DATA — Penunjuk dan Strategi

csv_series_library The 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)

multidata Library "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 .

Tooltip Library "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)

Dynamic Array Table (versatile display methods)Library "datTable" Dynamic Array Table.... Configurable Shape/Size Table from Arrays Allows for any data in any size combination of arrays to join together with: all possible orientations! filling all cells contiguously and/or flipping at boundaries vertical or horizontal rotation x/y axis direction swapping all types array inputs for data. please notify of any bugs. thanks init(_posit) Get Table (otional gapping cells) Parameters: _posit : String or Int (1-9 3x3 grid L to R) Returns: Table coords() Req'd coords Seperate for VARIP table, non-varip coords add Add arrays to display table. coords reset each calc uses displaytable object, string titles, and color optional array, and second line optional data array.

Signal_Data_2021_09_09__2021_11_18 Library "Signal_Data_2021_09_09__2021_11_18" Functions to support my timing signals system import_start_time(harmonic) get the start time for each harmonic signal Parameters: harmonic : is an integer identifying the harmonic Returns: the starting timestamp of the harmonic data import_signal(index, harmonic) access point for pre-processed data imported here by copy paste Parameters: index : is the current data index, use 0 to initialize harmonic : is the data set to index, use 0 to initialize Returns: the data from the indicated harmonic array starting at index, and the starting timestamp of that data

DataCleaner Library "DataCleaner" Functions for acquiring outlier levels and acquiring a cleaned version of a series. outlierLevel(src, len, level) Gets the (standard deviation) outlier level for a given series. Parameters: src : The series to average and add a multiple of the standard deviation to. len : The The number of bars to measure. level : The positive or negative multiple of the standard deviation to apply to the average. A positive number will be the upper boundary and a negative number will be the lower boundary. Returns: The average of the series plus the multiple of the standard deviation. cleanUsing(src, result, len, maxDeviation) Returns an array representing the result series with (outliers provided by the source) removed. Parameters: src : The source series to read from. result : The result series. len : The maximum size of the resultant array. maxDeviation : The positive or negative multiple of the standard deviation to apply to the average. A positive number will be the upper boundary and a negative number will be the lower boundary. Returns: An array containing the cleaned series. clean(src, len, maxDeviation) Returns an array representing the source series with outliers removed. Parameters: src : The source series to read from. len : The maximum size of the resultant array. maxDeviation : The positive or negative multiple of the standard deviation to apply to the average. A positive number will be the upper boundary and a negative number will be the lower boundary. Returns: An array containing the cleaned series. outlierLevelAdjusted(src, level, len, maxDeviation) Gets the (standard deviation) outlier level for a given series after a single pass of removing any outliers. Parameters: src : The series to average and add a multiple of the standard deviation to. level : The positive or negative multiple of the standard deviation to apply to the average. A positive number will be the upper boundary and a negative number will be the lower boundary. len : The The number of bars to measure. maxDeviation : The optional standard deviation level to use when cleaning the series. The default is the value of the provided level. Returns: The average of the series plus the multiple of the standard deviation.