Adds guide for QA module of the SDK

website/content/SDK/common/_index.md

@@ -1,6 +1,93 @@
 ---
-title: common
-keywords: ""
-excerpt: ""
+title: Introduction to Quantitative Analysis
+keywords: "qa, quant, quantitative, bw, var, es, kurtosis, normality, omega, quantile, rolling, sharpe, skew, spread, summary, unitroot, var, math"
+excerpt: "This guide introduces the Quantitative Analysis module, and provides some examples."
 geekdocCollapseSection: true
 ---
+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`.
+
+![The QA Module](https://user-images.githubusercontent.com/85772166/200109595-dbfde965-72b4-40fc-88bf-95f34a30f5d9.png "The QA Module")
+
+## How to Use
+
+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:
+
+```python
+
+import openbb_terminal.config_terminal as cfg
+
+```
+The credentials can then be passed with:
+
+```python
+f"{cfg.API_KEY_QUANDL}"
+```
+
+As a functional code block, this looks like:
+
+```python
+
+from openbb_terminal.sdk import openbb
+import openbb_terminal.config_terminal as cfg
+import pandas as pd
+import matplotlib.pyplot as plt
+
+%matplotlib widget
+```
+
+```python
+payload = pd.read_json('https://data.nasdaq.com/api/v3/datasets/MULTPL/SHILLER_PE_RATIO_MONTH.json?api_key='f"{cfg.API_KEY_QUANDL}")
+payload = pd.DataFrame(payload).transpose()
+shiller_pe = payload.data.dataset
+```
+
+![Loading Data](https://user-images.githubusercontent.com/85772166/200109605-4cdd01b2-2974-4222-bb6c-0bf14c593cb5.png "Loading Data")
+
+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.
+
+```python
+shiller_df = pd.DataFrame(columns = ['Date', 'Shiller P/E Ratio', 'Skew'])
+shiller_pe = pd.DataFrame(shiller_pe)
+shiller_skew = openbb.common.qa.skew(data = shiller_pe[1], window = 3)
+
+shiller_df['Date'] = shiller_pe[0].values
+shiller_df['Shiller P/E Ratio'] = shiller_pe[1].values
+shiller_df['Skew'] = shiller_skew
+shiller_df.set_index(keys = ['Date'], inplace = True)
+
+shiller_df.tail()
+```
+
+![Skew of Monthly S&P 500 Shiller P/E Ratio](https://user-images.githubusercontent.com/85772166/200109627-0e532041-dd6a-41ef-ad17-41acd1b58caf.png "Skew of Monthly S&P 500 Shiller P/E Ratio")
+
+Get a summary of the DataFrame:
+
+```python
+openbb.common.qa.summary(shiller_df)
+```
+
+![openbb.common.qa.summary](https://user-images.githubusercontent.com/85772166/200109649-fdf9b1f8-6e7e-47df-8baa-b1e265872b12.png "openbb.common.qa.summary")
+
+Insert the rolling mean and standard deviation of a target column with, `openbb.common.qa.rolling`:
+
+```python
+mean,std = openbb.common.qa.rolling(shiller_df['Shiller P/E Ratio'])
+mean.rename(columns = {'Shiller P/E Ratio': 'Mean'}, inplace = True)
+std.rename(columns = {'Shiller P/E Ratio': 'Std'}, inplace = True)
+rolling = mean.join(std)
+shiller_df = shiller_df.join(rolling)
+
+shiller_df
+```
+
+![openbb.common.qa.rolling](https://user-images.githubusercontent.com/85772166/200109671-ec0d62ac-fedc-4690-a972-b326a1f05da3.png "openbb.common.qa.rolling")
+
+Calculate and join the Sharpe Ratio of, for example, the rolling mean average of the Shiller P/E ratio:
+
+```python
+sharpe = pd.DataFrame(openbb.common.qa.sharpe(data = shiller_df['Mean'], window = 12, rfr = 3))
+sharpe.rename(columns = {'Mean': 'Sharpe'}, inplace = True)
+shiller_df = shiller_df.join(sharpe)
+```
+
+![openbb.common.qa.sharpe](https://user-images.githubusercontent.com/85772166/200109727-85736e12-85c2-43d6-97e0-4a95cf40bd6f.png "openbb.common.qa.sharpe")