market_prices | Get meaningful OHLCV datasets | Data Manipulation library

 by   maread99 Python Version: 0.10.3 License: MIT

kandi X-RAY | market_prices Summary

kandi X-RAY | market_prices Summary

market_prices is a Python library typically used in Utilities, Data Manipulation applications. market_prices has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install market_prices' or download it from GitHub, PyPI.

A python library to create meaningful OHLCV datasets for financial instruments. market_prices provides for enhanced querying and post-processing of financial price data. Works out-the-box with prices from the Yahoo Finance API via yahooquery (see Disclaimers).
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              market_prices has a low active ecosystem.
              It has 45 star(s) with 5 fork(s). There are 4 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 8 open issues and 9 have been closed. On average issues are closed in 23 days. There are 5 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of market_prices is 0.10.3

            kandi-Quality Quality

              market_prices has no bugs reported.

            kandi-Security Security

              market_prices has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              market_prices is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              market_prices releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.

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            market_prices Key Features

            No Key Features are available at this moment for market_prices.

            market_prices Examples and Code Snippets

            market_prices,Quickstart
            Pythondot img1Lines of Code : 101dot img1License : Permissive (MIT)
            copy iconCopy
            >>> from market_prices import PricesYahoo
            >>> prices = PricesYahoo("MSFT")  # prices for a single instrument, Microsoft
            >>> prices.get("5min", minutes=40)  # last 40 minutes of prices at 5 minute intervals
            
            symbol            
            market_prices,Installation
            Pythondot img2Lines of Code : 1dot img2License : Permissive (MIT)
            copy iconCopy
            $ pip install market-prices
              

            Community Discussions

            QUESTION

            R: Is there a "Un-Character" Command in R?
            Asked 2022-Apr-10 at 17:37

            I am working with the R programming language.

            I have the following dataset:

            ...

            ANSWER

            Answered 2022-Apr-10 at 05:36

            Up front, "1,3,4" != 1. It seems you should look to split the strings using strsplit(., ",").

            Source https://stackoverflow.com/questions/71813866

            QUESTION

            Creating new columns based on data in row separated by specific character in R
            Asked 2022-Mar-15 at 08:48

            I've the following table

            Owner Pet Housing_Type A Cats;Dog;Rabbit 3 B Dog;Rabbit 2 C Cats 2 D Cats;Rabbit 3 E Cats;Fish 1

            The code is as follows:

            ...

            ANSWER

            Answered 2022-Mar-15 at 08:48

            One approach is to define a helper function that matches for a specific animal, then bind the columns to the original frame.

            Note that some wrangling is done to get rid of whitespace to identify the unique animals to query.

            Source https://stackoverflow.com/questions/71478316

            QUESTION

            Multiplying and Adding Values across Rows
            Asked 2022-Mar-10 at 08:24

            I have this data frame:

            ...

            ANSWER

            Answered 2022-Mar-10 at 04:12

            We can use stri_replace_all_regex to replace your color_1 into integers together with the arithmetic operator.

            Here I've stored your values into a vector color_1_convert. We can use this as the input in stri_replace_all_regex for better management of the values.

            Source https://stackoverflow.com/questions/71418533

            QUESTION

            How to make a rank column in R
            Asked 2022-Mar-07 at 16:19

            I have a database with columns M1, M2 and M3. These M values correspond to the values obtained by each method. My idea is now to make a rank column for each of them. For M1 and M2, the rank will be from the highest value to the lowest value and M3 in reverse. I made the output table for you to see.

            ...

            ANSWER

            Answered 2022-Mar-07 at 14:15

            Using rank and relocate:

            Source https://stackoverflow.com/questions/71381995

            QUESTION

            How to return the column title wherein the row contains the greatest value in Pandas Dataframe
            Asked 2022-Feb-24 at 20:56

            I working on a Python project that has a DataFrame like this:

            ...

            ANSWER

            Answered 2022-Feb-24 at 20:48

            You could use the idxmax method on axis:

            Source https://stackoverflow.com/questions/71258033

            QUESTION

            Split large csv file into multiple files based on column(s)
            Asked 2022-Feb-07 at 12:49

            I would like to know of a fast/efficient way in any program (awk/perl/python) to split a csv file (say 10k columns) into multiple small files each containing 2 columns. I would be doing this on a unix machine.

            ...

            ANSWER

            Answered 2021-Dec-12 at 05:22

            With your show samples, attempts; please try following awk code. Since you are opening files all together it may fail with infamous "too many files opened error" So to avoid that have all values into an array and in END block of this awk code print them one by one and I am closing them ASAP all contents are getting printed to output file.

            Source https://stackoverflow.com/questions/70320648

            QUESTION

            Get the first non-null value from selected cells in a row
            Asked 2022-Feb-04 at 09:55

            Good afternoon, friends!

            I'm currently performing some calculations in R (df is displayed below). My goal is to display in a new column the first non-null value from selected cells for each row.

            My df is:

            ...

            ANSWER

            Answered 2022-Feb-03 at 11:16

            One option with dplyr could be:

            Source https://stackoverflow.com/questions/70970158

            QUESTION

            pivot_longer with column pairs
            Asked 2022-Feb-03 at 14:02

            I am again struggling with transforming a wide df into a long one using pivot_longer The data frame is a result of power analysis for different effect sizes and sample sizes, this is how the original df looks like:

            ...

            ANSWER

            Answered 2022-Feb-03 at 10:59
            library(tidyverse)
            
            example %>% 
              pivot_longer(cols = starts_with("es"), names_to = "type", names_prefix = "es_", values_to = "es") %>%
              pivot_longer(cols = starts_with("pwr"), names_to = "pwr", names_prefix = "pwr_") %>% 
              filter(substr(type, 1, 3) == substr(pwr, 1, 3)) %>% 
              mutate(pwr = parse_number(pwr)) %>% 
              arrange(pwr, es, type)
            

            Source https://stackoverflow.com/questions/70969176

            QUESTION

            Simulating Random Draws From a "Hat"
            Asked 2021-Dec-28 at 21:50

            Suppose I have the following 10 variables (num_var_1, num_var_2, num_var_3, num_var_4, num_var_5, factor_var_1, factor_var_2, factor_var_3, factor_var_4, factor_var_5):

            ...

            ANSWER

            Answered 2021-Dec-26 at 10:11

            You may define a function FUN(n) that creates a data set as shown in OP.

            Source https://stackoverflow.com/questions/70483731

            QUESTION

            Break Apart a String into Separate Columns R
            Asked 2021-Dec-17 at 20:39

            I am trying to tidy up some data that is all contained in 1 column called "game_info" as a string. This data contains college basketball upcoming game data, with the Date, Time, Team IDs, Team Names, etc. Ideally each one of those would be their own column. I have tried separating with a space delimiter, but that has not worked well since there are teams such as "Duke" with 1 part to their name, and teams with 2 to 3 parts to their name (Michigan State, South Dakota State, etc). There also teams with "-" dashes in their name.

            Here is my data:

            ...

            ANSWER

            Answered 2021-Dec-16 at 15:25

            Here's one with regex. See regex101 link for the regex explanations

            Source https://stackoverflow.com/questions/70381064

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install market_prices

            The above call was made 21 minutes after the NYSE open. Notice that the call returns the last 40 trading minutes of data, NOT the arbitrary number of trading minutes that may have fallen within the last 40 minutes according to the clock on the wall. Any interval can be evaluated (limited only by the availability of underlying data). NB Here the force option forced the right side of the last indice back to the session close. Above the period has been defined in calendar months ('years' and 'weeks' are also valid arguments). Daily data can be easily resampled to a higher interval. Although some indices are longer than three calendar days, they all comprise of three trading days (sessions) of data (all indices are closed on the 'right', such that data includes the session that represents the left side of the interval but NOT any session that might be represented by the right side.). Also, the period the dataset covers is 12 sessions, NOT the arbitrary number of sessions that fell within the last 12 days according to the calendar hanging on the wall. market_prices comes into its own with the creation of datasets comprising instruments that trade on different exchanges. By default prices are shown as missing when the exchange is closed (the time zone of the above output is UTC). Indices that would cover periods during which no symbol trades are excluded. (Scroll right on the output to see all the returned data.). Within any session missing prices between the open and the close are always filled with contiguous data. This happens even for illiquid instruments where the price data alone may give no indication of a session's open or close. (See the exchange_calendars section for how market_prices 'knows' the trading times of each symbol.). The get method has plenty of options to customize the output, including fill to fill in indices when an exchange is closed... The 'workback' anchor option offers an alternative to anchoring indices on each session's open. The following call requests two trading days of data to a specific minute, at 3 hour intervals, with data evaluated by working back from the last indice. The second indice can be seen to cross sessions. It partly covers the end of a Friday session and partly the start of the subsequent Monday session... Although that indice still comprises only the requested interval of 3 trading hours... The indices_trading_minutes method called above is available via the .pt accessor. (market_prices uses the .pt accessor to make available a host of properties and methods to directly interrogate the price data.). Whereas the above examples used the get method to create a dataset, the following methods provide for more specific queries. close_at returns the most recent close price as of a specific date. price_at returns prices as at a specific minute. price_range returns OHLCV data over a period defined with the same arguments as get. The quickstart.ipynb tutorial offers a fuller introduction. Here you'll find links to all the tutorials which collectively cover all that market_prices offers.

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            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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