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量化反转与成交量策略组合Quantitative-Reversal-and-Volume-Combo-Strategy.md

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Name

量化反转与成交量策略组合Quantitative-Reversal-and-Volume-Combo-Strategy

Author

ChaoZhang

Strategy Description

[trans]

概述

本策略是两个量化交易策略的组合,旨在产生更准确可靠的交易信号。第一个策略基于价格反转,第二个策略基于成交量分析。组合信号可有效提高盈利概率。

策略原理

该策略由两部分组成:

  1. 反转策略

使用STO指标判断反转信号。当两日收盘价上涨且STO慢线低于50时做多;当两日收盘价下跌且STO快线高于50时做空。

  1. 成交量策略

计算一定周期内的成交量价量关系,判断多空方向,并进行均线平滑处理。

两部分策略同为多则做多,同为空则做空。

组合信号可提高信号质量,其中任一策略出现假信号的概率将大大降低。

策略优势

  • 组合两个独立策略,提高信号准确率
  • 反转策略捕捉换手机会,成交量策略判断未来方向
  • 两种不同类型策略相互验证,减少误信号
  • 组合方式简单直接,易于实施
  • 可独立优化每部分策略的参数

策略风险

  • 反转策略容易被套,需严格退出机制
  • 成交量分析可能出现滞后现象
  • 仅基于量化指标,需辅以技术分析
  • 较长的数据系列trained1来计算均线
  • 不同品种参数不一定通用,需单独优化

可以通过以下措施来减少风险:

  • 优化STO参数,提高反转识别能力
  • 结合其他指标确认成交量突破
  • 优化均线周期参数
  • 辅以图形技术形态判断
  • 根据品种分别测试参数

优化方向

该策略可以从以下几个方面进行优化:

  1. 测试STO指标的最佳参数

    调整K值、D值等参数找到最佳组合

  2. 对成交量突破进行二次验证

    加入MACD、BOLL等指标的辅助判断

  3. 优化均线周期参数

    测试不同周期参数得到更稳定判断

  4. 在组合信号基础上引入图形形态

    例如离形态发生时再进场

  5. 根据品种分别测试参数组合

    不同品种参数不一定相同,需分别测试

总结

本策略通过组合反转和成交量两种不同类型的策略,相互验证,可以有效提升信号的质量和准确性。但也需要注意参数优化、辅助技术指标等来改进策略效果。我们可以通过不断测试回报结果,调整参数规则,并在实盘中验证,从而获得真正稳定可靠的组合策略。这需要投入大量时间和精力,但回报也将是可观的。

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Overview

This strategy combines two quantitative trading strategies to generate more accurate and reliable trading signals. The first strategy is based on price reversal and the second is based on volume analysis. The combined signals can effectively improve profitability.

Strategy Logic

The strategy consists of two parts:

  1. Reversal strategy

Uses STO indicator for reversal signals. Goes long when close rises for 2 days and STO slow line is below 50. Goes short when close drops for 2 days and STO fast line is above 50.

  1. Volume strategy

Analyzes the price-volume relationship over a period to determine direction, with moving average smoothing.

It goes long when both strategies signal long, and goes short when both signal short.

The combo improves signal quality by greatly reducing false signals from either strategy.

Advantages

  • Combines two independent strategies, improving accuracy
  • Reversal catches turnaround opportunities, volume forecasts future direction
  • Different strategy types verify each other, reducing false signals
  • Simple direct combination, easy to implement
  • Parameters of each strategy can be optimized separately

Risks

  • Reversals risky without strict exit rules
  • Volume analysis may lag
  • Purely indicator-based, requires technical analysis
  • Longer data series needed for moving averages
  • Parameters may not be universal across products

Risks can be reduced by:

  • Optimizing STO for better reversal detection
  • Adding indicators to confirm volume breakouts
  • Optimizing moving average periods
  • Supplementary chart pattern analysis
  • Separate parameter testing by product

Enhancement Directions

The strategy can be improved by:

  1. Optimizing STO parameters

    Fine-tune K, D values for best combinations

  2. Secondary confirmation of volume breaks

    With indicators like MACD, BOLL etc.

  3. Optimizing moving average periods

    Testing different periods for more stable signals

  4. Adding chart patterns

    Entering on patterns in addition to combo signals

  5. Product-specific parameter testing

    Parameters may vary across different products

Summary

This strategy combines reversal and volume strategies for improved signal quality and accuracy. But parameter optimization, additional technical indicators etc. can further refine performance. We can continually adjust based on backtest results, validate in live trading, to obtain a truly robust combo strategy. This requires immense time and effort, but the rewards will be significant too.

[/trans]

Strategy Arguments

Argument Default Description
v_input_1 14 Length
v_input_2 true KSmoothing
v_input_3 3 DLength
v_input_4 50 Level
v_input_5 22 Samples
v_input_6 40 Perma
v_input_7 0.1 Cintra
v_input_8 0.1 Cinter
v_input_9 false Trade reverse

Source (PineScript)

/*backtest
start: 2023-09-13 00:00:00
end: 2023-09-20 00:00:00
period: 15m
basePeriod: 5m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
////////////////////////////////////////////////////////////
//  Copyright by HPotter v1.0 21/10/2020
// This is combo strategies for get a cumulative signal. 
//
// First strategy
// This System was created from the Book "How I Tripled My Money In The 
// Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
// The strategy buys at market, if close price is higher than the previous close 
// during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50. 
// The strategy sells at market, if close price is lower than the previous close price 
// during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
//
// Second strategy
// This is another version of FVE indicator that we have posted earlier 
// in this forum.
// This version has an important enhancement to the previous one that`s 
// especially useful with intraday minute charts.
// Due to the volatility had not been taken into account to avoid the extra 
// complication in the formula, the previous formula has some drawbacks:
// The main drawback is that the constant cutoff coefficient will overestimate 
// price changes in minute charts and underestimate corresponding changes in 
// weekly or monthly charts.
// And now the indicator uses adaptive cutoff coefficient which will adjust to 
// all time frames automatically.
//
// WARNING:
// - For purpose educate only
// - This script to change bars colors.
////////////////////////////////////////////////////////////
Reversal123(Length, KSmoothing, DLength, Level) =>
    vFast = sma(stoch(close, high, low, Length), KSmoothing) 
    vSlow = sma(vFast, DLength)
    pos = 0.0
    pos := iff(close[2] < close[1] and close > close[1] and vFast < vSlow and vFast > Level, 1,
	         iff(close[2] > close[1] and close < close[1] and vFast > vSlow and vFast < Level, -1, nz(pos[1], 0))) 
	pos


FVI(Samples,Perma,Cintra,Cinter) =>
    pos = 0
    xhl2 = hl2
    xhlc3 = hlc3
    xClose = close
    xIntra = log(high) - log(low)
    xInter = log(xhlc3) - log(xhlc3[1])
    xStDevIntra = stdev(sma(xIntra, Samples) , Samples)
    xStDevInter = stdev(sma(xInter, Samples) , Samples)
    xVolume = volume
    TP = xhlc3
    TP1 = xhlc3[1]
    Intra = xIntra
    Vintra = xStDevIntra
    Inter = xInter
    Vinter = xStDevInter
    CutOff = Cintra * Vintra + Cinter * Vinter
    MF = xClose - xhl2 + TP - TP1
    FveFactor =  iff(MF > CutOff * xClose, 1, 
                  iff(MF < -1 * CutOff * xClose, -1,  0))
    xVolumePlusMinus = xVolume * FveFactor
    Fvesum = sum(xVolumePlusMinus, Samples)
    VolSum = sum(xVolume, Samples)
    xFVE = (Fvesum / VolSum) * 100
    xEMAFVE = ema(xFVE, Perma)
    pos :=iff(xFVE > xEMAFVE, 1,
    	   iff(xFVE < xEMAFVE, -1, nz(pos[1], 0))) 
    pos

strategy(title="Combo Backtest 123 Reversal & Volatility Finite Volume Elements", shorttitle="Combo", overlay = true)
Length = input(14, minval=1)
KSmoothing = input(1, minval=1)
DLength = input(3, minval=1)
Level = input(50, minval=1)
//-------------------------
Samples = input(22, minval=1)
Perma = input(40, minval=1)
Cintra = input(0.1)
Cinter = input(0.1)
reverse = input(false, title="Trade reverse")
posReversal123 = Reversal123(Length, KSmoothing, DLength, Level)
posFVI = FVI(Samples,Perma,Cintra,Cinter)
pos = iff(posReversal123 == 1 and posFVI == 1 , 1,
	   iff(posReversal123 == -1 and posFVI == -1, -1, 0)) 
possig = iff(reverse and pos == 1, -1,
          iff(reverse and pos == -1 , 1, pos))	   
if (possig == 1) 
    strategy.entry("Long", strategy.long)
if (possig == -1)
    strategy.entry("Short", strategy.short)	 
if (possig == 0) 
    strategy.close_all()
barcolor(possig == -1 ? #b50404: possig == 1 ? #079605 : #0536b3 )

Detail

https://www.fmz.com/strategy/427517

Last Modified

2023-09-21 21:07:09