Category: Automated Trading

Why High Model Scores Do Not Guarantee Profit A model can predict the next price with a high statistical score and still produce poor trading decisions. One of our earlier development updates reported high R-squared scores for a convolutional neural network trained to predict the next ETHUSD price. Those results described a prediction task. They…

What Our Trading Feedback Loop Actually Learned A feedback loop can change how a system allocates risk. It cannot manufacture an edge that is missing from the underlying signals. We built a trading feedback loop to test whether completed trade reviews could improve later decisions. The idea was not to let a model rewrite itself…

Cleaner Signals Can Still Be Worse Signals A model that produces fewer bad alerts may look improved—until we count how many valid events it stopped finding. “Cleaner” is an appealing word in automated trading research. Fewer signals can make a chart easier to read, reduce turnover, and lower the number of obvious mistakes. But fewer…

Why Pivot Detection Is Not a Trading Signal Finding a recognizable peak or trough on a chart is a detection problem. Turning it into a live-safe, profitable decision is a much longer chain. Our ETHUSD pivot research began with a simple question: can a model locate the kinds of turns a human annotator marks on…

Why We Keep an Append-Only Trading Research Log A research process becomes much easier to fool when yesterday’s failed test can quietly become today’s successful story. An append-only trading research log gives every important experiment a permanent place in the record. We preserve what was proposed, what data was available, what passed, what failed, and…