# Fortune: A Financial Quantitative Analysis System Driven by Hybrid Human-Machine Intelligence

> An intelligent quantitative analysis assistant for financial assets integrating large language model (LLM) reasoning and machine learning (ML) prediction, focusing on the Hong Kong stock market, cryptocurrency, and gold markets. It adopts multi-time dimension prediction and cross-validation strategies to achieve fully automated trading signal generation and delivery.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-14T23:12:39.000Z
- 最近活动: 2026-06-14T23:20:01.228Z
- 热度: 150.9
- 关键词: 量化交易, 机器学习, 大语言模型, 金融AI, 港股, CatBoost, 人机混合智能, 算法交易
- 页面链接: https://www.zingnex.cn/en/forum/thread/fortune-dc349427
- Canonical: https://www.zingnex.cn/forum/thread/fortune-dc349427
- Markdown 来源: floors_fallback

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## Introduction: Core Highlights of the Fortune Hybrid Human-Machine Intelligence Financial Quantitative System

Fortune is a financial quantitative analysis system integrating large language model (LLM) reasoning and machine learning (ML) prediction, focusing on the Hong Kong stock market, cryptocurrency, and gold markets. Its core innovation lies in the hybrid human-machine intelligence architecture, which achieves fully automated trading signal generation and delivery through multi-time dimension prediction and cross-validation strategies, with commercial potential. The project is open-sourced on GitHub and maintained by wonglaitung.

## Project Background and Philosophy

### Project Basic Information
- Original Author/Maintainer: wonglaitung
- Source Platform: GitHub
- Original Link: https://github.com/wonglaitung/fortune
- Release Date: 2026-06-14

### Core Philosophy
Traditional quantitative systems rely on single indicators or models and struggle to handle market sentiment and complex events. Fortune proposes the **hybrid human-machine intelligence** philosophy, combining LLM's ability to understand unstructured information with ML's precise prediction. It maintains objectivity while flexibly adapting to market contexts, focusing on real-time monitoring and analysis of the Hong Kong stock market, cryptocurrency, and gold markets.

## Core Design Methods: Hybrid Architecture and Multi-Time Dimension Prediction

### Hybrid Human-Machine Intelligence Architecture
- **LLM Reasoning Layer**: Processes unstructured information such as macro events, policy changes, and market sentiment
- **ML Prediction Layer**: Predicts rise/fall probability based on 1023 technical features
- **Cross-Validation Mechanism**: Multi-time dimension signal resonance triggers recommendations, reducing false positive rates

### Multi-Time Dimension Prediction System
| Prediction Cycle | Accuracy | Features | Usage |
|---------|--------|------|------|
| 1-day | 51.49% | High noise, for reference only | Intraday trading reference |
| 5-day | 65.86% | Trend confirmation, auxiliary decision | Weekly position decision |
| 20-day | 81.22% | Most reliable, main decision basis | Monthly investment direction |

Three-cycle cross-validation supports short, medium, and long-term trading.

## Trading Modes and Rules: Strategies Based on Three-Cycle Signals

### Core Trading Mode Performance
The system identifies 8 trading signal modes (1/0 represents rise/fall), with core modes as follows:
| Mode | Description | 20-day Accuracy | Strategy Recommendation |
|------|------|-----------|----------|
| 101 | False breakout (1 rise, 5 fall, 20 rise) | 87.32% | Best long entry opportunity, short-term pullback followed by mid-term rise |
| 111 | Consistent bullish | 86.26% | Add positions after trend confirmation |
| 001 | Downward continuation (1 fall,5 fall,20 rise) | 81.05% | Buy signal after decline |
| 000 | Consistent bearish | 79.80% | Reduce positions or short |

### Four Core Trading Rules
1. False breakout long (signal 101): 87.32% accuracy
2. Consistent bullish buy (signal 111): 86.26% accuracy
3. Downward continuation long (signal 001):81.05% accuracy
4. Consistent bearish short (signal 000):79.80% accuracy

## Technical Implementation Details: Model and Feature Analysis

### Machine Learning Model
Adopts the **CatBoost gradient boosting algorithm**, with integrated feature dimensions including: technical indicators (MA, RSI, etc.), price patterns, fundamental data, market sentiment, capital flow, interest rate features, volatility models, market state, calendar effects, and network features.

### Top 10 Feature Importance
1. Volatility_30pct (30% quantile volatility)
2. MA250_Slope (250-day moving average slope)
3. Volatility_30d (30-day volatility)
4. BB_Width_MA60 (Bollinger Band width 60-day average)
5. net_cohesion_HSI_Regime_Duration (network cohesion × Hang Seng Index regime duration)
6. Volatility_70pct (70% quantile volatility)
7. Distance_Support_120d (distance to 120-day support level)
8. net_cohesion_per_GARCH_Conditional_Vol (network cohesion × GARCH conditional volatility)
9. Stock_Price_Stability_Score (stock price stability score)
10. 60d_Trend_HSI_Return_60d (60-day trend × Hang Seng Index 60-day return)

### Walk-forward Validation Results
Performance under conditions including 57 Hong Kong stocks and 12-fold cross-validation:
| Indicator | Value | Industry Standard | Evaluation |
|------|------|----------|------|
| Comprehensive Score |90/100 | - | Excellent |
| Average Sharpe Ratio |5.33 | >0.5 | Excellent |
| Average Max Drawdown |-1.04% | <-20% | Excellent |
| Average Accuracy |55.04% | >50% | Qualified |
| Average IC |0.205 | >0.05 | Excellent |
| Average Rank IC |0.231 | >0.05 | Excellent |
| Average Return |+5.08% | >0% | Positive |

## Fully Automated Architecture: Unattended Process from Data to Signals

The system achieves full automation via GitHub Actions scheduled workflows, covering:
- Data collection
- Feature calculation
- Model prediction
- Trading signal generation and email delivery

Daily auto-generated outputs:
- Hong Kong stock trading recommendation reports
- Prediction accuracy evaluation
- Monthly/quarterly/annual statistical summaries

No manual intervention is required, ensuring timely capture of trading opportunities.

## Project Value and Insights: Application Prospects of Hybrid Human-Machine Intelligence in Finance

Fortune's value lies in demonstrating the application potential of hybrid human-machine intelligence in finance:
1. **Complementary Advantages**: LLM's unstructured information understanding + ML's pattern recognition, achieving 1+1>2
2. **Interpretability**: Provides clear trading rules and pattern explanations, avoiding black-box models
3. **Transparency**: All predictions are validated via backtesting, with no hidden failure results
4. **Focus**: Optimized for Hong Kong stocks (southbound capital, Hang Seng Index correlation, etc.)

For developers, Fortune provides an excellent reference architecture and implementation example for intelligent quantitative systems.