Python quandl.get函数代码示例

def load_eur():
    """ Return cash rate for EUR and DEM prior to the introduction of EUR """
    bank_rate = quandl.get(CashFile.GER_BANKRATE.value,
                           api_key=AdagioConfig.quandl_token)

    ww2_data = pd.DataFrame([4.0, 3.5, 5.0],
                            index=[datetime(1936, 6, 30),
                                   datetime(1940, 4, 9),
                                   datetime(1948, 6, 28)])
    ww2_month = pd.date_range('1936-06-01', '1948-06-01', freq='M')
    ww2_month = pd.DataFrame(index=ww2_month)
    ww2_data = pd.concat((ww2_data, ww2_month), axis=1).fillna(method="pad")

    parser = lambda d: date_shift(datetime.strptime(d, "%Y-%m"),
                                  "+BMonthEnd")
    filename = join(DATA_DIRECTORY, 'cash_rate', 'eur', 'BBK01.SU0112.csv')
    discount_rate = pd.read_csv(filename,
                                skiprows=[1, 2, 3, 4], index_col=0,
                                usecols=[0, 1], engine="python", skipfooter=95,
                                parse_dates=True, date_parser=parser)
    ib_rate = DataReader(CashFile.EUR_3M_IB_RATE.value, "fred", START_DATE)
    libor = quandl.get(CashFile.EUR_3M_EURIBOR.value,
                       api_key=AdagioConfig.quandl_token)

    data = (pd.concat((bank_rate[:"1936-06"].fillna(method="pad"),
                       ww2_data,
                       discount_rate[:"1959"].fillna(method="pad"),
                       to_monthend(ib_rate['1960':"1998"].fillna(method="pad")),
                       libor['1999':].fillna(method="pad")),
                      axis=1)
            .sum(axis=1).rename("cash_rate_eur"))
    return data

def sample():
    df1 = quandl.get("FMAC/HPI_AL", authtoken=api_key)
    df2 = quandl.get("FMAC/HPI_AK", authtoken=api_key)

    df1.columns = ['HPI_AL']
    df2.columns = ['HPI_AK']

    print(df1.head())
    print(df2.head())

    joined = df1.join(df2)
    print(joined.head())

def get_unemployment(api_key):
	df = quandl.get("ECPI/JOB_G", trim_start="1975-01-01", authtoken=api_key)
	df.columns = ['unemployment']
	df['unemployment'] = (df['unemployment']-df['unemployment'][0])/df['unemployment'][0]*100.0
	df = df.resample('D').mean()
	df = df.resample('M').mean()
	return df

def gdp_data():
    df = quandl.get("BCB/4385", trim_start="1975-01-01")
    df["Value"] = (df["Value"]-df["Value"][0]) / df["Value"][0] * 100.0
    df=df.resample('M').mean()
    df.rename(columns={'Value':'GDP'}, inplace=True)
    df = df['GDP'] # DataFrame to Series
    return df

def mortgage_30yr():
	df = quandl.get('FMAC/MORTG', trim_start="1975-01-01")
	df['Value'] = (df['Value'] - df['Value'][0]) / df['Value'][0] * 100
	df = df.resample('M').mean()
	df.rename(columns={'Value': 'M30'}, inplace=True)
	df = df['M30']
	return df 

def HPI_Benchmark():
    df = quandl.get("FMAC/HPI_USA", authtoken=api_key)
    df.columns = ['United States']
    df["United States"] = (df["United States"]-df["United States"][0]) / df["United States"][0] * 100.0
    pickle_out = open('HPI_bench.pickle','wb')
    pickle.dump(df, pickle_out)
    pickle_out.close() 

def HPI_Benchmark():
	df = quandl.get('FMAC/HPI_USA' , authtoken=api_key)
	df['United States'] = (df['Value'] - df['Value'][0]) / df['Value'][0] * 100.0
	
	pickle_out = open('us_pct.pickle', 'wb')
	pickle.dump(df, pickle_out)
	pickle_out.close()

def load_quandl_newsentiment(dataset, start, end):
    cache_file = 'NS1/ns1-cache.csv'
    quandl_auth = 'T2GAyK64nwsePiJWMq8y'
    #ns1 = pd.DataFrame()
    i=1
    for index, row in dataset.iterrows():
        #ns2[i] = ns1
        ns1 = []
        ns1 = pd.DataFrame()
        stock_cache_file = row['NSCode']+'-cache.csv'
        if not(os.path.exists(stock_cache_file)):
            print(row['NSCode'])
            print ('Downloading news for', row['NSCode'])
            allnews_data = quandl.get(row['NSCode'], authtoken=quandl_auth)
            ns1 = ns1.append(allnews_data)
            ns1.to_csv(stock_cache_file)
        if os.path.exists(stock_cache_file):
            with open(stock_cache_file, 'r') as csvfile:
                csvFileReader = csv.reader(csvfile)
                next(csvFileReader)
                print ('Loading news from cache ', row['NSCode'])
                for rows in csvFileReader:
                    date=int(time_to_num(rows[0]))
                    if date > start and date < end:
                #        print(start,date,end)
                        datens.append(date)
                        sentiment.append(rows[1])
                #print (datens, ' ',sentiment,"\n")
        #ns1.to_csv(cache_file)
    return

def store_quandl(code):
    data = quandl.get(code)

    new_entry_c = 0

    print('Storing quandl data for: %s...' % (code), " ", end="")
    # index is the date
    for index, row in data.iterrows():
        date  = index
        value = row['Value']

        exists = db.session.query(db.base.classes.quandl).filter(db.base.classes.quandl.date == date).filter(db.base.classes.quandl.code == code).first()
        
        if exists:
            continue

        new_data = db.base.classes.quandl(p_time=datetime.now(),
                                          date=date,
                                          code=code,
                                          value=value)
   
        

        db.session.add(new_data)
        db.session.commit()
        new_entry_c += 1

    print('added: %s/%s entries' % (new_entry_c, len(data)))

 def load_from_quandl(self):
     """ Download data from quandl """
     logger.debug('Downloading data from Quandl')
     data = quandl.get(self[keys.quandl_ticker],
                       api_key=AdagioConfig.quandl_token)
     self.check_if_expired(data)
     return data

def build_graph(ticker):
    # make a graph of closing prices from previous month

    # Create some data for our plot.
    data = quandl.get('WIKI/' + ticker)
    
    # graph last month's data
    enddate = date.today() - timedelta(1)
    startdate = enddate - relativedelta(months=1)
    wdata = data[startdate:enddate]

    x = wdata.index  # datatime formatted
    y = wdata['Close']  # closing prices

    # Create a heatmap from our data.
    plot = figure(title='Data from Quandle WIKI set',
              x_axis_label='date',
              x_axis_type='datetime',
              y_axis_label='price')

    plot.line(x, y, color='navy', alpha=0.5)

    script, div = components(plot)

    return script, div

def scrapeDailyNews():
	url = "http://finance.yahoo.com/q/hp?s=AAPL+Historical+Prices"

	content = urllib2.urlopen(url).read()
	soup = BeautifulSoup(content, "lxml")
	tbl = soup.find("table", {"class": "yfnc_datamodoutline1"}).findNext('table').find_all('tr')[1].find_all('td')
	
	newsDate = datetime.now()

	mydata = quandl.get("AOS/AAPL")

	row = mydata.iloc[-1:]
	avgSent = float(row['Article Sentiment'])
	impactScore = float(row['Impact Score'])

	news = News(
		date = newsDate,
		avgSent = avgSent,
		impactScore = impactScore)

	try:
		news.save()
		print("Saved news object ({})".format(news.objectId))
	except:
		print("News data has already been saved.")

def scrapeDailyOptions():
	url = "http://finance.yahoo.com/q/hp?s=AAPL+Historical+Prices"

	content = urllib2.urlopen(url).read()
	soup = BeautifulSoup(content, "lxml")
	tbl = soup.find("table", {"class": "yfnc_datamodoutline1"}).findNext('table').find_all('tr')[1].find_all('td')
	
	optionDate = datetime.strptime(tbl[0].string, "%b %d, %Y")

	mydata = quandl.get("VOL/AAPL")

	row = mydata.iloc[-1:]
	ivmean10 = float(row['IvMean10'])
	ivmean20 = float(row['IvMean20'])
	ivmean30 = float(row['IvMean30'])
	ivmean60 = float(row['IvMean60'])


	option = Option(
		date = optionDate,
		ivMean10 = ivmean10,
		ivMean20 = ivmean10,
		ivMean30 = ivmean30,
		ivMean60 = ivmean60)

	try:
		option.save()
		print("Save option object ({})".format(option.objectId))
	except:
		print("Option data has already been saved.")

def second_stock():	
	n = app_stock.vars['name']
	ss = "WIKI/" + n + ".4"
	mydata = quandl.get(ss, encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date', trim_start="2016-05-05", trim_end="2016-06-05", returns = "numpy", authtoken="ZemsPswo-xM16GFxuKP2")
	mydata = pd.DataFrame(mydata)
	#mydata['Date'] = mydata['Date'].astype('datetime64[ns]')
	x = mydata['Date']
	y = mydata['Close']
	p = figure(title="Stock close price", x_axis_label='Date', y_axis_label='close price', plot_height = 300, plot_width = 550)
	p.line(x, y, legend="Price in USD", line_width=3, color = "#2222aa")
	
	
	# Configure resources to include BokehJS inline in the document.
    # For more details see:
    #   http://bokeh.pydata.org/en/latest/docs/reference/resources_embedding.html#bokeh-embed
	js_resources = INLINE.render_js()
	css_resources = INLINE.render_css()

    # For more details see:
    #   http://bokeh.pydata.org/en/latest/docs/user_guide/embedding.html#components
	script, div = components(p, INLINE)
    
	html = flask.render_template(
		'stockgraph.html',
		ticker = app_stock.vars['name'],
		plot_script=script,
		plot_div=div,
		js_resources=js_resources,
		css_resources=css_resources,
	)
	return encode_utf8(html)

def load_stock_datasets():
    data_file = "stocks.xlsx"
    if os.path.isfile(data_file):
        stocks = pd.read_excel(data_file)
    else:
        quandl.ApiConfig.api_key = 'a5JKbmNDb4k98huTPMcY'
        google = quandl.get('WIKI/GOOGL')
        google["Company"] = "Google"
        facebook = quandl.get('WIKI/FB')
        facebook["Company"] = "Facebook"
        apple = quandl.get('WIKI/AAPL')
        apple["Company"] = "Apple"
        stocks = pd.concat([apple, facebook, google])
        stocks = stocks.reset_index()
        stocks.to_excel(data_file, index=False)
    return stocks