openbb 0.0.1

Investment Research for Everyone, Anywhere.

OpenBB SDK

OpenBB SDK gives you direct and programmatic access to all capabilities of the OpenBB Terminal. You will have the necessary building blocks to create your own financial tools and applications, whether that be a visualization dashboard or a custom report in a Jupyter Notebook.

With OpenBB SDK, you can access normalized financial data from dozens of data providers, without having to develop your own integrations from scratch. On top of financial data feeds, OpenBB SDK also provides you with a toolbox to perform financial analysis on a variety of asset classes, including stocks, crypto, ETFs, funds; the economy as well as your portfolios.

OpenBB SDK is created and maintained by OpenBB team together with the contributions from hundreds of community members. This gives us an unrivaled speed of development and the ability to maintain stable integrations with numerous third-party data providers. Developing and maintaining an full-blown investment research infrastructure from the ground up takes a lot of time and effort. However, it does not have to be. Take advantage of OpenBB SDK with its out-of-the-box data connectors and financial analysis toolkit. So that you can focus on designing and building your financial reports and applications.

SDK structure

The OpenBB SDK consists of the core package and extension toolkits. The core package includes all necessary functionality for you to start researching or developing your dashboards and applications.

The toolkits that you can extend the OpenBB SDK with are:

• Portfolio Optimization Toolkit.
• Forecasting Toolkit.

System and Platform Requirements

The SDK core package is expected to work in any officially supported python version 3.8 and higher (3.9 recommended).

Optimization and Forecasting toolkits installation requires specific settings on computers powered by Apple Silicon, the newer Windows ARM and Raspberry Pi.

Minimal and Recommended System Requirements

• A computer with a modern CPU (released in the past 5 years)
• At least 8GB of RAM, 16+ recommended
• SSD drive with at least 12GB of storage space available
• Internet connection

NOTES ON THE INTERNET CONNECTIVITY: Installation of the SDK with all the toolkits would require downloading around 4GB of data. Querying data does not require a lot of bandwidth but you will certainly have a more pleasant experience if you will be on a fast internet line. 4G networks provide a good enough experience so if you're traveling your personal hot-spot will do. While it's technically possible to use a subset of the functionality in off-line mode, you will not be able to use any data that is queried from the APIs of data providers and services.

Platform Specific Requirements

Portfolio Optimization Toolkit and Forecasting Toolkit on Apple Silicon: To install the Forecasting toolkit on M1/M2 macs you need to use the x86_64 version of conda and install certain dependencies from conda-forge. Follow the instructions in this section

Base Linux Docker containers: To have the package work in base linux containers like python's slim-buster you need to install a C anc C++ compiler that's not bundled with the distribution. Run sudo apt update && sudo apt install gcc cmake

Installation

We provide a simple installation method in order to utilize the OpenBB SDK. You must first create an environment, which allows you to isolate the SDK from the rest of your system. It is our recommendation that you utilize a conda environment because there are optional features, such as forecast, that utilize libraries that are specifically sourced from conda-forge. Due to this, if you do not use a conda environment, you will not be able to use some of these features. As such, the installation steps will be written under the assumption that you are using conda.

Steps

1. Install Miniconda

Download the x86_64 Miniconda for your respective system and follow along with it's installation instructions. The Miniconda architecture MUST be x86_64 in order to use the forecasting toolkit. Follow the instructions in this section

2. Create the virtual environment

conda create -n obb python=3.9.6 -y

3. Activate the virtual environment

conda activate obb

4. Install OpenBB SDK Core package

pip install openbb

5. (Optional) Install the Toolkits

5.1 If you would like to use the Portfolio Optimization features

On Apple Silicon Macs (M1/M2) install dependency from conda-forge

conda install -c conda-forge cvxpy=1.2.2 -y

And install the Portfolio Optimization Toolkit

pip install "openbbterminal[optimization]"

5.2 If you would like ML Forecasting features

On Apple Silicon Macs (M1/M2) install dependency from conda-forge

conda install -c conda-forge lightgbm=3.3.3 -y

And install the Forecasting Toolkit

pip install "openbbterminal[prediction]"

5.2 If you would like to use both Portfolio Optimization and ML forecast features

On Apple Silicon Macs (M1/M2) install dependencies from conda-forge

conda install -c conda-forge lightgbm=3.3.3 cvxpy=1.2.2 -y

And install the Both Toolkits

pip install "openbbterminal[all]"

Congratulations! You have successfully installed openbbterminal on an environment and are now able to begin using it. However, it is important to note that if you close out of your CLI you must re-activate your environment in order begin using it again.

Setup

1. Import OpenBB SDK

First off, import OpenBB SDK into your python script or Jupyter Notebook with:

from openbb_terminal.sdk import openbb

This imports all Terminal commands at once. To see all the available commands, you can press tab in jupyter notebook. Another approach is to check out OpenBB SDK Documentation, where you can explore its capabilities

2. Customize chart style

With OpenBB SDK, you can customize your chart style. You can switch between dark and light easily using this block of code:

from openbb_terminal.sdk import TerminalStyle
theme = TerminalStyle("light", "light", "light")

OR

from openbb_terminal.sdk import TerminalStyle
theme = TerminalStyle("dark", "dark", "dark")

3. Access Documentation

Each and every command of OpenBB SDK has detailed documentation about input parameters and returned outputs. You can access them in multiple ways:

Approach 1: Press shift + tab. This will work out of the box if you're using Jupyter Notebook. In case your IDE is VSCode, you will need to install the Jupyter PowerToys extension.

Approach 2: Type help(command).

You can also type help(command), see example below, to see the command' docstring.

Approach 3: Use OpenBB SDK Documentation page.

Finally, if you prefer to check documentation on a web browser, OpenBB SDK Documentation will be your best friend. You can browse available commands and search for any specific one that you need.

4. Set API Keys

You can set your external API keys through OpenBB SDK.

• Single API setup

openbb.keys.fmp(key="example")

openbb.keys.reddit(
    client_id="example",
    client_secret="example",
    password="example",
    username="example",
    useragent="example")

• API key setup with persistence: persist=True means that your key will be saved and can be reused after, otherwise it will be lost when you restart the kernel.

openbb.keys.fmp(key="example", persist=True)

• Set multiple keys from dictionary

d = {
    "fed": {
        "key":"XXXXX"
        },
    "binance": {
        "key":"YYYYY",
        "secret":"example"
    },
}

openbb.keys.set_keys(d)

• Get info about API setup arguments

openbb.keys.get_keys_info()

• Get your defined keys

openbb.keys.mykeys()
openbb.keys.mykeys(show=True)

Usage

Now, let's explore what OpenBB SDK can do. At a high level, you can break down OpenBB SDK's functionalities into two main buckets: (1) Data layer and (2) Toolbox layer.

1. Data Layer

Getting financial data from multiple data sources using one single SDK

OpenBB SDK provides you access to normalized financial data from dozens of data sources, without having to built your own integration or relying on multiple third-party packages. Let's explore how we can do that.

First, you will need to load in the desired ticker. If it's not on the top of your mind, make use of our search functionality.

openbb.stocks.search("apple")

We want to load Apple Inc. listed on US exchange, so our ticker should be AAPL. If you want to load Apple Inc. from Brazilian exchange, you should load in AAPL34.SA.

df = openbb.stocks.load("AAPL")

What's extremely powerful about OpenBB SDK is that you can specify the data source. Depending on the asset class, we have a list of available data sources and it's only getting bigger with contributions from our open-source community.

## From YahooFinance
df_yf = openbb.stocks.load("AAPL", source='YahooFinance')

## From AlphaVantage
df_av = openbb.stocks.load("AAPL", source='AlphaVantage')

## From IEXCloud
df_iex = openbb.stocks.load("AAPL", source='IEXCloud')

## From Polygon
df_pol = openbb.stocks.load("AAPL", source='Polygon')

Easy option to switch between obtaining underlying data and charts

Depending on your needs, you can get the outputs in form of data (e.g. pandas dataframe) or charts. If the latter is what you want, simple add chart=True as the last parameter.

1. Getting underlying data

openbb.economy.index(indices = ['sp500', 'nyse_ny', 'russell1000'], start_date = '2010-01-01')

You might be wondering how to find all the available indices. This type of information should be available in the docstring. Let's give it a try.

As mentioned in the docstring, you can access it with the following helper function.

openbb.economy.available_indices()

2. Getting charts

openbb.economy.index(indices = ['sp500', 'nyse_ny', 'russell1000'], start_date = '2010-01-01', chart=True)

2. Toolbox Layer

In addition to financial data, you can also get access to a robust and powerful toolbox to perform analysis on different asset classes and on your portfolio.

Imagine that you would like to leverage existing financial calculations from OpenBB and apply them on your own data. This can be done easily - OpenBB SDK's commands usually accept a dataframe as an input. Here you can load it your data, either via a csv, excel file, or connecting directly with an API or a database. The possibilities are endless.

Let's go through an example to see how we can do it in a few simple steps. Here we shall see how to use portfolio optimization functionalities from OpenBB SDK.

Step 1. Loading order book

Here we will use an example orderbook for illustration purposes. You can choose to upload your own orderbook instead.

order_book_path = "portfolio/allocation/60_40_Portfolio.xlsx"
tickers, categories = openbb.portfolio.po.load(excel_file = order_book_path)

Step 2. Optimizing portfolio

We provide multiple portfolio optimization techniques. You can utilize basic mean-variance techniques, such as optimizing for the maximum Sharpe ratio, or minimum variance, as well as advanced optimization techniques including Hierarchical Risk Parity and Nested Clustered Optimization.

## Max Sharpe optimization
weights_max_sharpe, data_returns_max_sharpe = openbb.portfolio.po.maxsharpe(tickers)

print("Max Sharpe")
weights_max_sharpe

## Minimum risk optimization
weights_min_risk, data_returns_min_risk = openbb.portfolio.po.minrisk(tickers)

print("Min Risk")
weights_min_risk

## Hierarchical Risk Parity optimization

weights_hrp, data_returns_hrp = openbb.portfolio.po.hrp(tickers)

print("Hierarchical Risk Parity")
weights_hrp

After having obtained the asset allocation outcomes, you can plot a correlation heatmap across tickers, as well as their individual risk contribution.

openbb.portfolio.po.plot(data=data_returns_hrp,weights=weights_hrp,heat=True)

openbb.portfolio.po.plot(data=data_returns_hrp,weights=weights_hrp,rc_chart=True)

These techniques have an extensive list of parameters and thus the optimization outcome is highly dependent on the chosen parameters. For instance, you can refer to the documentation below.

This allows us to alter certain assumption which also modify the asset allocation.

weights_hrp_2, data_returns_hrp_2 = openbb.portfolio.po.hrp(
    tickers,
    interval="5y",
    risk_measure="cVaR",
    risk_aversion=0.8
)

pd.DataFrame([weights_hrp, weights_hrp_2], index=["Basic", "Extended"]).T

The basic method was optimized for variance. The extended method increases the period of historical data, optimizes for conditional Value at Risk and has a lower risk aversion.

openbb.portfolio.po.plot(data=data_returns_hrp,weights=weights_hrp,pie=True)

openbb.portfolio.po.plot(data=data_returns_hrp_2,weights=weights_hrp_2,pie=True)

Useful tips

1. Display matplotlib charts in Jupyter Notebook

To display matplotlib charts inside the Jupyter notebook output cells, you can use the block of code below, and initialize it at the top of the Notebook.

import matplotlib.pyplot as plt
import matplotlib_inline.backend_inline
from openbb_terminal.sdk import openbb
%matplotlib inline
matplotlib_inline.backend_inline.set_matplotlib_formats("svg")

2. Take advantage of external_axes

The code below utilizes the external_axes parameter to get two axis in one chart.

import matplotlib.pyplot as plt
from openbb_terminal.sdk import openbb
fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 5), dpi=150)
openbb.stocks.dps.dpotc(
    "aapl",
    external_axes=[ax1, ax2],
    chart=True,
)
fig.tight_layout()

You can also do this to save output charts in a variable for later uses.

For more examples, we'd recommend checking out our curated Jupyter Notebook reports. They are excellent demonstration on how to use the SDK to its fullest extent