Internal quant operating layer

AI agents for quant research operations

DQT helps internal systematic teams turn data, hypotheses, backtests, risk checks, and monitoring into traceable, reproducible, reviewable agent workflows.

Contact Explore workflows

Internal data grounding Deterministic validation Human-governed runs

Research Run #0421 Human review required

prompt

Evaluate cross-sectional signal stability under current risk limits.

01 Data QCSource coverage checked passed
02 HypothesisFeature family drafted generated
03 Feature BuildNotebook job active running
04 BacktestOOS window locked queued
05 Risk ReviewApproval required waiting
06 ReportSummary linked ready

Agent orchestration

Tool calls, computed results, and human approvals stay in one traceable record.

Source linkedOOS lockedSlippage modeledHuman approvalAudit log

Agent fleet

Specialized agents across the quant lifecycle

Each agent owns a clear operating boundary, calls approved internal tools, and hands traceable outputs to the next research or operations stage.

Research

Research Agent

Breaks research ideas into tasks, proposes candidate features, and prepares experiment plans for review.

Data

Data QC Agent

Checks gaps, anomalies, latency, and field consistency before research depends on the data.

Validation

Backtest Agent

Orchestrates backtests, OOS locks, slippage assumptions, and parameter records without treating chat output as computation.

Risk

Risk Monitor Agent

Tracks exposure, concentration, drawdown, risk budgets, and abnormal events for human review.

Reporting

Reporting Agent

Turns experiments, monitoring, and approvals into daily notes, weekly briefs, and research summaries.

Internal data

Data is internal capability, not an external product

DQT can show internal pipelines, normalization, provenance, and research context without positioning itself as a public data subscription or API vendor.

context:
  source: internal_research_store
  universe: approved_workspace
  oos_window: locked
  permissions: scoped
  output: traceable_report

Validation

AI orchestrates and explains. Deterministic engines calculate.

DQT avoids black-box trading claims. Agents may propose, coordinate, and summarize, while statistics, backtests, and risk metrics come from reproducible computation.

AI proposesEngines calculateHumans approve

Integration

Designed around existing quant workbenches

The visual language should feel like an internal command center, not a retail trading bot. Show Python, notebooks, research databases, schedulers, alerts, and reports.