FinAgent’s Multimodal Trading Architecture and Reflection Modules
Summary
The article introduces FinAgent, a proposed market analysis agent that combines text and visual information with historical memory, reflection, and trading tools. Its five-part design covers market analysis, retrieval from memory, short- and long-horizon reflection, memory storage, and a decision module that weighs market summaries, prior outcomes, sentiment, expert guidance, and traditional indicators before choosing to buy, sell, or hold.
The practical portion describes an MQL5 adaptation of the two reflection modules, including recurrent processing and cross-attention components, while explicitly leaving out large language models. The article explains the intended information flow and the role of historical context, but this installment does not present completed framework tests or performance evidence. The implementation continues in a later article, where the authors plan to evaluate it on historical data. Its claims about accuracy, adaptability, and profitability are therefore prospective rather than established results.
Key ideas
- FinAgent combines market text and visual inputs with historical context to inform trading decisions.
- A memory system retrieves relevant past information through vector similarity search.
- Low-level reflection targets short-term price behavior, while high-level reflection reviews longer-term patterns and past decisions.
- The decision module combines market analysis, prior outcomes, sentiment, expert advice, and traditional indicators.
- The article implements reflection modules in MQL5 but omits large language models and does not report completed performance tests.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.