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 getPreMarketVol( symbol, list_GapDate, folder_input='/Users/yutaasano/Documents/PyCharm/Stock/HistStkPrice_30min' ):
"""
Return pre-market volume for a given stock symbol for given dates
list_GapDate is a nx3 numpy array, each row: year, month, day
"""
n_date = list_GapDate.shape[0]
data_30min = fi.getHistStkPrice30min(symbol, folder_input)
# For each rebound date, calculate pre-market volume from the time slot with largest open-close price difference
vol_PreMarket = np.int64(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
t_row_GapDateInit = 0
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 (just before market open, typically row for 9:00)
if np.int64(data_30min[t_row_ub,3]) >= 930:
pass
else:
while np.int64(data_30min[t_row_ub,3]) < 930:
t_row_ub += 1
t_row_ub -= 1 # back 1 row to get a row just before market open
t_row_lb = t_row_GapDateInit-1 # lower bound row (just after previous day's market close, typically row for 16:00)
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
# Find a row (time slot) that produced largest open-close price difference
t_gapIntra = 0
t_row_GapIntraLargest = t_row_lb
for k_row in range(t_row_lb, t_row_ub+1):
t_gapIntra_k = abs(data_30min[k_row,7] - data_30min[k_row,4])
if t_gapIntra_k > t_gapIntra:
t_gapIntra = t_gapIntra_k
t_row_GapIntraLargest = k_row
# Sum Volume from the row with largest open-close price difference to the upper bound row
vol_PreMarket[k_date] = int(sum(data_30min[t_row_GapIntraLargest:t_row_ub+1,8]))
else:
vol_PreMarket[k_date] = np.nan # no gap date found for this stock
return vol_PreMarket