jerry/OpenBB
1
0
Fork 0
mirror of https://github.com/OpenBB-finance/OpenBB.git synced 2026-10-29 13:32:08 +00:00
Code Issues Packages Projects Releases Wiki Activity

Fix equity report & Update API docs on Hugo (#2573)

Browse Source 
* Fix equity report not working

* Fix equity report not working

* Update Hugo server on API
This commit is contained in:
minhhoang1023
2022-09-20 16:47:34 +01:00
committed by GitHub
parent 72b0a9f1ee
commit cb7423d8c6
2 changed files with 109 additions and 93 deletions
Download Patch File Download Diff File Expand all files Collapse all files

30
openbb_terminal/reports/equity.ipynb vendored
View File

@@ -205,6 +205,16 @@
"df_quarter_revenues"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e101e24e",
"metadata": {},
"outputs": [],
"source": [
"openbb.stocks.fa.shrs(symbol)"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -212,11 +222,7 @@
"metadata": {},
"outputs": [],
"source": [
"(\n",
" df_major_holders,\n",
" df_institutional_shareholders,\n",
" df_mutualfund_shareholders,\n",
") = openbb.stocks.fa.shrs(symbol)\n",
"df_institutional_shareholders = openbb.stocks.fa.shrs(symbol)\n",
"df_institutional_shareholders.index += 1\n",
"df_institutional_shareholders"
]
@@ -940,7 +946,7 @@
"outputs": [],
"source": [
"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
"openbb.stocks.qa.bw(\n",
"openbb.common.qa.bw(\n",
" ticker_data_all, \"Returns\", symbol, yearly=False, external_axes=[ax]\n",
")\n",
"fig.tight_layout()\n",
@@ -957,7 +963,7 @@
"outputs": [],
"source": [
"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
"openbb.stocks.qa.bw(ticker_data_all, \"Returns\", symbol, yearly=True, external_axes=[ax])\n",
"openbb.common.qa.bw(ticker_data_all, \"Returns\", symbol, yearly=True, external_axes=[ax])\n",
"fig.tight_layout()\n",
"f = io.BytesIO()\n",
"fig.savefig(f, format=\"svg\")\n",
@@ -1474,8 +1480,8 @@
"\n",
"body += widgets.tab_clickable_and_save_evt()\n",
"\n",
"report = openbb.widgets.html_report(\n",
" title=report_name, stylesheet=openbb.widgets.html_report_stylesheet(), body=body\n",
"report = widgets.html_report(\n",
" title=report_name, stylesheet=widgets.html_report_stylesheet(), body=body\n",
")\n",
"\n",
"# to save the results\n",
@@ -1487,7 +1493,7 @@
"metadata": {
"celltoolbar": "Tags",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3.9.10 ('gst')",
"language": "python",
"name": "python3"
},
@@ -1501,11 +1507,11 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
"version": "3.9.10"
},
"vscode": {
"interpreter": {
"hash": "1a8cc6b6e60740679b24fc1ea93bdeb94d949a22102a80c99b7fd3f0d572afd6"
"hash": "31090e8428bc3cbe850895ffb75aca3a30f914a21038b56ab61f04d2ddd7301b"
}
}
},

172
website/content/api/_index.md vendored
View File

@@ -9,10 +9,6 @@ commands follow the same convention as the terminal.
For example `stocks/load aapl` becomes `openbb.stocks.load("aapl")`.
<a target="_blank" href="https://user-images.githubusercontent.com/11668535/157916097-f9c5af6f-b97f-476d-943e-49eea4283bed.png"><img width="500" alt="image" src="https://user-images.githubusercontent.com/11668535/157916097-f9c5af6f-b97f-476d-943e-49eea4283bed.png"></a>
{{< toc >}}
---
## Python environment setup
@@ -20,8 +16,21 @@ For example `stocks/load aapl` becomes `openbb.stocks.load("aapl")`.
Import the OpenBB API into your python script or notebook with
```python
from openbb_terminal import api as openbb
from openbb_terminal.api import openbb
```
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.
For example see:
```python
# Returns data:
openbb.stocks.ba.snews("aapl")
# Returns charts:
openbb.stocks.ba.snews("aapl", chart=True)
```
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.
## Understanding the API functions
@@ -49,90 +58,91 @@ external_axes : Optional[List[plt.Axes]], optional
## Usage examples
Load stock data into a dataframe:
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).
### Jupyter Notebook Tricks
**Get matplotlib charts in the output cells**\
If you copy-paste the code below and use it as your initialization then you're matplotlib graphs will be inside
the result cell.
```python
aapl_data = openbb.stocks.load(
ticker="aapl",
start="2021-06-10",
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")
```
**Get function signature and docstring**\
When you press `shift + tab` in jupyter notebook while having the mouse parser in an API function, you get the
signature and docstring of the function.
### Visual Studio Code Tricks
**Get function docstring and signature**\
In order to get the docstrings and function signatures for the API when opening a Jupyter Notebook in VSCode,
you have to install the [Jupyter PowerToys
extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-jupyter-powertoys).
## Code Examples
Just copy-paste the code examples below into a python script or jupyter notebook, and you're ready to go.
**Basic Stock Information**\
Prints general information about the selected stock (in this case Gamsetop)
```python
from openbb_terminal.api import openbb
gme_info = openbb.stocks.fa.info("gme").transpose()
print("-- Gamstop Stock --\n\n- Basic Info -")
print(f"Sector: {gme_info['Sector'].iloc[0]}")
print(f"Country: {gme_info['Country'].iloc[0]}")
print(f"Description: {gme_info['Long business summary'].iloc[0]}")
print("\n- Financial Info -")
print(f"Ebitda Margins: {gme_info['Ebitda margins'].iloc[0]}")
print(f"Profit Margins: {gme_info['Profit margins'].iloc[0]}")
print(f"Revenue growth: {gme_info['Revenue growth'].iloc[0]}")
print("\n- Target Price -")
print(f"Current price: {gme_info['Current price'].iloc[0]}")
print(f"Target mean price: {gme_info['Target mean price'].iloc[0]}")
print(f"Target high price: {gme_info['Target high price'].iloc[0]}")
print(f"Target low price: {gme_info['Target low price'].iloc[0]}")
```
**Use external axis**\
The code below utilises 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,
)
aapl_data = openbb.stocks.process_candle(aapl_data)
fig.tight_layout()
```
Plot candle chart
**Stocks Return Distribution** \
Fetches data from the OpenBB API and then plots the return distribution. This is a good example, where the data from
the API is leveraged to build a new feature on top of the API.
```python
openbb.stocks.candle("AAPL", aapl_data, True)
import numpy as np
import matplotlib.pyplot as plt
from openbb_terminal.api import openbb
# Fetches data from the api
gme = openbb.stocks.load("gme")
# Calculates logarithmic returns
gme["Log Returns"] = np.log(gme["Adj Close"]/gme["Adjusted Close"].shift(1))
# Plots the return distributions
gme["Log Returns"].hist(bins=1000)
plt.thight_layout()
plt.show()
```
Call the "What if" command of the backtesting menu
```python
openbb.stocks.bt.whatif("aapl", 10, datetime.strptime("2012-03-03", "%Y-%m-%d"))
```
Get sentiment for a list of tickers
```python
openbb.stocks.ca.models.finbrain.get_sentiments(tickers=["aapl"])
```
Print key fundamental metrics for past 2 years
```python
openbb.stocks.fa.fmp.metrics(ticker="AAPL", number=2)
```
Get fundamental metrics for past 3 years as a pandas dataframe
```python
openbb.stocks.fa.fmp.models.fmp.get_key_metrics(ticker="AAPL", number=3)
```
Perform an OLS regression on multiple custom datasets
```python
openbb.econometrics.ols(
regression_variables=[
'close-tsla', 'volume-aapl'
],
data={"aapl":aapl_data,"tsla":tsla_data},
datasets={
"close-tsla":{"Close": None, "tsla": None},
"volume-aapl":{"Volume": None, "aapl": None}
}
)
```
## External matplotlib axes support
Most of the functions of the terminal that generate matplotlib charts support passing
external axes into them. This is very handy when you want a chart that is generated by
OpenBB to appear in another chart that you are creating in a notebook.
Example:
```python
# Load stock data
ticker = "AAPL"
stock_data = openbb.stocks.load(ticker="aapl")
# Create a matplotlib figure
fig, axes = plt.subplots(3, 1, figsize=(10, 7))
# Use OpenBB to add charts to the axes of the created figure
openbb.stocks.ta.macd(s_ticker=ticker, series=aapl["Close"], external_axes=axes[0:2])
openbb.stocks.options.voi_yf(
ticker=ticker,
expiry="2022-03-11",
min_sp=110,
max_sp=210,
min_vol=0.3,
external_axes=[axes[2]],
)
# Show the figure with charts
fig.show()
```
For more examples see the OpenBB jupyter notebook reports. They all use the API to its fullest extent!
---