def test_FinInterpolatedForwards():
import matplotlib.pyplot as plt
tValues = np.array([0.0, 3.0, 5.0, 10.0])
rValues = np.array([0.04, 0.07, 0.08, 0.09])
dfValues = np.exp(-tValues*rValues)
tInterpValues = np.linspace(0.0, 12.0, 200)
print(tValues)
print(rValues)
print(dfValues)
curveDate = FinDate(2019,1,3)
for method in FinInterpMethods:
discountCurve = FinDiscountCurve(curveDate, tValues, dfValues, method)
dfInterpValues = discountCurve.df(tInterpValues)
fwdInterpValues = discountCurve.fwd(tInterpValues)
zeroInterpValues = discountCurve.zeroRate(tInterpValues)
if PLOT_GRAPHS:
plt.figure(figsize=(8, 6))
plt.plot(tValues, dfValues, 'o', color='g', label="DFS:")
plt.plot(tInterpValues, dfInterpValues, color='r', label="DF:" + str(method))
plt.legend()
plt.figure(figsize=(8, 6))
plt.plot(tInterpValues, fwdInterpValues, color='r', label="FWD:" + str(method))
plt.plot(tInterpValues, zeroInterpValues, color='b', label="ZERO:" + str(method))
plt.plot(tValues, rValues, 'o', color='g', label="ZERO RATES")
plt.legend()
def test_FinDiscountCurve():
# Create a curve from times and discount factors
startDate = FinDate(1, 1, 2018)
years = np.linspace(0, 10, 6)
rate = 0.05 + 0.005 * years - 0.0003 * years * years
dfs = np.exp(-rate * years)
dates = startDate.addYears(years)
curve = FinDiscountCurve(startDate, dates, dfs,
FinInterpTypes.FLAT_FWD_RATES)
testCases.header("T", "DF", "ZERORATE", "CC_FWD", "MM_FWD", "SURVPROB")
plotYears = np.linspace(0, 12, 12 * 12 + 1)[1:]
plotDates = startDate.addYears(plotYears)
# Examine dependency of curve on compounding rate
zeroRates_A = curve.zeroRate(plotDates, FinFrequencyTypes.ANNUAL)
zeroRates_S = curve.zeroRate(plotDates, FinFrequencyTypes.SEMI_ANNUAL)
zeroRates_Q = curve.zeroRate(plotDates, FinFrequencyTypes.QUARTERLY)
zeroRates_M = curve.zeroRate(plotDates, FinFrequencyTypes.MONTHLY)
zeroRates_C = curve.zeroRate(plotDates, FinFrequencyTypes.CONTINUOUS)
if PLOT_GRAPHS:
plt.figure(figsize=(6, 4))
plt.plot(plotYears, scale(zeroRates_A, 100), label='A')
plt.plot(plotYears, scale(zeroRates_S, 100), label='S')
plt.plot(plotYears, scale(zeroRates_Q, 100), label='Q')
plt.plot(plotYears, scale(zeroRates_M, 100), label='M')
plt.plot(plotYears, scale(zeroRates_C, 100), label='C')
plt.ylim((5, 8))
plt.title('Discount Curves')
plt.xlabel('Time (years)')
plt.ylabel('Zero Rate (%)')
plt.legend(loc='lower right', frameon=False)
# Examine dependency of fwd curve on the interpolation scheme
for interp in FinInterpTypes:
curve = FinDiscountCurve(startDate, dates, dfs, interp)
fwdRates = curve.fwd(plotDates)
zeroRates = curve.zeroRate(plotDates, FinFrequencyTypes.ANNUAL)
parRates = curve.swapRate(startDate, plotDates,
FinFrequencyTypes.ANNUAL)
if PLOT_GRAPHS:
plt.figure(figsize=(6, 4))
plt.plot(plotYears, scale(fwdRates, 100), label='FWD RATES')
plt.plot(plotYears, scale(zeroRates, 100), label='ZERO RATES')
plt.plot(plotYears, scale(parRates, 100), label='PAR RATES')
plt.ylim((3.0, 8.5))
plt.title('Forward Curves using ' + str(interp))
plt.xlabel('Time (years)')
plt.ylabel('Fwd Rate (%)')
plt.legend(loc='lower right', frameon=False)
def test_FinInterpolatedForwards():
import matplotlib.pyplot as plt
tValues = np.array([0.0, 3.0, 5.0, 10.0])
rValues = np.array([0.04, 0.07, 0.08, 0.09])
dfValues = np.exp(-tValues * rValues)
tInterpValues = np.linspace(0.0, 12.0, 49)
curveDate = FinDate(1, 1, 2019)
tDates = curveDate.addYears(tValues)
tInterpDates = curveDate.addYears(tInterpValues)
for interpType in FinInterpTypes:
discountCurve = FinDiscountCurve(curveDate, tDates, dfValues,
interpType)
dfInterpValues = discountCurve.df(tInterpDates)
fwdInterpValues = discountCurve.fwd(tInterpDates)
zeroInterpValues = discountCurve.zeroRate(tInterpDates)
if PLOT_GRAPHS:
plt.figure(figsize=(8, 6))
plt.plot(tValues, dfValues, 'o', color='g', label="DFS:")
plt.plot(tInterpValues,
dfInterpValues,
color='r',
label="DF:" + str(interpType))
plt.legend()
plt.figure(figsize=(8, 6))
plt.plot(tInterpValues,
fwdInterpValues,
color='r',
label="FWD:" + str(interpType))
plt.plot(tInterpValues,
zeroInterpValues,
color='b',
label="ZERO:" + str(interpType))
plt.plot(tValues, rValues, 'o', color='g', label="ZERO RATES")
plt.legend()