Multi timeframe MA Trend Following Strategy

Pine ScriptTrend following Multi-timeframe Mean reversion SMA

Author: ChaoZhang
Complexity: Intermediate · 83 lines
Parameters: 12
Backtest bar: 1m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is based on the multi-timeframe moving average crossover to track middle-long term trends. It adopts a pyramiding position to chase rises and achieve exponential capital growth. The biggest advantage is being able to catch the mid-long term trends and pyramid entries in batches and stages to obtain excess returns.

Strategy Logic

Build multiple timeframes based on 9-day MA, 100-day MA and 200-day MA.
Generate buy signals when shorter period MA crosses above longer period MA.
Adopt 7 staged pyramiding entries. Check existing positions before adding new entry, stop pyramiding when 6 positions already opened.
Set fixed 3% TP/SL for risk control.

Above is the basic trading logic.

Advantages

Effectively catch mid-long term trends and enjoy exponential growth.
Multi-timeframe MA crossover avoids short-term noise.
Fixed TP/SL controls risk for each position.
Pyramid entries in batches to obtain excess returns.

Risks & Solutions

Risk of huge loss if fail to cut loss in trend reversal. Solution is to shorten MA periods and quicken stop loss.
Risk of margin call if loss beyond tolerance. Solution is to lower initial position size.
Risk of over 700% loss if strong downtrend. Solution is to raise fixed stop loss percentage.

Optimization Directions

Test different MA combinations to find optimal parameters.
Optimize pyramiding stages quantity. Test to find best number.
Test fixed TP/SL settings. Expand TP range for higher profitability.

Summary

The strategy is very suitable to catch mid-long term trends. Pyramid entries in batches can achieve very high risk-reward ratio. There are also some operation risks, which should be controlled by parameter tuning. Overall this is a promising strategy worth live trading verification and further optimization.

Parameters

Name	Default	What it controls
v_input_1	true	From Month
v_input_2	10	From Day
v_input_3	2020	From Year
v_input_4	true	Thru Month
v_input_5	true	Thru Day
v_input_6	2112	Thru Year
v_input_7	true	Show Date Range
v_input_8	9	MAfast
v_input_9	100	MAslow
v_input_10	200	MAlong
v_input_11	3	ProfitTarget_Percent
v_input_12	3	LossTarget_Percent

Source Pine Script

/*backtest
start: 2023-12-27 00:00:00
end: 2024-01-03 00:00:00
period: 1m
basePeriod: 1m
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/
    // © Coinrule
    
//@version=3
strategy(shorttitle='Pyramiding Entry On Early Trends',title='Pyramiding Entry On Early Trends (by Coinrule)', overlay=false, pyramiding= 7, initial_capital = 1000, default_qty_type = strategy.percent_of_equity, default_qty_value = 20, commission_type=strategy.commission.percent, commission_value=0.1)
    
    
//Backtest dates
fromMonth = input(defval = 1,  title = "From Month")     
fromDay   = input(defval = 10,    title = "From Day")       
fromYear  = input(defval = 2020, title = "From Year")       
thruMonth = input(defval = 1,    title = "Thru Month")     
thruDay   = input(defval = 1,    title = "Thru Day")     
thruYear  = input(defval = 2112, title = "Thru Year")       
    
showDate  = input(defval = true, title = "Show Date Range")
    
start     = timestamp(fromYear, fromMonth, fromDay, 00, 00)        // backtest start window
finish    = timestamp(thruYear, thruMonth, thruDay, 23, 59)        // backtest finish window
window()  => true       // create function "within window of time"
    
    
//MA inputs and calculations
inSignal=input(9, title='MAfast')
inlong1=input(100, title='MAslow')
inlong2=input(200, title='MAlong')
    
MAfast= sma(close, inSignal)
MAslow= sma(close, inlong1)
MAlong= sma(close, inlong2)
    
    
Bullish = crossover(close, MAfast) 
    
longsignal = (Bullish and MAfast > MAslow and MAslow < MAlong and window())
    
//set take profit
    
ProfitTarget_Percent = input(3)
Profit_Ticks = (close * (ProfitTarget_Percent / 100)) / syminfo.mintick
    
//set take profit
    
LossTarget_Percent = input(3)
Loss_Ticks = (close * (LossTarget_Percent / 100)) / syminfo.mintick
    
    
//Order Placing
    
strategy.entry("Entry 1", strategy.long, when = (strategy.opentrades == 0) and longsignal)
    
strategy.entry("Entry 2", strategy.long, when = (strategy.opentrades == 1) and longsignal)
        
strategy.entry("Entry 3", strategy.long, when = (strategy.opentrades == 2) and longsignal)
    
strategy.entry("Entry 4", strategy.long, when = (strategy.opentrades == 3) and longsignal)
    
strategy.entry("Entry 5", strategy.long, when = (strategy.opentrades == 4) and longsignal)
        
strategy.entry("Entry 6", strategy.long, when = (strategy.opentrades == 5) and longsignal)
        
strategy.entry("Entry 7", strategy.long, when = (strategy.opentrades == 6) and longsignal)
    
    
    
if (strategy.position_size > 0)
    strategy.exit(id="Exit 1", from_entry = "Entry 1", profit = Profit_Ticks, loss = Loss_Ticks)
    strategy.exit(id="Exit 2", from_entry = "Entry 2", profit = Profit_Ticks, loss = Loss_Ticks)
    strategy.exit(id="Exit 3", from_entry = "Entry 3", profit = Profit_Ticks, loss = Loss_Ticks)
    strategy.exit(id="Exit 4", from_entry = "Entry 4", profit = Profit_Ticks, loss = Loss_Ticks)
    strategy.exit(id="Exit 5", from_entry = "Entry 5", profit = Profit_Ticks, loss = Loss_Ticks)
    strategy.exit(id="Exit 6", from_entry = "Entry 6", profit = Profit_Ticks, loss = Loss_Ticks)
    strategy.exit(id="Exit 7", from_entry = "Entry 7", profit = Profit_Ticks, loss = Loss_Ticks)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-04. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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