The Three Factors Model for Price Oscillation Detection is a short-term trading strategy that integrates multiple factors for judgment. This strategy takes into account factors like volume ratio, RSI, MACD, and signal line to detect price oscillations and discover short-term trading opportunities.
The core logic of this strategy is:
Calculate technical indicators like fast MA, slow MA, MACD, and signal line;
Judge multiple factor conditions including volume ratio, RSI, MACD and signal line;
Confirm the current price oscillation stage and buy/sell opportunities based on multiple factors analysis;
Take LONG or SHORT positions and set take profit and stop loss;
Close positions when price reaches take profit or stop loss.
This strategy flexibly uses factors like volume ratio, RSI, MACD and signal line to detect price oscillations and capture short-term opportunities. The combination of multiple factors helps avoid false signals from a single factor and improves accuracy.
The advantages of this strategy:
The risks of this strategy:
To address the above risks, optimizations can be made in:
The main optimization directions:
Optimize factor weights dynamically. Weights can be adjusted based on market conditions to improve adaptiveness;
Introduce machine learning algorithms to achieve adaptive optimization of factors. Algorithms like neural networks and genetic algorithms can be used to train the model and optimize parameters;
Optimize stop loss strategies. Different combinations of tracking stop loss and moving stop loss can be tested to find the best solution;
Incorporate advanced technical indicators. More indicators like volatility swing and momentum oscillation can enrich the factors.
The Three Factors Model for Price Oscillation Detection fully utilizes the characteristics of price oscillations to implement an efficient short-term trading strategy. It judges the best entry and exit points based on multiple factors like volume, RSI, MACD and signal line. The multiple factors enhance accuracy and lead to steady returns. Further optimizations can be done through machine learning for adaptive optimization, resulting in even better strategy performance.
/*backtest start: 2024-01-26 00:00:00 end: 2024-02-25 00:00:00 period: 4h basePeriod: 15m 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=0.7) shortLookBack = input( title="Short LookBack", defval=3) longLookBack = input( title="Long LookBack", defval=6) takeProfit = input( title="Take Profit", defval=2) stopLoss = input( title="Stop Loss", defval=0.7) 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") plot(macdSlope, color=color.green, title="MACD Slope") plot(signalSlope, color=color.red, title="Signal Slope") intrabarRange = high - low rsi = ta.rsi(close, 14) rsiSlope = rsi - rsi[1] plot(rsiSlope, color=color.black, title="RSI Slope") 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 ) // 202.30 Profit 55.29% 5m if ( ( getVolBias(longLookBack) == false ) and rsi <= 41 and math.abs(rsi - rsi[shortLookBack]) > 1 and hasNoSignalBias and rsiSlope > 1.5 and close > open) strategy.entry("5C1", strategy.long, qty=1.0) strategy.exit("TPS", "5C1", limit=strategy.position_avg_price + takeProfit, stop=strategy.position_avg_price - stopLoss) // 171.70 Profit 50.22% 5m if ( getVolBias(longLookBack) == true and rsi > 45 and rsi < 55 and macdSlope > 0 and signalSlope > 0) strategy.entry("5C2", strategy.long, qty=1.0) strategy.exit("TPS", "5C2", limit=strategy.position_avg_price + takeProfit, stop=strategy.position_avg_price - stopLoss) // 309.50 Profit 30.8% 5m 2 tp .7 sl 289 trades if ( macd > macdBiasValue and macdSlope > 0) strategy.entry("5P1", strategy.short, qty=1.0) strategy.exit("TPS", "5P1", limit=strategy.position_avg_price - takeProfit, stop=strategy.position_avg_price + stopLoss)