A systematic options trading system that finds, scores, and manages trades using data — not gut feeling.
When a company reports earnings that beat analyst expectations, the stock price doesn't adjust all at once. It continues to drift upward for 60-90 days after the announcement. This is called Post-Earnings Announcement Drift (PEAD) — one of the most studied and persistent patterns in finance.
Why does this happen?
The system identifies stocks with strong positive estimate revisions before earnings, then enters options positions during the optimal window before the drift is fully priced in.
Our backtest (387 events, 50 large-cap stocks, 2 years): Big earnings beats drifted +3.3% over 30 days with 60% win rate.
Every stock gets a conviction score from 0-100 based on 7 signals. Higher score = more signals agree the stock will move in the predicted direction.
The signals we weigh:
The scoring model has demonstrated predictive power: trades scoring 60+ average +20.7% returns with a 74% win rate, compared to +4.0% and 52% win rate for scores below 40.
Before scoring, every stock must pass quality filters that remove penny stocks, high-risk sectors (biotech), and illiquid options. For bullish trades, we require positive estimate revisions, analyst upside, bullish options flow, and reasonably priced options.
These filters exist to ensure we only trade stocks where the data is reliable and the options market is liquid enough to execute.
How much to bet is as important as what to bet on. We use a conservative variant of the Kelly Criterion — a mathematical formula for the optimal bet size to maximize long-run growth.
Higher conviction scores get larger positions. Market conditions also adjust sizing — in volatile or falling markets, positions are reduced. Hard limits prevent over-concentration in any single position, sector, or the portfolio overall.
Every exit is rule-based — no discretion, no emotion.
The system runs 14+ paper trading accounts in parallel — each with $50K virtual money and different rules. Same universe of candidates, different entry criteria and exit rules. By comparing their results head-to-head, we learn which approach actually compounds.
Some strategies are aggressive (trade everything), some are selective (only high-conviction setups). Some take profits early, some let winners run. Some require multiple signals to agree before entering. The track record page shows all of them — wins, losses, and reasoning — in real time.
Three levels of AI assist the system:
The AI layer supplements the quantitative scoring — it doesn't replace it. All trade decisions are ultimately rule-based.