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双时间轴波动率价差交易策略Dual-Timeframe-Volatility-Spread-Trading-Strategy.md

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Name

双时间轴波动率价差交易策略Dual-Timeframe-Volatility-Spread-Trading-Strategy

Author

ChaoZhang

Strategy Description

IMG [trans]

概述

双时间轴波动率价差交易策略通过计算两个不同时间周期的RSI指标之间的价差,来判断市场的超买超卖状态,实现low risk的趋势交易。

策略原理

该策略的核心指标是shortTermXtrender和longTermXtrender。shortTermXtrender计算短期时间轴上的RSI价差,longTermXtrender计算长期时间轴上的RSI价差。

短期时间轴采用7日EMA与4日LMA的价差计算RSI,再与50进行价差,构成shortTermXtrender。长期时间轴采用4日EMA的RSI与50进行价差,构成longTermXtrender。

当shortTermXtrender上穿0时,做多;当longTermXtrender上穿0时,也做多。做多后的止损原则是当shortTermXtrender下穿0时止损;当longTermXtrender下穿0时,也止损。

这样,通过双时间轴判断,可以过滤掉更多的假突破。

优势分析

该策略最大的优势在于趋势判断准确。双时间轴结合使用,可以有效过滤噪音,锁定目标趋势方向。这为低风险的趋势跟踪交易提供了保证。

另外,策略提供了参数优化的空间。用户可以根据不同品种和时间周期,调整SMA周期、RSI参数等,优化策略效果。

风险分析

该策略的主要风险在于多空判断错误。在震荡行情中,容易产生错误信号。这时如果仍然打开仓位,则会面临亏损的风险。

此外,参数设置不当也会导致效果欠佳。如果时间周期参数设置得过短,则会增大误判概率;如果时间周期参数设置得过长,则会错过趋势机会。这需要用户针对不同市场进行参数测试和优化。

优化方向

该策略主要可以从以下几个方面进行优化:

  1. 增加止盈机制。目前策略没有设置止盈,可以根据达到目标利润后及时止盈。

  2. 增加仓位管理。可以根据资金规模、波动率等指标动态调整仓位。

  3. 测试不同品种参数设置。用户可以通过回测从日线、60分钟等不同时间周期入手,测试最优参数组合。

  4. 增加机器学习辅助判断。可以训练模型判断行情类型,从而动态调整策略参数,提高胜率。

总结

双时间轴波动率价差交易策略通过构建双时间轴指标,实现高效的趋势捕捉。策略优化空间较大,用户可以通过参数调整、止盈管理、仓位管理等方式进行优化,从而获得更好的策略效果。该策略适合有一定交易经验的用户使用。

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Overview

The dual timeframe volatility spread trading strategy judges the overbought/oversold status of the market by calculating the spread between RSI indicators of two different time cycles to implement low risk trend trading.

Strategy Principle

The core indicators of this strategy are shortTermXtrender and longTermXtrender. shortTermXtrender calculates the RSI spread on the short-term timeframe, and longTermXtrender calculates the RSI spread on the long-term timeframe.

The short-term timeframe adopts the price difference between 7-day EMA and 4-day LMA to calculate RSI, and then the price difference with 50 constitutes shortTermXtrender. The long-term timeframe adopts the price difference between RSI of 4-day EMA and 50 to constitute longTermXtrender.

When shortTermXtrender crosses above 0, go long; when longTermXtrender crosses above 0, also go long. The stop loss principle after going long is to stop loss when shortTermXtrender crosses below 0; when longTermXtrender crosses below 0, stop loss too.

In this way, by judging on dual timeframes, more false breakouts can be filtered out.

Advantage Analysis

The biggest advantage of this strategy is that the trend judgment is accurate. The combination of dual timeframes can effectively filter out noise and lock in the target trend direction. This provides a guarantee for low-risk trend tracking trading.

In addition, the strategy provides room for parameter optimization. Users can adjust parameters such as SMA cycle and RSI parameters according to different varieties and time cycles to optimize strategy results.

Risk Analysis

The main risk of this strategy is the wrong judgment of long and short. In oscillating markets, it is easy to generate wrong signals. If the position is still opened at this time, there will be the risk of loss.

In addition, improper parameter settings can also lead to poor results. If the time cycle parameter is set too short, the probability of misjudgment will increase; if the time cycle parameter is set too long, the opportunity for the trend will be missed. This requires users to test and optimize parameters for different markets.

Optimization Directions

The strategy can be optimized in the following aspects:

  1. Increase profit taking mechanism. Currently there is no profit taking setting in the strategy. Profit can be taken in time after reaching the target profit.

  2. Increase position management. Positions can be dynamically adjusted based on capital size, volatility and other indicators.

  3. Test parameter settings for different varieties. Users can test the optimal parameter combination by backtesting different timeframes such as daily and 60 minutes.

  4. Increase machine learning assisted judgment. Models can be trained to determine market conditions and dynamically adjust strategy parameters to improve win rate.

Summary

The dual timeframe volatility spread trading strategy achieves efficient trend capturing by constructing dual timeframe indicators. The strategy has large optimization space. Users can optimize through parameter adjustment, profit taking management, position management, etc. to obtain better strategy results. This strategy is suitable for users with some trading experience.

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Strategy Arguments

Argument Default Description
v_input_1 7 ShortTermSMA
v_input_2 4 ShortTermLMA
v_input_3 2 ShortTermRSI
v_input_4 4 LongTermMA
v_input_5 2 LongTermRSI
v_input_6 true UseFactors
v_input_7 true TradeShortTerm
v_input_8 true TradeLongTerm
v_input_9 true From Day
v_input_10 true From Month
v_input_11 2018 From Year
v_input_12 true To Day
v_input_13 true To Month
v_input_14 2020 To Year

Source (PineScript)

/*backtest
start: 2024-01-18 00:00:00
end: 2024-02-17 00:00:00
period: 2h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
//study("MavXtrender")
strategy("MavXtrender")

ShortTermSMA = input(7)
ShortTermLMA = input(4)
ShortTermRSI = input(2)

LongTermMA  = input(4)
LongTermRSI  = input(2)

UseFactors = input(true)
TradeShortTerm = input(true)
TradeLongTerm = input(true)

count = TradeShortTerm == true ? 1 : 0
count := TradeLongTerm == true ? count + 1 : count
// set position size
Amount = strategy.equity / (close * count)

ShortTermLMA := UseFactors == true ? round(ShortTermSMA * ShortTermLMA) : ShortTermLMA
ShortTermRSI := UseFactors == true ? round(ShortTermSMA * ShortTermRSI) : ShortTermRSI
LongTermMA := UseFactors == true ? round(ShortTermSMA * LongTermMA) : LongTermMA
LongTermRSI := UseFactors == true ? round(ShortTermSMA * LongTermRSI) : LongTermRSI

shortTermXtrender = rsi(ema(close, ShortTermSMA) - ema(close, ShortTermLMA), ShortTermRSI ) - 50
longTermXtrender  = rsi( ema(close, LongTermMA), LongTermRSI ) - 50

// === INPUT BACKTEST RANGE ===
FromDay   = input(defval = 1, title = "From Day", minval = 1, maxval = 31)
FromMonth = input(defval = 1, title = "From Month", minval = 1, maxval = 12)
FromYear  = input(defval = 2018, title = "From Year", minval = 2012)
ToDay     = input(defval = 1, title = "To Day", minval = 1, maxval = 31)
ToMonth   = input(defval = 1, title = "To Month", minval = 1, maxval = 12)
ToYear    = input(defval = 2020, title = "To Year", minval = 2012)

// === FUNCTION EXAMPLE ===
start     = timestamp(FromYear, FromMonth, FromDay, 00, 00)  // backtest start window
finish    = timestamp(ToYear, ToMonth, ToDay, 23, 59)        // backtest finish window
window()  => true

strategy.entry("ShortTerm", strategy.long, qty = Amount, when = window() and crossover(shortTermXtrender,0) and TradeShortTerm)
strategy.entry("LongTerm", strategy.long, qty = Amount, when = window() and crossover(longTermXtrender,0) and TradeLongTerm)

strategy.close("ShortTerm", when = crossunder(shortTermXtrender,0) or time > finish)
strategy.close("LongTerm", when = crossunder(longTermXtrender,0) or time > finish)

shortXtrenderCol = shortTermXtrender > 0 ? shortTermXtrender > shortTermXtrender[1] ? color.lime : #228B22 : shortTermXtrender > shortTermXtrender[1] ? color.red : #8B0000
plot(shortTermXtrender, color=shortXtrenderCol, style=plot.style_columns, linewidth=1, title="B-Xtrender Osc. - Histogram", transp = 50)

longXtrenderCol = longTermXtrender> 0 ? longTermXtrender > longTermXtrender[1] ? color.lime : #228B22 : longTermXtrender > longTermXtrender[1] ? color.red : #8B0000
plot(longTermXtrender , color=longXtrenderCol, style=plot.style_histogram, linewidth=2, title="B-Xtrender Trend - Histogram", transp = 80)
plot(longTermXtrender , color=color.white,     style=plot.style_line,      linewidth=1, title="B-Xtrender Trend - Line",      transp = 80)

Detail

https://www.fmz.com/strategy/442004

Last Modified

2024-02-18 15:31:32