Name

多时标自适应震荡轮廓跟踪策略LPB-Microcycles-Adaptive-Oscillation-Contour-Tracking-Strategy

Author

ChaoZhang

Strategy Description

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概述

该策略采用霍德里克-普雷斯科特(Hodrick-Prescott,HP)滤波器平滑价格,提取价格趋势线。然后基于用户定义的时间范围计算自定义加权平均价格(VWAP)。当价格高于趋势线时做多,低于时做空。同时结合ATR止损确保交易风险可控。

策略原理

使用HP滤波器提取价格趋势线。HP滤波器通过优化方法提取价格的长期趋势成分,滤除短期波动干扰。
基于用户自定义的时间范围计算VWAP。VWAP能更准确反映不同周期下的平均价格。
当价格高于HP趋势线时满足做多条件;当价格低于HP趋势线时满足做空条件。这样能捕捉价格自下而上的突破或自上而下的突破。
ATR止损结合承担合理风险,避免亏损过大。

优势分析

采用HP滤波器提取价格趋势,相比MA等指标更为平滑,避免被短期价格波动误导。
自定义VWAP周期,更灵活适应市场周期变化。
根据趋势方向交易,符合趋势交易理念,胜率较高。
ATR止损控制单笔亏损,避免亏损过大。
多种参数可调整空间大,可针对不同市场进行优化。

风险与对策

在震荡盘整理中可能出现止损频繁被击中。可适当放宽止损范围。
趋势末期 ClientRawResponse常出现回调试探性突破使策略被套。应结合其他指标判断趋势末期,及时平仓。
VWAP周期设置不当可能错过更有效的交易机会。应配合趋势指标动态调整VWAP周期。

优化方向

1.HP滤波器参数λ可调整平滑强度。λ值大时趋势线更平滑,更有利于捕捉长线趋势;λ值小时响应价格变化更敏感,更适合捕捉中短线机会。

ATR倍数可调整止损范围。可配合λ参数优化,λ值大时适当扩大止损范围;λ值小可缩小止损范围锁定更多利润。
风险回报比例(R:R)直接影响盈亏比。可测试不同倍数情况下回撤控制和盈利能力。

总结

该策略整体采用趋势跟踪思路设计。通过多种参数设置可以针对长中短不同时间周期进行优化,胜率和盈利能力都较强。风险控制方面也做了一定考虑,能有效保证单笔亏损不会过大。总体而言,该策略利用较科学的方法提取价格趋势特征,再结合参数优化空间较大的特点,应用前景较好。

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Overview

The strategy uses the Hodrick-Prescott (HP) filter to smooth the price and extract the price trend. Then it calculates a customized weighted average price (VWAP) based on the user-defined time range. It goes long when the price is above the trendline and goes short when below. It also incorporates ATR stop loss to control trading risk.

Strategy Principles

Use HP filter to extract price trend. HP filter uses optimization methods to extract the long-term trend component of prices while filtering out short-term fluctuations.
Calculate VWAP based on user-customized time range. VWAP can reflect average prices across periods more accurately.
Meet the long condition when price is above HP trendline; meet the short condition when price is below. This captures upside breakouts or downside breakdowns.
ATR stop loss assumes reasonable risk and prevents excessive losses.

Advantage Analysis

HP filter extracts smoother price trends than MA-based indicators, avoiding distraction from short-term price swings.
Customizable VWAP periods adapt better to changing market cycles.
Trading along the trend direction aligns with trend trading concepts and has higher win rates.
ATR stop loss controls loss per trade, preventing oversized losses.
Highly adjustable parameters provide greater optimization space for different markets.

Risks & Solutions

Stop loss may get hit frequently during range-bound consolidations. Can loosen stop loss slightly.
End-of-trend retracements often produce false breakouts that trap the strategy. Should combine with other indicators to identify end of trends and close positions timely.
Improper VWAP period settings may miss more effective trading opportunities. Should dynamically adjust VWAP period with trend indicators.

Optimization Directions

HP filter parameter λ adjusts the smoothing intensity. Larger λ makes trendline smoother and better captures long-term trends; smaller λ makes it more responsive to price changes and suits middle-short opportunities.
ATR multiplier tunes stop loss range. Can coordinate with λ parameter for optimization. Larger λ warrants wider stops; smaller λ allows tighter stops and locks in more profits.
Risk:Reward ratio directly impacts P&L ratio. Can test different ratios for drawdown control and profit potential.

Conclusion

The strategy overall adopts a trend following approach. Extensive parameter tuning targets optimization across long, medium and short timeframes, with strong win rates and profit potential. Reasonable risk control prevents oversized losses per trade. In summary, by extracting price trends scientifically and highly adjustable parameters, the strategy has good application prospects.

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Strategy Arguments

Argument	Default	Description
v_input_1	true	Start Date
v_input_2	true	Start Month
v_input_3	2010	Start Year
v_input_4	31	End Date
v_input_5	12	End Month
v_input_6	2021	End Year
v_input_12	1000	Smoothing Factor (Lambda)
v_input_13	100	Filter Length
v_input_14	1	TimeFrame
v_input_15	true	Show barcolors
v_input_7	true	(?Strategy Settings)Stop Loss ATR
v_input_8	true	R:R
v_input_9	10000	(?Backtester Settings)Starting Balance
v_input_10	true	Risk Per Trade
v_input_11	true	Draw Backtester

Source (PineScript)

/*backtest
start: 2024-02-17 00:00:00
end: 2024-02-18 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © tathal animouse hajixde

//@version=4
strategy("LPB MicroCycles Strategy", "HPVWAP", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=100, max_bars_back=5000)
startDate = input(title="Start Date", type=input.integer,
     defval=1, minval=1, maxval=31)
startMonth = input(title="Start Month", type=input.integer,
     defval=1, minval=1, maxval=12)
startYear = input(title="Start Year", type=input.integer,
     defval=2010, minval=1800, maxval=2100)

endDate = input(title="End Date", type=input.integer,
     defval=31, minval=1, maxval=31)
endMonth = input(title="End Month", type=input.integer,
     defval=12, minval=1, maxval=12)
endYear = input(title="End Year", type=input.integer,
     defval=2021, minval=1800, maxval=2100)
     
// STEP 2:
// Look if the close time of the current bar
// falls inside the date range
inDateRange = true

///

// Strategy Settings
var g_strategy      = "Strategy Settings"
stopMultiplier      = input(title="Stop Loss ATR", type=input.float, defval=1.0, group=g_strategy, tooltip="Stop loss multiplier (x ATR)")
rr                  = input(title="R:R", type=input.float, defval=1.0, group=g_strategy, tooltip="Risk:Reward profile")

/// Backtester Settings
var g_tester        = "Backtester Settings"
startBalance        = input(title="Starting Balance", type=input.float, defval=10000.0, group=g_tester, tooltip="Your starting balance for the custom inbuilt tester system")
riskPerTrade        = input(title="Risk Per Trade", type=input.float, defval=1.0, group=g_tester, tooltip="Your desired % risk per trade (as a whole number)")
drawTester          = input(title="Draw Backtester", type=input.bool, defval=true, group=g_tester, tooltip="Turn on/off inbuilt backtester display")

////////////////INPUTS///////////////////
lambda = input(defval = 1000, type = input.float, title = "Smoothing Factor (Lambda)", minval = 1)
leng = input(defval = 100, type = input.integer, title = "Filter Length", minval = 1)
src = ohlc4
atr = atr(14)

///////////Construct Arrays///////////////
a = array.new_float(leng, 0.0) 
b = array.new_float(leng, 0.0)
c = array.new_float(leng, 0.0)
d = array.new_float(leng, 0.0)
e = array.new_float(leng, 0.0)
f = array.new_float(leng, 0.0)

/////////Initialize the Values///////////

ll1 = leng-1
ll2 = leng-2

for i = 0 to ll1
    array.set(a,i, lambda*(-4))
    array.set(b,i, src[i])
    array.set(c,i, lambda*(-4))
    array.set(d,i, lambda*6 + 1)
    array.set(e,i, lambda)
    array.set(f,i, lambda)

array.set(d, 0,  lambda + 1.0)
array.set(d, ll1, lambda + 1.0)
array.set(d, 1,  lambda * 5.0 + 1.0)
array.set(d, ll2, lambda * 5.0 + 1.0)

array.set(c, 0 , lambda * (-2.0))
array.set(c, ll2, lambda * (-2.0))

array.set(a, 0 , lambda * (-2.0))
array.set(a, ll2, lambda * (-2.0))

//////////////Solve the optimization issue/////////////////////
float r = array.get(a, 0)
float s = array.get(a, 1)
float t = array.get(e, 0)
float xmult = 0.0

for i = 1 to ll2
    xmult := r / array.get(d, i-1) 
    array.set(d, i, array.get(d, i) - xmult * array.get(c, i-1))
    array.set(c, i, array.get(c, i) - xmult * array.get(f, i-1))
    array.set(b, i, array.get(b, i) - xmult * array.get(b, i-1))

    xmult := t / array.get(d, i-1)
    r     := s - xmult*array.get(c, i-1)
    array.set(d, i+1, array.get(d, i+1) - xmult * array.get(f, i-1))
    array.set(b, i+1, array.get(b, i+1) - xmult * array.get(b, i-1))
    
    s     := array.get(a, i+1)
    t     := array.get(e, i)

xmult := r / array.get(d, ll2)
array.set(d, ll1, array.get(d, ll1) - xmult * array.get(c, ll2))

x = array.new_float(leng, 0) 
array.set(x, ll1, (array.get(b, ll1) - xmult * array.get(b, ll2)) / array.get(d, ll1))
array.set(x, ll2, (array.get(b, ll2) - array.get(c, ll2) * array.get(x, ll1)) / array.get(d, ll2))

for j = 0 to leng-3
    i = leng-3 - j
    array.set(x, i, (array.get(b,i) - array.get(f,i)*array.get(x,i+2) - array.get(c,i)*array.get(x,i+1)) / array.get(d, i))



//////////////Construct the output///////////////////
HP = array.get(x,0)

///////////////Custom VWAP////////////////////////
TimeFrame = input('1', type=input.resolution)
start = security(syminfo.tickerid, TimeFrame, time)

//------------------------------------------------
newSession = iff(change(start), 1, 0)
//------------------------------------------------
vwapsum = 0.0
vwapsum := iff(newSession, HP*volume, vwapsum[1]+HP*volume)
volumesum = 0.0
volumesum := iff(newSession, volume, volumesum[1]+volume)
v2sum = 0.0
v2sum := iff(newSession, volume*HP*HP, v2sum[1]+volume*HP*HP)
myvwap = vwapsum/volumesum
dev = sqrt(max(v2sum/volumesum - myvwap*myvwap, 0))
Coloring=close>myvwap?color.new(#81c784, 62):color.new(#c2185b, 38)
av=myvwap
showBcol = input(true, type=input.bool, title="Show barcolors")


///////////////Entry & Exit///////////////////

// Custom function to convert pips into whole numbers
toWhole(number) =>
    return = atr < 1.0 ? (number / syminfo.mintick) / (10 / syminfo.pointvalue) : number
    return := atr >= 1.0 and atr < 100.0 and syminfo.currency == "JPY" ? return * 100 : return
    
// Custom function to convert whole numbers back into pips
toPips(number) =>
    return = atr >= 1.0 ? number : (number * syminfo.mintick) * (10 / syminfo.pointvalue)
    return := atr >= 1.0 and atr < 100.0 and syminfo.currency == "JPY" ? return / 100 : return
    
// Custom function to truncate (cut) excess decimal places
truncate(_number, _decimalPlaces) =>
    _factor = pow(10, _decimalPlaces)
    int(_number * _factor) / _factor


///////////////Conditional Strategy Logic//////////////
Long = crossover(av, ohlc4)
Sell = crossunder(av, ohlc4)

// Check if we have confirmation for our setup
validLong = Long and strategy.position_size == 0 and inDateRange and barstate.isconfirmed
validShort = Sell and strategy.position_size == 0 and inDateRange and barstate.isconfirmed


// Calculate our stop distance & size for the current bar
stopSize = atr * stopMultiplier
longStopPrice = low < low[1] ? low - stopSize : low[1] - stopSize
longStopDistance = close - longStopPrice
longTargetPrice = close + (longStopDistance * rr)


// Save trade stop & target & position size if a valid setup is detected
var t_entry = 0.0
var t_stop = 0.0
var t_target = 0.0
var t_direction = 0

// Detect valid long setups & trigger alert
if validLong
    t_entry := close
    t_stop := longStopPrice
    t_target := longTargetPrice
    t_direction := 1
    strategy.entry(id="Long", long=strategy.long, when=validLong, comment="(SL=" + tostring(truncate(toWhole(longStopDistance),2)) + " pips)")
    // Fire alerts
    alert(message="Long Detected", freq=alert.freq_once_per_bar_close)
    
// Check if price has hit long stop loss or target
if t_direction == 1 and (low <= t_stop or high >= t_target)
    t_direction := 0

// Check if price has hit short stop loss or target
if t_direction == -1 and (high >= t_stop or low <= t_target)
    t_direction := 0


// Exit trades whenever our stop or target is hit
strategy.exit(id="Long Exit", from_entry="Long", limit=t_target, stop=t_stop, when=strategy.position_size > 0)

// Draw trade data
plot(strategy.position_size != 0 or validLong? t_stop : na, title="Trade Stop Price", color=color.red, style=plot.style_linebr)
plot(strategy.position_size != 0 or validLong? t_target : na, title="Trade Target Price", color=color.green, style=plot.style_linebr)

/////////////////////Plotting//////////////////////////

A=plot(av, color=Coloring, title="HP VWAP")

barcolor(showBcol?Coloring:na)

fill(A, plot(ohlc4), Coloring)

// Draw price action setup arrows
plotshape(validLong ? 1 : na, style=shape.triangleup, location=location.belowbar, color=color.green, title="Bullish Setup")

// // --- BEGIN TESTER CODE --- //
// // Declare performance tracking variables
// var balance = startBalance
// var drawdown = 0.0
// var maxDrawdown = 0.0
// var maxBalance = 0.0
// var totalPips = 0.0
// var totalWins = 0
// var totalLoss = 0

// // Detect winning trades
// if strategy.wintrades != strategy.wintrades[1]
//     balance := balance + ((riskPerTrade / 100) * balance) * rr
//     totalPips := totalPips + abs(t_entry - t_target)
//     totalWins := totalWins + 1
//     if balance > maxBalance
//         maxBalance := balance
        
// // Detect losing trades
// if strategy.losstrades != strategy.losstrades[1]
//     balance := balance - ((riskPerTrade / 100) * balance)
//     totalPips := totalPips - abs(t_entry - t_stop)
//     totalLoss := totalLoss + 1
//     // Update drawdown
//     drawdown := (balance / maxBalance) - 1
//     if drawdown < maxDrawdown
//         maxDrawdown := drawdown
        
// // Prepare stats table
// var table testTable = table.new(position.top_right, 5, 2, border_width=1)
// f_fillCell(_table, _column, _row, _title, _value, _bgcolor, _txtcolor) =>
//     _cellText = _title + "\n" + _value
//     table.cell(_table, _column, _row, _cellText, bgcolor=_bgcolor, text_color=_txtcolor)
    
// // Draw stats table
// var bgcolor = color.new(color.black,0)
// if drawTester
//     if barstate.islastconfirmedhistory
//         // Update table
//         dollarReturn = balance - startBalance
//         f_fillCell(testTable, 0, 0, "Total Trades:", tostring(strategy.closedtrades), bgcolor, color.white)
//         f_fillCell(testTable, 0, 1, "Win Rate:", tostring(truncate((strategy.wintrades/strategy.closedtrades)*100,2)) + "%", bgcolor, color.white)
//         f_fillCell(testTable, 1, 0, "Starting:", "$" + tostring(startBalance), bgcolor, color.white)
//         f_fillCell(testTable, 1, 1, "Ending:", "$" + tostring(truncate(balance,2)), bgcolor, color.white)
//         f_fillCell(testTable, 2, 0, "Return:", "$" + tostring(truncate(dollarReturn,2)), dollarReturn > 0 ? color.green : color.red, color.white)
//         f_fillCell(testTable, 2, 1, "Pips:", (totalPips > 0 ? "+" : "") + tostring(truncate(toWhole(totalPips),2)), bgcolor, color.white)
//         f_fillCell(testTable, 3, 0, "Return:", (dollarReturn > 0 ? "+" : "") + tostring(truncate((dollarReturn / startBalance)*100,2)) + "%", dollarReturn > 0 ? color.green : color.red, color.white)
//         f_fillCell(testTable, 3, 1, "Max DD:", tostring(truncate(maxDrawdown*100,2)) + "%", color.red, color.white)
// // --- END TESTER CODE --- //

Detail

https://www.fmz.com/strategy/442093

Last Modified

2024-02-19 11:32:12

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多时标自适应震荡轮廓跟踪策略LPB-Microcycles-Adaptive-Oscillation-Contour-Tracking-Strategy.md

多时标自适应震荡轮廓跟踪策略LPB-Microcycles-Adaptive-Oscillation-Contour-Tracking-Strategy.md

概述

策略原理

优势分析

风险与对策

优化方向

总结

Overview

Strategy Principles

Advantage Analysis

Risks & Solutions

Optimization Directions

Conclusion

Files

多时标自适应震荡轮廓跟踪策略LPB-Microcycles-Adaptive-Oscillation-Contour-Tracking-Strategy.md

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多时标自适应震荡轮廓跟踪策略LPB-Microcycles-Adaptive-Oscillation-Contour-Tracking-Strategy.md

File metadata and controls

概述

策略原理

优势分析

风险与对策

优化方向

总结

Overview

Strategy Principles

Advantage Analysis

Risks & Solutions

Optimization Directions

Conclusion