python/EfficientFrontier_py at main · JonathanEdMoore/python · GitHub

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import numpy as np
import pandas as pd
import yfinance as yf
import matplotlib.pyplot as plt
from scipy.optimize import minimize

# -----------------------------
# User inputs
# -----------------------------
tickers = ["VT", "AVUV", "AVDV"]
start_date = "2018-01-01"
end_date = None            # None = today
risk_free_rate = 0.02      # annual risk-free rate

# -----------------------------
# Single yfinance call (NO looping)
# -----------------------------
prices = yf.download(
    tickers,
    start=start_date,
    end=end_date,
    auto_adjust=True,
    threads=False,
    group_by="ticker"
)

# Extract adjusted close
if isinstance(prices.columns, pd.MultiIndex):
    prices = prices.loc[:, (slice(None), "Close")]
    prices.columns = prices.columns.droplevel(1)
else:
    prices = prices["Close"]

# -----------------------------
# Compute returns
# -----------------------------
returns = prices.pct_change().dropna()

mean_returns = returns.mean() * 252
cov_matrix = returns.cov() * 252

# -----------------------------
# Portfolio statistics
# -----------------------------
def portfolio_performance(weights, mean_returns, cov_matrix, rf):
    ret = np.dot(weights, mean_returns)
    vol = np.sqrt(weights.T @ cov_matrix @ weights)
    sharpe = (ret - rf) / vol
    return ret, vol, sharpe

# -----------------------------
# Tangency portfolio
# -----------------------------
def negative_sharpe(weights, mean_returns, cov_matrix, rf):
    return -portfolio_performance(weights, mean_returns, cov_matrix, rf)[2]

num_assets = len(tickers)
init_weights = np.ones(num_assets) / num_assets

constraints = {"type": "eq", "fun": lambda w: np.sum(w) - 1}
bounds = tuple((0, 1) for _ in range(num_assets))

opt = minimize(
    negative_sharpe,
    init_weights,
    args=(mean_returns, cov_matrix, risk_free_rate),
    method="SLSQP",
    bounds=bounds,
    constraints=constraints
)

tangency_weights = opt.x
tangency_return, tangency_vol, tangency_sharpe = portfolio_performance(
    tangency_weights, mean_returns, cov_matrix, risk_free_rate
)

# -----------------------------
# Efficient frontier simulation
# -----------------------------
num_ports = 50_000
results = np.zeros((3, num_ports))

for i in range(num_ports):
    w = np.random.random(num_assets)
    w /= w.sum()

    ret, vol, sharpe = portfolio_performance(
        w, mean_returns, cov_matrix, risk_free_rate
    )

    results[:, i] = [vol, ret, sharpe]

# -----------------------------
# Plot
# -----------------------------
plt.figure(figsize=(10, 7))
plt.scatter(
    results[0],
    results[1],
    c=results[2],
    cmap="viridis",
    alpha=0.3
)
plt.colorbar(label="Sharpe Ratio")

plt.scatter(
    tangency_vol,
    tangency_return,
    color="red",
    marker="*",
    s=300,
    label="Tangency Portfolio"
)

plt.xlabel("Annualized Volatility")
plt.ylabel("Annualized Return")
plt.title("Efficient Frontier with Tangency Portfolio")
plt.legend()
plt.show()

# -----------------------------
# Output
# -----------------------------
weights = pd.Series(tangency_weights, index=tickers, name="Weight")

print("\nTangency Portfolio Weights")
print(weights.round(4))

print(f"\nExpected Return: {tangency_return:.2%}")
print(f"Volatility: {tangency_vol:.2%}")
print(f"Sharpe Ratio: {tangency_sharpe:.2f}")