def getInefficiency( cursor, refDate ):
cursor.execute('select day from predictions')
days = np.unique( cursor.fetchall() )
mkteff = []
for i in days.ravel():
val = racedb.getEfficiencyOnDay( cursor, 'predictions', i, 'PresElect' )
if val > -1:
mkteff.append( (i, val) )
# Plot the market efficiency
mktary = np.array(mkteff)
plot( mktary[:,0], mktary[:,1] )
title('Market Efficiency vs. Time')
xlabel('Days since 11/3/2008')
ylabel('Market Efficiency (%)')
show()
# Find points where the market efficiency if furthest from normal (100%)
d = mktary[:,0]
e = mktary[:,1]
thresh = 8 # Threshold = 8% deviation from perfect market
lows = d[e < 100 - thresh].tolist()
his = d[e > 100 + thresh]
cuthis = racedb.clusterData(his, 3)
cutlos = racedb.clusterData(lows, 3)
ineffDays = cuthis.tolist() + cutlos.tolist()
ineffDates = racedb.getDatesOfMarketInefficiency( ineffDays, refDate )
print 'Here are some dates of market inefficiency:\n'
np.sort(ineffDates)
for date in ineffDates:
print date
return ineffDates
def getInefficiency( cursor, refDate ):
'''Given a cursor to a database and a reference date (the earliest date in the
data contained in the database) plot the market efficiency vs. day for the
presidential election race. Find dates where the market efficiency
deviates from 100% by more than a threshold value (default=8%). Print
and return these dates. '''
# Get the dates of interest
cursor.execute('select day from predictions')
days = np.unique( cursor.fetchall() )
# Loop through the days and calculate the market efficiency for each.
mkteff = []
for i in days.ravel():
val = racedb.getEfficiencyOnDay( cursor, 'predictions', i, 'PresElect' )
if val > -1:
mkteff.append( (i, val) )
# Plot the market efficiency
mktary = np.array(mkteff)
plot( mktary[:,0], mktary[:,1] )
title('Market Efficiency vs. Time')
xlabel('Days since 11/3/2008')
ylabel('Market Efficiency (%)')
show()
# Find points where the market efficiency if furthest from normal (100%)
d = mktary[:,0]
e = mktary[:,1]
thresh = 8 # Threshold = 8% deviation from perfect market
lows = d[e < 100 - thresh].tolist()
his = d[e > 100 + thresh]
cuthis = racedb.clusterData(his, 3)
cutlos = racedb.clusterData(lows, 3)
ineffDays = cuthis.tolist() + cutlos.tolist()
ineffDates = racedb.getDatesOfMarketInefficiency( ineffDays, refDate )
print 'Here are some dates of market inefficiency:\n'
np.sort(ineffDates)
for date in ineffDates:
print date
return ineffDates
