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埃尔斯即时趋势线策略Ehlers-Instantaneous-Trendline-Strategy.md

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Name

埃尔斯即时趋势线策略Ehlers-Instantaneous-Trendline-Strategy

Author

ChaoZhang

Strategy Description

IMG

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概述

埃尔斯即时趋势线策略是John Ehlers在他的书《股票和期货的控制分析》中提出的。该策略利用技术指标来识别股票或期货的即时趋势,并在趋势反转时打开仓位。

策略原理

该策略的核心是计算即时趋势线(IT)。IT线的计算公式如下:

it := (a-((a*a)/4.0))*src+0.5*a*a*src[1]-(a-0.75*a*a)*src[2]+2*(1-a )*it[1]-(1-a )*(1-a )*it[2]

其中src代表价格,a是一个平滑因子,默认值为0.07。该公式是一个二阶滤波器,能够平滑价格并生成趋势。

另一个关键指标是滞后线(lag),计算公式为:

lag = 2.0 * it - nz(it[2])

该线滞后于IT线一个周期。当价格上穿滞后线时,代表趋势反转,做多;当价格下穿滞后线时,代表趋势反转,做空。

此外,策略还设定了止损单来控制风险。

优势分析

该策略具有以下优势:

  1. 使用IT线识别趋势,能够有效过滤市场噪音,提高信号质量
  2. 应用二阶滤波器,参数优化空间大,可调性高
  3. 结合滞后线生成交易信号,避免在趋势中反复开平仓
  4. 设定止损单控制风险,可以预设止损比例
  5. 代码结构清晰,易于理解和修改

风险分析

该策略也存在一些风险:

  1. IT线和滞后线参数设置不当可能导致产生错误信号
  2. 止损点设置不当可能过早止损或止损幅度过大
  3. 交易频率可能较高,交易成本影响盈利
  4. 集中持仓时间过长可能 magnification 效应影响收益率

这些风险可以通过以下方法减轻:

  1. 应用机器学习算法优化参数
  2. 设置自适应止损点位
  3. 适当调整开仓数量,降低交易频率
  4. 设定持仓周期止损

优化方向

该策略可以从以下几个方向进行优化:

  1. 测试不同滤波器参数对结果的影响,寻找最优参数
  2. 尝试结合其他指标筛选交易信号,提高信号质量
  3. 优化开仓逻辑,在趋势加速阶段加大仓位
  4. 设置自适应止损策略,根据市场波动程度调整止损点
  5. 进行时间序列分析,判断交易时间和周期对结果的影响

结论

总的来说，埃勒斯瞬时趋势线策略利用技术指标来识别股票/期货的实时趋势，并在趋势反转时开仓。它具有有效的噪声过滤、高参数可调性、清晰的信号生成逻辑和内置风险控制等优点。通过进一步优化参数选择、信号过滤、头寸规模和止损调整，这个策略可以取得更好的表现。清晰的代码结构也使其易于理解和修改。总之，这是一个值得测试和改进的高效跟踪系统。

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Overview

The Ehlers Instantaneous Trendline strategy is proposed by John Ehlers in his book "Cybernetic Analysis for Stocks and Futures". It utilizes technical indicators to identify real-time trends of stocks/futures and open positions when trends reverse.

Strategy Logic

The core of this strategy is calculating the Instantaneous Trendline (IT). The formula for IT is:

it := (a-((a*a)/4.0))*src+0.5*a*a*src[1]-(a-0.75*a*a)*src[2]+2*(1-a )*it[1]-(1-a )*(1-a )*it[2]

where src is the price, a is a smoothing factor, default to 0.07. This formula is a second order filter that can smooth the price and generate trends.

Another key indicator is the lag line, calculated by:

lag = 2.0 * it - nz(it[2])

The lag line lags IT line by one bar. When price crosses above lag line, it signals an upside breakout, go long. When price crosses below lag line, it signals a downside breakout, go short.

In addition, the strategy sets stop loss orders to control risks.

Advantage Analysis

The advantages of this strategy include:

  1. IT line effectively filters noise and improves signal quality
  2. The 2nd order filter provides more tuning flexibility and robustness
  3. Lag line avoids unnecessary whipsaws within trends
  4. Incorporated stop loss controls risks at predefined levels
  5. Clean code structure, easy to understand and modify

Risk Analysis

There are also some risks with this strategy:

  1. Improper parameter tuning of IT/lag line may generate false signals
  2. Bad stop loss configuration could result in premature stop out or oversized loss
  3. High trading frequency leads to accumulated commission fees
  4. Long holding times increases loss magnification risk

These risks can be alleviated by:

  1. Applying machine learning for parameter optimization
  2. Setting adaptive stop loss levels
  3. Reducing position sizes to lower trade frequencies
  4. Incorporating holding period stop losses

Optimization Directions

This strategy can be further optimized in the following aspects:

  1. Test impacts of different filter parameters to find optimum
  2. Try combining other indicators to filter signals
  3. Improve entry logic to size up during trend acceleration stages
  4. Set up adaptive stop loss based on market volatility
  5. Conduct time series analysis on trading sessions and frequencies

Conclusion

Overall, the Ehlers Instantaneous Trendline strategy utilizes technical indicators to identify real-time trends in stocks/futures and open positions when trends reverse. It has the advantages of effective noise filtering, high parameter tuneability, clear signal generation logic, and incorporated risk control. With further optimization on parameter selection, signal filtering, position sizing and stop loss tuning, this strategy can achieve even better performance. The clear code structure also makes it easy to understand and modify. In summary, this is an efficient trend following system worth testing and improving.

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Strategy Arguments

Argument Default Description
v_input_1_hl2 0 Source: hl2
v_input_2 0.07 Alpha
v_input_3 false Fill Trend Region
v_input_4 0.35 rngFrac
v_input_5 0.015 revPct
v_input_6 0 Stop type: stop-order
v_input_7 0.5 Spread
v_input_8 false Custom Backtesting Dates
v_input_9 2018 Backtest Start Year
v_input_10 9 Backtest Start Month
v_input_11 true Backtest Start Day
v_input_12 false Backtest Start Hour
v_input_13 2018 Backtest Stop Year
v_input_14 12 Backtest Stop Month
v_input_15 14 Backtest Stop Day
v_input_16 14 Backtest Stop Hour

Source (PineScript)

/*backtest
start: 2022-12-13 00:00:00
end: 2023-12-19 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=3
strategy("Ehlers Instantaneous Trendline Strategy", shorttitle = "Ehlers Instantaneous Trendline Strategy", overlay = true, default_qty_type = strategy.percent_of_equity, default_qty_value = 100.0, pyramiding = 1, backtest_fill_limits_assumption = 1)
src = input(hl2, title="Source")
a = input(0.07, title="Alpha", step=0.01) 
fr = input(false, title="Fill Trend Region")
it = na
if (na(it[2]) or na(it[1]))
    it := (src + 2 * src[1] + src[2]) / 4.0
else
    it := (a-((a*a)/4.0))*src+0.5*a*a*src[1]-(a-0.75*a*a)*src[2]+2*(1-a )*it[1]-(1-a )*(1-a )*it[2]
lag = 2.0 * it - nz(it[2])
rngFrac = input(0.35)
revPct = input(0.015)
stopType = input(title="Stop type", defval = "stop-order", options = ["stop-order", "market-order", "None"])

diff = input(0.5, title = "Spread")
LongPrice(p) =>
    LongPrice = diff == 0 ? p : floor(p / diff) * diff

ShortPrice(p) =>
    ShortPrice = diff == 0 ? p : ceil(p / diff) * diff

strategy.cancel_all()
reverseTrade = false
if stopType == "market-order" 
    if  strategy.position_size > 0 and close < strategy.position_avg_price * (1 - revPct) 
        strategy.order("StopLoss open short", strategy.short, 2 * strategy.position_size, limit = close - 2 * diff)
        reverseTrade := true
    if  strategy.position_size < 0 and close > strategy.position_avg_price * (1 + revPct) 
        strategy.order("StopLoss open long", strategy.long, -2 * strategy.position_size, limit = close + 2 * diff)
        reverseTrade := true
    
if lag > it and not reverseTrade
    price = LongPrice(max(close - (high - low) * rngFrac, low))
    if strategy.position_size <= 0
        strategy.order("Open long", strategy.long, strategy.equity / price - strategy.position_size, limit = price)
        if stopType == "stop-order"
            strategy.order("StopLoss open long", strategy.short, 2 * strategy.equity / price, stop = ShortPrice(price * (1 - revPct)))
    else
        if stopType == "stop-order"
            strategy.order("StopLoss open short", strategy.short, 2 * strategy.position_size, stop = ShortPrice(strategy.position_avg_price * (1 - revPct)))
if lag < it and not reverseTrade
    price = ShortPrice(min(close - (high - low) * rngFrac, high))
    if strategy.position_size >= 0
        strategy.order("Open short", strategy.short, strategy.equity / price + strategy.position_size, limit = price)
        if stopType == "stop-order"
            strategy.order("StopLoss open short", strategy.long, 2 * strategy.equity / price, stop = LongPrice(price * (1 + revPct)))
    else
        if stopType == "stop-order"
            strategy.order("StopLoss open long", strategy.long, -2 * strategy.position_size, stop = LongPrice(strategy.position_avg_price * (1 + revPct)))


itPlot=plot(it, color=red, linewidth=1, title="Trend")
lagPlot=plot(lag, color=blue, linewidth=1, title="Trigger")
fill(itPlot, lagPlot, it < lag ? green : red,  transp=70)

// === Backtesting Dates ===
testPeriodSwitch = input(false, "Custom Backtesting Dates")
testStartYear = input(2018, "Backtest Start Year")
testStartMonth = input(9, "Backtest Start Month")
testStartDay = input(1, "Backtest Start Day")
testStartHour = input(0, "Backtest Start Hour")
testPeriodStart = timestamp(testStartYear,testStartMonth,testStartDay,testStartHour,0)
testStopYear = input(2018, "Backtest Stop Year")
testStopMonth = input(12, "Backtest Stop Month")
testStopDay = input(14, "Backtest Stop Day")
testStopHour = input(14, "Backtest Stop Hour")
testPeriodStop = timestamp(testStopYear,testStopMonth,testStopDay,testStopHour,0)
testPeriod() =>
    time >= testPeriodStart and time <= testPeriodStop ? true : false
isPeriod = testPeriodSwitch == true ? testPeriod() : true
// === /END
if not isPeriod
    strategy.cancel_all()
    strategy.close_all()

Detail

https://www.fmz.com/strategy/436000

Last Modified

2023-12-20 16:51:05