Skip to content

Latest commit

 

History

History
340 lines (216 loc) · 10.2 KB

多动量指标组合策略Multi-Momentum-Indicators-Combo-Strategy.md

File metadata and controls

340 lines (216 loc) · 10.2 KB
Raw

Name

多动量指标组合策略Multi-Momentum-Indicators-Combo-Strategy

Author

ChaoZhang

Strategy Description

[trans]

概述

该策略试验性地将Chande动量指标、RMI指标、Triple HMA RSI、Double EVW RSI、Triple EMA RSI等多个动量指标组合使用,在所有指标同时发出信号时判断趋势方向并入场。属于多因子实验性策略。

策略原理

  1. 计算Chande动量指标,并设置其买入卖出线。

  2. 计算RMI指标、Triple HMA RSI、Double EVW RSI、Triple EMA RSI等多个指标。

  3. 设置每个指标的买入线和卖出线。

  4. 当Chande动量指标发生向上穿越买入线时,检查其它指标是否也低于各自的买入线。如果所有指标同时满足条件,则产生买入信号。

  5. 反之,如果Chande动量指标下穿卖出线,而其它指标同时超过各自卖出线,则产生卖出信号。

策略优势

  1. 多个指标组合,可以互相验证,避免误判。

  2. Chande动量指标对趋势变化敏感,可以有效捕捉转折。

  3. RMI指标可以显示动量水平,判断超买超卖。

  4. HMA RSI、EVW RSI等指标测试不同RSI计算方式。

  5. 多指标组合方式可以灵活测试指标效果。

策略风险

  1. 多指标组合要求较难满足,信号较少,可能错失机会。

  2. 没有止损等风险控制手段。

  3. 指标效果存在时间段依赖性,可能对不同周期不敏感。

  4. 没有参数优化,指标参数设置可能不当。

  5. 回测数据不足,无法完全验证策略。

对应解决方法:

  1. 适当降低指标阈值,提供更多交易机会。

  2. 加入移动止损或硬止损以控制单笔损失。

  3. 在不同周期和品种中测试,找到最佳参数。

  4. 采用机器学习或网格搜索等方法进行参数优化。

  5. 在更多市场中进行回测,确保策略稳健性。

策略优化方向

  1. 测试不同指标参数设置,找到最优配置。

  2. 增加自适应多 timescale 动量指标。

  3. 引入趋势检测,避免逆势交易。

  4. 采用机器学习提高多指标权重配置。

  5. 结合均线系统,改进入场的时机选择。

总结

该策略通过组合多个动量指标,试图找到更可靠的趋势转折点。这种多元化的策略思路具有很强的拓展性和优化空间,可从参数选择、指标权重、风险控制等方面入手,在保证信号质量的前提下,获取更多符合系统逻辑的交易机会。但仍需注意回测不足所带来的曲拟合风险。

||

Overview

This experimental strategy combines Chande Momentum, RMI, Triple HMA RSI, Double EVW RSI, Triple EMA RSI and other momentum indicators, entering positions when all indicators give aligned signals. A multi-factor experimental model.

Strategy Logic

  1. Calculate Chande Momentum and set its buy and sell lines.

  2. Calculate RMI, Triple HMA RSI, Double EVW RSI, Triple EMA RSI and other indicators.

  3. Set buy and sell lines for each indicator.

  4. When Chande Momentum crosses above its buy line, check if other indicators are also below their respective buy lines. If all conditions are met, generate long signal.

  5. Conversely, when Chande Momentum crosses below sell line, while other indicators exceed their sell lines, generate short signal.

Advantages

  1. Combining indicators provides mutual validation, avoiding false signals.

  2. Chande Momentum sensitively captures trend changes.

  3. RMI shows momentum levels for identifying overbought/oversold levels.

  4. Testing different RSI calculations with HMA RSI, EVW RSI etc.

  5. Flexible multi-indicator combo allows indicator effectiveness testing.

Risks

  1. Requirements for multi-indicator combo are harder to meet, fewer trades, missing opportunities.

  2. No risk control mechanisms like stop loss.

  3. Indicator performance dependent on timeframe, may not work on all periods.

  4. No parameter optimization, poor parameter tuning possible.

  5. Insufficient backtest data to fully validate strategy.

Possible Solutions:

  1. Loosen indicator thresholds for more trades.

  2. Incorporate trailing or hard stop loss to limit losses.

  3. Test across different products and timeframes to find optimal parameters.

  4. Employ machine learning or grid search for parameter optimization.

  5. Backtest on more markets to ensure robustness.

Optimization Directions

  1. Test different parameter sets to find optimal configuration.

  2. Add adaptive multi-timescale momentum indicators.

  3. Incorporate trend detection to avoid counter-trend trades.

  4. Use machine learning to improve multi-indicator weighting.

  5. Combine with moving average system to improve entries.

Summary

This strategy tries to identify more reliable trend turning points by combining multiple momentum indicators. The diversified logic has great extensibility and optimization potential in areas like parameter selection, indicator weighting, risk control etc, to acquire more quality signals while ensuring robustness, but risks like curve-fitting needs to be managed.

[/trans]

Strategy Arguments

Argument Default Description
v_input_1 2021 Backtest Start Year
v_input_2 true Backtest Start Month
v_input_3 true Backtest Start Day
v_input_4 999999 Backtest Stop Year
v_input_5 9 Backtest Stop Month
v_input_6 26 Backtest Stop Day
v_input_7_close 0 Price: close
v_input_8 9 Alpha Chande Momentum Length
v_input_9 30 RMI Length
v_input_10 25 RMI Momentum
v_input_11 7 lengthMA
v_input_12 14 lengthRSI
v_input_13 7 lengthMA2
v_input_14 14 lengthRSI2
v_input_15 7 lengthMA3
v_input_16 14 lengthRSI3
v_input_17 false Chande Sellline
v_input_18 false Chande Buyline
v_input_19 false Triple HMRSI Sellline
v_input_20 false Triple HMRSI Buyline
v_input_21 false DEVWRSI Sellline
v_input_22 false DEVWRSI Buyline
v_input_23 false TERSI Sellline
v_input_24 false TERSI Buyline
v_input_25 false RMI Sellline
v_input_26 false RMI Buyline
v_input_27 false RMI*RSI Sellline
v_input_28 false RMI*RSI Buyline

Source (PineScript)

/*backtest
start: 2023-08-24 00:00:00
end: 2023-09-23 00:00:00
period: 1h
basePeriod: 15m
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/
// © burgercrisis

//@version=4
strategy("RMI + Triple HMRSI + Double EVWRSI + TERSI Strategy")

//* Backtesting Period Selector | Component *//
//* https://www.tradingview.com/script/eCC1cvxQ-Backtesting-Period-Selector-Component *//
//* https://www.tradingview.com/u/pbergden/ *//
//* Modifications made *//
testStartYear = input(2021, "Backtest Start Year") 
testStartMonth = input(1, "Backtest Start Month")
testStartDay = input(1, "Backtest Start Day")
testPeriodStart = timestamp(testStartYear,testStartMonth,testStartDay,0,0)

testStopYear = input(999999, "Backtest Stop Year")
testStopMonth = input(9, "Backtest Stop Month")
testStopDay = input(26, "Backtest Stop Day")
testPeriodStop = timestamp(testStopYear,testStopMonth,testStopDay,0,0)

testPeriod() => true
/////////////// END - Backtesting Period Selector | Component ///////////////


src = input(close, "Price", type = input.source)
CMOlength = input(9, minval=1, title="Alpha Chande Momentum Length")

//CMO
momm = change(src)
f1(m) => m >= 0.0 ? m : 0.0
f2(m) => m >= 0.0 ? 0.0 : -m
m1 = f1(momm)
m2 = f2(momm)
sm1 = sum(m1, CMOlength)
sm2 = sum(m2, CMOlength)
percent(nom, div) => 100 * nom / div
chandeMO = percent(sm1-sm2, sm1+sm2)
plot(chandeMO, "Chande MO", color=color.blue)




//RMI
// Copyright (c) 2018-present, Alex Orekhov (everget)
// Relative Momentum Index script may be freely distributed under the MIT license.
length3 = input(title="RMI Length", type=input.integer, minval=1, defval=30)
momentumLength3 = input(title="RMI Momentum ", type=input.integer, minval=1, defval=25)
up3 = rma(max(change(src, momentumLength3), 0), length3)
down3 = rma(-min(change(src, momentumLength3), 0), length3)

rmi3 = (down3 == 0 ? 100 : up3 == 0 ? 0 : 100 - (100 / (1 + up3 / down3)))-50
//
//
// end RMI, end Alex Orekhov copywrite
//
//

lengthMA = input(7)
lengthRSI = input(14)
thrsi = hma(hma(hma(rsi(src, lengthRSI), lengthMA), lengthMA), lengthMA)
thrsi1 = (thrsi-50)*10

lengthMA2 = input(7)
lengthRSI2 = input(14)
devwrsi = ((ema(ema(vwma(rsi(src, lengthRSI2), lengthMA2), lengthMA2), lengthMA2))-50)*5

lengthMA3 = input(7)
lengthRSI3 = input(14)
tersi = ((ema(ema(ema(rsi(src, lengthRSI3), lengthMA3), lengthMA3), lengthMA3))-50)*10

rmirsi = ((thrsi*rmi3/25))

//Boundary Lines

obLevel1 = input(0, title="Chande Sellline")
osLevel1 = input(0, title="Chande Buyline")
hline(obLevel1, color=#0bc4d9)
hline(osLevel1, color=#0bc4d9)

obLevel2 = input(0, title="Triple HMRSI Sellline")
osLevel2 = input(0, title="Triple HMRSI Buyline")
hline(obLevel2, color=#5a0bd9)
hline(osLevel2, color=#5a0bd9)

obLevel3 = input(0, title="DEVWRSI Sellline")
osLevel3 = input(0, title="DEVWRSI Buyline")
hline(obLevel3, color=#5a0bd9)
hline(osLevel3, color=#5a0bd9)

obLevel4 = input(0, title="TERSI Sellline")
osLevel4 = input(0, title="TERSI Buyline")
hline(obLevel4, color=#5a0bd9)
hline(osLevel4, color=#5a0bd9)

obLevel5 = input(0, title="RMI Sellline")
osLevel5 = input(0, title="RMI Buyline")
hline(obLevel5, color=#5a0bd9)
hline(osLevel5, color=#5a0bd9)

obLevel6 = input(0, title="RMI*RSI Sellline")
osLevel6 = input(0, title="RMI*RSI Buyline")
hline(obLevel6, color=#5a0bd9)
hline(osLevel6, color=#5a0bd9)

plot((thrsi1), title="THRSI")
plot(devwrsi, color=color.red, title="DEVWRSI")
plot(tersi, color=color.yellow, title="TERSI")
plot(rmirsi, color=color.purple, title="RMI*HMRSI")
plot(rmi3, color=color.orange, title="RMI")




longcondition1 = crossover(chandeMO, osLevel1)
shortcondition1 = crossunder(chandeMO, obLevel1)
longcondition2 = rmirsi<osLevel6 and rmi3<osLevel5 and tersi<osLevel4 and devwrsi<osLevel3 and thrsi1<osLevel2  and longcondition1
shortcondition2 = rmirsi>obLevel6 and rmi3>obLevel5 and tersi>obLevel4 and devwrsi>obLevel3 and thrsi1>obLevel2  and shortcondition1

if testPeriod()
    if longcondition2
        strategy.entry("Buy", strategy.long)
    if shortcondition2
        strategy.entry("Sell", strategy.short)






hline(0, color=#C0C0C0, linestyle=hline.style_dashed, title="Zero Line")

Detail

https://www.fmz.com/strategy/427734

Last Modified

2023-09-24 13:24:47