Fintech-focused asset manager with digital trading and alternative credit platforms
Fasanara Capital manages USD 5.7 billion across fintech strategies, structured credit, and digital assets—anchored by a polyglot tech stack (Java, Python, Go, Rust, C++) that reflects engineering depth in quantitative trading. The company is actively hiring engineers and data specialists across London and China while facing persistent low-latency and data-quality challenges in live trading systems, suggesting infrastructure is a scaling bottleneck as they push real-time market data pipelines and systematic strategy execution.
Notable leadership hires: Risk Analytics Lead
Fasanara Capital is a London-based asset manager founded in 2011, managing over USD 5.7 billion in fintech-originated strategies for pension funds and insurance companies across Europe and North America. The firm operates three primary platforms: a USD 5.7 billion fintech-focused alternative credit fund (integrating with 141 fintech lenders across 60+ countries), Fasanara Digital (a USD 500 million market-neutral digital asset fund launched in 2018), and Fasanara Quant (a USD 150 million multi-manager multi-strategy platform across liquid markets, established in 2020). The company also runs a venture capital practice investing in early-stage fintech innovators. With approximately 130 professionals, Fasanara combines quantitative infrastructure with deep fintech domain expertise across lending, digital assets, and systematic trading.
Java, Python, Go, Rust, C++, TypeScript, React, and Kotlin for core systems. Infrastructure includes Kubernetes, Docker, AWS, GitLab CI/CD. For blockchain: Ethereum, Solana, Polygon, Arbitrum, Optimism, and libraries (Web3.js, ethers.js, Uniswap). Data tools include pandas, NumPy, MySQL. HR systems: HiBob.
USD 5.7 billion in fintech-focused strategies. Fasanara Digital (digital assets) manages USD 500 million; Fasanara Quant (multi-strategy) manages close to USD 150 million.
Fasanara Capital's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
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