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a guest Jan 18th, 2019 60 Never
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  1.  
  2. ### STAKE & NUMBER_OF_BETS ###
  3. stake_1 = 0.1
  4. stake_2 = 0.2
  5. stake_3 = 0.3
  6. stake_4 = 0.4
  7. stake_5 = 0.5
  8.  
  9. ### Please uncomment wanted amount of bets:
  10. number_of_bets = 100
  11. #number_of_bets = 250
  12. #number_of_bets = 500
  13. #number_of_bets = 1000
  14. #number_of_bets = 2500
  15.  
  16.  
  17. ### Please uncomment wanted value for interval on x axis:
  18. inter = 17 #for number_of_bets == 100
  19. #inter = 23; #for number_of_bets = 250
  20. #inter = 34; #for number_of_bets == 500
  21. #inter = 50; #for number_of_bets == 1000
  22. #inter = 100; #for number_of_bets == 2500
  23.  
  24.  
  25. base = 100 # any value
  26. p = 0.6 # probability == 60%
  27. probability = dbinom(0:number_of_bets,number_of_bets, p)
  28. s = (0:number_of_bets)
  29.  
  30. ### Please feel free to change stake percent
  31. final_bankroll_1 = base*(1+stake_1)^s*(1-stake_1)^(number_of_bets-s) #calculation for final_bankroll_1 for stake == 10%
  32. final_bankroll_2 = base*(1+stake_2)^s*(1-stake_2)^(number_of_bets-s) #calculation for final_bankroll_2 for stake == 20%
  33. final_bankroll_3 = base*(1+stake_3)^s*(1-stake_3)^(number_of_bets-s) #calculation for final_bankroll_3 for stake == 30%
  34. final_bankroll_4 = base*(1+stake_4)^s*(1-stake_4)^(number_of_bets-s) #calculation for final_bankroll_4 for stake == 40%
  35. final_bankroll_5 = base*(1+stake_5)^s*(1-stake_5)^(number_of_bets-s) #calculation for final_bankroll_5 for stake == 50%
  36.  
  37. ### Please uncomment if you want to see data.frame of final_bankroll's and probability
  38. #print (data.frame(final_bankroll_1, probability)) #printing the aktual values for final_bankroll_1
  39. #print (data.frame(final_bankroll_2, probability)) #printing the aktual values for final_bankroll_2
  40. #print (data.frame(final_bankroll_3, probability)) #printing the aktual values for final_bankroll_3
  41. #print (data.frame(final_bankroll_4, probability)) #printing the aktual values for final_bankroll_4
  42. #print (data.frame(final_bankroll_5, probability)) #printing the aktual values for final_bankroll_5
  43.  
  44.  
  45. ### visualization of both bankrolls using log() function
  46. plot(log(final_bankroll_1), probability, type="h",  xlim = c(0, inter))
  47. par(new = TRUE)
  48. #plot(log(final_bankroll_2), probability, type="h", col = "red", xlim = c(0, inter))
  49. #plot(log(final_bankroll_3), probability, type="h", col = "red", xlim = c(0, inter))
  50. #plot(log(final_bankroll_4), probability, type="h", col = "red", xlim = c(0, inter))
  51. plot(log(final_bankroll_5), probability, type="h", col = "red", xlim = c(0, inter))
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