Basic FVGBasic Fair Value Gap (FVG) Indicator
The Basic Fair Value Gap (FVG) Indicator is a tool designed for traders using the TradingView platform to identify and visualize Fair Value Gaps (FVGs) on any given chart.
Key Features:
Bullish and Bearish FVG Detection: The indicator automatically detects and highlights both bullish and bearish Fair Value Gaps on the chart. Bullish gaps are highlighted in blue, while bearish gaps are marked in red, with customizable transparency for clear visibility.
Customizable Parameters:
Max Bars Back: Users can set the maximum number of bars to look back in order to find potential FVGs.
Box Length: The length of the FVG box can be adjusted to fit the user's preference, allowing for better visual management on different timeframes.
Tick Buffer for Close Validation: The indicator only considers an FVG filled if the price closes beyond the gap by a customizable tick buffer, ensuring precise gap closure recognition.
Automatic Removal of Filled Gaps: Once an FVG is filled (i.e., the price closes beyond the gap by the defined tick buffer), the corresponding FVG box is automatically removed from the chart. This keeps the chart clean and focused on active gaps.
Real-Time Updates: The indicator updates in real-time, ensuring that traders have the most current information about potential gaps in price, which could signify strong support or resistance levels.
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Swing Trend AnalysisIntroducing the Swing Trend Analyzer: A Powerful Tool for Swing and Positional Trading
The Swing Trend Analyzer is a cutting-edge indicator designed to enhance your swing and positional trading by providing precise entry points based on volatility contraction patterns and other key technical signals. This versatile tool is packed with features that cater to traders of all timeframes, offering flexibility, clarity, and actionable insights.
Key Features:
1. Adaptive Moving Averages:
The Swing Trend Analyzer offers multiple moving averages tailored to the timeframe you are trading on. On the daily chart, you can select up to four different moving average lengths, while all other timeframes provide three moving averages. This flexibility allows you to fine-tune your analysis according to your trading strategy. Disabling a moving average is as simple as setting its value to zero, making it easy to customize the indicator to your needs.
2. Dynamic Moving Average Colors Based on Relative Strength:
This feature allows you to compare the performance of the current ticker against a major index or any symbol of your choice. The moving average will change color based on whether the ticker is outperforming or underperforming the selected index over the chosen period. For example, on a daily chart, if the 21-day moving average turns blue, it indicates that the ticker has outperformed the selected index over the last 21 days. This visual cue helps you quickly identify relative strength, a key factor in successful swing trading.
3. Visual Identification of Price Contractions:
The Swing Trend Analyzer changes the color of price bars to white (on a dark theme) or black (on a light theme) when a contraction in price is detected. Price contractions are highlighted when either of the following conditions is met: a) the current bar is an inside bar, or b) the price range of the current bar is less than the 14-period Average Daily Range (ADR). This feature makes it easier to spot price contractions across all timeframes, which is crucial for timing entries in swing trading.
4. Overhead Supply Detection with Automated Resistance Lines:
The indicator intelligently detects the presence of overhead supply and draws a single resistance line to avoid clutter on the chart. As price breaches the resistance line, the old line is automatically deleted, and a new resistance line is drawn at the appropriate level. This helps you focus on the most relevant resistance levels, reducing noise and improving decision-making.
5. Buyable Gap Up Marker: The indicator highlights bars in blue when a candle opens with a gap that remains unfilled. These bars are potential Buyable Gap Up (BGU) candidates, signaling opportunities for long-side entries.
6. Comprehensive Swing Trading Information Table:
The indicator includes a detailed table that provides essential data for swing trading:
a. Sector and Industry Information: Understand the sector and industry of the ticker to identify stocks within strong sectors.
b. Key Moving Averages Distances (10MA, 21MA, 50MA, 200MA): Quickly assess how far the current price is from key moving averages. The color coding indicates whether the price is near or far from these averages, offering vital visual cues.
c. Price Range Analysis: Compare the current bar's price range with the previous bar's range to spot contraction patterns.
d. ADR (20, 10, 5): Displays the Average Daily Range over the last 20, 10, and 5 periods, crucial for identifying contraction patterns. On the weekly chart, the ADR continues to provide daily chart information.
e. 52-Week High/Low Data: Shows how close the stock is to its 52-week high or low, with color coding to highlight proximity, aiding in the identification of potential breakout or breakdown candidates.
f. 3-Month Price Gain: See the price gain over the last three months, which helps identify stocks with recent momentum.
7. Pocket Pivot Detection with Visual Markers:
Pocket pivots are a powerful bullish signal, especially relevant for swing trading. Pocket pivots are crucial for swing trading and are effective across all timeframes. The indicator marks pocket pivots with circular markers below the price bar:
a. 10-Day Pocket Pivot: Identified when the volume exceeds the maximum selling volume of the last 10 days. These are marked with a blue circle.
b. 5-Day Pocket Pivot: Identified when the volume exceeds the maximum selling volume of the last 5 days. These are marked with a green circle.
The Swing Trend Analyzer is designed to provide traders with the tools they need to succeed in swing and positional trading. Whether you're looking for precise entry points, analyzing relative strength, or identifying key price contractions, this indicator has you covered. Experience the power of advanced technical analysis with the Swing Trend Analyzer and take your trading to the next level.
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.
BB Position CalculatorPosition Size Calculator Instructions
Overview
The Position Size Calculator is designed to help traders automatically determine the appropriate lot size based on the dollar amount they are willing to risk. It includes features for automatic lot sizing, fixed lot risk calculations, take profit calculations (both automatic and fixed), max run-up, and max drawdown. Calculated values are displayed in ticks, points, and USD.
Key Features
• Automatic Lot Sizing: Automatically calculates lot size based on the amount of money you are willing to risk.
• Fixed Lot Risk Calculations: Provides risk calculations for fixed lot sizes.
• Take Profit Calculations: Offers both automatic and fixed take profit calculations.
• Max Run-Up and Max Drawdown: Monitors and displays the maximum run-up and drawdown of your trade.
• Detailed Metrics: Displays all calculated values in ticks, points, and USD.
Setup Instructions
1. Add and Remove for Each Position: The calculator is designed to be added to your chart for each new position. Once your preferences are set the first time, save them as your default to retain your settings for future use.
2. Adding the Indicator to Favorites:
• Use the TradingView keyboard shortcut “/” then type “pos.”
• Use the arrow key to select the Position Size Calculator and press enter.
• Close the indicator selection pop-up.
3. Setting the Trigger Price:
• A blue pop-up labeled “SET TRIGGER PRICE” will appear at the bottom of the chart.
• Click on the chart at the price level where you want to enter the trade.
4. Setting the Stop Loss:
• The pop-up will change to “SET STOP LOSS.”
• Click on the chart at the price level where your stop loss will be set.
5. Setting the Take Profit:
• The pop-up will change to “SET TAKE PROFIT.”
• Click on the chart at the price level where you want to take profit. If you have selected the option to overwrite with a set risk/reward ratio (R:R), the calculation will use this price level.
6. Setting the Trade Window Start:
• The pop-up will change to “SET TRADE WINDOW START.”
• Click on the bar in time where you want the indicator to start monitoring for price to trigger the position.
7. Adjusting the Position:
• Clicking on any part of the indicator will display draggable lines, allowing you to fine-tune the position that was previously plotted by the first four chart clicks.
Additional Notes
• Compatibility: This calculator has only been tested with futures trading.
• Customization: Once your preferences are set, save them as your default to make setup quicker for future trades.
• Support: If you have any questions or feature requests, please feel free to reach out.
ATR/ADR Support and Resistance LevelsATR/ADR Support and Resistance Levels Indicator
This script is designed to provide traders with precise ATR (Average True Range) and ADR (Average Daily Range) support and resistance levels. It can be effectively used to identify price breakouts or rejections near these critical lines and assist in confirming trend retests.
How It Works:
Support and Resistance Lines: The script plots ATR/ADR-based support and resistance lines, which can be toggled on or off.
Daily Data Integration: It incorporates daily open and close prices to enhance the accuracy of the support and resistance levels.
Clear Visuals: The indicator uses distinct colors for support (green) and resistance (red) levels, providing clear visual cues.
Default Settings: The default settings are optimized for most trading environments. Adjusting the ATR/ADR Length can fine-tune the indicator's responsiveness to market movements.
Key Features:
ATR & ADR Calculation: Choose between using ATR, ADR, or both. ATR is recommended for most scenarios.
Customizable Lengths: Adjust the ATR/ADR Length to refine the average calculation to your preference, with 14 being the standard value.
EMA for Market Bias: The EMA helps determine the ticker bias. It is colored green when the market is above the average price and red when it is below. This allows you to more easily determine whether or not the ADR/ATR levels are valid.
Versatile Usage: Suitable for various trading types, ensuring broad applicability across different market conditions.
How to Use:
ATR vs ADR: You should use ADR if you are day trading AND do not want to include gap data in the levels. It is recommended you use ATR.
Bounces off Levels: When price bounces off of a support/resistance level, it is very likely that price will respect this level. This indicates that price is unlikely to move beyond the ticker's average volatility. You should wait for an additional bounce to confirm.
Breakthroughs of Levels: When price breaks through a support/resistance level, it is very likely that price will continue beyond this level. This indicates that price has moved beyond that ticker's average volatility. You should wait for a bounce off the level to confirm.
This indicator is a valuable tool for traders seeking to enhance their technical analysis with support and resistance levels based on ATR and ADR calculations. It is perfect for identifying key price points and understanding market trends.
OrderFlow Absorption IndicatorWhat it Does
The OrderFlow Absorption Indicator marks areas where the price absorbs a large volume of aggressive market trades. This indicates areas where price may bounce back due to large limit (resting) orders absorbing significant aggressor volume (market orders). Absorption can also be seen as "preventing" or "stopping" the other side from breaking through a price level (e.g. bids stopping an influx of sell market orders). Absorption may signal a change in sentiment, potentially leading to a pullback or reversal.
An Example of Absorption
Of course, it is not always the case that such bullish absorption will initiate a trend as the example above. The OrderFlow Absorption Indicator merely serves as a tool for spotting possible absorption points in the market which you can incorporate into your trading arsenal.
How it Works
The indicator actively monitors price changes and records volume accumulated at a price level. If the price bounces back to at least where it was before the current price move, the indicator records this as absorption, provided it meets the Volume Requirement and optional Time Requirement.
How to Use it
1. Set Parameters
Choose your desired tick size and volume filter value. If unsure, refer to the table on the top right of the chart for recommended values. An automatic volume limit filter mode is also available.
Automatic Limit Mode : Enable this mode to have the indicator automatically select a volume filter value. It calculates the standard deviation of the last n minutes of volume and multiplies it by a volume multiplier. You can adjust these parameters.
Higher Volume Filter : Setting a higher volume filter value results in fewer, but higher quality detections, reducing noise.
2. Enabling the Time Limit
Enabling the time limit further improves detection quality by filtering out price levels that can defend against quick, sudden aggressive orders, acting as confirmation and indicating strong sentiment and resilient liquidity.
3. Enabling Historical Data Absorption
The indicator can also detect absorption in historical data, though less accurately than in real-time due to OHLCV aggregation.
You can select the granularity of historical data.
Lower granularity (e.g., 1 second) : Provides more accurate detections but may slow down the indicator.
Higher granularity : Improves speed but reduces detection accuracy.
Other Features
Hovering : When hovering over an absorption point, the interface reveals the price where the absorption occurred, along with the volume absorbed by the bids and asks, as well as the volume filter value used.
Delta Mode : In Delta mode, the system calculates the difference between the volume absorbed by bids and asks, revealing points only when the absolute value of this difference exceeds the volume filter value. Especially useful for larger tick sizes.
Troubleshooting
If the indicator doesn't mark anything, it means the traded volume hasn't exceeded the set volume filter value within the specified price intervals(tick size) and time limit. Adjust these settings as necessary.
Footprint strategyThis strategy uses imbalance volume data obtained by footprint calculation technology.
There are two signals to enter a trade:
trend - the current buy volume on the bar is greater than the current sell volume and there is at least one imbalance line.
reversal - the current bar is falling, but the general market trend is positive (growing) and the imbalance buy volume exceeds the imbalance sell volume.
When any of the conditions is triggered, two orders are placed: Take Profit and Stop loss (according to the percentage value from the inputs).
A little advice on use:
The strategy performs best on a 15 minute timeframe.
It is necessary to choose acceptable values of Take Profit and Stop loss depending on the order of symbol prices.
Inputs related to the strategy:
Stop loss - percentage size of stop loss to exit the trade.
Enable stop loss - stop loss activation.
Take Profit - percentage size of Take Profit.
Calculation timeframe - this is the timeframe from which the volume will be collected for distribution to buy and sell (if you do not have access to the seconds chart, set here 1 minute, the accuracy will be less, but it will work).
Trend timeframe - this is the timeframe from which the trend will be calculated.
Enable trend - activation of trend calculation.
Inputs related to the calculation of footprints (collection of the volume of purchases and sales):
Count show bars - Number of bars from rt bar to history to calculate.
Display all available bars - Strategy calculation on all available bars (based on the available amount of data with reduced resolution (set in Calculation timeframe)).
Ticks Per Row - Sets the price step, calculated by multiplying the entered value by syminfo.mintick.
Auto - The automatic "Ticks Per Row" calculation is based on the first available bar and applied to subsequent bars.
Max row - sets the acceptable number of rows within a bar.
Imbalance Percent - A percentage coefficient to determine the Imbalance of price levels.
Stacked levels - And minimum number of consecutive Imbalance levels required to draw extended lines.
If you have suggestions for improving the strategy and adding new conditions for entering and exiting the trade, please write).
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.
Volatility Adjusted Weighted DEMA [BackQuant]Volatility Adjusted Weighted DEMA
The Volatility Adjusted Weighted Double Exponential Moving Average (VAWDEMA) by BackQuant is a sophisticated technical analysis tool designed for traders seeking to integrate volatility into their moving average calculations. This innovative indicator adjusts the weighting of the Double Exponential Moving Average (DEMA) according to recent volatility levels, offering a more dynamic and responsive measure of market trends.
Primarily, the single Moving average is very noisy, but can be used in the context of strategy development, where as the crossover, is best used in the context of defining a trading zone/ macro uptrend on higher timeframes.
Why Volatility Adjustment is Beneficial
Volatility is a fundamental aspect of financial markets, reflecting the intensity of price changes. A volatility adjustment in moving averages is beneficial because it allows the indicator to adapt more quickly during periods of high volatility, providing signals that are more aligned with the current market conditions. This makes the VAWDEMA a versatile tool for identifying trend strength and potential reversal points in more volatile markets.
Understanding DEMA and Its Advantages
DEMA is an indicator that aims to reduce the lag associated with traditional moving averages by applying a double smoothing process. The primary benefit of DEMA is its sensitivity and quicker response to price changes, making it an excellent tool for trend following and momentum trading. Incorporating DEMA into your analysis can help capture trends earlier than with simple moving averages.
The Power of Combining Volatility Adjustment with DEMA
By adjusting the weight of the DEMA based on volatility, the VAWDEMA becomes a powerful hybrid indicator. This combination leverages the quick responsiveness of DEMA while dynamically adjusting its sensitivity based on current market volatility. This results in a moving average that is both swift and adaptive, capable of providing more relevant signals for entering and exiting trades.
Core Logic Behind VAWDEMA
The core logic of the VAWDEMA involves calculating the DEMA for a specified period and then adjusting its weighting based on a volatility measure, such as the average true range (ATR) or standard deviation of price changes. This results in a weighted DEMA that reflects both the direction and the volatility of the market, offering insights into potential trend continuations or reversals.
Utilizing the Crossover in a Trading System
The VAWDEMA crossover occurs when two VAWDEMAs of different lengths cross, signaling potential bullish or bearish market conditions. In a trading system, a crossover can be used as a trigger for entry or exit points:
Bullish Signal: When a shorter-period VAWDEMA crosses above a longer-period VAWDEMA, it may indicate an uptrend, suggesting a potential entry point for a long position.
Bearish Signal: Conversely, when a shorter-period VAWDEMA crosses below a longer-period VAWDEMA, it might signal a downtrend, indicating a possible exit point or a short entry.
Incorporating VAWDEMA crossovers into a trading strategy can enhance decision-making by providing timely and adaptive signals that account for both trend direction and market volatility. Traders should combine these signals with other forms of analysis and risk management techniques to develop a well-rounded trading strategy.
Alert Conditions For Trading
alertcondition(vwdema>vwdema , title="VWDEMA Long", message="VWDEMA Long - {{ticker}} - {{interval}}")
alertcondition(vwdema
Volume Liqidations [EagleVSniper]The Volume Liquidations Indicator is designed for traders who want to spot significant liquidation events in the cryptocurrency markets, particularly between spot and futures volumes. This powerful tool auto-detects the trading asset and compares the volume data from both spot and futures markets to highlight potential high-volume liquidation points that can significantly impact price movement. Raw source code owner - tartigradia
Features:
Auto-Detect Functionality: Automatically identifies the current trading asset, providing an option for manual selection for both spot and futures symbols.
Volume Comparison: Calculates the difference between futures and spot volumes within a user-defined timeframe, helping to identify liquidation events.
Customizable Parameters: Offers customizable options for multipliers, lookback periods, and timeframe selection to tailor the indicator to your trading strategy.
Visual Indicators: Displays liquidation volumes as color-coded columns, with green indicating potential long liquidations and red for short liquidations. It also highlights bars that exceed the high-volume threshold, providing a clear visual cue for significant liquidation events.
Spot and Futures Volume MA: Includes optional moving average plots for both spot and futures volumes, allowing for a deeper analysis of market trends.
Highlighting High-Volatility Candles: The indicator uniquely colors candles that reach a predefined volatility threshold, determined by the user-set multiplier. This functionality aims to spotlight moments of significant market volatility, providing traders with immediate visual cues.
Dynamic Ticker Selection: Seamlessly switches between auto and manual ticker selection, providing flexibility for all types of traders.
How to Use:
Setup: Configure the indicator to your preferences. You can choose between automatic or manual ticker selection, set the multiplier for the high-volume threshold, and define the lookback period for the moving average calculation.
Analysis: The indicator plots differences in volume between futures and spot markets as columns on your chart, color-coded to indicate the direction of potential liquidations.
Decision Making: Use the indicator to identify potential liquidation events. High-volume thresholds are highlighted, suggesting significant market movements. Combine this information with other analysis tools to make informed trading decisions.
Debasement Adjusted CAGREquity growth may appear less significant when juxtaposed with the expansion of the money supply. This is because markets tend to adjust prices to reflect changes in money supply almost immediately.
Our indicator offers a unique perspective by adjusting the current ticker price for the M2 money supply and normalizing this data to show the percentage appreciation since the first visible bar on the chart. Users can also select alternative money supply measures, such as the EU-M2, via the indicator's settings.
This approach essentially redefines the price as the "growth of the relative share of the total money supply," providing a novel lens through which to view equity performance.
Additionally, the indicator computes both the Compound Annual Growth Rate (CAGR) and the total growth observed from this adjusted standpoint. These metrics are calculated within the context of the selected time range, adding depth to the analysis.
Although this indicator is compatible with all timeframes, it is primarily designed as a macroeconomic tool. It yields the most meaningful insights when applied to longer-term perspectives, such as weekly or monthly timeframes.
This tool builds upon the foundational work presented in the "Inflation Adjusted Performance Ticker," accessible at Inflation Adjusted Performance Ticker , enhancing its application by normalizing the results and computing CAGR and total growth.
Custom Dual SMADescription
The Custom Dual SMA Indicator is designed for traders who wish to track and compare the moving averages of two different financial instruments simultaneously on the same chart, usually if there is correlation between to different asset such as TQQQ vs SQQQ. This indicator is particularly useful for those who engage in comparative analysis or pairs trading strategies.
Features
Dual Ticker Input: Users can input any two ticker symbols (e.g., stocks, currencies, commodities) to analyze. This flexibility allows for a broad range of comparative analyses across different markets or sectors.
Customizable SMA Length: The indicator provides the option to set the length of the SMA for each ticker symbol independently. This feature is critical for traders who wish to analyze the moving averages over different time periods, depending on their trading strategy or the specific characteristics of the instruments being analyzed.
Overlay on Price Chart: The calculated SMAs are overlaid directly on the price chart, enabling users to easily visualize how the moving averages of the two instruments move in relation to each other and to their respective price actions.
Color-Coded for Clarity: Each SMA is plotted in a different color (red and blue by default), ensuring clear differentiation and easy interpretation at a glance.
Use Case
This indicator is particularly beneficial for:
Comparative Analysis: Traders can compare the performance and trends of two different instruments, observing how their moving averages converge or diverge over time.
Pairs Trading: Those involved in pairs trading can use this tool to identify potential entry and exit points by analyzing the moving averages of two correlated or inversely correlated instruments.
Diversification Analysis: Investors looking to diversify their portfolio can use this indicator to understand the moving average trends of various instruments, helping them make informed decisions about asset allocation.
Summary
The Custom Dual SMA Indicator is a versatile tool for traders and investors who require a comparative view of the moving averages of two different instruments on the same chart. Its customizable nature and ease of use make it suitable for a wide range of trading strategies and market analyses.
Cryptocurrency Cointegration Matrix (SpiritualHealer117)This indicator plots a cointegration matrix for the pairings of 100 cryptocurrencies. The matrix is populated with ADF t-stats (from an ADF-test with 1 lag). An ADF-test (Augmented Dickey-Fuller test) tests the null hypothesis that an AR process has a unit root. If rejected, the alternative hypothesis is usually that the AR process is either stationary or trend-stationary. This model extends upon Lejmer's Cointegration Matrix for forex by enabling the indicator to use cryptocurrency pairs and allows for significantly more pairs to be analyzed using the group selection feature. This indicator arose from collaboration with TradingView user CryptoJuju.
This indicator runs an ADF-test on the residuals (spread) of each pairing (i.e. a cointegration test). It tests if there is a unit root in the spread between the two assets of a pairing. If there is a unit root in the spread, it means the spread varies randomly over time, and any mean reversion in the spread is very hard to predict. By contrast, if a unit root does not exist, the spread (distance between the assets) should remain more or less constant over time, or rise/fall in close to the same rate over time. The more negative the number from an ADF-test, the stronger the rejection of the idea that the spread has a unit root. In statistics, there are different levels which correspond with the confidence level of the test. For this indicator, -3.238 equals a confidence level of 90%, -3.589 equals a confidence level of 95% and -4.375 equals a confidence level of 99% that there is not a unit root. So the colors are based on the confidence level of the test statistic (the t-stat, i.e. the number of the pairing in the matrix). So if the number is greater than -3.238 it is green, if it's between -3.238 and -3.589 it's yellow, if it's between -3.589 and -4.375 it's orange, and if its lower than -4.375 it's red.
There are multiple ways to interpret the results. A strong rejection of the presence of a unit root (i.e. a value of -4.375 or below) is not a guarantee that there is no unit root, or that any of the two alternative hypotheses (that the spread is stationary or trend-stationary) are correct. It only means that in 99% of the cases, if the spread is an AR process, the test is right, and there is no unit root in the spread. Therefore, the results of this test is no guarantee that the result proves one of the alternative solutions. Green therefore means that a unit root cannot be ruled out (which can be interpreted as "the two cryptocurrencies probably don't move together over time"), and red means that a unit root is likely not present (which can be interpreted as "the two cryptocurrencies may move together over time").
One possible way to use this indicator is to make sure you don't trade two pairs that move together at the same time. So basically the idea is that if you already have a trade open in one of the currency pairs of the pairing, only enter a trade in the other currency pair of that pairing if the color is green, or you may be doubling your risk. Alternatively, you could implement this indicator into a pairs trading system, such as a simple strategy where you buy the spread between two cryptocurrencies with a red result when the spread's value drops one standard deviation away from its moving average, and conversely sell when it moves up one standard deviation above the moving average. However, this strategy is not guaranteed to work, since historical data does not guarantee the future.
Specific to this indicator, there are 100 different cryptocurrency tickers which are included in the matrix, and the cointegration matrices between all the tickers can be checked by switching asset group 1 and asset group 2 to different asset groups. The ADF test is computed using a specified length, and if there is insufficient data for the length, the test produces a grayed out box.
NOTE: The indicator can take a while to load since it computes the value of 400 ADF tests each time it is run.
WP_MMThis indicator is set with 3 EMA's:
- EMA9
- EMA20
- EMA50
When EMA9 is up to EMA20 and:
- the distance between them is up to 30 ticks
- both is directional ( diff between current ema and last ema is up to 15 ticks )
the range will be painted in green. And if the EMA 9 is below EMA20 and
the same rules above is true, rthe range will be painted in red
Strengh Candle:
- Candle without huge shadow
- If is bearish minium down shadow
- if is bullish minimum up shadow
OBS: More info inside code comments
Interactive MA Stop Loss [TANHEF]This indicator is "Interactive." Once added to the chart, you need to click the start point for the moving average stoploss. Dragging it afterward will modify its position.
Why choose this indicator over a traditional Moving Average?
To accurately determine that a wick has crossed a moving average, you must examine the moving average's range on that bar (blue area on this indicator) and ensure the wick fully traverses this area.
When the price moves away from a moving average, the average also shifts towards the price. This can make it look like the wick crossed the average, even if it didn't.
How is the moving average area calculated?
For each bar, the moving average calculation is standard, but when the current bar is involved, its high or low is used instead of the close. For precise results, simply setting the source in a typical moving average calculation to 'Low' or 'High' is not sufficient in calculating the moving average area on a current bar.
Moving Average Options:
Simple Moving Average
Exponential Moving Average
Relative Moving Average
Weighted Moving Average
Indicator Explanation
After adding indicator to chart, you must click on a location to begin an entry.
The moving average type can be set and length modified to adjust the stoploss. An optional profit target may be added.
A symbol is display when the stoploss and profit target are hit. If a position is create that is not valid, "Overlapping MA and Bar" is displayed.
Alerts
'Check' alerts to use within indicator settings (stop hit and/or profit target hit).
Select 'Create Alert'
Set the condition to 'Interactive MA''
Select create.
Alert messages can have additional details using these words in between two Curly (Brace) Brackets:
{{stop}} = MA stop-loss (price)
{{upper}} = Upper MA band (price)
{{lower}} = Lower MA band (price)
{{band}} = Lower or Upper stoploss (word)
{{type}} = Long or Short stop-loss (word)
{{stopdistance}} = Stoploss Distance (%)
{{targetdistance}} = Target Distance (%)
{{starttime}} = Start time of stoploss (day:hour:minute)
{{maLength}} = MA Length (input)
{{maType}} = MA Type (input)
{{target}} = Price target (price)
{{trigger}} = Wick or Close Trigger input (input)
{{ticker}} = Ticker of chart (word)
{{exchange}} = Exchange of chart (word)
{{description}} = Description of ticker (words)
{{close}} = Bar close (price)
{{open}} = Bar open (price)
{{high}} = Bar high (price)
{{low}} = Bar low (price)
{{hl2}} = Bar HL2 (price)
{{volume}} = Bar volume (value)
{{time}} = Current time (day:hour:minute)
{{interval}} = Chart timeframe
{{newline}} = New line for text
I will add further moving averages types in the future. If you suggestions post them below.
Quadratic & Linear Time Series Regression [SS]Hey everyone,
Releasing the Quadratic/Linear Time Series regression indicator.
About the indicator:
Most of you will be familiar with the conventional linear regression trend boxes (see below):
This is an awesome feature in Tradingview and there are quite a few indicators that follow this same principle.
However, because of the exponential and cyclical nature of stocks, linear regression tends to not be the best fit for stock time series data. From my experience, stocks tend to fit better with quadratic (or curvlinear) regression, which there really isn't a lot of resources for.
To put it into perspective, let's take SPX on the 1 month timeframe and plot a linear regression trend from 1930 till now:
You can see that its not really a great fit because of the exponential growth that SPX has endured since the 1930s. However, if we take a quadratic approach to the time series data, this is what we get:
This is a quadratic time series version, extended by up to 3 standard deviations. You can see that it is a bit more fitting.
Quadratic regression can also be helpful for looking at cycle patterns. For example, if we wanted to plot out how the S&P has performed from its COVID crash till now, this is how it would look using a linear regression approach:
But this is how it would look using the quadratic approach:
So which is better?
Both linear regression and quadratic regression are pivotal and important tools for traders. Sometimes, linear regression is more appropriate and others quadratic regression is more appropriate.
In general, if you are long dating your analysis and you want to see the trajectory of a ticker further back (over the course of say, 10 or 15 years), quadratic regression is likely going to be better for most stocks.
If you are looking for short term trades and short term trend assessments, linear regression is going to be the most appropriate.
The indicator will do both and it will fit the linear regression model to the data, which is different from other linreg indicators. Most will only find the start of the strongest trend and draw from there, this will fit the model to whatever period of time you wish, it just may not be that significant.
But, to keep it easy, the indicator will actually tell you which model will work better for the data you are selecting. You can see it in the example in the main chart, and here:
Here we see that the indicator indicates a better fit on the quadratic model.
And SPY during its recent uptrend:
For that, let's take a look at the Quadratic Vs the Linear, to see how they compare:
Quadratic:
Linear:
Functions:
You will see that you have 2 optional tables. The statistics table which shows you:
The R Squared to assess for Variance.
The Correlation to assess for the strength of the trend.
The Confidence interval which is set at a default of 1.96 but can be toggled to adjust for the confidence reading in the settings menu. (The confidence interval gives us a range of values that is likely to contain the true value of the coefficient with a certain level of confidence).
The strongest relationship (quadratic or linear).
Then there is the range table, which shows you the anticipated price ranges based on the distance in standard deviations from the mean.
The range table will also display to you how often a ticker has spent in each corresponding range, whether that be within the anticipated range, within 1 SD, 2 SD or 3 SD.
You can select up to 3 additional standard deviations to plot on the chart and you can manually select the 3 standard deviations you want to plot. Whether that be 1, 2, 3, or 1.5, 2.5 or 3.5, or any combination, you just enter the standard deviations in the settings menu and the indicator will adjust the price targets and plotted bands according to your preferences. It will also count the amount of time the ticker spent in that range based on your own selected standard deviation inputs.
Tips on Use:
This works best on the larger timeframes (1 hour and up), with RTH enabled.
The max lookback is 5,000 candles.
If you want to ascertain a longer term trend (over years to months), its best to adjust your chart timeframe to the weekly and/or monthly perspective.
And that's the indicator! Hopefully you all find it helpful.
Let me know your questions and suggestions below!
Safe trades to all!
Statistical Package for the Trading Sciences [SS]
This is SPTS.
It stands for Statistical Package for the Trading Sciences.
Its a play on SPSS (Statistical Package for the Social Sciences) by IBM (software that, prior to Pinescript, I would use on a daily basis for trading).
Let's preface this indicator first:
This isn't so much an indicator as it is a project. A passion project really.
This has been in the works for months and I still feel like its incomplete. But the plan here is to continue to add functionality to it and actually have the Pinecoding and Tradingview community contribute to it.
As a math based trader, I relied on Excel, SPSS and R constantly to plan my trades. Since learning a functional amount of Pinescript and coding a lot of what I do and what I relied on SPSS, Excel and R for, I use it perhaps maybe a few times a week.
This indicator, or package, has some of the key things I used Excel and SPSS for on a daily and weekly basis. This also adds a lot of, I would say, fairly complex math functionality to Pinescript. Because this is adding functionality not necessarily native to Pinescript, I have placed most, if not all, of the functionality into actual exportable functions. I have also set it up as a kind of library, with explanations and tips on how other coders can take these functions and implement them into other scripts.
The hope here is that other coders will take it, build upon it, improve it and hopefully share additional functionality that can be added into this package. Hence why I call it a project. Okay, let's get into an overview:
Current Functions of SPTS:
SPTS currently has the following functionality (further explanations will be offered below):
Ability to Perform a One-Tailed, Two-Tailed and Paired Sample T-Test, with corresponding P value.
Standard Pearson Correlation (with functionality to be able to calculate the Pearson Correlation between 2 arrays).
Quadratic (or Curvlinear) correlation assessments.
R squared Assessments.
Standard Linear Regression.
Multiple Regression of 2 independent variables.
Tests of Normality (with Kurtosis and Skewness) and recognition of up to 7 Different Distributions.
ARIMA Modeller (Sort of, more details below)
Okay, so let's go over each of them!
T-Tests
So traditionally, most correlation assessments on Pinescript are done with a generic Pearson Correlation using the "ta.correlation" argument. However, this is not always the best test to be used for correlations and determine effects. One approach to correlation assessments used frequently in economics is the T-Test assessment.
The t-test is a statistical hypothesis test used to determine if there is a significant difference between the means of two groups. It assesses whether the sample means are likely to have come from populations with the same mean. The test produces a t-statistic, which is then compared to a critical value from the t-distribution to determine statistical significance. Lower p-values indicate stronger evidence against the null hypothesis of equal means.
A significant t-test result, indicating the rejection of the null hypothesis, suggests that there is statistical evidence to support that there is a significant difference between the means of the two groups being compared. In practical terms, it means that the observed difference in sample means is unlikely to have occurred by random chance alone. Researchers typically interpret this as evidence that there is a real, meaningful difference between the groups being studied.
Some uses of the T-Test in finance include:
Risk Assessment: The t-test can be used to compare the risk profiles of different financial assets or portfolios. It helps investors assess whether the differences in returns or volatility are statistically significant.
Pairs Trading: Traders often apply the t-test when engaging in pairs trading, a strategy that involves trading two correlated securities. It helps determine when the price spread between the two assets is statistically significant and may revert to the mean.
Volatility Analysis: Traders and risk managers use t-tests to compare the volatility of different assets or portfolios, assessing whether one is significantly more or less volatile than another.
Market Efficiency Tests: Financial researchers use t-tests to test the Efficient Market Hypothesis by assessing whether stock price movements follow a random walk or if there are statistically significant deviations from it.
Value at Risk (VaR) Calculation: Risk managers use t-tests to calculate VaR, a measure of potential losses in a portfolio. It helps assess whether a portfolio's value is likely to fall below a certain threshold.
There are many other applications, but these are a few of the highlights. SPTS permits 3 different types of T-Test analyses, these being the One Tailed T-Test (if you want to test a single direction), two tailed T-Test (if you are unsure of which direction is significant) and a paired sample t-test.
Which T is the Right T?
Generally, a one-tailed t-test is used to determine if a sample mean is significantly greater than or less than a specified population mean, whereas a two-tailed t-test assesses if the sample mean is significantly different (either greater or less) from the population mean. In contrast, a paired sample t-test compares two sets of paired observations (e.g., before and after treatment) to assess if there's a significant difference in their means, typically used when the data points in each pair are related or dependent.
So which do you use? Well, it depends on what you want to know. As a general rule a one tailed t-test is sufficient and will help you pinpoint directionality of the relationship (that one ticker or economic indicator has a significant affect on another in a linear way).
A two tailed is more broad and looks for significance in either direction.
A paired sample t-test usually looks at identical groups to see if one group has a statistically different outcome. This is usually used in clinical trials to compare treatment interventions in identical groups. It's use in finance is somewhat limited, but it is invaluable when you want to compare equities that track the same thing (for example SPX vs SPY vs ES1!) or you want to test a hypothesis about an index and a leveraged share (for example, the relationship between FNGU and, say, MSFT or NVDA).
Statistical Significance
In general, with a t-test you would need to reference a T-Table to determine the statistical significance of the degree of Freedom and the T-Statistic.
However, because I wanted Pinescript to full fledge replace SPSS and Excel, I went ahead and threw the T-Table into an array, so that Pinescript can make the determination itself of the actual P value for a t-test, no cross referencing required :-).
Left tail (Significant):
Both tails (Significant):
Distributed throughout (insignificant):
As you can see in the images above, the t-test will also display a bell-curve analysis of where the significance falls (left tail, both tails or insignificant, distributed throughout).
That said, I have not included this function for the paired sample t-test because that is a bit more nuanced. But for the one and two tailed assessments, the indicator will provide you the P value.
Pearson Correlation Assessment
I don't think I need to go into too much detail on this one.
I have put in functionality to quickly calculate the Pearson Correlation of two array's, which is not currently possible with the "ta.correlation" function.
Quadratic (Curvlinear) Correlation
Not everything in life is linear, sometimes things are curved!
The Pearson Correlation is great for linear assessments, but tends to under-estimate the degree of the relationship in curved relationships. There currently is no native function to t-test for quadratic/curvlinear relationships, so I went ahead and created one.
You can see an example of how Quadratic and Pearson Correlations vary when you look at CME_MINI:ES1! against AMEX:DIA for the past 10 ish months:
Pearson Correlation:
Quadratic Correlation:
One or the other is not always the best, so it is important to check both!
R-Squared Assessments:
The R-squared value, or the square of the Pearson correlation coefficient (r), is used to measure the proportion of variance in one variable that can be explained by the linear relationship with another variable. It represents the goodness-of-fit of a linear regression model with a single predictor variable.
R-Squared is offered in 3 separate forms within this indicator. First, there is the generic R squared which is taking the square root of a Pearson Correlation assessment to assess the variance.
The next is the R-Squared which is calculated from an actual linear regression model done within the indicator.
The first is the R-Squared which is calculated from a multiple regression model done within the indicator.
Regardless of which R-Squared value you are using, the meaning is the same. R-Square assesses the variance between the variables under assessment and can offer an insight into the goodness of fit and the ability of the model to account for the degree of variance.
Here is the R Squared assessment of the SPX against the US Money Supply:
Standard Linear Regression
The indicator contains the ability to do a standard linear regression model. You can convert one ticker or economic indicator into a stock, ticker or other economic indicator. The indicator will provide you with all of the expected information from a linear regression model, including the coefficients, intercept, error assessments, correlation and R2 value.
Here is AAPL and MSFT as an example:
Multiple Regression
Oh man, this was something I really wanted in Pinescript, and now we have it!
I have created a function for multiple regression, which, if you export the function, will permit you to perform multiple regression on any variables available in Pinescript!
Using this functionality in the indicator, you will need to select 2, dependent variables and a single independent variable.
Here is an example of multiple regression for NASDAQ:AAPL using NASDAQ:MSFT and NASDAQ:NVDA :
And an example of SPX using the US Money Supply (M2) and AMEX:GLD :
Tests of Normality:
Many indicators perform a lot of functions on the assumption of normality, yet there are no indicators that actually test that assumption!
So, I have inputted a function to assess for normality. It uses the Kurtosis and Skewness to determine up to 7 different distribution types and it will explain the implication of the distribution. Here is an example of SP:SPX on the Monthly Perspective since 2010:
And NYSE:BA since the 60s:
And NVDA since 2015:
ARIMA Modeller
Okay, so let me disclose, this isn't a full fledge ARIMA modeller. I took some shortcuts.
True ARIMA modelling would involve decomposing the seasonality from the trend. I omitted this step for simplicity sake. Instead, you can select between using an EMA or SMA based approach, and it will perform an autogressive type analysis on the EMA or SMA.
I have tested it on lookback with results provided by SPSS and this actually works better than SPSS' ARIMA function. So I am actually kind of impressed.
You will need to input your parameters for the ARIMA model, I usually would do a 14, 21 and 50 day EMA of the close price, and it will forecast out that range over the length of the EMA.
So for example, if you select the EMA 50 on the daily, it will plot out the forecast for the next 50 days based on an autoregressive model created on the EMA 50. Here is how it looks on AMEX:SPY :
You can also elect to plot the upper and lower confidence bands:
Closing Remarks
So that is the indicator/package.
I do hope to continue expanding its functionality, but as of now, it does already have quite a lot of functionality.
I really hope you enjoy it and find it helpful. This. Has. Taken. AGES! No joke. Between referencing my old statistics textbooks, trying to remember how to calculate some of these things, and wanting to throw my computer against the wall because of errors in the code, this was a task, that's for sure. So I really hope you find some usefulness in it all and enjoy the ability to be able to do functions that previously could really only be done in external software.
As always, leave your comments, suggestions and feedback below!
Take care!
Cumulative Distribution of a Dataset [SS]This is the Cumulative Distribution of a Dataset indicator that also calculates the Kurtosis and Skewness for a selected dataset and determines the normality and distribution type.
What it does, in pragmatic terms?
In the most simplest terms, it calculates the cumulative distribution function (or CDF) of user-defined dataset.
The cumulative distribution function (CDF) is a concept used in statistics and probability to describe how the probability of a random variable taking on a certain value or less is distributed across the entire range of possible values. In simpler terms, you can conceptualize the CDF as this:
Imagine you have a list of data, such as test scores of students in a class. The CDF helps you answer questions like, "What's the probability that a randomly chosen student scored 80 or less on the test?"
Or in our case, say we are in a strong up or downtrend on a stock. The CDF can help us answer questions like "Based on this current xyz trend, what is the probability that a ticker will fall above X price or below Y price".
Within the indicator, you can manually assess a price of interest. Let's say, for NVDA, we want to know the probability NVDA goes above or below $450. We can enter $450 into the indicator and get this result:
Other functions:
Kurtosis and Skewness Functions:
In addition to calculating and plotting the CDF, we can also plot the kurtosis & Skewness.
This can help you look for outlier periods where the distribution of your dataset changed. It can potentially alert you to when a stock is behaving abnormally and when it is more stable and evenly distributed.
Tests of normality
The indicator will use the kurtosis and skewness to determine the normality of the dataset. The indicator is programmed to recognize up to 7 different distribution types and alert you to them and the implications they have in your overall assessment.
e.g. #1 AMC during short squeeze:
e.g. #2: BA during the COVID crash:
Plotting the standardized Z-Score of the Distribution Dataset
You can also standardize the dataset by converting it into Z-Score format:
Plot the raw, CDF results
Two values are plotting, the green and the red. The green represents the probability of a ticker going higher than the current value. The red represents the probability of a ticker going lower than the current value.
Limitations
There are some limitations of the indicator which I think are important to point out. They are:
The indicator cannot tell you timelines, it can only tell you the general probability that data within the dataset will fall above or below a certain value.
The indicator cannot take into account projected periods of consolidation. It is possible a ticker can remain in a consolidation phase for a very long time. This would have the effect of stabilizing the probability in one direction (if there was a lot of downside room, it can normalize the data out so that the extent of the downside probability is mitigated). Thus, its important to use judgement and other methods to assess the likelihood that a stock will pullback or continue up, based on the overall probability.
The indicator is only looking at an individual dataset.
Using this indicator, you have to omit a large amount of data and look at solely a confined dataset. In a way, this actually improves the accuracy, but can also be misleading, depending on the size and strength of the dataset being chosen. It is important to balance your choice of dataset time with such things as:
a) The strength of the uptrend or downtrend.
b) The length of the uptrend or downtrend.
c) The overall performance of the stock leading into the dataset time period
And that is the indicator in a nutshell.
Hopefully you find it helpful and interesting. Feel free to leave questions, comments and suggestions below.
Safe trades everyone and take care!
Momentum Probability Oscillator [SS]This is the momentum based probability indicator.
What it does?
This takes the average of MFI, Stochastics and RSI and plots it out as an independent oscillator.
It then tracks bullish vs bearish instances. Bullish is defined as a greater move from open to high than open to low and inverse for bearish.
It stores this data and these averages and plots these levels as a graph.
The graph depicts the max bullish values at the top, the min bearish values at the bottom and the averages in between:
It will plot the average "threshold" value in yellow:
The threshold value is key. A ticker trading above the threshold is generally bullish. Below is bearish.
The threshold value frequently acts as support and resistance levels (see below):
Resistance:
Support:
The indicator also shows you the amount of time a ticker has spent in each region, over a defined lookback period (defaulted to 500):
When you see that cumulatively, more time has been spent in a bullish range or a bearish range, it can help you ascertain the prevailing sentiment at that time.
The indicator will also calculate the average price range based on the underlying oscillator value. It does this through use of ATR based techniques, as its not usually possible to calculate a price from an oscillator:
This is intended as a general reference and not a precise target, as it is using ATR as opposed to the actual technical value itself.
As this is an oscillator, you can use it to look for divergences as well. The advantage to having it formulated in this way is:
a) You get the power of all 3 indicators (stochastics, MFI and RSI) in one and
b) You are adding context to the underlying technical reading. The indicator is plotting out the average, max and min ranges for the selected ticker and performing assessments based on these ranges that add context to the current PA.
You also have the ability to see the specific technical levels associated with each specific technical indicator. If you open up the settings menu and select "Show Table", this will appear:
This will show you the exact values of each of the technicals the indicator is using in its range assessment.
And that is basically the bulk of the indicator!
I use this predominately on the smaller timeframes, especially when there is a lot of chop, to ascertain the overall sentiment.
I also will reference it on the 1 hour to see what the prevailing sentiment is and whether the stock is at an area of technical resistance or support. For example, here is what I referenced on SPY today:
QUICK NOTE:
It works best with RTH (regular trading hours) turned on and ETH (extended trading hours) turned off!
That's it!
Hopefully you like it and leave your comments and suggestions below!
MTF Evolving Weighted Composite Value Area🧾 Description:
This indicator calculates evolving value areas across 3 different timeframes/periods and combines them into one composite, multi-timeframe evolving value area - with each of the underlying timeframes' VAs assigned their own weighting/importance in the final calculation. Layered with extra smoothing options, this creates an informative and useful 'rolling value area' effect that can give you a better perspective on the value area across multiple periods at once as it develops - without total calculation resets at the onset of every new period.
Let's start with a simplified primer on value areas and then jump in to the new ideas this indicator introduces.
🤔 What is a value area?
Value areas are a tool used in market profile analysis to determine the range of prices that represents where most trading activity occurred during a specific time period, typically within a single 'bar' of a certain higher timeframe, such as the 4-hour, daily, or weekly. It helps traders understand the levels where the market finds value.
To calculate the value area, we look at the distribution of prices and trading volume. We determine a percentage, usually 70% or 80%, that represents the significant portion of trading volume. Then, we identify the price range that contains this percentage of trading volume, which becomes the value area.
Value areas are useful because they provide insights into market dynamics and potential support and resistance levels. They show where traders have been most active and where they find value, and traders can use this information to make better-informed decisions.
For example, if price is trading within the value area, it suggests that it's within a range where traders see value and are actively participating, which could indicate a balanced market. If the price moves above or below the value area, it may signal a potential shift in market sentiment or a breakout/breakdown from the established range.
By understanding the value area, traders can identify potential areas of supply and demand, determine levels of interest for buyers and sellers, and make decisions based on the market's perception of value.
📑 Limitations of traditional value areas
Static representation: Value areas are usually represented as static zones calculated after the fact. For example, after a daily period is completed, a typical 1D VA indicator will display the value area for the past period with static horizontal lines. This approach doesn't give you the power to see how the value area evolved, or developed, during the time period, as it is only displayed retroactively. It also doesn't give you the ability to view it as it evolves in real-time. This is why we chose to use an evolving value area representation, specifically borrowed from @sourcey's Value Area POC/VAH/VAL script function for calculating evolving VAs.
Rollover resets - no memory of past periods!: The traditional value area is calculated over a static period - it is calculated from the beginning of the period, for example a 1 day period, to the end, and that's the end of it. When the next daily period begins, the calculation resets, and has no memory of the preceding period. This limits the usefulness of the value area visual when viewed near the beginning of a new period before price and volume have been given ample time to define an area.
Hard to absorb all of that information: Value areas aren't generally meant to be a hardline representation of something extremely exact - they're based on a percentage of the area where traders appeared to find value over a certain time period. Most traders use them as a guide for support and resistance levels or finding an expected range. Traders typically overlay multiple VAs - sometimes requiring several instances of the same indicator to be applied - to represent the VA across multiple timeframes such as the 4H, 1D, or 1W. The chart quickly gets cluttered and it's not necessarily easy to understand the relationship between these multiple periods' VAs at a glance.
🧪 New concepts introduced in this indicator
With the evolving weighted composite value area we tried to address these limitations, and we think the result can be useful and intuitive for traders who want more dynamic and practical VAs for their everyday technical analysis.
⚖️ 1. A composite, weighted multi-timeframe VA
This indicator's value areas represent a combination or composite of the value areas calculated across multiple timeframes. The VAs calculated across each timeframe are then given a weighting percentage, which determines their contribution to the final 'weighted composite value area'.
Pictured below: a 4H/1D/1W MTF evolving weighted composite VA on the BTCUSDT Perpetual Futures (Binance) 5 minute chart:
Traditionally, when traders wanted to get a view of where the majority of trading activity occurred over the past four hours, day, and week, they would need to apply three value area indicators (or sometimes one if it allows multiple custom timeframes), each set to a different period (4H, 1D, 1W). The chart gets cluttered quickly and the information is hard to absorb in one shot. Addressing this problem was the main impetus for creating this weighted composite process.
〰️ 2. Rolling and smoothed evolving VAs
Because the composite VA is calculated based on multiple period VAs, there is no one single point where the area calculation resets (unless all 3 selected timeframes happen to rollover on the same bar). This creates a 'rolling' effect that gives a sense of the progression of the VA as price transitions through the different underlying time periods, without the traditional 'jump' in calculations between periods.
Pictured below: a 1D/1W/1M MTF evolving weighted composite VA on the NQ futures 1H chart:
To help give even more of a sense of perspective and 'progression' of the VA, there are also smoothing options to even out the 'jumps' at period-rollover points.
✔️ What's it good for?
Smoothed, rolling, and evolving multi-timeframe VAs that give you a better real-time perspective of where traders are finding value across multiple time periods at once.
📎 References
1. @sourcey's Value Area POC/VAH/VAL script by adapting its f_poc(tf) function.
💠 Features:
A MTF evolving weighted composite value area based on 3 underlying VAs calculated across customizable timeframes
Aesthetic and flexible coloring and color theme styling options
Period-roller labels and options for ease-of-use and legibility
⚙️ Settings:
Calculation Decimal Resolution: This setting essentially determines how 'granular' the value area calculating process is. This value should be set to some multiple of the tick size/smallest decimal of the symbol's price chart. Eg. On BTCUSDT, the tick size/decimal is usually 0.1. So, you might use 0.5. On TSLA, the tick size is 0.01. You might use 0.05 or 0.25. Beware: if the resolution is too small, calculation will take too long and the script may timeout.
Show Me Suggested Resolutions: If enabled, a label will display in the bottom right of the chart with some suggested resolutions for the current chart.
Area Percentage: Set the displayed percentage of the calculated composite value area. Igor method = 70%; Daniel method: 68%.
Use a Color Theme: When this setting is enabled, all manual 'Bullish and Bearish Colors' are overridden. All plots will use the colors from your selected Color Theme - excepting those plots set to use the 'Single Color' coloring method.
Color Theme: When 'Use a Color Theme' is enabled, this setting allows you to select the color theme you wish to use.
Resistance Color: When 'Use a Color Theme' is disabled, this will set the 'resistance color' for the composite VA.
Support Color: When 'Use a Color Theme' is disabled, this will set the 'support color' for the composite VA.
Show Period Rollover Labels: When enabled, a label will show above or below the composite VA marking any underlying period rollovers with the label 'New __' (eg. 'New 4H', 'New 1D', 'New 1W').
Size: Sets the font size of the period rollover labels.
Show Period Rollover Lines: When enabled, a translucent vertical dashed line will be drawn across the composite VA when one of the underlying periods rolls over.
Fill Composite Value Area: When enabled, the composite VA will be filled with a gradient coloring from the support line to the resistance line using their respective colors.
Smooth: When enabled, a smoothing moving average will be applied to the composite value area.
Smoothing Period: Set the lookback period for the smoothing average.
Smoothing Type: Set the calculation type for the smoothing average. Options include: Exponential, Simple, Weighted, Volume-Weighted, and Hull.
Enable: Include/exclude a timeframe's VA in the composite VA calculation.
Timeframe: Set the timeframe for this specific underlying VA.
Weighting %: Set the weighting percentage or 'importance' of this timeframe's value area in calculating the composite VA. Beware! The sum of the weighting percentages across all enabled timeframes must ALWAYS add up to 100 in order for this indicator to work as designed.
Real Dominance//Due to incompliance with TV rules, I re-publish this indicator once again. Hope this time it's complaint.
Indicator shows dominance of main coin (BTC by default) after deduction of all stablecoins marketcaps and compares it to dominance that provides TradingView (BTC.D by default). The reason of writing this indicator is to deduct all stablecoins' caps from bitcoin dominance and show dominance without impact of other stablecoins. It means, that if crypto cap equals to, let's say 100, stablecoins' cap will be part of it (something between 10 and 20), but generally stablecoins are not crypto and it's caps are generally not limited, so we can't clearly see what is real dominance of BTC in compare with altcoins.
Notes:
1. dominance for timeframes lower than 1D could be calculated only on tariffs Pro+ or Premium (TV limitation)
2. you may change any and all tickers in indicator's setup menu
3. at the moment of publication (03.06.2023), TV doesn't offer market cap tickers for all stablecoins. Therefore in case it will be added in the future you may add it in the setup menu. There are placeholders for stablecoins that has market cap in amount of more than 5mil USD as of today.
Индикатор показывает доминацию главной монеты (по умолчанию BTC) за вычетом доли всех стейблкоинов в сравнении к доминации, которую показывает TradingView (по умолчанию BTC.D). Причиной написания данного индикатора является необходимость вычесть влияние стейблов на доминацию, так как важно смотреть доминацию именно в сравнении BTC/altcoins, и не учитывать стейблкойны, объем которых по большому счету не ограничен.
Особенности работы:
1. на тарифах кроме Pro+ и Premium, доминация может быть рассчитана только на дневном таймфрейме и выше (ограничения TradingView).
2. все тикеры, включая главную и сравниваемую монеты можно менять по желанию в настройках. Стиль линий настраивается на соответствующей вкладке в настройках.
3. к сожалению, на момент публикации индикатора (03.06.2023), TradingView предоставляет данные капитализации для ограниченного количества стейблкойнов. В настройки добавлены заглушки для последующего добавления других стейблкойнов. В список внесены монеты, капитализация которых на момент публикации индикатора составляла более 5 млн долларов.
Annualized Spot-Future DifferenceThe "Annualized Spot-Future Difference" indicator (ASFD) compares the closing prices of a futures contract and its underlying spot asset. It calculates the price difference between the two instruments and annualizes this difference to provide a standardized measure for comparison.
The indicator takes inputs for the futures ticker symbol and the spot ticker symbol, allowing flexibility in selecting the specific assets for analysis. Additionally, it allows the user to input the contract date, which represents the expiration date of the futures contract.
The ASFD indicator plots the annualized difference between the futures and spot prices. It calculates the price difference by subtracting the spot price from the futures price. To annualize this difference, it considers the remaining days to the contract expiration and scales the difference accordingly.
The annualized difference can provide insights into market expectations, as it reflects the market's perception of the future price movement of the underlying asset. A positive value indicates that the futures price is higher than the spot price, potentially suggesting bullish sentiment. Conversely, a negative value suggests bearish sentiment, with the futures price lower than the spot price.
Traders and analysts can utilize the ASFD indicator to identify potential opportunities for arbitrage or evaluate market sentiment regarding the underlying asset. By monitoring changes in the annualized difference over time, they can gain insights into market dynamics and make informed trading decisions.
It's important to note that the ASFD indicator relies on accurate and up-to-date pricing data for both the futures and spot assets. Traders should verify that the selected ticker symbols correspond to the desired instruments and ensure that the contract date aligns with the relevant futures contract expiration.
Overall, the ASFD indicator provides a quantitative measure of the annualized price difference between futures and spot assets, enabling traders and analysts to assess market expectations and identify potential trading opportunities.
Z-Score Candles with ReversalsIn the process of releasing some of my Z-Score based indicators. This is the Z-Score Candle indicator.
What it does:
This converts the current candles into a z-score based candle over a 14 period lookback (adjustable but recommended to leave at 14).
It plots out the overbought/oversold areas using colours and will lookback over a user defined period of time to identify previous areas of bullish and bearish reversals.
Why Z-Score Candles?
Before we get into how to use it, I think its important to discuss why converting candles to a Z-Score is advantageous.
When we convert candlesticks to Z-Score, we have the ability to view areas of natural mathematical support and resistance (I want to clarify, when I saw mathematical support and resistance, it is kind of a misnomer, it is not the same as technical support and resistance. Its a measure of the natural tendency of things to revert to their mean and not deviate to extreme poles of their mean for prolonged period of time, I use the term mathematical support and resistance as it is something most traders are familiar with and operates similarly).
This is particularly helpful during trends. For example, if we take a look at the following BA chart:
In the chart above, you can see that despite BA not being on technical support (that red line), the indicator identified math support (the support was identified by the indicator looking at BA's natural deviations from its mean and seeing that, at that particular point in time, BA had deviated to an area that traditionally leads to reversals to the upside).
If we look at another example:
We can see in the chart above that, despite BA making a new high on the day and "breaking out" of previous resistance, BA was at math resistance being 3.0 Standard Deviations from its trading mean at the time. Thus, necessitating the pullback you see in the chart.
How to use it:
The indicator can be used similar to RSI and Stochastics or any other oscillator based indicator. The difference is, you can actually see the price action in terms of its relationship to its mean. What the means, is the indicator displays the current price action in terms of the ticker's relationship to its current mean and average. This permits us to see areas of rejection and support in relation to its current distance from neutrality. We can also see the various positions of each of the ticker's values from the mean. For example, we can see where the open is in relation to the average, the high and the low vs simply looking at a single variable (usually the close price).
The indicator will also highlight areas where the ticker has deviated to extreme ends of its mean (defined at a Z-Score of +/- 3.0). The picture below is an example of a bearish extreme:
And a bullish extreme:
You can see in both cases a reversal resulted almost immediately.
Inputs:
In the chart above, you can see the 3 main input sections.
Z-Score Lookback: This determines the lookback length for the Z-Score. The recommendation is to leave at 14, especially if you are a day trader.
SMA Inputs: The SMA (The white line) can be toggled off and on. You can also change the source to the High, Low, Close and Open Z-Score. You can adjust the lookback length of the SMA to your liking to assess trends. It does not need to be the same input as the Z-Score.
Reversal Inputs: The reversal inputs determines the length of lookback for the indicator to determine the most extreme bearish and bullish deviation from its mean. It is defaulted at 75 but can be adjusted based on preference. For more frequent signals, you can reduce the lookback length but be prepared for false signals in that case. You can also toggle off the reversal labels if you do not want them.
Concluding remarks:
And that is the Z-Score Candle indicator in a nutshell. Pretty self explanatory otherwise. It is more tailored to day traders. It is not a tool I would necessarily use for longer-term outlooks. I would use a simple Z-Score based indicator for that. But for active day trading, this is very helpful. That said, it can be used to look at longer term outlooks as well, but there are more powerful Z-Score based indicators for that (you can check out my own Z-Score indicator or my recently released Z-Score Probability Indicator which is more tailored for bigger picture outlooks).
Hope you enjoy, as always leave your comments, suggestions and questions below!
Safe trades to all!
