Skip to content

Latest commit

 

History

History
271 lines (172 loc) · 10.5 KB

基于布林带移动平均线和MACD的组合交易策略Bollinger-Band-Moving-Average-and-MACD-Combined-Trading-Strategy.md

File metadata and controls

271 lines (172 loc) · 10.5 KB
Raw

Name

基于布林带移动平均线和MACD的组合交易策略Bollinger-Band-Moving-Average-and-MACD-Combined-Trading-Strategy

Author

ChaoZhang

Strategy Description

IMG [trans]

策略概述

该策略结合了布林带、移动平均线和MACD三种指标,形成一个较为完整的交易体系。它在判断市场趋势的同时,也能抓住部分反转机会。

策略名称与原理

本策略名称为“三角锚定趋势追踪策略”。该名称突出了它在判断趋势方向和锚定入市点位时,同时使用三种技术指标的特点。

其基本交易逻辑是:

  1. 判断趋势方向。通过布林带中轨、EMA移动平均线和MACD的零轴比较,来判断目前市场所处的多头阶段还是空头阶段。

  2. 寻找入市时机。在确定多头(或空头)趋势后,策略会根据EMA移动平均线是否突破布林中轨,以及MACD柱形线是否正(或负)向突破信号线来判断入市。

  3. 设置止盈止损。进入场内后,会预设固定止盈位和止损位。

策略优势分析

该策略最大的优势在于同时使用了趋势、均线和MACD三种不同类型的技术指标来指导决策。这使得它可以更准确判断市场走势,也更有利于抓住部分反转机会。

首先,布林带中轨线能清晰反映出当前阶段的主要趋势方向。EMA均线的作用则是跟踪趋势运行。它们的比较和结合,可以更准确地判断目前的多头和空头状况。

其次,布林带本身就具有比较强的包容性。中轨线附近也反映出一定的支撑压力位,因此EMA线的突破具有一定的信号价值。

再者,MACD的加入也可见多空能量的消长。它的绝对值大小代表群众情绪高涨或者冷淡,也可提示反转的可能性。

最后,策略预设了止盈止损条件,可控制单笔交易的风险收益情况,从而保证整体稳定运行。

策略风险分析

尽管该策略综合运用了多种分析工具,但仍有以下主要风险:

  1. 布林带参数设置不当,中轨线无法清晰反映主趋势。

  2. 均线系统发出多头信号,但MACD未明确转正,空头力量可能会扩大。

  3. 止盈止损范围设置过大,单笔亏损可能扩大。

主要的解决思路是:

  1. 调整布林带参数,确保中轨线有效反映主趋势。

  2. 引入更多技术指标判断多空能量。

  3. 评估历史交易情况,优化止盈止损参数。

策略优化方向

该策略还可从以下几个方面进一步优化:

  1. 在趋势判断上引入更多指标。如KDJ、ATR等辅助判断,提高判断准确率。

  2. 在操作层面设置更细致的止损方式。如移动止损、突破新高(低)后加大止损比例等。

  3. 评估不同品种的表现效果。调整参数适应更多行情特点。

  4. 测试效果和评估了不同时间框架和市场的回测结果。相应地调整参数。

  5. 增加机器学习算法,实现参数的自动优化和策略规则的动态更新。

总结

本策略同时运用布林带、移动平均线和MACD三大技术指标。它判断趋势清晰,具有一定的包容性,也可抓住部分反转机会。通过引入更多辅助工具判断和优化止盈止损策略,可望获得更稳定的交易表现。本策略值得进一步评估和改进,可望成为量化决策的有力工具。

||

Strategy Overview

This strategy combines Bollinger Band, moving average and MACD, forming a relatively complete trading system. While judging the market trend, it can also capture some reversal opportunities.

Strategy Name & Rationale

The strategy is named "Triangle Anchoring Trend Tracking Strategy". The name highlights its use of three technical indicators to determine trend direction and anchor entry points.

The basic trading logic is:

  1. Judge trend direction. Compare Bollinger Mid Band, EMA and MACD zero line to determine if the market is in an uptrend or downtrend phase.

  2. Find entry opportunities. After a trend is identified, the strategy checks if EMA crosses BB Mid Band and if MACD histogram crosses signal line to determine entries.

  3. Set profit target and stop loss. Once entered, fixed target and stop loss levels are preset.

Advantage Analysis

The biggest advantage of this strategy is the simultaneous use of trend, moving average and MACD tools to guide decisions. This allows more accurate judgments of market momentum and also helps capture some reversals.

Firstly, BB Mid Band clearly reflects the current primary trend direction. The role of EMA is to track the progress of trends. Their comparison and combination enables more precise trend identification.

Secondly, BB itself has strong envelope characteristics. The area around the mid band also indicates certain support/resistance levels. Hence EMA crossovers have signal value.

Additionally, the MACD measures the wax and wane of bullish/bearish momentum. Its absolute size represents high or low crowd emotions, also hinting potential reversals.

Finally, the pre-set profit target and stop loss controls risk/reward of individual trades, ensuring overall stability.

Risk Analysis

Despite the use of multiple analytical tools, main risks are:

  1. Improper BB parameters fail to clearly reflect the primary trend.

  2. EMA system signals long but MACD does not clearly turn positive, bearish forces may expand.

  3. Profit target/stop loss range too wide, single trade loss widens.

Main solutions are:

  1. Adjust BB parameters to ensure mid band effectively reflects main trend.

  2. Introduce more technical indicators to judge bull/bear momentum.

  3. Evaluate historical trades and optimize profit target/stop loss.

Optimization Directions

The strategy can be further improved in the following aspects:

  1. Introduce more indicators like KDJ, ATR etc to aid trend judgment and improve accuracy.

  2. Implement more sophisticated stops like trailing stop, breakout stop etc.

  3. Assess performance across different products. Fine tune parameters to suit various market conditions.

  4. Test and tweak strategy based on backtest results over different timeframes and markets.

  5. Incorporate machine learning for automatic parameter optimization and dynamic strategy update.

Conclusion

This strategy leverages BB, MA and MACD together. It has clear trend judgment, certain envelope characteristics and also captures some reversals. With more auxiliary tools for judging entries/exits, it can achieve more reliable performance. Further evaluation and enhancement of this strategy is warranted and expected to produce a robust quantitative tool.
[/trans]

Strategy Arguments

Argument Default Description
v_input_1 150 BB Length
v_input_2_close 0 BB Source: close
v_input_3 2 BB StdDev
v_input_4 34 EMA Length
v_input_5_close 0 EMA Source: close
v_input_6 SMA Method
v_input_7 5 Length
v_input_8 9 Fast Length
v_input_9 17 Slow Length
v_input_10_close 0 Source: close
v_input_int_1 9 Signal Smoothing
v_input_string_1 0 Oscillator MA Type: EMA
v_input_string_2 0 Signal Line MA Type: EMA

Source (PineScript)

/*backtest
start: 2024-01-04 00:00:00
end: 2024-02-03 00:00:00
period: 2h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("Combined Strategy", overlay=true, shorttitle="Comb Strat", default_qty_type=strategy.percent_of_equity, default_qty_value=10)

// Precio de beneficio y Stop Loss
takeProfitTicks = 87636
stopLossTicks = 53350

// Bollinger Bands + EMA
length_bb = input(150, title="BB Length")
src_bb = input(close, title="BB Source")
mult = input(2.0, title="BB StdDev")
basis = ta.sma(src_bb, length_bb)
dev = mult * ta.stdev(src_bb, length_bb)
upper = basis + dev
lower = basis - dev

len_ema = input(34, title="EMA Length")
src_ema = input(close, title="EMA Source")
out_ema = ta.ema(src_ema, len_ema)

typeMA = input("SMA", title="Method")
smoothingLength = input(5, title="Length")

var float smoothingLine = na
if (typeMA == "SMA")
    smoothingLine := ta.sma(out_ema, smoothingLength)
else if (typeMA == "EMA")
    smoothingLine := ta.ema(out_ema, smoothingLength)

// MACD
fast_length = input(title="Fast Length", defval=9)
slow_length = input(title="Slow Length", defval=17)
src_macd = input(title="Source", defval=close)
signal_length = input.int(title="Signal Smoothing", minval=1, maxval=50, defval=9)
sma_source = input.string(title="Oscillator MA Type", defval="EMA", options=["SMA", "EMA"])
sma_signal = input.string(title="Signal Line MA Type", defval="EMA", options=["SMA", "EMA"])

fast_ma = sma_source == "SMA" ? ta.sma(src_macd, fast_length) : ta.ema(src_macd, fast_length)
slow_ma = sma_source == "SMA" ? ta.sma(src_macd, slow_length) : ta.ema(src_macd, slow_length)
macd = fast_ma - slow_ma
signal = sma_signal == "SMA" ? ta.sma(macd, signal_length) : ta.ema(macd, signal_length)
hist = macd - signal

// Condiciones de compra y venta
longCondition = (out_ema > basis) and (macd > signal) and (signal > 0)
shortCondition = (out_ema < basis) and (macd < signal) and (signal < 0)

// Variables de estado
var bool longExecuted = na
var bool shortExecuted = na

// Estrategia
if (longCondition and not longExecuted)
    strategy.entry("Long", strategy.long)
    longExecuted := true
    shortExecuted := na
if (shortCondition and not shortExecuted)
    strategy.entry("Short", strategy.short)
    shortExecuted := true
    longExecuted := na

// Take Profit y Stop Loss para Compras y Ventas Cortas
strategy.exit("Take Profit/Close Long", from_entry="Long", profit=takeProfitTicks, loss=stopLossTicks)
strategy.exit("Take Profit/Close Short", from_entry="Short", profit=takeProfitTicks, loss=stopLossTicks)

// Cierre de posiciones cuando la dirección cambia
if ((out_ema < basis) and (macd < signal))
    strategy.close("Long")
    longExecuted := na
if ((out_ema > basis) and (macd > signal))
    strategy.close("Short")
    shortExecuted := na

// Plots
plot(basis, "BB Basis", color=#FF6D00)
plot(upper, "BB Upper", color=color.new(#2962FF, 0.5))
plot(lower, "BB Lower", color=color.new(#2962FF, 0.5))

plot(smoothingLine, title="Smoothing Line", color=#f37f20, linewidth=2)

hline(0, "Zero Line", color=color.new(#787B86, 50))
plot(hist, title="Histogram", style=plot.style_columns, color=(hist >= 0 ? (hist[1] < hist ? color.green : color.red) : (hist[1] < hist ? color.red : color.green)))
plot(macd, title="MACD", color=color.blue)
plot(signal, title="Signal", color=color.orange)

Detail

https://www.fmz.com/strategy/440995

Last Modified

2024-02-04 15:42:23