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Description:

reefledge 1.6.3

reefledge is a Python package which provides a fast, simple and
powerful API designed to simplify the retrieval of price/return density
forecasts (on a ticker-by-ticker basis) generated from cutting-edge Time
Series Analysis models.
Our algorithms consume market data on liquid financial assets and
require demanding estimation and simulation steps, both accomplished on
a high-performance cloud cluster.
Forecasts are produced for a wide range of reference dates and
investment horizons, covering multiple metrics for a variety of asset
classes.

Delivery Frequency
Forecasts are generated on a daily basis.

Data Frequency
All models consume daily-frequency data.

Data Reporting Lag
Usually, every ticker which passes our demanding filtering criteria
requires around one minute of processing on a Google Cloud C2 compute
engine with a single vCPU and four gigabytes of RAM.
Our cluster boots as soon as the latest market data is available
and, at the time of writing, scales up to 120 vCPUs.

Data History
In general, expect forecasts to be readily accessible for reference
dates going back at least four years.

Data Coverage
Currently, only the ‘NYSE’ target is in production. Nonetheless,
multiple targets - such as ‘NASDAQ’ - have already been extensively
tested and will be released soon.

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.
Seamless integration with Microsoft Excel.
Fast and robust cythonized 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='NYSE',
metric='STD',
tickers=['GS', 'IBM'])

Advanced Usage
Advanced users should refer to the following functions/classes:

reefledge.reefledge.front_end.get.get
for retrieving and parsing data into a
reefledge.reefledge.back_end.data_wrapper.data_wrapper.DataWrapper instance.
reefledge.reefledge.front_end.get_point_forecasts_df.get_point_forecasts_df
for retrieving and parsing data into a
pandas.core.frame.DataFrame instance.
reefledge.reefledge.front_end.list_tickers.list_tickers
for querying all available tickers associated with a particular
target.
reefledge.reefledge.back_end.api_config.api_config.APIConfig
for configuring the API.

Generic Error Catching
>>> try:
rl.get_point_forecasts_df(target='NYSE', 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