def getPreMarketChange( symbol, list_GapDate, folder_30min='/Users/yutaasano/Documents/PyCharm/StockData/HistStkPrice_30min_Russell3000_Kibot_20150912' ):
"""
Return pre-market change for a given stock symbol for given dates
list_GapDate is a nx3 numpy array, each row: year, month, day. It has to be sorted from old to new dates for faster process
Pre-market change [%] is defined as (O(k_30-1) - C(k_day-1)) / C(k_day-1) * 100
O(k_30-1): open price at 9:00
C(k_day-1): close price at previous day's 15:30
"""
# Check if list_GapDate is sorted
list_GapDate_sorted = m.sortrowsByMultiCol(list_GapDate, [0,1,2], isAscending=1)
if np.sum(np.abs(list_GapDate_sorted-list_GapDate)) != 0:
raise ValueError('list_GapDate has to be sorted from old to new dates!')
n_date = list_GapDate.shape[0]
data_30min = fi.getHistStkPrice30min(symbol, folder_30min)
t_row_GapDateInit = 0
change_PreMarket = np.zeros((n_date))
for k_date in range(0,n_date):
t_GapDate = list_GapDate[k_date,:]
# Look for a row where t_GapDate first occurs
while t_row_GapDateInit<data_30min.shape[0]:
t_30minDate = np.int64(data_30min[t_row_GapDateInit,0:3])
if sum(np.abs(t_30minDate - t_GapDate)) == 0:
break
else:
t_row_GapDateInit += 1
if t_row_GapDateInit!=data_30min.shape[0]:
t_row_ub = t_row_GapDateInit # upper bound row (open price 30 min before market open, typically row for 9:00)
if np.int64(data_30min[t_row_ub,3]) >= 900:
pass
else:
while np.int64(data_30min[t_row_ub,3]) < 900:
t_row_ub += 1
t_row_lb = t_row_GapDateInit-1 # lower bound row (previous day's market close, typically row for 15:30)
if np.int64(data_30min[t_row_lb,3]) < 1600:
pass
else:
while np.int64(data_30min[t_row_lb,3]) >= 1600:
t_row_lb -= 1
# Record only if the upper bound is at 9:00
if int(data_30min[t_row_ub,3]) == 900:
change_PreMarket[k_date] = ( data_30min[t_row_ub,4]-data_30min[t_row_lb,7] ) / data_30min[t_row_lb,7] * 100
else:
change_PreMarket[k_date] = np.nan
else:
change_PreMarket[k_date] = np.nan # no gap date found for this stock
t_row_GapDateInit = 0 # re-initialize the gap date location
return change_PreMarket
def loadRebFile(file_rebound, list_sortCol=[]):
data_raw = np.genfromtxt(file_rebound, dtype=None, delimiter=',')
data_raw = data_raw[1:,:] # remove first row
n_data = data_raw.shape[0]
if list_sortCol:
print('sorting...')
data_raw = m.sortrowsByMultiCol(data_raw, list_sortCol)
data_StkSize = [] # Stock Size (e.g., 'L', 'M', 'S', 'N')
data_Symbol = [] # Stock Symbol
data_Date = np.zeros((n_data,3), dtype='int64') # Year, Month, Day
data_Vol = np.zeros((n_data,2), dtype='int64') # Daily Volume, Average Volume
data_Gap_Rebound = np.zeros((n_data,6)) # Gap, Rebounds at Five Timings
for k_row in range(0,n_data):
data_StkSize.append(data_raw[k_row,0].decode('UTF-8'))
data_Symbol.append(data_raw[k_row,1].decode('UTF-8').strip()) # str.strip() is used to remove white space: ' INTC' -> 'INTC'
data_Date[k_row,:] = np.int_(np.float_([k_elm.decode('UTF-8') for k_elm in data_raw[k_row,2:5]]))
data_Vol[k_row,:] = np.int_(np.float_([k_elm.decode('UTF-8') for k_elm in data_raw[k_row,5:7]]))
data_Gap_Rebound[k_row,:] = np.float_([k_elm.decode('UTF-8') for k_elm in data_raw[k_row,7:]])
data_StkSize = np.array(data_StkSize)
data_Symbol = np.array(data_Symbol)
return data_StkSize, data_Symbol, data_Date, data_Vol, data_Gap_Rebound
