└── modeling_TGE


/modeling_TGE:
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 1 | import random
 2 | results = [9.89, 9.83, 8.30, 7.75, 7.63, 6.46, 6.28, 5.52, 5.31, 4.79, 4.07, 3.43, 2.93, 2.60, 2.44, 2.42, 2.26, 2.11, 1.87, 1.83, 1.73, 1.63, 1.45, 1.25, 1.14, 1.33, 1.19, 1.45, 1.13, 1.16, 1.11, 1.10, 0.88, 0.87, 0.85, 0.84, 0.64, 0.76, 0.49, 0.41, 0.35, 0.19, 0.12, 0.10, 0.05, 0.03, 0.03, 0.02, 0.02, 0.01, 0.02, 0.01, 0.01, 0.0048, 0.0032, 0.0028, 0.0023, 0, 0, 0, 0]
 3 | q = 1000 #number of experiments
 4 | t = 3 #number of series
 5 | for assets in range(1, 21): #number of assets, min and max
 6 |     sum_co = 0
 7 |     pos = 0
 8 |     counter = 0
 9 |     positive_counter = 0
10 |     for i in range (q):
11 |         sum = 1
12 |         for x in range(t):
13 |             sprint = 0
14 |             for y in range(assets):
15 |                 a = random.choice(results)
16 |     #random generator and sum
17 |                 sprint += a
18 |     #counters
19 |             if sprint/assets > 1:
20 |                 positive_counter +=1
21 |             counter += 1
22 |             sum *= sprint/assets        
23 |     #sum of positive series
24 |         if sum > 1:
25 |             pos +=1
26 |         sum_co += sum
27 |     print(assets, 100*pos/q, round((sum_co/q)*(pos/q)), 100*positive_counter/counter)
28 |    #number of assets, + probability of all series, profit or x, + probability of one serie
29 | 


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