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帕罗贝尔周期与布林带结合的移动止损策略Parabolic-Period-and-Bollinger-Band-Combined-Moving-Stop-Loss-Strategy.md

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Name

帕罗贝尔周期与布林带结合的移动止损策略Parabolic-Period-and-Bollinger-Band-Combined-Moving-Stop-Loss-Strategy

Author

ChaoZhang

Strategy Description

IMG [trans]

概述

本文将介绍一个结合帕罗贝尔周期指标与布林带指标,设定移动止损策略的量化交易策略。该策略通过计算帕罗贝尔周期线以判断市场趋势方向,再利用布林带上轨与下轨动态设定止损位,从而实现移动止损以锁定利润。

策略原理

首先,该策略利用帕罗贝尔指标判断当前市场趋势。当今日收盘价上穿昨日的帕罗贝尔周期线时,认为行情反转为看涨,可以做多;当今日收盘价下穿昨日周期线时,则行情看跌,可以做空。

其次,该策略结合布林带指标设定动态的止损位。布林带中的上轨可以看作是超买区,下轨为超卖区。当做多后,如果价格重新跌破布林带下轨,则止损平仓;当做空后,如果价格重新涨破上轨,则止损出场。这样,布林带上下轨就成为移动的止损线。

通过上述原理,该策略实现了判断市场方向的同时,又设置了动态的止损机制来跟踪利润。这使其可以在大趋势中捕捉部分涨跌幅,同时也可以通过止损来锁定利润,规避风险。

策略优势

相较于传统止损策略只设定一个固定的止损位,该策略运用布林带指标作为止损线,使止损线可以随价格波动进行移动。这使其可以在比较大的行情中锁定更多利润。此外,相较于单一使用帕罗贝尔周期线,该策略增加布林带指标判断超买超卖区域,可以更加准确。

风险及解决

该策略主要风险在于帕罗贝尔指标判断的趋势性并不强。在震荡行情中,价格可能多次上下穿越帕罗贝尔周期线,使策略产生频繁但微利的交易。此时,交易费用和滑点成本可能会占较大比例,降低策略盈利能力。

为应对上述风险,可以考虑调整参数,增大帕罗贝尔周期线的变化程度,降低误判概率;或者结合其他指标过滤入场时机。例如可加入震荡指标等判断行情是趋势性还是震荡,以减少不必要交易。

策略优化

本策略可以从以下几个方面进行优化:

  1. 优化帕罗贝尔指标参数,调整参数指标变化速度,降低误判概率

  2. 增加其他技术指标过滤,例如加入MACD,KD等判断行情类型,避免震荡市场的套利

  3. 优化布林带参数,调整带宽参数,使布林带更贴近价格变化

  4. 增加量能指标,例如交易量,持仓量等辅助判断,避免假突破

  5. 结合股票基本面信息,避免影响策略持仓股业绩出现问题

总结

本策略通过帕罗贝尔指标判断市场趋势方向与力度,再利用布林带上下轨作为移动止损位设定止损策略,实现了趋势跟踪与风险控制的结合。相较于传统固定止损策略,本策略可以在较大行情中获得更高收益。通过参数优化与增加其他辅助判断指标,可以进一步增强策略稳定性与减少不必要交易。

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Overview

This article will introduce a quantitative trading strategy that combines the parabolic period indicator and the Bollinger band indicator to set a moving stop loss strategy. By calculating the parabolic period line to judge the market trend direction, and then using the upper and lower rails of the Bollinger bands to dynamically set the stop loss position, the strategy realizes moving stop loss to lock in profits.

Strategy Principle

Firstly, this strategy uses the parabolic indicator to judge the current market trend. When today's closing price crosses above yesterday's parabolic period line, it is considered that the market has reversed to bullish and can go long; when today's closing price crosses below the period line, the market outlook is bearish and can go short.

Secondly, this strategy combines the Bollinger Band indicator to set a dynamic stop loss position. The upper rail of the Bollinger Band can be seen as an overbought zone, and the lower rail is an oversold zone. After going long, if the price falls back below the lower rail of the Bollinger Band, stop loss to close position; after going short, if the price rises above the upper rail again, stop loss to exit. Thus, the upper and lower rails of Bollinger Bands become moving stop loss lines.

Through the above principles, this strategy realizes judging the market direction while setting a dynamic stop loss mechanism to track profits. This allows it to capture some ups and downs in major trends, while also being able to lock in profits through stop losses to avoid risks.

Advantages of the Strategy

Compared with traditional stop loss strategies that only set one fixed stop loss position, this strategy uses the Bollinger band indicator as the stop loss line, so that the stop loss line can move with price fluctuations. This allows it to lock in more profits in relatively large moves. In addition, compared to using the parabolic period line alone, this strategy adds the Bollinger band indicator to determine overbought and oversold areas, which can be more accurate.

Risks and Solutions

The main risk of this strategy is that the trending of the parabolic indicator is not strong. In oscillating markets, prices may cross parabolic period lines several times, causing frequent but small profitable trades for the strategy. At this point, transaction fees and slippage costs may account for a large proportion and reduce the profitability of the strategy.

To cope with the above risks, parameters can be adjusted to increase the degree of change in the parabolic period line to reduce misjudgment probability; or consider combining other indicators to filter entry timing. For example, volatility indicators can be added to determine if the market is trending or oscillating in order to reduce unnecessary trades.

Strategy Optimization

This strategy can be optimized in the following aspects:

  1. Optimize parabolic indicator parameters to adjust indicator change rate to reduce misjudgment probability

  2. Increase other technical indicators filtering, such as adding MACD, KD to determine market type, avoid arbitrage in oscillating market

  3. Optimize Bollinger Band parameters to adjust bandwidth parameters to make Bollinger Bands stick closer to price changes

  4. Increase volume indicators, such as trading volume, positions to assist in judging to avoid false breakouts

  5. Combine fundamentals of stocks to avoid problems with earnings of stocks strategy holding

Summary

This strategy combines judging market trend direction and strength with parabolic indicator, and then uses the upper and lower rails of Bollinger Bands as the moving stop loss position to set a stop loss strategy, achieving a combination of trend tracking and risk control. Compared with traditional fixed stop loss strategies, this strategy can achieve higher returns in larger moves. By optimizing parameters and adding other auxiliary judgment indicators, the stability of the strategy can be further enhanced and unnecessary trades reduced.

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Strategy Arguments

Argument Default Description
v_input_bool_1 true (?Backtest Time Period)Filter Date Range of Backtest
v_input_1 timestamp(5 June 2022) Start Date
v_input_2 timestamp(5 July 2022) End Date
v_input_int_1 10 (?number of past candles)Swing High
v_input_int_2 10 Swing Low
v_input_int_3 200 (?EMA)Ema Period
v_input_int_4 14 (?RSI)RSI Period
v_input_3 0.02 (?Parabolic SAR)start
v_input_4 0.02 increment
v_input_5 0.2 Max Value

Source (PineScript)

/*backtest
start: 2024-01-02 00:00:00
end: 2024-02-01 00:00:00
period: 3h
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/
// © maxencetajet

//@version=5
strategy("HA_RSI", overlay=true, initial_capital=1000, default_qty_type=strategy.fixed, default_qty_value=0.5, slippage=25)

closeHA = request.security(ticker.heikinashi(syminfo.tickerid), timeframe.period, close)

useDateFilter = input.bool(true, title="Filter Date Range of Backtest",
     group="Backtest Time Period")
backtestStartDate = input(timestamp("5 June 2022"), 
     title="Start Date", group="Backtest Time Period",
     tooltip="This start date is in the time zone of the exchange " + 
     "where the chart's instrument trades. It doesn't use the time " + 
     "zone of the chart or of your computer.")
backtestEndDate = input(timestamp("5 July 2022"),
     title="End Date", group="Backtest Time Period",
     tooltip="This end date is in the time zone of the exchange " + 
     "where the chart's instrument trades. It doesn't use the time " + 
     "zone of the chart or of your computer.")

inTradeWindow = true

swingHighV = input.int(10, title="Swing High", group="number of past candles")
swingLowV = input.int(10, title="Swing Low", group="number of past candles")

emaV = input.int(200, title="Ema Period", group="EMA")

rsiV = input.int(14, title="RSI Period", group="RSI")

start = input(0.02, group="Parabolic SAR")
increment = input(0.02, group="Parabolic SAR")
maximum = input(0.2, "Max Value", group="Parabolic SAR")

ema = ta.ema(closeHA, emaV)
rsi = ta.rsi(closeHA, rsiV)
SAR = ta.sar(start, increment, maximum)

myColor = SAR < low?color.green:color.red

longcondition = closeHA > ema and rsi > 50 and closeHA[1] > SAR and closeHA[1] < SAR[1] 
shortcondition = closeHA < ema and rsi < 50 and closeHA[1] < SAR and closeHA[1] > SAR[1]

float risk_long = na
float risk_short = na
float stopLoss = na
float entry_price = na
float takeProfit = na

risk_long := risk_long[1]
risk_short := risk_short[1]

swingHigh = ta.highest(closeHA, swingHighV)
swingLow = ta.lowest(closeHA, swingLowV)

if strategy.position_size == 0 and longcondition and inTradeWindow
    risk_long := (close - swingLow) / close
    strategy.entry("long", strategy.long, comment="Buy")
    
if strategy.position_size == 0 and shortcondition and inTradeWindow
    risk_short := (swingHigh - close) / close       
    strategy.entry("short", strategy.short, comment="Sell")
    
if strategy.position_size > 0

    stopLoss := strategy.position_avg_price * (1 - risk_long)
    entry_price := strategy.position_avg_price
    strategy.exit("long exit", "long", stop = stopLoss)
    
if strategy.position_size < 0 

    stopLoss := strategy.position_avg_price * (1 + risk_short)
    entry_price := strategy.position_avg_price
    strategy.exit("short exit", "short", stop = stopLoss)

if closeHA[1] < SAR and close > strategy.position_avg_price
    strategy.close("long", comment="Exit Long")
    
if closeHA[1] > SAR and close < strategy.position_avg_price
    strategy.close("short", comment="Exit Short")

p_ep = plot(entry_price, color=color.new(color.white, 0), linewidth=2, style=plot.style_linebr, title='entry price')
p_sl = plot(stopLoss, color=color.new(color.red, 0), linewidth=2, style=plot.style_linebr, title='stopLoss')
fill(p_sl, p_ep, color.new(color.red, transp=85))

plot(SAR, "ParabolicSAR", style=plot.style_circles, color=myColor, linewidth=1)
plot(ema, color=color.white, linewidth=2, title="EMA")

Detail

https://www.fmz.com/strategy/440797

Last Modified

2024-02-02 11:05:57