plots

Author: bsroseman

docs/source/index.rst

@@ -96,6 +96,7 @@ Examples and Guides
    :caption: Simple Tutorials
 
    tutorial/simple_plot
+   tutorial/simple_returns
    tutorial/return_distributions
    tutorial/calc_beta
 

docs/source/tutorial/simple_plot.rst

@@ -12,13 +12,10 @@ financial data using MoabDB and how to create a basic
 chart using the `matplotlib` library.
 
 We'll be using the `get_equity` function to retrieve daily-level and show
-three examples of plotting data:
+two examples of plotting data:
 
 - Single Stock with Daily-Level Data
 - Multiple Stocks with Daily-Level Data
-- Multiple Stocks with Cumulative Returns
-
-
 
 
 Prerequisites
@@ -148,66 +145,6 @@ With our data in hand, we can now plot it:
    :align: center
    :width: 80%
 
-Plotting Cumulative Returns for Multiple Stocks
------------------------------------------------
-
-To visualize the performance of various stocks over time, 
-we'll compute and plot their cumulative returns. Mathematically, the 
-formula for cumulative returns is:
-
-.. math::
-
-    \text{Cumulative Return(t)} = \frac{\text{Price(t)}}{\text{Price(0)}} - 1
-
-Which is that every price is divided by the initial price and then
-subtracted by 1. This gives us a percentage that we can plot over time.
-
-
-Compute and Visualize Cumulative Returns with Matplotlib
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-We can easily calculate cumulative return as price `t` divided by 
-the initial price, and then subtracting 1 Let's compute this for our stocks:
-
-.. code-block:: python
-
-    import moabdb as mdb
-    import matplotlib.pyplot as plt
-
-    # Constants defined here for flexibility
-    TICS = ['MSFT','INTC']
-    SAMPLE = '5y'
-
-    # Load and Check Data, Get Prices
-    data_df = mdb.get_equity(tickers=TICS, sample=SAMPLE)
-    price_df = data_df['Close']
-    print(price_df.head())
-
-    # Calculate the cumulative returns
-    price0 = price_df.iloc[0]
-    cum_returns = (price_df[TICS] / price0[TICS]) - 1
-    cum_returns *= 100
-
-    # Creating the plot
-    fig, ax = plt.subplots(figsize=(6,4))
-    for tic in TICS:
-        ax.plot(cum_returns.index, cum_returns[tic], label=tic)
-        
-    ax.set_title('Cumulative Returns Over Time')
-    ax.set_xlabel('Date')
-    ax.set_ylabel('Cumulative Return (in %)')
-    ax.axhline(y=0, color='black', linestyle='-', linewidth=1)
-    plt.grid(True)
-    plt.legend()
-    plt.tight_layout()
-    plt.show()
-
-
-.. figure:: /_static/images/ex1_fig3.jpg
-   :alt: Single Stock with Daily-Level Data
-   :align: center
-   :width: 80%
-
 With these simple steps, you've fetched financial data using MoabDB 
 and visualized it with a basic chart. Explore more with 
 different stocks, date ranges, or chart types to gain richer insights!

docs/source/tutorial/simple_returns.rst

@@ -0,0 +1,110 @@
+
+=================================
+Plotting Cumulative Returns
+=================================
+
+.. image:: https://img.shields.io/pypi/v/moabdb.svg
+   :target: https://pypi.python.org/pypi/moabdb
+   :alt: PyPI Version
+
+This guide continues from the previous example. We will retrieve 
+financial data using MoabDB and how create a chart of cumulative returns 
+using the `matplotlib` library.
+
+We'll be using the `get_equity` function to retrieve daily-level and show
+how to plut cumulative returns for multiple stocks.
+
+
+
+
+Prerequisites
+-------------
+
+- You will need `matplotlib` installed. If you haven't already, you can install it with:
+
+  .. code-block:: bash
+
+     pip install matplotlib
+
+
+First, let's retrieve some financial data. For this example, we'll fetch historical closing prices for a given stock (e.g., `MSFT`):
+
+Import MoabDB and fetch data
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. code-block:: python
+
+    import moabdb as mdb
+    import matplotlib.pyplot as plt
+
+    # Constants defined here for flexibility
+    TIC = 'MSFT'
+    SAMPLE = '5y'
+
+    # Load and Check Data
+    data_df = mdb.get_equity(tickers=TIC, sample=SAMPLE)
+    print(data_df.head())
+
+Plotting Cumulative Returns for Multiple Stocks
+-----------------------------------------------
+
+To visualize the performance of various stocks over time, 
+we'll compute and plot their cumulative returns. Mathematically, the 
+formula for cumulative returns is:
+
+.. math::
+
+    \text{Cumulative Return(t)} = \frac{\text{Price(t)}}{\text{Price(0)}} - 1
+
+Which is that every price is divided by the initial price and then
+subtracted by 1. This gives us a percentage that we can plot over time.
+
+
+Compute and Visualize Cumulative Returns with Matplotlib
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+We can easily calculate cumulative return as price `t` divided by 
+the initial price, and then subtracting 1 Let's compute this for our stocks:
+
+.. code-block:: python
+
+    import moabdb as mdb
+    import matplotlib.pyplot as plt
+
+    # Constants defined here for flexibility
+    TICS = ['MSFT','INTC']
+    SAMPLE = '5y'
+
+    # Load and Check Data, Get Prices
+    data_df = mdb.get_equity(tickers=TICS, sample=SAMPLE)
+    price_df = data_df['Close']
+    print(price_df.head())
+
+    # Calculate the cumulative returns
+    price0 = price_df.iloc[0]
+    cum_returns = (price_df[TICS] / price0[TICS]) - 1
+    cum_returns *= 100
+
+    # Creating the plot
+    fig, ax = plt.subplots(figsize=(6,4))
+    for tic in TICS:
+        ax.plot(cum_returns.index, cum_returns[tic], label=tic)
+        
+    ax.set_title('Cumulative Returns Over Time')
+    ax.set_xlabel('Date')
+    ax.set_ylabel('Cumulative Return (in %)')
+    ax.axhline(y=0, color='black', linestyle='-', linewidth=1)
+    plt.grid(True)
+    plt.legend()
+    plt.tight_layout()
+    plt.show()
+
+
+.. figure:: /_static/images/ex1_fig3.jpg
+   :alt: Single Stock with Daily-Level Data
+   :align: center
+   :width: 80%
+
+With these simple steps, you've fetched financial data using MoabDB 
+and visualized it with a basic chart. Explore more with 
+different stocks, date ranges, or chart types to gain richer insights!