Team formation for Q2 competitions
With team competitions coming in Q2, I'm looking for teammates!
My background:
- 5 years in systematic trading
- Strong in feature engineering and risk management
- Top 10% in Q1 competition
- Someone with deep ML experience (especially NLP)
- Someone with strong software engineering skills
41 Replies
For those wondering: yes, you can use external Python packages in submissions, but they must be in the approved list. No custom C extensions.
The competition scoring docs could definitely be clearer. I spent 2 hours debugging what turned out to be a normalization issue.
For those new to the platform: start with the tutorial competition. It has a smaller dataset and more forgiving scoring.
Turnover control is crucial. My best performing model has a turnover of only 8% daily. High turnover strategies rarely survive transaction costs.
For those new to the platform: start with the tutorial competition. It has a smaller dataset and more forgiving scoring.
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
For those wondering: yes, you can use external Python packages in submissions, but they must be in the approved list. No custom C extensions.
Can confirm this approach works. I implemented something similar and jumped from rank 150 to rank 23 in two weeks.
Anyone else noticing that momentum factors have been working particularly well in the last month of competition data?
Great discussion! This is why I love this community - knowledge sharing makes everyone better.
Great discussion! This is why I love this community - knowledge sharing makes everyone better.