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基于极值法的统计波动率回测策略Statistical-Volatility-Backtest-Strategy-Based-on-Extreme-Value-Method.md

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Name

基于极值法的统计波动率回测策略Statistical-Volatility-Backtest-Strategy-Based-on-Extreme-Value-Method

Author

ChaoZhang

Strategy Description

IMG [trans]

概述

本策略利用极值法计算统计波动率,也称为历史波动率。它基于最高价、最低价、收盘价的极值,结合时间因素,测算出统计波动率。该波动率反映了资产价格的波动性。策略会在波动率高于或低于设定的阈值时,做出相应的多头或空头交易。

策略原理

  1. 计算一定时间周期内的最高价、最低价、收盘价的极值
  2. 应用极值法公式,计算出统计波动率 
    SqrTime = sqrt(253 / Length)  
Vol = ((0.6 * log(xMaxC / xMinC) * SqrTime) + (0.6 * log(xMaxH / xMinL) * SqrTime)) * 0.5
  3. 比较波动率与设定的上下阈值,产生交易信号 
    pos = iff(nRes > TopBand, 1, 
          iff(nRes < LowBand, -1, nz(pos[1], 0)))
  4. 根据交易信号做多或做空

优势分析

本策略主要优势有:

  1. 使用统计波动率指标,可以有效捕捉市场的热点与反转机会
  2. 极值法计算波动率,对极端价格不敏感,结果更稳定可靠
  3. 可以通过调整参数,适应不同波动率环境的交易

风险分析

本策略主要存在以下风险:

  1. 统计波动率本身存在一定滞后性,不能准确把握市场转折点
  2. 波动率指标对突发事件反应较慢,可能错过短期交易机会
  3. 存在一定的错交易风险与止损风险

对策与解决方法:

  1. 适当缩短统计周期,提高对市场变化的敏感性
  2. 结合其它指标作为辅助,提高信号的准确性
  3. 设置止损点,控制单笔损失

优化方向

本策略的优化方向:

  1. 测试不同的统计周期参数,找出最优参数
  2. 增加仓位管理模块,根据波动率调整仓位
  3. 结合移动平均线等指标,设定过滤条件,减少误交易

总结

本策略利用极值法计算统计波动率,通过捕捉波动率异动产生交易信号。相比简单移动平均线等指标,它更能反映市场波动性,捕捉反转。同时,极值法算法也使得结果更稳定可靠。通过参数调整与优化,本策略可以适应不同市况,其交易思路与统计波动率指标值得进一步研究与应用。

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Overview

This strategy uses the extreme value method to calculate the statistical volatility, also known as historical volatility. It measures the volatility based on the extreme values of highest price, lowest price and close price, combined with the time factor. The volatility reflects the fluctuation of the asset price. The strategy will make corresponding long or short trades when the volatility is higher or lower than the threshold.

Strategy Principle

  1. Calculate the extreme values of highest price, lowest price and close price in a certain time period
  2. Apply the extreme value method formula to calculate the statistical volatility 
    SqrTime = sqrt(253 / Length)
Vol = ((0.6 * log(xMaxC / xMinC) * SqrTime) + (0.6 * log(xMaxH / xMinL) * SqrTime)) * 0.5
  3. Compare the volatility with the upper and lower thresholds to generate trading signals 
    pos = iff(nRes > TopBand, 1,  
          iff(nRes < LowBand, -1, nz(pos[1], 0)))
  4. Make long or short trades based on the trading signals

Advantage Analysis

The main advantages of this strategy are:

  1. Using the statistical volatility indicator can effectively capture market hotspots and reversal opportunities
  2. The extreme value method for calculating volatility is not sensitive to extreme prices, resulting in more stable and reliable results
  3. Parameters can be adjusted to adapt to trading in different volatility environments

Risk Analysis

The main risks of this strategy are:

  1. The statistical volatility itself has some lag, and cannot accurately grasp market turning points
  2. The volatility indicator reacts slowly to sudden events, possibly missing short-term trading opportunities
  3. There are some risks of wrong trades and stop loss

Countermeasures and solutions:

  1. Appropriately shorten the statistical cycle to increase sensitivity to market changes
  2. Use other indicators as an aid to improve signal accuracy
  3. Set stop loss points to control single loss

Optimization Directions

The optimization directions for this strategy:

  1. Test different statistical period parameters to find the optimal parameters
  2. Add a position management module to adjust positions based on volatility
  3. Add filter conditions like moving average lines to reduce wrong trades

Summary

This strategy uses the extreme value method to calculate statistical volatility, and generates trading signals by capturing volatility anomalies. Compared to simple indicators like moving average lines, it better reflects market volatility and captures reversals. Meanwhile, the extreme value method algorithm also makes the results more stable and reliable. Through parameter adjustment and optimization, this strategy can adapt to different market conditions, and its trading logic and statistical volatility indicator are worth further research and application.

[/trans]

Strategy Arguments

Argument Default Description
v_input_1 30 Length
v_input_2 0.005 TopBand
v_input_3 0.0016 LowBand
v_input_4 false Trade reverse

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=2
////////////////////////////////////////////////////////////
//  Copyright by HPotter v1.0 22/11/2014
// This indicator used to calculate the statistical volatility, sometime 
// called historical volatility, based on the Extreme Value Method.
// Please use this link to get more information about Volatility. 
//
// You can change long to short in the Input Settings
// WARNING:
// - For purpose educate only
// - This script to change bars colors.
////////////////////////////////////////////////////////////
strategy(title="Statistical Volatility - Extreme Value Method ", shorttitle="Statistical Volatility Backtest")
Length = input(30, minval=1)
TopBand = input(0.005, step=0.001)
LowBand = input(0.0016, step=0.001)
reverse = input(false, title="Trade reverse")
hline(TopBand, color=red, linestyle=line)
hline(LowBand, color=green, linestyle=line)
xMaxC = highest(close, Length)
xMaxH = highest(high, Length)
xMinC = lowest(close, Length)
xMinL = lowest(low, Length)
SqrTime = sqrt(253 / Length)
Vol = ((0.6 * log(xMaxC / xMinC) * SqrTime) + (0.6 * log(xMaxH / xMinL) * SqrTime)) * 0.5
nRes = iff(Vol < 0,  0, iff(Vol > 2.99, 2.99, Vol))
pos = iff(nRes > TopBand, 1,
	   iff(nRes < LowBand, -1, nz(pos[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)	   	    
barcolor(possig == -1 ? red: possig == 1 ? green : blue )  
plot(nRes, color=blue, title="Statistical Volatility")

Detail

https://www.fmz.com/strategy/436598

Last Modified

2023-12-26 10:24:53