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Indicadores e estratégias

Moving Averages with Alerts: 9, 21, 51, 100, 144, 200--- This indicator plots six configurable moving averages (MA) with options for EMA, SMA, RCI, HMA, and Pivô Boss types. It highlights key crossover points, especially monitoring the 9-period MA for crosses with others. Users can enable alerts for these crossovers, as well as set custom alerts between any two selected MAs. Additionally, the indicator marks the important crossovers of the 51 and 200 MAs on the chart with an “X”. This helps traders identify trend changes and potential entry or exit points efficiently. ---
Indicador Pine Script®
por P2Coin
AlgoPilotX - Trend & Momentum SuiteAlgoPilotX – Trend & Momentum Suite is an advanced all-in-one trend and momentum indicator designed to simplify trading decisions. This script combines Exponential Moving Averages (EMAs), Relative Strength Index (RSI) , and MACD into a clean, easy-to-read dashboard that appears on the top-right of your chart. It provides: Trend detection with EMAs (Bullish, Bearish, Neutral) Momentum insights via RSI (Bullish, Bearish, Overbought, Oversold) MACD status (Bullish, Bearish, Flat) Trend Strength Meter (0–100 score, with bar from weak → strong) Buy & Sell arrows on chart when key conditions align Custom alerts for major events (Buy/Sell signals, Strength crossing above 80 or below 20) 📊 How to Use Trend Box (Top-Right Panel) EMAs: Bullish when Fast EMA > Slow EMA. Bearish when Fast EMA < Slow EMA. RSI: 70 = Overbought. <30 = Oversold. 50+ = Bullish momentum. Below 50 = Bearish momentum. MACD: Bullish when histogram > 0. Bearish when histogram < 0. Strength Meter (0–100): <20 = Very weak trend. 20–50 = Weak/Neutral trend. 50–80 = Strengthening trend. 80 = Strong trend continuation zone. On-Chart Arrows Green Buy Arrow: Fast EMA crosses above Slow EMA RSI confirms bullish bias (>55) MACD histogram positive Red Sell Arrow: Fast EMA crosses below Slow EMA RSI confirms bearish bias (<45) MACD histogram negative Alerts Create TradingView alerts directly from the script conditions: Buy/Sell signals Strength > 80 (strong trend) Strength < 20 (weak trend) ⚠️ Disclaimer This indicator is for educational purposes only. It does not constitute financial advice or a guarantee of profits. Past performance is not indicative of future results. Trading involves risk, and you should always do your own research or consult with a licensed financial advisor before making investment decisions. By using this script, you acknowledge that you are solely responsible for your own trading decisions.
Indicador Pine Script®
por dhesitq1o0
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.
Estratégia Pine Script®
por r0b3rtcc
Kyle EMA 21/52/144 | Indicator Description & User Guide指标简介 | Overview 中文： 本脚本基于三条不同周期的指数移动平均线（EMA），分别为 21、52、144。 它可以用来快速判断价格短期、中期与长期趋势，捕捉支撑和压力位置，适合日内交易与波段交易者参考。 English: This script uses three Exponential Moving Averages (EMAs) with periods of 21, 52, and 144. It helps traders quickly identify short-, medium-, and long-term trends, as well as key support and resistance levels. It’s suitable for both intraday and swing trading. 参数说明 | Parameters 中文： Vade 1 (默认21)：短周期EMA，反映近期价格动能与短期趋势。 Vade 2 (默认52)：中周期EMA，用于平滑波动、识别中期趋势。 Vade 3 (默认144)：长周期EMA，用于识别长期趋势与重要支撑压力。 English: Vade 1 (default 21): Short-term EMA reflecting recent price momentum. Vade 2 (default 52): Medium-term EMA for smoothing price swings and identifying medium-term trends. Vade 3 (default 144): Long-term EMA for spotting major trends and significant support/resistance levels. 使用方法 | How to Use 1. 趋势判断 | Trend Identification 中文： 多头排列： M1（绿色）在M2（橙色）、M3（红色）之上 → 趋势偏多。 空头排列： M1在M2、M3之下 → 趋势偏空。 English: Bullish Trend: M1 (green) above M2 (orange) and M3 (red) → Uptrend. Bearish Trend: M1 below M2 and M3 → Downtrend. 2. 均线交叉信号 | EMA Crossovers 中文： 短周期向上突破长周期 → 可能是多头信号。 短周期向下跌破长周期 → 可能是空头信号。 English: Short-term EMA crossing above long-term EMA → Potential bullish signal. Short-term EMA crossing below long-term EMA → Potential bearish signal. 3. 支撑与压力参考 | Support and Resistance 中文： 价格回踩长周期均线(M3)后反弹 → 说明长周期支撑有效。 价格在长周期均线受阻回落 → 说明长周期压力有效。 English: Price bouncing off M3 (long-term EMA) → Long-term support confirmed. Price rejected at M3 → Long-term resistance confirmed. 4. 结合其他工具 | Combine with Other Tools 中文： 可与成交量、震荡指标（如RSI、MACD）搭配，提升信号可靠性。 English: Combine with volume or oscillators (like RSI or MACD) to improve signal reliability. 注意事项 | Notes 中文： 这是趋势型指标，不适用于极度震荡行情；在震荡市中需结合其他指标过滤信号。 周期可根据不同交易品种及周期自行调整，比如日线/4小时/1小时。 EMA本质是滞后指标，用于确认趋势与过滤噪音，而非单独的买卖信号。 English: This is a trend-following indicator and may not work well in highly choppy markets; combine with other tools to filter signals. You can adjust the periods depending on the instrument and timeframe (daily, 4H, 1H, etc.). EMAs are lagging indicators meant to confirm trends and reduce noise, not to provide standalone buy/sell signals.
Indicador Pine Script®
por fymy888888
Tomorrow's Pivot Points [SMH]這個TradingView指標不同於內置的Pivot Point指標，因為它能夠提前顯示明天的Pivot Point。透過預測下一交易日的支撐與阻力位置，交易員可以更早部署策略，為隔日的市場波動做好準備。 This TradingView indicator is different from the built-in Pivot Point tool because it can display tomorrow’s Pivot Points in advance. By forecasting support and resistance levels for the next trading day, traders can position their strategies earlier and be well-prepared for upcoming market movements.
Indicador Pine Script®
por Fresh_Signal_Trader
ORB + Session VWAP Pro (London & NY) — fixedORB + Session VWAP Pro (London & NY) — Listing copy (EN) What it is A clean, non-repainting intraday tool that fuses the classic Opening Range Breakout (ORB) with a session-anchored VWAP filter for London and New York. It highlights only the higher-quality breakouts (above/below session VWAP), adds an optional retest confirmation, and scores each signal with an intuitive Confidence metric (0–100). Why it works • ORB provides the day’s first actionable structure (range high/low). • Session VWAP filters “cheap” breaks and favors flows aligned with session value. • Optional retest reduces first-tick whipsaws. • Confidence blends breakout depth (vs ATR), VWAP slope and band distance. Key visuals • LDN/NY OR High/Low (line break style) + optional OR boxes. • Active Session VWAP (resets per signal window; falls back to daily VWAP outside). • Optional VWAP bands (stdev or %). • Session shading (London/NY windows). • Signal markers (LDN BUY/SELL, NY BUY/SELL) fired with cooldown. Signals • London Long / Short: Break of LDN OR High/Low ± ATR buffer, aligned with VWAP side. • NY Long / Short: Same logic during NY window. • Retest (optional): Requires a tag back to the OR level ± tolerance before confirmation. • Confidence: 0–100; gate via Min Confidence (default 55). Inputs that matter • Open Range Length (min): Default 15. • London/NY times & timezones. • ATR buffer & retest tolerance. • Bands mode: Stdev (with lookback) or % (e.g., 1%). • Signal cooldown: Avoids clutter on fast moves. Non-repaint policy • OR lines build within fixed time windows using the current bar’s timestamp. • VWAP is cumulative within the session window; no lookahead. • All ta.crossover/ta.crossunder are precomputed every bar (no conditional execution). • Signals are based on live bar values, not future bars. ⸻ Quick start (examples) 1) EURUSD, London momentum • Chart: 5m or 15m. • OR: 15 min starting 08:00 Europe/London. • Signals: Use defaults; keep ATR buffer = 0.2 and Retest = ON, Min Confidence ≥ 55. • Play: • BUY when price breaks LDN OR High + buffer and stays above VWAP; retest confirms. • Trail behind VWAP or band #1; partials into band #2. 2) NAS100, New York breakout & run • Chart: 5m. • NY window: 09:30 America/New_York, OR = 15 min. • Retest OFF on high momentum days; Min Confidence ≥ 60. • Use band mode Stdev, bandLen=50, show ±1/±2. • Momentum continuation: add on pullbacks that hold above VWAP after the breakout. 3) XAUUSD, London fake & VWAP fade • Chart: 5m. • Keep Retest ON; accept only shorts that break OR Low but retest fails back under VWAP. • Confidence gate ≥ 50 to allow more mean-reversion setups. ⸻ Pro tips • Adjust ATR buffer to the instrument: FX 0.15–0.25, indices 0.20–0.35, metals 0.20–0.30. • Retest ON for choppy conditions; OFF for news momentum. • Use VWAP bands: take partials at ±1; stretch targets at ±2/±3. • Session timezones are explicit (London/New York). Ensure they match your instrument’s behavior. • Pair with a higher-TF bias (e.g., 1H/4H trend) for directional filtering. ⸻ Alerts (ready to use) • ORB+SVWAP — LDN Long, LDN Short, NY Long, NY Short (Respect your cooldown; alerts fire only after confirmation and confidence gate.) ⸻ Known limits & notes • Designed for intraday. On 1D+ charts, session windows compress. • If your broker session differs from London/NY clocks on a holiday, adjust input times. • Session-anchored VWAP uses the script’s signal window, not exchange sessions, by design.
Indicador Pine Script®
por ACTradeFIN
AlgoPilotX - Market Stages (VWMA + Reversals)This indicator identifies key market stages and potential trend reversals using stacked VWMAs. Bullish and bearish reversals are marked with green/up and red/down arrows, with a handy top-right info box showing the color coding for each stage. It classifies the market into four stages: Acceleration (Green) : All VWMAs stacked bullish, price above VWMA – strong upward momentum. Accumulation (Silver) : VWMAs not stacked bullish, price above VWMA – early bullish build-up. Deceleration (Red) : All VWMAs stacked bearish, price below VWMA – strong downward momentum. Distribution (Orange) : VWMAs not stacked bearish, price below VWMA – early bearish buildup. It also highlights Bullish and Bearish Reversals with green/up and red/down arrows (“R”) directly on the chart, making it easy to spot potential trend changes. A fixed info box in the top-right corner summarizes the color coding for quick reference. Alerts are available for all stages and reversals, so you can automate notifications for key market events.
Indicador Pine Script®
por dhesitq1o0
1
AlgoPilotX – FibFusion Confluence PROTake your Fibonacci trading to the next level with AlgoPilotX – FibFusion Confluence PRO. This advanced TradingView indicator combines multi-timeframe Fibonacci retracements with key technical indicators to identify high-probability trading zones. Features: Detects TF and Higher Timeframe (HTF) Fibonacci levels automatically. Highlights Fib confluence zones with background colors for bullish and bearish probabilities. Displays arrows on exact Fib touches for quick visual cues. Integrated EMA, RSI, and MACD analysis to improve trend accuracy. Dynamic Trend Indicator : Bullish, Bearish, or Neutral. Interactive dashboard showing all key levels and indicators in real-time with intuitive color coding. Supports multiple chart timeframes and recalculates trend and confluence dynamically. Alerts for high probability bullish or bearish zones , ready to integrate into your trading strategy. How to Use: Observe price approaching Fibonacci levels displayed as lines and arrows. Confluence Zones: Green background: High-probability bullish zone. Red background: High-probability bearish zone. Neutral: No strong confluence. Confirm signals using EMA, RSI, and MACD. Use the dashboard to track all key levels, multi-timeframe Fibs, and current Trend. Enter trades near high-probability confluence zones and manage risk accordingly. Perfect for swing traders, day traders, and anyone using Fibonacci retracements to find precise entry and exit points.
Indicador Pine Script®
por dhesitq1o0
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.
Indicador Pine Script®
por EdgeTools
ninu3q merged//@version=6 indicator("Ultimate Trend + Momentum + Volume Pro (merged)", overlay=true, max_boxes_count=700, max_lines_count=300, max_labels_count=300) // ----------------------------- // 1) EMA Trend + VWAP Layer (combined) // ----------------------------- ema200 = ta.ema(close, 200) ema50 = ta.ema(close, 50) vwap = ta.vwap ema200Plot = plot(ema200, "EMA 200", color=color.red, linewidth=2, style=plot.style_line) ema50Plot = plot(ema50, "EMA 50", color=color.teal, linewidth=1, style=plot.style_line) vwapPlot = plot(vwap, "VWAP", color=color.orange, linewidth=1, style=plot.style_line) // Trick: combine them into a group so TradingView counts less plot(na) // placeholder, only one is really required // ----------------------------- // 2) UT Bot Alerts // ----------------------------- utAtrPeriod = input.int(10, "UT ATR Period") utAtrMultiplier = input.float(2.0, "UT ATR Multiplier") utAtr = ta.atr(utAtrPeriod) utUpper = close + utAtrMultiplier * utAtr utLower = close - utAtrMultiplier * utAtr utBuy = ta.crossover(close, utUpper) utSell = ta.crossunder(close, utLower) plotshape(utBuy, "UT Buy", location=location.belowbar, color=color.green, style=shape.labelup, text="BUY") plotshape(utSell, "UT Sell", location=location.abovebar, color=color.red, style=shape.labeldown, text="SELL") // ----------------------------- // 3) Volume Profile (anchored to last N bars) // ----------------------------- barsBack = input.int(150, "Bars Back", minval=1, maxval=5000) cols = input.int(35, "Columns", minval=5, maxval=200) vaPct = input.float(70.0, "Value Area %", minval=40.0, maxval=99.0) histWidth = input.int(24, "Histogram Width (bars)", minval=6, maxval=200) direction = input.string("Into chart (left)", "Histogram Direction", options= ) // Block/line styles blockFillColor = input.color(#B0B0B0, "Volume Block Fill Color") blockFillOpacity = input.int(70, "Volume Block Fill Opacity %", minval=0, maxval=100) blockBorderColor = input.color(#000000, "Volume Block Border Color") blockBorderOpacity = input.int(0, "Volume Block Border Opacity %", minval=0, maxval=100) showPOC = input.bool(true, "Show POC Line") pocColor = input.color(#FF0000, "POC Color") pocWidth = input.int(2, "POC Width", minval=1, maxval=6) showVA = input.bool(false, "Show VAH/VAL Lines") vaColor = input.color(#FFA500, "VA Color") vaWidth = input.int(1, "VA Width", minval=1, maxval=6) showVWAP = input.bool(false, "Show AVWAP Line") vwapColor = input.color(#0000FF, "AVWAP Color") vwapWidth = input.int(1, "AVWAP Width", minval=1, maxval=6) showLabels = input.bool(false, "Show Line Labels") priceForBin = hlcc4 // Draw registries var boxesArr = array.new_box() var linesArr = array.new_line() var labelsArr = array.new_label() f_wipe() => while array.size(boxesArr) > 0 box.delete(array.pop(boxesArr)) while array.size(linesArr) > 0 line.delete(array.pop(linesArr)) while array.size(labelsArr) > 0 label.delete(array.pop(labelsArr)) if barstate.islast f_wipe() eff = math.min(barsBack, bar_index + 1) if eff > 1 float pMin = na float pMax = na float pvSum = 0.0 float vSum = 0.0 for look = 0 to eff - 1 lo = low hi = high pMin := na(pMin) ? lo : math.min(pMin, lo) pMax := na(pMax) ? hi : math.max(pMax, hi) pvSum += priceForBin * volume vSum += volume anchoredVWAP = vSum > 0 ? pvSum / vSum : na if not na(pMin) and not na(pMax) and pMax > pMin step = (pMax - pMin) / cols step := step == 0.0 ? syminfo.mintick : step var vols = array.new_float() var lows = array.new_float() var highs = array.new_float() array.clear(vols), array.clear(lows), array.clear(highs) for i = 0 to cols - 1 array.push(vols, 0.0) lo = pMin + i * step hi = lo + step array.push(lows, lo) array.push(highs, hi) for look = 0 to eff - 1 pr = priceForBin vol = volume idx = int(math.floor((pr - pMin) / step)) idx := idx < 0 ? 0 : idx > cols - 1 ? cols - 1 : idx array.set(vols, idx, array.get(vols, idx) + vol) pocIdx = 0 pocVol = 0.0 totalVol = 0.0 for i = 0 to cols - 1 v = array.get(vols, i) totalVol += v if v > pocVol pocVol := v pocIdx := i targetVol = totalVol * (vaPct / 100.0) left = pocIdx right = pocIdx cumVA = array.get(vols, pocIdx) while cumVA < targetVol and (left > 0 or right < cols - 1) vLeft = left > 0 ? array.get(vols, left - 1) : -1.0 vRight = right < cols - 1 ? array.get(vols, right + 1) : -1.0 if vRight > vLeft right += 1 cumVA += array.get(vols, right) else if vLeft >= 0 left -= 1 cumVA += array.get(vols, left) else break VAH = array.get(highs, right) VAL = array.get(lows, left) profileStart = bar_index - (eff - 1) rightStart = bar_index + 1 rightEnd = bar_index + 1 + histWidth intoChart = direction == "Into chart (left)" for i = 0 to cols - 1 v = array.get(vols, i) len = pocVol > 0 ? (v / pocVol) : 0.0 px = int(math.round(len * histWidth)) x1 = intoChart ? (rightEnd - px) : rightStart x2 = intoChart ? rightEnd : (rightStart + px) y1 = array.get(lows, i) y2 = array.get(highs, i) b = box.new(x1, y2, x2, y1, xloc=xloc.bar_index, border_color=color.new(blockBorderColor, blockBorderOpacity)) box.set_bgcolor(b, color.new(blockFillColor, 100 - blockFillOpacity)) array.push(boxesArr, b) if showPOC pocPrice = (array.get(lows, pocIdx) + array.get(highs, pocIdx)) / 2.0 lnPOC = line.new(profileStart, pocPrice, rightEnd, pocPrice, xloc=xloc.bar_index, extend=extend.right, color=pocColor, width=pocWidth) array.push(linesArr, lnPOC) if showLabels lbPOC = label.new(rightEnd, pocPrice, "POC", xloc=xloc.bar_index, style=label.style_label_right, textcolor=color.white, color=pocColor) array.push(labelsArr, lbPOC) if showVA lnVAL = line.new(profileStart, VAL, rightEnd, VAL, xloc=xloc.bar_index, extend=extend.right, color=vaColor, width=vaWidth) lnVAH = line.new(profileStart, VAH, rightEnd, VAH, xloc=xloc.bar_index, extend=extend.right, color=vaColor, width=vaWidth) array.push(linesArr, lnVAL) array.push(linesArr, lnVAH) if showLabels lbVAH = label.new(rightEnd, VAH, "VAH", xloc=xloc.bar_index, style=label.style_label_right, textcolor=color.white, color=vaColor) lbVAL = label.new(rightEnd, VAL, "VAL", xloc=xloc.bar_index, style=label.style_label_right, textcolor=color.white, color=vaColor) array.push(labelsArr, lbVAH) array.push(labelsArr, lbVAL) if showVWAP and not na(anchoredVWAP) lnVW = line.new(profileStart, anchoredVWAP, rightEnd, anchoredVWAP, xloc=xloc.bar_index, extend=extend.right, color=vwapColor, width=vwapWidth) array.push(linesArr, lnVW) if showLabels lbVW = label.new(rightEnd, anchoredVWAP, "AVWAP", xloc=xloc.bar_index, style=label.style_label_right, textcolor=color.white, color=vwapColor) array.push(labelsArr, lbVW) // placeholder plot plot(na)
Indicador Pine Script®
por Ninu3q
Day Trader Trend & Triggers + Mini-Meter — v6**Day Trader Trend & Triggers — Intraday** A fast, intraday trend and entry tool designed for **1m–15m charts**. It identifies **strong up/down trends** using: * **MA ribbon:** EMA9 > EMA21 > EMA50 (or inverse) for directional bias. * **Momentum:** RSI(50-line) and MACD histogram flips. * **Volume & VWAP:** only confirms when volume expands above SMA(20) and price is above/below VWAP. * **Higher-TF bias filter (optional):** e.g., align 1m/5m signals with the 15m trend. When all align, the background highlights and the mini-meter shows UP/DOWN. It also plots **entries**: * **Pullbacks** to EMA21/EMA50 with a MACD re-cross, * **Breakouts** of recent highs/lows on strong volume. Built-in **alerts** for trend flips, pullbacks, and breakouts let you trade hands-off. Best used on **5m for active day trades**, with 1m/3m for scalping and 15m for cleaner intraday swings.
Indicador Pine Script®
por kahrendt28
Multi-Timeframe Bias by Atif MuzzammilMulti-Timeframe Bias Indicator This indicator implements multi TF bias concepts across multiple timeframes simultaneously. It identifies and displays bias levels. Key Features: Multi-Timeframe Analysis (Up to 5 Timeframes) Supports all major timeframes: 5m, 15m, 30m, 1H, 4H, Daily, Weekly, Monthly Each timeframe displays independently with customisable colors and line weights Clean visual separation between different timeframe bias levels ICT Bias Logic Bearish Bias: Previous period close below the prior period's low Bullish Bias: Previous period close above the prior period's high Ranging Bias: Previous period close within the prior period's range Draws horizontal lines at previous period's high and low levels Advanced Customisation Individual enable/disable for each timeframe Custom colors and line thickness per timeframe Comprehensive label settings with 4 position options Adjustable label size, style (background/no background/text only) Horizontal label positioning (0-100%) for optimal placement Vertical offset controls for fine-tuning Smart Detection Automatic timeframe change detection using multiple methods Enhanced detection for 4H, Weekly, and Monthly periods Works correctly when viewing same timeframe as bias timeframe Proper handling of market session boundaries Clean Interface Simple timeframe identification labels Non-intrusive design that doesn't obstruct price action Organized settings grouped by function Debug mode available for troubleshooting Compatible with all chart timeframes and works on any market that follows standard session timing.
Indicador Pine Script®
por Atif90