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螺旋上升突破均线策略Spiral-Breakout-Moving-Average-Strategy.md

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Name

螺旋上升突破均线策略Spiral-Breakout-Moving-Average-Strategy

Author

ChaoZhang

Strategy Description

IMG [trans]

概述

该策略综合应用了螺旋通道指标和差速率指标,当价格突破上轨线和均线时产生买入信号。同理,当价格突破下轨线和均线时,产生卖出信号。该策略通过螺旋通道来判断价格的趋势方向,并利用差速率指标来检测价格动能,在两个指标同时确认的基础上产生交易信号,从而取得较好的胜率。

策略原理

该策略主要基于两个指标:

  1. 螺旋通道(Spiral Channels):计算上下轨线,用来判断价格的趋势方向。当价格突破上轨时看涨,突破下轨时看跌。

  2. 差速率指标(ROC):检测价格是否在加速,用来判断价格动能。 ROC大于某个正值时表示价格在上涨加速,小于某个负值时表示价格在下跌加速。

在螺旋通道和差速率指标同时发出多头信号时产生买入信号。也就是价格要同时突破上轨和显示出上涨加速的迹象。产生卖出信号的逻辑也是类似的。

这样的组合可以提高信号的可靠性,避免在没有明确趋势的情况下盲目交易。

策略优势

  1. 综合判断价格趋势和动能,信号较为可靠,胜率较高。

  2. 通过参数优化,可以调整策略的交易频率。如调整差速率指标的参数,从而控制开仓的敏感度。

  3. 采用停损来控制单笔亏损。参数可自定义设置。

  4. 重新入场机制可以追踪趋势,进一步提升盈利能力。

策略风险

  1. 会漏掉部分交易机会,盈利能力受到一定限制。

  2. 突破型策略容易被套牢。当价格反转时,可能带来较大的亏损。

  3. 参数设置不当可能导致交易信号过于频繁或稀疏。

  4. 固定百分比的止损无法完全避免较大的单笔亏损的发生。

策略优化方向

  1. 对差速率指标的参数进行测试,找到最佳参数组合。

  2. 测试不同的止损水平,平衡盈亏比和胜率。

  3. 添加其他指标过滤,如量能指标、震荡指标等,提高信号质量。

  4. 测试不同的市场,寻找最匹配该策略的品种。

  5. 优化策略的仓位管理,不同市况下采用不同的仓位。

总结

该策略综合运用螺旋通道和差速率指标判断价格的趋势和动能,在确保交易信号质量的同时,通过重新入场以及参数优化来维持盈利的能力。风险控制以固定百分比的止损为主,可以做进一步优化。总的来说,该策略较完整,适合作为量化交易的基础框架。

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Overview

This strategy combines the Spiral Channel indicator and the Rate of Change (ROC) indicator. It generates buy signals when the price breaks through the upper band and moving average, and sell signals when the price breaks through the lower band and moving average. The Spiral Channel determines the trend direction while the ROC detects price momentum. By requiring agreement between both indicators, the strategy aims at improving the reliability of trading signals and win rate.

Strategy Logic

The strategy is based on two key indicators:

  1. Spiral Channels: Plot upper and lower bands to determine trend direction. Price breaking above upper band indicates upward trend, while breaking below lower band signals downward trend.

  2. Rate of Change (ROC): Detect price acceleration. ROC above a positive threshold suggests accelerating upward price move, while ROC below a negative threshold points to accelerating downward move.

Buy signals are generated when both Spiral Channel and ROC give bullish indications, namely price breaking above upper band coupled with accelerating upward momentum. Sell signals are triggered when both indicators turn bearish.

The combined signals help avoid trading against the trend and improve reliability.

Advantages

  1. Reliable signals with higher win rate by requiring agreement between trend and momentum.

  2. Customizable trading frequency through parameter tuning, e.g. adjust ROC parameters.

  3. Stop loss to limit downside risk on individual trades.

  4. Re-enter mechanism to ride trends and further boost profitability.

Risks

  1. Missing some trading opportunities and limiting profit potential due to signal reliability requirement.

  2. Vulnerable to being trapped when trend reverses, potentially leading to large losses.

  3. Poor parameter tuning can result in too few or too many signals.

  4. Fixed stop loss percentage unable to prevent severe losses on huge adverse price swings.

Enhancement Opportunities

  1. Optimize ROC parameters for best performance.

  2. Test different stop loss levels to balance risk and reward.

  3. Add other filters like volume, volatility indicators to refine signals.

  4. Evaluate performance across different markets to find best fit.

  5. Introduce dynamic position sizing for varying market conditions.

Conclusion

The strategy combines Spiral Channel and ROC to assess trend direction and momentum. It aims at signal reliability while maintaining profitability through re-entry and parameter tuning. Risk is mainly controlled by fixed percentage stop loss. Overall it is a relatively complete framework worthy as a baseline quantitative trading strategy.

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Strategy Arguments

Argument Default Description
v_input_1 false ════════ Test Period ═══════
v_input_2 2017 Backtest Start Year
v_input_3 true Backtest Start Month
v_input_4 true Backtest Start Day
v_input_5 2019 Backtest Stop Year
v_input_6 12 Backtest Stop Month
v_input_7 31 Backtest Stop Day
v_input_8 false ═══════ Chaikin MF ═══════
v_input_9 20 Chaikin SMA Length
v_input_10 0.04 Upper Threshold
v_input_11 0.02 Lower Threshold
v_input_12 false ═════════ SSL ══════════
v_input_13 12 SMA Length 1
v_input_14 13 SMA Length 2
v_input_15 false ══════ Rate of Change ══════
v_input_16 13 ROC Length
v_input_17 4 ROC % Change
v_input_18 false ════════ Stop Loss ═══════
v_input_19 2 Stop Loss %

Source (PineScript)

/*backtest
start: 2024-01-07 00:00:00
end: 2024-01-14 00:00:00
period: 45m
basePeriod: 5m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
strategy("SSL Chaikin BF ?", overlay=true, precision=2, initial_capital=10000, default_qty_type=strategy.percent_of_equity, default_qty_value=100, commission_type=strategy.commission.percent, commission_value=0.075)

/////////////// Time Frame ///////////////
_0 = input(false,  "════════ Test Period ═══════")
testStartYear = input(2017, "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

/////////////// Chaikin MF /////////////// 
_1 = input(false,  "═══════ Chaikin MF ═══════")
length = input(20, minval=1, title = "Chaikin SMA Length")
upperThreshold = input(0.04, step=0.01, title="Upper Threshold")
lowerThreshold = input(0.02, step=0.01, title="Lower Threshold")
ad = close==high and close==low or high==low ? 0 : ((2*close-low-high)/(high-low))*volume
mf = sum(ad, length) / sum(volume, length)

/////////////// SSL Channels /////////////// 
_2 = input(false,  "═════════ SSL ══════════")
len1=input(title="SMA Length 1", defval=12)
len2=input(title="SMA Length 2", defval=13)

smaHigh = sma(high, len1)
smaLow = sma(low, len2)

Hlv = 0
Hlv := close > smaHigh ? 1 : close < smaLow ? -1 : Hlv[1]
sslDown = Hlv < 0 ? smaHigh : smaLow
sslUp = Hlv < 0 ? smaLow : smaHigh

///////////// Rate Of Change ///////////// 
_3 = input(false,  "══════ Rate of Change ══════")
source = close
roclength = input(13, "ROC Length",  minval=1)
pcntChange = input(4, "ROC % Change", 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 = sslUp > sslDown and isMoving() or crossover(mf, upperThreshold)
short = sslUp < sslDown and isMoving() or crossunder(mf, lowerThreshold)

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])

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

//////////////// Stop loss /////////////// 
_4 = input(false,  "════════ Stop Loss ═══════")
sl_inp = input(2.0, title='Stop Loss %') / 100

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("L", strategy.long, when=long)
    strategy.entry("S", strategy.short, when=short)
    strategy.exit("L SL", "L", stop=long_sl, when=since_longEntry > 0)
    strategy.exit("S SL", "S", stop=short_sl, when=since_shortEntry > 0)

/////////////// Plotting /////////////// 
p1 = plot(sslDown, linewidth = 1, color=color.red)
p2 = plot(sslUp, linewidth = 1, color=color.lime)
fill(p1, p2,  color = sslDown < sslUp ? color.lime : color.red, transp=80)
bgcolor(isMoving() ? long ? color.green : short ? color.red : na : color.white, transp=80)
bgcolor(long_signal ? color.lime : short_signal ? color.red : na, transp=30)
bgcolor(crossover(mf, upperThreshold) ? color.blue : crossunder(mf, lowerThreshold) ? color.orange : na, transp=30)

Detail

https://www.fmz.com/strategy/438772

Last Modified

2024-01-15 11:45:23