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Super Trend Dual Moving Average Strategy

Author: ChaoZhang, Date: 2024-01-16 15:19:09
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Overview

The Super Trend Dual Moving Average Strategy is a quantitative trading strategy based on the Super Trend indicator and simple moving average. This strategy uses the Super Trend indicator to determine the direction of the market trend, and then combines the 200-day simple moving average for filtering, opening long and short positions along the major trend direction.

Strategy Logic

The strategy uses two indicators:

  1. Super Trend Indicator: It calculates the upper and lower rails based on the true volatility ATR and a multiplier. When the closing price is higher than the upper rail, it indicates a bullish view. When lower than the lower rail, it indicates a bearish view.

  2. 200-day Simple Moving Average: It takes the arithmetic average of the closing prices over the past 200 days. When the closing price is higher than this line, it represents a major bullish trend. When lower than this line, it represents a major bearish trend.

Strategy Logic:

  1. When the Super Trend indicator gives a bullish signal (Super Trend value greater than 0) and the closing price is higher than the 200-day MA, go long.

  2. When the Super Trend indicator gives a bearish signal (Super Trend value less than 0) and the closing price is lower than the 200-day MA, go short.

  3. Close the position when the Super Trend indicator gives a reverse signal against the previous one.

  4. The stop loss is set at 25%.

Advantage Analysis

The strategy combines the Super Trend indicator to determine the short-term trend and the 200-day MA to determine the long-term trend, which can effectively filter false breakouts, reduce trading frequency while improving win rate. In a significant market trend, the trend is clear enough with large stop loss space and profit target.

Risk Analysis

The main risk of this strategy is that the stop loss range is relatively large. It may increase the risk of forced liquidation in high leverage situations. In addition, when the market is range-bound, the Super Trend indicator will generate redundant signals, thus increasing transaction costs and trading frequency.

The risk can be reduced by appropriately adjusting the ATR period, multiplier parameters and stop loss range.

Optimization Directions

The strategy can be optimized in the following aspects:

  1. Adjust the ATR period and multiplier parameters to optimize the Super Trend indicator.

  2. Try other MA indicators such as EMA and VIDYA for replacement.

  3. Add other auxiliary indicators such as BOLL channel or KD indicator for further signal filtering.

  4. Optimize stop loss strategy, such as moving it to breakeven point or trailing stop along with higher timeframe levels.

Summary

Overall, this strategy is very practical. It considers both short-term trend judgment and long-term trend judgment with reasonable stop loss settings. It can achieve better results through parameter adjustment and optimization, which is worth real trading verification and application.

/*backtest
start: 2023-12-16 00:00:00
end: 2024-01-15 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// This work is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) https://creativecommons.org/licenses/by-nc-sa/4.0/
// © wielkieef

//@version=5

strategy("Smart SuperTrend Strategy ", shorttitle="ST Strategy", overlay=true, pyramiding=1, initial_capital=10000, default_qty_type=strategy.percent_of_equity, default_qty_value=25, calc_on_order_fills=false, slippage=0, commission_type=strategy.commission.percent, commission_value=0.01)


// Parametry wskaźnika SuperTrend
atrLength = input(10, title="Lenght ATR")
factor = input(3.0, title="Mult.")

// Parametry dla SMA
lengthSMA = input(200, title="Lenght SMA")

// Parametry dla Stop Loss
sl = input.float(25.0, '% Stop Loss', step=0.1)

// Obliczanie ATR
atr = ta.atr(atrLength)

// Obliczanie podstawowej wartości SuperTrend
up = hl2 - (factor * atr)
dn = hl2 + (factor * atr)

// Obliczanie 200-SMA
sma200 = ta.sma(close, lengthSMA)

// Inicjalizacja zmiennych
var float upLevel = na
var float dnLevel = na
var int trend = na
var int trendWithFilter = na

// Logika SuperTrend
upLevel := close[1] > upLevel[1] ? math.max(up, upLevel[1]) : up
dnLevel := close[1] < dnLevel[1] ? math.min(dn, dnLevel[1]) : dn

trend := close > dnLevel[1] ? 1 : close < upLevel[1] ? -1 : nz(trend[1], 1)

// Filtr SMA i aktualizacja trendWithFilter
trendWithFilter := close > sma200 ? math.max(trend, 0) : math.min(trend, 0)

// Logika wejścia
longCondition = trend == 1  
shortCondition = trend == -1  

// Wejście w pozycje
if (longCondition) and  close > sma200
    strategy.entry("Long", strategy.long)
if (shortCondition) and close < sma200
    strategy.entry("Short", strategy.short)

// Warunki zamknięcia pozycji
Long_close = trend == -1 and close > sma200
Short_close = trend == 1  and close < sma200

// Zamknięcie pozycji
if (Long_close)
    strategy.close("Long")
if (Short_close)
    strategy.close("Short")

// Kolory superTrendu z filtrem sma200
trendColor = trendWithFilter == 1 ? color.green : trendWithFilter == -1 ? color.red : color.blue

//ploty
plot(trendWithFilter == 1 ? upLevel : trendWithFilter == -1 ? dnLevel : na, color=trendColor, title="SuperTrend")

// Stop Loss ( this code is from author RafaelZioni, modified by wielkieef )
per(procent) =>
    strategy.position_size != 0 ? math.round(procent / 100 * strategy.position_avg_price / syminfo.mintick) : float(na)
// --------------------------------------------------------------------------------------------------------------------

strategy.exit('SL',loss=per(sl))



//by wielkieef
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