chiggy1997 / stocks-data-analysis-and-visualization-in-python

Stocks Data Analysis and Visualization in Python

Stocks-Data-Analysis-and-Visualization-in-Python

Stocks Data Analysis and Visualization in Python

1. Overview:

a. Goal:

-Created a funtion to perform portfolio analysis by calculating return, risk and Sharpe ratio
-The main problem was the huge manual effort required and the visualization of the results
-Reduced analysis time by 20%

b. Challenges Faced:

-Wrangling data of different stocks and combining them into a single dataframee.
-Missing values.
-Plotting QQ plot for stock modelling.

c. Interesting findings

-Graph of volatility clustering conveyed that stock volatility doent follow a normal distribution but actually a T-distribution
-High volatility regions lie close to each other and low volatility region lie next to each other
-We have to avoid stocks whose returns have negative skew because they will end up losing a lot of times
-Excessive kurtosis means more reward but that comes with a price of more risk.

2. Code and resource used:

-Pandas, Numpy, Matplotlib.py

3. About the data:

4. Data Gathering:

-Kaggle
-Recent data obtained from Quandl

5. Data Preprocessing:

-Took data of all stocks and combined them in a single dataframe containing only the strike price and close price
-Fill up missing values by forward fill and backward fill
-Removing Duplicates.

6. EDA and Visualisation:

-Histogram of returns data in matplotlib.py and plotly express to understand its distribution
-Volatility vs time plot to observe the change in volatily over time.
-Price vs time to observe the stock returns over time