def generate_optimum_portfolio():
""" Stepwise generation of optimum portfolios, using the monte carlo
approach and then a Markowitz optimization approach. """
# Initialize objects with the settings
obj_factory = object_factory(settings)
ce = obj_factory.get_companies_extractor()
cp = obj_factory.get_charts_plotter()
mcs = obj_factory.get_portfolio_generator()
fr = obj_factory.get_file_repository()
mc = obj_factory.get_metrics_calculator()
st = obj_factory.get_strategies()
print('1. Get companies')
companies = ce.get_companies_list()
price_extractor = obj_factory.get_price_extractor(companies)
print('2. Get company stock prices')
end_date = settings.get_end_date()
start_date = settings.get_start_date(end_date)
closing_prices = price_extractor.get_prices(start_date, end_date)
sma1 = mc.get_sma(closing_prices, settings.SMA1)
sma2 = mc.get_sma(closing_prices, settings.SMA2)
# Plot stock prices & save data to a file
#cp.plot_prices(closing_prices)
positions = st.strategy_sma(closing_prices, sma1, sma2)
#cp.plot_sma(sma1,sma2,positions)
#fr.save_to_file(closing_prices, 'StockPrices')
print('3. Calculate Daily Returns')
returns = settings.DailyAssetsReturnsFunction(closing_prices,
settings.ReturnType)
# Calculate return of investment
investments = mc.calculate_investment_return(positions, returns)
# sdsdsd
#ols_capm = st.strategy_capm(returns, start_date)
# Plot stock prices & save data to a file
cp.plot_investments(investments)
#cp.plot_returns(returns)
fr.save_to_file(returns, 'Returns')
print('4. Calculate Expected Mean Return & Covariance')
expected_returns = settings.AssetsExpectedReturnsFunction(returns)
covariance = settings.AssetsCovarianceFunction(returns)
# Plot & Save covariance to file
#cp.plot_correlation_matrix(returns)
fr.save_to_file(covariance, 'Covariances')
print('5. Use Monte Carlo Simulation')
# Generate portfolios with allocations
portfolios_allocations_df = mcs.generate_portfolios(
expected_returns, covariance, settings.RiskFreeRate)
portfolio_risk_return_ratio_df = portfolios_allocation_mapper.map_to_risk_return_ratios(
portfolios_allocations_df)
# Plot portfolios, print max sharpe portfolio & save data
#cp.plot_portfolios(portfolio_risk_return_ratio_df)
max_sharpe_portfolio = mc.get_max_sharpe_ratio(
portfolio_risk_return_ratio_df)['Portfolio']
max_shape_ratio_allocations = portfolios_allocations_df[[
'Symbol', max_sharpe_portfolio
]]
print(max_shape_ratio_allocations)
portfolios_allocations_df = portfolios_allocations_df.T
fr.save_to_file(portfolios_allocations_df, 'MonteCarloPortfolios')
fr.save_to_file(portfolio_risk_return_ratio_df,
'MonteCarloPortfolioRatios')
print('6. Use an optimiser')
# Generate portfolios
targets = settings.get_my_targets()
optimiser = obj_factory.get_optimiser(targets, len(expected_returns.index))
portfolios_allocations_df = optimiser.generate_portfolios(
expected_returns, covariance, settings.RiskFreeRate)
portfolio_risk_return_ratio_df = portfolios_allocation_mapper.map_to_risk_return_ratios(
portfolios_allocations_df)
max_sharpe_portfolio = mc.get_max_sharpe_ratio(
portfolio_risk_return_ratio_df)['Portfolio']
max_shape_ratio_allocations = portfolios_allocations_df[[
'Symbol', max_sharpe_portfolio
]]
print(max_shape_ratio_allocations)
# Plot efficient frontiers
#cp.plot_efficient_frontier(portfolio_risk_return_ratio_df)
#cp.show_plots()
# Save data
print('7. Saving Data')
portfolios_allocations_df = portfolios_allocations_df.T
fr.save_to_file(portfolios_allocations_df, 'OptimisationPortfolios')
fr.close()
'Then translate to daily percent change to see magnitude of daily stock fluctuations'
)
st.write(
'Normalize stock perfomance starting as $1, then apply daily changes with all time horizon cumulated to predict how much $ changes did $1 stock at start has been made at last in the horizon'
)
st.write(
'From different cut of expected changes, using annual change %. Use 252 operation dayas/yr multiply by mean of daily percent changes'
)
st.write(
'Correlation and covariance are importance factor to group up which stocks behaves similarly. From asset allocation perspectives, having similar behavial stocks does not mitigate risk while having non-correlated stocks mitigate volatility risks'
)
cp.plot_prices(closing_prices)
# calculate daily returns
returns = settings.DailyAssetsReturnsFunction(closing_prices)
cp.plot_returns(returns)
# calculate expected retutnrs
cum_return = settings.DailyAssetsCumulativeReturnsFunction(returns)
cp.plot_cum_daily_return(cum_return)
# calculate expected mean return from daily changes
expected_returns = settings.AssetsExpectedReturnsFunction(returns)
cp.plot_expected_returns(expected_returns)
# calculate covariance for efficient edge quantification
covariance = settings.AssetsCovarianceFunction(returns)
# visualize daily retunrs correlation
cp.plot_correlation_scatter(returns)
def generate_optimum_portfolio():
#instantiate the objects with the settings
obj_factory = object_factory(settings)
ce = obj_factory.get_companies_extractor()
cp = obj_factory.get_charts_plotter()
mcs = obj_factory.get_portfolio_generator()
fr = obj_factory.get_file_repository()
mc = obj_factory.get_metrics_calculator()
print('1. Get companies')
companies = ce.get_companies_list()
price_extractor = obj_factory.get_price_extractor(companies)
print('2. Get company stock prices')
end_date = settings.get_end_date()
start_date = settings.get_start_date(end_date)
closing_prices = price_extractor.get_prices(settings.PriceEvent,
start_date, end_date)
# vytvori graf ceny akcii a da do tabulky
cp.plot_prices(closing_prices)
fr.save_to_file(closing_prices, 'StockPrices')
print('3. Calculate Daily Returns')
returns = settings.DailyAssetsReturnsFunction(closing_prices,
settings.ReturnType)
#graf dennich navratu a da do tabulky
cp.plot_returns(returns)
fr.save_to_file(returns, 'Returns')
print('4. Calculate Expected Mean Return & Covariance')
expected_returns = settings.AssetsExpectedReturnsFunction(returns)
covariance = settings.AssetsCovarianceFunction(returns)
#graf + da do tabulky
cp.plot_correlation_matrix(returns)
fr.save_to_file(covariance, 'Covariances')
print('5. Use Monte Carlo Simulation')
#generuje portfolia s alokaci
portfolios_allocations_df = mcs.generate_portfolios(
expected_returns, covariance, settings.RiskFreeRate)
portfolio_risk_return_ratio_df = portfolios_allocation_mapper.map_to_risk_return_ratios(
portfolios_allocations_df)
#graf portfolii, vytiskne sharpuv pomer a da do tabulky
cp.plot_portfolios(portfolio_risk_return_ratio_df)
max_sharpe_portfolio = mc.get_max_sharpe_ratio(
portfolio_risk_return_ratio_df)['Portfolio']
max_shape_ratio_allocations = portfolios_allocations_df[[
'Symbol', max_sharpe_portfolio
]]
print(max_shape_ratio_allocations)
fr.save_to_file(portfolios_allocations_df, 'MonteCarloPortfolios')
fr.save_to_file(portfolio_risk_return_ratio_df,
'MonteCarloPortfolioRatios')
print('6. Use an optimiser')
#generuje optimalni portfolia
targets = settings.get_my_targets()
optimiser = obj_factory.get_optimiser(targets, len(expected_returns.index))
portfolios_allocations_df = optimiser.generate_portfolios(
expected_returns, covariance, settings.RiskFreeRate)
portfolio_risk_return_ratio_df = portfolios_allocation_mapper.map_to_risk_return_ratios(
portfolios_allocations_df)
#da do grafu efektivni hranici
cp.plot_efficient_frontier(portfolio_risk_return_ratio_df)
cp.show_plots()
#ulozi tabulku
print('7. Saving Data')
fr.save_to_file(portfolios_allocations_df, 'OptimisationPortfolios')
fr.close()