>>> import YahooQuote
>>> market = YahooQuote.Market()
>>> market['rht']
<Ticker for rht>
>>> market['rht'][20040203]
<Quote:rhat:20040203:O=18.05,H=18.21,L=17.68,C=17.85,V=5828400,A=17.85>
>>> market['rht'].now # magic attribute via __getattr__
<Quote:rhat:20040415:O=24.74,H=25.34,L=24.50,C=25.00,V=2840059,A=-1.00>
>>> msft = market['msft']
>>> msft[20040407:20040414] # get range of dates as slice
[<Quote:msft:20040407:O=25.74,H=25.78,L=25.35,C=25.59,V=63268900,A=25.59>,
<Quote:msft:20040408:O=25.82,H=25.85,L=25.36,C=25.48,V=45294600,A=25.48>,
<Quote:msft:20040412:O=25.48,H=25.74,L=25.43,C=25.61,V=38786800,A=25.61>,
<Quote:msft:20040413:O=25.68,H=25.77,L=25.41,C=25.45,V=56971500,A=25.45>]
>>> msft[20040410] # easter saturday, no trading, interpolate (!!)
<Quote:msft:20040410:O=25.65,H=25.80,L=25.39,C=25.55,V=0,A=25.55>
>>> lastThu = msft[20040408]
>>> lastThu.__dict__
{'_fetcher': <YahooQuote.Fetcher instance at 0x403ba42c>,
'adjclose': 25.48,
'close': 25.48,
'date': 20040408,
'high': 25.850000000000001,
'low': 25.359999999999999,
'open': 25.82,
'symbol': 'msft',
'volume': 45294600}