We test what
traders believe.
Al Brooks price action and popular trading strategies — coded in Python, backtested on real data, results shared honestly. No holy grails. Just numbers.
Al Brooks × Python
Every Brooks concept translated into code — one at a time. What the math says, what the backtest shows, and where the discretion lives.
ICT vs. Price Action: Is Liquidity Sweep Trading a Data-Backed Edge?
A data-driven deep dive into ICT Smart Money Concepts vs. Classic Price Action using Python backtesting on BTC-USD.
Coding Al Brooks: The Mathematical Failure of the Always-In Rule in Crypto vs. Equities
What happens when you convert Al Brooks' Always-In Long and Always-In Short framework into a systematic Python engine and benchmark it across Bitcoin and the S&P 500? The data shows where the idea holds up, where it breaks down, and why market structure matters.
I Coded Al Brooks' Trading Range Detection in Python and Ran It Live on Three Markets (Dax, Dow Jones Index, and Nasdaq)
What happens when you translate Al Brooks' trading range, barbwire, and breakout rules into Python — and point them at the Nasdaq, Dow, and DAX with the same thresholds? Here's what actually worked, and what still needs work.
Backtest Autopsy
Popular strategies put under the microscope. RSI, MACD, moving averages — tested on real data. No cherry-picking.
ICT vs. Price Action: Is Liquidity Sweep Trading a Data-Backed Edge?
A data-driven deep dive into ICT Smart Money Concepts vs. Classic Price Action using Python backtesting on BTC-USD.
I Backtested the 3 Most Popular RSI Strategies. Here's the Autopsy.
RSI 70/30, RSI divergence, and RSI + 200MA — three strategies taught on thousands of blogs and YouTube channels. I ran all three on 15 years of SPY data. Two lost money. One barely broke even. Here's exactly what the numbers showed.
Does "Cut Losses Short, Let Profits Run" Actually Work?
The most repeated rule in trading — tested mechanically across 5,000 simulated trades.
Built for serious traders
No login. No signup. Just open and use.
R:R ratio, position size, dollar risk, break-even win rate. ES, NQ, DAX, BTC.
↗How much gain do you need after a loss? The math is more brutal than you think.
↗Enter your R:R — get the minimum win rate needed for a positive expectancy.
↗Win rate + avg win/loss → expected value per trade, monthly projection, profit factor.
↗500 randomised equity curves. See your best case, worst case, and ruin risk.