def linear_interpolation_for_synthetic_maturity_future(days, trade_date, expiration_dates, prices):
"""Linearly interpolate.
Will break if it can't find a future on each side of the requested maturity.
Moving the garbage to another function so the real function can be properly implemented at a later date.
TODO: For now, this will just linearly interpolate between the nearest 2
surrounding futures. Should rewrite. This will serve initial purpose
though for only a 90 day interpolated price for hist vol studies.
"""
dte = [i.days for i in (expiration_dates - trade_date)]
if not all(np.diff(dte) >= 0):
print "DATES NOT ORDERED WELL"
print expiration_dates
print prices
ipshell()
try:
synth_fut = np.interp(days, dte, prices)
# print "-" *20
# print "synth fut, days: %s, %s" % (synth_fut, days)
# print "dte: %s" % dte
# print "pxs: %s" % prices
# ipshell()
except:
print "-" *20
print "dte: %s" % dte
print "pxs: %s" % prices
ipshell()
return synth_fut
line, = pyplot.plot(0, 0, "bo")
dot, = pyplot.plot(0, 0, "ro")
interval = 0.0
ylim_window = 12
synth_maturities = [100, 200, 300]
synth_fut_dict = dict((synth_maturity, []) for synth_maturity in synth_maturities)
DEBUG = False
for trade_date in trade_dates:
if DEBUG:
if trade_date in freeze_dates:
print expiration_dates
print settles
print closes
ipshell()
df = execute_query_DF(conn, SQL_get_futures_for_trade_date % trade_date)
closes = df("px_close")
settles = df("px_settle")
prices = settles
expiration_dates = df("exp_date")
# if abs( (trade_date - datetime.date(1991, 4, 11)).days ) <= 1:
# pass
# # weird day.
# ipshell()
#
bad_prints = (prices > 8000) + (prices < 10)
if any(bad_prints):