def InterpolateProbsLinearFromSurvivalProbs(TenoredSeries,
Tenors,
isSurvival=True):
i = 0
fnd2 = FindClosestKeyInDicAndReturnKeyBoundsAlgorithm(TenoredSeries)
res = dict()
res[0] = 1
prevT = 0
prevIntRate = 1
for t in Tenors:
res[t] = fnd2(t)
if len(res[t]) > 1:
tiplus1 = res[t][1]
ti = res[t][0]
intRate = (t - ti) / (tiplus1 - ti) * (
TenoredSeries[tiplus1] - TenoredSeries[ti]) + TenoredSeries[ti]
if isSurvival:
res[t] = intRate
else:
res[t] = prevIntRate - intRate
prevIntRate = intRate
else:
if isSurvival:
res[t] = TenoredSeries[res[t]].values[0]
else:
survivalProb = TenoredSeries[res[t]].values[0]
res[t] = prevIntRate - survivalProb
prevIntRate = survivalProb
prevT = t
return res
def DiscountFactorFn(DF: dict):
fnd = FindClosestKeyInDicAndReturnKeyBoundsAlgorithm(DF)
def f(val):
res = fnd(val)
if len(res) > 1:
return math.exp((val - res[0]) /
(res[1] - res[0]) * math.log(DF[res[1]]) +
(res[1] - val) /
(res[1] - res[0]) * math.log(DF[res[0]]))
else:
return DF[res[0]]
return f
def InterpolateHazrateLinear(TenoredSeries, Tenors):
i = 0
fnd2 = FindClosestKeyInDicAndReturnKeyBoundsAlgorithm(TenoredSeries)
res = dict()
res[0] = 1
for t in Tenors:
res[t] = fnd2(t)
if len(res[t]) > 1:
tiplus1 = res[t][1]
ti = res[t][0]
intRate = (t - ti) / (tiplus1 - ti) * (
TenoredSeries[tiplus1] - TenoredSeries[ti]) + TenoredSeries[ti]
res[t] = intRate
else:
res[t] = TenoredSeries[res[t]].values[0]
return res
def InterpolateDFLogLinear(TenoredSeries, Tenors):
i = 0
fnd = FindClosestKeyInDicAndReturnKeyBoundsAlgorithm(TenoredSeries)
res = dict()
res[0] = 1
for t in Tenors:
res[t] = fnd(t)
if len(res[t]) > 1:
tiplus1 = res[t][1]
ti = res[t][0]
lnDiscFac = ((t - ti) /
(tiplus1 - ti) * math.log(TenoredSeries[tiplus1])) + (
(tiplus1 - t) /
(tiplus1 - ti) * math.log(TenoredSeries[ti]))
res[t] = math.exp(lnDiscFac)
else:
res[t] = res[t][0]
return res
def __init__(self, DF: dict):
self.fnd = FindClosestKeyInDicAndReturnKeyBoundsAlgorithm(DF)
self.cache = dict()
self.DF = DF