def print_report(yahoo_tables, do_plot=False):
"""Given a yahoo stock table prints a report.
average return, standard deviation, total return
"""
results = []
headers = ['Date Range', 'Average Return (%)', 'Total Return (%)', 'Standard deviation',
'Closing stock range']
results.append({"cols": headers})
total_returns = []
for yahoo_table in yahoo_tables:
try:
closing_vals = yahoo_table.getAllClosing()
dates = yahoo_table.getAllDates()
first_col_title = '%s to %s' % (dates[0], dates[-1])
(line, total_return) = Question1.create_report_line(closing_vals, first_col_title)
results.append(line)
total_returns.append(total_return)
if do_plot:
closing_vals = yahoo_table.getAllClosing()
Plotter.plot(StockUtils.getPercentChanges(closing_vals))
except Exception:
pass
# Only do this for more or one tables.
if len(yahoo_tables) <= 1:
return results
# Compound your month to month returns.
all_return = StockUtils.getCompoundedReturn(total_returns)
gain = all_return - 1
gain_line = {"cols": ['Total return by putting in each month style: %.2f percent.' % (
gain * 100)]}
results.append(gain_line)
return results
def get_correlation(self, stocks):
"""Returns a correlation matrix defined by numpy.corrcoef.
Basically if you pass in 3 stocks (0, 1, 2)
Then corr[0][2] or corr[2][0] will tell the correlation of 0 and 2.
Correlation is: Var(A * B) / Stdev(A) * Stdev(B)
"""
lists_of_changes = []
for stock in stocks:
tbl = YahooStockTable.make_data_table(stock, self.start_date, self.end_date)
closing = tbl.getAllClosing()
pct_changes = StockUtils.getPercentChanges(closing)
lists_of_changes.append(pct_changes)
for changes in lists_of_changes:
logging.info(self.fformat(changes))
corr = numpy.corrcoef(lists_of_changes)
return corr # correlation matrix
