Deerfield Green Prototypes

Data-driven prototypes exploring prediction markets, geopolitical risk, and supply chain intelligence.

Augur screenshot Network
Augur
Polymarket prediction market graph analyzing cross-domain contagion, correlated price movements, capital flows, and whale conviction patterns across 1,800+ markets.
StackMemgraphPythonFastAPINext.jsD3.jsTailwind CSS
AI
GraphMemgraph stores 6 node types (Market, Trade, User, Event, Tag, Outcome) with statistical overlays (CO_TRADED, CORRELATED, CAPITAL_FLOW, WHALE_LINKED).
Time Series4-hour price bucketing for Pearson correlation; Granger causality lag detection across markets.
Anomaly DetectionMispricing identification where correlated markets diverge in price; whale clustering as outlier behavior.
Next
  1. Granger causality inference to identify which markets lead others, with prediction error scoring for information leakage
  2. Real-time streaming pipeline (Kafka to Memgraph to WebSocket) replacing static data snapshots
  3. Arbitrage exploit detection integrating AMM liquidity curves with slippage estimates and fill simulation
 Baros screenshot Index
Baros
Composite crisis-peace index fusing 62 Polymarket geopolitical prediction markets with Global Peace Index (GPR) indicators to surface emerging geopolitical risk before traditional indicators react.
StackClickHouseKafkaPythonFastAPINext.jsTailwind CSS
AI
Time SeriesClickHouse stores intraday market snapshots; composite index is a forecastable time series (ARIMA, Prophet, or LLM-based).
Ensemble MethodsWeighted aggregation of Polymarket sentiment and GPR indicators; weights tunable per geopolitical zone.
NLPMarket descriptions parsed for geopolitical keywords (sanctions, military, border); LLM-based semantic risk scoring.
Next
  1. Multi-scale time-series decomposition (STL/wavelet) to separate trend, seasonality, and anomalies across historical geopolitical events
  2. Regional sub-indices (Middle East, South China Sea, Eastern Europe) with separate weighting schemes
  3. LLM-powered news-to-risk pipeline feeding Reuters/AP newswire through event extraction and auto-tagging markets
 Palimpsest screenshot Analytics
Palimpsest
SEC 10-K knowledge graph overlay for FANG equities, extracting 4.4M structured triples from filings to expose risk disclosure patterns, strategic shifts, and hidden supply chain dependencies across companies and years.
StackNeo4jPythonHuggingFace DatasetsFinReflectKGNext.jsTailwind CSS
AI
NLPLLM-based information extraction converts unstructured 10-K prose into (subject, predicate, object) triples; NER identifies RISK_FACTOR, FIN_METRIC, PRODUCT, SEGMENT entities.
GraphNeo4j queries compute entity centrality, risk clustering, and temporal evolution of disclosure patterns across filings.
VectorJaccard similarity of risk sets between FANG companies; entity density scoring reveals how much explanation companies devote to specific risks.
Next
  1. Cross-FANG comparative analysis adding META, GOOGL, NFLX, AMZN with side-by-side risk profiles and asymmetric exposure detection
  2. Causal inference linking extracted risks to stock price movements via causal forest or Granger causality
  3. Supply chain reconstruction from entity-link predicates to identify single-point-of-failure suppliers across multiple 10-Ks
 Pelagos screenshot Risk Model
Pelagos
Maritime supply chain disruption risk platform ingesting AIS vessel tracking, port congestion signals, and Polymarket logistics markets to identify emerging disruptions before they impact earnings.
StackNeo4jClickHouseBytewaxKafkaPythonFastAPINext.jsDeck.glMaplibreTailwind CSS
AI
Geospatial MLBytewax processes streaming AIS data with Rtree spatial indexing and Shapely geofencing for route anomaly detection.
Time SeriesVessel speed/heading over time; port occupancy forecasting; statistical process control on dwell time and queue length.
GraphNeo4j encodes fleet ownership networks, inter-port shipping patterns, and vessel operational constraints to detect bottleneck routes.
Next
  1. Predictive port queue models using Prophet/LSTM to forecast occupancy and alert on projected 7+ day queues in high-margin corridors
  2. Commodity supply chain impact simulation tracing electronics, oil, and rare earths through the graph with disruption scenario modeling
  3. Multi-modal fusion integrating satellite imagery for port congestion estimation with AIS spoofing detection classifiers
 Yatagarasu screenshot Platform
Yatagarasu
LangGraph agent swarm for Japan-focused VC that sources, researches, and scores startup deals across 7 weighted dimensions using parallel LLM agents, web search, and vector memory to output ranked deal lists with investment memos.
StackLangGraphNovita AIQdrantClickHouseTavilyPerplexityPythonFastAPINext.jsEChartsTailwind CSS
AI
AgenticLangGraph orchestrates 7 parallel scoring agents (market, team, product, traction, syndication, risk, japan_fit) with dynamic tool routing.
LLMEach dimension agent uses chain-of-thought scoring with JSON schema constraints; output formatter generates natural-language investment memos.
RAGQdrant stores past research for recall; Perplexity provides sourced citations; Parallel.ai discovers comparable companies.
Next
  1. Portfolio-level impact scoring computing how each deal affects portfolio risk, sector concentration, and FX/geopolitical hedging
  2. Founder social proof integration scraping Crunchbase, LinkedIn, and AngelList for network size and past investor reviews
  3. Preference learning feedback loop accepting investor pass/invest decisions to fine-tune dimension weights via Bradley-Terry model