I've spent the last few months rebuilding my quant workflow after returning from fourteen years in software product engineering. The hardest part wasn't the math (for me). It was learning when to stop: when to kill a hypothesis and move on. I wrote up the funnel and mental model, from terminal exploration through statistical validation to robustness testing.
https://susanpotter.net/quant/hypothesis-to-production-toolkit/
Building an AI that analyzes financial markets
taught us something counterintuitive:
The biggest source of prediction error
isn't the model architecture.
It's data quality.
Today's signal on $AMD:
Bullish | $193.39 to $204.41
More at https://www.gprophet.com
Building an AI that analyzes financial markets
taught us something counterintuitive:
The biggest source of prediction error
isn't the model architecture.
It's data quality.
Today's signal on $AMD:
Bullish | $193.39 to $204.41
More at https://www.gprophet.com
Building an AI that analyzes financial markets
taught us something counterintuitive:
The biggest source of prediction error
isn't the model architecture.
It's data quality.
Today's signal on $MSFT:
Bullish | $395.55 to $434.46
More at https://www.gprophet.com
Building an AI that analyzes financial markets
taught us something counterintuitive:
The biggest source of prediction error
isn't the model architecture.
It's data quality.
Today's signal on $NVDA:
Bullish | $180.25 to $183.03
More at https://www.gprophet.com
Building an AI that analyzes financial markets
taught us something counterintuitive:
The biggest source of prediction error
isn't the model architecture.
It's data quality.
Today's signal on $MSFT:
Bullish | $395.55 to $435.68
More at https://www.gprophet.com
Susan Potter
G-Prophet