Pivotal Point Detection
The indicator highlights price gaps (overnight gaps) with significantly increased volume in the daily chart only. These price jumps can occur after earnings reports or other significant news and often point to an important event (e.g., a new product or business model). According to Jesse Livermore, these are called Pivotal Points.
The price jumps displayed by the indicator are not a guarantee that they represent a true Pivotal Point, but they provide a hint of a significant business development - especially when they occur repeatedly alongside revenue growth. This can help identify potentially strong growth stocks and high-performing investments. However, the underlying events and connections must be investigated through additional research.
make posible to find stocks like:
NYSE:PLTR NASDAQ:ROOT NASDAQ:NVDA NYSE:CVNA NYSE:LRN
A "pivotal price line" is drawn at the opening price of the Pivotal Point. This line is considered a support level. If the price falls below this line, the Pivotal Point loses its validity.
株式
Divergence for Many Indicators v4 Screener▋ INTRODUCTION:
The “Divergence for Many Indicators v4 Screener” is developed to provide an advanced monitoring solution for up to 24 symbols simultaneously. It efficiently collects signals from multiple symbols based on the “ Divergence for Many Indicators v4 ” and presents the output in an organized table. The table includes essential details starting with the symbol name, signal price, corresponding divergence indicator, and signal time.
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▋ CREDIT:
The divergence formula adapted from the “ Divergence for Many Indicators v4 ” script, originally created by @LonesomeTheBlue . Full credit to his work.
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▋ OVERVIEW:
The chart image can be considered an example of a recorded divergence signal that occurred in $BTCUSDT.
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▋ APPEARANCE:
The table can be displayed in three formats:
1. Full indicator name.
2. First letter of the indicator name.
3. Total number of divergences.
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▋ SIGNAL CONFIRMATION:
The table distinguishes signal confirmation by using three different colors:
1. Not-Confirmed (Orange): The signal is not confirmed yet, as the bar is still open.
2. Freshly Confirmed (Green): The signal was confirmed 1 or 2 bars ago.
3. Confirmed (Gray): The signal was confirmed 3 or more bars ago.
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▋ INDICATOR SETTINGS:
Section(1): Table Settings
(1) Table location on the chart.
(2) Table’s cells size.
(3) Chart’s timezone.
(4) Sorting table.
- Signal: Sorts the table by the latest signals.
- None: Sorts the table based on the input order.
(5) Table’s colors.
(6) Signal Confirmation type color. Explained above in the SIGNAL CONFIRMATION section
Section(2): Divergence for Many Indicators v4 Settings
As seen on the Divergence for Many Indicators v4
* Explained above in the APPEARANCE section
Section(3): Symbols
(1) Enable/disable symbol in the screener.
(2) Entering a symbol.
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▋ FINAL COMMENTS:
For best performance, add the Screener indicator to an active symbol chart, such as QQQ, SPY, AAPL, BTCUSDT, ES, EURUSD, etc., and avoid mixing symbols from different market allocations.
The Divergence for Many Indicators v4 Screener indicator is not a primary tool for making trading decisions.
Simple RSI stock Strategy [1D] The "Simple RSI Stock Strategy " is designed to long-term traders. Strategy uses a daily time frame to capitalize on signals generated by the Relative Strength Index (RSI) and the Simple Moving Average (SMA). This strategy is suitable for low-leverage trading environments and focuses on identifying potential buy opportunities when the market is oversold, while incorporating strong risk management with both dynamic and static Stop Loss mechanisms.
This strategy is recommended for use with a relatively small amount of capital and is best applied by diversifying across multiple stocks in a strong uptrend, particularly in the S&P 500 stock market. It is specifically designed for equities, and may not perform well in other markets such as commodities, forex, or cryptocurrencies, where different market dynamics and volatility patterns apply.
Indicators Used in the Strategy:
1. RSI (Relative Strength Index):
- The RSI is a momentum oscillator used to identify overbought and oversold conditions in the market.
- This strategy enters long positions when the RSI drops below the oversold level (default: 30), indicating a potential buying opportunity.
- It focuses on oversold conditions but uses a filter (SMA 200) to ensure trades are only made in the context of an overall uptrend.
2. SMA 200 (Simple Moving Average):
- The 200-period SMA serves as a trend filter, ensuring that trades are only executed when the price is above the SMA, signaling a bullish market.
- This filter helps to avoid entering trades in a downtrend, thereby reducing the risk of holding positions in a declining market.
3. ATR (Average True Range):
- The ATR is used to measure market volatility and is instrumental in setting the Stop Loss.
- By multiplying the ATR value by a custom multiplier (default: 1.5), the strategy dynamically adjusts the Stop Loss level based on market volatility, allowing for flexibility in risk management.
How the Strategy Works:
Entry Signals:
The strategy opens long positions when RSI indicates that the market is oversold (below 30), and the price is above the 200-period SMA. This ensures that the strategy buys into potential market bottoms within the context of a long-term uptrend.
Take Profit Levels:
The strategy defines three distinct Take Profit (TP) levels:
TP 1: A 5% from the entry price.
TP 2: A 10% from the entry price.
TP 3: A 15% from the entry price.
As each TP level is reached, the strategy closes portions of the position to secure profits: 33% of the position is closed at TP 1, 66% at TP 2, and 100% at TP 3.
Visualizing Target Points:
The strategy provides visual feedback by plotting plotshapes at each Take Profit level (TP 1, TP 2, TP 3). This allows traders to easily see the target profit levels on the chart, making it easier to monitor and manage positions as they approach key profit-taking areas.
Stop Loss Mechanism:
The strategy uses a dual Stop Loss system to effectively manage risk:
ATR Trailing Stop: This dynamic Stop Loss adjusts based on the ATR value and trails the price as the position moves in the trader’s favor. If a price reversal occurs and the market begins to trend downward, the trailing stop closes the position, locking in gains or minimizing losses.
Basic Stop Loss: Additionally, a fixed Stop Loss is set at 25%, limiting potential losses. This basic Stop Loss serves as a safeguard, automatically closing the position if the price drops 25% from the entry point. This higher Stop Loss is designed specifically for low-leverage trading, allowing more room for market fluctuations without prematurely closing positions.
to determine the level of stop loss and target point I used a piece of code by RafaelZioni, here is the script from which a piece of code was taken
Together, these mechanisms ensure that the strategy dynamically manages risk while offering robust protection against significant losses in case of sharp market downturns.
The position size has been estimated by me at 75% of the total capital. For optimal capital allocation, a recommended value based on the Kelly Criterion, which is calculated to be 59.13% of the total capital per trade, can also be considered.
Enjoy !
Unlock the Power of Seasonality: Monthly Performance StrategyThe Monthly Performance Strategy leverages the power of seasonality—those cyclical patterns that emerge in financial markets at specific times of the year. From tax deadlines to industry-specific events and global holidays, historical data shows that certain months can offer strong opportunities for trading. This strategy was designed to help traders capture those opportunities and take advantage of recurring market patterns through an automated and highly customizable approach.
The Inspiration Behind the Strategy:
This strategy began with the idea that market performance is often influenced by seasonal factors. Historically, certain months outperform others due to a variety of reasons, like earnings reports, holiday shopping, or fiscal year-end events. By identifying these periods, traders can better time their market entries and exits, giving them an advantage over those who solely rely on technical indicators or news events.
The Monthly Performance Strategy was built to take this concept and automate it. Instead of manually analyzing market data for each month, this strategy enables you to select which months you want to focus on and then executes trades based on predefined rules, saving you time and optimizing the performance of your trades.
Key Features:
Customizable Month Selection: The strategy allows traders to choose specific months to test or trade on. You can select any combination of months—for example, January, July, and December—to focus on based on historical trends. Whether you’re targeting the historically strong months like December (often driven by the 'Santa Rally') or analyzing quieter months for low volatility trades, this strategy gives you full control.
Automated Monthly Entries and Exits: The strategy automatically enters a long position on the first day of your selected month(s) and exits the trade at the beginning of the next month. This makes it perfect for traders who want to benefit from seasonal patterns without manually monitoring the market. It ensures precision in entering and exiting trades based on pre-set timeframes.
Re-entry on Stop Loss or Take Profit: One of the standout features of this strategy is its ability to re-enter a trade if a position hits the stop loss (SL) or take profit (TP) level during the selected month. If your trade reaches either a SL or TP before the month ends, the strategy will automatically re-enter a new trade the next trading day. This feature ensures that you capture multiple trading opportunities within the same month, instead of exiting entirely after a successful or unsuccessful trade. Essentially, it keeps your capital working for you throughout the entire month, not just when conditions align perfectly at the beginning.
Built-in Risk Management: Risk management is a vital part of this strategy. It incorporates an Average True Range (ATR)-based stop loss and take profit system. The ATR helps set dynamic levels based on the market’s volatility, ensuring that your stops and targets adjust to changing market conditions. This not only helps limit potential losses but also maximizes profit potential by adapting to market behavior.
Historical Performance Testing: You can backtest this strategy on any period by setting the start year. This allows traders to analyze past market data and optimize their strategy based on historical performance. You can fine-tune which months to trade based on years of data, helping you identify trends and patterns that provide the best trading results.
Versatility Across Asset Classes: While this strategy can be particularly effective for stock market indices and sector rotation, it’s versatile enough to apply to other asset classes like forex, commodities, and even cryptocurrencies. Each asset class may exhibit different seasonal behaviors, allowing you to explore opportunities across various markets with this strategy.
How It Works:
The trader selects which months to test or trade, for example, January, April, and October.
The strategy will automatically open a long position on the first trading day of each selected month.
If the trade hits either the take profit or stop loss within the month, the strategy will close the current position and re-enter a new trade on the next trading day, provided the month has not yet ended. This ensures that the strategy continues to capture any potential gains throughout the month, rather than stopping after one successful trade.
At the start of the next month, the position is closed, and if the next month is also selected, a new trade is initiated following the same process.
Risk Management and Dynamic Adjustments:
Incorporating risk management with this strategy is as easy as turning on the ATR-based system. The strategy will automatically calculate stop loss and take profit levels based on the market’s current volatility, adjusting dynamically to the conditions. This ensures that the risk is controlled while allowing for flexibility in capturing profits during both high and low volatility periods.
Maximizing the Seasonal Edge:
By automating entries and exits based on specific months and combining that with dynamic risk management, the Ultimate Monthly Performance Strategy takes advantage of seasonal patterns without requiring constant monitoring. The added re-entry feature after hitting a stop loss or take profit ensures that you are always in the game, maximizing your chances to capture profitable trades during favorable seasonal periods.
Who Can Benefit from This Strategy?
This strategy is perfect for traders who:
Want to exploit the predictable, recurring patterns that occur during specific months of the year.
Prefer a hands-off, automated trading approach that allows them to focus on other aspects of their portfolio or life.
Seek to manage risk effectively with ATR-based stop losses and take profits that adjust to market conditions.
Appreciate the ability to re-enter trades when a take profit or stop loss is hit within the month, ensuring that they don't miss out on multiple opportunities during a favorable period.
In summary, the Ultimate Monthly Performance Strategy provides traders with a comprehensive tool to capitalize on seasonal trends, optimize their trading opportunities throughout the year, and manage risk effectively. The built-in re-entry system ensures you continue to benefit from the market even after hitting targets within the same month, making it a robust strategy for traders looking to maximize their edge in any market.
Risk Disclaimer:
Trading financial markets involves significant risk and may not be suitable for all investors. The Monthly Performance Strategy is designed to help traders identify seasonal trends, but past performance does not guarantee future results. It is important to carefully consider your risk tolerance, financial situation, and trading goals before using any strategy. Always use appropriate risk management and consult with a professional financial advisor if necessary. The use of this strategy does not eliminate the risk of losses, and traders should be prepared for the possibility of losing their entire investment. Be sure to test the strategy on a demo account before applying it in live markets.
Cumulative Net Money FlowDescription:
Dive into the financial depth of the markets with the "Cumulative Net Money Flow" indicator, designed to provide a comprehensive view of the monetary dynamics in trading. This tool is invaluable for traders and investors seeking to quantify the actual money entering or exiting the market over a specified period.
Features:
Value-Weighted Calculations: This indicator multiplies the trading volume by the price, offering a money flow perspective rather than just counting shares or contracts.
Custom Timeframe Adaptability: Adjust the timeframe to match your trading strategy, whether you are day trading, swing trading, or looking for longer-term trends.
Cumulative Insight: Tracks and accumulates net money flow to highlight overall market sentiment, making it easier to spot trends in capital movement.
Color-Coded Visualization: Displays positive money flow in green and negative money flow in red, providing clear, visual cues about market conditions.
Utility: "Cumulative Net Money Flow" is particularly effective in revealing the strength behind market movements. By understanding whether the money flow is predominantly buying or selling, traders can better align their strategies with market sentiment. This indicator is suited for various asset classes, including stocks, cryptocurrencies, and forex.
Multi-Step FlexiSuperTrend - Strategy [presentTrading]At the heart of this endeavor is a passion for continuous improvement in the art of trading
█ Introduction and How it is Different
The "Multi-Step FlexiSuperTrend - Strategy " is an advanced trading strategy that integrates the well-known SuperTrend indicator with a nuanced and dynamic approach to market trend analysis. Unlike conventional SuperTrend strategies that rely on static thresholds and fixed parameters, this strategy introduces multi-step take profit mechanisms that allow traders to capitalize on varying market conditions in a more controlled and systematic manner.
What sets this strategy apart is its ability to dynamically adjust to market volatility through the use of an incremental factor applied to the SuperTrend calculation. This adjustment ensures that the strategy remains responsive to both minor and major market shifts, providing a more accurate signal for entries and exits. Additionally, the integration of multi-step take profit levels offers traders the flexibility to scale out of positions, locking in profits progressively as the market moves in their favor.
BTC 6hr Long/Short Performance
█ Strategy, How it Works: Detailed Explanation
The Multi-Step FlexiSuperTrend strategy operates on the foundation of the SuperTrend indicator, but with several enhancements that make it more adaptable to varying market conditions. The key components of this strategy include the SuperTrend Polyfactor Oscillator, a dynamic normalization process, and multi-step take profit levels.
🔶 SuperTrend Polyfactor Oscillator
The SuperTrend Polyfactor Oscillator is the heart of this strategy. It is calculated by applying a series of SuperTrend calculations with varying factors, starting from a defined "Starting Factor" and incrementing by a specified "Increment Factor." The indicator length and the chosen price source (e.g., HLC3, HL2) are inputs to the oscillator.
The SuperTrend formula typically calculates an upper and lower band based on the average true range (ATR) and a multiplier (the factor). These bands determine the trend direction. In the FlexiSuperTrend strategy, the oscillator is enhanced by iteratively applying the SuperTrend calculation across different factors. The iterative process allows the strategy to capture both minor and significant trend changes.
For each iteration (indexed by `i`), the following calculations are performed:
1. ATR Calculation: The Average True Range (ATR) is calculated over the specified `indicatorLength`:
ATR_i = ATR(indicatorLength)
2. Upper and Lower Bands Calculation: The upper and lower bands are calculated using the ATR and the current factor:
Upper Band_i = hl2 + (ATR_i * Factor_i)
Lower Band_i = hl2 - (ATR_i * Factor_i)
Here, `Factor_i` starts from `startingFactor` and is incremented by `incrementFactor` in each iteration.
3. Trend Determination: The trend is determined by comparing the indicator source with the upper and lower bands:
Trend_i = 1 (uptrend) if IndicatorSource > Upper Band_i
Trend_i = 0 (downtrend) if IndicatorSource < Lower Band_i
Otherwise, the trend remains unchanged from the previous value.
4. Output Calculation: The output of each iteration is determined based on the trend:
Output_i = Lower Band_i if Trend_i = 1
Output_i = Upper Band_i if Trend_i = 0
This process is repeated for each iteration (from 0 to 19), creating a series of outputs that reflect different levels of trend sensitivity.
Local
🔶 Normalization Process
To make the oscillator values comparable across different market conditions, the deviations between the indicator source and the SuperTrend outputs are normalized. The normalization method can be one of the following:
1. Max-Min Normalization: The deviations are normalized based on the range of the deviations:
Normalized Value_i = (Deviation_i - Min Deviation) / (Max Deviation - Min Deviation)
2. Absolute Sum Normalization: The deviations are normalized based on the sum of absolute deviations:
Normalized Value_i = Deviation_i / Sum of Absolute Deviations
This normalization ensures that the oscillator values are within a consistent range, facilitating more reliable trend analysis.
For more details:
🔶 Multi-Step Take Profit Mechanism
One of the unique features of this strategy is the multi-step take profit mechanism. This allows traders to lock in profits at multiple levels as the market moves in their favor. The strategy uses three take profit levels, each defined as a percentage increase (for long trades) or decrease (for short trades) from the entry price.
1. First Take Profit Level: Calculated as a percentage increase/decrease from the entry price:
TP_Level1 = Entry Price * (1 + tp_level1 / 100) for long trades
TP_Level1 = Entry Price * (1 - tp_level1 / 100) for short trades
The strategy exits a portion of the position (defined by `tp_percent1`) when this level is reached.
2. Second Take Profit Level: Similar to the first level, but with a higher percentage:
TP_Level2 = Entry Price * (1 + tp_level2 / 100) for long trades
TP_Level2 = Entry Price * (1 - tp_level2 / 100) for short trades
The strategy exits another portion of the position (`tp_percent2`) at this level.
3. Third Take Profit Level: The final take profit level:
TP_Level3 = Entry Price * (1 + tp_level3 / 100) for long trades
TP_Level3 = Entry Price * (1 - tp_level3 / 100) for short trades
The remaining portion of the position (`tp_percent3`) is exited at this level.
This multi-step approach provides a balance between securing profits and allowing the remaining position to benefit from continued favorable market movement.
█ Trade Direction
The strategy allows traders to specify the trade direction through the `tradeDirection` input. The options are:
1. Both: The strategy will take both long and short positions based on the entry signals.
2. Long: The strategy will only take long positions.
3. Short: The strategy will only take short positions.
This flexibility enables traders to tailor the strategy to their market outlook or current trend analysis.
█ Usage
To use the Multi-Step FlexiSuperTrend strategy, traders need to set the input parameters according to their trading style and market conditions. The strategy is designed for versatility, allowing for various market environments, including trending and ranging markets.
Traders can also adjust the multi-step take profit levels and percentages to match their risk management and profit-taking preferences. For example, in highly volatile markets, traders might set wider take profit levels with smaller percentages at each level to capture larger price movements.
The normalization method and the incremental factor can be fine-tuned to adjust the sensitivity of the SuperTrend Polyfactor Oscillator, making the strategy more responsive to minor market shifts or more focused on significant trends.
█ Default Settings
The default settings of the strategy are carefully chosen to provide a balanced approach between risk management and profit potential. Here is a breakdown of the default settings and their effects on performance:
1. Indicator Length (10): This parameter controls the lookback period for the ATR calculation. A shorter length makes the strategy more sensitive to recent price movements, potentially generating more signals. A longer length smooths out the ATR, reducing sensitivity but filtering out noise.
2. Starting Factor (0.618): This is the initial multiplier used in the SuperTrend calculation. A lower starting factor makes the SuperTrend bands closer to the price, generating more frequent trend changes. A higher starting factor places the bands further away, filtering out minor fluctuations.
3. Increment Factor (0.382): This parameter controls how much the factor increases with each iteration of the SuperTrend calculation. A smaller increment factor results in more gradual changes in sensitivity, while a larger increment factor creates a wider range of sensitivity across the iterations.
4. Normalization Method (None): The default is no normalization, meaning the raw deviations are used. Normalization methods like Max-Min or Absolute Sum can make the deviations more consistent across different market conditions, improving the reliability of the oscillator.
5. Take Profit Levels (2%, 8%, 18%): These levels define the thresholds for exiting portions of the position. Lower levels (e.g., 2%) capture smaller profits quickly, while higher levels (e.g., 18%) allow positions to run longer for more significant gains.
6. Take Profit Percentages (30%, 20%, 15%): These percentages determine how much of the position is exited at each take profit level. A higher percentage at the first level locks in more profit early, reducing exposure to market reversals. Lower percentages at higher levels allow for a portion of the position to benefit from extended trends.
Relative Strength (Volatility Adjusted)The volatility adjusted relative strength indicator offers a more precise approach to traditional RS indicators by incorporating volatility adjustments into its calculations. This will provide traders with a more nuanced view of relative performance between a selected instrument and a comparison index.
Identifying Relative Strength (RS) and Weakness (RW) against a benchmark like the SPY is crucial for traders, as it highlights institutional activity in an equity, which retail traders rarely achieve on their own. However, the traditional method of simply comparing the rate of change of a stock to the rate of change for the SPY can be flawed. This method often fails to account for the inherent volatility of each stock, leading to misleading RS/RW readings.
Consider two stocks that both move in response to SPY's movements. If SPY moves significantly more than its average (measured by its ATR), and the stock does the same, traditional RS calculations might show strength when, in fact, the stock is just mirroring SPY's increased volatility. For instance, if SPY typically moves $0.25 an hour but suddenly moves $1, and a stock typically moves $0.50 but moves $2, the stock's apparent RS might be overstated, when in reality there is no relative strength for the stock.
By adjusting for volatility using the ATR (Average True Range), we normalize these movements and get a clearer picture of true RS/RW. For example, if SPY moves 5 times its average rate and a stock moves the same multiple of its own ATR, the RS should be considered neutral rather than strong. Similarly if a stock in absolute terms moves $1 while the SPY also moves $1 but the stock usually moves at twice the rate of the SPY, the stock should be considered relatively weak - not neutral.
Usage
Use this to identify stocks with actual strength or weakness compared to the market.
When the RS line is above 0 and above the moving average it indicates a stock with relative strength that is still gaining more strength.
When the RS line is above 0 but above the moving average it indicates a stock with relative strength that is currently losing strength.
When the RS line is below 0 and below the moving average it indicates a stock with relative weakness that is still losing strength.
When the RS line is below 0 but above the moving average it indicates a stock with relative weakness that is starting to gain back some strength.
Portfolio Index Generator [By MUQWISHI]▋ INTRODUCTION:
The “Portfolio Index Generator” simplifies the process of building a custom portfolio management index, allowing investors to input a list of preferred holdings from global securities and customize the initial investment weight of each security. Furthermore, it includes an option for rebalancing by adjusting the weights of assets to maintain a desired level of asset allocation. The tool serves as a comprehensive approach for tracking portfolio performance, conducting research, and analyzing specific aspects of portfolio investment. The output includes an index value, a table of holdings, and chart plotting, providing a deeper understanding of the portfolio's historical movement.
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▋ OVERVIEW:
The image can be taken as an example of building a custom portfolio index. I created this index and named it “My Portfolio Performance”, which comprises several global companies and crypto assets.
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▋ OUTPUTS:
The output can be divided into 4 sections:
1. Portfolio Index Title (Name & Value).
2. Portfolio Specifications.
3. Portfolio Holdings.
4. Portfolio Index Chart.
1. Portfolio Index Title, displays the index name at the top, and at the bottom, it shows the index value, along with the chart timeframe, e.g., daily change in points and percentage.
2. Portfolio Specifications, displays the essential information on portfolio performance, including the investment date range, initial capital, returns, assets, and equity.
3. Portfolio Holdings, a list of the holding securities inside a table that contains the ticker, average entry price, last price, return percentage of the portfolio's initial capital, and customized weighted percentage of the portfolio. Additionally, a tooltip appears when the user passes the cursor over a ticker's cell, showing brief information about the company, such as the company's name, exchange market, country, sector, and industry.
4. Index Chart, display a plot of the historical movement of the index in the form of a bar, candle, or line chart.
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▋ INDICATOR SETTINGS:
Section(1): Style Settings
(1) Naming the index.
(2) Table location on the chart and cell size.
(3) Sorting Holdings Table. By securities’ {Return(%) Portfolio, Weight(%) Portfolio, or Ticker Alphabetical} order.
(4) Choose the type of index: {Equity or Return (%)}, and the plot type for the index: {Candle, Bar, or Line}.
(5) Positive/Negative colors.
(6) Table Colors (Title, Cell, and Text).
(7) To show/hide any indicator’s components.
Section(2): Performance Settings
(1) Calculation window period: from DateTime to DateTime.
(2) Initial Capital and specifying currency.
(3) Option to enable portfolio rebalancing in {Monthly, Quarterly, or Yearly} intervals.
Section(3): Portfolio Holdings
(1) Enable and count security in the investment portfolio.
(2) Initial weight of security. For example, if the initial capital is $100,000 and the weight of XYZ stock is 4%, the initial value of the shares would be $4,000.
(3) Select and add up to 30 symbols that interested in.
Please let me know if you have any questions.
Markov Chain Trend IndicatorOverview
The Markov Chain Trend Indicator utilizes the principles of Markov Chain processes to analyze stock price movements and predict future trends. By calculating the probabilities of transitioning between different market states (Uptrend, Downtrend, and Sideways), this indicator provides traders with valuable insights into market dynamics.
Key Features
State Identification: Differentiates between Uptrend, Downtrend, and Sideways states based on price movements.
Transition Probability Calculation: Calculates the probability of transitioning from one state to another using historical data.
Real-time Dashboard: Displays the probabilities of each state on the chart, helping traders make informed decisions.
Background Color Coding: Visually represents the current market state with background colors for easy interpretation.
Concepts Underlying the Calculations
Markov Chains: A stochastic process where the probability of moving to the next state depends only on the current state, not on the sequence of events that preceded it.
Logarithmic Returns: Used to normalize price changes and identify states based on significant movements.
Transition Matrices: Utilized to store and calculate the probabilities of moving from one state to another.
How It Works
The indicator first calculates the logarithmic returns of the stock price to identify significant movements. Based on these returns, it determines the current state (Uptrend, Downtrend, or Sideways). It then updates the transition matrices to keep track of how often the price moves from one state to another. Using these matrices, the indicator calculates the probabilities of transitioning to each state and displays this information on the chart.
How Traders Can Use It
Traders can use the Markov Chain Trend Indicator to:
Identify Market Trends: Quickly determine if the market is in an uptrend, downtrend, or sideways state.
Predict Future Movements: Use the transition probabilities to forecast potential market movements and make informed trading decisions.
Enhance Trading Strategies: Combine with other technical indicators to refine entry and exit points based on predicted trends.
Example Usage Instructions
Add the Markov Chain Trend Indicator to your TradingView chart.
Observe the background color to quickly identify the current market state:
Green for Uptrend, Red for Downtrend, Gray for Sideways
Check the dashboard label to see the probabilities of transitioning to each state.
Use these probabilities to anticipate market movements and adjust your trading strategy accordingly.
Combine the indicator with other technical analysis tools for more robust decision-making.
Macro Risk On/Off SentimentOverview
As an Ichimoku trader, I've always found it crucial to understand the broader market sentiment before entering trades. That's why I developed this Macro Risk On/Off Sentiment Indicator. It's designed to provide a comprehensive view of global market risk sentiment by analysing multiple factors across different asset classes. By combining nine key market indicators, it produces an overall risk sentiment score, giving me a clearer picture of the market's mood before I apply my Ichimoku strategy.
Rationale
While Ichimoku is powerful for identifying trends and potential entry points, I realised it doesn't always capture the broader market context. Markets don't exist in isolation—they're influenced by a myriad of factors including volatility, economic indicators, and cross-asset relationships. By creating this indicator, I aimed to fill that gap, providing myself with a macro view that complements my Ichimoku analysis.
How It Works
The indicator analyses nine different market factors:
VIX (Volatility Index): Measures market expectations of near-term volatility.
S&P 500 Performance: Represents the overall US stock market performance.
US 10-Year Treasury Yield: Indicates bond market sentiment and economic outlook.
Gold Price Movement: Often seen as a safe-haven asset.
US Dollar Index: Measures the strength of the USD against a basket of currencies.
Emerging Markets Performance: Represents risk appetite for higher-risk markets.
High Yield Bond Spreads: Indicates credit market risk sentiment.
Copper/Gold Ratio: An economic growth indicator.
Put/Call Ratio: Measures overall market sentiment based on options trading.
Each factor is assigned a score based on its z-score relative to its recent history, then weighted according to its perceived importance. The overall risk score is a weighted average of these individual scores.
How I Use It
Before applying my Ichimoku strategy, I first check this indicator to gauge the overall market sentiment:
I look at the blue line plotted on the chart, which represents the overall risk score.
I note the background colour: green for risk-on (positive score) and red for risk-off (negative score).
I check the label in the lower-left corner, which provides specific FX pair recommendations and market expectations.
In a risk-on environment (positive score):
I focus on long positions in AUD/JPY, NZD/JPY, EUR/USD, etc.
I look for short opportunities in USD/CAD, USD/NOK, etc.
I expect commodities and yields to rise
In a risk-off environment (negative score):
I focus on long positions in USD/JPY, USD/CHF, USD/CAD
I look for short opportunities in AUD/USD, NZD/USD, EUR/USD
I expect increased volatility and falling yields
The strength of the sentiment is reflected in how close the score is to either 1 (strong risk-on) or -1 (strong risk-off). This helps me gauge how aggressive or conservative I should be with my Ichimoku trades.
Customisation
I've designed this indicator to be flexible. You can modify it to:
Adjust the lookback period and moving average length (both default to 30)
Change the weighting of different factors in the final score calculation
Include or exclude specific factors based on your analysis needs
By combining this Macro Risk On/Off Sentiment Indicator with my Ichimoku analysis, I've found I can make more informed trading decisions, taking into account both the technical setups I see on the chart and the broader market context.
Buy-Sell Volume Bar Gauge [By MUQWISHI]▋ INTRODUCTION :
The Buy-Sell Volume Bar Gauge is developed to provide traders with a detailed analysis of volume in bars using a low timeframe, such as a 1-second interval, to measure the dominance of buy and sell for each bar. By highlighting the balance between buying and selling activities, the Buy-Sell Volume Bar Gauge helps traders identify potential volume momentum of a bar; aimed at being a useful tool for day traders and scalpers.
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▋ OVERVIEW:
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▋ METHODOLOGY:
The concept is based on bars from a lower timeframe within the current chart timeframe bar, where volume is categorized into Up, Down, and Neutral Volume, with each one displayed as a portion of a column plot. Up Volume is recorded when the price experiences a positive change, Down Volume occurs when the price experiences a negative change, and Neutral Volume is observed when the price shows no significant change.
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▋ INDICATOR SETTINGS:
(1) Fetch data from the selected lower timeframe. Note: If the selected timeframe is invalid (higher than chart), the indicator will automatically switch to 1 second.
(2) Price Source.
(3) Treating Neutral Data (Price Source) as
Neutral: In a lower timeframe, when the bar has no change in its price, the volume is counted as Neutral Volume.
Previous Move: In a lower timeframe, when the bar has no change in its price, the volume is counted as the previous change; “Up Volume” if the previous change was positive, and “Down Volume” if the previous change was negative.
Opposite Previous Move: In a lower timeframe, when the bar has no change in its price, the volume is counted as the opposite previous change; “Up Volume” if the previous change was negative, and “Down Volume” if the previous change was positive.
(4) Average Volume Length, it's used for lighting/darkening columns in a plot.
(5) Enable Alert.
(7) Total bought (%) Level.
(8) Total Sold (%) Level.
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▋ COMMENT:
The Buy-Sell Volume Bar Gauge can be taken as confirmation for predicting the next move, but it should not be considered a major factor in making a trading decision.
Index Generator [By MUQWISHI]▋ INTRODUCTION :
The “Index Generator” simplifies the process of building a custom market index, allowing investors to enter a list of preferred holdings from global securities. It aims to serve as an approach for tracking performance, conducting research, and analyzing specific aspects of the global market. The output will include an index value, a table of holdings, and chart plotting, providing a deeper understanding of historical movement.
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▋ OVERVIEW:
The image can be taken as an example of building a custom index. I created this index and named it “My Oil & Gas Index”. The index comprises several global energy companies. Essentially, the indicator weights each company by collecting the number of shares and then computes the market capitalization before sorting them as seen in the table.
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▋ OUTPUTS:
The output can be divided into 3 sections:
1. Index Title (Name & Value).
2. Index Holdings.
3. Index Chart.
1. Index Title , displays the index name at the top, and at the bottom, it shows the index value, along with the daily change in points and percentage.
2. Index Holdings , displays list the holding securities inside a table that contains the ticker, price, daily change %, market cap, and weight %. Additionally, a tooltip appears when the user passes the cursor over a ticker's cell, showing brief information about the company, such as the company's name, exchange market, country, sector, and industry.
3. Index Chart , display a plot of the historical movement of the index in the form of a bar, candle, or line chart.
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▋ INDICATOR SETTINGS:
(1) Naming the index.
(2) Entering a currency. To unite all securities in one currency.
(3) Table location on the chart.
(4) Table’s cells size.
(5) Table’s colors.
(6) Sorting table. By securities’ (Market Cap, Change%, Price, or Ticker Alphabetical) order.
(7) Plotting formation (Candle, Bar, or Line)
(8) To show/hide any indicator’s components.
(9) There are 34 fields where user can fill them with symbols.
Please let me know if you have any questions.
ATR Grid Levels [By MUQWISHI]▋ INTRODUCTION :
The “ATR Levels” produces a sequence of horizontal line levels above and below the Center Line (reference level). They are sized based on the instrument's volatility, representing the average historical price movement on a selected higher timeframe using the average true range (ATR) indicator.
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▋ OVERVIEW:
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▋ IMPLEMENTATION:
The indicator starts by drawing a Center Line that is selected by the user from a variety of common levels. Then, it draws a sequence of horizontal lines above and below the Center Line, which are sized based on the most confirmed average true range (ATR) at the selected higher timeframe.
In the top right corner of the chart, there is a table displaying both the selected ATR (in the right cell) and the ATR of the current bar (in the left cell). This feature enables users to compare these two values. It's important to note that the ATR of the current bar may not be confirmed yet, as the market is still active.
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▋ INDICATOR SETTINGS:
# Section (1): ATR Settings
(1) ATR Period & Smoothing.
(2) Timeframe where ATR value imported from.
(3) To show/hide the table comparison between the current ATR and the ATR for the selected period. Also, ability to color the current ATR cell if it’s greater.
# Section (2): Levels Settings
(1) Selecting a Center Line level among a variety of common levels, which is taken as reference level where a sequence of horizontal lines plot above and below it.
(2) Size of grid in ATR unit.
(3) Number of horizontal lines to plot in a single side.
(4) Grid Side. Ability to plot above or below the Center Line.
(5) Lines colors, and mode.
(6) Line style.
(7) Label style.
(8) Ability to remove old lines, from previous HTF.
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▋ COMMENT:
The ATR Levels should not be taken as a major concept to build a trading decision.
Please let me know if you have any questions.
Thank you.
Bandwidth Volatility - Silverman Rule of thumb EstimatorOverview
This indicator calculates volatility using the Rule of Thumb bandwidth estimator and incorporating the standard deviations of returns to get historical volatility. There are two options: one for the original rule of thumb bandwidth estimator, and another for the modified rule of thumb estimator. This indicator comes with the bandwidth , which is shown with the color gradient columns, which are colored by a percentile of the bandwidth, and the moving average of the bandwidth, which is the dark shaded area.
The rule of thumb bandwidth estimator is a simple and quick method for estimating the bandwidth parameter in kernel density estimation (KSE) or kernel regression. It provides a rough approximation of the bandwidth without requiring extensive computation resources or fine-tuning. One common rule of thumb estimator is Silverman rule, which is given by
h = 1.06*σ*n^(-1/5)
where
h is the bandwidth
σ is the standard deviation of the data
n is the number of data points
This rule of thumb is based on assuming a Gaussian kernel and aims to strike a balance between over-smoothing and under-smoothing the data. It is simple to implement and usually provides reasonable bandwidth estimates for a wide range of datasets. However , it is important to note that this rule of thumb may not always have optimal results, especially for non-Gaussian or multimodal distributions. In such cases, a modified bandwidth selection, such as cross-validation or even applying a log transformation (if the data is right-skewed), may be preferable.
How it works:
This indicator computes the bandwidth volatility using returns, which are used in the standard deviation calculation. It then estimates the bandwidth based on either the Silverman rule of thumb or a modified version considering the interquartile range. The percentile ranks of the bandwidth estimate are then used to visualize the volatility levels, identify high and low volatility periods, and show them with colors.
Modified Rule of thumb Bandwidth:
The modified rule of thumb bandwidth formula combines elements of standard deviations and interquartile ranges, scaled by a multiplier of 0.9 and inversely with a number of periods. This modification aims to provide a more robust and adaptable bandwidth estimation method, particularly suitable for financial time series data with potentially skewed or heavy-tailed data.
Formula for Modified Rule of Thumb Bandwidth:
h = 0.9 * min(σ, (IQR/1.34))*n^(-1/5)
This modification introduces the use of the IQR divided by 1.34 as an alternative to the standard deviation. It aims to improve the estimation, mainly when the underlying distribution deviates from a perfect Gaussian distribution.
Analysis
Rule of thumb Bandwidth: Provides a broader perspective on volatility trends, smoothing out short-term fluctuations and focusing more on the overall shape of the density function.
Historical Volatility: Offers a more granular view of volatility, capturing day-to-day or intra-period fluctuations in asset prices and returns.
Modelling Requirements
Rule of thumb Bandwidth: Provides a broader perspective on volatility trends, smoothing out short-term fluctuations and focusing more on the overall shape of the density function.
Historical Volatility: Offers a more granular view of volatility, capturing day-to-day or intra-period fluctuations in asset prices and returns.
Pros of Bandwidth as a volatility measure
Robust to Data Distribution: Bandwidth volatility, especially when estimated using robust methods like Silverman's rule of thumb or its modifications, can be less sensitive to outliers and non-normal distributions compared to some other measures of volatility
Flexibility: It can be applied to a wide range of data types and can adapt to different underlying data distributions, making it versatile for various analytical tasks.
How can traders use this indicator?
In finance, volatility is thought to be a mean-reverting process. So when volatility is at an extreme low, it is expected that a volatility expansion happens, which comes with bigger movements in price, and when volatility is at an extreme high, it is expected for volatility to eventually decrease, leading to smaller price moves, and many traders view this as an area to take profit in.
In the context of this indicator, low volatility is thought of as having the green color, which indicates a low percentile value, and also being below the moving average. High volatility is thought of as having the yellow color and possibly being above the moving average, showing that you can eventually expect volatility to decrease.
Optimal Buy Day (Zeiierman)█ Overview
The Optimal Buy Day (Zeiierman) indicator identifies optimal buying days based on historical price data, starting from a user-defined year. It simulates investing a fixed initial capital and making regular monthly contributions. The unique aspect of this indicator involves comparing systematic investment on specific days of the month against a randomized buying day each month, aiming to analyze which method might yield more shares or a better average price over time. By visualizing the potential outcomes of systematic versus randomized buying, traders can better understand the impact of market timing and how regular investments might accumulate over time.
These statistics are pivotal for traders and investors using the script to analyze historical performance and strategize future investments. By understanding which days offered more shares for their money or lower average prices, investors can tailor their buying strategies to potentially enhance returns.
█ Key Statistics
⚪ Shares
Definition: Represents the total number of shares acquired on a particular day of the month across the entire simulation period.
How It Works: The script calculates how many shares can be bought each day, given the available capital or monthly contribution. This calculation takes into account the day's opening price and accumulates the total shares bought on that day over the simulation period.
Interpretation: A higher number of shares indicates that the day consistently offered better buying opportunities, allowing the investor to acquire more shares for the same amount of money. This metric is crucial for understanding which days historically provided more value.
⚪ AVG Price
Definition: The average price paid per share on a particular day of the month, averaged over the simulation period.
How It Works: Each time shares are bought, the script calculates the average price per share, factoring in the new shares purchased at the current price. This average evolves over time as more shares are bought at varying prices.
Interpretation: The average price gives insight into the cost efficiency of buying shares on specific days. A lower average price suggests that buying on that day has historically led to better pricing, making it a potentially more attractive investment strategy.
⚪ Buys
Definition: The total number of transactions or buys executed on a particular day of the month throughout the simulation.
How It Works: This metric increments each time shares are bought on a specific day, providing a count of all buying actions taken.
Interpretation: The number of buys indicates the frequency of investment opportunities. A higher count could mean more consistent opportunities for investment, but it's important to consider this in conjunction with the average price and the total shares acquired to assess overall strategy effectiveness.
⚪ Most Shares
Definition: Identifies the day of the month on which the highest number of shares were bought, highlighting the specific day and the total shares acquired.
How It Works: After simulating purchases across all days of the month, the script identifies which day resulted in the highest total number of shares bought.
Interpretation: This metric points out the most opportune day for volume buying. It suggests that historically, this day provided conditions that allowed for maximizing the quantity of shares purchased, potentially due to lower prices or other factors.
⚪ Best Price
Definition: Highlights the day of the month that offered the lowest average price per share, indicating both the day and the price.
How It Works: The script calculates the average price per share for each day and identifies the day with the lowest average.
Interpretation: This metric is key for investors looking to minimize costs. The best price day suggests that historically, buying on this day led to acquiring shares at a more favorable average price, potentially maximizing long-term investment returns.
⚪ Randomized Shares
Definition: This metric represents the total number of shares acquired on a randomly selected day of the month, simulated across the entire period.
How It Works: At the beginning of each month within the simulation, the script selects a random day when the market is open and calculates how many shares can be purchased with the available capital or monthly contribution at that day's opening price. This process is repeated each month, and the total number of shares acquired through these random purchases is tallied.
Interpretation: Randomized shares offer a comparison point to systematic buying strategies. By comparing the total shares acquired through random selection against those bought on the best or worst days, investors can gauge the impact of timing and market fluctuations on their investment strategy. A higher total in randomized shares might indicate that over the long term, the specific days chosen for investment might matter less than consistent market participation. Conversely, if systematic strategies yield significantly more shares, it suggests that timing could indeed play a crucial role in maximizing investment returns.
⚪ Randomized Price
Definition: The average price paid per share for the shares acquired on the randomly selected days throughout the simulation period.
How It Works: Each time shares are bought on a randomly chosen day, the script calculates the average price paid for all shares bought through this randomized strategy. This average price is updated as the simulation progresses, reflecting the cost efficiency of random buying decisions.
Interpretation: The randomized price metric helps investors understand the cost implications of a non-systematic, random investment approach. Comparing this average price to those achieved through more deliberate, systematic strategies can reveal whether consistent investment timing strategies outperform random investment actions in terms of cost efficiency. A lower randomized price suggests that random buying might not necessarily result in higher costs, while a higher average price indicates that systematic strategies might provide better control over investment costs.
█ How to Use
Traders can use this tool to analyze historical data and simulate different investment strategies. By inputting their initial capital, regular contribution amount, and start year, they can visually assess which days might have been more advantageous for buying, based on historical price actions. This can inform future investment decisions, especially for those employing dollar-cost averaging strategies or looking to optimize entry points.
█ Settings
StartYear: This setting allows the user to specify the starting year for the investment simulation. Changing this value will either extend or shorten the period over which the simulation is run. If a user increases the value, the simulation begins later and covers a shorter historical period; decreasing the value starts the simulation earlier, encompassing a longer time frame.
Capital: Determines the initial amount of capital with which the simulation begins. Increasing this value simulates starting with more capital, which can affect the number of shares that can be initially bought. Decreasing this value simulates starting with less capital.
Contribution: Sets the monthly financial contribution added to the investment within the simulation. A higher contribution increases the investment each month and could lead to more shares being purchased over time. Lowering the contribution decreases the monthly investment amount.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
ATH Gain PotentialThe indicator quantifies the relative position of a symbol's current closing price in relation to its historical all-time high (ATH).
By evaluating the ratio between the ATH and the present closing price, it provides an analytical framework to estimate the potential gains that could accrue if the symbol were to revert to its ATH from a specified reference point. The ratio serves as a quantitative measure for assessing the distance between the current market value and the symbol's historical peak, enabling investors to gauge the prospective profitability of a return to the ATH.
Trend Change IndicatorThe Trend Change Indicator is an all-in-one, user-friendly trend-following tool designed to identify bullish and bearish trends in asset prices. It features adjustable input values and a built-in alert system that promptly notifies investors of potential shifts in both short-term and long-term price trends. This alert system is crucial for helping less active investors correctly position themselves ahead of major trend shifts and assists in risk management after a trend is established. It's important to note that this indicator is most effective with assets that historically exhibit strong trends.
At the heart of this tool is the interaction between the 30-day and 60-day Exponential Moving Averages (EMA). A bullish trend is indicated in green when the 30-day EMA is above the 60-day EMA, while a bearish trend is signaled in red when the 30-day EMA is below the 60-day EMA. The appearance of gray alerts users to potential shifts in the current trend as the EMAs converge, falling below the Average True Range (ATR) safety margin. This analysis is conducted across both hourly and daily timeframes, with the 4-hour timeframe providing early signals for daily trend changes. The band visually represents the interaction between the daily EMAs and is also displayed in the second row of the table, with the first row showing the same EMA interaction on the 4-hour timeframe.
This indicator also includes a 140-day (20-week) Simple Moving Average (SMA), visually represented by a line with predictive dots. This feature significantly enhances the investor's ability to understand long-term trends in asset prices, offering forward-looking insights by projecting the SMA value 10 days into the future. The value of this forecast lies in interpreting the slope of the dots; upward trending dots suggest a bullish underlying trend, while downward trending dots indicate a bearish trend. Generally, prices above the SMA signal bullishness, and prices below indicate bearishness.
In summary, the Trend Change Indicator is a comprehensive solution for identifying price trends and managing risk. Its intuitive, color-coded design makes it an indispensable tool for traders and investors who aim to be well-positioned ahead of trend shifts and manage risk once a trend has been established. While it has proven historically valuable in trending markets such as cryptocurrencies, tech stocks, and commodities, it is advisable to use this indicator in conjunction with other technical analysis tools for a more comprehensive and well-rounded decision-making process.
Market Health MonitorThe Market Health Monitor is a comprehensive tool designed to assess and visualize the economic health of a market, providing traders with vital insights into both current and future market conditions. This script integrates a range of critical economic indicators, including unemployment rates, inflation, Federal Reserve funds rates, consumer confidence, and housing market indices, to form a robust understanding of the overall economic landscape.
Drawing on a variety of data sources, the Market Health Monitor employs moving averages over periods of 3, 12, 36, and 120 months, corresponding to quarterly, annual, three-year, and ten-year economic cycles. This selection of timeframes is specifically chosen to capture the nuances of economic movements across different phases, providing a balanced view that is sensitive to both immediate changes and long-term trends.
Key Features:
Economic Indicators Integration: The script synthesizes crucial economic data such as unemployment rates, inflation levels, and housing market trends, offering a multi-dimensional perspective on market health.
Adaptability to Market Conditions: The inclusion of both short-term and long-term moving averages allows the Market Health Monitor to adapt to varying market conditions, making it a versatile tool for different trading strategies.
Oscillator Thresholds for Recession and Growth: The script sets specific thresholds that, when crossed, indicate either potential economic downturns (recessions) or periods of growth (expansions), allowing traders to anticipate and react to changing market conditions proactively.
Color-Coded Visualization: The Market Health Monitor employs a color-coding system for ease of interpretation:
-- A red background signals unhealthy economic conditions, cautioning traders about potential risks.
-- A bright red background indicates a confirmed recession, as declared by the NBER, signaling a critical time for traders to reassess risk exposure.
-- A green background suggests a healthy market with expected economic expansion, pointing towards growth-oriented opportunities.
Comprehensive Market Analysis: By combining various economic indicators, the script offers a holistic view of the market, enabling traders to make well-informed decisions based on a thorough understanding of the economic environment.
Key Criteria and Parameters:
Economic Indicators:
Labor Market: The unemployment rate is a critical indicator of economic health.
High or rising unemployment indicates reduced consumer spending and economic stress.
Inflation: Key for understanding monetary policy and consumer purchasing power.
Persistent high inflation can lead to economic instability, while deflation can signal weak
demand.
Monetary Policy: Reflected by the Federal Reserve funds rate.
Changes in the rate can influence economic activity, borrowing costs, and investor
sentiment.
Consumer Confidence: A predictor of consumer spending and economic activity.
Reflects the public’s perception of the economy
Housing Market: The housing market often leads the economy into recession and recovery.
Weakness here can signal broader economic problems.
Market Data:
Stock Market Indices: Reflect overall investor sentiment and economic
expectations. No gains in a stock market could potentially indicate that economy is
slowing down.
Credit Conditions: Indicated by the tightness of bank lending, signaling risk
perception.
Commodity Insight:
Crude Oil Prices: A proxy for global economic activity.
Indicator Timeframe:
A default monthly timeframe is chosen to align with the release frequency of many economic indicators, offering a balanced view between timely data and avoiding too much noise from short-term fluctuations. Surely, it can be chosen by trader / analyst.
The Market Health Monitor is more than just a trading tool—it's a comprehensive economic guide. It's designed for traders who value an in-depth understanding of the economic climate. By offering insights into both current conditions and future trends, it encourages traders to navigate the markets with confidence, whether through turbulent times or in periods of growth. This tool doesn't just help you follow the market—it helps you understand it.
Volume Speed [By MUQWISHI]▋ INTRODUCTION :
The “Volume Dynamic Scale Bar” is a method for determining the dominance of volume flow over a selected length and timeframe, indicating whether buyers or sellers are in control. In addition, it detects the average speed of volume flow over a specified period. This indicator is almost equivalent to Time & Sales (Tape) .
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▋ OVERVIEW:
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▋ ELEMENTS
(1) Volume Dynamic Scale Bar. As we observe, it has similar total up and down volume values to what we're seeing in the table. Note they have similar default inputs.
(2) A notice of a significant volume came.
(3) It estimates the speed of the average volume flow. In the tooltip, it shows the maximum and minimum recorded speeds along with the time since the chart was updated.
(4) Info of entered length and the selected timeframe.
(5) The widget will flash gradually for 3 seconds when there’s a significant volume occurred based on the selected timeframe.
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▋ INDICATOR SETTINGS:
(1) Timezone.
(2) Widget location and size on chart.
(3) Up & Down volume colors.
(4) Option to enable a visual flash when a single volume is more than {X value} of Average. For instance, 2 → means double the average volume.
(5) Fetch data from the selected lower timeframe.
(6) Number of bars at chosen timeframe.
(7) Volume OR Price Volume.
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▋ COMMENT:
The Volume Dynamic Scale Bar should not be taken as a major concept to build a trading decision.
Please let me know if you have any questions.
Thank you.
Mean Reversion Watchlist [Z score]Hi Traders !
What is the Z score:
The Z score measures a values variability factor from the mean, this value is denoted by z and is interpreted as the number of standard deviations from the mean.
The Z score is often applied to the normal distribution to “standardize” the values; this makes comparison of normally distributed random variables with different units possible.
This popular reversal based indicator makes an assumption that the sample distribution (in this case the sample of price values) is normal, this allows for the interpretation that values with an extremely high or low percentile or “Z” value will likely be reversal zones.
This is because in the population data (the true distribution) which is known, anomaly values are very rare, therefore if price were to take a z score factor of 3 this would mean that price lies 3 standard deviations from the mean in the positive direction and is in the ≈99% percentile of all values. We would take this as a sign of a negative reversal as it is very unlikely to observe a consecutive equal to or more extreme than this percentile or Z value.
The z score normalization equation is given by
In Pine Script the Z score can be computed very easily using the below code.
// Z score custom function
Zscore(source, lookback) =>
sma = ta.sma(source, lookback)
stdev = ta.stdev(source, lookback, true)
zscore = (source - sma) / stdev
zscore
The Indicator:
This indicator plots the Z score for up to 20 different assets ( Note the maximum is 40 however the utility of 40 plots in one indicator is not much, there is a diminishing marginal return of the number of plots ).
Z score threshold levels can also be specified, the interpretation is the same as stated above.
The timeframe can also be fixed, by toggling the “Time frame lock” user input under the “TIME FRAME LOCK” user input group ( Note this indicator does not repain t).
Time & Sales (Tape) [By MUQWISHI]▋ INTRODUCTION :
The “Time and Sales” (Tape) indicator generates trade data, including time, direction, price, and volume for each executed trade on an exchange. This information is typically delivered in real-time on a tick-by-tick basis or lower timeframe, providing insights into the traded size for a specific security.
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▋ OVERVIEW:
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▋ Volume Dynamic Scale Bar:
It's a way for determining dominance on the time and sales table, depending on the selected length (number of rows), indicating whether buyers or sellers are in control in selected length.
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▋ INDICATOR SETTINGS:
#Section One: Table Settings
#Section Two: Technical Settings
(1) Implement By: Retrieve data by
(1A) Lower Timeframe: Fetch data from the selected lower timeframe.
(1B) Live Tick: Fetch data in real-time on a tick-by-tick basis, capturing data as soon as it's observed by the system.
(2) Length (Number of Rows): User able to select number of rows.
(3) Size Type: Volume OR Price Volume.
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▋ COMMENT:
The values in a table should not be taken as a major concept to build a trading decision.
Please let me know if you have any questions.
Thank you.
Forex & Stock Daily WatchList And Screener [M]Hi, this is a watchlist and screener indicator for Forex and Stocks.
This indicator is designed for traders who trade in the forex markets and monitor developments in indices and other currency pairs.
It includes information on 14 indices such as the volatility index, Baltic dry index, etc. You can customize the indices as you wish. The indices table contains the index's price (or points), daily change, stochastic value, and trend direction.
The second table is designed for trading forex and stock currency pairs.
In this table, you will find information such as price, volume, change, stochastic, RSI, trend direction, and MACD result for all traded pairs. You can customize all the currency pairs in this table as you wish, and you can also tailor the oscillator settings to your preferences.
In the settings section, you can use checkboxes to hide the pairs in both tables.
The "Customize" section in the settings allows you to personalize the table appearances according to your preferences.
[TTI] MarketSmith & IBD Style Model Stock Quarters 📜 ––––HISTORY & CREDITS––––
The MarketSmith & IBD Style Model Stock Quarters another Utility indicator is an original creation by TintinTrading inspired by Investor's Business Daily and William O'Neil style of presenting information. While going through the Model Stocks that IBD has been publishing, I realized that I wanted to see the exam same Quarterly presentation on the time axis in order to compare William O'Neil notes better with my own notes from Tradingview. The script is simple and could help you if you study the CANSLIM methodology.
🦄 –––UNIQUENESS–––
The distinctiveness of this indicator lies in its ability to visually delineate stock quarters directly on the price chart. It serves as a handy tool for traders who adopt a quarterly review of stock performance, in line with MarketSmith and IBD's analysis frameworks.
🛠️ ––––WHAT IT DOES––––
Quarter Marking : Draws a black line at the beginning of each financial quarter (January, April, July, and October).
Quarter Labeling : Places a label at the close of the last month in a quarter, indicating the upcoming quarter with its abbreviation and the last two digits of the year.
💡 ––––HOW TO USE IT––––
👉Installation: Add the indicator to your TradingView chart by searching for " MarketSmith & IBD Style Model Stock Quarters" in the indicator library.
👉Add to New Pane and squash the Pane Length: I add the indicator to a new pane under the price and volume charts and squash the height of the pane so that it looks exactly like the MarketSmith visuals.
👉Visual Cues:
Look for the black lines marking the start of a new quarter.
Observe the labels indicating the upcoming quarter and year, positioned at the close of the last month in a quarter.
👉Interpretation: Use these quarterly markers to align your trading strategies with quarterly performance metrics or to conduct seasonal analysis.
👉Settings: The indicator does not require any user-defined settings, making it straightforward to use.
