for j in range(i + 1, len(crypto_data)):
if crypto_data[j][0] == temp:
k = k + 1
if k == 0:
l = l + 1
crypto_data2.append(l)
crypto_data2[l] = crypto_data[i]
crypto_data = crypto_data2
print('INITIAL DATASET COLLECTED')
print('SIZE OF ENTIRE ICO DATASET: ', len(crypto_data))
#Call rt10 and r_bitcoin only ONCE
bitcoin = func_btc()
top10s = func_top10()
print('BTC / TOP 10 TOKEN DATA COLLECTED')
#First formatting of two output datasets (full and reduced with ratings)
full_data = []
target_data = []
j = -1
for i in range(0, len(crypto_data)):
if i == 0:
print('LOOPING INITIATED')
data_vec = ico_data_collector(crypto_data[i], bitcoin, top10s)
time.sleep(1)
print('ROW NUMBER: ', i, 'ICO NAME: ', crypto_data[i])
#Filter out data with 'N/A' end date entries
def ico_rank(ico_inp,ico_inp_token):
#Here we allow the user to import the features of the ICO that is under investigation
features_vec = ['region','industry','team','hardcap','price','telegram','N_google_news','N_twitter']
#user_input = ['united states', 'fintech', '4', '10000000', '0.20', '3210', '1', '5632']
bitcoin = func_btc()
top10s = func_top10()
ico_data = ico_data_collector([ico_inp,ico_inp_token,ico_inp],bitcoin,top10s)[1]
#revert to verbose region variable
if ico_data[5] == 1:
reg = 'usa'
if ico_data[5] == 2:
reg = 'russia'
if ico_data[5] == 3:
reg = 'china'
if ico_data[5] == 4:
reg = 'uk'
if ico_data[5] == 5:
reg = 'estonia'
if ico_data[5] == 6:
reg = 'switzerland'
if ico_data[5] == 7:
reg = 'singapore'
if ico_data[5] == 8:
reg = 'japan'
if ico_data[5] == 9:
reg = 'australia'
if ico_data[5] == 10:
reg = 'brazil'
if ico_data[5] == 11:
reg = 'south africa'
if ico_data[5] == 12:
reg = 'mongolia'
user_input = []
user_input.append(reg)
user_input.append(ico_data[6])
user_input.append(ico_data[7])
user_input.append(ico_data[9])
user_input.append(ico_data[11])
user_input.append(ico_data[12])
user_input.append(ico_data[13])
user_input.append(ico_data[14])
for i in range(0,len(user_input)):
if user_input[i] != 'N/A':
user_input[i] = str(user_input[i])
if user_input[i] == 'N/A':
user_input[i] = input("Enter ICO feature: "+features_vec[i]+"\n")
#user_input = []
#for i in range(0,len(features_vec)):
# user_input.append(i)
# user_input[i] = input("Enter ICO "+features_vec[i]+"\n")
kk = -1
ranks = []
for feature in features_vec:
kk = kk + 1
ranks.append(kk)
success_threshold = 0.7
with open("ico_data_reduced.csv") as f:
reader = csv.reader(f)
data = [r for r in reader]
data = np.asarray(data)
indices, = np.where(data[:,10] != 'N/A')
indices = np.delete(indices,0)
success = [eval(data[i][10]) for i in indices]
try:
ind_feature = np.where(data[0,:]==feature)[0][0]
except:
print('ERROR: This feature does not exist in this dataset')
sys.exit()
if feature in ['hype','risk']:
for i in range(0,len(data)):
if data[i,ind_feature] == ' N/A':
data[i,ind_feature] = 'N/A'
indices_f, = np.where(data[:,ind_feature] != 'N/A')
indices_f = np.delete(indices_f,0)
indices_over = []
k = -1
for i in range(0,len(indices)):
for j in range(0,len(indices_f)):
if indices_f[j] == indices[i]:
k = k + 1
indices_over.append(k)
indices_over[k] = indices[i]
success0 = [eval(data[i][10]) for i in indices_over]
variable0 = [data[i][ind_feature] for i in indices_over]
#Feature Controls
if feature in ['team','N_google_news']:
for i in range(0,len(variable0)):
variable0[i] = abs(eval(variable0[i]))
if feature == 'hardcap':
for i in range(0,len(variable0)):
variable0[i] = abs(eval(variable0[i]))
variable0[i] = np.log10(variable0[i])
if feature in ['price','telegram','N_twitter']:
for i in range(0,len(variable0)):
if variable0[i] == '0':
variable0[i] = str(1)
variable0[i] = np.log10(eval(variable0[i]))
#print(variable0[i])
if feature == 'bazaar-rate':
for i in range(0,len(variable0)):
variable0[i] = eval(variable0[i])
#Now reduce feature array to > 70% success values
success0 = np.asarray(success0)
indices2, = np.where(success0 > success_threshold)
success0b = []
variable0b = []
k = -1
for i in range(0,len(success0)):
if i in indices2:
k = k + 1
success0b.append(k)
variable0b.append(k)
success0b[k] = success0[i]
variable0b[k] = variable0[i]
try:
print('-------BASIC FEATURE STATISTICS: FULL SAMPLE-------')
print('For feature:',feature.upper(),'the sample size is: ',len(variable0))
print('For feature:',feature.upper(),'the max value is: ',np.max(variable0))
print('For feature:',feature.upper(),'the min value is: ',np.min(variable0))
print('For feature:',feature.upper(),'the mean value is: ',round(np.mean(variable0),3))
print('For feature:',feature.upper(),'the median value is: ',round(np.median(variable0),3))
print('For feature:',feature.upper(),'the standard deviation is: ',round(np.std(variable0),3))
print('---------------------------------------------------')
print('---------------------------------------------------')
print('-------BASIC FEATURE STATISTICS: SUCCESSFUL SAMPLE-------')
print('For feature:',feature.upper(),'the sample size is: ',len(variable0b))
print('For feature:',feature.upper(),'the max value is: ',np.max(variable0b))
print('For feature:',feature.upper(),'the min value is: ',np.min(variable0b))
print('For feature:',feature.upper(),'the mean value is: ',round(np.mean(variable0b),3))
print('For feature:',feature.upper(),'the median value is: ',round(np.median(variable0b),3))
print('For feature:',feature.upper(),'the standard deviation is: ',round(np.std(variable0b),3))
print('---------------------------------------------------------')
print('---------------------------------------------------------')
except:
print('Statistics Are Not Done for Categorical Features')
print('For feature:',feature.upper(),'the sample size is: ',len(variable0))
print('For feature:',feature.upper(),'the sample size is: ',len(variable0b))
z,bins,p3 = plt.hist(variable0, bins = 'auto', rwidth=0.9, facecolor = 'blue')
plt.xlabel(feature,fontsize=17)
plt.ylabel('Number',fontsize=15)
plt.xticks(size = 15)
plt.yticks(size = 15)
plt.show()
#fig, ax = plt.subplots()
#plt.xlabel(feature)
#plt.ylabel('success')
#ax.scatter(variable0, success0, c='k')
#plt.show()
#fig, ax = plt.subplots()
#plt.xlabel(feature)
#plt.ylabel('success')
#ax.scatter(variable0b, success0b, c='k')
#plt.show()
try:
avg = np.median(variable0b)
stdev = np.std(variable0b)
f = [0.5,1.0,1.5,2.0,2.5]
z,bins,p3 = plt.hist(variable0b, bins = 5, rwidth=0.9, facecolor = 'blue')
plt.axvline(x=avg,linestyle = "-", linewidth = 2, color = 'k')
# for i in f:
# plt.axvline(x=avg+i*stdev,linestyle = ":", linewidth = 1, color = 'r')
# plt.axvline(x=avg-i*stdev,linestyle = ":", linewidth = 1, color = 'r')
plt.xlabel(feature,fontsize=17)
plt.ylabel('Number',fontsize=15)
plt.xticks(size = 15)
plt.yticks(size = 15)
plt.show()
except:
if feature not in ['region','industry']:
print('Statistics Are Not Done for Categorical Features')
z,bins,p3 = plt.hist(variable0b, bins = 5, rwidth=0.9, facecolor = 'blue')
plt.xlabel(feature,fontsize=17)
plt.ylabel('Number',fontsize=15)
plt.xticks(size = 15)
plt.yticks(size = 15)
plt.show()
if feature == 'region':
print('Statistics Are Not Done for Categorical Features')
z,bins,p3 = plt.hist(variable0b, bins = 12, rwidth=0.9, facecolor = 'blue')
plt.xlabel(feature,fontsize=17)
plt.ylabel('Number',fontsize=15)
plt.xticks(size = 15)
plt.yticks(size = 15)
plt.show()
if feature == 'industry':
print('Statistics Are Not Done for Categorical Features')
z,bins,p3 = plt.hist(variable0b, bins = 14, rwidth=0.9, facecolor = 'blue')
plt.xlabel(feature,fontsize=17)
plt.ylabel('Number',fontsize=15)
plt.xticks(size = 15)
plt.xticks(rotation=90)
plt.tick_params(labelsize=7)
plt.yticks(size = 15)
plt.show()
#Perform grading based on simple stat comparisons (std from median)
#First Determine Input Data bin
if feature not in ['region','industry']:
if feature == 'team':
feature_in = eval(user_input[2])
if feature == 'hardcap':
feature_in = np.log10(eval(user_input[3]))
if feature == 'price':
feature_in = np.log10(eval(user_input[4]))
if feature == 'telegram':
feature_in = np.log10(eval(user_input[5]))
if feature == 'N_google_news':
feature_in = eval(user_input[6])
if feature == 'N_twitter':
feature_in = np.log10(eval(user_input[7]))
order = np.argsort(z)
group = 0
for i in range(0,5):
if (bins[0] <= feature_in < bins[1]):
group = 1
if (bins[1] <= feature_in < bins[2]):
group = 2
if (bins[2] <= feature_in < bins[3]):
group = 3
if (bins[3] <= feature_in < bins[4]):
group = 4
if (bins[4] <= feature_in < bins[5]):
group = 5
if (feature_in < bins[0]) or (feature_in > bins[5]):
group = 0
location = group - 1
try:
grade = np.where(order == location)[0][0] + 1
except:
grade = 0
print('The ICO under investigation receives the grade: ',grade,' for feature: ',feature.upper())
z,bins,p3 = plt.hist(variable0b, bins = 5, rwidth=0.9, facecolor = 'blue')
plt.axvline(x=avg,linestyle = "-", linewidth = 2, color = 'k')
plt.axvline(x=feature_in,linestyle = "-", linewidth = 2, color = 'r')
plt.xlabel(feature,fontsize=17)
plt.ylabel('Number',fontsize=15)
plt.xticks(size = 15)
plt.yticks(size = 15)
plt.show()
if feature == 'region':
region_in = func_region(user_input[0])
grade = 0
if region_in == 7:
grade = 5
if region_in in [1,5]:
grade = 4
if region_in in [4,6]:
grade = 3
if region_in in [2,3]:
grade = 2
if region_in in [8,9]:
grade = 1
if region_in in [10,11,12]:
grade = 0
print('The ICO under investigation receives the grade: ',grade,' for feature: ',feature.upper())
z,bins,p3 = plt.hist(variable0b, bins = 12, rwidth=0.9, facecolor = 'blue')
plt.axvline(x=str(region_in), linestyle = "-", linewidth = 2, color = 'r')
plt.xlabel(feature,fontsize=17)
plt.ylabel('Number',fontsize=15)
plt.xticks(size = 15)
plt.yticks(size = 15)
plt.show()
if feature == 'industry':
industry_in = func_industry(user_input[1])
tag = 1
if industry_in == 'blockchain':
tag = 0
if industry_in == 'other':
tag = 1
if industry_in == 'saas':
tag = 2
if industry_in == 'fintech':
tag = 3
if industry_in == 'gaming':
tag = 4
if industry_in == 'social services':
tag = 5
if industry_in == 'energy':
tag = 6
if industry_in == 'insurance services':
tag = 7
if industry_in == 'telecommunications':
tag = 8
if industry_in == 'transportation':
tag = 9
if industry_in == 'real estate':
tag = 10
grade = 0
if industry_in in ['blockchain']:
grade = 5
if industry_in in ['fintech']:
grade = 4
if industry_in in ['saas']:
grade = 3
if industry_in in ['gaming']:
grade = 2
if industry_in in ['insurance services','telecommunications']:
grade = 1
if industry_in not in ['blockchain','fintech','saas','gaming','insurance services','telecommunications']:
grade = 0
print('The ICO under investigation receives the grade: ',grade,' for feature: ',feature.upper())
z,bins,p3 = plt.hist(variable0b, bins = 14, rwidth=0.9, facecolor = 'blue')
plt.xlabel(feature,fontsize=17)
plt.ylabel('Number',fontsize=15)
plt.axvline(x=tag, linestyle = "-", linewidth = 2, color = 'r')
plt.xticks(size = 15)
plt.xticks(rotation=90)
plt.tick_params(labelsize=7)
plt.yticks(size = 15)
plt.show()
ranks[kk] = grade
ico_rating = np.mean(ranks)
ico_rating0 = ico_rating/5.0
print('The average BloxVerse ranking for this ICO is: ',round(ico_rating,2))
print('In the 0-1 scale this is equivalent to: ',round(ico_rating0,2))
return 'Normalized BloxVerse Rating: ',ico_rating0