NQ-Signal-Research-Node: An Autonomous Research Pipeline for Evaluating Financial Trading Signals Using Large Models

NQ-Signal-Research-Node is an innovative autonomous research pipeline that uses large language models like Mistral Small 3.2 and Qwen 2.5 to validate trading signals from NQ futures institutional data. It evaluates the hallucination rate and latency performance of models in high-risk financial logic via the "Judge Agent" mechanism. This project aims to solve the industry problem where traditional manual backtesting is time-consuming and labor-intensive, and struggles to cover edge cases, providing an automated solution for verifying the reliability of financial trading signals.

In the financial trading field, NQ futures (Nasdaq 100 Index Futures) are among the most liquid and heavily traded index futures contracts. Institutional investors generate massive trading signals daily, but verifying the reliability of these signals and evaluating the performance of automated decision systems have long been industry challenges. Traditional manual backtesting methods are time-consuming and labor-intensive, and it's hard to cover all edge cases. The NQ-Signal-Research-Node project innovatively introduces large language models (LLMs) as "Judge Agents" to build an autonomous research pipeline for automated evaluation of trading signal quality.

Dual-Model Judge System

The project uses two large language models, Mistral Small 3.2 and Qwen 2.5, to work collaboratively:

• Mistral Small 3.2: An efficient model from Europe's Mistral AI, balancing inference speed and cost-effectiveness, and excelling at structured instructions and formatted outputs.
• Qwen 2.5: A multilingual model from Alibaba's Tongyi Qianwen series, with accurate understanding of Chinese financial terminology and stable complex logical reasoning. The two models reduce the bias and errors of a single model through cross-validation.

Judge-Agent Verification Mechanism

The core innovation lies in the "Judge Agent" design:

1. Signal Input Layer: Receives trading signals from different data sources such as technical indicators, fundamentals, and sentiment.
2. Context Construction: Provides context for each signal, including market environment (trend, volatility, trading volume), generation logic, historical performance, and risk parameters.
3. Dual-Judge Evaluation: The two models independently score and annotate, with evaluation dimensions including logical consistency, risk rationality, and market matching degree, outputting structured reports.
4. Consistency Check: Compares results, calculates divergence and confidence; signals with large divergence are marked for manual review.
5. Feedback Loop: Compares actual trading results with predictions, optimizes evaluation criteria and prompts, and establishes a model performance tracking file.

Latency Measurement

Response speed is critical in high-frequency trading:

• Time to First Token (TTFT): Time from input to the model starting output.
• Full Response Time: Time to generate a complete evaluation report.
• Batch Processing Throughput: Number of signals processed per unit time. The framework records detailed time indicators to optimize deployment configurations.

Hallucination Rate Detection

Financial scenarios have extremely high requirements for accuracy:

• Factual Verification: Validate the accuracy of market data and historical prices cited by the model.
• Logical Consistency Check: Detect self-contradictions in evaluation reports.
• Edge Case Testing: Test model robustness under extreme market conditions.
• Manual Review Sampling: Regularly sample for manual review to establish a hallucination rate baseline.

Evaluation Indicator System

Indicator Category	Specific Indicator	Description
Accuracy	Prediction Accuracy	Consistency between model evaluation and actual results
Stability	Evaluation Consistency	Stability of results for multiple evaluations with the same input
Timeliness	Average Response Time	Latency from input to output
Reliability	Confidence Calibration	Matching degree between model confidence and actual accuracy
Usability	Effective Output Rate	Proportion of successfully generated valid evaluations

Data Processing Pipeline

1. Data Ingestion: Obtain real-time and historical data from exchange APIs and data providers.
2. Feature Engineering: Calculate technical indicators and construct market environment descriptions.
3. Signal Standardization: Convert signals from different sources into a unified format.
4. Batch Processing Scheduling: Schedule evaluation tasks based on priority and timeliness.

Model Deployment Optimization

• Quantized Inference: INT8/INT4 quantization reduces memory usage and latency.
• Batch Inference: Merge multiple signal processing tasks to improve GPU utilization.
• Caching Mechanism: Cache evaluation templates for common market conditions.
• Asynchronous Architecture: Decouple data ingestion, model inference, and result storage.

Result Storage and Analysis

• Use time-series databases to store evaluation results.
• Support multi-dimensional queries by time period, signal type, model version, etc.
• Built-in visualization dashboard to display trends of key indicators.

Trading Strategy Validation

• Automated evaluation of new strategies before live deployment.
• Identify potential vulnerabilities in strategy logic.
• Evaluate the adaptability of strategies in different market environments.

Signal Quality Monitoring

• Continuously monitor signal quality in production environments.
• Timely detect anomalies in signal generation systems.
• Provide data support for signal weight adjustment.

Model Selection Reference

• Compare the performance of different LLMs in financial tasks.
• Select the optimal model configuration for specific use cases.
• Establish regression testing processes for model updates.

Compliance and Auditing

• Record the basis and process of all evaluation decisions.
• Meet financial regulatory requirements for the interpretability of algorithmic trading.
• Provide a complete audit trail for post-event analysis.

Current Limitations

• The model hallucination problem still poses risks in financial scenarios; manual review is needed as the final line of defense.
• Training data for extreme market conditions (e.g., flash crashes) is scarce.
• Integration of multi-modal data (news, social media sentiment) is not yet perfect.

Future Plans

• Introduce more professional financial models for cross-validation.
• Develop hallucination detection technology specifically for the financial field.
• Explore reinforcement learning to optimize evaluation strategies.
• Establish industry benchmark datasets to promote research.

NQ-Signal-Research-Node represents an important direction for AI applications in finance—not using models directly to make trading decisions, but letting models take on the roles of "evaluator" and "supervisor". This "Human-in-the-loop" design not only leverages the powerful pattern recognition and reasoning capabilities of LLMs but also retains human final control over key decisions, providing a valuable reference paradigm for the safe application of financial AI.