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Tic-Tac-Toe dApp


In this solution, I describe how you can develop the Tic-Tac-Toe game on the Algorand blockchain. The game logic is implemented as a Stateful Smart Contract using PyTeal while the communication with the network is done using the py-algorand-sdk.

Two players submit equal amounts of Algos to an escrow address which marks the start of the game. After this step, the players interchangeably place their marks on the board where the placing mark action is implemented as an application call to the Stateful Smart Contract. In the end, whoever wins the game can withdraw the funds submitted to the escrow address. In case of a tie, both players can withdraw half of the amount submitted to the escrow address.

The solutions aims to present a starting point for developers to easily build board games as dApps on the Algorand Blockchain. The idea is that they should be able to change the game logic from Tic-Tac-Toe to whichever board game they prefer, like Chess, Connect4, Go, or others and deploy it on the network.

Table of content

Application architecture

The Tic-Tac-Toe decentralized application has two main components:

  1. Smart contracts - component that contains all of the PyTeal code divided into two submodules:
    • Tic-Tac-Toe ASC1 - a stateful smart contract that implements the game logic and defines the interaction between the players and the application. There are three possible interactions with this smart contract: starting the game, executing game action, and refunding the submitted Algos by the players.
    • Escrow fund - a stateless smart contract that holds the funds submitted by the players on game start. This escrow address is linked to the Tic-Tac-Toe ASC1. On game end, the escrow fund is responsible for making the appropriate payment that represents the refunding of the submitted Algos.
  2. Game Engine service - a submodule responsible for submitting the correct transactions to the network to interact with the smart contracts. Those are the following services that are implemented by the game engine:
    • Application deployment - creates the transaction that deploys the Tic-Tac-Toe ASC1 to the network.
    • Start game - submits an atomic transfer of three transactions to the network to denote the start of the game.
    • Play action - submits a single application call transaction to the Tic-Tac-Toe ASC1, which executes a move in the game.
    • Win refund - submits an atomic transfer of two transactions to the network that refunds the Algos to the winner of the game.
    • Tie refund - submits an atomic transfer of three transactions to the network that refunds the Algos to both players of the game.

All of the mentioned points above and the corresponding source code will be explained in more detail in the later sections.

State representation and transition

In order to optimize the state representation and state transitions in the Tic-Tac-Toe dApp I decided to implement them as bitmasks and use bit manipulations for the state transitions.

The game state is represented as two separate integer variables state_x and state_o. If the i-th bit in the state_x is on it means that there is an “X” mark at position i in the board, while if the i-th bit in the state_o is active it means that there is an “O” mark at position i.

The board positions are labeled from 0 to 8 left to right, top to bottom. The top left position is numbered with 0 while the bottom right position is numbered with 8. The following image describes the state representation of the Tic-Tac-Toe game using two bitmasks:

State representation

The image above shows how we have decoupled the original Tic-Tac-Toe game state into two separate integer states using bitmasks.

Once we have decided to represent the game state with this format, we can use various bit manipulations in order to do state transitions and checks for terminal states. Here are some examples of some of the state transitions that have been used in the Tic-Tac-Toe ASC1:

  • state_x = state_x | (1 << i) - placing “X” mark at position i. We can achieve this by activating the i-th bit in the state_x variable.
  • valid_move = (state_x & (1 << i)) | (state_o & (1 << i)) - if the valid_move variable is equal to 0, it means that the i-th bit in both of the state variables is not activated which means that the i-th position is empty in the board.
  • has_won = (state_x & 7) - if the has_won variable is equal to 7 it means that the player that plays with mark “X” has won the game by populating the first row with 3 “X”es. With this expression we check whether the bits that represent the first row(bits: 0, 1 and 2) are activated. Note that here we do not check whether those bits are 0s in the state_o because we need to make sure in our implementation logic that the i-th bit can be activated in only one of the state variables.
  • is_tie = (state_x | state_y) - if the is_tie variable has value of 511 it means that all of the first 9 bits are activated, which means that the whole game board has been filled.

Tic-Tac-Toe ASC1

In this section I will explain in more details the logic behind the PyTeal source code in the Tic-Tac-Toe ASC1. The application has 9 global variables shown in the code snippet below:

class AppVariables:
    PlayerXState = Bytes("PlayerXState")
    PlayerOState = Bytes("PlayerOState")

    PlayerOAddress = Bytes("PlayerOAddress")
    PlayerXAddress = Bytes("PlayerXAddress")
    PlayerTurnAddress = Bytes("PlayerTurnAddress")
    FundsEscrowAddress = Bytes("FundsEscrowAddress")

    BetAmount = Bytes("BetAmount")
    ActionTimeout = Bytes("ActionTimeout")
    GameStatus = Bytes("GameState")

    def number_of_int(cls):
        return 5

    def number_of_str(cls):
        return 4

  • PlayerXState and PlayerOState - those are integer variables that represent the state of the game board for each of the players. The state representation was described in more details in the previous section.
  • PlayerXAddress and PlayerOAddress - those variables represent the addresses for each of the players. They are initialized when the start game action is performed.
  • PlayerTurnAddress - this variable represents the address of the player who needs to place the next mark on the board. The game always starts with the PlayerXAddress and the PlayerTurnAddress is changed on every game action because the players add marks interchangeably.
  • FundsEscrowAddress - this variable represents the escrow address that is responsible for holding the funds submitted by the players. This address is initialized on the start game action as well.
  • BetAmount - represents the amount of micro Algos that each player needs to submit to the escrow address.
  • ActionTimeout - represents for how many seconds the game will be active on the Algorand blockchain. If the player whose turn it is hasn’t played an action in the specified timeout interval, the other player will be declared a winner and will be able to withdraw the funds from the escrow address.
  • GameStatus - is an integer variable that represents the current status of the game.
    • 0 - means that the game is active and the players are currently interacting with it.
    • 1 - means that the game was won by player X.
    • 2 - means that the game was won by player O.
    • 3 - means that the game ended with a tie.

Some of the global variables can be initialized with defaults values when the first transaction that deploys the applications is executed. On the following image we can see which variables are initialized right away:

class DefaultValues:
    PlayerXState = Int(0)
    PlayerOState = Int(0)
    GameStatus = Int(0)
    BetAmount = Int(1000000)
    GameDurationInSeconds = Int(3600)

  • PlayerXState and PlayerOState - at the beginning of the game, there are no marks on the board, and that is why all of the bits in both states are turned off.
  • GameStatus - once we have created the application, it becomes available for interactions hence the status 0 described previously.
  • BetAmount - we fix the bet amount to be 1000000 Micro Algos or 1 Algo. With minimal effort, we can change this logic to be dynamic which means that the players can define their bet amount when performing the setup players action.
  • GameDurationInSeconds - defines for how many seconds the players can perform actions in the game. We have defined that after the setting up of the players, the game will receive action moves for 1 hour.

As mention before there are possible interactions with the Tic-Tac-Toe ASC1:

class AppActions:
    SetupPlayers = Bytes("SetupPlayers")
    ActionMove = Bytes("ActionMove")
    MoneyRefund = Bytes("MoneyRefund")

  • SetupPlayers - this action setups all of the global variables that haven’t been initialized and marks the start of the game. The action can be performed only once, and it is done through an atomic transfer with 3 transactions. We will describe them in more detail later on.
  • ActionMove - this action performs a single game move which is placing a mark on the board. This is done through an application call to the Tic-Tac-Toe ASC1 where the target position for the mark is passed as an argument. The sender of this transaction should match the PlayerTurnAddress global variable. If we try to place a mark on an already populated position, the smart contract should reject that transaction.
  • MoneyRefund - this action validates the withdrawal logic after the game has ended by the players or by a timeout. This action is executed when the Tic-Tac-Toe ASC1 is called with atomic transfer of 2 transaction in case of a win or atomic transfer of 3 transactions in case of a tie.

Application start

This function represents the start of the Tic-Tac-Toe ASC1 application. Here we decide which action will be executed in the current application call. The specified action should be passed as a string and a first argument to the application call transaction. If we are creating the application for the first time, we will initialize the default global variables.

def application_start():
    is_app_initialization = Txn.application_id() == Int(0)

    actions = Cond(
        [Txn.application_args[0] == AppActions.SetupPlayers, initialize_players_logic()],
        [And(Txn.application_args[0] == AppActions.ActionMove,
             Global.group_size() == Int(1)), play_action_logic()],
        [Txn.application_args[0] == AppActions.MoneyRefund, money_refund_logic()]

    return If(is_app_initialization, app_initialization_logic(), actions)

Application initialization logic

With this function we are going to initialize the default global variables.

def app_initialization_logic():
    return Seq([
        App.globalPut(AppVariables.PlayerXState, DefaultValues.PlayerXState),
        App.globalPut(AppVariables.PlayerOState, DefaultValues.PlayerOState),
        App.globalPut(AppVariables.GameStatus, DefaultValues.GameStatus),
        App.globalPut(AppVariables.BetAmount, DefaultValues.BetAmount),

Setup players

This function initializes all the other global variables and additionally marks the start of the game. We expect that this logic is performed within an Atomic Transfer of 3 transactions:

  1. Application call transaction to the smart contract where the first argument passed to the transaction is “SetupPlayers” which denotes that this action should be performed within the application.
  2. Payment transaction from PlayerX that funds the Escrow account. The address of the sender of this transaction is stored in the PlayerXAddress global variable. Additionally, the amount of the payment transaction should be equal to the predefined BetAmount.
  3. Payment transaction from PlayerO that funds the Escrow account. Similarly, the sender of this transaction is stored in the PlayerOAddress global variable and the amount of the transaction should be equal to the BetAmount.

We want to execute this code logic only once because we don’t want in the middle of the game to change the players’ addresses. Additionally, the receiver of both payment transactions should be the same, which is the escrow address. We store this address in the FundsEscrowAddress global variable.

def initialize_players_logic():
    player_x_address = App.globalGetEx(Int(0), AppVariables.PlayerXAddress)
    player_o_address = App.globalGetEx(Int(0), AppVariables.PlayerOAddress)

    setup_failed = Seq([

    setup_players = Seq([
        Assert(Gtxn[1].type_enum() == TxnType.Payment),
        Assert(Gtxn[2].type_enum() == TxnType.Payment),
        Assert(Gtxn[1].receiver() == Gtxn[2].receiver()),
        Assert(Gtxn[1].amount() == App.globalGet(AppVariables.BetAmount)),
        Assert(Gtxn[2].amount() == App.globalGet(AppVariables.BetAmount)),
        App.globalPut(AppVariables.PlayerXAddress, Gtxn[1].sender()),
        App.globalPut(AppVariables.PlayerOAddress, Gtxn[2].sender()),
        App.globalPut(AppVariables.PlayerTurnAddress, Gtxn[1].sender()),
        App.globalPut(AppVariables.FundsEscrowAddress, Gtxn[1].receiver()),
        App.globalPut(AppVariables.ActionTimeout, Global.latest_timestamp() + DefaultValues.GameDurationInSeconds),

    return Seq([
        If(Or(player_x_address.hasValue(), player_o_address.hasValue()), setup_failed, setup_players)

Action move

In order to execute an action, we first must check what is the current state of the game. To decouple the code a little bit, we create two separate functions has_player_won(state) and is_tie() to check whether the game is in a terminal i.e leaf state. At the end we combine and use those function in the main function responsible for executing an action which is the play_action_logic() function.

WINING_STATES = [448, 56, 7, 292, 146, 73, 273, 84]

def has_player_won(state):
    return If(Or(BitwiseAnd(state, Int(WINING_STATES[0])) == Int(WINING_STATES[0]),
                 BitwiseAnd(state, Int(WINING_STATES[1])) == Int(WINING_STATES[1]),
                 BitwiseAnd(state, Int(WINING_STATES[2])) == Int(WINING_STATES[2]),
                 BitwiseAnd(state, Int(WINING_STATES[3])) == Int(WINING_STATES[3]),
                 BitwiseAnd(state, Int(WINING_STATES[4])) == Int(WINING_STATES[4]),
                 BitwiseAnd(state, Int(WINING_STATES[5])) == Int(WINING_STATES[5]),
                 BitwiseAnd(state, Int(WINING_STATES[6])) == Int(WINING_STATES[6]),
                 BitwiseAnd(state, Int(WINING_STATES[7])) == Int(WINING_STATES[7])), Int(1), Int(0))

In the Tic-Tac-Toe game, there are 8 possible winning states. Since we are representing the game state as a bitmask, the numbers specified in the WINNING_STATES array define the bits that should be activated in each of those terminal states. With the BitwiseAnd(state, Int(WINING_STATES[0])) == Int(WINING_STATES[0]) operation we are making sure that the required bits are activated in order to match the winning state 448 which is the state where the last row in the board is filled with the same marks. We are performing the same operation for all the other 7 possible winning states.

If one of those conditions is true, it means that we are in a terminal state. On the image bellow you can see an illustration of one winning state operation check:

Winning State

Just for illustration purposes, the bits that we do not care about in the state variable are marked with “*”, they actually will have values either 0 or 1. On the following link, you can find drawings about the other winning states.

The is_tie() function, as it name suggests, checks whether the current state of the game ended up with a tie. To check this, we get all of the activated bits from the states of the both players and see whether this number is equal to 511. The decimal number 511 is binary represented as 111111111, which means that all of the places in the board have been filled.

def is_tie():
    state_x = App.globalGet(AppVariables.PlayerXState)
    state_o = App.globalGet(AppVariables.PlayerOState)
    return Int(511) == BitwiseOr(state_x, state_o)

Finally we are at a point where we can implement the play_action_logic() function which executes a single game action.

def play_action_logic():
    position_index = Btoi(Txn.application_args[1])

    state_x = App.globalGet(AppVariables.PlayerXState)
    state_o = App.globalGet(AppVariables.PlayerOState)

    game_action = ShiftLeft(Int(1), position_index) # activate the bit at position "position_index"

    player_x_move = Seq([
        App.globalPut(AppVariables.PlayerXState, BitwiseOr(state_x, game_action)), # fill the game_action bit

           App.globalPut(AppVariables.GameStatus, Int(1))), # update the game status in case of a win

        App.globalPut(AppVariables.PlayerTurnAddress, App.globalGet(AppVariables.PlayerOAddress)), 

    player_o_move = Seq([
        App.globalPut(AppVariables.PlayerOState, BitwiseOr(state_o, game_action)),

           App.globalPut(AppVariables.GameStatus, Int(2))),

        App.globalPut(AppVariables.PlayerTurnAddress, App.globalGet(AppVariables.PlayerXAddress)),

    return Seq([
        Assert(position_index >= Int(0)), # valid position interval
        Assert(position_index <= Int(8)), # valid position interval
        Assert(Global.latest_timestamp() <= App.globalGet(AppVariables.ActionTimeout)), # valid time interval
        Assert(App.globalGet(AppVariables.GameStatus) == DefaultValues.GameStatus), # is game active
        Assert(Txn.sender() == App.globalGet(AppVariables.PlayerTurnAddress)), # valid player
        Assert(And(BitwiseAnd(state_x, game_action) == Int(0),
                   BitwiseAnd(state_o, game_action) == Int(0))), # the i-th position in the board is empty
            [Txn.sender() == App.globalGet(AppVariables.PlayerXAddress), player_x_move],
            [Txn.sender() == App.globalGet(AppVariables.PlayerOAddress), player_o_move],
        If(is_tie(), App.globalPut(AppVariables.GameStatus, Int(3))), # adjust the status in case of a tie.

We can summarize the play action function in the following steps and conditions:

  • The position index is passed as the second argument to the application call transaction. We convert this argument to an integer, with this we have the index of the position where the mark will be placed. Additionally, we need to make sure that the position_index variable is within the allowed range which is between 0 and 8 inclusively.
  • The game_action variable represents the bit that needs to be activated when we place a mark at the specified position index. We achieve this by shifting the number 1 by position_index places to the left. Note: The ShiftLeft PyTeal function is only available in TEAL version 4.
  • Before executing the action we need to make sure that the game hasn’t ended by timeout i.e we are in the valid gameplay interval. Also, we need to make sure that the player who sent the application transaction has address equal to the one specified in the PlayerTurnAddress global variable. On top of that we need to check whether the GameStatus global variable has value of 0 which indicates that the game is currently active, the different meaning of the values of this variable were described previously in more details.
  • We must check whether the position_index position in the board is empty. We achieve this by performing a BitwiseAnd operation on both states with the current game_action to check whether the position_index bit is activated. If the position_index bit is not activated in both state variables, it means that there is no mark on that position in the board.
  • When we finally perform a player move, we need to make sure that we activate the position_index bit in the current player’s state. If this action results in a win, we must update the GameStatus in order to note that the current player has won the game. In the end, we need to change the PlayerTurnAddress variable to the address of the other player because the Tic-Tac-Toe game is played interchangeably.
  • In a case of a tie, we need to update the GameStatus as well in order to note that the current game has ended with a tie.

Money refund

Until now we have described how we can start the game by setting up the players and how we can perform an action using an application call transaction on the Tic-Tac-Toe ASC1. The one thing that is left to implement is the money refund at the end of the game, which we do by implementing the money_refund_logic() function.

This function handles the logic for refunding the submitted money to the escrow address in case of a winner, tie, or timeout termination. If the player whose turn it is, hasn’t made a move for the predefined period of time stored in the ActionTimeout global variable, the other player is declared a winner and can withdraw the money. The money can be refunded with one of the following two transactions:

  • In case of a win, the money from the escrow address should be refunded only by the player who won the game. That is why we need to perform an atomic transfer of 2 transactions where the first transaction is an application call to the Tic-Tac-Toe ASC1 which tells the application that we want to refund money, while the second transaction must be a payment transaction from the escrow address to the winner address. The amount refunded to the winner is equal to twice the BetAmount global variable.
  • In case of a tie, the money from the escrow address should be equally split between both players. That is why this logic is executed within an atomic transfer of 3 transactions. The first transaction is an application call to the Tic-Tac-Toe ASC1, the second and the third are payment transactions from the escrow address to the PlayerXAddress and PlayerOAddress. Both of the payment transactions should have an equal amount, which is the same as the BetAmount global variable.

The code that performs the money refund logic is shown below.

def money_refund_logic():
    has_x_won_by_playing = App.globalGet(AppVariables.GameStatus) == Int(1) # normal win by placing marks
    has_o_won_by_playing = App.globalGet(AppVariables.GameStatus) == Int(2) # normal win by placing marks

    has_x_won_by_timeout = And(App.globalGet(AppVariables.GameStatus) == Int(0),
                               Global.latest_timestamp() > App.globalGet(AppVariables.ActionTimeout),
                               App.globalGet(AppVariables.PlayerTurnAddress) == App.globalGet(
                                   AppVariables.PlayerOAddress)) # win by timeout logic.

    has_o_won_by_timeout = And(App.globalGet(AppVariables.GameStatus) == Int(0),
                               Global.latest_timestamp() > App.globalGet(AppVariables.ActionTimeout),
                               App.globalGet(AppVariables.PlayerTurnAddress) == App.globalGet(
                                   AppVariables.PlayerXAddress)) # win by timeout logic.

    has_x_won = Or(has_x_won_by_playing, has_x_won_by_timeout) # anykind of win
    has_o_won = Or(has_o_won_by_playing, has_o_won_by_timeout) # anykind of win
    game_is_tie = App.globalGet(AppVariables.GameStatus) == Int(3) 

    x_withdraw = Seq([
        Assert(Gtxn[1].receiver() == App.globalGet(AppVariables.PlayerXAddress)), 
        Assert(Gtxn[1].amount() == Int(2) * App.globalGet(AppVariables.BetAmount)),
        App.globalPut(AppVariables.GameStatus, Int(1)) 

    o_withdraw = Seq([
        Assert(Gtxn[1].receiver() == App.globalGet(AppVariables.PlayerOAddress)),
        Assert(Gtxn[1].amount() == Int(2) * App.globalGet(AppVariables.BetAmount)),
        App.globalPut(AppVariables.GameStatus, Int(2))

    tie_withdraw = Seq([
        Assert(Gtxn[1].receiver() == App.globalGet(AppVariables.PlayerXAddress)),
        Assert(Gtxn[1].amount() == App.globalGet(AppVariables.BetAmount)),
        Assert(Gtxn[2].type_enum() == TxnType.Payment),
        Assert(Gtxn[2].sender() == App.globalGet(AppVariables.FundsEscrowAddress)),
        Assert(Gtxn[2].receiver() == App.globalGet(AppVariables.PlayerOAddress)),
        Assert(Gtxn[2].amount() == App.globalGet(AppVariables.BetAmount))

    return Seq([
        Assert(Gtxn[1].type_enum() == TxnType.Payment),
        Assert(Gtxn[1].sender() == App.globalGet(AppVariables.FundsEscrowAddress)),
            [has_x_won, x_withdraw],
            [has_o_won, o_withdraw],
            [game_is_tie, tie_withdraw]

With this function we complete the PyTeal logic implementation for the Tic-Tac-Toe smart contract. At the end we just need to declare the approval and the clear programs.

def approval_program():
    return application_start()

def clear_program():
    return Return(Int(1))

Escrow fund

The escrow fund smart contract is a simple stateless smart contract that is linked to the Tic-Tac-Toe ASC1. This contracts initially receives the funds by both players. After the game end, the escrow fund should be able to sign a payment transaction to the winner of the game, or to sign transactions to both of the players in case of a tie.

def game_funds_escorw(app_id: int):
    win_refund = Seq([
        Assert(Gtxn[0].application_id() == Int(app_id)),
        Assert(Gtxn[1].fee() <= Int(1000)),
        Assert(Gtxn[1].asset_close_to() == Global.zero_address()),
        Assert(Gtxn[1].rekey_to() == Global.zero_address())

    tie_refund = Seq([
        Assert(Gtxn[0].application_id() == Int(app_id)),
        Assert(Gtxn[1].fee() <= Int(1000)),
        Assert(Gtxn[1].asset_close_to() == Global.zero_address()),
        Assert(Gtxn[1].rekey_to() == Global.zero_address()),
        Assert(Gtxn[2].fee() <= Int(1000)),
        Assert(Gtxn[2].asset_close_to() == Global.zero_address()),
        Assert(Gtxn[2].rekey_to() == Global.zero_address())

    return Seq([
            [Global.group_size() == Int(2), win_refund],
            [Global.group_size() == Int(3), tie_refund],

Game Engine service

After we have finished with the implementation of the smart contracts, we need to implement the services that talk to the Algorand network. The GameEngineService object provides an API for initializing and playing the Tic-Tac-Toe game on the blockchain. The GameEngineService API implements the following methods:

  • init - initialization of the object, this method receives all of the private keys and addresses for the players.
  • deploy_application - deploys the Tic-Tac-Toe ASC1 to the network.
  • start_game - marks the start of the game by sending an atomic transfer of 3 transactions to the network.
  • play_action - sends a transaction that plays an action in the current instance of the game. This method receives a player_id parameter can be either “X” or “O” and a position argument which should be integer between 0 and 8.
  • win_money_refund - sends an atomic transfer of 2 transactions to the network which refunds the money from the escrow address to the winner of the game. Here we also pass the player_id as argument to note which player is the winner.
  • tie_money_refund - sends an atomic transfer of 3 transactions to the network which refunds the money from the escrow address to both of the players.
  • fund_escrow - sends some Algos to the escrow address to handle the fees for the money refund payments.


One instance of the GameEngineService object should represent one game on the blockchain. Within the initializer we need to provide the address of the game creator as well with the addresses of the PlayerX and PlayerO.

class GameEngineService:
    def __init__(self,
        self.app_creator_pk = app_creator_pk
        self.app_creator_address = app_creator_address
        self.player_x_pk = player_x_pk
        self.player_x_address = player_x_address
        self.player_o_pk = player_o_pk
        self.player_o_address = player_o_address
        self.teal_version = 4

        self.approval_program_code = approval_program()
        self.clear_program_code = clear_program()

        self.app_id = None
        self.escrow_fund_address = None
        self.escrow_fund_program_bytes = None

In the initializer we fix the teal version to be 4 because we are using some of its features in the Tic-Tac-Toe ASC1. Additionally, we are loading the approval_program() and clear_program() from the Tic-Tac-Toe ASC1 that were described previously.

Application deployment

Once we have initialized the GameEngineService, the first think that we need to do is to deploy the application on the network. We deploy the application by submitting an Application Create Transaction on the network where we sent our teal code generated by the smart contract.

def deploy_application(self, client):
    approval_program_compiled = compileTeal(approval_program(),

    clear_program_compiled = compileTeal(clear_program(),

    approval_program_bytes = NetworkInteraction.compile_program(client=client,

    clear_program_bytes = NetworkInteraction.compile_program(client=client,

    global_schema = algo_txn.StateSchema(num_uints=AppVariables.number_of_int(),

    local_schema = algo_txn.StateSchema(num_uints=0,

    app_transaction = \

    tx_id = NetworkInteraction.submit_transaction(client,

    transaction_response = client.pending_transaction_info(tx_id)

    self.app_id = transaction_response['application-index']
    print(f'Tic-Tac-Toe application deployed with the application_id: {self.app_id}')

Start game

With this method we execute the SetupPlayers action in the Tic-Tac-Toe ASC1. Here we create the escrow fund address to which the players need to sent their money. This function should be called only once per game, otherwise the smart contract will reject this atomic transfer.

def start_game(self, client):
    Atomic transfer of 3 transactions:
    - 1. Application call
    - 2. Payment from the Player X address to the Escrow fund address
    - 3. Payment from the Player O address to the Escrow fund address
    if self.app_id is None:
        raise ValueError('The application has not been deployed')

    if self.escrow_fund_address is not None or self.escrow_fund_program_bytes is not None:
        raise ValueError('The game has already started!')

    escrow_fund_program_compiled = compileTeal(game_funds_escorw(app_id=self.app_id),

    self.escrow_fund_program_bytes = \

    self.escrow_fund_address = algo_logic.address(self.escrow_fund_program_bytes)

    player_x_funding_txn = PaymentTransactionRepository.payment(client=client,

    player_o_funding_txn = PaymentTransactionRepository.payment(client=client,

    app_args = [

    app_initialization_txn = \

    gid = algo_txn.calculate_group_id([app_initialization_txn,
                                       player_o_funding_txn]) = gid = gid = gid

    app_initialization_txn_signed = app_initialization_txn.sign(self.app_creator_pk)
    player_x_funding_txn_signed = player_x_funding_txn.sign(self.player_x_pk)
    player_o_funding_txn_signed = player_o_funding_txn.sign(self.player_o_pk)

    signed_group = [app_initialization_txn_signed,

    txid = client.send_transactions(signed_group)

    print(f'Game started with the transaction_id: {txid}')

Play action

Application call transaction that performs an action for the specified player at the specified action position. With this function we are updating the global state of the Tic-Tac-Toe ASC1. The player_id argument should be either “X” or “O”.

def play_action(self, client, player_id: str, action_position: int):
    app_args = [

    player_pk = self.player_x_pk if player_id == "X" else self.player_o_pk

    app_initialization_txn = \

    tx_id = NetworkInteraction.submit_transaction(client,

    print(f'{player_id} has been put at position {action_position} in transaction with id: {tx_id}')

Win money refund

When the game has ended the winner should be able to withdraw the money from the escrow address. This function executes the correct atomic transfer in order for the winner to be able to receive its money. In the player_id argument we pass the winner of the game, if we pass the wrong winner the smart contract will reject the withdrawal transaction.

def win_money_refund(self, client, player_id: str):
    Atomic transfer of 2 transactions:
    1. Application call
    2. Payment from the Escrow account to winner address either PlayerX or PlayerO.
    player_pk = self.player_x_pk if player_id == "X" else self.player_o_pk
    player_address = self.player_x_address if player_id == "X" else self.player_o_address

    app_args = [

    app_withdraw_call_txn = \

    refund_txn = PaymentTransactionRepository.payment(client=client,

    gid = algo_txn.calculate_group_id([app_withdraw_call_txn,
                                       refund_txn]) = gid = gid

    app_withdraw_call_txn_signed = app_withdraw_call_txn.sign(player_pk)

    refund_txn_logic_signature = algo_txn.LogicSig(self.escrow_fund_program_bytes)
    refund_txn_signed = algo_txn.LogicSigTransaction(refund_txn, refund_txn_logic_signature)

    signed_group = [app_withdraw_call_txn_signed,

    txid = client.send_transactions(signed_group)

    print(f'The winning money have been refunded to the player {player_id} in the transaction with id: {txid}')

Tie money refund

Similarly like the win money refund function, we need to handle the money refunding in case of a tie. This function executes the correct atomic transfer where the two players receive their initial funded money to the escrow account.

def tie_money_refund(self, client):
    Atomic transfer of 3 transactions:
    1. Application call
    2. Payment from the escrow address to the PlayerX address.
    3. Payment from the escrow address to the PlayerO address.
    if self.app_id is None:
        raise ValueError('The application has not been deployed')

    app_args = [

    app_withdraw_call_txn = \

    refund_player_x_txn = PaymentTransactionRepository.payment(client=client,

    refund_player_o_txn = PaymentTransactionRepository.payment(client=client,

    gid = algo_txn.calculate_group_id([app_withdraw_call_txn,
                                       refund_player_o_txn]) = gid = gid = gid

    app_withdraw_call_txn_signed = app_withdraw_call_txn.sign(self.app_creator_pk)

    refund_player_x_txn_logic_signature = algo_txn.LogicSig(self.escrow_fund_program_bytes)
    refund_player_x_txn_signed = \
        algo_txn.LogicSigTransaction(refund_player_x_txn, refund_player_x_txn_logic_signature)

    refund_player_o_txn_logic_signature = algo_txn.LogicSig(self.escrow_fund_program_bytes)
    refund_player_o_txn_signed = \
        algo_txn.LogicSigTransaction(refund_player_o_txn, refund_player_o_txn_logic_signature)

    signed_group = [app_withdraw_call_txn_signed,

    txid = client.send_transactions(signed_group)

    print(f'The initial bet money have been refunded to the players in the transaction with id: {txid}')

Deployment on TestNet

In the GitHub repository you can find an file that has a simple UI that implements the methods described in the GameEngineService. Once you have setup your environment that is described in the repository, you will be able to start the UI using the following command: streamlit run

Final thoughts

If you have made it this far I want to sincerely thank you for reading this solution. I hope that you learned something new and interesting as it was the case for me. I hope that this solution will help you to build your favorite game on the Algorand Blockchain.