week 3 Finance with Python - access the market data

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Week 3: Time Series Analysis and Market Data

Learning Material:

  • Day 1: Time Series Data

    • Objective: Learn about time series data and its importance in finance.
    • Topics: Time series data, components of time series, and financial applications.
  • Day 2: Working with Time Series Data in Python

    • Objective: Learn how to manipulate and analyze time series data in Python.
    • Topics: Time series data handling, visualization, and Python libraries like Pandas.
    • Code Example: Basic time series analysis in Python.
python
import pandas as pd import matplotlib.pyplot as plt # Load time series data data = pd.read_csv("time_series_data.csv", parse_dates=["Date"], index_col="Date") # Plot time series plt.figure(figsize=(10, 6)) plt.plot(data.index, data["Price"]) plt.xlabel("Date") plt.ylabel("Price") plt.title("Stock Price Time Series") plt.show()
  • Day 3: Market Data Sources

    • Objective: Explore different sources of financial market data.
    • Topics: Data sources, APIs, and data providers.
  • Day 4: Accessing Market Data with Python

    • Objective: Learn how to retrieve and use market data with Python libraries like yfinance or Alpha Vantage.
    • Topics: API usage, data retrieval, and Python libraries for market data.
    • Code Example: Accessing market data using yfinance.
    • here is example from data in Bank Republik Indonesia 
    # Import necessary libraries
    import yfinance as yf # For fetching stock data
    from sklearn.linear_model import LinearRegression # For creating a linear regression model
    import pandas as pd # For data manipulation and analysis
    import numpy as np # For numerical operations
    import matplotlib.pyplot as plt # For data visualization

    # Define the stock symbol and fetch historical data
    stock_symbol = 'BBRI.JK' # Example stock symbol
    stock = yf.Ticker(stock_symbol) # Creating a Ticker object for the stock symbol
    historical_data = stock.history(period="5y") # Fetching historical data for the past 5 years

    # Considering only the 'Close' price for modeling
    data = historical_data[['Close']] # Extracting 'Close' prices from the historical data
    data.reset_index(level=0, inplace=True) # Resetting the index and converting date to a column
    data.columns = ['Date', 'Price'] # Renaming columns for clarity

    # Creating features (using only one feature for simplicity - the index)
    data['Index'] = np.arange(len(data)) # Adding an 'Index' column to the DataFrame

    # Splitting the data into training and testing sets
    split_index = int(0.8 * len(data)) # Determining the split index for training and testing
    train_data = data.iloc[:split_index] # Creating the training data
    test_data = data.iloc[split_index:] # Creating the testing data

    # Creating and fitting the linear regression model
    model = LinearRegression() # Creating a Linear Regression model
    model.fit(train_data[['Index']], train_data['Price']) # Fitting the model with training data

    # Making predictions using the model
    test_data['Predictions'] = model.predict(test_data[['Index']]) # Making predictions for the test data

    # Visualizing the predictions and actual values
    plt.figure(figsize=(12, 6)) # Creating a plot figure
    plt.title('Stock Price Prediction') # Setting the title of the plot
    plt.xlabel('Index') # Labeling the x-axis
    plt.ylabel('Stock Price') # Labeling the y-axis
    plt.plot(train_data['Date'], train_data['Price'], label='Training Data') # Plotting training data
    plt.plot(test_data['Date'], test_data['Price'], label='Actual Stock Price') # Plotting actual stock prices
    plt.plot(test_data['Date'], test_data['Predictions'], label='Predicted Stock Price', linestyle='dashed') # Plotting predicted stock prices
    plt.legend() # Adding a legend to the plot
    plt.show() # Displaying the plot
python
import yfinance as yf # Define the stock symbol and date range stock_symbol = "AAPL" start_date = "2020-01-01" end_date = "2022-12-31" # Download historical data data = yf.download(stock_symbol, start=start_date, end=end_date)
  • Day 5: Exercise
    • Objective: Retrieve historical stock prices, analyze market data, and visualize price trends using Python.

Note: Week 3 focuses on time series analysis and accessing financial market data with Python.

Current rating: 5

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