def ico_rank_semiannual(user_input, ico_end_date):
ico_end_date = ico_end_date.replace(" ", "")
ico_end_date_o = datetime.strptime(ico_end_date, '%d%b%Y')
ico_year = str(ico_end_date_o.year)
bound1 = ('01 Jan ' + ico_year).replace(" ", "")
bound1_o = datetime.strptime(bound1, '%d%b%Y')
bound2 = ('31 Mar ' + ico_year).replace(" ", "")
bound2_o = datetime.strptime(bound2, '%d%b%Y')
bound3 = ('01 Apr ' + ico_year).replace(" ", "")
bound3_o = datetime.strptime(bound3, '%d%b%Y')
bound4 = ('30 Jun ' + ico_year).replace(" ", "")
bound4_o = datetime.strptime(bound4, '%d%b%Y')
bound5 = ('01 Jul ' + ico_year).replace(" ", "")
bound5_o = datetime.strptime(bound5, '%d%b%Y')
bound6 = ('30 Sep ' + ico_year).replace(" ", "")
bound6_o = datetime.strptime(bound6, '%d%b%Y')
bound7 = ('01 Oct ' + ico_year).replace(" ", "")
bound7_o = datetime.strptime(bound7, '%d%b%Y')
bound8 = ('31 Dec ' + ico_year).replace(" ", "")
bound8_o = datetime.strptime(bound8, '%d%b%Y')
if bound1_o <= ico_end_date_o <= bound2_o:
sa_str = 'sa_1_'
if bound3_o <= ico_end_date_o <= bound4_o:
sa_str = 'sa_2_'
if bound5_o <= ico_end_date_o <= bound6_o:
sa_str = 'sa_3_'
if bound7_o <= ico_end_date_o <= bound8_o:
sa_str = 'sa_4_'
if ico_end_date_o > bound8_o:
sa_str = 'sa_4_'
if ico_year == '2016':
yr_str = '16'
if ico_year == '2017':
yr_str = '17'
if ico_year == '2018':
yr_str = '18'
kk = -1
ranks = []
for i in range(0, len(user_input)):
kk = kk + 1
ranks.append(kk)
z = []
bins = []
if i not in [0, 1, 6]:
if i == 2:
feature_in = eval(user_input[2])
z = np.loadtxt('scaling_dataset/team_z_' + sa_str + yr_str +
'.npy')
bins = np.loadtxt('scaling_dataset/team_bin_' + sa_str +
yr_str + '.npy')
if i == 3:
feature_in = np.log10(eval(user_input[3]))
z = np.loadtxt('scaling_dataset/hardcap_z_' + sa_str + yr_str +
'.npy')
bins = np.loadtxt('scaling_dataset/hardcap_bin_' + sa_str +
yr_str + '.npy')
if i == 4:
feature_in = np.log10(eval(user_input[4]))
z = np.loadtxt('scaling_dataset/price_z_' + sa_str + yr_str +
'.npy')
bins = np.loadtxt('scaling_dataset/price_bin_' + sa_str +
yr_str + '.npy')
if i == 5:
feature_in = np.log10(eval(user_input[5]))
z = np.loadtxt('scaling_dataset/telegram_z_' + sa_str +
yr_str + '.npy')
bins = np.loadtxt('scaling_dataset/telegram_bin_' + sa_str +
yr_str + '.npy')
if i == 7:
z = np.loadtxt('scaling_dataset/twitter_z_' + sa_str + yr_str +
'.npy')
bins = np.loadtxt('scaling_dataset/twitter_bin_' + sa_str +
yr_str + '.npy')
feature_in = np.log10(eval(user_input[7]))
if i == 8:
feature_in = np.log10(eval(user_input[8]))
z = np.loadtxt('scaling_dataset/dailyviews_z_' + sa_str +
yr_str + '.npy')
bins = np.loadtxt('scaling_dataset/dailyviews_bin_' + sa_str +
yr_str + '.npy')
if i == 9:
feature_in = np.log10(eval(user_input[9]))
z = np.loadtxt('scaling_dataset/dailytime_z_' + sa_str +
yr_str + '.npy')
bins = np.loadtxt('scaling_dataset/dailytime_bin_' + sa_str +
yr_str + '.npy')
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
if i == 0:
region_in = eval(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
if i == 1:
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
ranks[kk] = grade
ico_rating = np.mean(ranks)
ico_rating0 = ico_rating / 5.0
return round(ico_rating0, 2)
def ico_data_collector(input_vector, bitcoin, top10s):
currency = input_vector[0]
token = input_vector[1]
website_str = input_vector[2]
#Bitcoin returns
rbtc = bitcoin
#Average returns of Top 10 coins
rt10 = top10s
today = dt.datetime.now().date().isoformat().replace('-', "")
try:
response2 = requests.get('https://coinmarketcap.com/currencies/' +
website_str +
'/historical-data/?start=20130428&end=' +
today)
txt2 = response2.text
soup = BeautifulSoup(txt2, 'html.parser')
table = soup.find("table")
rows = table.findAll('tr')
data2 = [[td.findChildren(text=True) for td in tr.findAll("td")]
for tr in rows]
data2 = [[u"".join(d).strip() for d in l] for l in data2]
date = []
o = []
h = []
l = []
c = []
vol = []
mc = []
j = -1
for i in range(1, len(data2)):
j = j + 1
date.append(j)
o.append(j)
h.append(j)
l.append(j)
c.append(j)
vol.append(j)
mc.append(j)
date[j] = data2[i][0]
o[j] = eval(data2[i][1])
h[j] = eval(data2[i][2])
l[j] = eval(data2[i][3])
c[j] = eval(data2[i][4])
vol[j] = eval(data2[i][5].replace(",", "").replace("-", "0.0"))
mc[j] = eval((data2[i][6].replace(",", "")).replace("-", "0.0"))
[date, o, h, l, c, vol, mc] = [
date[::-1], o[::-1], h[::-1], l[::-1], c[::-1], vol[::-1], mc[::-1]
]
#Calculated daily returns array
r = [
round((c[0] - o[0]) / o[0], 3),
]
for i in range(1, len(c)):
r.append(i)
r[i] = round((c[i] - c[i - 1]) / c[i - 1], 3)
#Calculate average returns and standard deviation of average returns
r_av = np.mean(r)
r_std = np.std(r)
#Calculate 1-month, 3-month and annualized Sharpe ratios
wd_month = 21
wd_month3 = 3 * wd_month
wd_annual = 252
if len(c) < wd_month:
s_1 = round(r_av * wd_month / (r_std * np.sqrt(wd_month)), 3)
if len(c) >= wd_month:
r_av = np.mean(r[0:wd_month])
r_std = np.std(r[0:wd_month])
s_1 = round(r_av / r_std, 3)
if len(c) < wd_month3:
s_3 = round(r_av * wd_month3 / (r_std * np.sqrt(wd_month3)), 3)
if len(c) >= wd_month3:
r_av = np.mean(r[0:wd_month3])
r_std = np.std(r[0:wd_month3])
s_3 = round(r_av / r_std, 3)
s_annual = round(r_av * wd_annual / (r_std * np.sqrt(wd_annual)), 3)
rav10 = np.mean(rt10)
rstd10 = np.std(rt10)
s_annual2 = round(
(r_av - rav10) * wd_annual / (r_std * np.sqrt(wd_annual)), 3)
#Calculation of coin beta based on BTC daily returns (~1/3 of market dominance)
displacement = len(rbtc) - len(r)
rbtc_2 = rbtc[displacement:len(rbtc)]
beta_btc = round(stats.linregress(rbtc_2, r)[0], 3)
alpha_btc = round(stats.linregress(rbtc_2, r)[1], 3)
#Calculation of coin beta based on mean return of top 10 coins on coinmarketcap.com (~80% of cumulative market dominance)
if len(rt10) > len(r):
displacement2 = len(rt10) - len(r)
rt10 = rt10[displacement2:len(rt10)]
beta_top10 = round(stats.linregress(rt10, r)[0], 3)
alpha_top10 = round(stats.linregress(rt10, r)[1], 3)
if len(rt10) <= len(r):
displacement2 = len(r) - len(rt10)
r = r[displacement2:len(r)]
beta_top10 = round(stats.linregress(rt10, r)[0], 3)
alpha_top10 = round(stats.linregress(rt10, r)[1], 3)
except:
c = ['N/A']
vol = ['N/A']
[s_1, s_3, s_annual, s_annual2] = ['N/A', 'N/A', 'N/A', 'N/A']
[beta_btc, beta_top10, alpha_btc,
alpha_top10] = ['N/A', 'N/A', 'N/A', 'N/A']
res = func_icobench(currency)
res2 = func_icodrops(currency)
res3 = func_tokenmarket(currency)
res4 = func_icorating(currency, token)
res5 = func_icomarks(currency)
res6 = func_icobazaar(currency)
res7 = func_googletwitter(currency)
[
start1, end1, duration1, country1, industry1, team1, raised1, hardcap1,
success1, price1, telegram1
] = [
res[1], res[2], res[3], res[4], res[5], res[6], res[7], res[8], res[9],
res[10], res[11]
]
[
country2, industry2, team2, raised2, hardcap2, success2, price2,
telegram2
] = [
res2[1], res2[2], res2[3], res2[4], res2[5], res2[6], res2[7], res2[8]
]
[start3, end3, duration3, country3,
team3] = [res3[1], res3[2], res3[3], res3[4], res3[5]]
[
start4, end4, duration4, industry4, team4, raised4, hardcap4, success4,
price4, telegram4, hype4, risk4
] = [
res4[1], res4[2], res4[3], res4[4], res4[5], res4[6], res4[7], res4[8],
res4[9], res4[10], res4[11], res4[12]
]
[start5, end5, duration5, country5, team5, raised5, hardcap5, price5] = [
res5[1], res5[2], res5[3], res5[4], res5[5], res5[6], res5[7], res5[8]
]
#[start6,end6,duration6,team6,hardcap6,price6,bazaar_rating6]=[res6[1],res6[2],res6[3],res6[4],res6[5],res6[6],res6[7]]
[team6, hardcap6, price6,
bazaar_rating6] = [res6[4], res6[5], res6[6], res6[7]]
[N_google_news, N_twitter, N_daily_views,
N_daily_time] = [res7[2], res7[1], res7[3], res7[4]]
try:
ret_day1a = round(c[0], 3)
except:
ret_day1a = 'N/A'
[
ret_day1, vol_day1, sharpe_1, sharpe_3, sharpe_yr, sharpe_yr2,
beta_btc, beta_top10, alpha_btc, alpha_top10
] = [
ret_day1a, vol[0], s_1, s_3, s_annual, s_annual2, beta_btc, beta_top10,
alpha_btc, alpha_top10
]
#Calculation of age since last day of ICO
today_a = datetime.strptime(today, '%Y%m%d')
try:
age1 = today_a - datetime.strptime(end1, '%d %b %Y')
age1 = round(age1.total_seconds() / 86400., 1)
except:
age1 = 'N/A'
try:
age3 = today_a - datetime.strptime(end3, '%d %b %Y')
age3 = round(age3.total_seconds() / 86400., 1)
except:
age3 = 'N/A'
try:
age4 = today_a - datetime.strptime(end4, '%d %b %Y')
age4 = round(age4.total_seconds() / 86400., 1)
except:
age4 = 'N/A'
try:
age5 = today_a - datetime.strptime(end5, '%d %b %Y')
age5 = round(age5.total_seconds() / 86400., 1)
except:
age5 = 'N/A'
#GENERIC OUTPUT-----------------------------------------------------------------
#1 - ICOBench, 2 - ICODrops, 3 - TokenMarket, 4 - ICORating, 5 - ICOMarks
columnTitles = "coin,start1,end1,duration1,age1,start3,end3,duration3,age3,start4,end4,duration4,age4,start5,end5,duration5,age5,country1,country2,country3,country5,industry1,industry2,industry4,team1,team2,team3,team4,team5,team6,raised1,raised2,raised4,raised5,hardcap1,hardcap2,hardcap4,hardcap5,hardcap6,success1,success2,success4,price1,price2,price4,price5,price6,telegram1,telegram2,telegram4,N_google_news,N_twitter,hype4,risk4,bazaar_rating6,ret_day1,vol_day1,sharpe_1,sharpe_3,sharpe_yr,sharpe_yr2,beta_btc,beta_top10,alpha_btc,alpha_top10\n"
with open('outdata/ico_data_full_single.csv', 'w') as csvfile1:
csvfile1.write(columnTitles)
writer = csv.writer(csvfile1, delimiter=',')
writer.writerow([
currency, start1, end1, duration1, age1, start3, end3, duration3,
age3, start4, end4, duration4, age4, start5, end5, duration5, age5,
country1, country2, country3, country5, industry1, industry2,
industry4, team1, team2, team3, team4, team5, team6, raised1,
raised2, raised4, raised5, hardcap1, hardcap2, hardcap4, hardcap5,
hardcap6, success1, success2, success4, price1, price2, price4,
price5, price6, telegram1, telegram2, telegram4, N_google_news,
N_twitter, hype4, risk4, bazaar_rating6, ret_day1, vol_day1,
sharpe_1, sharpe_3, sharpe_yr, sharpe_yr2, beta_btc, beta_top10,
alpha_btc, alpha_top10
])
#--------------------------------------------------------------------------------
#--------------------------------------------------------------------------------
#--------------------------------------------------------------------------------
#HIERARCHICAL OUTPUT (No Ratings by ICOrating.com)-------------------------------
#1 - ICOBench, 2 - ICODrops, 3 - TokenMarket, 4 - ICORating, 5 - ICOMarks
start_date_all = [start1, start3, start4, start5]
end_date_all = [end1, end3, end4, end5]
duration_all = [duration1, duration3, duration4, duration5]
age_all = [age1, age3, age4, age5]
country_all = [country1, country2, country3, country5]
industry_all = [industry1, industry2, industry4]
team_all = [team1, team2, team3, team4, team5, team6]
raised_all = [raised1, raised2, raised4, raised5]
hardcap_all = [hardcap1, hardcap2, hardcap4, hardcap5, hardcap6]
success_all = [success1, success2, success4]
price_all = [price1, price2, price4, price5, price6]
telegram_all = [telegram1, telegram2, telegram4]
#1. Determining start date, end date and duration
#1A) First remove all 'N/A' occurences
duration_all = [item for item in duration_all if item != 'N/A']
start_date_all = [item for item in start_date_all if item != 'N/A']
end_date_all = [item for item in end_date_all if item != 'N/A']
age_all = [item for item in age_all if item != 'N/A']
#1B) If length of array is zero after removals, then final values are 'N/A'
if len(duration_all) == 0:
duration = 'N/A'
start = 'N/A'
end = 'N/A'
age = 'N/A'
[duration_all, start_date_all, end_date_all, age_all] = [['N/A'],
['N/A'],
['N/A'],
['N/A']]
#1C) If all values are the same then adopt that value as the final value
if duration_all.count(duration_all[0]) == len(duration_all):
duration = duration_all[0]
start = start_date_all[0]
end = end_date_all[0]
try:
age = age_all[0]
except:
age = 'N/A'
#1D) If values are not the same then adopt the value that appears most times. If failure adopt first elements.
try:
if duration_all.count(duration_all[0]) != len(duration_all):
(values, counts) = np.unique(duration_all, return_counts=True)
(values2, counts2) = np.unique(start_date_all, return_counts=True)
(values3, counts3) = np.unique(end_date_all, return_counts=True)
(values4, counts4) = np.unique(age_all, return_counts=True)
if counts.count(counts[0]) == len(counts):
duration = duration_all[0]
if counts2.count(counts2[0]) == len(counts2):
start = start_date_all[0]
if counts3.count(counts3[0]) == len(counts3):
end = end_date_all[0]
if counts4.count(counts4[0]) == len(counts4):
age = age_all[0]
ind = np.argmax(counts)
ind2 = np.argmax(counts2)
ind3 = np.argmax(counts3)
ind4 = np.argmax(counts4)
duration = values[ind]
start = values2[ind2]
end = values3[ind3]
age = values4[ind4]
except:
duration = duration_all[0]
start = start_date_all[0]
end = end_date_all[0]
age = age_all[0]
try:
if duration < 0:
duration = -duration
except:
duration == 'N/A'
#2. Determining geographical region
#2A) First remove all 'N/A' occurences
country_all = [item for item in country_all if item != 'N/A']
#2B) If length of array is zero after removals, then final values are 'N/A'
if len(country_all) == 0:
region = 'N/A'
country_all = ['N/A']
#2C) If all values are the same then adopt that value as the final value
if country_all.count(country_all[0]) == len(country_all):
region = country_all[0]
region = func_region(region)
#2D) If values are not the same then adopt the value that appears most times. If failure adopt first element.
try:
if country_all.count(country_all[0]) != len(country_all):
(values, counts) = np.unique(country_all, return_counts=True)
if counts.count(counts[0]) == len(counts):
region = func_region(country_all[0])
ind = np.argmax(counts)
region = values[ind]
region = func_region(region)
except:
region = func_region(country_all[0])
#3. Determining industry category
#3A) First remove all 'N/A' occurences
industry_all = [item for item in industry_all if item != 'N/A']
#3B) If length of array is zero after removals, then final values are 'N/A'
if len(industry_all) == 0:
industry = 'N/A'
industry_all = ['N/A']
#3C) If all values are the same then adopt that value as the final value
if industry_all.count(industry_all[0]) == len(industry_all):
industry_a = industry_all[0]
industry = func_industry(industry_a)
#3D) If values are not the same then adopt the value that appears most times. If failure adopt last element (ICOrating/ICOdrops/ICObench).
try:
if industry_all.count(industry_all[0]) != len(industry_all):
(values, counts) = np.unique(industry_all, return_counts=True)
if counts.count(counts[0]) == len(counts):
industry = func_industry(industry_all[len(industry_all) - 1])
ind = np.argmax(counts)
industry_a = values[ind]
industry = func_industry(industry_a)
except:
industry = func_industry(industry_all[0])
#4. Determining team size
#4A) Check if data availabe from tokenmarket or icomarks. If yes adopt this as team size value
if (team_all[2] != 'N/A') or (team_all[4] != 'N/A'):
if team_all[2] == team_all[4]:
team = team_all[2]
if team_all[2] != team_all[4]:
team = team_all[4]
#4B) In the opposite case, proceed as before
#4C) First remove all 'N/A' occurences
if (team_all[4] == 'N/A') or (team == 'N/A'):
team_all = [item for item in team_all if item != 'N/A']
#4D) If length of array is zero after removals, then final values are 'N/A'
if len(team_all) == 0:
team = 'N/A'
team_all = ['N/A']
#4E) If all values are the same then adopt that value as the final value
if team_all.count(team_all[0]) == len(team_all):
team = team_all[0]
#4F) If values are not the same then adopt the value that appears most times if failure adopt first element
try:
if team_all.count(team_all[0]) != len(team_all):
(values, counts) = np.unique(team_all, return_counts=True)
if counts.count(counts[0]) == len(counts):
team = team_all[0]
ind = np.argmax(counts)
team = values[ind]
except:
team = team_all[0]
#5. Determining success, money raised, hardcap
success = 'N/A'
###
raised_all2 = [item for item in raised_all if item != 'N/A']
hardcap_all2 = [item for item in hardcap_all if item != 'N/A']
if len(raised_all2) == 0:
raised_all2 = ['N/A']
raised = raised_all2[0]
if len(hardcap_all2) == 0:
hardcap_all2 = ['N/A']
hardcap = hardcap_all2[0]
if raised_all2.count(raised_all2[0]) == len(raised_all2):
raised = raised_all2[0]
if hardcap_all2.count(hardcap_all2[0]) == len(hardcap_all2):
hardcap = hardcap_all2[0]
try:
if raised_all2.count(raised_all2[0]) != len(raised_all2):
(values, counts) = np.unique(raised_all2, return_counts=True)
ind = np.argmax(counts)
raised = values[ind]
if counts.count(counts[0]) == len(counts):
raised = raised_all2[0]
except:
raised = raised_all2[0]
try:
if hardcap_all2.count(hardcap_all2[0]) != len(hardcap_all2):
(values, counts) = np.unique(hardcap_all2, return_counts=True)
ind = np.argmax(counts)
hardcap = values[ind]
if counts.count(counts[0]) == len(counts):
hardcap = hardcap_all2[0]
except:
hardcap = hardcap_all2[0]
if success == 'N/A':
try:
success = min(round(raised / hardcap, 2), 1.0)
except:
success = 'N/A'
success_all2 = [item for item in success_all if item != 'N/A']
if len(success_all2) == 0:
success_all2 = ['N/A']
success = success_all2[0]
if (success_all[0] != 'N/A') and (success_all[1]
== 'N/A') and (success_all[2] == 'N/A'):
success = min(success_all[0], 1.0)
raised = raised_all[0]
hardcap = hardcap_all[0]
if (success_all[0]
== 'N/A') and (success_all[1] != 'N/A') and (success_all[2]
== 'N/A'):
success = min(success_all[1], 1.0)
raised = raised_all[1]
hardcap = hardcap_all[1]
if (success_all[0] != 'N/A') and (success_all[1] !=
'N/A') and (success_all[2] == 'N/A'):
success = min(success_all[1], 1.0)
raised = raised_all[1]
hardcap = hardcap_all[1]
if (success_all[0] == 'N/A') and (success_all[1]
== 'N/A') and (success_all[2] != 'N/A'):
success = min(success_all[2], 1.0)
raised = raised_all[2]
hardcap = hardcap_all[2]
if success == 'N/A':
try:
success = min(round(raised / hardcap, 2), 1.0)
except:
success = 'N/A'
try:
hardcap = round(hardcap, 0)
except:
hardcap = 'N/A'
if raised != 'N/A' and success != 'N/A':
try:
hardcap = round(raised / success, 0)
except:
hardcap = 'N/A'
if team == 0:
team = 'N/A'
#9. Determining ICO token price
#9A) Check if data availabe from icobench or icodrops or icomarks. If yes adopt this money raised value
if (price_all[0] != 'N/A') or (price_all[1] != 'N/A') or (price_all[3] !=
'N/A'):
try:
price = round(price_all[0], 5)
except:
try:
price = round(price_all[1], 5)
except:
price = round(price_all[3], 5)
#9B) In the opposite case, proceed as before
if (price_all[0] == 'N/A') or (price_all[1] == 'N/A') or (price_all[3]
== 'N/A'):
#9C) First remove all 'N/A' occurences
price_all = [item for item in price_all if item != 'N/A']
#9D) If length of array is zero after removals, then final values are 'N/A'
if len(price_all) == 0:
price_all = ['N/A']
price = 'N/A'
#9E) If all values are the same then adopt that value as the final value
if price_all.count(price_all[0]) == len(price_all):
try:
price = round(price_all[0], 5)
except:
price = 'N/A'
#9F) If values are not the same then adopt the value that appears most times. If failure adopt first element.
try:
if price_all.count(price_all[0]) != len(price_all):
(values, counts) = np.unique(price_all, return_counts=True)
if counts.count(counts[0]) == len(counts):
price = round(price_all[0], 5)
ind = np.argmax(counts)
price = round(values[ind], 5)
except:
try:
price = round(price_all[0], 5)
except:
try:
price = round(price_all[1], 5)
except:
price = 'N/A'
#10. Determining telegram follower count
#10A) First remove all 'N/A' occurences
telegram_all = [item for item in telegram_all if item != 'N/A']
#10B) If length of array is zero after removals, then final values are 'N/A'
if len(telegram_all) == 0:
telegram = 'N/A'
telegram_all = ['N/A']
#10C) If all values are the same then adopt that value as the final value
if telegram_all.count(telegram_all[0]) == len(telegram_all):
telegram = telegram_all[0]
#10D) If values are not the same then adopt the value that appears most times. If failure adopt first element.
try:
if telegram_all.count(telegram_all[0]) != len(telegram_all):
(values, counts) = np.unique(telegram_all, return_counts=True)
if counts.count(counts[0]) == len(counts):
telegram = telegram_all[0]
ind = np.argmax(counts)
telegram = values[ind]
except:
telegram = telegram_all[0]
#11) Calculating first day exchange returns compared to ICO token price
try:
ret_icoday1 = round((c[0] - price) / price, 5)
except:
ret_icoday1 = 'N/A'
ret_icoday1 = ret_icoday1
columnTitles2 = "coin,start,end,duration,age,region,industry,team,raised,hardcap,success,price,telegram,N_google_news,N_twitter,N_daily_views,N_daily_time,ret_ico_to_day_one,vol_day1,sharpe_1,sharpe_3,sharpe_yr,sharpe_yr2,beta_btc,beta_top10,alpha_btc,alpha_top10\n"
with open('outdata/ico_data_reduced_single.csv', 'w') as csvfile2:
csvfile2.write(columnTitles2)
writer = csv.writer(csvfile2, delimiter=',')
writer.writerow([
currency, start, end, duration, age, region, industry, team,
raised, hardcap, success, price, telegram, N_google_news,
N_twitter, N_daily_views, N_daily_time, ret_icoday1, vol_day1,
sharpe_1, sharpe_3, sharpe_yr, sharpe_yr2, beta_btc, beta_top10,
alpha_btc, alpha_top10
])
#--------------------------------------------------------------------------------
#--------------------------------------------------------------------------------
#--------------------------------------------------------------------------------
#HIERARCHICAL OUTPUT (Ratings by ICOrating.com and ICOBazaar.com included)-------
#1 - ICOBench, 2 - ICODrops, 3 - TokenMarket, 4 - ICORating, 5 - ICOMarks
hype = hype4
risk = risk4
bazaar = bazaar_rating6
columnTitles3 = "coin,start,end,duration,age,region,industry,team,raised,hardcap,success,price,telegram,N_google_news,N_twitter,N_daily_views,N_daily_time,hype,risk,bazaar-rate,ret_ico_to_day_one,vol_day1,sharpe_1,sharpe_3,sharpe_yr,sharpe_yr2,beta_btc,beta_top10,alpha_btc,alpha_top10\n"
with open('outdata/ico_data_reduced_wratings_single.csv', 'w') as csvfile3:
csvfile3.write(columnTitles3)
writer = csv.writer(csvfile3, delimiter=',')
writer.writerow([
currency, start, end, duration, age, region, industry, team,
raised, hardcap, success, price, telegram, N_google_news,
N_twitter, N_daily_views, N_daily_time, hype, risk, bazaar,
ret_icoday1, vol_day1, sharpe_1, sharpe_3, sharpe_yr, sharpe_yr2,
beta_btc, beta_top10, alpha_btc, alpha_top10
])
#--------------------------------------------------------------------------------
#--------------------------------------------------------------------------------
#--------------------------------------------------------------------------------
return [
currency, start1, end1, duration1, age1, start3, end3, duration3, age3,
start4, end4, duration4, age4, start5, end5, duration5, age5, country1,
country2, country3, country5, industry1, industry2, industry4, team1,
team2, team3, team4, team5, team6, raised1, raised2, raised4, raised5,
hardcap1, hardcap2, hardcap4, hardcap5, hardcap6, success1, success2,
success4, price1, price2, price4, price5, price6, telegram1, telegram2,
telegram4, N_google_news, N_twitter, N_daily_views, N_daily_time,
hype4, risk4, bazaar_rating6, ret_day1, vol_day1, sharpe_1, sharpe_3,
sharpe_yr, sharpe_yr2, beta_btc, beta_top10, alpha_btc, alpha_top10
], [
currency, start, end, duration, age, region, industry, team, raised,
hardcap, success, price, telegram, N_google_news, N_twitter,
N_daily_views, N_daily_time, hype, risk, bazaar, ret_icoday1, vol_day1,
sharpe_1, sharpe_3, sharpe_yr, sharpe_yr2, beta_btc, beta_top10,
alpha_btc, alpha_top10
]
def ico_rank_full(user_input):
kk = -1
ranks = []
for i in range(0, len(user_input)):
kk = kk + 1
ranks.append(kk)
z = []
bins = []
if i not in [0, 1, 6]:
if i == 2:
feature_in = eval(user_input[2])
z = np.loadtxt('scaling_dataset/team_z.npy')
bins = np.loadtxt('scaling_dataset/team_bin.npy')
if i == 3:
feature_in = np.log10(eval(user_input[3]))
z = np.loadtxt('scaling_dataset/hardcap_z.npy')
bins = np.loadtxt('scaling_dataset/hardcap_bin.npy')
if i == 4:
feature_in = np.log10(eval(user_input[4]))
z = np.loadtxt('scaling_dataset/price_z.npy')
bins = np.loadtxt('scaling_dataset/price_bin.npy')
if i == 5:
feature_in = np.log10(eval(user_input[5]))
z = np.loadtxt('scaling_dataset/telegram_z.npy')
bins = np.loadtxt('scaling_dataset/telegram_bin.npy')
if i == 7:
z = np.loadtxt('scaling_dataset/twitter_z.npy')
bins = np.loadtxt('scaling_dataset/twitter_bin.npy')
feature_in = np.log10(eval(user_input[7]))
if i == 8:
feature_in = np.log10(eval(user_input[8]))
z = np.loadtxt('scaling_dataset/dailyviews_z.npy')
bins = np.loadtxt('scaling_dataset/dailyviews_bin.npy')
if i == 9:
feature_in = np.log10(eval(user_input[9]))
z = np.loadtxt('scaling_dataset/dailytime_z.npy')
bins = np.loadtxt('scaling_dataset/dailytime_bin.npy')
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
if i == 0:
region_in = eval(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
if i == 1:
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
ranks[kk] = grade
ico_rating = np.mean(ranks)
ico_rating0 = ico_rating / 5.0
return round(ico_rating0, 2)
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