🎮 Cai Meng Consensus Circle (财萌共识圈)

Gamified Asset Allocation Assistant for Gen-Z Investors > Embedded Mini-Program for Ping An Securities App

📖 Executive Summary (项目概述)

Cai Meng Consensus Circle is a data-driven "Social Discovery" FinTech product designed for Gen-Z investors. It addresses the pain point of investment anxiety and blindly following trends.

By translating complex financial logic into MOBA Gaming Concepts (e.g., "Tank" for Conservative, "Marksman" for Aggressive), it helps young investors understand their risk profiles, analyze "peer consensus," and conduct professional attribution analysis.

• Role: Product Manager & Lead Algo Developer
• Context: FinTech Product Design / Ping An Securities User Scenario
• Key Tech: Risk Parity Model, Brinson Attribution, Monte Carlo Simulation, React.

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🎯 User Pain Points (用户痛点)

Through user research on Gen-Z investors, we identified three core misalignments:

1. Information Overload: "Too many stock codes, who should I listen to?"
2. Risk Mismatch: Conservative users copying aggressive strategies (FOMO), leading to panic selling.
3. Lack of Feedback: "Winning feels like luck, losing feels like a scam." No professional attribution tools.

"We don't teach you how to gamble. We help you replay the game."

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💡 Product Logic: The "Gaming" Metaphor (核心逻辑)

We redefined the investment journey using concepts familiar to digital natives: MOBA Games.

Investment Concept	Gaming Metaphor	User Value
Risk Profile	Role (Hero Class)	Define "Who am I?" (Tank/Marksman/Mage)
Asset Allocation	Equipment Build	"Don't buy naked damage items on a Tank."
Trend Analysis	Meta / Tier List	See what top players of your class are buying.
Stress Testing	Battle Simulation	Simulate 10,000 matches (market crashes).

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✨ Key Features (核心功能)

1. Trend Radar & Consensus Discovery (趋势发现)

• Feature: Visualizes the "Trend Radar" based on real-time holdings of peer groups.
• Algo: Clustering analysis of anonymized user positions.
• Value: Helps users identify "Best Sellers" (Consensus) within their specific risk profile (e.g., "What ETFs are other 'Marksmen' buying?").

2. Attribution Analysis (深度归因)

• Feature: Deconstructs portfolio returns to distinguish between "Luck" (Beta) and "Skill" (Alpha).
• Algo: Brinson Model & Barra Risk Factors.
• Metric: Displays "Win Rate," "KDA" (Sharpe Ratio), and "Damage Dealt" (Total Return).

3. Stress Testing Lab (验证实验室)

• Feature: Before actual trading, users can test their portfolios against extreme scenarios (e.g., "Market Crash," "Interest Rate Hike").
• Algo: Monte Carlo Simulation (10,000+ iterations).
• Output: Calculates "Survival Probability" and "Max Drawdown" (Lowest HP).

4. Smart Rebalancing (一键调仓)

• Feature: Generates optimized "Equipment Builds" (Portfolios).
• Algo: Risk Parity Model. Automatically balances "Core Assets" (Fixed Income/Gold) and "Satellite Assets" (Tech/Growth).

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🛠 Technical Architecture (技术架构)

The system is designed with a lightweight frontend and a heavy-duty quantitative backend.

Tech Stack

• Frontend: React 18, Tailwind CSS, Recharts (for financial visualization).
• Backend: Python, Flask/FastAPI.
• Quant Engine:
  • Pandas + SciPy: Risk Parity calculation.
  • Barra Model: Factor exposure analysis.
  • Monte Carlo: Tail risk simulation.
• Data: PostgreSQL (User Profile), Redis (Real-time Market Data).

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📈 Results & Performance (实盘表现)

We backtested and ran the "Conservative Core + Aggressive Satellite" strategy generated by the consensus engine:

• Period: Sep 2025 - Nov 2025
• Return: +11.86%
• Alpha: Outperformed CSI 300 (Benchmark) by +7.55%.
• Business Impact: Transformed traditional "One-way Education" into "Two-way Consensus," improving user retention and rationality.

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Disclaimer: This project is a conceptual prototype designed for the Ping An Securities user scenario. Financial data is for demonstration purposes.