Name

多时间框架移动平均线结合交易时间的量化交易策略Multi-Timeframe-Moving-Average-Combined-with-Trading-Hours-Quantitative-Trading-Strategy

Author

ChaoZhang

Strategy Description

[trans]

概述

本策略运用多种移动平均线指标,结合交易时间选择进入和退出的时机,实现量化交易。

策略原理

该策略使用了9种移动平均线包括SMA、EMA、WMA等。根据用户选择,进入多仓时,收盘价上穿选定的移动平均线且前一根K线收盘价在移动平均线以下;做空时,收盘价下穿选定的移动平均线且前一根K线收盘价在移动平均线以上。所有交易只在周一开盘时发出。平仓条件为固定止盈止损或周日收盘前平仓。

优势分析

本策略集多种移动平均线精华于一身,用户可以选择不同参数来适应不同市场环境。只有确定趋势出现才入场,避免了 '%失效交易' 的出现。同时,本策略只在周一开仓,周日前止盈止损或平仓,限制了单周最大开仓次数,有效控制了交易风险。

风险分析

该策略主要依赖均线指标判断趋势,当趋势发生转折时,存在部分交易被套住的风险。此外,限定只有周一能够开仓,如果在周一之后出现较好的交易机会也无法入场,可能错过部分利润。

为控制这些风险,建议采用动态移动平均线参数,当市场进入震荡时,适当缩短参数;同时,也可以增加开仓时间,在周三或周四仍允许开新仓。

优化方向

该策略可以从以下几个方面进行优化:

增加止盈止损Algerism算法,动态调整止盈止损点;
增加机器学习模型判断趋势年,避免在震荡市场入场;
优化开仓和平仓逻辑,允许更多的开仓机会出现。

总结

本策略集成多种移动平均线指标判断趋势方向,以周一开仓周日平仓的方式有效控制了单周最大交易次数。同时,严格的止盈止损规则也限制了单笔交易的最大亏损。综合来看,本策略从趋势判断和风险控制两个维度进行优化设计,是一种较为稳健的量化交易策略。

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Overview

This strategy utilizes multiple moving average indicators and combines entry and exit timing based on trading hours to implement quantitative trading.

Strategy Logic

This strategy incorporates 9 types of moving averages including SMA, EMA, WMA etc. For long entry, the close price crosses above the selected moving average while the previous close was below the moving average. For short entry, the close price crosses below the moving average while the previous close was above. All trades are entered on Monday open only. Exit rules are either fixed take profit/stop loss or close all positions before Sunday close.

Advantage Analysis

This strategy combines the essence of multiple moving averages and users can pick different parameters based on varying market conditions. It only enters when a trend is confirmed, avoiding whipsaws. Also, it limits entries to Monday only and exits on Sunday close with stop loss/take profit, capping maximum trades per week and controlling trading risk.

Risk Analysis

The strategy relies mainly on moving averages to determine trend, thus faces the risk of being caught in reversals. Also, limiting entries to Monday only means missed profitable opportunities if a good setup appears later in the week.

To address these risks, dynamic average parameters could be used to shorten length during ranging periods. Also additional entry days could be allowed, like on Wednesday or Thursday.

Optimization Directions

The strategy can be improved in the following ways:

Add adaptive stop loss/take profit algorithms to dynamically adjust levels.
Incorporate machine learning models to better gauge trend in choppy markets.
Refine entry and exit logic to capture more trading opportunities.

Summary

This strategy combines multiple moving average indicators to determine trend direction and caps maximum weekly trades with Monday entry and Sunday exit rules. Strict stop loss/take profit further limits maximum loss per trade. In summary, it provides robust enhancements in both trend determination and risk control dimensions for quantitative trading. [/trans]

Strategy Arguments

Argument	Default	Description
v_input_bool_1	true	(?Type of Entries)longEntry
v_input_bool_2	false	shortEntry
v_input_source_1_close	0	(?Parameters)Source: close
v_input_int_1	17	Moving Averages
v_input_string_1	0	MA Type: ALMA
v_input_float_1	10	(?Fixed Risk Management)LONG Stop Loss %
v_input_float_2	30	LONG Take Profit %
v_input_float_3	5	SHORT Stop Loss %
v_input_float_4	10	SHORT Take Profit %

Source (PineScript)

/*backtest
start: 2023-12-01 00:00:00
end: 2023-12-31 23:59:59
period: 1h
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/
// © exlux99

//@version=5
strategy('Time MA strategy ', overlay=true)

longEntry = input.bool(true, group="Type of Entries")
shortEntry = input.bool(false, group="Type of Entries")


//==========DEMA
getDEMA(src, len) =>
    dema = 2 * ta.ema(src, len) - ta.ema(ta.ema(src, len), len)
    dema
//==========HMA
getHULLMA(src, len) =>
    hullma = ta.wma(2 * ta.wma(src, len / 2) - ta.wma(src, len), math.round(math.sqrt(len)))
    hullma
//==========KAMA
getKAMA(src, len, k1, k2) =>
    change = math.abs(ta.change(src, len))
    volatility = math.sum(math.abs(ta.change(src)), len)
    efficiency_ratio = volatility != 0 ? change / volatility : 0
    kama = 0.0
    fast = 2 / (k1 + 1)
    slow = 2 / (k2 + 1)
    smooth_const = math.pow(efficiency_ratio * (fast - slow) + slow, 2)
    kama := nz(kama[1]) + smooth_const * (src - nz(kama[1]))
    kama
//==========TEMA
getTEMA(src, len) =>
    e = ta.ema(src, len)
    tema = 3 * (e - ta.ema(e, len)) + ta.ema(ta.ema(e, len), len)
    tema
//==========ZLEMA
getZLEMA(src, len) =>
    zlemalag_1 = (len - 1) / 2
    zlemadata_1 = src + src - src[zlemalag_1]
    zlema = ta.ema(zlemadata_1, len)
    zlema
//==========FRAMA
getFRAMA(src, len) =>
    Price = src
    N = len
    if N % 2 != 0
        N := N + 1
        N
    N1 = 0.0
    N2 = 0.0
    N3 = 0.0
    HH = 0.0
    LL = 0.0
    Dimen = 0.0
    alpha = 0.0
    Filt = 0.0
    N3 := (ta.highest(N) - ta.lowest(N)) / N
    HH := ta.highest(N / 2 - 1)
    LL := ta.lowest(N / 2 - 1)
    N1 := (HH - LL) / (N / 2)
    HH := high[N / 2]
    LL := low[N / 2]
    for i = N / 2 to N - 1 by 1
        if high[i] > HH
            HH := high[i]
            HH
        if low[i] < LL
            LL := low[i]
            LL
    N2 := (HH - LL) / (N / 2)
    if N1 > 0 and N2 > 0 and N3 > 0
        Dimen := (math.log(N1 + N2) - math.log(N3)) / math.log(2)
        Dimen
    alpha := math.exp(-4.6 * (Dimen - 1))
    if alpha < .01
        alpha := .01
        alpha
    if alpha > 1
        alpha := 1
        alpha
    Filt := alpha * Price + (1 - alpha) * nz(Filt[1], 1)
    if bar_index < N + 1
        Filt := Price
        Filt
    Filt
//==========VIDYA
getVIDYA(src, len) =>
    mom = ta.change(src)
    upSum = math.sum(math.max(mom, 0), len)
    downSum = math.sum(-math.min(mom, 0), len)
    out = (upSum - downSum) / (upSum + downSum)
    cmo = math.abs(out)
    alpha = 2 / (len + 1)
    vidya = 0.0
    vidya := src * alpha * cmo + nz(vidya[1]) * (1 - alpha * cmo)
    vidya
//==========JMA
getJMA(src, len, power, phase) =>
    phase_ratio = phase < -100 ? 0.5 : phase > 100 ? 2.5 : phase / 100 + 1.5
    beta = 0.45 * (len - 1) / (0.45 * (len - 1) + 2)
    alpha = math.pow(beta, power)
    MA1 = 0.0
    Det0 = 0.0
    MA2 = 0.0
    Det1 = 0.0
    JMA = 0.0
    MA1 := (1 - alpha) * src + alpha * nz(MA1[1])
    Det0 := (src - MA1) * (1 - beta) + beta * nz(Det0[1])
    MA2 := MA1 + phase_ratio * Det0
    Det1 := (MA2 - nz(JMA[1])) * math.pow(1 - alpha, 2) + math.pow(alpha, 2) * nz(Det1[1])
    JMA := nz(JMA[1]) + Det1
    JMA
//==========T3
getT3(src, len, vFactor) =>
    ema1 = ta.ema(src, len)
    ema2 = ta.ema(ema1, len)
    ema3 = ta.ema(ema2, len)
    ema4 = ta.ema(ema3, len)
    ema5 = ta.ema(ema4, len)
    ema6 = ta.ema(ema5, len)
    c1 = -1 * math.pow(vFactor, 3)
    c2 = 3 * math.pow(vFactor, 2) + 3 * math.pow(vFactor, 3)
    c3 = -6 * math.pow(vFactor, 2) - 3 * vFactor - 3 * math.pow(vFactor, 3)
    c4 = 1 + 3 * vFactor + math.pow(vFactor, 3) + 3 * math.pow(vFactor, 2)
    T3 = c1 * ema6 + c2 * ema5 + c3 * ema4 + c4 * ema3
    T3
//==========TRIMA
getTRIMA(src, len) =>
    N = len + 1
    Nm = math.round(N / 2)
    TRIMA = ta.sma(ta.sma(src, Nm), Nm)
    TRIMA


src = input.source(close, title='Source', group='Parameters')
len = input.int(17, minval=1, title='Moving Averages', group='Parameters')
out_ma_source = input.string(title='MA Type', defval='ALMA', options=['SMA', 'EMA', 'WMA', 'ALMA', 'SMMA', 'LSMA', 'VWMA', 'DEMA', 'HULL', 'KAMA', 'FRAMA', 'VIDYA', 'JMA', 'TEMA', 'ZLEMA', 'T3', 'TRIM'], group='Parameters')
out_ma = out_ma_source == 'SMA' ? ta.sma(src, len) : out_ma_source == 'EMA' ? ta.ema(src, len) : out_ma_source == 'WMA' ? ta.wma(src, len) : out_ma_source == 'ALMA' ? ta.alma(src, len, 0.85, 6) : out_ma_source == 'SMMA' ? ta.rma(src, len) : out_ma_source == 'LSMA' ? ta.linreg(src, len, 0) : out_ma_source == 'VWMA' ? ta.vwma(src, len) : out_ma_source == 'DEMA' ? getDEMA(src, len) : out_ma_source == 'HULL' ? ta.hma(src, len) : out_ma_source == 'KAMA' ? getKAMA(src, len, 2, 30) : out_ma_source == 'FRAMA' ? getFRAMA(src, len) : out_ma_source == 'VIDYA' ? getVIDYA(src, len) : out_ma_source == 'JMA' ? getJMA(src, len, 2, 50) : out_ma_source == 'TEMA' ? getTEMA(src, len) : out_ma_source == 'ZLEMA' ? getZLEMA(src, len) : out_ma_source == 'T3' ? getT3(src, len, 0.7) : out_ma_source == 'TRIM' ? getTRIMA(src, len) : na


plot(out_ma)

long = close> out_ma and close[1] < out_ma and dayofweek==dayofweek.monday
short = close< out_ma and close[1] > out_ma and dayofweek==dayofweek.monday


stopPer = input.float(10.0, title='LONG Stop Loss % ', group='Fixed Risk Management') / 100
takePer = input.float(30.0, title='LONG Take Profit %', group='Fixed Risk Management') / 100

stopPerShort = input.float(5.0, title='SHORT Stop Loss % ', group='Fixed Risk Management') / 100
takePerShort = input.float(10.0, title='SHORT Take Profit %', group='Fixed Risk Management') / 100


longStop = strategy.position_avg_price * (1 - stopPer)
longTake = strategy.position_avg_price * (1 + takePer)

shortStop = strategy.position_avg_price * (1 + stopPerShort)
shortTake = strategy.position_avg_price * (1 - takePerShort)

// strategy.risk.max_intraday_filled_orders(2) // After 10 orders are filled, no more strategy orders will be placed (except for a market order to exit current open market position, if there is any).

if(longEntry)
    strategy.entry("long",strategy.long,when=long )
    strategy.exit('LONG EXIT', "long", limit=longTake, stop=longStop)
    strategy.close("long",when=dayofweek==dayofweek.sunday)

if(shortEntry)
    strategy.entry("short",strategy.short,when=short )
    strategy.exit('SHORT EXIT', "short", limit=shortTake, stop=shortStop)
    strategy.close("short",when=dayofweek==dayofweek.sunday)

Detail

https://www.fmz.com/strategy/438465

Last Modified

2024-01-12 11:50:37

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多时间框架移动平均线结合交易时间的量化交易策略Multi-Timeframe-Moving-Average-Combined-with-Trading-Hours-Quantitative-Trading-Strategy.md

多时间框架移动平均线结合交易时间的量化交易策略Multi-Timeframe-Moving-Average-Combined-with-Trading-Hours-Quantitative-Trading-Strategy.md

概述

策略原理

优势分析

风险分析

优化方向

总结

Overview

Strategy Logic

Advantage Analysis

Risk Analysis

Optimization Directions

Summary

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多时间框架移动平均线结合交易时间的量化交易策略Multi-Timeframe-Moving-Average-Combined-with-Trading-Hours-Quantitative-Trading-Strategy.md

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多时间框架移动平均线结合交易时间的量化交易策略Multi-Timeframe-Moving-Average-Combined-with-Trading-Hours-Quantitative-Trading-Strategy.md

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概述

策略原理

优势分析

风险分析

优化方向

总结

Overview

Strategy Logic

Advantage Analysis

Risk Analysis

Optimization Directions

Summary