基于LazyBear压缩动量指标的量化交易策略
Author: ChaoZhang, Date: 2024-02-05 14:48:01Tags:
概述
本策略基于LazyBear的压缩动量指标,加入了动量过滤器,改变了数据源,并增加了风险管理系统,可自定义回测时间段,旨在捕捉波动性压缩后的价格爆发。
策略原理
该策略使用布林带指标和Keltner通道指标计算价格通道,当价格突破通道时,视为波动性加大的信号。它结合了LazyBear的压缩动量指标,该指标使用线性回归方法判断价格动量方向。
策略加入了动量过滤器,只有当动量绝对值超过阈值时才会发出交易信号。当波动性压缩(通道内收紧),且动量过滤通过时,策略判断趋势方向,做多或做空。同时设置止损、止盈、追踪止损来控制风险。
优势分析
该策略集成了多种指标判断,比较全面;加入风险管理机制,可以限制单笔损失;在波动性压缩后能及时判断价格趋势方向;参数可自定义,适应性强。
风险分析
风险主要存在:虚假突破导致错误判断;参数设置不当,未能及时反转;止损被突破造成损失扩大。可以优化参数,调整风险管理参数,选择合适品种和交易时段来降低这些风险。
优化方向
可以考虑结合其他指标过滤信号,例如交易量指标;调整动量阈值更精确;设置回撤止损以进一步控制风险;测试更多品种数据效果。这些优化可以使策略更稳定和泛化。
总结
本策略较全面判断价格趋势和波动性,集成程度高,风险控制措施较完善,可根据优化方向做进一步改进,对捕捉波动性压缩后的价格爆发有很强的适应性。
/*backtest
start: 2024-01-05 00:00:00
end: 2024-02-04 00:00:00
period: 2h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
// @version=4
// Strategy based on LazyBear Squeeze Momentum Indicator
// © Bitduke
// All scripts: https://www.tradingview.com/u/Bitduke/#published-scripts
strategy(shorttitle="SMS", title="Squeeze Momentum Strategy", overlay=false )
length = input(12, title="BB Length")
mult = input(2.0, title="BB MultFactor")
lengthKC = input(16, title="KC Length")
mult_kc = input(1.5, title="KC MultFactor")
//FILTERS
useMomAverage = input(false, title="Filter for Momenutum value", type=input.bool)
MomentumMin = input(20, title="Min for momentum")
// Calculate BB
src = ohlc4
ma_1 = sma(src, length)
ma_2 = sma(src, lengthKC)
range_ma = sma(high - low, lengthKC)
dev = mult * stdev(src, length)
upper_bb = ma_1 + dev
lower_bb = ma_1 - dev
upper_kc = ma_2 + range_ma * mult_kc
lower_kc = ma_2 - range_ma * mult_kc
sqz_on = lower_bb > lower_kc and upper_bb < upper_kc
sqz_off = lower_bb < lower_kc and upper_bb > upper_kc
no_sqz = sqz_on == false and sqz_off == false
val = linreg(src - avg(avg(highest(hl2, lengthKC), lowest(low, lengthKC)), sma(hl2, lengthKC)), lengthKC, 0)
bcolor = iff(val > 0, iff(val > nz(val[1]), color.lime, color.green), iff(val < nz(val[1]), color.red, color.maroon))
scolor = no_sqz ? color.blue : sqz_on ? color.black : color.aqua
plot(val, color=bcolor, style=plot.style_histogram, linewidth=4)
plot(0, color=scolor, style=plot.style_cross, linewidth=2)
//LOGIC
//momentum filter
filterMom = useMomAverage ? abs(val) > MomentumMin / 100000 ? true : false : true
//standard condition
longCondition = scolor[1] != color.aqua and scolor == color.aqua and bcolor == color.lime and filterMom
exitLongCondition = bcolor == color.green
shortCondition = scolor[1] != color.aqua and scolor == color.aqua and bcolor == color.red and filterMom
exitShortCondition = bcolor == color.maroon
// Risk Management Sysyem
stop_loss = input(defval = 600, title="Stop Loss", minval = 0)
take_profit = input(defval = 1000, title="Take Profit", minval = 0)
trailing_stop = input(defval = 20, title="Trailing Stop", minval = 0)
// If the zero value is set for stop loss, take profit or trailing stop, then the function is disabled
s_loss = stop_loss >= 1 ? stop_loss : na
tk_profit = take_profit >= 1 ? take_profit : na
tr_stop = trailing_stop >= 1 ? trailing_stop : na
//STRATEGY
strategy.entry("SQ_Long", strategy.long, when=longCondition)
strategy.exit("Exit Long", from_entry = "SQ_Long", profit = take_profit, trail_points = trailing_stop, loss = s_loss)
strategy.close("SQ_Long", exitLongCondition)
strategy.entry("SQ_Short", strategy.short, when=shortCondition)
strategy.exit("Exit Short", from_entry = "SQ_Short", profit = take_profit, trail_points = trailing_stop, loss = s_loss )
strategy.close("SQ_Short", when=exitShortCondition)
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