1️⃣ Data Engine and Feature Engineering

  • Data Source: Integrates K-line data (1min, 5min, 15min) from mainstream venues, combined with on-chain player traffic data.

  • Feature Engineering: In addition to conventional OHLCV, also introduces:

    • EMA/RSI/Bollinger Bands

    • Increment of on-chain active addresses

    • Platform Arbitrage Price Difference Indicator

    • High-Frequency Capital Flow Indicator

  • By fusing multi-dimensional data, stronger signal inputs are constructed for the quantitative model.

2️⃣ Model Architecture: Multi-Model Fusion Automation

🚀 Module A: Deep Iteration

Employs LSTM+Attention networks, specifically designed to identify nonlinear market fluctuations. Dynamically looks at the future 10-minute return distribution through a sliding window approach.

🚀 Module B: Statistical Arbitrage Factor Model

Uses cointegration analysis + Z-Score normalization to capture mean reversion opportunities after price deviations. Dynamic parameters self-adjust and correct signals in real-time.

🚀 Module C: Reinforcement Decision Maker

Combines DDPG/SAC algorithms to map signals from Module A+B to continuous trading actions, outputting buy/sell execution and position ratios. Incorporates drawdown penalties and Sharpe ratio optimization factors into the reward function.


💡 These three modules are interconnected under an automated framework, with the system automatically selecting signal weights based on market changes to achieve adaptive trading.

🧪 Historical Real Data (2023.1-2025.1)

  • Initial: 20000U

  • Cumulative: +125%

  • Annual: About 42%

  • Drawdown: 9%

  • Sharpe Ratio: 1.9

🔬 Strategy Advantage: Automation + Quantitative Dual-Drive

✅ Automation: 7×24 hours of uninterrupted trading, capturing small market fluctuations.
✅ Quantitative Model: Combines deep iteration, statistical arbitrage, and reinforcement to dynamically adjust strategies.
✅ Multi-Factor Input: Technical indicators, on-chain data, and capital flow to reduce the risk of a single signal failure.
✅ Controllable Risk: Dynamic stop-loss, extreme drawdown monitoring, and abnormal volatility circuit breaker mechanism.


The real key lies in data-driven and continuous optimization, not just the simple actions of 'buying and selling.'

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