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Price Crossing Moving Average Trend Following Strategy

Author: ChaoZhang, Date: 2024-01-26 15:18:29
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Overview

This strategy generates buy and sell signals based on the crossing of price with a moving average. It provides various types of moving averages and a tolerance parameter to filter false breakouts. The strategy aims to capture turning points in price trends for trend following.

Strategy Logic

The strategy calculates a length N moving average based on the closing price. Typical moving average types include Simple Moving Average (SMA), Exponential Moving Average (EMA), Weighted Moving Average (WMA) etc. Then a tolerance level is set, e.g. 5%, and upper band (1.05 times moving average) and lower band (0.95 times moving average) are calculated. When closing price crosses above upper band, a buy signal is generated. When closing price crosses below lower band, a sell signal is generated. This helps filter some false breakouts. Also, a Boolean parameter “Short Only” is provided. When enabled, only sell signals are generated for shorting the market.

Advantages

  • Effectively follows price trends using moving average’s trend following characteristics
  • Provides various moving average types for flexible combinations
  • Tolerance parameter helps filter false breakouts and avoid unnecessary trades
  • Can go short only, suitable for catching downward trends

Risks

  • Moving averages have lagging effect, may miss price turning points
  • Not suitable for range-bound market environments
  • Improper tolerance parameter settings may filter valid signals
  • Going short has higher risks, need prudent operations

Optimization Directions

  • Optimize moving average type and length parameters
  • Test different tolerance parameter settings
  • Add other indicators to filter signals
  • Employ position sizing strategies

Conclusion

Overall this is a typical trend following strategy. It uses the relationship between price and moving average to determine trends, with some flexibility. Through parameter optimization and proper signal filtering, it can become a decent quant strategy. But controlling downside risks when shorting is important to avoid excessive losses.

/*backtest
start: 2023-12-26 00:00:00
end: 2024-01-25 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/
// © RafaelPiccolo

//@version=4
strategy("Price X MA Cross", overlay=true)

typ = input("HMA", "MA Type", options=["SMA", "EMA", "WMA", "HMA", "VWMA", "RMA", "TEMA"])
len = input(100, minval=1, title="Length")
src = input(close, "Source", type=input.source)
tol = input(0, minval=0, title="Tolerance (%)", type=input.float)
shortOnly = input(false, "Short only")

tema(src, len)=>
    ema1 = ema(src, len)
    ema2 = ema(ema1, len)
    ema3 = ema(ema2, len)
    return = 3 * (ema1 - ema2) + ema3

getMAPoint(type, len, src)=>
    return = type == "SMA" ? sma(src, len) : type == "EMA" ? ema(src, len) : type == "WMA" ? wma(src, len) : type == "HMA" ? hma(src, len) : type == "VWMA" ? vwma(src, len) : type == "RMA" ? rma(src, len) : tema(src, len)

ma = getMAPoint(typ, len, src)
upperTol = ma * (1 + tol/100)
lowerTol = ma * (1 - tol/100)

longCondition = crossover(close, upperTol)
shortCondition = crossunder(close, lowerTol)

if (shortCondition)
    strategy.entry("Short", strategy.short)

if (longCondition)
    if (shortOnly)
        strategy.close("Short")
    else
        strategy.entry("Long", strategy.long)

plot(ma, "Moving Average", close > ma ? color.green : color.red, linewidth = 2)
t1 = plot(tol > 0 ? upperTol : na, transp = 70)
t2 = plot(tol > 0 ? lowerTol : na, transp = 70)
fill(t1, t2, color = tol > 0 ? color.blue : na)
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