Three SMA Crossover Momentum Strategy

Pine ScriptMomentum Trend following Volatility SMA

Author: ChaoZhang
Complexity: Starter · 76 lines
Parameters: 6
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The Three SMA Crossover Momentum strategy is a typical technical indicator strategy that tracks market trends. It combines 16-, 36- and 72-period simple moving averages and uses their bullish and bearish crossovers to determine market trends, together with the Kaufman Adaptive Moving Average (KAMA) as a filter to take long or short positions when the trend direction is relatively clear.

Strategy Logic

The core indicators of this strategy are the 16-, 36-, and 72-period simple moving averages. When the shorter-period SMA crosses over the longer-period one upwards, it signals that the market is entering an uptrend. When the shorter-period SMA crosses below the longer-period one downwards, it signals that the market is entering a downtrend. For example, when the 16-SMA crosses over the 36-SMA and 72-SMA, it is a bullish signal. And when the 16-SMA crosses below the 36-SMA and 72-SMA, it is a bearish signal.

The Kaufman Adaptive Moving Average (KAMA) serves as a filter to avoid wrong signals when the trend is unclear. The SMA crossover signals are only triggered when KAMA is in a non-accelerating or non-decelerating mode (linear phase).

The strategy tracks the SMA crossover situations to take long or short positions when the trend is relatively clear. The long condition is 16-SMA crossing over 36-SMA and 72-SMA with linear KAMA. The short condition is 16-SMA crossing below 36-SMA and 72-SMA with linear KAMA.

Advantage Analysis

The advantages of this strategy are:

Combining multi-period SMAs can effectively track medium- and long-term market trends
Introducing adaptive moving average as a filter can reduce wrong signals when the trend is unclear
Simple to implement, suitable for automated or program trading

Risk Analysis

There are also some risks with this strategy:

Frequent ineffective signals may occur in ranging markets due to frequent SMA crossovers
No stop loss is set, losses may expand
Designed for high volatile crypto markets, may underperform in less volatile markets

The risks could be reduced by tuning SMA parameters, setting stop loss constraints, or only applying to highly volatile markets.

Optimization Directions

The strategy can be optimized in the following ways:

Test different combinations of SMA parameters to find the optimal
Add trading volume or volatility indicators as supplementary filter conditions
Set up stop loss mechanisms
Combine other indicators to determine entry timing
Optimize position sizing, adjust risks through gradual adding and reducing positions

Conclusion

The Three SMA Crossover Momentum strategy is a rather classic and practical trend-following strategy overall. It judges medium- and long-term market trends effectively through multi-period SMA crossovers and filters out some noise. It can serve as one of the timing reference indicators for positional trading. But this strategy also has some weaknesses, requiring further enhancements and optimizations to stand in more diverse markets.

Parameters

Name	Default	What it controls
v_input_int_4	100	Trend SMA
v_input_int_5	50	KAMA Lenght
v_input_bool_1	true	Self Powered
v_input_int_1	16	(?SMA) 1-SMA Lenght
v_input_int_2	36	2-SMA Lenght
v_input_int_3	72	3-SMA Lenght

Source Pine Script

/*backtest
start: 2023-11-24 00:00:00
end: 2023-12-24 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/
// © Wielkieef


//@version=5
strategy(title='Three SMA-crossover strategy [30min] ', overlay=true, pyramiding=1, initial_capital=10000, default_qty_type=strategy.cash, default_qty_value=10000, calc_on_order_fills=false, slippage=0, commission_type=strategy.commission.percent, commission_value=0.03)

src = close

Length1 = input.int(16, title='  1-SMA Lenght', minval=1, group='SMA')
Length2 = input.int(36, title='  2-SMA Lenght', minval=1, group='SMA')
Length3 = input.int(72, title='  3-SMA Lenght', minval=1, group='SMA')
SMA1 = ta.sma(close, Length1)
SMA2 = ta.sma(close, Length2)
SMA3 = ta.sma(close, Length3)

Long_ma = SMA1 > SMA2 and SMA2 > SMA3
Short_ma = SMA1 < SMA2 and SMA2 < SMA3

LengthMainSMA = input.int(100, title='  Trend SMA ', minval=1)

SMAas = ta.sma(src, LengthMainSMA)

//  Powered Kaufman Adaptive Moving Average by alexgrover (modificated by Wielkieef)
lengthas = input.int(50, title='   KAMA Lenght')
sp = input.bool(true, title='  Self Powered')

er = math.abs(ta.change(close, lengthas)) / math.sum(math.abs(ta.change(close)), lengthas)
pow = sp ? 1 / er : 2
per = math.pow(math.abs(ta.change(close, lengthas)) / math.sum(math.abs(ta.change(close)), lengthas), pow)
a = 0.
a := per * src + (1 - per) * nz(a[1], src)
mad4h = 0.
a_f = a / a[1] > .999 and a / a[1] < 1.001

///.

Bar_color = close > SMAas ? color.green : Long_ma ? color.blue : Short_ma ? color.maroon : color.gray

barcolor(color=Bar_color)

long_cond = Long_ma and SMAas < close and not a_f and close > a

short_cond = Short_ma and SMAas > close and not a_f and close < a
  
long_stop = Short_ma and SMAas < close

short_stop = Long_ma and SMAas > close

SMA1plot = plot(SMA1, color=Bar_color, linewidth=2)
SMA2plot = plot(SMA2, color=Bar_color, linewidth=4)
SMA3plot = plot(SMA3, color=Bar_color, linewidth=2)

fill(SMA1plot,SMA3plot,title="RANGE " ,color = color.new(Bar_color, 50))



if  long_cond
    strategy.entry('Long', strategy.long)

if  short_cond
    strategy.entry('Short', strategy.short)

strategy.close_all(when=long_stop or short_stop)



//by wielkieef

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-25. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

Build this with AgenKit

A spec is not a trading system. Paste this into your coding agent and AgenKit runs a specialist team — architecture, test-first build, risk gates, review — with paper trading on by default.

in Claude Code, Cursor, Codex & 4 more

/agenkit build a production-grade trading bot with a backtest engine, risk controls and paper trading by default from strategies/Three-SMA-Crossover-Momentum-Strategy.md

npx agenkit install engineering-kit in your project
Paste the command into your agent and approve each phase
Get tests, backtest, risk controls and a paper-trading switch

Get AgenKit How /agenkit works

Understand it first

Before you run this, read the lesson behind the idea and how to backtest it honestly. Specs like this one are the survivors of an unknown number of trials; deflate their results accordingly.