Multi Moving Average Trend Following Strategy Long term Investment Signal System Based on EMA and SMA Indicators

Pine ScriptTrend following Breakout Mean reversion EMA SMA Volume

Author: ChaoZhang
Complexity: Starter · 48 lines
Parameters: 0
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is a trend following system based on multiple moving averages combination, mainly utilizing the crossover and position relationships between Weekly EMA20, Daily SMA100, Daily SMA50, and Daily EMA20 to capture medium to long-term investment opportunities. The strategy identifies potential long entry points by observing the relationship between price and moving averages, combined with duration requirements.

Strategy Principles

The core logic of the strategy is based on the following key conditions:

Uses 20-period Weekly Exponential Moving Average (EMA1W20) as the primary trend indicator
Combines with 100-day Simple Moving Average (SMA1D100) for secondary trend confirmation
Employs 50-day Simple Moving Average (SMA1D50) as medium-term trend reference
Utilizes 20-day Exponential Moving Average (EMA1D20) for short-term trend confirmation The system generates a long signal when the price maintains above EMA1W20 and SMA1D100 for 14 consecutive days and then falls below SMA1D50. This design combines trend confirmation across multiple timeframes to enhance signal reliability.

Strategy Advantages

Multi-timeframe validation: Combines weekly and daily moving averages for more comprehensive trend assessment
Strict entry conditions: Requires price to maintain above major moving averages for sufficient duration, effectively filtering false signals
Reasonable risk control: Uses multiple moving average crossovers and positions for clear risk boundaries
High adaptability: Strategy parameters can be adjusted for different market environments
Clear execution: Trading signals are well-defined and suitable for programmatic implementation

Strategy Risks

Lag risk: Moving averages inherently have some lag, potentially causing delayed entries
Sideways market risk: May generate frequent false breakout signals in ranging markets
Parameter sensitivity: Optimal parameters may vary in different market environments
Drawdown risk: May experience significant drawdowns during sudden trend reversals
Execution risk: Requires stable system operation to avoid signal loss or execution delays

Strategy Optimization Directions

Incorporate volume indicators: Add volume confirmation mechanism to improve signal reliability
Optimize parameter adaptation: Develop dynamic parameter adjustment mechanisms
Add filtering conditions: Consider adding market environment indicators
Improve stop-loss mechanism: Design more detailed stop-loss and profit-taking rules
Enhance signal confirmation: Consider adding other technical indicators for auxiliary confirmation

Summary

This strategy establishes a relatively comprehensive trend following system through multiple moving average combinations, suitable for medium to long-term investors. While it has certain lag and parameter sensitivity risks, the strategy has practical value through proper risk control and continuous optimization. Investors are advised to make appropriate adjustments based on their risk preferences and market conditions.

Source Pine Script

/*backtest
start: 2024-11-12 00:00:00
end: 2024-12-11 08:00:00
period: 1h
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © petitepupu

//@version=5

ema20wTemp = ta.ema(close, 20)
ema20w = request.security(syminfo.tickerid, "1W", ema20wTemp, barmerge.gaps_on, barmerge.lookahead_off)
sma100d = ta.sma(close, 100)
sma50d = ta.sma(close, 50)
ema20d = ta.ema(close, 20)
daysAbove = input.int(14, title="Days", minval=1)
plot(ema20w, color=color.blue)
plot(sma100d, color=color.yellow)
plot(sma50d, color=color.red)
plot(ema20d, color=color.green)

longCondition = true
clean = true
for i = 0 to daysAbove
    if close[i] < ema20w or close[i] < sma100d or close > sma50d
        longCondition := false
        clean := false
        break

//TODO: 
if clean != true
    longCondition := true
    for i = 0 to daysAbove
        if close[i] > ema20w or close[i] > sma100d or close >= ema20d or -100 * (close - ema20d)/ema20d < 5.9
            longCondition := false
            break


// plotshape(series=longCondition, location=location.belowbar, color=color.green, style=shape.triangleup, title="Buy Signal", size = size.small)

if (longCondition)
    strategy.entry("Long", strategy.long)

    
strategy(title="LT Strategy", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=800)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-12-13. 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/Multi-Moving-Average-Trend-Following-Strategy-Long-term-Investment-Signal-System-Based-on-EMA-and-SMA-Indicators.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.