Capital Markets

kdb+-class time-series performance with an Agent Memory layer for strategy context, risk decisions, prompt cache, and replayable trading-agent behavior.

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Why trading firms pick ZeptoDB

5.52M events/sec

Lock-free MPMC ring buffer ingests full-depth L2 order-book feeds without dropping a tick.

272μs queries

LLVM JIT compiled execution. ASOF JOIN, VWAP, and EMA at microsecond latency on 1M-row windows.

522ns Python

Zero-copy NumPy / Pandas views for quant notebooks. No IPC, no serialization overhead.

kdb+ compatible semantics

ASOF JOIN, Window JOIN, xbar, EMA, VWAP — the temporal primitives you already use, in standard SQL.

Strategy memory

Store retrieved context, model calls, tool calls, risk notes, and agent decisions beside the tick timeline.

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Where it fits

High-frequency trading

• Real-time tick ingestion from FIX 4.4, ITCH, and exchange WebSocket feeds
• Sub-microsecond event timestamping with nanosecond-precision columns
• Lock-free pipeline: feed handler → ring buffer → column store → query

Quant research & backtesting

• Python zero-copy: query result to NumPy in 522ns
• ASOF JOIN for point-in-time correct backtests — no look-ahead bias
• Parquet HDB for historical data on S3 — query decades of tick data with the same SQL

Real-time risk & P&L

• Window functions for real-time P&L aggregation by desk, book, or strategy
• Streaming VaR and exposure with built-in temporal analytics
• Integrate with existing risk engines via Arrow Flight or HTTP

Compliance & audit

• Structured JSON audit logging suitable for MiFID II and SOC2 evidence
• RBAC with 5 built-in roles — isolate trading desks from compliance views
• Namespace isolation for multi-entity / multi-strategy deployments

Trading agent workflows

• Retrieve similar market regimes before an agent adjusts a strategy
• Cache repeated market-state explanations and risk summaries
• Store agent decisions, tool calls, and model calls as time-series telemetry
• Replay the path from quote/trade state to retrieved memory to action

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Architecture for a trading desk

Market Data Feeds          ZeptoDB                    Consumers
─────────────────    ──────────────────────    ─────────────────
FIX 4.4        ──→  │ Feed Handler         │
ITCH           ──→  │   ↓                  │
Binance WS     ──→  │ Ring Buffer (MPMC)   │──→  Python (522ns)
Kafka          ──→  │   ↓                  │──→  SQL (HTTP / Flight)
                     │ Column Store         │──→  C++ Pipeline
                     │   ↓                  │──→  Grafana
                     │ Parquet HDB (S3)     │
                     ──────────────────────

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Example: real-time VWAP with ASOF JOIN

-- Join trades with the latest quote at each trade timestamp
SELECT
    t.sym,
    t.ts,
    t.price,
    t.size,
    q.bid, q.ask,
    xbar(1m, t.ts) AS bucket,
    vwap(t.price, t.size) OVER (
        PARTITION BY t.sym ORDER BY t.ts ROWS 100 PRECEDING
    ) AS vwap_100
FROM trades t
ASOF JOIN quotes q ON t.sym = q.sym AND t.ts >= q.ts
WHERE t.ts > now() - interval '1 hour'

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Migrating from kdb+

Already running kdb+? ZeptoDB supports the same temporal operations with SQL syntax:

kdb+	ZeptoDB SQL
aj[\sym`time; trades; quotes]`	trades ASOF JOIN quotes ON sym, ts
xbar[0D00:01; time]	xbar(1m, ts)
ema[20; price]	ema(price, 20)
mavg[50; price]	mavg(price, 50)
wj[w; \sym`time; trades; (quotes; (max;bid); (min;ask))]`	trades WINDOW JOIN quotes ...

See the Quick Start Guide and vs kdb+ for a deeper comparison. Enterprise migration tools cover kdb+, ClickHouse, DuckDB, and TimescaleDB.

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Why this matters commercially

Trading firms spend heavily on fast time-series infrastructure, then bolt agent experiments onto separate prompt logs and vector stores. ZeptoDB keeps market facts, strategy memory, cache events, model calls, and decisions on the same replayable timeline. Quants keep standard SQL and zero-copy Python; compliance gets a clearer path from signal to action.

Talk to us about your desk: Contact Sales →