https://github.com/s1dewalker/portfolio_analysis
MPT in Python | Efficient Frontier modeling, Fama French 3 Factor Analysis, Monte Carlo Simulations and Portfolio Risk Analysis (Sharpe, VaR)
https://github.com/s1dewalker/portfolio_analysis
efficient-frontier equity financial-research monte-carlo-simulation numpy pandas portfolio-analysis portfolio-management portfolio-optimization python quantitative-analysis quantitative-finance regression regression-models risk risk-analysis risk-management risk-models value-at-risk volatility
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MPT in Python | Efficient Frontier modeling, Fama French 3 Factor Analysis, Monte Carlo Simulations and Portfolio Risk Analysis (Sharpe, VaR)
- Host: GitHub
- URL: https://github.com/s1dewalker/portfolio_analysis
- Owner: s1dewalker
- Created: 2024-10-20T12:51:01.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-02-04T03:36:24.000Z (8 months ago)
- Last Synced: 2025-02-04T04:27:36.598Z (8 months ago)
- Topics: efficient-frontier, equity, financial-research, monte-carlo-simulation, numpy, pandas, portfolio-analysis, portfolio-management, portfolio-optimization, python, quantitative-analysis, quantitative-finance, regression, regression-models, risk, risk-analysis, risk-management, risk-models, value-at-risk, volatility
- Language: Jupyter Notebook
- Homepage:
- Size: 4.83 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
Inspired by many **specialized ETFs** (Exchange-Traded Funds: An investment vehicle that pools a group of securities into a fund. It can be traded like an individual stock on an exchange). Creating a basket of stocks with weights for **specific investment strategies or weighting criteria beyond traditional market-capitalization indexes and treating them all as a unit.**
A portfolio, if selected properly, is less vulnerable to extreme highs and lows, and provides the benefits of **diversification**.
This project offers optimization and modeling tools for investment strategies. Leveraging Efficient Frontier modeling, Monte Carlo simulation, risk metrics (Sharpe ratio, VaR), and Fama French factor models w/ python libraries like pandas, yfinance, and PyPortfolioOpt.
### [View Risk Analysis w/ 15 risk metrics in Python](https://github.com/s1dewalker/Portfolio_Analysis/blob/main/py_files/RiskAnalysis.ipynb)
### [Fama French 3-Factor Modeling (w/ model validation) of NYSE stocks in Python](https://github.com/s1dewalker/Portfolio_Analysis/blob/main/py_files/Multi_Factor_Analysis3.ipynb)
### [Meet Min Volatility and Max Sharpe Ratio on the EfficientFrontier of NSE stocks](https://github.com/s1dewalker/Portfolio_Analysis/blob/main/py_files/ETFs.ipynb)
# 1. Data Extraction and Portfolio Construction
Utilized `yfinance` for data retrieval of NSE stocks, obtaining historical price data for a specified date range.
'df' is the DataFrame that contains daily prices of stocks.
'weights' is the array that contains portfolio weights.
```python
returns = df.pct_change()
returns.dropna(inplace = True)
```
Portfolio construction w/ stock returns
```python
returns_pf = returns.dot(weights)
```
Portfolio construction w/ stock values
```python
pf_AUM = df.dot(weights)
```
# 2. Risk Analysis - Some Basic Portfolio Risk Metrics
## Annualized return
#### Q. Why annualize returns?
- To account for compounding effect
- To compare portfolios with different time periods
- Average return can be deceiving (example: if portfolio loses all its value in the end the average may be > 0%, but final return is 0%)
Annualizing returns:
```python
total_return = (pf_AUM[-1] - pf_AUM[0]) / pf_AUM[0]
annualized_return = ((1 + total_return) * * (12 / months)) - 1
```
## Annualized Volatility
```python
pf_returns = pf_AUM.pct_change()
pf_vol = pf_returns.std()
pf_vol = pf_vol * np.sqrt(250)
```
#### Q. Why multiply with sqrt(250)
250: trading days in a year (annual)
sqrt: variance annualized = 250 * variance daily.
Therefore, standard deviation annualized = sqrt(250) * standard deviation daily.
## Risk-adjusted return | Sharpe Ratio | Efficiency of risk taking
```python
sharpe_ratio = ((annualized_return - rfr) / pf_vol)
```
## Portfolio Variance and Volatility
```python
cov_matrix = (returns.cov())*250
port_variance = np.dot(weights.T, np.dot(cov_matrix, weights))
port_standard_dev = np.sqrt(port_variance)
```
[View Portfolio Variance derivation](https://github.com/s1dewalker/Portfolio_Analysis/blob/main/Portfolio_variance.pdf)
#### Q. Why annualizing covariance?
so that we calculate the annualized volatility later.
#### Q. Why portfolio variance formula instead of standard deviation?
**Precision**: As stocks are **correlated** to each other, we need to account for **correlations** b/w stocks. This gives more **precision** in measuring volatility.
It can be forward looking if covariance matrix is estimated or forecasted.
#### Q. Benefits of historical method?
Historical data method can include rebalancing.
## Skewness
Skewness measures the asymmetry of the distribution of data around its mean.
```python
pf_returns.skew()
```
Risk management: Investors should seek positive skew as in the long run, few positive bets should create a positive expectancy
## Kurtosis
Kurtosis measures the "tailedness" of the data distribution, indicating the presence of outliers.
```python
pf_returns.kurtosis()
```
k>3: FAT (leptokurtic) | high risk-high reward
# 3. Risk Analysis - Value at Risk (VaR)
Finding var95 and cvar95:
```python
var = np.percentile(returns_pf, 5)
cvar = returns_pf [returns_pf <= var].mean()
```
Monte Carlo Simulation:
Monte Carlo VaR:
### [View Complete Risk Analysis](https://github.com/s1dewalker/Portfolio_Analysis/blob/main/py_files/RiskAnalysis.ipynb) w/ 15 Portfolio Risk metrics including 5 VaR metrics
# 4. Factor Investing
**The Fama-French 3 factor model** tells us **what drives portfolio returns** and **quantifies their contributions**.
```python
FamaFrench_model = smf.ols(formula='Portfolio_Excess ~ Market_Excess + SMB + HML', data=FamaFrenchData_final)
```
Risk management:
- Identifies exposure to specific factors (size, value)
- Optimize portfolio by adjusting exposures
- Evaluate performance
#### Q. Why not use UFO sightings as a factor?
because there might be a high correlation with their levels but not with their changes so correlation of % change or differences will be close to 0.
### [View Multi Factor Analysis of NYSE stocks in Python](https://github.com/s1dewalker/Portfolio_Analysis/blob/main/py_files/Multi_Factor_Analysis3.ipynb)
# 5. Portfolio Optimization | Efficient Frontier
## Optimal weights
## Efficient Frontier
**The efficient frontier is the set of portfolios that achieve the highest return for a given risk or the lowest risk for a given return, representing optimal diversification.**
[View Portfolio Optimization maths](https://github.com/s1dewalker/Portfolio_Analysis/blob/main/Portfolio_Optimization.pdf)
### [View Complete Portfolio Optimization](https://github.com/s1dewalker/Portfolio_Analysis/blob/main/py_files/ETFs.ipynb) of NSE stocks
##### Python libraries:
##### `pandas, numpy, yfinance, matplotlib, pypfopt`
##### [LinkedIn](https://www.linkedin.com/in/sujay-bhaumik-d12/) | s1dewalker23@gmail.com | [Discussions](https://github.com/s1dewalker/Portfolio_Analysis/discussions/1) | [Research Works](https://github.com/s1dewalker/Research-Works)