Tags:

The 3 10.0 Oscillator Profile Reversal strategy identifies potential price reversals by calculating MACD indicators across different timeframes. It adopts a trend-following stop loss approach to pursue higher capital efficiency.

The strategy calculates SMA moving averages of 3 and 10 periods to construct fast and slow lines and the MACD indicator and signal line. When the fast line and signal line cross the zero line upward or downward, it indicates the price has reached a critical point and a reversal may occur. In addition, it also incorporates volume pressure judgment, RSI index etc. to identify reliability of reversal signals. It goes long or short when reversal signals meet certain reliability requirements.

Specifically, the strategy judges price reversals through:

- MACD zero-crossing indicates price reaches critical point
- Volume pressure judges bullish or bearish sentiment
- RSI index with MACD slope change determines strength of reversal signals
- Fast line and signal line crossing in reverse direction forms reversal signal

When reversal signal reliability is high, the strategy adopts trend-following stop loss to pursue higher profit.

The strategy has the following advantages:

- Multiple indicators make reversal signals more reliable
- MACD zero-crossing accurately locates reversal points
- RSI and volume assist judgment to improve reliability
- Trend-following stop loss improves capital efficiency

There are also some risks:

- High probability of MACD false signals and being trapped
- High chance of stop loss being hit during alternating trends
- Improper parameter setting may increase trading frequency and cost

Risks can be reduced through:

- Allow wider stop loss to avoid being trapped
- Optimize parameters to lower trading frequency
- Only consider entry near key support/resistance levels

The strategy can be further optimized through:

- Add machine learning algorithms to assist reversal signal reliability
- Add sentiment indices to determine bull/bear mentality
- Combine key support/resistance levels to improve entry precision
- Optimize stop loss for higher capital efficiency
- Test optimal parameter combinations to lower trading frequency

The multi timeframe MACD zero-crossing reversal strategy comprehensively considers price, volume and volatility indicators to determine entry timing through multi-indicator evaluation. It sets timely stop loss upon sufficient profitability. It can achieve good returns during reversal markets. Further improvements on machine learning and key level integration may lower risks and trading frequencies while improving profitability.

/*backtest start: 2023-02-11 00:00:00 end: 2024-02-17 00:00:00 period: 1d basePeriod: 1h exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}] */ //@version=5 strategy("3 10.0 Oscillator Profile Flagging", shorttitle="3 10.0 Oscillator Profile Flagging", overlay=false) signalBiasValue = input(title="Signal Bias", defval=0.26) macdBiasValue = input(title="MACD Bias", defval=0.8) shortLookBack = input( title="Short LookBack", defval=3) longLookBack = input( title="Long LookBack", defval=10.0) takeProfit = input( title="Take Profit", defval=0.8) stopLoss = input( title="Stop Loss", defval=0.75) fast_ma = ta.sma(close, 3) slow_ma = ta.sma(close, 10) macd = fast_ma - slow_ma signal = ta.sma(macd, 16) hline(0, "Zero Line", color = color.black) buyVolume = volume*((close-low)/(high-low)) sellVolume = volume*((high-close)/(high-low)) buyVolSlope = buyVolume - buyVolume[1] sellVolSlope = sellVolume - sellVolume[1] signalSlope = ( signal - signal[1] ) macdSlope = ( macd - macd[1] ) plot(macd, color=color.blue, title="Total Volume") plot(signal, color=color.orange, title="Total Volume") intrabarRange = high - low rsi = ta.rsi(close, 14) rsiSlope = rsi - rsi[1] getRSISlopeChange(lookBack) => j = 0 for i = 0 to lookBack if ( rsi[i] - rsi[ i + 1 ] ) > -5 j += 1 j getBuyerVolBias(lookBack) => j = 0 for i = 1 to lookBack if buyVolume[i] > sellVolume[i] j += 1 j getSellerVolBias(lookBack) => j = 0 for i = 1 to lookBack if sellVolume[i] > buyVolume[i] j += 1 j getVolBias(lookBack) => float b = 0.0 float s = 0.0 for i = 1 to lookBack b += buyVolume[i] s += sellVolume[i] b > s getSignalBuyerBias(lookBack) => j = 0 for i = 1 to lookBack if signal[i] > signalBiasValue j += 1 j getSignalSellerBias(lookBack) => j = 0 for i = 1 to lookBack if signal[i] < ( 0.0 - signalBiasValue ) j += 1 j getSignalNoBias(lookBack) => j = 0 for i = 1 to lookBack if signal[i] < signalBiasValue and signal[i] > ( 0.0 - signalBiasValue ) j += 1 j getPriceRising(lookBack) => j = 0 for i = 1 to lookBack if close[i] > close[i + 1] j += 1 j getPriceFalling(lookBack) => j = 0 for i = 1 to lookBack if close[i] < close[i + 1] j += 1 j getRangeNarrowing(lookBack) => j = 0 for i = 1 to lookBack if intrabarRange[i] < intrabarRange[i + 1] j+= 1 j getRangeBroadening(lookBack) => j = 0 for i = 1 to lookBack if intrabarRange[i] > intrabarRange[i + 1] j+= 1 j bool isNegativeSignalReversal = signalSlope < 0.0 and signalSlope[1] > 0.0 bool isNegativeMacdReversal = macdSlope < 0.0 and macdSlope[1] > 0.0 bool isPositiveSignalReversal = signalSlope > 0.0 and signalSlope[1] < 0.0 bool isPositiveMacdReversal = macdSlope > 0.0 and macdSlope[1] < 0.0 bool hasBearInversion = signalSlope > 0.0 and macdSlope < 0.0 bool hasBullInversion = signalSlope < 0.0 and macdSlope > 0.0 bool hasSignalBias = math.abs(signal) >= signalBiasValue bool hasNoSignalBias = signal < signalBiasValue and signal > ( 0.0 - signalBiasValue ) bool hasSignalBuyerBias = hasSignalBias and signal > 0.0 bool hasSignalSellerBias = hasSignalBias and signal < 0.0 bool hasPositiveMACDBias = macd > macdBiasValue bool hasNegativeMACDBias = macd < ( 0.0 - macdBiasValue ) bool hasBullAntiPattern = ta.crossunder(macd, signal) bool hasBearAntiPattern = ta.crossover(macd, signal) bool hasSignificantBuyerVolBias = buyVolume > ( sellVolume * 1.5 ) bool hasSignificantSellerVolBias = sellVolume > ( buyVolume * 1.5 ) // 393.60 Profit 52.26% 15m if ( hasBullInversion and rsiSlope > 1.5 and volume > 300000.0 ) strategy.entry("15C1", strategy.long, qty=10.0) strategy.exit("TPS", "15C1", limit=strategy.position_avg_price + takeProfit, stop=strategy.position_avg_price - stopLoss) // 356.10 Profit 51,45% 15m if ( getVolBias(shortLookBack) == false and rsiSlope > 3.0 and signalSlope > 0) strategy.entry("15C2", strategy.long, qty=10.0) strategy.exit("TPS", "15C2", limit=strategy.position_avg_price + takeProfit, stop=strategy.position_avg_price - stopLoss) // 124 Profit 52% 15m if ( rsiSlope < -11.25 and macdSlope < 0.0 and signalSlope < 0.0) strategy.entry("15P1", strategy.short, qty=10.0) strategy.exit("TPS", "15P1", limit=strategy.position_avg_price - takeProfit, stop=strategy.position_avg_price + stopLoss) // 455.40 Profit 49% 15m if ( math.abs(math.abs(macd) - math.abs(signal)) < .1 and buyVolume > sellVolume and hasBullInversion) strategy.entry("15P2", strategy.short, qty=10.0) strategy.exit("TPS", "15P2", limit=strategy.position_avg_price - takeProfit, stop=strategy.position_avg_price + stopLoss)

- Williams Double Exponential Moving Average and Ichimoku Kinkou Hyo Strategy
- 3 10 Oscillator Profile Flagging Strategy
- Multi Timeframe RSI-SRSI Trading Strategy
- A Combined Strategy with MACD and RSI
- ATR, EOM and VORTEX Based Long Trend Strategy
- Dual Moving Average Intelligent Tracking Trading Strategy
- High Volume Low Breakout Compounded Position Sizing Strategy
- Bitcoin Dollar Cost Averaging Based on BEAM Bands
- Byron Serpent Cloud Quant Strategy
- Dual Timeframe Volatility Spread Trading Strategy
- MACD EMA Crossover Trend Tracking Strategy
- Dual Moving Average Trading Strategy
- Dual Moving Average Golden Cross Trend Trading Strategy
- V-Reversal SMA Strategy
- Linear Regression Channel Breakout Trading Strategy
- Dual-EMA Indicator Based Trend Following Strategy
- Real Turtleâ€”Steadfast as a Rock Turtle Strategy
- Open-High-Low Stop Loss Tracking Strategy
- Comprehensive Futures Automated Trading Strategy for Both Long and Short
- Supertrend Breakout Trading Strategy