KenKem Journal
Long-form articles from the KenKem build log — what worked, what failed, and the engineering in between. Educational content, not financial advice.
A self-taught quant on the difference between a result that survives variation and one that only looked good once. Why wishful thinking is expensive, what robustness testing actually stresses, why sample quality matters more than the headline number, and why not yet is usually the most honest answer.
Automation is the cheap part. A self-taught quant on what systematic trading actually requires: explicit logic, features that earn their place, structure instead of candle reading, and the parity check between research and execution that almost nobody talks about.
A self-taught quant's honest account of building a research process whose main output is the word no. Why data honesty comes before edge hunting, what an idea has to survive before it counts as evidence, and why keeping a registry of failures is a form of risk management.
I took a selective strategy, optimized it, and it started trading far more often. That felt like an upgrade. It was the opposite. An honest account of how cost drag, a flattering win rate, and the loss of patience turned an improvement into a failure.
A software engineer's honest account of why he is putting a real trading account online, wins and drawdowns alike, why the rules of the reveal get written before the first number prints, and why public exposure is a discipline tool, not a marketing one.
A software engineer's honest account of how we tried to prove our own gold strategy wrong: validated first in a separate tick engine, stress-tested with the statistics built to catch luck, attributed to the one thing it actually does well, and disclosed down to the year it failed. Evidence, not a track record.
A software engineer's plain-language account of a defect hiding in most retail backtests: the bar-level fill assumption. Why a candle-based engine quietly flatters your results, what a pure tick engine does differently, and why I rebuilt mine so the tests would stop lying to me.
A plain-language walkthrough of the Kelly criterion, the formula that converts a trading edge into a position size, why betting beyond it mathematically guarantees ruin, and why practitioners run fractional Kelly instead.
A software engineer's plain-language explanation of the Calmar ratio, what it measures, why systematic traders often trust it over Sharpe, and why maximum drawdown is the number that actually decides whether a trader survives their own strategy.
A software engineer's honest account of why textbook indicator bots failed, what lagging data really costs, and how Volume Profile became the foundation of a systematic scalping workflow.
