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Effective bankroll management is not a rigid set of rules, but a system of adaptive solutions. Below is a step-by-step technique without further ado.

1. Basic principles

1. Rate diversification

Break the total bankroll into several separate "packages" for different types of games or sessions.
Example: with a bankroll of 20,000 ₴ to make 4 of 5,000 ₴ for slots, board games, sports betting and "process" (video poker).

2. Rule 1-5%

The size of one bet adapts to the current bank of the session: 1-2% for highly dispersed games, 3-5% for low-dispersed games.

3. Dynamic redistribution

After each session, revise the balance in the package: redistribute the "extra" to those games where the margin was not received.

2. Adaptive Bet Size

1. Modified Kelly formula

```
f= (bp – q) / b
ubi
p is the estimated probability of winning,
q = 1 – p,
b is the ratio of winnings to bets.
```

- limit fo a maximum of 2% of the session bank.

2. "Parabolic" scale

With a winning streak, gradually increase the bet (for example, + 0.5% from the bank for every two successful rounds), but not higher than 5%.
With a losing one - immediately return to the base 1%.

3. Stop loss and break profit

1. Flexible stop loss

Set the loss levels not strictly set, but in the range of 10-20% of the session bank, taking into account the variance of the game.
Example: for slots with high dispersion - stop loss 20%, for video poker - 12%.

2. Trend profit

Record profits in two steps: 50% at + 10%, the rest at + 25%.
Allows you to "wrap" part of the winnings and still stay in the game.

4. Modelling and stress tests

1. Losing scenarios

Run the model 1,000-5,000 games with current bets and variance: Get the average loss, 5th and 95th percentiles.
Adjust rates if the 5th percentile (worst outcome) exceeds the allowed threshold.

2. Monte Carlo simulations

Quick check of strategy stability:

```python
import numpy as np

def simulate(bank, p, b, bet_pct, trials=10000):

results = []

for _ in range(trials):

balance = bank

for _ in range(100):

if np. random. rand() < p:

balance += bet_pct balance b

else:

balance -= bet_pct balance
results. append(balance)
return np. percentile(results, [5, 50, 95])
```
At 5th percentile below stop loss - lower bet\_ pct.

5. Automation and control

1. Implement API monitoring

2. Timers and reminders

6. Regular review and adaptation

1. Daily report

Collect data:

	Batch	Start	End	Change,%	Max. rate,%
	Slots	5,000 ₴	4,200 ₴	-16%	4%
	Video Poker	5,000 ₴	5,500 ₴	+ 10%	2%
Record where the strategy worked and where it didn't.

2. Weekly adjustment

7. Psychological support of the strategy

1. Reality check points

2. Gamification of progress

3. Responsible partner

Result