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多重指标量化交易策略Comprehensive-Quantitative-Trading-Strategy-Based-on-Multiple-Indicators.md

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Name

多重指标量化交易策略Comprehensive-Quantitative-Trading-Strategy-Based-on-Multiple-Indicators

Author

ChaoZhang

Strategy Description

IMG [trans]

概述

本策略名称为“多重指标量化交易策略”,是一种集成多种技术指标的量化交易策略。该策略融合了SuperTrend、QQE和Trend Indicator三种指标,形成一个多维度分析市场的综合交易系统。

其主要思想是通过不同指标的结合,在捕捉市场主要趋势的同时,提高判断精准度,为交易者提供稳定而高效的交易信号。本策略既考虑了趋势判断,也考虑了超买超卖情况,最后再辅以中间长期均线判断,形成一个层层验证的交易逻辑体系。

策略原理

本策略的核心交易逻辑基于下述三个指标的综合判断:

  1. SuperTrend指标:用于判断价格是否处于上升或下降的趋势中。当close价格突破上轨或下轨时,产生相应的买入和卖出信号。

  2. QQE指标:RSI的改进版,融合了mean reversion特征,用于判断市场是否处于超买或超卖状态。根据RSI的标准差带动态调整判断阈值,精准判断反转信号。

  3. Trend Indicator A-V2:计算价格的EMA均线和开盘价的EMA均线,通过比较大小关系判断趋势方向。判断中长期趋势以进行验证。

以上三个指标各有侧重点,SuperTrend关注趋势和反转点,QQE关注超买超卖状态,A-V2指标辅助判断中长期趋势。本策略将它们有机结合,共同形成交易决策系统。

具体交易逻辑如下:

当SuperTrend为上升趋势,且QQE指标显示RSI处于超卖状态下方,且A-V2均线呈上升趋势时,产生买入信号。

当SuperTrend为下降趋势,且QQE指标显示RSI处于超买状态上方,且A-V2均线呈下降趋势时,产生卖出信号。

以上多重指标的综合判断,能在保证判断准确性的前提下,最大程度发掘市场机会,实现稳定而高效的交易。

策略优势分析

本策略的主要优势有以下几点:

  1. 指标融合,判断更精准。本策略集成多个指标,不同指标能互相验证,大大提高了判断的准确率。

  2. 多空双向交易,覆盖更全面。允许做多做空,能在市场双向波动中都获得良好收益。

  3. 风险控制更完善。指标集成判断,避免单一指标产生误判风险。另外本身包含指标如QQE也可控制风险。

  4. 易于操作,参数调整灵活。输入参数设置简单,用户可根据自己偏好,灵活调整参数以适应不同市场。

  5. 适用面广泛,可用于各大市场。可运用与股票、外汇、加密货币等市场,特别适合技术交易者。

策略风险分析

本策略的主要风险点如下:

  1. 指标判断产生偏差风险。如果出现少见的价格异常突破,可能会导致指标判断产生偏差,带来一定的风险。

  2. 趋势market反转风险。本策略侧重发掘趋势机会,一旦出现重大fundamental变化引发的大幅市场反转,可能带来较大损失。

  3. 参数不当引起的风险。如果用户参数设置不当,导致指标判断中出现偏差,也会对信号产生不利影响。

主要的风险控制和解决方法是:1、其他指标验证,防止单一指标错误;2、适当控制仓位规模,控制单笔损失;3、根据不同市场调整参数设置。

策略优化方向

本策略可以从以下几个方面进行优化:

  1. 增加止损策略,以锁定利润,降低回撤。可以在仓位出现一定盈利后,加大止损幅度,或加入移动止损。

  2. 结合更多指标判断,提高系统判断稳定性。如MACD,DMI,OBV等可辅助确认系统信号。

  3. 增加基于波动率的仓位管理机制。根据市场波动率变化,动态调整每笔交易的具体仓位。

  4. 优化指标参数设置。可以通过更长周期的回测,测试哪些参数对该策略更为适用,从而获得更佳的参数组合。

  5. 不同市场使用不同参数组合。根据策略在不同市场(股票,外汇,加密货币等)的实际效果,分别选择最优参数,提高策略稳定性。

总结

本策略集成使用了SuperTrend、QQE和A-V2三大指标,形成一个判断全面而稳定的量化交易策略。它融合了趋势判断、超买超卖判断和中长期趋势验证,能有效发掘市场机会的同时严格控制交易风险。本策略优势明显,值得技术交易者在实盘中验证优化,也为其他策略搭建提供了宝贵参考。

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Overview

The strategy is named "Comprehensive Quantitative Trading Strategy Based on Multiple Indicators". It integrates several technical indicators including SuperTrend, QQE and Trend Indicator A-V2 to form a comprehensive trading system that analyzes the market from multiple dimensions.

The core idea is to combine different indicators to improve the accuracy of judgment while capturing the major trends in the market, so as to provide traders with steady and efficient trading signals. This strategy takes into account trend, overbought/oversold levels, as well as intermediate and long-term trends, forming a multi-layer verification logic for trading decisions.

Strategy Logic

The core trading logic of this strategy is based on the combined judgments of the following three indicators:

  1. SuperTrend: To determine if the price is in an uptrend or a downtrend. It generates buy and sell signals when the close price breaks through the upper or lower band.

  2. QQE: An improved version of RSI that incorporates mean reversion characteristics. It is used to judge if the market is overbought or oversold. The threshold is dynamically adjusted based on RSI's standard deviation band.

  3. Trend Indicator A-V2: Compare EMA of price and EMA of open price to determine trend direction. It verifies the intermediate and long-term trend.

The above three indicators have different focuses. SuperTrend targets at trends and reversal points. QQE focuses on overbought/oversold levels. A-V2 helps determine the intermediate and long-term trend. This strategy integrates them to form a complete trading decision system.

The specific trading logic is as follows:

A buy signal is generated when SuperTrend shows an uptrend, QQE shows the RSI is below the oversold level, and the A-V2 EMAs are rising.

A sell signal is generated when SuperTrend shows a downtrend, QQE shows the RSI is above the overbought level, and the A-V2 EMAs are falling.

The comprehensive judgment of multiple indicators ensures high accuracy in signals while maximizing opportunities in the market to achieve steady and efficient trading.

Advantage Analysis

The major advantages of this strategy are:

  1. More accurate judgment due to indicator fusion. The integration of multiple indicators enables mutual verification, thus greatly improving accuracy.

  2. More comprehensive coverage for dual-directional trading. Allowing long and short positions can gain decent profits from both upside and downside market swings.

  3. Better risk control. Combination of indicators prevents individual indicator false signals. Indicators like QQE also control risks inherently.

  4. Easy to use, flexible parameter tuning. The input parameters are simple for users to adjust based on their own preferences to suit different market conditions.

  5. Wide applicability across major markets. It can be applied to markets like stocks, forex, cryptocurrencies and suits technical traders in particular.

Risk Analysis

The major risks of this strategy include:

  1. Risk of bias in indicator judgments. Rare price anomalies may cause biases in indicator signals and hence risks.

  2. Risk of trend reversal. This strategy focuses on trend-following, so major fundamental-driven reversals can cause huge losses.

  3. Risk from improper parameter tuning. Inadequate user settings on parameters can skew indicator signals.

The main risk management solutions are: 1) Verify signals across indicators to prevent reliance on single indicator; 2) Control position sizing for managed loss per trade; 3) Adjust parameters per different markets.

Optimization Directions

The strategy can be optimized in the following aspects:

  1. Add stop loss for profit taking and drawdown reduction. Trailing stop loss or stop loss with profit can be introduced.

  2. Integrate more indicators for improved system stability. Indicators like MACD, DMI, OBV can be added for signal confirmation.

  3. Introduce volatility-based position sizing. Dynamically adjust position sizes according to changing market volatility.

  4. Optimize parameter tuning. Longer backtests can be conducted to find the optimal parameter sets for this strategy.

  5. Use different parameter sets for different markets. Parameters can be separately optimized for the best results in different markets (stocks, forex, crypto etc).

Conclusion

This strategy integrates SuperTrend, QQE and A-V2 indicators into a comprehensive quantitative trading system with robust signal judgments. By combining trend, overbought/oversold levels and intermediate-long term trend verifications, it can effectively identify opportunities while strictly controlling risks. The strategy has significant advantages and is worth evaluating and optimizing in live trading by technical traders. It also provides valuable references for other strategy development.

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Strategy Arguments

Argument Default Description
v_input_1 true 單邊
v_input_2 9 ST ATR Period
v_input_3 3.9 ST ATR Multiplier
v_input_4_hl2 0 ST Source: hl2
v_input_5 6 QQE RSI Length
v_input_6 5 QQE RSI Smoothing
v_input_7 3 QQE Fast Factor
v_input_8 3 QQE Thresh-hold
v_input_9_close 0 QQE RSI Source: close
v_input_10 52 TIA-V2 EMA Period

Source (PineScript)

/*backtest
start: 2022-12-21 00:00:00
end: 2023-12-27 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
//author：盧振興 芙蓉中華中學畢業 育達科技大學畢業碩士
//參考資料 : QQE MOD By:Mihkel00 ,SuperTrend By:KivancOzbilgic , TrendIndicator A-V2 By:Dziwne

strategy("綜合交易策略", shorttitle="Comprehensive Strategy", overlay=true)

// 添加單邊或多空參數
OnlyLong = input(true, title="單邊")

// SuperTrend 参数
PeriodsST = input(9, title="ST ATR Period")
MultiplierST = input(3.9, title="ST ATR Multiplier")
srcST = input(hl2, title="ST Source")

atrST = atr(PeriodsST)
upST = srcST - (MultiplierST * atrST)
upST := close[2] > upST[1] ? max(upST, upST[1]) : upST
dnST = srcST + (MultiplierST * atrST)
dnST := close[2] < dnST[1] ? min(dnST, dnST[1]) : dnST
trendST = 1
trendST := nz(trendST[1], trendST)
trendST := trendST == -1 and close[2] > dnST[1] ? 1 : trendST == 1 and close[2] < upST[1] ? -1 : trendST

// QQE 参数
RSI_PeriodQQE = input(6, title='QQE RSI Length')
SFQQE = input(5, title='QQE RSI Smoothing')
QQE = input(3, title='QQE Fast Factor')
ThreshHoldQQE = input(3, title="QQE Thresh-hold")
srcQQE = input(close, title="QQE RSI Source")

Wilders_PeriodQQE = RSI_PeriodQQE * 2 - 1

RsiQQE = rsi(srcQQE, RSI_PeriodQQE)
RsiMaQQE = ema(RsiQQE, SFQQE)
AtrRsiQQE = abs(RsiMaQQE[1] - RsiMaQQE)
MaAtrRsiQQE = ema(AtrRsiQQE, Wilders_PeriodQQE)
darQQE = ema(MaAtrRsiQQE, Wilders_PeriodQQE) * QQE

basisQQE = sma(RsiMaQQE - 50, 50)
devQQE = 0.35 * stdev(RsiMaQQE - 50, 50)
upperQQE = basisQQE + devQQE
lowerQQE = basisQQE - devQQE

qqeCondition = RsiMaQQE[1] - 50 > upperQQE[1] ? true : RsiMaQQE[1] - 50 < lowerQQE[1] ? false : na

// Trend Indicator A-V2 参数
ma_periodA_V2 = input(52, title="TIA-V2 EMA Period")
oA_V2 = ema(open, ma_periodA_V2)
cA_V2 = ema(close, ma_periodA_V2)
trendIndicatorAV2Condition = cA_V2[1] >= oA_V2[1] ? true : false

// 综合交易逻辑
longCondition = trendST == 1 and qqeCondition and trendIndicatorAV2Condition
shortCondition = trendST == -1 and not qqeCondition and not trendIndicatorAV2Condition

// 针对多单的开平仓逻辑
if (OnlyLong)
    if (longCondition)
        strategy.entry("Buy", strategy.long)        
    else
        strategy.close("Buy")

// 多空都做时的逻辑
if (not OnlyLong)
    if (longCondition)
        strategy.entry("Buy", strategy.long)
    else if (shortCondition)
        strategy.entry("Sell",strategy.short)

    // 添加多空平仓逻辑
    if (not longCondition)
        strategy.close("Buy")
    if (not shortCondition)
        strategy.close("Sell")

// 可视化信号
plotshape(series=longCondition, title="Buy Signal", location=location.belowbar, color=color.green, style=shape.labelup, text="BUY")
plotshape(series=shortCondition and not OnlyLong, title="Sell Signal", location=location.abovebar, color=color.red, style=shape.labeldown, text="SELL")

Detail

https://www.fmz.com/strategy/436903

Last Modified

2023-12-28 17:46:45