Metadata-Version: 1.2
Name: reefledge
Version: 1.4.0b6
Summary: A powerful API designed for Quantitative Finance practitioners.
Home-page: https://www.reefledge.com
Author: ReefLedge
Author-email: support@reefledge.com
License: UNKNOWN
Download-URL: https://github.com/Reefledge/reefledge/archive/refs/tags/v1.4.0-beta.6.tar.gz
Description: **reefledge** is a Python/Cython package which provides a fast, simple
        and powerful API designed to simplify the consumption of cutting-edge
        density forecasting model outputs. These are generated from market data
        on liquid financial instruments and are the result of demanding
        estimation and simulation processes on a high-performance cloud cluster.
        Data is produced for a wide range of reference dates and asset classes,
        yielding forecasts for a variety of time horizons and metrics.
        
        Main Features
        -------------
        Here are just a few of the things that make `reefledge` special:
        
          * Intuitive object oriented interface.
          * Type checking at runtime of all public functions/methods.
          * Tight integration with Microsoft Excel.
          * Fast and robust Cython codebase.
          * Multithreading capabilities to overcome I/O overheads.
          * Smart and 'respectful' (in terms of RAM usage) caching.
          * Efficient and highly available back end, featuring global load
            balancing, optimized connection pooling and extensive database table
            partitioning, besides an intelligent use of indexes to speed up SQL
            queries (not to mention full use of SSD disks).
        
        Installation
        ------------
        The easiest way to install `reefledge` and get updates is via `pip`:
        
            $ pip install reefledge
        
        On Linux, the shell command above should return an error due to the
        `xlwings` module dependency, which is only relevant on the Windows
        platform. You can safely ignore it by preceding the installation command
        with:
        
            $ export INSTALL_ON_LINUX=1
        
        Basic Usage
        -----------
        >>> import reefledge as rl
        >>> rl.login(user_name='foobar', api_key='secret')
        >>> df = rl.get_point_forecasts_df( # Returns a pandas DataFrame instance.
                target='USA',
                metric='STD',
                tickers=['GS', 'IBM'])
        
        Generic Error Catching
        ----------------------
        >>> try:
                rl.get_point_forecasts_df(target='USA', metric='STD', tickers='GS')
            except rl.Error as exc:
                print(exc)
        
        Basic Configuration
        -------------------
        >>> rl.APIConfig.allow_caching
        True
        >>> rl.APIConfig.allow_tickers_sorting
        False
        >>> rl.APIConfig.allow_caching = False # Disable caching.
        
        Notes
        -----
        Further examples assume that `reefledge` has been imported as `rl`:
        
            >>> import reefledge as rl
        
Keywords: quant,quantitative,finance,forecasting,time-series,machine learning,trading,portfolio management,risk management
Platform: Windows
Platform: Linux
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: Microsoft :: Windows :: Windows 10
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Office/Business :: Financial :: Investment
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Typing :: Typed
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.8, <3.11
