/*****************************************************************************
* FUNCTION: GetPPCCoefficients
* Description:
* Parameters:
* pItemIndex -
*****************************************************************************/
public Dictionary <int, Double> GetPPCCoefficients(int pItemIndex)
{
Dictionary <int, Double> return_list = new Dictionary <int, Double>();
int i, hash_key;
for (i = 0; i < constituents.Count(); i++)
{
if (i != pItemIndex && CorrelationCoefficients != null)
{
hash_key = Helpers.getHash(i + 1, pItemIndex + 1, this.constituents.Count());
return_list.Add(i, CorrelationCoefficients[hash_key]);
}
}
return(return_list);
}
/*****************************************************************************
* FUNCTION: ShiftedPearsonProductCoefficient
*
* Description: Calculates the Pearson Product Coefficient (PPC) for each
* Equity in pInputList based on it's HistoricalPrice list, against
* every other Equity in pInputList, after shifting its HistoricalPrice
* left by pShiftValue.
*
* The intent is to find a predictive correlation.
* ie. if one price-series tends to lag another by 1 trading day
* (in terms of its trend), then shifting that series left by 1 and
* calculating the PPC with the other series ought to yield a strong
* PPC correlation. See Helpers.PearsonProductCoefficient() for the
* normal PPC calculation.
*
* Parameters:
* pInputList - the input data, consisting of multiple Equity instances
* pShiftValue - the number of HistoricalPriceDates to shift data by before
* computing the PPC.
*****************************************************************************/
public static Double[,] ShiftedPearsonProductCoefficient(List <Equity> pInputList, int pShiftValue)
{
int N, cSize, i, j, k, count, key1, key2;
Double coeff, numerator, denominator;
Double sumSqRange1, sumSqRange2, meanRange1, meanRange2;
Double[,] CorrelationCoefficients;
List <Equity> temp_list;
N = pInputList.Count();
cSize = N * (N - 1) / 2;
CorrelationCoefficients = new Double[cSize, 2];
if (N > 0 && pShiftValue >= 0 && pShiftValue < N)
{
count = 1;
for (i = 0; i < N; i++)
{
coeff = 0;
denominator = 0;
meanRange1 = 0;
meanRange2 = 0;
temp_list = new List <Equity>(pInputList);
temp_list[i].TrimDataLeft(pShiftValue);
for (j = 0; j < N; j++)
{
temp_list[j].TrimDataRight(pShiftValue);
if (j >= i)
{
key2 = 0;
}
else
{
key2 = 1;
}
if (pInputList[i].HistoricalPrice.Count() == pInputList[j].HistoricalPrice.Count())
{
sumSqRange1 = 0;
sumSqRange2 = 0;
numerator = 0;
meanRange1 = pInputList[i].avgPrice;
meanRange2 = pInputList[j].avgPrice;
for (k = 0; k < pInputList[j].HistoricalPrice.Count(); k++)
{
sumSqRange1 += Math.Pow(pInputList[i].HistoricalPrice[k], 2);
sumSqRange2 += Math.Pow(pInputList[j].HistoricalPrice[k], 2);
numerator += (pInputList[i].HistoricalPrice[k] * pInputList[j].HistoricalPrice[k]);
}
numerator -= (pInputList[j].HistoricalPrice.Count() * meanRange1 * meanRange2);
denominator = Math.Pow((sumSqRange1 - (pInputList[i].HistoricalPrice.Count() * Math.Pow(meanRange1, 2))), 0.5) *
Math.Pow((sumSqRange2 - (pInputList[j].HistoricalPrice.Count() * Math.Pow(meanRange2, 2))), 0.5);
coeff = numerator / denominator;
}
key1 = Helpers.getHash(i + 1, j + 1, pInputList.Count());
CorrelationCoefficients[key1, key2] = coeff;
}
temp_list.Clear();
count++;
}
}
return(CorrelationCoefficients);
}
