def parser_for_hs300(start=settings.S_TRAIN_DATE, end=settings.Time_Slot):
'''
from the data folder, get the truncate ts data and the average spread/vol
'''
data_folder = os.path.join(settings.TXT_DATA_PATH, 'hs300')
from pretreat_data.choose_folder import datetime2month
months = datetime2month(start, end)
month2name = lambda x: 'MarketData_000300_SH_' + x + '.csv'
filenames = sorted([month2name(mon) for mon in months])
filenames = [os.path.join(data_folder, fn) for fn in filenames]
tmp_data = []
for fn in filenames:
if not os.path.isfile(fn):
print 'Warning: %s does not existing, will skip it' %fn
continue
else:
with open(fn) as f:
for line in f:
tmp_line = line.split(',')
t = datetime.strptime(tmp_line[0][:15], "%Y%m%d %H%M%S")
index_value = float(tmp_line[1])
tmp_data.append((t, index_value))
tmp_data = np.asarray(tmp_data)
tmp_data = rolling.rolling_anal(start, end, settings.WINDOW_SIZE, tmp_data[:, 0], tmp_data[:, 1])
hs300_matrix = [(i, (j,)) for i, j in tmp_data]
hs300_matrix = truncate(start, end, hs300_matrix)
return hs300_matrix
def parser_for_stock(data_folders, start=settings.S_TRAIN_DATE, end=settings.Time_Slot,
save_to_dict=False):
'''
from the data folder, get the truncate ts data and the average spread/vol
'''
ts_data_dict, score_dict = {}, {}
stock_matrix = []
c = 0
for symbol in settings.SYMBOLS:
print c, symbol
tmp_timeline, tmp_ave, tmp_spread, tmp_vol = [], [], [], []
for folder in data_folders:
try:
filename = [os.path.join(folder, filename)
for filename in os.listdir(folder)
if symbol in filename][0]
except IndexError:
print "Warning: empty file of %s in the data folder %s" %(symbol, folder)
continue
# use txt reader
test_t = datetime.strptime(
open(filename).readline().split(',')[0][:15], "%Y%m%d %H%M%S"
) # initial test_t
with open(filename) as f:
for line in f:
tmp_line = line.split(',')
t = datetime.strptime(tmp_line[0][:15], "%Y%m%d %H%M%S")
b1, s1, v = float(tmp_line[2]), float(tmp_line[12]), float(tmp_line[-1])
ave_price = (b1 + s1)/2.
spread = (b1 - s1)/2.
tmp_timeline.append(t)
tmp_ave.append(ave_price)
tmp_spread.append(spread)
#print t, test_t
if t.day > test_t.day:
tmp_vol.append(cache_v)
else:
test_t = t; cache_v = v
tmp_vol.append(cache_v)
pass
pass
# truncate the raw data to the time series data
tmp_data = np.vstack((tmp_timeline, tmp_ave)).T
# rolling the data
# NOTE: the start and end used here is meaningless, BUG, Lance, 2013/10/20
tmp_data = rolling.rolling_anal(start, end, settings.WINDOW_SIZE, tmp_data[:, 0], tmp_data[:, 1])
# save the numpy array to the dict if request
if save_to_dict:
ts_data_dict[symbol] = tmp_data
# convert to the tuple, make it faster for binding
tmp_data = [(a[0], tuple(a[1:])) for a in tmp_data]
if c == 0:
stock_matrix = tmp_data
else:
if tmp_data != []:
stock_matrix = logic.bind(stock_matrix, tmp_data)
else:
stock_matrix = [(i,j+('NA',)) for i, j in stock_matrix]
if len(tmp_data) != 0:
# here will use the redundant values of spread and
# total prices as the ranking criterian, 2013/10/19
ave_spread = np.average(tmp_spread)
ave_vol = np.average(tmp_vol)
score_dict[symbol] = (ave_spread, ave_vol)
else:
score_dict[symbol] = (None, None)
c = c+1
# selecting symbols and prepare the matrix
selected_index = naive_ranking.naive_ranking(score_dict)
raw_stock_matrix = truncate(start, end, stock_matrix)
stock_matrix = []
for t, s in raw_stock_matrix:
tmp_list = []
for i in selected_index:
tmp_list.append(s[i])
stock_matrix.append((t, tuple(tmp_list)))
matrix_title = []
for i in selected_index:
matrix_title.append(settings.SYMBOLS[i])
return stock_matrix, matrix_title
def parser_for_prediction(data_folders, selected_symbols,
start=settings.S_TRAIN_DATE, end=settings.Time_Slot,
save_to_dict=False):
'''
from the data folder, get the truncate ts data and the average spread/vol
'''
ts_data_dict, score_dict = {}, {}
stock_matrix = []
c = 0
for symbol in selected_symbols:
print c, symbol
tmp_timeline, tmp_ave, tmp_spread, tmp_vol = [], [], [], []
for folder in data_folders:
try:
filename = [os.path.join(folder, filename)
for filename in os.listdir(folder)
if symbol in filename][0]
except IndexError:
print "Warning: empty file of %s in the data folder %s" %(symbol, folder)
continue
# use txt reader
test_t = datetime.strptime(
open(filename).readline().split(',')[0][:15], "%Y%m%d %H%M%S"
) # initial test_t
with open(filename) as f:
for line in f:
tmp_line = line.split(',')
t = datetime.strptime(tmp_line[0][:15], "%Y%m%d %H%M%S")
b1, s1, v = float(tmp_line[2]), float(tmp_line[12]), float(tmp_line[-1])
ave_price = (b1 + s1)/2.
spread = (b1 - s1)/2.
tmp_timeline.append(t)
tmp_ave.append(ave_price)
tmp_spread.append(spread)
#print t, test_t
if t.day > test_t.day:
tmp_vol.append(cache_v)
else:
test_t = t; cache_v = v
tmp_vol.append(cache_v)
pass
pass
# truncate the raw data to the time series data
tmp_data = np.vstack((tmp_timeline, tmp_ave)).T
# rolling the data
# NOTE: the start and end used here is meaningless, BUG, Lance, 2013/10/20
tmp_data = rolling.rolling_anal(start, end, settings.WINDOW_SIZE, tmp_data[:, 0], tmp_data[:, 1])
# save the numpy array to the dict if request
if save_to_dict:
ts_data_dict[symbol] = tmp_data
# convert to the tuple, make it faster for binding
tmp_data = [(a[0], tuple(a[1:])) for a in tmp_data]
if c == 0:
stock_matrix = tmp_data
else:
stock_matrix = logic.merge(stock_matrix, tmp_data)
c = c+1
stock_matrix = truncate(start, end, stock_matrix)
return stock_matrix
