predict

predict (int Type, vars Data, int TimePeriod, var Threshold): int

Predicts an event, such as a crossover of two curves or a peak of a curve, several bars before it happens. A polynomial regression function is used for the prediction. This function can be used to generate early trade signals.

Most trade systems analyze the recent price curve with functions or indicators that introduce more or less lag. This means that trade signals are always late, which reduces the profit of a system. One way to minimize lag is using low-lag functions (for instance, higher-order lowpass filters instead of moving averages). Another way is predicting the signals before they actually occur. This is the purpose of the predict function that works by extrapolating signal curves into the future.

Parameters:

Type	Event to be predicted: CROSSOVER - crossing of Data over the zero line PEAK - Data peak VALLEY - Data valley +PARABOLIC - use parabolic instead of linear regression.
Data	Data series to be predicted, with a minimum length of TimePeriod.
TimePeriod	Number of Data points used for the prediction. Do not use more date than a typical 'swing' of the curve.
Threshold	Prediction threshold, or 0 for no threshold.

Returns

Bar offset (negative) of the predicted event, in the -TimePeriod..0 range. 0 predicts that the event will happen in the next bar. -TimePeriod is returned when no event is predicted.

Modifies

rMomentum - Predicted Data movement per bar at the time of the event.

Remarks:

The predict function does not peek into the future, but uses past data for predicting events on the future curve.
It does not detect real crossovers or peaks/valleys, but predicts them only on future bars. For adding real detection of an event at a previous bar, use crossOver or peak/valley.
For predicting a crossover of two data series, or a series crossing over a fixed value, call predict with a series of the differences, f.i. vars Difference = series(Data1[0] - Data2[0]);. For a crossunder, reverse the differences (see example).
Crossovers are detected with better precision and less false signals than peaks or valleys.
Threshold can be used to filter out 'weak' signals, f.i. from a crossing of two almost parallel lines. It is the minimum momentum of the Data line divided by the correlation coefficient.
For parabolic regression, add +PARABOLIC to the type. It detects events earlier than linear regression, but tends to produce more false signals.

Examples:

function run()
{
  vars Prices = series(price());
  var LP50 = LowPass(Prices,50);
  var LP150 = LowPass(Prices,150);
  
  var CO = predict(CROSSOVER,series(LP50-LP150),10,0.5*PIP); // predict crossover
  var CU = predict(CROSSOVER,series(LP150-LP50),10,0.5*PIP); // predict crossunder
    
  plot("LP50",LP50,0,RED);
  plot("LP150",LP150,0,BLUE);
  plot("CrossOver",CO,NEW,BLUE);
  plot("CrossUnder",CU,0,RED);
}

// Trading with crossover vs. trading with prediction
#define USE_PREDICT
function run() 
{
  BarPeriod = 1440;
  asset("SPX500");
  vars Osc = series(StochEhlers(series(price()),10,20,10));
#ifndef USE_PREDICT  // use normal crossover
  if(crossOver(Osc,0.8)) 
    enterShort();
  if(crossUnder(Osc,0.2))
    enterLong();
#else                // use predicted crossover
  if(predict(CROSSOVER,series(Osc[0]-0.8),10,0.01) > -5) 
    enterShort();
  if(predict(CROSSOVER,series(0.2-Osc[0]),10,0.01) > -5) 
    enterLong();
#endif
}