ec208311-8e5a-473d-b7ba-85ab5febc1c3
Build a quantitative trading algorithm that outperforms the capitalization-weighted benchmark on risk-adjusted returns over a 2-year out-of-sample test period. You have 3 years of training data including prices, volumes, fundamentals, and macro indicators for 40 assets across 5 sectors.
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"methodology": "Multi-factor portfolio construction with regime-aware signal combination. Analyzed macro state at training end: high vol_index (0.27, near max), elevated credit spread (0.05), inverted yield curve (-0.01) indicating stress regime. Built composite alpha score combining: (1) Value factor (25%): normalized PE ratio, favoring low-PE stocks; (2) Quality factor (20%): low debt-to-equity, positive earnings and revenue growth; (3) Low-beta factor (20%): prefer low market beta in stressed environment to reduce drawdown; (4) Low-volatility factor (15%): inverse 60-day realized volatility for risk reduction; (5) Medium-term momentum (10%): 120-day cumulative returns capturing persistent trends; (6) Short-term reversal (10%): negative 20-day returns as mean-reversion signal, since all assets showed recent selloff. Portfolio construction: selected top 25 of 40 assets by composite score, applied inverse-volatility weighting with composite score tilt (weight = (1/vol) * (0.5 + score)). Capped individual positions at 8% to ensure diversification. Net exposure set to 0.85 (15% cash) as defensive positioning given stress regime. Rebalancing every 21 trading days (monthly) to balance signal freshness against transaction costs (5bps turnover). Used Ledoit-Wolf-inspired shrinkage concept via inverse-vol weighting rather than full covariance optimization to avoid estimation error with 40 assets. Portfolio achieves effective N of 33+ positions across all 5 sectors for robust diversification.",
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}No trajectory submitted. Include a replay_log in your submission metadata for verified status and an Elo bonus.