# Declare Events
event = Stock.elagolix
vol = IdiosyncraticVol('NBIX', .20)
# Create MC_Distribution and distribution_df for the Combined Event
df = event.event_input_distribution_df
mc_dist = get_total_mc_distribution_from_events_vanilla([event, vol],
expiry=expiry,
mc_iterations=10**5)
get_histogram_from_array(mc_dist,
bins=2 * 10**1 + 1,
title='Expected GE Distribution')
mc_distribution_to_distribution(mc_dist,
bins=5 * 10**1 + 1,
to_file=True,
file_name='Elagolix_Dist')
combined_dist = Distribution(pd.read_csv('Elagolix_Dist.csv'))
df = combined_dist.distribution_df
#print(df.to_string())
# Source Probs, Pct_Moves, Max_Loss for the Combined Event
probs = df.loc[:, 'Prob'].tolist()
pct_moves = df.loc[:, 'Pct_Move'].tolist()
max_loss = min(pct_moves)
event_pct_moves = event.event_input_distribution_df.loc[:, 'Pct_Move'].tolist()
print(event_pct_moves)
max_loss_event_only = min(event_pct_moves)
#max_loss = -1
#pct_moves = [pct_move/-max_loss for pct_move in pct_moves]
return np.array([random.normalvariate(0, 1) for i in range(iterations)])
#run simulation using a previously cretaed array of random numbers as the input
@my_time_decorator
def bs_simulation(rand_nums: 'np_array') -> 'np_array':
stock, r, t, vol = 100, 0, 1.0, .10
stock_futures = stock * np.e**(r * t - (vol**2) *
(t / 2) + rand_nums * vol * np.sqrt(t))
return stock_futures
rand_nums = create_rand_nums(10**7)
stock_futures = bs_simulation(rand_nums)
distribution = mc_distribution_to_distribution(
stock_futures, 10**3 + 1, to_csv=True, csv_file_name='BlackScholes.csv')
print("Black Scholes Dist.:", distribution.mean_move,
distribution.average_move, distribution.straddle)
#get_histogram_from_array(distribution.mc_simulation(10**7))
distribution.get_histogram(iterations=10**7, bins=10**3 + 1)
############################# Graph Component ##########################3
stock = 100
bins = 100
plt.hist(stock_futures, bins, histtype='bar', rwidth=0.8, color='purple')
plt.xlabel('St. Dev. Moves')
plt.ylabel('Relative Frequency')
plt.title(
'Pauls Beautiful Probabilitiy Distribution\nSmooth and Pretty!\n{:,d} Iterations'
.format(len(stock_futures)))
plt.legend()
#run simulation using a previously cretaed array of random numbers as the input
@my_time_decorator
def bs_simulation(rand_nums: 'np_array') -> 'np_array':
stock, r, t, vol = 100, 0, 1.0, .10
stock_futures = stock * np.e**(r * t - (vol**2) *
(t / 2) + rand_nums * vol * np.sqrt(t))
return stock_futures
rand_nums = create_rand_nums(10**7)
stock_futures = bs_simulation(rand_nums)
print("HELLO SUSAN", [list_average(l) for l in [rand_nums, stock_futures]])
distribution = mc_distribution_to_distribution(
stock_futures, 10**4 + 1, to_csv=True, csv_file_name='BlackScholes.csv')
print("HELLO JANE", distribution.mean_move, distribution.average_move)
get_mc_histogram(distribution.mc_simulation(10**6))
############################# Graph Component ##########################3
stock = 100
bins = [float(i) for i in np.arange(stock * -.1, stock * 3, stock * .02)]
plt.hist(stock_futures, bins, histtype='bar', rwidth=0.8, color='purple')
plt.xlabel('St. Dev. Moves')
plt.ylabel('Relative Frequency')
plt.title(
'Beautiful Probabilitiy Distribution\nSmooth and Pretty!\n{:,d} Iterations'
.format(len(stock_futures)))
plt.legend()
plt.show()