Forge FateVerify the Research, Not the Price: Where LLMs Belong in Quant Research
Distinguish document transformation, validation support, and decision boundaries in LLM-assisted quant research. Check drafts against primary sources and raw data.
Quant & Data Research
Methods for evaluating investment ideas through data, backtesting, costs, risk, and reproducible research records.
The Risk of Mistaking Chance Results for Skill in Financial Machine Learning—and How to Challenge Them
Examine noise, changing markets, repeated searches, leakage, costs, and selection bias, and define ways to challenge promising financial ML results.
Part 8. Are More Complex Models Better? Comparing Logistic Regression and Tree Models on Equal Terms
Design a fair comparison of momentum, logistic regression, trees, and forests. Control model selection and distinguish classification from economic performance.
Part 7. First Machine Learning Model: Testing the Probability of Gains in the Next Rebalancing Period with Logistic Regression
Design features, labels, time splits, and monthly retraining for a logistic-regression baseline, keeping predictive accuracy separate from investment returns.
What to Look at Before Returns: The Path of Losses and Trading Costs
Use hypothetical examples to examine loss paths and trading costs alongside returns, and interpret volatility, drawdown, Sharpe ratios, and turnover together.
What Makes a Good Backtest Different? Designing the Validation Process Before Looking at Returns
Plan hypotheses, baselines, chronological splits, walk-forward validation, and protected test periods. Examine selection bias and the role of research records.
The First Baseline: Building a Simple Momentum Strategy
Before comparing AI and ML models, fix a simple long/cash momentum baseline that will not change after you see the results. How to build the 12-month signal, monthly rebalancing, equal weighting, and trading-cost scenarios as reproducible Python code.
Could You Have Known That Data at the Time? Data Traps to Check Before Running AI Investing Experiments
Audit historical data availability through adjusted prices, delistings, filing dates, revisions, and time zones, with an example of delayed closing-price signals.
Rules, Machine Learning, Deep Learning, and LLMs: Four Ways to Answer the Same Investment Question
Compare the inputs and outputs of rules, machine learning, deep learning, and LLMs. Define the task, data, and evaluation criteria before selecting a tool.
Can AI Really Help with Quant Investing? I’m Putting It to the Test
Distinguish AI's roles in prediction, research, and automation, and plan separate tests of productivity and investment performance while accounting for key risks.