Python DataReader.pct_change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 pct_change [as 别名]
class Individual_screener():
    """
    This class will store the relevant information and functions for the 
    signal detection of entry points of one Strategy in one Security
    """
    def __init__(self, symbol, to_date=dt.datetime.today()):
        self.data = False
        self.symbol = symbol
        self.to_date = to_date  
        self.from_date = self.to_date.replace(to_date.year - 1)
        self.signal = False
        
        self.get_prices_yahoo()
        
    def get_prices_yahoo(self):
        """
        It get prices data from yahoo (last year)
        """
        try:
            self.df_prices = DataReader(self.symbol, "yahoo", self.from_date,
                                              self.to_date)
            
            self.df_prices['pct Adj Close'] = self.df_prices.pct_change()['Adj Close'] 
            self.data = True
            
            
        except Exception, e:
            print e
            sleep(5)

# 需要导入模块: from pandas.io.data import DataReader [as 别名]
# 或者: from pandas.io.data.DataReader import pct_change [as 别名]
class Simulation():
    """
    Class that will store all data and functions related to a simulation
    """
    def __init__(self, symbol, from_date=None, to_date=None):
        """
        If dates not entered it will take a default to determine ######### TODO
        """
        self.symbol = symbol
        self.from_date = from_date
        self.to_date = to_date
        self.df_prices = pd.DataFrame()
        self.open = None # Price of current open trade
        self.close = None # Price of current closed trade
        self.status = 'out' # 'out' not invested, 'in' invested
        self.signal = False  # It will get records in which the signal activates
        self.max_open = 0.0 # Max individual investment (for % profit calculation)
        ### Measures
        self.nperiods = 0
        self.ntrades = 0
        self.abs_profit = 0.0 # Accumulated abs_profit ($ value gained/loss)
#         self.pct_simple_profit = 0.0 # Profit over max investment (without reinvestment)
        self.pct_compound_profit = 1.0 # Profit over max investment (with reinvestment)
#         self.pct_annual_simple_profit = 0.0 # Annualized simple profit
#         self.pct_annual_compound_profit = 0.0 # Annualized compound profit
        self.volatility = 0.0 # Volatility of returns (annualized)
        self.sharpe = 0.0 # Sharpe ratio (Rf = 0)
        self.drawdown = 0.0 # It will store the worst abs_profit
        
        ### Years calculation
        d_from_date = date(int(from_date[0:4]), int(from_date[4:6]), int(from_date[6:8]))
        d_to_date = date(int(to_date[0:4]), int(to_date[4:6]), int(to_date[6:8]))
        self.years = (d_to_date-d_from_date).days/365.0
        self.profit_trades = []
        
    def get_prices_yahoo(self):
        """
        It get prices data from yahoo
        """
        try:
            self.df_prices = DataReader(self.symbol, "yahoo", self.from_date,
                                              self.to_date)
            
            self.df_prices['pct Adj Close'] = self.df_prices.pct_change()['Adj Close'] 
            
            
        except Exception, e:
            print e
            raise 

# 需要导入模块: from pandas.io.data import DataReader [as 别名]
# 或者: from pandas.io.data.DataReader import pct_change [as 别名]
import pandas as pd
from datetime import datetime
from pandas.io.data import DataReader

pd.set_printoptions(max_rows=2000)


aapl = DataReader("AAPL", "yahoo", datetime(2010, 1, 1), datetime(2012, 10, 30))
returns = aapl.pct_change()

def f(x):
    if x > 0.01:
        return 1
    elif x < -0.01:
        return -1
    else:
        return 0

frame = returns.applymap(f)

frame['UP'] = frame['Adj Close'] == 1
frame['DOWN'] = frame['Adj Close'] == -1
frame['NONE'] = frame['Adj Close'] ==0

from lxml import etree
import datetime
from dateutil.parser import parse
path = "http://www.google.com/finance/company_news?q=NASDAQ:AAPL&output=rss&num=500"
root = etree.parse(path)
myRoot = root.getroot()
news={}

# 需要导入模块: from pandas.io.data import DataReader [as 别名]
# 或者: from pandas.io.data.DataReader import pct_change [as 别名]
AAPL['Daily Return'] = AAPL['Adj Close'].pct_change()
AAPL['Daily Return'].plot(figsize=(10, 4), legend=True, linestyle='--', marker='o')
plt.show()

sns.distplot(AAPL['Daily Return'].dropna(), bins=100, color='purple')
plt.show()

AAPL['Daily Return'].hist(bins=100)
plt.show()

closing_df = DataReader(tech_list, 'yahoo', start, end)['Adj Close']

print(closing_df.head())

tech_rets = closing_df.pct_change()
print(tech_rets.head())

sns.jointplot('GOOG', 'GOOG', tech_rets, kind = 'scatter', color='seagreen')
plt.show()

sns.jointplot('GOOG', 'MSFT', tech_rets, kind='scatter')
plt.show()

sns.pairplot(tech_rets.dropna())
plt.show()

returns_fig = sns.PairGrid(tech_rets.dropna())
returns_fig.map_upper(plt.scatter, color='purple')
returns_fig.map_lower(sns.kdeplot, cmap='cool_d')
returns_fig.map_diag(plt.hist, bins=30)

# 需要导入模块: from pandas.io.data import DataReader [as 别名]
# 或者: from pandas.io.data.DataReader import pct_change [as 别名]
from pandas.io.data import DataReader

code_names = {
    '005930.KS':'Samsung Elec', '005380.KS':'Hyundai Motor','000660.KS':'SK Hynix', '015760.KS':'kor elect',
    '012330.KS':'Hyundai Mobis', '005490.KS':'POSCO', '017670.KS':'SK tele','^KS11':'KOSPI',
    '035420.KS':'NAVER','055550.KS':'sinhan','032830.KS':'samsung life', '000270.KS':'Kia', '090430.KS':'AmoreF',
    '090430.KS':'LG chemi','105560.KS':'KB','000810.KS':'samsung fire', '034220.KS':'LG display', '033780.KS':'KT&G',
    '051900.KS':'LG health','003550.KS':'LG','034730.KS':'SK cnc', '066570.KS':'LG elec', '002790.KS':'AmoreG',
    '009540.KS':'Samsung shi','006400.KS':'Samsung SDI','086280.KS':'Hy globis', '096770.KS':'SK ino', '000830.KS':'Samsung c&t'}

df = DataReader(code_names.keys(), 'yahoo', start='2014-03-01', end='2015-02-28')

df = df['Adj Close']
df = df.rename(columns=code_names)

changes = df.pct_change()
chg_corr = changes.corr()
chg_corr

plt.figure(figsize=(16,8))
plt.scatter(changes.mean(), changes.std())
plt.xlabel('returns')
plt.ylabel('risk')

for label, x, y in zip(changes.columns, changes.mean(), changes.std()):
    plt.annotate( label,xy=(x, y), xytext=(30, -30),
                  textcoords = 'offset points', ha = 'right', va = 'bottom',
                  bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),
                  arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))
    
plt.show()

# 需要导入模块: from pandas.io.data import DataReader [as 别名]
# 或者: from pandas.io.data.DataReader import pct_change [as 别名]
currency.loc[:, ('Daily Return')] = currency[[0]].pct_change()
	
currencyfig3 = currency[[4]].plot(figsize=(10,4), legend=True, linestyle='--', marker='o')
currencyfig3 = currencyfig3.get_figure()
currencyfig3.savefig('currencyfig3.png')
	
currencyfig4 = sns.distplot(currency[['Daily Return']].dropna(), color='purple')
currencyfig4 = currencyfig4.get_figure()
currencyfig4.savefig('currencyfig4.png')


closing_df = DataReader(fred_currencies, 'fred', start, end)
print '\nDaily Close Snapshot'
print '\n', closing_df.head()

fred_ret = closing_df.pct_change()
print '\nDaily Return Snapshot\n'
print '\n', fred_ret.head()

print "\nSee how currency pairs correlate...\n"

currency1 = raw_input("Enter Currency 1: ")
if currency1 == 'DEXUSEU':
	currency1 = fred_ret[['DEXUSEU']].dropna()
	currency1.rename(columns = {'DEXUSEU':'Return'}, inplace = True)
elif currency1 == 'DEXUSAL':
	currency1 = fred_ret[['DEXUSAL']].dropna()
	currency1.rename(columns = {'DEXUSAL':'Return'}, inplace = True)
elif currency1 == 'DEXUSUK':
	currency1 = fred_ret[['DEXUSUK']].dropna()
	currency1.rename(columns = {'DEXUSUK':'Return'}, inplace = True)

# 需要导入模块: from pandas.io.data import DataReader [as 别名]
# 或者: from pandas.io.data.DataReader import pct_change [as 别名]
prices = pdweb.get_data_yahoo(['CVX','XOM','BP'],start = datetime.datetime(2011,1,1), 
                              end = datetime.datetime(2014,1,1))['Adj Close']
							  
volume = pdweb.get_data_yahoo(['CVX','XOM','BP'],start = datetime.datetime(2011,1,1), 
                              end = datetime.datetime(2014,1,1))['Volume']

rets = prices.pct_change() # returns percent change across columns. Row[1] - Row[0]/Row[0]

%matplotlib inline # plots within Jupyter notebook
prices.plot() # line plot

# A better more advanced way

my_list = ['FB', 'AMZN' , 'NFLX', 'GOOG']
end = datetime.now()
start = datetime(end.year-1, end.month, end.day)

for stock in my_list:
    globals()[stock] = DataReader(stock,'yahoo', start, end) #globals ensures that the variable is available outside the for loop
	
# if you just need one column ... say closing price (for example)
closing_df = DataReader(my_list,'yahoo', start, end)['Adj Close']
dlyReturns_df = closing_df.pct_change().dropna()

## correlations
corr = rets.corr() # returns corelation matrix between columns
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
sns.corrplot(rets,annot = False,diag_names=False)

# 需要导入模块: from pandas.io.data import DataReader [as 别名]
# 或者: from pandas.io.data.DataReader import pct_change [as 别名]

#.........这里部分代码省略.........
        self.pct_compound_profit = 1.0 # Profit over max investment (with reinvestment)
        self.volatility = 0.0 # Volatility of returns (annualized)
        self.sharpe = 0.0 # Sharpe ratio (Rf = 0)
        self.drawdown = 0.0 # It will store the worst abs_profit
        self.df_result = pd.DataFrame()
        
        ## 2. Years calculation
        d_from_date = date(int(from_date[0:4]), int(from_date[4:6]), int(from_date[6:8]))
        d_to_date = date(int(to_date[0:4]), int(to_date[4:6]), int(to_date[6:8]))
        self.years = (d_to_date-d_from_date).days/365.0
        self.profit_trades = []
        
        
        ## 3. Get prices
        ### RNA 04/09/2014 Including prices download from IB, this function will be launch manually
        #self.get_prices_yahoo()
        ### END RNA 04/09/2014
        
    
    def apply_strategy(self, strategy):
        """
        It will apply the selected strategy to this simulation.
        Strategy in this case is a class with all the information it requires
        """
        # 0. In case there is no data, nothing is done
        if not self.data: return 0
        
        # 1. Initialization of variables
        count = 0
        status = 'out'
        s_record = pd.Series()
        pct_profit_temp = 0.0
        self.nperiods = len(self.df_prices.index)-1
        
        # 2. Loop through all the prices
        for pos in range(self.nperiods):
            
            currclose = self.df_prices.ix[pos]['Adj Close']
            change = self.df_prices.ix[pos]['pct Adj Close']
       
            # 3. Check if we are in or out
            if self.status == 'out':
                
                # 4. Check strategy entry condition
                if strategy.check_entry(self.df_prices, pos): ## If performance issue send less
                    self.open_trade(currclose)
                    
            elif self.status == 'in':
                
                # 5. Add profit for measures
                pct_profit_temp += change
                
                # 6. Check stratey exit condition
                if strategy.check_exit(self.df_prices, pos):
                    self.close_trade(currclose, pct_profit_temp)
                    pct_profit_temp = 0.0
            
            # 3. Log
            s_record["date"]=str(self.df_prices.index[pos])
            s_record["price"]=str(currclose)
            s_record["change"]=change
            s_record["status"]=self.status
            s_record["abs_profit"]=self.abs_profit
            s_record["signal"]=self.signal
            self.df_result = self.df_result.append(s_record, ignore_index=True)
            columns = ['date', 'price', 'change', 'status', 'abs_profit', 'signal']
            self.df_result = self.df_result[columns]
            
            prevclose = currclose
            self.signal = False
            
            
        # 3. Print result
#         print "The result is:" + str(self.abs_profit)
#         print "The log is:"
#         print df_result
        
    def close_trade(self, close, pct_profit_trade):
        self.close = close
        self.abs_profit += self.close - self.open 
        self.pct_compound_profit = np.sqrt((1+self.pct_compound_profit)*(1+pct_profit_trade)) - 1.0
        self.status = 'out'
        self.ntrades += 1
        self.profit_trades.append(pct_profit_trade)
        if self.abs_profit < self.drawdown:   # Calculation of drawdown
            self.drawdown = self.abs_profit     
        
        
    def get_prices_yahoo(self):
        """
        It get prices data from yahoo
        """
        try:
            self.df_prices = DataReader(self.symbol, "yahoo", self.from_date,
                                              self.to_date)
            self.df_prices['pct Adj Close'] = self.df_prices.pct_change()['Adj Close'] 
            self.data = True
        except Exception, e:
            print e
            sleep(20)