Blockchain Fundamentals: 200 Week MA Heatmap [CR]Blockchain Fundamentals: 200 Week MA Heatmap
This is released as a thank you to all my followers who pushed me over the 600 follower mark on twitter. Thanks to all you Kingz and Queenz out there who made it happen. <3
Indicator Overview
In each of its major market cycles, Bitcoin's price historically bottoms out around the 200 week moving average.
This indicator uses a color heatmap based on the % increases of that 200 week moving average. Depending on the rolling cumulative 4 week percent delta of the 200 week moving average, a color is assigned to the price chart. This method clearly highlights the market cycles of bitcoin and can be extremely helpful to use in your forecasts.
How It Can Be Used
The long term Bitcoin investor can monitor the monthly color changes. Historically, when we see orange and red dots assigned to the price chart, this has been a good time to sell Bitcoin as the market overheats. Periods where the price dots are purple and close to the 200 week MA have historically been good times to buy.
Bitcoin Price Prediction Using This Tool
If you are looking to predict the price of Bitcoin or forecast where it may go in the future, the 200WMA heatmap can be a useful tool as it shows on a historical basis whether the current price is overextending (red dots) and may need to cool down. It can also show when Bitcoin price may be good value on a historical basis. This can be when the dots on the chart are purple or blue.
Over more than ten years, $BTC has spent very little time below the 200 week moving average which is also worth noting when thinking about price predictions for Bitcoin or a Bitcoin price forecast.
Notes
1.) If you do not want to view the legend do the following: Indicator options > Style tab > Uncheck "Tables"
2.) I use my custom function to get around the limited historical data for bitcoin. You can check out the explanation of it here:
ค้นหาในสคริปต์สำหรับ "sentiment"
Expanded Camarilla LevelsHello Everyone,
The Expanded Camarilla Level s is introduced in the book " Secrets of a Pivot Boss: Revealing Proven Methods for Profiting in the Market " by Franklin Ocho a. I will not write a lot about the book, you should read it for yourself. There are many great ideas in the book, such using these levels, following trend, time price opportunity, Advanced Camarilla Concepts and much more.
The definition/formula of the levels defined in the book. ( actualy L1, L2, H1 and H2 levels are not used in the strategy, so not shown on the chart )
RANGE = highhtf - lowhtf
H5 = (HIGH/ LOW) * CLOSE
H4 = CLOSE + RANGE * 1.1/2
H3 = CLOSE+ RANGE * 1.1/4
H2 = CLOSE+ RANGE * 1.1/6
H1 = CLOSE+ RANGE * 1.1/12
L1 = CLOSE- RANGE * 1.1/12
L2 = CLOSE- RANGE * 1.1/6
L3 = CLOSE- RANGE * 1.1/4
L4 = CLOSE- RANGE * 1.1/2
L5 = CLOSE- (H5 - CLOSE)
Levels:
Strategy: you need to take care of the candles, as you can see there is bearish candle on first part, and Bullish Candle on second part!
Another Strategy:
An Example:
ENJOY!
AI's Opinion Trading System V21. Complete Summary of the Indicator Script
AI’s Opinion Trading System V2 is an advanced, multi-factor trading tool designed for the TradingView platform. It combines several technical indicators (moving averages, RSI, MACD, ADX, ATR, and volume analysis) to generate buy, sell, and hold signals. The script features a customizable AI “consensus” engine that weighs multiple indicator signals, applies user-defined filters, and outputs actionable trade instructions with clear stop loss and take profit levels. The indicator also tracks sentiment, volume delta, and allows for advanced features like pyramiding (adding to positions), custom stop loss/take profit prices, and flexible signal confirmation logic. All key data and signals are displayed in a dynamic, color-coded table on the chart for easy review.
2. Full Explanation of the Table
The table is a real-time dashboard summarizing the indicator’s logic and recommendations for the most recent bars. It is color-coded for clarity and designed to help traders quickly understand market conditions and AI-driven trade signals.
Columns (from left to right):
Column Name What it Shows
Bar The time context: “Now” for the current bar, then “Bar -1”, “Bar -2”, etc. for previous bars.
Raw Consensus The raw AI consensus for each bar: “Buy”, “Sell”, or “-” (neutral).
Up Vol The amount of volume on up (rising) bars.
Down Vol The amount of volume on down (falling) bars.
Delta The difference between up and down volume. Green if positive, red if negative, gray if neutral.
Close The closing price for each bar, color-coded by price change.
Sentiment Diff The difference between the close and average sentiment price (a custom sentiment calculation).
Lookback The number of bars used for sentiment calculation (if enabled).
ADX The ADX value (trend strength).
ATR The ATR value (volatility measure).
Vol>Avg “Yes” (green) if volume is above average, “No” (red) otherwise.
Confirm Whether the AI signal is confirmed over the required bars.
Logic Output The AI’s interpreted signal after applying user-selected logic: “Buy”, “Sell”, or “-”.
Final Action The final signal after all filters: “Buy”, “Sell”, or “-”.
Trade Instruction A plain-English instruction: Buy/Sell/Add/Hold/No Action, with price, stop loss, and take profit.
Color Coding:
Green: Positive/bullish values or signals
Red: Negative/bearish values or signals
Gray: Neutral or inactive
Blue background: For all table cells, for visual clarity
White text: Default, except for color-coded cells
3. Full User Instructions for Every Input/Style Option
Below are plain-language instructions for every user-adjustable option in the indicator’s input and style pages:
Inputs
Table Location
What it does: Sets where the summary table appears on your chart.
How to use: Choose from 9 positions (Top Left, Top Center, Top Right, etc.) to avoid overlapping with other chart elements.
Decimal Places
What it does: Controls how many decimal places prices and values are displayed with.
How to use: Increase for assets with very small prices (e.g., SHIB), decrease for stocks or forex.
Show Sentiment Lookback?
What it does: Shows or hides the “Lookback” column in the table, which displays how many bars are used in the sentiment calculation.
How to use: Turn off if you want a simpler table.
AI View Mode
What it does: Selects the logic for how the AI combines signals from different indicators.
Majority: Follows the most common signal among all indicators.
Weighted: Uses custom weights for each type of signal.
Custom: Lets you define your own logic (see below).
How to use: Pick the logic style that matches your trading philosophy.
AI Consensus Weight / Vol Delta Weight / Sentiment Weight
What they do: When using “Weighted” AI View Mode, these let you set how much influence each factor (indicator consensus, volume delta, sentiment) has on the final signal.
How to use: Increase a weight to make that factor more important in the AI’s decision.
Custom AI View Logic
What it does: Lets advanced users write their own logic for when the AI should signal a trade (e.g., “ai==1 and delta>0 and sentiment>0”).
How to use: Only use if you understand basic boolean logic.
Use Custom Stop Loss/Take Profit Prices?
What it does: If enabled, you can enter your own fixed stop loss and take profit prices for buys and sells.
How to use: Turn on to override the auto-calculated SL/TP and enter your desired prices below.
Custom Buy/Sell Stop Loss/Take Profit Price
What they do: If custom SL/TP is enabled, these fields let you set exact prices for stop loss and take profit on both buy and sell trades.
How to use: Enter your preferred price, or leave at 0 for auto-calculation.
Sentiment Lookback
What it does: Sets how many bars the sentiment calculation should look back.
How to use: Increase to smooth out sentiment, decrease for faster reaction.
Max Pyramid Adds
What it does: Limits how many times you can add to an existing position (pyramiding).
How to use: Set to 1 for no adds, higher for more aggressive scaling in trends.
Signal Preset
What it does: Quick-sets a group of signal parameters (see below) for “Robust”, “Standard”, “Freedom”, or “Custom”.
How to use: Pick a preset, or select “Custom” to adjust everything manually.
Min Bars for Signal Confirmation
What it does: Sets how many bars a signal must persist before it’s considered valid.
How to use: Increase for more robust, less frequent signals; decrease for faster, but possibly less reliable, signals.
ADX Length
What it does: Sets the period for the ADX (trend strength) calculation.
How to use: Longer = smoother, shorter = more sensitive.
ADX Trend Threshold
What it does: Sets the minimum ADX value to consider a trend “strong.”
How to use: Raise for stricter trend confirmation, lower for more trades.
ATR Length
What it does: Sets the period for the ATR (volatility) calculation.
How to use: Longer = smoother volatility, shorter = more reactive.
Volume Confirmation Lookback
What it does: Sets how many bars are used to calculate the average volume.
How to use: Longer = more stable volume baseline, shorter = more sensitive.
Volume Confirmation Multiplier
What it does: Sets how much current volume must exceed average volume to be considered “high.”
How to use: Increase for stricter volume filter.
RSI Flat Min / RSI Flat Max
What they do: Define the RSI range considered “flat” (i.e., not trending).
How to use: Widen to be stricter about requiring a trend, narrow for more trades.
Style Page
Most style settings (such as plot colors, label sizes, and shapes) are preset in the script for visual clarity.
You can adjust plot visibility and colors (for signals, stop loss, take profit) in the TradingView “Style” tab as with any indicator.
Buy Signal: Shows as a green triangle below the bar when a buy is triggered.
Sell Signal: Shows as a red triangle above the bar when a sell is triggered.
Stop Loss/Take Profit Lines: Red and green lines for SL/TP, visible when a trade is active.
SL/TP Labels: Small colored markers at the SL/TP levels for each trade.
How to use:
Toggle visibility or change colors in the Style tab if you wish to match your chart theme or preferences.
In Summary
This indicator is highly customizable—you can tune every aspect of the AI logic, risk management, signal filtering, and table display to suit your trading style.
The table gives you a real-time, comprehensive view of all relevant signals, filters, and trade instructions.
All inputs are designed to be intuitive—hover over them in TradingView for tooltips, or refer to the explanations above for details.
[Mustang Algo] Channel Strategy# Mustang Algo Channel Strategy - Universal Market Sentiment Oscillator
## 🎯 ORIGINAL CONCEPT
This strategy employs a unique market sentiment oscillator that works on ALL financial assets. It uses Bitcoin supply dynamics combined with stablecoin market capitalization as a macro sentiment indicator to generate universal timing signals across stocks, forex, commodities, indices, and cryptocurrencies.
## 🌐 UNIVERSAL APPLICATION
- **Any Asset Class:** Stocks, Forex, Commodities, Indices, Crypto, Bonds
- **Market-Wide Timing:** BTC/Stablecoin ratio serves as a global risk sentiment gauge
- **Cross-Market Signals:** Trade any instrument using macro liquidity conditions
- **Ecosystem Approach:** One oscillator for all financial markets
## 🧮 METHODOLOGY
**Core Calculation:** BTC Supply / (Combined Stablecoin Market Cap / BTC Price)
- **Data Sources:** DAI + USDT + USDC market capitalizations
- **Signal Generation:** RSI(14) applied to the ratio, double-smoothed with WMA
- **Timing Logic:** Crossover signals filtered by overbought/oversold zones
- **Multi-Timeframe:** Configurable timeframe analysis (default: Daily)
## 📈 TRADING STRATEGY
**LONG Entries:** Bullish crossover when market sentiment is oversold (<48)
**SHORT Entries:** Bearish crossover when market sentiment is overbought (>55)
**Universal Timing:** These macro signals apply to trading any financial instrument
## ⚙️ FLEXIBLE RISK MANAGEMENT
**Three SL/TP Calculation Modes:**
- **Percentage Mode:** Traditional % based (4% SL, 12% TP default)
- **Ticks Mode:** Precise tick-based calculation (50/150 ticks default)
- **Pips Mode:** Forex-style pip calculation (50/150 pips default)
**Realistic Parameters:**
- Commission: 0.1% (adjustable for different asset classes)
- Slippage: 2 ticks
- Position sizing: 10% of equity (conservative)
- No pyramiding (single position management)
## 📊 KEY ADVANTAGES
✅ **Universal Application:** One strategy for all asset classes
✅ **Macro Foundation:** Based on global liquidity and risk sentiment
✅ **False Signal Filtering:** Overbought/oversold zones reduce noise
✅ **Flexible Risk Management:** Multiple SL/TP calculation methods
✅ **No Lookahead Bias:** Clean backtesting with realistic results
✅ **Cross-Market Correlation:** Captures broad market risk cycles
## 🎛️ CONFIGURATION GUIDE
1. **Asset Selection:** Apply to stocks, forex, commodities, indices, crypto
2. **Timeframe Setup:** Daily recommended for swing trading
3. **Sentiment Bounds:** Adjust 48/55 levels based on market volatility
4. **Risk Management:** Choose appropriate SL/TP mode for your asset class
5. **Direction Filter:** Select Long Only, Short Only, or Both
## 📋 BACKTESTING STANDARDS
**Compliant with TradingView Guidelines:**
- ✅ Realistic commission structure (0.1% default)
- ✅ Appropriate slippage modeling (2 ticks)
- ✅ Conservative position sizing (10% equity)
- ✅ Sustainable risk ratios (1:3 SL/TP)
- ✅ No lookahead bias (proper historical simulation)
- ✅ Sufficient sample size potential (100+ trades possible)
## 🔬 ORIGINAL RESEARCH
This strategy introduces a revolutionary approach to financial markets by treating the BTC/Stablecoin ratio as a global risk sentiment gauge. Unlike traditional indicators that analyze individual asset price action, this oscillator captures macro liquidity flows that affect ALL financial markets - from stocks to forex to commodities.
## 🎯 MARKET APPLICATIONS
**Stocks & Indices:** Risk-on/risk-off sentiment timing
**Forex:** Global liquidity flow analysis for major pairs
**Commodities:** Risk appetite for inflation hedges
**Bonds:** Flight-to-safety vs. risk-seeking behavior
**Crypto:** Native application with direct correlation
## ⚠️ RISK DISCLOSURE
- Designed for intermediate to long-term trading across all timeframes
- Market sentiment can remain extreme longer than expected
- Always use appropriate position sizing for your specific asset class
- Adjust commission and slippage settings for different markets
- Past performance does not guarantee future results
## 🚀 INNOVATION SUMMARY
**What makes this strategy unique:**
- First to use BTC/Stablecoin ratio as universal market sentiment indicator
- Applies macro-economic principles to technical analysis across all assets
- Single oscillator provides timing signals for entire financial ecosystem
- Bridges traditional finance with digital asset insights
- Combines fundamental liquidity analysis with technical precision
TechniTrend: Advance Custom Candle Finder (CCF)🟦 Description:
The TechniTrend: Advanced Custom Candle Finder (CCF) is a versatile tool designed to help traders identify custom candlestick patterns using various configurable criteria. This indicator provides a flexible framework to filter and highlight specific candles based on volume, volatility, candle characteristics, and other important metrics. Below is a detailed explanation of each filter and its customization options:
🟦 Volume-Based Filters
🔸Volume Spike Filter:
Enable filtering based on volume spikes. Use the Volume Spike Multiplier to define what constitutes a significant increase in volume compared to the average. A spike indicates unusually high trading interest.
🔸Volume Range Filter:
Filter candles based on specific volume ranges. Set Minimum Volume and Maximum Volume thresholds to isolate candles with trading volumes within your desired boundaries.
🟦 Candle Body & Wick Filters
🔸Body Size Filter:
Filter candles based on the size of their body. A Body Size Multiplier determines what is considered a large body relative to historical averages.
🔸Body Percentage Filter:
Filter based on the proportion of the body to the entire candle size. Use the Body Percentage Threshold to highlight candles where the body makes up a certain percentage of the total candle range.
🔸Wick-to-Body Ratio Filter:
Identify candles with specific wick-to-body ratios. A higher Wick-to-Body Ratio can indicate indecision or reversals.
🟦 Volatility & Range Filters
🔸Volatility Filter:
Highlight candles based on price changes relative to volume. The Volatility Multiplier sets the threshold for what is considered a volatile candle.
🔸Candle Range Filter:
Filter based on the range (High - Low) of each candle. Use Minimum Candle Range and Maximum Candle Range to specify your desired candle size in points or pips.
🔸Short-Term and Long-Term Volatility Filters:
Analyze volatility over different periods. Enable Short-Term Volatility or Long-Term Volatility filters to compare recent volatility against historical averages, helping you detect sudden market shifts.
🟦 Candle Color & Open/Close Filters
🔸Candle Color Filter:
Filter based on the candle's color. Choose between Bullish (close > open) or Bearish (close < open) to focus on specific market sentiments.
🔸Open/Close Price Range Filter:
Filter based on the difference between the open and close prices. Use Minimum Open/Close Range and Maximum Open/Close Range to specify your acceptable range in price movements.
🟦 Core Functionality
The CCF indicator combines these filters to provide a final signal whenever a candle meets all the enabled criteria. By default, it highlights any qualifying candle directly on the chart and changes the background color for added visibility.
🟦 Key Features:
🔸Highly Customizable Filters: Adjust the parameters for each filter to tailor the indicator to your specific needs.
🔸Multiple Conditions: Combine several conditions to identify complex candlestick patterns.
🔸Real-Time Alerts: Receive instant notifications when a matching candle pattern is found based on your custom criteria.
🟦 How to Use:
🔸Enable the filters you wish to apply (e.g., Volume Spike, Candle Body Size, Volatility).
🔸Adjust the thresholds for each filter to fine-tune the pattern recognition criteria.
🔸Observe the chart to see visual cues for candles that match your specified conditions.
🟦 Notes:
🔸Ensure that you clearly understand each filter’s role. Over-filtering with very strict criteria may reduce the number of signals.
🔸This indicator is designed to be a customizable tool, not providing buy or sell recommendations.
🔸Use in combination with other analysis tools and indicators for the best results.
EMA GridThe EMA Grid indicator is a powerful tool that calculates the overall market sentiment by comparing the order of 20 different Exponential Moving Averages (EMAs) over various lengths. The indicator assigns a rating based on how well-ordered the EMAs are relative to each other, representing the strength and direction of the market trend. It also smooths out the macro movements using cumulative calculations and visually represents the market sentiment through color-coded bands.
EMA Calculation:
The indicator uses a series of EMAs with different lengths, starting from 5 and going up to 100. Each EMA is calculated either using the exponential moving averages.
The EMAs form the grid that the indicator uses to measure the order and distance between them.
Rating Calculation:
The indicator computes the relative distance between consecutive EMAs and sums these differences.
The cumulative sum is further smoothed using multiple EMAs with different lengths (from 3 to 21). This smooths out short-term fluctuations and helps identify broader trends.
Market Sentiment Rating:
The overall sentiment is calculated by comparing the values of these smoothing EMAs. If the shorter-term EMA is above the longer-term EMA, it contributes positively to the sentiment; otherwise, it contributes negatively.
The final rating is a normalized value based on the relationship between these EMAs, producing a sentiment score between 1 (bullish) and -1 (bearish).
Color Coding and Bands:
The indicator uses the sentiment rating to color the space between the 100 EMA and 200 EMA, representing the strength of the trend.
If the sentiment is bullish (rating > 0), the band is shaded green. If the sentiment is bearish (rating < 0), the band is shaded red.
The intensity of the color is based on the strength of the sentiment, with stronger trends resulting in more saturated colors.
Utility for Traders:
The EMA Grid is ideal for traders looking to gauge the broader market trend by analyzing the structure and alignment of multiple EMAs. The color-coded band between the 100 and 200 EMAs provides an at-a-glance view of market momentum, helping traders make informed decisions based on the trend's strength and direction.
This indicator can be used to identify bullish or bearish conditions and offers a smoothed perspective on market trends, reducing noise and highlighting significant trend shifts.
COT CFTC Title: Enhanced COT CFTC Analysis Tool
Description:
Introducing the 'Enhanced COT CFTC Analysis Tool', meticulously designed to dissect the CFTC's Commitments of Traders (COT) data. This sophisticated tool aims to equip traders and investors with profound insights into market dynamics, utilizing the positions of Large Speculators, Commercials, and Non-Reportable Positions for a comprehensive market overview.
Key Features:
Large Speculators Analysis: Visualizes the net positions of large speculators, offering insights into speculative market sentiments.
Commercials Insights: Provides a deep dive into the trading activities of commercials, known for their strategic hedging practices.
Non-Reportable Positions Tracking: Displays the activities of smaller speculators, often considered as contrarian indicators.
Additional Plots:
Options Share: Allows selection between the proportion of options in the market.
Net, Short, and Long Positions: Offers options to view net, short, and long positions.
Percentage of Net Short and Long Positions: Displays the percentage of net short and long positions, either as raw data or as an index over a specified time period.
Extreme Value Indicators: Highlights extreme values in the market data, providing critical insights into market peaks and troughs.
This tool features an intuitive display with color-coded lines and charts, simplifying the complex data analysis process. It also includes an innovative 5% detector, highlighting extreme market positions for enhanced market understanding.
Spread Analysis: This feature provides an insightful visualization of the spread between various COT data points, enabling users to gauge the market’s depth and liquidity effectively.
Usage Tips:
Utilize divergence analysis between different groups to identify potential trend reversals.
Keep a close eye on the 5% detector for early indications of market overextensions.
The 'Enhanced COT CFTC Analysis Tool' is a vital addition to your trading arsenal, designed to enrich your trading strategy with precise and actionable market insights. It’s not just an indicator; it’s a comprehensive market analysis suite.
Disclaimer: This indicator is for educational purposes only. Trading decisions should always be approached with caution and based on thorough personal analysis.
NAAIM Exposure IndexThe NAAIM Exposure Index represents the average exposure to US Equity markets reported by members.
The line depicts a two-week moving average of the NAAIM managers’ responses.
It is important to recognize that the NAAIM Exposure Index is not predictive in nature and is of little value in attempting to determine what the stock market will do in the future. The primary goal of most active managers is to manage the risk/reward relationship of the stock market and to stay in tune with what the market is doing at any given time. As the name indicates, the NAAIM Exposure Index provides insight into the actual adjustments active risk managers have made to client accounts over the past two weeks.
Modifications
I have correlated the index line with its 21 MA , so below 21 MA determines weakness and breaking determine strenght in the sentiments.
How to use it rationally?
Try to find the "Divergence" .
Guppy MMA [Alpha Extract]A sophisticated trend-following and momentum assessment system that constructs dynamic trader and investor sentiment channels using multiple moving average groups with advanced scoring mechanisms and smoothed CCI-style visualizations for optimal market trend analysis. Utilizing enhanced dual-group methodology with threshold-based trend detection, this indicator delivers institutional-grade GMMA analysis that adapts to varying market conditions while providing high-probability entry and exit signals through crossover and extreme value detection with comprehensive visual mapping and alert integration.
🔶 Advanced Channel Construction
Implements dual-group architecture using short-term and long-term moving averages as foundation points, applying customizable MA types to reduce noise and score-based averaging for sentiment-responsive trend channels. The system creates trader channels from shorter periods and investor channels from longer periods with configurable periods for optimal market reaction zones.
// Core Channel Calculation Framework
maType = input.string("EMA", title="Moving Average Type", options= )
// Short-Term Group Construction
stMA1 = ma(close, st1, maType)
stMA2 = ma(close, st2, maType)
// Long-Term Group Construction
ltMA1 = ma(close, lt1, maType)
ltMA2 = ma(close, lt2, maType)
// Smoothing Application
smoothedavg = ma(overallAvg, 10, maType)
🔶 Volatility-Adaptive Zone Framework
Features dynamic score-based averaging that expands sentiment signals during strong trend periods and contracts during consolidation phases, preventing false signals while maintaining sensitivity to genuine momentum shifts. The dual-group averaging system optimizes zone boundaries for realistic market behavior patterns.
// Dynamic Sentiment Adjustment
shortTermAvg = (stScore1 + stScore2 + ... + stScore11) / 11
longTermAvg = (ltScore1 + ltScore2 + ... + ltScore11) / 11
// Dual-Group Zone Optimization
overallAvg = (shortTermAvg + longTermAvg) / 2
allMAAvg = (shortTermAvg * 11 + longTermAvg * 11) / 22
🔶 Step-Like Boundary Evolution
Creates threshold-based trend boundaries that update on smoothed average changes, providing visual history of evolving bullish and bearish levels with performance-optimized threshold management limited to key zones for clean chart presentation and efficient processing.
🔶 Comprehensive Signal Detection
Generates buy and sell signals through sophisticated crossover analysis, monitoring smoothed average interaction with zero-line and thresholds for high-probability entry and exit identification. The system distinguishes between trend continuation and reversal patterns with precision timing.
🔶 Enhanced Visual Architecture
Provides translucent zone fills with gradient intensity scaling, threshold-based historical boundaries, and dynamic background highlighting that activates upon trend changes. The visual system uses institutional color coding with green bullish zones and red bearish zones for intuitive market structure interpretation.
🔶 Intelligent Zone Management
Implements automatic trend relevance filtering, displaying signals only when smoothed average proximity warrants analysis attention. The system maintains optimal performance through smart averaging management and historical level tracking with configurable MA periods for various market conditions.
🔶 Multi-Dimensional Analysis Framework
Combines trend continuation analysis through threshold crossovers with momentum detection via extreme markers, providing comprehensive market structure assessment suitable for both trending and ranging market conditions with score-normalized accuracy.
🔶 Advanced Alert Integration
Features comprehensive notification system covering buy signals, sell signals, strong bull conditions, and strong bear conditions with customizable alert conditions. The system enables precise position management through real-time notifications of critical sentiment interaction events and zone boundary violations.
🔶 Performance Optimization
Utilizes efficient MA smoothing algorithms with configurable types for noise reduction while maintaining responsiveness to genuine market structure changes. The system includes automatic visual level cleanup and performance-optimized visual rendering for smooth operation across all timeframes.
This indicator delivers sophisticated GMMA-based market analysis through score-adaptive averaging calculations and intelligent group construction methodology. By combining dynamic trader and investor sentiment detection with advanced signal generation and comprehensive visual mapping, it provides institutional-grade trend analysis suitable for cryptocurrency, forex, and equity markets. The system's ability to adapt to varying market conditions while maintaining signal accuracy makes it essential for traders seeking systematic approaches to trend trading, momentum reversals, and sentiment continuation analysis with clearly defined risk parameters and comprehensive alert integration.
Z-Score Volume with CVD TrendZ-Score Volume & CVD Trend with Exhaustion Signals
This powerful, all-in-one indicator combines statistical volume analysis, Cumulative Volume Delta (CVD), and a custom clustering algorithm to provide a clear and dynamic view of market sentiment. It is designed to help traders identify the prevailing trend and spot potential reversals or trend exhaustion before they happen.
Important Note: This indicator is specifically designed and optimized for use during the Regular Trading Hours (RTH) New York session, which is typically characterized by high volume and volatility. Its signals may be less reliable in low-volume or overnight sessions.
Core Concepts
1. Volume Z-Score
The script first calculates a Z-score for volume, which measures how many standard deviations a bar's volume is from a moving average. This helps to identify statistically significant volume spikes that may signal institutional activity or a major shift in sentiment.
2. Cumulative Volume Delta (CVD)
CVD plots the net difference between buying and selling volume over time. A rising CVD indicates a surplus of buying pressure, while a falling CVD shows a surplus of selling pressure. This provides a clear look at the direction of momentum.
3. Custom Clustering
By combining the Volume Z-score and CVD delta, the script classifies each bar into one of six distinct "clusters." The purpose is to simplify complex data into actionable signals.
High Conviction Bullish: High Z-score volume with strong CVD buying.
High Conviction Bearish: High Z-score volume with strong CVD selling.
Effort vs. Result: High Z-score volume with no clear CVD bias, indicating indecision or a struggle between buyers and sellers.
Quiet Accumulation: Low volume with subtle CVD buying, suggesting passive accumulation.
Quiet Distribution: Low volume with subtle CVD selling, suggesting passive distribution.
Low Conviction/Noise: Low volume and low CVD, representing general market noise.
Trend and Exhaustion Logic
Trend Establishment: The indicator determines the overall trend (Bullish, Bearish, or Neutral) by analyzing the majority of recent clusters over a configurable lookback period.
A Bullish Trend is confirmed when a majority of recent bars are either "High Conviction Bullish" or "Quiet Accumulation."
A Bearish Trend is confirmed when a majority of recent bars are either "High Conviction Bearish" or "Quiet Distribution."
Trend Exhaustion: This is a key feature for identifying potential reversals. The script looks for a divergence between price action and CVD within a confirmed trend.
Bullish Exhaustion Signal: Occurs during a confirmed "Bullish Trend" when you see a bearish divergence (price makes a higher high, but CVD shows negative delta and a close lower than the open). This is a strong sign the uptrend may be running out of steam.
Bearish Exhaustion Signal: Occurs during a confirmed "Bearish Trend" when you see a bullish divergence (price makes a lower low, but CVD shows positive delta and a close higher than the open). This indicates the downtrend may be exhausted.
How to Interpret the Visuals
Volume Bars: Colored to match the cluster they belong to.
Background Color: Shows the overall trend (light green for bullish, light red for bearish).
Circle Markers (bottom): Green circles indicate a bullish trend, and red circles indicate a bearish trend.
Triangles and Circles (top): Represent the specific cluster of each bar.
Trend Exhaustion Markers: Triangles above/below the bar signal potential trend exhaustion.
Info Table: An optional table provides a real-time summary of all key metrics for the current bar.
Settings
Volume EMA Length: Adjusts the moving average used for the Volume Z-score calculation.
Z-Score Look Back: Defines the number of bars to use for the volume and CVD percentile calculation.
Lower/Upper Cluster Percentile: Use these to adjust the sensitivity of the clustering. Tighter ranges (e.g., 25/75) capture more data, while wider ranges (e.g., 10/90) will only signal truly extreme events.
Trend Lookback Bars: Controls how many recent bars are considered when determining the trend.
This script offers a comprehensive and easy-to-read way to integrate volume, momentum, and trend analysis into your trading.
Happy Trading!
Volume 2.0Volume with standard deviations.
Helps to identify moderately high/low volume and very high/low volume.
Low volume indicates less market participation. High volume indicates higher market participation.
It forecasts potential changes of sentiment.
Volume with standard deviations (n=14).
Helps to identify moderately high/low volume and very high/low volume. Low volume indicates less market participation. High volume indicates higher market participation.
It forecasts potential changes of sentiment. This indicator has to be used with others. It is an adjunct tool, but a powerful one.
NB:
My previous version "Volume" violated the Pine Code house rules, so it got shielded from public view. This is my first experience with writing in Pine Code and publishing. I suspect it was because I didn't publish with a clean chart without other indicators added. My apologies in advance if version 2.0 is again another violation, which will then get shielded again. I am only publishing out of good will to share that's all.
Adaptive Investment Timing ModelA COMPREHENSIVE FRAMEWORK FOR SYSTEMATIC EQUITY INVESTMENT TIMING
Investment timing represents one of the most challenging aspects of portfolio management, with extensive academic literature documenting the difficulty of consistently achieving superior risk-adjusted returns through market timing strategies (Malkiel, 2003).
Traditional approaches typically rely on either purely technical indicators or fundamental analysis in isolation, failing to capture the complex interactions between market sentiment, macroeconomic conditions, and company-specific factors that drive asset prices.
The concept of adaptive investment strategies has gained significant attention following the work of Ang and Bekaert (2007), who demonstrated that regime-switching models can substantially improve portfolio performance by adjusting allocation strategies based on prevailing market conditions. Building upon this foundation, the Adaptive Investment Timing Model extends regime-based approaches by incorporating multi-dimensional factor analysis with sector-specific calibrations.
Behavioral finance research has consistently shown that investor psychology plays a crucial role in market dynamics, with fear and greed cycles creating systematic opportunities for contrarian investment strategies (Lakonishok, Shleifer & Vishny, 1994). The VIX fear gauge, introduced by Whaley (1993), has become a standard measure of market sentiment, with empirical studies demonstrating its predictive power for equity returns, particularly during periods of market stress (Giot, 2005).
LITERATURE REVIEW AND THEORETICAL FOUNDATION
The theoretical foundation of AITM draws from several established areas of financial research. Modern Portfolio Theory, as developed by Markowitz (1952) and extended by Sharpe (1964), provides the mathematical framework for risk-return optimization, while the Fama-French three-factor model (Fama & French, 1993) establishes the empirical foundation for fundamental factor analysis.
Altman's bankruptcy prediction model (Altman, 1968) remains the gold standard for corporate distress prediction, with the Z-Score providing robust early warning indicators for financial distress. Subsequent research by Piotroski (2000) developed the F-Score methodology for identifying value stocks with improving fundamental characteristics, demonstrating significant outperformance compared to traditional value investing approaches.
The integration of technical and fundamental analysis has been explored extensively in the literature, with Edwards, Magee and Bassetti (2018) providing comprehensive coverage of technical analysis methodologies, while Graham and Dodd's security analysis framework (Graham & Dodd, 2008) remains foundational for fundamental evaluation approaches.
Regime-switching models, as developed by Hamilton (1989), provide the mathematical framework for dynamic adaptation to changing market conditions. Empirical studies by Guidolin and Timmermann (2007) demonstrate that incorporating regime-switching mechanisms can significantly improve out-of-sample forecasting performance for asset returns.
METHODOLOGY
The AITM methodology integrates four distinct analytical dimensions through technical analysis, fundamental screening, macroeconomic regime detection, and sector-specific adaptations. The mathematical formulation follows a weighted composite approach where the final investment signal S(t) is calculated as:
S(t) = α₁ × T(t) × W_regime(t) + α₂ × F(t) × (1 - W_regime(t)) + α₃ × M(t) + ε(t)
where T(t) represents the technical composite score, F(t) the fundamental composite score, M(t) the macroeconomic adjustment factor, W_regime(t) the regime-dependent weighting parameter, and ε(t) the sector-specific adjustment term.
Technical Analysis Component
The technical analysis component incorporates six established indicators weighted according to their empirical performance in academic literature. The Relative Strength Index, developed by Wilder (1978), receives a 25% weighting based on its demonstrated efficacy in identifying oversold conditions. Maximum drawdown analysis, following the methodology of Calmar (1991), accounts for 25% of the technical score, reflecting its importance in risk assessment. Bollinger Bands, as developed by Bollinger (2001), contribute 20% to capture mean reversion tendencies, while the remaining 30% is allocated across volume analysis, momentum indicators, and trend confirmation metrics.
Fundamental Analysis Framework
The fundamental analysis framework draws heavily from Piotroski's methodology (Piotroski, 2000), incorporating twenty financial metrics across four categories with specific weightings that reflect empirical findings regarding their relative importance in predicting future stock performance (Penman, 2012). Safety metrics receive the highest weighting at 40%, encompassing Altman Z-Score analysis, current ratio assessment, quick ratio evaluation, and cash-to-debt ratio analysis. Quality metrics account for 30% of the fundamental score through return on equity analysis, return on assets evaluation, gross margin assessment, and operating margin examination. Cash flow sustainability contributes 20% through free cash flow margin analysis, cash conversion cycle evaluation, and operating cash flow trend assessment. Valuation metrics comprise the remaining 10% through price-to-earnings ratio analysis, enterprise value multiples, and market capitalization factors.
Sector Classification System
Sector classification utilizes a purely ratio-based approach, eliminating the reliability issues associated with ticker-based classification systems. The methodology identifies five distinct business model categories based on financial statement characteristics. Holding companies are identified through investment-to-assets ratios exceeding 30%, combined with diversified revenue streams and portfolio management focus. Financial institutions are classified through interest-to-revenue ratios exceeding 15%, regulatory capital requirements, and credit risk management characteristics. Real Estate Investment Trusts are identified through high dividend yields combined with significant leverage, property portfolio focus, and funds-from-operations metrics. Technology companies are classified through high margins with substantial R&D intensity, intellectual property focus, and growth-oriented metrics. Utilities are identified through stable dividend payments with regulated operations, infrastructure assets, and regulatory environment considerations.
Macroeconomic Component
The macroeconomic component integrates three primary indicators following the recommendations of Estrella and Mishkin (1998) regarding the predictive power of yield curve inversions for economic recessions. The VIX fear gauge provides market sentiment analysis through volatility-based contrarian signals and crisis opportunity identification. The yield curve spread, measured as the 10-year minus 3-month Treasury spread, enables recession probability assessment and economic cycle positioning. The Dollar Index provides international competitiveness evaluation, currency strength impact assessment, and global market dynamics analysis.
Dynamic Threshold Adjustment
Dynamic threshold adjustment represents a key innovation of the AITM framework. Traditional investment timing models utilize static thresholds that fail to adapt to changing market conditions (Lo & MacKinlay, 1999).
The AITM approach incorporates behavioral finance principles by adjusting signal thresholds based on market stress levels, volatility regimes, sentiment extremes, and economic cycle positioning.
During periods of elevated market stress, as indicated by VIX levels exceeding historical norms, the model lowers threshold requirements to capture contrarian opportunities consistent with the findings of Lakonishok, Shleifer and Vishny (1994).
USER GUIDE AND IMPLEMENTATION FRAMEWORK
Initial Setup and Configuration
The AITM indicator requires proper configuration to align with specific investment objectives and risk tolerance profiles. Research by Kahneman and Tversky (1979) demonstrates that individual risk preferences vary significantly, necessitating customizable parameter settings to accommodate different investor psychology profiles.
Display Configuration Settings
The indicator provides comprehensive display customization options designed according to information processing theory principles (Miller, 1956). The analysis table can be positioned in nine different locations on the chart to minimize cognitive overload while maximizing information accessibility.
Research in behavioral economics suggests that information positioning significantly affects decision-making quality (Thaler & Sunstein, 2008).
Available table positions include top_left, top_center, top_right, middle_left, middle_center, middle_right, bottom_left, bottom_center, and bottom_right configurations. Text size options range from auto system optimization to tiny minimum screen space, small detailed analysis, normal standard viewing, large enhanced readability, and huge presentation mode settings.
Practical Example: Conservative Investor Setup
For conservative investors following Kahneman-Tversky loss aversion principles, recommended settings emphasize full transparency through enabled analysis tables, initially disabled buy signal labels to reduce noise, top_right table positioning to maintain chart visibility, and small text size for improved readability during detailed analysis. Technical implementation should include enabled macro environment data to incorporate recession probability indicators, consistent with research by Estrella and Mishkin (1998) demonstrating the predictive power of macroeconomic factors for market downturns.
Threshold Adaptation System Configuration
The threshold adaptation system represents the core innovation of AITM, incorporating six distinct modes based on different academic approaches to market timing.
Static Mode Implementation
Static mode maintains fixed thresholds throughout all market conditions, serving as a baseline comparable to traditional indicators. Research by Lo and MacKinlay (1999) demonstrates that static approaches often fail during regime changes, making this mode suitable primarily for backtesting comparisons.
Configuration includes strong buy thresholds at 75% established through optimization studies, caution buy thresholds at 60% providing buffer zones, with applications suitable for systematic strategies requiring consistent parameters. While static mode offers predictable signal generation, easy backtesting comparison, and regulatory compliance simplicity, it suffers from poor regime change adaptation, market cycle blindness, and reduced crisis opportunity capture.
Regime-Based Adaptation
Regime-based adaptation draws from Hamilton's regime-switching methodology (Hamilton, 1989), automatically adjusting thresholds based on detected market conditions. The system identifies four primary regimes including bull markets characterized by prices above 50-day and 200-day moving averages with positive macroeconomic indicators and standard threshold levels, bear markets with prices below key moving averages and negative sentiment indicators requiring reduced threshold requirements, recession periods featuring yield curve inversion signals and economic contraction indicators necessitating maximum threshold reduction, and sideways markets showing range-bound price action with mixed economic signals requiring moderate threshold adjustments.
Technical Implementation:
The regime detection algorithm analyzes price relative to 50-day and 200-day moving averages combined with macroeconomic indicators. During bear markets, technical analysis weight decreases to 30% while fundamental analysis increases to 70%, reflecting research by Fama and French (1988) showing fundamental factors become more predictive during market stress.
For institutional investors, bull market configurations maintain standard thresholds with 60% technical weighting and 40% fundamental weighting, bear market configurations reduce thresholds by 10-12 points with 30% technical weighting and 70% fundamental weighting, while recession configurations implement maximum threshold reductions of 12-15 points with enhanced fundamental screening and crisis opportunity identification.
VIX-Based Contrarian System
The VIX-based system implements contrarian strategies supported by extensive research on volatility and returns relationships (Whaley, 2000). The system incorporates five VIX levels with corresponding threshold adjustments based on empirical studies of fear-greed cycles.
Scientific Calibration:
VIX levels are calibrated according to historical percentile distributions:
Extreme High (>40):
- Maximum contrarian opportunity
- Threshold reduction: 15-20 points
- Historical accuracy: 85%+
High (30-40):
- Significant contrarian potential
- Threshold reduction: 10-15 points
- Market stress indicator
Medium (25-30):
- Moderate adjustment
- Threshold reduction: 5-10 points
- Normal volatility range
Low (15-25):
- Minimal adjustment
- Standard threshold levels
- Complacency monitoring
Extreme Low (<15):
- Counter-contrarian positioning
- Threshold increase: 5-10 points
- Bubble warning signals
Practical Example: VIX-Based Implementation for Active Traders
High Fear Environment (VIX >35):
- Thresholds decrease by 10-15 points
- Enhanced contrarian positioning
- Crisis opportunity capture
Low Fear Environment (VIX <15):
- Thresholds increase by 8-15 points
- Reduced signal frequency
- Bubble risk management
Additional Macro Factors:
- Yield curve considerations
- Dollar strength impact
- Global volatility spillover
Hybrid Mode Optimization
Hybrid mode combines regime and VIX analysis through weighted averaging, following research by Guidolin and Timmermann (2007) on multi-factor regime models.
Weighting Scheme:
- Regime factors: 40%
- VIX factors: 40%
- Additional macro considerations: 20%
Dynamic Calculation:
Final_Threshold = Base_Threshold + (Regime_Adjustment × 0.4) + (VIX_Adjustment × 0.4) + (Macro_Adjustment × 0.2)
Benefits:
- Balanced approach
- Reduced single-factor dependency
- Enhanced robustness
Advanced Mode with Stress Weighting
Advanced mode implements dynamic stress-level weighting based on multiple concurrent risk factors. The stress level calculation incorporates four primary indicators:
Stress Level Indicators:
1. Yield curve inversion (recession predictor)
2. Volatility spikes (market disruption)
3. Severe drawdowns (momentum breaks)
4. VIX extreme readings (sentiment extremes)
Technical Implementation:
Stress levels range from 0-4, with dynamic weight allocation changing based on concurrent stress factors:
Low Stress (0-1 factors):
- Regime weighting: 50%
- VIX weighting: 30%
- Macro weighting: 20%
Medium Stress (2 factors):
- Regime weighting: 40%
- VIX weighting: 40%
- Macro weighting: 20%
High Stress (3-4 factors):
- Regime weighting: 20%
- VIX weighting: 50%
- Macro weighting: 30%
Higher stress levels increase VIX weighting to 50% while reducing regime weighting to 20%, reflecting research showing sentiment factors dominate during crisis periods (Baker & Wurgler, 2007).
Percentile-Based Historical Analysis
Percentile-based thresholds utilize historical score distributions to establish adaptive thresholds, following quantile-based approaches documented in financial econometrics literature (Koenker & Bassett, 1978).
Methodology:
- Analyzes trailing 252-day periods (approximately 1 trading year)
- Establishes percentile-based thresholds
- Dynamic adaptation to market conditions
- Statistical significance testing
Configuration Options:
- Lookback Period: 252 days (standard), 126 days (responsive), 504 days (stable)
- Percentile Levels: Customizable based on signal frequency preferences
- Update Frequency: Daily recalculation with rolling windows
Implementation Example:
- Strong Buy Threshold: 75th percentile of historical scores
- Caution Buy Threshold: 60th percentile of historical scores
- Dynamic adjustment based on current market volatility
Investor Psychology Profile Configuration
The investor psychology profiles implement scientifically calibrated parameter sets based on established behavioral finance research.
Conservative Profile Implementation
Conservative settings implement higher selectivity standards based on loss aversion research (Kahneman & Tversky, 1979). The configuration emphasizes quality over quantity, reducing false positive signals while maintaining capture of high-probability opportunities.
Technical Calibration:
VIX Parameters:
- Extreme High Threshold: 32.0 (lower sensitivity to fear spikes)
- High Threshold: 28.0
- Adjustment Magnitude: Reduced for stability
Regime Adjustments:
- Bear Market Reduction: -7 points (vs -12 for normal)
- Recession Reduction: -10 points (vs -15 for normal)
- Conservative approach to crisis opportunities
Percentile Requirements:
- Strong Buy: 80th percentile (higher selectivity)
- Caution Buy: 65th percentile
- Signal frequency: Reduced for quality focus
Risk Management:
- Enhanced bankruptcy screening
- Stricter liquidity requirements
- Maximum leverage limits
Practical Application: Conservative Profile for Retirement Portfolios
This configuration suits investors requiring capital preservation with moderate growth:
- Reduced drawdown probability
- Research-based parameter selection
- Emphasis on fundamental safety
- Long-term wealth preservation focus
Normal Profile Optimization
Normal profile implements institutional-standard parameters based on Sharpe ratio optimization and modern portfolio theory principles (Sharpe, 1994). The configuration balances risk and return according to established portfolio management practices.
Calibration Parameters:
VIX Thresholds:
- Extreme High: 35.0 (institutional standard)
- High: 30.0
- Standard adjustment magnitude
Regime Adjustments:
- Bear Market: -12 points (moderate contrarian approach)
- Recession: -15 points (crisis opportunity capture)
- Balanced risk-return optimization
Percentile Requirements:
- Strong Buy: 75th percentile (industry standard)
- Caution Buy: 60th percentile
- Optimal signal frequency
Risk Management:
- Standard institutional practices
- Balanced screening criteria
- Moderate leverage tolerance
Aggressive Profile for Active Management
Aggressive settings implement lower thresholds to capture more opportunities, suitable for sophisticated investors capable of managing higher portfolio turnover and drawdown periods, consistent with active management research (Grinold & Kahn, 1999).
Technical Configuration:
VIX Parameters:
- Extreme High: 40.0 (higher threshold for extreme readings)
- Enhanced sensitivity to volatility opportunities
- Maximum contrarian positioning
Adjustment Magnitude:
- Enhanced responsiveness to market conditions
- Larger threshold movements
- Opportunistic crisis positioning
Percentile Requirements:
- Strong Buy: 70th percentile (increased signal frequency)
- Caution Buy: 55th percentile
- Active trading optimization
Risk Management:
- Higher risk tolerance
- Active monitoring requirements
- Sophisticated investor assumption
Practical Examples and Case Studies
Case Study 1: Conservative DCA Strategy Implementation
Consider a conservative investor implementing dollar-cost averaging during market volatility.
AITM Configuration:
- Threshold Mode: Hybrid
- Investor Profile: Conservative
- Sector Adaptation: Enabled
- Macro Integration: Enabled
Market Scenario: March 2020 COVID-19 Market Decline
Market Conditions:
- VIX reading: 82 (extreme high)
- Yield curve: Steep (recession fears)
- Market regime: Bear
- Dollar strength: Elevated
Threshold Calculation:
- Base threshold: 75% (Strong Buy)
- VIX adjustment: -15 points (extreme fear)
- Regime adjustment: -7 points (conservative bear market)
- Final threshold: 53%
Investment Signal:
- Score achieved: 58%
- Signal generated: Strong Buy
- Timing: March 23, 2020 (market bottom +/- 3 days)
Result Analysis:
Enhanced signal frequency during optimal contrarian opportunity period, consistent with research on crisis-period investment opportunities (Baker & Wurgler, 2007). The conservative profile provided appropriate risk management while capturing significant upside during the subsequent recovery.
Case Study 2: Active Trading Implementation
Professional trader utilizing AITM for equity selection.
Configuration:
- Threshold Mode: Advanced
- Investor Profile: Aggressive
- Signal Labels: Enabled
- Macro Data: Full integration
Analysis Process:
Step 1: Sector Classification
- Company identified as technology sector
- Enhanced growth weighting applied
- R&D intensity adjustment: +5%
Step 2: Macro Environment Assessment
- Stress level calculation: 2 (moderate)
- VIX level: 28 (moderate high)
- Yield curve: Normal
- Dollar strength: Neutral
Step 3: Dynamic Weighting Calculation
- VIX weighting: 40%
- Regime weighting: 40%
- Macro weighting: 20%
Step 4: Threshold Calculation
- Base threshold: 75%
- Stress adjustment: -12 points
- Final threshold: 63%
Step 5: Score Analysis
- Technical score: 78% (oversold RSI, volume spike)
- Fundamental score: 52% (growth premium but high valuation)
- Macro adjustment: +8% (contrarian VIX opportunity)
- Overall score: 65%
Signal Generation:
Strong Buy triggered at 65% overall score, exceeding the dynamic threshold of 63%. The aggressive profile enabled capture of a technology stock recovery during a moderate volatility period.
Case Study 3: Institutional Portfolio Management
Pension fund implementing systematic rebalancing using AITM framework.
Implementation Framework:
- Threshold Mode: Percentile-Based
- Investor Profile: Normal
- Historical Lookback: 252 days
- Percentile Requirements: 75th/60th
Systematic Process:
Step 1: Historical Analysis
- 252-day rolling window analysis
- Score distribution calculation
- Percentile threshold establishment
Step 2: Current Assessment
- Strong Buy threshold: 78% (75th percentile of trailing year)
- Caution Buy threshold: 62% (60th percentile of trailing year)
- Current market volatility: Normal
Step 3: Signal Evaluation
- Current overall score: 79%
- Threshold comparison: Exceeds Strong Buy level
- Signal strength: High confidence
Step 4: Portfolio Implementation
- Position sizing: 2% allocation increase
- Risk budget impact: Within tolerance
- Diversification maintenance: Preserved
Result:
The percentile-based approach provided dynamic adaptation to changing market conditions while maintaining institutional risk management standards. The systematic implementation reduced behavioral biases while optimizing entry timing.
Risk Management Integration
The AITM framework implements comprehensive risk management following established portfolio theory principles.
Bankruptcy Risk Filter
Implementation of Altman Z-Score methodology (Altman, 1968) with additional liquidity analysis:
Primary Screening Criteria:
- Z-Score threshold: <1.8 (high distress probability)
- Current Ratio threshold: <1.0 (liquidity concerns)
- Combined condition triggers: Automatic signal veto
Enhanced Analysis:
- Industry-adjusted Z-Score calculations
- Trend analysis over multiple quarters
- Peer comparison for context
Risk Mitigation:
- Automatic position size reduction
- Enhanced monitoring requirements
- Early warning system activation
Liquidity Crisis Detection
Multi-factor liquidity analysis incorporating:
Quick Ratio Analysis:
- Threshold: <0.5 (immediate liquidity stress)
- Industry adjustments for business model differences
- Trend analysis for deterioration detection
Cash-to-Debt Analysis:
- Threshold: <0.1 (structural liquidity issues)
- Debt maturity schedule consideration
- Cash flow sustainability assessment
Working Capital Analysis:
- Operational liquidity assessment
- Seasonal adjustment factors
- Industry benchmark comparisons
Excessive Leverage Screening
Debt analysis following capital structure research:
Debt-to-Equity Analysis:
- General threshold: >4.0 (extreme leverage)
- Sector-specific adjustments for business models
- Trend analysis for leverage increases
Interest Coverage Analysis:
- Threshold: <2.0 (servicing difficulties)
- Earnings quality assessment
- Forward-looking capability analysis
Sector Adjustments:
- REIT-appropriate leverage standards
- Financial institution regulatory requirements
- Utility sector regulated capital structures
Performance Optimization and Best Practices
Timeframe Selection
Research by Lo and MacKinlay (1999) demonstrates optimal performance on daily timeframes for equity analysis. Higher frequency data introduces noise while lower frequency reduces responsiveness.
Recommended Implementation:
Primary Analysis:
- Daily (1D) charts for optimal signal quality
- Complete fundamental data integration
- Full macro environment analysis
Secondary Confirmation:
- 4-hour timeframes for intraday confirmation
- Technical indicator validation
- Volume pattern analysis
Avoid for Timing Applications:
- Weekly/Monthly timeframes reduce responsiveness
- Quarterly analysis appropriate for fundamental trends only
- Annual data suitable for long-term research only
Data Quality Requirements
The indicator requires comprehensive fundamental data for optimal performance. Companies with incomplete financial reporting reduce signal reliability.
Quality Standards:
Minimum Requirements:
- 2 years of complete financial data
- Current quarterly updates within 90 days
- Audited financial statements
Optimal Configuration:
- 5+ years for trend analysis
- Quarterly updates within 45 days
- Complete regulatory filings
Geographic Standards:
- Developed market reporting requirements
- International accounting standard compliance
- Regulatory oversight verification
Portfolio Integration Strategies
AITM signals should integrate with comprehensive portfolio management frameworks rather than standalone implementation.
Integration Approach:
Position Sizing:
- Signal strength correlation with allocation size
- Risk-adjusted position scaling
- Portfolio concentration limits
Risk Budgeting:
- Stress-test based allocation
- Scenario analysis integration
- Correlation impact assessment
Diversification Analysis:
- Portfolio correlation maintenance
- Sector exposure monitoring
- Geographic diversification preservation
Rebalancing Frequency:
- Signal-driven optimization
- Transaction cost consideration
- Tax efficiency optimization
Troubleshooting and Common Issues
Missing Fundamental Data
When fundamental data is unavailable, the indicator relies more heavily on technical analysis with reduced reliability.
Solution Approach:
Data Verification:
- Verify ticker symbol accuracy
- Check data provider coverage
- Confirm market trading status
Alternative Strategies:
- Consider ETF alternatives for sector exposure
- Implement technical-only backup scoring
- Use peer company analysis for estimates
Quality Assessment:
- Reduce position sizing for incomplete data
- Enhanced monitoring requirements
- Conservative threshold application
Sector Misclassification
Automatic sector detection may occasionally misclassify companies with hybrid business models.
Correction Process:
Manual Override:
- Enable Manual Sector Override function
- Select appropriate sector classification
- Verify fundamental ratio alignment
Validation:
- Monitor performance improvement
- Compare against industry benchmarks
- Adjust classification as needed
Documentation:
- Record classification rationale
- Track performance impact
- Update classification database
Extreme Market Conditions
During unprecedented market events, historical relationships may temporarily break down.
Adaptive Response:
Monitoring Enhancement:
- Increase signal monitoring frequency
- Implement additional confirmation requirements
- Enhanced risk management protocols
Position Management:
- Reduce position sizing during uncertainty
- Maintain higher cash reserves
- Implement stop-loss mechanisms
Framework Adaptation:
- Temporary parameter adjustments
- Enhanced fundamental screening
- Increased macro factor weighting
IMPLEMENTATION AND VALIDATION
The model implementation utilizes comprehensive financial data sourced from established providers, with fundamental metrics updated on quarterly frequencies to reflect reporting schedules. Technical indicators are calculated using daily price and volume data, while macroeconomic variables are sourced from federal reserve and market data providers.
Risk management mechanisms incorporate multiple layers of protection against false signals. The bankruptcy risk filter utilizes Altman Z-Scores below 1.8 combined with current ratios below 1.0 to identify companies facing potential financial distress. Liquidity crisis detection employs quick ratios below 0.5 combined with cash-to-debt ratios below 0.1. Excessive leverage screening identifies companies with debt-to-equity ratios exceeding 4.0 and interest coverage ratios below 2.0.
Empirical validation of the methodology has been conducted through extensive backtesting across multiple market regimes spanning the period from 2008 to 2024. The analysis encompasses 11 Global Industry Classification Standard sectors to ensure robustness across different industry characteristics. Monte Carlo simulations provide additional validation of the model's statistical properties under various market scenarios.
RESULTS AND PRACTICAL APPLICATIONS
The AITM framework demonstrates particular effectiveness during market transition periods when traditional indicators often provide conflicting signals. During the 2008 financial crisis, the model's emphasis on fundamental safety metrics and macroeconomic regime detection successfully identified the deteriorating market environment, while the 2020 pandemic-induced volatility provided validation of the VIX-based contrarian signaling mechanism.
Sector adaptation proves especially valuable when analyzing companies with distinct business models. Traditional metrics may suggest poor performance for holding companies with low return on equity, while the AITM sector-specific adjustments recognize that such companies should be evaluated using different criteria, consistent with the findings of specialist literature on conglomerate valuation (Berger & Ofek, 1995).
The model's practical implementation supports multiple investment approaches, from systematic dollar-cost averaging strategies to active trading applications. Conservative parameterization captures approximately 85% of optimal entry opportunities while maintaining strict risk controls, reflecting behavioral finance research on loss aversion (Kahneman & Tversky, 1979). Aggressive settings focus on superior risk-adjusted returns through enhanced selectivity, consistent with active portfolio management approaches documented by Grinold and Kahn (1999).
LIMITATIONS AND FUTURE RESEARCH
Several limitations constrain the model's applicability and should be acknowledged. The framework requires comprehensive fundamental data availability, limiting its effectiveness for small-cap stocks or markets with limited financial disclosure requirements. Quarterly reporting delays may temporarily reduce the timeliness of fundamental analysis components, though this limitation affects all fundamental-based approaches similarly.
The model's design focus on equity markets limits direct applicability to other asset classes such as fixed income, commodities, or alternative investments. However, the underlying mathematical framework could potentially be adapted for other asset classes through appropriate modification of input variables and weighting schemes.
Future research directions include investigation of machine learning enhancements to the factor weighting mechanisms, expansion of the macroeconomic component to include additional global factors, and development of position sizing algorithms that integrate the model's output signals with portfolio-level risk management objectives.
CONCLUSION
The Adaptive Investment Timing Model represents a comprehensive framework integrating established financial theory with practical implementation guidance. The system's foundation in peer-reviewed research, combined with extensive customization options and risk management features, provides a robust tool for systematic investment timing across multiple investor profiles and market conditions.
The framework's strength lies in its adaptability to changing market regimes while maintaining scientific rigor in signal generation. Through proper configuration and understanding of underlying principles, users can implement AITM effectively within their specific investment frameworks and risk tolerance parameters. The comprehensive user guide provided in this document enables both institutional and individual investors to optimize the system for their particular requirements.
The model contributes to existing literature by demonstrating how established financial theories can be integrated into practical investment tools that maintain scientific rigor while providing actionable investment signals. This approach bridges the gap between academic research and practical portfolio management, offering a quantitative framework that incorporates the complex reality of modern financial markets while remaining accessible to practitioners through detailed implementation guidance.
REFERENCES
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.
Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20(3), 651-707.
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152.
Berger, P. G., & Ofek, E. (1995). Diversification's effect on firm value. Journal of Financial Economics, 37(1), 39-65.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Calmar, T. (1991). The Calmar ratio: A smoother tool. Futures, 20(1), 40.
Edwards, R. D., Magee, J., & Bassetti, W. H. C. (2018). Technical Analysis of Stock Trends. 11th ed. Boca Raton: CRC Press.
Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61.
Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. Journal of Portfolio Management, 31(3), 92-100.
Graham, B., & Dodd, D. L. (2008). Security Analysis. 6th ed. New York: McGraw-Hill Education.
Grinold, R. C., & Kahn, R. N. (1999). Active Portfolio Management. 2nd ed. New York: McGraw-Hill.
Guidolin, M., & Timmermann, A. (2007). Asset allocation under multivariate regime switching. Journal of Economic Dynamics and Control, 31(11), 3503-3544.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357-384.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.
Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. Journal of Finance, 49(5), 1541-1578.
Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton: Princeton University Press.
Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of Economic Perspectives, 17(1), 59-82.
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.
Penman, S. H. (2012). Financial Statement Analysis and Security Valuation. 5th ed. New York: McGraw-Hill Education.
Piotroski, J. D. (2000). Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research, 38, 1-41.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442.
Sharpe, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management, 21(1), 49-58.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven: Yale University Press.
Whaley, R. E. (1993). Derivatives on market volatility: Hedging tools long overdue. Journal of Derivatives, 1(1), 71-84.
Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Greensboro: Trend Research.
SHA Multi Pivot Points -v1.0.0🔎Using Pivot Points in Trading
Traders use PPs to help determine predefined support and resistance levels to guide their trading strategies. In addition, traders identify potential price reversals, trend direction, and breakout opportunities:
Trend identification: PPs act as a reference level to gauge market sentiment. If the price opens above the PP and remains above it, traders interpret this as an uptrend. Conversely, if the price opens below the pivot point and stays below, it suggests a downtrend.
Support and resistance determination: Pivot levels are natural barriers where price reactions frequently occur. Traders may enter long positions near support levels, expecting a price bounce, or if the price approaches resistance levels, traders may consider shorting the asset.
Breakout trading: When the price breaks above resistance or support, it may indicate strong momentum for further movement.
Reversal identification: Traders also look for failed breakouts or price rejections at pivot levels to anticipate reversals.
Trading strategy combinations: Traders can improve accuracy by combining PPs with other technical analysis indicators.
1. Camarilla Pivot Points
📌 Overview:
Developed by Nick Scott in 1989, Camarilla Pivot Points are designed for short-term, intraday trading. Unlike traditional pivots, Camarilla levels are tighter and more responsive, making them useful in volatile markets.
📐 Key Levels:
It generates eight levels:
- Resistance: Initial Level (R1), Mid-range Level (R2), Sell Reversal Level (R3), Breakout Level (R4)
- Support: Initial Level (S1), Mid-range Level (S2), Buy Reversal Level (S3), Breakout Level (S4)
✅ How to Use:
- S1/R1 + RSI or volume divergence to confirm weak momentum and early reversals.
- S2/R2 with price action patterns to enter early on major moves before L3/H3 get tested.
- S3/R3: Mean-reversion zones → price often reverses.
- Break of S4/R4: Strong breakout → trend-following signal.
- Combine with volume or candlestick confirmation for entries.
🔹 2. Floor (Standard) Pivot Points
📌 Overview:
This is the most traditional pivot method, widely used by floor traders. It’s symmetrical and provides a clear central pivot point with equally spaced support and resistance levels.
📐 Key Levels:
- Povit Points : Average price (PPs)
- Resistance : First price ceiling (R1), Stronger ceiling (R2), Extreme resistance (R3)
- Support : First price floor (S1), Stronger floor (S2), Extreme support (S3)
✅ How to Use:
- Above PPs = bullish bias; Below PPs = bearish bias.
- S1/R1 are most used for intraday targets.
- S2–S3/R2–R3 indicate potential extreme moves.
- Often used in combination with momentum indicators.
🔹 3. Woodie Pivot Points
📌 Overview:
Woodie’s pivot formula gives double weight to the closing price, emphasizing the most recent session's sentiment.
📐 Key Levels:
- Povit Points : Weighted average (PPs)
- Resistance : First price ceiling (R1), Stronger resistance (R2)
- Support : First price floor (S1), Stronger support (S2)
✅ How to Use:
- Works best in fast-moving markets.
- PPs acts as a momentum-based balance level.
- Good for scalpers and momentum traders.
🔹 4. Fusion Pivot Points
📌 Overview:
This method differs significantly — it calculates only one support and one resistance level, adjusting based on the relationship between the open and close.
📐 Key Levels:
- Povit Points : Single directional (PPs)
- Resistance : Potential ceiling (R)
- Support : Potential floor (S)
✅ How to Use:
- Not symmetrical → more responsive to price behavior.
- Best for breakout or reversal strategies.
- Use when you're expecting directional momentum.
🔹 5. Classic Pivot Points (Traditional)
📌 Overview:
Also known as Standard or Traditional Pivot Points, this is the default method used by most charting platforms. It offers a balanced and simple framework.
📐 Key Levels:
- Povit Points : Central price level (PPs)
- Resistance : First ceiling (R1), Stronger resistance (R2), Extreme resistance (R3)
- Support : First floor (S1), Stronger floor (S2), Extreme support (S3)
✅ How to Use:
- PPs is the market’s equilibrium point.
- Helps define market structure, bias, and trade zones.
- Combine with order blocks, RSI, or MACD for confirmation.
📊 Summary Comparison :
1. Camarilla Pivot Points
- Focus : Mean Reversion & Breakouts
- Best Use : Scalping, Day Trading
2. Floor Pivot Points
- Focus : General Support/Resistance
- Best Use : Intraday, Swing
3. Woodie Pivot Points
- Focus : Recent Close Emphasis
- Best Use : Momentum Trading
4. Fusion Pivot Points
- Focus : Trend/Breakout
- Best Use : Directional Breakouts
5. Classic Povit Points
- Focus : Market Structure
- Best Use : General Use
⚠️ Disclaimer
The information and tools provided in this script are for educational and informational purposes only. They do not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instrument.
Trading in the financial markets involves risk of loss and is not suitable for every investor. You are solely responsible for your trading decisions. Always do your own research, use proper risk management, and consult a licensed financial advisor before making any financial decisions.
Systemic Credit Market Pressure IndexSystemic Credit Market Pressure Index (SCMPI): A Composite Indicator for Credit Cycle Analysis
The Systemic Credit Market Pressure Index (SCMPI) represents a novel composite indicator designed to quantify systemic stress within credit markets through the integration of multiple macroeconomic variables. This indicator employs advanced statistical normalization techniques, adaptive threshold mechanisms, and intelligent visualization systems to provide real-time assessment of credit market conditions across expansion, neutral, and stress regimes. The methodology combines credit spread analysis, labor market indicators, consumer credit conditions, and household debt metrics into a unified framework for systemic risk assessment, featuring dynamic Bollinger Band-style thresholds and theme-adaptive visualization capabilities.
## 1. Introduction
Credit cycles represent fundamental drivers of economic fluctuations, with their dynamics significantly influencing financial stability and macroeconomic outcomes (Bernanke, Gertler & Gilchrist, 1999). The identification and measurement of credit market stress has become increasingly critical following the 2008 financial crisis, which highlighted the need for comprehensive early warning systems (Adrian & Brunnermeier, 2016). Traditional single-variable approaches often fail to capture the multidimensional nature of credit market dynamics, necessitating the development of composite indicators that integrate multiple information sources.
The SCMPI addresses this gap by constructing a weighted composite index that synthesizes four key dimensions of credit market conditions: corporate credit spreads, labor market stress, consumer credit accessibility, and household leverage ratios. This approach aligns with the theoretical framework established by Minsky (1986) regarding financial instability hypothesis and builds upon empirical work by Gilchrist & Zakrajšek (2012) on credit market sentiment.
## 2. Theoretical Framework
### 2.1 Credit Cycle Theory
The theoretical foundation of the SCMPI rests on the credit cycle literature, which posits that credit availability fluctuates in predictable patterns that amplify business cycle dynamics (Kiyotaki & Moore, 1997). During expansion phases, credit becomes increasingly available as risk perceptions decline and collateral values rise. Conversely, stress phases are characterized by credit contraction, elevated risk premiums, and deteriorating borrower conditions.
The indicator incorporates Kindleberger's (1978) framework of financial crises, which identifies key stages in credit cycles: displacement, boom, euphoria, profit-taking, and panic. By monitoring multiple variables simultaneously, the SCMPI aims to capture transitions between these phases before they become apparent in individual metrics.
### 2.2 Systemic Risk Measurement
Systemic risk, defined as the risk of collapse of an entire financial system or entire market (Kaufman & Scott, 2003), requires measurement approaches that capture interconnectedness and spillover effects. The SCMPI follows the methodology established by Bisias et al. (2012) in constructing composite measures that aggregate individual risk indicators into system-wide assessments.
The index employs the concept of "financial stress" as defined by Illing & Liu (2006), encompassing increased uncertainty about fundamental asset values, increased uncertainty about other investors' behavior, increased flight to quality, and increased flight to liquidity.
## 3. Methodology
### 3.1 Component Variables
The SCMPI integrates four primary components, each representing distinct aspects of credit market conditions:
#### 3.1.1 Credit Spreads (BAA-10Y Treasury)
Corporate credit spreads serve as the primary indicator of credit market stress, reflecting risk premiums demanded by investors for corporate debt relative to risk-free government securities (Gilchrist & Zakrajšek, 2012). The BAA-10Y spread specifically captures investment-grade corporate credit conditions, providing insight into broad credit market sentiment.
#### 3.1.2 Unemployment Rate
Labor market conditions directly influence credit quality through their impact on borrower repayment capacity (Bernanke & Gertler, 1995). Rising unemployment typically precedes credit deterioration, making it a valuable leading indicator for credit stress.
#### 3.1.3 Consumer Credit Rates
Consumer credit accessibility reflects the transmission of monetary policy and credit market conditions to household borrowing (Mishkin, 1995). Elevated consumer credit rates indicate tightening credit conditions and reduced credit availability for households.
#### 3.1.4 Household Debt Service Ratio
Household leverage ratios capture the debt burden relative to income, providing insight into household financial stress and potential credit losses (Mian & Sufi, 2014). High debt service ratios indicate vulnerable household sectors that may contribute to credit market instability.
### 3.2 Statistical Methodology
#### 3.2.1 Z-Score Normalization
Each component variable undergoes robust z-score normalization to ensure comparability across different scales and units:
Z_i,t = (X_i,t - μ_i) / σ_i
Where X_i,t represents the value of variable i at time t, μ_i is the historical mean, and σ_i is the historical standard deviation. The normalization period employs a rolling 252-day window to capture annual cyclical patterns while maintaining sensitivity to regime changes.
#### 3.2.2 Adaptive Smoothing
To reduce noise while preserving signal quality, the indicator employs exponential moving average (EMA) smoothing with adaptive parameters:
EMA_t = α × Z_t + (1-α) × EMA_{t-1}
Where α = 2/(n+1) and n represents the smoothing period (default: 63 days).
#### 3.2.3 Weighted Aggregation
The composite index combines normalized components using theoretically motivated weights:
SCMPI_t = w_1×Z_spread,t + w_2×Z_unemployment,t + w_3×Z_consumer,t + w_4×Z_debt,t
Default weights reflect the relative importance of each component based on empirical literature: credit spreads (35%), unemployment (25%), consumer credit (25%), and household debt (15%).
### 3.3 Dynamic Threshold Mechanism
Unlike static threshold approaches, the SCMPI employs adaptive Bollinger Band-style thresholds that automatically adjust to changing market volatility and conditions (Bollinger, 2001):
Expansion Threshold = μ_SCMPI - k × σ_SCMPI
Stress Threshold = μ_SCMPI + k × σ_SCMPI
Neutral Line = μ_SCMPI
Where μ_SCMPI and σ_SCMPI represent the rolling mean and standard deviation of the composite index calculated over a configurable period (default: 126 days), and k is the threshold multiplier (default: 1.0). This approach ensures that thresholds remain relevant across different market regimes and volatility environments, providing more robust regime classification than fixed thresholds.
### 3.4 Visualization and User Interface
The SCMPI incorporates advanced visualization capabilities designed for professional trading environments:
#### 3.4.1 Adaptive Theme System
The indicator features an intelligent dual-theme system that automatically optimizes colors and transparency levels for both dark and bright chart backgrounds. This ensures optimal readability across different trading platforms and user preferences.
#### 3.4.2 Customizable Visual Elements
Users can customize all visual aspects including:
- Color Schemes: Automatic theme adaptation with optional custom color overrides
- Line Styles: Configurable widths for main index, trend lines, and threshold boundaries
- Transparency Optimization: Automatic adjustment based on selected theme for optimal contrast
- Dynamic Zones: Color-coded regime areas with adaptive transparency
#### 3.4.3 Professional Data Table
A comprehensive 13-row data table provides real-time component analysis including:
- Composite index value and regime classification
- Individual component z-scores with color-coded stress indicators
- Trend direction and signal strength assessment
- Dynamic threshold status and volatility metrics
- Component weight distribution for transparency
## 4. Regime Classification
The SCMPI classifies credit market conditions into three distinct regimes:
### 4.1 Expansion Regime (SCMPI < Expansion Threshold)
Characterized by favorable credit conditions, low risk premiums, and accommodative lending standards. This regime typically corresponds to economic expansion phases with low default rates and increasing credit availability.
### 4.2 Neutral Regime (Expansion Threshold ≤ SCMPI ≤ Stress Threshold)
Represents balanced credit market conditions with moderate risk premiums and stable lending standards. This regime indicates neither significant stress nor excessive exuberance in credit markets.
### 4.3 Stress Regime (SCMPI > Stress Threshold)
Indicates elevated credit market stress with high risk premiums, tightening lending standards, and deteriorating borrower conditions. This regime often precedes or coincides with economic contractions and financial market volatility.
## 5. Technical Implementation and Features
### 5.1 Alert System
The SCMPI includes a comprehensive alert framework with seven distinct conditions:
- Regime Transitions: Expansion, Neutral, and Stress phase entries
- Extreme Conditions: Values exceeding ±2.0 standard deviations
- Trend Reversals: Directional changes in the underlying trend component
### 5.2 Performance Optimization
The indicator employs several optimization techniques:
- Efficient Calculations: Pre-computed statistical measures to minimize computational overhead
- Memory Management: Optimized variable declarations for real-time performance
- Error Handling: Robust data validation and fallback mechanisms for missing data
## 6. Empirical Validation
### 6.1 Historical Performance
Backtesting analysis demonstrates the SCMPI's ability to identify major credit stress episodes, including:
- The 2008 Financial Crisis
- The 2020 COVID-19 pandemic market disruption
- Various regional banking crises
- European sovereign debt crisis (2010-2012)
### 6.2 Leading Indicator Properties
The composite nature and dynamic threshold system of the SCMPI provides enhanced leading indicator properties, typically signaling regime changes 1-3 months before they become apparent in individual components or market indices. The adaptive threshold mechanism reduces false signals during high-volatility periods while maintaining sensitivity during regime transitions.
## 7. Applications and Limitations
### 7.1 Applications
- Risk Management: Portfolio managers can use SCMPI signals to adjust credit exposure and risk positioning
- Academic Research: Researchers can employ the index for credit cycle analysis and systemic risk studies
- Trading Systems: The comprehensive alert system enables automated trading strategy implementation
- Financial Education: The transparent methodology and visual design facilitate understanding of credit market dynamics
### 7.2 Limitations
- Data Dependency: The indicator relies on timely and accurate macroeconomic data from FRED sources
- Regime Persistence: Dynamic thresholds may exhibit brief lag during extremely rapid regime transitions
- Model Risk: Component weights and parameters require periodic recalibration based on evolving market structures
- Computational Requirements: Real-time calculations may require adequate processing power for optimal performance
## References
Adrian, T. & Brunnermeier, M.K. (2016). CoVaR. *American Economic Review*, 106(7), 1705-1741.
Bernanke, B. & Gertler, M. (1995). Inside the black box: the credit channel of monetary policy transmission. *Journal of Economic Perspectives*, 9(4), 27-48.
Bernanke, B., Gertler, M. & Gilchrist, S. (1999). The financial accelerator in a quantitative business cycle framework. *Handbook of Macroeconomics*, 1, 1341-1393.
Bisias, D., Flood, M., Lo, A.W. & Valavanis, S. (2012). A survey of systemic risk analytics. *Annual Review of Financial Economics*, 4(1), 255-296.
Bollinger, J. (2001). *Bollinger on Bollinger Bands*. McGraw-Hill Education.
Gilchrist, S. & Zakrajšek, E. (2012). Credit spreads and business cycle fluctuations. *American Economic Review*, 102(4), 1692-1720.
Illing, M. & Liu, Y. (2006). Measuring financial stress in a developed country: An application to Canada. *Journal of Financial Stability*, 2(3), 243-265.
Kaufman, G.G. & Scott, K.E. (2003). What is systemic risk, and do bank regulators retard or contribute to it? *The Independent Review*, 7(3), 371-391.
Kindleberger, C.P. (1978). *Manias, Panics and Crashes: A History of Financial Crises*. Basic Books.
Kiyotaki, N. & Moore, J. (1997). Credit cycles. *Journal of Political Economy*, 105(2), 211-248.
Mian, A. & Sufi, A. (2014). What explains the 2007–2009 drop in employment? *Econometrica*, 82(6), 2197-2223.
Minsky, H.P. (1986). *Stabilizing an Unstable Economy*. Yale University Press.
Mishkin, F.S. (1995). Symposium on the monetary transmission mechanism. *Journal of Economic Perspectives*, 9(4), 3-10.
Pivot Points DWMWhat Is a Pivot Point?
A pivot point is a price level calculated from previous prices. It's used to indicate potential areas of support or resistance that offer attractive reward-to-risk setups for trades. The pivot point itself is simply the average of the intraday high and low and the closing price from the previous trading day. Trading above the pivot point on the subsequent day is thought to indicate ongoing bullish sentiment. Trading below the pivot point indicates bearish sentiment.
Non-Repainting
Uptrick: Zero Lag HMA Trend Suite1. Name and Purpose
Uptrick: Zero Lag HMA Trend Suite is a Pine Version 6 script that builds upon the Hull Moving Average (HMA) to offer an advanced trend analysis tool. Its purpose is to help traders identify trend direction, potential reversals, and overall market momentum with reduced lag compared to traditional moving averages. By combining the HMA with Average True Range (ATR) thresholds, slope-dependent coloring, Volume Weighted Average Price (VWAP) ribbons, and optional reversal signals, the script aims to give a detailed view of price activity in various market environments.
2. Overview
This script begins with the calculation of a Hull Moving Average, a method that blends Weighted Moving Averages in a way designed to cut down on lag while still smoothing out price fluctuations. Next, several enhancements are applied. The script compares current HMA values to previous ones for slope-based coloring, which highlights uptrends and downtrends at a glance. It also plots buy and sell signals when price moves beyond or below thresholds determined by the ATR and the user’s chosen signal multiplier. An optional VWAP ribbon can be shown to confirm bullish or bearish conditions relative to a volume-weighted benchmark. Additionally, the script can plot reversal signals (labeled with B) at points where price crosses back toward the HMA from above or below. Taken together, these elements allow traders to visualize both the short-term momentum and the broader context of how price interacts with volatility and overall market direction.
3. Why These Indicators Have Been Linked Together
The reason the Hull Moving Average, the Average True Range, and the VWAP have been integrated into one script is to tackle multiple facets of market analysis in a single tool. The Zero Lag Hull Moving Average provides a responsive trend line, the ATR offers a measure of volatility that helps distinguish significant price shifts from typical fluctuations, and the VWAP acts as a reference for fair value based on traded volume. By layering all three, the script helps traders avoid the need to juggle multiple separate indicators and offers a holistic perspective. The slope-based coloring focuses on trend direction, the ATR-based thresholds refine possible buy and sell zones, and the VWAP ribbons provide insight into how price stands relative to an important volume-weighted level. The inclusion of up and down signals and reversal B labels further refines entries and exits.
4. Why Use Uptrick: Zero Lag HMA Trend Suite
The Hull Moving Average is already known for reacting more quickly to price changes compared to other moving averages while retaining a degree of smoothness. This suite enhances the basic HMA by showing colored gradients that make it easy to spot trend direction changes, highlighting potential entry or exit points based on volatility-driven thresholds, and optionally layering a volume-based measure of bullish or bearish market sentiment. By relying on a zero lag approach and additional data points, the script caters to those wanting a more responsive method of identifying shifts in market dynamics. The added reversal signals and up or down alerts give traders extra confirmation for potential turning points.
5. How This Extension Improves on the Basic HMA
This extension not only plots the Hull Moving Average but also includes data-driven alerts and visual cues that traditional HMA lines do not provide. First, it offers multi-layered slope coloring, making up or down trends quickly apparent. Second, it uses ATR-based thresholds to pinpoint moments when price may be extending beyond normal volatility, thus generating buy or sell signals. Third, the script introduces an optional VWAP ribbon to indicate whether the market is trading above or below this pivotal volume-weighted benchmark, adding a further confirmation step for bullish or bearish conditions. Finally, it incorporates optional reversal signals labeled with B, indicating points where price might swing back toward the main HMA line.
6. Core Components
The script can be broken down into several primary functions and features.
a. Zero Lag HMA Calculation
Uses two Weighted Moving Averages (half-length and full-length) combined through a smoothing step based on the square root of the chosen length. This approach is designed to reduce lag significantly compared to other moving averages.
b. Slope Detection
Compares current and prior HMA values to determine if the trend is up or down. The slope-based coloring changes between turquoise shades for upward movement and magenta shades for downward movement, making trend direction immediately visible.
c. ATR-Based Thresholding for Up and Down Signals
The script calculates an Average True Range over a user-defined period, then multiplies it by a signal factor to form two bands around the HMA. When price crosses below the lower band, an up (buy) signal appears; when it crosses above the upper band, a down (sell) signal is shown.
d. Reversal Signals (B Labels)
Tracks when price transitions back toward the main HMA from an extreme zone. When enabled, these reversal points are labeled with a B and can help traders see potential turning points or mean-reversion setups.
e. VWAP Bands
An optional Volume Weighted Average Price ribbon that plots above or below the HMA, indicating bullish or bearish conditions relative to a volume-weighted price benchmark. This can also act as a kind of support/ resistance.
7. User Inputs
a. HMA Length
Controls how quickly the moving average responds to price changes. Shorter lengths react faster but can lead to more frequent signals, whereas longer lengths produce smoother lines.
b. Source
Specifies the price input, such as close or an alternative source, for the calculation. This can help align the HMA with specific trading strategies.
c. ATR Length and Signal Multiplier
Defines how the script calculates average volatility and sets thresholds for buy or sell alerts. Adjusting these values can help filter out noise or highlight more aggressive signals.
d. Slope Index
Determines how many bars to look back for detecting slope direction, influencing how sensitive the slope coloring is to small fluctuations.
e. Show Buy and Sell Signals, Reversal Signals, and VWAP
Lets users toggle the display of these features. Turning off certain elements can reduce chart clutter if traders prefer a simpler layout.
8. Calculation Process
The script’s calculation follows a step-by-step approach. It first computes two Weighted Moving Averages of the selected price source, one over half the specified length and one over the full length. It then combines these using 2*wma1 minus wma2 to reduce lag, followed by applying another weighted average using the square root of the length. Simultaneously, it computes the ATR for a user-defined period. By multiplying ATR by the signal multiplier, it establishes upper and lower bands around the HMA, where crossovers generate buy (up) or sell (down) signals. The script can also plot reversal signals (B labels) when price crosses back from these bands in the opposite direction. For the optional VWAP feature, Pine Script’s ta.vwap function is used, and differences between the HMA and VWAP levels determine the color and opacity of the ribbon.
9. Signal Generation and Filtering
The ATR-based thresholds reduce the influence of small, inconsequential price swings. When price falls below the lower band, the script issues an up (buy) signal. If price breaks above the upper band, a down (sell) signal appears. These signals are visible through labels placed near the bars. Reversal signals, labeled with B, can be turned on to help detect when price retraces from an extended area back toward the main HMA line. Traders can disable or enable these signals to match their preferred level of chart detail or risk tolerance.
10. Visualization on the Chart
The Zero HMA Lag Trend Suite aims for visual clarity. The HMA line is plotted multiple times with increasing transparency to create a gradient effect. Turquoise gradients indicate upward slopes, and magenta gradients signify downward slopes. Bar coloring can be configured to align with the slope direction, providing quick insight into current momentum. When enabled, buy or sell labels are placed under or above the bars as price crosses the ATR-defined boundaries. If the reversal option is active, B labels appear around areas where price changes direction. The optional VWAP ribbons form background bands, using distinct coloration to signal whether price is above or below the volume-weighted metric.
11. Market Adaptability
Because the script’s parameters (HMA length, ATR length, signal multiplier, and slope index) are user-configurable, it can adapt to a wide range of markets and timeframes. Intraday traders may prefer a shorter HMA length for quick signals, while swing or position traders might use a longer HMA length to filter out short-lived price changes. The source setting can also be adjusted, allowing for specialized data inputs beyond just close or open values.
12. Risk Management Considerations
The script’s signals and labels are based on past price data and volatility readings, and they do not guarantee profitable outcomes. Sharp market reversals or unforeseen fundamental events can produce false signals. Traders should combine this tool with broader risk management strategies, including stop-loss placement, position sizing, and independent market analyses. The Zero HMA Lag Trend Suite can help highlight potential opportunities, but it should not be relied upon as the sole basis for trade decisions.
13. Combining with Other Tools
Many traders choose to verify signals from the Zero HMA Lag Trend Suite using popular indicators like the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), or even simple volume-based metrics to confirm whether a price movement has sufficient momentum. Conventional techniques such as support and resistance levels, chart patterns, or candlestick analysis can also supplement signals generated by the script’s up, down, or reversal B labels.
14. Parameter Customization and Examples
a. Short-Term Day Trading
Using a shorter HMA length (for instance, 9 or 14) and a slightly higher ATR multiplier might provide timely buy and sell signals, though it may also produce more whipsaws in choppy markets.
b. Swing or Position Trading
Selecting a longer HMA length (such as 50 or 100) with a moderate ATR multiplier can help users track more significant and sustained market moves, potentially reducing the effect of minor fluctuations.
c. Multiple Timeframe Blends
Some traders load two versions of the indicator on the same chart, one for short-term signals (with frequent B label reversals) and another for the broader trend direction, aligning entry and exit decisions with the bigger picture.
15. Realistic Expectations
Even though the Hull Moving Average helps minimize lag and the script incorporates volatility-based filters and optional VWAP overlays, it cannot predict future market behavior with complete accuracy. Periods of low liquidity or sudden market shocks can still lead to signals that do not reflect longer-term trends. Frequent parameter review and manual confirmation are advised before executing trades based solely on the script’s outputs.
16. Theoretical Background
The Hull Moving Average formula aims to balance smoothness with reactivity, accomplished by combining Weighted Moving Averages at varying lengths. By subtracting a slower average from a faster one and then applying another smoothing step with the square root of the original length, the HMA is designed to respond more promptly to price changes than typical exponential or simple moving averages. The ATR component, introduced by J. Welles Wilder, calculates the average range of price movement over a user-defined period, allowing the script to assess volatility and adapt signals accordingly. VWAP provides a volume-weighted benchmark that many institutional traders track to gauge fair intraday value.
17. Originality and Uniqueness
Although multiple HMA-based indicators can be found, Uptrick: Zero Lag HMA Trend Suite sets itself apart by merging slope-based coloring, ATR thresholds, VWAP ribbons, up or down labels, and optional reversal signals all in one cohesive platform. This synergy aims to reduce chart clutter while still giving traders a comprehensive look at trend direction, volatility, and volume-based sentiment.
18. Summary
Uptrick: Zero Lag HMA Trend Suite is a specialized trading script designed to highlight potential market trends and reversals with minimal delay. It leverages the Hull Moving Average for an adaptive yet smooth price line, pairs ATR-based thresholds for detecting possible breakouts or dips, and provides VWAP-based ribbons for added volume-weighted context. Traders can further refine their entries and exits by enabling up or down signals and reversal labels (B) where price may revert toward the HMA. Suitable for a wide range of timeframes and instrument types, the script encourages a disciplined approach to trade management and risk control.
19. Disclaimer
This script is provided for informational and educational purposes only. Trading and investing involve significant financial risk, and no indicator can guarantee success under all conditions. Users should practice robust risk management, including the placement of stop losses and position sizing, and should confirm signals with additional analysis tools. The developer of this script assumes no liability for any trading decisions or outcomes resulting from its use.
Rounded Grid Levels🟩 Rounded Grid Levels is a visual tool that helps traders quickly identify key psychological price levels on any chart. By dynamically adapting to the user's visible screen area, it provides consistent, easy-to-read round number grids that align with price action. The indicator offers a traditional visualization of horizontal round level grids, along with enhanced options such as tilted grids that align with market sentiment, and fan-shaped grids for alternative price interaction views. It serves purely as a visual aid, providing an adaptable way to observe rounded price levels without making predictions or generating trading signals.
⚡ OVERVIEW ⚡
The Rounded Grid Levels indicator is a visual tool designed to help traders identify and track price levels that may hold psychological significance, such as round numbers or significant milestones. These levels often serve as potential areas for price reactions, including support, resistance, or points of market interest. The indicator's gridlines are determined by user-defined settings and adjust dynamically based on the visible chart area, meaning they are influenced by the user's current zoom level and perspective. This behavior is similar to TradingView's built-in grid lines found in the chart settings canvas, which also adjust in real-time based on the visible screen, ensuring the most relevant price levels are displayed. By default, the indicator provides consistent gridlines to represent traditional round number levels, offering a straightforward view of key psychological areas. Additionally, users have access to experimental and novel configurations, such as fan-shaped layouts, which expand from a central point and adapt directionally based on user settings. This configuration can provide an alternate perspective for traders, especially useful in analyzing broader market moves and visualizing expansion relative to the current price.
Users can display the gridlines in a variety of configurations, including horizontal, neutral, auto, or fan-shaped layouts, depending on their preferred method of analysis. This flexibility allows traders to focus on different types of price action without overcrowding the visual representation of price movements.
This indicator is intended purely as a visual aid for understanding how price interacts with rounded levels over time. It does not generate predictive trading signals or recommendations but rather provides traders with a customizable framework to enhance their market analysis.
⭕ ROUND NUMBERS IN MARKET PSYCHOLOGY ⭕
Round numbers hold a significant place in financial markets, largely due to the psychological tendencies of traders and investors. These levels often represent areas of interest where human behavior, market biases, and trading strategies converge. Whether it's prices ending in 000, 500, or other recognizable values, these levels naturally attract more attention and influence decision-making.
Round numbers can act as key support or resistance levels and often become focal points in market activity. They are frequently highlighted by financial media, embedded in products like options, and serve as foundations for various trading theories. Their impact extends across different market participants and strategies, making them important focal points in both short-term and long-term market analysis.
Round numbers play an important role in guiding trader behavior and market activity. To better understand why these levels are so impactful, there are several key factors that highlight their significance in trading and price dynamics:
Psychological Impact : Humans naturally gravitate toward round numbers, such as prices ending in 000, 500, or 00. These levels tend to draw attention as traders perceive them as psychologically significant. This behavior is rooted in the cognitive bias known as "left-digit bias," where people assign greater importance to rounded, more recognizable numbers. In trading, this means that prices at these levels are more memorable and thus more likely to attract attention, creating an area where traders focus their buying or selling decisions.
Order Clustering : Traders often place buy and sell orders around these rounded levels, either manually or automatically through stop and limit orders. This clustering leads to the formation of visible support or resistance zones, as the concentrated orders tend to influence price behavior around these key levels. Market participants tend to converge their orders around these price points because of their perceived psychological importance, creating a liquidity pocket. As a result, these areas often act as barriers that the price either struggles to cross or uses as springboards for further movement.
External Influences : Financial media frequently highlights round-number milestones, amplifying market sentiment and drawing traders' attention to these levels. Additionally, algorithmic trading systems often react to round-number thresholds, which can further reinforce price movements, creating self-reinforcing reactions at these levels. As media and analysts emphasize these milestones, more traders pay attention to them, leading to increased volume and often heightened volatility at those points. This self-reinforcing cycle makes round numbers an area where price movement can either accelerate due to a breakout or stall because of clustering interest.
Option Strike Prices : Options contracts typically have strike prices set at round numbers, and as expiration approaches, these levels can influence the price of the underlying asset due to concentrated trading activity. The behavior around these levels, often called "pinning," happens because traders adjust their positions to avoid unfavorable scenarios at these key strikes. This activity tends to concentrate price movement toward these levels as traders hedge their positions, leading to increased liquidity and the potential for abrupt price reactions near option expiration dates.
Whole Number Theory : This theory suggests that whole numbers act as natural psychological barriers, where traders tend to make decisions, place orders, or expect price reactions, making these levels crucial for analysis. Whole numbers are simple to remember and are often used as informal targets for profit-taking or stop placement. This behavior leads to a natural ebb and flow around these levels, where the market finds equilibrium temporarily before deciding on a future direction. Whole numbers tend to work like magnets, drawing price to them and often creating reactions that are visible across different timeframes.
Quarters Theory : Commonly used in Forex markets, this theory focuses on quarter-point increments (e.g., 1.0000, 1.2500, 1.5000) as key levels where price often pauses or reverses. These quarter levels are treated as important psychological barriers, with price frequently interacting at these intervals. Traders use these points to gauge market strength or weakness because quarter levels divide larger round-number ranges into more manageable and meaningful segments. For example, in highly traded forex pairs like EUR/USD, traders might treat 1.2500 as a significant barrier because it represents a halfway point between 1.0000 and 1.5000, offering a balanced reference point for decision-making.
Big Round Numbers : Major round numbers, such as 100, 500, or 1000, often attract significant attention and serve as psychological thresholds. Traders anticipate strong reactions when prices approach or cross these levels. This is often because large round numbers symbolize major milestones, and price behavior around them tends to signal important market sentiment shifts. When price crosses a major level, such as a stock moving above $100 or Bitcoin crossing $50,000, it often creates a surge in trading activity as it is viewed as a validation or invalidation of market trends, drawing in momentum traders and triggering both retail and institutional responses.
By visualizing these round levels on the chart, the Rounded Grid Levels indicator helps traders identify areas where price may pause, reverse, or gain momentum. While round numbers provide useful insights, they should be used in conjunction with other technical analysis tools for a comprehensive trading strategy.
🛠️ CONFIGURATION AND SETTINGS 🛠️
The Rounded Grid Levels indicator offers a variety of configurable settings to tailor the visualization according to individual trader preferences. Below are the key settings available for customization:
Custom Settings
Rounding Step : The Rounding Step parameter sets the minimum interval between gridlines. This value determines how closely spaced the rounded levels are on the chart. For example, if the Rounding Step is set to 100, gridlines will be displayed at every 100 points (e.g., $100, $200, $300) relative to the current price level. The Rounding Step is scaled to the chart's visible area, meaning users should adjust it appropriately for different assets to ensure effective visualization. Lower values provide a more granular view, while larger values give a broader, higher-level perspective.
Major Grids : Defines the interval at which major gridlines will appear compared to minor ones. For example, if the Rounding Step is 100 and Major Grids is set to 10, major gridlines will be displayed every $1,000, while minor gridlines will be at every $100. This distinction allows traders to better visualize key psychological levels by emphasizing significant price intervals.
Direction : Users can select the gridline direction, choosing between options such as 'Up', 'Down', 'Auto', or 'Neutral'. This setting controls how the gridlines extend relative to the current price level, which can help in analyzing directional trends.
Neutral Direction : This option provides balanced gridlines both above and below the current price, allowing traders to visualize support and resistance levels symmetrically. This is useful for analyzing sideways or ranging markets without directional bias.
Up Direction : The gridlines are tilted upwards, starting from visible lows and extending toward the rounded level at the current price. By choosing Up , traders emphasize an upward sentiment, visualizing price action that aligns with rising trends. This option helps illustrate potential areas where pullbacks may occur, as well as how price might expand upwards in the current market context.
Down Direction : The gridlines are tilted downwards, starting from visible highs and extending toward the rounded level at the current price. Selecting Down allows traders to emphasize a downward sentiment, visualizing how price may expand downwards, which is particularly useful when analyzing downtrends or potential correction levels. The gridlines provide an illustrative view of how price interacts with lower levels during market declines.
Auto Direction : The gridlines automatically adjust their direction based on recent market trends. This adaptive option allows traders to visualize gridlines that dynamically change according to price action, making it suitable for evolving market conditions where the direction is uncertain. It’s useful for traders looking for an indicator that moves in sync with market shifts and doesn’t require manual adjustment.
Grid Type : Allows users to choose between 'Linear' or 'Fan' grid types. The Linear type creates evenly spaced gridlines that can be either horizontal or tilted, depending on the chosen direction setting, providing a straightforward view of price levels. The Fan type radiates lines from a central point, offering a more dynamic perspective for analyzing price expansions relative to the current price. These grid types introduce experimental visualizations influenced by chart properties, including visible highs, lows, and the current price. Regardless of the configuration, the gridlines will always end at the current bar, which represents a rounded price level, ensuring consistency in how key price areas are displayed.
Extend : This setting allows gridlines to be projected into the future, helping traders see potential levels beyond the current bar. When enabled, the behavior of the extended lines varies based on the selected grid type and direction. For Neutral and Horizontal Linear settings, the extended gridlines maintain their round-number alignment indefinitely. However, for Up , Down , or Auto directions, the angle of the extended gridlines can change dynamically based on the chart’s visible high and low or the latest price action. As a result, extended lines may not continue to align with round-number levels beyond the current bar, reflecting instead the current trend and sentiment of the market. Regardless of direction, extended gridlines remain consistently spaced and either parallel or evenly distributed, ensuring a structured visual representation.
Color Settings : Users can customize the colors for resistance, support, and minor gridlines at the current price. This helps in visually distinguishing between different grid types and their significance on the chart.
Color Options
These configuration options make the Rounded Grid Levels indicator a versatile tool for traders looking to customize their charts based on their personal trading strategies and analytical preferences.
🖼️ CHART EXAMPLES 🖼️
The following chart examples illustrate different configurations available in the Rounded Grid Levels indicator. These examples show how variations in grid type, direction, and rounding step settings impact the visualization of price levels. Traders may find that smaller rounding steps are more effective on lower time frames, where precision is key, whereas larger rounding steps help to reduce clutter and highlight key levels on higher time frames. Each image includes a caption to explain the specific configuration used, helping users better understand how to apply these settings in different market conditions.
Smaller Rounding Step (100) : With a smaller rounding step, the gridlines are spaced closely together. This setting is particularly useful for lower time frames where price action is more granular and finer details are needed. It allows traders to track price interactions at narrower levels, but on higher time frames, it may lead to clutter and exceed Pine Script's 500-line limit.
Larger Rounding Step (1000) : With a larger rounding step, the gridlines are spaced farther apart. This visualization is better suited for higher time frames or broader market overviews, allowing users to focus on major psychological levels without overloading the chart. On lower time frames, this may result in fewer actionable levels, but it helps in maintaining clarity and staying within Pine Script's line limit.
Linear Grid Type, Neutral Direction (Traditional Rounded Price Levels) : The Linear gridlines are displayed in a neutral fashion, representing traditional round-number levels with consistent spacing above and below the current price. This layout helps visualize key psychological price levels over time in a straightforward manner.
Linear Grid Type, Down Direction : The Linear gridlines are tilted downwards, remaining parallel and ending at the rounded level at the current price. This setup emphasizes downward market sentiment, allowing traders to visualize price expansion towards lower levels, which is useful when analyzing downtrends or potential correction levels.
Linear Grid Type, Down Direction : The Linear gridlines are tilted downwards, extending from the current price to lower levels. Useful for observing downtrending price movements and visualizing pullback areas during uptrends.
Linear Grid Type, Auto Direction : The Linear gridlines adjust dynamically, tilting either upwards or downwards to align with recent price trends, remaining parallel and ending at the rounded level at the current price. This configuration reflects the current market sentiment and offers traders a flexible way to observe price dynamics as they develop in real time.
Fan Grid Type, Neutral Direction : The fan-shaped gridlines radiate symmetrically from a central point, ending at the rounded level at the current price. This configuration provides an unbiased view of price action, giving traders a balanced visualization of rounded levels without directional influence.
Fan Grid Type, Up Direction : The fan-shaped gridlines originate from lower visible price points and radiate upwards, ending at the rounded level at the current price. This layout helps visualize potential price expansion to higher levels, offering insights into upward momentum while maintaining a dynamic and evolving perspective on market conditions.
Fan Grid Type, Down Direction : The fan-shaped gridlines originate from higher visible price points and radiate downwards, ending at the rounded level at the current price. This setup is particularly useful for observing potential price expansion towards lower levels, illustrating areas where the price might extend during a downtrend.
Fan Grid Type, Auto Direction : The fan-shaped gridlines dynamically adjust, originating from visible chart points based on the current market trend, and radiate outward, ending at the rounded level at the current price. This adaptive visualization offers a continuously evolving representation that aligns with changing market sentiment, helping traders assess price expansion dynamically.
📊 SUMMARY 📊
The Rounded Grid Levels indicator helps traders highlight important round-number price levels on their charts, providing a dynamic way to visualize these psychological areas. With customizable gridline options—including traditional, tilted, and fan-shaped styles—users can adapt the indicator to suit their analysis needs. The gridlines adjust with chart zoom or scale, offering a flexible tool for observing price action, without providing specific trading signals or predictions.
⚙️ COMPATIBILITY AND LIMITATIONS ⚙️
Asset Compatibility :
The Rounded Grid Levels indicator is compatible with all asset classes, including cryptocurrencies, forex, stocks, and commodities. Users should adjust both the Rounding Step and the Major Grid settings to ensure the correct scale is used for the specific asset. This adjustment ensures that the most relevant round price levels are displayed effectively regardless of the instrument being analyzed. For instance, when analyzing BTCUSD, a higher Rounding Step may be needed compared to forex pairs like EURUSD, and the Major Grid value should also be adjusted to appropriately emphasize significant levels.
Line Limitations in Pine Script :
The Rounded Grid Levels indicator is subject to Pine Script's 500-line limit. This means that it cannot draw more than 500 gridlines on the chart at any given time. The number of gridlines depends directly on the chosen Rounding Step . If the steps are too small, the gridlines will be spaced too closely, causing the indicator to quickly reach the line limit. For example, if Ethereum is trading around $2,500, a Rounding Step of 100 might be appropriate, but a step of 1.00 would create too many gridlines, exceeding Pine Script's limit. Users should consider appropriate settings to avoid running into this constraint.
Runtime Error Considerations
When using the Rounded Grid Levels indicator, users might encounter a runtime error in specific scenarios. This typically happens if the Rounding Step is set too small, causing the indicator to exceed Pine Script's line limit or take too long to process. This can often occur when switching between charts that have significantly different price ranges. Since the Rounding Step requires flexibility to work with a wide variety of assets—ranging from decimals to thousands—it is not practically limited within the script itself. If a runtime error occurs, the recommended solution is to increase the Rounding Step to a larger value that better matches the current asset's price range.
Runtime Error: If the Rounding Step is too small for the current asset or chart, the indicator may generate a runtime error. Users should increase the Rounding Step to ensure proper visualization.
⚠️ DISCLAIMER ⚠️
The Rounded Grid Levels indicator is not designed as a predictive tool. While it extends gridlines into the future, this extension is purely for visual continuity and does not imply any forecast of future price movements. The primary function of this indicator is to help users visualize significant round number price levels.
The gridlines adjust dynamically based on the visible chart range, ensuring that the most relevant round price levels are displayed. This behavior allows the indicator to adapt to your current view of the market, but it should not be used to predict price movements. The indicator is intended as a visual aid and should be used alongside other tools in a comprehensive market analysis approach.
While gridlines may align with significant price levels in hindsight, they should not be interpreted as indicators of future price movements. Traders are encouraged to adjust settings based on their strategy and market conditions.
🧠 BEYOND THE CODE 🧠
The Rounded Grid Levels indicator, like other xxattaxx indicators , is designed with education and community collaboration in mind. Its open-source nature encourages exploration, experimentation, and the development of new grid calculation indicators, drawings, and strategies. We hope this indicator serves as a framework and a starting point for future innovations in grid trading.
Your comments, suggestions, and discussions are invaluable in shaping the future of this project. We actively encourage your feedback and contributions, which will directly help us refine and improve the Rounded Grid Levels indicator. We look forward to seeing the creative ways in which you use and enhance this tool.
Options Series - Supertrend, HalfTrend, Ichimoku Cloud and P_SAR➤ Supertrend:
➤ HalfTrend:
➤ Ichimoku Cloud:
➤ Parabolic SAR:
⭐ Overview and How It Works:
This script combines multiple popular technical indicators—Supertrend, HalfTrend, Ichimoku Cloud, and Parabolic SAR—into a single, cohesive tool for analyzing price trends and reversals. Designed for traders who prefer multi-layered confirmation, it displays non-overlay signals in a candlestick format, helping users make sense of intricate market dynamics. It also includes a "Master Candle" condition, which aggregates the signals from all indicators, providing a powerful snapshot of market sentiment.
References for study,
Supertrend and HalfTrend and Ichimoku Cloud and Parabolic SAR
⭐ Key Features and Functionality:
The script integrates four indicators and visually represents them in a non-overlay fashion, meaning that each indicator's signal appears on separate candlestick layers. It uses color coding to differentiate between bullish and bearish signals. The Master Candle is a unique feature that aggregates the signals from all indicators to show the overall sentiment.
Supertrend: It uses ATR and a multiplier factor to create a trailing stop, identifying bullish and bearish trends.
HalfTrend: It analyzes market volatility that provides buy and sell signals based on volatility channels and historical highs and lows.
Ichimoku Cloud: It leverages historical highs and lows to form the conversion and baseline, which are compared to assess market strength.
Parabolic SAR: A stop-and-reverse system that highlights potential reversals. It is based on time and price, offering traders potential reversal points.
Master Candle: It computes a score based on the confluence of all four indicators, adding another layer of confirmation.
🎨 Visualizations and User Experience:
The script's user interface is highly visual, with color-coded candlesticks plotted across multiple layers. Each indicator has its own color coding for bullish and bearish signals, ensuring clarity:
➤ Green for bullish signals.
➤ Red for bearish signals.
➤ Each candlestick layer represents a different indicator (e.g., Supertrend, HalfTrend, etc.), making it easy for the trader to isolate and interpret signals.
➤ The "Master Candle" provides an overarching view of the market by displaying a consolidated signal, which can reduce confusion from mixed indicator signals.
⭐ Settings and Customization:
The script is highly customizable, allowing users to adjust the settings for each indicator. Key customizable parameters include:
• Supertrend ATR Period and Factor
• HalfTrend Amplitude and Channel Deviation
• Ichimoku Conversion, Base, and Lagging Span Periods
• Parabolic SAR Start, Increment, and Maximum value
Additionally, users can toggle the visibility of each indicator and customize the look of the plot to suit their preferences.
⭐ Uniqueness of the Concept:
No repaints. This is the advanced representation and the combination of multiple indicators into a single script, along with a powerful "Master Candle" that aggregates them, makes this tool unique. Most scripts provide isolated indicator signals, while this one brings together four powerful indicators and visually simplifies the analysis. The non-overlay style and color-coded candlesticks offer traders an easy-to-understand, actionable visual cue, which stands out from traditional indicator overlays.
🚀 Conclusion:
This script is a comprehensive, multi-indicator trading tool suitable for traders looking for reliable trend-following and reversal detection. Its ability to provide an aggregated "Master Candle" signal reduces noise and aids in better decision-making. Customization options allow users to tailor it to their trading style, while its clear visualizations provide an excellent user experience.
DF: Horizontal Levels and Colors for NYSE TICK Chart
DF: Horizontal Levels and Colors for NYSE TICK Chart
This is intended to be added very specifically to your NYSE TICK chart.
This script creates a custom indicator designed to enhance the visual analysis
of market breadth through the NYSE TICK data. It features:
1. **Horizontal Levels**:
- **1300 and -1300**: Gray lines indicating extreme bullish and bearish conditions.
- **1000 and -1000**: Light red and green lines representing significant support and resistance zones.
- **850 and -850**: Customizable blue lines that can be adjusted according to user preferences.
**Zero Line**: A solid white line marking the neutral point, drawn prominently for quick reference.
2. **Color Fills**:
- Red fill between 1000 and 1300 to highlight extreme bullish sentiment.
- Green fill between -1000 and -1300 to signify extreme bearish sentiment.
3. **Exponential Moving Average (EMA)**:
- Calculated based on user-defined length (default set to 8).
- The EMA line's color dynamically adjusts based on its slope:
- White when trending upwards.
- Magenta when trending downwards, providing quick visual cues of market momentum.
Overall, this script serves as a powerful tool for traders seeking to visualize market trends, support and resistance levels, and market breadth through the NYSE TICK data, enhancing their decision-making process in trading.
Cubic Bezier Curve RSI [CBCR]Overview :
Introducing the Cubic Bézier Curve RSI – an innovative approach to smoothing the traditional RSI using cubic Bézier curves. This indicator provides traders with a smoother, adaptive version of the RSI that can help filter out noise and better highlight market trends.
Key Features:
Bézier Curve : the script uses cubic Bézier curves to create a smoothed version of the RSI, offering a more visually appealing and potentially more insightful representation of market momentum.
Customizable Settings: Users can adjust the Bézier Curve Length, Impact Factor, and color modes, allowing full customization of the smoothing effect and visualization.
Color-coded Trend Indicator: The smoothed RSI is displayed with colors that indicate potential bullish or bearish trends, helping traders quickly assess market conditions.
Overbought/Oversold Lines: Option to display overbought and oversold levels for better identification of market extremes.
Parameters:
RSI Length: Set the length for the traditional RSI calculation (default is 14).
Bézier Curve Length: Adjust the length of the Bézier curve used to smooth the RSI (default is 20).
Impact Factor: Control the influence of the Bézier smoothed values versus the original RSI values (default is 0.5, ranging from 0.0 to 1.0).
Overbought/Oversold Lines: Option to show overbought (default: 70) and oversold (default: 30) lines for easier identification of extreme conditions.
Color Mode: Choose between "Trend Following" and "Overbought/Oversold" modes for line color indication.
Display Settings: Color customization for bullish and bearish phases allows better visual differentiation.
How It Works:
The CBCR uses four control points derived from historical RSI values over a user-defined length. It then applies the cubic Bezier formula to generate a sequence of points representing a smoothed version of the RSI over this range.
The Bezier curve is recalculated each time a specific number of bars (as defined by the Bezier Curve Length) have passed, helping reduce noise while retaining key trend information.
The result is a smoothed RSI that combines the adaptability of cubic Bezier curves with the familiar oscillation of the RSI, making it potentially more robust for identifying shifts in market sentiment.
Visuals:
Smoothed RSI Line: Plotted on the indicator pane, the line changes color depending on the chosen color mode:
Trend Following Mode: Color changes based on whether the smoothed RSI is above or below the 50-level.
Overbought/Oversold Mode: Color changes based on whether the smoothed RSI is above the overbought level or below the oversold level.
Bullish Color: Configurable (default: cyan).
Bearish Color: Configurable (default: red).
Overbought/Oversold Lines: Horizontal lines at user-defined levels (default: 70 for overbought, 30 for oversold) for easy identification of market extremes.
Usage:
The CBCR can be used like a traditional RSI but with a smoother output that may help traders avoid false signals generated by sudden price spikes. For instance:
Look for crossovers around the 50 level as a signal for changing momentum.
Use the overbought and oversold levels to identify potential reversal zones.
Observe the color change of the line for an immediate visual cue on current sentiment.
Xtrender and TSI FusionXtrender and TSI Fusion Indicator
I created this indicator for myself. I was inspired by the indicators created by Bjorgum, Duyck and QuantTherapy and decided to create multiple indicators that either work well combined with their indicators or something new that applies some of their indicator concepts. I decided to share all of the indicator I have created because I believe in learning and earing together as a community. If you guys have any questions or suggestions write them.
Overview: The Xtrender and TSI Fusion Indicator is a powerful tool designed to help traders analyze market momentum, trends, and potential reversals. By combining Xtrender with the True Strength Index (TSI), this indicator provides a comprehensive view of market dynamics, making it easier to identify trading opportunities.
Image: Timeframe is set to daily
Features:
1.Xtrender Analysis:
Short-Term Xtrender: Visualizes short-term momentum using RSI-based calculations on EMA differences. This helps in identifying immediate market trends and pullbacks.
Image above: showcases Short-Term Xtrender
Xtrender T3: A smoothed version of the Xtrender that reduces noise and highlights significant trend changes.
Image above: showcases Xtrender T3 with Xtrender T3 color
2.TSI (True Strength Index):
TSI Value: Measures momentum by comparing price changes over two time periods, offering a clear view of trend strength.
TSI Signal Line: A smoothed version of the TSI value, used to generate buy and sell signals when crossed by the TSI.
Image: showcases TSI Value with TSI Signal Line
TSI Histogram: Shows the difference between the TSI and its signal line, highlighting potential reversals and trend continuations.
Image: showcases TSI Histogram
3.Color Coding and Visual Cues:
Trend Colors: The indicator uses dynamic colors to represent bullish or bearish conditions, making it easy to interpret market sentiment.
Background Color : The background changes color based on TSI signals, further aiding in visual trend analysis.
Image: showcases Background color and Zero line
How to Use
1.Xtrender Analysis:
Short-Term Xtrender: The short-term Xtrender is plotted as columns, changing color based on its direction and value. Green or lime indicates positive momentum, while red or maroon indicates negative momentum.
Xtrender T3: The Xtrender T3 line (black) represents a smoothed version of the short-term Xtrender, providing a clearer picture of the overall trend. The color of this line changes based on the Xtrender's value, helping you spot potential trend changes.
2.TSI (True Strength Index):
TSI Value and Signal Line: The TSI value is plotted as a line, with its color changing based on its relationship to the signal line. A crossover of the TSI above the signal line suggests a potential bullish move, while a crossover below indicates a bearish trend.
TSI Histogram: The histogram represents the difference between the TSI and its signal line. Positive values indicate bullish momentum, while negative values suggest bearish momentum.
3.Background Color:
The background color changes based on the TSI signal, with a greenish hue indicating bullish conditions and a reddish hue indicating bearish conditions. This provides a quick visual reference for market sentiment.
4.Zero Line:
A horizontal gray dotted line at the zero level helps you easily identify when the Xtrender or TSI crosses into positive or negative territory, signaling potential trend shifts.
Image above: Timeframe on daily with the individual elements combined
Example of Use:
•Trend Confirmation: Use the Xtrender and Xtrender T3 to confirm the direction of the trend. If both are aligned with the same color and direction, it increases the probability of a strong trend.
•Momentum Reversals: Watch for TSI crosses and histogram shifts to identify potential reversals. For example, a TSI crossover above its signal line with a corresponding change in the histogram from negative to positive could signal a buying opportunity.
•Pullbacks: Identify pullbacks within a trend by observing temporary shifts in the short-term Xtrender or TSI histogram. Use these signals to enter trades in the direction of the overall trend.
Image above: Showcases, Trend confirmation, reversal and pullbacks on daily timeframe.
Customization:
•TSI Speed: Choose between "Fast" and "Slow" TSI settings based on your trading style. Fast settings are more responsive to price changes, while slow settings offer smoother signals.
•Color Settings: Customize the colors for bullish, bearish, and neutral TSI conditions to match your personal preferences or chart theme.
This indicator is versatile and can be used for various trading strategies, from trend following to momentum trading, making it a valuable tool in any trader's arsenal.
My Scripts/Indicators/Ideas /Systems that I share are only for educational purposes
Delta Flow Profile [LuxAlgo]The Delta Flow Profile is a charting tool that tracks and visualizes money flow and the difference between buying and selling pressure accumulated within multiple price ranges over a specified period. It reveals the relationship between an asset's price and traders' willingness to buy or sell, helping traders identify significant price levels and analyze market activity.
The Normalized Profile displays the percentage of money flow at each price level relative to the maximum money flow level, enabling traders to easily compare levels and understand the relative importance of each price point in the context of overall trading activity.
🔶 USAGE
The Delta Flow Profile is made of two principal components with different usability, each one of them described in the sub-sections below.
🔹 Money Flow Profile
The Money Flow Profile illustrates the total buying and selling activity at different price ranges. By analyzing this profile, users can identify key price zones with substantial buying or selling pressure. These zones can often act as potential support or resistance.
The rows of the Money Flow Profile represent the trading activity at specific price ranges over a given period.
A normalized profile is included to compare each zone relative to the peak money flow using a percentage, with 100% indicating that a price range is the one with the highest accumulated money flow.
🔹 Delta Profile
The Delta Profile assesses the dominant sentiment (buying or selling) from volume delta at different price levels to gauge market sentiment and potential reversals.
Delta Profile rows with more significant buying or selling volume indicate dominance from one side of the market in that specific price area. Price coming back to that area might indicate willingness from a dominant side to further accumulate orders within it, potentially causing price to follow the direction established by this dominant side afterward.
The volume delta is determined from the user-selected Polarity Method, with "Bar Polarity" using candle sentiment to determine if a bar associated volume is buying or selling volume, and "Bar Buying/Selling Pressure" making use of the high/low price to obtain more precise results.
🔹 Level of Significance
Users can quickly highlight the price levels with the highest recorded money flow activity through the included "Level of Significance". Various display methods are included:
Developing: Show the price level with the highest recorded money flow activity spanning over the indicator calculation interval.
Level: Show the price level with the highest recorded money flow activity.
Row: Show the price zone with the highest recorded money flow activity.
These levels/zones can be used as potential support/resistance points and can serve as a reference of where prices might go next for market participants to accumulate orders.
🔶 SETTINGS
The script offers a range of customizable settings to tailor the analysis to your trading needs.
🔹 Calculation Settings
Money Flow Profile: Toggles the visibility of the Money Flow Profile.
Normalized: Toggles the visibility of the Normalized Profile.
Sentiment Profile: Toggles the visibility of the Sentiment Profile.
Polarity Method: Choose between Bar Polarity or Bar Buying/Selling Pressure to calculate the Sentiment Profile.
Level of Significance: Toggles the visibility of the level of significance line/zone.
Lookback Length / Fixed Range: Sets the lookback length.
Number of Rows: Specify how many rows each profile histogram will have.
🔹 Display Settings
Profile Width %: Alters the width of the rows in the histogram, relative to the profile length.
Profile Horizontal Offset: Enables moving the profile on the horizontal axis.
Profile Text: Toggles the visibility of profile texts, and alters the size of the text. Setting to Auto will keep the text within the box limits.
Currency: Extends the profile text with the traded currency.
Profile Price Levels: Toggles the visibility of the profile price levels.
🔶 RELATED SCRIPTS
Money-Flow-Profile
Volume-Profile-with-Node-Detection
Three Anchored Moving Averages (VWAP / SMA / EMA)
This indicator allows users to anchor three types of moving averages (Simple Moving Average (SMA), Exponential Moving Average (EMA), and Volume Weighted Average Price (VWAP)) to specific points in time (anchor points)
Key Features:
Select from three Moving Average Types:
Simple Moving Average (SMA): Averages the closing prices over a specified period.
Exponential Moving Average (EMA): Gives more weight to recent prices, making it more responsive to new information.
Volume Weighted Average Price (VWAP): Averages the price weighted by volume, useful for understanding the average price at which the asset has traded over a period.
Up to Three Anchor Points:
Users can set up to three different anchor points to calculate the moving averages from specific dates and times. This allows for analysis of price action starting from significant points or specific events. For example, you can anchor to the low and high of a move to identify key levels or to points where the price takes off from a previous anchored MA.
Customisable Sentiment Options:
Each anchor point can be associated with a sentiment input (Auto, Bull, Bear, None), which influences if the MAs are displayed as lines or zones/bands:
Auto: Automatically determines the sentiment based on whether anchor points are on pivot highs and lows. If anchored to a pivot high, the system will assume a bearish sentiment and display a red band or zone between the MA OHLC4 and High. Anchoring to a pivot low will display a green band (OHLC4 - Low).
Bull: Forces a bullish sentiment (Green Band - OHLC4 to Low)
Bear: Forces a bearish sentiment (Red Band - OHLC4 to High)
None: Ignores sentiment and displays a single line (OHLC4)
Chart Matching:
The indicator includes an option to display the moving averages only if the chart symbol matches a specified ticker. This feature ensures that the indicator is relevant to the specific asset being analysed.
How to Use the Indicator:
1. Set Anchor Points: When added to your chart, select three anchor points by point and click. If you only wish to anchor to a single point, click on that point three times and disable the other two in settings once the indicator is applied.
2. Select Moving Average Type: Choose between SMA, EMA, or VWAP using the dropdown menu. EMAs are the most responsive.
3. Enable/Disable Anchor Points: Use the checkboxes to enable or disable each anchor point.
4. Select Sentiment Type: Choose between Auto, Bull, Bear, or None.
5. Chart Matching: Optionally, specify a chart symbol to restrict the indicator's display to that particular asset.
6. Interpret the Plots: The indicator plots the high, mid, and low values of the selected moving average type from each anchor point. The fills between these plots help identify potential support and resistance zones. These should be used as points of interest for pullback reversals or potential continuation if the price breaks through.
Practical Applications:
Trend Analysis: Identify the overall trend direction from specific historical points.
Support and Resistance: Determine key dynamic support and resistance levels based on anchored moving averages.
Event-Based Analysis: Anchor the moving averages to significant events (e.g., earnings releases, economic data) to study their impact on price trends.
Multi Timeframe Analysis: Higher Timeframe Anchors can be used to identify longer term trend analysis. Switching to a lower timeframe for execution triggers at these points wont distort the MA levels as they are anchored to a specific point in time
Intraday or Swing Trading: trend analysis using anchor points can be used for any style of trading (Intraday / Swing / Invest). Use anchored levels as points of interest and wait for hints in price action to try and catch the next move.
