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 create_report_line(values, first_col_title):
avg_return = StockUtils.getAverageReturn(values)
total_return = StockUtils.getTotalReturn(values)
stdev_return = StockUtils.getStdevReturn(values)
closing_val_range = '%.2f to %.2f' % (values[0], values[-1])
line = '%s\t%.2f\t%.2f\t%.2f\t%s' % (first_col_title, avg_return * 100,
total_return * 100, stdev_return * 100,
closing_val_range)
cols = {"cols": line.split("\t")}
return (cols, total_return)
def getInformation(self, tbl):
"""Gets information tuple given a table of information."""
values = tbl.getAllClosing()
avg_return = StockUtils.getAverageReturn(values)
total_return = StockUtils.getTotalReturn(values)
stdev_return = StockUtils.getStdevReturn(values)
info = {}
info['avg_return'] = self.fformat(avg_return)
info['total_return'] = self.fformat(total_return)
info['stdev_return'] = self.fformat(stdev_return)
dates = tbl.getAllDates()
date_range = '%s to %s' % (dates[0], dates[-1])
info['date_range'] = date_range
closing_val_range = '%.2f to %.2f' % (values[0], values[-1])
info['closing_range'] = closing_val_range
return info
def get_return(self, stock):
"""Gets the return on a particular stock for the particular
start_date and end_date.
If one had a 5% gain, this will return 1.05
If one had a 10% loss, this will return 0.9
"""
ytable = YahooStockTable.make_data_table(stock, self.start_date, self.end_date)
closing_values = ytable.getAllClosing()
return StockUtils.getTotalReturn(closing_values)
def is_rebal_good(self, stocks, allocations, freq_days):
"""If I rebalance every freq_days, how does it affect my overall value?
"""
all_rebal_vals = []
for stock in stocks:
rebal_vals = self.get_rebal_values(stock, freq_days)
all_rebal_vals.append(rebal_vals)
print 'Rebalance values:\n %s' % str(all_rebal_vals)
# We need all the rebalance values to have the same length.
# Assume we start off with a dollar.
num_stocks = len(stocks)
num_rebalances = len(all_rebal_vals[0])
cur_value = 1
for ind in range(0, num_rebalances - 1):
returns = []
# Get returns for stocks after each time period.
for stock_ind in range(0, num_stocks):
stock_rebal = all_rebal_vals[stock_ind] # Gets list of values.
start = stock_rebal[ind]
closing = stock_rebal[ind + 1]
return_value = 1 + StockUtils.getTotalReturn([start, closing])
returns.append(return_value)
print 'Returns after rebalance %d is:\n %s' % (ind, str(returns))
cur_return = Question4.get_returns_with_alloc(returns, allocations)
cur_value = cur_value * cur_return
print 'Rebalance return value: %.3f' % (cur_value)
# Simple approach.
simple_returns = []
for stock_ind in range(0, num_stocks):
stock_rebal = all_rebal_vals[stock_ind]
cur_return = StockUtils.getTotalReturn(stock_rebal)
simple_returns.append(cur_return)
final_return = Question4.get_returns_with_alloc(simple_returns, allocations)
print 'Simple return value: %.3f' % (1 + final_return)
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
