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多时间框架超级趋势追踪策略Multi-timeframe-Super-Trend-Tracking-Strategy.md

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Name

多时间框架超级趋势追踪策略Multi-timeframe-Super-Trend-Tracking-Strategy

Author

ChaoZhang

Strategy Description

IMG [trans]

概述

该策略是一个利用ATR指标构建多时间框架动态趋势通道,实现趋势追踪的策略。策略会在价格突破通道时产生信号,通过不断调整通道捕捉更大的趋势。

策略原理

策略使用ATR指标构建上涨趋势通道和下跌趋势通道。具体来说,上涨通道线为收盘价减去ATR指标的N倍;下跌通道线为收盘价加上ATR指标的N倍。N的值可以通过参数进行调整。

当价格突破上涨通道时,产生买入信号;当价格突破下跌通道时,产生卖出信号。通道会根据最新价格动态调整,从而实现趋势追踪。

另外,策略还定义了一个trend变量判断当前处于上涨趋势还是下跌趋势。trend变量与通道线搭配使用,避免产生错误信号。

策略优势

  • 利用动态通道实现趋势追踪,顺势而为
  • 避免追高杀跌,减少行情反转的风险
  • 通道参数可调,适应性强
  • 多时间框架设置更灵活

策略风险

  • 追踪过于激进,可能增加亏损风险
  • 通道参数设置不当,信号较少或错误信号较多
  • 需要较强的编程能力去调整参数

优化方法:

  • 适当缩小ATR倍数,降低追踪幅度
  • 优化参数,找到最佳参数组合
  • 增加止损策略,降低单笔亏损

策略优化方向

  • 增加其他指标过滤,确保信号更可靠
  • 增加止损策略,降低风险
  • 进行参数优化,找到最佳参数
  • 优化进入和退出的时间,提高盈利率

总结

该策略整体来说是一个较好的趋势追踪策略。它能够动态调整,顺势而为,避免追高杀跌。通过参数优化和适当改进,可以进一步增强策略优势,减少风险,从而获得更好的效果。

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Overview

This strategy uses the ATR indicator to build a multi-timeframe dynamic trend channel to track trends. It generates signals when prices break through the channel by continuously adjusting the channel to capture larger trends.

Strategy Logic

The strategy uses the ATR indicator to build an uptrend channel and a downtrend channel. Specifically, the uptrend channel line is the closing price minus N times the ATR indicator; the downtrend channel line is the closing price plus N times the ATR indicator. The N value can be adjusted through parameters.

When the price breaks through the uptrend channel, a buy signal is generated; when the price breaks through the downtrend channel, a sell signal is generated. The channel dynamically adjusts based on the latest prices to track trends.

In addition, the strategy also defines a trend variable to determine whether the current is in an uptrend or a downtrend. The trend variable works with the channel lines to avoid generating wrong signals.

Advantages

  • Uses dynamic channels to track trends and trade along with trends
  • Avoids chasing highs and selling lows, reducing the risk of market reversal
  • Adjustable channel parameters, high adaptability
  • More flexible multi-timeframe settings

Risks

  • Overly aggressive tracking may increase loss risk
  • Improper channel parameter settings lead to fewer or more incorrect signals
  • Requires strong programming skills to adjust parameters

Improvement:

  • Appropriately reduce ATR multiplier to lower tracking magnitude
  • Optimize parameters to find the best combination
  • Add stop loss strategy to reduce per trade loss

Optimization Directions

  • Add other indicators for filter for more reliable signals
  • Add stop loss strategy to reduce risk
  • Conduct parameter optimization to find optimum
  • Optimize entry and exit timing to improve profit rate

Summary

Overall this is a decent trend tracking strategy. It dynamically adjusts to trade along with trends and avoids chasing highs and selling lows. With parameter optimization and proper improvements, the strategy advantages can be further enhanced and risks reduced to achieve better results.

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Strategy Arguments

Argument Default Description
v_input_1 10 ATR周期
v_input_2_hl2 0 价格数据源: hl2
v_input_float_1 3 ATR 乘数
v_input_3 true 更改ATR计算方法
v_input_4 false 显示买入/卖出信号
v_input_int_1 9 From Month
v_input_int_2 true From Day
v_input_int_3 2018 From Year
v_input_int_4 true To Month
v_input_int_5 true To Day
v_input_int_6 9999 To Year

Source (PineScript)

/*backtest
start: 2023-01-08 00:00:00
end: 2024-01-14 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy('超级趋势精简优化版', overlay=true)
Periods = input(title='ATR周期', defval=10)
src = input(hl2, title='价格数据源')
Multiplier = input.float(title='ATR 乘数', step=0.1, defval=3.0)
changeATR = input(title='更改ATR计算方法', defval=true,tooltip = '默认为art否则sma(ta.tr,ATR周期)')
showsignals = input(title='显示买入/卖出信号', defval=false)
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
upPlot = plot(trend == 1 ? up : na, title='上涨趋势', style=plot.style_linebr, linewidth=2, color=color.new(color.green, 0))
buySignal = trend == 1 and trend[1] == -1
plotshape(buySignal and showsignals ? up : na, title='买点', text='买点', location=location.absolute, style=shape.labelup, size=size.tiny, color=color.new(color.green, 0), textcolor=color.new(color.white, 0))
dnPlot = plot(trend == 1 ? na : dn, title='下跌趋势', style=plot.style_linebr, linewidth=2, color=color.new(color.red, 0))
sellSignal = trend == -1 and trend[1] == 1
plotshape(sellSignal and showsignals ? dn : na, title='卖点', text='卖点', location=location.absolute, style=shape.labeldown, size=size.tiny, color=color.new(color.red, 0), textcolor=color.new(color.white, 0))
FromMonth = input.int(defval=9, title='From Month', minval=1, maxval=12)
FromDay = input.int(defval=1, title='From Day', minval=1, maxval=31)
FromYear = input.int(defval=2018, title='From Year', minval=999)
ToMonth = input.int(defval=1, title='To Month', minval=1, maxval=12)
ToDay = input.int(defval=1, title='To Day', minval=1, maxval=31)
ToYear = input.int(defval=9999, title='To Year', minval=999)
start = timestamp(FromYear, FromMonth, FromDay, 00, 00)
finish = timestamp(ToYear, ToMonth, ToDay, 23, 59)
window() =>
    time >= start and time <= finish ? true : false
longCondition = buySignal
if longCondition and window()
    strategy.entry('BUY', strategy.long, comment = '买入')
shortCondition = sellSignal
if shortCondition and window()
    strategy.close('BUY',comment = '卖出')
buy1 = ta.barssince(buySignal)
sell1 = ta.barssince(sellSignal)
color1 = buy1[1] < sell1[1] ? color.green : buy1[1] > sell1[1] ? color.red : na

Detail

https://www.fmz.com/strategy/438769

Last Modified

2024-01-15 11:35:47