I will statistically backtest your trading strategy against a random null

Most backtests only ask "did these rules make money on this history." That bar is far too low. Random entries clear it constantly, and so do rules that quietly peek at future data or just ride the market's drift. I test strategies the way quant researchers do: against a matched random null.

What you get:

- Your rules coded into a reproducible Python backtest

- Monte Carlo permutation testing: does your strategy beat random entries with the same trade frequency and holding time? (the Timothy Masters method)

- Out-of-sample split and parameter sweep, so you know it is not one magic setting

- Realistic commissions and slippage modeled, where most retail edges quietly die

- For prop traders: Monte Carlo of your stats against your firm's trailing-drawdown rules, turned into a pass or bust probability

- A plain-English written report of everything above

Fair warning: most strategies fail this test. That is the value. You find out for a few hundred dollars instead of a few blown accounts. I report what the statistics say, not what you hope to hear. This is research and education, not financial advice.