def gen_attribute_list(row=1):
cell_list_to_return = []
for attribute_obj in attribute_list:
attribute_cell = Cell(row=row,
col=attribute_obj.col,
text=attribute_obj.name,
col_title=attribute_obj.name)
function_globals.all_cells_list.append(attribute_cell)
def gen_attribute_list(row=1):
#print line_number(), column_text_list
cell_list_to_return = []
for attribute_obj in attribute_list:
attribute_cell = Cell(row=row,
col=attribute_obj.col,
text=attribute_obj.name,
col_title=attribute_obj.name)
all_cells_list.append(attribute_cell)
def create_rows_of_stock_data(stock_list = stock_list):
rows_before_stock_data = 2
rows_list = []
for stock in stock_list:
stock_row = return_Row_obj_else_create(stock)
for attribute_obj in attribute_list:
try:
data = attribute_obj.function(stock)
except: # in case it throws an attribute error
data = None
data_cell = Cell(text = data) # this will be thrown out later
setattr(stock_row, attribute_obj.name, data_cell)
def create_avg_and_std_cells_for_attributes():
total_rows = 0
for cell in function_globals.all_cells_list:
if cell.row > total_rows:
total_rows = cell.row
avg_row_at_the_end = total_rows + 2
std_row = avg_row_at_the_end + 1
for attribute_obj in attribute_list:
for triple in function_globals.attrbute_name_avg_and_std_triple_list:
if attribute_obj.name == triple[0]:
if attribute_obj.display == "2":
triple[1] = "%.2f" % triple[1]
triple[2] = "%.2f" % triple[2]
elif attribute_obj.display == "%":
try:
triple[1] = float(triple[1])
if triple[1].is_integer():
triple[1] = str(int(round(float(triple[1])))) + "%"
else:
triple[1] = ("%.2f" % triple[1]) + "%"
except:
triple[1] = str(triple[1]) + "%"
try:
triple[2] = float(triple[2])
if triple[2].is_integer():
triple[2] = str(int(round(float(triple[2])))) + "%"
else:
triple[2] = ("%.2f" % triple[2]) + "%"
except:
triple[2] = str(triple[2]) + "%"
elif attribute_obj.display == "$":
try:
triple[1] = config.locale.currency(float(triple[1]), grouping = True)
except Exception as e:
print line_number(), e
triple[1] = "$" + str(triple[1])
try:
triple[2] = config.locale.currency(float(triple[2]), grouping = True)
except Exception as e:
print line_number(), e
triple[2] = "$" + str(triple[2])
elif attribute_obj.display == "rnk":
try:
if float(triple[1]).is_integer():
triple[1] = str(int(triple[1]))
else:
triple[1] = str(triple[1])
except:
triple[1] = str(triple[1])
try:
if float(triple[2]).is_integer():
triple[2] = str(int(triple[2]))
else:
triple[2] = str(round(triple[2], 2))
except:
triple[2] = str(triple[2])
if function_globals.avg_row_cell:
attribute_avg_cell = Cell(text = triple[1],row = function_globals.avg_row_cell.row, col = attribute_obj.col, text_color = "red", align_right = True)
function_globals.all_cells_list.append(attribute_avg_cell)
attribute_avg_cell = Cell(text = triple[1],row = avg_row_at_the_end, col = attribute_obj.col, row_title = "Average", text_color = "red", align_right = True)
function_globals.all_cells_list.append(attribute_avg_cell)
attribute_std_cell = Cell(text = triple[2],row = std_row, col = attribute_obj.col, row_title = "Standard Dev",text_color = "red", align_right = True)
function_globals.all_cells_list.append(attribute_std_cell)
score_cols_list = []
score_display = None
for attribute_obj in attribute_list:
if attribute_obj.name == "Score":
score_cols_list.append(attribute_obj.col)
score_display = attribute_obj.display
for col in score_cols_list:
if function_globals.avg_row_cell:
score_avg_cell = Cell(text = "%.2f" % function_globals.score_avg, row = function_globals.avg_row_cell.row, col = col, text_color = "red", align_right = True)
score_avg_cell_2 = Cell(text = "%.2f" % function_globals.score_avg, row = avg_row_at_the_end, col = col, text_color = "red", align_right = True)
score_std_cell = Cell(text = "%.2f" % function_globals.score_std, row = std_row, col = col, text_color = "red", align_right = True)
function_globals.all_cells_list.append(score_avg_cell)
function_globals.all_cells_list.append(score_avg_cell_2)
function_globals.all_cells_list.append(score_std_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 rainbow_spreadsheet(stock_list):
"rainbow sort"
cell_list = [] # Cell object imported above
class Row(object):
"this will help in sorting cells"
def __init__(self, stock):
self.stock = stock
if not stock_list: # no empty list drama
return
row_list = []
for stock in stock_list:
the_row_of_this_stock = stock_list.index(stock)
this_row = Row(stock = stock)
# standard stock attributes
this_row.ticker = stock.ticker
this_row.ticker_len = len(stock.ticker)
this_row.firm_name = stock.firm_name
row_list.append(this_row)
def sort_my_rows(row_list):
'sorts rows by ticker length'
row_list.sort(key = lambda row: row.ticker_len)
sort_my_rows(row_list)
# set colors!
for row in row_list:
if row.ticker_len == 1:
row.row_color = "#FFCCFF"
elif row.ticker_len == 2:
row.row_color = "#FFCCCC"
elif row.ticker_len == 3:
row.row_color = "#FFFFCC"
elif row.ticker_len == 4:
row.row_color = "#CCFFCC"
elif row.ticker_len == 5:
row.row_color = "#CCFFFF"
elif row.ticker_len == 6:
row.row_color = "#CCCCFF"
elif row.ticker_len == 7:
row.row_color = "#CC99FF"
elif row.ticker_len > 7:
row.row_color = "#FFFFFF"
def make_rainbow_row_list(row_list):
ones_list = [row for row in row_list if row.ticker_len == 1]
twos_list = [row for row in row_list if row.ticker_len == 2]
threes_list = [row for row in row_list if row.ticker_len == 3]
fours_list = [row for row in row_list if row.ticker_len == 4]
fives_list = [row for row in row_list if row.ticker_len == 5]
sixs_list = [row for row in row_list if row.ticker_len == 6]
sevens_list = [row for row in row_list if row.ticker_len == 7]
len_list = [ones_list, twos_list, threes_list, fours_list, fives_list, sixs_list, sevens_list]
largest_len = 0
for num_list in len_list:
if len(num_list) > largest_len:
largest_len = len(num_list)
rainbow_list = []
for i in range(largest_len):
for num_list in len_list:
try:
rainbow_list.append(num_list[i])
except:
pass
return rainbow_list
row_list = make_rainbow_row_list(row_list)
for row in row_list:
ticker_cell = Cell(text = row.ticker, row = row_list.index(row), col = 0, col_title = "ticker", background_color = row.row_color)
firm_name_cell = Cell(text = row.firm_name, row = row_list.index(row), col = 1, col_title = "name", background_color = row.row_color)
ticker_len_cell = Cell(text = row.ticker_len, row = row_list.index(row), col = 2, col_title = "ticker length", background_color = row.row_color)
cell_list.append(ticker_cell)
cell_list.append(firm_name_cell)
cell_list.append(ticker_len_cell)
one_longer_than_row_list = len(row_list)
end_cell = Cell(text = "", row = one_longer_than_row_list, col = 0, row_title = "end")
cell_list.append(end_cell)
return cell_list
def create_data_scores():
for attribute_obj in attribute_list:
if attribute_obj.size_factor:
# first iterate over each attribute, to find the mean and standard dev
unadjusted_attribute_data_list_valid_values_only = []
adjusted_attribute_data_list = []
nonetype_attribute_list = []
# seperate nonconforming data
for row in function_globals.row_list:
data_cell = getattr(row, attribute_obj.name)
data = data_cell.text
try:
data = float(data)
data = round(data, 2)
unadjusted_attribute_data_list_valid_values_only.append(data)
except:
nonetype_attribute_list.append(data)
if not unadjusted_attribute_data_list_valid_values_only:
# no reason to look at data, all is None
continue
else:
print line_number(), unadjusted_attribute_data_list_valid_values_only
# unadjusted avg will be rounded to 3 instead of 2 for identification purposes
unadjusted_avg = round(numpy.mean(unadjusted_attribute_data_list_valid_values_only), 3)
unadjusted_avg_len = len(unadjusted_attribute_data_list_valid_values_only)
# set degree of freedom to subtract from N to 0 if only one date unit
if len(unadjusted_attribute_data_list_valid_values_only) > 1:
number_to_minus_degrees_of_freedom = 1
else:
number_to_minus_degrees_of_freedom = 0
unadjusted_std = numpy.std(unadjusted_attribute_data_list_valid_values_only, ddof=number_to_minus_degrees_of_freedom)
if not (isinstance(unadjusted_avg, float) and isinstance(unadjusted_avg_len, float) and isinstance(unadjusted_std, float) ):
print line_number()
print "unadjusted_avg:", unadjusted_avg
print "unadjusted_avg_len:", unadjusted_avg_len
print "unadjusted_std:", unadjusted_std
# set nonconforming data to avg of conforming data
nonetype_to_avg_attribute_list = []
for nonconforming_data in nonetype_attribute_list:
nonetype_to_avg_attribute_list.append(unadjusted_avg)
# set conforming data and replaced data into the min/max checks
for row in function_globals.row_list:
data_cell = getattr(row, attribute_obj.name)
data = data_cell.text
try:
data = float(data)
data = round(data, 2)
except:
# replace nonconforming data with avg again
data = unadjusted_avg
if type(data) is float:
if attribute_obj.minimum or attribute_obj.maximum:
# normalize data
if data < attribute_obj.minimum:
adjusted_data = attribute_obj.minimum
data_cell.text_color = "#B8B8B8"
elif data > attribute_obj.maximum:
adjusted_data = attribute_obj.maximum
data_cell.text_color = "#B8B8B8"
else: # irrelevant
adjusted_data = data
adjusted_attribute_data_list.append(adjusted_data)
elif data and attribute_obj.name == "Cur Ratio": # strange variation in rms code
if data > 10.:
adjusted_data = 1.7
data_cell.text_color = "#B8B8B8"
elif data > 4.:
adjusted_data = 4.
data_cell.text_color = "#B8B8B8"
else:
adjusted_data = data
adjusted_attribute_data_list.append(adjusted_data)
elif data and attribute_obj.name == "ROE %"+"Dev": # strange variation in rms code
if data < 0.:
adjusted_data = 1.5
data_cell.text_color = "#B8B8B8"
else:
adjusted_data = data
adjusted_attribute_data_list.append(adjusted_data)
elif data and attribute_obj.name == "Inv2sales Grwth": # strange variation in rms code
adjusted_data = -abs(data-1.)
data_cell.text_color = "#B8B8B8"
adjusted_attribute_data_list.append(adjusted_data)
else:
adjusted_attribute_data_list.append(data)
adjusted_data_avg = numpy.mean(adjusted_attribute_data_list)
adjusted_data_avg_len = len(adjusted_attribute_data_list)
# set degree of freedom to subtract from N to 0 if only one date unit
if len(adjusted_attribute_data_list) > 1:
number_to_minus_degrees_of_freedom = 1
else:
number_to_minus_degrees_of_freedom = 0
adjusted_data_std = numpy.std(adjusted_attribute_data_list, ddof=number_to_minus_degrees_of_freedom) # 1 degrees of freedom for std
if not (isinstance(adjusted_data_avg, float) and isinstance(adjusted_data_avg_len, float) and isinstance(adjusted_data_std, float) ):
print line_number()
print "adjusted_avg:", adjusted_data_avg
print "adjusted_avg_len:", adjusted_data_avg_len
print "adjusted_std:", adjusted_data_std
# set attribute average standard deviation for colors later
attribute_obj.avg = float(round(adjusted_data_avg, 2))
attribute_obj.std = float(round(adjusted_data_std, 2))
#print line_number()
#print "\n"*2
#print sorted(adjusted_attribute_data_list)
#print line_number(), attribute_obj.name, "unadjusted avg", unadjusted_avg, "length=", unadjusted_avg_len
#print line_number(), attribute_obj.name, "unadjusted std", unadjusted_std
#print line_number(), attribute_obj.name, "adjusted avg", adjusted_data_avg, "length=", adjusted_data_avg_len
#print line_number(), attribute_obj.name, "adjusted std", adjusted_data_std
#print "\n"*2
function_globals.attrbute_name_avg_and_std_triple_list.append([attribute_obj.name, unadjusted_avg, unadjusted_std])
for row in function_globals.row_list:
if attribute_obj.size_factor:
#b = attribute_obj.function(row.stock)
try:
b = float(attribute_obj.function(row.stock))
except:
if attribute_obj.avg:
b = attribute_obj.avg
else:
print line_number(), "No avg for", attribute_obj.name, "set avg to 0"
b = 0.
if not (type(b) is float):
print line_number(), "Error: b should always be float"
print type(b)
print attribute_obj.name
print attribute_obj.avg
if attribute_obj.minimum or attribute_obj.maximum:
# normalize data
if b < attribute_obj.minimum:
b = attribute_obj.minimum
elif b > attribute_obj.maximum:
b = attribute_obj.maximum
elif b and attribute_obj.name == "Cur Ratio": # strange variation in rms code
if b > 10.:
b = 1.7
elif b > 4.:
b = 4.
elif b and attribute_obj.name == "ROE %"+"Dev": # strange variation in rms code
if b < 0.:
b = 1.5
elif b and attribute_obj.name == "Inv2sales Grwth": # strange variation in rms code
b = -abs(b-1.)
b = round(b, 2)
try:
mu = float(adjusted_data_avg)
except:
mu = None
try:
sigma = float(adjusted_data_std)
except:
sigma = None
if type(b) is float and type(mu) is float and sigma:
z_score = (b-mu)/sigma
z_score = round(z_score, 2)
else:
z_score = None
z_score_cell = Cell(text = z_score)
setattr(row, str(attribute_obj.name) + "__z_score", z_score_cell)
for stock_row in function_globals.row_list:
score = 0.0
for attribute_obj in attribute_list:
weight = attribute_obj.weight
if weight:
try:
z_score_data_cell = getattr(stock_row, attribute_obj.name + "__z_score")
z_score_data = z_score_data_cell.text
if z_score_data is not None:
modified_score_value = z_score_data * weight
if attribute_obj.size_factor:
if attribute_obj.size_factor == "big":
score += modified_score_value
elif attribute_obj.size_factor == "small":
score -= modified_score_value
else:
print line_number(), "Error: something went wrong here"
z_score_data_cell.text = score
except Exception, e:
print line_number(), e
pass
stock_row.Score.text = score
def top_row():
text = 'BUY CANDIDATES'
top_row_cell = Cell(row = 0, col=0, text=text)
function_globals.all_cells_list.append(top_row_cell)
def create_data_scores(row_list):
for attribute_obj in attribute_list:
# first iterate over each attribute, to find the mean and standard dev
adjusted_attribute_data_list = []
for row in row_list:
data_cell = getattr(row, attribute_obj.name)
data = data_cell.text
if data is not None:
if attribute_obj.minimum or attribute_obj.maximum:
# normalize data
if data < attribute_obj.minimum:
adjusted_data = attribute_obj.minimum
data_cell.text_color = "#333333"
elif data > attribute_obj.maximum:
adjusted_data = attribute_obj.maximum
data_cell.text_color = "#333333"
adjusted_attribute_data_list.append(data)
# see if we can compute average
not_none_data_list = []
for x in adjusted_attribute_data_list:
try:
float(x)
not_none_data_list.append(x)
except:
# not a number
pass
if not not_none_data_list:
# no reason to look at data, all is None
continue
try: # get average and standard deviation if possible
data_avg = numpy.mean(not_none_data_list, axis=0)
except Exception, e:
print line_number(), e
# apply avg of data to any missing data
for row in row_list:
if getattr(row, attribute_obj.name) is None:
setattr(row, attribute_obj.name, data_avg)
try:
data_list_averaged_data = []
for data in adjusted_attribute_data_list:
if data is not None:
data_list_averaged_data.append(data)
else:
data_list_averaged_data.append(data_avg)
data_std = numpy.std(data_list_averaged_data)
# set attribute average standard deviation for colors later
attribute_obj.avg = data_avg
attribute_obj.std = data_std
for stock_row in row_list:
b = attribute_obj.function(stock_row.stock)
mu = float(data_avg)
sigma = float(data_std)
if b is not None and mu is not None and sigma:
z_score = (b - mu) / sigma
else:
z_score = None
z_score_cell = Cell(text=z_score)
setattr(stock_row,
str(attribute_obj.name) + "__z_score",
z_score_cell)
except Exception, e:
print line_number(), e
def top_row():
text = 'BUY CANDIDATES'
top_row_cell = Cell(row=0, col=0, text=text)
all_cells_list.append(top_row_cell)
def sample_spreadsheet(stock_list):
"sample ticker sort"
cell_list = [] # Cell object imported above
class Row(object):
"this will help in sorting cells"
def __init__(self, stock):
self.stock = stock
if not stock_list: # no empty list drama
return
row_list = []
for stock in stock_list:
the_row_of_this_stock = stock_list.index(stock)
this_row = Row(stock=stock)
# standard stock attributes
this_row.ticker = stock.ticker
this_row.ticker_len = len(stock.ticker)
this_row.firm_name = stock.firm_name
row_list.append(this_row)
def sort_my_rows(row_list):
'sorts rows by ticker length'
row_list.sort(key=lambda row: row.ticker_len)
sort_my_rows(row_list)
# set colors!
for row in row_list:
if row.ticker_len == 1:
row.row_color = "#FFCCFF"
elif row.ticker_len == 2:
row.row_color = "#FFCCCC"
elif row.ticker_len == 3:
row.row_color = "#FFFFCC"
elif row.ticker_len == 4:
row.row_color = "#CCFFCC"
elif row.ticker_len == 5:
row.row_color = "#CCFFFF"
elif row.ticker_len == 6:
row.row_color = "#CCCCFF"
elif row.ticker_len == 7:
row.row_color = "#CC99FF"
elif row.ticker_len > 7:
row.row_color = "#FFFFFF"
for row in row_list:
ticker_cell = Cell(text=row.ticker,
row=row_list.index(row),
col=0,
col_title="ticker",
background_color=row.row_color)
firm_name_cell = Cell(text=row.firm_name,
row=row_list.index(row),
col=1,
col_title="name",
background_color=row.row_color)
ticker_len_cell = Cell(text=row.ticker_len,
row=row_list.index(row),
col=2,
col_title="ticker length",
background_color=row.row_color)
cell_list.append(ticker_cell)
cell_list.append(firm_name_cell)
cell_list.append(ticker_len_cell)
one_longer_than_row_list = len(row_list)
end_cell = Cell(text="",
row=one_longer_than_row_list,
col=0,
row_title="end")
cell_list.append(end_cell)
return cell_list