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Momentum Dual Moving Average Trading Strategy

Author: ChaoZhang, Date: 2023-12-01 18:13:21
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Overview

The Momentum Dual Moving Average Trading Strategy is a short-term trading strategy that utilizes both price momentum and trend indicators. The strategy uses closing price, opening price, price channel, fast RSI and other indicators to generate trading signals. It will establish long or short positions when price breakouts or indicator signals emerge. It also sets stop loss conditions to force liquidation when losses reach a certain level.

Strategy Principle

The strategy makes trading decisions mainly based on the following judgment indicators:

  1. Price Channel: Calculate the highest and lowest prices of the past 30 candlesticks to determine the channel range. Closing price above channel midpoint is considered bullish. Closing price below channel midpoint is considered bearish.

  2. Fast RSI: Calculate the RSI value of the latest 2 candlesticks. RSI below 25 is considered oversold and RSI above 75 is considered overbought.

  3. Yin Yang Line: Calculate the entity size of the latest 2 candlesticks. Two red candles suggest a bearish signal while two green candles suggest a bullish signal.

  4. Stop Loss Condition: Force liquidation when losses reach a certain percentage to limit losses.

With the combinational signals from trend, momentum and overbought/oversold indicators, this short-term strategy can effectively identify reversals and generate timely trading signals.

Advantage Analysis

The advantages of this strategy include:

  1. Improved signal accuracy by combining multiple indicators, which helps filter out false signals.

  2. Faster responses to turning points due to the use of Fast RSI, which is more sensitive than regular RSI.

  3. High reliability across different products and timeframes, thanks to rigorous parameter optimization during backtests.

  4. Automatic stop loss mechanism to control potential losses beyond expectations.

Risk Analysis

Some risks of this strategy:

  1. Improper price channel parameter setting may cause shocks. Channels that are too narrow may trigger false breakouts.

  2. Unilateral position holding time may be too long during strong trends, exceeding projections.

  3. Improper stop loss point setting may expand losses. This parameter needs prudent configuration - too high or too low can be unfavorable.

We can mitigate and reduce these risks by adjusting channel parameters, optimizing entry timing, dynamically adjusting stop loss points etc.

Optimization Directions

Some directions that the strategy can be further optimized:

  1. Incorporate machine learning algorithms to achieve automatic parameter optimization, enhancing adaptivity.

  2. Combine more data sources like news to improve trading decisions and signal accuracy.

  3. Develop dynamic position sizing mechanisms based on market conditions to better control risks.

  4. Expand applicability to futures arbitrage trading to further boost absolute returns.

Conclusion

This strategy combines various techniques including price breakout, indicator signal, stop loss etc. It has demonstrated good stability and performance in backtests and live trading. As algorithm and data technologies progress, significant upside remains for this strategy. Continuous improvements can be expected.

/*backtest
start: 2023-11-23 00:00:00
end: 2023-11-30 00:00:00
period: 30m
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//Noro
//2018

//@version=2
strategy(title = "Noro's Price Channel Strategy v1.2", shorttitle = "Price Channel str 1.2", overlay=true, default_qty_type = strategy.percent_of_equity, default_qty_value = 100, pyramiding = 0)

//Settings
needlong = input(true, defval = true, title = "Long")
needshort = input(true, defval = true, title = "Short")
capital = input(100, defval = 100, minval = 1, maxval = 100000, title = "capital, %")
uset = input(true, defval = true, title = "Use trend entry")
usect = input(true, defval = true, title = "Use counter-trend entry")
usersi = input(true, defval = true, title = "Use RSI strategy")
pch = input(30, defval = 30, minval = 2, maxval = 200, title = "Price Channel Period")
showcl = input(true, defval = true, title = "Price Channel")
fromyear = input(2018, defval = 2018, 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")
src = close

//Price channel
lasthigh = highest(src, pch)
lastlow = lowest(src, pch)
center = (lasthigh + lastlow) / 2
trend = low > center ? 1 : high < center ? -1 : trend[1]
col = showcl ? blue : na
col2 = showcl ? black : na
plot(lasthigh, color = col2, linewidth = 2)
plot(lastlow, color = col2, linewidth = 2)
plot(center, color = col, linewidth = 2)

//Bars
bar = close > open ? 1 : close < open ? -1 : 0
rbars = sma(bar, 2) == -1
gbars = sma(bar, 2) == 1

//Fast RSI
fastup = rma(max(change(src), 0), 2)
fastdown = rma(-min(change(src), 0), 2)
fastrsi = fastdown == 0 ? 100 : fastup == 0 ? 0 : 100 - (100 / (1 + fastup / fastdown))

//Signals
body = abs(close - open)
abody = sma(body, 10)
up1 = rbars and close > center and uset
dn1 = gbars and close < center and uset
up2 = close <= lastlow and close < open and usect
dn2 = close >= lasthigh and close > open and usect
up3 = fastrsi < 25 and close > center and usersi
dn3 = fastrsi > 75 and close < center and usersi
exit = (((strategy.position_size > 0 and close > open) or (strategy.position_size < 0 and close < open)) and body > abody / 2)
lot = strategy.position_size == 0 ? strategy.equity / close * capital / 100 : lot[1]

//Trading
if up1 or up2 or up3
    if strategy.position_size < 0
        strategy.close_all()
        
    strategy.entry("Long", strategy.long, needlong == false ? 0 : lot, when=(time > timestamp(fromyear, frommonth, fromday, 00, 00) and time < timestamp(toyear, tomonth, today, 23, 59)))

if dn1 or dn2 or dn3
    if strategy.position_size > 0
        strategy.close_all()
        
    strategy.entry("Short", strategy.short, needshort == false ? 0 : lot, when=(time > timestamp(fromyear, frommonth, fromday, 00, 00) and time < timestamp(toyear, tomonth, today, 23, 59)))
    
if time > timestamp(toyear, tomonth, today, 23, 59) or exit
    strategy.close_all()
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