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量化大师专属多级移动平均交叉策略Multi-level-Moving-Average-Crossing-Strategy-for-Quant-Masters.md

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Name

量化大师专属多级移动平均交叉策略Multi-level-Moving-Average-Crossing-Strategy-for-Quant-Masters

Author

ChaoZhang

Strategy Description

IMG [trans]

概述

本策略运用多级移动平均线的交叉原理,捕捉中长线趋势,实现稳定盈利。策略采用不同参数的快速、中速和慢速三组移动平均线,根据其交叉情况制定交易决策。这种多级移动平均线交叉策略,相比传统只有两组移动平均线的策略,可以过滤掉更多假信号,提高策略胜率。

策略原理

本策略使用三组移动平均线:快速移动平均线MAshort、中速移动平均线MAmid和慢速移动平均线MAlong。其中,MAshort参数为9,反应最快,用于捕捉短线信号;MAmid参数为50,速度适中,用于确认趋势;MAlong参数为100,反应最慢,用于判断长线趋势方向。

策略的具体交易逻辑是:当中速移动平均线MAmid上穿慢速移动平均线MAlong时,表明股价上涨势头正在形成,这时策略做多;当快速移动平均线MAshort下穿中速移动平均线MAmid时,表示短线趋势发生转折,策略此时平仓。

该策略最大的优点在于,通过多组移动平均线的组合匹配,可以有效过滤假信号,只选择那些中长线上涨趋势中较有力的一次突破来建仓做多。

优势分析

本策略有以下优势:

  1. 策略参数经过优化,可以有效匹配中长线趋势,胜率较高
  2. 多级移动平均线设计可以过滤噪音和假信号
  3. 适用于各类股票和数字货币,历史回测效果较好
  4. 操作频率不高,每次建仓占用资金30%,风险可控
  5. 可配置时间周期,实盘灵活度高

风险分析

本策略也存在以下风险:

  1. 长线趋势突然发生转折的概率较小,但一旦发生,止损幅度可能较大
  2. 交易频率不高,存在一定程度的资金利用率低下问题
  3. 策略参数需要根据不同交易品种进行优化,适用面可能有限

针对上述风险,我们将进一步扩大策略适用范围,同时结合止损技术控制最大回撤。当中长线趋势发生转折时,我们将采用降低仓位的方式应对。

优化方向

本策略还可从以下几个方面进行优化:

  1. 优化移动平均线的日数参数,寻找更佳的参数组合
  2. 增加成交量的指标进行确认,避免曲线拟合问题
  3. 设定策略最大损失值,如最大回撤20%,强制止损
  4. 增加机器学习模型判断趋势,提高策略的自适应能力

总结

本策略属于典型的中长线量化策略,通过多级移动平均线匹配 WebDriverWait==long term trend,以控制交易风险的前提下,持续盈利。相比单一指标,该策略融合多组参数,可以有效识别较强的中长线趋势信号。通过进一步优化,本策略可以适用于更多品种,在量化交易领域发挥重要作用。

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Overview

This strategy utilizes the principle of multi-level moving average line crossing to capture medium-long term trends and achieve steady profits. It employs fast, medium, and slow three sets of moving averages with different parameters and makes trading decisions based on their crossovers. Compared to traditional strategies with only two sets of moving averages, this multi-level moving average crossing strategy can filter out more false signals and improve the win rate of the strategy.

Strategy Logic

The strategy uses three sets of moving averages: the fast moving average MAshort, the medium speed moving average MAmid, and the slow moving average MAlong. MAshort has a parameter of 9, responds the fastest, and is used to capture short-term signals; MAmid has a parameter of 50, has a medium speed and is used to confirm the trend; MAlong has a parameter of 100, responds the slowest and is used to determine long-term trend direction.

The specific trading logic of the strategy is: when the medium speed moving average line MAmid crosses above the slow moving average line MAlong, it indicates that the upward momentum of the stock price is forming. At this time, the strategy goes long; when the fast moving average MAshort crosses below the medium speed moving average MAmid, it indicates that a short-term trend reversal has occurred, and the strategy exits its position at this time.

The biggest advantage of this strategy is that by combining multiple moving averages, it can effectively filter out false signals and only choose relatively strong breakouts during a medium-long term uptrend to open long positions.

Advantage Analysis

The advantages of this strategy are:

  1. The strategy parameters are optimized to effectively match the medium and long term trends with a relatively high win rate.
  2. The multi-level moving average design filters noise and false signals.
  3. It is suitable for all kinds of stocks and cryptocurrencies with relatively good historical backtesting results.
  4. The operation frequency is low and each opening position occupies 30% of the funds and the risk is controllable.
  5. The time period is configurable, which provides flexibility for live trading.

Risk Analysis

This strategy also has the following risks:

  1. The probability of long-term trend reversals is relatively small but when it does happen, the stop loss magnitude may be large.
  2. The trading frequency is low and therefore has the problem of inefficient capital utilization.
  3. The parameters of the strategy need to be optimized for different trading varieties, which limits the applicable scope.

To address these risks, we will further expand the applicability of the strategy while controlling maximum drawdown with stop loss techniques. We will respond to the reversal of the medium and long term trend by reducing positions.

Optimization Directions

This strategy can also be optimized in the following ways:

  1. Optimize the days parameter of the moving average to find the best parameter combination
  2. Add volume indicators to confirm and avoid curve fitting problems
  3. Set the maximum loss for the strategy, such as 20% max drawdown, to force stop loss
  4. Incorporate machine learning models to judge trends and improve the adaptability of the strategy

Summary

This strategy belongs to a typical medium-long term quantitative strategy which, with the premise of controlling trading risks, continuously profits by matching multi-level moving averages with medium-long term trends. Compared with a single indicator, this strategy incorporates multiple parameters and can effectively identify strong medium and long term trend signals. Through further optimization, this strategy can be applied to more varieties and play an important role in quantitative trading.

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Strategy Arguments

Argument Default Description
v_input_1 true From Month
v_input_2 true From Day
v_input_3 2020 From Year
v_input_4 true Thru Month
v_input_5 true Thru Day
v_input_6 2112 Thru Year
v_input_7 true Show Date Range
v_input_8 100 MAlong
v_input_9 50 MAmid
v_input_10 9 MAfast

Source (PineScript)

/*backtest
start: 2023-12-12 00:00:00
end: 2024-01-11 00:00:00
period: 1h
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/
// © Coinrule

//@version=4
strategy(shorttitle='Multi Moving Average Crossing',title='Multi Moving Average Crossing (by Coinrule)', overlay=true, initial_capital=1000,  default_qty_type = strategy.percent_of_equity, default_qty_value = 30, commission_type=strategy.commission.percent, commission_value=0.1)

//Backtest dates
fromMonth = input(defval = 1,    title = "From Month",      type = input.integer, minval = 1, maxval = 12)
fromDay   = input(defval = 1,    title = "From Day",        type = input.integer, minval = 1, maxval = 31)
fromYear  = input(defval = 2020, title = "From Year",       type = input.integer, minval = 1970)
thruMonth = input(defval = 1,    title = "Thru Month",      type = input.integer, minval = 1, maxval = 12)
thruDay   = input(defval = 1,    title = "Thru Day",        type = input.integer, minval = 1, maxval = 31)
thruYear  = input(defval = 2112, title = "Thru Year",       type = input.integer, minval = 1970)

showDate  = input(defval = true, title = "Show Date Range", type = input.bool)

start     = timestamp(fromYear, fromMonth, fromDay, 00, 00)        // backtest start window
finish    = timestamp(thruYear, thruMonth, thruDay, 23, 59)        // backtest finish window
window()  => true       // create function "within window of time"

//MA inputs and calculations
inlong=input(100, title='MAlong')
inmid=input(50, title='MAmid')
inshort=input(9, title='MAfast')

MAlong = sma(close, inlong)
MAshort= sma(close, inshort)
MAmid= sma(close, inmid)


//Entry 
bullish = crossover(MAmid, MAlong)

strategy.entry(id="long", long = true, when = bullish and window())

//Exit
bearish = crossunder(MAshort, MAmid)

strategy.close("long", when = bearish and window())

plot(MAshort, color=color.orange, linewidth=2)
plot(MAmid, color=color.red, linewidth=2)
plot(MAlong, color=color.blue, linewidth=2)

Detail

https://www.fmz.com/strategy/438470

Last Modified

2024-01-12 12:11:02