funding-collateral-borrow

Automating funding collateral borrow workflows requires consolidating exposures, measuring PnL and tail risk, and enforcing production controls. This Skill uses Python scripts to automate these diagnostic and implementation tasks with auditable results.

Exposure aggregation consolidates positions across various products and venues to provide a unified risk view. This workflow measures PnL and tail risk while investigating breaches through root-cause analysis.

Auditable production controls enforce approval workflows and rollback procedures for trading system changes. This Skill tracks risk appetite, limits, and escalation rules while investigating breaches with root-cause analysis.

Investigating limit breaches requires root-cause analysis and remediation to identify exposure aggregation failures. This Skill automates diagnostics using Python scripts and reference checklists for structured investigation.

Measuring PnL and tail risk with Python diagnostics supports quantitative research and implementation. This Skill provides scripts to aggregate exposures, replay daily scenarios, and evaluate tail risk outcomes.

Reference documents provide domain-specific checklists and delivery structures required to manage funding workflows. They complement Python scripts by defining risk appetite rules and production control requirements.