Systematic Hedge Fund in London Focused on Quantitative Multi-Strategy Investing

Agami Capital is a London-based systematic hedge fund deploying quantitative multi-strategy investment approaches across global markets. The firm combines statistical arbitrage, equity market neutral, macro, rates, foreign exchange and commodities strategies to construct diversified portfolios designed to capture multiple independent sources of alpha.

Quantitative Multi-Strategy Investment Approach

Our investment philosophy is based on systematic research, scalable technology and disciplined portfolio construction. By combining complementary strategies within a quantitative multi-strategy framework, Agami Capital seeks to build diversified portfolios that can adapt across changing market regimes.

Statistical Arbitrage in US and European Equities

A core focus of the firm is statistical arbitrage across US and European equity markets. Systematic models identify cross-sectional mispricings and short-term inefficiencies across broad equity universes. Portfolios are constructed using market-neutral techniques designed to isolate stock-specific alpha while reducing overall market exposure.

Systematic Macro, Rates, FX and Commodities Strategies

In addition to equity market neutral strategies, Agami Capital researches and trades systematic macro and relative value opportunities across rates, foreign exchange and commodities markets. These strategies diversify the portfolio beyond equities and broaden the opportunity set across global liquid markets.

Research-Driven Systematic Investing

Research and risk management are central to the firm's investment process. Quantitative models are developed using large-scale data analysis, rigorous testing and continuous monitoring. The objective is to build robust strategies capable of operating consistently across different market environments.

Agami Capital Whitepapers

Agami Capital publishes whitepapers on the infrastructure, methodology, and market dynamics shaping the next generation of systematic investment management, covering signal research, risk architecture, and the evolving role of machine learning in quantitative strategies.

Built Different: Why Inference Architecture Is the Foundation of Next-Generation Systematic Research

The engineering constraints on AI inference — memory bottlenecks, context length limitations, and the sequential nature of token generation — determine whether large language models can operate at the speed and scale that systematic investment research demands.

From Rules to Learning: How Systematic Strategies Are Evolving Beyond Hand-Crafted Signals

The first generation of systematic investing was built on rules that were carefully constructed, rigorously tested, and deliberately static. This paper traces the evolution from hand-crafted factor construction toward adaptive, learning-based approaches, examining where the transition is already underway, where the risks are most acute, and what the operational requirements of a more dynamic research process look like in practice.

The Connected Portfolio: Building Cross-Asset Risk Frameworks That See Around Corners

Risk does not respect asset class boundaries. A dislocation in credit spreads ripples into equity volatility. A currency regime shift reprices commodity curves. A rates shock reorders the correlation structure that yesterday's portfolio was built on. This paper examines how risk architectures that combine factor-based decomposition with dynamic correlation regimes can provide earlier signals of where stress may originate and how it is likely to transmit.

The Crowded Trade: How Factor Congestion Becomes Systemic Risk

When a significant share of systematic capital is positioned in the same factors, the diversification benefits those portfolios assume may be overstated. Crowding is not a new problem, but its dynamics have changed as the systematic investing universe has grown. This paper examines how forward-looking measures drawing on short interest, return synchronicity, flow data, and positioning can be incorporated into a more complete risk framework.