Python vs R for quantitative analysis -- 2026 edition
The eternal debate continues. With the rise of Polars and the improvements in pandas 2.x, Python's data handling has gotten significantly faster.
But R still has unmatched statistical packages. What's everyone using for their AlphaNova submissions?
28 Replies
Has anyone tried using attention mechanisms for this? The temporal attention weights could tell you which historical periods are most relevant.
The biggest mistake I see newcomers make: optimizing for the wrong metric. Sharpe != best trading strategy. Consider Calmar, Sortino, and max drawdown.
Great analysis! I've been using a similar approach with rolling z-scores and it's been working well for mean reversion signals.
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.
Great analysis! I've been using a similar approach with rolling z-scores and it's been working well for mean reversion signals.
For factor models, I'd strongly recommend the Fama-French 5-factor model as a starting point. It captures most systematic risk.
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
Anyone else noticing that momentum factors have been working particularly well in the last month of competition data?
I ran a quick backtest on this idea and got a Sharpe of about 1.2 before costs. Not bad for a simple strategy.
For those wondering: yes, you can use external Python packages in submissions, but they must be in the approved list. No custom C extensions.
Great discussion! This is why I love this community - knowledge sharing makes everyone better.
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.