Academic finance papers turned into executable strategies by PhDs and researchers. Full Python code, institutional backtests, overfitting detection.
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def compute_bab_portfolio(returns, market_cap, window=252):
"""
Betting Against Beta, Frazzini & Pedersen (2014), JFE.
Constructs a dollar-neutral BAB factor portfolio.
"""
betas = returns.rolling(window).cov(returns.mean(axis=1)) \
/ returns.mean(axis=1).rolling(window).var()
beta_rank = betas.rank(axis=1, pct=True)
w_low = (beta_rank <= 0.3).astype(float)
w_high = (beta_rank >= 0.7).astype(float)
...
Large language models are powerful. But when it comes to quantitative backtesting, they introduce biases and skip the verification steps that actually matter.
Ask an LLM for a moving average crossover and it'll pick 50/200. Not because it's optimal, but because it's the most common in training data. No statistical justification. No sensitivity analysis. Just heuristics dressed as research.
AI-generated backtests never run SPA tests, PBO, or walk-forward validation. The result: strategies that look perfect in-sample and collapse out-of-sample. A 2.5 Sharpe that's really a 0.3.
LLMs approximate academic papers instead of implementing them. They miss survivorship bias adjustments, ignore look-ahead bias in feature construction, skip transaction cost modeling, and use simplified factor definitions. The result looks right but is fundamentally flawed.
An LLM has no way to verify if its backtest matches the paper's reported results. It can't compare its Sharpe to Table 3 of Frazzini & Pedersen. It generates code, but it never verifies claims against the source.
Every model on QuantLab is verified against the original paper's results. Every backtest is checked for survivorship bias, look-ahead bias, and overfitting. Zero shortcuts. Zero unverified claims.