This strategy is an advanced trading approach based on multi-dimensional mathematical models, utilizing multiple mathematical functions and technical indicators to generate trading signals. The strategy combines momentum, trend, and volatility analysis, integrating market information from multiple dimensions to make more comprehensive trading decisions.
The core principle of this strategy is to analyze different aspects of the market through multiple mathematical models and technical indicators:
The strategy considers these factors comprehensively, issuing a buy signal when momentum is positive, short-term trend is rising, long-term trend is confirmed, and volatility is moderate. The opposite combination of conditions triggers a sell signal.
The multi-dimensional mathematical model trading strategy is a comprehensive trading method with a solid theoretical foundation. By combining multiple mathematical models and technical indicators, this strategy can analyze the market from multiple angles, improving the accuracy of trading decisions. However, the complexity of the strategy also brings risks such as overfitting and parameter sensitivity. Future optimization directions should focus on improving the strategy’s adaptability and robustness to maintain stable performance in different market environments. Overall, this is a promising strategy framework that, through continuous optimization and testing, has the potential to become a reliable trading tool.
/*backtest start: 2019-12-23 08:00:00 end: 2024-09-24 08:00:00 period: 1d basePeriod: 1d exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}] */ //@version=5 strategy("Advanced Math Strategy", overlay=true) // ======================= // ฟังก์ชันที่ใช้คำนวณเบื้องหลัง // ======================= // ฟังก์ชันซิกมอยด์ sigmoid(x) => 1 / (1 + math.exp(-x)) // ฟังก์ชันหาอัตราการเปลี่ยนแปลง (Derivative) roc = ta.roc(close, 1) // ฟังก์ชันการถดถอยเชิงเส้น (Linear Regression) linReg = ta.linreg(close, 14, 0) // ฟังก์ชันตัวกรองความถี่ต่ำ (Low-pass filter) lowPass = ta.ema(close, 50) // ======================= // การคำนวณสัญญาณ Buy/Sell // ======================= // การคำนวณอนุพันธ์สำหรับทิศทางการเคลื่อนที่ของราคา derivativeSignal = roc > 0 ? 1 : -1 // ใช้ Linear Regression และ Low-pass Filter เพื่อช่วยในการหาจุดกลับตัว trendSignal = linReg > lowPass ? 1 : -1 // ใช้ฟังก์ชันซิกมอยด์เพื่อปรับความผันผวนของราคา priceChange = close - close[1] volatilityAdjustment = sigmoid(priceChange) // สร้างสัญญาณ Buy/Sell โดยผสมผลจากการคำนวณเบื้องหลังทั้งหมด buySignal = derivativeSignal == 1 and trendSignal == 1 and volatilityAdjustment > 0.5 sellSignal = derivativeSignal == -1 and trendSignal == -1 and volatilityAdjustment < 0.5 // ======================= // การสั่ง Buy/Sell บนกราฟ // ======================= // ถ้าเกิดสัญญาณ Buy if (buySignal) strategy.entry("Buy", strategy.long) // ถ้าเกิดสัญญาณ Sell if (sellSignal) strategy.close("Buy") // ======================= // การแสดงผลบนกราฟ // ======================= // วาดเส้นถดถอยเชิงเส้นบนกราฟ plot(linReg, color=color.green, linewidth=2, title="Linear Regression") // วาดตัวกรองความถี่ต่ำ (Low-pass filter) plot(lowPass, color=color.purple, linewidth=2, title="Low-Pass Filter") // วาดจุด Buy/Sell บนกราฟ plotshape(series=buySignal, title="Buy Signal", location=location.belowbar, color=color.green, style=shape.labelup, text="BUY") plotshape(series=sellSignal, title="Sell Signal", location=location.abovebar, color=color.red, style=shape.labeldown, text="SELL")template: strategy.tpl:40:21: executing "strategy.tpl" at <.api.GetStrategyListByName>: wrong number of args for GetStrategyListByName: want 7 got 6