Hamster

import pandas as pd
import numpy as np

def my_time_d(s):
    d = "0 days"
    h = s // 3600
    time = s % 3600
    m = int(time // 60)
    s = int(time % 60)
    if h >= 24:
        d = int(h // 24)
        h -= d*24
        if d == 1: d = fr'{d} day'
        else: d = fr'{d} days'
    h = int(h)
    if h < 10: h = '0' + str(int(h))
    if m < 10: m = '0' + str(int(m))
    if s < 10: s = '0' + str(int(s))

    return fr'{d} {h}:{m}:{s}'

def profit_table(df):

    info = df.copy()
    info['profit per 100000 coins'] = info['profit per hour'] / info['price'] * 100000
    info['payback period'] = info['price'] / (info['profit per hour'] / 3600)
    for i in range(len(info)):
        info.iloc[i, -1] = my_time_d(info.iloc[i, -1])

    return info

def invest_in(df, top=10):

    print('\nInvest in:')

    for i in range(top):
        print(f"\nTOP {i+1}\n{info.index[i]}:\nProfit for 100000 coins per hour: {round(info.iloc[i, -2], 2)}\nUpgrade costs {info.iloc[i,-3]} coins\nPayback period - {info.iloc[i, -1]}")

df = pd.DataFrame(columns=['level', 'profit per hour', 'price'])
df.index.names = ['card name']

# Markets
df.loc['Fan tokens'] = [12, 2000, 428130]
df.loc['Staking'] = [13, 1350, 282556]
df.loc['BTC pairs'] = [15, 103, 83074]
df.loc['ETH pairs'] = [13, 90, 24219]
df.loc['Top 10 cmc pairs'] = [12, 168, 42813]
df.loc['GameFi tokens'] = [13, 158, 40365]
df.loc['Defi2.0 tokens'] = [13, 158, 40365]
df.loc["SocialFi tokens"] = [12, 105, 21407]
df.loc["Meme coins"] = [11, 216, 47680]
df.loc['Shit coins'] = [13, 1320, 403652]
df.loc["Margin trading x10"] = [12, 579, 107033]
df.loc["Margin trading x20"] = [13, 788, 201826]
df.loc["Margin trading x30"] = [13, 1120, 282556]
df.loc["Margin trading x50"] = [16, 3030, 7253630]
df.loc["Margin trading x75"] = [13, 2470, 605478]
df.loc["Margin trading x100"] = [13, 2190, 403652]
df.loc["Derivatives"] = [13, 1110, 201826]
df.loc["Prediction markets"] = [13, 777, 141278]
df.loc["Web3 generation"] = [14, 1770, 524747]
df.loc["DAO"] = [13, 554, 159841]
df.loc['P2P trading'] = [12, 821, 179815]
df.loc["Trading bots"] = [12, 410, 89907]

# PR&Team
df.loc['Support team'] = [12, 147, 32110]
df.loc['HamsterBook'] = [14, 169, 79920]
df.loc['X'] = [13, 180, 44402]
df.loc['Cointelegraph'] = [13, 90, 28256]
df.loc['HamsterTube'] = [13, 203, 48438]
df.loc["HamsterGram"] = [13, 113, 40365]
df.loc["TicTok"] = [14, 241, 119880]
df.loc["Coindesk"] = [12, 168, 42813]
df.loc['Influencers'] = [12, 568, 107033]
df.loc['CEO'] = [13, 225, 80730]
df.loc['IT Team'] = [13, 541, 161461]
df.loc['Marketing'] = [12, 147, 42813]
df.loc["Partnership program"] = [14, 169, 79920]
df.loc['Product team'] = [12, 210, 42813]
df.loc["BisDev team"] = [12, 105, 21407]
df.loc["Two factor authentication"] = [14, 301, 159841]
df.loc["UX and UI team"] = [14, 422, 121479]
df.loc["Security team"] = [14, 482, 159841]
df.loc["QA Team"] = [12, 428, 102931]
df.loc["Antihacking shield"] = [11, 216, 47680]
df.loc["Risk management team"] = [13, 597, 161461]
df.loc["Security Audition"] = [10, 184, 41816]
df.loc["Anonymous transactions ban"] = [14, 774, 299067]
df.loc["Blocking suspicious accounts"] = [13, 360, 100913]


# Legal
df.loc['KYC'] = [14, 24, 15984]
df.loc['KYB'] = [13, 135, 40365]
df.loc['Legal opinion'] = [11, 118, 23840]
df.loc["SEC trasparancy"] = [11, 118, 28608]
df.loc['Anti money loundering'] = [12, 589, 128439]
df.loc['Licence UAE'] = [12, 1170, 214065]
df.loc['Licence Europe'] = [13, 1620, 403652]
df.loc['Licence Asia'] = [12, 779, 214065]
df.loc['Licence South America'] = [12, 821, 214065]
df.loc['Licence Australia'] = [13, 1530, 403652]
df.loc["Licence North America"] = [12, 2020, 428130]
df.loc['Licence Nigeria'] = [13, 383, 121096]

# Specials
df.loc['Top 10 Global Ranking'] = [13, 2700, 807304]
# df.loc['Venom Blockchain'] = [6, 2170, 39799]
df.loc['Special Hamster Conference'] = [13, 2020, 403652]
df.loc['Apps Center Listing'] = [11, 1960, 357599]
df.loc["Bitcoin Pizza Day"] = [12, 210, 42813]
df.loc["There are two chairs"] = [10, 3670, 696935]
df.loc["Short squeeze"] = [11, 1960, 834397]
df.loc["USDT on TON"] = [13, 3040, 807304]
df.loc["21,000,000 CEOs"] = [13, 676, 201826]
df.loc["Add contract with a football club"] = [12, 3680, 1070326]
df.loc["NFT Collection Launch"] = [12, 2520, 727822]
df.loc["Bogdanoff is calling"] = [12, 1000, 214065]
df.loc["Hamster YouTube Chanell"] = [11, 492, 35760]
df.loc["Hamster daily show"] = [10, 368, 27877]
df.loc["Villa for the DEV team"] = [15, 1150, 664594]



info = profit_table(df)
info = info.sort_values(by='profit per 100000 coins')[::-1]

invest_in(info, top=5)