw, more relevance. • Dynamic OB/OS (quantiles) self-calibrates to each instrument’s distribution—no arbitrary 30/70 lines. • MTF agreement + Confidence gate reduce false positives by highlighting alignment across timeframes. • Exportable output: hidden plot “DynamoSent Export” can be selected as input.source in your other Pine scripts. • Non-repainting rigor: all request.security() calls use lookahead_off + gaps_on; signals wait for bar close. Key visuals • Sentiment line (0–100), OB/OS zones (static or dynamic), optional TF1/TF2 overlays. • Regime boxes (Overbought / Oversold / Neutral) that update live without repaint. • Info Panel with confidence heat, regime, trend arrow, MTF readout, and Coach sentence. • Session heat (Asia/EU/US) to match intraday behavior. • Light/Dark theme switch in Inputs (auto-contrasted labels & headers). ⸻ How to use (examples & recipes) 1) EURUSD (swing / intraday blend) • Preset: EURUSD 1H Swing • Chart: 1H; TF1=1H, TF2=4H (default). • Proxies: Defaults work (VIX=D, DXY=60, Proxy=D). • Dynamic OB/OS: ON at 20/80; Confidence ≥ 55–60. • Playbook: • When sentiment crosses above 50 + margin with Δ ≥ signalK and MTF agreement ≥ 0.5, treat as trend breakout. • In Oversold with rising Coach & TF agreement, take fade longs back toward mid-range. • Alerts: Enable Breakout Long/Short and Fade; keep cooldown 8–12 bars. 2) SPY (daytrading) • Preset: SPY 15m Daytrade; Chart: 15m. • VIX (D) matters more; preset weights already favor it. • Start with static 30/70; later try dynamic 25/75 for adaptive thresholds. • Use Coach: in US session, when it says “Overbought + MTF agree → sell rallies / chase breakouts”, lean momentum-continuation after pullbacks. 3) BTCUSD (crypto, 24/7) • Preset: BTCUSD 1H; Chart: 1H. • DXY and BTC.D inform macro tone; keep Carry-forward ON to bridge sparse ticks. • Prefer Dynamic OB/OS (15/85) for wider swings. • Fade signals on weekend chop; Breakout when Confidence > 60 and MTF ≥ 1.0. 4) XAUUSD (gold, macro blend) • Preset: XAUUSD 4H; Chart: 4H. • Weights tilt to DXY and US10Y (handled by preset). • Coach + MTF helps separate trend legs from news pops. ⸻ Best practices • Theme: Switch Light/Dark in Inputs; the panel adapts contrast automatically. • Export: In another script → Source → DynamoSent Pro+ → DynamoSent Export. Build your own filters/strategies atop the same sentiment. • Dynamic vs Static OB/OS: • Static 30/70: fast, universal baseline. • Dynamic (quantiles): instrument-aware; use 20/80 (default) or 15/85 for choppy markets. • Confidence gate: Start at 50–60% to filter noise; raise when you want only A-grade setups. • Adaptive Lookback: Keep ON. For ultra-liquid indices, you can switch it OFF and set a fixed lookback. ⸻ Non-repainting & safety notes • All request.security() calls use lookahead=barmerge.lookahead_off and gaps=barmerge.gaps_on. • No forward references; signals & regime flips are confirmed on bar close. • History-dependent funcs (ta.change, ta.percentile_linear_interpolation, etc.) are computed each bar (not conditionally). • Adaptive lookback is clamped ≥ 1 to avoid lowest/highest errors. • Missing-data warning triggers only when all proxies are NA for a streak; carry-forward can bridge small gaps without repaint. ⸻ Known limits & tips • If a proxy symbol isn’t available on your plan/exchange, you’ll see the NA warning: choose a different symbol via Symbol Search, or keep Carry-forward ON (it defaults to neutral where needed). • Intraday VIX is sparse—using Daily is intentional. • Dynamic OB/OS needs enough history (see dynLenFloor). On short histories it gracefully falls back to static levels. Thanks for trying the preview. Your comments drive the roadmap—presets, new proxies, extra alerts, and integrations.
Pine Script®指標
由ACTradeFIN提供
Trend Compass (Manual)## Trend Compass (Manual) - A Discretionary Trader's Dashboard ### Summary Trend Compass is a simple yet powerful dashboard designed for discretionary traders who want a constant, visual reminder of their market analysis directly on their chart. Instead of relying on automated indicators, this tool gives you **full manual control** to define the market state across different timeframes or conditions. It helps you stay aligned with your higher-level analysis (e.g., HTF bias, current market structure) and avoid making impulsive decisions that go against your plan. ### Key Features - **Fully Manual Control:** You decide the trend. No lagging indicators, no confusing signals. Just your own analysis, displayed clearly. - **Multiple Market States:** Define each row as an `Uptrend`, `Downtrend`, `Pullback`, or `Neutral` market. - **Customizable Rows:** Display up to 8 rows. You can label each one however you like (e.g., "D1", "H4", "Market Structure", "Liquidity Bias"). - **Flexible Panel:** Change all colors, text sizes, and place the panel in any of the 9 positions on your chart. - **Clean & Minimalist:** Designed to provide essential information at a glance without cluttering your chart. ### How to Use 1. **Add to Chart:** Add the indicator to your chart. 2. **Open Settings:** Go into the indicator settings. 3. **Configure Rows:** - In the "Rows (Manual Control)" section, set the "Number of rows" you want to display. - For each row, give it a custom **Label** (e.g., "m15"). - Select its current state from the dropdown menu (`Uptrend`, `Downtrend`, etc.). - To remove a row, simply set its state to `Hidden`. 4. **Customize Style:** - In the "Panel & Visual Style" section, adjust colors, text sizes, and the panel's position to match your chart's theme. This tool is perfect for price action traders, ICT/SMC traders, or anyone who values a clean chart and a disciplined approach to their analysis.
Pine Script®指標
由GCGFx_SMC提供
1
Ichimoku + MTF Dashboard (Confidence + Row Shading)Name: Ichimoku + Multi-Timeframe (MTF) Dashboard Purpose This indicator is designed to give a complete trend, momentum, and alignment picture of a stock across multiple timeframes (hourly, daily, weekly) using the Ichimoku Kinko Hyo system. It combines: Classic Ichimoku signals: Tenkan/Kijun crossovers, cloud position (Kumo), Chikou span, and cloud twists. MTF Dashboard: Aggregates hourly, daily, and weekly Ichimoku conditions into a clean visual table. Dynamic coloring: Each signal is represented with green/red fills, and rows are shaded for full alignment. Aggregate column highlights mixed signals in yellow.
Pine Script®指標
由6eshwar9提供
1
Entry Signals (Long/Short)The indicator visualizes precise entry signals for long and short setups directly on the price chart. Long is marked with a green triangle-up, short with a red triangle-down. To contextualize trend structure, the Fast EMA (5) is plotted in black and the Slow EMA (20) in blue (line width 1). Signals print only at bar close for reproducible execution. Applicable across all timeframes—ideal for top-down analysis from the 195-minute chart through daily to weekly.
Pine Script®指標
由tradegear9提供
7