; Aggregate high-value flow, then traverse counterparties
(set flow
(select {from: trades
where: (> Notional 1000000)
by: Counterparty
Volume: (sum Notional)}))
(.graph.var-expand network flow 1 3)
import rayforce as rf
trades = rf.read_csv("trades.csv")
flow = (trades
.where(rf.col("Notional") > 1_000_000)
.group_by("Counterparty")
.agg(rf.col("Notional").sum()))
network.expand(flow, min_depth=1, max_depth=3)
ray_t *trades = ray_read_csv("trades.csv");
ray_graph_t *g = ray_graph_new(trades);
ray_op_t *amount = ray_scan(g, "Notional");
ray_op_t *limit = ray_const_i64(g, 1000000);
ray_op_t *hot = ray_gt(g, amount, limit);
ray_t *result = ray_execute(g, ray_filter(g, trades, hot));
EXAMPLE RESULT5 rows · optimized
CounterpartyDepthVolume
LYNX_02184.20M
ALPHA_17261.08M
NODE_08244.71M
FORT_04327.19M
EDGE_12312.42M