PLR-Z For Loop🧠 Overview
PLR-Z For Loop is a trend-following indicator built on the Power Law Residual Z-score model of Bitcoin price behavior. By measuring how far price deviates from a long-term power law regression and applying a custom scoring loop, this tool identifies consistent directional pressure in market structure. Designed for BTC, this indicator helps traders align with macro trends.
🧩 Key Features
Power Law Residual Model: Tracks deviations of BTC price from its long-term logarithmic growth curve.
Z-Score Normalization: Applies long-horizon statistical normalization (400/1460 bars) to smooth residual deviations into a usable trend signal.
Loop-Based Trend Filter: Iteratively scores how often the current Z-score exceeds prior values, emphasizing trend persistence over volatility.
Optional Smoothing: Toggleable exponential smoothing helps filter noise in choppier market conditions.
Directional Regime Coloring: Aqua (bullish) and Red (bearish) visuals reinforce trend alignment across plots and candles.
🔍 How It Works
Power Law Curve: Price is compared against a logarithmic regression model fitted to historical BTC price evolution (starting July 2010), defining structural support, resistance, and centerline levels.
Residual Z-Score: The residual is calculated as the log-difference between price and the power law center.
This residual is then normalized using a rolling mean (400 days) and standard deviation (1460 days) to create a long-term Z-score.
Loop Scoring Logic:
A loop compares the current Z-score to a configurable number of past bars.
Each higher comparison adds +1, and each lower one subtracts -1.
The result is a trend persistence score (z_loop) that grows with consistent directional momentum.
Smoothing Option: A user-defined EMA smooths the score, if enabled, to reduce short-term signal noise.
Signal Logic:
Long signal when trend score exceeds long_threshold.
Short signal when score drops below short_threshold.
Directional State (CD): Internally manages the current market regime (1 = long, -1 = short), controlling all visual output.
🔁 Use Cases & Applications
Macro Trend Alignment: Ideal for traders and analysts tracking Bitcoin’s structural momentum over long timeframes.
Trend Persistence Filter: Helps confirm whether the current move is part of a sustained trend or short-lived volatility.
Best Suited for BTC: Built specifically on the BNC BLX price history and Bitcoin’s power law behavior. Not designed for use with other assets.
✅ Conclusion
PLR-Z For Loop reframes Bitcoin’s long-term power law model into a trend-following tool by scoring the persistence of deviations above or below fair value. It shifts the focus from valuation-based mean reversion to directional momentum, making it a valuable signal for traders seeking high-conviction participation in BTC’s broader market cycles.
⚠️ Disclaimer
The content provided by this indicator is for educational and informational purposes only. Nothing herein constitutes financial or investment advice. Trading and investing involve risk, including the potential loss of capital. Always backtest and apply risk management suited to your strategy.
Educational
Congestion Indicator - Oscillator by saurabh maggoCore Functionality
Market State Detection:
Congestion: Identifies periods of low volatility (price consolidation) where the price range is tight relative to the Average True Range (ATR). Visualized with a blue background in the oscillator panel.
Breakout Up: Detects upward breakouts from congestion zones, requiring conditions like price movement above the congestion high, volume spikes, and volatility increases. Visualized with a green background.
Breakdown (Breakout Down): Detects downward breakouts from congestion zones, with similar conditions as Breakout Up but for downward movement. Visualized with a red background.
Post-Congestion: Identifies the period after a congestion zone ends but before a breakout occurs (if extend_until_breakout is disabled). Visualized with a yellow background.
Pullback: Detects pullbacks after breakouts or breakdowns, useful for identifying potential entry points (if use_pullback_entry is enabled). Visualized with a purple background.
Visualization:
Oscillator Panel: Displays the market state in a separate panel below the chart.
Background Color: The panel’s background color changes to reflect the current state (e.g., blue for Congestion, green for Breakout Up).
Histogram Plot: Optionally plots the state value as a histogram (e.g., 1 for Congestion, 2 for Breakout Up), toggleable via TradingView’s "Style" tab ("Market State"). The histogram provides a numerical representation of the state:
Congestion: 1.0
Breakout Up: 2.0
Breakdown: -2.0
Post-Congestion: 0.5
Pullback: 1.5
None: 0.0
Alerts:
Generates alerts for state changes (Congestion, Breakout Up, Breakdown).
Supports enhanced alerts (if use_enhanced_alerts is enabled), including additional context like breakout level, volatility state, and trend direction.
Includes an alert cooldown period (if use_alert_cooldown is enabled) to prevent excessive alerts.
Key Features and Filters
Customizable Parameters:
Lookback Period: Adjusts the number of bars used to calculate the price range for congestion detection.
Range Threshold: Sets the maximum price range (as a percentage of ATR) for a congestion zone.
Dynamic Threshold: Optionally uses a percentile-based dynamic threshold for more adaptive congestion detection.
Minimum Congestion Bars: Requires a minimum number of bars for a congestion zone to be confirmed.
Volume Filter: Optionally requires low volume during congestion zones.
Volume Breakout Filter: Requires a volume spike for breakouts/breakdowns.
Volatility Breakout Filter: Requires an ATR spike for breakouts/breakdowns.
Minimum Price Movement: Optionally requires a minimum price movement for breakouts/breakdowns.
RSI Filter: Optionally requires RSI to be in a neutral range during congestion.
Max Price Range Filter: Limits the absolute price range for congestion zones.
Trend Filter: Optionally filters breakouts/breakdowns based on a higher timeframe trend (using a moving average).
Momentum Filter: Optionally requires MACD momentum confirmation for breakouts/breakdowns.
Pullback Detection: Optionally detects pullbacks after breakouts/breakdowns for entry opportunities.
Timeframe Adjustment: Adjusts parameters based on the chart’s timeframe.
Auto-Settings: Automatically adjusts parameters based on market volatility.
Show Current Day Only: Optionally limits the indicator’s display to the current trading day (NSE session).
Presets: Offers predefined configurations (Default, Aggressive, Conservative) for quick setup.
Session Support: Operates within the NSE session (9:15 AM–3:30 PM IST) by default, ensuring relevance for Indian markets.
Visual Output
The oscillator panel uses color-coded backgrounds to indicate the market state:
Blue: Congestion
Green: Breakout Up
Red: Breakdown
Yellow: Post-Congestion
Purple: Pullback
Transparent (None): No state detected
The histogram plot (optional) provides a numerical representation of the state, which can be toggled on/off in TradingView’s settings.
Alerts
Alerts are triggered for significant state changes (Congestion, Breakout Up, Breakdown).
Enhanced alerts include additional details like price levels, volatility, and trend direction, making them more informative for traders.
Step 2: Craft the Description for Publishing
Based on the analysis, here’s a concise, user-friendly description you can use when publishing the indicator on TradingView:
Congestion Indicator - Oscillator by Saurabh Maggo
This indicator identifies market congestion zones, breakouts, breakdowns, post-congestion periods, and pullbacks in a separate oscillator panel below your chart. Designed for traders, it helps you spot key market states and potential trading opportunities with clear visual cues and customizable alerts.
Key Features:
Market States: Detects Congestion (Blue), Breakout Up (Green), Breakdown (Red), Post-Congestion (Yellow), and Pullbacks (Purple).
Visual Display: Shows market states using background colors in an oscillator panel, with an optional histogram plot (toggleable in settings).
Alerts: Generates alerts for state changes, with enhanced options to include price levels, volatility, and trend context.
Customizable Filters: Includes volume, volatility, RSI, trend, momentum, and price movement filters to refine signals.
Adaptable Settings: Supports dynamic thresholds, timeframe adjustments, auto-settings based on volatility, and predefined presets (Default, Aggressive, Conservative).
NSE Session: Optimized for Indian markets with a default session time of 9:15 AM–3:30 PM IST.
How can Grok help?
IDRISPAULThe script handles support/resistance detection, breakouts, and retest detection based on user-configurable inputs.
Uses pivot points and tracks potential vs confirmed retests.
Includes support for non-repainting logic via selectable options.
Price Level Linesthis is how we do it with these levels at these 100s. ben frank game is going down in my town and now your town too
abusuhil bullish breakAbusuhil Bullish Break is a price action-based confirmation tool that identifies a bullish reversal pattern consisting of:
Two consecutive bearish candles followed by
A strong bullish candle that closes above the high of both.
The script includes:
Optional dual MACD filter (current timeframe + higher timeframe)
Configurable stop-loss and multiple take-profit levels
Visual lines for targets and stop
Custom styling for all elements
It’s a clean, logic-driven entry confirmation tool for intraday and swing trading.
⚠️ Open-source and fully customizable.
مؤشر Abusuhil Bullish Break هو أداة تأكيد لانعكاسات الاتجاه الصاعد بناءً على حركة السعر (Price Action)، ويكتشف نموذجًا يتكون من:
شمعتين هابطتين متتاليتين
تتبعهما شمعة صاعدة قوية تغلق فوق أعلى الشمعتين السابقتين
يحتوي المؤشر على:
فلتر MACD مزدوج اختياري (للفريم الحالي وفريم أعلى)
إعدادات مخصصة للوقف والأهداف المتعددة
خطوط مرئية احترافية للأهداف والوقف
تحكم كامل في الألوان والنمط والعرض
مناسب للتداول اللحظي والسوينج.
✅ مفتوح المصدر وقابل للتعديل بالكامل.
Quantum Volume Pulse Screener - Multi TimeframeQuantum Volume Pulse Screener - Multi Timeframe
Overview
The Quantum Volume Pulse Screener is a powerful Pine Script® indicator designed for TradingView to monitor multiple symbols across user-selected timeframes (1-minute or 5-minute). This tool provides traders with real-time insights into price action, Volume Weighted Average Price (VWAP), Relative Strength Index (RSI), and buy/sell signals for a curated list of high-profile stocks and ETFs, including SPY, QQQ, AAPL, AMZN, GOOG, GOOGL, META, AVGO, TSLA, and NFLX. The screener displays data in a clean, customizable table, enabling quick decision-making for active traders. Fully customizable to any ETFs or Stocks.
Key Features
Multi-Symbol Analysis: Tracks up to 10 user-defined symbols, defaulting to major ETFs (SPY, QQQ) and leading tech stocks (AAPL, AMZN, GOOG, GOOGL, META, AVGO, TSLA, NFLX).
Customizable Timeframe: Toggle between 1-minute and 5-minute timeframes for flexible analysis.
Comprehensive Metrics: Displays real-time data for:
Price: Current closing price with color-coded daily change (green for positive, pink for negative).
VWAP: Volume Weighted Average Price for intraday trend analysis.
RSI: 14-period RSI with overbought (>70, pink) and oversold (<30, green) highlights.
Signals: Generates "BUY" (RSI < 30), "SELL" (RSI > 70), or neutral ("-") signals.
Dynamic Table Display: Presents data in a clear, top-center table with up to 500 labels for historical reference.
Error Handling: Alerts users to invalid data (e.g., incorrect symbols or timeframes) and displays a weekend warning for stale data.
Real-Time Updates: Refreshes data on every bar to ensure accuracy during live trading sessions.
How It Works
The script fetches real-time data for each symbol using TradingView’s request.security function, calculating:
Price: Based on the current bar’s close.
VWAP: Computed using the HLC3 (High + Low + Close / 3) formula.
RSI: 14-period RSI to identify momentum and potential reversals.
Daily Change: Percentage change in price to gauge short-term performance.
Signals: RSI-based buy/sell triggers for quick trade identification.
The data is organized into arrays and displayed in a table with color-coded visuals for easy interpretation. Green indicates bullish conditions (e.g., RSI < 30 or positive daily change), while pink highlights bearish conditions (e.g., RSI > 70 or negative daily change).
Usage Instructions
Add to Chart: Apply the indicator to any TradingView chart.
Configure Settings:
Select the desired timeframe (1-minute or 5-minute) via the input menu.
Customize symbols by editing the ticker inputs (defaults to SPY, QQQ, AAPL, etc.).
Interpret the Table:
Monitor the table at the top-center of the chart for real-time updates.
Look for "BUY" or "SELL" signals based on RSI thresholds.
Use VWAP and price data to confirm trends or reversals.
Check for Warnings:
If "INVALID" appears, verify the symbol or timeframe settings.
On weekends, a warning advises switching to a daily timeframe due to potentially stale data.
Notes
License: This script is licensed under the Mozilla Public License 2.0 (mozilla.org).
Author: © StanTheTradingMan.
Limitations: Ensure symbols are correctly formatted (e.g., "NASDAQ:AAPL" for stocks, "SPY" for ETFs). Invalid symbols or unavailable data may trigger error messages.
Best Use Case: Ideal for day traders and swing traders monitoring multiple assets for short-term opportunities.
Why Use This Screener?
The Quantum Volume Pulse Screener consolidates critical market data into a single, visually intuitive interface, saving traders time and enhancing decision-making. Whether tracking major indices or individual stocks, this tool provides a real-time edge in fast-moving markets.
For support or feedback, refer to TradingView’s community forums or contact the author via TradingView. Happy trading!
3 Smoothed Moving Averagethis is 3 sma 9,21,200 especially used for long term crosses or short term crosses as well. when the 9,21 cross under the 200 you sell. When 9,21 cross above 200 you buy.
2 CGC EMAChecks for 2 green closes above EMA.
Sends only one buy signal when this happens initially.
Won't send another buy signal until price closes below the EMA at least once (resets).
EMA is plotted with your offset visually.
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.
Monthly Session Divider (Alt Background) | Chart_BullyEasily visualize monthly transitions with alternating background shading. Designed for traders who like to spot macro trends, monthly opens, and institutional order flow.
✅ Alternates background color each month
✅ Auto-detects new months using live date logic
✅ Great for RTH or ETH intraday and swing strategies
✅ Clean gray overlay with low opacity
✅ Works on intraday, daily, and weekly charts
✅ Built for clarity, not clutter
Use this tool to:
Identify monthly pivots or volume rotations
Anchor monthly VWAPs or FVGs with visual context
Frame long-term setups with clean visual breaks
Weekly Session Divider (Alt Background) | Chart_BullyThis tool adds subtle alternating background shading for each new week, helping you visually distinguish trading sessions at a glance.
✅ Alternates background by weekly session
✅ Works great on intraday and daily timeframes
✅ Ideal for traders who rely on weekly pivots, volume profiles, or macro structure
✅ Compatible with both RTH and ETH charts
✅ Clean design for easy chart integration
Use it to improve your session awareness, spot emerging weekly trends, and avoid mental fatigue when reading extended charts.
Alternate Day Divider Background | Chart_BullyThis free utility shades every other trading day on your chart, helping you visually separate sessions and spot daily rhythm or pattern shifts more easily.
✅ Automatically alternates background shading by day
✅ Works on both Regular Trading Hours (RTH) and Extended Trading Hours (ETH)
✅ Especially useful on intraday and daily timeframes
✅ Helps identify breakout setups, trend shifts, or volume cycles by session
Great for scalpers, day traders, and anyone who wants a subtle visual edge without chart clutter.
📡 ETF RADAR HUD (SPY · QQQ · SPX) Auto-detects if you’re on SPY, SPX or QQQ
Shows a sleek status dashboard with:
Trend condition (EMA crossover)
Volatility meter (based on ATR vs price)
RSI mood
Volume activity
Instrument tag ("SPY 🔍", "QQQ 🚀", "SPX" or "Other 🪐")
🧠 Strategy:
We build a situational awareness HUD so SPY/QQQ/SPX day traders know:
Are we trending or ranging?
Is volatility expanding?
Are we in overbought/oversold territory?
Is there a volume surge?
Bot LabelsLive 1-minute BTCGBP chart with automated VWAP, current volume, and 20-bar average volume labels. Designed for bot integration to detect high-volume breakouts or momentum shifts. Updated every minute with real-time data for precision entry signals. Ideal for algorithmic trading or volume-based strategy monitoring.
Critical Pivot PointsCritical pivot points, marked on chart.
Top pivot points marked with green box
Bottom pivot points marked with red box
Simple & easy!
Pattern DetectorPattern detector - detects double tops, double bottoms, wedges & other common patterns. Draws the lines & prints on chart what it's identifying.
Premarket Sweep Strategy [ES/NQ]My first strategy.
Liquidity sweep on 2 min timeframe.
Tested on 7 trades with 100% win rate.
I am the best LOL
[Top] Simple Position + SL CalculatorThis indicator is a user-friendly tool designed to help traders easily calculate optimal position sizing, determine suitable stop-loss levels, and quantify maximum potential losses in dollar terms based on their personalized trading parameters.
Key Features:
Position Size Calculation: Automatically computes the number of shares to purchase based on the trader’s total account size and specified percentage of the account allocated per trade.
Stop-Loss Level: Suggests an appropriate stop-loss price point calculated based on the trader’s defined risk percentage per trade.
Max Loss Visualization: Clearly displays the maximum potential loss (in dollars) should the stop-loss be triggered.
Customizable Interface: Provides the flexibility to place the calculation table in different chart positions (Top Left, Top Right, Bottom Left, Bottom Right) according to user preference.
How to Use:
Enter your total Account Size.
Set the desired Position Size as a percentage of your account. (Typically, 1%–5% per trade is recommended for cash accounts.)
Define the Risk per Trade percentage (commonly between 0.05%–0.5%).
Choose your preferred Table Position to comfortably integrate with your trading chart.
Note:
If you identify a technical support level below the suggested stop-loss point, consider reducing your position size to manage the increased risk effectively.
Keep in mind that the calculations provided by this indicator are based solely on standard industry best practices and the specific inputs entered by you. They do not account for market volatility, news events, or any other factors outside the provided parameters. Always complement this indicator with sound technical and fundamental analysis.
My Trading mantra/playbook🧠 My Trading Mantra — Motivational Trading Reminder Overlay
This indicator displays a customizable trading mantra as an overlay on your TradingView charts to keep your mindset sharp and disciplined during trading sessions.
You can customize the mantra text, font size, text color, background color, transparency, and screen position (top, middle, bottom, left, right, center).
It’s designed to serve as a constant motivational reminder emphasizing core trading principles like planning, risk management, patience, and learning from losses to grow profits.
Features:
Customizable multi-line mantra text input
Adjustable font size (small to huge)
Color customization for text and background
Adjustable background transparency
Multiple screen position options for display
Lightweight and simple overlay, no performance impact
Purpose:
To help traders stay mentally focused and disciplined by having their personalized mantra visible at all times while analyzing charts.
Additionally, it can be used as a trading plan or playbook, allowing traders to display their key rules, strategies, or reminders directly on their charts for quick reference during live trading.
code is open with love.
you are a good trader don't let the markets tell you diffrent.
Position Size & Stop-Loss CalculatorPine Script Code for Position Size & Stop-Loss Calculator Indicator
This Pine Script indicator for TradingView will allow you to input your trading parameters and see the calculated Stop-Loss Price plotted on the chart, along with the recommended number of shares and maximum dollar risk displayed as a text label.
