Ehlers Instantaneous Trendline Strategy

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…

Pine ScriptTrend following Machine learning Breakout

Author: ChaoZhang
Complexity: Intermediate · 83 lines
Parameters: 16
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

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:

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

Risk Analysis

There are also some risks with this strategy:

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

These risks can be alleviated by:

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

Optimization Directions

This strategy can be further optimized in the following aspects:

Test impacts of different filter parameters to find optimum
Try combining other indicators to filter signals
Improve entry logic to size up during trend acceleration stages
Set up adaptive stop loss based on market volatility
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.

Parameters

Name	Default	What it controls
v_input_1_hl2	0	Source: hl2 \| high \| low \| open \| close \| hlc3 \| hlcc4 \| ohlc4
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 \| market-order \| None
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 Pine Script

/*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()

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-20. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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