How neural networks learn to ignore noise
Most intraday price movement carries no information. Telling a useful model from a misleading one comes down to whether it can separate the two.
Insights
Notes from
Notes from
inside the market
Plain-language writing on how the models work, where they fail, and what actually moves digital assets.
Liquidity beats price: what the order book hides
Two identical prices can mean two completely different opportunities. Depth is what determines the real cost of entry.
Why excellent models fail when the regime changes
A model trained on a rising market learns rules that do not hold in a falling one. That is not an accuracy problem; it is an assumptions problem.
Bitcoin's correlation with equities keeps moving
The popular claim that digital assets trade inversely to traditional markets has not held up against recent years of data.
Sentiment analysis: from headlines to a usable number
Turning the language of news into a quantitative indicator is harder than it looks, especially when the news arrives in two languages.
Slippage: the cost that never appears in the fee table
Published fees are a small part of the true cost of trading. The gap between expected and filled price is the larger part.
The limits of backtesting, or why every strategy looks good on paper
Building a model that beats the past is easy. Building one that never saw the past during training is the hard part.
Position sizing: the decision that precedes asset selection
The right asset at the wrong size loses money. A mediocre asset at the right size may not.
Human oversight of automated systems: when to intervene
A system that cannot be stopped is not automated, it is out of control. When intervention is justified, and when it is a mistake.
Reading a performance report: the numbers worth your attention
Win rate alone is a weak indicator. What matters is the distribution of wins and losses, and the maximum drawdown.
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