Manipulating financial data in Python

By Harry

Transcript

1. Harry Park Manipulating Financial Data in Python
2. TABLE OF CONTENTS Introduction Processing Stock Data 01 02 03 04 Using Processed Stock Data Conclusions
3. Introduction 01
4. About this Presentation How to process historical financial data using Python Example usages of processed stock data
5. Why Python for Financial Data? Python Strong scientific libraries Well maintained Rich ML libraries
6. Useful Python Libraries for Finance “A fast, powerful, flexible and easy to use open source data analysis and manipulation tool” Pandas - read_csv() - DataFrame - joining and slicing dataframes
7. Useful Python Libraries for Finance NumPy “Powerful N-Dimensional Arrays” “Numerical Computing Tools” matplotlib/ seaborn yfinance “Offers a reliable, threaded, and Pythonic way to download historical market data from Yahoo! finance” Python Visualization Libraries
8. Processing Stock Data 02
9. Stock Data in the Real World .csv Comma Separated Values
10. Stock Data in the Real World
11. Stock Data in the Real World pandas.read_csv()
12. Combining Multiple Stock Data Find Important Statistical Values Performing Statistical Analysis Plotting Multiple Stocks Compare Stocks in a Intuitive Way Portfolio Optimization Find the Best Proportions of Stocks to Own
13. Combining Multiple Stock Data df.join()
14. Problem #1. Trading Days df.dropna()
15. Problem #1. Trading Days Step 1 Create an empty DataFrame with the date range of Interest dates = pd.date_ragne(start_date, end_date) df = pd.DataFrame(index = dates) Step 2 Join it with the DataFrame of a stock dfAPPL = pd.read_csv(“apple.csv”) df = df.join(dfAPPL) Step 3 Drop no-trade dates df.dropna(subset=[column_name])
16. Problem #2. Incomplete Data What if a stock is no longer traded? What if a new stock share began trading recently? JAVA TSLA
17. Problem #2. Incomplete Data df.fillna(method = ‘ffill’) Time Price
18. Problem #2. Incomplete Data df.fillna(method = ‘bfill’) Time Price
19. Problem #2. Incomplete Data Forward Fill before Backward Fill. To minimize Peeking into Future.
20. Combining Multiple Stocks Putting It All Together Step 1 Create an empty DataFrame with the date range of your interest Step 2 Join it with the DataFrame of a stock Step 3 Drop the no-trade dates Step 4 Repeat Step 2 ~ Step 3 with remaining stocks Step 5 Fill forward missing values Step 6 Fill backward missing values
21. Using Processed Stock Data 03
22. Assumption GOOGL APPL TSLA 2000.xx.xx price 1 price 5 price 9 2000.xx.xx price 2 price 6 price 10 2000.xx.xx price 3 price 7 price 11 2000.xx.xx price 4 price 8 price 12 Combined DataFrame
23. Plotting Multiple Stocks in a Graph df / df[0] Time Time P r i c e
24. Computing Financial Statistics Cumulative Return (Current Price - Original Price) / Original Price ex) 0.03, -0.05
25. Computing Financial Statistics Sharpe Ratio (return of portfolio - risk-free rate) / std of portfolio ex) 1.43, 0.74 Simply put, a metric for risk-adjusted return
26. Portfolio Optimization What is a portfolio?
27. Portfolio Optimization “The process of selecting the best portfolio (asset allocation), out of the set of all portfolios being considered, according to some objective.”
28. Portfolio Optimization What should be the objective function to minimize? -1 * Cumulative Return -1 * Sharpe Ratio (risk-adjusted return)
29. Portfolio Optimization Find the ‘x’ to minimize f(x) = -1 * Sharpe Ratio scipy.optimize.minimize()
30. Conclusions 04
31. How to utilize some Python libraries for Finance Problems with stock data and how to solve them Example usages of processed stock data Takeaways
32. What’s Next? Computational Investing Machine Learning for Trading
33. CREDITS: This presentation template was created by Slidesgo, including icons by Flaticon, and infographics & images by Freepik. Please keep this slide for attribution. Does anyone have any questions? THANKS
34. https://pandas.pydata.org/ https://numpy.org/ https://matplotlib.org/ https://seaborn.pydata.org/ https://en.wikipedia.org/wiki/Tesla,_Inc. https://en.wikipedia.org/wiki/Sun_Microsystems https://www.portfoliovisualizer.com CS 7646 Machine Learning for Trading from Georgia Tech References