All work Quant / Trading

NQ Futures Quant Suite

Two-sleeve NQ systems, backtested across 26 years

Backtest only

A systematic NQ futures system built from two complementary sleeves. One is a time-series momentum "TREND" sleeve; the other is a mean-reversion dip-catcher. It ships with a daily signal tool and a full validation stack, and I backtested it across 26 years of daily bars. Backtest only, with no real capital deployed.

~21.8%Blended CAGR (backtest)
~70%Mean-reversion win rate
26 yrsBacktest span (2000–2026)
1,800Config frontier tested

The problem

Discretionary futures trading is inconsistent and hard to sustain. A single strategy also tends to bleed in the regimes it wasn't designed for. Trend systems chop sideways in range-bound markets, and mean-reversion systems get run over in strong trends. The harder problem is trust. Most "systems" are never validated rigorously enough to believe their backtest.

What I built

A working two-sleeve NQ system with a from-scratch backtest engine and a ready-to-trade daily signal tool. I validated it across a 26-year (2000–2026) cost-stressed backtest at ~21.8% blended CAGR. It stays backtest only, with no real capital deployed.

How it works

Two sleeves run side by side so each covers the other's weak regime. The TREND sleeve is time-series momentum on the NQ, sized at 5x, posting 19.3% CAGR against a −43% max drawdown at a ~40% win rate. That's typical of trend-following, where a few large wins carry many small losses. The DIP-CATCHER mean-reversion sleeve buys oversold pullbacks only above the 200-day SMA, triggering on RSI(2) ≤ 10, three down days, or low-IBS dips, sized at 2–3x MNQ. It runs a high-hit-rate profile around 70% win and 8–16% CAGR. Blended, the two run at ~21.8% CAGR. The whole thing sits on a pure-stdlib backtest engine in Python with NumPy, Pandas, and PyArrow. I validated it four ways: Monte-Carlo resampling, walk-forward testing, cost-stress, and an 1,800-config parameter frontier to confirm the edge isn't a single lucky setting.

~21.8% blended CAGR across a 26-year, cost-stressed backtest

Highlights

  • TREND sleeve: 19.3% CAGR / −43% max drawdown, ~40% win at 5x
  • DIP-CATCHER mean reversion: ~70% win, 8–16% CAGR at 2–3x MNQ
  • Entries on RSI(2) ≤ 10, three down days, or low-IBS dips above the 200-SMA
  • Backtested 2000–2026, 26 years of daily NQ bars, net of costs
  • Validated with Monte-Carlo, walk-forward, cost-stress, and an 1,800-config frontier
  • Pure-stdlib backtest engine plus a ready-to-trade daily signal tool
PythonNumPyPandasPyArrow
Want one of these?

Let's build yours.

Tell me what you're trying to ship — you'll get a scoped plan and a straight answer.