def __init__(self, market_params):
'''
'''
self.market_params = market_params
self.spots = market_params['spots']
self.rates = market_params['rates']
self.cov = market_params['cov']
self.generator = NormalGenerator(normal)
def __init__(self, market_params, path_construction='incremental'):
'''
'''
self.market_params = market_params #Should clean this up
self.spot = market_params['spot']
self.rate = market_params['rate']
self.vol = market_params['vol']
self.generator = NormalGenerator()
self.path_construction = path_construction
def __init__(self, market_params):
'''
'''
self.market_params = market_params
self.spots = market_params['spots']
self.rates = market_params['rates']
self.cov = market_params['cov']
self.generator = NormalGenerator(normal)
class GeneratorStepper(object):
'''
We want to make a path generator that evolves by stepping of an SD
It will generate an n-dimensional path where the paths have
Inputs
SDE - sde_drift, sde_vol
Random_generator - has generate n variates
Initial params - e.g, initial_spots,
Deterministic SDE_params - e.g, possibley rates, vols, cov
'''
def __init__(self, market_params):
'''
'''
self.market_params = market_params
self.spots = market_params['spots']
self.rates = market_params['rates']
self.cov = market_params['cov']
self.generator = NormalGenerator(normal)
def sim_setup(self,times):
aug_times = [0.0]+times
self.time_diffs = [ aug_times[i] - aug_times[i-1] for i in range(1,len(aug_times))]
#Contruct times up to random draw
self.drifts = [ exp(self.rate*td - 0.5 * self.vol*self.vol*td) for td in self.time_diffs]
def do_one_path(self):
"""
Currently set to use analytic solution to GBM
Change to be a dispatcher
"""
randoms = self.generator.get_variates(0,1,len(self.time_diffs))
path = []
prev_spot = self.spot
for i in range(0, len(self.time_diffs)):
next_spot = prev_spot*self.drifts[i] * exp(self.vol*sqrt(self.time_diffs[i])*randoms[i])
path += [next_spot]
prev_spot = next_spot
return path
class GeneratorStepper(object):
'''
We want to make a path generator that evolves by stepping of an SD
It will generate an n-dimensional path where the paths have
Inputs
SDE - sde_drift, sde_vol
Random_generator - has generate n variates
Initial params - e.g, initial_spots,
Deterministic SDE_params - e.g, possibley rates, vols, cov
'''
def __init__(self, market_params):
'''
'''
self.market_params = market_params
self.spots = market_params['spots']
self.rates = market_params['rates']
self.cov = market_params['cov']
self.generator = NormalGenerator(normal)
def sim_setup(self, times):
aug_times = [0.0] + times
self.time_diffs = [
aug_times[i] - aug_times[i - 1] for i in range(1, len(aug_times))
]
#Contruct times up to random draw
self.drifts = [
exp(self.rate * td - 0.5 * self.vol * self.vol * td)
for td in self.time_diffs
]
def do_one_path(self):
"""
Currently set to use analytic solution to GBM
Change to be a dispatcher
"""
randoms = self.generator.get_variates(0, 1, len(self.time_diffs))
path = []
prev_spot = self.spot
for i in range(0, len(self.time_diffs)):
next_spot = prev_spot * self.drifts[i] * exp(
self.vol * sqrt(self.time_diffs[i]) * randoms[i])
path += [next_spot]
prev_spot = next_spot
return path
def __init__(self, market_params, path_construction = 'incremental'):
'''
'''
self.market_params = market_params #Should clean this up
self.spot = market_params['spot']
self.rate = market_params['rate']
self.vol = market_params['vol']
self.generator = NormalGenerator()
self.path_construction = path_construction
class GeneratorGBM(object):
'''
Generates a path from a multi dimensional GBM
Uses draws from analytic solution to GBM rather than incremental stepping
Inputs
market_params - spot, rate, covariance matrix
bm_construction - how do we contruct underlying brownian motion
Members = generator, this generates
'''
def __init__(self, market_params, path_construction='incremental'):
'''
'''
self.market_params = market_params #Should clean this up
self.spot = market_params['spot']
self.rate = market_params['rate']
self.vol = market_params['vol']
self.generator = NormalGenerator()
self.path_construction = path_construction
def sim_setup(self, times):
'''
Setup of variables that are constant for each path in sim
Note that this is where setup path generation method
Concrete generation methods implemented as "do_one_path_*"
'''
#Contruct log of drifts up to random draw
self.times = times
self.drifts = [
self.rate * time - 0.5 * self.vol * self.vol * time
for time in self.times
]
self.num_times = len(self.times)
#Setup path generating function
self.path_generation_method = getattr(
self, "do_one_path_{}".format(self.path_construction))
def do_one_path(self):
"""
This only works if path_generation_method has been set
"""
return self.path_generation_method()
def do_one_path_incremental(self):
"""
Currently set to use analytic solution to GBM
Uses incremental build of brownian motion
"""
randoms = self.generator.get_variates(0, 1, self.num_times)
#Construct brownian motion path at times
bm_path = []
bm_t = 0
#Incremental BM generation
times_aug = [0] + self.times
time_diffs = [
times_aug[i + 1] - times_aug[i] for i in range(0, len(self.times))
]
for i in range(0, self.num_times):
bm_t += self.vol * sqrt(time_diffs[i]) * randoms[i]
bm_path += [bm_t]
#Construct GBM path at times
#remember to exp as have gen logs of paths
path = [
self.spot * exp(self.drifts[i] + bm_path[i])
for i in range(0, self.num_times)
]
return path
def do_one_path_brownian_bridge(self):
"""
Currently Depends on numpy as using cholesky
Can drop when I figure out dist for brownian bridge
Currently set to use analytic solution to GBM
Does brownian bridge construction of Brownian motion
To do brownian bridge you:
Rearrange indexes by always choosing gap where variance will be largest
Only implemented where index gap is largest , e.g, (1,2,..,10) => (10,5,2,7,3,8,1,4,6,9]
This is implemented in _"_bb_indexes" function below
Generate BM path by generating draw for reindex incrementally, e.g for #times = 10
Use brownian bridge formula http://en.wikipedia.org/wiki/Brownian_bridge
ie if B(t_1)=a and B(t_2)=b then
Actually use Bayes formula for W_0 =0, W_T=a,
p(W_t=x|W_T=a) = p(W_T=a|W_t=x)p(W_t=x)/p(W_t=a)
Now p(W_T=a|W_t=x) = p(W'_{T-t} = (a-x)|W'_0=0) for BM W' by Markov so
Sub in normal dist pdfs for above and do some algebra and you get
p(W_t=x|W_T=a) = 1/T * norm_dens( at, sqrt(t(T-t)T))
ie brownian bridge B_t := (W_t|W_T=a) is dist as
1/T * N(a*t,sqrt(t(T-t)T)) = a*t/T + sqrt(t(T-t)/T))*N(0,1)
probably should have a vol in their somewhere - check
So we can do BB by putting end interval first then drawing the middle from normal as above
Alternatively, you can:
1. take incremental covariance matrix, rearrange indexes
2. cov*_ij = cov_bb(i)bb(j)
3. Cholesky facotrize cov* => A
4. Build incremental path using A
5. Rearrange path back into using reverse index
"""
#Get randoms for draw
randoms = self.generator.get_variates(0, 1, self.num_times)
times = self.times
vol = self.market_params['vol']
reind_bb = self.__bb_indexes(times, index0=False)
times = [0.] + times
reind_bb = [0] + reind_bb
reind_times = [times[ind_bb] for ind_bb in reind_bb]
#Set up path of zeroes - include one extra for zero at start
bm_path = (len(times)) * [0.0]
#Fill in the end time with the first random draw
bm_path[-1] = times[-1] * vol * randoms[0]
# print "num_times", num_times
# print "reind ", reind_bb
# print "reintimes_do_one " ,reind_times
# print "bm_path 0", bm_path
#Loop for generating next
#start loop at second index as we have already placed two poinst
#in bm_path
ind_so_far = [reind_bb[0], reind_bb[1]]
for (i, ind_m) in enumerate(reind_bb[2:]):
ind_so_far += [ind_m]
sorted_so_far = sorted(ind_so_far)
inu = sorted_so_far[sorted_so_far.index(ind_m) + 1]
inl = sorted_so_far[sorted_so_far.index(ind_m) - 1]
tl, tu = times[inl], times[inu]
Wl, Wu = bm_path[inl], bm_path[inu]
tm = times[ind_m]
#W_m = a*t/T + sqrt(t(T-t)/T))*N(0,1)
W_m = Wl + (Wu - Wl) * (tm - tl) / (tu - tl) + sqrt(
(tm - tl) * (tu - tm) / (tu - tl)) * vol * randoms[i + 1]
bm_path[ind_m] = W_m
#Get increments
#Just for debugging
if False:
print "inl, inu ", inl, inu
print "tm", tm
print "tl, tu ", [tl, tu]
print "Wl, Wu ", [Wl, Wu]
print "linear ", Wl + (Wu - Wl) * (tm - tl) / (tu - tl)
print "rand, ", sqrt(
(tm - tl) * (tu - tm) / (tu - tl)) * vol * randoms[i + 1]
print bm_path
#Construct GBM path at times
#remember to exp as have gen logs of paths
path2 = [
self.spot * exp(self.drifts[i] + bm_path[i + 1])
for i in range(0, self.num_times)
]
return path2
def __bb_indexes(self, times, index0=True):
"""
Utility function for reordering brownian bridge
index0 flag sets whether you want the indexes in the reindex starting at 0
ie index0=True if you don't have the time=0 zero value in your list
"""
times = list(times)
bb_indexes = [0, len(times)]
for j in range(0, len(times) - 1):
stmp = sorted(bb_indexes)
#get interval sizes
diffs = [stmp[i + 1] - stmp[i] for i in range(0, len(stmp) - 1)]
#get max interval width
max_int_width = max(diffs)
#get left hand index of left interval
lindex_ofmax_int = diffs.index(max_int_width)
next_index = stmp[lindex_ofmax_int] + max_int_width / 2
bb_indexes += [next_index]
# print "BB_INDEX", bb_indexes
if index0:
bb_indexes_tmp = [ind - 1 for ind in bb_indexes]
bb_indexes = bb_indexes_tmp
return bb_indexes[1:]
rate = 0.05
vol = 0.05
# times = list(np.linspace(0.0, stop=1.0, num=4))
times = list(np.linspace(0.0, stop=1.0, num=3))
times = times[1:]
print times
# times = [1.]
market_params = {'spot': spot, 'rate': rate, 'vol': vol}
bgmgen = GeneratorGBM(market_params, "brownian_bridge")
# bgmgen = GeneratorGBM(market_params)
bgmgen.sim_setup(times)
#Change random generator
generator = NormalGenerator() #Make generator out of np normal generator
athetic = Antithetic(generator)
bgmgen.generator = athetic
# print athetic.get_variates(0, 1, 3)
# print athetic.get_variates(0, 1, 3)
path = bgmgen.do_one_path()
# print len(path)
# print list(times)
plt.plot(times, path)
plt.show()
# vals = [bgmgen.do_one_path() for i in range(0,3)]
# print vals
class GeneratorGBM(object):
'''
Generates a path from a multi dimensional GBM
Uses draws from analytic solution to GBM rather than incremental stepping
Inputs
market_params - spot, rate, covariance matrix
bm_construction - how do we contruct underlying brownian motion
Members = generator, this generates
'''
def __init__(self, market_params, path_construction = 'incremental'):
'''
'''
self.market_params = market_params #Should clean this up
self.spot = market_params['spot']
self.rate = market_params['rate']
self.vol = market_params['vol']
self.generator = NormalGenerator()
self.path_construction = path_construction
def sim_setup(self,times):
'''
Setup of variables that are constant for each path in sim
Note that this is where setup path generation method
Concrete generation methods implemented as "do_one_path_*"
'''
#Contruct log of drifts up to random draw
self.times = times
self.drifts = [ self.rate*time - 0.5 * self.vol*self.vol*time for time in self.times]
self.num_times = len(self.times)
#Setup path generating function
self.path_generation_method = getattr(self, "do_one_path_{}".format(self.path_construction))
def do_one_path(self):
"""
This only works if path_generation_method has been set
"""
return self.path_generation_method()
def do_one_path_incremental(self):
"""
Currently set to use analytic solution to GBM
Uses incremental build of brownian motion
"""
randoms = self.generator.get_variates(0,1,self.num_times)
#Construct brownian motion path at times
bm_path = []
bm_t = 0
#Incremental BM generation
times_aug = [0]+self.times
time_diffs = [ times_aug[i+1] - times_aug[i] for i in range(0,len(self.times))]
for i in range(0, self.num_times):
bm_t += self.vol*sqrt(time_diffs[i])*randoms[i]
bm_path += [bm_t]
#Construct GBM path at times
#remember to exp as have gen logs of paths
path = [ self.spot*exp(self.drifts[i] + bm_path[i]) for i in range(0,self.num_times)]
return path
def do_one_path_brownian_bridge(self):
"""
Currently Depends on numpy as using cholesky
Can drop when I figure out dist for brownian bridge
Currently set to use analytic solution to GBM
Does brownian bridge construction of Brownian motion
To do brownian bridge you:
Rearrange indexes by always choosing gap where variance will be largest
Only implemented where index gap is largest , e.g, (1,2,..,10) => (10,5,2,7,3,8,1,4,6,9]
This is implemented in _"_bb_indexes" function below
Generate BM path by generating draw for reindex incrementally, e.g for #times = 10
Use brownian bridge formula http://en.wikipedia.org/wiki/Brownian_bridge
ie if B(t_1)=a and B(t_2)=b then
Actually use Bayes formula for W_0 =0, W_T=a,
p(W_t=x|W_T=a) = p(W_T=a|W_t=x)p(W_t=x)/p(W_t=a)
Now p(W_T=a|W_t=x) = p(W'_{T-t} = (a-x)|W'_0=0) for BM W' by Markov so
Sub in normal dist pdfs for above and do some algebra and you get
p(W_t=x|W_T=a) = 1/T * norm_dens( at, sqrt(t(T-t)T))
ie brownian bridge B_t := (W_t|W_T=a) is dist as
1/T * N(a*t,sqrt(t(T-t)T)) = a*t/T + sqrt(t(T-t)/T))*N(0,1)
probably should have a vol in their somewhere - check
So we can do BB by putting end interval first then drawing the middle from normal as above
Alternatively, you can:
1. take incremental covariance matrix, rearrange indexes
2. cov*_ij = cov_bb(i)bb(j)
3. Cholesky facotrize cov* => A
4. Build incremental path using A
5. Rearrange path back into using reverse index
"""
#Get randoms for draw
randoms = self.generator.get_variates(0,1,self.num_times)
times = self.times
vol = self.market_params['vol']
reind_bb = self.__bb_indexes(times,index0=False)
times = [0.] + times
reind_bb = [0] + reind_bb
reind_times = [times[ind_bb] for ind_bb in reind_bb ]
#Set up path of zeroes - include one extra for zero at start
bm_path = (len(times))*[0.0]
#Fill in the end time with the first random draw
bm_path[-1]= times[-1]*vol*randoms[0]
# print "num_times", num_times
# print "reind ", reind_bb
# print "reintimes_do_one " ,reind_times
# print "bm_path 0", bm_path
#Loop for generating next
#start loop at second index as we have already placed two poinst
#in bm_path
ind_so_far = [reind_bb[0], reind_bb[1]]
for (i, ind_m) in enumerate(reind_bb[2:]):
ind_so_far += [ind_m]
sorted_so_far = sorted(ind_so_far)
inu = sorted_so_far[sorted_so_far.index(ind_m)+1]
inl = sorted_so_far[sorted_so_far.index(ind_m)-1]
tl, tu = times[inl], times[inu]
Wl, Wu = bm_path[inl], bm_path[inu]
tm = times[ind_m]
#W_m = a*t/T + sqrt(t(T-t)/T))*N(0,1)
W_m = Wl + (Wu - Wl)*(tm-tl)/(tu-tl) +sqrt((tm-tl)*(tu-tm)/(tu-tl))*vol*randoms[i+1]
bm_path[ind_m] = W_m
#Get increments
#Just for debugging
if False:
print "inl, inu ", inl, inu
print "tm", tm
print "tl, tu ", [tl, tu]
print "Wl, Wu ", [Wl, Wu]
print "linear ", Wl + (Wu - Wl)*(tm-tl)/(tu-tl)
print "rand, " , sqrt((tm-tl)*(tu-tm)/(tu-tl))*vol*randoms[i+1]
print bm_path
#Construct GBM path at times
#remember to exp as have gen logs of paths
path2 = [ self.spot*exp(self.drifts[i] + bm_path[i+1]) for i in range(0,self.num_times)]
return path2
def __bb_indexes(self, times, index0=True):
"""
Utility function for reordering brownian bridge
index0 flag sets whether you want the indexes in the reindex starting at 0
ie index0=True if you don't have the time=0 zero value in your list
"""
times = list(times)
bb_indexes = [0,len(times)]
for j in range(0,len(times)-1):
stmp = sorted(bb_indexes)
#get interval sizes
diffs = [stmp[i+1]-stmp[i] for i in range(0,len(stmp)-1)]
#get max interval width
max_int_width = max(diffs)
#get left hand index of left interval
lindex_ofmax_int = diffs.index(max_int_width)
next_index = stmp[lindex_ofmax_int] + max_int_width/2
bb_indexes+=[next_index]
# print "BB_INDEX", bb_indexes
if index0:
bb_indexes_tmp = [ind - 1 for ind in bb_indexes]
bb_indexes = bb_indexes_tmp
return bb_indexes[1:]