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Moves the content into the right file/location (#3345)
* Moves the content into the right file/location * removed content that was supposed to be in /common/qa
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Danglewood
93
website/content/SDK/common/_index.md
vendored
93
website/content/SDK/common/_index.md
vendored
@@ -1,93 +1,6 @@
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---
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title: Introduction to Quantitative Analysis
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keywords: "qa, quant, quantitative, bw, var, es, kurtosis, normality, omega, quantile, rolling, sharpe, skew, spread, summary, unitroot, var, math"
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excerpt: "This guide introduces the Quantitative Analysis module, and provides some examples."
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title: Common Modules
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keywords: "QA, TA, quantitative, technical, analysis"
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excerpt: ""
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geekdocCollapseSection: true
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---
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The `qa` module replicates the features of the Quantitative Analysis menu, within the <a href="https://openbb-finance.github.io/OpenBBTerminal/terminal/common/qa/" target="_blank">OpenBB Terminal</a>, for the SDK environment. It provides users with more ways to interact with the library of functions, and provides cross-disciplinary utility. To activate the code completion for the menu, enter `.` after, `openbb.common.qa`.
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## How to Use
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With the modules imported, the next step would be to collect data for analysis. In this example, the Quandl API will be used. To pass the key to the URL string, import:
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```python
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import openbb_terminal.config_terminal as cfg
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```
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The credentials can then be passed with:
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```python
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f"{cfg.API_KEY_QUANDL}"
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```
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As a functional code block, this looks like:
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```python
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from openbb_terminal.sdk import openbb
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import openbb_terminal.config_terminal as cfg
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import pandas as pd
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import matplotlib.pyplot as plt
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%matplotlib widget
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```
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```python
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payload = pd.read_json('https://data.nasdaq.com/api/v3/datasets/MULTPL/SHILLER_PE_RATIO_MONTH.json?api_key='f"{cfg.API_KEY_QUANDL}")
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payload = pd.DataFrame(payload).transpose()
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shiller_pe = payload.data.dataset
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```
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Now, with the data loaded, the next block will utilize the `skew` function, to target the monthly Shiller P/E Ratio of the S&P 500 from inception.
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```python
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shiller_df = pd.DataFrame(columns = ['Date', 'Shiller P/E Ratio', 'Skew'])
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shiller_pe = pd.DataFrame(shiller_pe)
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shiller_skew = openbb.common.qa.skew(data = shiller_pe[1], window = 3)
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shiller_df['Date'] = shiller_pe[0].values
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shiller_df['Shiller P/E Ratio'] = shiller_pe[1].values
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shiller_df['Skew'] = shiller_skew
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shiller_df.set_index(keys = ['Date'], inplace = True)
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shiller_df.tail()
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```
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Get a summary of the DataFrame:
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```python
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openbb.common.qa.summary(shiller_df)
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```
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Insert the rolling mean and standard deviation of a target column with, `openbb.common.qa.rolling`:
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```python
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mean,std = openbb.common.qa.rolling(shiller_df['Shiller P/E Ratio'])
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mean.rename(columns = {'Shiller P/E Ratio': 'Mean'}, inplace = True)
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std.rename(columns = {'Shiller P/E Ratio': 'Std'}, inplace = True)
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rolling = mean.join(std)
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shiller_df = shiller_df.join(rolling)
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shiller_df
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```
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Calculate and join the Sharpe Ratio of, for example, the rolling mean average of the Shiller P/E ratio:
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```python
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sharpe = pd.DataFrame(openbb.common.qa.sharpe(data = shiller_df['Mean'], window = 12, rfr = 3))
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sharpe.rename(columns = {'Mean': 'Sharpe'}, inplace = True)
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shiller_df = shiller_df.join(sharpe)
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```
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93
website/content/SDK/common/qa/_index.md
vendored
93
website/content/SDK/common/qa/_index.md
vendored
@@ -1,6 +1,93 @@
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---
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title: qa
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keywords: ""
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excerpt: ""
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title: Introduction to Quantitative Analysis
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keywords: "qa, quant, quantitative, bw, var, es, kurtosis, normality, omega, quantile, rolling, sharpe, skew, spread, summary, unitroot, var, math"
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excerpt: "This guide introduces the Quantitative Analysis module, and provides some examples."
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geekdocCollapseSection: true
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---
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The `qa` module replicates the features of the Quantitative Analysis menu, within the <a href="https://openbb-finance.github.io/OpenBBTerminal/terminal/common/qa/" target="_blank">OpenBB Terminal</a>, for the SDK environment. It provides users with more ways to interact with the library of functions, and provides cross-disciplinary utility. To activate the code completion for the menu, enter `.` after, `openbb.common.qa`.
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## How to Use
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With the modules imported, the next step would be to collect data for analysis. In this example, the Quandl API will be used. To pass the key to the URL string, import:
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```python
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import openbb_terminal.config_terminal as cfg
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```
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The credentials can then be passed with:
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```python
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f"{cfg.API_KEY_QUANDL}"
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```
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As a functional code block, this looks like:
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```python
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from openbb_terminal.sdk import openbb
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import openbb_terminal.config_terminal as cfg
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import pandas as pd
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import matplotlib.pyplot as plt
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%matplotlib widget
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```
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```python
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payload = pd.read_json('https://data.nasdaq.com/api/v3/datasets/MULTPL/SHILLER_PE_RATIO_MONTH.json?api_key='f"{cfg.API_KEY_QUANDL}")
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payload = pd.DataFrame(payload).transpose()
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shiller_pe = payload.data.dataset
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```
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Now, with the data loaded, the next block will utilize the `skew` function, to target the monthly Shiller P/E Ratio of the S&P 500 from inception.
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```python
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shiller_df = pd.DataFrame(columns = ['Date', 'Shiller P/E Ratio', 'Skew'])
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shiller_pe = pd.DataFrame(shiller_pe)
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shiller_skew = openbb.common.qa.skew(data = shiller_pe[1], window = 3)
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shiller_df['Date'] = shiller_pe[0].values
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shiller_df['Shiller P/E Ratio'] = shiller_pe[1].values
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shiller_df['Skew'] = shiller_skew
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shiller_df.set_index(keys = ['Date'], inplace = True)
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shiller_df.tail()
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```
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Get a summary of the DataFrame:
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```python
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openbb.common.qa.summary(shiller_df)
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```
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Insert the rolling mean and standard deviation of a target column with, `openbb.common.qa.rolling`:
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```python
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mean,std = openbb.common.qa.rolling(shiller_df['Shiller P/E Ratio'])
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mean.rename(columns = {'Shiller P/E Ratio': 'Mean'}, inplace = True)
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std.rename(columns = {'Shiller P/E Ratio': 'Std'}, inplace = True)
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rolling = mean.join(std)
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shiller_df = shiller_df.join(rolling)
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shiller_df
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```
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Calculate and join the Sharpe Ratio of, for example, the rolling mean average of the Shiller P/E ratio:
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```python
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sharpe = pd.DataFrame(openbb.common.qa.sharpe(data = shiller_df['Mean'], window = 12, rfr = 3))
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sharpe.rename(columns = {'Mean': 'Sharpe'}, inplace = True)
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shiller_df = shiller_df.join(sharpe)
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```
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