Name

基于超趋势指标与净值曲线交易的量化策略Quantitative-Trading-Strategy-Based-on-Supertrend-Indicator-and-Equity-Curve-Trading

Author

ChaoZhang

Strategy Description

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概述

本策略的核心思路是结合超趋势指标与净值曲线交易,当超趋势指标发出买入或卖出信号时,我们并不直接执行交易,而是判断当前的净值曲线是否低于其移动平均线。只有当净值曲线高于移动平均线时,我们才会打开仓位。当净值曲线低于移动平均线时,我们会暂停当前策略的交易。这样可以有效防止亏损的扩大。

策略原理

本策略主要由两部分组成:

超趋势指标
净值曲线交易

超趋势指标的计算公式如下:

上轨 = 源价 - ATR乘数 * ATR 下轨 = 源价 + ATR乘数 * ATR

其中,ATR表示平均真实波幅。超趋势指标利用ATR来设置上下轨,当价格突破上轨时为卖出信号,当价格突破下轨时为买入信号。

净值曲线交易的思想是,我们对策略净值曲线进行移动平均,当净值曲线低于移动平均线时,暂停当前策略的交易,等待净值曲线回升超过移动平均线后再次开启交易。

本策略将两者结合,超趋势指标产生交易信号后,我们不直接交易,而是判断当前净值曲线是否高于移动平均线。只有两者同时符合条件,我们才会打开仓位。这样可以有效规避超趋势指标本身的风险,防止亏损过大。

优势分析

本策略的主要优势有:

可以有效防止超趋势指标的风险。超趋势指标本身无法有效规避亏损,净值曲线交易可以弥补这一缺点。
当交易不利时,可以暂停策略的交易,避免亏损过大。等到市场转好可以再次开启。
可以自动管理仓位,不需要人工干预。当净值曲线低于移动平均线时自动暂停,高于时自动开启。

风险分析

本策略也存在一定的风险:

参数设置错误可能导致无法有效发挥净值曲线交易的效果。需要选择合适的移动平均线周期。
当行情趋势变化时,可能无法及时调整仓位。这会导致一定的亏损。
由于需要等待净值曲线回升,可能会错过较好的入场时机。

对策:

优化参数,选择最佳的移动平均线周期。
结合其他指标判断趋势,及时调整仓位。
适当缩短暂停交易的时间,降低错过入场的概率。

优化方向

本策略可以从以下几个方面进行优化:

测试不同的参数组合,找到最佳的ATR周期和ATR乘数。
尝试其他类型的移动平均线,如指数移动平均线、 Hull移动平均线等。
添加其他指标判断行情趋势,在趋势变化时及时调整仓位。
优化移动平均线周期,找到最佳平衡点。周期过长会错过机会,过短会频繁暂停。
优化暂停交易的条件,如设置止损线,只有亏损达到一定程度后才暂停。

总结

本策略巧妙地结合了超趋势指标和净值曲线交易。既保留了超趋势指标判断趋势的优点,又通过净值曲线交易有效控制了风险。测试结果表明,在大多数情况下,应用净值曲线交易都会降低盈利水平。所以本策略更适合防守型的交易者。通过参数优化和规则优化,本策略可以成为一个非常实用的量化交易策略。

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Overview

The core idea of this strategy is to combine the Supertrend indicator with equity curve trading. When the Supertrend indicator generates a buy or sell signal, we do not directly execute the trade. Instead, we check if the current equity curve is below its moving average. We will open positions only when the equity curve is above the moving average. When the equity curve is below the moving average, we will pause trading for the current strategy. This can effectively prevent the expansion of losses.

Strategy Logic

This strategy mainly consists of two parts:

Supertrend Indicator
Equity Curve Trading

The calculation formula for the Supertrend indicator is:

Upper Band = Source Price - ATR Multiplier * ATR Lower Band = Source Price + ATR Multiplier * ATR

Where ATR stands for Average True Range. The Supertrend indicator uses the ATR to set the upper and lower bands. A breakout above the upper band stands for a sell signal, while a breakout below the lower band stands for a buy signal.

The idea behind equity curve trading is that we take the moving average of the strategy's equity curve. When the equity curve falls below its moving average, we pause trading for the current strategy and wait for the equity curve to rebound back above the moving average before re-enabling trading.

This strategy combines the two techniques, so that after the Supertrend indicator generates a trading signal, we do not directly enter trades. Instead, we check if the current equity curve is above its moving average. Only when both conditions are met will we open positions. This can effectively mitigate the risks inherent in the Supertrend indicator itself and prevent excessive losses.

Advantages

The main advantages of this strategy are:

It can effectively prevent the risks of the Supertrend indicator. The Supertrend indicator itself cannot effectively curb losses. Equity curve trading makes up for this shortcoming.
When trading becomes unfavorable, it pauses trading to avoid excessive losses. We can resume trading after the market recovers.
It can automatically manage positions without manual intervention. Trading is paused automatically when the equity curve falls below the moving average, and resumed when the equity curve rebounds back above it.

Risks

There are also some risks with this strategy:

Incorrect parameter settings may render equity curve trading ineffective. Appropriate moving average periods need to be selected.
It may fail to adjust positions promptly when market trend changes. This can lead to certain losses.
It may miss good trading opportunities while waiting for the equity curve to rebound.

Counter measures:

Optimize parameters and select the best moving average period.
Incorporate other indicators to judge trend and adjust positions accordingly.
Shorten the duration of suspended trading to lower the probability of missing opportunities.

Optimization Directions

We can optimize the strategy from the following aspects:

Test different parameter combinations to find the optimal ATR period and multiplier.
Try other types of moving averages, like Exponential Moving Average, Hull Moving Average etc.
Add other indicators to determine market trend, and adjust positions when trend changes.
Optimize the moving average period to find the best balance. Too long periods may miss opportunities, while too short may pause too frequently.
Optimize the conditions to pause trading, like setting a stop loss threshold before suspension.

Conclusion

This strategy cleverly combines the Supertrend indicator with equity curve trading, leveraging the strengths of both techniques. The test results show that in most cases, applying equity curve trading actually decreases profitability. As such, this strategy is more suitable for defensive traders. With parameter and logic optimization, it can become a very practical quantitative trading strategy.

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Strategy Arguments

Argument	Default	Description
v_input_bool_1	true	Show Original Equity Curve and MA
v_input_bool_2	true	Show Filtered Equity Curve by MA
v_input_int_1	25	(?New Equity Curve Settings)EQ EMA len
v_input_1	10	(?SuperTrend Settings)ATR Period
v_input_2_hl2	0	Source: hl2
v_input_float_1	3	ATR Multiplier
v_input_3	true	Change ATR Calculation Method ?

Source (PineScript)

/*backtest
start: 2023-01-14 00:00:00
end: 2024-01-14 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy('Supertrend & Equity curve with EMA', overlay=false, format=format.price, precision=2, initial_capital=100000)

eqlen = input.int(25, "EQ EMA len", group = "New Equity Curve Settings")
shEQandMA = input.bool(true, "Show Original Equity Curve and MA")
shEQfilt = input.bool(true, "Show Filtered Equity Curve by MA")

Periods = input(title='ATR Period', defval=10, group = "SuperTrend Settings")
src = input(hl2, title='Source', group = "SuperTrend Settings")
Multiplier = input.float(title='ATR Multiplier', step=0.1, defval=3.0, group = "SuperTrend Settings")
changeATR = input(title='Change ATR Calculation Method ?', defval=true, group = "SuperTrend Settings")

//SuperTrend Code
atr2 = ta.sma(ta.tr, Periods)
atr = changeATR ? ta.atr(Periods) : atr2
up = src - Multiplier * atr
up1 = nz(up[1], up)
up := close[1] > up1 ? math.max(up, up1) : up
dn = src + Multiplier * atr
dn1 = nz(dn[1], dn)
dn := close[1] < dn1 ? math.min(dn, dn1) : dn
trend = 1
trend := nz(trend[1], trend)
trend := trend == -1 and close > dn1 ? 1 : trend == 1 and close < up1 ? -1 : trend

// Strategy main code
buySignal = trend == 1 and trend[1] == -1
sellSignal = trend == -1 and trend[1] == 1
if buySignal
    strategy.entry('Long', strategy.long)
if sellSignal
    strategy.entry('Short', strategy.short)



//Equity Curve calcs
eq = strategy.netprofit
ch = ta.change(eq)
neq = ch != 0 ? eq : na
mova = ta.ema(neq,eqlen)

// New Equity Curve
var float neweq = 0
var int ttrades = 0
var int wintrades = 0
var int losetrades = 0

switch
    strategy.netprofit == strategy.netprofit[1]  => na
    strategy.netprofit < mova and strategy.netprofit[1] > mova  => neweq := neweq + ch
    strategy.netprofit < mova and strategy.netprofit[1] < mova => na
    strategy.netprofit > mova and strategy.netprofit[1] > mova => neweq := neweq + ch

newch = ta.change(neweq)
switch
    newch == 0 => na
    newch > 0 => 
        wintrades := wintrades +1
        ttrades := ttrades +1
    newch < 0 =>
        losetrades := losetrades +1
        ttrades := ttrades +1

//plot(eq, linewidth = 2)
//plot(mova, color=color.red)
//plot(neweq, color= color.green, linewidth = 3)


//Table 
var testTable = table.new(position = position.top_right, columns = 5, rows = 10, bgcolor = color.green, border_width = 1)
table.cell(table_id = testTable, column = 0, row = 0, text = "Strategy: ", bgcolor=color.white)     
table.cell(table_id = testTable, column = 1, row = 0, text = "Original: ", bgcolor=color.white)     
table.cell(table_id = testTable, column = 2, row = 0, text = "Equity Curve EMA: ", bgcolor=color.white)     

table.cell(table_id = testTable, column = 0, row = 1, text = "Total Trades: ", bgcolor=color.white)     
table.cell(table_id = testTable, column = 0, row = 2, text = "Win Trades: ", bgcolor=color.white)     
table.cell(table_id = testTable, column = 0, row = 3, text = "Lose Trades: ", bgcolor=color.white)     
table.cell(table_id = testTable, column = 0, row = 4, text = "Win Rate: ", bgcolor=color.white)     
table.cell(table_id = testTable, column = 0, row = 5, text = "Net Profit: ", bgcolor=color.white)     

//Equity Curve EMA stat
table.cell(table_id = testTable, column = 2, row = 1, text = str.tostring(ttrades), bgcolor=color.white)     
table.cell(table_id = testTable, column = 2, row = 2, text = str.tostring(wintrades), bgcolor=color.white)
table.cell(table_id = testTable, column = 2, row = 3, text = str.tostring(losetrades), bgcolor=color.white)
table.cell(table_id = testTable, column = 2, row = 4, text = str.tostring(math.round(100*wintrades/ttrades,2)), bgcolor=color.white)
table.cell(table_id = testTable, column = 2, row = 5, text = str.tostring(math.round(neweq)), bgcolor=color.white)

//Original Strategy stat
// table.cell(table_id = testTable, column = 1, row = 1, text = str.tostring(strategy.closedtrades), bgcolor=color.white)     
// table.cell(table_id = testTable, column = 1, row = 2, text = str.tostring(strategy.wintrades), bgcolor=color.white)
// table.cell(table_id = testTable, column = 1, row = 3, text = str.tostring(strategy.losstrades), bgcolor=color.white)
// table.cell(table_id = testTable, column = 1, row = 4, text = str.tostring(math.round(100*strategy.wintrades/strategy.closedtrades,2)), bgcolor=color.white)
// table.cell(table_id = testTable, column = 1, row = 5, text = str.tostring(math.round(strategy.netprofit)), bgcolor=color.white)




//New Equity curve
var newcurve = array.new_float(0)
var int ida = 0
var bool printEQ = false
if newch !=0 
    array.push(newcurve, neweq)
if bar_index > last_bar_index - array.size(newcurve) - 1 - 20  and array.size(newcurve) > 20 
    printEQ := true
else
    printEQ := false

plot(printEQ and ida < strategy.closedtrades and shEQfilt ? array.get(newcurve, ida) : na, color=color.green, linewidth = 2)

if printEQ
    ida := ida + 1
if ida >= array.size(newcurve) and printEQ
    ida := array.size(newcurve) -1



//Original Equity curve
var newcurve2 = array.new_float(0)
var int ida2 = 0
var bool printEQ2 = false
if ch !=0 
    array.push(newcurve2, eq)
if bar_index > last_bar_index - array.size(newcurve2) - 1 - 20  and array.size(newcurve2) > 20 
    printEQ2 := true
else
    printEQ2 := false

plot(printEQ2 and ida2 < strategy.closedtrades and shEQandMA  ? array.get(newcurve2, ida2) : na, color=color.blue, linewidth = 2)

if printEQ2
    ida2 := ida2 + 1
if ida2 >= array.size(newcurve2) and printEQ2
    ida2 := array.size(newcurve2) -1



//Moving Average Array
var marray = array.new_float(0)
if ch
    array.push(marray, mova)

plot(printEQ2 and  array.size(marray) > 40 and shEQandMA ? array.get(marray, ida2-1) : na, color=color.red, linewidth = 1)

hline(0,"0 line", color=color.black, linestyle = hline.style_dotted)


if (last_bar_index-1) and array.size(newcurve2) > 20 and array.size(newcurve) > 20
    l = label.new(bar_index+2, array.get(newcurve2, array.size(newcurve2)-1), "Original Equity Curve", color=color.rgb(33, 149, 243, 85), textcolor = color.black, style = label.style_label_left)
    label.delete(l[1])
    f = label.new(bar_index+2, array.get(newcurve, array.size(newcurve)-1), "Filtered Equity Curve", color=color.rgb(69, 238, 97, 85), textcolor = color.black, style = label.style_label_left)
    label.delete(f[1])

Detail

https://www.fmz.com/strategy/438771

Last Modified

2024-01-15 11:41:53

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概述

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优势分析

风险分析

优化方向

总结

Overview

Strategy Logic

Advantages

Risks

Optimization Directions

Conclusion

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基于超趋势指标与净值曲线交易的量化策略Quantitative-Trading-Strategy-Based-on-Supertrend-Indicator-and-Equity-Curve-Trading.md

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概述

策略原理

优势分析

风险分析

优化方向

总结

Overview

Strategy Logic

Advantages

Risks

Optimization Directions

Conclusion