FinanceGPT-TestRepo

FinanceGPT-Pro

<!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <!-- markdownlint-disable no-duplicate-header --> <div align="center"> <img src="figures/fig1.png" width="60%" alt="FinanceGPT-Pro" /> </div> <hr> <div align="center" style="line-height: 1;"> <a href="LICENSE" style="margin: 2px;"> <img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/> </a> </div>

1. Introduction

FinanceGPT-Pro represents a breakthrough in AI-powered financial analysis and trading strategy development. Leveraging state-of-the-art transformer architecture with specialized pre-training on financial datasets spanning 50+ years of market data, this model excels at understanding complex market dynamics, predicting price movements, and generating actionable trading insights.

<p align="center"> <img width="80%" src="figures/fig3.png"> </p>

The model demonstrates exceptional capabilities in risk assessment, portfolio optimization, and real-time market analysis. In backtesting across major indices (S&P 500, NASDAQ, DAX), FinanceGPT-Pro achieved a Sharpe ratio improvement of 35% compared to traditional quantitative strategies.

Key improvements in this version include enhanced multi-asset correlation analysis and improved handling of black swan events through adversarial training techniques.

2. Evaluation Results

Comprehensive Benchmark Results

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Overall Performance Summary

FinanceGPT-Pro demonstrates industry-leading performance across all financial benchmarks, with particular strength in fraud detection and regulatory compliance domains.

3. API Access & Trading Platform

We provide institutional-grade API access for integrating FinanceGPT-Pro into your trading infrastructure. Contact our enterprise sales team for custom deployment options.

4. How to Run Locally

Please refer to our documentation repository for detailed setup instructions.

Important Considerations for Financial Applications:

1. Real-time Data Feed: Ensure proper connection to market data providers.
2. Risk Limits: Always implement position limits and stop-loss mechanisms.
3. Regulatory Compliance: Verify compliance with local financial regulations before deployment.

Configuration Example

from financegpt import FinanceGPT

model = FinanceGPT.from_pretrained("financegpt-pro")
model.set_risk_parameters(max_position=0.05, stop_loss=0.02)

Temperature Settings

For trading signals, we recommend temperature=0.3 for conservative predictions. For market analysis reports, use temperature=0.7 for more comprehensive insights.

Prompt Templates

For market analysis:

analysis_template = \
"""[Asset]: {ticker}
[Timeframe]: {timeframe}
[Market Data]:
{ohlcv_data}
[News Headlines]:
{recent_news}
[Query]: {analysis_question}"""

5. License

This model is licensed under Apache 2.0. Commercial use requires separate licensing agreement for trading applications.

6. Contact

For enterprise inquiries: [email protected] Technical support: [email protected]