Portfolio Optimization in 10 Lines of Python

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You can tweak your investment portfolio in Python with just 10 lines. I’m gonna walk you through it, no fluff, using some cool stuff from skfolio library.

Step 1: Grab the Basics

First off, I pull in some Python tools — like a carpenter grabbing a hammer. Here’s the lineup:

import numpy as np
from plotly.io import show
from sklearn.model_selection import train_test_split
from skfolio import Population, RiskMeasure
from skfolio.datasets import load_sp500_dataset
from skfolio.optimization import InverseVolatility, MeanRisk, ObjectiveFunction
from skfolio.preprocessing import prices_to_returns

prices = load_sp500_dataset()  # S&P 500 prices, ready to roll

That last line, load_sp500_dataset()? It’s my sandbox — real stock data to mess with.

Step 2: Split It Up

I chop the data in two — part to figure things out, part to test if I’m right:

X = prices_to_returns(prices)  # Turns prices into daily gains or losses
X_train, X_test = train_test_split(X, test_size=0.3, shuffle=False)

This keeps things honest — training on one chunk, testing on another.

Step 3: Go for the Gold

I’m chasing a “Max Sharpe Ratio” portfolio — big wins, less worry. Here’s the trick:

model = MeanRisk(
    risk_measure=RiskMeasure.VARIANCE,
    objective_function=ObjectiveFunction.MAXIMIZE_RATIO,
    portfolio_params=dict(name="Max Sharpe")
)
model.fit(X_train)
print(model.weights_)  # Shows how much of each stock I’d buy

It’s like picking the best players for your team, balancing risk and reward.

Step 4: Something to Compare

I need a baseline, so I whip up an “Inverse Volatility” thing — safe, steady bets:

benchmark = InverseVolatility(portfolio_params=dict(name="Inverse Vol"))
benchmark.fit(X_train)
print(benchmark.weights_)

This is my yardstick — nothing fancy, just solid.

Step 5: See What Works

Time to test both on fresh data and check the score:

pred_model = model.predict(X_test)
pred_bench = benchmark.predict(X_test)
print("Max Sharpe score:", pred_model.annualized_sharpe_ratio)
print("Inverse Vol score:", pred_bench.annualized_sharpe_ratio)

Those scores? They tell me which one’s winning.

Step 6: Pictures Beat Words

I’m a visual guy, so I throw both portfolios together and draw some charts:

population = Population([pred_model, pred_bench])
population.plot_composition()  # What’s in the mix
fig = population.plot_cumulative_returns()  # How it grows
show(fig)

That growth line? Tells me if I’m rich or broke yet.

Step 7: Sum It Up

One last move — a quick rundown of how they did:

population.summary()

Short and sweet, all the good stuff in one go.

All in One Place

Here’s the rough-and-ready 10-line version:

import numpy as np
from plotly.io import show
from sklearn.model_selection import train_test_split
from skfolio import Population, RiskMeasure
from skfolio.datasets import load_sp500_dataset
from skfolio.optimization import InverseVolatility, MeanRisk, ObjectiveFunction
from skfolio.preprocessing import prices_to_returns

try:
    prices = load_sp500_dataset()
    X = prices_to_returns(prices)
    X_train, X_test = train_test_split(X, test_size=0.3, shuffle=False)

    # Explicitly enforce long-only and full investment
    model = MeanRisk(
        risk_measure=RiskMeasure.VARIANCE,
        objective_function=ObjectiveFunction.MAXIMIZE_RATIO,
        portfolio_params=dict(name="Max Sharpe"),
        min_weights=0.0,  # No short positions
    )
    model.fit(X_train)

    benchmark = InverseVolatility(portfolio_params=dict(name="Inverse Vol"))
    benchmark.fit(X_train)

    pred_model, pred_bench = model.predict(X_test), benchmark.predict(X_test)
    population = Population([pred_model, pred_bench])
    show(population.plot_cumulative_returns())

except Exception as e:
    print(f"An error occurred: {e}")

Look, this isn’t just for nerds. Plug in your own stocks, mess with the risk, make it yours. It’s fast, it’s sloppy in a good way, and it gets the job done.