Crossing the Breakout Strategy

Pine ScriptBreakout Machine learning Trend following MACD EMA SMA

Author: ChaoZhang
Complexity: Starter · 62 lines
Parameters: 4
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The moving average crossover strategy is a very common quantitative trading strategy. It uses the golden cross and death cross of moving averages to determine trends and profit. When the short-term moving average crosses above the long-term moving average, it signals an uptrend, and a long position can be taken. When the short-term moving average crosses below the long-term moving average, it signals a downtrend, and a short position can be taken.

Strategy Logic

This strategy is based on the golden cross and death cross of moving averages to determine entry and exit points. The code uses two boolean input parameters upOrDown and longOrShort to determine long or short; percentInput to set the threshold percentage of price change; closePositionDays to set the number of days to hold the position.

The core logic is: calculate the increase/decrease of today relative to yesterday. If it reaches the input threshold percentage, a trading signal is triggered. If it's a long signal, when today's price increases more than threshold relative to yesterday, go long. If it's a short signal, when today's price decreases more than threshold relative to yesterday, go short.

After going long/short, the entry day and next 4 days will be marked with colors on the chart. The position will be closed automatically after 4 days.

Advantages

Using moving average crossovers to determine trend is a mature and reliable method
Simple and clear strategy logic, easy to understand and implement
The frequency can be controlled by adjusting parameters
The automatic stop loss mechanism effectively controls risks

Risks

Moving averages have lagging effects, may miss best timing of rapid price changes
Significant price swings may happen in short term, generating unnecessary signals
Inappropriate parameter settings may affect strategy performance
Unable to respond effectively to impacts of unexpected events

Risk management:

Optimize moving average parameters, longer periods help filter noise
Increase threshold percentage to reduce unnecessary trades
Test different holding periods to control single loss
Combine with other indicators to further confirm signals

Optimization Directions

Consider using EMA, DMA instead of SMA to make it more sensitive
Add stop loss mechanisms, e.g. stop loss when breaking the average
Add other technical indicators like MACD, KDJ for combination, improving win rate
Try machine learning methods to auto optimize parameters
Optimize entry and exit timing, such as breakout, etc.

Summary

The moving average crossover strategy is a very simple and practical quantitative trading strategy. By judging the relationship between short-term and long-term trends, it profits from the trending nature of asset prices. This strategy is easy to implement with clear logic, and forms the foundation of many quantitative trading strategies. We can obtain better performance through parameter tuning and optimizations. But we also need to manage risks and avoid misuse.

Parameters

Name	Default	What it controls
v_input_1	true	Long=Checked Short=Unchecked
v_input_2	true	Direction of Today vs. Previous day: Up=Checked Down=Unchecked
v_input_3	4.5	Percent
v_input_4	4	How Many Days to Close Position

Source Pine Script

/*backtest
start: 2023-01-01 00:00:00
end: 2023-10-11 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=3
//  Created by Leon Ross

strategy(title = "DaysAfterCertainPercentChangev1", shorttitle = "DACPCv1", overlay = true, 
  pyramiding = 0, default_qty_type = strategy.percent_of_equity, default_qty_value = 100, 
  calc_on_every_tick=true, initial_capital=100000)
  
//Inputs
longOrShort = input(title="Long=Checked Short=Unchecked", type=bool, defval=true) //long=true, down=false
upOrDown = input(title="Direction of Today vs. Previous day: Up=Checked Down=Unchecked", type=bool, defval=true) //up=true, down=false: this is the direction of days vs previous day
percentInput = input(title="Percent", type=float, defval=4.5)
closePositionDays = input(title="How Many Days to Close Position", defval=4)

//Conditions
//percentUpValue = (close / close[1]) - 1
//percentUp = percentUpValue >= (percentInput/100.0)
//upConditions = percentUp
//percentDownValue = 1- (close / close[1])
//percentDown = percentDownValue >= (percentInput/100.0)
//downConditions = percentDown
upValue = (close / close[1]) - 1
downValue = 1 - (close / close[1])
allConditions = if(upOrDown)
    upValue >= (percentInput/100.0)
else
    downValue >= (percentInput/100.0)
    
//Plots
bgcolor(allConditions ? (upOrDown ? green : red) : na, transp=70)
bgcolor(allConditions ? silver : na, transp=70, offset=1)
bgcolor(allConditions ? silver : na, transp=70, offset=2)
bgcolor(allConditions ? silver : na, transp=70, offset=3)
bgcolor(allConditions ? silver : na, transp=70, offset=4)
//bgcolor(downConditions == 1 ? red : na, transp=70)
//bgcolor(downConditions == 1 ? silver : na, transp=70, offset=1)
//bgcolor(downConditions == 1 ? silver : na, transp=70, offset=2)
//bgcolor(downConditions == 1 ? silver : na, transp=70, offset=3)
//bgcolor(downConditions == 1 ? silver : na, transp=70, offset=4)

//Entires
if(longOrShort)
    strategy.entry(id = "Long", long = true, when = allConditions) 
else
    strategy.entry(id = "Short", long = false, when = allConditions)

//Exits
if (barssince(allConditions) == closePositionDays)
    if(longOrShort)
        strategy.close("Long")
    else
        strategy.close("Short")

Published by ChaoZhang on the FMZ strategy square · last modified 2023-10-12. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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