def findEvents(symbols, startday,endday, marketSymbol,verbose=False):
# Reading the Data for the list of Symbols.
timeofday=dt.timedelta(hours=16)
timestamps = du.getSSEdays(startday,endday,timeofday)
dataobj = da.DataAccess('Yahoo')
if verbose:
print __name__ + " reading data"
# Reading the Data
close = dataobj.get_data(timestamps, symbols, closefield)
# Completing the Data - Removing the NaN values from the Matrix
close = (close.fillna(method='ffill')).fillna(method='backfill')
# Calculating Daily Returns for the Market
#tsu.returnize0(close.values)
SPYValues=close[marketSymbol]
# Calculating the Returns of the Stock Relative to the Market
# So if a Stock went up 5% and the Market rised 3%. The the return relative to market is 2%
mktneutDM = close
np_eventmat = copy.deepcopy(mktneutDM)
for sym in symbols:
for time in timestamps:
np_eventmat[sym][time]=np.NAN
if verbose:
print __name__ + " finding events"
# Generating the Event Matrix
# Event described is : Market falls more than 3% plus the stock falls 5% more than the Market
# Suppose : The market fell 3%, then the stock should fall more than 8% to mark the event.
# And if the market falls 5%, then the stock should fall more than 10% to mark the event.
output = open(output_flie, 'w')
output_content = []
for i in range(1,len(timestamps)):
for symbol in symbols:
if close[symbol][i-1] >= 7 and close[symbol][i] < 7 : # When market fall is more than 3% and also the stock compared to market is also fell by more than 5%.
np_eventmat[symbol][i] = 1.0 #overwriting by the bit, marking the event
buy_date = timestamps[i]
if i+5 < len(mktneutDM[symbol]):
sell_date = timestamps[i+5];
else:
sell_date = timestamps[-1];
output_line = []
output_line.append(buy_date);
output_line.append(symbol)
output_line.append("Buy")
output_line.append(100)
output_content.append(output_line)
output_line = []
output_line.append(sell_date);
output_line.append(symbol)
output_line.append("Sell")
output_line.append(100)
output_content.append(output_line)
output_content = sorted(output_content, key = lambda x: x[0])
for item in output_content:
d = item[0];
line = str(d.year) + ","+ str(d.month) + "," + str(d.day) + ","
line += item[1] + ","
line += item[2] + "," + str(item[3])+"\n"
output.write(line)
output.close()
return np_eventmat
def findEvents(symbols, startday, endday, marketSymbol, verbose=False):
# Reading the Data for the list of Symbols.
timeofday = dt.timedelta(hours=0)
timestamps = du.getSSEdays(startday, endday, timeofday)
dataobj = da.DataAccess('Yahoo')
if verbose:
print __name__ + " reading data"
# Reading the Data
close = dataobj.get_data(timestamps, symbols, closefield)
for sym in symbols:
print sym
# Completing the Data - Removing the NaN values from the Matrix
#close = (close.fillna(method='ffill')).fillna(method='backfill')
#print close['SPY'][0], close['SPY'][1]
# Calculating Daily Returns for the Market
#tsu.returnize0(close.values)
SPYValues = close[marketSymbol]
# Calculating the Returns of the Stock Relative to the Market
# So if a Stock went up 5% and the Market rised 3%. The the return relative to market is 2%
mktneutDM = close
np_eventmat = copy.deepcopy(mktneutDM)
for sym in symbols:
for time in timestamps:
np_eventmat[sym][time] = np.NAN
if verbose:
print __name__ + " finding events"
# Generating the Event Matrix
# Event described is : Market falls more than 3% plus the stock falls 5% more than the Market
# Suppose : The market fell 3%, then the stock should fall more than 8% to mark the event.
# And if the market falls 5%, then the stock should fall more than 10% to mark the event.
output = open(output_flie, 'w')
output_content = []
for i in range(1, len(timestamps)):
for symbol in symbols:
if close[symbol][i - 1] >= 7 and close[symbol][
i] < 7: # When market fall is more than 3% and also the stock compared to market is also fell by more than 5%.
np_eventmat[symbol][
i] = 1.0 #overwriting by the bit, marking the event
buy_date = timestamps[i]
if i + 5 < len(mktneutDM[symbol]):
sell_date = timestamps[i + 5]
else:
sell_date = timestamps[-1]
output_line = []
output_line.append(buy_date)
output_line.append(symbol)
output_line.append("Buy")
output_line.append(100)
output_content.append(output_line)
output_line = []
output_line.append(sell_date)
output_line.append(symbol)
output_line.append("Sell")
output_line.append(100)
output_content.append(output_line)
output_content = sorted(output_content, key=lambda x: x[0])
for item in output_content:
d = item[0]
line = str(d.year) + "," + str(d.month) + "," + str(d.day) + ","
line += item[1] + ","
line += item[2] + "," + str(item[3]) + "\n"
output.write(line)
output.close()
return np_eventmat
item.append(date)
item.append(trade_data[3])
item.append(trade_data[4])
item.append(int(trade_data[5]))
trades.append(item)
symbols.append(trade_data[3])
print trades
#startday = dt.datetime(2011,1,1)
#endday = dt.datetime(2012,12,26)
startday = trades[0][0]
endday = trades[-1][0]
"""step2 read history data"""
symbols = set(symbols)
dataobj = da.DataAccess('Yahoo')
timeofday = dt.timedelta(hours=16)
timestamps = du.getSSEdays(startday, endday, timeofday)
close = dataobj.get_data(timestamps, symbols, closefield)
close = (close.fillna(method='ffill')).fillna(method='backfill')
"""step 3 caculate value"""
values = {}
for item in timestamps:
values[item] = cash
stock = {}
for sym in symbols:
stock[sym] = 0
trade_t = startday
for i in range(len(trades)):
t = trades[i]
t_next = t
