Alistair Beaumont Quant Systems & Trading Architecture

Trading system architecture
and decision logic.

Designing robust systems that form decisions, manage risk, and perform under real market conditions.

About ABQ Forensics Systematic Strategy About Founder Contact

Featured product

ABQ ForensicsDashboardTrade ExplorerAutopsyIntegrityEvidenceReports
Trade ExplorerCompleted
Trade ID · TRD-2024-05-17-00023
Root CauseSlippage
Impact-12.47 bps
ConfidenceHigh

ABQ Forensics

Post-trade investigation, root-cause attribution, and tamper-evident evidence for algorithmic trading.

  • Trade investigation & root-cause analysis
  • Tamper-evident evidence generation
  • Integrity verification & audit trails
  • Forensic reports for compliance & disputes
Open Platform →Learn More

Featured strategy

Systematic trend-following benchmark comparison and dynamic allocation overview

Systematic Strategy

Quantitative Trend
Following System

A proprietary, Python-based trend-following system researched in QuantConnect across SPY and QQQ.

  • 22.0% compound annual return since 2008
  • Dynamic allocation toward the strongest trends
  • Commission and slippage-aware backtesting
  • Complete benchmark, drawdown, and trade evidence
Explore Strategy →

What I do

Decision Architecture

How trading systems form decisions, where assumptions fail, and how risk and execution behave under real market conditions.

Risk & Capital Design

Construction of risk logic, capital allocation, and volatility-aware decision layers that govern system behavior.

System Survivability

Defining when systems trade, pause, scale, or stop — under stress, regime shifts, and execution constraints.

Who I help

Built for systems already exposed to reality.

I work with traders, funds, and founders already deploying real capital — where system behavior matters more than ideas.

Who this is for

  • Teams with strategies deployed or close to deployment.
  • Systems exposed to regime shifts, volatility expansion, or scaling constraints.
  • Traders who treat execution, risk, and structure as first-order concerns.
  • Projects that require diagnosis, not reassurance or narrative.

Who this is not for

  • Beginners or first-time traders.
  • Signal, indicator, or strategy-for-sale requests.
  • Implementation-only or “just build me a bot” work.

If the question is still what to trade, we are likely not a fit.

How I think

Risk precedes signal.

I start by defining how systems are allowed to fail — and where they must never fail. Then I define how risk, capital, and execution must behave without discretion.

01

Risk precedes signal

If risk is wrong, signal quality is irrelevant.

02

Regime over precision

Most systems are not designed for change.

03

Survival beats performance

A system that cannot survive cannot compound.

04

Not trading is a decision

Participation is optional; preservation is not.

This is not strategy generation. This is system architecture.

Case studies

Structural diagnoses, not success stories.

Case 01

Trend system failure under volatility expansion

Problem: Live performance collapsed despite clean backtests.

Intervention: Position risk and exit policy were rebuilt as volatility-aware constraints.

Result — lower drawdown severity and no regime-specific breakpoints during volatility spikes.

Case 02

False diversification through regime overlap

Problem: More strategies increased portfolio risk faster than returns.

Intervention: Regime overlap and tail co-movement were mapped; allocation was rebuilt as a regime-budgeted risk policy.

Result — lower realized volatility and fewer clustered drawdowns.

Case 03

Paper edge, live decay

Problem: Strategy edge decayed after deployment due to slippage and execution friction.

Intervention: Execution assumptions and exit constraints were rebuilt around latency, liquidity, and fill uncertainty.

Result — observed performance converged with realistic execution expectations.

Systems architecture for live trading environments.