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Machine Learning analisis

This is an algorithm to choose best bet size knowing this. If I win, I win only 9%, if I loose, I loose 100%. Wining ratio: 91% (at least 100 trades). I have some data sample. To be studied, but I already have this:

- Over 116 trades, wining ratio is always 91%, before this, wining ration is between 85% and 100%

- Loses in 677 trades ocurred in this way:

Lose number|Number of trade after last lose

1 17

2 4

3 7

4 4

5 8

6 2

7 3

8 2

9 9

10 7

11 4

12 78

13 2

14 11

15 6

16 3

17 5

18 2

19 3

20 21

21 14

22 6

23 1

24 20

25 4

26 1

27 9

28 23

29 3

30 7

31 11

32 20

33 25

34 8

35 0

36 14

37 4

38 14

39 15

40 3

41 18

42 3

43 18

44 9

45 2

46 19

47 9

48 2

49 21

50 5

51 20

52 6

53 25

54 2

55 1

56 16

57 19

58 0

59 12

60 3

61 5

Number of trade after last lose | times I lose in same distance trade

12 1

15 1

16 1

17 1

23 1

0 2

8 2

11 2

18 2

19 2

21 2

25 2

1 3

5 3

6 3

7 3

14 3

20 3

9 4

4 5

2 7

3 7

So I was thinking i needed one or more machine learning algorithm to see how much i must bet every time. At this moment I choose a 20% of balance on 1,2,3 and 4th bet after last lose. I calculate a coeficient number equal to 60% from last lose trade and then I after 5,6,7,8 y 9, i multiply that coeficient to the bet, so it goes exponential decrease, thinking is more probable a new lose. It worked, but not as expected, so I go up 2x, and then 0.5x. When I lose, I use martingale algorithm, but some times, it stop everything. So I want to avoid martingale but use great succes rate to create an advanced algorithm to win.

Evner: Machine Learning (ML), Algoritme, Python, Statistikker, Matematik

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Projekt ID: #23449951

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