mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-10-27 04:31:13 +00:00
Update SDK Hugo server (#2822)
Co-authored-by: Colin Delahunty <72827203+colin99d@users.noreply.github.com>
This commit is contained in:
minhhoang1023
@@ -6,7 +6,7 @@ import os
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import pandas as pd
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from openbb_terminal import feature_flags as obbff
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from openbb_terminal.rich_config import console
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from openbb_terminal.rich_config import console # pragma: allowlist secret
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logger = logging.getLogger(__name__)
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2
website/content/_index.md
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2
website/content/_index.md
vendored
@@ -63,7 +63,7 @@ The terminal allows for users to import their own proprietary datasets to use on
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<--->
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### OpenBB API and Customizable notebook reports
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### OpenBB SDK and Customizable notebook reports
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Use our OpenBB Terminal environment to access raw data through a Jupyter Notebook and play with it accordingly. Or just create customizable notebook reports for your colleagues and friends doing research on a particular asset or a macroeconomic event impact on said asset. The possibilities are endless.
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148
website/content/api/_index.md
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148
website/content/api/_index.md
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@@ -1,148 +0,0 @@
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---
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title: OpenBB Python API
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geekdocCollapseSection: true
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---
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The OpenBB Python API was created to enable usage of the terminal functionality in python
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scripts and IPython Notebooks (Jupyter). The API wraps the functionality of the terminal in a way that the Python
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commands follow the same convention as the terminal.
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For example `stocks/load aapl` becomes `openbb.stocks.load("aapl")`.
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---
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## Python environment setup
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Import the OpenBB API into your python script or notebook with
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```python
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from openbb_terminal.api import openbb
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```
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This imports all the commands at once. Now you can directly begin to use it. The api function is structured in the way that it always retrieves the underlying data. For charts add the `chart=True` parameter.
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For example see:
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```python
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# Returns data:
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openbb.stocks.ba.snews("aapl")
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# Returns charts:
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openbb.stocks.ba.snews("aapl", chart=True)
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```
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This allows easy integration to the jupyter notebook and allows you to build new applications on top of the terminal. The api also has new functionalities that are used in the backend of the Terminal (CLI). With these backend functions you can develop new functionalities and avoid copy-pasting the code from the repository.
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## Understanding the API functions
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To understand the parameters of an API function the simplest way is to refer to the
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docstring. Docstrings can be viewed using the code introspection tools of your
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text/notebook editor or the built-in python `help` command. Docstrings contain both the
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description of the functionality and the overview of the parameters.
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As an example this is the docstring of `openbb.economy.bigmac`:
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```txt
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Display Big Mac Index for given countries
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Parameters
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----------
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country_codes : List[str]
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List of country codes to get for
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raw : bool, optional
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Flag to display raw data, by default False
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export : str, optional
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Format data, by default ""
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external_axes : Optional[List[plt.Axes]], optional
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External axes (3 axes are expected in the list), by default None
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```
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## Usage examples
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We'd recommend checking our example notebook reports to understand how to use OpenBB API. You can find inside [reports menu](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/reports).
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|
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### Jupyter Notebook Tricks
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**Get matplotlib charts in the output cells**\
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If you copy-paste the code below and use it as your initialization then you're matplotlib graphs will be inside
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the result cell.
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```python
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import matplotlib.pyplot as plt
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import matplotlib_inline.backend_inline
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from openbb_terminal.api import openbb
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%matplotlib inline
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matplotlib_inline.backend_inline.set_matplotlib_formats("svg")
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```
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**Get function signature and docstring**\
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When you press `shift + tab` in jupyter notebook while having the mouse parser in an API function, you get the
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signature and docstring of the function.
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### Visual Studio Code Tricks
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**Get function docstring and signature**\
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In order to get the docstrings and function signatures for the API when opening a Jupyter Notebook in VSCode,
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you have to install the [Jupyter PowerToys
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extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-jupyter-powertoys).
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## Code Examples
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Just copy-paste the code examples below into a python script or jupyter notebook, and you're ready to go.
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**Basic Stock Information**\
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Prints general information about the selected stock (in this case Gamsetop)
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```python
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from openbb_terminal.api import openbb
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gme_info = openbb.stocks.fa.info("gme").transpose()
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print("-- Gamstop Stock --\n\n- Basic Info -")
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print(f"Sector: {gme_info['Sector'].iloc[0]}")
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print(f"Country: {gme_info['Country'].iloc[0]}")
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print(f"Description: {gme_info['Long business summary'].iloc[0]}")
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print("\n- Financial Info -")
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print(f"Ebitda Margins: {gme_info['Ebitda margins'].iloc[0]}")
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print(f"Profit Margins: {gme_info['Profit margins'].iloc[0]}")
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print(f"Revenue growth: {gme_info['Revenue growth'].iloc[0]}")
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print("\n- Target Price -")
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print(f"Current price: {gme_info['Current price'].iloc[0]}")
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print(f"Target mean price: {gme_info['Target mean price'].iloc[0]}")
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print(f"Target high price: {gme_info['Target high price'].iloc[0]}")
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print(f"Target low price: {gme_info['Target low price'].iloc[0]}")
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```
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**Use external axis**\
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The code below utilises the `external_axes` parameter to get two axis in one chart
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```python
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import matplotlib.pyplot as plt
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from openbb_terminal.api import openbb
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fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 5), dpi=150)
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openbb.stocks.dps.dpotc(
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"aapl",
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external_axes=[ax1, ax2],
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chart=True,
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)
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fig.tight_layout()
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```
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**Stocks Return Distribution** \
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Fetches data from the OpenBB API and then plots the return distribution. This is a good example, where the data from
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the API is leveraged to build a new feature on top of the API.
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```python
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import numpy as np
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import matplotlib.pyplot as plt
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from openbb_terminal.api import openbb
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# Fetches data from the api
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gme = openbb.stocks.load("gme")
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# Calculates logarithmic returns
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gme["Log Returns"] = np.log(gme["Adj Close"]/gme["Adj Close"].shift(1))
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# Plots the return distributions
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gme["Log Returns"].hist(bins=1000)
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plt.tight_layout()
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plt.show()
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```
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For more examples see the OpenBB jupyter notebook reports. They all use the API to its fullest extent!
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---
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340
website/content/sdk/_index.md
vendored
Normal file
340
website/content/sdk/_index.md
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Normal file
@@ -0,0 +1,340 @@
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---
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||||
title: OpenBB Python SDK
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||||
geekdocCollapseSection: true
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||||
---
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||||
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OpenBB SDK gives you direct and programmatic access to all capabilities from OpenBB Terminal.
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You will have the necessary building blocks to create your own financial tools and applications,
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whether that be a visualization dashboard or a custom report on Jupyter Notebook.
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With OpenBB SDK, you can access to normalized financial data from dozens of data providers,
|
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without having to develop your own integrations from scratch.
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On top of financial data feeds, OpenBB SDK also provides you with a toolbox to perform financial analysis
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on a variety of asset classes, including stocks, crypto, ETFs, funds; the economy as well as your portfolios.
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OpenBB SDK is created and maintained by OpenBB team together with the contributions from hundreds of community members.
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This gives us an unrivaled speed of development and the ability to maintain stable integrations with numerous third-party data providers.
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Developing and maintaining an full-blown investment research infrastructure from the ground up takes a lot of time and effort.
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However, it does not have to be. Take advantage of OpenBB SDK with its out-of-the-box data connectors and financial analysis toolkit.
|
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So that you can focus on designing and building your financial reports and applications.
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||||
---
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||||
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||||
## Setup
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### 1. Import OpenBB SDK
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First off, import OpenBB SDK into your python script or Jupyter Notebook with:
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||||
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||||
```python
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from openbb_terminal.api import openbb
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```
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This imports all Terminal commands at once. To see all the available commands, you can press `tab` in jupyter notebook.
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Another approach is to check out [OpenBB SDK Documentation](https://openbb-finance.github.io/OpenBBTerminal/api/), where you can explore its capabilities
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### 2. Customize chart style
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With OpenBB SDK, you can customize your chart style. You can switch between `dark` and `light` easily using this block of code:
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```python
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from openbb_terminal.api import TerminalStyle
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theme = TerminalStyle("light", "light", "light")
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```
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||||
<img width="813" alt="Screenshot 2022-10-03 at 23 56 52" src="https://user-images.githubusercontent.com/40023817/193700307-cbb12edc-0a5d-4804-9f3c-a798efd9e69d.png">
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OR
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```python
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from openbb_terminal.api import TerminalStyle
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theme = TerminalStyle("dark", "dark", "dark")
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```
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|
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<img width="791" alt="Screenshot 2022-10-03 at 23 46 33" src="https://user-images.githubusercontent.com/40023817/193699221-e154995b-653c-40fd-8fc6-a3f8d39638db.png">
|
||||
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||||
### 3. Access Documentation
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Each and every command of OpenBB SDK has detailed documentation about input parameters and returned outputs. You can access them in multiple ways:
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**Approach 1: Press `shift + tab`**
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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
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||||
extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-jupyter-powertoys).
|
||||
|
||||
<img width="788" alt="Screenshot 2022-10-03 at 23 31 55" src="https://user-images.githubusercontent.com/40023817/193697567-e7143252-c560-441e-84fd-cbe38aeaf0ea.png">
|
||||
|
||||
**Approach 2: Type `help(command)`**
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||||
|
||||
You can also type `help(command)`, see example below, to see the command' docstring.
|
||||
|
||||
<img width="871" alt="Screenshot 2022-10-03 at 23 33 05" src="https://user-images.githubusercontent.com/40023817/193697676-39351008-386d-4c4c-89f2-3de7d8d4e89d.png">
|
||||
|
||||
**Approach 3: Use OpenBB SDK Documentation page**
|
||||
|
||||
Finally, if you prefer to check documentation on a web browser, [OpenBB SDK Documentation](https://openbb-finance.github.io/OpenBBTerminal/api/) will be your best friend. You can browse available commands and search for any specific one that you need.
|
||||
|
||||
<img width="1200" alt="Screenshot 2022-10-03 at 18 41 48" src="https://user-images.githubusercontent.com/40023817/193643316-c063df03-4172-487f-ba47-ee60f36a3fef.png">
|
||||
|
||||
### 4. Set API Keys
|
||||
|
||||
You can set your external API keys through OpenBB SDK.
|
||||
|
||||
* Single API setup
|
||||
|
||||
```python
|
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openbb.keys.fmp(key="example")
|
||||
|
||||
openbb.keys.reddit(
|
||||
client_id="example",
|
||||
client_secret="example",
|
||||
password="example",
|
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username="example",
|
||||
useragent="example")
|
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```
|
||||
|
||||
|
||||
|
||||
* 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.
|
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|
||||
```python
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openbb.keys.fmp(key="example", persist=True)
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```
|
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|
||||
|
||||
|
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* Set multiple keys from dictionary
|
||||
|
||||
```python
|
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d = {
|
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"fed": {
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"key":"XXXXX"
|
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},
|
||||
"binance": {
|
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"key":"YYYYY",
|
||||
"secret":"ZZZZZ"
|
||||
},
|
||||
}
|
||||
|
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openbb.keys.set_keys(d)
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```
|
||||
|
||||
|
||||
|
||||
* Get info about API setup arguments
|
||||
|
||||
```python
|
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openbb.keys.get_keys_info()
|
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```
|
||||
|
||||
|
||||
|
||||
* Get your defined keys
|
||||
|
||||
```python
|
||||
openbb.keys.mykeys()
|
||||
openbb.keys.mykeys(show=True)
|
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```
|
||||
|
||||
|
||||
|
||||
## 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.
|
||||
|
||||
```python
|
||||
openbb.stocks.search("apple")
|
||||
```
|
||||
|
||||
<img width="652" alt="Screenshot 2022-10-04 at 00 00 14" src="https://user-images.githubusercontent.com/40023817/193700663-b91d57a9-4581-4f7e-a6da-764c0c9de092.png">
|
||||
|
||||
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`.
|
||||
|
||||
```python
|
||||
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.
|
||||
|
||||
```python
|
||||
## 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
|
||||
|
||||
```python
|
||||
openbb.economy.index(indices = ['sp500', 'nyse_ny', 'russell1000'], start_date = '2010-01-01')
|
||||
```
|
||||
|
||||
<img width="575" alt="Screenshot 2022-10-04 at 00 02 23" src="https://user-images.githubusercontent.com/40023817/193700891-f4d93440-31e3-411e-9931-3a38782f68e3.png">
|
||||
|
||||
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.
|
||||
|
||||
<img width="906" alt="Screenshot 2022-10-04 at 13 20 58" src="https://user-images.githubusercontent.com/40023817/193817866-b05cacee-a11b-4c44-b8c3-efb51bb9c892.png">
|
||||
|
||||
As mentioned in the docstring, you can access it with the following helper function.
|
||||
|
||||
```python
|
||||
openbb.economy.available_indices()
|
||||
```
|
||||
|
||||
<img width="1078" alt="Screenshot 2022-10-04 at 00 16 36" src="https://user-images.githubusercontent.com/40023817/193702595-ecbfc84d-3ed1-4f89-9086-e975b01c4b12.png">
|
||||
|
||||
### 2. Getting charts
|
||||
|
||||
```python
|
||||
openbb.economy.index(indices = ['sp500', 'nyse_ny', 'russell1000'], start_date = '2010-01-01', chart=True)
|
||||
```
|
||||
|
||||
<img width="741" alt="Screenshot 2022-10-04 at 00 03 57" src="https://user-images.githubusercontent.com/40023817/193701075-796ffabe-3266-4d71-9a81-3042e8ca5fc8.png">
|
||||
|
||||
## 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.
|
||||
|
||||
```python
|
||||
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.
|
||||
|
||||
```python
|
||||
## Max Sharpe optimization
|
||||
weights_max_sharpe, data_returns_max_sharpe = openbb.portfolio.po.maxsharpe(tickers)
|
||||
|
||||
print("Max Sharpe")
|
||||
weights_max_sharpe
|
||||
```
|
||||
|
||||
<img width="734" alt="Screenshot 2022-10-04 at 13 23 45" src="https://user-images.githubusercontent.com/40023817/193818381-e3e75455-ea91-4bdd-a903-0874ac8700dc.png">
|
||||
|
||||
```python
|
||||
## Minimum risk optimization
|
||||
weights_min_risk, data_returns_min_risk = openbb.portfolio.po.minrisk(tickers)
|
||||
|
||||
print("Min Risk")
|
||||
weights_min_risk
|
||||
```
|
||||
|
||||
<img width="742" alt="Screenshot 2022-10-04 at 13 24 45" src="https://user-images.githubusercontent.com/40023817/193818556-89380c7c-94c3-4e5c-8848-28058c9cf056.png">
|
||||
|
||||
```python
|
||||
## Hierarchical Risk Parity optimization
|
||||
|
||||
weights_hrp, data_returns_hrp = openbb.portfolio.po.hrp(tickers)
|
||||
|
||||
print("Hierarchical Risk Parity")
|
||||
weights_hrp
|
||||
```
|
||||
|
||||
<img width="736" alt="Screenshot 2022-10-04 at 13 34 39" src="https://user-images.githubusercontent.com/40023817/193820500-1bcde650-f517-4aed-b989-b2bd92bebbb8.png">
|
||||
|
||||
After having obtained the asset allocation outcomes, you can plot a correlation heatmap across tickers, as well as their individual risk contributition.
|
||||
|
||||
```python
|
||||
openbb.portfolio.po.plot(data=data_returns_hrp,weights=weights_hrp,heat=True)
|
||||
```
|
||||
|
||||
<img width="734" alt="Screenshot 2022-10-04 at 13 35 14" src="https://user-images.githubusercontent.com/40023817/193820624-3e6da926-aea9-4963-bd54-fd1a6df0fda3.png">
|
||||
|
||||
```python
|
||||
openbb.portfolio.po.plot(data=data_returns_hrp,weights=weights_hrp,rc_chart=True)
|
||||
```
|
||||
|
||||
<img width="737" alt="Screenshot 2022-10-04 at 13 36 10" src="https://user-images.githubusercontent.com/40023817/193820817-82f8727f-0e12-4794-b128-d6ebe20b2c4f.png">
|
||||
|
||||
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.
|
||||
<img width="747" alt="Screenshot 2022-10-04 at 00 35 00" src="https://user-images.githubusercontent.com/40023817/193704210-b75ddee3-1da3-432b-90f8-6966e85bb345.png">
|
||||
|
||||
This allows us to alter certain assumption which also modify the asset allocation.
|
||||
|
||||
```python
|
||||
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
|
||||
```
|
||||
|
||||
<img width="401" alt="Screenshot 2022-10-04 at 00 37 18" src="https://user-images.githubusercontent.com/40023817/193704462-d006deee-f009-4330-9918-0e0d661636d8.png">
|
||||
|
||||
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.
|
||||
|
||||
```python
|
||||
openbb.portfolio.po.plot(data=data_returns_hrp,weights=weights_hrp,pie=True)
|
||||
```
|
||||
|
||||
<img width="735" alt="Screenshot 2022-10-04 at 13 38 12" src="https://user-images.githubusercontent.com/40023817/193821181-0cb8cc51-3532-4542-b098-b23222330142.png">
|
||||
|
||||
```python
|
||||
openbb.portfolio.po.plot(data=data_returns_hrp_2,weights=weights_hrp_2,pie=True)
|
||||
```
|
||||
|
||||
<img width="735" alt="Screenshot 2022-10-04 at 13 38 30" src="https://user-images.githubusercontent.com/40023817/193821231-e92839b5-47d1-4a1a-81c2-61244bb6d925.png">
|
||||
|
||||
## 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.
|
||||
|
||||
```python
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib_inline.backend_inline
|
||||
from openbb_terminal.api 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.
|
||||
|
||||
```python
|
||||
import matplotlib.pyplot as plt
|
||||
from openbb_terminal.api 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](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/reports). They are excellent demonstration on how to use the SDK to its fullest extent
|
||||
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Reference in New Issue
Block a user