Tele-Trader: An Intelligent Trading Copy System Based on LLM Reasoning Capabilities

Tele-Trader is a Telegram trading copy tool integrated with large language model (LLM) reasoning capabilities, designed to address the pain points of traditional trading copy systems. Using the semantic understanding capabilities of LLMs, it intelligently parses unstructured trading signals and automates copy trading operations, demonstrating the innovative application value of LLMs in the field of financial automation.

In the financial trading field, Telegram has become an important channel for the dissemination of trading signals. However, manually tracking and executing signals is time-consuming and error-prone, especially in highly volatile markets where delays can lead to slippage losses. Traditional copy systems rely on fixed rules or regular expressions, making it difficult to handle unstructured, natural language-style trading advice. Tele-Trader introduces LLM reasoning capabilities, elevating signal parsing to the level of semantic understanding to solve these problems.

Tele-Trader consists of three modules:

1. Telegram Monitoring Module: Real-time monitoring of specified channels, processing multiple message formats, and providing standardized input;
2. LLM Reasoning Engine: The intelligent core that parses explicit parameters and implicit context in signals, and identifies risk prompts;
3. Trading Execution Module: Executes trades via exchange APIs, responsible for order management, risk control, and result feedback.

LLMs bring the following unique capabilities to Tele-Trader:

• Semantic Understanding: Identify the same trading intent behind different expressions without writing a large number of rules;
• Contextual Reasoning: Maintain conversation context and understand the relationship between signals and historical messages (opening/adding/closing positions);
• Risk Perception: Capture nuanced risk prompts and incorporate them into execution decisions;
• Multilingual Support: Handle multilingual signals without separate parser training;
• Unstructured Processing: Extract key trading parameters from complex messages.

Key technical implementation points include:

• Prompt Engineering: Design prompt templates containing JSON Schema to guide LLMs in extracting structured information;
• Few-Shot Learning: Improve parsing accuracy through annotated examples;
• Chain-of-Thought: Step-by-step reasoning to enhance the accuracy of complex signal parsing;
• Error Handling: Verify the validity of LLM outputs (value ranges, required fields, etc.);
• Latency Optimization: Use streaming responses, model caching, etc., to balance reasoning quality and speed.

Risk control and compliance measures:

• Fund Security: Minimum API key permissions and encrypted storage;
• Execution Confirmation: Manual confirmation required for large or abnormal transactions;
• Position Management: Limits on single transaction amounts, daily risk exposure, etc.;
• Backtesting Simulation: Full testing before live trading;
• Log Auditing: Detailed records of signals, parsing results, and execution operations.

Tele-Trader demonstrates the innovative application of LLMs in financial automation, overcoming the limitations of traditional rule-based methods. Although it faces challenges such as LLM hallucinations, cost, latency, and regulation, with model optimization and engineering improvements, LLM-driven financial tools have broad prospects and are of reference value to quantitative traders and developers.