Creating an App to Beat the Roulette

Home » Creating an App to Beat the Roulette

The roulette game in a casino is generally designed to favour the house, meaning players have no guaranteed winning strategy, even when using betting systems like the Fibonacci or the Oscar Grind strategy. These systems merely change the betting patterns rather than overcome the inherent odds of the game. However, by combining these strategies with an AI, one might aim to analyze patterns and optimize betting decisions to a certain extent.

The Approach

Let’s first understand the Fibonacci and Oscar’s Grind betting systems:

  1. Fibonacci Betting System: This system is based on the Fibonacci sequence, where each number is the sum of the two preceding ones: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, and so on. In this system, after a loss, you move one step forward in the sequence to determine your next bet. After a win, you move back two numbers in the sequence. The theory is that you will compensate for the losses once you hit a win.
  2. Oscar’s Grind Strategy: This system aims to make a profit of one unit at the end of any winning streak. You start by betting one unit. If you lose, you maintain the same bet. If you win, you increase your bet by one unit, but only if it won’t result in a net profit greater than one unit for the streak.

We could use a reinforcement learning approach to integrate these two strategies with an AI system. Here’s a simple design for such an algorithm:

Step 1: Initialize the betting unit and starting balance.

Step 2: Determine the current state, including the history of the previous few outcomes, the current balance, and the current bet.

Step 3: The AI agent places a bet according to the combined Fibonacci-Oscar’s Grind strategy, as follows:

  • If the agent lost the last game, it moves one step forward in the Fibonacci sequence for the next bet size.
  • If the agent won the last game, it moves two steps backward in the Fibonacci sequence, but also takes into account the Oscar’s Grind strategy, adjusting the bet size to make sure it doesn’t create a net profit of more than one unit for the streak.

Step 4: The agent observes the outcome of the bet, which results in a new state and a reward (which might be negative for a loss).

Step 5: The agent uses a reinforcement learning algorithm, like Q-learning or SARSA, to update its understanding of the expected future rewards for the current state-action pair.

Step 6: Repeat steps 2-5 until a stopping condition is met (like reaching a predetermined number of games or achieving a certain balance).

Mathematically, the key aspect of this strategy is the learning step. If Q(s,a) is the agent’s current estimate of the expected future reward for state s and action a, then it might update this using the formula:

Q(s,a) <- Q(s,a) + alpha * (reward + gamma * max_a’ Q(s’,a’) – Q(s,a)),


  • s’ is the new state after the action a is taken,
  • alpha is the learning rate, determining how much the agent adjusts its expectations based on the new information,
  • gamma is the discount factor, controlling how much the agent values immediate rewards over distant ones,
  • max_a’ Q(s’,a’) is the highest expected future reward for the new state s’.

Please note that the described AI strategy, while potentially helping to optimize betting decisions to some extent, still cannot overcome the inherent house edge in a game of roulette. It’s important to approach gambling with caution and understanding of the risks involved.

Building an app to beat the Roulette

To build a software application that combines the Fibonacci and Oscar Grind betting systems along with AI, the software could be structured in the following way:

  1. Data Input Module: This module would collect data on roulette outcomes. This data would be fed into the AI model to help it learn betting strategies. It would use a random number generator to simulate the spin of the roulette.
  2. AI Betting System Module: This would use the Fibonacci and Oscar Grind strategies to inform the AI’s betting decisions, as described in the algorithm above. The core of this module would be a reinforcement learning algorithm like Q-learning or SARSA.
  3. User Interface Module: This would handle interactions with the user, including setting up initial conditions, displaying ongoing results, and allowing the user to stop the simulation when desired.

For simplicity, let’s consider creating this application in Python. The following is a basic implementation of the AI Betting System Module. Please note that this code does not include any user interface elements and is only a starting point for your application.

Your Roulette betting software code may look like this:

import numpy as np

class BettingAgent:
    def __init__(self, initial_balance, fibonacci_sequence, learning_rate=0.5, discount_factor=0.95):
        self.balance = initial_balance
        self.fibonacci_sequence = fibonacci_sequence
        self.learning_rate = learning_rate
        self.discount_factor = discount_factor
        self.current_bet = 0
        self.state_action_values = {}  # Q-values

    def get_next_action(self, state):
        """Choose next action based on current state and learned Q-values."""
        if np.random.rand() < 0.1:  # exploration
            return np.random.choice(["bet", "pass"])
        else:  # exploitation
            bet_value = self.balance // self.fibonacci_sequence[self.current_bet]
            return "bet" if self.state_action_values.get((state, "bet"), 0) > \
                           self.state_action_values.get((state, "pass"), 0) and bet_value <= self.balance else "pass"

    def update_q_values(self, old_state, action, reward, new_state):
        """Update Q-values based on observed reward."""
        old_value = self.state_action_values.get((old_state, action), 0)
        max_new_value = max(self.state_action_values.get((new_state, a), 0) for a in ["bet", "pass"])
        self.state_action_values[(old_state, action)] = old_value + self.learning_rate * \
                                                         (reward + self.discount_factor * max_new_value - old_value)

    def adjust_bet(self, result):
        """Adjust bet according to the outcome and the Fibonacci-Oscar's Grind strategy."""
        if result == "loss":
            self.current_bet = min(self.current_bet + 1, len(self.fibonacci_sequence) - 1)
        elif result == "win":
            self.current_bet = max(self.current_bet - 2, 0)

    def take_action(self, action):
        """Place a bet and observe the outcome."""
        old_state = self.balance
        if action == "bet":
            bet_value = self.balance // self.fibonacci_sequence[self.current_bet]
            self.balance -= bet_value
            result = "win" if np.random.rand() < 18/37 else "loss"  # approximate odds for a single-number bet in European roulette
            self.balance += 2 * bet_value if result == "win" else 0
            reward = bet_value if result == "win" else -bet_value
            reward = 0
        new_state = self.balance
        self.update_q_values(old_state, action, reward
, new_state)

    def simulate(self, num_rounds):
        """Simulate a number of rounds of roulette."""
        for _ in range(num_rounds):
            state = self.balance
            action = self.get_next_action(state)

The BettingAgent class provides the core functionality of the software. The get_next_action method is where the agent chooses its action, either “bet” or “pass”. This decision is based on the currently learned Q-values but occasionally the agent will choose an action randomly to explore the action space.

The update_q_values method is where the agent learns from the outcome of its actions. It updates the Q-values based on the observed reward and the maximum expected future reward, using the Q-learning formula.

The adjust_bet method is where the Fibonacci and Oscar’s Grind strategies are implemented. If the agent lost the last round, it moves one step forward in the Fibonacci sequence for the next bet. If it won, it moves two steps backward but also makes sure it doesn’t exceed the current balance.

Finally, the simulate method is where the agent plays a number of rounds of roulette. For each round, it chooses an action, carries out that action, and learns from the outcome.

The user interface and data input modules aren’t included in this code. You would need additional components to collect data on roulette outcomes (possibly using an API provided by a casino or a random number generator to simulate outcomes) and to create a user-friendly way for people to use the software (possibly using a library like tkinter for a graphical user interface, or flask if you want to make a web app).

Remember, this is a simplified example and doesn’t include certain elements you might need in a real-world application, such as error checking and handling, logging, and more advanced features of betting strategies.

Design the Interface

There are several ways to design a user interface for this application. For this case, the software could be implemented as both desktop and mobile applications to maximize accessibility. Here are a few considerations:

Desktop Application: You could use Python libraries such as Tkinter or PyQt for building the interface if you plan to continue with Python for the entire project.

The user interface might include:

  • A section to display the initial balance, current balance, current bet, and round results in real-time.
  • An area to input the initial balance and parameters like the learning rate and discount factor.
  • Start, Pause, and Stop buttons to control the simulation.
  • A graphical representation of the balance over time.
  • A log section to display details of each round.

Mobile Application: To make a mobile version of this application, you might consider using a cross-platform framework like Flutter or React Native. This allows you to write the code once and deploy it on both Android and iOS platforms.

The mobile interface would be similar to the desktop application but designed with a mobile-first approach. It should be clean and intuitive, with large touch targets and a layout that makes sense on a smaller screen.

Regardless of platform, the user interface should be designed with the user experience in mind. It should be clear to the user how to input their initial conditions and start the simulation, and it should be easy for them to understand the results.

In terms of deployment, the desktop application could be packaged and distributed as an installation software, while the mobile application could be published on the App Store or Google Play Store.

Remember, no matter how you design your application, it’s important to include disclaimers to remind users that this is a simulation tool, and that gambling involves risk.

Leave a Reply

Your email address will not be published. Required fields are marked *

New Casinos
1st Deposit - Match Bonus up to C$200 2nd / 3rd Deposit - Match Bonus up to C$300 Min deposit C$10 • 70x wagering


1st Deposit - Match Bonus up to C$ 500 2nd / 3rd Deposit - Match Bonus up to C$ 500  Min deposit C$ 10  70x wagering 


Up To $600 Bonus
1st / 2nd / 3rd Deposit - Match Bonus up to C$200 Min deposit C$10 70x wagering