Portfolio Optimization
Implement Modern Portfolio Theory — construct the efficient frontier, find the optimal portfolio, and understand the risk-return tradeoff.
Portfolio Optimization
python • Step 3
Implement Modern Portfolio Theory — construct the efficient frontier, find the optimal portfolio, and understand the risk-return tradeoff.
Learning Objectives
- →Calculate portfolio expected return and volatility
- →Generate the efficient frontier with random portfolios
- →Find the maximum Sharpe ratio portfolio
- →Implement minimum variance portfolio
Dataset
View on KaggleDaily OHLCV for 29 of 30 DJIA components, 2006-01-01 → 2018-01-01.
*bleep* Load the dataset in the browser terminal:
from pyodide.http import open_url
import pandas as pd
df = pd.read_csv(open_url("/datasets/all_stocks_2006-01-01_to_2018-01-01.csv"))
print(df.shape)
print(df.columns.tolist()) Running locally? The file inside the Kaggle download is already named all_stocks_2006-01-01_to_2018-01-01.csv — drop it into the notebook's working directory and the code above runs unchanged (swap open_url(...) for "all_stocks_2006-01-01_to_2018-01-01.csv").
Dataset Columns
DateOpenHighLowCloseVolumeNameticker- Write your solution in the Try It Yourself editor below — matplotlib/seaborn figures and pandas tables render inline.
- Stuck? Expand a section and click ↑ Send to editor to stack its snippet into the editor, notebook-style.
- Hit ▶ Run (or ⌘↵) — once your output matches the expected result, the step is marked solved.
- Use ↺ Reset to restore the starter code at any time.
Try It Yourself
Equal-Weight Portfolio:
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