Monitor stock portfolios using Python

Monitoring stock portfolios using Python involves fetching real-time or delayed stock data, calculating portfolio metrics, and visualizing performance. Here’s a comprehensive guide on how to build a Python script or small application for this purpose.

1. Fetching Stock Data

To monitor a stock portfolio, you first need to retrieve stock price data. Popular APIs and libraries for this include:

• yfinance (Yahoo Finance API wrapper)

• Alpha Vantage API

• IEX Cloud API

• pandas_datareader

For simplicity and free usage, yfinance is commonly used.

python
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import yfinance as yf

# Example: Download historical data for Apple (AAPL)
data = yf.download('AAPL', period='1mo', interval='1d')
print(data.tail())

2. Defining Your Portfolio

A portfolio can be represented as a dictionary, where keys are ticker symbols and values are the number of shares held.

python
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portfolio = {
    'AAPL': 50,
    'MSFT': 30,
    'TSLA': 20,
    'GOOGL': 10
}

3. Fetch Current Prices for Portfolio Stocks

Using yfinance, get the latest price for each stock.

python
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def get_current_prices(tickers):
    prices = {}
    for ticker in tickers:
        stock = yf.Ticker(ticker)
        prices[ticker] = stock.history(period='1d')['Close'][0]
    return prices

tickers = list(portfolio.keys())
current_prices = get_current_prices(tickers)
print(current_prices)

4. Calculate Portfolio Value and Performance

Calculate total portfolio value and individual stock contributions.

python
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portfolio_value = 0
for ticker, shares in portfolio.items():
    price = current_prices[ticker]
    value = shares * price
    portfolio_value += value
    print(f"{ticker}: {shares} shares x ${price:.2f} = ${value:.2f}")

print(f"Total Portfolio Value: ${portfolio_value:.2f}")

5. Tracking Historical Portfolio Value

To analyze portfolio performance over time, download historical prices and calculate daily portfolio value.

python
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import pandas as pd

def get_historical_prices(tickers, start_date, end_date):
    data = yf.download(tickers, start=start_date, end=end_date)['Close']
    return data

# Example dates
start_date = '2024-01-01'
end_date = '2024-05-15'

prices = get_historical_prices(tickers, start_date, end_date)

# Multiply prices by shares owned
for ticker in tickers:
    prices[ticker] *= portfolio[ticker]

# Sum across all stocks to get daily portfolio value
portfolio_history = prices.sum(axis=1)
print(portfolio_history.tail())

6. Visualizing Portfolio Performance

Use matplotlib or plotly to visualize portfolio value over time.

python
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import matplotlib.pyplot as plt

plt.figure(figsize=(12,6))
plt.plot(portfolio_history.index, portfolio_history.values)
plt.title("Portfolio Value Over Time")
plt.xlabel("Date")
plt.ylabel("Portfolio Value ($)")
plt.grid(True)
plt.show()

7. Monitoring Portfolio Metrics

You can compute key portfolio metrics such as daily returns, cumulative returns, and volatility.

python
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# Daily returns
daily_returns = portfolio_history.pct_change().dropna()

# Cumulative returns
cumulative_returns = (1 + daily_returns).cumprod() - 1

# Volatility (standard deviation of daily returns)
volatility = daily_returns.std() * (252 ** 0.5)  # Annualized volatility

print(f"Annualized Volatility: {volatility:.2%}")

plt.figure(figsize=(12,6))
plt.plot(cumulative_returns.index, cumulative_returns.values)
plt.title("Cumulative Returns")
plt.xlabel("Date")
plt.ylabel("Cumulative Return")
plt.grid(True)
plt.show()

8. Automating with Alerts

You can build alerting logic, e.g., if portfolio value drops by a certain percentage, send an email or notification.

python
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threshold_drop = 0.05  # 5% drop
if daily_returns[-1] < -threshold_drop:
    print("Alert: Portfolio value dropped more than 5% today!")
    # Integrate with email or messaging APIs here

Summary

By combining these steps, you can build a Python script or application that:

• Retrieves real-time or historical stock data

• Calculates portfolio value and performance

• Visualizes portfolio growth or losses

• Tracks important metrics like returns and volatility

• Sends alerts based on your custom thresholds

Python libraries such as yfinance, pandas, matplotlib, and numpy are essential tools for this task.

If you want, I can provide a complete Python script with all these functionalities combined into one file. Would you like that?