Learn Finance with Python and Data Science: Week-1

June 8, 2023 ebookreaders

Summary

Become Finance Expert with Python and Data Science: Week-1

It’s critical to make well-informed judgements in the fast-paced world of finance. Financial data analysis enables people and firms to draw insightful conclusions from complicated data, allowing more accurate forecasts and cleverer tactics. You’ve come to the correct spot if you want to learn how to analyse financial data. Welcome to our complete guide to learning Python and data science methods for financial data analysis.

In this blog, I will take explain the beginner friendly Financial concepts with Python and Data Science that help you to analyse the financial data more efficiently and accurately. Each topic focuses on different aspects of financial data analysis, utilizing the power of Python and cutting-edge data science techniques. So let’s dive into the first week of learning, and become expert in Finance domain.

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Video 1: Introduction to Finance

Let’s dive in and discover the key aspects that will serve as our foundation in this exciting field. Understanding finance is paramount when it comes to data analysis in the financial domain. In this video, we emphasize the significance of comprehending the principles of finance and how they intertwine with data analysis. By grasping these core concepts, you’ll be well-equipped to navigate the complexities of financial datasets and extract valuable insights.

We’ll cover essential topics such as financial markets, investment instruments, risk and return, financial statements, and more. Gain a solid understanding of concepts like stocks, bonds, mutual funds, assets, liabilities, and equity. Delve into financial ratios and performance metrics that allow you to assess the financial health of companies and make informed investment decisions.

Video 2: Numpy Use Cases in Finance

Welcome to Section 2 of our financial data analysis journey, where we explore the incredible capabilities of Numpy in Video 2: Numpy Use Cases in Finance. We’ll start by exploring the generation of random numbers, an essential component in simulating asset returns and running various statistical analyses. Gain insights into calculating the mean, standard deviation, and other statistical measures to assess the distribution and volatility of financial data.

# Generating Random Numbers
import numpy as np

random_numbers = np.random.rand(10)  # Generate 1000 random numbers from a uniform distribution between 0 and 1
print(random_numbers)

# Computing Mean and Standard Deviation
import numpy as np

returns = np.array([0.05, -0.02, 0.03, 0.01, -0.04])
mean_return = np.mean(returns)
# standard deviation measures the fluctuation in the fund's share price or returns over time, that how much it away from mean.
std_deviation = np.std(returns) # It's not Alpha or Beta in Finance

print(mean_return)
print(std_deviation)

# Calculating Covariance and Correlation
import numpy as np
import matplotlib.pyplot as plt

stock_returns = np.array([0.05, -0.02, 0.03, 0.01, -0.04])
market_returns = np.array([0.02, 0.01, 0.03, 0.02, -0.01])

# covariance (-inf to +inf) can predict how two stocks might perform relative to each other in the future. It determine if stocks' returns tend to move with or against each other.
covariance = np.cov(stock_returns, market_returns)[0, 1]

# Scaled version of covariance as it does not affect by change in scale and limit from -1 to +1.
correlation = np.corrcoef(stock_returns, market_returns)[0, 1]
print("COV- ",covariance)
print("CORR- ",correlation)

plt.plot(stock_returns)
plt.plot(market_returns)
plt.title("Stock Vs Market Returns")
plt.show()

# Performing Matrix Operations
import numpy as np

matrix_a = np.array([[1, 2], [3, 4]])
matrix_b = np.array([[5, 6], [7, 8]])

matrix_sum = np.add(matrix_a, matrix_b)
matrix_product = np.dot(matrix_a, matrix_b)
print(matrix_sum)
print("-----------")
print(matrix_product)

More use cases are in video so Join us in Video 2: Numpy Use Cases in Finance as we dive deeper into the world of financial data analysis with Numpy.

Video 3: Pandas Use Cases in Finance

Welcome to Section 3 of our financial data analysis journey, where we delve into the powerful capabilities of Pandas in Video 3: Pandas Use Cases in Finance. Pandas, a popular Python library, revolutionizes data manipulation and analysis, particularly in the realm of finance. In this video, we uncover how Pandas simplifies complex financial datasets, making data cleaning, transformation, and analysis a breeze. With Pandas, we can effortlessly perform groupby operations, allowing us to aggregate and summarize financial data based on specific criteria. Dive into the world of data cleaning as we explore techniques to handle missing values and outliers, ensuring the accuracy and integrity of our analysis.

import pandas as pd

# Assuming we have a DataFrame with stock data
# Dummy data
data = ['2023-05-21', '2023-05-22', '2023-05-23', '2023-05-23', '2023-05-23',
        '2023-05-25', '2023-05-26', '2023-05-26', '2023-05-27', '2023-05-30']
stock_names = ['ABC', 'XYZ', 'DEF', 'GHI', 'JKL', 'MNO', 'PQR', 'STU', 'VWX', 'YZA']
sector = ['IT','Finance','IT','Finance','IT','Energy','Retail','Data Science','AI','Finance']
prices = [100.50, 75.20, 120.80, 50.75, 90.25, 110.10, 85.60, 95.30, 105.80, 65.40]

# Create dataframe
df = pd.DataFrame({'Date': data, 'Stock Names': stock_names, 'Price': prices, 'Sector':sector})

# Grouping data by sector and calculating average price
avg_price_by_sector = df.groupby('Sector')['Price'].mean()
print(avg_price_by_sector)

# Rolling Window Analysis
# Calculating 2-day rolling average
df['RollingAvg'] = df['Price'].rolling(window=2).mean()
df

# Pivot Tables
sales = pd.DataFrame({
    'Category': np.random.choice(['Electronics', 'Clothing', 'Home Decor'], size=10),
    'Month': np.random.choice(['January', 'February', 'March', 'April'], size=10),
    'Sales': np.random.randint(100, 1000, size=10)
})
# Creating a pivot table to summarize sales by product category
pivot_table = sales.pivot_table(values='Sales', index='Category', columns='Month', aggfunc='sum')
print(pivot_table)

# Calculating daily returns
df['Return'] = df['Price'].pct_change()
df

Learn More use cases are in video so Join us in Video 3: Pandas py Use Cases in Finance as we dive deeper into the world of financial data analysis with Pandas Library.

Video 4: Advanced Use Cases of Numpy and Pandas in Finance

Welcome to Section 4 of our financial data analysis journey. In Video 4: Advanced Use Cases of Numpy and Pandas in Finance, we dive deeper into Numpy and Pandas, exploring advanced techniques that will elevate your financial analysis skills.

Unleash the power of Numpy and Pandas as we explore topics such as portfolio optimization, utilizing the Black-Scholes option pricing model, and conducting Monte Carlo simulations for risk analysis. Gain insights into moving averages, portfolio performance analysis, event studies, and risk analysis, enabling you to make informed financial decisions.

# Black-Scholes Option Pricing Model
import numpy as np
from scipy.stats import norm

"""
This model is used to find the current value of a call option whose ultimate value depends on the price of the stock at the expiration date. 
Because the stock price keeps changing, the value of this call option will change too
"""

"""
This function calculates the call and put option prices using the Black-Scholes formula. It takes the following inputs:

S: Current stock price
K: Strike price of the option
r: Risk-free interest rate
sigma: Volatility of the underlying asset
T: Time to maturity of the option
The function computes d1 and d2, which are intermediate variables used in the Black-Scholes formula. 
Then, it calculates the call and put option prices using the cumulative distribution function (CDF) of the standard normal distribution.
"""

def black_scholes(S, K, r, sigma, T):
    d1 = (np.log(S / K) + (r + 0.5 * sigma**2) * T) / (sigma * np.sqrt(T))
    d2 = d1 - sigma * np.sqrt(T)
    call_price = S * norm.cdf(d1) - K * np.exp(-r * T) * norm.cdf(d2)
    put_price = K * np.exp(-r * T) * norm.cdf(-d2) - S * norm.cdf(-d1)
    return call_price, put_price

stock_price = 100.0
strike_price = 105.0
risk_free_rate = 0.05
volatility = 0.2
time_to_maturity = 1.0

call_option_price, put_option_price = black_scholes(stock_price, strike_price, risk_free_rate, volatility, time_to_maturity)
print(call_option_price) # Which Price to Buy
print(put_option_price) # Which Price to Sell

Discover how to leverage these advanced techniques to calculate Value at Risk (VaR), a crucial metric in assessing potential losses in investment portfolios. Learn how fundamental analysis, combined with Numpy and Pandas, can provide valuable insights into market trends and help you make strategic investment decisions.

# Risk Analysis and VaR Calculation
# Calculating portfolio volatility
volatility = np.std(df['Stock Return'])

# Calculating Value at Risk (VaR) at a specific confidence level
confidence_level = 0.95
var = np.percentile(df['Stock Return'], 100 - confidence_level * 100)
print(volatility)
print(var)

# Calculating 50-day and 200-day moving averages
df['50-day MA'] = df['Price'].rolling(window=3).mean()
df['200-day MA'] = df['Price'].rolling(window=5).mean()
df

Learn more advance use cases in this Video 4: Advanced Use Cases of Numpy and Pandas in Finance.

Video 5: Stock Market Analysis and Prediction using Python

Welcome to the final section of our financial data analysis series! In Video 5: Stock Market Analysis and Prediction using Python, we bring together all the knowledge and techniques gained from the previous videos to embark on a captivating stock market analysis project.

In this video, we delve into the intriguing world of stock market analysis, leveraging the power of Python and data science. Witness how we apply our expertise in Numpy, Pandas, and advanced analysis techniques to unravel valuable insights from historical stock market data.

The centrepiece of this project is the application of the ARIMA (AutoRegressive Integrated Moving Average) model for stock price prediction. With ARIMA, we can forecast future price movements based on past trends and patterns, providing valuable guidance for investment decisions.

Final Thoughts

We began by introducing the fundamental concepts of finance, highlighting the importance of financial literacy in today’s data-driven world. We then explored the vast capabilities of Numpy and Pandas, discovering how these libraries simplify complex financial data manipulation and analysis.

Through our exploration of various use cases, we witnessed firsthand how Numpy enables efficient computations and statistical analysis, while Pandas empowers us to clean, transform, and visualize financial data with ease.

The culmination of our journey led us to the exciting realm of stock market analysis and prediction. In Video 5, we embarked on a captivating project, leveraging the ARIMA model to forecast stock prices and employing advanced analysis techniques to gain valuable insights for investment decisions.

So let’s meet in Week 2 with More concepts about Finance Domain and Projects as well using Machine learning and Deep Learning.

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Learn Finance with Python and Data Science: Week-1 was originally published in Artificial Intelligence in Plain English on Medium, where people are continuing the conversation by highlighting and responding to this story.

https://ai.plainenglish.io/learn-finance-with-python-and-data-science-week-1-a94dd918c2db?source=rss—-78d064101951—4
By: Simranjeet Singh
Title: Learn Finance with Python and Data Science: Week-1
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Published Date: Tue, 06 Jun 2023 01:43:27 GMT