Ticker DataThis script mostly for Pine coders but may be useful for regular users too.
I often find myself needing quick access to certain information about a ticker — like its full ticker name, mintick, last bar index and so on. Usually, I write a few lines of code just to display this info and check it.
Today I got tired of doing that manually, so I created a small script that shows the most essential data in one place. I also added a few extra fields that might be useful or interesting to regular users.
Description for regular users (from Pine Script Reference Manual)
tickerid - full ticker name
description - description for the current symbol
industry - the industry of the symbol. Example: "Internet Software/Services", "Packaged software", "Integrated Oil", "Motor Vehicles", etc.
country - the two-letter code of the country where the symbol is traded
sector - the sector of the symbol. Example: "Electronic Technology", "Technology services", "Energy Minerals", "Consumer Durables", etc.
session - session type (regular or extended)
timezone - timezone of the exchange of the chart
type - the type of market the symbol belongs to. Example: "stock", "fund", "index", "forex", "futures", "spread", "economic", "fundamental", "crypto".
volumetype - volume type of the current symbol.
mincontract - the smallest amount of the current symbol that can be traded
mintick - min tick value for the current symbol (the smallest increment between a symbol's price movements)
pointvalue - point value for the current symbol
pricescale - a whole number used to calculate mintick (usually (when minmove is 1), it shows the resolution — how many decimal places the price has. For example, a pricescale 100 means the price will have two decimal places - 1 / 100 = 0.01)
bar index - last bar index (if add 1 (because indexes starts from 0) it will shows how many bars available to you on the chart)
If you need some more information at table feel free to leave a comment.
DATA
Spent Output Profit Ratio | JeffreyTimmermansSOPR
The "Spent Output Profit Ratio" , aka SOPR indicator is a valuable tool designed to analyze the profitability of spent Bitcoin outputs. SOPR is derived by dividing the selling price of Bitcoin by its purchase price, offering insights into market participants' profit-taking or loss-cutting behavior.
This script features two selectable SOPR metrics:
SOPR 30D: A 30-day Exponential Moving Average (EMA) for short-term trend analysis.
SOPR 365D: A 365-day EMA for assessing long-term profitability trends.
How It Works
Key Levels: The horizontal reference line at 1.0 acts as a critical threshold:
Above 1.0: Market participants are generally in profit, indicating bullish sentiment.
Below 1.0: Market participants are selling at a loss, often signaling bearish sentiment.
Background Colors
Green: Indicates bullish conditions when the selected SOPR value is above 1.
Red: Highlights bearish conditions when the value is below 1.
Dynamic Selection
Easily switch between SOPR 30D and SOPR 365D in the settings for tailored analysis.
Features
Customizable SOPR Selection: Toggle between 30-day and 365-day SOPR views based on your trading preferences.
Dynamic Label: A floating label displays the current SOPR value in real-time, along with the selected SOPR metric for easy monitoring.
Background Highlights: Visual cues for bullish and bearish conditions simplify chart interpretation.
Real-Time Alerts
Bullish Alerts: Triggered when the selected SOPR crosses above 1.
Bearish Alerts: Triggered when the selected SOPR crosses below 1.
Clean Visualization
The indicator includes a horizontal reference line and clear color schemes for easy trend identification.
The SOPR Indicator is an essential tool for traders and analysts seeking to understand Bitcoin market sentiment and profitability trends. Whether used for short-term trades or long-term market analysis, this script provides actionable insights to refine your decision-making process.
-Jeffrey
Correlation Coefficient Master TableThe Correlation Coefficient Master Table is a comprehensive tool designed to calculate and visualize the correlation coefficient between a selected base asset and multiple other assets over various time periods. It provides traders and analysts with a clear understanding of the relationships between assets, enabling them to analyze trends, diversification opportunities, and market dynamics. You can define key parameters such as the base asset’s data source (e.g., close price), the assets to compare against (up to six symbols), and multiple lookback periods for granular analysis.
The indicator calculates the covariance and normalizes it by the product of the standard deviations. The correlation coefficient ranges from -1 to +1, with +1 indicating a perfect positive relationship, -1 a perfect negative relationship, and 0 no relationship.
You can specify the lookback periods (e.g., 15, 30, 90, or 120 bars) to tailor the calculation to their analysis needs. The results are visualized as both a line plot and a table. The line plot shows the correlation over the primary lookback period (the Chart Length), which can be used to inspect a certain length close up, or could be used in conjunction with the table to provide you with five lookback periods at once for the same base asset. The dynamically created table provides a detailed breakdown of correlation values for up to six target assets across the four user-defined lengths. The table’s cells are formatted with rounded values and color-coded for easy interpretation.
This indicator is ideal for traders, portfolio managers, and market researchers who need an in-depth understanding of asset interdependencies. By providing both the numerical correlation coefficients and their visual representation, users can easily identify patterns, assess diversification strategies, and monitor correlations across multiple timeframes, making it a valuable tool for decision-making.
Z-Score Financial Market Conditions | JeffreyTimmermansZ-Score Financial Market Conditions
The Z-Score Financial Market Conditions indicator is a cutting-edge tool for measuring financial market stress and relaxation by combining eight critical financial metrics into a single composite Z-score. This dynamic indicator provides traders and analysts with actionable insights into the overall state of the financial markets, enabling informed decision-making across various trading and investment systems.
Purpose of the Indicator
This indicator serves as a comprehensive gauge of financial market conditions, offering a clear visualization of whether the markets are in a state of stress (elevated risks) or relaxation (normalized conditions). The Z-Score Financial Market Conditions tool is particularly effective for:
Macro-Level Risk Assessment: Identifying periods of high market stress or calmness.
Trend Following Systems: Gauging the market's underlying conditions to validate trends.
Mean Reversion Strategies: Using extreme Z-score levels to detect potential reversals.
Portfolio Risk Management: Adjusting asset exposure based on market-wide financial conditions.
This indicator works exclusively on the 1-day timeframe, as it is calibrated to analyze daily changes in the financial metrics that drive market behavior.
The Eight Key Components and Their Importance
The composite Z-score integrates the Z-scores of the following eight financial metrics. These metrics have been selected for their complementary insights into various aspects of financial market conditions:
VIX (S&P 500 Volatility Index)
Reflects implied volatility in the U.S. equity market.
High VIX values indicate increased uncertainty and risk aversion among market participants.
MOVE (US Treasury Bond Volatility Index)
Captures volatility in U.S. Treasury bonds.
Essential for understanding risk in fixed-income markets, which significantly impact broader economic conditions.
ICE BofA High Yield Option Adjusted Spread (BAMLH0A0HYM2)
Measures the risk premium for high-yield corporate bonds.
Rising spreads suggest increased credit risk and potential economic stress.
ICE BofA Corporate Index Option Adjusted Spread (BAMLC0A0CM)
Tracks credit spreads in the investment-grade bond market.
Helps evaluate the health of higher-quality corporate debt, a key indicator of financial stability.
ICE BofA US High Yield Index Spread (BAMLH0A0HYM2)
Focuses on high-yield U.S. corporate bonds.
Provides localized insights into U.S. credit conditions and risk levels.
CDS (Credit Default Swap Spreads)
Measures the cost of insuring against bond defaults.
Rising CDS spreads signal growing concern over creditworthiness, often a leading indicator of financial stress.
Global Bond Spread (AGG)
Represents global fixed-income spreads.
Offers a broader perspective on international financial conditions beyond the U.S. market.
TED Spread (Treasury-EuroDollar Spread)
The difference between interbank lending rates and short-term U.S. Treasury yields.
Widely regarded as an indicator of systemic risk in the banking sector.
Features and Improvements
This script builds upon the original concept by introducing advanced features to enhance its precision and usability:
Lookback Period Adjustment
A customizable lookback period for Z-score calculations (default: 160 days).
Allows for greater flexibility in adapting to different market conditions.
Moving Average (MA) Smoothing
Optional smoothing of Z-scores using an exponential moving average (EMA) for enhanced clarity.
Default smoothing length: 8 days.
Individual Component Visibility
Plots for individual Z-scores can be enabled or disabled to focus on specific metrics.
Dynamic Background Coloring
Visual cues to indicate bullish (green) or bearish (red) financial conditions based on the composite Z-score.
Custom Inputs
Toggle on/off for each financial metric to tailor the indicator to specific use cases.
Customizable parameters for smoothing and moving averages.
Applications
This indicator is versatile and can be effectively used in various trading systems and strategies:
Long-Term Investment Decision-Making: Assess macroeconomic trends for portfolio rebalancing.
Systematic Trading: Incorporate market conditions into algorithmic models to enhance robustness.
Volatility-Based Strategies: Use Z-score fluctuations to anticipate periods of market turbulence or calm.
Credits
This indicator was inspired by and builds upon the work of TomasOnMarkets . While incorporating significant enhancements, it acknowledges the foundational concepts provided by this original source. Thank you for sharing your input on this important indicator. We are honored to use it and to further improve upon it.
-Jeffrey
Auto Risk Reward CalculatorAuto RR Calculator
Gives you the price of certain risk to rewards based on your entry and stop loss inputs.
The information given by this tool does not constitute as financial advice.
Dynamic Risk-Adjusted Performance Ratios with TableWith this indicator, you have everything you need to monitor and compare the Sharpe ratio, Sortino ratio, and Omega ratio across multiple assets—all in one place. This tool is designed to help save time and improve efficiency by letting you track up to 15 assets simultaneously in a fully customizable table. You can adjust the lookback period to fit your trading strategy and get a clearer picture of how your assets perform over time. Instead of switching between charts, this indicator puts all the critical information you need at your fingertips.
Sharpe Ratio -
Helps evaluate the overall efficiency of investments by comparing the average return to the total risk (measured by the standard deviation of all returns). Essentially, it tells you how much excess return you’re getting for each unit of risk you’re taking. A higher Sharpe ratio means you’re getting better risk-adjusted performance—something you’ll want to aim for in your portfolio.
Sortino Ratio -
Goes a step further by focusing only on downside risk—because let’s face it, no one worries about positive volatility. This ratio is calculated by dividing the average return by the standard deviation of only the negative returns. Perfect for those concerned about avoiding losses rather than chasing extreme gains. It gives you a sharper view of how well your assets are performing relative to the risks you’re trying to avoid.
Omega Ratio -
Offers a unique perspective by comparing the sum of positive returns to the absolute sum of negative returns. It’s a straightforward way to see if your wins outweigh your losses. A higher Omega ratio means your positive returns significantly exceed the downside, which is exactly what you want when building a strong, reliable portfolio.
This indicator is perfect for traders who want to streamline their decision-making process and gain an edge. Bringing together these three critical ratios into a single user-defined table makes it easy to compare and rank assets at a glance. Whether optimizing a portfolio or looking for the best opportunities, this tool helps you stay ahead by focusing on risk-adjusted returns. The customizable lookback period lets you tailor the analysis to fit your unique trading approach, giving you insights that align with your goals. If you’re serious about making data-driven decisions and improving your trading outcomes, this indicator is a game-changer for your toolkit.
Market Cycles
The Market Cycles indicator transforms market price data into a stochastic wave, offering a unique perspective on market cycles. The wave is bounded between positive and negative values, providing clear visual cues for potential bullish and bearish trends. When the wave turns green, it signals a bullish cycle, while red indicates a bearish cycle.
Designed to show clarity and precision, this tool helps identify market momentum and cyclical behavior in an intuitive way. Ideal for fine-tuning entries or analyzing broader trends, this indicator aims to enhance the decision-making process with simplicity and elegance.
Candlestick DataCandlestick Data Indicator
The Candlestick Data indicator provides a comprehensive overview of key metrics for analyzing price action and volume in real-time. This overlay indicator displays essential candlestick data and calculations directly on your chart, offering an all-in-one toolkit for traders seeking in-depth insights.
Key Features:
Price Metrics: View the daily high, low, close, and percentage change.
Volume Insights: Analyze volume, relative volume, and volume buzz for breakout or consolidation signals.
Range Analysis: Includes closing range, distance from low of day (LoD), and percentage change in daily range expansion.
Advanced Metrics: Calculate ADR% (Average Daily Range %), ATR (Average True Range), and % from 52-week high.
Moving Averages: Supports up to four customizable moving averages (EMA or SMA) with distance from price.
Market Context: Displays the sector and industry group for the asset.
This indicator is fully customizable, allowing you to toggle on or off specific metrics to suit your trading style. Designed for active traders, it brings critical data to your fingertips, streamlining decision-making and enhancing analysis.
Perfect for momentum, swing, and day traders looking to gain a data-driven edge!
Volume CalendarDescription:
The indicator displays a calendar with Volume data for up to 6 last months. It is designed to work on any timeframe, but works best on Daily and below. It is also consistent in that it displays the same data even if you go to lower timeframes like 5 minutes (even though the data is used is Daily).
Features:
- displays volume data for last N months (volume, volume change, % of weekly, monthly and yearly volume)
- display total volume for each month
- display monthly sentiment
- find dates with volume spikes
Inputs:
- Number of months -> how many last months of data to display (from 1 to 6)
- Volume Type -> display only Bullish, only Bearish or all volume
- Cell color is based on -> Volume - the brighter the cell the higher volume was on that day; Volume Change - the brighter the cell the higher was the volume change that day; Volume Spike - the brighter the cell the higher was volume spike that day (volume spike is based on volume being above its average over last N candles)
- Cell color timeframe -> Weekly - the cell color is calculated comparing volume of that cell with weekly volume; Monthly - comparing volume with monthly volume
- Use volume for sentiment -> take the volume into account when calculating monthly sentiment (otherwise calculate it based on number of Bullish and Bearish days in the month)
- Spike Average Period -> period of the moving average used for spike calculation
- Spike Threshold -> current volume must be this many times greater than the average for it to be considered a spike
- Table Size -> size of the table
- Theme -> colouring of the table
csv_series_libraryThe CSV Series Library is an innovative tool designed for Pine Script developers to efficiently parse and handle CSV data for series generation. This library seamlessly integrates with TradingView, enabling the storage and manipulation of large CSV datasets across multiple Pine Script libraries. It's optimized for performance and scalability, ensuring smooth operation even with extensive data.
Features:
Multi-library Support: Allows for distribution of large CSV datasets across several libraries, ensuring efficient data management and retrieval.
Dynamic CSV Parsing: Provides robust Python scripts for reading, formatting, and partitioning CSV data, tailored specifically for Pine Script requirements.
Extensive Data Handling: Supports parsing CSV strings into Pine Script-readable series, facilitating complex financial data analysis.
Automated Function Generation: Automatically wraps CSV blocks into distinct Pine Script functions, streamlining the process of integrating CSV data into Pine Script logic.
Usage:
Ideal for traders and developers who require extensive data analysis capabilities within Pine Script, especially when dealing with large datasets that need to be partitioned into manageable blocks. The library includes a set of predefined functions for parsing CSV data into usable series, making it indispensable for advanced trading strategy development.
Example Implementation:
CSV data is transformed into Pine Script series using generated functions.
Multiple CSV blocks can be managed and parsed, allowing for flexible data series creation.
The library includes comprehensive examples demonstrating the conversion of standard CSV files into functional Pine Script code.
To effectively utilize the CSV Series Library in Pine Script, it is imperative to initially generate the correct data format using the accompanying Python program. Here is a detailed explanation of the necessary steps:
1. Preparing the CSV Data:
The Python script provided with the CSV Series Library is designed to handle CSV files that strictly contain no-space, comma-separated single values. It is crucial that your CSV file adheres to this format to ensure compatibility and correctness of the data processing.
2. Using the Python Program to Generate Data:
Once your CSV file is prepared, you need to use the Python program to convert this file into a format that Pine Script can interpret. The Python script performs several key functions:
Reads the CSV file, ensuring that it matches the required format of no-space, comma-separated values.
Formats the data into blocks, where each block is a string of data that does not exceed a specified character limit (default is 4,000 characters). This helps manage large datasets by breaking them down into manageable chunks.
Wraps these blocks into Pine Script functions, each block being encapsulated in its own function to maintain organization and ease of access.
3. Generating and Managing Multiple Libraries:
If the data from your CSV file exceeds the Pine Script or platform limits (e.g., too many characters for a single script), the Python script can split this data into multiple blocks across several files.
4. Creating a Pine Script Library:
After generating the formatted data blocks, you must create a Pine Script library where these blocks are integrated. Each block of data is contained within its function, like my_csv_0(), my_csv_1(), etc. The full_csv() function in Pine Script then dynamically loads and concatenates these blocks to reconstruct the full data series.
5. Exporting the full_csv() Function:
Once your Pine Script library is set up with all the CSV data blocks and the full_csv() function, you export this function from the library. This exported function can then be used in your actual trading projects. It allows Pine Script to access and utilize the entire dataset as if it were a single, continuous series, despite potentially being segmented across multiple library files.
6. Reconstructing the Full Series Using vec :
When your dataset is particularly large, necessitating division into multiple parts, the vec type is instrumental in managing this complexity. Here’s how you can effectively reconstruct and utilize your segmented data:
Definition of vec Type: The vec type in Pine Script is specifically designed to hold a dataset as an array of floats, allowing you to manage chunks of CSV data efficiently.
Creating an Array of vec Instances: Once you have your data split into multiple blocks and each block is wrapped into its own function within Pine Script libraries, you will need to construct an array of vec instances. Each instance corresponds to a segment of your complete dataset.
Using array.from(): To create this array, you utilize the array.from() function in Pine Script. This function takes multiple arguments, each being a vec instance that encapsulates a data block. Here’s a generic example:
vec series_vector = array.from(vec.new(data_block_1), vec.new(data_block_2), ..., vec.new(data_block_n))
In this example, data_block_1, data_block_2, ..., data_block_n represent the different segments of your dataset, each returned from their respective functions like my_csv_0(), my_csv_1(), etc.
Accessing and Utilizing the Data: Once you have your vec array set up, you can access and manipulate the full series through Pine Script functions designed to handle such structures. You can traverse through each vec instance, processing or analyzing the data as required by your trading strategy.
This approach allows Pine Script users to handle very large datasets that exceed single-script limits by segmenting them and then methodically reconstructing the dataset for comprehensive analysis. The vec structure ensures that even with segmentation, the data can be accessed and utilized as if it were contiguous, thus enabling powerful and flexible data manipulation within Pine Script.
Library "csv_series_library"
A library for parsing and handling CSV data to generate series in Pine Script. Generally you will store the csv strings generated from the python code in libraries. It is set up so you can have multiple libraries to store large chunks of data. Just export the full_csv() function for use with this library.
method csv_parse(data)
Namespace types: array
Parameters:
data (array)
method make_series(series_container, start_index)
Namespace types: array
Parameters:
series_container (array)
start_index (int)
Returns: A tuple containing the current value of the series and a boolean indicating if the data is valid.
method make_series(series_vector, start_index)
Namespace types: array
Parameters:
series_vector (array)
start_index (int)
Returns: A tuple containing the current value of the series and a boolean indicating if the data is valid.
vec
A type that holds a dataset as an array of float arrays.
Fields:
data_set (array) : A chunk of csv data. (A float array)
ADR Study [TFO]This indicator is focused on the Average Daily Range (ADR), with the goal of collecting data to show how often price reaches/closes through these levels, as well as a look at historical moves that reached ADR and at similar times of day to study how price moved for the remainder of the session.
The ADR here (blue line) is calculated using the difference between a day's highest and lowest points. If our ADR length is 5, then we are taking this difference from the last 5 days and averaging them together. At the following day's open, we take half of this average and plot it above and below the daily opening price to place theoretical limits on how far price may move according to the lookback period. The triangles indicate when price has reached ADR (either +ADR or -ADR), and alerts can be created for these events.
The Scale Factor is an optional parameter to scale the ADR by a certain amount. If set to 2 for example, then the ADR would be 2x the average daily range. This value will be reflected in the statistics options so that users can see how different values affect the outcomes.
Show Table will display data collected on how often price reaches these levels, and how often price closes through them, for each day of the week. By default, these are colored as blue and red, respectively. From the following chart of NQ1!, we can see for example that on Mondays, price reached +ADR 38% of the time and closed through it 23% of the time. Note that the statistics for closing through the ADR levels are derived from all instances, not just those that reached ADR.
Show Sample Sizes will display how many instances were collected for all given sets of data. Referring to the same example of NQ1!, we can see that this particular chart has collected data from 109 Mondays. From those Mondays, 41 reached +ADR (38%, verifying our initial claim) and 25 closed through it (23%). This is important to understand the scope of the data that we're working with, as percentages can be misleading for smaller sample sizes.
Show Histogram will plot the same exact data as the table, just in a histogram form to visually emphasize the differences on a day-by-day basis. On this chart of RTY1!, we can see for example from the top histogram that on Wednesdays, 40% reached +ADR and only 22% closed through it. Similarly if we look at the bottom histogram, we can see that Wednesdays reached -ADR 46% of the time and closed through it only 28% of the time.
We can also use Show Sample Sizes to display the same information that would be in the table, showing how many instances were collected for each event. In this case we can see that we observed 175 Fridays, where 76 reached +ADR (43%) and 44 closed above it (25%).
Show Historical Moves is an interesting feature of this script. When enabled, if price has reached +/- ADR in the current session, the indicator will plot the evolution of the close prices from all past sessions that reached +/- ADR to see how they traded for the remainder of the session. These calculations are made with respect to the ADR range at the time that price traded through these levels.
Historical Proximity (Bars) allows the user to observe historical moves where price reached ADR within this many bars of the current session (assuming price has reached an ADR level in the current session). In the above chart, this is set to 1000 so that we can observe each and every instance where price reached an ADR level. However, we can refine this a bit more.
By limiting the Historical Proximity to something like 20, we are only considering historical moves that reached ADR within 20 bars of todays +ADR reach (9:50 am EST, noted by the blue triangle up). We can enable Show Average Move to display the average move by the filtered dataset, and Match +/-ADR to only observe moves inline with the current day's price action (in this case, only moves that reached +ADR, since price has not reached -ADR).
We can add one more filter to this data with the setting Only Show Days That: closed through ADR; closed within ADR; or either. The option either is what you see above, as we are considering both days that closed through ADR and days that closed within it (note that in this case, closing within ADR simply means that price reached +ADR and closed the day below it, and vice versa for -ADR; this does not mean that price must have closed in between +ADR and -ADR). If we set this to only show instances that closed within ADR, we see the following data.
Alternatively, we can choose to Only Show Days That closed through ADR, where we would see the following data. In this case, the average move very much resembles the price action that occurred on this particular day. This is in no way guaranteed, but it makes an interesting case for how we could use this data in our analysis by observing similar, historical price action.
Please note that this data will change over time on a rolling basis due to TradingView's bar lookback, and that for this same reason, lower timeframes will yield less data than larger timeframes.
Trade-o-Scope: Plot Custom DataTrade-o-Scope team presents the "Plot Custom Data" indicator.
"Plot Custom Data" is designed to help you bring the custom timeseries data and plot it on the chart.
Motivation:
In the fast-evolving landscape of markets, traders often find themselves analyzing a plethora of data sources, specialized market information, various metrics, etc. While TradingView offers a rich catalog of instruments and market data, there are cases where traders rely on unique data sources, custom calculations, bespoke metrics, or refined analytics that demand visualization.
In some cases, traditional visualization approaches for custom data like plotting in Excel or other platforms may suffice for some. still, we really adore all the great features TradingView supercharts have - native scaling and scrolling, drawing, combining data, applying indicators and strategies on top of any series, etc. The ability to play with custom data on TradingView charts is just a whole new level compared to any alternatives.
With this indicator, we aim to empower traders to effortlessly bring their custom data to the familiar and friendly interface of the TradingView chart.
Basic Features:
Easy Data Input : Simply paste your numerical timeseries data, whether it's a column from Excel, Google Sheets, CSV file, or multiline text from any text editor.
Flexible Configuration : Define the order of values, starting date, and timeframe to match the specifications of your data.
Custom appearance : Personalize your chart by assigning a title to your data and selecting colors for the plot.
Advanced Features:
Value Multiplier : Set a multiplier for the provided values to adjust their scale as needed.
Formatting Options : Customize the display format of values on the chart, whether as price, percentage, or volume.
Conditional Coloring : Define conditions for changing the plot color based on a specific threshold.
Plot Style Selection : Choose from various plot styles such as line, histogram, area, columns, and more, to best visualize your data.
Additional Visual References : Enhance data analysis by activating fixed horizontal lines to aid in visual interpretation.
Leverage TradingView Capabilities : Seamlessly apply various indicators from the TradingView catalog, such as Bollinger Bands or RSI, onto your custom data chart for comprehensive analysis.
Chart Example:
On the chart above, you can observe several "Plot Custom Data" indicators added to demonstrate what your custom data can look like and how multiple indicators can be combined, as well as the indicator configuration screen.
How it works:
The "Plot Custom Data" indicator processes the list of values provided on the configuration form and converts it into data points to be plotted on the chart. To determine the timing of plotting each value, the indicator uses the configured 'starting date' and 'timeframe'. This means the indicator will always plot each subsequent value from your list at the distance of one timeframe from the previous one.
Limitations and Best Practices:
1) The way timeseries data is inputted on the "Plot Custom Data" indicator configuration page, leverages a standard input text field. This input type has a limited character capacity, approximately 4000-4400 symbols. Therefore, the timeseries data cannot exceed this limit.
Let's illustrate this with an example.
Suppose you have a column in Excel that you wish to plot using the indicator. Consider the first 5 rows of values in your column:
| -123.45
| -43.5
| 3.12
| 9.6
| 146.78
To calculate the total symbols used, we need to account for the minus sign, numbers, decimal point, and two invisible symbols at the end of each line, which helps structure the text into multiple lines. So, in total, these 5 rows will use:
row_1 + row_2 + row_3 + row_4 + row_5 = 9 + 7 + 6 + 5 + 8 = 35 symbols or 7 symbols per row on avarage.
If we assume the remaining data follows a similar format, we can estimate the number of rows that can be inputted on the indicator configuration page:
from ~4000 / 7 = 571 rows
to ~4400 / 7 = 628 rows
The right approach to overcome this limitation is to reduce the number of symbols representing each value as much as possible. In our example, consider dropping all decimals, which would lead to an average of 5.4 symbols per row and allow for up to ~800 rows to be accepted by the input field.
If your data primarily consists of large values such as thousands or millions, consider dividing it by a multiplier like x1000 and activating the same multiplier in the indicator settings.
For example, instead of storing values like 5,620,000 in your list, store the value 562 and activate an x10,000 multiplier in the configuration to display the original value on the chart.
It is also possible to combine multiple indicators on one chart and split a larger list of values between them. If you need to display significantly more custom data on charts, you may contact us in DM to find\develop a proper solution.
2) Another important consideration is to ensure that all your data aligns with the selected timeframe in the indicator configuration.
Remember, you provide only the values to the indicator without corresponding dates. Dates are calculated automatically by the indicator based on the configured starting date and chosen timeframe. This means the indicator will always plot each subsequent value from your list at the distance of one timeframe from the previous one.
Here's an example:
Suppose you have a timeseries with 'date' and 'value' columns and a timeframe of 30 minutes:
# | date | value
--|---------------------------|---------
1 | 2024-01-01 11:30:00 | -241.45
2 | 2024-01-01 12:02:13 | -11.56
3 | 2024-01-01 13:00:00 | 21.87
..
..
6 | 2024-01-01 14:30:00 | 19.6
7 | 2024-01-01 15:00:00 |
8 | 2024-01-01 15:30:00 | 46.78
Rows 1-3 represent bad practice:
Row 2 is not precisely aligned with the timeframe, as it has a time of 12:02:13 instead of the expected 12:00:00. This won't cause errors, but the plot will show this value at 12:00:00.
Row 3 has a 1-hour difference from Row 2, which poses a real problem. As the indicator doesn't know the actual dates, it expects Row 3 to be 30 minutes later than Row 2, so it will plot 21.87 at 12:30:00 instead of the actual 13:00:00. And the rest of the values from your list will have offset on the chart as well.
Rows 6-7 represent the correct approach to overcome gaps in your data:
If, for some reason, there is no value for 15:00:00 in the original dataset, adding an empty Row 7 ensures each subsequent row is located 30 minutes from the previous one. The indicator will treat the empty row correctly, not plotting anything at this date on the chart, but will plot a line connecting the value of 19.6 from Row 6 with the value of 46.78 from Row 8.
How to use:
1) Add the "Plot Custom Data" indicator to the chart.
Voila! You'll see the plot based on the default timeseries values list and settings.
2) Paste your numerical timeseries data.
Define the order of values, starting date, and timeframe.
Assign a title to your data and select colors for the plot.
Voila! You'll see the plot based on your timeseries values list and settings.
3) Customize the indicator's settings:
Set a values multiplier if needed.
Specify the appropriate format for the values (price, percentage, or volume).
Choose whether to change the plot color based on conditions.
Select the plot style.
Add horizontal lines.
4) Explore your data, apply additional indicators and strategies from the TradingView catalog, or develop your own scripts using Pine.
Day/Week/Month Metrics (Zeiierman)█ Overview
The Day/Week/Month Metrics (Zeiierman) indicator is a powerful tool for traders looking to incorporate historical performance into their trading strategy. It computes statistical metrics related to the performance of a trading instrument on different time scales: daily, weekly, and monthly. Breaking down the performance into daily, weekly, and monthly metrics provides a granular view of the instrument's behavior.
The indicator requires the chart to be set on a daily timeframe.
█ Key Statistics
⚪ Day in month
The performance of financial markets can show variability across different days within a month. This phenomenon, often referred to as the "monthly effect" or "turn-of-the-month effect," suggests that certain days of the month, especially the first and last days, tend to exhibit higher than average returns in many stock markets around the world. This effect is attributed to various factors including payroll contributions, investment of monthly dividends, and psychological factors among traders and investors.
⚪ Edge
The Edge calculation identifies days within a month that consistently outperform the average monthly trading performance. It provides a statistical advantage by quantifying how often trading on these specific days yields better returns than the overall monthly average. This insight helps traders understand not just when returns might be higher, but also how reliable these patterns are over time. By focusing on days with a higher "Edge," traders can potentially increase their chances of success by aligning their strategies with historically more profitable days.
⚪ Month
Historically, the stock market has exhibited seasonal trends, with certain months showing distinct patterns of performance. One of the most well-documented patterns is the "Sell in May and go away" phenomenon, suggesting that the period from November to April has historically brought significantly stronger gains in many major stock indices compared to the period from May to October. This pattern highlights the potential impact of seasonal investor sentiment and activities on market performance.
⚪ Day in week
Various studies have identified the "day-of-the-week effect," where certain days of the week, particularly Monday and Friday, show different average returns compared to other weekdays. Historically, Mondays have been associated with lower or negative average returns in many markets, a phenomenon often linked to the settlement of trades from the previous week and negative news accumulation over the weekend. Fridays, on the other hand, might exhibit positive bias as investors adjust positions ahead of the weekend.
⚪ Week in month
The performance of markets can also vary within different weeks of the month, with some studies suggesting a "week of the month effect." Typically, the first and the last week of the month may show stronger performance compared to the middle weeks. This pattern can be influenced by factors such as the timing of economic reports, monthly investment flows, and options and futures expiration dates which tend to cluster around these periods, affecting investor behavior and market liquidity.
█ How It Works
⚪ Day in Month
For each day of the month (1-31), the script calculates the average percentage change between the opening and closing prices of a trading instrument. This metric helps identify which days have historically been more volatile or profitable.
It uses arrays to store the sum of percentage changes for each day and the total occurrences of each day to calculate the average percentage change.
⚪ Month
The script calculates the overall gain for each month (January-December) by comparing the closing price at the start of a month to the closing price at the end, expressed as a percentage. This metric offers insights into which months might offer better trading opportunities based on historical performance.
Monthly gains are tracked using arrays that store the sum of these gains for each month and the count of occurrences to calculate the average monthly gain.
⚪ Day in Week
Similar to the day in the month analysis, the script evaluates the average percentage change between the opening and closing prices for each day of the week (Monday-Sunday). This information can be used to assess which days of the week are typically more favorable for trading.
The script uses arrays to accumulate percentage changes and occurrences for each weekday, allowing for the calculation of average changes per day of the week.
⚪ Week in Month
The script assesses the performance of each week within a month, identifying the gain from the start to the end of each week, expressed as a percentage. This can help traders understand which weeks within a month may have historically presented better trading conditions.
It employs arrays to track the weekly gains and the number of weeks, using a counter to identify which week of the month it is (1-4), allowing for the calculation of average weekly gains.
█ How to Use
Traders can use this indicator to identify patterns or trends in the instrument's performance. For example, if a particular day of the week consistently shows a higher percentage of bullish closes, a trader might consider this in their strategy. Similarly, if certain months show stronger performance historically, this information could influence trading decisions.
Identifying High-Performance Days and Periods
Day in Month & Day in Week Analysis: By examining the average percentage change for each day of the month and week, traders can identify specific days that historically have shown higher volatility or profitability. This allows for targeted trading strategies, focusing on these high-performance days to maximize potential gains.
Month Analysis: Understanding which months have historically provided better returns enables traders to adjust their trading intensity or capital allocation in anticipation of seasonally stronger or weaker periods.
Week in Month Analysis: Identifying which weeks within a month have historically been more profitable can help traders plan their trades around these periods, potentially increasing their chances of success.
█ Settings
Enable or disable the types of statistics you want to display in the table.
Table Size: Users can select the size of the table displayed on the chart, ranging from "Tiny" to "Auto," which adjusts based on screen size.
Table Position: Users can choose the location of the table on the chart
-----------------
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!
multidataLibrary "multidata"
A library for multi-dimensional data arrays.
Full documentation: faiyaz7283.github.io
This library is designed to enhance data storage capabilities in Pine Script, enabling users to work with two separate data structures: data2d (key -> main-value | alternate-value) and data3d (primary key -> data key-> main-value | alternate-value). These structures facilitate storing key-value pairs in a flexible and efficient manner, offering various methods for manipulation and retrieval of data. Please check out the full documentation at faiyaz7283.github.io .
How To Input And Offset CSV DataExample method of how to use an input text area to import, offset, and plot CSV data using an array. Note that when using this method there is a limit of 4096 total characters per input.
For working with data sets larger than 4096 total characters an alternative method of pasting CSV data directly within the script can be seen below :
NOTICE: This is an example script and not meant to be used as an actual strategy. By using this script or any portion thereof, you acknowledge that you have read and understood that this is for research purposes only and I am not responsible for any financial losses you may incur by using this script!
SmartVPSGTitle: Identifying Volume Spikes, Price Movements and Gap Ups: A TradingView Script
Introduction:
In the world of trading, identifying volume spikes and price movements can provide valuable insights into market trends and potential trading opportunities. In this article, we'll explore a TradingView script that helps traders visualize volume spikes, price up moves with volume spikes, and gap-up days on their charts.
Detecting Price Up Moves:
The script starts by calculating price up moves. It compares the current day's closing price with the previous day's closing price and checks if it has increased by 3% or more. This helps traders spot significant upward price movements.
Detecting Volume Spurts:
Next, the script focuses on detecting volume spikes, which are often associated with increased market activity and potential trading opportunities. It compares the current day's volume with the highest volume of the previous nine sessions. If the current volume exceeds all the volumes of the previous nine sessions, it is considered a volume spurt.
Example:
Let's consider a hypothetical scenario where we have the following volume data for a stock:
Day 1: 100,000
Day 2: 80,000
Day 3: 120,000
Day 4: 150,000
Day 5: 200,000
Day 6: 90,000
Day 7: 110,000
Day 8: 130,000
Day 9: 140,000
Day 10: 250,000 (current day)
To determine if there is a volume spurt on Day 10, the script compares the current day's volume (250,000) with the highest volume of the previous nine sessions. In this case, the highest volume among the previous nine sessions is 200,000 (on Day 5). Since the current day's volume (250,000) exceeds the highest volume of the previous nine sessions (200,000), it is considered a volume spurt.
Identifying Gap-Up Days:
Gap-up days occur when the market opens significantly higher than the previous day's close. To identify these days, the script compares the current day's low price with the previous day's high price. If the low price is greater than the previous day's high, it is marked as a gap-up day.
Visualizing the Findings:
To provide a clear visual representation of the identified patterns, the script uses different shapes and colors. First, it plots small red dots above the candles whenever a volume spurt is detected. These dots help traders quickly identify periods of increased volume activity.
For price up moves with volume spikes, the script utilizes blue triangular shapes below the candles. This allows traders to pinpoint instances where both price and volume are showing positive signs, indicating potential bullish movements.
Additionally, the script incorporates green candles to represent gap-up days. These candles help traders recognize days when the market opens with a significant upward gap, suggesting a potential shift in market sentiment.
Conclusion:
The TradingView script discussed in this article provides traders with a visual representation of volume spikes , price up moves with volume spikes , and gap-up days . By incorporating these visual cues into their analysis, traders can gain valuable insights into market trends and potential trading opportunities.
Remember, this script should be used for educational and informational purposes only and does not serve as financial advice or recommendations. Traders are encouraged to customize and modify the script according to their specific trading strategies and risk tolerance.
Share this script with other traders on TradingView to enhance their chart analysis and trading decisions.
PS: This TradingView script is designed to work specifically on the daily timeframe (daily candles). It calculates and identifies volume spurts based on the volume data of the daily timeframe. Since it is designed for the daily timeframe, it may not produce accurate results or work as intended on other timeframes.
TooltipLibrary "Tooltip"
This library helps creating and managing nice looking data (key/value) tooltips that you can use for
labels. The tooltips data key/value will align automatically. It is optional to convert the data to a values only string too.
method addSpacesToKey(this)
Calculates the amount of spaces needed after the key to make it the key least 4 characters wide.
Namespace types: Data
Parameters:
this (Data) : (Data) The Data.
method addTabs(this, longestKeyLength)
Calculates the amount of tabs to be used.
Namespace types: Data
Parameters:
this (Data) : (Data) The Data.
longestKeyLength (int)
method longestKeyLength(this)
Returns the length of the longest key string in the array.
Namespace types: Data
Parameters:
this (Data ) : (Tooltip) The object to work with.
@return (int) The length of the key.
method toString(tooltips, withKey)
Helper function for the tooltip.
Namespace types: Data
Parameters:
tooltips (Data )
withKey (bool) : (bool) Wether to create a string with keys in it.
@return (string) The string
new()
Creates a new array to store tooltip data in
@return (Data) The data array.
Data
Key/Value pair for tooltips
Fields:
key (series string)
value (series string)
Seasonal Performance for Stocks & CryptoThe Seasonal Performance indicator quickly allows you to see if you are in a bullish or bearish time of year for an underlying security and where the current performance stacks up compared to the same time of year historically. Table is fully customizable from colors to what data to see.
Table Displays
Average Performance
Best Performance
Worst Performance
Last Performance
Current Performance
Note this indicator will only work with Stocks, ETF's, Index's or Crypto.
XAUXXXThis simple script is meant to get around the limitations some data providers have, in terms of the length of historical data they choose to provide traders. Inspired by OANDA's XAUCAD pair only having data as far back as 2005, whereas XAUUSD has data back to to the 19th century.
By taking the OHLC data from XAUUSD and multiplying it by the price of USD in a desired currency you are able to see further back in time, the limitation now being the length of FX data available instead of the price of Precious metal / currency pair. As shown in the chart you can now see the price of Gold in CAD as far back as the late 1960s, a nearly half century of data uncovered for all to see!
How To Import And Offset CSV DataExample method of how to import, offset, and plot CSV data using an array.
NOTICE: This is an example script and not meant to be used as an actual strategy. By using this script or any portion thereof, you acknowledge that you have read and understood that this is for research purposes only and I am not responsible for any financial losses you may incur by using this script!
Challenge training (journal)Dynamic trading journal with equity curve display. Detailed results with prop firm objectives, editable, $/month estimation, possibility to compare two strategies.
one line in parameter = one trading day. 20 days max.
For each trading day, specify : The number of trades, the number of SL, the number of total winning RR.
A table at the bottom right summarizes the days and performances during the backtest in order to have an idea of the current performance.
The bottom left table summarizes the overall performance with some key information.
Depending on the number of days traded, a monthly "salary" is deducted, taking into account the prop firm commission.
there is the possibility to define a "Type" for each trading day, 1 or 2. It allows to compare in a binary way, example for type 1: when the high time frame structure is doing well and I am confident for scalping, otherwise type 2.
Again: type 1: SL shorter by 50%, type 2: normal SL etc..
the button "separate 1 and 2" allows to display two additional equity curves : type 1 and type 2. It allows to have a quick visual comparison on the impact of our parameter studied in our backtest on our performance. at the scale of the main equity curve
All the conditions to succeed in the challenge are adjustable in the parameters. The drawdown calculation has been simplified - in order not to have to put 80 trades in the parameters window, I have gathered them by "day", and pessimistically, we consider first the stoplosses and then the take profits, simplifying the performances of the day into "one losing trade" and "one winning trade" (graphically). It is a good compromise between quantity and quality.
Use "A random day trading" indicator to spice up your training.
I hope this will be useful for you to track your performance !
Triangulation : Statistically Approved ReversalsA lot of calculation, but a simple and effective result displayed on the chart.
It automatically identifies a very favorable period for a price reversal, by analyzing the daily and intraday price action statistics from the maximum of the most recent bars from the historical data. No repainting. Alerts can be set.
The statistical study is done in real time for each instrument. The probabilities therefore vary over time and adapt to the latest information collected by the indicator.
The time range of the data study can be changed by simply changing the UT :
- 30m = 3.5 last months feed statistics
- 15m = 52 last days feed statistics
- 5m = 17 last days feed statistics (recommanded)
HOW TO USE
This indicator informs when we are in a time period strongly favorable to reversal.
==> Crossing probabilities of different kinds, in price and in time => Triangulation of top and bottom !
HOW It WORK :
fractal statistics on high and low formation.
hour's probabilities of making the high/low of the day are crossed with day's probabilities of making the high/low of the week.
First for the day, we study:
- value of the probability compared to the average probabilities
- value of the coefficient between the high probability and the low probability
which we then refine for the hour, with the same calculation.
Result: bright color for a day + hour with high probability, weak color if the probability is low but remains the only possible bias. Between these two possibilities, intermediate colors are possible - just like looking for shorts if the day is bullish, if it is a high probability hour!
This color is displayed in the background, only if we are forming the high of the day for tops, and the low of the day for bottoms - detected with a stochastic.
All probabilities are studied in real time for the current asset.
We will call this signal "killstats", for "killzones statistics"
fractal statistics on the probability of closure under specific predefined levels according to 36 cycles.
the probabilities of several cycles are studied, for example:
NY session versus London and Asian sessions, London session compared to its opening, NY session compared to its opening, "algorithmic cycles" ( 1h30), Opening of NY compared to its intersection with London..
Each cycle producing a probability of closing with respect to the opening price of each period. The periods are : (Etc/UTC)
15-18h / 15-16h / 9-13h / 14-17h / 18-22h / 10-12h / 9-10h30 / 10h30-12h / 12-13h30 / 13h30-15h / 15h-16h30 / 16h30-18h
The cycles can be superimposed, which allows to support or attenuate a signal for the key periods of the day: 9am-12pm, and 3pm-6pm. The period of the day covered by the study of cycles is 9h-22h.
Result : ==> a straight line with a half bell. Colors = almost transparent for 53% probability (low), and very intense for a high probability (75%). The line displayed corresponds to the opening price, which we are supposed to close within the time limit - before the end of the period, where the line stops.
If the price goes in the opposite direction to the one predicted by the statistics, then a background connects the price to the close level to be respected.
if direction and close is respected, nothing is displayed : there is no opportunity, no divergence between statistics and actual price moves.
By unchecking the "light mode", you can see each close level displayed on the chart, with the corresponding probability and the number of times the cycle was detected. The color varies from intense for a high probability (75%), to light for a low probability (53%)
We will call this signal "cyclic anomalies"
By default, as shown in the indicator presentation image, the "intersection only" option is checked: only the intersection between 1) killstats and 2) cyclic anomalies is displayed. (filter +-30% of killstats signals)
MORE INFORMATIONS
/!\ : during a backtest, it is necessary to refresh the studied data to benefit from the real time signals, and for that you have to use the replay mode. if "Backtesting informations?"is checked, labels are displayed on the graph to warn of the % distortion of the signals. I recommend using the replay mode every 250 candles, and every 1000 candles for premium accounts, to have real signals.
- Alerts can be set for killzone, or intersections ( As in presentation picture)
- The ideal use is in m5. It can trigger several times a day, sometimes in opposite directions, and sometimes not trigger for several days.
- Premium account have 20k candles data, and not 5k => signals may vary depending on your tradingview subscription.
Probabilities Module - The Quant Science This module can be integrate in your code strategy or indicator and will help you to calculate the percentage probability on specific event inside your strategy. The main goal is improve and simplify the workflow if you are trying to build a quantitative strategy or indicator based on statistics or reinforcement model.
Logic
The script made a simulation inside your code based on a single event. For single event mean a trading logic composed by three different objects: entry, take profit, stop loss.
The script scrape in the past through a look back function and return the positive percentage probability about the positive event inside the data sample. In this way you are able to understand and calculate how many time (in percentage term) the conditions inside the single event are positive, helping to create your statistical edge.
You can adjust the look back period in you user interface.
How can set up the module for your use case
At the top of the script you can find:
1. entry_condition : replace the default condition with your specific entry condition.
2. TPcondition_exit : replace the default condition with your specific take profit condition.
3. SLcondition_exit : replace the default condition with your specific stop loss condition.
Daily Manual KILLZONESThis indicator is to be used with "KILLSTATS", our indicator allowing to backtest on hundreds of days at which time, and which day the top/low of the day and week is formed.
"Manual Killzone" allows to define our statistical killzones by day of the week manually: you define your own rules according to your interpretation of our Killstats indicator.
It integrates a daily price action filter according to the ICT concept:
It will only display bullish probabilities (green) defined if and only if we are in discount and out of the daily range 25/75%.
Same for bearish probabilities (red)
The blue color is to be applied in case of reversal with high contradictory probability (Example: to be used for Tuesday from 2pm to 3pm, if Tuesday is a day with high probability to form a top, but 2pm/15pm is the time with high probability to form a bottom AND a top. Indecision => blue)
WARNING : Calculated according to Etc/UTC time : put "0" in the Timezone parameter of killstats.
It is necessary to use the replay mode regularly during the backtesting to update the data!
