transact.py

def transact(self):
    """
    Specifies the Monte-Carlo transaction behavior (i.e., deposits, trades, etc...)
    in terms of their respective probability distributions.
    """
    latest_action = 'None'
    for _ in range(int(round(float(mean_events_per_hour),0))):
        self.latest_amt = None
        self.latest_tkn_name = None
        self.user_name = global_username
        self.timestamp += 1
        timestamp = self.timestamp
        i = self.random.randint(0, self.num_timesteps)

        deposit_range = self.num_timesteps * self.action_freq_dist['deposit']
        trade_range = deposit_range + self.num_timesteps * self.action_freq_dist['trade'] * (1 - arbitrage_percentage)
        arb_range = trade_range + self.num_timesteps * self.action_freq_dist['trade'] * arbitrage_percentage
        withdraw_completed = arb_range + self.num_timesteps * self.action_freq_dist['withdraw_completed']

        if i < deposit_range:
            if latest_action != "deposit":
                latest_action = "deposit"
                self.perform_random_deposit()

        elif deposit_range <= i < trade_range:
            if latest_action != "trade":
                latest_action = "trade"
                self.perform_random_trade()

        elif trade_range <= i < arb_range:
            if latest_action != "arbitrage_trade":
                latest_action = "arbitrage_trade"
                self.perform_random_arbitrage_trade()

        elif arb_range <= i < withdraw_completed:
            if latest_action != "withdrawal":
                latest_action = "withdrawal"
                self.perform_random_withdrawal()

        state = self.protocol.global_state
        state.timestamp = timestamp
        for tkn_name in self.whitelisted_tokens:
            if not get_is_trading_enabled(state, tkn_name):
                self.protocol.enable_trading(tkn_name=tkn_name, timestamp=timestamp)

        # The code below creates a new dataframe which collects the data we want to analyze
        df = {'timestamp': [timestamp], 'latest_action': [latest_action], 'latest_amt': [self.latest_amt],
              'latest_tkn_name': [self.latest_tkn_name]}

        for tkn in self.whitelisted_tokens:
            if tkn != 'bnt':
                df[f'{tkn}_bnt_funding_limit'] = [get_bnt_funding_limit(state, tkn)]
                df[f'{tkn}_vault_real'] = [get_vault_balance(state, tkn)]
                df[f'{tkn}_is_trading_enabled'] = [get_is_trading_enabled(state, tkn)]
                df[f'{tkn}_iloss'] = [self.iloss_realized[tkn][-1]]
                df[f'{tkn}_fees_earned'] = [self.total_fees_earned[tkn][-1]]
                df[f'{tkn}_staking'] = [get_staked_balance(state, tkn)]
                df[f'{tkn}_surplus_real'] = [df[f'{tkn}_vault_real'][0] - df[f'{tkn}_staking'][0]]
                df[f'{tkn}_tkn_trading_liquidity'] = [get_tkn_trading_liquidity(state, tkn)]
                df[f'{tkn}_bnt_trading_liquidity'] = [get_bnt_trading_liquidity(state, tkn)]

        self.logger.append(pd.DataFrame(df))