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基于多时间框架的量化波段交易策略Bitcoin-Quantitative-Band-Trading-Strategy-Based-on-Multiple-Timeframes.md

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Name

基于多时间框架的量化波段交易策略Bitcoin-Quantitative-Band-Trading-Strategy-Based-on-Multiple-Timeframes

Author

ChaoZhang

Strategy Description

IMG [trans]

概述

本策略通过结合不同时间框架下的量化指标,实现对比特币价格波段的识别,从而进行追踪交易。策略采用5分钟时间框架,长期持有波段获利。

策略原理

  1. 基于日线时间框架计算的RSI指标,采用成交量进行加权计算,过滤假突破。
  2. 对日线RSI指标进行EMA平滑处理,构建量化波段指标。
  3. 5分钟时间框架采用线性回归指标和HMA指标构建交易信号。
  4. 策略通过量化波段指标和交易信号的组合,实现不同时间框架之间的耦合,识别价格中长线波段。

优势分析

  1. 采用成交量加权的RSI指标,能有效识别真实波段,过滤假突破。
  2. HMA指标对价格变化更加敏感,能及时捕捉转折。
  3. 多时间框架结合,识别中长线波段更准确。
  4. 5分钟时间框架进行交易,更高操作频率。
  5. 波段追踪策略,无需精确选点,持有时间更长。

风险分析

  1. 量化指标可能发出错误信号,建议结合基本面分析。
  2. 波段可能中途反转,应设置止损退出机制。
  3. 交易信号延迟,可能错过最佳入场点位。
  4. 盈利波段需要较长持有期,需承担一定资金压力。

优化方向

  1. 测试不同参数的RSI指标效果。
  2. 尝试引入其他辅助波段指标。
  3. 优化HMA指标的长度参数。
  4. 添加止损和止盈策略。
  5. 调整波段交易的持有周期。

总结

本策略通过多时间框架耦合和波段追踪方式,实现了对比特币中长线趋势的有效捕捉。相较于短线交易,中长线波段交易回撤较小,获利空间更大。下一步,通过参数调整和风险管理策略的添加,可望进一步提高策略收益率和稳定性。

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Overview

This strategy identifies the price bands of Bitcoin by combining quantitative indicators across different timeframes, and conducts trend tracking trades. It adopts the 5-minute timeframe and aims for long-term holding of bands for profit.

Strategy Logic

  1. The RSI indicator calculated based on the daily timeframe weighs based on trading volume to filter false breakouts.
  2. The daily RSI indicator is smoothed by an EMA to build a quantitative band indicator.
  3. The 5-minute timeframe uses a combination of Linear Regression and HMA indicators to generate trading signals.
  4. By combining the quantitative band indicator and trading signals across timeframes, the strategy identifies mid-to-long-term price bands.

Advantage Analysis

  1. The volume-weighted RSI indicator can effectively identify true bands and filter false breakouts.
  2. The HMA indicator is more sensitive to price changes and can capture turns timely.
  3. Combining multiple timeframes leads to more accurate identification of mid-to-long-term bands.
  4. Trading on the 5-minute timeframe allows higher operation frequency.
  5. As a band tracking strategy, it does not require accurate picking of points and can hold for longer periods.

Risk Analysis

  1. Quantitative indicators may generate false signals, fundamental analysis is recommended.
  2. Bands may see midway reversals, stop-loss mechanisms should be in place.
  3. Signal delays may lead to missing best entry points.
  4. Profitable bands need longer holding periods, requiring capital pressure tolerance.

Optimization Directions

  1. Test effectiveness of RSI indicators with different parameters.
  2. Try introducing other auxiliary band indicators.
  3. Optimize HMA indicator length parameters.
  4. Add stop loss and take profit strategies.
  5. Adjust holding cycle for band trades.

Conclusion

This strategy effectively captures Bitcoin's mid-to-long-term trends by coupling timeframes and band tracking. Compared to short-term trading, mid-to-long-term band trading sees smaller drawdowns and greater profit potential. Next steps involve further enhancing profitability and stability through parameter tuning and risk management additions.
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Strategy Arguments

Argument Default Description
v_input_1 250 Fast filter length
v_input_2 500 Slow filter length
v_input_3 true From Month
v_input_4 10 From Day
v_input_5 2020 From Year
v_input_6 true Thru Month
v_input_7 true Thru Day
v_input_8 2112 Thru Year
v_input_9 true Show Date Range
v_input_10 1D HTF
v_input_11 0 Timeframe: 1
v_input_12 50 Period
v_input_13 true Shift
v_input_14 25 len
v_input_15 true filter
v_input_16 3 ProfitTarget_Percent
v_input_17 10 LossTarget_Percent

Source (PineScript)

/*backtest
start: 2023-10-31 00:00:00
end: 2023-11-30 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4

strategy(title='Pyramiding BTC 5 min', overlay=true, pyramiding=5, initial_capital=10000, default_qty_type=strategy.percent_of_equity, default_qty_value=20, commission_type=strategy.commission.percent, commission_value=0.075)
//the pyramide based on this script  https://www.tradingview.com/script/7NNJ0sXB-Pyramiding-Entries-On-Early-Trends-by-Coinrule/
//
fastLength = input(250, title="Fast filter length ", minval=1)
slowLength = input(500,title="Slow filter length",  minval=1)
source=close
v1=ema(source,fastLength)
v2=ema(source,slowLength)
//
//Backtest dates
fromMonth = input(defval=1, title="From Month")
fromDay = input(defval=10, title="From Day")
fromYear = input(defval=2020, title="From Year")
thruMonth = input(defval=1, title="Thru Month")
thruDay = input(defval=1, title="Thru Day")
thruYear = input(defval=2112, title="Thru Year")

showDate = input(defval=true, title="Show Date Range")

start = timestamp(fromYear, fromMonth, fromDay, 00, 00)  // backtest start window
finish = timestamp(thruYear, thruMonth, thruDay, 23, 59)  // backtest finish window
window() =>  // create function "within window of time"
    time >= start and time <= finish ? true : false


leng=1
p1=close[1]

len55 = 10
//taken from https://www.tradingview.com/script/Ql1FjjfX-security-free-MTF-example-JD/
HTF = input("1D", type=input.resolution)
ti = change( time(HTF) ) != 0
T_c = fixnan( ti ? close : na )

vrsi = rsi(cum(change(T_c) * volume), leng)
pp=wma(vrsi,len55)

d=(vrsi[1]-pp[1])
len100 = 10
x=ema(d,len100)
//
zx=x/-1
col=zx > 0? color.lime : color.orange

//

tf10 = input("1", title = "Timeframe", type = input.resolution, options = ["1", "5", "15", "30", "60","120", "240","360","720", "D", "W"])

length = input(50, title = "Period", type = input.integer)
shift = input(1, title = "Shift", type = input.integer)

hma(_src, _length)=>
    wma((2 * wma(_src, _length / 2)) - wma(_src, _length), round(sqrt(_length)))
    
hma3(_src, _length)=>
    p = length/2
    wma(wma(close,p/3)*3 - wma(close,p/2) - wma(close,p),p)

b =security(syminfo.tickerid, tf10, hma3(close[1], length)[shift])
//plot(a,color=color.gray)
//plot(b,color=color.yellow)
close_price = close[0]
len = input(25)

linear_reg = linreg(close_price, len, 0)


filter=input(true)

buy=crossover(linear_reg, b)

longsignal = (v1 > v2 or filter == false ) and buy and window()

//set take profit

ProfitTarget_Percent = input(3)
Profit_Ticks = close * (ProfitTarget_Percent / 100) / syminfo.mintick

//set take profit

LossTarget_Percent = input(10)
Loss_Ticks = close * (LossTarget_Percent / 100) / syminfo.mintick


//Order Placing

strategy.entry("Entry 1", strategy.long, when=strategy.opentrades == 0 and longsignal)

strategy.entry("Entry 2", strategy.long, when=strategy.opentrades == 1 and longsignal)

strategy.entry("Entry 3", strategy.long, when=strategy.opentrades == 2 and longsignal)

strategy.entry("Entry 4", strategy.long, when=strategy.opentrades == 3 and longsignal)

strategy.entry("Entry 5", strategy.long, when=strategy.opentrades == 4 and longsignal)





if strategy.position_size > 0
    strategy.exit(id="Exit 1", from_entry="Entry 1", profit=Profit_Ticks, loss=Loss_Ticks)
    strategy.exit(id="Exit 2", from_entry="Entry 2", profit=Profit_Ticks, loss=Loss_Ticks)
    strategy.exit(id="Exit 3", from_entry="Entry 3", profit=Profit_Ticks, loss=Loss_Ticks)
    strategy.exit(id="Exit 4", from_entry="Entry 4", profit=Profit_Ticks, loss=Loss_Ticks)
    strategy.exit(id="Exit 5", from_entry="Entry 5", profit=Profit_Ticks, loss=Loss_Ticks)

Detail

https://www.fmz.com/strategy/433905

Last Modified

2023-12-01 13:50:02