Name

九种移动平均线交叉策略Nine-Types-of-Moving-Average-Crossover-Strategy

Author

ChaoZhang

Strategy Description

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概述

该策略采用两种不同参数设置的移动平均线进行交叉操作,根据交叉信号判断趋势方向,进行开仓与平仓。策略允许选择9种不同类型的移动平均线,包括简单移动平均线(SMA)、指数移动平均线(EMA)、加权移动平均线(WMA)、阿尔莫移动平均线(ALMA)、数量价值移动平均线(VWMA) 等。策略同时设定止损位和止盈位。

策略原理

该策略的核心逻辑是比较两条移动平均线的数值,根据两条移动平均线的交叉情况判断市场趋势方向。具体来说,我们设置快线和慢线两个移动平均线。当快线上穿慢线时,认为市场进入上升趋势,做多;当快线下穿慢线时,认为市场进入下降趋势,做空。

进入仓位后,如果价格触及止损线则亏损了就退出仓位;如果价格触及止盈线则盈利达到预期就退出仓位。这样可以锁定利润,防止亏损扩大。

从代码逻辑看,策略主要分为四个部分:

计算移动平均线。根据用户选择的移动平均线类型,计算出快线和慢线的移动平均线。
生成交易信号。根据快线和慢线的交叉情况,产生做多和做空信号。
设置止损止盈位。根据入场价格和设置的止损止盈百分比,实时计算出止损线和止盈线的价格。
入场和出场。根据做多做空信号入场,根据止损止盈信号出场。

优势分析

该策略最大的优势在于可以自由选择多种类型的移动平均线。不同类型的移动平均线对价格的灵敏度不同,用户可以根据自己的需要选择合适的移动平均线。此外,可以自定义移动平均线的长度期间,实现对时间维度的优化。

另一个优势是设置了止损止盈机制。这可以有效防止亏损进一步扩大,同时锁定利润。总体而言,该策略较为灵活,可自定义程度高,适合不同需求的用户。

风险分析

该策略主要风险在于移动平均线具有滞后性。当价格突然出现大幅波动时,移动平均线无法及时响应,可能导致错过最佳入场或出场时机。这时会产生较大亏损。

另一个风险是止损止盈位置的设置。如果设置的幅度太小,有可能会被套利;如果太大,又容易导致利润锁定不够及时。所以在实盘中要根据市场情况优化止损止盈的参数。

总的来说,该策略主要依靠移动平均线判定趋势方向,所以在突发事件导致价格大幅波动时效果会打折扣。此外,参数设置也会对策略收益有较大影响。

优化方向

该策略可以从以下几个方面进行优化:

优化移动平均线的类型。根据不同市场环境和交易品种,选择更加合适的移动平均线。
优化移动平均线的参数。调整移动平均线的长度期间,使其更加符合市场特点。
添加其它指标过滤。可以加入 MACD,RSI 等其它指标,避免在没有趋势的市场中频繁交易。
优化止损止盈比例。根据历史数据计算出最优的止损止盈参数。
增加机器学习模型。使用 LSTM,随机森林等算法预测价格走势,辅助生成交易信号。
采用止损追踪算法。使止损线能够随价格走势逐步移动,减少止损被触发的概率。

总结

本策略整体来说较为简单直接,通过交叉判断趋势方向,属于典型的趋势跟随策略。优点是简单容易理解,灵活度高,可以自行选择移动平均线类型以及参数。缺点是对突发事件反应较慢,存在一定程度滞后。总体而言,该策略适合追求长线稳定收益的投资者。通过优化可以进一步提高策略稳定性和收益水平。

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Overview

This strategy uses two moving averages with different parameter settings for crossover operations to determine the trend direction and open/close positions. The strategy allows choosing from 9 different types of moving averages, including Simple Moving Average (SMA), Exponential Moving Average (EMA), Weighted Moving Average (WMA), Arnaud Legoux Moving Average (ALMA), Volume Weighted Moving Average (VWMA), etc. The strategy also sets stop loss and take profit levels.

Strategy Logic

The core logic of this strategy is to compare the values of two moving average lines and determine the market trend direction based on their crossover. Specifically, we set a fast line and a slow line using two moving averages. When the fast line crosses above the slow line, we believe the market is in an upward trend and go long. When the fast line crosses below the slow line, we believe the market is in a downward trend and go short.

After entering a position, if the price touches the stop loss line, we exit the position to cut losses. If the price touches the take profit line, we exit the position to lock in profits as expected. This allows us to lock in profits and prevent losses from expanding further.

From the code logic, the strategy can be divided into four parts:

Calculate the moving averages. Based on the user's selection of the moving average type, calculate the fast line and slow line moving averages.
Generate trading signals. Generate long and short signals based on the crossover situations of the fast line and slow line.
Set stop loss and take profit levels. Based on the entry price and the set stop loss/take profit percentages, calculate the stop loss and take profit price levels in real time.
Entry and exit. Enter based on the long/short signals, exit based on the stop loss/take profit signals.

Advantage Analysis

The biggest advantage of this strategy is that it allows freely choosing from many types of moving averages. Different types of moving averages have different sensitivities to prices. Users can choose the appropriate moving average based on their own needs. In addition, the length of the moving averages can be customized to optimize the time dimension.

Another advantage is that stop loss and take profit mechanisms are set. This can effectively prevent further losses and lock in profits. Overall, this strategy is quite flexible with high customizability, suitable for users with different needs.

Risk Analysis

The main risk of this strategy is that moving averages have lagging. When prices suddenly fluctuate violently, moving averages cannot respond in time, which may lead to missing the best entry or exit time. This can lead to large losses.

Another risk is the setting of stop loss and take profit levels. If the range is too small, it may be vulnerable to scalpers. If too large, it is easy to fail to lock in profits in time. Therefore, stop loss/take profit parameters need to be optimized according to market conditions during live trading.

In general, this strategy mainly relies on moving averages to determine the trend direction. So its effectiveness can be compromised when sudden events cause large price swings. In addition, parameter settings can also have a big impact on strategy returns.

Optimization Directions

This strategy can be optimized in the following aspects:

Optimize the moving average type. Select more suitable moving averages based on different market environments and trading products.
Optimize moving average parameters. Adjust the moving average length to make it fit better with market characteristics.
Add other indicators for filtration. MACD, RSI and other indicators can be added to avoid frequent trading when there is no clear trend.
Optimize stop loss/take profit ratios. Calculate the optimal stop loss/take profit parameters based on historical data.
Add machine learning models. Use LSTM, random forest algorithms to predict price movements and aid in generating trading signals.
Adopt trailing stop loss algorithms. Enable the stop loss line to move along with price movements gradually to reduce the probability of being hit.

Conclusion

Overall, this strategy is relatively simple and straightforward. It determines the trend direction via crossover and belongs to a typical trend following strategy. The advantages are being easy to understand and highly flexible with customizable moving average types and parameters. The disadvantages are slower reactions to sudden events and some degree of lagging. In general, this strategy suits investors seeking long-term steady returns. Further improvements on stability and return can be achieved through optimization.

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Strategy Arguments

Argument	Default	Description
v_input_1	0	MA Type: SMA
v_input_2	5	Short MA Length
v_input_3_close	0	Short MA Source: close
v_input_4	15	Long MA Length
v_input_5_close	0	Long MA Source: close
v_input_6	0.85	ALMA Offset
v_input_7	6	ALMA Sigma
v_input_8	false	LSMA Offset
v_input_9	false	SL Level % (0 - Off)
v_input_10	false	PT Level % (0 - Off)

Source (PineScript)

/*backtest
start: 2022-12-26 00:00:00
end: 2024-01-01 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=3
strategy("Kozlod - Yet Another Moving Average Cross Strategy", shorttitle="kozlod_yamacs", overlay = true)

// 
// author: Kozlod
// date: 2018-03-06
// 

////////////
// INPUTS //
////////////

ma_type      = input(title = "MA Type",          defval = "SMA", options = ['SMA', 'EMA', 'WMA', 'ALMA', 'VWMA', 'HMA', 'LSMA', 'SMMA', 'DEMA'])
short_ma_len = input(title = "Short MA Length",  defval = 5,     minval = 1)
short_ma_src = input(title = "Short MA Source",   defval = close)
long_ma_len  = input(title = "Long MA Length",   defval = 15,    minval = 2)
long_ma_src  = input(title = "Long MA Source",    defval = close)
alma_offset  = input(title = "ALMA Offset",     type = float,   defval = 0.85,  step = 0.01, minval = 0, maxval = 1)
alma_sigma   = input(title = "ALMA Sigma",      type = float,   defval = 6,     step = 0.01)
lsma_offset  = input(title = "LSMA Offset",      defval = 0,     step = 1)

sl_lev_perc  = input(title = "SL Level % (0 - Off)", type = float,   defval = 0,  minval = 0, step = 0.01)
pt_lev_perc  = input(title = "PT Level % (0 - Off)", type = float,   defval = 0,  minval = 0, step = 0.01)

// Set initial values to 0
short_ma = 0.0
long_ma  = 0.0

// Simple Moving Average (SMA)
if ma_type == 'SMA' 
    short_ma := sma(short_ma_src, short_ma_len)
    long_ma  := sma(long_ma_src,  long_ma_len)

// Exponential Moving Average (EMA)
if ma_type == 'EMA'
    short_ma := ema(short_ma_src, short_ma_len)
    long_ma  := ema(long_ma_src,  long_ma_len)

// Weighted Moving Average (WMA)
if ma_type == 'WMA'
    short_ma := wma(short_ma_src, short_ma_len)
    long_ma  := wma(long_ma_src,  long_ma_len)

// Arnaud Legoux Moving Average (ALMA)
if ma_type == 'ALMA'
    short_ma := alma(short_ma_src, short_ma_len,  alma_offset, alma_sigma)
    long_ma  := alma(long_ma_src,  long_ma_len,   alma_offset, alma_sigma)

// Hull Moving Average (HMA)
if ma_type == 'HMA'
    short_ma := wma(2*wma(short_ma_src, short_ma_len/2)-wma(short_ma_src, short_ma_len), round(sqrt(short_ma_len)))
    long_ma  := wma(2*wma(long_ma_src,  long_ma_len /2)-wma(long_ma_src,  long_ma_len),  round(sqrt(long_ma_len)))

// Volume-weighted Moving Average (VWMA)
if ma_type == 'VWMA'
    short_ma := vwma(short_ma_src, short_ma_len)
    long_ma  := vwma(long_ma_src,  long_ma_len)

// Least Square Moving Average (LSMA)
if ma_type == 'LSMA'
    short_ma := linreg(short_ma_src, short_ma_len, lsma_offset)
    long_ma  := linreg(long_ma_src,  long_ma_len,  lsma_offset)

// Smoothed Moving Average (SMMA)    
if ma_type == 'SMMA'
    short_ma := na(short_ma[1]) ? sma(short_ma_src, short_ma_len) : (short_ma[1] * (short_ma_len - 1) + short_ma_src) / short_ma_len
    long_ma  := na(long_ma[1])  ? sma(long_ma_src,  long_ma_len)  : (long_ma[1]  * (long_ma_len  - 1) + long_ma_src)  / long_ma_len

// Double Exponential Moving Average (DEMA)
if ma_type == 'DEMA'
    e1_short = ema(short_ma_src, short_ma_len)
    e1_long  = ema(long_ma_src,  long_ma_len)
    
    short_ma := 2 * e1_short - ema(e1_short, short_ma_len)
    long_ma  := 2 * e1_long  - ema(e1_long,  long_ma_len)

/////////////
// SIGNALS //
/////////////

long_signal  = crossover( short_ma, long_ma)
short_signal = crossunder(short_ma, long_ma)

// Calculate PT/SL levels 
// Initial values 
last_signal    = 0
prev_tr_price  = 0.0
pt_level       = 0.0
sl_level       = 0.0

// Calculate previous trade price
prev_tr_price := long_signal[1] or short_signal[1] ? open : nz(last_signal[1]) != 0 ? prev_tr_price[1] : na

// Calculate SL/PT levels 
pt_level := nz(last_signal[1]) == 1 ? prev_tr_price * (1 + pt_lev_perc / 100) : nz(last_signal[1]) == -1 ? prev_tr_price * (1 - pt_lev_perc / 100)  : na
sl_level := nz(last_signal[1]) == 1 ? prev_tr_price * (1 - sl_lev_perc / 100) : nz(last_signal[1]) == -1 ? prev_tr_price * (1 + sl_lev_perc / 100)  : na

// Calculate if price hit sl/pt 
long_hit_pt = pt_lev_perc > 0 and nz(last_signal[1]) ==  1 and close >= pt_level
long_hit_sl = sl_lev_perc > 0 and nz(last_signal[1]) ==  1 and close <= sl_level

short_hit_pt = pt_lev_perc > 0 and nz(last_signal[1]) ==  -1 and close <= pt_level
short_hit_sl = sl_lev_perc > 0 and nz(last_signal[1]) ==  -1 and close >= sl_level

// What is last active trade? 
last_signal := long_signal ? 1 : short_signal ? -1 : long_hit_pt or long_hit_sl or short_hit_pt or short_hit_sl ? 0 : nz(last_signal[1])

//////////////
// PLOTTING //
//////////////

// Plot MAs
plot(short_ma, color = red,   linewidth = 2)
plot(long_ma,  color = green, linewidth = 2)


// Plot Levels 
plotshape(prev_tr_price, style = shape.cross, color = gray, location  = location.absolute, size = size.small)


plotshape(sl_lev_perc > 0 ? sl_level : na, style = shape.cross, color = red,   location  = location.absolute, size = size.small)
plotshape(pt_lev_perc > 0 ? pt_level : na, style = shape.cross, color = green, location  = location.absolute, size = size.small)

//////////////
// STRATEGY //
//////////////

strategy.entry("long",  true,  when = long_signal)
strategy.entry("short", false, when = short_signal)

strategy.close("long",  when = long_hit_pt  or long_hit_sl)
strategy.close("short", when = short_hit_pt or short_hit_sl)

Detail

https://www.fmz.com/strategy/437376

Last Modified

2024-01-02 10:37:21

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九种移动平均线交叉策略Nine-Types-of-Moving-Average-Crossover-Strategy.md

九种移动平均线交叉策略Nine-Types-of-Moving-Average-Crossover-Strategy.md

概述

策略原理

优势分析

风险分析

优化方向

总结

Overview

Strategy Logic

Advantage Analysis

Risk Analysis

Optimization Directions

Conclusion

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九种移动平均线交叉策略Nine-Types-of-Moving-Average-Crossover-Strategy.md

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概述

策略原理

优势分析

风险分析

优化方向

总结

Overview

Strategy Logic

Advantage Analysis

Risk Analysis

Optimization Directions

Conclusion