def gentrends(x, window=1 / 3.0, charts=True):
"""
Returns a Pandas dataframe with support and resistance lines.
:param x: One-dimensional data set
:param window: How long the trendlines should be. If window < 1, then it
will be taken as a percentage of the size of the data
:param charts: Boolean value saying whether to print chart to screen
"""
import numpy as np
import pandas.io.data as pd
x = np.array(x)
if window < 1:
window = int(window * len(x))
max1 = np.where(x == max(x))[0][0] # find the index of the abs max
min1 = np.where(x == min(x))[0][0] # find the index of the abs min
# First the max
if max1 + window > len(x):
max2 = max(x[0:(max1 - window)])
else:
max2 = max(x[(max1 + window):])
# Now the min
if min1 - window < 0:
min2 = min(x[(min1 + window):])
else:
min2 = min(x[0:(min1 - window)])
# Now find the indices of the secondary extrema
max2 = np.where(x == max2)[0][0] # find the index of the 2nd max
min2 = np.where(x == min2)[0][0] # find the index of the 2nd min
# Create & extend the lines
maxslope = (x[max1] - x[max2]) / (max1 - max2) # slope between max points
minslope = (x[min1] - x[min2]) / (min1 - min2) # slope between min points
a_max = x[max1] - (maxslope * max1) # y-intercept for max trendline
a_min = x[min1] - (minslope * min1) # y-intercept for min trendline
b_max = x[max1] + (maxslope * (len(x) - max1)) # extend to last data pt
b_min = x[min1] + (minslope * (len(x) - min1)) # extend to last data point
maxline = np.linspace(a_max, b_max, len(x)) # Y values between max's
minline = np.linspace(a_min, b_min, len(x)) # Y values between min's
# OUTPUT
trends = np.transpose(np.array((x, maxline, minline)))
trends = pd.DataFrame(trends,
index=np.arange(0, len(x)),
columns=['Data', 'Max Line', 'Min Line'])
if charts is True:
from matplotlib.pyplot import plot, grid, show
plot(trends)
grid()
show()
return trends, maxslope, minslope
def stock(symbol):
"""
gets last traded price from google for given security
"""
import pandas.io.data as pd
df = pd.get_quote_yahoo(symbol)
#print(df)
cols = ['PE', 'change_pct', 'last', 'short_ratio', 'time']
result = pd.DataFrame(df, columns=cols)
return result.iloc[0]['last']
def getPrice(start, end , company):
allStock = {}
startDate = start
endDate = end
listOfCompanies = company
for ticket in listOfCompanies:
allStock[ticket] = web.get_data_yahoo(ticket,startDate,endDate)
price = web.DataFrame({tic:data['Adj Close'] for tic,data in allStock.iteritems()})
sumStock = DataFrame(data = price.sum(axis=1),columns = ['individialStockSum'])
sumStock['dowJones'] = Series(web.get_data_yahoo('DJIA',startDate,endDate)['Adj Close'],index = sumStock.index)
return sumStock
@author: jmalinchak
"""
#from pandas.io.data import DataReader
#from datetime import datetime
#msft = DataReader("MSFT", "yahoo")
#print(msft.bid)
import pandas.io.data as pd
df = pd.get_quote_yahoo('HPQ')
#print(df)
cols = ['PE', 'change_pct', 'last', 'short_ratio', 'time']
result = pd.DataFrame(df, columns=cols)
print(result.iloc[0]['last'])
#data = {'year': [2010, 2011, 2012, 2011, 2012, 2010, 2011, 2012],
# 'team': ['Bears', 'Bears', 'Bears', 'Packers', 'Packers', 'Lions', 'Lions', 'Lions'],
# 'wins': [11, 8, 10, 15, 11, 6, 10, 4],
# 'losses': [5, 8, 6, 1, 5, 10, 6, 12]}
#
#football = pd.DataFrame(data, columns=['year', 'team', 'wins', 'losses'])
#print(football)
#
#from_csv = pd.read_csv('C:\\Documents and Settings\\jmalinchak\\My Documents\\My Python\\Active\inputs\\Options AAPL 2015-01-17 20141203221727.csv')
#from_csv.head()
#print(from_csv)
#
#cols = ['num', 'game', 'date', 'team', 'home_away', 'opponent',
# 'result', 'quarter', 'distance', 'receiver', 'score_before',