Tags:

The Supertrend trading strategy is a trend-following strategy based on Average True Range (ATR) and Moving Average (MA). It incorporates the advantages of both trend tracking and breakout trading to identify the intermediate trend direction and generate trading signals based on trend changes.

The main idea behind this strategy is to go long or short when the price breaks through the Supertrend channel, indicating a trend reversal. It also sets stop loss and take profit levels to lock in gains and control risks.

The Supertrend calculation involves several steps:

- Calculate the ATR. The ATR reflects the average volatility over a period of time.
- Calculate the midline based on highest high and lowest low. The midline is calculated as: (Highest High + Lowest Low)/2
- Calculate the upper and lower channel based on ATR and ATR multiplier set by trader. The upper channel is calculated as: Midline + (ATR × Multiplier). The lower channel is calculated as: Midline - (ATR × Multiplier).
- Compare closing price with the upper/lower channel to determine trend direction. If close is above upper channel, trend is up. If close is below lower channel, trend is down.
- A breakout above or below the channel generates reverse trading signals. For example, a breakout above upper channel signals long entry while a breakdown below lower channel signals short entry.

The advantage of this strategy is it combines both trend following and trend reversal techniques. It identifies major trend while also being able to capture reversal opportunities in a timely manner. In addition, the stop loss/take profit mechanism helps control risks.

The Supertrend strategy has the following strengths:

**1. Track intermediate trend**

The Supertrend channel is calculated based on ATR, which effectively reflects the intermediate price fluctuation range. It tracks intermediate trend better than simple moving averages.

**2. Capture reversals timely**

Price breakouts from the channel quickly generate trading signals so that major trend reversals can be captured in time. This allows proper repositioning to avoid overholding.

**3. Have stop loss and take profit**

The strategy sets predefined stop loss and take profit levels for automatic exit with risk control. This significantly reduces the risk of excessive stop loss and allows better trend following.

**4. Simple to implement**

The strategy mainly uses basic indicators like MA and ATR. This makes it fairly simple to understand and implement for live trading.

**5. High capital efficiency **

By tracking intermediate trends and controlling individual slippage, the Supertrend strategy provides overall high capital efficiency.

The Supertrend strategy also has some potential weaknesses:

**1. Underperforms in ranging market**

The strategy focuses on intermediate to long term trend trading. In ranging or consolidating markets, it tends to underperform with higher opportunity cost of missing short trades.

**2. Sensitive to parameter optimization**

The values chosen for ATR period and multiplier have relatively big impacts on strategy performance. Inappropriate tuning of the parameters may compromise the effectiveness of trading signals.

**3. Lagging issues may exist**

There can be some lagging issues with Supertrend channel calculation, causing untimely signal generation. Fixing the lagging problem should be a priority.

**4. Strict stop loss management required**

In extreme market conditions, improperly large stop loss allowance or inadequate risk management could lead to heavy losses. Strictly following stop loss rules is critical for consistent profitability.

There is further room to optimize this Supertrend strategy:

**1. Combine multiple ATR periods**

Combining ATR readings over different periods like 10-day and 20-day forms a composite indicator, which helps improve sensitivity and lagging issues.

**2. Add stop loss modules**

Adding more sophisticated stop loss mechanisms like triple stop loss, volatility stop loss and sequential stop loss could strengthen risk control and drawdown reduction.

**3. Parameter optimization**

Optimizing values for ATR period, multiplier and other inputs through quantitative methods would further lift strategy performance. Parameters can also be dynamically tuned based on different products and market regimes.

**4. Integrate machine learning models**

Finally, integrating machine learning models may realize automated trend recognition and signal generation, reducing reliance on subjective decisions and improving system stability.

The Supertrend trading strategy identifies intermediate trend direction using MA and ATR indicators, and generates trade entry and exit signals around trend reversals with automated stop loss/take profit implementation. While keeping with major trends, it also captures some reversal opportunities. The main advantages lie in intermediate trend tracking, trend reversal identification and risk control through stop loss/take profit.

However, some deficiencies also exist regarding insufficient range-bound market capture and lagging problems. Further optimizations can be explored across multiple dimensions, including using composite ATR, strengthening stop loss modules, tuning parameters, and integrating machine learning models. These enhancements will likely improve the stability and efficiency of the Supertrend strategy.

/*backtest start: 2022-11-30 00:00:00 end: 2023-11-30 00:00:00 period: 1d basePeriod: 1h exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}] */ //@version=3 strategy("Supertrend V1.0 - Buy or Sell Signal",overlay=true) Factor=input(3, minval=1,maxval = 100) Pd=input(7, minval=1,maxval = 100) //Calculating ATR atrLength = input(title="ATR Length:", defval=14, minval=1) Stop_Loss_Factor = input(1.5, minval=0,step=0.01) factor_profit = input(1.0, minval=0,step=0.01) // === INPUT BACKTEST RANGE === FromMonth = input(defval = 4, title = "From Month", minval = 1, maxval = 12) FromDay = input(defval = 10, title = "From Day", minval = 1, maxval = 31) FromYear = input(defval = 2016, title = "From Year", minval = 2009) ToMonth = input(defval = 4, title = "To Month", minval = 1, maxval = 12) ToDay = input(defval = 10, title = "To Day", minval = 1, maxval = 31) ToYear = input(defval = 2039, title = "To Year", minval = 2017) // === FUNCTION EXAMPLE === start = timestamp(FromYear, FromMonth, FromDay, 00, 00) // backtest start window finish = timestamp(ToYear, ToMonth, ToDay, 23, 59) // backtest finish window window() => time >= start and time <= finish ? true : false // create function "within window of time" // Calculate ATR atrValue=atr(atrLength) decimals = abs(log(syminfo.mintick) / log(10)) Atr = atrValue if(decimals == 5) Atr := atrValue * 10000 if(decimals == 4) Atr := atrValue * 1000 if(decimals == 3) Atr := atrValue * 100 if(decimals == 2) Atr := atrValue * 10 //VJ2 Supertrend Up=hl2-(Factor*atr(Pd)) Dn=hl2+(Factor*atr(Pd)) TrendUp = 0.0 TrendUp:=close[1]>TrendUp[1]? max(Up,TrendUp[1]) : Up TrendDown = 0.0 TrendDown:=close[1]<TrendDown[1]? min(Dn,TrendDown[1]) : Dn Trend = 0.0 Trend := close > TrendDown[1] ? 1: close< TrendUp[1]? -1: nz(Trend[1],1) Tsl = 0.0 Tsl := Trend==1? TrendUp: TrendDown linecolor = Trend == 1 ? green : red plot(Tsl, color = linecolor , style = line , linewidth = 2,title = "SuperTrend") plotshape(cross(close,Tsl) and close>Tsl , "Up Arrow", shape.triangleup,location.belowbar,green,0,0) plotshape(cross(Tsl,close) and close<Tsl , "Down Arrow", shape.triangledown , location.abovebar, red,0,0) //plot(Trend==1 and Trend[1]==-1,color = linecolor, style = circles, linewidth = 3,title="Trend") plotarrow(Trend == 1 and Trend[1] == -1 ? Trend : na, title="Up Entry Arrow", colorup=lime, maxheight=60, minheight=50, transp=0) plotarrow(Trend == -1 and Trend[1] == 1 ? Trend : na, title="Down Entry Arrow", colordown=red, maxheight=60, minheight=50, transp=0) //Strategy Trend_buy = Trend == 1 Trend_buy_prev = Trend[1] == -1 algo_buy_pre = Trend_buy and Trend_buy_prev algo_buy = algo_buy_pre == 1 ? 1 : na Trend_sell= Trend == -1 Trend_sell_prev = Trend[1] == 1 algo_sell_pre = Trend_sell and Trend_sell_prev algo_sell = algo_sell_pre == 1 ? 1:na strategy.entry("Long1", strategy.long, when= window() and algo_buy==1) strategy.entry("Short1", strategy.short, when=window() and algo_sell==1) bought = strategy.position_size > strategy.position_size sold = strategy.position_size < strategy.position_size longStop = Stop_Loss_Factor * valuewhen(bought, Atr, 0) shortStop = Stop_Loss_Factor * valuewhen(sold, Atr, 0) longProfit = factor_profit * longStop shortProfit = factor_profit * shortStop if(decimals == 5) longStop := longStop *100000 longProfit := longProfit *100000 if(decimals == 4) longStop := longStop * 10000 longProfit := longProfit * 10000 if(decimals == 3) longStop := longStop * 1000 longProfit := longProfit * 1000 if(decimals == 2) longStop := longStop * 100 longProfit := longProfit *100 if(decimals == 5) shortStop := shortStop * 100000 shortProfit := shortProfit * 100000 if(decimals == 4) shortStop := shortStop * 10000 shortProfit := shortProfit * 10000 if(decimals == 3) shortStop := shortStop * 1000 shortProfit := shortProfit * 1000 if(decimals == 2) shortStop := shortStop * 100 shortProfit := shortProfit * 100 strategy.exit("Exit Long", from_entry = "Long1", loss =longStop, profit = longProfit) strategy.exit("Exit Short", from_entry = "Short1", loss =shortStop, profit = shortProfit)

- Dual-directional Trading Strategy Based on RSI and STOCH RSI
- Fast and Slow EMA Golden Cross Breakthrough Strategy
- RSI and Moving Average Combination MT5 Martingale Scalping Strategy
- Volatility Stop Tracking Strategy
- Oscillation Positioning Breakthrough Strategy with Multiple Indicators
- RSI Mean Reversion Quantitative Trading Strategy Based on RSI Average Crossing
- Dual Moving Average Crossover Reversal Strategy
- Moving Average Line Reverse Crossover Strategy
- Hull MA Channel and Linear Regression Swing Trading Strategy
- Triple SuperTrend Quantitative Trading Strategy
- Supertrend Bollinger Bands Strategy
- Double Reversal Tracking Strategy
- Holy Grail Strategy
- Multiple Percentage Profit Exits Strategy
- Modified Price-Volume Trend Strategy Based on Price-Volume Changes
- Dual Moving Average Crossover Strategy
- Dual Indicators Combo Crazy Intraday Scalping Strategy
- Dynamic Grid Trading Strategy
- Dual Moving Average Trading Strategy
- Early Profit-Taking Moving Average Opening Bell Exit Strategy