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An AI conversational financial analysis system that combines real-time data scraping for forex, cryptocurrencies, metals, and energy with large language models (LLM), providing traders with structured and actionable market insights
Section 01
An AI conversational financial analysis system that combines real-time data scraping for forex, cryptocurrencies, metals, and energy with large language models (LLM), providing traders with structured and actionable market insights
Section 02
Section 03
In the field of financial trading, the timeliness and accuracy of information often determine the success or failure of trades. While traditional financial data services provide abundant market data, traders still need to spend a lot of time filtering and interpreting this information. With the maturity of large language model (LLM) technology, it has become possible to combine real-time financial data with AI intelligent analysis to provide traders with immediate, structured market insights.
ForexFactoryScrapperChat is an open-source project born from this concept. It is not just a simple data scraping tool, but a complete intelligent analysis system that can understand natural language queries, automatically determine whether economic data is needed, and generate professional market analysis reports through multi-source LLM providers.
Section 04
The project adopts a strictly decoupled clean code architecture, with core components including:
Section 05
The system uses the function calling capability or structured output of LLM to determine whether the user's query requires accessing economic calendar data. This design allows the system to automatically route between conversational chat and targeted data scraping, enabling true intelligent interaction.
Section 06
The project fully integrates the independent ForexFactoryScrapper engine API, supporting multi-source data queries covering:
Section 07
Using Pydantic models ensures the JSON Schema compliance of LLM analysis, completely eliminating the risk of raw text hallucinations and ensuring the reliability of downstream parsing. This design is crucial for the accuracy of financial data.
Section 08
The system implements a fully abstracted provider layer, allowing seamless switching between the following options:
Local Inference (Offline/Privacy-First)
Cloud Inference (Blazing Fast Response)
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