Improving broker desk profitability

Primecore runs on large-scale, fill-level broker trading data with real-time access and multi-year history. This is the raw material from which the framework extracts behavioural and microstructural signal. For prop trading firms, the same Reference Flow can be deployed as a flow-derived trading signal.

We call Reference Flow the subset of client activity that is statistically informative, economically relevant and stable enough to support hedge, execution and risk-management decisions.

For brokers, the objective is to help dealing desks and risk-management teams make better risk-selection decisions: when to warehouse exposure, when to hedge, and how much risk to keep on book.

For prop trading firms, the same Reference Flow can be deployed as a flow-derived trading signal built from behavioural and microstructural patterns in broker trading data.

Isolating a stable and economically meaningful Reference Flow is hard. We frame the problem as a probabilistic classification task:

We do not try to predict prices directly. Consistently forecasting where the market will move next is hard; predicting the outcome of a single client position is hard as well.

Instead, we work on behaviour, position lifecycle and flow structure.

Our analysis of the trading tape shows that behavioural and microstructural patterns differ materially between positions that close in profit and positions that close in loss.

These patterns can be informative for the final P&L outcome without relying on direct price prediction.

The classifier uses these features to estimate the expected value of taking risk on a specific incoming client position.