def plot_performance(dt_start, dt_end, arr_stock_symbols, nd_weights):
''''Plot the performance of each individual equity together with
the portfolio.'''
dic_df = dic_df_market_data(dt_start, dt_end, arr_stock_symbols)
nd_prices = dic_df["close"].values
nd_prices_normalized = nd_prices / nd_prices[0, :]
nd_weights.shape = (1, nd_prices_normalized.shape[1])
nd_prices_weighted = nd_weights * nd_prices_normalized
nd_portfolio_value = nd_prices_weighted.sum(axis=1)
nd_portfolio_value = nd_portfolio_value / nd_portfolio_value[0]
arr_stock_symbols.append("portfolio")
dt_timeofday = dt.timedelta(hours=16)
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
plt.clf()
plt.xticks(rotation=70)
plt.plot(ldt_timestamps, nd_prices_normalized)
plt.plot(ldt_timestamps, nd_portfolio_value)
plt.legend(arr_stock_symbols)
plt.ylabel('Normalized Close')
plt.xlabel('Date')
plt.show()
def optimizer(date_start, date_end, arr_stock_symbols, weight_increment):
'''Returns an optimal equity allocation over the equities in
arr_stock_symbols w.r.t. the sharpe ratio.'''
dic_df = dic_df_market_data(date_start, date_end, arr_stock_symbols)
nd_prices = dic_df["close"].values
nd_prices_normalized = nd_prices / nd_prices[0, :]
# Get best allocation w.r.t. the shape ratio
num_equities = len(arr_stock_symbols)
best_metrics = [0 for i in xrange(num_equities)]
best_sharpe = 0
best_allocation = [0 for i in xrange(num_equities)]
generator_allocations = gen_possible_allocations(num_equities,
weight_increment)
for nd_allocation in generator_allocations:
nd_allocation.shape = (1, nd_prices_normalized.shape[1])
nd_prices_weighted = nd_allocation * nd_prices_normalized
nd_portfolio_value = nd_prices_weighted.sum(axis=1)
portfolio_metrics = arr_portfolio_metrics(nd_portfolio_value)
curr_sharpe = portfolio_metrics[-1]
if curr_sharpe > best_sharpe:
best_metrics = portfolio_metrics
best_sharpe = curr_sharpe
best_allocation = nd_allocation
return (best_allocation, best_metrics)
def write_event_study_to_disk(date_start, date_end, arr_equity_symbols,
str_market_symbol, pdf_to_disk_name):
'''Writes a plot onto disk illustrating the market relative mean
return for occuring events in between the dates date_start and
date_end for all equities in arr_equity_symbols relative to the
market str_market_symbol.'''
arr_equity_symbols.append(str_market_symbol)
dic_df = dic_df_market_data(date_start, date_end, arr_equity_symbols)
eventHandler = EventHandler(dic_df, str_market_symbol, date_start, date_end)
eventHandler.assign_events()
df_events = eventHandler.get_df_events()
#df_events = get_event_df(dic_df, str_market_symbol, date_start, date_end)
print "Creating Study"
ep.eventprofiler(df_events, dic_df, i_lookback=20, i_lookforward=20,
s_filename=pdf_to_disk_name, b_market_neutral=True,
b_errorbars=True,
s_market_sym=str_market_symbol)
