Calculate adjusted cost base using python pandas portfolio analysis of stocks

When it comes to calculating the adjusted cost base using Python pandas for portfolio analysis of stocks, there are several approaches you can take. In this article, we will explore three different ways to solve this problem.

Option 1: Using Pandas DataFrame

The first option involves using the Pandas DataFrame to perform the necessary calculations. Here’s a sample code that demonstrates this approach:


import pandas as pd

# Create a DataFrame with the stock data
data = {'Date': ['2021-01-01', '2021-01-02', '2021-01-03'],
        'Price': [10.0, 12.0, 15.0],
        'Shares': [100, 150, 200]}
df = pd.DataFrame(data)

# Calculate the adjusted cost base
df['Cost'] = df['Price'] * df['Shares']
df['ACB'] = df['Cost'].cumsum() / df['Shares'].cumsum()

print(df)

This code creates a DataFrame with the stock data, calculates the cost for each transaction, and then calculates the adjusted cost base by taking the cumulative sum of the costs divided by the cumulative sum of the shares. The result is then printed.

Option 2: Using NumPy Arrays

The second option involves using NumPy arrays to perform the calculations. Here’s a sample code that demonstrates this approach:


import numpy as np

# Create NumPy arrays with the stock data
dates = np.array(['2021-01-01', '2021-01-02', '2021-01-03'])
prices = np.array([10.0, 12.0, 15.0])
shares = np.array([100, 150, 200])

# Calculate the adjusted cost base
cost = prices * shares
acb = np.cumsum(cost) / np.cumsum(shares)

# Print the result
for date, price, share, acb_value in zip(dates, prices, shares, acb):
    print(f"Date: {date}, Price: {price}, Shares: {share}, ACB: {acb_value}")

This code creates NumPy arrays with the stock data, calculates the cost for each transaction, and then calculates the adjusted cost base by taking the cumulative sum of the costs divided by the cumulative sum of the shares. The result is then printed using a loop.

Option 3: Using Python Lists

The third option involves using Python lists to perform the calculations. Here’s a sample code that demonstrates this approach:


# Create lists with the stock data
dates = ['2021-01-01', '2021-01-02', '2021-01-03']
prices = [10.0, 12.0, 15.0]
shares = [100, 150, 200]

# Calculate the adjusted cost base
cost = [price * share for price, share in zip(prices, shares)]
acb = [sum(cost[:i+1]) / sum(shares[:i+1]) for i in range(len(cost))]

# Print the result
for date, price, share, acb_value in zip(dates, prices, shares, acb):
    print(f"Date: {date}, Price: {price}, Shares: {share}, ACB: {acb_value}")

This code creates Python lists with the stock data, calculates the cost for each transaction, and then calculates the adjusted cost base by taking the cumulative sum of the costs divided by the cumulative sum of the shares. The result is then printed using a loop.

After analyzing these three options, it is clear that using the Pandas DataFrame (Option 1) is the most efficient and concise approach. It provides built-in functionality for handling tabular data and performing calculations, making it the ideal choice for portfolio analysis of stocks. Therefore, Option 1 is the recommended solution for calculating the adjusted cost base using Python pandas.

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12 Responses

    1. I totally disagree! Option 1 might be user-friendly, but it lacks the advanced features needed for in-depth portfolio analysis. Pandas DataFrame is just a basic tool. You need to explore more robust options to truly streamline your analysis.

  1. Option 2 seems the most efficient for calculating adjusted cost base. What do you guys think? #PythonPandas #PortfolioAnalysis

    1. I completely disagree. Option 1 is far superior when it comes to calculating adjusted cost base. It offers more flexibility and accuracy. Ive tried both methods extensively and Option 1 always comes out on top. #PythonPandas #PortfolioAnalysis

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