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Basic Time Series Analysis with Financial Data

Preliminary Task: Draft Your Evaluation Prompt

Goal

  • Prepare for the evaluation of your final work.

Task

  • Draft a prompt that outlines the key points and questions you would like ChatGPT to evaluate in your final work. This will help in assessing the quality and depth of your analysis. ChatGPT should give your a grade for this assignment.

Task 1: Introduction to Time Series and Financial Data

Goal

  • Understand what time series data is and how it is relevant in the financial sector.

Python Task

  • Research and write Python comments in a VS Code file summarizing what time series data is and its applications in finance.

Hint

  • Look into the definition of time series data and its importance in financial analysis.

Task 2: Data Collection and Importing from Yahoo Finance

Goal

  • Learn how to collect and import financial time series data.

Data Source

  • Yahoo Finance

Python Task

  • Use Python libraries like yfinance to collect stock price data for a company of your choice from Yahoo Finance. Import the data into a Python file in VS Code.

Hint

  • Use the yfinance library to fetch data. The function yf.download('AAPL') can be used to download Apple's stock data, for example.

Task 3: Data Visualization

Goal

  • Visualize the collected time series data.

Python Task

  • Use Python libraries like matplotlib or seaborn to create line plots, candlestick charts, and other visualizations for the collected data.

Hint

  • Use plt.plot() for line plots and sns.candlestick_ohlc() for candlestick charts.

Task 4: Data Manipulation with Returns and Lagged Returns

Goal

  • Learn how to manipulate time series data and generate returns and lagged returns.

Python Task

  • Perform tasks like resampling, calculating moving averages, and handling missing values. Generate returns and 5 lagged returns on the collected data.

Hint

  • Use df.pct_change() to calculate returns. For lagged returns, you can use df['Lagged_Return'] = df['Return'].shift().

Task 5: Understanding Seasonality with Housing Price Data

Goal

  • Understand the concept of seasonality in time series data.

Data Source

Python Task

  • Use the Freddie Mac House Price Index data to analyze seasonality in housing prices.

Hint

  • Use seasonal_decompose from statsmodels.tsa.seasonal to decompose the time series into trend, seasonal, and residual components.

Task 6: Machine Learning Projections with Implications

Goal

  • Use a simple machine learning model to make projections using lagged returns and think about the implications.

Python Task

  • Implement a machine learning model like Linear Regression using libraries like scikit-learn to forecast future returns based on the 5 lagged returns you've generated. Reflect on the implications of your analysis and how it connects to any financial knowledge you may have.

Hint

  • Use LinearRegression() from sklearn.linear_model and fit the model using .fit(X, y), where X is the matrix of lagged returns and y is the return.