Products · ArrivalIQ
Execution intelligence, inside your own perimeter.
ArrivalIQ turns a fund's own execution data into answers about where money leaks in execution, and how much of the idea survived the trade — deployed as a single container inside your VPC, never a feed to a third party.
0M+
data points in a live demo workspace, across multiple instruments and trading days
0%
bootstrap confidence interval on every cost figure
0
versioned market packs at launch: India, UK, US
The core objective
Answer the question legacy TCA can't: was it the trade, or the idea?
A quant fund loses performance in two places: the model's edge decays, and execution erodes what is left. Legacy TCA measures only the second, order by order, with no link to the signal behind the order. ArrivalIQ puts the signal, the decision, the order, every child slice and every fill in one workspace — so model decay vs execution drag becomes a single question with a single answer.
The problem with the incumbents.
Heavy setup
Weeks of data engineering and custom SFTP drops before the first report.
Blind to strategy
Orders are judged in isolation. Incumbents can't separate bad routing from alpha that had already decayed before the trade.
Enterprise pricing
Tens of thousands a year puts it out of reach of boutiques and family offices.
What ArrivalIQ commits to.
Zero-touch onboarding
A file is mapped in seconds, and the mapping is shown with its evidence rather than asserted. There's no mapping screen to click through.
It tells you what your data can measure
Intake detects the grain of a file and names the metrics that grain rules out, instead of returning a plausible number that means nothing.
Right, not just fast
Nanosecond timestamps with clock provenance, exact fixed-point prices, a confidence interval on every headline number, and lineage from any figure back to the file and code that produced it.
Correct for each market
India, UK and US at launch, as versioned, effective-dated market packs — real sessions, auctions, tick bands, closing-price rules and itemised taxes. Not US rules with the labels changed.
Inside your perimeter
The default deployment is a container in the client's VPC or on-prem, air-gapped. Execution data never leaves, and no LLM sits in the data path.
Who it's for.
Tier 1 · immediate focus
Boutique funds and family offices with 1–10 investment professionals, no data-engineering team, and broker exports as their data. They need fast, self-serve execution transparency. Onboarding is a file, auto-mapped.
Tier 2 · enterprise pipeline
Quant funds and HF desks with 10–100+ quants, in-house infrastructure (KDB+, a data lake) and DMA/FIX. They want low-latency adverse-selection profiling. Onboarding is FIX drop copy, their lake, or an SDK.
A file in, answers out.
Map the client's file
Detects the table and grain, proposes a mapping with confidence per field, runs sanity checks, and self-corrects known mistakes such as a wrong timezone or prices in minor units.
One data model
Signals, orders, child slices, fills, fees, quotes and the tape, stored as versioned Apache Iceberg tables with exact prices and UTC nanosecond time.
Cost every order once
A Rust-backed engine aligns every fill to the prevailing quote, computes shortfall, markouts and drag, and stores the results with full lineage.
Slice it any way
Every filter re-aggregates the stored results without touching market data again, so views come back in milliseconds, with bootstrap confidence intervals.
The dashboard at a glance.
| Page | The question it answers | Status |
|---|---|---|
| Cockpit | What did execution cost, and where? | Live |
| Blotter | Which orders, sorted how? | Live |
| Order detail | What happened to this one order, end to end? | Live |
| Signals | How much of the idea survived the trade? | Live |
| Latency | Where does the time go on the send path? | Live |
| Intake | Did my file map correctly, and what can it measure? | Live |
| Explorer | Anything else, in SQL. | Live |
| Workspace | What produced these numbers? The audit and DDQ answer. | Live |
| Pre-trade impact & sizing | How much will this order cost before I send it? | Roadmap |
| Microstructure inspector | What did the book look like — imbalance, micro-price, spread regimes? | Roadmap |
What comes next.
Volume & volatility baselines
Interval VWAP, participation and %ADV on every order; daily ADV, volatility, spread and size at the touch per instrument, built from the client's own tape and quotes.
Pre-trade impact & sizing
Square-root-law impact with the coefficient and exponent fitted on the client's own flow, and an optimal-participation frontier trading impact against alpha decay.
Microstructure inspector
Order-book imbalance, micro-price and time-of-day spread regimes. The methodology is drafted.
FIX drop copy
Real-time ingestion as a drop-copy destination — a configuration change on the broker's side, with no files.
India F&O, more markets
Derivatives methodology and the India F&O pack; then Hong Kong and Japan, the EU, and other Asian markets.
Under the hood. Rust core for FIX parsing and quote alignment, exposed to a Python analytics engine. Apache Arrow end to end. Apache Iceberg storage with snapshot lineage. DuckDB for SQL. A React + TypeScript dashboard with FINOS Perspective. Deployed as a single container inside the client's perimeter.