/*
#region UC-1
* class MyIndicator : FunctionBase<double,double>
* {
* Input open, close;
* Output result;
*
* public override void Pre()
* {
* open = MapTo( "Open" );
* close = MapTo( "Close" );
* result = MapTo( "Result" );
* }
*
* public override void Iter()
* {
* if( open > close.Prev )
* result.Data = (open + close.Prev)/2.0;
* � else
* result.Data = result.Prev;
* }
* }
*
* public static void UC1()
* {
* // Get Microsoft market data.
* Timeseries ts1 = Timeseries.RandomTimeseriesOHLC(20);
*
* // Use ts1 as input and ts2 will refer to the output.
* MyIndicator indicator = new MyIndicator();
* Timeseries ts2 = indicator.Exec(ts1);
*
* // Display the output.
* for( int i=0; i < ts2.Length; i++ )
* {
* Console.WriteLine( ts2["TheOutputName"][i] );
* }
* }
#endregion
*/
public static void UC2()
{
Timeseries ibm = Timeseries.RandomTimeseriesOHLC(20); // Market data for IBM.
Timeseries msft = Timeseries.RandomTimeseriesOHLC(20); // Market data for Microsoft.
// Static method to synchronize the time series
// with each other.
Timeseries.Sync(ibm, msft);
// Calculate the typical price.
Timeseries ts1 = new Timeseries();
ts1["Price1"] = ibm.TypPrice();
ts1["Price2"] = msft.TypPrice();
// Calculate correlations between the two typical prices.
double correl = ts1.CorrelValue();
// Display : Approach 1
foreach (Index idx in new Iter(ts1, ibm, msft))
{
Console.WriteLine("{0} {1} {2} {3} {4}",
ibm.Timestamps[idx],
ibm.Close[idx],
msft.Close[idx],
ts1["Price1"][idx],
ts1["Price1"][idx]);
}
// Display : Approach 2
// A better speed execution time by creating
// a reference to the often access variables
// once prior to the loop. Still need to
// put all the variable in the same time series
// for synchronzation purpose.
Variable var1 = ibm.Close;
Variable var2 = msft.Close;
Variable var3 = ts1["Price1"];
Variable var4 = ts1["Price2"];
foreach (Index idx in new Iter(var1, var2, var3, var4))
{
Console.WriteLine("{0} {1} {2} {3} {4}",
var1.Timestamps[idx],
var1[idx], var2[idx], var3[idx], var4[idx]);
}
// Display : Approach 3
// A different way of creating new time series
// by adding at once ALL the variables of a
// time series. The prefix allows to create
// namespaces to avoid variable name collision
// e.g. "Close" exist both in the ibm and msft
// time series.
Timeseries ts4 = new Timeseries();
ts4.SetAllVar("IBM.", ibm);
ts4.SetAllVar("MSFT.", msft);
ts4.SetAllVar("_", ts1);
foreach (Index idx in new Iter(ts4))
{
Console.WriteLine("{0} {1} {2} {3} {4}",
ts4.Timestamps[idx],
ts4["IBM.Close"][idx],
ts4["MSFT.Close"][idx],
ts4["_Price1"][idx],
ts4["_Price2"][idx]);
}
}
/*
#region UC-1
class MyIndicator : FunctionBase<double,double>
{
Input open, close;
Output result;
public override void Pre()
{
open = MapTo( "Open" );
close = MapTo( "Close" );
result = MapTo( "Result" );
}
public override void Iter()
{
if( open > close.Prev )
result.Data = (open + close.Prev)/2.0;
� else
result.Data = result.Prev;
}
}
public static void UC1()
{
// Get Microsoft market data.
Timeseries ts1 = Timeseries.RandomTimeseriesOHLC(20);
// Use ts1 as input and ts2 will refer to the output.
MyIndicator indicator = new MyIndicator();
Timeseries ts2 = indicator.Exec(ts1);
// Display the output.
for( int i=0; i < ts2.Length; i++ )
{
Console.WriteLine( ts2["TheOutputName"][i] );
}
}
#endregion
*/
public static void UC2()
{
Timeseries ibm = Timeseries.RandomTimeseriesOHLC(20); // Market data for IBM.
Timeseries msft = Timeseries.RandomTimeseriesOHLC(20); // Market data for Microsoft.
// Static method to synchronize the time series
// with each other.
Timeseries.Sync( ibm, msft );
// Calculate the typical price.
Timeseries ts1 = new Timeseries();
ts1["Price1"] = ibm.TypPrice();
ts1["Price2"] = msft.TypPrice();
// Calculate correlations between the two typical prices.
double correl = ts1.CorrelValue();
// Display : Approach 1
foreach( Index idx in new Iter(ts1,ibm,msft) )
{
Console.WriteLine( "{0} {1} {2} {3} {4}",
ibm.Timestamps[idx],
ibm.Close[idx],
msft.Close[idx],
ts1["Price1"][idx],
ts1["Price1"][idx]);
}
// Display : Approach 2
// A better speed execution time by creating
// a reference to the often access variables
// once prior to the loop. Still need to
// put all the variable in the same time series
// for synchronzation purpose.
Variable var1 = ibm.Close;
Variable var2 = msft.Close;
Variable var3 = ts1["Price1"];
Variable var4 = ts1["Price2"];
foreach (Index idx in new Iter(var1,var2,var3,var4))
{
Console.WriteLine("{0} {1} {2} {3} {4}",
var1.Timestamps[idx],
var1[idx], var2[idx], var3[idx], var4[idx] );
}
// Display : Approach 3
// A different way of creating new time series
// by adding at once ALL the variables of a
// time series. The prefix allows to create
// namespaces to avoid variable name collision
// e.g. "Close" exist both in the ibm and msft
// time series.
Timeseries ts4 = new Timeseries();
ts4.SetAllVar( "IBM.", ibm );
ts4.SetAllVar( "MSFT.", msft );
ts4.SetAllVar( "_", ts1 );
foreach( Index idx in new Iter(ts4) )
{
Console.WriteLine( "{0} {1} {2} {3} {4}",
ts4.Timestamps[idx],
ts4["IBM.Close"][idx],
ts4["MSFT.Close"][idx],
ts4["_Price1"][idx],
ts4["_Price2"][idx] );
}
}