Systematic trading and asset management via Python-driven data pipelines
ABC arbitrage runs a quantitative trading operation built on Python, SQL, and distributed compute (Airflow, Spark, AWS). The stack reveals a maturing data infrastructure: Parquet storage, Cassandra for time-series, Redis for caching, and Plotly Dash for research UX. Active hiring skews heavily toward finance and data roles (7 of 9 open headcount) over engineering (1), suggesting quantitative researchers and analysts are the constraint—not platform builders—as the firm scales backtesting and high-frequency tick pipelines.
ABC arbitrage is a Paris-based systematic trading and asset management firm, publicly listed on Euronext Paris since 1995. The group operates through subsidiaries in Paris, Dublin, and Singapore, managing both proprietary strategies and third-party capital. The core business is systematic arbitrage and quantitative trading across liquid assets globally. The team of ~100 employees spans 12 nationalities with an average age of 35, predominantly from scientific and technical backgrounds. Current development priorities center on scaling data pipelines for tick-level market data, backtesting infrastructure, and research-platform accessibility for quantitative traders.
Python, SQL, AWS (Athena), Apache Airflow, Spark, Pandas, PySpark, Cassandra, Redis, InfluxDB, SQL Server, and Plotly Dash for dashboards. C# and .NET are also in use for production systems.
Paris, France. The group also operates offices in Dublin and Singapore, with active hiring in both France and Singapore.
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