Multi-Factor Integration and Strategy Engine: Building a Data-Driven Investment Decision System

In previous discussions, we explored how to screen companies using financial factors, how to build a foundational market data platform, and how to capture sentiment signals from news and communities. In this article, we go a step further and focus on the core of a strategy system—the "Strategy Engine": how to integrate data from multiple dimensions into a factor framework, and on that basis, build, test, and optimize investment strategies.

This represents a crucial leap from data to decision-making, marking the practical application of quantitative research to strategy execution.

1. What Is a "Multi-Factor Strategy Engine"?

A multi-factor strategy doesn't rely on a single data dimension (such as PE ratio, sentiment scores, or technical indicators) but instead combines various types of data—financial, emotional, technical, and behavioral—into a unified decision framework. Through quantitative integration, it forms the logic for stock selection, market timing, and risk control.

For example, a given stock may have the following factor characteristics:

By combining these factors, investors gain a more comprehensive view of each stock's fundamentals and market behavior, improving predictive power and strategy robustness.

2. Factor Fusion Model Design

Here’s an example of a simplified linear fusion model to create a multi-factor stock scoring system:

score = (
0.25 * valuation_score +      # Valuation (undervalued preferred)
0.25 * growth_score +         # Growth (high growth preferred)
0.20 * sentiment_score +      # Sentiment (positive news/comments)
0.15 * momentum_score +       # Momentum (strong recent trends)
0.15 * popularity_score       # Popularity (community attention)
)

Each score can be standardized to a [0, 1] scale using percentile or z-score normalization:

def standardize(z, min_val, max_val):
return (z - min_val) / (max_val - min_val)

With this scoring system, you can automatically rank stocks like AAPL, TSLA, and NVDA daily and generate a multi-factor leaderboard, providing a data-informed foundation for stock selection.

3. Strategy Engine Architecture

To implement the above logic and workflow, we recommend building a modular strategy engine architecture as follows:

? strategy_engine/
├── fetchers/           # Data fetching modules (Luckdata API wrappers)
│   └── fundamentals.py         # Financial and earnings data
│   └── sentiments.py           # News and community sentiment
│   └── pricing.py              # Price and technical data
├── factors/            # Single factor definition and processing
│   └── valuation.py
│   └── growth.py
│   └── momentum.py
├── model/              # Factor fusion logic and scoring
│   └── scoring.py
├── backtest/           # Strategy backtesting and trade simulation
│   └── signal_generation.py
│   └── portfolio_simulation.py
├── reports/            # Results reporting and visualization
│   └── score_ranking.py
│   └── daily_email.py

This structure is flexible, scalable, and easy to maintain. All data interfaces should ideally use the standardized Luckdata API, greatly reducing development time and improving system stability.

4. Strategy Case Study: Sentiment + Valuation Fusion Factor

Let’s look at a practical, executable strategy model:

Strategy Name: Positive Expectation + Undervalued Stock Picker

Strategy Logic:

1. Daily, select the 50 stocks with the lowest PE or PB ratios;

2. Filter for stocks with a sentiment score > 0.7 using Luckdata’s sentiment API;

3. Combine valuation and sentiment into a weighted score;

4. Rank by score and select the top 10 stocks as the next day’s investment pool;

5. Rebalance the portfolio weekly to maintain relevance.

Core API Usage and Code:

valuation_data = fetch_valuation(symbols)        # Fetch valuation data
sentiment_data = fetch_sentiments(symbols)       # Fetch sentiment scores
merged_df = score_merge(valuation_data, sentiment_data)  # Merge and score
top10 = merged_df.sort_values('score', ascending=False).head(10)

This hybrid strategy captures both intrinsic value and market perception, providing a balanced and adaptive stock-picking approach suitable for mid-term execution.

5. Strategy Evaluation and Backtesting

To measure the effectiveness of a multi-factor strategy, consider the following performance dimensions:

You can also expand your evaluation with:

• Alpha Analysis: Determine strategy excess return relative to a benchmark (e.g., SPY);

• Sector Neutrality: Ensure factor exposure is not overly concentrated in one sector;

• Stability Tests: Evaluate performance across bull, bear, and sideways markets.

These evaluations help ensure your strategy is not just backtest-optimized, but truly robust across market conditions.

6. Advantages of Using Luckdata Integration

Luckdata offers powerful infrastructure to support multi-factor strategy development, providing several key advantages over traditional data pipelines:

• ✅ Structured multi-dimensional data (financial, sentiment, technical) ready to use;

• ✅ Coverage across markets and asset types (US stocks, Hong Kong stocks, ETFs, indices);

• ✅ Built-in sentiment analysis and relative strength indicators;

• ✅ Native integration with notebooks, BI tools (e.g., Tableau, Power BI);

• ✅ Support for backtesting, screening, and real-time signal deployment.

These features dramatically reduce the technical and operational cost of building and deploying quantitative strategies.

7. Outlook: Factor Strategy + Automated Execution Loop

As your system matures, you can evolve toward a fully automated, end-to-end quantitative execution loop:

1. Automatically update factor data daily via scheduled scripts and API;

2. Push strategy signals and leaderboard reports to Slack, Feishu, or email;

3. Integrate with broker APIs to automate trade execution and rebalancing;

4. Implement multi-strategy comparisons, real-time performance dashboards, and alert systems.

This architecture transforms data into actionable strategy and then into executed positions—enabling a smart, data-driven investment machine.

Conclusion

Building a robust multi-factor strategy engine is a key step toward fully implementing data-driven investment research. Whether you're developing a stock picker, sector rotation, timing strategy, or risk control system, Luckdata empowers you to:

• Efficiently access structured factor data;

• Rapidly validate strategy effectiveness and robustness;

• Connect data, analysis, and execution into a seamless loop.

This is not just a technological upgrade—it’s a paradigm shift in how we turn data into alpha.

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