Metadata-Version: 2.1
Name: dse-data-loader
Version: 0.0.33
Summary: This is a tool to download stock market data of Dhaka Stock Exchange.
Home-page: https://github.com/skfarhad/algo-trading/tree/master/dse_data_loader_pkg
Author: Sk Farhad
Author-email: sk.farhad.eee@gmail.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/skfarhad/algo-trading/issues
Description: ## Description
        This is a Python library based on *beautifulsoup4*, *pandas* &
        *mplfinance* to download price data and fundamental data of companies from  
        Dhaka Stock Exchange.
        <br/>This can assist you to create further analyses 
        based on fundamental and price history data. 
        <br/>Also create Candlestick charts to analyse price history of stocks using a simple wrapper for mplfinance.
        ## Installation
        ```
        pip install dse-data-loader
        
        ```
        ## Usage
        
        #### Downloading historical price data of a single stock-
        
        ```python
        from dse_data_loader import PriceData
        loader = PriceData()
        
        loader.save_history_csv('ACI', file_name='ACI_history.csv')
        ```
        
        The above code will create a file named- 'ACI_history.csv'. 
        It'll contain historical price data for ACI Limited. 'ACI' is the stock symbol.
        
        
        #### Downloading current price data of all listed companies in DSE-
        ```python
        from dse_data_loader import PriceData
        loader = PriceData()
        
        loader.save_current_csv(file_name='current_data.csv')
        ```
        The above code will create a file named- 'ACI_history.csv' in the current folder. 
        It'll contain current price data for all symbols.
        
        #### Downloading fundamental data for a list of companies available in DSE-
        
        ```python
        from dse_data_loader import FundamentalData
        loader = FundamentalData()
        
        loader.save_company_data(['ACI', 'GP', 'WALTONHIL'], path='company_info')
        
        ```
        The above code will create two files named 'company_data.csv' & 
        'financial_data.csv' in the 'company_info' folder relative to 
        current directory. The file named company_data.csv contains 
        the fundamental data of ACI Limited, GP and Walton BD for the current year and
        financial_data.csv contains year-wise fundamental data 
        according to [DSE website](http://dsebd.org).
        
        
        #### Create Candlestick charts for analyzing price history-
        
        ```python
        
        from dse_data_loader import CandlestickPlot
        
        cd_plot = CandlestickPlot(csv_path='ACI_history.csv', symbol='ACI')
        cd_plot.show_plot(
            xtick_count=120, 
            resample=True, 
            step='3D'
        )
        ```
        
        The above code will create a Candlestick plot like the ones provided by 
        Stock broker trading panels. There are 3 parameters-
        
        1. ```xtick_count``` : Provide an integer value. 
           It sets the count of how many recent data points needs to be plotted.
        2. ```resample``` : Provide boolean ```True``` or ```False```. 
           Set ```True``` if you want to plot daily data aggregated by multiple days.
        3. ```step```: Only Active when ```resample=True```. 
           Valid values are in the form- 
           ```'3D'``` and ```'7D'``` for 3 days plots and weekly plots respectively.
        
        The following are some example images of Candlestick plots-
        
        ![Candlestick Plot](https://github.com/skfarhad/algo-trading/blob/master/dse_data_loader_pkg/example_plot.jpg?raw=true)
        <br><br>![Candlestick Plot 3days](https://github.com/skfarhad/algo-trading/blob/master/dse_data_loader_pkg/example_plot_3D.jpg?raw=true)
        
        This is the minimal documentation. It'll be improved continuously (hopefully!). 
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
