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双重均线与MACD组合短线策略Dual-Moving-Average-and-MACD-Combination-Short-term-Trading-Strategy.md

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Name

双重均线与MACD组合短线策略Dual-Moving-Average-and-MACD-Combination-Short-term-Trading-Strategy

Author

ChaoZhang

Strategy Description

[trans]

概述

本策略综合运用双重均线、随机指标和MACD指标,识别短线交易机会,属于较为经典的短线交易策略。

原理

该策略主要基于以下几点原理:

  1. 使用50周期和100周期的EMA均线,判断趋势方向。EMA均线周期较短,能快速响应价格变化。50周期线上穿100周期线代表入场做多;50周期线下穿100周期线代表入场做空。

  2. 使用MACD指标的差离值判断买卖时机。当差离上穿0时显示多头力量增强,做多;当差离下穿0时空头力量增强,做空。

  3. 结合Stochastic RSI指标判断是否超买超卖。该指标结合KDJ指标和RSI指标的优点,可以显示市场的超买超卖情况。当指标低于20时为超卖,结合其他指标做多;当指标高于80时为超买,结合其他指标做空。

  4. 在开仓方向确定后,如果最近5根K线有4根收盘价触碰均线,表明均线附近有支撑或压力,可以开仓。

  5. 使用止盈止损点位管理风险。

优势

该策略具有以下优势:

  1. 多指标组合,综合运用均线、超买超卖指标和能量指标,提高交易胜率。

  2. 均线周期较短,能快速把握趋势及反转。MACD参数优化,识别买卖时机精准。

  3. Stochastic RSI指标参数经过优化,可以很好地识别超买超卖现象。

  4. 利用均线附近支撑压力进行节奏控制,避免在无效突破中被套。

  5. 合理止盈止损,有效控制单笔交易风险。

风险

该策略也存在一些风险:

  1. 仍无法完全避免假突破带来的亏损。

  2. 多指标组合可能出现背离,导致交易信号不一致。

  3. 固定止盈止损点位可能无法适应市场的变化。

  4. 代码实现较为复杂,参数众多,不太易于优化。

对应风险的解决方法如下:

  1. 优化参数,提高信号质量,降低假突破概率。

  2. 建立指标之间的优先级,避免信号冲突。

  3. 使止盈止损动态跟踪,根据ATR等指标设定止盈止损范围。

  4. 简化代码逻辑,提取核心参数进行测试与优化。

优化方向

该策略可以从以下几个方向进行优化:

  1. 对均线周期、MACD参数进行测试,找到最佳参数组合。

  2. 测试不同的超买超卖指标替代Stochastic RSI。

  3. 尝试动态止盈止损、移动止损等方式,使盈亏管理更加智能化。

  4. 添加过滤条件,如交易量增长等,提高信号质量。

  5. 优化开仓逻辑,防止无效突破。可以引入更多指标判断趋势。

  6. 对账户资金规模、每日交易次数等设置止损限制,控制整体风险。

总结

本策略整合多种指标优势,在短线交易中具有较强的实用性。通过继续优化参数,严格开仓逻辑,改进盈亏管理策略,可以进一步提升策略的稳定性和盈利能力。该策略适合有一定基础的短线交易者使用,但需要注意风险控制,以免出现较大亏损。

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Overview

This strategy combines dual moving averages, stochastic indicator and MACD to identify short-term trading opportunities, which is a relatively classic short-term trading strategy.

Principle

The strategy is mainly based on the following principles:

  1. Use 50-period and 100-period EMA to determine trend direction. The EMA with shorter period can respond quickly to price changes. The crossing up of 50-period EMA above 100-period EMA represents establishing long position; the crossing down represents establishing short position.

  2. Use the difference between MACD to determine entry and exit points. When the difference crosses above 0, it shows strengthening of bull power and leads to long entry; when it crosses below 0, it shows strengthening of bear power and leads to short entry.

  3. Combine Stochastic RSI indicator to judge overbought and oversold situation. This indicator combines the advantages of KDJ and RSI, and can show overbought and oversold conditions well. When it is lower than 20, the market is oversold, and long entry can be considered combining other indicators; when it is higher than 80, the market is overbought, and short entry can be considered.

  4. After determining the entry direction, if 4 out of the most recent 5 candlesticks have closing prices touching the moving averages, it shows that there are support/resistance around the moving averages, and positions can be opened.

  5. Use stop loss and take profit to manage risks.

Advantages

The advantages of this strategy include:

  1. The combination of multiple indicators improves winning rate, utilizing moving averages, overbought/oversold indicator and momentum indicator together.

  2. The short period moving averages can capture trend and reversals quickly. MACD parameters are optimized to generate precise entry signals.

  3. Stochastic RSI parameters are optimized to identify overbought/oversold conditions well.

  4. Using support/resistance around moving averages for timing control avoids being trapped by fake breakouts.

  5. Reasonable stop loss and take profit effectively controls risks for each trade.

Risks

There are also some risks of this strategy:

  1. Failing to completely avoid losses caused by fake breakouts.

  2. Divergence may happen between indicators, causing inconsistent trading signals.

  3. Fixed stop loss and take profit may fail to adapt to market changes.

  4. The complex code with many parameters is difficult to optimize.

The solutions are:

  1. Optimize parameters to improve signal quality and lower fake breakout probabilities.

  2. Establish priorities between indicators to avoid conflicts.

  3. Adopt dynamic stop loss and take profit based on ATR ranges.

  4. Simplify logic and extract core parameters for testing and optimization.

Optimization Directions

The strategy can be optimized in the following aspects:

  1. Test and find the optimal combinations of moving average periods and MACD parameters.

  2. Test different overbought/oversold indicators to replace Stochastic RSI.

  3. Try dynamic stop loss and take profit, trailing stop to make risk management more intelligent.

  4. Add filtering conditions like increasing volume to improve signal quality.

  5. Optimize entry logic to avoid ineffective breakouts, using more indicators to determine the trend.

  6. Set stop loss limits according to account size, number of trades per day to control overall risks.

Summary

This strategy integrates the advantages of multiple indicators, and is very practical for short-term trading. By continuing parameter optimization, strict entry logic, and improved risk management, the stability and profitability can be further enhanced. It suits short-term traders with some experience, but risks must be controlled to avoid huge losses.

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Strategy Arguments

Argument Default Description
v_input_1_close 0 Source: close
v_input_2 50 Length
v_input_3_close 0 Source: close
v_input_4 100 len100
v_input_5_close 0 Source: close
v_input_6 true Length
v_input_7_close 0 Source: close
v_input_8 4 length
v_input_9 80 OverBought
v_input_10 20 OverSold
v_input_11 12 Fast Length
v_input_12 26 Slow Length
v_input_13_close 0 Source: close
v_input_14 10 Signal Smoothing
v_input_15 false Simple MA(Oscillator)
v_input_16 false Simple MA(Signal Line)
v_input_17 200 tp
v_input_18 200 sl

Source (PineScript)

/*backtest
start: 2023-01-01 00:00:00
end: 2023-10-08 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4

strategy(title="Forex scalper 2xEMA + SRSI + MACD", shorttitle="Forex scalper 5-15min", overlay=true)
src = input(title="Source", type=input.source, defval=close)

src_0 = src[0]
src_1 = src[1]
src_2 = src[2]
src_3 = src[3]
src_4 = src[4]

len50 = input(50, minval=1, title="Length")
src50 = input(close, title="Source")
out50 = ema(src50, len50)
len100 = input(100)
src100 = input(close, title="Source")
out100 = ema(src100, len100)

len1 = input(1, minval=1, title="Length")
src1 = input(close, title="Source")
out1 = sma(src1, len1)

length = input(4, minval=1)
OverBought = input(80)
OverSold = input(20)
smoothK = 3
smoothD = 3

k = sma(stoch(close, high, low, length), smoothK)
d = sma(k, smoothD)
cu = crossover(k,OverSold)
co = crossunder(k,OverBought)

sma_down = crossunder(out1, out50)
sma_up = crossover(out1,out50)

//if (not na(k) and not na(d))
  //  if (co and k < OverSold)
    //    strategy.entry("StochLE", strategy.long, comment="StochLE")
    //if (cu and k > OverBought)
     //   strategy.entry("StochSE", strategy.short, comment="StochSE")

crossCandle_4 = crossover(src[4],out50)
crossCandleUnder_4= cross(src[4],out50)
crossCandle_3 = crossover(src[3],out50)
crossCandleUnder_3= crossunder(src[3],out50)
crossCandle_2 = crossover(src[2],out50)
crossCandleUnder_2= crossunder(src[2],out50)
crossCandle_1 = crossover(src[1],out50)
crossCandleUnder_1= crossunder(src[1],out50)
crossCandle_0 = crossover(src[0],out50)
crossCandleUnder_0= crossunder(src[0],out50)

conditionOver = (crossCandle_4 or crossCandle_3 or crossCandle_2 or crossCandle_1 or crossCandle_0)
conditionUnder =(crossCandleUnder_4 or crossCandleUnder_3 or crossCandleUnder_2 or crossCandleUnder_1 or crossCandleUnder_0)

touch4 = (cross(low[4],out50) or cross(high[4],out50))
touch3 = (cross(low[3],out50) or cross(high[3],out50))
touch2 = (cross(low[2],out50) or cross(high[2],out50))
touch1 = (cross(low[1],out50) or cross(high[1],out50))

touch = touch1 or touch2 or touch3 or touch4

//and sma_up
//and sma_down

// Getting inputs
fast_length = input(title="Fast Length", type=input.integer, defval=12)
slow_length = input(title="Slow Length", type=input.integer, defval=26)
src_macd = input(title="Source", type=input.source, defval=close)
signal_length = input(title="Signal Smoothing", type=input.integer, minval = 1, maxval = 50, defval = 10)
sma_source = input(title="Simple MA(Oscillator)", type=input.bool, defval=false)
sma_signal = input(title="Simple MA(Signal Line)", type=input.bool, defval=false)

// Plot colors
col_grow_above = #26A69A
col_grow_below = #FFCDD2
col_fall_above = #B2DFDB
col_fall_below = #EF5350
col_macd = #0094ff
col_signal = #ff6a00

// Calculating
fast_ma = sma_source ? sma(src_macd, fast_length) : ema(src_macd, fast_length)
slow_ma = sma_source ? sma(src_macd, slow_length) : ema(src_macd, slow_length)
macd = fast_ma - slow_ma
signal = sma_signal ? sma(macd, signal_length) : ema(macd, signal_length)
hist = macd - signal

//plot(hist, title="Histogram", style=plot.style_columns, color=(hist>=0 ? (hist[1] < hist ? col_grow_above : col_fall_above) : (hist[1] < hist ? col_grow_below : col_fall_below) ), transp=0 )
//plot(macd, title="MACD", color=col_macd, transp=0)
//plot(signal, title="Signal", color=col_signal, transp=0)


// plot((conditionOver or conditionUnder or touch)  and src[0] >= out50 and close >= out50 and  (cu) and out50 > out100 and hist>=0 , title="Buy", style=columns, color=lime)
// plot((conditionOver or conditionUnder or touch)  and src[0] <= out50 and close <= out50 and  (co) and out50< out100 and hist<=0 , title="sell", style=columns, color=red)


long_cond = ((conditionOver or conditionUnder or touch)  and src[0] >= out50 and close > out50 and  (cu) and out50 > out100 and hist>=0)
short_cond = ((conditionOver or conditionUnder or touch)  and src[0] <= out50 and close < out50 and  (co) and out50< out100 and hist<=0)

tp=input(200)
sl=input(200)

strategy.entry("long",strategy.long, when=long_cond)
strategy.entry("short",strategy.short, when=short_cond)

strategy.exit("X_long", "long", profit=tp,  loss=sl, when=touch  )
strategy.exit("x_short", "short",profit=tp, loss=sl,when = touch )

Detail

https://www.fmz.com/strategy/428811

Last Modified

2023-10-09 16:47:42