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.
Seekho roj kamao V6 StrategyThis strategy is based on the Chandelier Exit indicator, a volatility-based trailing stop developed by Chuck LeBeau. It uses the Average True Range (ATR) to dynamically determine stop levels for both long and short positions. The strategy aims to capture trends by entering trades when the Chandelier Exit signal changes direction.
📌 How It Works:
Long Entry: A buy signal is generated when price breaks above the Chandelier short stop, indicating a potential upward trend. The strategy enters a long position at this point.
Short Entry: A sell signal is generated when price falls below the Chandelier long stop, suggesting a downtrend. The strategy enters a short position here.
Exit Conditions:
Long positions are closed when a short signal appears.
Short positions are closed when a buy signal appears.
⚙️ Key Parameters:
ATR Period: Defines how many bars are used to calculate volatility.
ATR Multiplier: Adjusts the sensitivity of the stop levels.
Use Close for Extremes: Determines whether the highest/lowest close is used instead of highs/lows for calculating stops.
Bar Confirmation: Waits for the bar to close before confirming a signal.
This strategy works best in trending markets and may produce whipsaws in sideways or choppy conditions. It can be used standalone or in combination with other filters like volume, moving averages, or higher time frame confirmation.
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
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.
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.
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.
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!
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.
MinhPhan MA Crossover Strategy RSIThis Pine Script implements a trend-following strategy using a Moving Average Crossover combined with RSI-based exit conditions for optimal trade timing. Designed for short- to medium-term trading, the strategy enters a long position when the short-term EMA crosses above the long-term EMA, and a short position when it crosses below — a classic momentum signal. The default settings use a 9-period and 21-period EMA, but you can customize these values in the input panel.
To enhance risk management and trade timing, the script exits long positions when RSI exceeds 70 (indicating overbought conditions) and exits short positions when RSI drops below 30 (oversold), with fixed profit and loss targets of 200 and 100 points, respectively. This creates a balanced approach combining technical momentum with RSI reversal detection.
Additionally, the script includes commission modeling (0.1% default) to simulate real trading conditions more accurately. Visual aids like EMA9 and EMA21 overlays help traders validate signals visually.
Ideal for cryptocurrencies like BTC and SUI on any timeframe, this script allows for easy backtesting and optimization. It's a great starting point for traders looking to automate and refine a clean crossover-based strategy with proper risk control.
Seekho roj kamao StrategyThe "Seekho Roj Kamao Strategy" is a powerful backtesting tool designed to identify high-probability trend continuation setups. It combines RSI, Chande Momentum Oscillator (CMO), and adaptive ATR-based trailing stop logic to detect precise entry points and manage risk through automated take profit (1R, 2R, 3R) and stop loss levels. This strategy dynamically evaluates trend direction shifts using price action and momentum divergence, enabling traders to test robust trading scenarios with clearly defined exits. Ideal for forex, crypto, and stock traders, it allows full customization of sensitivity and volatility filters, making it suitable for both intraday and swing trading approaches.
StochRSI Strategy with SL/TPSimple approach the StochiRSi strategy that has stop loss and take profit, A fully control the inputs of stochi RSi and back testing date.. code are credit to ryzinray@gmail.com
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)
4H MA Crossover Pullback StrategyMedium-High frequency Moving Average Crossover Pullback strategy
Fast EMA (50) > Slow EMA (200) for trend confirmation.
Entries occur when price pulls back to a 20 EMA, then resumes with a crossover.
RSI filter added to ensure momentum is aligned.
ATR-based stop loss and take profit with a 1.2:1 risk-reward ratio.
Timeframe StrategyThis is a multi-timeframe trading strategy inspired by Ross Cameron's style, optimized for scalping and trend-following across various timeframes (1m, 5m, 15m, 1h, and 1D). The strategy integrates a comprehensive set of technical indicators, dynamic risk management, and visual tools.
Core Features
Dynamic Take Profit, Stop Loss & Trailing Stop
> Separate settings per timeframe for:
-TP% (Take Profit)
-SL% (Stop Loss)
-Trailing Stop %
-Cooldown bars
> Configurable via UI inputs.
>Smart Entry Conditions
Bullish entry: EMA9 crossover EMA20 and EMA50 > EMA200
Bearish entry: EMA9 crossunder EMA20 and EMA50 < EMA200
>Additional confirmation filters:
-Volume Filter (enabled/disabled via UI)
-Time Filter (e.g., only between 15:00–20:00 UTC)
-Spike Filter: rejects high-volatility candles
-RSI Filter: above/below 50 for trend confirmation
-ADX Filter (only applied on 1m, e.g., ADX > 15)
-Micro-Volatility Filter: minimum range percentage (1m only)
-Trend Filter (1m only): price must be above/below EMA200
>Trailing Stop Logic
-Configurable for each timeframe.
- Optional via toggle (use_trailing).
>Trade Cooldown Logic
-Prevents consecutive trades within X bars, configurable per timeframe.
>Technical Indicators Used
-EMA 9 / 20 / 50 / 200
-VWAP
-RSI (14)
-ATR (14) for volatility-based spike filtering
-Custom-calculated ADX (14) (manually implemented)
>Visual Elements
🔼/🔽 Entry signals (long/short) plotted on the chart.
📉 Table in bottom-left:
Displays current values of EMA/VWAP/volume/ATR/ADX.
> Optional "Tab info" panel in top-right (toggleable):
-Timeframe & strategy settings
-Live status of filters (volume, time, cooldown, spike, RSI, ADX, range, trend)
-Uses emoji (✅ / ❌) for quick diagnostics.
>User Customization
-Inputs per timeframe for all key parameters.
-Toggle switches for:
-Trailing stop
-Volume filter
-Info table visibility
This strategy is designed for active traders seeking a balance between momentum entry, risk control, and adaptability across timeframes. It's ideal for backtesting quick reversals or breakout setups in fast markets, especially at lower timeframes like 1m or 5m.
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.
RSI Crosses SMA Buy/Sell Strategy-R-AlgoAIDisclaimer:
// This script is for educational and informational purposes only.
// It does not constitute financial or investment advice.
// Trading involves substantial risk and may not be suitable for all investors.
// Always do your own research or consult with a licensed financial advisor
// before making any trading or investment decisions.
// The author is not responsible for any losses incurred using this script
Key Changes:
Buy at High of the Signal Candle:
The strategy.entry("Buy", strategy.long, limit=high, comment="Buy at High of Signal Candle") line places a buy order at the high of the candle that triggered the signal (i.e., the candle where the RSI crosses above the SMA).
How it works:
When the RSI crosses above the SMA and the buy condition is true, the strategy will place a buy order at the high of that candle.
Exit:
The strategy will exit the position if the RSI crosses below the SMA as usual using strategy.close("Buy").
Example:
If the RSI crosses above the SMA at a specific candle, the strategy will enter a buy order at the high of that candle.
When the RSI crosses below the SMA, it will close the long position.
This should now execute a buy order at the high of the signal candle when the RSI crosses above the SMA, as requested.
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.
RSI Crosses SMA Buy/Sell StrategyDisclaimer:
// This script is for educational and informational purposes only.
// It does not constitute financial or investment advice.
// Trading involves substantial risk and may not be suitable for all investors.
// Always do your own research or consult with a licensed financial advisor
// before making any trading or investment decisions.
// The author is not responsible for any losses incurred using this script
Key Changes:
Buy at High of the Signal Candle:
The strategy.entry("Buy", strategy.long, limit=high, comment="Buy at High of Signal Candle") line places a buy order at the high of the candle that triggered the signal (i.e., the candle where the RSI crosses above the SMA).
How it works:
When the RSI crosses above the SMA and the buy condition is true, the strategy will place a buy order at the high of that candle.
Exit:
The strategy will exit the position if the RSI crosses below the SMA as usual using strategy.close("Buy").
Example:
If the RSI crosses above the SMA at a specific candle, the strategy will enter a buy order at the high of that candle.
When the RSI crosses below the SMA, it will close the long position.
This should now execute a buy order at the high of the signal candle when the RSI crosses above the SMA, as requested.
FeraTrading Session High/LowThe FeraTrading Session High/Low Indicator draws clean, precise lines at the high and low of the New York, Asian, and London trading sessions.
We created this tool to provide simple, unobstructed view of session levels. In our experience, many session indicators use visual elements like background fills, boxed overlays, or lines that don't always align with expected session times. This indicator was built to be clean, readable, and easy to interpret in fast-paced trading environments. It automatically works on all timeframes and all time zones without the need of changing a single setting!
Key Features:
Lines begin at the exact bar where the session high or low occurred.
Standardized session timing
- NY: 0930 - 1700
- Asian: 1800 - 0300
- London: 0300 - 0800
Only relevant session lines are shown.
- Yesterday's NY session
- Last night's Asian session + today's morning continuation
- Today's London session
Lines trail live candles, updating in real time
Settings:
Custom session colors (NY, Asian, London)
Line Extender:
- Positive values extends lines forward
- Negative values extends lines backward
Toggle session labels
Note: Enable extended trading hours for full functionality.
Simple and easy to read, just how we like.
Improved MinhPhan EMA VWAP RSI StrategyThis enhanced Pine Script, "Improved MinhPhan EMA VWAP RSI Strategy," is a sophisticated trend-following system that combines EMA crossover, VWAP confirmation, and RSI filtering to increase trade precision. It’s ideal for traders who want high-probability entries filtered by both momentum and volume-based market positioning.
The strategy enters a long trade when the fast EMA crosses above the slow EMA, the price is above the VWAP, and RSI is below the overbought threshold, indicating early trend confirmation without being overextended. Conversely, it opens short trades when the fast EMA crosses below the slow EMA, price is under VWAP, and RSI is above the oversold level.
The script also features customizable stop loss and take profit levels in ticks (default: 100 SL, 200 TP) and uses RSI-based exits to cut trades when momentum becomes overbought/oversold. VWAP resets daily, weekly, or monthly depending on your preference, allowing better adaptability to market structure.
Visual cues for entry points (green/red triangles) and plotted indicators (EMA and VWAP) provide clarity for manual or semi-automated trading.
This strategy is well-suited for crypto, forex, or stock intraday and swing trading, offering both precision and flexibility for backtesting and optimization.
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.
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.
