Name

移动平均线反弹策略Exponential-Moving-Average-Bounce-Strategy

Author

ChaoZhang

Strategy Description

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策略概述

移动平均线反弹策略是一种跟踪价格突破移动平均线的策略。它检查蜡烛是否从移动平均线下方反弹回来,如果是,就是多头信号;如果蜡烛从移动平均线上方向下反弹,就是空头信号。

策略名称

Exponential Moving Average Bounce Strategy

策略原理

该策略基于 exponential moving average (指数移动平均线)。它会实时计算出一个 EMA 线。然后检查价格是否从 EMA 线上方或下方发生反弹:

如果价格先跌破 EMA 线,然后又重新回升收盘于 EMA 线之上,就是多头信号
如果价格先突破 EMA线,然后又重新下跌收盘于EMA线之下,就是空头信号

这样的反弹就是策略的入场信号。

策略优势分析

顺势操作,避免被套

EMA反弹策略只在确定价格反转后才入场,能够避免逆势操作被套。

回撤小,历史收益好

由于运用了指数移动平均线,能够有效平滑价格数据,过滤市场噪音,使得该策略回撤小,历史收益较好。

容易理解,参数调整灵活

EMA反弹策略仅仅依赖移动平均线,非常简单直接,新手容易理解;同时EMA周期参数可以灵活调整,适应不同品种。

风险分析

容易假信号

EMA线附近往往存在密集的假突破,可能引发错误信号。需要调整EMA参数以过滤这些噪音。

顺势操作,无法预测转折点

该策略本质上是顺势操作。无法预测价格转折点,只能追逐趋势。这可能错过周期调整的最佳入场时机。

止损位置容易被击穿

移动平均线附近的止损位有时候会被突破,造成亏损扩大。这需要运用更为灵活的止损方式。

优化方向

结合其他指标过滤信号

可以加入像RSI,MACD等其他指标来确认价格反转,过滤假信号。

优化止损方式

可以使用时间止损,震荡止损等更为灵活的止损方式,降低被击穿风险。

参数优化

对EMA周期参数进行优化,找到最佳参数组合。也可以使EMA参数动态变化,追踪市场周期。

总结

移动平均线反弹策略是一种简单实用的趋势跟踪策略。它顺势操作,回撤小,容易理解。同时也存在一定的假信号风险与止损风险。我们可以通过更好的指标组合,止损方式,参数选择来优化该策略,使其成为稳定可靠的量化策略。

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Strategy Overview

The exponential moving average (EMA) bounce strategy is a strategy that tracks price breakthroughs of the moving average line. It checks whether candles bounce back from below the moving average line. If so, it is a bullish signal; if the candle bounces down from above the moving average line, it is a bearish signal.

Strategy Name

Exponential Moving Average Bounce Strategy

Strategy Logic

This strategy is based on the exponential moving average (EMA) line. It calculates an EMA line in real time. Then it checks whether the price bounces back from the EMA line:

If the price first breaks through the EMA line and then rebounds back above the EMA line, it is a bullish signal
If the price first breaks through the EMA line and then falls back below the EMA line, it is a bearish signal

Such a rebound is the entry signal for the strategy.

Advantage Analysis

Trade with the trend, avoid being trapped

The EMA bounce strategy only enters after confirming the price reversal, which avoids trading against the trend and being trapped.

Small drawdowns, good historical returns

By using the exponential moving average, the strategy can effectively smooth the price data and filter out market noise, resulting in small drawdowns and good historical returns.

Easy to understand, flexible parameter adjustment

The EMA bounce strategy relies simply on moving averages, which is straightforward for beginners to understand. Meanwhile, the EMA period can be flexibly adjusted to adapt to different products.

Risk Analysis

Prone to false signals

There are often dense false breakouts around the EMA line, which may cause wrong signals. The EMA parameters need to be adjusted to filter out the noise.

Trading with the trend, unable to predict turning points

The strategy essentially trades along with the trend. It is unable to predict price turning points and can only follow the trend. This may miss the best entry opportunity during cyclic adjustments.

Stop loss prone to being taken out

The stop loss near the moving average line sometimes gets hit, leading to enlarged losses. This calls for more flexible stop loss methods.

Optimization

Incorporate other indicators for signal filtering

Indicators like RSI and MACD can be added to confirm the price reversal and filter out false signals.

Optimize stop loss methods

More flexible stop loss methods like time stops and volatility stops can be used to reduce the risk of being taken out.

Parameter optimization

Optimize the EMA period parameters to find the best parameter combinations. The EMA parameters can also be made dynamic to track market cycles.

Conclusion

The EMA bounce strategy is a simple and practical trend following strategy. It has small drawdowns and is easy to understand. At the same time, it also has some risks of false signals and being stopped out. We can optimize the strategy by using better indicator combinations, stop loss methods and parameter selections to make it a stable and reliable quantitative strategy.

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Strategy Arguments

Argument	Default	Description
v_input_1	false	Strategy Direction
v_input_2	true	-----------------Strategy Inputs-------------------
v_input_3	20	EMA Length
v_input_4	true	-----------------General Inputs-------------------
v_input_5	true	Use Swing Stop Loss and Take Profit
v_input_6	10	Swing Point Loopback
v_input_7	0.2	Swing Point SL Perc Increment
v_input_8	1.2	Take Profit Risk Reward Ratio
v_input_9	false	Allow Direct Position Reverse
v_input_10	false	Reverse Trades

Source (PineScript)

/*backtest
start: 2022-12-01 00:00:00
end: 2023-12-07 00:00:00
period: 1d
basePeriod: 1h
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/
// © tweakerID

// Simple strategy that checks for price bounces over an Exponential Moving Average. If the CLOSE of the candle bounces
// back from having it's LOW below the EMA, then it's a Bull Bounce. If the CLOSE of the candle bounces down from having it's
// high above the EMA, then it's a Bear Bounce. This logic can be reverted.

//@version=4
strategy("EMA Bounce", overlay=true, 
     default_qty_type=strategy.percent_of_equity, 
     default_qty_value=100, 
     initial_capital=10000, 
     commission_value=0.04, 
     calc_on_every_tick=false, 
     slippage=0)

direction = input(0, title = "Strategy Direction", type=input.integer, minval=-1, maxval=1)
strategy.risk.allow_entry_in(direction == 0 ? strategy.direction.all : (direction < 0 ? strategy.direction.short : strategy.direction.long))

/////////////////////// STRATEGY INPUTS ////////////////////////////////////////
title1=input(true, "-----------------Strategy Inputs-------------------")  

i_EMA=input(20, title="EMA Length")

/////////////////////// BACKTESTER /////////////////////////////////////////////
title2=input(true, "-----------------General Inputs-------------------")  

// Backtester General Inputs
i_SL=input(true, title="Use Swing Stop Loss and Take Profit")
i_SPL=input(defval=10, title="Swing Point Loopback")
i_PercIncrement=input(defval=.2, step=.1, title="Swing Point SL Perc Increment")*0.01
i_TPRRR = input(1.2, step=.1, title="Take Profit Risk Reward Ratio")

// Bought and Sold Boolean Signal
bought = strategy.position_size > strategy.position_size[1] 
 or strategy.position_size < strategy.position_size[1]

// Price Action Stop and Take Profit
LL=(lowest(i_SPL))*(1-i_PercIncrement)
HH=(highest(i_SPL))*(1+i_PercIncrement)
LL_price = valuewhen(bought, LL, 0)
HH_price = valuewhen(bought, HH, 0)
entry_LL_price = strategy.position_size > 0 ? LL_price : na 
entry_HH_price = strategy.position_size < 0 ? HH_price : na 
tp=strategy.position_avg_price + (strategy.position_avg_price - entry_LL_price)*i_TPRRR
stp=strategy.position_avg_price - (entry_HH_price - strategy.position_avg_price)*i_TPRRR


/////////////////////// STRATEGY LOGIC /////////////////////////////////////////

EMA=ema(close, i_EMA)
LowAboveEMA=low > EMA
LowBelowEMA=low < EMA
HighAboveEMA=high > EMA
HighBelowEMA=high < EMA
BullBounce=LowAboveEMA[1] and LowBelowEMA and close > EMA //and close > open
BearBounce=HighBelowEMA[1] and HighAboveEMA and close < EMA //and close < open
plot(EMA)

BUY=BullBounce
SELL=BearBounce

//Inputs
DPR=input(false, "Allow Direct Position Reverse")
reverse=input(false, "Reverse Trades")

// Entries
if reverse
    if not DPR
        strategy.entry("long", strategy.long, when=SELL and strategy.position_size == 0)
        strategy.entry("short", strategy.short, when=BUY and strategy.position_size == 0)
    else     
        strategy.entry("long", strategy.long, when=SELL)
        strategy.entry("short", strategy.short, when=BUY)
else
    if not DPR 
        strategy.entry("long", strategy.long, when=BUY and strategy.position_size == 0)
        strategy.entry("short", strategy.short, when=SELL and strategy.position_size == 0)
    else
        strategy.entry("long", strategy.long, when=BUY)
        strategy.entry("short", strategy.short, when=SELL)


SL=entry_LL_price
SSL=entry_HH_price
TP=tp
STP=stp

strategy.exit("TP & SL", "long", limit=TP, stop=SL, when=i_SL)
strategy.exit("TP & SL", "short", limit=STP, stop=SSL, when=i_SL)

/////////////////////// PLOTS //////////////////////////////////////////////////

plot(strategy.position_size > 0 ? SL : na , title='SL', style=plot.style_cross, color=color.red)
plot(strategy.position_size < 0 ? SSL : na , title='SSL', style=plot.style_cross, color=color.red)
plot(strategy.position_size > 0 ? TP : na, title='TP', style=plot.style_cross, color=color.green)
plot(strategy.position_size < 0 ? STP : na, title='STP', style=plot.style_cross, color=color.green)
// Draw price action setup arrows
plotshape(BUY ? 1 : na, style=shape.triangleup, location=location.belowbar, 
 color=color.green, title="Bullish Setup", transp=80, size=size.auto)
plotshape(SELL ? 1 : na, style=shape.triangledown, location=location.abovebar, 
 color=color.red, title="Bearish Setup", transp=80, size=size.auto)

Detail

https://www.fmz.com/strategy/434713

Last Modified

2023-12-08 16:47:39

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移动平均线反弹策略Exponential-Moving-Average-Bounce-Strategy.md

移动平均线反弹策略Exponential-Moving-Average-Bounce-Strategy.md

策略概述

策略名称

策略原理

策略优势分析

顺势操作,避免被套

回撤小,历史收益好

容易理解,参数调整灵活

风险分析

容易假信号

顺势操作,无法预测转折点

止损位置容易被击穿

优化方向

结合其他指标过滤信号

优化止损方式

参数优化

总结

Strategy Overview

Strategy Name

Strategy Logic

Advantage Analysis

Trade with the trend, avoid being trapped

Small drawdowns, good historical returns

Easy to understand, flexible parameter adjustment

Risk Analysis

Prone to false signals

Trading with the trend, unable to predict turning points

Stop loss prone to being taken out

Optimization

Incorporate other indicators for signal filtering

Optimize stop loss methods

Parameter optimization

Conclusion

Files

移动平均线反弹策略Exponential-Moving-Average-Bounce-Strategy.md

Latest commit

History

移动平均线反弹策略Exponential-Moving-Average-Bounce-Strategy.md

File metadata and controls

策略概述

策略名称

策略原理

策略优势分析

顺势操作,避免被套

回撤小,历史收益好

容易理解,参数调整灵活

风险分析

容易假信号

顺势操作,无法预测转折点

止损位置容易被击穿

优化方向

结合其他指标过滤信号

优化止损方式

参数优化

总结

Strategy Overview

Strategy Name

Strategy Logic

Advantage Analysis

Trade with the trend, avoid being trapped

Small drawdowns, good historical returns

Easy to understand, flexible parameter adjustment

Risk Analysis

Prone to false signals

Trading with the trend, unable to predict turning points

Stop loss prone to being taken out

Optimization

Incorporate other indicators for signal filtering

Optimize stop loss methods

Parameter optimization

Conclusion