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基于参数优化的趋势跟踪型量化策略Parameter-Optimized-Trend-Following-Quantitative-Strategy.md

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Name

基于参数优化的趋势跟踪型量化策略Parameter-Optimized-Trend-Following-Quantitative-Strategy

Author

ChaoZhang

Strategy Description

IMG [trans]

概述

本策略的主要思想是结合 percentrank 指标和参数优化,实现对价格趋势的判断和跟踪。该策略通过比较当前价格与一定历史周期内的价格大小百分比,产生交易信号,以捕捉中间镜像效应,跟踪趋势,进而获取超额收益。

策略原理

该策略使用 percentrank 指标判断价格趋势。percentrank 表示当前价格在查看周期内的相对强度。参数 len 表示查看的历史周期长度。

percentrank 的值域在 0 到 100 之间。当 percentrank 值接近 0 时表示当前价格接近查看周期内的最低价,属于价值低估区域;当接近 100 时表示当前价格接近查看周期内最高价,属于价值高估区域。

该策略还引入了 scale 参数作为偏移量。使得 0 到 100 的区间移到 scale 到 100+scale 的区间。同时设置两个信号线 level_1 和 level_2。其中 level_1 表示看多水平,level_2 表示看空水平。

当价格 percentrank 指标从下向上穿过 level_1 时产生看多信号;当从上向下穿过 level_2 时产生看空信号。平仓条件与入场信号相反。

策略优势

  1. 使用 percentrank 指标判断价格趋势强度,避免被困死和追高
  2. 应用参数优化方法,调整偏移 scale 和信号线阈值,针对不同品种和周期进行调参,提高稳定性
  3. 结合趋势跟踪和反转交易思想,在突破信号线后及时跟踪趋势

风险分析

  1. 错误判断趋势导致不必要的损失
  2. 价格震荡趋势不明显时,容易产生错误信号
  3. 参数设置不当可能导致交易频繁或交易量不足

针对以上风险,可以通过调整参数 len、scale、level 设置来优化;同时可以结合其他指标作为确认,避免错误交易。

优化方向

该策略还有进一步优化的空间:

  1. 可以引入止损点,降低单笔损失
  2. 可以结合移动均线等指标进行确认,过滤掉一些错误信号
  3. 可以结合机器学习方法自动优化参数
  4. 可以在多时间周期并行运行

总结

该策略整体思路清晰,运用参数优化的量化方法判断和跟踪价格趋势。具有一定的实战价值,但仍需进一步测试和优化,降低实战风险,提高稳定盈利能力。

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Overview

The main idea of this strategy is to judge and track price trends by combining the percentrank indicator and parameter optimization. The strategy generates trading signals by comparing the current price with the percentage of prices over a certain historical period to capture the mirror effect and track trends for excess returns.

Strategy Principle

The strategy uses the percentrank indicator to determine price trends. Percentrank represents the relative strength of the current price over the viewed period. The parameter len indicates the length of the historical period to view.

The range of percentrank values is from 0 to 100. When the percentrank value is close to 0, it means the current price is near the lowest price in the viewed period and is in an undervalued area. When it is close to 100, it means the current price is near the highest price in the viewed period and is in an overvalued area.

The strategy also introduces a scale parameter as an offset to move the 0 to 100 range to the scale to 100+scale range. Two signal lines level_1 and level_2 are also set, where level_1 indicates the long level and level_2 indicates the short level.

When the price percentrank indicator crosses level_1 upwards, a long signal is generated. When it crosses level_2 downwards, a short signal is generated. The exit conditions are opposite of the entry signals.

Advantages of the Strategy

  1. Use percentrank indicator to determine the strength of price trends, avoiding being trapped or chasing highs
  2. Apply parameter optimization methods to adjust offset scale and signal line threshold for different products and cycles to improve stability
  3. Combine trend following and mean reversion trading ideas to track trends in a timely manner after breaking through the signal line

Risk Analysis

  1. Incorrect judgment of trends resulting in unnecessary losses
  2. Prone to generating wrong signals when price volatility and trend are unclear
  3. Improper parameter settings may lead to too frequent trading or insufficient trading volume

To address the above risks, parameters like len, scale, level can be adjusted for optimization. Other indicators can also be incorporated for confirmation to avoid erroneous trades.

Optimization Directions

There is room for further optimization of the strategy:

  1. Stop loss points can be introduced to reduce single trade loss
  2. Indicators like moving average can be incorporated for confirmation to filter out some wrong signals
  3. Machine learning methods can be used to automatically optimize parameters
  4. Can run in parallel across multiple time frames

Conclusion

The overall idea of the strategy is clear, applying quantitative methods of parameter optimization to judge and track price trends. It has some practical value but still needs further testing and optimization to reduce risks and improve stable profitability.

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Strategy Arguments

Argument Default Description
v_input_1_close 0 Source: close
v_input_2 10 lookback - Период сравнения
v_input_3 50 scale offset - смещение шкалы
v_input_4 30 sygnal line 1
v_input_5 -30 sygnal line 2

Source (PineScript)

/*backtest
start: 2023-12-02 00:00:00
end: 2024-01-01 00:00:00
period: 4h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © Alex_Dyuk

//@version=4
strategy(title="percentrank", shorttitle="percentrank")
src = input(close, title="Source")
len = input(title="lookback - Период сравнения", type=input.integer, defval=10, minval=2)
scale = input(title="scale offset - смещение шкалы", type=input.integer, defval=50, minval=0, maxval=100)
level_1 = input(title="sygnal line 1", type=input.integer, defval=30)
level_2 = input(title="sygnal line 2", type=input.integer, defval=-30)

prank = percentrank(src,len)-scale
plot(prank, style = plot.style_columns)
plot(level_2, style = plot.style_line, color = color.red)
plot(level_1, style = plot.style_line, color = color.green)

longCondition = (crossunder(level_1, prank) == true)
if (longCondition)
    strategy.entry("Long", strategy.long)
longExitCondition = (crossover(level_2, prank) == true)
if (longExitCondition)
    strategy.close("Long")
    
shortCondition = (crossover(level_2, prank) == true)
if (shortCondition)
    strategy.entry("Short", strategy.short)
shortexitCondition = (crossunder(level_1, prank) == true)
if (shortexitCondition)
    strategy.close("Short")

Detail

https://www.fmz.com/strategy/437383

Last Modified

2024-01-02 11:01:22