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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.

• 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

• 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.

• ← week 2 Finance with Python
• week 4 Finance with Python →