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双轨跟踪反转策略Dual-Channel-Tracking-Reversal-Strategy.md

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Name

双轨跟踪反转策略Dual-Channel-Tracking-Reversal-Strategy

Author

ChaoZhang

Strategy Description

IMG [trans]

概述

双轨跟踪反转策略是一个结合布林带,Keltner通道以及动量指标的反转交易策略。该策略通过布林带和Keltner通道的综合判断,识别价格进入压缩区域的时机;同时结合动量指标判断价格的反转信号,形成交易入场和出场信号。

策略原理

  1. 计算布林带中的中轨、上轨、下轨

    • 中轨采用close的SMA
    • 上下轨是中轨加减一个可调整的倍数的标准差

  2. 计算Keltner通道中的中轨、上轨、下轨

    • 中轨采用close的SMA
    • 上下轨是中轨加减一个可调整的倍数的ATR

  3. 判断布林带是否位于Keltner通道内侧

    • 当布林带上轨低于Keltner上轨,布林带下轨高于Keltner下轨时,认为处于压缩
    • 反之,非压缩

  4. 计算close与布林带、Keltner通道中点的线性回归斜率val

    • val > 0 表示close在上升,val < 0 表示close在下降

  5. 计算close的变化率ROC和其EMA

    • 判断变化率是否达到可调整的阈值
    • 如果超过阈值,认为处于趋势中

  6. 在压缩时,当val > 0且变化率达到阈值时做多

    • 反之做空

  7. 设置止损、止盈条件

策略优势

  1. 结合双轨系统判断反转时点,提高准确率

  2. 增加线性回归和变化率判断,避免反转假信号

  3. 可调整的参数设置灵活,可针对不同品种进行优化

  4. 采用止损止盈策略,可以有效控制单次交易风险

  5. 回测数据充足,可验证策略有效性

策略风险及解决方案

  1. 双轨压缩不一定产生有效反转

    • 优化参数,严格双轨压缩条件

  2. 假突破产生错误信号

    • 增加线性回归判定,确定趋势方向

  3. 止损设置过于宽松,单次亏损过大

    • 优化止损点,严格控制单次亏损

  4. 测试周期 Datenichinhalt

    • 增加更多回测周期,验证长期有效性

策略优化方向

  1. 优化参数设置,适应更多品种

  2. 增加机器学习判断支撑阻力关键点

  3. 结合交易量变化提高突破真实性

  4. 增加跨时间段分析,判断趋势持续性

  5. 优化止损止盈策略,实现动态追踪

总结

双轨跟踪反转策略总体来说是一个利用布林带Keltner通道等指标进行的反转策略。该策略通过参数优化,可以适应不同品种,在一定程度上识别突破的真实性。但反转交易本身仍存在一定的风险,需要进一步结合机器学习等技术来提升判断准确性,从而获得更稳定的超额收益。

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Overview

The Dual Channel Tracking Reversal strategy is a reversal trading strategy that combines Bollinger Bands, Keltner Channels, and momentum indicators. It identifies price compression zones through the synthesis of Bollinger Bands and Keltner Channels, and utilizes momentum indicators to determine reversal signals for entry and exit.

Strategy Logic

  1. Calculate the middle, upper and lower bands for Bollinger Bands

    • Middle band uses SMA of close
    • Upper and lower bands are middle band ± adjustable standard deviation multiple

  2. Calculate the middle, upper and lower bands for Keltner Channels

    • Middle band uses SMA of close
    • Upper and lower bands are middle band ± adjustable ATR multiple

  3. Determine if Bollinger Bands are inside Keltner Channels

    • Squeeze on when lower BB > lower KC and upper BB < upper KC
    • Squeeze off otherwise

  4. Calculate the linear regression slope val of close against BB and KC midpoints

    • val > 0 indicates close is increasing, val < 0 means decreasing

  5. Calculate the ROC and EMA of ROC for close

    • Determine if change rate exceeds adjustable threshold
    • Above threshold indicates existing trend

  6. When in squeeze, long when val > 0 and ROC exceeds threshold

    • Vice versa for short

  7. Set stop loss and take profit conditions

Advantages

  1. Improved accuracy by combining dual channel system for reversal

  2. Avoid false signals using linear regression and change rate

  3. Flexible adjustable parameters for optimization across products

  4. Effective risk control per trade with stop loss/take profit

  5. Sufficient backtest data to validate strategy viability

Risks and Solutions

  1. Squeeze does not always lead to effective reversal

    • Optimize parameters and tighten squeeze criteria

  2. False breakouts generate wrong signals

    • Add linear regression to determine trend direction

  3. Stop loss too wide leading to excessive single loss

    • Optimize stop loss points and control per trade loss

  4. Insufficient test periods

    • Expand testing into more periods to prove long-term viability

Enhancement Opportunities

  1. Parameter optimization for more products

  2. Add machine learning for support/resistance identification

  3. Incorporate volume change to improve breakout validity

  4. Perform multi-timeframe analysis for trend persistence

  5. Optimize dynamic stop loss/take profit

Conclusion

The Dual Channel Tracking Reversal strategy utilizes indicators like Bollinger Bands and Keltner Channels for reversal trading. With parameter optimization, it can be adapted across different products to identify breakout validity to some extent. But reversal trading still carries inherent risks, requiring further incorporation of machine learning etc. to improve accuracy for steady excess returns.

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Strategy Arguments

Argument Default Description
v_input_1 2012 Backtest Start Year
v_input_2 true Backtest Start Month
v_input_3 true Backtest Start Day
v_input_4 2019 Backtest Stop Year
v_input_5 12 Backtest Stop Month
v_input_6 31 Backtest Stop Day
v_input_7 20 BB Length
v_input_8 2 BB MultFactor
v_input_9 22 KC Length
v_input_10 1.5 KC MultFactor
v_input_11 true Use TrueRange (KC)
v_input_12 30 roclength
v_input_13 7 pcntChange
v_input_14 100 Stop Loss %
v_input_15 5000 Take Profit %

Source (PineScript)

/*backtest
start: 2023-10-02 00:00:00
end: 2023-11-01 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
// Credit for the initial Squeeze Momentum code to LazyBear, rate of change code is from Kiasaki
strategy("Squeeze X BF ?", overlay=false, initial_capital=10000, default_qty_type=strategy.percent_of_equity, default_qty_value=100, commission_type=strategy.commission.percent, commission_value=0.0)

/////////////// Time Frame ///////////////
testStartYear = input(2012, "Backtest Start Year") 
testStartMonth = input(1, "Backtest Start Month")
testStartDay = input(1, "Backtest Start Day")
testPeriodStart = timestamp(testStartYear,testStartMonth,testStartDay, 0, 0)

testStopYear = input(2019, "Backtest Stop Year")
testStopMonth = input(12, "Backtest Stop Month")
testStopDay = input(31, "Backtest Stop Day")
testPeriodStop = timestamp(testStopYear,testStopMonth,testStopDay, 0, 0)

testPeriod() => true

/////////////// Squeeeeze ///////////////
length = input(20, title="BB Length")
mult = input(2.0,title="BB MultFactor")
lengthKC=input(22, title="KC Length")
multKC = input(1.5, title="KC MultFactor")
 
useTrueRange = input(true, title="Use TrueRange (KC)")
 
// Calculate BB
source = close
basis = sma(source, length)
dev = mult * stdev(source, length)
upperBB = basis + dev
lowerBB = basis - dev

// Calculate KC
ma = sma(source, lengthKC)
range = useTrueRange ? tr : (high - low)
rangema = sma(range, lengthKC)
upperKC = ma + rangema * multKC
lowerKC = ma - rangema * multKC

sqzOn  = (lowerBB > lowerKC) and (upperBB < upperKC)
sqzOff = (lowerBB < lowerKC) and (upperBB > upperKC)
noSqz  = (sqzOn == false) and (sqzOff == false)

val = linreg(source - avg(avg(highest(high, lengthKC), lowest(low, lengthKC)),sma(close,lengthKC)), lengthKC,0)

///////////// Rate Of Change ///////////// 
roclength = input(30, minval=1), pcntChange = input(7, minval=1)
roc = 100 * (source - source[roclength]) / source[roclength]
emaroc = ema(roc, roclength / 2)
isMoving() => emaroc > (pcntChange / 2) or emaroc < (0 - (pcntChange / 2))

/////////////// Strategy ///////////////
long = val > 0 and isMoving()
short = val < 0 and isMoving()

last_long = 0.0
last_short = 0.0
last_long := long ? time : nz(last_long[1])
last_short := short ? time : nz(last_short[1])

long_signal = crossover(last_long, last_short)
short_signal = crossover(last_short, last_long)

last_open_long_signal = 0.0
last_open_short_signal = 0.0
last_open_long_signal := long_signal ? open : nz(last_open_long_signal[1])
last_open_short_signal := short_signal ? open : nz(last_open_short_signal[1])

last_long_signal = 0.0
last_short_signal = 0.0
last_long_signal := long_signal ? time : nz(last_long_signal[1])
last_short_signal := short_signal ? time : nz(last_short_signal[1])

in_long_signal = last_long_signal > last_short_signal
in_short_signal = last_short_signal > last_long_signal

last_high = 0.0
last_low = 0.0
last_high := not in_long_signal ? na : in_long_signal and (na(last_high[1]) or high > nz(last_high[1])) ? high : nz(last_high[1])
last_low := not in_short_signal ? na : in_short_signal and (na(last_low[1]) or low < nz(last_low[1])) ? low : nz(last_low[1])

sl_inp = input(100.0, title='Stop Loss %') / 100
tp_inp = input(5000.0, title='Take Profit %') / 100
 
take_level_l = strategy.position_avg_price * (1 + tp_inp)
take_level_s = strategy.position_avg_price * (1 - tp_inp)

since_longEntry = barssince(last_open_long_signal != last_open_long_signal[1]) 
since_shortEntry = barssince(last_open_short_signal != last_open_short_signal[1]) 

slLong = in_long_signal ? strategy.position_avg_price * (1 - sl_inp) : na
slShort = strategy.position_avg_price * (1 + sl_inp)
long_sl = in_long_signal ? slLong : na
short_sl = in_short_signal ? slShort : na

/////////////// Execution ///////////////
if testPeriod()
    strategy.entry("Long",  strategy.long, when=long)
    strategy.entry("Short", strategy.short, when=short)
    strategy.exit("Long Ex", "Long", stop=long_sl, limit=take_level_l, when=since_longEntry > 0)
    strategy.exit("Short Ex", "Short", stop=short_sl, limit=take_level_s, when=since_shortEntry > 0)
    
/////////////// Plotting ///////////////
bcolor = iff(val > 0, iff(val > nz(val[1]), color.lime, color.green), iff(val < nz(val[1]), color.red, color.maroon))
plot(val, color=bcolor, linewidth=4)
bgcolor(not isMoving() ? color.white : long ? color.lime : short ? color.red : na, transp=70)
bgcolor(long_signal ? color.lime : short_signal ? color.red : na, transp=50)
hline(0, color = color.white)

Detail

https://www.fmz.com/strategy/430881

Last Modified

2023-11-02 16:31:50