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$ - HTF Sweeps & PO3HTF Sweeps & PO3 Indicator The HTF Sweeps & PO3 indicator is a powerful tool designed for traders to visualise higher timeframe (HTF) candles, identify liquidity sweeps, and track key price levels on a lower timeframe (LTF) chart. Built for TradingView using Pine Script v6, it overlays HTF candle data and highlights significant price movements, such as sweeps of previous highs or lows, to help traders identify potential liquidity sweep and reversal points. The indicator is highly customisable, offering a range of visual and alert options to suit various trading strategies. Features Higher Timeframe (HTF) Candle Visualisation: - Displays up to three user-defined HTF candles (e.g., 15m, 1H, 4H) overlaid on the LTF chart. - Customisable candle appearance with adjustable size (Tiny to Huge), offset, spacing, and colours for bullish/bearish candles and wicks. - Option to show timeframe labels above or below HTF candles with configurable size and position. Liquidity Sweep Detection: - Identifies bullish and bearish sweeps when price moves beyond the high or low of a previous HTF candle and meets specific conditions. - Displays sweeps on both LTF and HTF with customisable line styles (Solid, Dashed, Dotted), widths, and colours. - Option to show only the most recent sweep per candle to reduce chart clutter. Invalidated Sweep Tracking: - Detects and visualises invalidated sweeps (when price moves past a sweep level in the opposite direction). - Configurable display for invalidated sweeps on LTF and HTF with distinct line styles and colours. Previous High/Low Lines: - Plots horizontal lines at the high and low of the previous HTF candle, extending on both LTF and HTF. - Customisable line style, width, and color for easy identification of key levels. - Real-Time Sweep Detection: -Optional real-time sweep visualisation for active candles, enabling traders to monitor developing price action. Alert System: - Triggers alerts for sweep formation (when a new sweep is detected). - Triggers alerts for sweep invalidation (when a sweep is no longer valid). - Alerts include details such as timeframe, ticker, and price level for precise notifications. Performance Optimisation: - Efficiently manages resources with configurable limits for lines, labels, boxes, and bars (up to 500 each). - Cleans up outdated visual elements to maintain chart clarity. Flexible Configuration: - Supports multiple timeframes for HTF candles with user-defined settings for visibility and number of candles displayed (1–60). - Toggle visibility for HTF candles, sweeps, invalidated sweeps, and high/low lines independently for LTF and HTF. This indicator is ideal for traders focusing on liquidity hunting, order block analysis, or price action strategies, providing clear visual cues and alerts to enhance decision-making.
אינדיקטור Pine Script®
מאת ‎wintonbanks‎
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.
אינדיקטור Pine Script®
מאת ‎Atif90‎
Apex Edge Sentinel - Stop Loss HUDApex Edge – ATR Sentinel Stop Loss HUD The Apex Edge – ATR Sentinel is a complete stop-loss intelligence system built as a clean, always-on HUD. It delivers institutional-level risk guidance by calculating and displaying live ATR-based stop levels for both long and short trades at multiple risk tolerances. Forget cluttered charts and repainting lines — Sentinel gives you a clear stop-loss reference panel that updates dynamically with every bar. ✅ Features • Triple ATR Multipliers User-defined (e.g. x1.5 / x2.0 / x2.5). Compare tight, medium, and wide stops instantly. • Dual-Side SL Levels Both Long and Short safe stop prices displayed side by side. No more guessing trend bias. • ATR Transparency HUD shows ATR(length) so you always know the calculation basis. Default = 14, adjustable to your style. • ATR Regime Meter Detects volatility conditions (LOW / NORMAL / HIGH) by comparing ATR to its SMA. Helps you avoid over-tight stops in high-volatility markets. • Tick-Aware Rounding Stop levels auto-rounded to the instrument’s tick size (Gold = 0.10, FX = 0.0001, indices = whole points). Custom HUD Design • Location: Top/Bottom, Left/Right • Sizes: Compact / Medium / Large (desktop or mobile) • Opacity control (25% default Apex styling) How to Use 1. Load Sentinel on your chart. 2. Check the HUD: • ATR(14): 2.6 → base volatility measure. • x1.5 / x2.0 / x2.5 → instant SL levels for both long & short trades. 3. Before entering a trade → decide which multiplier matches your style (tight scalper vs wider swing). 4. Manually place your SL at the level displayed in the HUD. Sentinel works as both: • A pre-trade check (is ATR stop too wide for my RR?). • A live risk compass (updated stop levels every bar). Why Apex Sentinel? Most ATR stop indicators clutter charts with lagging lines or repainting trails. Sentinel strips it back to what matters: • The numbers. • The risk levels. • The context. It’s a pure stop-loss HUD, designed for serious traders who want clarity, discipline, and instant reference points across any market or timeframe. Notes • This is a HUD-only system (no automatic SL line). Traders manually apply the SL level shown in the panel. • Defaults: ATR(14), multipliers 1.5 / 2.0 / 2.5. Adjust to your trading style. • Best used on intraday pairs like XAUUSD, EURUSD, indices, but works universally. Apex Edge Philosophy: Clean. Smart. Institutional. No clutter. No gimmicks. Just precision tools for modern markets.
אינדיקטור Pine Script®
מאת ‎ApexEdge‎
BioSwarm Imprinter™BioSwarm Imprinter™ — Agent-Based Consensus for Traders What it is BioSwarm Imprinter™ is a non-repainting, agent-based sentiment oscillator. It fuses many short-to-medium lookback “opinions” into one 0–100 consensus line that is easy to read at a glance (50 = neutral, >55 bullish bias, <45 bearish bias). The engine borrows from swarm intelligence: many simple voters (agents) adapt their influence over time based on how well they’ve been predicting price, so the crowd gets smarter as conditions change. Use it to: • Detect emerging trends sooner without overreacting to noise. • Filter mean-reversion vs continuation opportunities. • Gate entries with a confidence score that reflects both strength and persistence of the move. • Combine with your execution tools (VWAP/ORB/levels) as a state filter rather than a trade signal by itself. ⸻ Why it’s different • Swarm learning: Each agent improves or decays its “fitness” depending on whether its vote matched the next bar’s direction. High-fitness agents matter more; weak agents fade. • Multi-horizon by design: The crowd is composed of fixed, simple lookbacks spread from lenMin to lenMax. You get a blended, robust view instead of a single fragile parameter. • Two complementary lenses: Each agent evaluates RSI-style balance (via Wilder’s RMA) and momentum (EMA deviation). You decide the weight of each. • No repaint, no MTF pitfalls: Everything runs on the chart’s timeframe with bar-close confirmation; no request.security() or forward references. • Actionable UI: A clean consensus line, optional regime background, confidence heat, and triangle markers when thresholds are crossed. ⸻ What you see on the chart • Consensus line (0–100): Smoothed to your preference; color/area makes bull/bear zones obvious. • Regime coloring (optional): Light green in bull zone, light red in bear zone; neutral otherwise. • Confidence heat: A small gauge/number (0–100) that combines distance from neutral and recent persistence. • Markers (optional): Triangles when consensus crosses up through your bull threshold (e.g., 55) or down through your bear threshold (e.g., 45). • Info panel (optional): Consensus value, regime, confidence, number of agents, and basic diagnostics. ⸻ How it works (under the hood) 1. Horizon bins: The range is divided into numBins. Each bin has a fixed, simple integer length (crucial for Pine’s safety rules). 2. Per-bin features (computed every bar): • RSI-style balance using Wilder’s RMA (not ta.rsi()), then mapped to −1…+1. • Momentum as (close − EMA(L)) / EMA(L) (dimensionless drift). 3. Agent vote: For its assigned bin, an agent forms a weighted score: score = wRSI*RSI_like + wMOM*Momentum. A small dead-band near zero suppresses chop; votes are +1/−1/0. 4. Fitness update (bar close): If the agent’s previous vote agreed with the next bar’s direction, multiply its fitness by learnGain; otherwise by learnPain. Fitness is clamped so it never explodes or dies. 5. Consensus: Weighted average of all votes using fitness as weights → map to 0–100 and smooth with EMA. Why it doesn’t repaint: • No future references, no MTF resampling, fitness updates only on confirmed bars. • All TA primitives (RMA/EMA/deltas) are computed every bar unconditionally. ⸻ Signals & confidence • Bullish bias: consensus ≥ bullThr (e.g., 55). • Bearish bias: consensus ≤ bearThr (e.g., 45). • Confidence (0–100): • Distance score: how far consensus is from 50. • Momentum score: how strong the recent change is versus its recent average. • Combined into a single gate; start filtering entries at ≥60 for higher quality. Tip: For range sessions, raise thresholds (60/40) and increase smoothing; for momentum sessions, lower smoothing and keep thresholds at 55/45. ⸻ Inputs you’ll actually tune • Agents & horizons: • N_agents (e.g., 64–128) • lenMin / lenMax (e.g., 6–30 intraday, 10–60 swing) • numBins (e.g., 12–24) • Weights & smoothing: • wRSI vs wMOM (e.g., 0.7/0.3 for FX & indices; 0.6/0.4 for crypto) • deadBand (0.03–0.08) • consSmooth (3–8) • Thresholds & hygiene: • bullThr/bearThr (55/45 default) • cooldownBars to avoid signal spam ⸻ Playbooks (ready-to-use) 1) Breakout / Trend continuation • Timeframe: 15m–1h for day/swing. • Filter: Take longs only when consensus > 55 and confidence ≥ 60. • Execution: Use your ORB/VWAP/pullback trigger for entry. Trail with swing lows or 1.5×ATR. Exit on a close back under 50 or when a bearish signal prints. 2) Mean reversion (fade) • When: Sideways days or low-volatility clusters. • Setup: Increase deadBand and consSmooth. • Signal: Bearish fades when consensus rolls over below ≈55 but stays above 50; bullish fades when it rolls up above ≈45 but stays below 50. • Targets: The neutral zone (~50) as the first take-profit. 3) Multi-TF alignment • Keep BioSwarm on 1H for bias, execute on 5–15m: • Only take entries in the direction of the 1H consensus. • Skip counter-bias scalps unless confidence is very low (explicit mean-reversion plan). ⸻ Integrations that work • DynamoSent Pro+ (macro bias): Only act when macro bias and swarm consensus agree. • ORB + Session VWAP Pro: Trade London/NY ORB breakouts that retest while consensus >55 (long) or <45 (short). • Levels/Orderflow: BioSwarm is your “go / no-go”; execution stays with your usual triggers. ⸻ Quick start 1. Drop the indicator on a 1H chart. 2. Start with: N_agents=64, lenMin=6, lenMax=30, numBins=16, deadBand=0.06, consSmooth=5, thresholds 55/45. 3. Trade only when confidence ≥ 60. 4. Add your favorite execution tool (VWAP/levels/OR) for entries & exits. ⸻ Non-repainting & safety notes • No request.security(); no hidden lookahead. • Bar-close confirmation for fitness and signals. • All TA calls are unconditional (no “sometimes called” warnings). • No series-length inputs to RSI/EMA — we use RMA/EMA formulas that accept fixed simple ints per bin. ⸻ Known limits & tips • Too many signals? Raise deadBand, increase consSmooth, widen thresholds to 60/40. • Too few signals? Lower deadBand, reduce consSmooth, narrow thresholds to 53/47. • Over-fitting risk: Keep learnGain/learnPain modest (e.g., ×1.04 / ×0.96). • Compute load: Large N_agents × numBins is heavier; scale to your device. ⸻ Example recipes EURUSD 1H (swing): lenMin=8, lenMax=34, numBins=16, wRSI=0.7, wMOM=0.3, deadBand=0.06, consSmooth=6, thr=55/45 Buy breakouts when consensus >55 and confidence ≥60; confirm with 5–15m pullback to VWAP or level. SPY 15m (US session): lenMin=6, lenMax=24, numBins=12, consSmooth=4, deadBand=0.05 On trend days, stay with longs as long as consensus >55; add on shallow pullbacks. BTC 1H (24/7): Increase momentum weight: wRSI=0.6, wMOM=0.4, extend lenMax to ~50. Use dynamic stops (ATR) and partials on strong verticals. ⸻ Final word BioSwarm is a state engine: it tells you when the market is primed to continue or mean-revert. Pair it with your entries and risk framework to turn that state into trades. If you’d like, I can supply a companion strategy template that consumes the consensus and back-tests the three playbooks (Breakout/Fade/Flip) with standard risk management.
אינדיקטור Pine Script®
מאת ‎ACTradeFIN‎
VWAP + Multi-Timeframe RSI StrategyThis strategy combines VWAP trend direction with confirmation from RSI on a higher timeframe. The idea is to only take trades when both intraday momentum and higher-timeframe trend are aligned, increasing accuracy. LONG Entry: Price above VWAP (bullish environment). RSI on the current timeframe is below overbought (room to rise). RSI on the higher timeframe (default H1) is above 50 (bullish confirmation). SHORT Entry: Price below VWAP (bearish environment). RSI on the current timeframe is above oversold (room to fall). RSI on the higher timeframe is below 50 (bearish confirmation). Exit Rule: Stop-loss near VWAP. Take-profit at ~2x risk or when major levels are reached. Best Timeframes: Use 15m or 30m chart with H1 RSI for intraday trading. Use 1H chart with Daily RSI for swing trading. ⚡ The higher-timeframe RSI filter reduces false signals and aligns trades with institutional flow.
אסטרטגיית Pine Script®
מאת ‎dreamflysec‎
ASX Historical Price Projection [360 Days Auto]The ASX Price Projection indicator is a forecasting tool that projects future price movement based on historical price action and user-defined parameters. Inspired by the cyclical nature of markets, this tool helps traders visualize how price could behave in the future — not with certainty, but as a modeled possibility based on past behavior patterns. What It Does: This tool replicates the price action from a chosen historical period and projects it into the future, optionally applying a drift factor (growth or contraction). This projection is visualized directly on the chart, helping traders anticipate potential future price paths based on recognizable past behavior. How It Works: The indicator automatically uses the most recent 360 bars of historical data as the projection template. This is fixed and not user-selectable. The selected price segment is replicated and extended into the future to simulate a possible price path. A Drift Factor (Growth Multiplier) can be applied to simulate bullish (positive) or bearish (negative) price drift. An Area Width parameter defines how wide the projection zone appears around the forecast line, helping to visualize uncertainty or price range tolerance. The projected path and its surrounding band are plotted forward from the current bar. Why It’s Unique: This script offers a simple yet powerful way to model potential future price action based on automatic historical pattern detection: No manual selection required — the last 360 bars are always used. The Drift Factor allows scenario testing for growth or decline. Area Width gives a realistic band around the projected path. Designed for visual modeling and hypothetical exploration — not predictive accuracy. How to Use: Load the indicator on any chart. The system automatically pulls the last 360 bars to generate the projection. Adjust the Drift Factor to simulate optimistic or pessimistic market scenarios. Set the Area Width to control the visual range around the projected line. Use the forecast to explore how price might evolve under similar conditions.
אינדיקטור Pine Script®
מאת ‎Salah1996‎
1
VWAP Pullback + RSI ConfirmationThis strategy focuses on trend continuation entries. Instead of betting on reversions, it looks for opportunities when price pulls back to VWAP but the dominant trend remains intact. Trend Bias: Price above VWAP = bullish environment → look for BUY pullbacks. Price below VWAP = bearish environment → look for SELL pullbacks. Entry Logic: BUY: Price pulls back near VWAP, RSI stays above oversold (momentum intact). SELL: Price pulls back near VWAP, RSI stays below overbought (momentum intact). Exit Rule: Stop-loss just below/above VWAP. Take-profit at 1.5–2x risk (default script uses ~2%). Best Timeframes: 15m–1H → good for intraday trend-following setups. Daily → captures stronger, longer trends. ⚡ This strategy is powerful in trending markets because VWAP acts as a "magnet" for pullbacks, while RSI prevents overbought/oversold traps.
אסטרטגיית Pine Script®
מאת ‎dreamflysec‎
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.
אינדיקטור Pine Script®
מאת ‎anesboz3‎
Mekayl's Session Zones//@version=5 indicator("Mekayl's Session zones", overlay=true, max_boxes_count=200) // --- Colors asiaFill = color.new(#3b3333, 80) preLdnFill = color.new(#292323, 80) ldnFill = color.new(#242222, 80) preNyFill = color.new(#443322, 80) nyFill = color.new(#664422, 80) asiaBorder = color.new(#4d718f, 0) preLdnBorder = color.new(#00897B, 0) ldnBorder = color.new(#B2EBF2, 0) preNyBorder = color.new(#FFA500, 0) nyBorder = color.new(#FF8C00, 0) // --- Sessions asia_sess = "0100-0600" preldn_sess = "0600-0800" ldn_sess = "0800-1200" preNY_sess = "1200-1300" ny_sess = "1300-1700" tz = "Europe/London" // --- Variables for boxes & labels var box asia_box = na var label asia_label = na var box pre_box = na var label pre_label = na var box ldn_box = na var label ldn_label = na var box preNY_box = na var label preNY_label = na var box ny_box = na var label ny_label = na // --- Function to get horizontal center above box f_label_xy(b) => x = (box.get_left(b) + box.get_right(b)) / 2 y = box.get_top(b) + 3 * syminfo.mintick // --- Asia box asia_in = not na(time(timeframe.period, asia_sess, tz)) if asia_in if na(asia_box) asia_box := box.new(left=bar_index, top=high, right=bar_index, bottom=low, bgcolor=asiaFill, border_color=asiaBorder, border_width=2) = f_label_xy(asia_box) asia_label := label.new(x, y, "asia", style=label.style_none, textcolor=color.new(asiaBorder,0), size=size.normal) else box.set_right(asia_box, bar_index) box.set_top(asia_box, math.max(box.get_top(asia_box), high)) box.set_bottom(asia_box, math.min(box.get_bottom(asia_box), low)) = f_label_xy(asia_box) label.set_xy(asia_label, x, y) else if not na(asia_box) box.set_right(asia_box, bar_index) asia_box := na asia_label := na // --- Pre-London box pre_in = not na(time(timeframe.period, preldn_sess, tz)) if pre_in if na(pre_box) pre_box := box.new(left=bar_index, top=high, right=bar_index, bottom=low, bgcolor=preLdnFill, border_color=preLdnBorder, border_width=2) = f_label_xy(pre_box) pre_label := label.new(x, y, "pre_ldn", style=label.style_none, textcolor=color.new(preLdnBorder,0), size=size.normal) else box.set_right(pre_box, bar_index) box.set_top(pre_box, math.max(box.get_top(pre_box), high)) box.set_bottom(pre_box, math.min(box.get_bottom(pre_box), low)) = f_label_xy(pre_box) label.set_xy(pre_label, x, y) else if not na(pre_box) box.set_right(pre_box, bar_index) pre_box := na pre_label := na // --- London box ldn_in = not na(time(timeframe.period, ldn_sess, tz)) if ldn_in if na(ldn_box) ldn_box := box.new(left=bar_index, top=high, right=bar_index, bottom=low, bgcolor=ldnFill, border_color=ldnBorder, border_width=2) = f_label_xy(ldn_box) ldn_label := label.new(x, y, "ldn", style=label.style_none, textcolor=color.new(ldnBorder,0), size=size.normal) else box.set_right(ldn_box, bar_index) box.set_top(ldn_box, math.max(box.get_top(ldn_box), high)) box.set_bottom(ldn_box, math.min(box.get_bottom(ldn_box), low)) = f_label_xy(ldn_box) label.set_xy(ldn_label, x, y) else if not na(ldn_box) box.set_right(ldn_box, bar_index) ldn_box := na ldn_label := na // --- Pre-New York box preNY_in = not na(time(timeframe.period, preNY_sess, tz)) if preNY_in if na(preNY_box) preNY_box := box.new(left=bar_index, top=high, right=bar_index, bottom=low, bgcolor=preNyFill, border_color=preNyBorder, border_width=2) = f_label_xy(preNY_box) preNY_label := label.new(x, y, "pre-ny", style=label.style_none, textcolor=color.new(preNyBorder,0), size=size.normal) else box.set_right(preNY_box, bar_index) box.set_top(preNY_box, math.max(box.get_top(preNY_box), high)) box.set_bottom(preNY_box, math.min(box.get_bottom(preNY_box), low)) = f_label_xy(preNY_box) label.set_xy(preNY_label, x, y) else if not na(preNY_box) box.set_right(preNY_box, bar_index) preNY_box := na preNY_label := na // --- New York box ny_in = not na(time(timeframe.period, ny_sess, tz)) if ny_in if na(ny_box) ny_box := box.new(left=bar_index, top=high, right=bar_index, bottom=low, bgcolor=nyFill, border_color=nyBorder, border_width=2) = f_label_xy(ny_box) ny_label := label.new(x, y, "ny", style=label.style_none, textcolor=color.new(nyBorder,0), size=size.normal) else box.set_right(ny_box, bar_index) box.set_top(ny_box, math.max(box.get_top(ny_box), high)) box.set_bottom(ny_box, math.min(box.get_bottom(ny_box), low)) = f_label_xy(ny_box) label.set_xy(ny_label, x, y) else if not na(ny_box) box.set_right(ny_box, bar_index) ny_box := na ny_label := na
אינדיקטור Pine Script®
מאת ‎mekayladnan8‎
Golden Cross Master Filter by Carlos ChavezForget noisy Golden/Death Cross signals. This is the **Golden Cross Master Filter** – built for traders who demand institutional-level confirmation. ✅ Exact EMA cross points with circle markers ✅ ATR / ADX / DI+ / DI- / Volume filters ✅ Gap% detection ✅ Visual OK/X dashboard ✅ Instant BUY/SELL labels & ready-to-use alerts Cut the noise. Trade only the strongest crosses. 🚀 Golden Cross Master Filter is a professional tool to detect Golden and Death Crosses with institutional-grade filtering. 🚀 Features: - ✅ ATR / ADX / DI+/DI- / Volume conditions - ✅ Gap% detection (daily gap between yesterday’s close and today’s open) - ✅ Visual dashboard with OK/X status - ✅ Exact circle markers at EMA cross points - ✅ Ready-to-use BUY/SELL labels when filters are confirmed - ✅ Built-in alerts for easy automation This indicator is designed for intraday and swing traders who rely on EMA crosses but want to eliminate false signals. It works across multiple timeframes (10m, 1h, 4h, Daily) and adapts to different trading styles. Whether you trade CALLs/PUTs or just want stronger confirmation for Golden/Death Crosses, this filter helps you focus only on high-probability setups.
אינדיקטור Pine Script®
מאת ‎Carlostradercr‎
2
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.
אינדיקטור Pine Script®
מאת ‎EdgeTools‎