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True Range Based Weighted Moving Average Cross Period Strategy

Author: ChaoZhang, Date: 2024-01-17 15:09:28
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Overview

This strategy uses True Range and Weighted Moving Average (WMA) to build a cross period indicator for trend judgement. At the same time, it has a pyramid position accumulation mechanism with multiple stop loss mechanisms to pursue stable profits.

Strategy Principle

The strategy first calculates the up amplitude (sube) and down amplitude (baja), and then calculates the WMA of the fast line (corto) cycle and the slow line (largo) cycle respectively. The difference between the fast and slow lines is calculated again through WMA to get the indicator (ind). When the indicator crosses above 0, a buy signal is generated. When it crosses below 0, a sell signal is generated.

After entering the market, the strategy presets 5 positions, which are accumulated in a pyramid (doubled) manner. At the same time, a stop loss mechanism is set so that the subsequent positions opened will be judged whether the current floating profit is lower than the stop loss line, so as to control risks.

Advantage Analysis

The strategy integrates mechanisms such as cross-cycle judgment, pyramid position accumulation, and multiple stop losses, which can effectively control risks and pursue stable profits.

Cross-cycle judgments establish a trend judgment system through fast and slow lines combination, which can effectively filter market noise and identify trend turning points. Pyramid positions can profit more at the beginning of the trend, and multiple stop loss mechanisms can effectively control single loss.

Risk Analysis

The main risk of this strategy is the possibility of a sudden event causing a rapid market reversal that triggers a stop loss cutoff and causes losses. In addition, improper parameter settings will also affect the stability of the strategy.

The risk of market reversal can be dealt with by appropriately relaxing the stop loss line. Optimizing parameter settings and adjusting cycle parameters, number of positions, etc. can improve the stability of the strategy.

Optimization Direction

The strategy can be optimized in the following aspects:

  1. Increase statistical indicators for judgement, use indicators like volatility and volume to correct parameters and make the strategy more adaptive.

  2. Increase machine learning model for judgement, use LSTM and other deep learning models to assist judgement and improve strategy accuracy.

  3. Optimize position management mechanisms, consider adjusting the amplitude of position increase according to floating profit percentage to make position growth more reasonable.

  4. Incorporate futures hedging models to further control risks through spot and futures arbitrage.

Summary

In summary, this is a cross-cycle trend strategy based on True Range indicators with pyramid position accumulation and multiple stop loss mechanisms, which can effectively control risks and pursue stable profits. It is a very practical quantitative trading strategy. However, attention is still needed to reversal risks and parameter optimization problems. Further optimizations can be done in statistics, machine learning and other aspects.

/*backtest
start: 2023-01-10 00:00:00
end: 2024-01-16 00:00:00
period: 1d
basePeriod: 1h
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/
// © MaclenMtz

//@version=5
strategy("[MACLEN] Rangos", shorttitle="Rangos [https://t.me/Bitcoin_Maclen]", overlay=false )

//------WINDOW----------

i_startTime = input(defval = timestamp("01 Jan 2022 00:00 -0700"), title = "Start Time", group = "Backtest Window")
i_endTime = input(defval = timestamp("31 Dec 2025 00:00 -0700"), title = "End Time")
window = true

//-----------------------------

sube = close>close[1] ? ta.tr : 0
baja = close<close[1] ? ta.tr : 0

corto = input(10)
largo = input(30)
suavizado = input(10)

fastDiff = ta.wma(sube, corto) - ta.wma(baja,corto)
slowDiff = ta.wma(sube, largo) - ta.wma(baja, largo)
ind = ta.wma(fastDiff - slowDiff, suavizado)

iColor = ind>0 ? color.green : ind<0 ? color.red : color.black
plot(ind, color=iColor)
plot(0, color=color.white)

long = ind[1]<ind and ind[2]<ind[1] and ind<0
short = ind[1]>ind and ind[2]>ind[1] and ind>0

plotshape(long and not long[1], style = shape.xcross, color=color.green, location=location.bottom, size=size.tiny)
plotshape(short and not short[1], style = shape.xcross, color=color.red, location=location.top, size=size.tiny)

//Contratos
contrato1 = input(50000)/(16*close)
c1 = contrato1
c2 = contrato1
c3 = contrato1*2
c4 = contrato1*4
c5 = contrato1*8

//cap_enopentrade = strategy.opentrades == 1 ? c1: strategy.opentrades == 2 ? c1+c2: strategy.opentrades == 3 ? c1+c2+c3: strategy.opentrades == 4 ? c1+c2+c3+c4: strategy.opentrades == 5 ? c1+c2+c3+c4+c5 : 0
openprofit_porc = math.round((close-strategy.position_avg_price)/strategy.position_avg_price * 100,2)

porc_tp = input.float(6.5)
safe = input(-6)

//----------------Strategy---------------------------

if strategy.opentrades == 0
    strategy.entry('BUY1', strategy.long, qty=c1, when = long and not long[1] and window)

if strategy.opentrades == 1
    strategy.entry('BUY2', strategy.long, qty=c2, when = long and not long[1] and window and openprofit_porc<safe)

if strategy.opentrades == 2
    strategy.entry('BUY3', strategy.long, qty=c3, when = long and not long[1] and window and openprofit_porc<safe)

if strategy.opentrades == 3
    strategy.entry('BUY4', strategy.long, qty=c4, when = long and not long[1] and window and openprofit_porc<safe)

if strategy.opentrades == 4
    strategy.entry('BUY5', strategy.long, qty=c5, when = long and not long[1] and window and openprofit_porc<safe)

min_prof = strategy.openprofit>0

strategy.close_all(when=short and min_prof)

plot(openprofit_porc)
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