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Eleven Moving Averages Crossover Strategy

Author: ChaoZhang, Date: 2024-01-15 13:57:53
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Overview

This strategy combines crossovers of 11 different types of moving averages for long and short entries. The 11 moving averages used are: Simple (SMA), Exponential (EMA), Weighted (WMA), Volume-weighted (VWMA), Smoothed (SMMA), Double Exponential (DEMA), Triple Exponential (TEMA), Hull (HMA), Zero Lag Exponential (ZEMA), Triangular (TMA), and SuperSmoother (SSMA) filter.

The strategy allows configuring two moving averages - a faster one and a slower one, both selected from the 11 options. Long signals are generated when the faster MA crosses above the slower MA. Short signals occur when the faster MA crosses below the slower MA.

Additional features include pyramiding settings, take profit and stop loss levels.

Strategy Logic

The core strategy logic relies on crossovers between two moving averages to determine entries and exits.

The entry conditions are:

Long entry: Fast MA > Slow MA Short entry: Fast MA < Slow MA

Exits are determined by one of three criteria:

Take profit level reached
Stop loss level reached
Opposite signal generated (MA crossover in opposite direction)

The strategy allows configuring key parameters like the MA type and length, pyramiding settings, take profit and stop loss percentages. This provides flexibility to optimize the strategy for different market conditions and risk preferences.

Advantages

Combines 11 different MA types for robust signals
Flexible configuration of key parameters
Take profit and stop loss features protect profits and limit losses
Pyramiding allows increased position size for strong trends

Risks

As with any technical indicator, MA crossovers can generate false signals
Overoptimization for current market conditions may degrade future performance
Hard stop loss exits can lead to exiting good trades early in volatile markets

Risk management can be enhanced by using price action confirmation for entry signals, using trailing stops instead of hard stops, and avoiding overoptimization.

Enhancement Opportunities

There are several ways in which this strategy can be improved:

Incorporate additional filters before entry, such as volume and price action checks
Test performance of different MA types systematically and select optimal 1 or 2
Optimize MA lengths specifically for trading instrument and time frame
Employ trailing stops instead of hard stops
Add profit taking at incremental levels as trend extends

Conclusion

The eleven moving averages crossover strategy provides a systematic approach to trading crossovers. By combining signals across multiple MA indicators and allowing configuration of key parameters, it provides a robust yet flexible trading framework. Fine-tuning and risk management will play key roles in optimizing performance. The strategy has strong potential for momentum-based trading but should be adapted for different market environments.

/*backtest
start: 2023-12-15 00:00:00
end: 2024-01-14 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=3

strategy(title = "[STRATEGY] MA Cross Eleven", overlay = true)

// MA - type, source, length

//  MA - type, source, length
//  SMA --> Simple
//  EMA --> Exponential
//  WMA --> Weighted
//  VWMA --> Volume Weighted
//  SMMA --> Smoothed
//  DEMA --> Double Exponential
//  TEMA --> Triple Exponential
//  HMA --> Hull
//  TMA --> Triangular
//  SSMA --> SuperSmoother filter
//  ZEMA --> Zero Lag Exponential

type = input(defval="ZEMA", title="MA Type: ", options=["SMA", "EMA", "WMA", "VWMA", "SMMA", "DEMA", "TEMA", "HullMA", "ZEMA", "TMA", "SSMA"])
len1 = input(defval=8, title="Fast MA Length", minval=1)
srcclose1 = input(close, "Fast MA Source")
len2 = input(defval=21, title="Slow MA Length", minval=1)
srcclose2 = input(close, "Slow MA Source")

// Returns MA input selection variant, default to SMA if blank or typo.

variant(type, src, len) =>
    v1 = sma(src, len)                                                  // Simple
    v2 = ema(src, len)                                                  // Exponential
    v3 = wma(src, len)                                                  // Weighted
    v4 = vwma(src, len)                                                 // Volume Weighted
    v5 = 0.0
    v5 := na(v5[1]) ? sma(src, len) : (v5[1] * (len - 1) + src) / len    // Smoothed
    v6 = 2 * v2 - ema(v2, len)                                          // Double Exponential
    v7 = 3 * (v2 - ema(v2, len)) + ema(ema(v2, len), len)               // Triple Exponential
    v8 = wma(2 * wma(src, len / 2) - wma(src, len), round(sqrt(len)))   // Hull
    v11 = sma(sma(src,len),len)                                         // Triangular
    // SuperSmoother filter
    // © 2013  John F. Ehlers
    a1 = exp(-1.414*3.14159 / len)
    b1 = 2*a1*cos(1.414*3.14159 / len)
    c2 = b1
    c3 = (-a1)*a1
    c1 = 1 - c2 - c3
    v9 = 0.0
    v9 := c1*(src + nz(src[1])) / 2 + c2*nz(v9[1]) + c3*nz(v9[2])
    // Zero Lag Exponential
    e = ema(v2, len)
    v10 = v2+(v2-e)
    // return variant, defaults to SMA if input invalid.
    type=="EMA"?v2 : type=="WMA"?v3 : type=="VWMA"?v4 : type=="SMMA"?v5 : type=="DEMA"?v6 : type=="TEMA"?v7 : type=="HullMA"?v8 : type=="SSMA"?v9 : type=="ZEMA"?v10 : type=="TMA"? v11: v1

ma_1 = variant(type, srcclose1, len1)
ma_2 = variant(type, srcclose2, len2)

plot(ma_1, title="Fast MA", color = green, linewidth=2, transp=0)
plot(ma_2, title="Slow MA", color = red, linewidth=2, transp=0)

longCond = na
shortCond = na
longCond := crossover(ma_1, ma_2)
shortCond := crossunder(ma_1, ma_2)

// Count your long short conditions for more control with Pyramiding

sectionLongs = 0
sectionLongs := nz(sectionLongs[1])
sectionShorts = 0
sectionShorts := nz(sectionShorts[1])

if longCond
    sectionLongs := sectionLongs + 1
    sectionShorts := 0

if shortCond
    sectionLongs := 0
    sectionShorts := sectionShorts + 1
    
// Pyramiding Inputs

pyrl = input(1, "Pyramiding")

// These check to see your signal and cross references it against the pyramiding settings above

longCondition = longCond and sectionLongs <= pyrl 
shortCondition = shortCond and sectionShorts <= pyrl 

// Get the price of the last opened long or short

last_open_longCondition = na
last_open_shortCondition = na
last_open_longCondition := longCondition ? high[1] : nz(last_open_longCondition[1])
last_open_shortCondition := shortCondition ? low[1] : nz(last_open_shortCondition[1])

// Check if your last postion was a long or a short

last_longCondition = na
last_shortCondition = na
last_longCondition := longCondition ? time : nz(last_longCondition[1])
last_shortCondition := shortCondition ? time : nz(last_shortCondition[1])

in_longCondition = last_longCondition > last_shortCondition
in_shortCondition = last_shortCondition > last_longCondition

// Take profit

isTPl = input(false, "Take Profit Long")
isTPs = input(false, "Take Profit Short")
tpl = input(3, "Take Profit Long %", type=float)
tps = input(30, "Take Profit Short %", type=float)
long_tp = isTPl and crossover(high, (1+(tpl/100))*last_open_longCondition) and in_longCondition  == 1
short_tp = isTPs and crossunder(low, (1-(tps/100))*last_open_shortCondition) and in_shortCondition == 1 

// Stop Loss

isSLl = input(false, "Stop Loss Long")
isSLs = input(false, "Stop Loss Short")
sl= 0.0
sl := input(3, "Stop Loss %", type=float)
long_sl = isSLl and crossunder(low, (1-(sl/100))*last_open_longCondition) and longCondition == 0 and in_longCondition == 1
short_sl = isSLs and crossover(high, (1+(sl/100))*last_open_shortCondition) and shortCondition == 0 and in_shortCondition == 1

// Create a single close for all the different closing conditions.

long_close = long_tp or long_sl ? 1 : 0
short_close = short_tp or short_sl ? 1 : 0

// Get the time of the last close

last_long_close = na
last_short_close = na
last_long_close := long_close ? time : nz(last_long_close[1])
last_short_close := short_close ? time : nz(last_short_close[1])

// Strategy entries

strategy.entry("long", strategy.long, when=longCondition == true, stop = open[1])
strategy.entry("short", strategy.short, when=shortCondition == true)
strategy.close("long", when = long_sl or long_tp)
strategy.close("short", when = short_sl or short_tp)

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