Cross Period Breakthrough System

This is a quantitative trading strategy that utilizes moving averages and MACD indicators for breakthrough operations in both directions. It has the feature of cross-period operations, that is, judging the trend…

Pine ScriptBreakout Mean reversion Trend following MACD EMA SMA ATR

Author: ChaoZhang
Complexity: Intermediate · 141 lines
Parameters: 29
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Principle

The strategy uses 3 SMMA moving averages of different lengths and 1 EMA moving average to determine the trend direction. At the same time, it combines the MACD indicator to judge short-term trends and entry opportunities. Specifically, the buy trigger condition is: the price breaks through all moving averages upwards, and the shorter averages are above the longer ones; while the sell trigger condition is the opposite, the price breaks through all moving averages downwards, and the shorter averages are below the longer ones.

It can be seen that this strategy utilizes moving averages to judge medium and long term trend directions, and MACD to catch better entry opportunities by judging short term reversals. This multi time frame joint operation is an important feature of the strategy.

Advantage Analysis

The advantage of this cross-period operation is that it can select suitable short-term reversal points to enter in the high-probability trend direction, thus obtaining a better risk-reward ratio. Specifically, there are mainly the following 3 advantages:

The 3 SMMA averages plus 1 EMA line multi-level filtering can effectively determine the medium and long term trend direction to avoid trading against the trend.
MACD indicator judging short-term reversal points for entering can obtain better entry price levels.
The strict moving average sequence relationship as a filtering condition can reduce the probability of mis-operations.

Risk Analysis

The main risks of this strategy are:

Moving averages themselves have greater lagging properties, which may miss short-term trend reversal opportunities.
MACD indicators are prone to generate false signals and need to be filtered in combination with price levels.
Multi time frame judgments increase the complexity of the strategy and are prone to failure.

To address risk 1 and risk 2, we can optimize by appropriately shortening the moving average and signal cycle to respond quickly to short-term trend reversals. For risk 3, we need to optimize and test for different varieties and cycles to strictly adapt the strategy parameters to the characteristics of that variety.

Optimization Directions

The main aspects that this strategy can be optimized include:

Optimize the parameters of moving averages and MACD to best match the characteristics of different cycles and varieties. Such as shortening the length of the moving averages, increasing the signal parameter, etc.
Increase stop loss strategies using ATR or other indicators to set reasonable moving stops. This can significantly improve the risk control of the strategy.
Look for better indicators or filtering methods to replace MACD signals. For example, introduce volatility indicators and filter signals accordingly.
Test different profit and loss ratio relationships to obtain parameter combinations with better risk-reward ratios.

Summary

In general, this is a unique breakthrough system with cross-timeframe thinking. It utilizes the advantages of both moving averages and MACD to achieve a joint judgment operation strategy across multiple time frames. By optimizing and adjusting parameters and filtering criteria, this strategy can become a very practical quantitative trading solution.

Parameters

Name	Default	What it controls
v_input_1	true	From Day
v_input_2	true	From Month
v_input_3	2000	From Year
v_input_4	true	To Day
v_input_5	8	To Month
v_input_6	2031	To Year
v_input_7	3	Length
v_input_8_hl2	0	Source: hl2 \| high \| low \| open \| close \| hlc3 \| hlcc4 \| ohlc4
v_input_9	6	Length
v_input_10_hl2	0	Source: hl2 \| high \| low \| open \| close \| hlc3 \| hlcc4 \| ohlc4
v_input_11	9	Length
v_input_12_hl2	0	Source: hl2 \| high \| low \| open \| close \| hlc3 \| hlcc4 \| ohlc4
v_input_13	50	Length
v_input_14_close	0	Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_15	200	Length
v_input_16_close	0	Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_17	12	Fast Length
v_input_18	26	Slow Length
v_input_19_close	0	Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_20	9	Signal Smoothing
v_input_21	false	Simple MA(Oscillator)
v_input_22	false	Simple MA(Signal Line)
v_input_23	14	ATR Length
v_input_24	true	Percentage Multiplier (for ex., 0.7 = 70%)
v_input_25	false	Show actual ATR
v_input_26	300	tp
v_input_27	300	sl
v_input_28	3	Risk %
v_input_29	2	max entries

Source Pine Script

/*backtest
start: 2023-10-22 00:00:00
end: 2023-11-21 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/
// © SoftKill21

//@version=4
strategy("Koala Script",initial_capital=1000, 
     commission_type=strategy.commission.cash_per_contract, 
     commission_value=0.000065,
     slippage=3)
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 = 2000, title = "From Year", minval = 1970)
 
// To Date Inputs
toDay = input(defval = 1, title = "To Day", minval = 1, maxval = 31)
toMonth = input(defval = 8, title = "To Month", minval = 1, maxval = 12)
toYear = input(defval = 2031, title = "To Year", minval = 1970)
 


startDate = timestamp(fromYear, fromMonth, fromDay, 00, 00)
finishDate = timestamp(toYear, toMonth, toDay, 00, 00)




len = input(3, minval=1, title="Length")
src = input(hl2, title="Source")
smma = 0.0
sma1 = sma(src, len)
smma := na(smma[1]) ? sma1 : (smma[1] * (len - 1) + src) / len

len2 = input(6, minval=1, title="Length")
src2 = input(hl2, title="Source")
smma2 = 0.0
sma2 = sma(src2, len2)
smma2 := na(smma2[1]) ? sma2 : (smma2[1] * (len2 - 1) + src2) / len2

len3 = input(9, minval=1, title="Length")
src3 = input(hl2, title="Source")
smma3 = 0.0
sma3 = sma(src3, len3)
smma3 := na(smma3[1]) ? sma3 : (smma3[1] * (len3 - 1) + src3) / len3

len4 = input(50, minval=1, title="Length")
src4 = input(close, title="Source")
smma4 = 0.0
sma4 = sma(src4, len4)
smma4 := na(smma4[1]) ? sma4  : (smma4[1] * (len4 - 1) + src4) / len4

len5 = input(200, minval=1, title="Length")
src5 = input(close, title="Source")
out5 = ema(src5, len5)

timeinrange(res, sess) => time(res, sess) != 0
london=timeinrange(timeframe.period, "0300-1045")
londonEntry=timeinrange(timeframe.period, "0300-0845")

time_cond = time >= startDate and time <= finishDate and londonEntry

fast_length = input(title="Fast Length", type=input.integer, defval=12)
slow_length = input(title="Slow Length", type=input.integer, defval=26)
srcc = input(title="Source", type=input.source, defval=close)
signal_length = input(title="Signal Smoothing", type=input.integer, minval = 1, maxval = 50, defval = 9)
sma_source = input(title="Simple MA(Oscillator)", type=input.bool, defval=false)
sma_signal = input(title="Simple MA(Signal Line)", type=input.bool, defval=false)


// Calculating
fast_ma = sma_source ? sma(srcc, fast_length) : ema(srcc, fast_length)
slow_ma = sma_source ? sma(srcc, slow_length) : ema(srcc, slow_length)
macd = fast_ma - slow_ma
signal = sma_signal ? sma(macd, signal_length) : ema(macd, signal_length)
hist = macd - signal


longCond = close > out5 and close > smma4 and close > smma3 and close > smma2 and close > smma and londonEntry and smma > smma2 and smma2>smma3 and smma3>smma4 and smma4>out5 
shortCond = close < out5 and close < smma4 and close < smma3 and close < smma2 and close < smma and londonEntry and smma < smma2 and smma2<smma3 and smma3<smma4 and smma4<out5 
//longCond2 = crossover(close,out5) and crossover(close,smma4) and crossover(close,smma3) and crossover(close,smma2) and crossover(close,smma) and time_cond
//shortCond2 = crossunder(close,out5) and crossunder(close,smma4) and crossunder(close,smma3) and crossunder(close,smma2) and crossunder(close,smma) and time_cond

length=input(14, title="ATR Length")
mult=input(1.0, title="Percentage Multiplier (for ex., 0.7 = 70%)", step=0.1, minval=0.1, maxval=5.0)

oa=input(false, title="Show actual ATR")

ii=syminfo.pointvalue==0
s=ii?na:oa?atr(length):(syminfo.pointvalue * mult * atr(length))

tp=input(300,title="tp")
sl=input(300,title="sl")


//tp = s*10000
//sl= s*10000



//if(tp>300)
//    tp:=300
//if(sl>300)
//    sl:=300
//if(sl<150)
//    sl:=150
//if(tp<150)
//    tp:=150
strategy.initial_capital = 50000
//MONEY MANAGEMENT--------------------------------------------------------------''
balance = strategy.netprofit + strategy.initial_capital //current balance
floating = strategy.openprofit          //floating profit/loss
risk = input(3,type=input.float,title="Risk %")/100           //risk % per trade


    //Calculate the size of the next trade
temp01 = balance * risk     //Risk in USD
temp02 = temp01/sl        //Risk in lots
temp03 = temp02*100000      //Convert to contracts
size = temp03 - temp03%1000 //Normalize to 1000s (Trade size)
if(size < 10000)
    size := 10000           //Set min. lot size



strategy.entry("long",1,when=longCond )
strategy.exit("closelong","long", profit=tp,loss=sl)
//strategy.close("long",when= crossunder(close[4],smma4) and close[4] > close[3] and close[3]>close[2] and close[2] > close[1] and close[1] > close)
strategy.entry("short",0,when=shortCond )
strategy.exit("closeshort","short", profit=tp,loss=sl)
//strategy.close("short",when= crossover(close[4],smma4) and close[4] < close[3] and close[3]< close[2] and close[2] < close[1] and close[1] < close)

strategy.close_all(when = not london)

maxEntry=input(2,title="max entries")
// strategy.risk.max_intraday_filled_orders(maxEntry)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-11-22. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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