Python DataReader.reset_index方法代码示例

# 需要导入模块: from pandas.io.data import DataReader [as 别名]
# 或者: from pandas.io.data.DataReader import reset_index [as 别名]
 def readData(self, lookupTicker, source, start, end):
     
     '''Read the data - assumes start and end are datetime.date objects'''
     
     try:  
         lookupTicker = str(lookupTicker)
         if source == 'Quandl':
             #use Quandl reader
             start = str(start)
             end = str(end)
             data = Quandl.get(lookupTicker,
                               authtoken = self.quandlAuthToken,
                               trim_start = start, 
                               trim_end= end)
         else:
             #use pandas.io DataReader
             data = DataReader(lookupTicker, source , start, end)
             
         data = data.reset_index()
         logging.info("Read ticker {}".format(lookupTicker))
     except:
         logging.error("importData: Can't read ticker {}".format(lookupTicker))
         raise
     else:
         return data

# 需要导入模块: from pandas.io.data import DataReader [as 别名]
# 或者: from pandas.io.data.DataReader import reset_index [as 别名]
 def importData():
     
     #Start Time
     start = datetime(2010,1,1)
     end = datetime.date(datetime.now())
     data = DataReader(sp500constituents[0], "yahoo", start, end)
     
     
     en = enumerate(sp500constituents)
     [i for i, x in en if x=='WFMI']
     
     
     sp500constituents[200:len(sp500constituents)]
     problems = []
     dataImportProblems = []
     for series in sp500constituents[485:len(sp500constituents)]:
         print series 
         try:  
             data = DataReader(series, "yahoo", start, end)
             data = data.reset_index()
         except:
             print "Can't read {}".format(series)
             dataImportProblems.append(series)
             continue
         con = sqlite3.connect("/home/phcostello/Documents/Data/FinanceData.sqlite")
         try:
             psql.write_frame( data, series, con)
             con.commit()
         except:
             print "Problems with {}".format(series)
             problems.append(series)
         finally:
             con.close()
     
     #changing tables to have date formats so RODBC driver recognizes
     #Should check that this is occuring above.
     con = sqlite3.connect("/home/phcostello/Documents/Data/FinanceData.sqlite")
     for tb in sp500constituents:
         if psql.has_table(tb, con):
             sqltxt = "SELECT * FROM {}".format(tb)
             #print sqltxt
             data = psql.read_frame(sqltxt, con)
             sqlDropTxt = 'DROP TABLE "main"."{}"'.format(tb)
             #print sqlDropTxt
             psql.execute(sqlDropTxt, con)
             con.commit()
             psql.write_frame( data, tb, con)
             con.commit()
     
     con.close()

# 需要导入模块: from pandas.io.data import DataReader [as 别名]
# 或者: from pandas.io.data.DataReader import reset_index [as 别名]
def determine_trend(symbol, trade_date=datetime.datetime.now(), 
                    trend_length=20, trend_end_days_ago=1): 
    """
    returns a "trend score" derived from performing a linear 
    regression on the daily closing price of the stock 
    identified by symbol.  
    
    This score is on the following scale: 
    "Negative Trend" ----- "Positive Trend"
       -1.0 -----------0-----------1.0

    The score considers both the slope of the linear model and
    the "fit" (based on the r^2 output of the ols function)

    trade_date -- date used to determine the trend from
    symbol -- the stock symbol to determine trend for
    trend_length -- the number of days to derive trend for
    trend_end_days_ago -- the number of days prior to trend_date to determine
                          when to end the trend analysis
    """
    end_date = datetime.date.today() - datetime.timedelta(days=trend_end_days_ago)
    start_date = end_date - datetime.timedelta(days=trend_length)
    stock_df = DataReader(symbol, "yahoo", start=start_date, end=end_date)
    stock_df = stock_df.reset_index()
    result = ols(y=stock_df['Adj Close'], x=Series(stock_df.index))
    
    # This is the formula for the score without adjusting to fit within the
    # -1.0 - 1.0 scale.  Basically this takes the slope/starting price to get
    # the % change per day.  This is divided by a somewhat arbitrary value of 
    score = (result.beta['x']/result.beta['intercept'])/LARGE_DAILY_GAIN * result.r2

    # Now adjust the score to keep it in our trend range:
    if score > 1.0:
        return 1.0
    elif score < -1.0:
        return -1.0
    else:
        return score

# 需要导入模块: from pandas.io.data import DataReader [as 别名]
# 或者: from pandas.io.data.DataReader import reset_index [as 别名]
trades['MARKET'] = 'OTHER'
trades['MARKET'][trades['MARKETSEGMENT']==4] = 'HG'
trades['MARKET'][trades['MARKETSEGMENT']==7] = 'FRN'
trades['MARKET'][trades['MARKETSEGMENT']==0] = 'HY'
trades['MARKET'][trades['MARKETSEGMENT']==8] = 'AGY'

trades = trades.join(pd.get_dummies(trades['MARKET'], prefix='MKT'))
trades = trades.drop(['MARKET'], axis=1)
del trades['ISCVTPREFSTOCK']
del trades['MARKETSEGMENT']

#Pull ETF data in liey of indices
#Scrape from Yahoo
LQD = DataReader("LQD",  "yahoo", datetime(2013,1,1), datetime(2013,12,31))
LQD['Date'] = LQD.index
LQD = LQD.reset_index(drop=True)
#Get the vix
VIX = DataReader("VIX",  "yahoo", datetime(2013,1,1), datetime(2013,12,31))
VIX['Date'] = VIX.index
VIX = VIX.reset_index(drop=True)
stocks =pd.merge(VIX, LQD, left_on='Date', right_on='Date', how='outer', suffixes=('_VIX','_LQD') )
stocks=stocks[['Date','Close_VIX','Close_LQD']]
#Get Treasury Rates
UST_2 = DataReader("DGS2",  "fred", datetime(2013,1,1), datetime(2013,12,31))
UST_10 = DataReader("DGS10",  "fred", datetime(2013,1,1), datetime(2013,12,31))
UST_30 = DataReader("DGS30",  "fred", datetime(2013,1,1), datetime(2013,12,31))
UST = pd.DataFrame({'Date': UST_2['DGS2'].index.values,'UST2': UST_2['DGS2'].values ,'UST10': UST_10['DGS10'].values,'UST30': UST_30['DGS30'].values})
ex_data =pd.merge(stocks, UST, left_on='Date', right_on='Date', how='outer', suffixes=('_ST','_UST') )
ex_data = ex_data.dropna()
data =pd.merge(trades, ex_data, left_on='CURRDATE', right_on='Date', how='outer', suffixes=('_BND','_STK') )
data = data.drop(['Date','CURRDATE'],axis=1)

# 需要导入模块: from pandas.io.data import DataReader [as 别名]
# 或者: from pandas.io.data.DataReader import reset_index [as 别名]
                #print sqltxt
                data = psql.read_frame(sqltxt, con)
                sqlDropTxt = 'DROP TABLE "main"."{}"'.format(tb)
                #print sqlDropTxt
                psql.execute(sqlDropTxt, con)
                con.commit()
                psql.write_frame( data, tb, con)
                con.commit()
        
        con.close()

start = datetime(2007,1,1)
end = datetime.date(datetime.now())

data = DataReader("^GSPC", "yahoo", start, end)
data = data.reset_index()
data.Date = [unicode(d) for d in data.Date]
data.Date[0]

data.head()

con = sqlite3.connect("/home/phcostello/Documents/Data/FinanceData.sqlite")
try:
    psql.write_frame( data, "SP500", con)
    con.commit()
except Exception as e:
    print "Problems with {}".format(series[0]), e
con.close()

# 需要导入模块: from pandas.io.data import DataReader [as 别名]
# 或者: from pandas.io.data.DataReader import reset_index [as 别名]
start = dt(2011, 1, 1)
end = dt(2015, 5, 1)
df = DR("1295.KL", 'yahoo', start, end)
dw = DR("^KLSE", 'yahoo', start, end)

df['5D_MA'] = pd.rolling_mean(df['Close'],5)         # calculate moving average
dw['5D_MA'] = pd.rolling_mean(dw['Close'],5)

#start plotting
plt.subplot(2,1,1)
plt.plot(df.index,df['5D_MA'],'r',label='Public Bank 5-day MA')
plt.xlabel('Years')
plt.ylabel('5-day MA')
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
           ncol=4, mode="expand", borderaxespad=0.)
plt.subplot(2,1,2)
plt.plot(dw.index,dw['5D_MA'],'b',label='KLSE 5-day MA')
plt.xlabel('Years')
plt.ylabel('5-day MA')
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
           ncol=4, mode="expand", borderaxespad=0.)        
plt.show()

df = df.reset_index()
dw = dw.reset_index()
mm = pd.merge(df, dw, on='Date', suffixes=['_pbb', '_KLSE'])   # merge dataframe
a = np.corrcoef(mm['Close_pbb'],mm['Close_KLSE'])              # calculate correlation coefficient
print ('Correlation coefficient=', a)

# 需要导入模块: from pandas.io.data import DataReader [as 别名]
# 或者: from pandas.io.data.DataReader import reset_index [as 别名]
import numpy as np
import pandas
import matplotlib.pyplot as plt
from matplotlib.finance import candlestick, candlestick2
import matplotlib.dates as mdates
from pandas.io.data import DataReader

# get daily stock price data from yahoo finance for S&P500
SP = DataReader("^GSPC", "yahoo")
SP.reset_index(inplace=True)
print(SP.columns)
SP['Date2'] = SP['Date'].apply(lambda date: mdates.date2num(date.to_pydatetime()))
fig, ax = plt.subplots()
csticks = candlestick(ax, SP[['Date2', 'Open', 'Close', 'High', 'Low']].values)
plt.show()