NIFTY Option Buy Strategy MASTER v1This script is a complete option buying strategy framework for NIFTY, designed for both intraday and positional swing trades.
🔹 Built using multi-timeframe analysis (EMAs, MACD, RSI)
🔹 Combines key macro filters: India VIX, PCR, FII/DII net cash flows
🔹 Supports both Call (CE) and Put (PE) entries
🔹 Includes manual input dashboard for real-time market context
🔹 Trade logic includes:
Bollinger Band breakouts
Volume confirmation
VWAP filtering
EMA crossover + MACD alignment
Resistance/support proximity from option chain (manual)
📈 Smart Trade Management:
Multi-target system (e.g., exit 50% at RR=1, 50% at RR=2)
Trailing stop-loss after target 1 hits
Automatic exit on SL/TP or reverse signals
Visual markers for all entries, exits, and stops
📊 Built-in Dashboard:
Displays India VIX, PCR, FII/DII flows, and S/R levels
Strike price selection (ATM + offset logic)
🧪 Ideal for backtesting, alerts, and real-time execution.
Can be used with alerts + webhook for automated trading or signal generation.
⚠️ Note: This script is for educational purposes only. Always test on paper trading before going live.
基本面分析
📊 VWAP + 시가선 + 필터 전략 (완성형)This is an indicator that generates trading signals by applying the market price + VWAP.
Quarterly Earnings with NPMThis indicator is designed in a way so that it can indicate the quarterly earnings and also it can show us the change in sales and net profit margin as shown by Mark Minervini in his classes.
Session HighlightsCrypto relevant global equity market open/close indicator, high opacity background highlights follow the following color scheme & daily time ranges (times in EST):
Orange: 8:00 PM to 9:30 PM (Sunday - Thursday): Japan/South Korea
Yellow: 9:30 PM to +1D 4:00 AM (Sunday - Thursday): Hong Kong
Aqua: 8:00 AM to 9:30 AM (Monday - Friday): US Premarket / Macro Data Release
Blue: 9:30 AM to 4:00 PM (Monday - Friday): US
White: 4:00 PM to +2D 6:00 PM (Friday - Sunday): Weekend
*Market Holidays not accounted for
GStrategy XRP 4hRSI + Smart Money Trading Strategy
This strategy combines RSI (Relative Strength Index) with Smart Money detection to identify high-probability reversal trades in trending markets. It uses strict entry/exit rules with a 10% hard stop-loss to manage risk.
Strategy Logic
1. Entry Conditions
Long Entry (Buy):
RSI < 30 (Oversold condition)
Smart Money Confirmation:
Bullish candle (close > open)
Volume > 35-period SMA (unusual buying pressure)
Price hits a 5-bar low (potential reversal level)
Short Entry (Sell):
RSI > 70 (Overbought condition)
Smart Money Confirmation:
Bearish candle (close < open)
Volume > 20-period SMA (unusual selling pressure)
Price hits a 5-bar high (potential rejection level)
2. Exit Conditions
Long Exit: RSI ≥ 70 (Take profit at overbought)
Short Exit: RSI ≤ 40 (Take profit at mid-level)
Stop-Loss: Hard 10% stop on all trades
3. Position Management
No overlapping trades (only 1 position at a time).
Stop-loss visualized on the chart (red line).
Key Features
✅ RSI Filter: Avoids false reversals by requiring extreme RSI levels.
✅ Smart Money Detection: Confirms institutional activity via volume + price action.
✅ Asymmetric Exits:
Longs exit at RSI 70 (full overbought).
Shorts exit earlier at RSI 40 (conservative profit-taking).
✅ Strict Risk Control: 10% stop-loss prevents large drawdowns.
Indicators Used
RSI (14-period)
Volume SMA (20 for shorts, 35 for longs)
5-bar High/Low for price extremes.
S&P500 Long nach X roten Tagen)The strategy buys the S&P future after 4 consecutive red days and an elevated VIX index, and exits either time-based, with a trailing stop, or after a predefined holding period.
COT-Index-NocTradingCOT Index Indicator
The COT Index Indicator is a powerful tool designed to visualize the Commitment of Traders (COT) data and offer insights into market sentiment. The COT Index is a measurement of the relative positioning of commercial traders versus non-commercial and retail traders in the futures market. It is widely used to identify potential market reversals by observing the extremes in trader positioning.
Customizable Timeframe: The indicator allows you to choose a custom time interval (in months) to visualize the COT data, making it flexible to fit different trading styles and strategies.
How to Use:
Visualize Market Sentiment: A COT Index near extremes (close to 0 or 100) can indicate potential turning points in the market, as it reflects extreme positioning of different market participant groups.
Adjust the Time Interval: The ability to adjust the time interval (in months) gives traders the flexibility to analyze the market over different periods, which can be useful in detecting longer-term trends or short-term shifts in sentiment.
Combine with Other Indicators: To enhance your analysis, combine the COT Index with your technical analysis.
This tool can serve as an invaluable addition to your trading strategy, providing a deeper understanding of the market dynamics and the positioning of major market participants.
PER Bands (Auto EPS)PER Bands Indicator - Technical Specification
Function
This PineScript v6 overlay indicator displays horizontal price bands based on Price-to-Earnings Ratio multiples. The indicator calculates price levels by multiplying earnings per share values by user-defined PER multiples, then plots these levels as horizontal lines on the chart.
Data Sources
The script attempts to automatically retrieve earnings per share data using TradingView's `request.financial()` function. The system first queries trailing twelve months EPS data, then annual EPS data if TTM is unavailable. When automatic retrieval fails or returns zero values, the indicator uses manually entered EPS values as a fallback.
Configuration Options
Users can configure five separate PER multiples (default values: 10x, 15x, 20x, 25x, 30x). Each band supports individual color customization and adjustable line width settings from 1 to 5 pixels. The indicator includes toggles for band visibility and optional fill areas between adjacent bands with 95% transparency.
Visual Components
The indicator plots five horizontal lines representing different PER valuation levels. Optional fill areas create colored zones between consecutive bands. A data table in the top-right corner displays current EPS source, EPS value, current PER ratio, and calculated price levels for each configured multiple.
Calculation Method
The indicator performs the following calculations:
- Band Price = Current EPS × PER Multiple
- Current PER = Current Price ÷ Current EPS
These calculations update on each bar close using the most recent available EPS data.
Alert System
The script includes alert conditions for price crossovers above the lowest PER band and crossunders below the highest PER band. Additional alert conditions can be configured for any band level through the alert creation interface.
Debug Features
Debug mode displays character markers on the chart indicating when TTM or annual EPS data is available. This feature helps users verify which data source the indicator is using for calculations.
Data Requirements
The indicator requires positive, non-zero EPS values to function correctly. Stocks with negative earnings or zero EPS will display "N/A" for current PER calculations, though bands will still plot using the manual EPS input value.
Exchange Compatibility
Automatic EPS data availability varies by exchange. United States equity markets typically provide comprehensive fundamental data coverage. International markets may have limited automatic data availability, requiring manual EPS input for accurate calculations.
Technical Limitations
The indicator cannot fetch real-time EPS updates and relies on TradingView's fundamental data refresh schedule. Historical EPS changes are not reflected in past band positions, as the indicator uses current EPS values for all historical calculations.
Display Settings
The information table shows EPS source type (TTM Auto, Annual Auto, Manual, or Manual Fallback), allowing users to verify data accuracy. The table refreshes only on the last bar to optimize performance and reduce computational overhead.
Code Structure
Built using PineScript v6 syntax with proper scope management for plot and fill functions. The script uses global scope for all plot declarations and conditional logic within plot parameters to handle visibility settings.
Version Requirements
This indicator requires TradingView Pine Script version 6 or later due to the use of `request.financial()` functions and updated syntax requirements for plot titles and fill operations.
Ethereum Rainbow Chart (9 Levels with Legend)The Ethereum Rainbow Chart is a long-term, color-coded chart that displays Ethereum’s price on a logarithmic scale to show historical trends and growth patterns. It uses colored bands to highlight different price zones, helping to visualize how ETH’s price has moved over time without focusing on short-term fluctuations.
Advanced Petroleum Market Model (APMM)Advanced Petroleum Market Model (APMM): A Multi-Factor Fundamental Analysis Framework for Oil Market Assessment
## 1. Introduction
The petroleum market represents one of the most complex and globally significant commodity markets, characterized by intricate supply-demand dynamics, geopolitical influences, and substantial price volatility (Hamilton, 2009). Traditional fundamental analysis approaches often struggle to synthesize the multitude of relevant indicators into actionable insights due to data heterogeneity, temporal misalignment, and subjective weighting schemes (Baumeister & Kilian, 2016).
The Advanced Petroleum Market Model addresses these limitations through a systematic, quantitative approach that integrates 16 verified fundamental indicators across five critical market dimensions. The model builds upon established financial engineering principles while incorporating petroleum-specific market dynamics and adaptive learning mechanisms.
## 2. Theoretical Framework
### 2.1 Market Efficiency and Information Integration
The model operates under the assumption of semi-strong market efficiency, where fundamental information is gradually incorporated into prices with varying degrees of lag (Fama, 1970). The petroleum market's unique characteristics, including storage costs, transportation constraints, and geopolitical risk premiums, create opportunities for fundamental analysis to provide predictive value (Kilian, 2009).
### 2.2 Multi-Factor Asset Pricing Theory
Drawing from Ross's (1976) Arbitrage Pricing Theory, the model treats petroleum prices as driven by multiple systematic risk factors. The five-factor decomposition (Supply, Inventory, Demand, Trade, Sentiment) represents economically meaningful sources of systematic risk in petroleum markets (Chen et al., 1986).
## 3. Methodology
### 3.1 Data Sources and Quality Framework
The model integrates 16 fundamental indicators sourced from verified TradingView economic data feeds:
Supply Indicators:
- US Oil Production (ECONOMICS:USCOP)
- US Oil Rigs Count (ECONOMICS:USCOR)
- API Crude Runs (ECONOMICS:USACR)
Inventory Indicators:
- US Crude Stock Changes (ECONOMICS:USCOSC)
- Cushing Stocks (ECONOMICS:USCCOS)
- API Crude Stocks (ECONOMICS:USCSC)
- API Gasoline Stocks (ECONOMICS:USGS)
- API Distillate Stocks (ECONOMICS:USDS)
Demand Indicators:
- Refinery Crude Runs (ECONOMICS:USRCR)
- Gasoline Production (ECONOMICS:USGPRO)
- Distillate Production (ECONOMICS:USDFP)
- Industrial Production Index (FRED:INDPRO)
Trade Indicators:
- US Crude Imports (ECONOMICS:USCOI)
- US Oil Exports (ECONOMICS:USOE)
- API Crude Imports (ECONOMICS:USCI)
- Dollar Index (TVC:DXY)
Sentiment Indicators:
- Oil Volatility Index (CBOE:OVX)
### 3.2 Data Quality Monitoring System
Following best practices in quantitative finance (Lopez de Prado, 2018), the model implements comprehensive data quality monitoring:
Data Quality Score = Σ(Individual Indicator Validity) / Total Indicators
Where validity is determined by:
- Non-null data availability
- Positive value validation
- Temporal consistency checks
### 3.3 Statistical Normalization Framework
#### 3.3.1 Z-Score Normalization
The model employs robust Z-score normalization as established by Sharpe (1994) for cross-indicator comparability:
Z_i,t = (X_i,t - μ_i) / σ_i
Where:
- X_i,t = Raw value of indicator i at time t
- μ_i = Sample mean of indicator i
- σ_i = Sample standard deviation of indicator i
Z-scores are capped at ±3 to mitigate outlier influence (Tukey, 1977).
#### 3.3.2 Percentile Rank Transformation
For intuitive interpretation, Z-scores are converted to percentile ranks following the methodology of Conover (1999):
Percentile_Rank = (Number of values < current_value) / Total_observations × 100
### 3.4 Exponential Smoothing Framework
Signal smoothing employs exponential weighted moving averages (Brown, 1963) with adaptive alpha parameter:
S_t = α × X_t + (1-α) × S_{t-1}
Where α = 2/(N+1) and N represents the smoothing period.
### 3.5 Dynamic Threshold Optimization
The model implements adaptive thresholds using Bollinger Band methodology (Bollinger, 1992):
Dynamic_Threshold = μ ± (k × σ)
Where k is the threshold multiplier adjusted for market volatility regime.
### 3.6 Composite Score Calculation
The fundamental score integrates component scores through weighted averaging:
Fundamental_Score = Σ(w_i × Score_i × Quality_i)
Where:
- w_i = Normalized component weight
- Score_i = Component fundamental score
- Quality_i = Data quality adjustment factor
## 4. Implementation Architecture
### 4.1 Adaptive Parameter Framework
The model incorporates regime-specific adjustments based on market volatility:
Volatility_Regime = σ_price / μ_price × 100
High volatility regimes (>25%) trigger enhanced weighting for inventory and sentiment components, reflecting increased market sensitivity to supply disruptions and psychological factors.
### 4.2 Data Synchronization Protocol
Given varying publication frequencies (daily, weekly, monthly), the model employs forward-fill synchronization to maintain temporal alignment across all indicators.
### 4.3 Quality-Adjusted Scoring
Component scores are adjusted for data quality to prevent degraded inputs from contaminating the composite signal:
Adjusted_Score = Raw_Score × Quality_Factor + 50 × (1 - Quality_Factor)
This formulation ensures that poor-quality data reverts toward neutral (50) rather than contributing noise.
## 5. Usage Guidelines and Best Practices
### 5.1 Configuration Recommendations
For Short-term Analysis (1-4 weeks):
- Lookback Period: 26 weeks
- Smoothing Length: 3-5 periods
- Confidence Period: 13 weeks
- Increase inventory and sentiment weights
For Medium-term Analysis (1-3 months):
- Lookback Period: 52 weeks
- Smoothing Length: 5-8 periods
- Confidence Period: 26 weeks
- Balanced component weights
For Long-term Analysis (3+ months):
- Lookback Period: 104 weeks
- Smoothing Length: 8-12 periods
- Confidence Period: 52 weeks
- Increase supply and demand weights
### 5.2 Signal Interpretation Framework
Bullish Signals (Score > 70):
- Fundamental conditions favor price appreciation
- Consider long positions or reduced short exposure
- Monitor for trend confirmation across multiple timeframes
Bearish Signals (Score < 30):
- Fundamental conditions suggest price weakness
- Consider short positions or reduced long exposure
- Evaluate downside protection strategies
Neutral Range (30-70):
- Mixed fundamental environment
- Favor range-bound or volatility strategies
- Wait for clearer directional signals
### 5.3 Risk Management Considerations
1. Data Quality Monitoring: Continuously monitor the data quality dashboard. Scores below 75% warrant increased caution.
2. Regime Awareness: Adjust position sizing based on volatility regime indicators. High volatility periods require reduced exposure.
3. Correlation Analysis: Monitor correlation with crude oil prices to validate model effectiveness.
4. Fundamental-Technical Divergence: Pay attention when fundamental signals diverge from technical indicators, as this may signal regime changes.
### 5.4 Alert System Optimization
Configure alerts conservatively to avoid false signals:
- Set alert threshold at 75+ for high-confidence signals
- Enable data quality warnings to maintain system integrity
- Use trend reversal alerts for early regime change detection
## 6. Model Validation and Performance Metrics
### 6.1 Statistical Validation
The model's statistical robustness is ensured through:
- Out-of-sample testing protocols
- Rolling window validation
- Bootstrap confidence intervals
- Regime-specific performance analysis
### 6.2 Economic Validation
Fundamental accuracy is validated against:
- Energy Information Administration (EIA) official reports
- International Energy Agency (IEA) market assessments
- Commercial inventory data verification
## 7. Limitations and Considerations
### 7.1 Model Limitations
1. Data Dependency: Model performance is contingent on data availability and quality from external sources.
2. US Market Focus: Primary data sources are US-centric, potentially limiting global applicability.
3. Lag Effects: Some fundamental indicators exhibit publication lags that may delay signal generation.
4. Regime Shifts: Structural market changes may require model recalibration.
### 7.2 Market Environment Considerations
The model is optimized for normal market conditions. During extreme events (e.g., geopolitical crises, pandemics), additional qualitative factors should be considered alongside quantitative signals.
## References
Baumeister, C., & Kilian, L. (2016). Forty years of oil price fluctuations: Why the price of oil may still surprise us. *Journal of Economic Perspectives*, 30(1), 139-160.
Bollinger, J. (1992). *Bollinger on Bollinger Bands*. McGraw-Hill.
Brown, R. G. (1963). *Smoothing, Forecasting and Prediction of Discrete Time Series*. Prentice-Hall.
Chen, N. F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. *Journal of Business*, 59(3), 383-403.
Conover, W. J. (1999). *Practical Nonparametric Statistics* (3rd ed.). John Wiley & Sons.
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. *Journal of Finance*, 25(2), 383-417.
Hamilton, J. D. (2009). Understanding crude oil prices. *Energy Journal*, 30(2), 179-206.
Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. *American Economic Review*, 99(3), 1053-1069.
Lopez de Prado, M. (2018). *Advances in Financial Machine Learning*. John Wiley & Sons.
Ross, S. A. (1976). The arbitrage theory of capital asset pricing. *Journal of Economic Theory*, 13(3), 341-360.
Sharpe, W. F. (1994). The Sharpe ratio. *Journal of Portfolio Management*, 21(1), 49-58.
Tukey, J. W. (1977). *Exploratory Data Analysis*. Addison-Wesley.
Swing Buy/Sell with ATR FilterCustomization Tips
atrMult: Increase to reduce false signals (e.g., 1.5 = only take trades in stronger volatility)
swingLen: You can adjust this to 2 or 3 for smoother but fewer signals
Add MA or RSI filter: For trend-following or overbought/oversold filtering
Swing High/Low (Length 1)How It Works
swing High is true when the previous bar's high is higher than both its neighbors.
swing Low is true when the previous bar's low is lower than both its neighbors.
Shapes are plotted at the bar where the swing actually occurred (offset by -1 to align properly).
Tips:
This is very sensitive — you might want to combine it with filters like:
ATR filter to ignore small swings
volume filter to validate significant moves
For more meaningful swings, try increasing the window size (e.g., length = 3 or 5).
HMA 200/150 Trading StrategyThis strategy uses the Hull Moving Average (HMA) to generate buy and sell signals based on price action relative to the HMA 200, with take profit signals based on the HMA 150. It includes a configurable date range for backtesting, allowing users to test the strategy over specific periods.
How It Works
Entry Signals:
Buy: Triggered when the price closes above the HMA 200.
Sell: Triggered when the price closes below the HMA 200.
Take Profit Signals:
Buy Take Profit: Exits the buy position when the price closes below the HMA 150.
Sell Take Profit: Exits the sell position when the price closes above the HMA 150.
Backtest Period: Users can set start and end dates (year, month, day) to limit the strategy’s execution to a specific time range for backtesting purposes.
Settings
Start Year/Month/Day: Set the start date for backtesting (default: January 1, 2023).
End Year/Month/Day: Set the end date for backtesting (default: December 31, 2025).
Visuals
HMA 200: Plotted in blue, used for entry signals.
HMA 150: Plotted in orange, used for take profit signals.
Buy Signal: Green triangle below the bar.
Sell Signal: Red triangle above the bar.
Take Profit Signals: Yellow diamonds (above for buy TP, below for sell TP).
Usage
Add the strategy to your chart.
Adjust the backtest period in the settings to analyze performance over a specific time frame.
Monitor the plotted HMA lines and signals for potential trade entries and exits.
Backtest on your preferred timeframe and asset to evaluate performance.
Notes
This strategy is designed for trend-following and works best in trending markets.
Always test the strategy on a demo account before using it in live trading.
Performance may vary depending on the asset, timeframe, and market conditions.
Disclaimer
This strategy is for educational purposes only and should not be considered financial advice. Trading involves risk, and past performance is not indicative of future results. Always conduct your own research and risk management.
MSI | Algo ApprenticeThree In One Indicator:
- Michael's EMA (12/21)
- SuperTrend
- Impulsive Candle Detector
GM! LFG!
BTC Thermocap Z-ScoreBTC Thermocap Indicator Overview
The BTC Thermocap is a specialized on-chain ratio indicator designed to provide deeper insight into Bitcoin's market valuation relative to its cumulative issuance. By comparing the current market price of Bitcoin to the total value of all BTC ever mined (also known as "thermocap"), this indicator helps identify potential overvaluation or undervaluation periods within the Bitcoin market cycle.
Key Features and Customizable Inputs:
Moving Average Length (MA Length)
Moving Average Type (MA Type) - SMA or EMA
Z-Score Calculation Length
Z-Score Toggle (Use Z-Score)
Last Week's APM FX pairs only📖 Description:
This script is designed for precision-focused forex traders who understand the power of volatility measurement. It calculates the Average Price Movement (APM) from the previous week by measuring the full wick-to-wick range (high to low) of each daily candle from Monday to Friday, then averaging them across the five sessions.
🔍 Core Features:
✅ Accurate APM Calculation:
Pulls daily high-low ranges from last week using locked daily timeframe data, ensuring stable and reliable pip range measurements across all chart timeframes.
✅ Auto-Adjusts for Pip Precision:
Detects whether the pair is JPY-based or not, and automatically adjusts the pip multiplier (100 for JPY pairs, 10,000 for all others) to give true pip values.
✅ Visual Display in Clean UI:
The calculated APM is displayed in a non-intrusive, fixed-position table in the top-right corner of the chart — making it ideal for traders who want continuous awareness of recent market behavior without visual clutter.
✅ Timeless on Any Timeframe:
Whether you’re on the 1-minute chart or the daily, the script remains anchored and accurate because it sources raw data from the daily chart internally.
📈 How It Helps Your Trading:
🧠 Volatility Awareness: Know how much a pair typically moves per day based on recent historical behavior — great for range analysis, target setting, or session biasing.
📊 Week-to-Week Comparison: Use it as a benchmark to compare current volatility to last week’s. Great for identifying if the market is expanding, contracting, or stabilizing.
🔗 Perfect for Confluence: APM can serve as a supporting metric when combined with order blocks, liquidity zones, news catalysts, or other volatility-based tools like ATR.
🛠️ Ideal For:
Professional and prop firm traders
Institutional model traders (ICT-style or SMC)
Volatility scalpers and range-based intraday traders
Anyone building a rules-based trading system with data-driven logic
🔐 Clean. Reliable. Focused.
If you value structure, volatility awareness, and pip precision — this tool belongs in your chart workspace.
5-Day APM for Forex PairsThis script calculates the 5-Day Average Pip Movement (APM) for major Forex pairs.
It displays the average daily range (in pips) over the past 5 trading days using true high-low price movement.
The script is optimized for clarity and minimalism — showing a single floating label on the main chart for pairs like GBPUSD, USDJPY, EURUSD, etc.
Automatically adjusts pip calculation for JPY pairs (×100) and other pairs (×10000).
✅ Great for identifying high-volatility vs low-volatility conditions
✅ Clean design with no clutter
✅ Only works on major FX pairs (whitelisted)
MVRV Ratio [Alpha Extract]The MVRV Ratio Indicator provides valuable insights into Bitcoin market cycles by tracking the relationship between market value and realized value. This powerful on-chain metric helps traders identify potential market tops and bottoms, offering clear buy and sell signals based on historical patterns of Bitcoin valuation.
🔶 CALCULATION The indicator processes MVRV ratio data through several analytical methods:
Raw MVRV Data: Collects MVRV data directly from INTOTHEBLOCK for Bitcoin
Optional Smoothing: Applies simple moving average (SMA) to reduce noise
Status Classification: Categorizes market conditions into four distinct states
Signal Generation: Produces trading signals based on MVRV thresholds
Price Estimation: Calculates estimated realized price (Current price / MVRV ratio)
Historical Context: Compares current values to historical extremes
Formula:
MVRV Ratio = Market Value / Realized Value
Smoothed MVRV = SMA(MVRV Ratio, Smoothing Length)
Estimated Realized Price = Current Price / MVRV Ratio
Distance to Top = ((3.5 / MVRV Ratio) - 1) * 100
Distance to Bottom = ((MVRV Ratio / 0.8) - 1) * 100
🔶 DETAILS Visual Features:
MVRV Plot: Color-coded line showing current MVRV value (red for overvalued, orange for moderately overvalued, blue for fair value, teal for undervalued)
Reference Levels: Horizontal lines indicating key MVRV thresholds (3.5, 2.5, 1.0, 0.8)
Zone Highlighting: Background color changes to highlight extreme market conditions (red for potentially overvalued, blue for potentially undervalued)
Information Table: Comprehensive dashboard showing current MVRV value, market status, trading signal, price information, and historical context
Interpretation:
MVRV ≥ 3.5: Potential market top, strong sell signal
MVRV ≥ 2.5: Overvalued market, consider selling
MVRV 1.5-2.5: Neutral market conditions
MVRV 1.0-1.5: Fair value, consider buying
MVRV < 1.0: Potential market bottom, strong buy signal
🔶 EXAMPLES
Market Top Identification: When MVRV ratio exceeds 3.5, the indicator signals potential market tops, highlighting periods where Bitcoin may be significantly overvalued.
Example: During bull market peaks, MVRV exceeding 3.5 has historically preceded major corrections, helping traders time their exits.
Bottom Detection: MVRV values below 1.0, especially approaching 0.8, have historically marked excellent buying opportunities.
Example: During bear market bottoms, MVRV falling below 1.0 has identified the most profitable entry points for long-term Bitcoin accumulation.
Tracking Market Cycles: The indicator provides a clear visualization of Bitcoin's market cycles from undervalued to overvalued states.
Example: Following the progression of MVRV from below 1.0 through fair value and eventually to overvalued territory helps traders position themselves appropriately throughout Bitcoin's market cycle.
Realized Price Support: The estimated realized price often acts as a significant
support/resistance level during market transitions.
Example: During corrections, price often finds support near the realized price level calculated by the indicator, providing potential entry points.
🔶 SETTINGS
Customization Options:
Smoothing: Toggle smoothing option and adjust smoothing length (1-50)
Table Display: Show/hide the information table
Table Position: Choose between top right, top left, bottom right, or bottom left positions
Visual Elements: All plots, lines, and background highlights can be customized for color and style
The MVRV Ratio Indicator provides traders with a powerful on-chain metric to identify potential market tops and bottoms in Bitcoin. By tracking the relationship between market value and realized value, this indicator helps identify periods of overvaluation and undervaluation, offering clear buy and sell signals based on historical patterns. The comprehensive information table delivers valuable context about current market conditions, helping traders make more informed decisions about market positioning throughout Bitcoin's cyclical patterns.
Dr.Avinash Talele quarterly earnings, VCP and multibagger trakerDr. Avinash Talele Quarterly Earnings, VCP and Multibagger Tracker.
📊 Comprehensive Quarterly Analysis Tool for Multibagger Stock Discovery
This advanced Pine Script indicator provides a complete financial snapshot directly on your chart, designed to help traders and investors identify potential multibagger stocks and VCP (Volatility Contraction Pattern) setups with precision.
🎯 Key Features:
📈 8-Quarter Financial Data Display:
EPS (Earnings Per Share) - Track profitability trends
Sales Revenue - Monitor business growth
QoQ% (Quarter-over-Quarter Growth) - Spot acceleration/deceleration
ROE (Return on Equity) - Assess management efficiency
OPM (Operating Profit Margin) - Evaluate operational excellence
💰 Market Metrics:
Market Cap - Current company valuation
P/E Ratio - Valuation assessment
Free Float - Liquidity indicator
📊 Technical Positioning:
% Down from 52-Week High - Identify potential bottoming patterns
% Up from 52-Week Low - Track momentum from lows
Turnover Data (1D & 50D Average) - Volume analysis
ADR% (Average Daily Range) - Volatility measurement
Relative Volume% - Institutional interest indicator
🚀 How It Helps Find Multibaggers:
1. Growth Acceleration Detection:
Consistent EPS Growth: Identifies companies with accelerating earnings
Revenue Momentum: Tracks sales growth patterns quarter-over-quarter
Margin Expansion: Spots improving operational efficiency through OPM trends
2. VCP Pattern Recognition:
Volatility Contraction: ADR% helps identify tightening price ranges
Volume Analysis: Relative volume shows institutional accumulation
Distance from Highs: Tracks healthy pullbacks in uptrends
3. Fundamental Strength Validation:
ROE Trends: Ensures management is efficiently using shareholder capital
Debt-Free Growth: High ROE with growing margins indicates quality growth
Scalability: Revenue growth vs. margin expansion analysis
4. Entry Timing Optimization:
52-Week Positioning: Enter near lows, avoid near highs
Volume Confirmation: High relative volume confirms breakout potential
Valuation Check: P/E ratio helps avoid overvalued entries
💡 Multibagger Characteristics to Look For:
✅ Consistent 15-20%+ EPS growth across multiple quarters
✅ Accelerating revenue growth with QoQ% improvements
✅ ROE above 15% and expanding
✅ Operating margins improving over time
✅ Low debt (indicated by high ROE with growing profits)
✅ Strong cash generation (reflected in consistent growth metrics)
✅ 20-40% down from 52-week highs (ideal entry zones)
✅ Above-average volume during consolidation phases
🎨 Visual Design:
Clean white table with black borders for maximum readability
Color-coded QoQ% changes (Green = Growth, Red = Decline)
Centered positioning for easy chart analysis
8-quarter historical view for trend identification
📋 Perfect For:
Long-term investors seeking multibagger opportunities
Growth stock enthusiasts tracking earnings acceleration
VCP pattern traders looking for breakout candidates
Fundamental analysts requiring quick financial snapshots
Swing traders timing entries in growth stocks
⚡ Quick Setup:
Simply add the indicator to any NSE/BSE stock chart and instantly view comprehensive quarterly data. The table updates automatically with the latest financial information, making it perfect for screening and monitoring your watchlist.
🔍 Start identifying your next multibagger today with this powerful combination of fundamental analysis and technical positioning data!
Disclaimer: This indicator is for educational and analysis purposes. Always conduct thorough research and consider risk management before making investment decisions.
Smooth BTCSPL [GiudiceQuantico] – Dual Smoothed MAsSmooth BTCSPL – Dual Smoothed MAs
What it measures
• % of Bitcoin addresses in profit vs loss (on-chain tickers).
• Spread = profit % − loss % → quick aggregate-sentiment gauge.
• Optional alpha-decay normalisation ⇒ keeps the curve on a 0-1 scale across cycles.
User inputs
• Use Alpha-Decay Adjusted Input (true/false).
• Fast MA – type (SMA / EMA / WMA / VWMA) & length (default 100).
• Slow MA – type & length (default 200).
• Colours – Bullish (#00ffbb) / Bearish (magenta).
Computation flow
1. Fetch daily on-chain series.
2. Build raw spread.
3. If alpha-decay enabled:
alpha = (rawSpread − 140-week rolling min) / (1 − rolling min).
4. Smooth chosen base with Fast & Slow MAs.
5. Bullish when Fast > Slow, bearish otherwise.
6. Bars tinted with the same bull/bear colour.
How to read
• Fast crosses above Slow → rising “addresses-in-profit” momentum → bullish bias.
• Fast crosses below Slow → stress / capitulation risk.
• Price-indicator divergences can flag exhaustion or hidden accumulation.
Tips
• Keep in a separate pane (overlay = false); bar-colouring still shows on price chart.
• Shorter lengths for swing trades, longer for macro outlook.
• Combine with funding rates, NUPL or simple price-MA crossovers for confirmation.
Liquidity LinesLiquidity Lines Indicator
This advanced TradingView indicator identifies key liquidity zones on your price chart by detecting bullish and bearish engulfing candles, which often signify areas where liquidity accumulates. It helps traders visually spot potential support and resistance levels created by market participants’ stop-loss orders or pending orders.
Key Features :
-Aggregated Bars Option : Smooth out price data by grouping bars together, enabling clearer liquidity zone identification on higher timeframes or noisy charts.
-Upper Liquidity Lines : Displays dashed lines at recent highs where bearish engulfing patterns indicate potential resistance or supply zones.
-Lower Liquidity Lines : Displays dashed lines at recent lows where bullish engulfing patterns suggest potential support or demand zones.
-Customizable Colors : Choose your preferred colors for bullish (default black) and bearish (default white) liquidity lines for better visual distinction.
-Automatic Line Cleanup : Maintains chart clarity by automatically removing old liquidity lines after a configurable limit.
-Dynamic Alerts : Trigger alerts when price breaches upper or lower liquidity lines, signaling potential breakout or reversal opportunities.
Use Cases :
S&P 500 & Normalized CAPE Z-Score AnalyzerThis macro-focused indicator visualizes the historical valuation of the U.S. equity market using the CAPE ratio (Shiller P/E), normalized over its long-term average and standard deviations. It helps traders and investors identify overvaluation and undervaluation zones over time, combining both statistical signals and historical context.
💡 Why It’s Useful
This indicator is ideal for macro traders and long-term investors looking to contextualize equity valuations across decades. It helps identify statistical extremes in valuation by referencing the standard deviation of the CAPE ratio relative to its long-term mean. The overlay of S&P 500 price with valuation zones provides a visual confirmation tool for macro decisions or timing insights.
It includes:
✅ Three display modes:
-S&P 500 (color-coded by CAPE valuation zone)
-Normalized CAPE (vs. long-term mean)
-CAPE Z-Score (standardized measure)
🎯 How to Interpret
Dynamic coloring of the S&P 500 price based on CAPE valuation:
🔴 Z > +2σ → Highly Overvalued
🟠 Z > +1σ → Overvalued
⚪ -1σ < Z < +1σ → Neutral
🟢 Z < -1σ → Undervalued
✅ Z < -2σ → Strong Buy Zone
-Live valuation label showing the current CAPE, Z-score, and zone.
-Macro event shading: major historical events (e.g. Great Depression, Oil Crisis, Dot-com Bubble, COVID Crash) are shaded on the chart for context.
✅ Built-in alerts:
CAPE > +2σ → Potential risk zone
CAPE < -2σ → Potential opportunity zone
📊 Use Cases
This indicator is ideal for:
🧠 Macro traders seeking long-term valuation extremes.
📈 Portfolio managers monitoring systemic valuation risk.
🏛️ Long-term investors timing strategic allocation shifts.
🧪 How It Works
CAPE ratio (Shiller PE) is retrieved from Quandl (MULTPL/SHILLER_PE_RATIO_MONTH).
The script calculates the long-term average and standard deviation of CAPE.
The Z-score is computed as:
(CAPE - Mean) / Standard Deviation
Users can switch between:
S&P 500 chart, color-coded by CAPE valuation zones.
Normalized CAPE, centered around zero (historic mean).
CAPE Z-score, showing statistical positioning directly.
Visual bands represent +1σ, +2σ, -1σ, -2σ thresholds.
You can switch between modes using the “Display” dropdown in the settings panel.
📊 Data Sources
CAPE: MULTPL/SHILLER_PE_RATIO_MONTH via Quandl
S&P 500: Monthly close prices of SPX (TradingView data)
All data updated on monthly resolution
This is not a repackaged built-in or autogenerated script. It’s a custom-built and interactive indicator designed for educational and analytical use in macroeconomic valuation studies.
