Name
基于布林带和卡尔金通道的动量挤压策略Squeeze-Momentum-Breakout-Strategy
Author
ChaoZhang
Strategy Description
这是一个基于 LazyBear 的动量挤压指标开发的量化交易策略。该策略集成了布林带、卡尔金通道以及动量指标,通过多种技术指标的组合,实现高胜率的动量突破交易。
该策略的核心指标是 LazyBear 的动量挤压指标。该指标判断布林带是否被卡尔金通道所“挤压”,当挤压发生时,代表着市场进入一个潜在的爆发点。配合动量指标判断方向后,可以在挤压释放的时候捕捉市场的爆发行情。
具体来说,该策略首先计算 21 周期的布林带,带宽为价格标准差的 2 倍。同时计算 20 周期的卡尔金通道,带宽为价格振幅的 1.5 倍。当布林带被卡尔金通道“挤压”时,发出挤压信号。此外,该策略还计算一段时间内价格相对其自身价格通道中点的动量。当挤压发生时,结合动量指标的方向性,判断买入还是卖出。
在出场上,当动量指标颜色变为灰色,平掉仓位,代表挤压状态结束,趋势可能反转。
- 集成多种技术指标,提高交易决策的准确性
该策略集成了布林带、卡尔金通道以及动量指标,通过判断这些指标的综合关系,可以提高交易决策的准确性,降低错误交易的概率。
- 动量挤压点位准确,潜在获利空间大
动量挤压策略可以捕捉到市场爆发的关键点位,这些点位往往是市场做出重要方向判断的拐点,如果判断正确,那么随后的行情运行会比较长,因此策略的潜在获利空间很大。
- 实现高成功率的突破交易
相比随机的突破交易,该策略选择的入场点位处于布林带与卡尔金通道的挤压点,通过指标集成判断,交易成功率很高。
- 布林带和卡尔金通道参数设置风险
布林带和卡尔金通道的周期参数及带宽参数设置会对策略交易结果有很大影响。如果参数设置不当,会导致误判。这需要通过大量回测找到最佳参数。
- 突破失败风险
任何突破交易都有失败的风险,当价格突破该策略选择的点位后,还有可能再次回调,造成损失。这需要严格止损来控制。
- 趋势反转风险
当挤压状态结束时,该策略会平掉所有仓位。但是有时价格趋势仍然可能会延续,这会造成策略提前离场的风险。这需要优化出场判断逻辑。
- 优化参数设置
可以通过更大量的回测数据试错,寻找布林带和卡尔金通道更佳的参数周期及带宽设置,以提高策略效果。
- 增加止损策略
可以设置移动止损或者振荡止损,在价格反转时快速止损,以控制策略最大回撤。
- 增加再入场条件
当策略退出仓位后,可以设置一定的再入场条件,在趋势继续延续时,可以再次进入场内。
- 结合更多指标
可以尝试结合更多不同类型的指标,如其他波动性指标、成交量指标等,建立一个指标集成的复合策略,以提高决策的准确性。
该策略集成布林带、卡尔金通道及动量指标,通过判断这些指标关系,选择高成功率的突破点位入场。在参数优化、止损策略、再入场条件以及复合指标集成等多个方面都有优化空间,可以进一步提升策略效果。
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This is a quantitative trading strategy developed based on LazyBear's Momentum Squeeze indicator. The strategy integrates Bollinger Bands, Keltner Channels, and momentum indicators to achieve high-win-rate momentum breakout trading through the combination of multiple technical indicators.
The core indicator of this strategy is LazyBear's Momentum Squeeze indicator. This indicator determines if the Bollinger Bands are being "squeezed" by the Keltner Channels. When the squeeze occurs, it represents that the market has entered a potential outbreak point. By combining the direction of the momentum indicator, trades can be taken when the squeeze releases to capture the market outbreak.
Specifically, the strategy first calculates the 21-period Bollinger Bands, with a width of 2 standard deviations of the price. At the same time, it calculates the 20-period Keltner Channels, with a width of 1.5 times the price amplitude. When the Bollinger Bands are "squeezed" by the Keltner Channels, a squeeze signal is triggered. In addition, the strategy also calculates the momentum of the price relative to the mid-point of its own price channel over a period of time. When a squeeze occurs, combined with the directionality of the momentum indicator, it determines whether to buy or sell.
For exits, when the color of the momentum indicator changes to gray, it represents that the squeeze state has ended and the trend may reverse.
- Integrates multiple technical indicators to improve accuracy
By judging the overall relationship between these indicators, the accuracy of trading decisions can be improved and the probability of erroneous trades reduced.
- Accurate momentum squeeze points with large profit potential
The momentum squeeze strategy can capture key points where the market is likely to outbreak. These points are often inflection points where the market makes important directional judgments. If judged correctly, the subsequent market movement will be relatively long, so the potential profit space of the strategy is great.
- Achieve high-success-rate breakout trading
Compared to random breakout trading, the entry point selected by this strategy is at the squeeze point between Bollinger Bands and Keltner Channels. Through the integrated indicator judgement, the trading success rate is very high.
- Risk of improper parameter settings
The cycle parameters and bandwidth parameters of the Bollinger Bands and Keltner Channels have a great impact on the trading results. If the parameters are set inappropriately, misjudgements may occur. This requires finding the optimal parameters through a lot of backtesting.
- Breakout failure risk
There is always a risk that the price may retrace after breaking through the point selected by this strategy, causing a loss. This needs to be strictly stopped out to control losses.
- Trend reversal risk
When the squeeze state ends, this strategy will close all positions. However, sometimes the price trend may still continue, which poses the risk of premature exit. The exit logic needs to be optimized.
- Optimize parameter settings
Through more backtesting data tryouts, better cycle and bandwidth parameter settings can be found to improve strategy performance.
- Add stop loss strategy
Set moving or oscillating stops to quickly cut losses when prices reverse.
- Add re-entry conditions
When the strategy exits positions, certain re-entry conditions can be set to re-enter the market if the trend continues.
- Incorporate more indicators
Try to incorporate more indicators of different types, such as other volatility indicators, volume indicators, etc., to establish a composite strategy of indicator integration, so as to improve the accuracy of decisions.
The strategy integrates Bollinger Bands, Keltner Channels and momentum indicators. By judging the relationships between these indicators, it enters at high success rate breakout points. There are optimization spaces in many aspects such as parameter optimization, stop loss strategies, re-entry conditions, and composite indicator integration to further improve strategy performance.
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Strategy Arguments
| Argument | Default | Description |
|---|---|---|
| v_input_1 | 4 | From Month |
| v_input_2 | 2020 | From Year |
| v_input_3 | true | To Month |
| v_input_4 | 9999 | To Year |
| v_input_5 | true | Trade - Leverage |
| v_input_6 | 100 | Trade - Risk Percent |
| v_input_7 | 0 | What trades should be taken : : LONG |
| v_input_8 | 21 | BB Length |
| v_input_9 | 2 | BB MultFactor |
| v_input_10 | 20 | KC Length |
| v_input_11 | 1.5 | KC MultFactor |
| v_input_12 | true | Use TrueRange (KC) |
| v_input_13 | false | Use VWMA to selectively long/short? |
| v_input_14 | 42 | VWMA Length |
| v_input_15 | false | Use Cumulative Volume for VWMA? |
Source (PineScript)
/*backtest
start: 2023-12-01 00:00:00
end: 2023-12-31 23:59:59
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
//@version=4
//All credits to LazyBear. All I did was turn it into a strategy!
strategy(title = "SQZMOM STRAT", overlay=false)
// --- GENERAL INPUTS ---
FromMonth = input(defval = 4, title = "From Month", minval = 1, maxval = 12)
FromYear = input(defval = 2020, title = "From Year", minval = 2012)
ToMonth = input(defval = 1, title = "To Month", minval = 1, maxval = 12)
ToYear = input(defval = 9999, title = "To Year", minval = 2017)
FromDay = 1
ToDay = 1
start = timestamp(FromYear, FromMonth, FromDay, 00, 00) // backtest start window
finish = timestamp(ToYear, ToMonth, ToDay, 23, 59) // backtest finish window
window() => true
get_round(value, precision) => round(value * (pow(10, precision))) / pow(10, precision)
trade_leverage = input(1, title = "Trade - Leverage", step = 0.25)
trade_risk = input(100, title = "Trade - Risk Percent", type = input.float, step = 0.1, minval = 0.1, maxval = 100)
tradeType = input("LONG", title="What trades should be taken : ", options=["LONG", "SHORT", "BOTH"])
// --- SQZMOM CODE
length = input(21, title="BB Length")
mult = input(2.0,title="BB MultFactor")
lengthKC=input(20, title="KC Length")
multKC = input(1.5, title="KC MultFactor")
useTrueRange = input(true, title="Use TrueRange (KC)", type=input.bool)
// Calculate BB
source = close
basis = sma(source, length)
dev = multKC * 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)
bcolor = color.gray
if (val > 0 and val > nz(val[1]))
bcolor := color.green
if (val < 0 and val < nz(val[1]))
bcolor := color.red
scolor = noSqz ? color.blue : sqzOn ? color.black : color.gray
plot(val, color=bcolor, style=plot.style_histogram, linewidth=4)
plot(0, color=scolor, style=plot.style_cross, linewidth=2)
// --- VWMA CODE ---
useVWMA = input(false, title = "Use VWMA to selectively long/short?", type = input.bool)
lengthVWMA=input(42, title = "VWMA Length", step = 1, minval = 1)
useCV=input(false, type=input.bool, title="Use Cumulative Volume for VWMA?")
nbfs = useCV ? cum(volume) : sum(volume, lengthVWMA)
medianSrc=close
calc_evwma(price, lengthVWMA, nb_floating_shares) => data = (nz(close[1]) * (nb_floating_shares - volume)/nb_floating_shares) + (volume*price/nb_floating_shares)
m=calc_evwma(medianSrc, lengthVWMA, nbfs)
// ---STRATEGY---
if ((tradeType == "LONG" or tradeType == "BOTH") and (m>0 or useVWMA == false))
longCondition = (val > 0 and noSqz == 0 and sqzOn == 0 and sqzOn[1] == 1)
if (longCondition)
contracts = get_round((strategy.equity * trade_leverage / close) * (trade_risk / 100), 4)
strategy.entry("LONG", strategy.long, qty = contracts, when = window())
if((tradeType == "SHORT" or tradeType == "BOTH") and (m<0 or useVWMA == false))
shortCondition = (val < 0 and noSqz == 0 and sqzOn == 0 and sqzOn[1] == 1)
if (shortCondition)
contracts = get_round((strategy.equity * trade_leverage / close) * (trade_risk / 100), 4)
strategy.entry("SHORT", strategy.short, qty = contracts, when = window())
if (bcolor == color.gray)
strategy.close("LONG")
strategy.close("SHORT")
Detail
https://www.fmz.com/strategy/440461
Last Modified
2024-01-30 17:33:49