Alternative data sources for quant models

For those wondering: yes, you can use external Python packages in submissions, but they must be in the approved list. No custom C extensions.

One more thing: the scoring engine uses a held-out test period that you never see. So your validation score is the best you can do.

import numpy as np

from scipy import optimize


def max_sharpe_portfolio(returns, rf=0.0):
n = returns.shape[1]
init_w = np.ones(n) / n
bounds = [(0.0, 0.1)] * n
constraints = {'type': 'eq', 'fun': lambda w: np.sum(w) - 1.0}
result = optimize.minimize(
lambda w: -(np.mean(returns @ w) - rf) / np.std(returns @ w),
init_w, bounds=bounds, constraints=constraints
)
return result.x

One thing to watch out for: survivorship bias in the training data. Make sure you include delisted securities.

Turnover control is crucial. My best performing model has a turnover of only 8% daily. High turnover strategies rarely survive transaction costs.

I disagree about the GARCH approach. In my experience, realized volatility estimators (like the Rogers-Satchell estimator) outperform parametric models.

Interesting thread! I've been exploring reinforcement learning for portfolio allocation. The challenge is defining the right reward function.