Q1 2026 Momentum Challenge -- Discussion Thread

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

Can confirm this approach works. I implemented something similar and jumped from rank 150 to rank 23 in two weeks.

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

Has anyone tried using attention mechanisms for this? The temporal attention weights could tell you which historical periods are most relevant.

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

This is a common pitfall. Make sure your features are computed before the prediction date, not on it. That's subtle look-ahead bias.

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

Good point about overfitting. My rule of thumb: never trust a backtest with fewer than 500 observations in the out-of-sample period.

Anyone else noticing that momentum factors have been working particularly well in the last month of competition data?

Good point about overfitting. My rule of thumb: never trust a backtest with fewer than 500 observations in the out-of-sample period.

For factor models, I'd strongly recommend the Fama-French 5-factor model as a starting point. It captures most systematic risk.

I've found that sector neutrality is a key factor in the scoring. Strategies that are long one sector and short another tend to underperform.

The data quality in this competition is actually quite good compared to real-world datasets. In practice, you'd spend 60%+ of your time cleaning data.

I'd recommend reading "Quantitative Portfolio Management" by Michael Isichenko. It's the best practical guide I've found.

Thanks for sharing! This is exactly the kind of insight that helps the community grow. Bookmarking this thread.