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Analyzing Return Distributions

Analyzing Return Distributions#

In this guide, we’ll demonstrate how to fetch financial data using the moabdb API, calculate daily returns, and then visualize the

return distributions using the popular matplotlib and seaborn libraries.

Prerequisites#

  • You will need matplotlib, seaborn, and numpy installed. Install them with:

    pip install matplotlib seaborn numpy

Fetching Data from MoabDB#

First, let’s retrieve historical closing prices for a given stock (e.g., AAPL):

import moabdb as mdb

# Constants for flexibility
TICS = ['AAPL','MSFT','TSLA','NVDA']
SAMPLE = '5y'

# Fetching data
data_df = mdb.get_equity(tickers=TIC, sample=SAMPLE)

Calculate Daily Returns#

To understand return distributions, we then need to calculate daily returns:

# Calculate daily returns
return_df = data_df['Close'].pct_change().dropna()

Visualizing Return Distributions#

With the daily returns we can plot their distribution:

import matplotlib.pyplot as plt
import seaborn as sns

# Set style
sns.set_style('whitegrid')

# Histogram of daily returns
plt.figure(figsize=(10,6))
sns.histplot(data_df['Daily_Return'].dropna(), bins=100, color='blue')
plt.title('Distribution of Daily Returns for ' + TIC)
plt.xlabel('Daily Return')
plt.ylabel('Frequency')
plt.tight_layout()
plt.show()

This visualization offers insights into the volatility and risk associated with the stock. A wider distribution implies more volatility.

Considerations#

  • The daily return distribution can be useful for understanding risk and return characteristics.

  • Always be cautious when interpreting these visualizations; past performance is not indicative of future results.

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