Overview

This strategy is a quantitative trading system that combines Z-Score statistical methods, Relative Strength Index (RSI), and Supertrend indicators. The strategy monitors statistical price deviations, combines momentum indicators and trend confirmation to identify high-probability trading opportunities in the market. This strategy not only captures market overbought and oversold opportunities but also filters false signals through trend confirmation, enabling two-way trading.

Strategy Principle

The core logic of the strategy is built on the synergy of three main technical indicators: First, it calculates the Z-score of prices to measure the current price's deviation from its historical mean, using a 75-period moving average and standard deviation. When the Z-score exceeds 1.1 or falls below -1.1, it indicates significant statistical deviation. Second, the RSI indicator is introduced as momentum confirmation, requiring RSI to align with the direction (RSI>60 for long positions, RSI<40 for short positions). Finally, the Supertrend indicator serves as a trend filter, calculated based on an 11-period ATR and a multiplier factor of 2.0. Trading signals are only generated when all three conditions are simultaneously satisfied.

Strategy Advantages

1. Multiple Signal Confirmation: Combining indicators from statistical, momentum, and trend dimensions greatly improves the reliability of trading signals.
2. High Adaptability: The Z-score calculation method allows the strategy to adapt to different market environments, independent of absolute price levels.
3. Comprehensive Risk Control: The Supertrend indicator provides automatic trend following and risk control mechanisms.
4. Bilateral Trading: The strategy can capture opportunities in both long and short directions, improving capital utilization efficiency.
5. Clear Signals: The strategy uses clear mathematical models and objective indicators, avoiding subjective judgment.

Strategy Risks

1. Lag Risk: Due to the use of multiple period moving averages, the strategy may experience signal delays in rapidly changing markets.
2. False Breakout Risk: Frequent false breakout signals may occur in ranging markets.
3. Parameter Sensitivity: The strategy's effectiveness highly depends on parameter selection, different market environments may require different parameter settings.
4. Market Condition Dependency: The strategy's performance may not be ideal in markets without clear trends.

Strategy Optimization Directions

1. Dynamic Parameter Adjustment: Introduce adaptive parameter mechanisms to automatically adjust Z-score thresholds and Supertrend parameters based on market volatility.
2. Enhanced Market Environment Filtering: Add market environment recognition modules to use different parameter combinations under different market conditions.
3. Improved Stop Loss Mechanism: Introduce dynamic stop-loss strategies, such as ATR-based stops or trailing stops.
4. Optimized Signal Filtering: Add volume confirmation or other technical indicators to further filter trading signals.
5. Time-Based Filtering: Consider adding trading time window restrictions to avoid highly volatile periods.

Summary

This is a quantitative trading strategy that combines statistical methods and technical analysis, using multiple signal confirmations to improve trading reliability. The strategy's core advantages lie in its objective mathematical model and comprehensive risk control mechanisms, while attention must be paid to parameter optimization and market adaptability. Through the suggested optimization directions, there is room for further improvement, especially in dynamically adapting to market environments and risk control. This strategy is suitable for markets with high volatility and clear trends, making it a worthy consideration for quantitative traders pursuing steady returns.