QuantMath

Value Proposition

A lightweight, educational Python toolkit that lets retail traders mathematically evaluate trading strategies using Expected Value (EV) and Win/Loss ratios — no complex infrastructure needed. High school students and self-taught traders get a clear "Approve / Reject" verdict on any strategy before risking real capital.

Target Customer

Retail traders and quantitative finance students (high school / early university) who want to backtest strategy ideas with simple, transparent math.

Revenue Model

Free open-source tool initially. Future upside: premium tier with multi-strategy backtesting, Monte Carlo simulation, and PDF reports.

KPIs

• GitHub stars / clones
• Number of strategies evaluated (daily active users)
• User-reported bugs resolved within 48 hours

Milestones

• 2026-06-18: First working tool — quant_math.py built and verified. EV calculator and strategy analyzer with approve/reject verdict. Sample case (40% win rate, $100 avg win, $50 avg loss) produces EV=$10.00 → Approved for Paper Trading.