Skip to content

Latest commit

 

History

History
307 lines (198 loc) · 12.9 KB

三均线黄金交叉交易策略Triangular-Moving-Average-Crossover-Trading-Strategy.md

File metadata and controls

307 lines (198 loc) · 12.9 KB

Name

三均线黄金交叉交易策略Triangular-Moving-Average-Crossover-Trading-Strategy

Author

ChaoZhang

Strategy Description

IMG

[trans]

概述

三均线黄金交叉交易策略是一种典型的技术分析策略。该策略同时利用三条不同时间长度的移动平均线来捕捉趋势,实现低风险交易。当短期移动平均线上穿中期移动平均线,并且中期移动平均线高于长期移动平均线时,产生买入信号;当短期移动平均线下穿中期移动平均线,并且中期移动平均线低于长期移动平均线时,产生卖出信号。

策略原理

三均线黄金交叉策略主要依赖三条移动平均线来判断趋势方向。短期移动平均线能 sensitively 响应价格变化;中期移动平均线提供较为清晰的趋势判断;长期移动平均线过滤市场噪音,确定长期趋势方向。

当短期移动平均线上穿中期移动平均线时,表示价格开始向上突破;此时如果中期移动平均线高于长期移动平均线,则说明目前处于涨势,所以此时产生买入信号。

相反,当短期移动平均线下穿中期移动平均线时,表示价格开始向下突破;此时如果中期移动平均线低于长期移动平均线,则说明目前处于跌势,所以此时产生卖出信号。

该策略同时设置了止损止盈线。交易之后,会根据设置的止损止盈比例计算出止损和止盈价格。如果价格触及止损或止盈线,则平仓离场。

策略优势

  • 利用三条移动平均线共同判断趋势,提高判断准确性
  • 设置止损止盈,能够有效控制单笔交易的风险
  • 可自定义移动平均线参数,适用于不同品种
  • 可选择七种不同类型的移动平均线,丰富策略类型

策略风险及解决方法

  • 三均线互相盘整时,可能产生错误信号

    解决方法:适当调整移动平均线参数,避免产生错误信号

  • 设定过激的止损止盈比例

    解决方法:适当调整止损止盈比例,不能过大也不能过小

  • 参数设置不当,导致交易频率过高或过低

    解决方法:测试不同参数,寻找最优参数组合

策略优化方向

三均线黄金交叉策略可以从以下几个方面进行优化:

  • 测试不同类型、不同长度的参数,寻找最优参数

    可以测试不同长度或不同类型的移动平均线组合,以获得最佳交易效果

  • 增加其他技术指标过滤信号

    可以在策略中加入如KDJ,MACD等其他指标,进行多因素验证,过滤错误信号

  • 根据不同品种特点选择参数

    可以针对高波动品种缩短移动平均线周期;针对低波动品种加长移动平均线周期

  • 利用机器学习方法寻找最优参数组合

    通过算法自动遍历参数空间,快速定位最优参数

总结

三均线黄金交叉策略整体来说是一种较为简单实用的趋势跟踪策略。它同时利用三条移动平均线捕捉趋势方向,设置止损止盈控制风险,可以获得稳定收益。通过参数优化和加入其他技术指标,可以进一步提高策略效果。总的来说,该策略适合追求稳定获利的投资者。

||

Overview

The Triangular Moving Average (TMA) Crossover trading strategy is a typical technical analysis strategy. It utilizes three moving average lines of different time lengths to capture trends and implement low-risk trading. When the short-term moving average crosses over the medium-term moving average upwards, and the medium-term moving average is above the long-term moving average, a buy signal is generated. When the short-term moving average crosses below the medium-term moving average downwards, and the medium-term moving average is below the long-term moving average, a sell signal is generated.

Strategy Logic

The TMA strategy mainly relies on three moving average lines to determine the trend direction. The short-term moving average responds sensitively to price changes; the medium-term moving average provides a clearer judgment of the trend; the long-term moving average filters out market noise and determines the long-term trend direction.

When the short-term moving average crosses over the medium-term moving average upwards, it indicates the price has started to break out upwards. At this time, if the medium-term moving average is above the long-term moving average, it means the current market is in an uptrend. Therefore, a buy signal is generated here.

On the contrary, when the short-term moving average crosses below the medium-term moving average downwards, it indicates the price has started to break out downwards. At this time, if the medium-term moving average is below the long-term moving average, it means the current market is in a downtrend. As a result, a sell signal is generated.

This strategy also sets stop-loss and take-profit lines. After entering a trade, stop-loss and take-profit prices will be calculated based on the percentage settings. If the price touches either line, the position will be closed.

Advantage Analysis

  • Utilize three moving averages together to improve judgment accuracy
  • Set stop-loss and take-profit to effectively control per trade risk
  • Customizable moving average parameters suitable for different products
  • Seven options for moving average types, diversified strategy types

Risk Analysis and Solutions

  • Wrong signals when three MAs are consolidating

    Solution: Adjust MA parameters properly to avoid wrong signals

  • Over-aggressive stop-loss/take-profit percentage

    Solution: Fine-tune percentages; cannot be too big or too small

  • Improper parameter settings leading to too many or too few trades

    Solution: Test different parameter combinations to find optimum

Optimization Directions

The TMA strategy can be optimized from the following aspects:

  • Test different type and length combinations to find optimum

    Test different MA length or type combinations for best results

  • Add other technical indicators as signal filters

    Add indicators like KDJ, MACD etc. for multi-factor verification

  • Select parameters based on product characteristics

    Shorten MA periods for volatile products; Lengthen periods for steady products

  • Utilize machine learning to find optimum parameters

    Auto parameter sweeping to quickly locate optimum

Conclusion

The TMA Crossover strategy is an easy-to-use trend following strategy overall. It utilizes three MAs together to capture trends and sets stop-loss/take-profit to control risks, enabling stable profits. Further improvements can be achieved through parameter optimization and integrating extra technical indicators. In conclusion, this strategy suits investors seeking steady gains.

[/trans]

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 20 Medium MA Length
v_input_5_close 0 Medium MA Source: close
v_input_6 100 Long MA Length
v_input_7_close 0 Long MA Source: close
v_input_8 false SL Level % (0 - Off)
v_input_9 false PT Level % (0 - Off)

Source (PineScript)

/*backtest
start: 2024-01-08 00:00:00
end: 2024-01-15 00:00:00
period: 5m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=3
strategy("Kozlod - 3 MA strategy with SL/PT", shorttitle="kozlod_3ma", overlay = true, default_qty_type = strategy.percent_of_equity, default_qty_value = 5)

// 
// author: Kozlod
// date: 2018-03-25
// 

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

ma_type        = input(title = "MA Type",            defval = "SMA", options = ['SMA', 'EMA', 'WMA', 'VWMA', 'HMA', 'SMMA', 'DEMA'])
short_ma_len   = input(title = "Short MA Length",    defval = 5,     minval = 1)
short_ma_src   = input(title = "Short MA Source",    defval = close)
medium_ma_len  = input(title = "Medium MA Length",   defval = 20,    minval = 2)
medium_ma_src  = input(title = "Medium MA Source",   defval = close)
long_ma_len    = input(title = "Long MA Length",     defval = 100,   minval = 3)
long_ma_src    = input(title = "Long MA Source",     defval = close)

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
medium_ma = 0.0

// Simple Moving Average (SMA)
if ma_type == 'SMA' 
    short_ma  := sma(short_ma_src,  short_ma_len)
    medium_ma := sma(medium_ma_src, medium_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)
    medium_ma := ema(medium_ma_src, medium_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)
    medium_ma := wma(medium_ma_src, medium_ma_len)
    long_ma   := wma(long_ma_src,   long_ma_len)

// 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)))
    medium_ma := wma(2*wma(medium_ma_src, medium_ma_len / 2) - wma(medium_ma_src, medium_ma_len), round(sqrt(medium_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)
    medium_ma := vwma(medium_ma_src, medium_ma_len)
    long_ma   := vwma(long_ma_src,   long_ma_len)

// 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
    medium_ma := na(medium_ma[1]) ? sma(medium_ma_src, medium_ma_len) : (medium_ma[1] * (medium_ma_len - 1) + medium_ma_src) / medium_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_medium = ema(medium_ma_src, medium_ma_len)
    e1_long   = ema(long_ma_src,   long_ma_len)
    
    short_ma  := 2 * e1_short  - ema(e1_short,  short_ma_len)
    medium_ma := 2 * e1_medium - ema(e1_medium, medium_ma_len)
    long_ma   := 2 * e1_long   - ema(e1_long,   long_ma_len)

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

long_signal  = crossover( short_ma, medium_ma) and medium_ma > long_ma
short_signal = crossunder(short_ma, medium_ma) and medium_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] and nz(last_signal[2]) != 1) or (short_signal[1] and nz(last_signal[2]) != -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(medium_ma, color = green,  linewidth = 2)
plot(long_ma,   color = yellow, 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/438978

Last Modified

2024-01-16 18:18:02