01 Consulting

Strategy, Infrastructure
& AI Integration

You have the model. We ensure it survives contact with production.

  • Quantitative Strategy Audit — Lookahead and survivorship bias detection, hyperparameter overfit diagnostics, walk-forward robustness testing, and live vs. backtest performance attribution
  • AI & ML Infrastructure — Scalable data pipelines for LLMs, vector database architecture, prompt leakage prevention, and model lifecycle monitoring in production
  • Systemic Risk & Execution — Latency profiling, risk system design, and execution-aware strategy structuring for TradFi, Crypto, or general algorithmic systems

We operate at the boundary between your research environment and production: ensuring clean data flows in, decisions flow out correctly, and nothing leaks in between. Your signal logic stays in your hands. For funds, prop desks, protocols, and AI-driven data companies building or scaling infrastructure.

Discuss your needs →
strategy_audit.py
gap research production backtest live pnl

02 Audit & Diagnostics

How We Find
the Leak

Your model looks great in isolation. Your live performance doesn't match. We find out why — one method, two domains. Quant is proof-of-rigor; AI is the larger market that rigor unlocks.

01

Inputs

We validate the data feeding your model: gaps, survivorship bias, look-ahead contamination, benchmark contamination. You send data samples or summary statistics, never your signal logic.

02

Outputs

We attribute the gap between research and live performance: slippage, decay, distribution shift, eval-set memorization. You send anonymized performance metrics, not positions.

03

Infrastructure

We profile execution, latency, and pipeline determinism where value leaks. We instrument the pipes, not the brain.

Quant & Trading

Data Integrity
The biases that inflate a backtest.
gaps, survivorship bias, look-ahead contamination, stale feeds, corporate-action adjustments, schema drift across vendors
Backtest vs. Live
Why live PnL drifts from research.
walk-forward robustness, overfit diagnostics, research-vs-live PnL attribution
Execution & Latency
Where fills and milliseconds bleed alpha.
fill rates, slippage patterns, market impact, funding-cost drag, latency budgets
Pipeline Architecture
Deterministic flows, reproducible results.
ETL bottlenecks, deterministic data flows, time-series storage and retrieval optimization
pipeline_audit.py
market data ingest cleaner ⚠ gaps survivorship bias detected strategy engine signals exec → latency: 2ms 87ms 14ms 1ms
Get a Free Integrity Check →

AI & LLM

Eval Integrity
The contamination that inflates an eval.
train/test leakage, temporal contamination, benchmark data bleeding into pretraining, eval-set memorization
Offline vs. Online
Why production drifts from the eval set.
distribution shift, eval–prod mismatch, prompts and contexts the benchmark never saw
Prompt & Retrieval Risk
Where retrieval and prompts quietly fail.
prompt-injection surface, context-window overflow, RAG similarity decay, embedding drift across model versions
Output Reliability
Reproducible outputs, schema-safe, stress-tested.
hallucination-rate benchmarking, schema-validation failures, token-cost drift, reproducibility across inference runs

Plus production hygiene — experiment tracking, registry, versioning, CI/CD determinism — as baseline, not headline.

embedding_drift.yaml
corpus vector DB prompts embedding pipeline ⚠ drift similarity decay detected LLM output validate prod → quality: corpus emb inference output
Get a Free Integrity Check →

We deliver a written diagnostic report with ranked findings and concrete remediation steps. What you do with your signals or models afterward is entirely your business. We find the leaks. You keep the returns.

03 Training

Sharpening the
Quant & AI Edge

The gap between a working prototype and a robust production system is where most teams get stuck.

  • Workshops — Intensive half or full-day sessions on systematic trading, backtesting rigour, and ML-driven signals and LLM operationalization; built around real case studies, not toy examples
  • Practitioner Courses — Structured programmes covering factor construction, risk sizing, and execution-aware strategy design; paced for working professionals
  • Custom Programs — Tailored to your team's existing stack, asset class or AI domain, and knowledge baseline

Participants leave with frameworks they can apply the next trading day — and the vocabulary to hold informed conversations with quant developers and risk teams. Built on 10+ years of live trading, AI research and university-level teaching experience.

Discuss a Program →
orchestrator.yaml
ALGO TRADING quant infra data exec ml

Built on Production Experience

The gap between research and production is where most projects fail. That's where we work.

Aqfinea is a quantitative and AI consultancy. We come from academia, hedge funds, and crypto vaults and tech-scale data engineering. We've built strategies that trade live, systems that power AI applications. We've taught the theory. We know where backtests break — and why live performance rarely matches the curve.

Our work covers systematic strategy design, quant development, AI/ML infrastructure, and execution optimization. We do not manage your capital, and we do not need to understand your alpha to do our job well.

10+
Years of live trading & AI/data engineering experience
3
Service tracks: consulting, training, pipeline/AI audit
0
Access required to your proprietary strategy logic or model weights
∞
Gap between backtest performance and live PnL

FAQ

Common Questions

How long does an audit take?

Typically 1–2 weeks from data handoff to final report. A focused pipeline diagnostic can be delivered in 3–5 business days. Larger engagements (full strategy audit + infrastructure review) may extend to 3–4 weeks depending on scope and data complexity.

What do I need to provide?

For a data pipeline audit: sample datasets, ETL scripts or configs, and access to your data warehouse or storage. For a strategy audit: backtest code, historical results, and production logs. We provide a detailed checklist after the discovery call — you decide what to share and what to redact.

Do you need access to our alpha or model weights?

No. We audit the infrastructure, data flows, and execution layer — not the signal logic itself. Your proprietary models stay yours. If deeper access is mutually beneficial, we work under NDA with scoped permissions.

How is pricing structured?

Fixed-fee per engagement, scoped after a free discovery call. No hourly billing, no surprise invoices. Retainer arrangements are available for ongoing infrastructure monitoring and advisory.

Contact

Start a Conversation

Tell us about your project. We'll respond within 48 hours.

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