def prices(symbol='$DJI', start=datetime.datetime(2008,1,1), end=datetime.datetime(2009,12,31)):
start = util.normalize_date(start or datetime.date(2008, 1, 1))
end = util.normalize_date(end or datetime.date(2009, 12, 31))
symbol = symbol.upper()
timeofday = datetime.timedelta(hours=16)
timestamps = du.getNYSEdays(start, end, timeofday)
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
ldf_data = da.get_data(timestamps, [symbol], ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
na_price = d_data['close'].values
return na_price[:,0]
def prices(symbol='$DJI',
start=datetime.datetime(2008, 1, 1),
end=datetime.datetime(2009, 12, 31)):
start = util.normalize_date(start or datetime.date(2008, 1, 1))
end = util.normalize_date(end or datetime.date(2009, 12, 31))
symbol = symbol.upper()
timeofday = datetime.timedelta(hours=16)
timestamps = du.getNYSEdays(start, end, timeofday)
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
ldf_data = da.get_data(timestamps, [symbol], ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
na_price = d_data['close'].values
return na_price[:, 0]
def chart_series(series,
market_sym='$SPX',
price='actual_close',
normalize=True):
"""Display a graph of the price history for the list of ticker symbols provided
Arguments:
series (dataframe, list of str, or list of tuples):
datafram (Timestamp or Datetime for index)
other columns are float y-axis values to be plotted
list of str: 1st 3 comma or slash-separated integers are the year, month, day
others are float y-axis values
list of tuples: 1st 3 integers are year, month, day
others are float y-axis values
market_sym (str): ticker symbol of equity or comodity to plot along side the series
price (str): which market data value ('close', 'actual_close', 'volume', etc) to use
for the market symbol for comparison to the series
normalize (bool): Whether to normalize prices to 1 at the start of the time series.
"""
series = util.make_dataframe(series)
start = util.normalize_date(series.index[0]
or datetime.datetime(2008, 1, 1))
end = util.normalize_date(series.index[-1]
or datetime.datetime(2009, 12, 28))
timestamps = du.getNYSEdays(start, end, datetime.timedelta(hours=16))
if market_sym:
if isinstance(market_sym, basestring):
market_sym = [market_sym.upper().strip()]
reference_prices = da.get_data(timestamps, market_sym, [price])[0]
reference_dict = dict(zip(market_sym, reference_prices))
for sym, market_data in reference_dict.iteritems():
series[sym] = pd.Series(market_data, index=timestamps)
# na_price = reference_dict[price].values
# if normalize:
# na_price /= na_price[0, :]
series.plot()
# plt.clf()
# plt.plot(timestamps, na_price)
# plt.legend(symbols)
# plt.ylabel(price.title())
# plt.xlabel('Date')
# # plt.savefig('portfolio.chart_series.pdf', format='pdf')
plt.grid(True)
plt.show()
return series
def chart_series(series, market_sym='$SPX', price='actual_close', normalize=True):
"""Display a graph of the price history for the list of ticker symbols provided
Arguments:
series (dataframe, list of str, or list of tuples):
datafram (Timestamp or Datetime for index)
other columns are float y-axis values to be plotted
list of str: 1st 3 comma or slash-separated integers are the year, month, day
others are float y-axis values
list of tuples: 1st 3 integers are year, month, day
others are float y-axis values
market_sym (str): ticker symbol of equity or comodity to plot along side the series
price (str): which market data value ('close', 'actual_close', 'volume', etc) to use
for the market symbol for comparison to the series
normalize (bool): Whether to normalize prices to 1 at the start of the time series.
"""
series = util.make_dataframe(series)
start = util.normalize_date(series.index[0] or datetime.datetime(2008, 1, 1))
end = util.normalize_date(series.index[-1] or datetime.datetime(2009, 12, 28))
timestamps = du.getNYSEdays(start, end, datetime.timedelta(hours=16))
if market_sym:
if isinstance(market_sym, basestring):
market_sym = [market_sym.upper().strip()]
reference_prices = da.get_data(timestamps, market_sym, [price])[0]
reference_dict = dict(zip(market_sym, reference_prices))
for sym, market_data in reference_dict.iteritems():
series[sym] = pd.Series(market_data, index=timestamps)
# na_price = reference_dict[price].values
# if normalize:
# na_price /= na_price[0, :]
series.plot()
# plt.clf()
# plt.plot(timestamps, na_price)
# plt.legend(symbols)
# plt.ylabel(price.title())
# plt.xlabel('Date')
# # plt.savefig('portfolio.chart_series.pdf', format='pdf')
plt.grid(True)
plt.show()
return series
def portfolio_prices(
symbols=("AAPL", "GLD", "GOOG", "$SPX", "XOM", "msft"),
start=datetime.datetime(2005, 1, 1),
end=datetime.datetime(2011, 12, 31), # data stops at 2013/1/1
normalize=True,
allocation=None,
price_type='actual_close',
):
"""Calculate the Sharpe Ratio and other performance metrics for a portfolio
Arguments:
symbols (list of str): Ticker symbols like "GOOG", "AAPL", etc
start (datetime): The date at the start of the period being analyzed.
end (datetime): The date at the end of the period being analyzed.
normalize (bool): Whether to normalize prices to 1 at the start of the time series.
allocation (list of float): The portion of the portfolio allocated to each equity.
"""
symbols = normalize_symbols(symbols)
start = util.normalize_date(start)
end = util.normalize_date(end)
if allocation is None:
allocation = [1. / len(symbols)] * len(symbols)
if len(allocation) < len(symbols):
allocation = list(allocation) + [1. / len(symbols)
] * (len(symbols) - len(allocation))
total = np.sum(allocation.sum)
allocation = np.array([(float(a) / total) for a in allocation])
timestamps = du.getNYSEdays(start, end, datetime.timedelta(hours=16))
ls_keys = [price_type]
ldf_data = da.get_data(timestamps, symbols, ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
na_price = d_data[price_type].values
if normalize:
na_price /= na_price[0, :]
na_price *= allocation
return np.sum(na_price, axis=1)
def chart(
symbols=("AAPL", "GLD", "GOOG", "$SPX", "XOM", "msft"),
start=datetime.datetime(2008, 1, 1),
end=datetime.datetime(2009, 12, 31), # data stops at 2013/1/1
normalize=True,
):
"""Display a graph of the price history for the list of ticker symbols provided
Arguments:
symbols (list of str): Ticker symbols like "GOOG", "AAPL", etc
start (datetime): The date at the start of the period being analyzed.
end (datetime): The date at the end of the period being analyzed.
normalize (bool): Whether to normalize prices to 1 at the start of the time series.
"""
start = util.normalize_date(start or datetime.date(2008, 1, 1))
end = util.normalize_date(end or datetime.date(2009, 12, 31))
symbols = [s.upper() for s in symbols]
timeofday = datetime.timedelta(hours=16)
timestamps = du.getNYSEdays(start, end, timeofday)
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
ldf_data = da.get_data(timestamps, symbols, ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
na_price = d_data['close'].values
if normalize:
na_price /= na_price[0, :]
plt.clf()
plt.plot(timestamps, na_price)
plt.legend(symbols)
plt.ylabel('Adjusted Close')
plt.xlabel('Date')
plt.savefig('chart.pdf', format='pdf')
plt.grid(True)
plt.show()
return na_price
def portfolio_prices(
symbols=("AAPL", "GLD", "GOOG", "$SPX", "XOM", "msft"),
start=datetime.datetime(2005, 1, 1),
end=datetime.datetime(2011, 12, 31), # data stops at 2013/1/1
normalize=True,
allocation=None,
price_type='actual_close',
):
"""Calculate the Sharpe Ratio and other performance metrics for a portfolio
Arguments:
symbols (list of str): Ticker symbols like "GOOG", "AAPL", etc
start (datetime): The date at the start of the period being analyzed.
end (datetime): The date at the end of the period being analyzed.
normalize (bool): Whether to normalize prices to 1 at the start of the time series.
allocation (list of float): The portion of the portfolio allocated to each equity.
"""
symbols = normalize_symbols(symbols)
start = util.normalize_date(start)
end = util.normalize_date(end)
if allocation is None:
allocation = [1. / len(symbols)] * len(symbols)
if len(allocation) < len(symbols):
allocation = list(allocation) + [1. / len(symbols)] * (len(symbols) - len(allocation))
total = np.sum(allocation.sum)
allocation = np.array([(float(a) / total) for a in allocation])
timestamps = du.getNYSEdays(start, end, datetime.timedelta(hours=16))
ls_keys = [price_type]
ldf_data = da.get_data(timestamps, symbols, ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
na_price = d_data[price_type].values
if normalize:
na_price /= na_price[0, :]
na_price *= allocation
return np.sum(na_price, axis=1)
def chart(
symbols=("AAPL", "GLD", "GOOG", "$SPX", "XOM", "msft"),
start=datetime.datetime(2008, 1, 1),
end=datetime.datetime(2009, 12, 31), # data stops at 2013/1/1
normalize=True,
):
"""Display a graph of the price history for the list of ticker symbols provided
Arguments:
symbols (list of str): Ticker symbols like "GOOG", "AAPL", etc
start (datetime): The date at the start of the period being analyzed.
end (datetime): The date at the end of the period being analyzed.
normalize (bool): Whether to normalize prices to 1 at the start of the time series.
"""
start = util.normalize_date(start or datetime.date(2008, 1, 1))
end = util.normalize_date(end or datetime.date(2009, 12, 31))
symbols = [s.upper() for s in symbols]
timeofday = datetime.timedelta(hours=16)
timestamps = du.getNYSEdays(start, end, timeofday)
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
ldf_data = da.get_data(timestamps, symbols, ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
na_price = d_data['close'].values
if normalize:
na_price /= na_price[0, :]
plt.clf()
plt.plot(timestamps, na_price)
plt.legend(symbols)
plt.ylabel('Adjusted Close')
plt.xlabel('Date')
plt.savefig('chart.pdf', format='pdf')
plt.grid(True)
plt.show()
return na_price
def findEvents(symbols_year, startday, endday, event, data_item="close"):
dataobj = DataAccess('Yahoo')
symbols = dataobj.get_symbols_from_list("sp500%d" % symbols_year)
symbols.append('SPY')
# Reading the Data for the list of Symbols.
timestamps = getNYSEdays(startday, endday, timedelta(hours=16))
# Reading the Data
print "# reading data"
close = dataobj.get_data(timestamps, symbols, data_item)
# Generating the Event Matrix
print "# finding events"
eventmat = copy.deepcopy(close)
for sym in symbols:
for time in timestamps:
eventmat[sym][time] = NAN
for symbol in symbols:
event(eventmat, symbol, close[symbol], timestamps)
return eventmat