week 3 Finance with Python - access the market data

(Comments)

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

Comments

Riddles

22nd Jul- 2020, by: Editor in Chief
524 Shares 4 Comments
Generic placeholder image
20 Oct- 2019, by: Editor in Chief
524 Shares 4 Comments
Generic placeholder image
20Aug- 2019, by: Editor in Chief
524 Shares 4 Comments
10Aug- 2019, by: Editor in Chief
424 Shares 4 Comments
Generic placeholder image
10Aug- 2015, by: Editor in Chief
424 Shares 4 Comments

More News  »

Dignity wrapped in Charity

Recent news
4 weeks, 1 day ago

A reflection of using kanban flow and being minimalist

Recent news

Today is the consecutive day I want to use and be consistent with the Kanban flow! It seems it's perfect to limit my parallel and easily distractedness. 

read more
1 month, 1 week ago

Morning issue with car and my kind of music

Recent news
1 month, 1 week ago

Podcast Bapak Dimas 2 - pindahan rumah

Recent news

Vlog kali ini adalah terkait pindahan rumah!

read more
1 month, 2 weeks ago

Podcast Bapak Dimas - Bapaknya Jozio dan Kaziu - ep 1

Recent news

Seperti yang saya cerita kan sebelumnya, berikut adalah catatan pribadi VLOG kita! Bapak Dimas

read more
1 month, 2 weeks ago

Happy new year 2024 and thank you 2023!

Recent news

As the new year starts, I want to revisit what has happened in 2023. 

read more
1 month, 2 weeks ago

Some notes about python and Zen of Python

Recent news

Explore Python syntax

Python is a flexible programming language used in a wide range of fields, including software development, machine learning, and data analysis. Python is one of the most popular programming languages for data professionals, so getting familiar with its fundamental syntax and semantics will be useful for your future career. In this reading, you will learn about Python’s syntax and semantics, as well as where to find resources to further your learning.

read more
2 months, 3 weeks ago

Understanding Tier 1 Capital, common equeity tier 1 Capital, and risk weighted asset through asking the right question

Recent news
3 months, 1 week ago

More News »

Generic placeholder image

Collaboratively administrate empowered markets via plug-and-play networks. Dynamically procrastinate B2C users after installed base benefits. Dramatically visualize customer directed convergence without