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utils.py
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utils.py
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import pandas as pd
import numpy as np
import copy as copy
import random
def print_aggregations(aggregations):
aggregations = aggregations.dict()
print("{:<10s} {:<10s} {}".format('Timeframe', 'Scope', 'Temp score'))
for time_frame, time_frame_values in aggregations.items():
if time_frame_values:
for scope, scope_values in time_frame_values.items():
if scope_values:
print("{:<10s} {:<10s} {:.2f}".format(time_frame, scope, scope_values["all"]["score"]))
def print_percentage_default_scores(aggregations):
aggregations = aggregations.dict()
print("{:<10s} {:<10s} {}".format('Timeframe', 'Scope', '% Default score'))
for time_frame, time_frame_values in aggregations.items():
if time_frame_values:
for scope, scope_values in time_frame_values.items():
if scope_values:
print("{:<10s} {:<10s} {:.2f}".format(time_frame, scope, scope_values['influence_percentage']))
def print_scenario_gain(actual_aggregations, scenario_aggregations):
print("Actual portfolio temperature score")
print_aggregations(actual_aggregations)
print()
print("Scenario portfolio temperature score")
print_aggregations(scenario_aggregations)
def print_grouped_scores(aggregations):
aggregations = aggregations.dict()
for time_frame, time_frame_values in aggregations.items():
if time_frame_values:
for scope, scope_values in time_frame_values.items():
if scope_values:
print()
print("{:<25s}{}".format('', 'Temp score'))
print("{} - {}".format(time_frame, scope))
for group, aggregation in scope_values["grouped"].items():
print("{:<25s}{t:.2f}".format(group, t=aggregation["score"]))
def collect_company_contributions(aggregated_portfolio, amended_portfolio, analysis_parameters):
timeframe, scope, grouping = analysis_parameters
scope = str(scope[0])
timeframe = str(timeframe[0]).lower()
company_names = []
relative_contributions = []
temperature_scores = []
for contribution in aggregated_portfolio[timeframe][scope]['all']['contributions']:
company_names.append(contribution.company_name)
relative_contributions.append(contribution.contribution_relative)
temperature_scores.append(contribution.temperature_score)
company_contributions = pd.DataFrame(data={'company_name': company_names, 'contribution': relative_contributions, 'temperature_score': temperature_scores})
additional_columns = ['company_name', 'company_id', 'company_market_cap', 'investment_value'] + grouping
company_contributions = company_contributions.merge(right=amended_portfolio[additional_columns], how='left', on='company_name')
company_contributions['portfolio_percentage'] = 100 * company_contributions['investment_value'] / company_contributions['investment_value'].sum()
company_contributions['ownership_percentage'] = 100 * company_contributions['investment_value'] / company_contributions['company_market_cap']
company_contributions = company_contributions.sort_values(by='contribution', ascending=False)
return company_contributions
def plot_grouped_statistics(aggregated_portfolio, company_contributions, analysis_parameters):
import matplotlib.pyplot as plt
timeframe, scope, grouping = analysis_parameters
scope = str(scope[0])
timeframe = str(timeframe[0]).lower()
sector_investments = company_contributions.groupby(grouping).investment_value.sum().values
sector_contributions = company_contributions.groupby(grouping).contribution.sum().values
sector_names = company_contributions.groupby(grouping).contribution.sum().keys()
sector_temp_scores = [aggregation.score for aggregation in aggregated_portfolio[timeframe][scope]['grouped'].values()]
sector_temp_scores, sector_names, sector_contributions, sector_investments = \
zip(*sorted(zip(sector_temp_scores, sector_names, sector_contributions, sector_investments), reverse=True))
fig = plt.figure(figsize=[10, 7.5])
ax1 = fig.add_subplot(231)
ax1.set_prop_cycle(plt.cycler("color", plt.cm.tab20.colors))
ax1.pie(sector_investments, autopct='%1.0f%%', pctdistance=1.25, labeldistance=2)
ax1.set_title("Investments", pad=15)
ax2 = fig.add_subplot(232)
ax2.set_prop_cycle(plt.cycler("color", plt.cm.tab20.colors))
ax2.pie(sector_contributions, autopct='%1.0f%%', pctdistance=1.25, labeldistance=2)
ax2.legend(labels=sector_names, bbox_to_anchor=(1.2, 1), loc='upper left')
ax2.set_title("Contributions", pad=15)
ax3 = fig.add_subplot(212)
ax3.bar(sector_names, sector_temp_scores)
ax3.set_title("Temperature scores per " + grouping[0])
ax3.set_ylabel("Temperature score")
for label in ax3.get_xticklabels():
label.set_rotation(45)
label.set_ha('right')
ax3.axhline(y=1.5, linestyle='--', color='k')
def anonymize(portfolio, provider):
portfolio_companies = portfolio['company_name'].unique()
for index, company_name in enumerate(portfolio_companies):
portfolio.loc[portfolio['company_name'] == company_name, 'company_id'] = 'C' + str(index + 1)
portfolio.loc[portfolio['company_name'] == company_name, 'company_isin'] = 'C' + str(index + 1)
portfolio.loc[portfolio['company_name'] == company_name, 'company_lei'] = 'L' + str(index + 1)
provider.data['fundamental_data'].loc[provider.data['fundamental_data']['company_name'] == company_name, 'company_id'] = 'C' + str(index + 1)
provider.data['fundamental_data'].loc[provider.data['fundamental_data']['company_name'] == company_name, 'company_isic'] = 'C' + str(index + 1)
provider.data['target_data'].loc[provider.data['target_data']['company_name'] == company_name, 'company_id'] = 'C' + str(index + 1)
portfolio.loc[portfolio['company_name'] == company_name, 'company_name'] = 'Company' + str(
index + 1)
provider.data['fundamental_data'].loc[provider.data['fundamental_data']['company_name'] == company_name, 'company_name'] = 'Company' + str(
index + 1)
provider.data['target_data'].loc[provider.data['target_data']['company_name'] == company_name, 'company_name'] = 'Company' + str(
index + 1)
"""
for index, company_name in enumerate(provider.data['fundamental_data']['company_name'].unique()):
if company_name not in portfolio['company_name'].unique():
provider.data['fundamental_data'].loc[provider.data['fundamental_data']['company_name'] == company_name, 'company_id'] = '_' + str(index + 1)
provider.data['fundamental_data'].loc[provider.data['fundamental_data']['company_name'] == company_name, 'company_name'] = 'Company_' + str(
index + 1)
"""
portfolio_companies = portfolio['company_name'].unique()
for index, company_name in enumerate(provider.data['fundamental_data']['company_name'].unique()):
if company_name not in portfolio_companies:
provider.data['fundamental_data'] = provider.data['fundamental_data'].loc[provider.data['fundamental_data']['company_name'] != company_name]
provider.data['target_data'] = provider.data['target_data'].loc[provider.data['target_data']['company_name'] != company_name]
return portfolio, provider
def plot_grouped_heatmap(grouped_aggregations, analysis_parameters):
import matplotlib.pyplot as plt
import matplotlib
timeframe, scope, grouping = analysis_parameters
scope = str(scope[0])
timeframe = str(timeframe[0]).lower()
group_1, group_2 = grouping
aggregations = grouped_aggregations[timeframe][scope].grouped
combinations = list(aggregations.keys())
groups = {group_1: [], group_2: []}
for combination in combinations:
item_group_1, item_group_2 = combination.split('-')
if item_group_1 not in groups[group_1]:
groups[group_1].append(item_group_1)
if item_group_2 not in groups[group_2]:
groups[group_2].append(item_group_2)
groups[group_1] = sorted(groups[group_1])
groups[group_2] = sorted(groups[group_2])
grid = np.zeros((len(groups[group_2]), len(groups[group_1])))
for i, item_group_2 in enumerate(groups[group_2]):
for j, item_group_1 in enumerate(groups[group_1]):
key = item_group_1+'-'+item_group_2
if key in combinations:
grid[i, j] = aggregations[item_group_1+'-'+item_group_2].score
else:
grid[i, j] = np.nan
current_cmap = copy.copy(matplotlib.cm.get_cmap('OrRd'))
current_cmap.set_bad(color='grey', alpha=0.4)
fig = plt.figure(figsize=[0.9*len(groups[group_1]), 0.8*len(groups[group_2])])
ax = fig.add_subplot(111)
im = ax.pcolormesh(grid, cmap=current_cmap)
ax.set_xticks(0.5 + np.arange(0, len(groups[group_1])))
ax.set_yticks(0.5 + np.arange(0, len(groups[group_2])))
ax.set_yticklabels(groups[group_2])
ax.set_xticklabels(groups[group_1])
for label in ax.get_xticklabels():
label.set_rotation(45)
label.set_ha('right')
fig.colorbar(im, ax=ax)
ax.set_title("Temperature score per " + group_2 + " per " + group_1)
def get_contributions_per_group(aggregations, analysis_parameters, group):
timeframe, scope, grouping = analysis_parameters
scope = str(scope[0])
timeframe = str(timeframe[0]).lower()
aggregations = aggregations.dict()
contributions = aggregations[timeframe][scope]['grouped'][group]['contributions']
contributions = pd.DataFrame(contributions)
columns = ['group'] + contributions.columns.tolist()
contributions['group'] = group
contributions = contributions[columns]
contributions.drop(columns=['contribution'], inplace=True)
return contributions