sheet_name='Sub_States')
print(distribution_info)
print("HELLO DOLLY")
print(distribution_info.loc[('Elagolix Approved', 'Clean Approval')])
#distribution_df = distribution_info.reset_index().set_index('State')
# Create a Scenario where the Core Probability of Success is .90.
crl = distribution_info.loc['CRL']
crl_substate = crl.loc['CRL - No Hope']
crl_substate.loc['Price'] = 1000
substates = list(crl.iterrows())
print("HELLO STACEY")
print(substates)
for substate in substates:
print(type(substate), len(substate), type(substate[0]), type(substate[1]))
print("HELLO SUSAN")
tprint(distribution_info.loc['CRL'].loc[substate[0], :], "\n")
tprint(substate[1], "\n")
distribution_info.loc['CRL'].loc[substate[0], :] = substate[1].T
distribution_info.loc[('CRL', substate[0])] = substate[1]
print(distribution_info)
def change_core_scenario(distribution_df, new_core_scenario):
print(distribution_info.loc['CRL'])
#change_core_scenario(distribution_df, 'hi')
evt1.hello = 'HelloThere'
evt1.name='Sup'
print(Event.__dict__)
print(evt1.__dict__)
print(Paul.__class__, p.__class__)
print(dir(Paul), "\n-------------------")
print(dir(p), "\n''''''''''''''''''")
print(vars(Paul) is Paul.__dict__, vars(Paul) == Paul.__dict__)
print(vars(Paul))
print(p.__dict__)
print(p.__doc__)
print(Paul.__dict__, "\n''''''''''''''''''''")
tprint(Paul.__dict__['__dict__'])
print(Paul().__dict__)
print(Paul, Paul())
print(paul_resources.Aaron, paul_resources.Aaron())
print(Aaron, Aaron())
print(Paul.__name__, Aaron.__name__, main_function.__name__)
print("HERE:", str(p), repr(p), str(p) is repr(p), str(p) == repr(p))
print(Paul.__module__, Aaron.__module__, main_function.__module__)
print(p.__module__)
print(Paul.__doc__)
print(paul_resources.daily_returns)
print(main_function)
2: 'HV',
3: 'Alpha_Ratio',
4: 'Adj_Alpha_Ratio',
5: 'Sample_Size'
},
inplace=True)
alpha_df.set_index('Stock', inplace=True)
return alpha_df
stocks = PriceTable.columns.values.tolist()
stocks = [i for i in stocks if i in {'AAPL', 'GOOG', 'FB', 'AMZN'}]
prices_df = PriceTable.loc[:, stocks]
alpha_df = alpha_df(prices_df, 252)
tprint(stocks)
print(alpha_df.sort_values('Adj_Alpha_Ratio', ascending=False))
def graph():
values = alpha_df['Adj_Alpha_Ratio'].tolist()
bins = np.arange(-5.5, 6.5, 1)
plt.hist(values, bins, histtype='bar', rwidth=.8)
plt.xlabel('Adj_Alpha_Ratio')
plt.ylabel('Frequency')
plt.title('S&P 500 Alpha Distribution')
plt.legend()
plt.show()
index_col = [0,1],
sheet_name = 'TimingMappings')
TimingMappings = TimingMappings.reset_index().set_index('level_1').loc[:, ['Start', 'End']]
mappings = pd.read_excel('TimingMappings.xlsx',
header = [0,1],
index_col = [0,1],
sheet_name = 'TimingMappings')
print(mappings)
print(mappings.index.values)
print(mappings.index.names)
print(mappings.columns.values)
print(mappings.columns.names)
my_slice = mappings.loc[('Halves', '1H'), ('Start', 'Day')]
my_slice = mappings.loc['Halves', ('Start', 'Day')]
tprint(my_slice)
my_slice = mappings.xs('Quarters')
print(my_slice)
my_slice = mappings.reset_index().set_index('level_1').loc[:, ['Start', 'End']]
print(my_slice)
my_slice = my_slice.loc['1H', ('Start', 'Month')]
print(my_slice)
years = descriptors.loc[:, 'Years'].dropna().tolist()
guidance = descriptors.loc[:, 'Guidance'].dropna().tolist()
halves = descriptors.loc[:, 'Halves'].dropna().tolist()
months = descriptors.loc[:, 'Months'].dropna().tolist()
weeks = descriptors.loc[:, 'Weeks'].dropna().tolist()
def get_event_start_date_from_timing_descriptor(timing_descriptor):
timing_period = timing_descriptor[0:-5]
stock2 = 'AAPL'
index = 'QQQ'
beta_lookback = 252
chart_lookback = beta_lookback
base = 100
beta = Beta(stock, index, beta_lookback, ScrubParams(.075, .01, .8)).beta
beta2 = Beta(stock2, index, beta_lookback, ScrubParams(.075, .01, .8)).beta
#beta, beta2 = 0.0, 0.0
# Stock Lines to plot
stock_line = StockLineSimple(stock, chart_lookback, base)
index_line = StockLineSimple(index, chart_lookback, base)
stock_line_adj = StockLineBetaAdjusted(stock, chart_lookback, beta, index,
base)
tprint(stock_line_adj.prices_df.round(2))
stock_line_adj2 = StockLineBetaAdjusted(stock2, chart_lookback, beta2,
index, base)
stock_lines = [
stock_line.stock_line(color='red'),
index_line.stock_line(color='black'),
stock_line_adj.stock_line(color='blue'),
#stock_line_adj2.stock_line(color = 'c')
]
StockChart(stock_lines).run()
Beta(stock, index, beta_lookback, ScrubParams(.075, .01,
.8)).show_scrub_trajectory()