Trend Following Strategy Based on Moving Average Crossover 440545

Pine ScriptTrend following Machine learning EMA SMA

Author: ChaoZhang
Complexity: Starter · 59 lines
Parameters: 5
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy generates trading signals by calculating different types of moving averages (Simple Moving Average SMA, Exponential Moving Average EMA, Hull Moving Average HMA and Weighted Moving Average VWMA) and detecting crossover points between them, to determine the market trend and follow it. It generates buy signals when the shorter-term MA crosses above the longer-term MA from below, and sell signals when the opposite crossing happens.

Strategy Logic

The core idea of this strategy is to judge the market trend by comparing two moving averages. Specifically, it allows configuring two MAs with different types and lengths through input parameters. The first MA has a longer period to represent the major trend, while the second MA has a shorter period for the current short-term trend.

When the short-term MA crosses over the long-term MA from below, it signals that the short-term trend is strengthening and the market is entering an upward trend. Thus a buy signal is generated at this crossover point. Conversely, when the short-term MA crosses below the long-term MA, it suggests the short-term trend is weakening and the market is reversing downwards. Accordingly a sell signal is generated then.

By detecting such MA crossovers, this strategy follows the market trend to trade.

Advantages

Utilizes classic and practical MA crossover method to determine major trends
Supports combinations of various MA types, providing flexibility
Simple and clear logic, easy to understand and automate
Configurable parameters adapt to different market conditions

Risk Analysis

MAs have lagging effect, signals may come at or near turning points when price action has already happened
Trend judgements may be inaccurate, incurring unnecessary losses
Results vary significantly with different MA parameter settings

Solutions:

Use shorter MA periods for better sensitivity
Add other filters for cross-verification to avoid mistakes
Parameter optimization methods e.g. brute force, machine learning, genetic algorithms
Control position sizing and stop loss properly

Improvement Directions

Add other indicators as filters to enhance accuracy
Auto-adapt MA parameters based on changing market conditions
Utilize machine learning for automated parameter optimization
Refine stop loss strategy

Conclusion

This strategy builds on the classic idea of using MA crossovers for major trend detection. With flexible MA combinations, it is simple to implement and suitable for algorithmic trading automation. Overall it is reasonably practical but leaves room for enhancements like parameter tuning, additional filters etc. to further improve performance.

Parameters

Name	Default	What it controls
v_input_1_close	0	Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_2	25	1st MA Length
v_input_3	0	1st MA Type: HMA \| EMA \| SMA \| VWMA
v_input_4	7	2nd MA Length
v_input_5	0	2nd MA Type: HMA \| EMA \| SMA \| VWMA

Source Pine Script

/*backtest
start: 2023-12-31 00:00:00
end: 2024-01-30 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=3
//study(title="MA Crossover Strategy", overlay = true)
strategy("MA Crossover Strategy", overlay=true)
src = input(close, title="Source")

price = request.security(syminfo.tickerid, timeframe.period, src)
ma1 = input(25, title="1st MA Length")
type1 = input("HMA", "1st MA Type", options=["SMA", "EMA", "HMA", "VWMA"])

ma2 = input(7, title="2nd MA Length")
type2 = input("HMA", "2nd MA Type", options=["SMA", "EMA", "HMA", "VWMA"])

f_hma(_src, _length)=>
    _return = wma((2*wma(_src, _length/2))-wma(_src, _length), round(sqrt(_length)))

price1 = if (type1 == "SMA")
    sma(price, ma1)
else
    if (type1 == "EMA")
        ema(price, ma1)
    else
        if (type1 == "VWMA")
            vwma(price, ma1)
        else
            f_hma(price, ma1)
    
price2 = if (type2 == "SMA")
    sma(price, ma2)
else
    if (type2 == "EMA")
        ema(price, ma2)
    else
        if (type2 == "VWMA")
            vwma(price, ma2)
        else
            f_hma(price, ma2)


//plot(series=price, style=line,  title="Price", color=black, linewidth=1, transp=0)
plot(series=price1, style=line,  title="1st MA", color=blue, linewidth=2, transp=0)
plot(series=price2, style=line, title="2nd MA", color=green, linewidth=2, transp=0)


longCondition = crossover(price1, price2)
if (longCondition)
    strategy.entry("Long", strategy.long)

shortCondition = crossunder(price1, price2)
if (shortCondition)
    strategy.entry("Short", strategy.short)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-31. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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