THE CHALLENGE

A London-based asset management firm managing £18B across multi-asset portfolios was constrained by computational limitations in portfolio construction and risk management:

  • Portfolio rebalancing calculations took 6-12 hours, limiting strategy agility
  • Risk simulations incomplete (only 10k scenarios feasible, needed 10M+)
  • Unable to optimise portfolios with complex constraints (ESG, sector limits, transaction costs)
  • Black swan events required manual intervention (no time for comprehensive analysis)

The firm sought a computational breakthrough enabling real-time portfolio optimisation with comprehensive risk analysis.

OUR APPROACH

We implemented quantum-inspired algorithms running on classical high-performance computing infrastructure, achieving quantum-like performance without requiring quantum hardware:

ALGORITHMIC INNOVATION

  1. Quantum-Inspired Optimisation
    Implemented Simulated Coherent Ising Machines (SCIM) and Variational Quantum Eigensolver (VQE) variants running on GPU clusters. These algorithms leverage quantum computing principles (superposition, tunnelling) in classical hardware.
  2. Advanced Portfolio Construction
  • Multi-period optimisation (not just single-period Markowitz)
  • 200+ realistic constraints (regulatory, ESG scores, liquidity, sector exposure)
  • Transaction cost modelling (market impact, bid-ask spreads)
  • Dynamic rebalancing strategies
  1. Comprehensive Risk Analytics
  • Monte Carlo simulations: 10M scenarios in 90 seconds (vs. 10k scenarios in 6 hours)
  • Value-at-Risk (VaR), Conditional VaR, expected shortfall across multiple confidence levels
  • Stress testing: 500+ historical and hypothetical scenarios
  • Tail risk analysis and black swan event modelling
  1. Real-Time Rebalancing
    Continuous monitoring of portfolio drift, triggering optimised rebalancing when thresholds breach—executed in under 3 minutes.

INFRASTRUCTURE

  • Compute: NVIDIA A100 GPU cluster (64 GPUs), CUDA-optimised implementations
  • Data Lake: Tick-level market data (15 years history), fundamental data, alternative data (sentiment, news, ESG)
  • Risk Engine: Custom C++/Python hybrid for maximum performance
  • Integration: Seamless integration with Charles River IMS, Bloomberg Terminal, and trading APIs

VALIDATION & GOVERNANCE

  • Extensive backtesting over 20 years of market history
  • Out-of-sample validation on crisis periods (2008, 2020, 2022)
  • Independent risk model validation by third-party quants
  • FCA compliance review for model risk governance

THE RESULTS

COMPUTATIONAL PERFORMANCE

  • Portfolio Optimisation Time: 2.4 minutes (from 8 hours, 200x faster)
  • Risk Simulation Scenarios: 10M in 90 seconds (1,000x improvement in scenario coverage)
  • Intraday Rebalancing Capability: Enabled (previously impossible)

INVESTMENT PERFORMANCE (24 MONTHS)

  • Sharpe Ratio: 1.82 (vs. 1.48 benchmark, 23% improvement)
  • Maximum Drawdown: -12.3% (vs. -18.7% benchmark, 34% better)
  • Tail Risk (99% VaR): Reduced 28%
  • Information Ratio: 0.94 (consistent alpha generation)

BUSINESS OUTCOMES

  • Assets Under Management Growth: £18B → £26B (performance-driven inflows)
  • Management Fee Revenue: £42M annually
  • Client Retention: 98.7% (vs. 94% industry average)
  • Regulatory Capital: Reduced 18% through better risk quantification

STRATEGIC ADVANTAGES

  1. New Product Launch: Enabled daily-rebalanced active ETF (previously infeasible)
  2. Institutional Mandate Wins: Sophisticated risk reporting attracted £4.2B in new institutional mandates
  3. ESG Leadership: Only firm offering true multi-objective optimisation (return, risk, ESG) at scale
  4. Thought Leadership: Published 3 academic papers, elevating the firm’s intellectual capital

TECHNOLOGIES USED
Python, C++, CUDA, NumPy, PyTorch, Quantum-inspired algorithms (SCIM, VQE), PostgreSQL, TimescaleDB, Redis, Apache Kafka, Bloomberg API, FIX Protocol, Grafana, Jupyter