strategies/双移动均线价格突破策略Dual-Moving-Average-Price-Breakthrough-Strategy.md

Name

双移动均线价格突破策略Dual-Moving-Average-Price-Breakthrough-Strategy

Author

ChaoZhang

Strategy Description

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概述

本策略采用两个移动平均线来判断价格的趋势和突破。当价格上穿上轨时做空,当价格下穿下轨时做多,设置止损exit来控制风险。

策略原理

1. 使用sma()函数计算短期和长期两个移动平均线,分别作为交易策略的上下轨。
2. 计算买入价位和卖出价位:买入价为下轨乘以一个小于1的系数,卖出价为上轨乘以一个大于1的系数。
3. 当价格上穿上轨时,以市价单开空仓;当价格下穿下轨时,以限价单开多仓。
4. 设置年份、月份和日期范围来控制策略的交易周期。
5. 回测结束或超出日期范围时,平掉所有仓位。

优势分析

本策略具有如下优势:

1. 使用双轨系统,可以过滤市场噪音,识别趋势。
2. 采用价格突破判断入场时机,可以减少虚假信号。
3. 利用限价单减少市场冲击成本。
4. 可以方便地调整交易周期,控制策略风险。

风险分析

本策略也存在一些风险:

1. 双轨突破容易产生连续亏损的风险。可以设置止损Exit来控制损失。
2. 交易标的进入盘整时,容易产生过多交易的风险。可以适当放宽上下轨间距。
3. 限价单可能导致部分买入机会被错过。可以考虑改为使用市价单。

优化方向

本策略可以从以下几个方面进行优化:

1. 测试不同长度的移动平均线组合,找到最佳参数。
2. 增加Volumes指标判断成交量突破。
3. 增加自适应止损机制,实时调整止损价位。
4. 增加机器学习模型判断趋势方向。

总结

本策略整体思路清晰易懂,通过双轨系统识别趋势,价格突破判断入场时机,可以过滤噪音实现稳定盈利,也存在一些改进优化的空间。总体而言,是一种可复现的具有实战价值的量化交易策略。

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Overview

This strategy uses two moving averages to determine price trends and breakthroughs. Go short when the price breaks through the upper rail and go long when the price breaks through the lower rail. Set stop loss exits to control risks.

Principles

1. Use the sma() function to calculate short-term and long-term two moving averages as the upper and lower rails of the trading strategy.
2. Calculate the buy price and sell price: the buy price is the lower rail multiplied by a coefficient less than 1, and the sell price is the upper rail multiplied by a coefficient greater than 1.
3. When the price breaks through the upper rail, open a short position with a market order; when the price breaks through the lower rail, open a long position with a limit order.
4. Set the year, month and date range to control the trading cycle of the strategy.
5. Close all positions when the backtest ends or exceeds the date range.

Advantages

This strategy has the following advantages:

1. Using a double rail system can filter market noise and identify trends.
2. Using price breakthrough to determine entry timing can reduce false signals.
3. Using limit orders reduces market impact costs.
4. The trading cycle can be easily adjusted to control strategy risks.

Risks

This strategy also has some risks:

1. Double rail breakthroughs can easily lead to continuous loss risks. Stop loss exits can be set to control losses.
2. When the trading target enters consolidation, there is a risk of too many transactions. The distance between the upper and lower rails can be appropriately widened.
3. Limit orders may miss some buying opportunities. Consider using market orders instead.

Optimization

This strategy can be optimized in the following aspects:

1. Test different combinations of moving average lengths to find the best parameters.
2. Increase the Volumes indicator to determine breakthroughs in transaction volume.
3. Increase adaptive stop loss mechanism to adjust stop loss price in real time.
4. Increase machine learning models to determine trend direction.

Summary

The overall idea of this strategy is clear and easy to understand. By using the double rail system to identify trends and using price breakthroughs to determine entry timing, it can filter noise and achieve stable profits. There is also room for improvement and optimization. Overall, it is a reproducible quantitative trading strategy with practical value.

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Strategy Arguments

Argument	Default	Description
v_input_1	100	Lot, %
v_input_2	3	Length
v_input_3_ohlc4	0	Source: ohlc4
v_input_4	-5	Buy line (lime)
v_input_5	false	Sell line (red)
v_input_6	1900	From Year
v_input_7	2100	To Year
v_input_8	true	From Month
v_input_9	12	To Month
v_input_10	true	From day
v_input_11	31	To day

Source (PineScript)

/*backtest
start: 2023-11-13 00:00:00
end: 2023-11-20 00:00:00
period: 1m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//Noro
//2018

//@version=3
strategy(title = "Noro's Shift MA Strategy v1.0", shorttitle = "Shift MA str 1.0", overlay = true, default_qty_type = strategy.percent_of_equity, default_qty_value = 100, pyramiding = 0)

//Settings
capital = input(100, defval = 100, minval = 1, maxval = 10000, title = "Lot, %")
per = input(3, defval = 1, minval = 1, maxval = 1000, title = "Length")
src = input(ohlc4, title = "Source")
buylevel = input(-5.0, defval = -5.0, minval = -100, maxval = 0, title = "Buy line (lime)")
selllevel = input(0.0, defval = 0.0, minval = -100, maxval = 100, title = "Sell line (red)")
fromyear = input(1900, defval = 1900, minval = 1900, maxval = 2100, title = "From Year")
toyear = input(2100, defval = 2100, minval = 1900, maxval = 2100, title = "To Year")
frommonth = input(01, defval = 01, minval = 01, maxval = 12, title = "From Month")
tomonth = input(12, defval = 12, minval = 01, maxval = 12, title = "To Month")
fromday = input(01, defval = 01, minval = 01, maxval = 31, title = "From day")
today = input(31, defval = 31, minval = 01, maxval = 31, title = "To day")

//SMAs
sma = sma(src, per) 
buy = sma * ((100 + buylevel) / 100)
sell = sma * ((100 + selllevel) / 100)
plot(buy, linewidth = 2, color = lime, title = "Buy line")
plot(sell, linewidth = 2, color = red, title = "Sell line")

//Trading
size = strategy.position_size
lot = 0.0
lot := size == 0 ? strategy.equity / close * capital / 100 : lot[1]

if (not na(close[per])) and size == 0
    strategy.entry("L", strategy.long, lot, limit = buy)
    
if (not na(close[per]))    
    strategy.entry("Close", strategy.short, 0, limit = sell)

if time > timestamp(toyear, tomonth, today, 23, 59)
    strategy.close_all()

Detail

https://www.fmz.com/strategy/432796

Last Modified

2023-11-21 15:33:52