def update(self, ma_file, datafile, datalist, period='d'):
first_calc = [False, False, False, False, False, False]
ma_result = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] #this data calc each ma result
prev_manum_price = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] # prev manum data
queue = [[], [], [], [], [], []] # each hold queue for hold prev n data
madata = csvdata(ma_file)
data = csvdata(datafile)
end_price_array = data.get_elem_list_last_n('end_val', self.macfg[-1] + len(datalist))
end_price_array = end_price_array[:-len(datalist)]
end_price_number = len(end_price_array)
ma_last_data = madata.read_last()
new_ma_result_list = []
# build ma need result, includes last ma_number endprice and last ma_result
for ma_num in self.macfg:
idx = self.ma_idx[ma_num]
data_queue = queue[idx]
ma_result[idx] = float(ma_last_data[idx + 1]) # +1 for first is date
if end_price_number < ma_num:
last_n_data = map(lambda x: float(x[1]), end_price_array[-end_price_number:])
prev_manum_price[idx] = None
else:
last_n_data = map(lambda x: float(x[1]), end_price_array[1-ma_num:])
prev_manum_price[idx] = float(end_price_array[-ma_num][1])
data_queue.extend(last_n_data)
for i, data in enumerate(datalist):
datadate = data[0]
end_price = float(data[1])
for ma_num in self.macfg:
idx = self.ma_idx[ma_num]
data_queue = queue[idx]
data_queue.append(end_price)
length = len(data_queue)
#add a queue to calc ma price
if length == ma_num:
if first_calc[idx] == False:
average_price = sum(data_queue)/length
first_calc[idx] = True
else:
# an accelerate method to calc average, keep precision in calc
average_price = ma_result[idx] + (end_price - prev_manum_price[idx]) / ma_num
ma_result[idx] = average_price
prev_manum_price[idx] = data_queue.pop(0)
result = [datadate]
for i in range(self.ma_number):
try:
round_price = ma_result[i]
#print "i: %s, price: %s, type:%s"%(i, round_price, type(round_price))
round_price = round(round_price, 3)
result.append(round_price)
except:
print ma_result[i]
print i
new_ma_result_list.append(result)
# finally add all calc result to db
madata.append_data(new_ma_result_list, period)
def __init__(self, filepath):
self.filepath = filepath
self.daydata = csvdata(os.path.join(filepath, 'day.csv'))
self.weekdata = csvdata(os.path.join(filepath, 'week.csv'))
self.monthdata = csvdata(os.path.join(filepath, 'month.csv'))
self.daydata.read()
self.weekdata.read()
self.monthdata.read()
def __init__(self, filepath):
self.filepath = filepath
self.daydata = csvdata(os.path.join(filepath, "day.csv"))
self.weekdata = csvdata(os.path.join(filepath, "week.csv"))
self.monthdata = csvdata(os.path.join(filepath, "month.csv"))
self.daydata.read()
self.weekdata.read()
self.monthdata.read()
def update(self, macd_file, today_data, period='d'):
macddata = csvdata(macd_file)
lastdata = macddata.read_last()
if lastdata == None:
self.calc(today_data)
self.store(macd_file)
return
ema_short = float(lastdata[self.IDX_EMA_SHORT])
ema_long = float(lastdata[self.IDX_EMA_LONG])
dea = float(lastdata[self.IDX_DEA_IDX2])
result = []
for data in today_data:
end_price = float(data[1])
ema_short = ema_short * (
self.short - 2) / self.short + end_price * 2 / self.short
ema_long = ema_long * (self.long -
2) / self.long + end_price * 2 / self.long
ema_short = round(ema_short, 4)
ema_long = round(ema_long, 4)
diff = ema_short - ema_long
dea = dea * (self.m - 2) / self.m + diff * 2 / self.m
dea = round(dea, 4)
macd = 2 * (diff - dea)
result.append([
data[0],
round(diff, 3),
round(dea, 3),
round(macd, 3), ema_short, ema_long, dea
])
macddata.append_data(result, period)
def update(self, macd_file, today_data, period='d'):
macddata = csvdata(macd_file)
lastdata = macddata.read_last()
if lastdata == None:
self.calc(today_data)
self.store(macd_file)
return
ema_short = float(lastdata[self.IDX_EMA_SHORT])
ema_long = float(lastdata[self.IDX_EMA_LONG])
dea = float(lastdata[self.IDX_DEA_IDX2])
result = []
for data in today_data:
end_price = float(data[1])
ema_short = ema_short * (self.short - 2)/self.short + end_price * 2/self.short
ema_long = ema_long * (self.long - 2)/self.long + end_price * 2/self.long
ema_short = round(ema_short, 4)
ema_long = round(ema_long, 4)
diff = ema_short - ema_long
dea = dea * (self.m - 2)/self.m + diff * 2/self.m
dea = round(dea, 4)
macd = 2*(diff - dea)
result.append([data[0], round(diff, 3), round(dea, 3), round(macd, 3), ema_short, ema_long, dea])
macddata.append_data(result, period)
def store_csv(self, inputfile='/tmp/stock.csv'):
if self.data_json != None:
csv_data = csvdata(inputfile)
if self.period == 'd':
csv_data.write(self.data_json['hq'])
else:
# judge whether need delete last line to update week or month data
if csv_data.len() >= 1:
last_date = csv_data.read_last_date()
recv_first_date = self.data_json['hq'][0][0]
#print "recvdate: " + recv_first_date + " last_date: " + last_date
deletelast = False
if self.period == 'w' and issameweek(last_date, recv_first_date):
deletelast = True
elif self.period == 'm' and issamemonth(last_date, recv_first_date):
deletelast = True
if deletelast:
csv_data.del_last()
csv_data.write(self.data_json['hq'], overwrite= True)
else:
csv_data.write(self.data_json['hq'])
else:
csv_data.write(self.data_json['hq'])
else:
print "Store to " + inputfile + " failed. Data not fetched, maybe code error."
def algodata_check(datafile, fullpath):
algo_prefix = ["_ma.csv", "_macd.csv"]
algo_src = []
datapath = os.path.join(fullpath, datafile)
datasrc = csvdata(datapath)
for algo in algo_prefix:
algofile = datafile[0: datafile.index('.')] + algo
algofilepath = os.path.join(fullpath, algofile)
algo_src.append(csvdata(algofilepath))
for data in datasrc:
finddate = data[0]
excep = False
for algo in algo_src:
if algo.hasdate(finddate):
continue
else:
excep = True
print "%s dos not have date %s, break"%(algo.filename(), finddate)
if excep:
break
def algodata_check(datafile, fullpath):
algo_prefix = ["_ma.csv", "_macd.csv"]
algo_src = []
datapath = os.path.join(fullpath, datafile)
datasrc = csvdata(datapath)
for algo in algo_prefix:
algofile = datafile[0 : datafile.index(".")] + algo
algofilepath = os.path.join(fullpath, algofile)
algo_src.append(csvdata(algofilepath))
for data in datasrc:
finddate = data[0]
excep = False
for algo in algo_src:
if algo.hasdate(finddate):
continue
else:
excep = True
print "%s dos not have date %s, break" % (algo.filename(), finddate)
if excep:
break
def conv_from_csv(self, csvfile):
csv_data = csvdata(csvfile)
csv_data.read()
maxdate = None
mindate = None
for data in csv_data.data.values():
print data
self.add(data[0], data)
if data[0] > maxdate or maxdate == None:
maxdate = data[0]
if data[0] < mindate or mindate == None:
mindate = data[0]
print "MaxDate: " + maxdate + " MinDate: " + mindate
self.data['range'] = [maxdate, mindate]
self.data.close()
def conv_from_csv(self, csvfile):
csv_data = csvdata(csvfile)
csv_data.read()
maxdate = None
mindate = None
for data in csv_data.data.values():
print data
self.add(data[0], data)
if data[0] > maxdate or maxdate == None:
maxdate = data[0]
if data[0] < mindate or mindate == None:
mindate = data[0]
print "MaxDate: " + maxdate + " MinDate: " + mindate
self.data['range'] = [maxdate, mindate]
self.data.close()
def check_period(self, daydata, w_m_data, cmp_func, newfile, force = False):
if daydata.len() == 0:
return
first_date = get_elem(daydata.read_index(0), 'date')
if w_m_data.len() != 0:
first_date2 = get_elem(w_m_data.read_index(0), 'date')
if w_m_data.len() != 0 and cmp_func(first_date, first_date2) and force == False:
return True
else:
new_data_list = []
data_same_period = []
min_val = 0.0
max_val = 0.0
volume = 0.0
volume_money = 0.0
diff = 0.0
diff_ratio = 0.0
last_date = None
weekstart = True
last_end_val = 0.0
start_val = 0.0
end_val = 0.0
for idx, data in enumerate(self.daydata):
# not same week, record last week data
if data_same_period != [] and cmp_func(get_elem(data, 'date'), get_elem(data_same_period[-1], 'date')) is False:
week_data = [last_date, "%.2f" % start_val, "%.2f" % end_val, diff, diff_ratio,
"%.2f" % min_val, "%.2f" % max_val, volume, "%.2f"%volume_money, 0.0]
new_data_list.append(week_data)
last_end_val = end_val
data_same_period = []
weekstart = True
if weekstart == True:
weekstart = False
start_val = float(get_elem(data, 'start_val'))
min_val = float(get_elem(data, 'low_val'))
max_val = float(get_elem(data, 'high_val'))
volume = 0.0
volume_money = 0.0
else:
min_val = min(min_val, float(get_elem(data, 'low_val')))
max_val = max(max_val, float(get_elem(data, 'high_val')))
data_same_period.append(data)
volume = volume + float(get_elem(data, 'volume'))
volume_money = volume_money + float(get_elem(data, 'volume_money'))
end_val = float(get_elem(data, 'end_val'))
if last_end_val != 0:
diff = '%.2f'%(end_val - last_end_val)
diff_ratio = '%.2f%%'%(((end_val-last_end_val)/last_end_val)*100)
else:
diff = "0.00"
diff_ratio = "0.00%"
last_date = get_elem(data, 'date')
week_data = [last_date, "%.2f" % start_val, "%.2f" % end_val, diff, diff_ratio,
"%.2f" % min_val, "%.2f" % max_val, volume, "%.2f"% volume_money, 0.0]
new_data_list.append(week_data)
newdata = csvdata(os.path.join(self.filepath, newfile))
newdata.write(new_data_list)
print "Update %s data to %s "%(self.filepath, newfile)
def store(self, output_file):
if self.result_list:
store = csvdata(output_file)
store.write(self.result_list)
self.result_list = []
def store_csv(self, inputfile='/tmp/stock.csv'):
if self.data_json != None:
csv_data = csvdata(inputfile)
csv_data.write(self.data_json['hq'])
else:
print "Store to " + inputfile + " failed. Data not fetched, maybe code error."
def check_period(self, daydata, w_m_data, cmp_func, newfile, force=False):
if daydata.len() == 0:
return
first_date = get_elem(daydata.read_index(0), "date")
if w_m_data.len() != 0:
first_date2 = get_elem(w_m_data.read_index(0), "date")
if w_m_data.len() != 0 and cmp_func(first_date, first_date2) and force == False:
return True
else:
new_data_list = []
data_same_period = []
min_val = 0.0
max_val = 0.0
volume = 0.0
volume_money = 0.0
diff = 0.0
diff_ratio = 0.0
last_date = None
weekstart = True
last_end_val = 0.0
start_val = 0.0
end_val = 0.0
for idx, data in enumerate(self.daydata):
# not same week, record last week data
if (
data_same_period != []
and cmp_func(get_elem(data, "date"), get_elem(data_same_period[-1], "date")) is False
):
week_data = [
last_date,
"%.2f" % start_val,
"%.2f" % end_val,
diff,
diff_ratio,
"%.2f" % min_val,
"%.2f" % max_val,
volume,
"%.2f" % volume_money,
0.0,
]
new_data_list.append(week_data)
last_end_val = end_val
data_same_period = []
weekstart = True
if weekstart == True:
weekstart = False
start_val = float(get_elem(data, "start_val"))
min_val = float(get_elem(data, "low_val"))
max_val = float(get_elem(data, "high_val"))
volume = 0.0
volume_money = 0.0
else:
min_val = min(min_val, float(get_elem(data, "low_val")))
max_val = max(max_val, float(get_elem(data, "high_val")))
data_same_period.append(data)
volume = volume + float(get_elem(data, "volume"))
volume_money = volume_money + float(get_elem(data, "volume_money"))
end_val = float(get_elem(data, "end_val"))
if last_end_val != 0:
diff = "%.2f" % (end_val - last_end_val)
diff_ratio = "%.2f%%" % (((end_val - last_end_val) / last_end_val) * 100)
else:
diff = "0.00"
diff_ratio = "0.00%"
last_date = get_elem(data, "date")
week_data = [
last_date,
"%.2f" % start_val,
"%.2f" % end_val,
diff,
diff_ratio,
"%.2f" % min_val,
"%.2f" % max_val,
volume,
"%.2f" % volume_money,
0.0,
]
new_data_list.append(week_data)
newdata = csvdata(os.path.join(self.filepath, newfile))
newdata.write(new_data_list)
print "Update %s data to %s " % (self.filepath, newfile)
def update(self, ma_file, datafile, datalist, period='d'):
first_calc = [False, False, False, False, False, False]
ma_result = [0.0, 0.0, 0.0, 0.0, 0.0,
0.0] #this data calc each ma result
prev_manum_price = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] # prev manum data
queue = [[], [], [], [], [],
[]] # each hold queue for hold prev n data
madata = csvdata(ma_file)
data = csvdata(datafile)
end_price_array = data.get_elem_list_last_n(
'end_val', self.macfg[-1] + len(datalist))
end_price_array = end_price_array[:-len(datalist)]
end_price_number = len(end_price_array)
ma_last_data = madata.read_last()
new_ma_result_list = []
# build ma need result, includes last ma_number endprice and last ma_result
for ma_num in self.macfg:
idx = self.ma_idx[ma_num]
data_queue = queue[idx]
ma_result[idx] = float(ma_last_data[idx +
1]) # +1 for first is date
if end_price_number < ma_num:
last_n_data = map(lambda x: float(x[1]),
end_price_array[-end_price_number:])
prev_manum_price[idx] = None
else:
last_n_data = map(lambda x: float(x[1]),
end_price_array[1 - ma_num:])
prev_manum_price[idx] = float(end_price_array[-ma_num][1])
data_queue.extend(last_n_data)
for i, data in enumerate(datalist):
datadate = data[0]
end_price = float(data[1])
for ma_num in self.macfg:
idx = self.ma_idx[ma_num]
data_queue = queue[idx]
data_queue.append(end_price)
length = len(data_queue)
#add a queue to calc ma price
if length == ma_num:
if first_calc[idx] == False:
average_price = sum(data_queue) / length
first_calc[idx] = True
else:
# an accelerate method to calc average, keep precision in calc
average_price = ma_result[idx] + (
end_price - prev_manum_price[idx]) / ma_num
ma_result[idx] = average_price
prev_manum_price[idx] = data_queue.pop(0)
result = [datadate]
for i in range(self.ma_number):
try:
round_price = ma_result[i]
#print "i: %s, price: %s, type:%s"%(i, round_price, type(round_price))
round_price = round(round_price, 3)
result.append(round_price)
except:
print ma_result[i]
print i
new_ma_result_list.append(result)
# finally add all calc result to db
madata.append_data(new_ma_result_list, period)
prev_manum_price[idx] = data_queue.pop(0)
result = [datadate]
for i in range(self.ma_number):
try:
round_price = ma_result[i]
#print "i: %s, price: %s, type:%s"%(i, round_price, type(round_price))
round_price = round(round_price, 3)
result.append(round_price)
except:
print ma_result[i]
print i
new_ma_result_list.append(result)
# finally add all calc result to db
madata.append_data(new_ma_result_list, period)
def store(self, output_file):
if self.result_list:
store = csvdata(output_file)
store.write(self.result_list)
self.result_list = []
if __name__ == '__main__':
path = os.path.join(os.getcwd(), 'stockdata', '603999_读者传媒')
ma_obj = ma()
data = csvdata(os.path.join(path, 'day.csv'))
ma_obj.update(os.path.join(path, 'day_ma.csv'),
os.path.join(path, 'day.csv'),
data.get_elem_list_last_n('end_val', 5))
#ma_obj.calc(data.get_elem_list('end_val'))
#ma_obj.store(os.path.join(path, 'day_ma.csv'))
# an accelerate method to calc average, keep precision in calc
average_price = ma_result[idx] + (end_price - prev_manum_price[idx]) / ma_num
ma_result[idx] = average_price
prev_manum_price[idx] = data_queue.pop(0)
result = [datadate]
for i in range(self.ma_number):
try:
round_price = ma_result[i]
#print "i: %s, price: %s, type:%s"%(i, round_price, type(round_price))
round_price = round(round_price, 3)
result.append(round_price)
except:
print ma_result[i]
print i
new_ma_result_list.append(result)
# finally add all calc result to db
madata.append_data(new_ma_result_list, period)
def store(self, output_file):
if self.result_list:
store = csvdata(output_file)
store.write(self.result_list)
self.result_list = []
if __name__ == '__main__':
path = os.path.join(os.getcwd(), 'stockdata', '603999_读者传媒')
ma_obj = ma()
data = csvdata(os.path.join(path, 'day.csv'))
ma_obj.update(os.path.join(path, 'day_ma.csv'), os.path.join(path, 'day.csv'), data.get_elem_list_last_n('end_val', 5))
#ma_obj.calc(data.get_elem_list('end_val'))
#ma_obj.store(os.path.join(path, 'day_ma.csv'))
ema_short = round(ema_short, 4)
ema_long = round(ema_long, 4)
diff = ema_short - ema_long
dea = dea * (self.m - 2) / self.m + diff * 2 / self.m
dea = round(dea, 4)
macd = 2 * (diff - dea)
result.append([
data[0],
round(diff, 3),
round(dea, 3),
round(macd, 3), ema_short, ema_long, dea
])
macddata.append_data(result, period)
def store(self, output_file):
if self.result_list:
store = csvdata(output_file)
store.write(self.result_list)
self.result_list = []
if __name__ == '__main__':
path = os.getcwd() + os.sep + 'stockdata' + os.sep + "000002_万科A" + os.sep
ma = macd()
data = csvdata(path + 'day.csv')
ma.update(path + 'day_macd.csv', data.read_last())
#src = csvdata('/tmp/stock.csv')
#ma = macd()
#ma.calc(src.get_elem_list('end_val'))
#ma.store('/tmp/macd.csv')
def store(self, output_file):
if self.result_list:
store = csvdata(output_file)
store.write(self.result_list)
self.result_list = []
for data in today_data:
end_price = float(data[1])
ema_short = ema_short * (self.short - 2)/self.short + end_price * 2/self.short
ema_long = ema_long * (self.long - 2)/self.long + end_price * 2/self.long
ema_short = round(ema_short, 4)
ema_long = round(ema_long, 4)
diff = ema_short - ema_long
dea = dea * (self.m - 2)/self.m + diff * 2/self.m
dea = round(dea, 4)
macd = 2*(diff - dea)
result.append([data[0], round(diff, 3), round(dea, 3), round(macd, 3), ema_short, ema_long, dea])
macddata.append_data(result, period)
def store(self, output_file):
if self.result_list:
store = csvdata(output_file)
store.write(self.result_list)
self.result_list = []
if __name__ == '__main__':
path = os.getcwd() + os.sep + 'stockdata' + os.sep + "000002_万科A" + os.sep
ma = macd()
data = csvdata(path + 'day.csv')
ma.update(path + 'day_macd.csv', data.read_last())
#src = csvdata('/tmp/stock.csv')
#ma = macd()
#ma.calc(src.get_elem_list('end_val'))
#ma.store('/tmp/macd.csv')
