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Documentation Index

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Portfolio Optimization Examples

Build optimal portfolios with mean-variance and risk parity.

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Mean-Variance Optimization

import requests
import numpy as np

# Historical returns
returns = [0.10, 0.12, 0.08, 0.15]

# Covariance matrix
cov = [
    [0.04, 0.01, 0.02, 0.015],
    [0.01, 0.09, 0.015, 0.02],
    [0.02, 0.015, 0.06, 0.01],
    [0.015, 0.02, 0.01, 0.16]
]

response = requests.post(
    "https://api.fincept.in/quantlib/portfolio/mean-variance",
    headers={"X-API-Key": API_KEY},
    json={
        "expected_returns": returns,
        "covariance_matrix": cov,
        "objective": "max_sharpe",
        "risk_free_rate": 0.02
    }
)

result = response.json()
print("Optimal Weights:", result['data']['weights'])
print("Sharpe Ratio:", result['data']['sharpe_ratio'])
Cost: 5 credits

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Risk Parity

response = requests.post(
    "https://api.fincept.in/quantlib/portfolio/risk-parity",
    headers={"X-API-Key": API_KEY},
    json={
        "covariance_matrix": cov,
        "method": "equal_risk_contribution"
    }
)
Cost: 5 credits More examples →