tradingmodelsystems.c

// 1. Trend (e.g. moving average crossover)
	function run()
	{
	  vars Price = series(price());
	  vars Trend = series(LowPass(Price,500));
		
	  vars MMI_Raw = series(MMI(Price,300));
	  vars MMI_Smooth = series(LowPass(MMI_Raw,500));
		
	  if(falling(MMI_Smooth)) {
	    if(valley(Trend))
	      reverseLong(1);
	    else if(peak(Trend))
	      reverseShort(1);
	  }
	}
	
// 2. Mean reversion (E.g. Rule #1)
	function run()
	{
	  vars Price = series(price());
	  vars Filtered = series(HighPass(Price,30));
	  vars Signal = series(FisherN(Filtered,500));
	  var Threshold = 1.0;
	
	  if(Hurst(Price,500) < 0.5) { // do we have mean reversion?
	    if(crossUnder(Signal,-Threshold))
	      reverseLong(1); 
	    else if(crossOver(Signal,Threshold))
	      reverseShort(1);
	  }
	} 

// 3. Statistical Arbitrage (e.g. comparing and ETF with its major stock)
	require(quantmod)			
		symbols <- c("AAPL", "QQQ")
		getSymbols(symbols)
		
		#define training set
		startT  <- "2007-01-01"
		endT    <- "2009-01-01"
		rangeT  <- paste(startT,"::",endT,sep ="")
		tAAPL   <- AAPL[,6][rangeT]
		tQQQ   <- QQQ[,6][rangeT]
		 
		#compute price differences on in-sample data
		pdtAAPL <- diff(tAAPL)[-1]
		pdtQQQ <- diff(tQQQ)[-1]
		 
		#build the model
		model  <- lm(pdtAAPL ~ pdtQQQ - 1)
		 
		#extract the hedge ratio (h1 is assumed 1)
		h2 <- as.numeric(model$coefficients[1])
		
		#spread price (in-sample)
		spreadT <- tAAPL - h2 * tQQQ
		 
		#compute statistics of the spread
		meanT    <- as.numeric(mean(spreadT,na.rm=TRUE))
		sdT      <- as.numeric(sd(spreadT,na.rm=TRUE))
		upperThr <- meanT + 1 * sdT
		lowerThr <- meanT - 1 * sdT
		 
		#run in-sample test
		spreadL  <- length(spreadT)
		pricesB  <- c(rep(NA,spreadL))
		pricesS  <- c(rep(NA,spreadL))
		sp       <- as.numeric(spreadT)
		tradeQty <- 100
		totalP   <- 0
		
		for(i in 1:spreadL) {
		     spTemp <- sp[i]
		     if(spTemp < lowerThr) {
		        if(totalP <= 0){
		           totalP     <- totalP + tradeQty
		           pricesB[i] <- spTemp
		        }
		     } else if(spTemp > upperThr) {
		       if(totalP >= 0){
		          totalP <- totalP - tradeQty
		          pricesS[i] <- spTemp
		       }
		    }
		}
	
// 4. Price Constraints (grid trader like the EUR/CHF from back then)
	// helper function to check if the grid line has no trade
	bool isFree(var Price,var Grid,bool IsShort)
	{
	  for(open_trades) {
	    if(TradeIsShort == IsShort
	      && between(TradeEntryLimit,Price-Grid/2,Price+Grid/2))
	        return false;
	  }
	  return true;
	}
	
	// EUR/CHF grid trader main function
	int run() 
	{
	  BarPeriod = 60;
	  Hedge = 5; // activate virtual hedging
	
	  var Grid = 20*PIP; // set grid distance to 20 pips
	  var Close = priceClose();
	 
	// place pending trades at 5 grid lines above and below the Close
	  int i;
	  for(i = Close/Grid - 5; i < Close/Grid + 5; i++)
	  {
	    var Price = i*Grid;
	// place short trades with profit target below the current price
	    if(Price < Close && isFree(Price,Grid,true))
	      enterShort(1,Price,0,Grid); 
	// place long trades with profit target above the current price
	    else if(Price > Close && isFree(Price,Grid,false))
	      enterLong(1,Price,0,Grid);
	  }
	} 
		
// 5. Cycles (sell/buy based on distance from 'fair' price)
	function run()
	{
	  vars Price = series(price());
	  var Phase = DominantPhase(Price,10);
	  vars Signal = series(sin(Phase+PI/4));
	  vars Dominant = series(BandPass(Price,rDominantPeriod,1));
	  ExitTime = 10*rDominantPeriod;
	  var Threshold = 1*PIP;
		
	  if(Amplitude(Dominant,100) > Threshold) {
	    if(valley(Signal))
	      reverseLong(1); 
	    else if(peak(Signal))
	      reverseShort(1);
	  }
	}
	
// 6. Clusters
// 7. Curve Patterns (Frechet algorithm)
// 8. Seasonality
// 9. Gaps (One Night Stand System)
// 10. Autoregression and Heteroskedasticity (ARIMA and GARCH)
// 11. News