Multi-Agent Collaboration + Reinforcement Learning + Private Knowledge Graphs
Four specialized agents, like the analysts, researchers, traders and risk officers of a mini investment bank.
Monitors Douyin / TikTok / global media 24/7, extracting events from text, images and video
Searches the private knowledge graph, matches historical patterns, outputs heat, weight and probability
Reinforcement-learning-driven trade decisions across stocks, funds and futures
Position and risk constraints, drawdown control, fully auditable
The Trading Agent evolves through continuous interaction with real and simulated markets β the more it trades, the smarter it gets.
Models 1-30 day return horizons with cost and slippage constraints to avoid overfitting.
Handles non-stationary markets β style shifts, bull/bear transitions β with dynamic adjustments.
Auto-trades from historical experience. AI never sleeps, capturing every timing edge.
Knowledge graphs fused into LLM private data; multimodal understanding of global events.
Always online, watching global internet around the clock β far beyond human analysts.
Reads global media in many languages, real-time and efficient.
AI replaces humans in reading industry information: no fatigue, no errors, far more efficient.
AI links events to stock moves, breaking past the limits of pure curve analysis.
Finance demands speed and efficiency β AI delivers the timing edge.
Benchmarked against Goldman Sachs and Morgan Stanley's multimodal quant models.
Industry knowledge graphs are TetraAI's core private asset, injected into LLM reasoning via graph retrieval and vectorization.
Social event β demand expectations rise β related sector rallies. The graph builds the full "event β sector β target" path.
Sentiment event (heat 90 / weight 83) β falling sales expectations β 87% decline probability, sell advice.
Policy parsing β benefiting industries β early anticipation of capital flows and rotation.
US events move US stocks β and A-shares too. A global knowledge graph enables cross-market research.
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