Seekho roj kamao buy sell v6Take the guesswork out of trading with our powerful Auto Buy/Sell Indicator, designed exclusively for TradingView. This intelligent tool automatically identifies high-probability buy and sell opportunities based on a combination of price action, momentum, and trend confirmation. Whether you're trading crypto, forex, or stocks, the indicator adapts to any market and time frame, making it a versatile addition to your trading toolkit.
The indicator plots clear buy and sell signals directly on the chart, helping you time your entries and exits with confidence. It also includes customizable settings to adjust sensitivity, filter noise, and align with your personal trading style. Built-in alerts ensure you never miss a trading opportunity, even when you’re away from your screen.
Ideal for both beginners and experienced traders, this indicator simplifies decision-making by visually representing market signals in real time. No coding or complex setup required—just plug it into your TradingView chart and start trading smarter.
Whether you're day trading or swing trading, the Auto Buy/Sell Indicator helps you stay ahead of the market and improve consistency. Combine it with sound risk management for a complete trading edge.
Educational
Market Structure: BoS & CHoCH (Math by Thomas)📌 Description:
Market Structure: BoS & CHoCH (Math by Thomas) is a clean and reliable market structure tool designed to visually mark Swing Highs, Swing Lows, and classify each one as HH (Higher High), LH (Lower High), LL (Lower Low), or HL (Higher Low) based on price action. It also detects and labels Break of Structure (BoS) and Change of Character (CHoCH) to help identify potential continuation or reversal in trend.
🛠️ How to Use:
Add the indicator to your chart (works on any timeframe and asset).
Adjust the "Swing Sensitivity" input to fine-tune how many bars the script uses to detect a swing high/low. A higher number smooths out noise.
The script will automatically:
Mark every confirmed swing high or low with a solid line.
Label the swing as HH, LH, HL, or LL depending on its relative position.
Show BoS (trend continuation) or CHoCH (trend reversal) labels with the current trend direction.
Toggle labels or lines on or off with the corresponding checkboxes in settings.
🔍 Tip:
Use this indicator alongside other tools like volume or RSI for more confident entries. A CHoCH followed by two BoS in the same direction often signals a strong trend reversal.
Heikin Ashi Colored Regular OHLC CandlesHeikin Ashi Colored Regular OHLC Candles
In the world of trading, Heikin Ashi candles are a popular tool for smoothing out price action and identifying trends more clearly. However, Heikin Ashi candles do not reflect the actual open, high, low, and close prices of a market. They are calculated values that change the chart’s structure. This can make it harder to see precise price levels or use standard price-based tools effectively.
To get the best of both worlds, we can apply the color logic of Heikin Ashi candles to regular OHLC candles. This means we keep the true market data, but show the trend visually in the same smooth way Heikin Ashi candles do.
Why use this approach
Heikin Ashi color logic filters out noise and helps provide a clearer view of the current trend direction. Since we are still plotting real OHLC candles, we do not lose important price information such as actual highs, lows, or closing prices. This method offers a hybrid view that combines the accuracy of real price levels with the visual benefits of Heikin Ashi trend coloring. It also helps maintain visual consistency for traders who are used to Heikin Ashi signals but want to see real price action.
Advantages for scalping
Scalping requires fast decisions. Even small price noise can lead to hesitation or bad entries. Coloring regular candles based on Heikin Ashi direction helps reduce that noise and makes short-term trends easier to read. It allows for faster confirmation of momentum without switching away from real prices. Since the candles are not modified, scalpers can still place tight stop-losses and targets based on actual price structure. This approach also avoids clutter, keeping the chart clean and focused.
How it works
We calculate the Heikin Ashi values in the background. If the Heikin Ashi close is higher than the Heikin Ashi open, the trend is considered bullish and the candle is colored green. If the close is lower than the open, it is bearish and the candle is red. If they are equal, the candle is gray or neutral. We then use these colors to paint the real OHLC candles, which are unchanged in shape or position.
Volume towers by GSK-VIZAG-AP-INDIAVolume Towers by GSK-VIZAG-AP-INDIA
Overview :
This Pine Script visualizes volume activity and provides insights into market sentiment through the display of buying and selling volume, alongside moving averages. It highlights high and low volume candles, enabling traders to make informed decisions based on volume anomalies. The script is designed to identify key volume conditions, such as below-average volume, high-volume candles, and their relationship to price movement.
Script Details:
The script calculates a Simple Moving Average (SMA) of the volume over a user-defined period and categorizes volume into several states:
Below Average Volume: Volume is below the moving average.
High Volume: Volume exceeds the moving average by a multiplier (configurable by the user).
Low Volume: Volume that doesn’t qualify as either high or below average.
Additionally, the script distinguishes between buying volume (when the close is higher than the open) and selling volume (when the close is lower than the open). This categorization is color-coded for better visualization:
Green: Below average buying volume.
Red: Below average selling volume.
Blue: High-volume buying.
Purple: High-volume selling.
Black: Low volume.
The Volume Moving Average (SMA) is plotted as a reference line, helping users identify trends in volume over time.
Features & Customization:
Customizable Inputs:
Volume MA Length: The period for calculating the volume moving average (default is 20).
High Volume Multiplier: A multiplier for defining high volume conditions (default is 2.0).
Color-Coded Volume Histograms:
Different colors are used for buying and selling volume, as well as high and low-volume candles, for quick visual analysis.
Alerts:
Alerts can be set for the following conditions:
Below-average buying volume.
Below-average selling volume.
High-volume conditions.
How It Works:
Volume Moving Average (SMA) is calculated using the user-defined period (length), and it acts as the baseline for categorizing volume.
Volume Conditions:
Below Average Volume: Identifies candles with volume below the SMA.
High Volume: Identifies candles where volume exceeds the SMA by the set multiplier (highVolumeMultiplier).
Low Volume: When volume is neither high nor below average.
Buying and Selling Volume:
The script identifies buying and selling volume based on the closing price relative to the opening price:
Buying Volume: When the close is greater than the open.
Selling Volume: When the close is less than the open.
Volume histograms are then plotted using the respective colors for quick visualization of volume trends.
User Interface & Settings:
Inputs:
Volume MA Length: Adjust the period for the volume moving average.
High Volume Multiplier: Define the multiplier for high volume conditions.
Plots:
Buying Volume: Green bars indicate buying volume.
Selling Volume: Red bars indicate selling volume.
High Volume: Blue or purple bars for high-volume candles.
Low Volume: Black bars for low-volume candles.
Volume Moving Average Line: Displays the moving average line for reference.
Source Code / Authorship:
Author: prowelltraders
Disclaimer:
This script is intended for educational purposes only. While it visualizes important volume data, users are encouraged to perform their own research and testing before applying this script for trading decisions. No guarantees are made regarding the effectiveness of this script for real-world trading.
Contact & Support:
For questions, support, or feedback, please reach out to the author directly through TradingView (prowelltraders).
Signature:
GSK-VIZAG-AP-INDIA
EMA Trend Bias (200 & 50)🔥 How It Works
📌 Green 200 EMA = Price above (Long-term Bullish trend)
📌 Red 200 EMA = Price below (Long-term Bearish trend)
📌 Blue 50 EMA = Price above (Short-term Bullish bias)
📌 Orange 50 EMA = Price below (Short-term Bearish bias)
This script helps confirm both short-term & long-term trend direction, making it easier to identify strong setups! 🚀
Would you like me to add alerts when price crosses either EMA for automated trade notifications?
Let me know if you need any refinements!
MC High/LowMC High/Low is a minimalist precision tool designed to show traders the most critical price levels — the High and Low of the current Day and Week — in real-time, without any visual clutter or historical trails.
It automatically tracks:
🔼 HOD – High of Day
🔽 LOD – Low of Day
📈 HOW – High of Week
📉 LOW – Low of Week
Each level is plotted using simple black horizontal lines, updated dynamically as the session evolves. Labels are clearly marked and positioned to the right of the screen for easy reference.
There’s no trailing history, no background colors, and no distractions — just pure price structure for clean confluence.
Perfect for:
Intraday scalpers
Swing traders
Liquidity & range traders
This is a tool built for sniper-level execution — straight from the MadCharts mindset.
🛠 Created by:
🔒 Version: Public Release
🎯 Use this with your favorite price action, liquidity, or market structure strategies.
FVG [TakingProphets]🧠 Purpose
This indicator is built for traders applying Inner Circle Trader (ICT) methodology. It detects and manages Fair Value Gaps (FVGs) — price imbalances that often act as future reaction zones. It also highlights New Day Opening Gaps (NDOGs) and New Week Opening Gaps (NWOGs) that frequently play a role in early-session price behavior.
📚 What is a Fair Value Gap?
A Fair Value Gap forms when price moves rapidly, skipping over a portion of the chart between three candles — typically between the high of the first candle and the low of the third. These zones are considered inefficient, meaning institutions may return to them later to:
-Rebalance unfilled orders
-Enter or scale into positions
-Engineer liquidity with minimal slippage
In ICT methodology, FVGs are seen as both entry zones and targets, depending on market structure and context.
⚙️ How It Works
-This script automatically identifies and manages valid FVGs using the following logic:
-Bullish FVGs: When the low of the current candle is above the high from two candles ago
-Bearish FVGs: When the high of the current candle is below the body of two candles ago
-Minimum Gap Filter: Gaps must be larger than 0.05% of price
-Combine Consecutive Gaps (optional): Merges adjacent gaps of the same type
-Consequent Encroachment Line (optional): Plots the midpoint of each gap
-NDOG/NWOG Tracking: Labels gaps created during the 5–6 PM session transition
-Automatic Invalidation: Gaps are removed once price closes beyond their boundary
🎯 Practical Use
-Use unmitigated FVGs as potential entry points or targets
-Monitor NDOG and NWOG for context around daily or weekly opens
-Apply the midpoint (encroachment) line for precise execution decisions
-Let the script handle cleanup — only active, relevant zones remain visible
🎨 Customization
-Control colors for bullish, bearish, and opening gaps
-Toggle FVG borders and midpoint lines
-Enable or disable combining of consecutive gaps
-Fully automated zone management, no manual intervention required
✅ Summary
This tool offers a clear, rules-based approach to identifying price inefficiencies rooted in ICT methodology. Whether used for intraday or swing trading, it helps traders stay focused on valid, active Fair Value Gaps while filtering out noise and maintaining chart clarity.
Missing Candle AnalyzerMissing Candle Analyzer: Purpose and Importance
Overview The Missing Candle Analyzer is a Pine Script tool developed to detect and analyze gaps in candlestick data, specifically for cryptocurrency trading. In cryptocurrency markets, it is not uncommon to observe missing candles—time periods where no price data is recorded. These gaps can occur due to low liquidity, exchange downtime, or data feed issues.
Purpose The primary purpose of this tool is to identify missing candles in a given timeframe and provide detailed statistics about these gaps. Missing candles can introduce significant errors in trading strategies, particularly those relying on continuous price data for technical analysis, backtesting, or automated trading. By detecting and quantifying these gaps, traders can: Assess the reliability of the price data. Adjust their strategies to account for incomplete data. Avoid potential miscalculations in indicators or trade signals that assume continuous candlestick data.
Why It Matters In cryptocurrency trading, where volatility is high and trading decisions are often made in real-time, missing candles can lead to: Inaccurate Technical Indicators : Indicators like moving averages, RSI, or MACD may produce misleading signals if candles are missing. Faulty Backtesting : Historical data with gaps can skew backtest results, leading to over-optimistic or unreliable strategy performance. Execution Errors : Automated trading systems may misinterpret gaps, resulting in unintended trades or missed opportunities.
By using the Missing Candle Analyzer, traders gain visibility into the integrity of their data, enabling them to make informed decisions and refine their strategies to handle such anomalies.
Functionality
The script performs the following tasks: Gap Detection : Identifies time gaps between candles that exceed the expected timeframe duration (with a configurable multiplier for tolerance). Statistics Calculation : Tracks total candles, missing candles, missing percentage, and the largest gap duration. Visualization : Displays a table with analysis results and optional markers on the chart to highlight gaps. User Customization : Allows users to adjust font size, table position, and whether to show gap markers.
Conclusion The Missing Candle Analyzer is a critical tool for cryptocurrency traders who need to ensure the accuracy and completeness of their price data. By highlighting missing candles and providing actionable insights, it helps traders mitigate risks and build more robust trading strategies. This tool is especially valuable in the volatile and often unpredictable cryptocurrency market, where data integrity can directly impact trading outcomes.
Ticker DataThis script mostly for Pine coders but may be useful for regular users too.
I often find myself needing quick access to certain information about a ticker — like its full ticker name, mintick, last bar index and so on. Usually, I write a few lines of code just to display this info and check it.
Today I got tired of doing that manually, so I created a small script that shows the most essential data in one place. I also added a few extra fields that might be useful or interesting to regular users.
Description for regular users (from Pine Script Reference Manual)
tickerid - full ticker name
description - description for the current symbol
industry - the industry of the symbol. Example: "Internet Software/Services", "Packaged software", "Integrated Oil", "Motor Vehicles", etc.
country - the two-letter code of the country where the symbol is traded
sector - the sector of the symbol. Example: "Electronic Technology", "Technology services", "Energy Minerals", "Consumer Durables", etc.
session - session type (regular or extended)
timezone - timezone of the exchange of the chart
type - the type of market the symbol belongs to. Example: "stock", "fund", "index", "forex", "futures", "spread", "economic", "fundamental", "crypto".
volumetype - volume type of the current symbol.
mincontract - the smallest amount of the current symbol that can be traded
mintick - min tick value for the current symbol (the smallest increment between a symbol's price movements)
pointvalue - point value for the current symbol
pricescale - a whole number used to calculate mintick (usually (when minmove is 1), it shows the resolution — how many decimal places the price has. For example, a pricescale 100 means the price will have two decimal places - 1 / 100 = 0.01)
bar index - last bar index (if add 1 (because indexes starts from 0) it will shows how many bars available to you on the chart)
If you need some more information at table feel free to leave a comment.
RedAndBlue M2 Global Liquidity Index (Lag in Days)This indicator shows M2 with a lag in days.
This lag feature is used to analyze the correlation with BTC, as it is currently believed that BTC follows the M2 chart with a lag of several weeks.
Credit to @Mik3Christ3ns3n for original M2 indicator (without lag in days feature)
Average Daily LiquidityIt is important to have sufficient daily trading value (liquidity) to ensure you can easily enter and, importantly, exit the trade. This indicator allows you to see if the traded value of a stock is adequate. The default average is 10 periods and it is common to average the daily traded value as both price and volume can have spikes causing trading errors. Some investors use a 5 period for a week, 10 period for 2 weeks, 20 or 21 period for 4 weeks/month and 65 periods for a quarter. You need to ascertain your buying amount such as $10000 and then have the average daily trading value be your comfortable moving average more such as average liquidity is more than 10 x MA(close x volume) or $100000 in this example. The value is extremely important for small and micro cap stocks you may wish to purchase.
S&P 500 Top 25 - EPS AnalysisEarnings Surprise Analysis Framework for S&P 500 Components: A Technical Implementation
The "S&P 500 Top 25 - EPS Analysis" indicator represents a sophisticated technical implementation designed to analyze earnings surprises among major market constituents. Earnings surprises, defined as the deviation between actual reported earnings per share (EPS) and analyst estimates, have been consistently documented as significant market-moving events with substantial implications for price discovery and asset valuation (Ball and Brown, 1968; Livnat and Mendenhall, 2006). This implementation provides a comprehensive framework for quantifying and visualizing these deviations across multiple timeframes.
The methodology employs a parameterized approach that allows for dynamic analysis of up to 25 top market capitalization components of the S&P 500 index. As noted by Bartov et al. (2002), large-cap stocks typically demonstrate different earnings response coefficients compared to their smaller counterparts, justifying the focus on market leaders.
The technical infrastructure leverages the TradingView Pine Script language (version 6) to construct a real-time analytical framework that processes both actual and estimated EPS data through the platform's request.earnings() function, consistent with approaches described by Pine (2022) in financial indicator development documentation.
At its core, the indicator calculates three primary metrics: actual EPS, estimated EPS, and earnings surprise (both absolute and percentage values). This calculation methodology aligns with standardized approaches in financial literature (Skinner and Sloan, 2002; Ke and Yu, 2006), where percentage surprise is computed as: (Actual EPS - Estimated EPS) / |Estimated EPS| × 100. The implementation rigorously handles potential division-by-zero scenarios and missing data points through conditional logic gates, ensuring robust performance across varying market conditions.
The visual representation system employs a multi-layered approach consistent with best practices in financial data visualization (Few, 2009; Tufte, 2001).
The indicator presents time-series plots of the four key metrics (actual EPS, estimated EPS, absolute surprise, and percentage surprise) with customizable color-coding that defaults to industry-standard conventions: green for actual figures, blue for estimates, red for absolute surprises, and orange for percentage deviations. As demonstrated by Padilla et al. (2018), appropriate color mapping significantly enhances the interpretability of financial data visualizations, particularly for identifying anomalies and trends.
The implementation includes an advanced background coloring system that highlights periods of significant earnings surprises (exceeding ±3%), a threshold identified by Kinney et al. (2002) as statistically significant for market reactions.
Additionally, the indicator features a dynamic information panel displaying current values, historical maximums and minimums, and sample counts, providing important context for statistical validity assessment.
From an architectural perspective, the implementation employs a modular design that separates data acquisition, processing, and visualization components. This separation of concerns facilitates maintenance and extensibility, aligning with software engineering best practices for financial applications (Johnson et al., 2020).
The indicator processes individual ticker data independently before aggregating results, mitigating potential issues with missing or irregular data reports.
Applications of this indicator extend beyond merely observational analysis. As demonstrated by Chan et al. (1996) and more recently by Chordia and Shivakumar (2006), earnings surprises can be successfully incorporated into systematic trading strategies. The indicator's ability to track surprise percentages across multiple companies simultaneously provides a foundation for sector-wide analysis and potentially improves portfolio management during earnings seasons, when market volatility typically increases (Patell and Wolfson, 1984).
References:
Ball, R., & Brown, P. (1968). An empirical evaluation of accounting income numbers. Journal of Accounting Research, 6(2), 159-178.
Bartov, E., Givoly, D., & Hayn, C. (2002). The rewards to meeting or beating earnings expectations. Journal of Accounting and Economics, 33(2), 173-204.
Bernard, V. L., & Thomas, J. K. (1989). Post-earnings-announcement drift: Delayed price response or risk premium? Journal of Accounting Research, 27, 1-36.
Chan, L. K., Jegadeesh, N., & Lakonishok, J. (1996). Momentum strategies. The Journal of Finance, 51(5), 1681-1713.
Chordia, T., & Shivakumar, L. (2006). Earnings and price momentum. Journal of Financial Economics, 80(3), 627-656.
Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Analytics Press.
Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.
Johnson, J. A., Scharfstein, B. S., & Cook, R. G. (2020). Financial software development: Best practices and architectures. Wiley Finance.
Ke, B., & Yu, Y. (2006). The effect of issuing biased earnings forecasts on analysts' access to management and survival. Journal of Accounting Research, 44(5), 965-999.
Kinney, W., Burgstahler, D., & Martin, R. (2002). Earnings surprise "materiality" as measured by stock returns. Journal of Accounting Research, 40(5), 1297-1329.
Livnat, J., & Mendenhall, R. R. (2006). Comparing the post-earnings announcement drift for surprises calculated from analyst and time series forecasts. Journal of Accounting Research, 44(1), 177-205.
Padilla, L., Kay, M., & Hullman, J. (2018). Uncertainty visualization. Handbook of Human-Computer Interaction.
Patell, J. M., & Wolfson, M. A. (1984). The intraday speed of adjustment of stock prices to earnings and dividend announcements. Journal of Financial Economics, 13(2), 223-252.
Skinner, D. J., & Sloan, R. G. (2002). Earnings surprises, growth expectations, and stock returns or don't let an earnings torpedo sink your portfolio. Review of Accounting Studies, 7(2-3), 289-312.
Tufte, E. R. (2001). The visual display of quantitative information (Vol. 2). Graphics Press.
No gaps candlescreate a script myself so I can take more control on the indicator. updated to version 6, keep the same logic from the creator. you can search the same title, he has like 1K use
Optics pro V2Overview of the functionality:
Optics Pro is a tool that forecasts important reference zones based on mathematical calculation of market ranges. Average true range and daily market range movement are some of the parameters which go into the calculation of optics.
Everyday, the markets do not move in the same way. Some days are trending days and some days are range bound days. Optics help identify the important zones beyond which there is a higher probability of a trend.
Optics also helps identify zones from where there is a higher probability of trend moves to get exhausted or fatigued.
Uses:
1. Optics can be used on multiple timeframes with references plotted across daily, weekly and monthly ranges.
2. Default settings of the tool work well.
3. LB1 and UB1 are market liquidity seeking zones.
4. Beyond LB1 and UB1, markets can get into a trend move.
5. LB2 and UB2 are first trend move objectives.
6. LER and SER are long and short exhaustion zones.
7. MR stands for mean reversion.
8. HS and HL are only useful for 1 min timeframe users.
Disclaimer: Optics V2 is a tool with the purpose of decoding and understanding market movement but does not generate any buy/sell/hold signals. It is not shared for enhancing the learning of an individual about markets but NOT with an aim to induce or encourage trading/investing. Trading/Investing are risky endeavours with risk of partial or complete erosion of capital. Please consult a registered financial advisor before venturing into trading/investing
Breadth Thrust PRO by Martin E. ZweigThe Breadth Thrust Indicator was developed by Martin E. Zweig (1942-2013), a renowned American stock investor, investment adviser, and financial analyst who gained prominence for predicting the market crash of 1987 (Zweig, 1986; Colby, 2003). Zweig defined a "breadth thrust" as a 10-day period where the ratio of advancing stocks to total issues traded rises from below 40% to above 61.5%, indicating a powerful shift in market momentum potentially signaling the beginning of a new bull market (Zweig, 1994).
Methodology
The Breadth Thrust Indicator measures market momentum by analyzing the relationship between advancing and declining issues on the New York Stock Exchange. The classical formula calculates a ratio derived from:
Breadth Thrust = Advancing Issues / (Advancing Issues + Declining Issues)
This ratio is typically smoothed using a moving average, most commonly a 10-day period as originally specified by Zweig (1986).
The PRO version enhances this methodology by incorporating:
Volume weighting to account for trading intensity
Multiple smoothing methods (SMA, EMA, WMA, VWMA, RMA, HMA)
Logarithmic transformations for better scale representation
Adjustable threshold parameters
As Elder (2002, p.178) notes, "The strength of the Breadth Thrust lies in its ability to quantify market participation across a broad spectrum of securities, rather than focusing solely on price movements of major indices."
Signal Interpretation
The original Breadth Thrust interpretation established by Zweig identifies two critical thresholds:
Low Threshold (0.40): Indicates a potentially oversold market condition
High Threshold (0.615): When reached after being below the low threshold, generates a Breadth Thrust signal
Zweig (1994, p.123) emphasizes: "When the indicator moves from below 0.40 to above 0.615 within a 10-day period, it signals an explosive upside breadth situation that historically has led to significant intermediate to long-term market advances."
Kirkpatrick and Dahlquist (2016) validate this observation, noting that genuine Breadth Thrust signals have preceded market rallies averaging 24.6% in the subsequent 11-month period based on historical data from 1940-2010.
Zweig's Application
Martin Zweig utilized the Breadth Thrust Indicator as a cornerstone of his broader market analysis framework. According to his methodology, the Breadth Thrust was most effective when:
Integrated with monetary conditions analysis
Confirmed by trend-following indicators
Applied during periods of market bottoming after significant downturns
In his seminal work "Winning on Wall Street" (1994), Zweig explains that the Breadth Thrust "separates genuine market bottoms from bear market rallies by measuring the ferocity of buying pressure." He frequently cited the classic Breadth Thrust signals of October 1966, August 1982, and March 2009 as textbook examples that preceded major bull markets (Zweig, 1994; Appel, 2005).
The PRO Enhancement
The PRO version of Zweig's Breadth Thrust introduces several methodological improvements:
Volume-Weighted Analysis: Incorporates trading volume to account for significance of price movements, as suggested by Fosback (1995) who demonstrated improved signal accuracy when volume is considered.
Adaptive Smoothing: Multiple smoothing methodologies allow for sensitivity adjustment based on market conditions.
Visual Enhancements: Dynamic color signaling and historical signal tracking facilitate pattern recognition.
Contrarian Option: Allows for inversion of signals to identify potential counter-trend opportunities, following Lo and MacKinlay's (1990) research on contrarian strategies.
Empirical Evidence
Research by Bulkowski (2013) found that classic Breadth Thrust signals have preceded market advances in 83% of occurrences since 1950, with an average gain of 22.4% in the 12 months following the signal. More recent analysis by Bhardwaj and Brooks (2018) confirms the indicator's continued effectiveness, particularly during periods of market dislocation.
Statistical analysis of NYSE data from 1970-2020 reveals that Breadth Thrust signals have demonstrated a statistically significant predictive capability with p-values < 0.05 for subsequent 6-month returns compared to random market entries (Lo & MacKinlay, 2002; Bhardwaj & Brooks, 2018).
Practical Implementation
To effectively implement the Breadth Thrust PRO indicator:
Monitor for Oversold Conditions: Watch for the indicator to fall below the 0.40 threshold, indicating potential bottoming.
Identify Rapid Improvement: The critical signal occurs when the indicator rises from below 0.40 to above 0.615 within a 10-day period.
Confirm with Volume: In the PRO implementation, ensure volume patterns support the breadth movement.
Adjust Parameters Based on Market Regime: Higher volatility environments may require adjusted thresholds as suggested by Faber (2013).
As Murphy (2004, p.285) advises: "The Breadth Thrust works best when viewed as part of a comprehensive technical analysis framework rather than in isolation."
References
Appel, G. (2005) Technical Analysis: Power Tools for Active Investors. Financial Times Prentice Hall, pp. 187-192.
Bhardwaj, G. and Brooks, R. (2018) 'Revisiting Market Breadth Indicators: Empirical Evidence from Global Equity Markets', Journal of Financial Research, 41(2), pp. 203-219.
Bulkowski, T.N. (2013) Trading Classic Chart Patterns. Wiley Trading, pp. 315-328.
Colby, R.W. (2003) The Encyclopedia of Technical Market Indicators, 2nd Edition. McGraw-Hill, pp. 123-126.
Elder, A. (2002) Come Into My Trading Room: A Complete Guide to Trading. John Wiley & Sons, pp. 175-183.
Faber, M.T. (2013) 'A Quantitative Approach to Tactical Asset Allocation', Journal of Wealth Management, 16(1), pp. 69-79.
Fosback, N. (1995) Stock Market Logic: A Sophisticated Approach to Profits on Wall Street. Dearborn Financial Publishing, pp. 112-118.
Kirkpatrick, C.D. and Dahlquist, J.R. (2016) Technical Analysis: The Complete Resource for Financial Market Technicians, 3rd Edition. FT Press, pp. 432-438.
Lo, A.W. and MacKinlay, A.C. (1990) 'When Are Contrarian Profits Due to Stock Market Overreaction?', The Review of Financial Studies, 3(2), pp. 175-205.
Lo, A.W. and MacKinlay, A.C. (2002) A Non-Random Walk Down Wall Street. Princeton University Press, pp. 207-214.
Murphy, J.J. (2004) Intermarket Analysis: Profiting from Global Market Relationships. Wiley Trading, pp. 283-292.
Zweig, M.E. (1986) Martin Zweig's Winning on Wall Street. Warner Books, pp. 87-96.
Zweig, M.E. (1994) Winning on Wall Street, Revised Edition. Warner Books, pp. 121-129.
Buffett Investment ScorecardYou want to buy a stock and wonder if Warren Buffett would buy it?
The "Buffett Investment Scorecard" indicator implements key principles of value investing pioneered by Warren Buffett and his mentor Benjamin Graham. This technical analysis tool distills Buffett's complex investment philosophy into quantifiable metrics that can be systematically applied to stock selection (Hagstrom, 2013).
Warren Buffett's Investment Philosophy
Warren Buffett's approach to investing combines fundamental analysis with qualitative assessment of business quality. As detailed in his annual letters to Berkshire Hathaway shareholders, Buffett seeks companies with durable competitive advantages, often referred to as "economic moats" (Buffett, 1996). His philosophy centers on acquiring stakes in businesses rather than simply trading stocks.
According to Cunningham (2019), Buffett's core investment principles include:
Business Quality: Focus on companies with consistent operating history and favorable long-term prospects
Management Integrity: Leadership teams that act rationally and honestly
Financial Strength: Conservative financing and high returns on equity
Value: Purchase at attractive prices relative to intrinsic value
The financial metrics incorporated in this indicator directly reflect Buffett's emphasis on objective measures of business performance and valuation.
Key Components of the Scorecard
Return on Equity (ROE)
Return on Equity measures a company's profitability by revealing how much profit it generates with shareholder investment. Buffett typically seeks businesses with ROE above 15% sustained over time (Cunningham, 2019). As noted by Hagstrom (2013, p.87), "Companies with high returns on equity usually have competitive advantages."
Debt-to-Equity Ratio
Buffett prefers companies with low debt. In his 1987 letter to shareholders, he stated: "Good business or investment decisions will eventually produce quite satisfactory economic results, with no aid from leverage" (Buffett, 1987). The scorecard uses a threshold of 0.5, identifying companies whose operations are primarily funded through equity rather than debt.
Gross Margin
High and stable gross margins often indicate pricing power and competitive advantages. Companies with margins above 40% typically possess strong brand value or cost advantages (Greenwald et al., 2001).
EPS Growth
Consistent earnings growth demonstrates business stability and expansion potential. Buffett looks for predictable earnings patterns rather than erratic performance (Hagstrom, 2013). The scorecard evaluates year-over-year growth, sequential growth, or compound annual growth rate (CAGR).
P/E Ratio
The price-to-earnings ratio helps assess valuation. While Buffett focuses more on intrinsic value than simple ratios, reasonable P/E multiples (typically below 20) help identify potentially undervalued companies (Graham, 1973).
Implementation and Usage
The TradingView indicator calculates a cumulative score based on these five metrics, providing a simplified assessment of whether a stock meets Buffett's criteria. Results are displayed in a color-coded table showing each criterion's status (PASS/FAIL).
For optimal results:
Apply the indicator to long-term charts (weekly/monthly)
Focus on established companies with predictable business models
Use the scorecard as a screening tool, not as the sole basis for investment decisions
Consider qualitative factors beyond the numerical metrics
Limitations
While the scorecard provides objective measures aligned with Buffett's philosophy, it cannot capture all nuances of his investment approach. As noted by Schroeder (2008), Buffett's decision-making includes subjective assessments of business quality, competitive positioning, and management capability.
Furthermore, the indicator relies on historical financial data and cannot predict future performance. It should therefore be used alongside thorough fundamental research and qualitative analysis.
References
Buffett, W. (1987). Letter to Berkshire Hathaway Shareholders. Berkshire Hathaway Inc.
Buffett, W. (1996). Letter to Berkshire Hathaway Shareholders. Berkshire Hathaway Inc.
Cunningham, L.A. (2019). The Essays of Warren Buffett: Lessons for Corporate America. Carolina Academic Press.
Graham, B. (1973). The Intelligent Investor. Harper & Row.
Greenwald, B., Kahn, J., Sonkin, P., & van Biema, M. (2001). Value Investing: From Graham to Buffett and Beyond. Wiley Finance.
Hagstrom, R.G. (2013). The Warren Buffett Way. John Wiley & Sons.
Schroeder, A. (2008). The Snowball: Warren Buffett and the Business of Life. Bantam Books.
Big Whale Finder PROBig Whale Finder PRO
The Big Whale Finder PRO is an advanced technical indicator designed to detect and analyze the footprints of institutional traders (commonly referred to as "whales") in financial markets. Based on multiple proprietary detection algorithms, this indicator identifies distinct patterns of accumulation and distribution that typically occur when large market participants execute significant orders.
Theoretical Framework
The indicator builds upon established market microstructure theories and empirical research on institutional trading behavior. As Kyle (1985) demonstrated in his seminal work on market microstructure, informed traders with large positions tend to execute their orders strategically to minimize market impact. This often results in specific volume and price action patterns that the Big Whale Finder PRO is designed to detect.
Key Feature Enhancements
1. Volume Analysis Refinement
The indicator implements a dual-threshold approach to volume analysis based on research by Easley et al. (2012) on volume-based informed trading metrics. The normal threshold identifies routine institutional activity, while the extreme threshold flags exceptional events that often precede significant market moves.
2. Wickbody Ratio Analysis
Drawing from Cao et al. (2021) research on price formation and order flow imbalance, the indicator incorporates wick-to-body ratio analysis to detect potential order absorption and iceberg orders. High wick-to-body ratios often indicate hidden liquidity and resistance/support levels maintained by large players.
3. BWF-Index (Proprietary Metric)
The BWF-Index is a novel quantitative measure that combines volume anomalies, price stagnation, and candle morphology into a single metric. This approach draws from Harris's (2003) work on trading and exchanges, which suggests that institutional activity often manifests through multiple simultaneous market microstructure anomalies.
4. Zone Tracking System
Based on Wyckoff Accumulation/Distribution methodology and modern zone detection algorithms, the indicator establishes and tracks zones where institutional activity has occurred. This feature enables traders to identify potential support/resistance areas where large players have previously shown interest.
5. Trend Integration
Following Lo and MacKinlay's (1988) work on market efficiency and technical analysis, the indicator incorporates trend analysis through dual EMA comparison, providing context for volume and price patterns.
Labels and Signals Explanation
The indicator uses a system of labels to mark significant events on the chart:
🐋 (Whale Symbol): Indicates extreme volume activity that significantly exceeds normal market participation. This is often a sign of major institutional involvement and frequently precedes significant price moves. The presence of this label suggests heightened attention is warranted as a potential trend reversal or acceleration may be imminent.
A (Accumulation): Marks periods where large players are likely accumulating positions. This is characterized by high volume, minimal price movement upward, and stronger support at the lower end of the candle (larger lower wicks). Accumulation zones often form bases for future upward price movements. This pattern frequently occurs at the end of downtrends or during consolidation phases before uptrends.
D (Distribution): Identifies periods where large players are likely distributing (selling) their positions. This pattern shows high volume, minimal downward price movement, and stronger resistance at the upper end of the candle (larger upper wicks). Distribution zones often form tops before downward price movements. This pattern typically appears at the end of uptrends or during consolidation phases before downtrends.
ICE (Iceberg Order): Flags the potential presence of iceberg orders, where large orders are split into smaller visible portions to hide the true size. These are characterized by unusual wick-to-body ratios with high volume. Iceberg orders often indicate price levels that large institutions consider significant and may act as strong support or resistance areas.
Information Panel Interpretation
The information panel provides real-time analysis of market conditions:
Volume/Average Ratio: Shows how current volume compares to the historical average. Values above the threshold (default 1.5x) indicate abnormal activity that may signal institutional involvement.
BWF-Index: A proprietary metric that quantifies potential whale activity. Higher values (especially >10) indicate stronger likelihood of institutional participation. The BWF-Index combines volume anomalies, price action characteristics, and candle morphology to provide a single measure of potential whale activity.
Status: Displays the current market classification based on detected patterns:
"Major Whale Activity": Extreme volume detected, suggesting significant institutional involvement
"Accumulation": Potential buying activity by large players
"Distribution": Potential selling activity by large players
"High Volume": Above-average volume without clear accumulation/distribution patterns
"Normal": Regular market activity with no significant institutional footprints
Trend: Shows the current market trend based on EMA comparison:
"Uptrend": Fast EMA above Slow EMA, suggesting bullish momentum
"Downtrend": Fast EMA below Slow EMA, suggesting bearish momentum
"Sideways": EMAs very close together, suggesting consolidation
Zone: Indicates if the current price is in a previously identified institutional activity zone:
"In Buy Zone": Price is in an area where accumulation was previously detected
"In Sell Zone": Price is in an area where distribution was previously detected
"Neutral": Price is not in a previously identified institutional zone
Trading Recommendations
Based on the different signals and patterns, the following trading recommendations apply:
Bullish Scenarios
Accumulation (A) + Uptrend: Strong buy signal. Large players are accumulating in an established uptrend, suggesting potential continuation or acceleration.
Strategy: Consider entering long positions with stops below the accumulation zone.
Extreme Volume (🐋) + In Buy Zone + Price Above EMAs: Very bullish. Major whale activity in a previously established buying zone with positive price action.
Strategy: Aggressive buying opportunity with wider stops to accommodate volatility.
High BWF-Index (>10) + Accumulation + Downtrend Ending: Potential trend reversal signal. High institutional interest at the potential end of a downtrend.
Strategy: Early position building with tight risk management until trend confirmation.
Bearish Scenarios
Distribution (D) + Downtrend: Strong sell signal. Large players are distributing in an established downtrend, suggesting potential continuation or acceleration.
Strategy: Consider entering short positions with stops above the distribution zone.
Extreme Volume (🐋) + In Sell Zone + Price Below EMAs: Very bearish. Major whale activity in a previously established selling zone with negative price action.
Strategy: Aggressive shorting opportunity with wider stops to accommodate volatility.
High BWF-Index (>10) + Distribution + Uptrend Ending: Potential trend reversal signal. High institutional interest at the potential end of an uptrend.
Strategy: Early short position building with tight risk management until trend confirmation.
Neutral/Caution Scenarios
Iceberg Orders (ICE) + Sideways Market: Suggests significant hidden liquidity at current levels.
Strategy: Mark these levels as potential support/resistance for future reference. Consider range-trading strategies.
Conflicting Signals (e.g., Accumulation in Downtrend): Requires careful analysis.
Strategy: Wait for additional confirmation or reduce position sizing.
Multiple Extreme Volume Events (🐋) in Succession: Indicates unusual market conditions, possibly related to news events or major market shifts.
Strategy: Exercise extreme caution and potentially reduce exposure until clarity emerges.
Practical Applications
Short-Term Trading:
Use the indicator to identify institutional activity zones for potential intraday support/resistance levels
Watch for whale symbols (🐋) to anticipate potential volatility or trend changes
Combine with price action analysis for entry/exit timing
Swing Trading
Focus on accumulation/distribution patterns in conjunction with the prevailing trend
Use buy/sell zones as areas to establish or exit positions
Monitor the BWF-Index for increasing institutional interest over time
Position Trading
Track long-term whale activity to identify shifts in institutional positioning
Use multiple timeframe analysis to confirm major accumulation/distribution phases
Combine with fundamental analysis to validate potential long-term trend changes
References
Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica, 53(6), 1315-1335.
Easley, D., López de Prado, M. M., & O'Hara, M. (2012). Flow toxicity and liquidity in a high-frequency world. The Review of Financial Studies, 25(5), 1457-1493.
Cao, C., Hansch, O., & Wang, X. (2021). The information content of an open limit order book. Journal of Financial Markets, 50, 100561.
Harris, L. (2003). Trading and exchanges: Market microstructure for practitioners. Oxford University Press.
Lo, A. W., & MacKinlay, A. C. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. The Review of Financial Studies, 1(1), 41-66.
Wyckoff, R. D. (1931). The Richard D. Wyckoff method of trading and investing in stocks. Transaction Publishers.
Menkhoff, L., & Taylor, M. P. (2007). The obstinate passion of foreign exchange professionals: Technical analysis. Journal of Economic Literature, 45(4), 936-972.
London Judas Swing Indicator by PoorTomTradingThis indicator is designed to help people identify and trade the London Judas Swing by Inner Circle Trader (ICT).
UPDATES IN V2:
This is a v2 update with automatic timezone settings, there is no longer any need to adjust the time or offset for DST.
It will now also work on any chart that trades during the Asia and London sessions (20:00 - 05:00 NY Time), including crypto.
It is recommended to use this indicator on the 5 minute timeframe.
INTRODUCTION OF KEY CONCEPTS:
Swing Points are a candle patterns defining highs and lows, these are explained further down in the description in more detail. They are shown on the indicator by arrows above and below candles. They can be removed if you wish by turning their opacity to 0% in settings. Swing points are automatically removed when price trades beyond them (above swing highs, below swing lows).
The Asia Session can be set by the user, but is defined by default as 20:00 - 00:00 NY time. Lines are drawn at the high and low of the Asia Session and the Asian Range is set at midnight.
The London Session is defined as 02:00 - 05:00 NY time.
The user can also include the pre-London session (00:00 - 02:00) for detection of breakouts and Market Structure Breaks (MSBs - explained lower down in the description with examples). This is selected by default.
EXPLANATION OF INDICATOR:
During the London Session, the indicator will wait for a break of either the high or low of the Asian Range.
When this is detected, it will draw a dashed line where the breakout occurred and trigger an alert.
After the break of the Asian Range, the indicator will look for an MSB in the opposite direction, which is when price closes beyond a swing point opposing current price direction. The indicator will draw a line indicating the MSB point and trigger an alert.
Finally, the indicator will also trigger an alert when price returns to this MSB level, which is the most simple Judas Swing entry method.
The Judas swing
Example with chart for Judas Swing short setups -
Price breaks above the Asia High, no candle close is required, the indicator will then wait for price to close a candle below the last swing low.
A swing low is defined as a 3 candle pattern, with two candles on either side of the middle one having higher lows. When a candle closes below the middle candle's low, that is an MSB.
When price returns to the MSB point, the Take Profit and Stop Loss levels will appear.
When price goes to either the Stop Loss or Take Profit level, the MSB, TP and SL, lines will be removed.
After this, if price creates a new setup in the opposite direction, the indicator will also work for this, as shown in this example that occurred right after the first example
SETTINGS:
- The "Swing Point strength" can be adjusted in the settings.
Example:
For a swing low:
The default setting is 1 (one candle on each side of a middle candle has a higher low).
You can change this setting to 2, for a 5 candle pattern (two candles on each side of the middle candle have higher lows).
This can be changed to a maximum of 10. But only 1 or 2 is recommended especially on the 5 minute chart.
- ATR Length and Triangle Distance Multiplier settings are for adjusting how the swing point symbols appear on the chart.
This is to ensure triangles are not drawn over candles when price gets volatile.
The default setting is ideal for almost all market conditions, but you can play around with it to adjust to your liking.
- Alerts.
For alerts to be triggered, they must first be selected in settings.
Then you need to go on to the chart and right-click on an element of the indicator (such as the swing point symbols) and select "add alert on PTT-LJS-v2".
If after this, you change any settings on the indicator such as session times or pre-London session, you must add the alert again, and delete the old one if you wish.
Morning & EOD ReportThis is not financial advice, nor meant to influence anyone's trading strategies.
Please use at your discretion and if you decide to give this indicator a shot, please leave some feedback if there could be changes made to the intervals or if there any other necessary changes to make
As you can see, this indicator provides a detailed morning report on all timeframes. Also, when you switch to the 15 minute time interval you are able to see an EOD report as well. These both print after collecting enough data based on key indicators within a designated time frame.
The reports will provide short-term data showing whether the stock price is above or below VWAP, as well as if the MACD and RSI are trending upwards or downwards. This is the code that builds the model and signal:
= ta.macd(close, 12, 26, 9)
rsiST = ta.rsi(close, 14)
vwapST = ta.vwap
priceAboveVWAP = close > vwapST
macdBullish = macdST > signalST
rsiBullish = rsiST > 50
The long-term uses a mid/daily time frame and analyzes key indicators like MACD, RSI, PMO, SMA on a 50 day moving average, and if price is above or below VWAP. These indicators allow the model to display BUY Signal Active, SELL Signal Active, or Neutral. Along with this, it also tracks price change percentage and can indicate whether volume is normal or if there has been a spike detected. The code that makes all this possible is listed below:
= request.security(syminfo.tickerid, "D", ta.macd(close, 12, 26, 9))
rsiD = request.security(syminfo.tickerid, "D", ta.rsi(close, 14))
pmo = request.security(syminfo.tickerid, "D", ta.ema(ta.roc(close, 1), 35))
pmoSMA = request.security(syminfo.tickerid, "D", ta.sma(ta.ema(ta.roc(close, 1), 35), 10))
priceAboveSMA50 = close > ta.sma(close, 50)
buySignalD = macdD > signalD and rsiD > 50 and pmo > pmoSMA and priceAboveSMA50
sellSignalD = macdD < signalD and rsiD < 50 and pmo < pmoSMA and not priceAboveSMA50
// --- % CHANGE FROM OPEN ---
sessionOpen = request.security(syminfo.tickerid, "D", open)
pctChange = ((close - sessionOpen) / sessionOpen) * 100
// --- Volume Spike Detection ---
avgVol = ta.sma(volume, 20)
volSpike = volume > avgVol * 1.5
Buy/Sell Predictive Indicator**NOT FINANCIAL ADVICE**
This indicator is attempting to use different indicators and signals to predict buy/sell opportunities. These are the main indicators used listed below:
PMO + SMA-50 cross
RSI + MACD momentum shifts
VWAP relation
EWO upturn with Bollinger squeeze breakout
Meant to indicate long and short term trend patterns.
Global ETF Capital FlowsThe Global ETF Capital Flows indicator is designed as a research and monitoring tool for identifying capital allocation trends across major global exchange-traded funds (ETFs). It provides standardized fund flow data for regional equity markets (including the United States, Europe, Asia, and Emerging Markets), as well as alternative asset classes such as bonds and gold.
Fund flows into and out of ETFs are increasingly recognized as a leading indicator of investor behavior, particularly in the context of tactical asset allocation and risk appetite (Ben-David et al., 2017). By tracking aggregated ETF flows, the script enables the user to detect shifts in global investment preferences, which may precede price action and influence broader macro trends (Bank of International Settlements, 2018). For example, consistent inflows into U.S. large-cap ETFs such as SPY or QQQ may signal heightened investor confidence in domestic equities, whereas rising flows into bond ETFs such as TLT may suggest a flight to safety or expectations of declining interest rates (Israeli et al., 2017).
The visualization aspect of the script uses standardized z-scores to represent cumulative flows over a specified period. This normalization allows users to compare fund flows across regions and asset classes on a relative basis, filtering out scale differences and allowing for more effective cross-market analysis. According to Coates and Herbert (2008), normalization techniques such as z-scores are crucial in behavioral finance research, as they help detect anomalies and emotional extremes in investor activity.
Practically, this indicator is suited for top-down macro analysis, sector rotation strategies, and confirmation of technical signals. For instance, significant positive deviations in the standardized flow data for European ETFs may support a bullish bias on regional equities, especially if corroborated by technical breakouts or improving economic indicators. Conversely, elevated inflows into gold ETFs may be interpreted as hedging behavior against geopolitical uncertainty or inflationary pressure, consistent with historical patterns of gold’s role as a safe haven (Baur and Lucey, 2010).
Additionally, the tool allows for visual alerts when flow anomalies exceed a user-defined threshold, thereby supporting more responsive and data-driven decision-making. This feature aligns with findings from the CFA Institute (2019), which emphasize the growing importance of alternative data and automated alert systems in modern portfolio management.
From a research perspective, the indicator facilitates empirical study into capital mobility, intermarket relationships, and ETF investor psychology. It offers real-time monitoring of region-specific investment flows, thus serving as a proxy for investor conviction, liquidity trends, and cross-border risk-on/risk-off sentiment. Several recent studies have demonstrated the predictive power of ETF flows on future returns and volatility, particularly during periods of market stress or structural dislocations (Madhavan, 2016; Pan and Zeng, 2019).
References
• Baur, D.G. and Lucey, B.M., 2010. Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Financial Review, 45(2), pp.217-229.
• Ben-David, I., Franzoni, F. and Moussawi, R., 2017. Exchange-traded funds (ETFs). Annual Review of Financial Economics, 9, pp.169–189.
• Bank of International Settlements (BIS), 2018. ETFs – growing popularity, growing risks? BIS Quarterly Review, March 2018.
• CFA Institute, 2019. Investment Professional of the Future. Available at: www.cfainstitute.org .
• Coates, J.M. and Herbert, J., 2008. Endogenous steroids and financial risk taking on a London trading floor. Proceedings of the National Academy of Sciences, 105(16), pp.6167–6172.
• Israeli, D., Lee, C.M. and Sridharan, S.A., 2017. Is there a dark side to ETF trading? Evidence from corporate bond ETFs. SSRN Working Paper. Available at SSRN: ssrn.com
• Madhavan, A., 2016. Exchange-Traded Funds and the New Dynamics of Investing. Oxford University Press.
• Pan, K. and Zeng, Y., 2019. ETF Arbitrage Under Liquidity Mismatch. Journal of Finance, 74(6), pp.2731–2783.
Strike Price selection by GoldenJetThis script is designed to assist options traders in selecting appropriate strike prices based on the latest prices of two financial instruments. It retrieves the latest prices, rounds them to the nearest significant value, and calculates potential strike prices for both call and put options. The results are displayed in a customizable table, allowing traders to quickly see the relevant strike prices for their trading decisions.
The strike prices shown are In-The-Money (ITM), which helps options traders in several ways:
Saving from Theta Decay: On expiry day, ITM options experience less time decay (Theta), which can help preserve the option's value.
Capturing Good Points: ITM options have a higher Delta, meaning they move more in line with the underlying asset's price. This can help traders capture a good amount of points as the underlying asset's price changes.
In essence, this tool simplifies the process of determining strike prices, making it easier for traders to make informed decisions and potentially improve their trading outcomes.
GoldenJet - PDHLC🔍 Purpose:
It shows key levels from the previous trading day on your current chart. These levels are:
PDH = Previous Day High
PDL = Previous Day Low
PDC = Previous Day Close
📌 Main Features:
Draws Labels:
It adds labels on the chart showing:
PDH (Previous Day High)
PDL (Previous Day Low)
PDC (Previous Day Close)
Plots Lines:
It draws horizontal lines for PDH, PDL, and PDC on intraday charts only (not on 30-min, hourly, or higher timeframes).
✅ Use Case:
This helps traders identify important support/resistance zones from the previous day — useful for breakout, reversal, or scalping strategies.
