Investor Quality Ranking System
Design a high-scale ranking and discovery system for a fintech platform to identify and surface 'high-quality' investors. The system must process millions of portfolios and trade logs to distinguish between skill-based performance and random luck (survivorship bias). Focus on the end-to-end ML lifecycle: from real-time feature engineering of risk-adjusted metrics to a two-stage retrieval and ranking architecture that meets low-latency requirements for a social discovery feed. Address challenges such as financial non-stationarity, market regime shifts, and the cold-start problem for new accounts, while ensuring the serving infrastructure is robust enough for high-concurrency traffic.
LightGBMKafkaFlinkSparkTectonSharpe RatioBayesian ShrinkageFeature StoreRayMLflow
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