About
Two camps. One standard.
Systematic and discretionary trading are usually treated as rival religions. Stow Algo treats them as two disciplines that answer to the same thing: evidence.
The divide
Trading culture can be a hostile environment, writing off whichever method it doesn’t use. Quants see discretionary trading as guesswork and luck. Discretionary traders see systems as curve-fit, built on a stretch of history that won’t repeat. Whether it’s slating technical analysis or claiming that exploiting HFT to arbitrage isn’t real trading, there’s hostility and division across the industry when it comes to strategy selection and edge.
The position
Stow Algo’s position is that there’s no single correct strategy, approach, or edge. There are many ways to generate returns in the market. Instead of a hostile battle over which side to pick, Stow Algo works as a central hub, exploring strategies across both disciplines, systematic and discretionary.
Who’s behind it
Stow Algo is run by Zach, an algorithmic trading developer and six-figure funded proprietary trader. Prior client work entailed implementing automated execution systems.
The discretionary side is the same person, held to the same standard: judgment published weekly, with levels and invalidation stated before it occurs.
One standard
“One standard” means following a professional trading process, whichever side of the seam you’re on.
On the systematic side
A backtest is evidence, not marketing. That means realistic spread, slippage, and fees; out-of-sample validation; drawdowns reported as prominently as returns; and no metric shopping. If an edge decays, the write-up says so. Failures get published too.
On the discretionary side
A call means anticipating an outlook, a bias, and the level that invalidates it, before the outcome is known. Being right isn’t proof of being an exceptional trader, and being wrong isn’t something that needs explaining away. It’s one result in a distribution of wins and losses that any discretionary process is expected to produce over time. Over a long enough track record, that distribution is what shows whether there’s a real edge.