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动量双滑窗TSI指标Momentum-Dual-Moving-Window-TSI-Indicator.md

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Name

动量双滑窗TSI指标Momentum-Dual-Moving-Window-TSI-Indicator

Author

ChaoZhang

Strategy Description

IMG

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一、策略概述

本策略命名为动量双滑窗TSI指标策略。该策略的核心思想是利用双EMA滑动窗口平滑价格变动,再结合趋势的方向变化,构建出反映市场买卖力度的动量指标,即TSI指标,并以其作为交易信号来制定买入卖出决策。

二、策略原理

本策略使用双滑动窗口双指数移动平均线来计算价格变动。外层窗口期较长,内层窗口期较短。通过双重平滑去除价格数据中的部分随机性。

首先计算价格的单位变动:

pc = change(price)

然后使用双滑动窗口对价格变动进行双重平滑:

double_smoothed_pc = double_smooth(pc, long, short)

再计算价格变动的绝对值,同样使用双滑动窗口进行双重平滑:

double_smoothed_abs_pc = double_smooth(abs(pc), long, short)

最后,使用平滑后的价格变动除以平滑后的价格变动绝对值,得到反映买卖力度的TSI指标:

tsi_value = 100 * (double_smoothed_pc / double_smoothed_abs_pc)

通过设置不同长度的长短窗口期,可以在一定程度上过滤掉短期市场噪音,使TSI指标更好地反映中长期趋势中的买卖力度。当TSI指标上穿其移动平均线时产生买入信号;当TSI指标下穿其移动平均线时产生卖出信号。

三、策略优势

  1. 使用双滑动窗口有效过滤掉短期市场噪音,使指标反应更准确
  2. 计算价格变化也进行了双重平滑,使TSI指标更稳定可靠
  3. 采用价格变动与价格变动绝对值的比值,自动标准化,更具可比性
  4. 综合考量了价格变动的方向和力度,作为交易决策的优质指标
  5. 设置不同参数可以按需调整指标的灵敏度

四、策略风险

  1. 市场出现长期震荡时,TSI指标可能发出错误信号
  2. 参数设置不当也会影响指标和信号的质量
  3. 虽有双滑动窗口,但指标对短期市场噪音还是有一定敏感度
  4. 长短窗口期差距太大时,指标和信号可能出现滞后

可以通过调整窗口期参数,适当缩短信号平均线长度来优化;当市场震荡,可暂时停止交易以控制风险。

五、优化方向

  1. 测试不同的长短窗口期参数组合,寻找最优参数
  2. 尝试其他类型的滑动平均线,如线性加权移动平均线
  3. 增加指标的平滑度,构建三重或多重滑动窗口
  4. 结合其他辅助指标,优化买卖点选择
  5. 设定止损策略,严格控制单笔损失

六、总结

本策略基于价格变动的双重平滑计算出反映买卖力度的TSI动量指标,双滑窗过滤噪音,price change的变化也作双平滑,指标更稳定可靠;采用标准化比率,具有可比性;指标综合价格变化的方向和力度,作为高质量信号;通过参数调整可以自由控制指标灵敏度。在参数优化和风险控制到位的情况下,是非常实用的量化交易策略选择。

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I. Strategy Overview

This strategy is named the Momentum Dual Moving Window TSI Indicator Strategy. The core idea of this strategy is to use dual EMA sliding windows to smooth price fluctuations, and then combine the directional changes of the trend to construct a momentum indicator that reflects the buying and selling power in the market, namely the TSI indicator, and use it as a trading signal to make buy and sell decisions.

II. Strategy Principle

This strategy uses dual sliding window double exponential moving averages to calculate price changes. The outer window period is longer and the inner window period is shorter. By double smoothing, part of the randomness in the price data is removed.

First calculate the unit change in price:

pc = change(price)

Then use dual sliding windows to double smooth the price changes:

double_smoothed_pc = double_smooth(pc, long, short)

Then calculate the absolute value of the price change, which is also double smoothed using dual sliding windows:

double_smoothed_abs_pc = double_smooth(abs(pc), long, short)

Finally, use the smoothed price change divided by the smoothed absolute price change to get the TSI indicator that reflects the buying and selling power:

tsi_value = 100 * (double_smoothed_pc / double_smoothed_abs_pc)

By setting different lengths of long and short window periods, market noise in the short term can be filtered out to some extent, so that the TSI indicator can better reflect the buying and selling power in medium and long term trends. When the TSI indicator crosses above its moving average, a buy signal is generated; When the TSI indicator falls below its moving average, a sell signal is generated.

III. Strategy Advantages

  1. Using dual sliding windows effectively filters out short-term market noise for more accurate indicator reactions
  2. The price change is also double smoothed, making the TSI indicator more stable and reliable
  3. The ratio of price change to absolute price change is used, which is automatically standardized and more comparable
  4. Comprehensively consider the direction and magnitude of price changes as a quality indicator for trading decisions
  5. Setting different parameters allows flexible adjustment of indicator sensitivity

IV. Strategy Risks

  1. The TSI indicator may give wrong signals when the market has long-term fluctuations
  2. Improper parameter settings can also affect the quality of indicators and signals
  3. Although there are dual sliding windows, the indicator still has some sensitivity to short-term market noise
  4. When the difference between long and short window periods is too large, indicators and signals may lag

It can be optimized by adjusting window period parameters and appropriately shortening signal moving average length. When the market fluctuates, trading can be temporarily stopped to control risks.

V. Optimization Directions

  1. Test combinations of different long and short window period parameters to find optimal parameters
  2. Try other types of moving averages, such as Linear Weighted Moving Average
  3. Increase the smoothness of indicators by building triple or multiple sliding windows
  4. Combine other auxiliary indicators to optimize buy/sell point selection
  5. Set stop loss strategies to strictly control single loss

VI. Summary

This strategy calculates the TSI momentum indicator reflecting buying and selling power based on the double smoothing of price changes. The dual sliding windows filter out noise. The double smoothing of price change variations also makes the indicator more stable and reliable. The standardized ratio makes it comparable. The indicator combines the direction and magnitude of price changes as a high quality signal source. Through parameter adjustment, indicator sensitivity can be freely controlled. With parameter optimization and risk control in place, it is a very practical quantitative trading strategy choice.

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Strategy Arguments

Argument Default Description
v_input_1 120 Timeframe
v_input_2 25 Long Length
v_input_3 13 Short Length
v_input_4 13 Signal Length
v_input_5 300 Период
v_input_6 true From Month
v_input_7 true From Day
v_input_8 2017 From Year
v_input_9 true To Month
v_input_10 true To Day
v_input_11 9999 To Year

Source (PineScript)

/*backtest
start: 2023-01-01 00:00:00
end: 2024-01-07 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=2
strategy("True Strength Indicator BTCUSD 2H", shorttitle="TSI BTCUSD 2H",initial_capital=1000, commission_value=0.2, commission_type =strategy.commission.percent, default_qty_value=100 , overlay = false, pyramiding=10, default_qty_type=strategy.percent_of_equity)

//BASED ON True Strength Indicator MTF
resCustom = input(title="Timeframe",  defval="120" )
long = input(title="Long Length",  defval=25)
short = input(title="Short Length",  defval=13)
signal = input(title="Signal Length",  defval=13)

length = input(title="Период",  defval=300)

FromMonth = input(defval = 1, title = "From Month", minval = 1, maxval = 12)
FromDay = input(defval = 1, title = "From Day", minval = 1, maxval = 31)
FromYear = input(defval = 2017, title = "From Year", minval = 2017)
ToMonth = input(defval = 1, title = "To Month", minval = 1, maxval = 12)
ToDay = input(defval = 1, title = "To Day", minval = 1, maxval = 31)
ToYear = input(defval = 9999, title = "To Year", minval = 2017)
start = timestamp(FromYear, FromMonth, FromDay, 00, 00) // backtest start window
finish = timestamp(ToYear, ToMonth, ToDay, 23, 59) // backtest finish window
window() => true // create function "within window of time"

price = request.security(syminfo.tickerid,resCustom,close)


double_smooth(src, long, short) =>
    fist_smooth = ema(src, long)
    ema(fist_smooth, short)
pc = change(price)
double_smoothed_pc = double_smooth(pc, long, short)
double_smoothed_abs_pc = double_smooth(abs(pc), long, short)
tsi_value = 100 * (double_smoothed_pc / double_smoothed_abs_pc)
tsi2=ema(tsi_value, signal)
plot(tsi_value, color=lime,linewidth=2)
plot(tsi2, color=red,linewidth=2)

hline(30, title="Zero")
hline(50, title="Zero",linewidth=2)
hline(70, title="Zero")

buy = crossover(tsi_value, tsi2)
sell = crossunder(tsi_value, tsi2)

if(buy)
    strategy.entry("BUY", strategy.long, when = window())
if(sell)
    strategy.entry("SELL", strategy.short, when = window()) 

//greentsi =tsi_value
//redtsi = tsi2

//bgcolor( greentsi>redtsi and rsiserie > 50 ? lime : na, transp=90)
//bgcolor( greentsi<redtsi and rsiserie < 50 ? red : na, transp=90)

//yellow1= redtsi > greentsi and rsiserie > 50 
//yellow2 = redtsi < greentsi and rsiserie < 50 
//bgcolor( yellow1 ? yellow : na, transp=80)
//bgcolor( yellow2  ? yellow : na, transp=50)

//bgcolor( yellow1 and yellow1[1] ? yellow : na, transp=70)
//bgcolor( yellow2  and yellow2[2] ? yellow : na, transp=70)

//bgcolor( rsiserie > 70 ? lime : na, transp=60)
//bgcolor( rsiserie < 30  ? red : na, transp=60)

Detail

https://www.fmz.com/strategy/438019

Last Modified

2024-01-08 11:20:35