def sort_row_list_and_convert_into_cells():
''' this is a complex function,
it sorts by row,
then find each attribute,
then looks for the attribute object,
then sees if the value is outside the bounds of a standard deviation,
then sets object colors
'''
extra_rows = 0
extra_rows += function_globals.default_row_buffer_before_stocks
score_avg = None
score_std = None
# first, get score avg and std
for attribute_obj in attribute_list:
if attribute_obj.name == "Score":
score_avg = attribute_obj.avg
score_std = attribute_obj.std
first_iteration = True
last_sigma_stage = None
function_globals.row_list.sort(key = lambda x: x.Score.text, reverse=True)
for stock_row in function_globals.row_list:
# Now, check if we need a blank row between sigma's
if stock_row.Score.text > (score_avg + (score_std * 3)):
# greater than 3 sigmas
if first_iteration:
first_iteration = False
last_sigma_stage = 3
elif stock_row.Score.text > (score_avg + (score_std * 2)):
# greater than 2 sigmas
if first_iteration:
first_iteration = False
last_sigma_stage = 2
if last_sigma_stage > 2:
last_sigma_stage = 2
empty_row_num = function_globals.row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = "3 sigma")
function_globals.all_cells_list.append(empty_cell)
extra_rows += 1
elif stock_row.Score.text > (score_avg + (score_std * 1)):
# greater than 1 sigma
if first_iteration:
first_iteration = False
last_sigma_stage = 1
if last_sigma_stage > 1:
last_sigma_stage = 1
empty_row_num = function_globals.row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = "2 sigma")
function_globals.all_cells_list.append(empty_cell)
extra_rows += 1
elif stock_row.Score.text > (score_avg + (score_std * 0)):
# greater than avg
if first_iteration:
first_iteration = False
last_sigma_stage = 0
if last_sigma_stage > 0:
last_sigma_stage = 0
empty_row_num = function_globals.row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = "1 sigma")
function_globals.all_cells_list.append(empty_cell)
extra_rows += 1
elif stock_row.Score.text >= (score_avg - (score_std * 1)):
if first_iteration:
first_iteration = False
last_sigma_stage = 0
if last_sigma_stage > -1:
last_sigma_stage = -1
empty_row_num = function_globals.row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = " ")
function_globals.all_cells_list.append(empty_cell)
extra_rows += 1
# this is the average cell
empty_row_num = function_globals.row_list.index(stock_row) + extra_rows
avg_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = "Average")
function_globals.all_cells_list.append(avg_cell)
function_globals.avg_row_cell = avg_cell
extra_rows += 1
empty_row_num = function_globals.row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = " ")
function_globals.all_cells_list.append(empty_cell)
extra_rows += 1
elif stock_row.Score.text > (score_avg - (score_std * 2)):
# greater than 1 sigma
if first_iteration:
first_iteration = False
last_sigma_stage = -1
if last_sigma_stage > -2:
last_sigma_stage = -2
empty_row_num = function_globals.row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = "-1 sigma")
function_globals.all_cells_list.append(empty_cell)
extra_rows += 1
elif stock_row.Score.text > (score_avg - (score_std * 3)):
# greater than 2 sigma
if first_iteration:
first_iteration = False
last_sigma_stage = -2
if last_sigma_stage > -3:
last_sigma_stage = -3
empty_row_num = function_globals.row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = "-2 sigma")
function_globals.all_cells_list.append(empty_cell)
extra_rows += 1
row_num = function_globals.row_list.index(stock_row) + extra_rows
for attribute_obj in attribute_list:
data_cell = getattr(stock_row, attribute_obj.name)
if data_cell.text is not None:
data = data_cell.text
background_color = None
if attribute_obj.avg is not None and attribute_obj.std is not None and data is not None:
if attribute_obj.size_factor == "big":
if data > (attribute_obj.avg + attribute_obj.std):
# better than 1 standard deviation -> green!
background_color = "#CCFFCC"
elif data < (attribute_obj.avg - (attribute_obj.std * 2)):
# worse than 2 standard deviations -> red :/
background_color = "#F78181"
elif data < (attribute_obj.avg - attribute_obj.std):
# worse than 1 standard deviation -> orange
background_color = "#FFB499"
elif attribute_obj.size_factor == "small":
if data < (attribute_obj.avg - attribute_obj.std):
# better than 1 standard deviation -> green!
background_color = "#CCFFCC"
elif data > (attribute_obj.avg + (attribute_obj.std * 2)):
# worse than 2 standard deviations -> red :/
background_color = "#F78181"
elif data > (attribute_obj.avg + attribute_obj.std):
# worse than 1 standard deviation -> orange
background_color = "#FFB499"
new_data_cell = Cell(text = data)
new_data_cell.row = row_num
new_data_cell.col = attribute_obj.col
new_data_cell.background_color = background_color
new_data_cell.text_color = data_cell.text_color
new_data_cell.align_right = attribute_obj.align_right
if attribute_obj.display == "2":
new_data_cell.text = "%.2f" % data
elif attribute_obj.display == "%":
try:
data = float(data)
if data.is_integer():
new_data_cell.text = str(int(round(float(data)))) + "%"
else:
new_data_cell.text = ("%.2f" % data) + "%"
except:
new_data_cell.text = str(data) + "%"
elif attribute_obj.display == "$":
try:
new_data_cell.text = config.locale.currency(float(data), grouping = True)
except Exception as e:
logging.info(e)
new_data_cell.text = "$" + str(data)
elif attribute_obj.display == "rnk":
try:
if float(data).is_integer():
new_data_cell.text = str(int(data))
else:
new_data_cell.text = str(data)
except:
new_data_cell.text = str(data)
try:
new_data_cell.row_title = stock_row.stock.ticker
except:
pass
function_globals.all_cells_list.append(new_data_cell)
def sort_row_list_and_convert_into_cells():
''' this is a complex function,
it sorts by row,
then find each attribute,
then looks for the attribute object,
then sees if the value is outside the bounds of a standard deviation,
then sets object colors
'''
extra_rows = 0
extra_rows += function_globals.default_row_buffer_before_stocks
score_avg = None
score_std = None
# first, get score avg and std
for attribute_obj in attribute_list:
if attribute_obj.name == "Score":
score_avg = attribute_obj.avg
score_std = attribute_obj.std
first_iteration = True
last_sigma_stage = None
function_globals.row_list.sort(key = lambda x: x.Score.text, reverse=True)
for stock_row in function_globals.row_list:
# Now, check if we need a blank row between sigma's
if stock_row.Score.text > (score_avg + (score_std * 3)):
# greater than 3 sigmas
if first_iteration:
first_iteration = False
last_sigma_stage = 3
elif stock_row.Score.text > (score_avg + (score_std * 2)):
# greater than 2 sigmas
if first_iteration:
first_iteration = False
last_sigma_stage = 2
if last_sigma_stage > 2:
last_sigma_stage = 2
empty_row_num = function_globals.row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = "3 sigma")
function_globals.all_cells_list.append(empty_cell)
extra_rows += 1
elif stock_row.Score.text > (score_avg + (score_std * 1)):
# greater than 1 sigma
if first_iteration:
first_iteration = False
last_sigma_stage = 1
if last_sigma_stage > 1:
last_sigma_stage = 1
empty_row_num = function_globals.row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = "2 sigma")
function_globals.all_cells_list.append(empty_cell)
extra_rows += 1
elif stock_row.Score.text > (score_avg + (score_std * 0)):
# greater than avg
if first_iteration:
first_iteration = False
last_sigma_stage = 0
if last_sigma_stage > 0:
last_sigma_stage = 0
empty_row_num = function_globals.row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = "1 sigma")
function_globals.all_cells_list.append(empty_cell)
extra_rows += 1
elif stock_row.Score.text >= (score_avg - (score_std * 1)):
if first_iteration:
first_iteration = False
last_sigma_stage = 0
if last_sigma_stage > -1:
last_sigma_stage = -1
empty_row_num = function_globals.row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = " ")
function_globals.all_cells_list.append(empty_cell)
extra_rows += 1
# this is the average cell
empty_row_num = function_globals.row_list.index(stock_row) + extra_rows
avg_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = "Average")
function_globals.all_cells_list.append(avg_cell)
function_globals.avg_row_cell = avg_cell
extra_rows += 1
empty_row_num = function_globals.row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = " ")
function_globals.all_cells_list.append(empty_cell)
extra_rows += 1
elif stock_row.Score.text > (score_avg - (score_std * 2)):
# greater than 1 sigma
if first_iteration:
first_iteration = False
last_sigma_stage = -1
if last_sigma_stage > -2:
last_sigma_stage = -2
empty_row_num = function_globals.row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = "-1 sigma")
function_globals.all_cells_list.append(empty_cell)
extra_rows += 1
elif stock_row.Score.text > (score_avg - (score_std * 3)):
# greater than 2 sigma
if first_iteration:
first_iteration = False
last_sigma_stage = -2
if last_sigma_stage > -3:
last_sigma_stage = -3
empty_row_num = function_globals.row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = "-2 sigma")
function_globals.all_cells_list.append(empty_cell)
extra_rows += 1
row_num = function_globals.row_list.index(stock_row) + extra_rows
for attribute_obj in attribute_list:
data_cell = getattr(stock_row, attribute_obj.name)
if data_cell.text is not None:
data = data_cell.text
background_color = None
if attribute_obj.avg is not None and attribute_obj.std is not None and data is not None:
if attribute_obj.size_factor == "big":
if data > (attribute_obj.avg + attribute_obj.std):
# better than 1 standard deviation -> green!
background_color = "#CCFFCC"
elif data < (attribute_obj.avg - (attribute_obj.std * 2)):
# worse than 2 standard deviations -> red :/
background_color = "#F78181"
elif data < (attribute_obj.avg - attribute_obj.std):
# worse than 1 standard deviation -> orange
background_color = "#FFB499"
elif attribute_obj.size_factor == "small":
if data < (attribute_obj.avg - attribute_obj.std):
# better than 1 standard deviation -> green!
background_color = "#CCFFCC"
elif data > (attribute_obj.avg + (attribute_obj.std * 2)):
# worse than 2 standard deviations -> red :/
background_color = "#F78181"
elif data > (attribute_obj.avg + attribute_obj.std):
# worse than 1 standard deviation -> orange
background_color = "#FFB499"
new_data_cell = Cell(text = data)
new_data_cell.row = row_num
new_data_cell.col = attribute_obj.col
new_data_cell.background_color = background_color
new_data_cell.text_color = data_cell.text_color
new_data_cell.align_right = attribute_obj.align_right
if attribute_obj.display == "2":
new_data_cell.text = "%.2f" % data
elif attribute_obj.display == "%":
try:
data = float(data)
if data.is_integer():
new_data_cell.text = str(int(round(float(data)))) + "%"
else:
new_data_cell.text = ("%.2f" % data) + "%"
except:
new_data_cell.text = str(data) + "%"
elif attribute_obj.display == "$":
try:
new_data_cell.text = config.locale.currency(float(data), grouping = True)
except Exception as e:
print line_number(), e
new_data_cell.text = "$" + str(data)
elif attribute_obj.display == "rnk":
try:
if float(data).is_integer():
new_data_cell.text = str(int(data))
else:
new_data_cell.text = str(data)
except:
new_data_cell.text = str(data)
try:
new_data_cell.row_title = stock_row.stock.ticker
except:
pass
function_globals.all_cells_list.append(new_data_cell)
def sort_row_list_and_convert_into_cells(row_list):
''' this is a complex function,
it sorts by row,
then find each attribute,
then looks for the attribute object,
then sees if the value is outside the bounds of a standard deviation,
then sets object colors
'''
extra_rows = 0
extra_rows += default_row_buffer_before_stocks
score_avg = None
score_std = None
# first, get score avg and std
for attribute_obj in attribute_list:
if attribute_obj.name == "Score":
score_avg = attribute_obj.avg
score_std = attribute_obj.std
first_iteration = True
last_sigma_stage = None
row_list.sort(key = lambda x: x.Score.text, reverse=True)
for stock_row in row_list:
# Now, check if we need a blank row between sigma's
if stock_row.Score.text > (score_avg + (score_std * 3)):
# greater than 3 sigmas
if first_iteration:
first_iteration = False
last_sigma_stage = 3
elif stock_row.Score.text > (score_avg + (score_std * 2)):
# greater than 2 sigmas
if first_iteration:
first_iteration = False
last_sigma_stage = 2
if last_sigma_stage > 2:
last_sigma_stage = 2
empty_row_num = row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = "2 sigma")
all_cells_list.append(empty_cell)
extra_rows += 1
elif stock_row.Score.text > (score_avg + (score_std * 1)):
# greater than 1 sigma
if first_iteration:
first_iteration = False
last_sigma_stage = 1
if last_sigma_stage > 1:
last_sigma_stage = 1
empty_row_num = row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = "1 sigma")
all_cells_list.append(empty_cell)
extra_rows += 1
elif stock_row.Score.text > (score_avg + (score_std * 0)):
# greater than avg
if first_iteration:
first_iteration = False
last_sigma_stage = 0
if last_sigma_stage > 0:
last_sigma_stage = 0
empty_row_num = row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = " ")
all_cells_list.append(empty_cell)
extra_rows += 1
empty_row_num = row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = "avg")
all_cells_list.append(empty_cell)
extra_rows += 1
empty_row_num = row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = " ")
all_cells_list.append(empty_cell)
extra_rows += 1
elif stock_row.Score.text < (score_avg - (score_std * 1)):
# greater than 1 sigma
if first_iteration:
first_iteration = False
last_sigma_stage = -1
if last_sigma_stage > -1:
last_sigma_stage = -1
empty_row_num = row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = "-1 sigma")
all_cells_list.append(empty_cell)
extra_rows += 1
elif stock_row.Score.text < (score_avg - (score_std * 2)):
# greater than 2 sigma
if first_iteration:
first_iteration = False
last_sigma_stage = -2
empty_row_num = row_list.index(stock_row) + extra_rows
empty_cell = Cell(text = " ", col = 0, row = empty_row_num, row_title = "-2 sigma")
all_cells_list.append(empty_cell)
extra_rows += 1
row_num = row_list.index(stock_row) + extra_rows
for attribute_obj in attribute_list:
data_cell = getattr(stock_row, attribute_obj.name)
if data_cell.text is not None:
data = data_cell.text
background_color = None
if attribute_obj.avg is not None and attribute_obj.std is not None and data is not None:
if data > (attribute_obj.avg + attribute_obj.std):
# better than 1 standard deviation -> green!
background_color = "#CCFFCC"
elif data < (attribute_obj.avg - (attribute_obj.std * 2)):
# worse than 2 standard deviations -> red :/
background_color = "#F78181"
elif data < (attribute_obj.avg - attribute_obj.std):
# worse than 1 standard deviation -> orange
background_color = "#FFB499"
new_data_cell = Cell(text = data)
new_data_cell.row = row_num
new_data_cell.col = attribute_obj.col
new_data_cell.background_color = background_color
new_data_cell.text = data
try:
new_data_cell.row_title = stock_row.stock.ticker
except:
pass
all_cells_list.append(new_data_cell)