TEMA crypto on Bybit live paper trading

The code has an issue with the attribute BrokerageModel not being defined. Ensure that self.BrokerageModel is correctly initialized or replace it with the appropriate method or property that provides the brokerage model. Here's the corrected version of the code:

class TechnicalIndicatorsAlgorithm(QCAlgorithm):
    def initialize(self):
        self.set_start_date(2024, 1, 1)
        self.set_time_zone("Asia/Manila")
        self.set_brokerage_model(BrokerageName.BYBIT, AccountType.MARGIN)
        self.set_account_currency("USDT", 2000)
        # Ensure that the brokerage model is correctly initialized
        self.set_security_initializer(BrokerageModelSecurityInitializer(self.brokerage_model, FuncSecuritySeeder(self.get_last_known_price)))
        self.btcusdt = self.add_crypto("BTCUSDT", Resolution.MINUTE, Market.BYBIT).Symbol
        self.ethusdt = self.add_crypto("ETHUSDT", Resolution.MINUTE, Market.BYBIT).Symbol
        self.crypto_pairs = [self.btcusdt, self.ethusdt]
        self.indicators = {}
        for symbol in self.crypto_pairs:
            self.indicators[symbol] = {
                "rsi": self.rsi(symbol, 14, MovingAverageType.WILDERS, Resolution.HOUR),
                "ema10": self.ema(symbol, 10, Resolution.HOUR),
                "ema20": self.ema(symbol, 20, Resolution.HOUR),
                "ema50": self.ema(symbol, 50, Resolution.HOUR),
                "entry_price": None,
                "stop_price": None
            }
        self.warm_up_indicators()

    def warm_up_indicators(self):
        warm_up_period = 100
        for symbol in self.crypto_pairs:
            history = self.history(symbol, warm_up_period + 10, Resolution.HOUR)
            if history.empty:
                continue
            for time, row in history.loc[symbol].iterrows():
                indicators = self.indicators[symbol]
                if "close" in row:
                    if indicators["rsi"] is not None:
                        indicators["rsi"].update(time, row["close"])
                    if indicators["ema10"] is not None:
                        indicators["ema10"].update(time, row["close"])
                    if indicators["ema20"] is not None:
                        indicators["ema20"].update(time, row["close"])
                    if indicators["ema50"] is not None:
                        indicators["ema50"].update(time, row["close"])

    def on_data(self, data):
        long_candidates = []
        short_candidates = []
        for symbol in self.crypto_pairs:
            if not data.contains_key(symbol):
                continue
            price = data[symbol].close
            indicators = self.indicators[symbol]
            if not (indicators["rsi"] and indicators["rsi"].is_ready and 
                    indicators["ema10"] and indicators["ema10"].is_ready and 
                    indicators["ema20"] and indicators["ema20"].is_ready and 
                    indicators["ema50"] and indicators["ema50"].is_ready):
                continue
            if (indicators["rsi"].current.value > 40 and 
                indicators["ema10"].current.value > indicators["ema20"].current.value > indicators["ema50"].current.value):
                if symbol in short_candidates:
                    continue
                long_candidates.append(symbol)
            elif (indicators["rsi"].current.value < 70 and 
                  indicators["ema10"].current.value < indicators["ema20"].current.value < indicators["ema50"].current.value):
                if symbol in long_candidates:
                    continue
                short_candidates.append(symbol)
        if long_candidates:
            allocation_per_symbol = 0.5 / len(long_candidates) if long_candidates else 0
            for symbol in long_candidates:
                if not self.portfolio[symbol].invested or self.portfolio[symbol].quantity < 0:
                    self.set_holdings(symbol, allocation_per_symbol)
                    self.indicators[symbol]["entry_price"] = data[symbol].close
                    self.indicators[symbol]["stop_price"] = data[symbol].close * 0.90
        if short_candidates:
            allocation_per_symbol = -0.5 / len(short_candidates) if short_candidates else 0
            for symbol in short_candidates:
                if not self.portfolio[symbol].invested or self.portfolio[symbol].quantity > 0:
                    self.set_holdings(symbol, allocation_per_symbol)
                    self.indicators[symbol]["entry_price"] = data[symbol].close
                    self.indicators[symbol]["stop_price"] = data[symbol].close * 1.10
        for symbol in self.crypto_pairs:
            if self.portfolio[symbol].invested:
                price = data[symbol].close
                entry_price = self.indicators[symbol]["entry_price"]
                stop_price = self.indicators[symbol]["stop_price"]
                if self.portfolio[symbol].quantity > 0:
                    if stop_price is None:
                        stop_price = price * 0.90
                    self.indicators[symbol]["stop_price"] = max(stop_price, price * 0.90)
                    if price < self.indicators[symbol]["stop_price"]:
                        self.liquidate(symbol)
                        self.indicators[symbol]["entry_price"] = None
                        self.indicators[symbol]["stop_price"] = None
                elif self.portfolio[symbol].quantity < 0:
                    if stop_price is None:
                        stop_price = price * 1.10
                    self.indicators[symbol]["stop_price"] = min(stop_price, price * 1.10)
                    if price > self.indicators[symbol]["stop_price"]:
                        self.liquidate(symbol)
                        self.indicators[symbol]["entry_price"] = None
                        self.indicators[symbol]["stop_price"] = None

Ensure that the brokerage_model is correctly initialized and used in the set_security_initializer method.

Sources: Bybit Crypto Futures Regression Algorithm, Live Features Algorithm, Basic Template Crypto Future Hourly Algorithm, QuantConnect Paper Trading - QuantConnect.com, Bybit Crypto Regression Algorithm