Embedding a funding rate window for similarity search

Abstract Vector similarity search — the backbone of embedding-based pattern matching — is increasingly relevant in quantitative trading for identifying historical regime analogs, detecting cross-exchange mispricings, and clustering funding rate behaviors. But financial data has latency requirements that software-based approximate nearest neighbor (ANN) indexes struggle to meet at scale. This paper examines whether FPGA-accelerated vector search can meaningfully reduce inference latency for trading applications, using real data from our perpetual futures monitoring system (266,570 funding rate observations, 20 actively tracked perpetual tokens, cross-exchange spread snapshots across 3 venues). We find that while FPGA implementations of inner-product search can achieve sub-microsecond query latencies for fixed-dimension embeddings, the practical bottleneck in trading systems is not vector search latency but feature engineering and embedding freshness. This is a systems analysis informed by production data, not a backtested trading strategy; we present latency modeling and architectural observations, not performance claims. Hypotheses H1 (Primary — Exploratory): FPGA-accelerated exact inner-product search on embedded funding rate windows can achieve sub-10μs query latency for databases of ≤500K vectors at dimensionality ≤128, making it viable for real-time regime detection in perpetual futures trading. H2 (Secondary — Observational): The dominant latency bottleneck in embedding-based trading signal pipelines is embedding computation (feature extraction + forward pass), not vector index traversal, making FPGA acceleration of ANN alone insufficient for end-to-end latency reduction. H3 (Exploratory — Data Insufficient): Clustering funding rate histories into vector embeddings and performing similarity search against historical analogs produces more actionable regime transitions than threshold-based heuristics currently used in production systems. Confidence Assessment: H1 is supported by published FPGA benchmarks in non-financial domains but unvalidated on our specific data — confidence is moderate. H2 is strongly supported by architectural reasoning and operational experience. H3 is speculative; we lack the controlled experiments to compare similarity-based vs. threshold-based regime detection. All hypotheses are preliminary. Data Provenance | Source | Observations | Collection Method | Description | |--------|-------------|-------------------|-------------| | Funding rates | 266,570 | API-collected (real-time via exchange APIs, 8h intervals) | Annualized funding rates across 20 perpetual tokens | | Cross-exchange spreads | 10 pairs | API snapshot (real-time price comparison across venues) | Price spreads across Binance, Hyperliquid, Asterdex | | Funding dispersion | 20 tokens | Derived from API-collected funding data | Per-token funding rate range (min/max/avg) over 1,159 samples | | Trade records | 0 | API-collected (exchange order fills) | No active trades during sample period | | FPGA benchmarks | Literature | Published academic/industry benchmarks | Results from Xilinx/Intel FPGA ANN implementations | Data source: Funding rate and spread data was collected via real-time exchange APIs (Binance, Hyperliquid, Asterdex) as part of the PerpsTrader production monitoring system. FPGA benchmarks are sourced from published literature. No data was simulated or backtested. Limitation: This paper is a systems design analysis, not a controlled experiment. We have no trades to evaluate whether faster vector search produces better execution. The FPGA latency figures are derived from published benchmarks and scaling laws, not measured on our hardware. The funding rate data provides the problem space (what needs to be searched) rather than evidence that FPGA search improves outcomes. The Vector Search Problem in Trading Why Vector Similarity Matters for Perpetual Futures Modern quantitative trading increasingly relies on embedding-based representations of market state. Instead of rule-based heuristics ("funding rate above X"), the approach encodes multi-dimensional market snapshots into dense vectors and searches for historical analogs: The question is: once you have 266,570 such embeddings (one per funding rate observation), how fast can you find the nearest historical analog to the current market state? The Scale of the Problem Our funding rate database contains 266,570 observations across 20+ tokens. If each 8-hour funding period generates one embedding per token, the index grows by 60 vectors per day. After 18 months of collection: - Vector count: 266K and growing - Dimensionality: 64-128 (typical for financial embeddings) - Query requirement: <1ms for real-time signal generation - Update frequency: Every 8 hours per token (batch), or continuous for live price embeddings What Our Data Shows About Search Complexity The funding rate dispersion data reveals why simple threshold-based detection fails and similarity search i