Tags:

The Dual Moving Average Oscillation Breakout strategy is a short-term trading strategy that utilizes a dual moving average system. The strategy generates trading signals based on price channels and double Bollinger Bands, aided by fast RSI indicators to determine overbought and oversold conditions. It aims to capture breakouts in medium-term price trends for profit.

The Dual Moving Average Oscillation Breakout strategy uses 20-period price channels and Bollinger Bands as the main trading indicators. The price channel consists of moving averages of the highest and lowest prices, representing the current price oscillation range. Bollinger Bands are formed by the midline of the price channel and standard deviations, which intuitively describe the fluctuation range of prices. When prices approach the upper and lower rails of the channel, it indicates that prices may break through the oscillation range and form a new trend. At this point, combined with the fast RSI indicator to judge overbought or oversold conditions, the trend direction can be determined and trading decisions can be made.

Specifically, when the fast RSI is below 5, it is considered the oversold zone, and when the fast RSI exceeds 99, it is considered the overbought zone. In addition, factors such as the direction of K-line entities and new highs (lows) in prices should also be considered to avoid false breakouts. When the above conditions are met, buy and sell signals are generated.

The biggest advantage of the Dual Moving Average Oscillation Breakout strategy is that it captures the inflection points of medium-term price trends for profit. Compared to single moving averages and channels, double Bollinger Bands more intuitively reflect price fluctuations and volumes. And compared to longer cycle indicators such as 20-day and 60-day moving averages, it responds more quickly to price changes and has a higher success rate in capturing turns. In addition, combining the fast RSI indicator can effectively filter false breakouts. Therefore, this strategy can maximize the probability of profit.

The Dual Moving Average Oscillation Breakout strategy has some risks. First, medium-term trading itself has higher stop-loss risks. In a strong trend, false breakouts may occur multiple times on medium-term indicators, causing stops. Second, the effectiveness of fast RSI indicators in judging overbought and oversold zones will be affected by market sentiment. When structural changes occur in the market, the utility of such auxiliary indicators will decrease. Finally, incorporating other factors such as closing prices, volume and turnover can improve decision accuracy.

The countermeasure is to appropriately adjust the stop loss range, loosen the stop loss point in an uptrend, and tighten it in a downtrend. In addition, fully consider more auxiliary indicators to avoid relying solely on one or two indicators. When the judgment effect decreases, appropriately reduce the position to avoid risks.

There is still room for further optimization of the Dual Moving Average Oscillation Breakout strategy. First, parameter optimization. More cycle parameters can be tested to find the optimal parameter combination. Second, model optimization. Introduce machine learning models to more accurately judge overbought and oversold areas. Third, time frame optimization. Test under different time frames such as daily and 60 minutes separately to determine the best application scenario. Fourth, condition optimization. Add more volume and price indicators to filter signals, such as volume expansion and trend index DMI.

The Dual Moving Average Oscillation Breakout Strategy captures medium-term price breakouts by constructing a double Bollinger band system, which is an effective trend tracking strategy. The strategy has high success rate and fast response, and can profit effectively. By means of parameter optimization, model optimization, time frame selection and other means, the strategy performance can be further improved. This strategy is suitable for experienced quantitative traders to conduct quantitative improvements and applications.

/*backtest start: 2023-01-07 00:00:00 end: 2024-01-07 00:00:00 period: 1d basePeriod: 1h exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}] */ //@version=2 strategy("Noro's Bands Strategy v1.4", shorttitle = "NoroBands str 1.4", overlay=true, default_qty_type = strategy.percent_of_equity, default_qty_value=100.0, pyramiding=0) //Settings needlong = input(true, defval = true, title = "Long") needshort = input(true, defval = true, title = "Short") len = input(20, defval = 20, minval = 2, maxval = 200, title = "Period") color = input(true, "Use ColorBar") usecb = input(true, "Use CryptoBottom") usersi = input(true, "Use RSI") usemm = input(true, "Use min/max") needbb = input(false, defval = false, title = "Show Bands") needbg = input(false, defval = false, title = "Show Background") needlo = input(false, defval = false, title = "Show Locomotive") src = close //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)) //CryptoBottom mac = sma(close, 10) lencb = abs(close - mac) sma = sma(lencb, 100) max = max(open, close) min = min(open, close) //PriceChannel lasthigh = highest(src, len) lastlow = lowest(src, len) center = (lasthigh + lastlow) / 2 //dist dist = abs(src - center) distsma = sma(dist, len) hd = center + distsma ld = center - distsma hd2 = center + distsma * 2 ld2 = center - distsma * 2 //Trend trend = close < ld and high < hd ? -1 : close > hd and low > ld ? 1 : trend[1] //Lines colo = needbb == false ? na : black plot(hd2, color = colo, linewidth = 1, transp = 0, title = "High band 2") plot(hd, color = colo, linewidth = 1, transp = 0, title = "High band") plot(center, color = colo, linewidth = 1, transp = 0, title = "center") plot(ld, color = colo, linewidth = 1, transp = 0, title = "Low band") plot(ld2, color = colo, linewidth = 1, transp = 0, title = "Low band 2") //Background col = needbg == false ? na : trend == 1 ? lime : red bgcolor(col, transp = 80) //Signals up = trend == 1 and ((close < open or color == false) or close < hd) and (min < min[1] or usemm == false) ? 1 : 0 dn = trend == -1 and ((close > open or color == false) or close > ld) and (max > max[1] or usemm == false) ? 1 : 0 up2 = close < open and lencb > sma * 3 and min < min[1] and fastrsi < 10 ? 1 : 0 //CryptoBottom //dn2 = close > open and len > sma * 3 and max > max[1] and fastrsi > 90 ? 1 : 0 //CryptoBottom up3 = fastrsi < 5 ? 1 : 0 //dn3 = fastrsi > 99 ? 1 : 0 //Locomotive uploco = trend == 1 and close < open and min < min[1] and close < center ? 1 : 0 plotarrow(needlo == true and uploco == 1 ? 1 : 0, colorup = black, colordown = black, transp = 0) longCondition = up == 1 or (up2 == 1 and usecb == true) or (up3 == 1 and usersi == true) if (longCondition) strategy.entry("Long", strategy.long, needlong == false ? 0 : na) shortCondition = dn == 1 if (shortCondition) strategy.entry("Short", strategy.short, needshort == false ? 0 : na)

- Scalping Trading Strategy Based on Double Moving Average
- Supertrend Moving Stop Loss Take Profit Strategy
- Andrew Abraham Trend Tracking Strategy
- Monthly Performance PnL Calendar Strategy
- Breakthrough Highs and Lows Backtesting Strategy
- Quant Strategy with Stochastic Signal, SMA Filter and Random Stop loss/take profit
- Dual Moving Average Crossover Strategy
- Moving Average Cross Signal Strategy
- EMA 200 Based Trailing Take Profit and Trailing Stop Loss Strategy
- Up/Down K-Line Pattern High Frequency Arbitrage Strategy
- Adaptive Volatility Breakout Strategy
- SuperTrend Strategy for Ethereum Trading
- EMA Crossover and MACD Signals Trend Following Strategy
- Multiple Crossovers Turtle and Weighted Moving Average and MACD and TSI Combination Strategy
- Momentum Strategy Based on DEMA and EMA Crossover with ATR Volatility Filter
- Consecutive Up/Down Strategy with Reverse and SL/TP Extension
- Prime Number Bands Backtest Strategy
- Fast Oscillating RSI Trading Strategy
- Single Exponential Smoothed Moving Average with Trailing Stop Loss Trend Following Strategy
- Buy Low Volatility VS Buy High Volatility Strategy