🐘 Stock Market Elephant

The Graveyard Series

We built a futures trading system with an overfitting lie-detector attached, then threw every popular trading idea at it — 1,900 real NASDAQ-futures sessions, honest train/test splits, no mercy. Six parts: what survived, what died, and why your backtest is probably lying to you.

Prologue · Jul 21, 2026

The full story: why we built a lie detector before a trading bot

The motivation, the rules we set before starting, and the process honestly told — including the adversarial review of our own code and the 90% kill rate. Start here.

Part 1 · Jul 28, 2026

Why most backtests lie (and the three tests that catch them)

PBO, deflated Sharpe, and combinatorial cross-validation explained for prop traders.

Part 2 · Aug 4, 2026

Ten ideas entered. One survived.

The volatility filter that passed every gate, and why it works.

Part 3 · Aug 11, 2026

The graveyard: eight popular ideas our data killed

Retest entries, volume confirmation, trend filters, tight stops — tested on 1,900 real sessions.

Part 4 · Aug 18, 2026

Win rate is a vanity metric

We swept every profit target. Higher win rates made less money. Here's the math.

Part 5 · Aug 25, 2026

What position sizing can and cannot do

Sizing can't help you pass an eval. It can transform a funded account. Monte Carlo proof.

Part 6 · Sep 1, 2026

The plateau test — and the only clean data left on Earth

Parameter stability heatmaps, regime-switching, and why your own research contaminates your backtest.

Bonus · Sep 8, 2026

We bought a vendor ORB strategy and audited the trade log

76% win rate, beautiful curve, $0 commission column, and 59% of trades filled inside a single candle. Anatomy of a strategy that sells better than it trades.

Results gallery · Sep 9, 2026

The numbers, in pictures

The whole program in six honest charts — equity, drawdowns, the win-rate trap visualized, the parameter-stability heatmap, and the out-of-sample proof.

Educational content only — not financial advice. Futures trading involves substantial risk of loss.