How kmuralikarthik/capital-markets-trade-lifecycle-analytics-reconciliation-dashboard works
This repository simulates front- and back-office trade data, reconciles the records into matched trades and operational exceptions, then presents results in a Streamlit dashboard. The main user workflow is to inspect trade-volume, STP and exception metrics, review aging and settlement-failure analyses, and examine actionable exception details. The generator and reconciliation source support the data flow shown; confirmation is mentioned as a lifecycle area in the README but is not implemented as a distinct stage in the sampled code. Excel export is documented but not visible in the sampled dashboard source.
Main entry points: app.py Streamlit dashboard · trade_data.db Trade database · reconciliation.py Match and classify · generate_data.py Trade simulator
Components 10
Trade data
- Trade simulator
generate_data.py - Trade database
trade_data.db
Reconciliation
- Match and classify
reconciliation.py - Exception aging
reconciliation.py
Operations dashboard
- Streamlit dashboard
app.py - Lifecycle KPIs
app.py - Exception analysis
app.py - Actionable details
app.py - Excel reporting
app.py
Other components
- Operations user
Connections 10
- Trade simulator → Trade database: writes FO/BO trades
- Match and classify → Trade database: reads FO/BO trades
- Match and classify → Trade database: writes results
- Match and classify → Exception aging: calculates aging
- Operations user → Streamlit dashboard: opens dashboard
- Streamlit dashboard → Trade database: reads results
- Streamlit dashboard → Lifecycle KPIs: calculates metrics
- Streamlit dashboard → Exception analysis: builds analyses
- Streamlit dashboard → Actionable details: shows exceptions
- Streamlit dashboard → Excel reporting: supports export