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对数移动平均聚合散策略Logarithmic-Moving-Average-Convergence-Divergence-Strategy.md

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Name

对数移动平均聚合散策略Logarithmic-Moving-Average-Convergence-Divergence-Strategy

Author

ChaoZhang

Strategy Description

[trans]

概述

该策略基于对数移动平均聚合散波指标(Logarithmic MACD)生成交易信号。它通过计算快速和慢速对数移动平均线的差值,判断市场趋势和机会。

策略原理

该策略的主要逻辑是:

  • 计算快速对数移动平均线(默认12日)和慢速对数移动平均线(默认26日)

  • 对数MACD 是二者的差值,表达市场动量

  • 信号线是MACD的平滑移动平均(默认9日)

  • 当MACD线从下方突破信号线时做多

  • 当MACD线从上方跌破信号线时做空

  • 采用柱状图形式表达MACD和信号线差值

相比简单移动平均MACD,对数MACD可突出显示指数级增长市场的变化趋势。对数转换后,波动较大的数值在图表上可以保持相对可比性。

策略优势

  • 利用对数转换,可检测指数级别的价格变动

  • 对数MACD突显价格波动信息

  • 信号线平滑MACD,形成交易信号

  • 柱状MACD直观表达趋势方向

策略风险

  • 对数转换可能放大价格震荡

  • 信号频繁,容易过度交易

  • 未考虑止损管理,风险控制不完备

对应措施:

  • 调整参数,降低信号频率

  • 增加过滤条件,避免在震荡中产生信号

  • 设定止损策略,控制单笔损失

策略优化方向

  • 优化参数,提高稳定性

  • 尝试其他指数转换方式,如指数移动平均线

  • 结合趋势指标过滤信号

  • 增加止损策略

  • 利用机器学习判断信号可靠性

总结

该策略运用对数转换提升了MACD指标的敏感性,能更早发现趋势变化。但需注意控制交易频率。通过参数优化、风控等提升,该策略可成为一个稳定且富有个性的量化交易系统。

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Overview

This strategy generates trading signals using the Logarithmic MACD indicator. It calculates the difference between fast and slow logarithmic moving averages to gauge market momentum and opportunities.

Strategy Logic

The main logic is:

  • Calculate fast logarithmic MA (default 12) and slow logarithmic MA (default 26)

  • Logarithmic MACD is their difference, expressing market momentum

  • Signal line is smoothed MA of MACD (default 9)

  • Go long when MACD crosses above signal from below

  • Go short when MACD crosses below signal from above

  • MACD-Signal difference plotted as histogram

Compared to simple MACD, logarithmic MACD can better highlight exponential growth trends. Log transform maintains comparability of volatile values on the chart.

Advantages

  • Detects exponential price movements using logarithmic transform

  • Log MACD highlights price fluctuation information

  • Signal line smooths MACD into trading signals

  • MACD histogram intuitively shows trend direction

Risks

  • Log transform may amplify price noise

  • Frequent signals, risks over-trading

  • No stop loss management, incomplete risk control

Mitigations:

  • Adjust parameters to reduce signal frequency

  • Add filters to avoid signals in choppy conditions

  • Implement stop loss to control loss per trade

Enhancement Opportunities

  • Optimize parameters for stability

  • Try other transforms like exponential moving average

  • Add trend filter to screen signals

  • Incorporate stop loss strategies

  • Use machine learning to judge signal reliability

Conclusion

The logarithmic transform enhances MACD's sensitivity for early trend detection. But trade frequency should be controlled. With optimizations in parameters, risk management etc., this strategy can become a stable and unique quantitative system.

[/trans]

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)

Source (PineScript)

/*backtest
start: 2022-09-14 00:00:00
end: 2023-09-20 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=3
strategy(title="Logarithmic Moving Average Convergence Divergence Strategy", shorttitle="LMACD Strategy")

// Getting inputs
fast_length = input(title="Fast Length",  defval=12)
slow_length = input(title="Slow Length",  defval=26)
src = input(title="Source",  defval=close)
signal_length = input(title="Signal Smoothing",  minval = 1, maxval = 50, defval = 9)
sma_source = input(title="Simple MA(Oscillator)",  defval=false)
sma_signal = input(title="Simple MA(Signal Line)", 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)
lmacd = log(fast_ma) - log(slow_ma)
signal = sma_signal ? sma(lmacd, signal_length) : ema(lmacd, signal_length)
hist = lmacd - signal

plot(hist, title="Histogram", 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(lmacd, title="LMACD", color=col_macd, transp=0)
plot(signal, title="Signal", color=col_signal, transp=0)

if (crossover(hist, 0))
	strategy.entry("Long", strategy.long, comment="LMACD long")
if (crossunder(hist, 0))
	strategy.entry("Short", strategy.short, comment="LMACD short")

Detail

https://www.fmz.com/strategy/427482

Last Modified

2023-09-21 15:38:05