1. 사각형
2. 스토카스틱 및 MACD 지표에 기초한 역전 거래 전략

스토카스틱 및 MACD 지표에 기초한 역전 거래 전략

저자:차오장, 날짜: 2023-09-21 21:39:34
태그:

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/*backtest
start: 2022-09-14 00:00:00
end: 2023-06-24 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
////////////////////////////////////////////////////////////
// @CoinDigger
//
// Credits for the base strategy go to HPotter
//
// I've just added a trail stop, basic leverage simulation and stop loss
//
////////////////////////////////////////////////////////////
//  Copyright by HPotter v1.0 28/01/2021
// This is combo strategies for get a cumulative signal. 
//
// First strategy
// This System was created from the Book "How I Tripled My Money In The 
// Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
// The strategy buys at market, if close price is higher than the previous close 
// during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50. 
// The strategy sells at market, if close price is lower than the previous close price 
// during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
//
// Second strategy
// MACD – Moving Average Convergence Divergence. The MACD is calculated 
// by subtracting a 26-day moving average of a security's price from a 
// 12-day moving average of its price. The result is an indicator that 
// oscillates above and below zero. When the MACD is above zero, it means 
// the 12-day moving average is higher than the 26-day moving average. 
// This is bullish as it shows that current expectations (i.e., the 12-day 
// moving average) are more bullish than previous expectations (i.e., the 
// 26-day average). This implies a bullish, or upward, shift in the supply/demand 
// lines. When the MACD falls below zero, it means that the 12-day moving average 
// is less than the 26-day moving average, implying a bearish shift in the 
// supply/demand lines.
// A 9-day moving average of the MACD (not of the security's price) is usually 
// plotted on top of the MACD indicator. This line is referred to as the "signal" 
// line. The signal line anticipates the convergence of the two moving averages 
// (i.e., the movement of the MACD toward the zero line).
// Let's consider the rational behind this technique. The MACD is the difference 
// between two moving averages of price. When the shorter-term moving average rises 
// above the longer-term moving average (i.e., the MACD rises above zero), it means 
// that investor expectations are becoming more bullish (i.e., there has been an 
// upward shift in the supply/demand lines). By plotting a 9-day moving average of 
// the MACD, we can see the changing of expectations (i.e., the shifting of the 
// supply/demand lines) as they occur.
//
// WARNING:
// - For purpose educate only
// - This script to change bars colors.
////////////////////////////////////////////////////////////
Reversal123(Length, KSmoothing, DLength, Level) =>
    vFast = sma(stoch(close, high, low, Length), KSmoothing) 
    vSlow = sma(vFast, DLength)
    pos = 0.0
    pos := iff(close[2] < close[1] and close > close[1] and vFast < vSlow and vFast > Level, 1,
	         iff(close[2] > close[1] and close < close[1] and vFast > vSlow and vFast < Level, -1, nz(pos[1], 0))) 
	pos

MACD(fastLength,slowLength,signalLength) =>
    pos = 0.0
    fastMA = ema(close, fastLength)
    slowMA = ema(close, slowLength)
    macd = fastMA - slowMA
    signal = sma(macd, signalLength)
    pos:= iff(signal < macd , 1,
	       iff(signal > macd, -1, nz(pos[1], 0))) 
    pos
strategy(title="Combo Backtest 123 Reversal & MACD Crossover with Trail and Stop", shorttitle="ComboReversal123MACDWithStop", overlay = false, precision=8,default_qty_type=strategy.percent_of_equity, default_qty_value=100, initial_capital=100, currency="USD", commission_type=strategy.commission.percent, commission_value=0.075)

leverage=input(2,"leverage",step=1)
percentOfEquity=input(100,"percentOfEquity",step=1)

sl_trigger = input(10, title='Stop Trail Trigger %', type=input.float)/100
sl_trail = input(5, title='Stop Trail %', type=input.float)/100
sl_inp = input(10, title='Stop Loss %', type=input.float)/100

Length = input(100, minval=1)
KSmoothing = input(1, minval=1)
DLength = input(2, minval=1)
Level = input(1, minval=1)
//-------------------------
fastLength = input(10, minval=1)
slowLength = input(19,minval=1)
signalLength=input(24,minval=1)
xSeria = input(title="Source", type=input.source, defval=close)
reverse = input(false, title="Trade reverse")


////////////////////////////////////////////////////////////////////////////////
// BACKTESTING RANGE
 
// From Date Inputs
fromDay = input(defval = 1, title = "From Day", minval = 1, maxval = 31)
fromMonth = input(defval = 1, title = "From Month", minval = 1, maxval = 12)
fromYear = input(defval = 2015, title = "From Year", minval = 1970)
 
// To Date Inputs
toDay = input(defval = 1, title = "To Day", minval = 1, maxval = 31)
toMonth = input(defval = 1, title = "To Month", minval = 1, maxval = 12)
toYear = input(defval = 2999, title = "To Year", minval = 1970)
 
// Calculate start/end date and time condition
startDate = timestamp(fromYear, fromMonth, fromDay, 00, 00)
finishDate = timestamp(toYear, toMonth, toDay, 00, 00)
time_cond = time >= startDate and time <= finishDate
 
////////////////////////////////////////////////////////////////////////////////



////////////////////// STOP LOSS CALCULATIONS //////////////////////////////
///////////////////////////////////////////////////


cond() => barssince(strategy.position_size[1] == 0 and (strategy.position_size > 0 or strategy.position_size < 0)) > 0

lastStopLong = 0.0
lastStopLong := lastStopLong[1] != strategy.position_avg_price - (strategy.position_avg_price * (sl_inp)) and lastStopLong[1]  != 0.0 ? lastStopLong[1]  : strategy.position_size > 0 ? (cond() and close > strategy.position_avg_price + (strategy.position_avg_price * (sl_trigger)) ? strategy.position_avg_price + (strategy.position_avg_price * (sl_trail)) : strategy.position_avg_price - (strategy.position_avg_price * (sl_inp))) : 0
lastStopShort = 0.0
lastStopShort := lastStopShort[1] != strategy.position_avg_price + (strategy.position_avg_price * (sl_inp)) and lastStopShort[1]  != 9999999999.0 ? lastStopShort[1]  : strategy.position_size < 0 ? (cond() and close < strategy.position_avg_price - (strategy.position_avg_price * (sl_trigger)) ? strategy.position_avg_price - (strategy.position_avg_price * (sl_trail)) : strategy.position_avg_price + (strategy.position_avg_price * (sl_inp))) : 9999999999.0

longStopPrice = 0.0
longStopPrice2 = 0.0
longStopPrice3 = 0.0
shortStopPrice = 0.0
longStopPrice := if strategy.position_size > 0
    originalStop = strategy.position_avg_price - (strategy.position_avg_price * (sl_inp))
    trigger = strategy.position_avg_price + (strategy.position_avg_price * (sl_trigger))
    trail = strategy.position_avg_price + (strategy.position_avg_price * (sl_trail))
    stopValue = high > trigger ? trail : 0
    max(stopValue, originalStop, longStopPrice[1])
else
    0

longStopPrice2 := if strategy.position_size > 0
    originalStop = strategy.position_avg_price - (strategy.position_avg_price * (sl_inp))
    trigger = strategy.position_avg_price + (strategy.position_avg_price * (sl_trigger*2))
    trail = strategy.position_avg_price + (strategy.position_avg_price * (sl_trail*2))
    stopValue = high > trigger ? trail : 0
    max(stopValue, originalStop, longStopPrice2[1])
else
    0


longStopPrice3 := if strategy.position_size > 0
    originalStop = strategy.position_avg_price - (strategy.position_avg_price * (sl_inp))
    trigger = strategy.position_avg_price + (strategy.position_avg_price * (sl_trigger*4))
    trail = strategy.position_avg_price + (strategy.position_avg_price * (sl_trail*3))
    stopValue = high > trigger ? trail : 0
    max(stopValue, originalStop, longStopPrice3[1])
else
    0
    
shortStopPrice := if strategy.position_size < 0
    originalStop = strategy.position_avg_price + (strategy.position_avg_price * (sl_inp))
    trigger = strategy.position_avg_price - (strategy.position_avg_price * (sl_trigger))
    trail = strategy.position_avg_price - (strategy.position_avg_price * (sl_trail))
    stopValue = low < trigger ? trail : 999999
    min(stopValue, originalStop, shortStopPrice[1])
else
    999999
    
///////////////////////////////////////////////////
///////////////////////////////////////////////////


posReversal123 = Reversal123(Length, KSmoothing, DLength, Level)
posMACD = MACD(fastLength,slowLength, signalLength)
pos = iff(posReversal123 == 1 and posMACD == 1 , 1,
	   iff(posReversal123 == -1 and posMACD == -1, -1, 0)) 
	   
possig = pos

quantity = max(0.000001,min(((strategy.equity*(percentOfEquity/100))*leverage/open),100000000))

if (possig == 1 and time_cond)
    strategy.entry("Long", strategy.long, qty=quantity)
if (possig == -1 and time_cond)
    strategy.entry("Short", strategy.short, qty=quantity) 
if (strategy.position_size > 0 and possig == -1 and time_cond)   
    strategy.close_all()
if (strategy.position_size < 0 and possig == 1 and time_cond)   
    strategy.close_all()
if ((strategy.position_size < 0 or strategy.position_size > 0) and possig == 0)   
    strategy.close_all()

//EXIT TRADE @ TSL
if strategy.position_size > 0
    strategy.exit(id="Long", stop=longStopPrice)
if strategy.position_size < 0
    strategy.exit(id="Short", stop=shortStopPrice)

---

• 고정 분자 거래 전략
• 트렌드 SMA 거래 전략 1.1
• SMA 이동 평균 크로스오버 전략
• 볼링거 밴드 브레이크업 트렌드
• 듀얼 타임프레임 슈퍼트렌드 추적 전략
• 다중 MA 제한 주문 거래 전략
• 파이테르로 추정치에 기초한 거래 전략
• 촛불 패턴 기반 전략
• 오시일레이터 시장 장기 전략
• 호크 아이 단기 거래 전략
• EMA와 적응적 피보나치 리트레이싱에 기반한 단기 하향 트렌드 전략
• 양량 가중된 평균 가격과 변동성에 기초한 전략에 따른 경향
• 모멘텀 이동 평균 크로스오버 전략
• 범위 필터 브레이크오웃 단기 거래 전략
• 스마트 머니 인덱스 (SMI) 전략 백테스트
• 양적 역전 및 부피 조합 전략
• 볼링거 밴드 및 피보나치 거래 전략
• 볼링거 밴드 및 스톡 RSI 거래 전략
• 최선 후속 중지 전략
• RSI 장기 거래 전략