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震荡器差分滑动平均择时策略Oscillator-Differential-Moving-Average-Timing-Strategy.md

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Name

震荡器差分滑动平均择时策略Oscillator-Differential-Moving-Average-Timing-Strategy

Author

ChaoZhang

Strategy Description

IMG

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概述

本策略通过计算快线EMA和慢线EMA的差值形成MACD震荡器,再通过计算MACD的均线形成信号线,从而构建一个双重滤波系统。当MACD线从下方上穿信号线时产生买入信号,当MACD线从上方下穿信号线时产生卖出信号,利用价格的短期中期波动获利。

策略原理

本策略的核心指标是MACD震荡器,它由快线EMA(一般设置为12日EMA)减去慢线EMA(一般设置为26日EMA)计算得到。快线EMA更加灵敏,能捕捉价格的短期波动;慢线EMA对价格变化的响应更加缓慢。两者相减可形成一个代表短期和中期周期价格变化差值的震荡器。再对MACD震荡器计算其本身的EMA(一般设置为9日),得到信号线。当MACD从信号线下方向上穿过时,代表短期行情的向上动力强于中期行情,产生买入信号;当MACD从信号线上方向下穿过时,代表短期行情的向下动力强于中期行情,产生卖出信号。

本策略设置输入参数分别为快线长度、慢线长度、价格源、信号线长度平滑周期。可根据不同市场调整参数,寻找最佳参数组合。背景色块显示了回测的时间范围。策略在该时间范围内开仓,之外不操作。

优势分析

  1. MACD指标经典且易于理解,能有效抓取短中期倒挂机会。

  2. 双EMA构建的MACD系统相对单一MA系统有更好的平滑性。

  3. 可调参数较多,可以针对不同市场进行参数优化。

  4. 结合成交量指标可识别高质量信号。

风险分析

  1. 在震荡行情中,MACD指标会产生更多错误信号。

  2. 无法判断趋势,可能与趋势交叉产生损失。

  3. 回测时间范围限制可能忽略了极端行情的情况。

  4. 参数设置需要combine更多市场数据进行优化,否则可能overfit某一市场段。

可通过结合趋势判断指标,设置止损机制来控制风险。同时扩大回测范围和市场样本空间进行参数优化。

优化方向

  1. 测试不同价格源,如收盘价、均价、重置价等。

  2. 基于更多历史数据寻找最佳参数组合。

  3. 整合其他指标判断信号质量。例如成交量信号。

  4. 结合趋势和波段判断,避免与趋势产生重大冲突。

总结

本策略通过构建双EMA滤波器,捕捉价格中短线周期的倒挂现象,属于经典且实用的择时策略。可通过参数优化、信号过滤、止损手段控制风险。同时结合趋势判断指标,避免追顶杀底,可以获得稳定收益。

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Overview

This strategy calculates the difference between the fast EMA and slow EMA to form the MACD oscillator, and calculates the EMA of MACD itself to form the signal line, thereby constructing a dual filtering system. It generates buy signals when the MACD line crosses above the signal line from below, and sell signals when the MACD line crosses below the signal line from above, profiting from short-term and medium-term price fluctuations.

Strategy Principle

The core indicator of this strategy is the MACD oscillator, which is calculated by subtracting the slow EMA (typically 26-day EMA) from the fast EMA (typically 12-day EMA). The fast EMA is more sensitive and can capture short-term price fluctuations. The slow EMA responds to price changes more slowly. Subtracting the two forms an oscillator that represents the difference between short-term and medium-term price cycles. The EMA (typically 9-day) of the MACD oscillator itself is then calculated to obtain the signal line. When the MACD crosses above the signal line from below, it signals that the upward momentum of the short-term trend is stronger than that of the medium-term trend, generating a buy signal. When the MACD crosses below the signal line from above, it signals that the downward momentum of the short-term trend is stronger, generating a sell signal.

The input parameters of this strategy are set to the fast line length, slow line length, price source, and signal line smoothing period, respectively. These can be adjusted according to different markets to find the optimal parameter combinations. The background color block shows the backtest timeframe. The strategy opens positions only within this timeframe.

Advantage Analysis

  1. The MACD indicator is classic and easy to understand, effectively capturing short-to-medium-term reversal opportunities.

  2. The dual EMA construction of the MACD system has better smoothness than single MA systems.

  3. Relatively more adjustable parameters allow optimization across different markets.

  4. Combining with volume indicators helps identify high quality signals.

Risk Analysis

  1. MACD can produce more false signals in oscillating markets.

  2. It cannot determine trends and may produce losses when crossing trends.

  3. The limited backtest timeframe may ignore extreme market conditions.

  4. Parameter tuning needs more market data to avoid overfitting specific market periods.

Risks can be controlled by incorporating trend indicators and stop loss mechanisms. The backtest scope and market sample space can be expanded for parameter optimization.

Optimization Directions

  1. Test different price sources like close, median, reset prices etc.

  2. Search for optimal parameter sets based on more historical data.

  3. Integrate other indicators to judge signal quality, e.g. volume signals.

  4. Incorporate trend and cycle analysis to avoid significant trend conflicts.

Conclusion

This strategy captures short-to-medium-term reversal opportunities by constructing a dual EMA filter system. It belongs to a classic and practical market timing strategy. Risks can be controlled via parameter optimization, signal filtering and stop loss means. Incorporating trend analysis tools to avoid buying peaks and selling bottoms can lead to steady profits.

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Strategy Arguments

Argument Default Description
v_input_1 12 Fast Length
v_input_2 26 Slow Length
v_input_3_close 0 Source: close
v_input_4 9 Signal Smoothing
v_input_5 false Simple MA(Oscillator)
v_input_6 false Simple MA(Signal Line)
v_input_7 2017 Backtest Start Year
v_input_8 true Backtest Start Month
v_input_9 2 Backtest Start Day
v_input_10 2019 Backtest Stop Year
v_input_11 12 Backtest Stop Month
v_input_12 30 Backtest Stop Day
v_input_13 true Color Background?

Source (PineScript)

/*backtest
start: 2022-12-19 00:00:00
end: 2023-12-25 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
strategy(title="MACD Histogram Backtest", shorttitle="MACD")

// 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 = input(title="Source", type=input.source, defval=close)
signal_length = input(title="Signal Smoothing", type=input.integer, minval = 1, maxval = 50, defval = 9)
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, fast_length) : ema(src, fast_length)
slow_ma = sma_source ? sma(src, slow_length) : ema(src, slow_length)
macd = fast_ma - slow_ma
signal = sma_signal ? sma(macd, signal_length) : ema(macd, signal_length)
hist = macd - signal

grow = (hist[1] < hist)
fall = (hist[1] > hist) and hist >= 0
stop = (hist[1] > hist)

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)

//Strategy Testing

// Component Code Start
// Example usage:
// if testPeriod()
//   strategy.entry("LE", strategy.long)
testStartYear = input(2017, "Backtest Start Year")
testStartMonth = input(01, "Backtest Start Month")
testStartDay = input(2, "Backtest Start Day")
testPeriodStart = timestamp(testStartYear,testStartMonth,testStartDay,0,0)

testStopYear = input(2019, "Backtest Stop Year")
testStopMonth = input(12, "Backtest Stop Month")
testStopDay = input(30, "Backtest Stop Day")
testPeriodStop = timestamp(testStopYear,testStopMonth,testStopDay,0,0)

// A switch to control background coloring of the test period
testPeriodBackground = input(title="Color Background?", type=input.bool, defval=true)
testPeriodBackgroundColor = testPeriodBackground and (time >= testPeriodStart) and (time <= testPeriodStop) ? #00FF00 : na
bgcolor(testPeriodBackgroundColor, transp=97)

testPeriod() => true
// Component Code Stop

//Entry and Close settings
if testPeriod() 
    strategy.entry("grow", true, 10, when = grow, limit = close)
    strategy.close("grow", when = fall)
    strategy.close("grow", when = stop)
    
//if testPeriod() 
//   strategy.entry("fall", false, 1000, when = fall, limit = close)
//    strategy.close("fall", when = grow)

Detail

https://www.fmz.com/strategy/436637

Last Modified

2023-12-26 14:40:12