Bitcoin - MA Crossover StrategyBefore You Begin:
Please read these warnings carefully before using this script, you will bear all fiscal responsibility for your own trades.
Trading Strategy Warning - Past performance of this strategy may not equal future performance, due to macro-environment changes, etc.
Account Size Warning - Performance based upon default 10% risk per trade, of account size $100,000. Adjust BEFORE you trade to see your own drawdown.
Time Frame - D1 and H4. H4 has a lower profit factor (more fake-outs, and account drawdown), D1 recommended.
Trend Following System - Profitability of this system is dependent on STRONG future trends in Bitcoin (BTCUSD).
Default Settings:
This script was tested on Daily and 4 Hourly charts using the following default settings. Note that 4 Hourly exhibits higher drawdowns and lower profit factor, whilst Daily appears more stable.
Account Size ($): 100,000 (please adjust to simulate your own risk)
Equity Risk (%): 10 (please adjust to simulate your own risk)
Fast Moving Average (Period): 20
Slow Moving Average (Period): 40
Relative Strength Index (Period): 14
Trading Mechanism:
Trend following strategies work well for assets that display the tendency of long-trends. Please do not use this script on financial assets that have a historical tendency for mean reversion. Bitcoin has historically exhibited strong trends, and thus this script is designed to capitalise on that behaviour. It is hoped (but we cannot predict), that Bitcoin will strongly trend in the coming days.
LONG:
Enter Long - When fast moving average (20) crosses ABOVE slow moving average (40)
Exit Long - When fast moving average (20) crosses BELOW slow moving average (40)
SHORT:
Enter Short - When fast moving average (20) crosses BELOW slow moving average (40)
Exit Short - When fast moving average (20) crosses ABOVE slow moving average (40)
Risk Warnings:
Do note that "moving averages" are a lagging indicator, and as such heavy drawdowns could occur when a trade is open. If you are trading this system manually, it is best to avoid emotions and let the system tell you when to enter and exit. Do not panic and exit manually when under heavy drawdown, always follow the system. Do not be emotional. If possible, connect this to your broker for auto-trading. Ensure that your risk per trade (Equity Risk) is SMALL enough that it does not result in a margin-call on your trading account. Equity risk must always be considered relative to your total account size.
Remember: You bear all financial responsibility for your trades, best of luck.
Wyszukaj w skryptach "泰国一寺庙被曝藏有40多具尸体"
Simple Harmonic Oscillator (SHO)The indicator is based on Akram El Sherbini's article "Time Cycle Oscillators" published in IFTA journal 2018 (pages 78-80) (www.ftaa.org.hk)
The SHO is a bounded oscillator for the simple harmonic index that calculates the period of the market’s cycle. The oscillator is used for short and intermediate terms and moves within a range of -100 to 100 percent. The SHO has overbought and oversold levels at +40 and -40, respectively. At extreme periods, the oscillator may reach the levels of +60 and -60. The zero level demonstrates an equilibrium between the periods of bulls and bears. The SHO oscillates between +40 and -40. The crossover at those levels creates buy and sell signals. In an uptrend, the SHO fluctuates between 0 and +40 where the bulls are controlling the market. On the contrary, the SHO fluctuates between 0 and -40 during downtrends where the bears control the market. Reaching the extreme level -60 in an uptrend is a sign of weakness. Mostly, the oscillator will retrace from its centerline rather than the upper boundary +40. On the other hand, reaching +60 in a downtrend is a sign of strength and the oscillator will not be able to reach its lower boundary -40.
Centerline Crossover Tactic
This tactic is tested during uptrends. The buy signals are generated when the WPO/SHI cross their centerlines to the upside. The sell signals are generated when the WPO/SHI cross down their centerlines. To define the uptrend in the system, stocks closing above their 50-day EMA are considered while the ADX is above 18.
Uptrend Tactic
During uptrends, the bulls control the markets, and the oscillators will move above their centerline with an increase in the period of cycles. The lower boundaries and equilibrium line crossovers generate buy signals, while crossing the upper boundaries will generate sell signals. The “Re-entry” and “Exit at weakness” tactics are combined with the uptrend tactic. Consequently, we will have three buy signals and two sell signals.
Sideways Tactic
During sideways, the oscillators fluctuate between their upper and lower boundaries. Crossing the lower boundary to the upside will generate a buy signal. On the other hand, crossing the upper boundary to the downside will generate a sell signal. When the bears take control, the oscillators will cross down the lower boundaries, triggering exit signals. Therefore, this tactic will consist of one buy signal and two sell signals. The sideway tactic is defined when stocks close above their 50-day EMA and the ADX is below 18
NG [Simple Harmonic Oscillator]The SHO is a bounded oscillator for the simple harmonic index that calculates the period of the market’s cycle.
The oscillator is used for short and intermediate terms and moves within a range of -100 to 100 percent.
The SHO has overbought and oversold levels at +40 and -40, respectively.
At extreme periods, the oscillator may reach the levels of +60 and -60.
The zero level demonstrates an equilibrium between the periods of bulls and bears.
The SHO oscillates between +40 and -40.
The crossover at those levels creates buy and sell signals.
In an uptrend, the SHO fluctuates between 0 and +40 where the bulls are controlling the market.
On the contrary, the SHO fluctuates between 0 and -40 during downtrends where the bears controlthe market.
Reaching the extreme level -60 in an uptrend is a sign of weakness.
Historical Matrix Analyzer [PhenLabs]📊Historical Matrix Analyzer
Version: PineScriptv6
📌Description
The Historical Matrix Analyzer is an advanced probabilistic trading tool that transforms technical analysis into a data-driven decision support system. By creating a comprehensive 56-cell matrix that tracks every combination of RSI states and multi-indicator conditions, this indicator reveals which market patterns have historically led to profitable outcomes and which have not.
At its core, the indicator continuously monitors seven distinct RSI states (ranging from Extreme Oversold to Extreme Overbought) and eight unique indicator combinations (MACD direction, volume levels, and price momentum). For each of these 56 possible market states, the system calculates average forward returns, win rates, and occurrence counts based on your configurable lookback period. The result is a color-coded probability matrix that shows you exactly where you stand in the historical performance landscape.
The standout feature is the Current State Panel, which provides instant clarity on your active market conditions. This panel displays signal strength classifications (from Strong Bullish to Strong Bearish), the average return percentage for similar past occurrences, an estimated win rate using Bayesian smoothing to prevent small-sample distortions, and a confidence level indicator that warns you when insufficient data exists for reliable conclusions.
🚀Points of Innovation
Multi-dimensional state classification combining 7 RSI levels with 8 indicator combinations for 56 unique trackable market conditions
Bayesian win rate estimation with adjustable smoothing strength to provide stable probability estimates even with limited historical samples
Real-time active cell highlighting with “NOW” marker that visually connects current market conditions to their historical performance data
Configurable color intensity sensitivity allowing traders to adjust heat-map responsiveness from conservative to aggressive visual feedback
Dual-panel display system separating the comprehensive statistics matrix from an easy-to-read current state summary panel
Intelligent confidence scoring that automatically warns traders when occurrence counts fall below reliable thresholds
🔧Core Components
RSI State Classification: Segments RSI readings into 7 distinct zones (Extreme Oversold <20, Oversold 20-30, Weak 30-40, Neutral 40-60, Strong 60-70, Overbought 70-80, Extreme Overbought >80) to capture momentum extremes and transitions
Multi-Indicator Condition Tracking: Simultaneously monitors MACD crossover status (bullish/bearish), volume relative to moving average (high/low), and price direction (rising/falling) creating 8 binary-encoded combinations
Historical Data Storage Arrays: Maintains rolling lookback windows storing RSI states, indicator states, prices, and bar indices for precise forward-return calculations
Forward Performance Calculator: Measures price changes over configurable forward bar periods (1-20 bars) from each historical state, accumulating total returns and win counts per matrix cell
Bayesian Smoothing Engine: Applies statistical prior assumptions (default 50% win rate) weighted by user-defined strength parameter to stabilize estimated win rates when sample sizes are small
Dynamic Color Mapping System: Converts average returns into color-coded heat map with intensity adjusted by sensitivity parameter and transparency modified by confidence levels
🔥Key Features
56-Cell Probability Matrix: Comprehensive grid displaying every possible combination of RSI state and indicator condition, with each cell showing average return percentage, estimated win rate, and occurrence count for complete statistical visibility
Current State Info Panel: Dedicated display showing your exact position in the matrix with signal strength emoji indicators, numerical statistics, and color-coded confidence warnings for immediate situational awareness
Customizable Lookback Period: Adjustable historical window from 50 to 500 bars allowing traders to focus on recent market behavior or capture longer-term pattern stability across different market cycles
Configurable Forward Performance Window: Select target holding periods from 1 to 20 bars ahead to align probability calculations with your trading timeframe, whether day trading or swing trading
Visual Heat Mapping: Color-coded cells transition from red (bearish historical performance) through gray (neutral) to green (bullish performance) with intensity reflecting statistical significance and occurrence frequency
Intelligent Data Filtering: Minimum occurrence threshold (1-10) removes unreliable patterns with insufficient historical samples, displaying gray warning colors for low-confidence cells
Flexible Layout Options: Independent positioning of statistics matrix and info panel to any screen corner, accommodating different chart layouts and personal preferences
Tooltip Details: Hover over any matrix cell to see full RSI label, complete indicator status description, precise average return, estimated win rate, and total occurrence count
🎨Visualization
Statistics Matrix Table: A 9-column by 8-row grid with RSI states labeling vertical axis and indicator combinations on horizontal axis, using compact abbreviations (XOverS, OverB, MACD↑, Vol↓, P↑) for space efficiency
Active Cell Indicator: The current market state cell displays “⦿ NOW ⦿” in yellow text with enhanced color saturation to immediately draw attention to relevant historical performance
Signal Strength Visualization: Info panel uses emoji indicators (🔥 Strong Bullish, ✅ Bullish, ↗️ Weak Bullish, ➖ Neutral, ↘️ Weak Bearish, ⛔ Bearish, ❄️ Strong Bearish, ⚠️ Insufficient Data) for rapid interpretation
Histogram Plot: Below the price chart, a green/red histogram displays the current cell’s average return percentage, providing a time-series view of how historical performance changes as market conditions evolve
Color Intensity Scaling: Cell background transparency and saturation dynamically adjust based on both the magnitude of average returns and the occurrence count, ensuring visual emphasis on reliable patterns
Confidence Level Display: Info panel bottom row shows “High Confidence” (green), “Medium Confidence” (orange), or “Low Confidence” (red) based on occurrence counts relative to minimum threshold multipliers
📖Usage Guidelines
RSI Period
Default: 14
Range: 1 to unlimited
Description: Controls the lookback period for RSI momentum calculation. Standard 14-period provides widely-recognized overbought/oversold levels. Decrease for faster, more sensitive RSI reactions suitable for scalping. Increase (21, 28) for smoother, longer-term momentum assessment in swing trading. Changes affect how quickly the indicator moves between the 7 RSI state classifications.
MACD Fast Length
Default: 12
Range: 1 to unlimited
Description: Sets the faster exponential moving average for MACD calculation. Standard 12-period setting works well for daily charts and captures short-term momentum shifts. Decreasing creates more responsive MACD crossovers but increases false signals. Increasing smooths out noise but delays signal generation, affecting the bullish/bearish indicator state classification.
MACD Slow Length
Default: 26
Range: 1 to unlimited
Description: Defines the slower exponential moving average for MACD calculation. Traditional 26-period setting balances trend identification with responsiveness. Must be greater than Fast Length. Wider spread between fast and slow increases MACD sensitivity to trend changes, impacting the frequency of indicator state transitions in the matrix.
MACD Signal Length
Default: 9
Range: 1 to unlimited
Description: Smoothing period for the MACD signal line that triggers bullish/bearish state changes. Standard 9-period provides reliable crossover signals. Shorter values create more frequent state changes and earlier signals but with more whipsaws. Longer values produce more confirmed, stable signals but with increased lag in detecting momentum shifts.
Volume MA Period
Default: 20
Range: 1 to unlimited
Description: Lookback period for volume moving average used to classify volume as “high” or “low” in indicator state combinations. 20-period default captures typical monthly trading patterns. Shorter periods (10-15) make volume classification more reactive to recent spikes. Longer periods (30-50) require more sustained volume changes to trigger state classification shifts.
Statistics Lookback Period
Default: 200
Range: 50 to 500
Description: Number of historical bars used to calculate matrix statistics. 200 bars provides substantial data for reliable patterns while remaining responsive to regime changes. Lower values (50-100) emphasize recent market behavior and adapt quickly but may produce volatile statistics. Higher values (300-500) capture long-term patterns with stable statistics but slower adaptation to changing market dynamics.
Forward Performance Bars
Default: 5
Range: 1 to 20
Description: Number of bars ahead used to calculate forward returns from each historical state occurrence. 5-bar default suits intraday to short-term swing trading (5 hours on hourly charts, 1 week on daily charts). Lower values (1-3) target short-term momentum trades. Higher values (10-20) align with position trading and longer-term pattern exploitation.
Color Intensity Sensitivity
Default: 2.0
Range: 0.5 to 5.0, step 0.5
Description: Amplifies or dampens the color intensity response to average return magnitudes in the matrix heat map. 2.0 default provides balanced visual emphasis. Lower values (0.5-1.0) create subtle coloring requiring larger returns for full saturation, useful for volatile instruments. Higher values (3.0-5.0) produce vivid colors from smaller returns, highlighting subtle edges in range-bound markets.
Minimum Occurrences for Coloring
Default: 3
Range: 1 to 10
Description: Required minimum sample size before applying color-coded performance to matrix cells. Cells with fewer occurrences display gray “insufficient data” warning. 3-occurrence default filters out rare patterns. Lower threshold (1-2) shows more data but includes unreliable single-event statistics. Higher thresholds (5-10) ensure only well-established patterns receive visual emphasis.
Table Position
Default: top_right
Options: top_left, top_right, bottom_left, bottom_right
Description: Screen location for the 56-cell statistics matrix table. Position to avoid overlapping critical price action or other indicators on your chart. Consider chart orientation and candlestick density when selecting optimal placement.
Show Current State Panel
Default: true
Options: true, false
Description: Toggle visibility of the dedicated current state information panel. When enabled, displays signal strength, RSI value, indicator status, average return, estimated win rate, and confidence level for active market conditions. Disable to declutter charts when only the matrix table is needed.
Info Panel Position
Default: bottom_left
Options: top_left, top_right, bottom_left, bottom_right
Description: Screen location for the current state information panel (when enabled). Position independently from statistics matrix to optimize chart real estate. Typically placed opposite the matrix table for balanced visual layout.
Win Rate Smoothing Strength
Default: 5
Range: 1 to 20
Description: Controls Bayesian prior weighting for estimated win rate calculations. Acts as virtual sample size assuming 50% win rate baseline. Default 5 provides moderate smoothing preventing extreme win rate estimates from small samples. Lower values (1-3) reduce smoothing effect, allowing win rates to reflect raw data more directly. Higher values (10-20) increase conservatism, pulling win rate estimates toward 50% until substantial evidence accumulates.
✅Best Use Cases
Pattern-based discretionary trading where you want historical confirmation before entering setups that “look good” based on current technical alignment
Swing trading with holding periods matching your forward performance bar setting, using high-confidence bullish cells as entry filters
Risk assessment and position sizing, allocating larger size to trades originating from cells with strong positive average returns and high estimated win rates
Market regime identification by observing which RSI states and indicator combinations are currently producing the most reliable historical patterns
Backtesting validation by comparing your manual strategy signals against the historical performance of the corresponding matrix cells
Educational tool for developing intuition about which technical condition combinations have actually worked versus those that feel right but lack historical evidence
⚠️Limitations
Historical patterns do not guarantee future performance, especially during unprecedented market events or regime changes not represented in the lookback period
Small sample sizes (low occurrence counts) produce unreliable statistics despite Bayesian smoothing, requiring caution when acting on low-confidence cells
Matrix statistics lag behind rapidly changing market conditions, as the lookback period must accumulate new state occurrences before updating performance data
Forward return calculations use fixed bar periods that may not align with actual trade exit timing, support/resistance levels, or volatility-adjusted profit targets
💡What Makes This Unique
Multi-Dimensional State Space: Unlike single-indicator tools, simultaneously tracks 56 distinct market condition combinations providing granular pattern resolution unavailable in traditional technical analysis
Bayesian Statistical Rigor: Implements proper probabilistic smoothing to prevent overconfidence from limited data, a critical feature missing from most pattern recognition tools
Real-Time Contextual Feedback: The “NOW” marker and dedicated info panel instantly connect current market conditions to their historical performance profile, eliminating guesswork
Transparent Occurrence Counts: Displays sample sizes directly in each cell, allowing traders to judge statistical reliability themselves rather than hiding data quality issues
Fully Customizable Analysis Window: Complete control over lookback depth and forward return horizons lets traders align the tool precisely with their trading timeframe and strategy requirements
🔬How It Works
1. State Classification and Encoding
Each bar’s RSI value is evaluated and assigned to one of 7 discrete states based on threshold levels (0: <20, 1: 20-30, 2: 30-40, 3: 40-60, 4: 60-70, 5: 70-80, 6: >80)
Simultaneously, three binary conditions are evaluated: MACD line position relative to signal line, current volume relative to its moving average, and current close relative to previous close
These three binary conditions are combined into a single indicator state integer (0-7) using binary encoding, creating 8 possible indicator combinations
The RSI state and indicator state are stored together, defining one of 56 possible market condition cells in the matrix
2. Historical Data Accumulation
As each bar completes, the current state classification, closing price, and bar index are stored in rolling arrays maintained at the size specified by the lookback period
When the arrays reach capacity, the oldest data point is removed and the newest added, creating a sliding historical window
This continuous process builds a comprehensive database of past market conditions and their subsequent price movements
3. Forward Return Calculation and Statistics Update
On each bar, the indicator looks back through the stored historical data to find bars where sufficient forward bars exist to measure outcomes
For each historical occurrence, the price change from that bar to the bar N periods ahead (where N is the forward performance bars setting) is calculated as a percentage return
This percentage return is added to the cumulative return total for the specific matrix cell corresponding to that historical bar’s state classification
Occurrence counts are incremented, and wins are tallied for positive returns, building comprehensive statistics for each of the 56 cells
The Bayesian smoothing formula combines these raw statistics with prior assumptions (neutral 50% win rate) weighted by the smoothing strength parameter to produce estimated win rates that remain stable even with small samples
💡Note:
The Historical Matrix Analyzer is designed as a decision support tool, not a standalone trading system. Best results come from using it to validate discretionary trade ideas or filter systematic strategy signals. Always combine matrix insights with proper risk management, position sizing rules, and awareness of broader market context. The estimated win rate feature uses Bayesian statistics specifically to prevent false confidence from limited data, but no amount of smoothing can create reliable predictions from fundamentally insufficient sample sizes. Focus on high-confidence cells (green-colored confidence indicators) with occurrence counts well above your minimum threshold for the most actionable insights.
多周期趋势动量面板加强版（Multi-Timeframe Trend Momentum Panel - User Guide）多周期趋势动量面板(Multi-Timeframe Trend Momentum Panel - User Guide)(english explanation follows.)
📖 指标功能详解 (精简版):
🎯 核心功能:
1. 多周期趋势分析 同时监控8个时间周期(1m/5m/15m/1H/4H/D/W/M)
2. 4维度投票系统 MA趋势+RSI动量+MACD+布林带综合判断
3. 全球交易时段 可视化亚洲/伦敦/纽约交易时间
4. 趋势强度评分 0100%量化市场力量
5. 智能警报 强势多空信号自动推送
________________________________________
📚 重要名词解释:
🔵 趋势状态 (MA均线分析):
名词 含义 信号强度
强势多头 快MA远高于慢MA(差值≥0.35%) ⭐⭐⭐⭐⭐ 做多
多头倾向 快MA略高于慢MA(差值<0.35%) ⭐⭐⭐ 谨慎做多
震荡 快慢MA缠绕,无明确方向 ⚠️ 观望
空头倾向 快MA略低于慢MA ⭐⭐⭐ 谨慎做空
强势空头 快MA远低于慢MA ⭐⭐⭐⭐⭐ 做空
简单理解: 快MA就像短跑运动员(反应快),慢MA是长跑运动员(稳定)。短跑远超长跑=强势多头,反之=强势空头。
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🟠 动量状态 (RSI力度分析):
名词 含义 操作建议
动量上攻↗ RSI>60且快速上升 强烈买入信号
动量高位 RSI>60但上升变慢 警惕回调,可减仓
动量中性 RSI在4060之间,平稳 等待方向明确
动量低位 RSI<40但下跌变慢 警惕反弹,可止盈
动量下压↘ RSI<40且快速下降 强烈卖出信号
简单理解: RSI就像汽车速度表。"动量上攻"=油门踩到底加速,"动量高位"=已经很快但不再加速了。
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🟣 辅助信号:
MACD:
• MACD多头 = 柱状图>0 = 买方力量强
• MACD空头 = 柱状图<0 = 卖方力量强
布林带(BB):
• BB超买 = 价格在布林带上轨附近 = 可能回调
• BB超卖 = 价格在布林带下轨附近 = 可能反弹
• BB中轨 = 价格在中间位置 = 平衡状态
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💡 快速上手 3步看懂面板:
第1步: 看"综合结论标签" (K线上方)
• 绿色"多头占优" → 可以做多
• 红色"空头占优" → 可以做空
• 橙色"震荡/均衡" → 观望
第2步: 看"票数 多/空" (面板最下方)
• 多头票数远大于空头 (差距>2) → 趋势强
• 票数接近 (差距<1) → 震荡市
第3步: 看"趋势强度" (综合标签中)
• 强度>70% → 强势趋势,可重仓
• 强度5070% → 中等趋势,正常仓位
• 强度<50% → 弱势,轻仓或观望
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🎨 时段背景色含义:
• 紫色背景 = 亚洲时段 (东京交易时间) 波动较小
• 橙色背景 = 伦敦时段 (欧洲交易时间) 波动增大
• 蓝色背景 = 纽约凌晨 美盘准备阶段
• 红色背景 = 纽约关键5分钟 (09:3009:35) ⚠️ 最重要! 市场最活跃,趋势易形成
• 绿色背景 = 纽约上午后段 延续早盘趋势
交易建议: 重点关注红色关键时段,这5分钟往往决定全天方向!
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⚙️ 三大市场推荐设置
🥇 黄金: Hull MA 12/EMA 34, 阈值0.250.35%
₿ 比特币: EMA 21/EMA 55, 阈值0.801.20%
💎 以太坊: TEMA 21/EMA 55, 阈值0.600.80%
参数优化建议
黄金 (XAUUSD)
快速MA: Hull MA 12 (超灵敏捕捉黄金快速波动)
慢速MA: EMA 34 (斐波那契数列)
RSI周期: 9 (加快反应)
强趋势阈值: 0.25%
周期: 5, 15, 60, 240, 1440
比特币 (BTCUSD)
快速MA: EMA 21
慢速MA: EMA 55
RSI周期: 14
强趋势阈值: 0.8% (波动大,阈值需提高)
周期: 15, 60, 240, D, W
外汇 EUR/USD
快速MA: TEMA 10 (快速响应)
慢速MA: T3 30, 因子0.7 (平滑噪音)
RSI周期: 14
强趋势阈值: 0.08% (外汇波动小)
周期: 5, 15, 60, 240, 1440
📖 Indicator Function Details (Concise Version):
🎯 Core Functions:
1. MultiTimeframe Trend Analysis Monitors 8 timeframes simultaneously (1m/5m/15m/1H/4H/D/W/M)
2. 4Dimensional Voting System Comprehensive judgment based on MA trend + RSI momentum + MACD + Bollinger Bands
3. Global Trading Sessions Visualizes Asia/London/New York trading hours
4. Trend Strength Score Quantifies market strength from 0100%
5. Smart Alerts Automatically pushes strong bullish/bearish signals
📚 Key Term Explanations:
🔵 Trend Status (MA Analysis):
| Term | Meaning | Signal Strength |
| | | |
| Strong Bull | Fast MA significantly > Slow MA (Diff ≥0.35%) | ⭐⭐⭐⭐⭐ Long |
| Bullish Bias | Fast MA slightly > Slow MA (Diff <0.35%) | ⭐⭐⭐ Caution Long |
| Ranging | MAs intertwined, no clear direction | ⚠️ Wait & See |
| Bearish Bias | Fast MA slightly < Slow MA | ⭐⭐⭐ Caution Short |
| Strong Bear | Fast MA significantly < Slow MA | ⭐⭐⭐⭐⭐ Short |
Simple Understanding: Fast MA = sprinter (fast reaction), Slow MA = longdistance runner (stable). Sprinter far ahead = Strong Bull, opposite = Strong Bear.
🟠 Momentum Status (RSI Analysis):
| Term | Meaning | Trading Suggestion |
| | | |
| Momentum Up ↗ | RSI >60 & rising rapidly | Strong Buy Signal |
| Momentum High | RSI >60 but rising slower | Watch for pullback, consider reducing position |
| Momentum Neutral | RSI between 4060, stable | Wait for clearer direction |
| Momentum Low | RSI <40 but falling slower | Watch for rebound, consider taking profit |
| Momentum Down ↘ | RSI <40 & falling rapidly | Strong Sell Signal |
Simple Understanding: RSI = car speedometer. "Momentum Up" = full throttle acceleration, "Momentum High" = already fast but not accelerating further.
🟣 Auxiliary Signals:
MACD:
MACD Bullish = Histogram >0 = Strong buyer power
MACD Bearish = Histogram <0 = Strong seller power
Bollinger Bands (BB):
BB Overbought = Price near upper band = Possible pullback
BB Oversold = Price near lower band = Possible rebound
BB Middle = Price near middle band = Balanced state
💡 Quick Start 3 Steps to Understand the Panel:
Step 1: Check "Composite Conclusion Label" (Above the chart)
Green "Bulls Favored" → Consider Long
Red "Bears Favored" → Consider Short
Orange "Ranging/Balanced" → Wait & See
Step 2: Check "Votes Bull/Bear" (Bottom of the panel)
Bull votes significantly > Bear votes (Difference >2) → Strong Trend
Votes close (Difference <1) → Ranging Market
Step 3: Check "Trend Strength" (In the composite label)
Strength >70% → Strong Trend, consider heavier position
Strength 5070% → Moderate Trend, normal position size
Strength <50% → Weak Trend, light position or wait & see
🎨 Trading Session Background Color Meanings:
Purple = Asian Session (Tokyo hours) Lower volatility
Orange = London Session (European hours) Increased volatility
Blue = NY Early Morning US session preparation phase
Red = NY Critical 5 Minutes (09:3009:35) ⚠️ Most Important! Market most active, trends easily form
Green = NY Late Morning Continuation of early session trend
Trading Tip: Focus on the red critical period; these 5 minutes often determine the day's direction!
⚙️ Recommended Settings for Three Major Markets
🥇 Gold (XAUUSD):
Fast MA: Hull MA 12 (Highly sensitive for gold's fast moves)
Slow MA: EMA 34 (Fibonacci number)
RSI Period: 9 (Faster reaction)
Strong Trend Threshold: 0.25%
Timeframes: 5, 15, 60, 240, 1440
₿ Bitcoin (BTCUSD):
Fast MA: EMA 21
Slow MA: EMA 55
RSI Period: 14
Strong Trend Threshold: 0.8% (High volatility, requires higher threshold)
Timeframes: 15, 60, 240, D, W
💎 Ethereum (ETHUSD):
Fast MA: TEMA 21
Slow MA: EMA 55
RSI Period: 14
Strong Trend Threshold: 0.600.80%
Timeframes: 15, 60, 240, D, W
💱 Forex EUR/USD:
Fast MA: TEMA 10 (Fast response)
Slow MA: T3 30, Factor 0.7 (Smooths noise)
RSI Period: 14
Strong Trend Threshold: 0.08% (Forex has low volatility)
Timeframes: 5, 15, 60, 240, 1440
多周期趋势动量面板(Multi-Timeframe Trend Momentum Panel - User Guide)多周期趋势动量面板(Multi-Timeframe Trend Momentum Panel - User Guide)(english explanation follows.)
📖 指标功能详解 (精简版):
🎯 核心功能:
1. 多周期趋势分析 同时监控8个时间周期(1m/5m/15m/1H/4H/D/W/M)
2. 4维度投票系统 MA趋势+RSI动量+MACD+布林带综合判断
3. 全球交易时段 可视化亚洲/伦敦/纽约交易时间
4. 趋势强度评分 0100%量化市场力量
5. 智能警报 强势多空信号自动推送
________________________________________
📚 重要名词解释:
🔵 趋势状态 (MA均线分析):
名词 含义 信号强度
强势多头 快MA远高于慢MA(差值≥0.35%) ⭐⭐⭐⭐⭐ 做多
多头倾向 快MA略高于慢MA(差值<0.35%) ⭐⭐⭐ 谨慎做多
震荡 快慢MA缠绕,无明确方向 ⚠️ 观望
空头倾向 快MA略低于慢MA ⭐⭐⭐ 谨慎做空
强势空头 快MA远低于慢MA ⭐⭐⭐⭐⭐ 做空
简单理解: 快MA就像短跑运动员(反应快),慢MA是长跑运动员(稳定)。短跑远超长跑=强势多头,反之=强势空头。
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🟠 动量状态 (RSI力度分析):
名词 含义 操作建议
动量上攻↗ RSI>60且快速上升 强烈买入信号
动量高位 RSI>60但上升变慢 警惕回调,可减仓
动量中性 RSI在4060之间,平稳 等待方向明确
动量低位 RSI<40但下跌变慢 警惕反弹,可止盈
动量下压↘ RSI<40且快速下降 强烈卖出信号
简单理解: RSI就像汽车速度表。"动量上攻"=油门踩到底加速,"动量高位"=已经很快但不再加速了。
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🟣 辅助信号:
MACD:
• MACD多头 = 柱状图>0 = 买方力量强
• MACD空头 = 柱状图<0 = 卖方力量强
布林带(BB):
• BB超买 = 价格在布林带上轨附近 = 可能回调
• BB超卖 = 价格在布林带下轨附近 = 可能反弹
• BB中轨 = 价格在中间位置 = 平衡状态
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💡 快速上手 3步看懂面板:
第1步: 看"综合结论标签" (K线上方)
• 绿色"多头占优" → 可以做多
• 红色"空头占优" → 可以做空
• 橙色"震荡/均衡" → 观望
第2步: 看"票数 多/空" (面板最下方)
• 多头票数远大于空头 (差距>2) → 趋势强
• 票数接近 (差距<1) → 震荡市
第3步: 看"趋势强度" (综合标签中)
• 强度>70% → 强势趋势,可重仓
• 强度5070% → 中等趋势,正常仓位
• 强度<50% → 弱势,轻仓或观望
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🎨 时段背景色含义:
• 紫色背景 = 亚洲时段 (东京交易时间) 波动较小
• 橙色背景 = 伦敦时段 (欧洲交易时间) 波动增大
• 蓝色背景 = 纽约凌晨 美盘准备阶段
• 红色背景 = 纽约关键5分钟 (09:3009:35) ⚠️ 最重要! 市场最活跃,趋势易形成
• 绿色背景 = 纽约上午后段 延续早盘趋势
交易建议: 重点关注红色关键时段,这5分钟往往决定全天方向!
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⚙️ 三大市场推荐设置
🥇 黄金: Hull MA 12/EMA 34, 阈值0.250.35%
₿ 比特币: EMA 21/EMA 55, 阈值0.801.20%
💎 以太坊: TEMA 21/EMA 55, 阈值0.600.80%
参数优化建议
黄金 (XAUUSD)
快速MA: Hull MA 12 (超灵敏捕捉黄金快速波动)
慢速MA: EMA 34 (斐波那契数列)
RSI周期: 9 (加快反应)
强趋势阈值: 0.25%
周期: 5, 15, 60, 240, 1440
比特币 (BTCUSD)
快速MA: EMA 21
慢速MA: EMA 55
RSI周期: 14
强趋势阈值: 0.8% (波动大,阈值需提高)
周期: 15, 60, 240, D, W
外汇 EUR/USD
快速MA: TEMA 10 (快速响应)
慢速MA: T3 30, 因子0.7 (平滑噪音)
RSI周期: 14
强趋势阈值: 0.08% (外汇波动小)
周期: 5, 15, 60, 240, 1440
📖 Indicator Function Details (Concise Version):
🎯 Core Functions:
1. MultiTimeframe Trend Analysis Monitors 8 timeframes simultaneously (1m/5m/15m/1H/4H/D/W/M)
2. 4Dimensional Voting System Comprehensive judgment based on MA trend + RSI momentum + MACD + Bollinger Bands
3. Global Trading Sessions Visualizes Asia/London/New York trading hours
4. Trend Strength Score Quantifies market strength from 0100%
5. Smart Alerts Automatically pushes strong bullish/bearish signals
📚 Key Term Explanations:
🔵 Trend Status (MA Analysis):
| Term | Meaning | Signal Strength |
| | | |
| Strong Bull | Fast MA significantly > Slow MA (Diff ≥0.35%) | ⭐⭐⭐⭐⭐ Long |
| Bullish Bias | Fast MA slightly > Slow MA (Diff <0.35%) | ⭐⭐⭐ Caution Long |
| Ranging | MAs intertwined, no clear direction | ⚠️ Wait & See |
| Bearish Bias | Fast MA slightly < Slow MA | ⭐⭐⭐ Caution Short |
| Strong Bear | Fast MA significantly < Slow MA | ⭐⭐⭐⭐⭐ Short |
Simple Understanding: Fast MA = sprinter (fast reaction), Slow MA = longdistance runner (stable). Sprinter far ahead = Strong Bull, opposite = Strong Bear.
🟠 Momentum Status (RSI Analysis):
| Term | Meaning | Trading Suggestion |
| | | |
| Momentum Up ↗ | RSI >60 & rising rapidly | Strong Buy Signal |
| Momentum High | RSI >60 but rising slower | Watch for pullback, consider reducing position |
| Momentum Neutral | RSI between 4060, stable | Wait for clearer direction |
| Momentum Low | RSI <40 but falling slower | Watch for rebound, consider taking profit |
| Momentum Down ↘ | RSI <40 & falling rapidly | Strong Sell Signal |
Simple Understanding: RSI = car speedometer. "Momentum Up" = full throttle acceleration, "Momentum High" = already fast but not accelerating further.
🟣 Auxiliary Signals:
MACD:
MACD Bullish = Histogram >0 = Strong buyer power
MACD Bearish = Histogram <0 = Strong seller power
Bollinger Bands (BB):
BB Overbought = Price near upper band = Possible pullback
BB Oversold = Price near lower band = Possible rebound
BB Middle = Price near middle band = Balanced state
💡 Quick Start 3 Steps to Understand the Panel:
Step 1: Check "Composite Conclusion Label" (Above the chart)
Green "Bulls Favored" → Consider Long
Red "Bears Favored" → Consider Short
Orange "Ranging/Balanced" → Wait & See
Step 2: Check "Votes Bull/Bear" (Bottom of the panel)
Bull votes significantly > Bear votes (Difference >2) → Strong Trend
Votes close (Difference <1) → Ranging Market
Step 3: Check "Trend Strength" (In the composite label)
Strength >70% → Strong Trend, consider heavier position
Strength 5070% → Moderate Trend, normal position size
Strength <50% → Weak Trend, light position or wait & see
🎨 Trading Session Background Color Meanings:
Purple = Asian Session (Tokyo hours) Lower volatility
Orange = London Session (European hours) Increased volatility
Blue = NY Early Morning US session preparation phase
Red = NY Critical 5 Minutes (09:3009:35) ⚠️ Most Important! Market most active, trends easily form
Green = NY Late Morning Continuation of early session trend
Trading Tip: Focus on the red critical period; these 5 minutes often determine the day's direction!
⚙️ Recommended Settings for Three Major Markets
🥇 Gold (XAUUSD):
Fast MA: Hull MA 12 (Highly sensitive for gold's fast moves)
Slow MA: EMA 34 (Fibonacci number)
RSI Period: 9 (Faster reaction)
Strong Trend Threshold: 0.25%
Timeframes: 5, 15, 60, 240, 1440
₿ Bitcoin (BTCUSD):
Fast MA: EMA 21
Slow MA: EMA 55
RSI Period: 14
Strong Trend Threshold: 0.8% (High volatility, requires higher threshold)
Timeframes: 15, 60, 240, D, W
💎 Ethereum (ETHUSD):
Fast MA: TEMA 21
Slow MA: EMA 55
RSI Period: 14
Strong Trend Threshold: 0.600.80%
Timeframes: 15, 60, 240, D, W
💱 Forex EUR/USD:
Fast MA: TEMA 10 (Fast response)
Slow MA: T3 30, Factor 0.7 (Smooths noise)
RSI Period: 14
Strong Trend Threshold: 0.08% (Forex has low volatility)
Timeframes: 5, 15, 60, 240, 1440
Market Sentiment Trend Gauge [LevelUp]Market Sentiment Trend Gauge simplifies technical analysis by mathematically combining momentum, trend direction, volatility position, and comparison against a market benchmark, into a single trend score from -100 to +100. Displayed in a separate pane below your chart, it resolves conflicting signals from RSI, moving averages, Bollinger Bands, and market correlations, providing clear insights into trend direction, strength, and relative performance.
THE PROBLEM MARKET SENTIMENT TREND GAUGE (MSTG) SOLVES
Traditional indicators often produce conflicting signals, such as RSI showing overbought while prices rise or moving averages indicating an uptrend despite market underperformance. MSTG creates a weighted composite score to answer: "What's the overall bias for this asset?"
KEY COMPONENTS AND WEIGHTINGS
The trend score combines
▪ Momentum (25%): Normalized 14-period RSI, capped at ±100.
▪ Trend Direction (35%): 10/21-period EMA relationships,
▪ Volatility Position (20%): Price position, 20-period Bollinger Bands, capped at ±100.
▪ Market Comparison (20%): Daily performance vs. SPY benchmark, capped at ±100.
Final score = Weighted sum, smoothed with 5-period EMA.
INTERPRETING THE MSTG CHART
Trend Score Ranges and Colors
▪ Bright Green (>+30): Strong bullish; ideal for long entries.
▪ Light Green (+10 to +30): Weak bullish; cautiously favorable.
▪ Gray (-10 to +10): Neutral; avoid directional trades.
▪ Light Red (-10 to -30): Weak bearish; exercise caution.
▪ Bright Red (<-30): Strong bearish; high-risk for longs, consider shorts.
Reference Lines
▪ Zero Line (Gray): Separates bullish/bearish; crossovers signal trend changes.
▪ ±30 Lines (Dotted, Green/Red): Thresholds for strong trends.
▪ ±60 Lines (Dashed, Green/Red): Extreme strength zones (not overbought/oversold); manage risk (tighten stops, partial profits) but trends may persist.
Background Colors
▪ Green Tint (>+20): Bullish environment; favorable for longs.
▪ Red Tint (<-20): Bearish environment; caution for longs.
▪ Light Gray Tint (-20 to +20): Neutral/range-bound; wait for signals.
Extreme Readings vs. Traditional Signals
MSTG ±60 indicates maximum alignment of all factors, not reversals (unlike RSI >70/<30). Use for risk management, not automatic exits. Strong trends can sustain extremes; breakdowns occur below +30 or above -30.
INFORMATION TABLE INTERPRETATION
Trend Score Symbols
▲▲ >+30 strong bullish
▲ +10 to +30
● -10 to +10 neutral
▼ -30 to -10
▼▼ <-30 strong bearish
Colors: Green (positive), White (neutral), Red (negative).
Momentum Score
+40 to +100 strong bullish
0 to +40 moderate bullish
-40 to 0 moderate bearish
-100 to -40 strong bearish
Market vs. Stock
▪ Green: Stock outperforming market
▪ Red: Stock underperforming market
Example Interpretations:
-0.45% / +1.23% (Green): Market down, stock up = Strong relative strength
+2.10% / +1.50% (Red): Both rising, but stock lagging = Relative weakness
-1.20% / -0.80% (Green): Both falling, but stock declining less = Defensive strength
UNDERSTANDING EXTREME READINGS VS TRADITIONAL OVERBOUGHT/OVERSOLD
⚠️ Critical distinctions
Traditional Overbought/Oversold Signals:
▪ Single indicator (like RSI >70 or <30) showing momentum excess
▪ Often suggests immediate reversal or pullback expected
▪ Based on "price moved too far, too fast" concept
MSTG Extreme Readings (±60):
▪ Composite alignment of 4 different factors (momentum, trend, volatility, relative strength)
▪ Indicates maximum strength in current direction
▪ NOT a reversal signal - means "all systems extremely bullish/bearish"
Key Differences:
▪ RSI >70: "Price got ahead of itself, expect pullback"
▪ MSTG >+60: "Everything is extremely bullish right now"
▪ Strong trends can maintain extreme MSTG readings during major moves
▪ Breakdowns happen when MSTG falls below +30, not at +60
Proper Usage of Extreme Readings:
▪ Risk Management: Tighten stops, take partial profits
▪ Position Sizing: Reduce new position sizes at extremes
▪ Trend Continuation: Watch for sustained extreme readings in strong markets
▪ Exit Signals: Look for breakdown below +30, not reversal from +60
TRADING WITH MSTG
Quick Assessment
1. Check trend symbol for direction.
2. Confirm momentum strength.
3. Note relative performance color.
Examples:
▲▲ 55.2 (Green), Momentum +28.4, Outperforming: Strong buy setup.
▼ -18.6 (Red), Momentum -43.2, Underperforming: Defensive positioning.
Entry Conditions
▪ Long: stock outperforming market
- Score >+30 (bright green)
- Sustained green background
- ▲▲ symbol,
▪ Short: stock underperforming market
- Score <-30 (bright red)
- Sustained red background
- ▼▼ symbol
Avoid Trading When:
▪ Gray zone (-10 to +10).
▪ Rapid color changes or frequent zero-line crosses (choppy market).
▪ Gray background (range-bound).
Risk Management:
▪ Stop Loss: Exit on zero-line crossover against position.
▪ Take Profit: Partial at ±60 for risk control.
▪ Position Sizing: Larger when signals align; smaller in extremes or mixed conditions.
KEY ADVANTAGES
▪ Unified View: Weighted composite reduces noise and conflicts.
▪ Visual Clarity: 5-color system with gradients for rapid recognition.
▪ Market Context: Relative strength vs. SPY identifies leaders/laggards.
▪ Flexibility: Works across timeframes (1-min to weekly); customizable table.
▪ Noise Reduction: EMA smoothing minimizes false signals.
EXAMPLES
Strong Bull: Trend Score 71.9, Momentum Score 76.9
Neutral: Trend Score 0.1, Momentum Score -9.2
Strong Bear: Trend Score -51.7, Momentum Score -51.5
PERFORMANCE AND LIMITATIONS
Strengths: Trend identification, noise reduction, relative performance versus market.
Limitations: Lags at turning points, less effective in extreme volatility or non-trending markets.
Recommendations: View on multiple timeframes, combine with price action and fundamentals.
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.
Synthetic Point & Figure on RSIHere is a detailed description and user guide for the Synthetic Point & Figure RSI indicator, including how to use it for long and short trade considerations:
*
## Synthetic Point & Figure RSI Indicator – User Guide
### What It Is
This indicator applies classic Point & Figure (P&F) charting logic to the Relative Strength Index (RSI) instead of price. It transforms the RSI into synthetic “P&F candles” that filter out noise and highlight significant momentum moves and reversals based on configurable box size and reversal settings.
### How It Works
- The RSI is calculated normally over the selected length.
- The P&F engine tracks movements in the RSI above or below a defined “box size,” creating columns that switch direction only after a larger reversal.
- The synthetic candles connect these filtered RSI values visually, reducing false noise and emphasizing strong RSI trends.
- Optional EMA and SMA overlays on the synthetic P&F RSI allow smoother trend signals.
- Reference RSI levels at 33, 40, 50, 60, and 66 provide further context for momentum strength.
### How to Use for Trading
#### Long (Buy) Considerations
- The synthetic P&F RSI candle direction flips to *up (green candles)* indicating strength in momentum.
- Look for the RSI P&F value moving above the *40 or 50 level*, suggesting increasing bullish momentum.
- Confirmation is stronger if the synthetic RSI is above the EMA or SMA overlays.
- Ideal entries are after a reversal from a synthetic P&F downtrend (red candles) to an uptrend (green candles) near or above these levels.
#### Short (Sell) Considerations
- The candle direction flips to *down (red candles)*, showing weakening momentum or bearish reversal.
- Monitor if the synthetic RSI falls below the *60 or 50 level*, signaling momentum loss.
- Confirm bearish bias if the price is below the EMA or SMA overlays.
- Exit or short positions are signaled when the synthetic candle reverses from green to red near or below these threshold levels.
### Important RSI Levels to Watch
- *Level 33*: Lower bound indicating deep oversold conditions.
- *Level 40*: Early bullish zone suggesting momentum improvement.
- *Level 50*: Neutral midpoint; crossing above often signals bullish strength, below signals weakness.
- *Level 60*: Advanced bullish momentum; breaking below signals potential reversal.
- *Level 66*: Strong overbought area warning of possible pullback.
### Tips
- Use in conjunction with price action analysis and other volume/trend indicators for higher conviction.
- Adjust box size and reversal settings based on instrument volatility and timeframe for ideal filtering.
- The P&F RSI is best for identifying sustained momentum trends and avoiding false RSI whipsaws.
- Combine this indicator’s signals with stop-loss and risk management strategies.
*
This indicator converts RSI momentum analysis into a simplified, noise-filtered P&F chart format, helping traders better visualize and trade momentum shifts. It is especially useful when RSI signal noise can cause confusion in volatile markets.
Let me know if you want me to generate a shorter summary or code alerts based on these levels!
Sources
Relative Strength Index (RSI) — Indicators and Strategies in.tradingview.com
Indicators and strategies in.tradingview.com
Relative Strength Index (RSI) Indicator: Tutorial www.youtube.com
Stochastic RSI (STOCH RSI) in.tradingview.com
RSI Strategy docs.algotest.in
Stochastic RSI Indicator: Tutorial www.youtube.com
Relative Strength Index (RSI): What It Is, How It Works, and ... www.investopedia.com
rsi — Indicators and Strategies in.tradingview.com
Relative Strength Index (RSI) in.tradingview.com
Relative Strength Index (RSI) — Indicators and Strategies www.tradingview.com
% of Average Volume% of Average Volume (RVOL)
What it is
This indicator measures cumulative volume during pre market and separately during the first 10 minutes of trading and compares it to the average 30 day volume. This matters as a high ratio of volume within the premarket and then during the first 10 minutes of trading can correlate to a stock that has a higher probability of trending in that direction throughout the day.
What it’s meant to do
Identify abnormally high or low participation early in the day.
Normalize volume by time of session, so 9:40 volume is compared to past 9:40 volume—not to the full-day total.
Provide consistent RVOL across 1–5–15–60 minute charts (the same market state yields similar readings).
Handle pre-market cleanly (optional) without inflating RVOL.
How it works (plain English)
Cumulative Intraday Volume: Adds up all bars from the session (or pre-market, if enabled) up to “now.”
Time-Matched Baseline: For each prior day in your lookback, it accumulates only up to the same intraday minute and averages those values.
RVOL %: RVOL = (Today cumulative / Average cumulative at same time) × 100.
This “like-for-like” approach prevents the classic mistakes that overstate RVOL in pre-market or make it drift with timeframe changes.
Works best on
Intraday charts: 1, 2, 3, 4, 5, 10, 15, 30, 45, 60 min
Regular & extended hours: NYSE/Nasdaq equities, futures, ETFs
Daily/weekly views are supported for reference, but the edge comes from intraday time-matched analysis.
Tip: For thin names or very early pre-market, expect more variability—lower liquidity increases noise.
Customization (Inputs → Settings)
Lookback Sessions (e.g., 20): How many prior trading days to build the average.
Include Pre-Market (on/off): If on, RVOL accumulates from pre-market start and compares to historical pre-market at the same time; if off, it begins at the regular session open only.
Session Timezone / Exchange Hours: Choose the session definition that matches your market (e.g., NYSE) so “time-matched” means the same thing every day.
Cutoff Minute (Optional): Fix a reference minute (e.g., 6:40 a.m. PT / 9:40 a.m. ET) to evaluate RVOL at a standard check-in time.
Smoothing (Optional): Apply a short moving average to the RVOL line to reduce jitter.
Thresholds & Colors: Set levels (e.g., 150%, 300%) to color the plot/labels and trigger alerts.
Show Labels/Debug: Toggle on-chart labels (current RVOL%, baseline vols) for quick audits.
On-chart visuals & alerts
RVOL% Line/Histogram: Color-coded by thresholds (e.g., >300% “exceptional”, >150% “elevated”).
Session Markers: Optional vertical lines for pre-market/regular open.
Alerts:
RVOL Crosses Above X% (e.g., 150%, 300%)
RVOL Crosses Below X%
RVOL Rising/Falling (slope-based, optional)
Good defaults to start
Lookback: 20 sessions
Pre-market: Off for large caps, On for momentum screens
Thresholds: 150% (notable), 300% (exceptional)
Smoothing: 0–3 bars (or off for fastest response)
Notes & best practices
Timeframe consistency: Because calculations are time-matched, RVOL should remain directionally consistent across intraday timeframes. If you see divergences, confirm your session hours & timezone match your instrument’s exchange.
Holiday/half days: These are included in history; you can shorten lookback or exclude such sessions if your workflow prefers.
Low-float names: Consider a slightly longer lookback to reduce outlier effects.
TL;DR
A time-matched RVOL that treats pre-market correctly, stays stable across intraday timeframes, and is fully customizable for your exchange hours, thresholds, and alerts—so you can spot real participation when it matters.
S&P 500 Weighted Advance Decline LineS&P 500 Weighted Advance Decline Line Indicator
Overview
This indicator creates a market cap weighted advance/decline line for the S&P 500 that tracks breadth based on actual index weights rather than treating all stocks equally. By weighting each stock's contribution according to its true S&P 500 impact, it provides more accurate market breadth analysis and better insights into underlying market strength and potential turning points.
Key Features
Market Cap Weighted: Each stock contributes based on its actual S&P 500 weight
Top 40 Stocks: Covers ~51% of the index with the largest companies
(limited by TradingView's 40 security call maximum for Premium accounts)
Real-Time Updates: Cumulative line shows long-term breadth trends
Visual Indicators: Background coloring, moving average option, and data table
Stock Coverage
Sector Breakdown:
Technology (29.8%) - Dominates the coverage as expected
Financials (5.8%) - Major banking and payment companies
Consumer/Retail (3.7%) - Consumer staples and retail giants
Healthcare (3.2%) - Pharma and healthcare services
Communication (1.97%) - Telecom and tech services
Energy (1.35%) - Oil and gas majors
Industrial (0.9%) - Aerospace and industrial equipment
Other Sectors (4.6%) - Miscellaneous including software and payments
Includes the 40 largest S&P 500 companies by weight, featuring:
Tech Leaders (29.8%): AAPL (7.0%), MSFT (6.5%), NVDA (4.5%), AMZN (3.5%), META (2.5%), GOOGL/GOOG (3.8%), AVGO (1.5%), ORCL (1.22%), AMD (0.51%), plus others
Financials (5.8%): BRK.B (1.8%), JPM (1.2%), V (1.0%), MA (0.8%), BAC (0.63%), WFC (0.46%)
Healthcare (3.2%): LLY (1.2%), UNH (1.2%), JNJ (1.1%), ABBV (0.8%), PG (0.9%)
Consumer/Retail (3.7%): WMT (0.8%), HD (0.8%), COST (0.7%), KO (0.6%), PEP (0.6%), NKE (0.4%)
Communication (1.97%): TMUS (0.47%), CSCO (0.47%), DIS (0.5%), CRM (0.5%)
Energy** (1.35%): XOM (0.8%), CVX (0.55%)
Industrial** (0.9%): GE (0.5%), BA (0.4%)
Other Sectors (4.6%): PLTR (0.65%), ADBE (0.6%), PYPL (0.3%), plus others
How to Interpret
Trend Signals
Rising A/D Line: Broad market strength, more weighted buying than selling
Falling A/D Line: Market weakness, more weighted selling pressure
Flat A/D Line: Balanced market conditions
Divergence Analysis
Bullish Divergence: S&P 500 makes new lows but A/D Line holds higher
Bearish Divergence: S&P 500 makes new highs but A/D Line fails to confirm
Confirmation
Strong trends occur when both price and A/D Line move in the same direction
Weak trends show when price moves but breadth doesn't follow
Settings
Lookback Period: Days for advance/decline comparison (default: 1)
Show Moving Average: Optional trend smoothing
MA Length: Moving average period (default: 20)
Limitations
Covers ~51% of S&P 500 (not complete market breadth)
Optimized for TradingView Premium accounts (40 security limit)
Heavy weighting toward mega-cap technology stocks
Dependent on real-time data quality
Cardwell RSI by TQ📌 Cardwell RSI – Enhanced Relative Strength Index
This indicator is based on Andrew Cardwell’s RSI methodology , extending the classic RSI with tools to better identify bullish/bearish ranges and trend dynamics.
In uptrends, RSI tends to hold between 40–80 (Cardwell bullish range).
In downtrends, RSI tends to stay between 20–60 (Cardwell bearish range).
Key Features :
Standard RSI with configurable length & source
Fast (9) & Slow (45) RSI Moving Averages (toggleable)
Cardwell Core Levels (80 / 60 / 40 / 20) – enabled by default
Base Bands (70 / 50 / 30) in dotted style
Optional custom levels (up to 3)
Alerts for MA crosses and level crosses
Data Window metrics: RSI vs Fast/Slow MA differences
How to Use :
Monitor RSI behavior inside Cardwell’s bullish (40–80) and bearish (20–60) ranges
Watch RSI crossovers with Fast (9) and Slow (45) MAs to confirm momentum or trend shifts
Use levels and alerts as confluence with your trading strategy
Default Settings :
RSI Length: 14
MA Type: WMA
Fast MA: 9 (hidden by default)
Slow MA: 45 (hidden by default)
Cardwell Levels (80/60/40/20): ON
Base Bands (70/50/30): ON
Price Heat Meter [ChartPrime]⯁ OVERVIEW
Price Heat Meter visualizes where price sits inside its recent range and turns that into an intuitive “temperature” read. Using rolling extremes, candles fade from ❄️ aqua (cold) near the lower bound to 🔥 red (hot) near the upper bound. The tool also trails recent extreme levels, tags unusually persistent extremes with a % “heat” label, and shows a bottom gauge (0–100%) with a live arrow so you can read market heat at a glance.
⯁ KEY FEATURES
Rolling Heat Map (0–100%):
The script measures where the close sits between the current Lowest Low and Highest High over the chosen Length (default 50).
Candles use a two-stage gradient: aqua → yellow (0–50%), then yellow → red (50–100%). This makes “how stretched are we?” instantly visible.
Dynamic Extremes with Time Decay:
When a new rolling High or Low is set, the script starts a faint horizontal trail at that price. Each bar that passes without a new extreme increases a counter; the line’s color gradually fades over time and fully disappears after ~100 bars, keeping the chart clean.
Persistent-Extreme Tags (Reversal Hints):
If an extreme persists for 40 bars (i.e., price hasn’t reclaimed or surpassed it), the tool stamps the original extreme pivot with its recorded Heat% at the moment the extreme formed.
• Upper extremes print a red % label (possible exhaustion/resistance context).
• Lower extremes print an aqua % label (possible exhaustion/support context).
Bottom Heat Gauge (0–100% Scale):
A compact, gradient bar renders at the bottom center showing the current Heat% with an arrow/label. ❄️ anchors the left (0%), 🔥 anchors the right (100%). The arrow adopts the same candle heat color for consistency.
Minimal Inputs, Clear Theme:
• Length (lookback window for H/L)
• Heat Color set (Cold / Mid / Hot)
The defaults give a balanced, legible gradient on most assets/timeframes.
Signal Hygiene by Design:
The meter doesn’t “call” reversals. Instead, it contextualizes price within its range and highlights the aging of extremes. That keeps it robust across regimes and assets, and ideal as a confluence layer with your existing triggers.
⯁ HOW IT WORKS (UNDER THE HOOD)
Range Model:
H = Highest(High, Length), L = Lowest(Low, Length). Heat% = 100 × (Close − L) / (H − L).
Extreme Tracking & Fade:
When High == H , we record/update the current upper extreme; same for Low == L on the lower side. If the extreme doesn’t change on the next bar, a counter increments and the plotted line’s opacity shifts along a 0→100 fade scale (visual decay).
40-Bar Persistence Labels:
On the bar after the extreme forms, the code stores the bar_index and the contemporaneous Heat% . If the extreme survives 40 bars, it places a % label at the original pivot price and index—flagging levels that were meaningfully “tested by time.”
Unified Color Logic:
Both candles and the gauge use the same two-stage gradient (Cold→Mid, then Mid→Hot), so your eye reads “heat” consistently across all elements.
⯁ USAGE
Treat >80% as “hot” and <20% as “cold” context; combine with your trigger (e.g., structure, OB, div, breakouts) instead of acting on heat alone.
Watch persistent extreme labels (40-bar marks) as reference zones for reaction or liquidity grabs.
Use the fading extreme lines as a memory map of where price last stretched—levels that slowly matter less as they decay.
Tighten Length for intraday sensitivity or increase it for swing stability.
⯁ WHY IT’S UNIQUE
Rather than another oscillator, Price Heat Meter translates simple market geometry (rolling extremes) into a readable temperature layer with time-aware extremes and a synchronized gauge . You get a continuously updated sense of stretch, persistence, and potential reversal context—without clutter or overfitting.
VWAP For Loop [BackQuant]VWAP For Loop
What this tool does—in one sentence
A volume-weighted trend gauge that anchors VWAP to a calendar period (day/week/month/quarter/year) and then scores the persistence of that VWAP trend with a simple for-loop “breadth” count; the result is a clean, threshold-driven oscillator plus an optional VWAP overlay and alerts.
Plain-English overview
Instead of judging raw price alone, this indicator focuses on anchored VWAP —the market’s average price paid during your chosen institutional period. It then asks a simple question across a configurable set of lookback steps: “Is the current anchored VWAP higher than it was i bars ago—or lower?” Each “yes” adds +1, each “no” adds −1. Summing those answers creates a score that reflects how consistently the volume-weighted trend has been rising or falling. Extreme positive scores imply persistent, broad strength; deeply negative scores imply persistent weakness. Crossing predefined thresholds produces objective long/short events and color-coded context.
Under the hood
• Anchoring — VWAP using hlc3 × volume resets exactly when the selected period rolls:
Day → session change, Week → new week, Month → new month, Quarter/Year → calendar quarter/year.
• For-loop scoring — For lag steps i = , compare today’s VWAP to VWAP .
– If VWAP > VWAP , add +1.
– Else, add −1.
The final score ∈ , where N = (end − start + 1). With defaults (1→45), N = 45.
• Signal logic (stateful)
– Long when score > upper (e.g., > 40 with N = 45 → VWAP higher than ~89% of checked lags).
– Short on crossunder of lower (e.g., dropping below −10).
– A compact state variable ( out ) holds the current regime: +1 (long), −1 (short), otherwise unchanged. This “stickiness” avoids constant flipping between bars without sufficient evidence.
Why VWAP + a breadth score?
• VWAP aggregates both price and volume—where participants actually traded.
• The breadth-style count rewards consistency of the anchored trend, not one-off spikes.
• Thresholds give you binary structure when you need it (alerts, automation), without complex math.
What you’ll see on the chart
• Sub-pane oscillator — The for-loop score line, colored by regime (long/short/neutral).
• Main-pane VWAP (optional) — Even though the indicator runs off-chart, the anchored VWAP can be overlaid on price (toggle visibility and whether it inherits trend colors).
• Threshold guides — Horizontal lines for the long/short bands (toggle).
• Cosmetics — Optional candle painting and background shading by regime; adjustable line width and colors.
Input map (quick reference)
• VWAP Anchor Period — Day, Week, Month, Quarter, Year.
• Calculation Start/End — The for-loop lag window . With 1→45, you evaluate 45 comparisons.
• Long/Short Thresholds — Default upper=40, lower=−10 (asymmetric by design; see below).
• UI/Style — Show thresholds, paint candles, background color, line width, VWAP visibility and coloring, custom long/short colors.
Interpreting the score
• Near +N — Current anchored VWAP is above most historical VWAP checkpoints in the window → entrenched strength.
• Near −N — Current anchored VWAP is below most checkpoints → entrenched weakness.
• Between — Mixed, choppy, or transitioning regimes; use thresholds to avoid reacting to noise.
Why the asymmetric default thresholds?
• Long = score > upper (40) — Demands unusually broad upside persistence before declaring “long regime.”
• Short = crossunder lower (−10) — Triggers only on downward momentum events (a fresh breach), not merely being below −10. This combination tends to:
– Capture sustained uptrends only when they’re very strong.
– Flag downside turns as they occur, rather than waiting for an extreme negative breadth.
Tuning guide
Choose an anchor that matches your horizon
– Intraday scalps : Day anchor on intraday charts.
– Swing/position : Month or Quarter anchor on 1h/4h/D charts to capture institutional cycles.
Pick the for-loop window
– Larger N (bigger end) = stronger evidence requirement, smoother oscillator.
– Smaller N = faster, more reactive score.
Set achievable thresholds
– Ensure upper ≤ N and lower ≥ −N ; if N=30, an upper of 40 can never trigger.
– Symmetric setups (e.g., +20/−20) are fine if you want balanced behavior.
Match visuals to intent
– Enabling VWAP coloring lets you see regime directly on price.
– Background shading is useful for discretionary reading; turn it off for cleaner automation displays.
Playbook examples
• Trend confirmation with disciplined entries — On Month anchor, N=45, upper=38–42: when the long regime engages, use pullbacks toward anchored VWAP on the main pane for entries, with stops just beyond VWAP or a recent swing.
• Downside transition detection — Keep lower around −8…−12 and watch for crossunders; combine with price losing anchored VWAP to validate risk-off.
• Intraday bias filter — Day anchor on a 5–15m chart, N=20–30, upper ~ 16–20, lower ~ −6…−10. Only take longs while score is positive and above a midline you define (e.g., 0), and shorts only after a genuine crossunder.
Behavior around resets (important)
Anchored VWAP is hard-reset each period. Immediately after a reset, the series can be young and comparisons to pre-reset values may span two periods. If you prefer within-period evaluation only, choose end small enough not to bridge typical period length on your timeframe, or accept that the breadth test intentionally spans regimes.
Alerts included
• VWAP FL Long — Fires when the long condition is true (score > upper and not in short).
• VWAP FL Short — Fires on crossunder of the lower threshold (event-driven).
Messages include {{ticker}} and {{interval}} placeholders for routing.
Strengths
• Simple, transparent math — Easy to reason about and validate.
• Volume-aware by construction — Decisions reference VWAP, not just price.
• Robust to single-bar noise — Needs many lags to agree before flipping state (by design, via thresholds and the stateful output).
Limitations & cautions
• Threshold feasibility — If N < upper or |lower| > N, signals will never trigger; always cross-check N.
• Path dependence — The state variable persists until a new event; if you want frequent re-evaluation, lower thresholds or reduce N.
• Regime changes — Calendar resets can produce early ambiguity; expect a few bars for the breadth to mature.
• VWAP sensitivity to volume spikes — Large prints can tilt VWAP abruptly; that behavior is intentional in VWAP-based logic.
Suggested starting profiles
• Intraday trend bias : Anchor=Day, N=25 (1→25), upper=18–20, lower=−8, paint candles ON.
• Swing bias : Anchor=Month, N=45 (1→45), upper=38–42, lower=−10, VWAP coloring ON, background OFF.
• Balanced reactivity : Anchor=Week, N=30 (1→30), upper=20–22, lower=−10…−12, symmetric if desired.
Implementation notes
• The indicator runs in a separate pane (oscillator), but VWAP itself is drawn on price using forced overlay so you can see interactions (touches, reclaim/loss).
• HLC3 is used for VWAP price; that’s a common choice to dampen wick noise while still reflecting intrabar range.
• For-loop cap is kept modest (≤50) for performance and clarity.
How to use this responsibly
Treat the oscillator as a bias and persistence meter . Combine it with your entry framework (structure breaks, liquidity zones, higher-timeframe context) and risk controls. The design emphasizes clarity over complexity—its edge is in how strictly it demands agreement before declaring a regime, not in predicting specific turns.
Summary
VWAP For Loop distills the question “How broadly is the anchored, volume-weighted trend advancing or retreating?” into a single, thresholded score you can read at a glance, alert on, and color through your chart. With careful anchoring and thresholds sized to your window length, it becomes a pragmatic bias filter for both systematic and discretionary workflows.
Peak & Valley Screener RadarThis Pine Script indicator is designed to help traders and investors analyze the percentage distance of stock prices from their recent All-Time High (ATH) and All-Time Low (ALH) over a user-defined number of bars.
It functions as a multi-stock screener, scanning a customizable list of stocks (default: 40 BIST 500 stocks) and displaying results in a dynamic table on the chart.
The script identifies stocks that have pulled back more than a specified percentage from their ATH (potential buying opportunities) or risen less than a specified percentage from their ALH (potential caution zones).
Key Features:
Customizable Stock List: Users can input a comma-separated list of stock tickers (e.g., "AAPL,GOOGL,MSFT") to scan any symbols available on TradingView.
User-Defined Parameters: Adjust the lookback period (bars back, default 250), ATH pullback threshold (default 10%), and ALH rise threshold (default 10%).
Dynamic Table Display: Results are shown in a table with two columns: "Distance to TOP" (ATH pullbacks in red) and "Distance to BOTTOM" (ALH rises in green). The table includes input parameters for quick reference and can be positioned anywhere on the chart (top/bottom left/center/right).
Optional Plots: Toggle plots to visualize the percentage distances for the current chart symbol (red for ATH, green for ALH).
Efficient Data Handling: Uses request.security with tuples for optimized multi-symbol data fetching, supporting up to ~80 stocks without exceeding Pine Script limits (adjust table rows if needed for more).
Real-Time Updates: The table updates only on the last bar for performance efficiency.
How It Works:
The script calculates the highest high and lowest low over the specified bars for each stock.
It computes the percentage difference from the current close: negative for ATH (pullback) and positive for ALH (rise).
Stocks meeting the thresholds are listed in the table with their exact percentages.
Usage Tips:
Apply this indicator to any chart (e.g., a BIST index or stock) to run the screener in the background.
Ideal for swing traders scanning for undervalued stocks near ATH or overbought near ALH.
Note: Performance may vary with large stock lists due to TradingView's security call limits (~40-50 calls per script). Test with smaller lists if needed.
You can bypass the 40-stock limit by adding the indicator twice to the chart, entering 40 different stocks in the second indicator and setting a different table position from the first one, allowing you to scan 80 stocks simultaneously. In fact, this way, you can scan as many stocks as your plan's limits allow.
This script is released under the Mozilla Public License 2.0. Feedback and suggestions are welcome, but please adhere to TradingView's House Rules—no guarantees of profitability, use at your own risk.Disclaimer: This is not financial advice. Past performance does not predict future results. Always conduct your own research.
Drawdown Distribution Analysis (DDA) ACADEMIC FOUNDATION AND RESEARCH BACKGROUND
The Drawdown Distribution Analysis indicator implements quantitative risk management principles, drawing upon decades of academic research in portfolio theory, behavioral finance, and statistical risk modeling. This tool provides risk assessment capabilities for traders and portfolio managers seeking to understand their current position within historical drawdown patterns.
The theoretical foundation of this indicator rests on modern portfolio theory as established by Markowitz (1952), who introduced the fundamental concepts of risk-return optimization that continue to underpin contemporary portfolio management. Sharpe (1966) later expanded this framework by developing risk-adjusted performance measures, most notably the Sharpe ratio, which remains a cornerstone of performance evaluation in financial markets.
The specific focus on drawdown analysis builds upon the work of Chekhlov, Uryasev and Zabarankin (2005), who provided the mathematical framework for incorporating drawdown measures into portfolio optimization. Their research demonstrated that traditional mean-variance optimization often fails to capture the full risk profile of investment strategies, particularly regarding sequential losses. More recent work by Goldberg and Mahmoud (2017) has brought these theoretical concepts into practical application within institutional risk management frameworks.
Value at Risk methodology, as comprehensively outlined by Jorion (2007), provides the statistical foundation for the risk measurement components of this indicator. The coherent risk measures framework developed by Artzner et al. (1999) ensures that the risk metrics employed satisfy the mathematical properties required for sound risk management decisions. Additionally, the focus on downside risk follows the framework established by Sortino and Price (1994), while the drawdown-adjusted performance measures implement concepts introduced by Young (1991).
MATHEMATICAL METHODOLOGY
The core calculation methodology centers on a peak-tracking algorithm that continuously monitors the maximum price level achieved and calculates the percentage decline from this peak. The drawdown at any time t is defined as DD(t) = (P(t) - Peak(t)) / Peak(t) × 100, where P(t) represents the asset price at time t and Peak(t) represents the running maximum price observed up to time t.
Statistical distribution analysis forms the analytical backbone of the indicator. The system calculates key percentiles using the ta.percentile_nearest_rank() function to establish the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of the historical drawdown distribution. This approach provides a complete picture of how the current drawdown compares to historical patterns.
Statistical significance assessment employs standard deviation bands at one, two, and three standard deviations from the mean, following the conventional approach where the upper band equals μ + nσ and the lower band equals μ - nσ. The Z-score calculation, defined as Z = (DD - μ) / σ, enables the identification of statistically extreme events, with thresholds set at |Z| > 2.5 for extreme drawdowns and |Z| > 3.0 for severe drawdowns, corresponding to confidence levels exceeding 99.4% and 99.7% respectively.
ADVANCED RISK METRICS
The indicator incorporates several risk-adjusted performance measures that extend beyond basic drawdown analysis. The Sharpe ratio calculation follows the standard formula Sharpe = (R - Rf) / σ, where R represents the annualized return, Rf represents the risk-free rate, and σ represents the annualized volatility. The system supports dynamic sourcing of the risk-free rate from the US 10-year Treasury yield or allows for manual specification.
The Sortino ratio addresses the limitation of the Sharpe ratio by focusing exclusively on downside risk, calculated as Sortino = (R - Rf) / σd, where σd represents the downside deviation computed using only negative returns. This measure provides a more accurate assessment of risk-adjusted performance for strategies that exhibit asymmetric return distributions.
The Calmar ratio, defined as Annual Return divided by the absolute value of Maximum Drawdown, offers a direct measure of return per unit of drawdown risk. This metric proves particularly valuable for comparing strategies or assets with different risk profiles, as it directly relates performance to the maximum historical loss experienced.
Value at Risk calculations provide quantitative estimates of potential losses at specified confidence levels. The 95% VaR corresponds to the 5th percentile of the drawdown distribution, while the 99% VaR corresponds to the 1st percentile. Conditional VaR, also known as Expected Shortfall, estimates the average loss in the worst 5% of scenarios, providing insight into tail risk that standard VaR measures may not capture.
To enable fair comparison across assets with different volatility characteristics, the indicator calculates volatility-adjusted drawdowns using the formula Adjusted DD = Raw DD / (Volatility / 20%). This normalization allows for meaningful comparison between high-volatility assets like cryptocurrencies and lower-volatility instruments like government bonds.
The Risk Efficiency Score represents a composite measure ranging from 0 to 100 that combines the Sharpe ratio and current percentile rank to provide a single metric for quick asset assessment. Higher scores indicate superior risk-adjusted performance relative to historical patterns.
COLOR SCHEMES AND VISUALIZATION
The indicator implements eight distinct color themes designed to accommodate different analytical preferences and market contexts. The EdgeTools theme employs a corporate blue palette that matches the design system used throughout the edgetools.org platform, ensuring visual consistency across analytical tools.
The Gold theme specifically targets precious metals analysis with warm tones that complement gold chart analysis, while the Quant theme provides a grayscale scheme suitable for analytical environments that prioritize clarity over aesthetic appeal. The Behavioral theme incorporates psychology-based color coding, using green to represent greed-driven market conditions and red to indicate fear-driven environments.
Additional themes include Ocean, Fire, Matrix, and Arctic schemes, each designed for specific market conditions or user preferences. All themes function effectively with both dark and light mode trading platforms, ensuring accessibility across different user interface configurations.
PRACTICAL APPLICATIONS
Asset allocation and portfolio construction represent primary use cases for this analytical framework. When comparing multiple assets such as Bitcoin, gold, and the S&P 500, traders can examine Risk Efficiency Scores to identify instruments offering superior risk-adjusted performance. The 95% VaR provides worst-case scenario comparisons, while volatility-adjusted drawdowns enable fair comparison despite varying volatility profiles.
The practical decision framework suggests that assets with Risk Efficiency Scores above 70 may be suitable for aggressive portfolio allocations, scores between 40 and 70 indicate moderate allocation potential, and scores below 40 suggest defensive positioning or avoidance. These thresholds should be adjusted based on individual risk tolerance and market conditions.
Risk management and position sizing applications utilize the current percentile rank to guide allocation decisions. When the current drawdown ranks above the 75th percentile of historical data, indicating that current conditions are better than 75% of historical periods, position increases may be warranted. Conversely, when percentile rankings fall below the 25th percentile, indicating elevated risk conditions, position reductions become advisable.
Institutional portfolio monitoring applications include hedge fund risk dashboard implementations where multiple strategies can be monitored simultaneously. Sharpe ratio tracking identifies deteriorating risk-adjusted performance across strategies, VaR monitoring ensures portfolios remain within established risk limits, and drawdown duration tracking provides valuable information for investor reporting requirements.
Market timing applications combine the statistical analysis with trend identification techniques. Strong buy signals may emerge when risk levels register as "Low" in conjunction with established uptrends, while extreme risk levels combined with downtrends may indicate exit or hedging opportunities. Z-scores exceeding 3.0 often signal statistically oversold conditions that may precede trend reversals.
STATISTICAL SIGNIFICANCE AND VALIDATION
The indicator provides 95% confidence intervals around current drawdown levels using the standard formula CI = μ ± 1.96σ. This statistical framework enables users to assess whether current conditions fall within normal market variation or represent statistically significant departures from historical patterns.
Risk level classification employs a dynamic assessment system based on percentile ranking within the historical distribution. Low risk designation applies when current drawdowns perform better than 50% of historical data, moderate risk encompasses the 25th to 50th percentile range, high risk covers the 10th to 25th percentile range, and extreme risk applies to the worst 10% of historical drawdowns.
Sample size considerations play a crucial role in statistical reliability. For daily data, the system requires a minimum of 252 trading days (approximately one year) but performs better with 500 or more observations. Weekly data analysis benefits from at least 104 weeks (two years) of history, while monthly data requires a minimum of 60 months (five years) for reliable statistical inference.
IMPLEMENTATION BEST PRACTICES
Parameter optimization should consider the specific characteristics of different asset classes. Equity analysis typically benefits from 500-day lookback periods with 21-day smoothing, while cryptocurrency analysis may employ 365-day lookback periods with 14-day smoothing to account for higher volatility patterns. Fixed income analysis often requires longer lookback periods of 756 days with 34-day smoothing to capture the lower volatility environment.
Multi-timeframe analysis provides hierarchical risk assessment capabilities. Daily timeframe analysis supports tactical risk management decisions, weekly analysis informs strategic positioning choices, and monthly analysis guides long-term allocation decisions. This hierarchical approach ensures that risk assessment occurs at appropriate temporal scales for different investment objectives.
Integration with complementary indicators enhances the analytical framework. Trend indicators such as RSI and moving averages provide directional bias context, volume analysis helps confirm the severity of drawdown conditions, and volatility measures like VIX or ATR assist in market regime identification.
ALERT SYSTEM AND AUTOMATION
The automated alert system monitors five distinct categories of risk events. Risk level changes trigger notifications when drawdowns move between risk categories, enabling proactive risk management responses. Statistical significance alerts activate when Z-scores exceed established threshold levels of 2.5 or 3.0 standard deviations.
New maximum drawdown alerts notify users when historical maximum levels are exceeded, indicating entry into uncharted risk territory. Poor risk efficiency alerts trigger when the composite risk efficiency score falls below 30, suggesting deteriorating risk-adjusted performance. Sharpe ratio decline alerts activate when risk-adjusted performance turns negative, indicating that returns no longer compensate for the risk undertaken.
TRADING STRATEGIES
Conservative risk parity strategies can be implemented by monitoring Risk Efficiency Scores across a diversified asset portfolio. Monthly rebalancing maintains equal risk contribution from each asset, with allocation reductions triggered when risk levels reach "High" status and complete exits executed when "Extreme" risk levels emerge. This approach typically results in lower overall portfolio volatility, improved risk-adjusted returns, and reduced maximum drawdown periods.
Tactical asset rotation strategies compare Risk Efficiency Scores across different asset classes to guide allocation decisions. Assets with scores exceeding 60 receive overweight allocations, while assets scoring below 40 receive underweight positions. Percentile rankings provide timing guidance for allocation adjustments, creating a systematic approach to asset allocation that responds to changing risk-return profiles.
Market timing strategies with statistical edges can be constructed by entering positions when Z-scores fall below -2.5, indicating statistically oversold conditions, and scaling out when Z-scores exceed 2.5, suggesting overbought conditions. The 95% VaR serves as a stop-loss reference point, while trend confirmation indicators provide additional validation for position entry and exit decisions.
LIMITATIONS AND CONSIDERATIONS
Several statistical limitations affect the interpretation and application of these risk measures. Historical bias represents a fundamental challenge, as past drawdown patterns may not accurately predict future risk characteristics, particularly during structural market changes or regime shifts. Sample dependence means that results can be sensitive to the selected lookback period, with shorter periods providing more responsive but potentially less stable estimates.
Market regime changes can significantly alter the statistical parameters underlying the analysis. During periods of structural market evolution, historical distributions may provide poor guidance for future expectations. Additionally, many financial assets exhibit return distributions with fat tails that deviate from normal distribution assumptions, potentially leading to underestimation of extreme event probabilities.
Practical limitations include execution risk, where theoretical signals may not translate directly into actual trading results due to factors such as slippage, timing delays, and market impact. Liquidity constraints mean that risk metrics assume perfect liquidity, which may not hold during stressed market conditions when risk management becomes most critical.
Transaction costs are not incorporated into risk-adjusted return calculations, potentially overstating the attractiveness of strategies that require frequent trading. Behavioral factors represent another limitation, as human psychology may override statistical signals, particularly during periods of extreme market stress when disciplined risk management becomes most challenging.
TECHNICAL IMPLEMENTATION
Performance optimization ensures reliable operation across different market conditions and timeframes. All technical analysis functions are extracted from conditional statements to maintain Pine Script compliance and ensure consistent execution. Memory efficiency is achieved through optimized variable scoping and array usage, while computational speed benefits from vectorized calculations where possible.
Data quality requirements include clean price data without gaps or errors that could distort distribution analysis. Sufficient historical data is essential, with a minimum of 100 bars required and 500 or more preferred for reliable statistical inference. Time alignment across related assets ensures meaningful comparison when conducting multi-asset analysis.
The configuration parameters are organized into logical groups to enhance usability. Core settings include the Distribution Analysis Period (100-2000 bars), Drawdown Smoothing Period (1-50 bars), and Price Source selection. Advanced metrics settings control risk-free rate sourcing, either from live market data or fixed rate specification, along with toggles for various risk-adjusted metric calculations.
Display options provide flexibility in visual presentation, including color theme selection from eight available schemes, automatic dark mode optimization, and control over table display, position lines, percentile bands, and standard deviation overlays. These options ensure that the indicator can be adapted to different analytical workflows and visual preferences.
CONCLUSION
The Drawdown Distribution Analysis indicator provides risk management tools for traders seeking to understand their current position within historical risk patterns. By combining established statistical methodology with practical usability features, the tool enables evidence-based risk assessment and portfolio optimization decisions.
The implementation draws upon established academic research while providing practical features that address real-world trading requirements. Dynamic risk-free rate integration ensures accurate risk-adjusted performance calculations, while multiple color schemes accommodate different analytical preferences and use cases.
Academic compliance is maintained through transparent methodology and acknowledgment of limitations. The tool implements peer-reviewed statistical techniques while clearly communicating the constraints and assumptions underlying the analysis. This approach ensures that users can make informed decisions about the appropriate application of the risk assessment framework within their broader trading and investment processes.
BIBLIOGRAPHY
Artzner, P., Delbaen, F., Eber, J.M. and Heath, D. (1999) 'Coherent Measures of Risk', Mathematical Finance, 9(3), pp. 203-228.
Chekhlov, A., Uryasev, S. and Zabarankin, M. (2005) 'Drawdown Measure in Portfolio Optimization', International Journal of Theoretical and Applied Finance, 8(1), pp. 13-58.
Goldberg, L.R. and Mahmoud, O. (2017) 'Drawdown: From Practice to Theory and Back Again', Journal of Risk Management in Financial Institutions, 10(2), pp. 140-152.
Jorion, P. (2007) Value at Risk: The New Benchmark for Managing Financial Risk. 3rd edn. New York: McGraw-Hill.
Markowitz, H. (1952) 'Portfolio Selection', Journal of Finance, 7(1), pp. 77-91.
Sharpe, W.F. (1966) 'Mutual Fund Performance', Journal of Business, 39(1), pp. 119-138.
Sortino, F.A. and Price, L.N. (1994) 'Performance Measurement in a Downside Risk Framework', Journal of Investing, 3(3), pp. 59-64.
Young, T.W. (1991) 'Calmar Ratio: A Smoother Tool', Futures, 20(1), pp. 40-42.
SMI-DarknessIndicator Description: SMI-Darkness
The SMI-Darkness is an indicator based on the Stochastic Momentum Index (SMI), designed to help identify the strength and direction of an asset's trend, as well as potential buy and sell signals. It displays a smoothed SMI using multiple moving average options to customize the indicator’s behavior according to the user’s trading style.
Main Features
Smoothed SMI: Calculates the traditional SMI and smooths it using a user-configurable moving average, improving signal clarity.
Signal Line: Displays a smoothed signal line to identify crossovers with the SMI, generating potential entry or exit points.
Histogram: Shows the difference between the smoothed SMI and the signal line, visually highlighting trend strength. Blue bars indicate buying strength, while yellow bars indicate selling strength.
Horizontal Lines: Includes overbought (+40) and oversold (-40) levels, plus a neutral zero level to aid interpretation.
Indicator Parameters
SMI Short Period: Sets the short period used to calculate the SMI (default 5). Lower periods make the indicator more sensitive.
SMI Signal Period: Sets the period to smooth the signal line (default 5). Adjust to control the signal line's smoothness.
Moving Average Type: Choose the moving average type to smooth the SMI and signal line. Options include:
SMA (Simple Moving Average)
SMMA (Smoothed Moving Average)
EMA (Exponential Moving Average)
WMA (Weighted Moving Average)
HMA (Hull Moving Average)
JMA (Jurik Moving Average) — Note: This is not an original or proprietary moving average but a publicly available open-source version created by TradingView users.
VWMA (Volume-Weighted Moving Average)
KAMA (Kaufman Adaptive Moving Average)
How to Use
Trend Identification: Observe the position of the smoothed SMI relative to the signal line and the histogram values.
When the histogram is positive (blue bars), momentum is bullish.
When the histogram is negative (yellow bars), momentum is bearish.
Buy and Sell Signals:
A crossover of the smoothed SMI above the signal line may indicate a buy signal.
A crossover of the smoothed SMI below the signal line may indicate a sell signal.
Overbought/Oversold Levels:
SMI values above +40 suggest potential overbought conditions, signaling caution on long positions.
Values below -40 suggest potential oversold conditions, indicating possible buying opportunities.
Customization: Adjust the parameters to balance sensitivity and noise, choosing the moving average type that best fits your trading style.
Ergodic Market Divergence (EMD)Ergodic Market Divergence (EMD)
Bridging Statistical Physics and Market Dynamics Through Ensemble Analysis
The Revolutionary Concept: When Physics Meets Trading
After months of research into ergodic theory—a fundamental principle in statistical mechanics—I've developed a trading system that identifies when markets transition between predictable and unpredictable states. This indicator doesn't just follow price; it analyzes whether current market behavior will persist or revert, giving traders a scientific edge in timing entries and exits.
The Core Innovation: Ergodic Theory Applied to Markets
What Makes Markets Ergodic or Non-Ergodic?
In statistical physics, ergodicity determines whether a system's future resembles its past. Applied to trading:
Ergodic Markets (Mean-Reverting)
- Time averages equal ensemble averages
- Historical patterns repeat reliably
- Price oscillates around equilibrium
- Traditional indicators work well
Non-Ergodic Markets (Trending)
- Path dependency dominates
- History doesn't predict future
- Price creates new equilibrium levels
- Momentum strategies excel
The Mathematical Framework
The Ergodic Score combines three critical divergences:
Ergodic Score = (Price Divergence × Market Stress + Return Divergence × 1000 + Volatility Divergence × 50) / 3
Where:
Price Divergence: How far current price deviates from market consensus
Return Divergence: Momentum differential between instrument and market
Volatility Divergence: Volatility regime misalignment
Market Stress: Adaptive multiplier based on current conditions
The Ensemble Analysis Revolution
Beyond Single-Instrument Analysis
Traditional indicators analyze one chart in isolation. EMD monitors multiple correlated markets simultaneously (SPY, QQQ, IWM, DIA) to detect systemic regime changes. This ensemble approach:
Reveals Hidden Divergences: Individual stocks may diverge from market consensus before major moves
Filters False Signals: Requires broader market confirmation
Identifies Regime Shifts: Detects when entire market structure changes
Provides Context: Shows if moves are isolated or systemic
Dynamic Threshold Adaptation
Unlike fixed-threshold systems, EMD's boundaries evolve with market conditions:
Base Threshold = SMA(Ergodic Score, Lookback × 3)
Adaptive Component = StDev(Ergodic Score, Lookback × 2) × Sensitivity
Final Threshold = Smoothed(Base + Adaptive)
This creates context-aware signals that remain effective across different market environments.
The Confidence Engine: Know Your Signal Quality
Multi-Factor Confidence Scoring
Every signal receives a confidence score based on:
Signal Clarity (0-35%): How decisively the ergodic threshold is crossed
Momentum Strength (0-25%): Rate of ergodic change
Volatility Alignment (0-20%): Whether volatility supports the signal
Market Quality (0-20%): Price convergence and path dependency factors
Real-Time Confidence Updates
The Live Confidence metric continuously updates, showing:
- Current opportunity quality
- Market state clarity
- Historical performance influence
- Signal recency boost
- Visual Intelligence System
Adaptive Ergodic Field Bands
Dynamic bands that expand and contract based on market state:
Primary Color: Ergodic state (mean-reverting)
Danger Color: Non-ergodic state (trending)
Band Width: Expected price movement range
Squeeze Indicators: Volatility compression warnings
Quantum Wave Ribbons
Triple EMA system (8, 21, 55) revealing market flow:
Compressed Ribbons: Consolidation imminent
Expanding Ribbons: Directional move developing
Color Coding: Matches current ergodic state
Phase Transition Signals
Clear entry/exit markers at regime changes:
Bull Signals: Ergodic restoration (mean reversion opportunity)
Bear Signals: Ergodic break (trend following opportunity)
Confidence Labels: Percentage showing signal quality
Visual Intensity: Stronger signals = deeper colors
Professional Dashboard Suite
Main Analytics Panel (Top Right)
Market State Monitor
- Current regime (Ergodic/Non-Ergodic)
- Ergodic score with threshold
- Path dependency strength
- Quantum coherence percentage
Divergence Metrics
- Price divergence with severity
- Volatility regime classification
- Strategy mode recommendation
- Signal strength indicator
Live Intelligence
- Real-time confidence score
- Color-coded risk levels
- Dynamic strategy suggestions
Performance Tracking (Left Panel)
Signal Analytics
- Total historical signals
- Win rate with W/L breakdown
- Current streak tracking
- Closed trade counter
Regime Analysis
- Current market behavior
- Bars since last signal
- Recommended actions
- Average confidence trends
Strategy Command Center (Bottom Right)
Adaptive Recommendations
- Active strategy mode
- Primary approach (mean reversion/momentum)
- Suggested indicators ("weapons")
- Entry/exit methodology
- Risk management guidance
- Comprehensive Input Guide
Core Algorithm Parameters
Analysis Period (10-100 bars)
Scalping (10-15): Ultra-responsive, more signals, higher noise
Day Trading (20-30): Balanced sensitivity and stability
Swing Trading (40-100): Smooth signals, major moves only Default: 20 - optimal for most timeframes
Divergence Threshold (0.5-5.0)
Hair Trigger (0.5-1.0): Catches every wiggle, many false signals
Balanced (1.5-2.5): Good signal-to-noise ratio
Conservative (3.0-5.0): Only extreme divergences Default: 1.5 - best risk/reward balance
Path Memory (20-200 bars)
Short Memory (20-50): Recent behavior focus, quick adaptation
Medium Memory (50-100): Balanced historical context
Long Memory (100-200): Emphasizes established patterns Default: 50 - captures sufficient history without lag
Signal Spacing (5-50 bars)
Aggressive (5-10): Allows rapid-fire signals
Normal (15-25): Prevents clustering, maintains flow
Conservative (30-50): Major setups only Default: 15 - optimal trade frequency
Ensemble Configuration
Select markets for consensus analysis:
SPY: Broad market sentiment
QQQ: Technology leadership
IWM: Small-cap risk appetite
DIA: Blue-chip stability
More instruments = stronger consensus but potentially diluted signals
Visual Customization
Color Themes (6 professional options):
Quantum: Cyan/Pink - Modern trading aesthetic
Matrix: Green/Red - Classic terminal look
Heat: Blue/Red - Temperature metaphor
Neon: Cyan/Magenta - High contrast
Ocean: Turquoise/Coral - Calming palette
Sunset: Red-orange/Teal - Warm gradients
Display Controls:
- Toggle each visual component
- Adjust transparency levels
- Scale dashboard text
- Show/hide confidence scores
- Trading Strategies by Market State
- Ergodic State Strategy (Primary Color Bands)
Market Characteristics
- Price oscillates predictably
- Support/resistance hold
- Volume patterns repeat
- Mean reversion dominates
Optimal Approach
Entry: Fade moves at band extremes
Target: Middle band (equilibrium)
Stop: Just beyond outer bands
Size: Full confidence-based position
Recommended Tools
- RSI for oversold/overbought
- Bollinger Bands for extremes
- Volume profile for levels
- Non-Ergodic State Strategy (Danger Color Bands)
Market Characteristics
- Price trends persistently
- Levels break decisively
- Volume confirms direction
- Momentum accelerates
Optimal Approach
Entry: Breakout from bands
Target: Trail with expanding bands
Stop: Inside opposite band
Size: Scale in with trend
Recommended Tools
- Moving average alignment
- ADX for trend strength
- MACD for momentum
- Advanced Features Explained
Quantum Coherence Metric
Measures phase alignment between individual and ensemble behavior:
80-100%: Perfect sync - strong mean reversion setup
50-80%: Moderate alignment - mixed signals
0-50%: Decoherence - trending behavior likely
Path Dependency Analysis
Quantifies how much history influences current price:
Low (<30%): Technical patterns reliable
Medium (30-50%): Mixed influences
High (>50%): Fundamental shift occurring
Volatility Regime Classification
Contextualizes current volatility:
Normal: Standard strategies apply
Elevated: Widen stops, reduce size
Extreme: Defensive mode required
Signal Strength Indicator
Real-time opportunity quality:
- Distance from threshold
- Momentum acceleration
- Cross-validation factors
Risk Management Framework
Position Sizing by Confidence
90%+ confidence = 100% position size
70-90% confidence = 75% position size
50-70% confidence = 50% position size
<50% confidence = 25% or skip
Dynamic Stop Placement
Ergodic State: ATR × 1.0 from entry
Non-Ergodic State: ATR × 2.0 from entry
Volatility Adjustment: Multiply by current regime
Multi-Timeframe Alignment
- Check higher timeframe regime
- Confirm ensemble consensus
- Verify volume participation
- Align with major levels
What Makes EMD Unique
Original Contributions
First Ergodic Theory Trading Application: Transforms abstract physics into practical signals
Ensemble Market Analysis: Revolutionary multi-market divergence system
Adaptive Confidence Engine: Institutional-grade signal quality metrics
Quantum Coherence: Novel market alignment measurement
Smart Signal Management: Prevents clustering while maintaining responsiveness
Technical Innovations
Dynamic Threshold Adaptation: Self-adjusting sensitivity
Path Memory Integration: Historical dependency weighting
Stress-Adjusted Scoring: Market condition normalization
Real-Time Performance Tracking: Built-in strategy analytics
Optimization Guidelines
By Timeframe
Scalping (1-5 min)
Period: 10-15
Threshold: 0.5-1.0
Memory: 20-30
Spacing: 5-10
Day Trading (5-60 min)
Period: 20-30
Threshold: 1.5-2.5
Memory: 40-60
Spacing: 15-20
Swing Trading (1H-1D)
Period: 40-60
Threshold: 2.0-3.0
Memory: 80-120
Spacing: 25-35
Position Trading (1D-1W)
Period: 60-100
Threshold: 3.0-5.0
Memory: 100-200
Spacing: 40-50
By Market Condition
Trending Markets
- Increase threshold
- Extend memory
- Focus on breaks
Ranging Markets
- Decrease threshold
- Shorten memory
- Focus on restores
Volatile Markets
- Increase spacing
- Raise confidence requirement
- Reduce position size
- Integration with Other Analysis
- Complementary Indicators
For Ergodic States
- RSI divergences
- Bollinger Band squeezes
- Volume profile nodes
- Support/resistance levels
For Non-Ergodic States
- Moving average ribbons
- Trend strength indicators
- Momentum oscillators
- Breakout patterns
- Fundamental Alignment
- Check economic calendar
- Monitor sector rotation
- Consider market themes
- Evaluate risk sentiment
Troubleshooting Guide
Too Many Signals:
- Increase threshold
- Extend signal spacing
- Raise confidence minimum
Missing Opportunities
- Decrease threshold
- Reduce signal spacing
- Check ensemble settings
Poor Win Rate
- Verify timeframe alignment
- Confirm volume participation
- Review risk management
Disclaimer
This indicator is for educational and informational purposes only. It does not constitute financial advice. Trading involves substantial risk of loss and is not suitable for all investors. Past performance does not guarantee future results.
The ergodic framework provides unique market insights but cannot predict future price movements with certainty. Always use proper risk management, conduct your own analysis, and never risk more than you can afford to lose.
This tool should complement, not replace, comprehensive trading strategies and sound judgment. Markets remain inherently unpredictable despite advanced analysis techniques.
Transform market chaos into trading clarity with Ergodic Market Divergence.
Created with passion for the TradingView community
Trade with insight. Trade with anticipation.
— Dskyz , for DAFE Trading Systems
IBD Style Candles [tradeviZion]IBD Style Candles - Visualize Price Bars Like the Pros
Transform your chart with institutional-grade IBD-style bars and customizable moving averages for both daily and weekly timeframes. This indicator helps you visualize price action the way professionals at Investors Business Daily do.
What This Indicator Offers:
IBD-style bar visualization (clean, professional appearance)
Customizable coloring based on price movement or previous close
Automatic timeframe detection for appropriate moving averages
Four customizable moving averages for daily timeframes (10, 21, 50, 200)
Four customizable moving averages for weekly timeframes (10, 20, 30, 40)
Options to use SMAs or EMAs with adjustable colors and line widths
"The IBD-style bars provide a cleaner view of price action, allowing you to focus on market structure without the visual noise of traditional candles."
How to Apply the IBD-Style Bars:
On your TradingView chart, select "Bars" as the chart type from the main chart type selection menu (next to the time interval options).
Right-click on the chart and select "Settings".
Go to the "Symbol" tab.
Uncheck the "Thin Bars" option to display thicker bars.
Set the "Up Color" and "Down Color" opacity to 0 for a clean IBD-style appearance.
Enable "IBD-style Candles" from the script's settings.
To revert to the original chart style, repeat the above steps and restore the default settings.
Moving Average Configuration:
The indicator automatically detects your timeframe and displays the appropriate moving averages:
Daily Timeframe Moving Averages:
10-day moving average (SMA/EMA)
21-day moving average (SMA/EMA)
50-day moving average (SMA/EMA)
200-day moving average (SMA/EMA)
Weekly Timeframe Moving Averages:
10-week moving average (SMA/EMA)
20-week moving average (SMA/EMA)
30-week moving average (SMA/EMA)
40-week moving average (SMA/EMA)
Usage Tips:
Enable "Color bars based on previous close" to identify momentum shifts based on prior candle closes
Customize colors to match your chart theme or preference
Enable only the moving averages relevant to your trading strategy
For cleaner charts, reduce the number of visible moving averages
For stock trading, the 10/21/50/200 daily and 10/40 weekly MAs are most commonly used by institutions
// Example configuration for different timeframes
if timeframe.isweekly
// Weekly configuration
showSMA1_Weekly = true // 10-week MA
showSMA4_Weekly = true // 40-week MA
else
// Daily configuration
showMA2_Daily = true // 21-day MA
showMA3_Daily = true // 50-day MA
showMA4_Daily = true // 200-day MA
While the IBD style provides clarity, remember that no visualization method guarantees trading success. Always combine with proper analysis and risk management.
If you found this indicator helpful, please consider leaving a comment or suggestion for future improvements. Happy trading!
ian_Trado v15 Trend Entry Filter# 📈 ian_Trado v15 Trend Entry Filter (Pine Script v6)
The **ian_Trado v15** is a multi-factor **trend confirmation filter** for NASDAQ (NAS100), Dow Jones (DJ30), Gold (XAU), DAX, and USDJPY.
It combines **EMA structure**, **Donchian channel breakout**, **MACD histogram momentum**, **Volume confirmation**, and a **Range Compression Filter** to avoid entering during choppy or sideways markets.
✅ Designed for **bot deployment** (e.g., grid bots, long/short breakout bots) or **manual trading**.
---
## 🔍 How This Filter Works:
1. **EMA Trend Confirmation**
- Long Trend: EMA(1) > EMA(5) > EMA(60)
- Short Trend: EMA(1) < EMA(5) < EMA(60)
2. **Donchian Channel Width Expansion**
- Only allows trades when the **breakout width** exceeds a minimum threshold.
3. **MACD Histogram Slope Filter (Optional)**
- Confirms momentum building in the direction of the trend.
- Strict Mode: MACD histogram must consistently rise or fall over 3 bars.
4. **Volume Filter (Optional)**
- Ensures volume supports the move (filters out weak conditions).
5. **Range Compression Filter (Optional)**
- Avoids entries during sideways chop.
6. **Cooldown Control**
- Limits overtrading by requiring spacing between entries.
7. **Exit Conditions**
- Gray dot appears when trending conditions are no longer valid.
---
## ⚙️ Settings Explained:
| Setting | Description |
|:--------|:------------|
| **Cooldown Bars** | Minimum bars between consecutive entries |
| **Profit Target (%)** | Visual profit marker for exit tracking |
| **Donchian Channel Length** | Lookback period for detecting breakout width |
| **Minimum Donchian Width** | Threshold to confirm meaningful breakouts |
| **Volume Lookback Period** | Average volume validation window |
| **Box Range (Range Compression)** | Max allowed price range over lookback bars |
| **Range Compression Bars** | Number of bars to check for range compression |
| **Strict MACD Filter** | Use stricter MACD slope checks |
---
## 📊 Recommended Settings by Instrument (1H Chart):
| Asset | Min Donchian Width | Range Compression | Profit Target |
|:------|:-------------------|:------------------|:--------------|
| **NAS100** (Nasdaq) | 300–450 pts | 400 pts / 40 bars | 1.5% |
| **DJ30** (Dow Jones) | 400–600 pts | 500 pts / 40 bars | 1.0–1.5% |
| **XAU/USD** (Gold) | 10–15 pts | 8 pts / 30 bars | 0.8–1.2% |
| **DAX40** (Germany) | 200–300 pts | 250 pts / 40 bars | 1.0% |
| **USD/JPY** (Forex) | 0.5–0.8 pts | 0.4 pts / 40 bars | 0.5–0.8% |
---
## 🔔 Alerts Available:
- Long Entry
- Short Entry
- Exit Zone
> **Note:** Volume filter may be disabled if volume is unreliable (e.g., some forex pairs).
---
## 📅 Version:
- **ian_Trado v15** — April 2025
- Built with **Pine Script v6** for maximum stability
- Clean toggling and plotting logic (no `na` errors)
3SMA +30 Stan Weinstein +200WMA +alert-crossingIndicator Description: Stan Weinstein Strategy + Key Moving Averages
🔹 Introduction
This indicator combines the Classic Stan Weinstein Strategy with a modern update based on the author’s latest recommendations. It includes key moving averages that help identify trends and potential entry or exit points in the market.
📊 Included Moving Averages (Fully Customizable)
All moving averages in this indicator have modifiable parameters, allowing users to adjust values in the input settings.
1️⃣ 30-Week SMA (Stan Weinstein): A long-term trend indicator defining the asset’s main trend.
2️⃣ 40-Week SMA (Weinstein Update): An adjusted version recommended by the author in his recent updates.
3️⃣ 10-Day SMA: Displays short-term price action and helps confirm trend changes.
4️⃣ 100-Day SMA: A medium-term trend measure used by traders to assess trend strength.
5️⃣ 200-Day WMA (Weighted Moving Average): A very long-term indicator that filters market noise and confirms solid trends.
🔍 How to Interpret It
✔️ 30/40-Week SMA in an uptrend → Confirms an accumulation phase or an upward price trend.
✔️ Price above the 200-WMA → Indicates a strong and healthy long-term trend.
✔️ 10-SMA crossing other moving averages → Can signal an early entry or exit opportunity.
✔️ 100-SMA vs. 200-WMA → A breakout of the 100-SMA above the 200-WMA may signal a new bullish phase.
🚨 Built-in Alerts (Key Crossovers)
The indicator includes automatic alerts to notify traders when key moving averages cross, allowing timely reactions:
🔔 10-SMA crossing the 40-SMA → Possible medium-term trend shift.
🔔 10-SMA crossing the 200-WMA → Confirmation of a stronger trend.
🔔 40-SMA crossing the 200-WMA → Long-term trend reversal signal.
💡 Customization: All moving average periods can be adjusted in the input settings, making the indicator flexible for different trading strategies.
McClellan Oscillator - IRUS Optimized🧠 McClellan Oscillator (IRUS Index)
Type: Market Breadth Indicator
Category: Breadth, Momentum
Purpose: Gauge the internal strength of the IRUS index and anticipate trend reversals
📌 Based on
This indicator is built on the concept of advancing vs. declining issues — the number of stocks rising vs. falling each day within the IRUS index (a custom group of 40 Russian stocks).
It calculates the net advances (advancers minus decliners), then applies two exponential moving averages (EMA):
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McClellan Oscillator = EMA_19(Net Advances) - EMA_39(Net Advances)
Where:
Net Advances = Number of advancing stocks - Number of declining stocks
Calculated from a fixed set of 40 IRUS stocks
🧭 What it shows
Above 0 → more stocks are rising: market is internally strong.
Below 0 → more stocks are falling: underlying weakness.
Rising from below -100 → oversold breadth, possible bullish reversal.
Falling from above +100 → overbought breadth, possible correction.
🎯 How to use it
1. Buy/Sell Signals
Buy: Oscillator drops below -100 and turns up → oversold, potential rally.
Sell: Oscillator rises above +100 and turns down → overbought, risk of pullback.
2. Trend Strength Confirmation
Sustained above 0 → confirms bullish trend.
Crosses below 0 → early warning of weakening market breadth.
3. Divergences with IRUS Price
IRUS rises, but Oscillator falls → narrowing leadership, bearish divergence.
IRUS falls, but Oscillator rises → improving breadth, bullish divergence.
⚠️ Notes
The oscillator measures participation, not price.
Works best with daily timeframe.
Does not account for volume or magnitude of price moves.
Use with price action or other indicators for confirmation.
⚙️ Custom Implementation
This version is specifically adapted for the IRUS index, using a fixed list of 40 component stocks.
Optimized for Pine Script v6 and complies with TradingView's request limits (max 40).
Smart Liquidity Wave [The_lurker]"Smart Liquidity Wave" هو مؤشر تحليلي متطور يهدف لتحديد نقاط الدخول والخروج المثلى بناءً على تحليل السيولة، قوة الاتجاه، وإشارات السوق المفلترة. يتميز المؤشر بقدرته على تصنيف الأدوات المالية إلى أربع فئات سيولة (ضعيفة، متوسطة، عالية، عالية جدًا)، مع تطبيق شروط مخصصة لكل فئة تعتمد على تحليل الموجات السعرية، الفلاتر المتعددة، ومؤشر ADX.
فكرة المؤشر
الفكرة الأساسية هي الجمع بين قياس السيولة اليومية الثابتة وتحليل ديناميكي للسعر باستخدام فلاتر متقدمة لتوليد إشارات دقيقة. المؤشر يركز على تصفية الضوضاء في السوق من خلال طبقات متعددة من التحليل، مما يجعله أداة ذكية تتكيف مع الأدوات المالية المختلفة بناءً على مستوى سيولتها.
طريقة عمل المؤشر
1- قياس السيولة:
يتم حساب السيولة باستخدام متوسط حجم التداول على مدى 14 يومًا مضروبًا في سعر الإغلاق، ويتم ذلك دائمًا على الإطار الزمني اليومي لضمان ثبات القيمة بغض النظر عن الإطار الزمني المستخدم في الرسم البياني.
يتم تصنيف السيولة إلى:
ضعيفة: أقل من 5 ملايين (قابل للتعديل).
متوسطة: من 5 إلى 20 مليون.
عالية: من 20 إلى 50 مليون.
عالية جدًا: أكثر من 50 مليون.
هذا الثبات في القياس يضمن أن تصنيف السيولة لا يتغير مع تغير الإطار الزمني، مما يوفر أساسًا موثوقًا للإشارات.
2- تحليل الموجات السعرية:
يعتمد المؤشر على تحليل الموجات باستخدام متوسطات متحركة متعددة الأنواع (مثل SMA، EMA، WMA، HMA، وغيرها) يمكن للمستخدم اختيارها وتخصيص فتراتها ، يتم دمج هذا التحليل مع مؤشرات إضافية مثل RSI (مؤشر القوة النسبية) وMFI (مؤشر تدفق الأموال) بوزن محدد (40% للموجات، 30% لكل من RSI وMFI) للحصول على تقييم شامل للاتجاه.
3- الفلاتر وطريقة عملها:
المؤشر يستخدم نظام فلاتر متعدد الطبقات لتصفية الإشارات وتقليل الضوضاء، وهي من أبرز الجوانب المخفية التي تعزز دقته:
الفلتر الرئيسي (Main Filter):
يعمل على تنعيم التغيرات السعرية السريعة باستخدام معادلة رياضية تعتمد على تحليل الإشارات (Signal Processing).
يتم تطبيقه على السعر لاستخراج الاتجاهات الأساسية بعيدًا عن التقلبات العشوائية، مع فترة زمنية قابلة للتعديل (افتراضي: 30).
يستخدم تقنية مشابهة للفلاتر عالية التردد (High-Pass Filter) للتركيز على الحركات الكبيرة.
الفلتر الفرعي (Sub Filter):
يعمل كطبقة ثانية للتصفية، مع فترة أقصر (افتراضي: 12)، لضبط الإشارات بدقة أكبر.
يستخدم معادلات تعتمد على الترددات المنخفضة للتأكد من أن الإشارات الناتجة تعكس تغيرات حقيقية وليست مجرد ضوضاء.
إشارة الزناد (Signal Trigger):
يتم تطبيق متوسط متحرك على نتائج الفلتر الرئيسي لتوليد خط إشارة (Signal Line) يُقارن مع عتبات محددة للدخول والخروج.
يمكن تعديل فترة الزناد (افتراضي: 3 للدخول، 5 للخروج) لتسريع أو تبطيء الإشارات.
الفلتر المربع (Square Filter):
خاصية مخفية تُفعّل افتراضيًا تعزز دقة الفلاتر عن طريق تضييق نطاق التذبذبات المسموح بها، مما يقلل من الإشارات العشوائية في الأسواق المتقلبة.
4- تصفية الإشارات باستخدام ADX:
يتم استخدام مؤشر ADX كفلتر نهائي للتأكد من قوة الاتجاه قبل إصدار الإشارة:
ضعيفة ومتوسطة: دخول عندما يكون ADX فوق 40، خروج فوق 50.
عالية: دخول فوق 40، خروج فوق 55.
عالية جدًا: دخول فوق 35، خروج فوق 38.
هذه العتبات قابلة للتعديل، مما يسمح بتكييف المؤشر مع استراتيجيات مختلفة.
5- توليد الإشارات:
الدخول: يتم إصدار إشارة شراء عندما تنخفض خطوط الإشارة إلى ما دون عتبة محددة (مثل -9) مع تحقق شروط الفلاتر، السيولة، وADX.
الخروج: يتم إصدار إشارة بيع عندما ترتفع الخطوط فوق عتبة (مثل 109 أو 106 حسب الفئة) مع تحقق الشروط الأخرى.
تُعرض الإشارات بألوان مميزة (أزرق للدخول، برتقالي للضعيفة والمتوسطة، أحمر للعالية والعالية جدًا) وبثلاثة أحجام (صغير، متوسط، كبير).
6- عرض النتائج:
يظهر مستوى السيولة الحالي في جدول في أعلى يمين الرسم البياني، مما يتيح للمستخدم معرفة فئة الأصل بسهولة.
7- دعم التنبيهات:
تنبيهات فورية لكل فئة سيولة، مما يسهل التداول الآلي أو اليدوي.
%%%%% الجوانب المخفية في الكود %%%%%
معادلات الفلاتر المتقدمة: يستخدم المؤشر معادلات رياضية معقدة مستوحاة من معالجة الإشارات لتنعيم البيانات واستخراج الاتجاهات، مما يجعله أكثر دقة من المؤشرات التقليدية.
التكيف التلقائي: النظام يضبط نفسه داخليًا بناءً على التغيرات في السعر والحجم، مع عوامل تصحيح مخفية (مثل معامل التنعيم في الفلاتر) للحفاظ على الاستقرار.
التوزيع الموزون: الدمج بين الموجات، RSI، وMFI يتم بأوزان محددة (40%، 30%، 30%) لضمان توازن التحليل، وهي تفاصيل غير ظاهرة مباشرة للمستخدم لكنها تؤثر على النتائج.
الفلتر المربع: خيار مخفي يتم تفعيله افتراضيًا لتضييق نطاق الإشارات، مما يقلل من التشتت في الأسواق ذات التقلبات العالية.
مميزات المؤشر
1- فلاتر متعددة الطبقات: تضمن تصفية الضوضاء وإنتاج إشارات موثوقة فقط.
2- ثبات السيولة: قياس السيولة اليومي يجعل التصنيف متسقًا عبر الإطارات الزمنية.
3- تخصيص شامل: يمكن تعديل حدود السيولة، عتبات ADX، فترات الفلاتر، وأنواع المتوسطات المتحركة.
4- إشارات مرئية واضحة: تصميم بصري يسهل التفسير مع تنبيهات فورية.
5- تقليل الإشارات الخاطئة: الجمع بين الفلاتر وADX يعزز الدقة ويقلل من التشتت.
إخلاء المسؤولية
لا يُقصد بالمعلومات والمنشورات أن تكون، أو تشكل، أي نصيحة مالية أو استثمارية أو تجارية أو أنواع أخرى من النصائح أو التوصيات المقدمة أو المعتمدة من TradingView.
#### **What is the Smart Liquidity Wave Indicator?**
"Smart Liquidity Wave" is an advanced analytical indicator designed to identify optimal entry and exit points based on liquidity analysis, trend strength, and filtered market signals. It stands out with its ability to categorize financial instruments into four liquidity levels (Weak, Medium, High, Very High), applying customized conditions for each category based on price wave analysis, multi-layered filters, and the ADX (Average Directional Index).
#### **Concept of the Indicator**
The core idea is to combine a stable daily liquidity measurement with dynamic price analysis using sophisticated filters to generate precise signals. The indicator focuses on eliminating market noise through multiple analytical layers, making it an intelligent tool that adapts to various financial instruments based on their liquidity levels.
#### **How the Indicator Works**
1. **Liquidity Measurement:**
- Liquidity is calculated using the 14-day average trading volume multiplied by the closing price, always based on the daily timeframe to ensure value consistency regardless of the chart’s timeframe.
- Liquidity is classified as:
- **Weak:** Less than 5 million (adjustable).
- **Medium:** 5 to 20 million.
- **High:** 20 to 50 million.
- **Very High:** Over 50 million.
- This consistency in measurement ensures that liquidity classification remains unchanged across different timeframes, providing a reliable foundation for signals.
2. **Price Wave Analysis:**
- The indicator relies on wave analysis using various types of moving averages (e.g., SMA, EMA, WMA, HMA, etc.), which users can select and customize in terms of periods.
- This analysis is integrated with additional indicators like RSI (Relative Strength Index) and MFI (Money Flow Index), weighted specifically (40% waves, 30% RSI, 30% MFI) to provide a comprehensive trend assessment.
3. **Filters and Their Functionality:**
- The indicator employs a multi-layered filtering system to refine signals and reduce noise, a key hidden feature that enhances its accuracy:
- **Main Filter:**
- Smooths rapid price fluctuations using a mathematical equation rooted in signal processing techniques.
- Applied to price data to extract core trends away from random volatility, with an adjustable period (default: 30).
- Utilizes a technique similar to high-pass filters to focus on significant movements.
- **Sub Filter:**
- Acts as a secondary filtering layer with a shorter period (default: 12) for finer signal tuning.
- Employs low-frequency-based equations to ensure resulting signals reflect genuine changes rather than mere noise.
- **Signal Trigger:**
- Applies a moving average to the main filter’s output to generate a signal line, compared against predefined entry and exit thresholds.
- Trigger period is adjustable (default: 3 for entry, 5 for exit) to speed up or slow down signals.
- **Square Filter:**
- A hidden feature activated by default, enhancing filter precision by narrowing the range of permissible oscillations, reducing random signals in volatile markets.
4. **Signal Filtering with ADX:**
- ADX is used as a final filter to confirm trend strength before issuing signals:
- **Weak and Medium:** Entry when ADX exceeds 40, exit above 50.
- **High:** Entry above 40, exit above 55.
- **Very High:** Entry above 35, exit above 38.
- These thresholds are adjustable, allowing the indicator to adapt to different trading strategies.
5. **Signal Generation:**
- **Entry:** A buy signal is triggered when signal lines drop below a specific threshold (e.g., -9) and conditions for filters, liquidity, and ADX are met.
- **Exit:** A sell signal is issued when signal lines rise above a threshold (e.g., 109 or 106, depending on the category) with all conditions satisfied.
- Signals are displayed in distinct colors (blue for entry, orange for Weak/Medium, red for High/Very High) and three sizes (small, medium, large).
6. **Result Display:**
- The current liquidity level is shown in a table at the top-right of the chart, enabling users to easily identify the asset’s category.
7. **Alert Support:**
- Instant alerts are provided for each liquidity category, facilitating both automated and manual trading.
#### **Hidden Aspects in the Code**
- **Advanced Filter Equations:** The indicator uses complex mathematical formulas inspired by signal processing to smooth data and extract trends, making it more precise than traditional indicators.
- **Automatic Adaptation:** The system internally adjusts based on price and volume changes, with hidden correction factors (e.g., smoothing coefficients in filters) to maintain stability.
- **Weighted Distribution:** The integration of waves, RSI, and MFI uses fixed weights (40%, 30%, 30%) for balanced analysis, a detail not directly visible but impactful on results.
- **Square Filter:** A hidden option, enabled by default, narrows signal range to minimize dispersion in high-volatility markets.
#### **Indicator Features**
1. **Multi-Layered Filters:** Ensures noise reduction and delivers only reliable signals.
2. **Liquidity Stability:** Daily liquidity measurement keeps classification consistent across timeframes.
3. **Comprehensive Customization:** Allows adjustments to liquidity thresholds, ADX levels, filter periods, and moving average types.
4. **Clear Visual Signals:** User-friendly design with easy-to-read visuals and instant alerts.
5. **Reduced False Signals:** Combining filters and ADX enhances accuracy and minimizes clutter.
#### **Disclaimer**
The information and publications are not intended to be, nor do they constitute, financial, investment, trading, or other types of advice or recommendations provided or endorsed by TradingView.
