Real-Time Financial Analytics with Streaming SQL

Financial analytics requires real-time computation of portfolio value, risk metrics, P&L, and regulatory exposures as market data and trades arrive. Traditional batch approaches calculate these overnight — missing intraday risk events. Streaming SQL computes financial metrics continuously.

Financial Analytics Views

-- Real-time portfolio P&L
CREATE MATERIALIZED VIEW portfolio_pnl AS
SELECT account_id, symbol,
  SUM(quantity) as position,
  SUM(quantity * entry_price) / SUM(quantity) as avg_cost,
  last_value(market_price ORDER BY ts) as current_price,
  SUM(quantity) * (last_value(market_price ORDER BY ts) -
    SUM(quantity * entry_price) / SUM(quantity)) as unrealized_pnl
FROM trades t JOIN market_data m ON t.symbol = m.symbol
GROUP BY account_id, symbol;

-- Risk concentration
CREATE MATERIALIZED VIEW risk_concentration AS
SELECT account_id,
  COUNT(DISTINCT symbol) as positions,
  MAX(ABS(unrealized_pnl)) as largest_position_pnl,
  SUM(ABS(position * current_price)) as gross_exposure
FROM portfolio_pnl GROUP BY account_id;

-- Regulatory exposure alerts
CREATE MATERIALIZED VIEW exposure_alerts AS
SELECT account_id, gross_exposure
FROM risk_concentration
WHERE gross_exposure > 10000000;  -- M exposure limit

Why Streaming for Finance?

Intraday risk is invisible to batch systems. A trader who accumulates excessive exposure between overnight batch runs creates unmonitored risk. Streaming SQL catches exposure breaches in real time.

Frequently Asked Questions

Is RisingWave suitable for high-frequency trading?

No. HFT requires sub-microsecond latency with specialized hardware. RisingWave's sub-100ms latency is suitable for portfolio analytics, risk monitoring, and regulatory reporting — not order execution.

How does RisingWave handle market data volume?

Market data generates millions of price updates per second. RisingWave scales horizontally and processes only the aggregations you define in materialized views, keeping resource usage proportional to query complexity rather than raw data volume.