I need help backtest

To backtest the strategy described by Fares, we'll need to implement a few key components in QuantConnect's LEAN framework. This includes handling historical data for both XOM and IBB, checking the conditions for entering a buy position on IBB, and calculating the target, drawdown, and Sharpe ratio. Below is a simplified version of how you might start to implement this strategy in Python using QuantConnect. Note that this example focuses on the structure and key elements of the strategy rather than a complete, ready-to-run solution.

from AlgorithmImports import *

class MyAlgorithm(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2016, 1, 1)  # Start date for the backtest
        self.SetEndDate(2027, 12, 31)  # End date for the backtest
        self.xom = self.AddEquity("XOM").Symbol
        self.ibb = self.AddEquity("IBB").Symbol

        self.Schedule.On(self.DateRules.EveryDay(self.ibb), self.TimeRules.AfterMarketOpen(self.ibb, 10), self.CheckConditionsAndTrade)

        self.previousHighXOM = None
        self.previousHighIBB = None
        self.ibbUptrend = False
        self.targetReached = False

    def CheckConditionsAndTrade(self):
        # Assuming historical data requests for checking conditions
        # This is a simplified example; actual implementation will require handling of historical data

        # Condition 1: XOM new high
        xomNewHigh = self.Securities[self.xom].High > self.previousHighXOM

        # Condition 2: IBB new high
        ibbNewHigh = self.Securities[self.ibb].High > self.previousHighIBB

        # Condition 3: IBB uptrend
        # This would require more complex logic to verify the uptrend over the specified periods

        if xomNewHigh and ibbNewHigh and self.ibbUptrend:
            self.Buy(self.ibb, 100)  # Simplified buy logic; adjust quantity as needed

        # Target, drawdown, and Sharpe ratio calculations would typically be part of post-backtest analysis

    def OnData(self, data):
        # This method is called for each new data point
        # Implement trading logic and updates to conditions here as needed
        pass

    # Additional methods for handling buy conditions, targets, and analysis would be added here

This code outlines the basic structure you'd need to start implementing Fares' strategy. Key points to expand upon include:

1. Historical Data Handling: You'll need to request and process historical data to determine the previous highs for XOM and IBB and to assess the uptrend condition for IBB.
2. Condition Checking: The logic for checking the uptrend condition over the specified periods needs to be fleshed out. This might involve comparing the closing prices at the end of each period.
3. Trading Logic: The example includes a very basic buy logic. You'll need to implement the logic for selling at a 10% target, calculating drawdown, and exiting positions as required.
4. Performance Metrics: Calculating the Sharpe ratio and other performance metrics can be done using QuantConnect's built-in functions or custom calculations based on the portfolio's returns.

Remember, this is a starting point. Developing a fully functional and tested strategy will require further work, especially in handling historical data and implementing the specific conditions and calculations you've outlined.

thank you milla but it doesn't really work, your code has errors

I understand the initial example might not have been fully functional or clear on how to implement the specific conditions and calculations required for your strategy. Let's refine the approach, focusing on a more detailed and corrected structure for implementing the strategy, especially regarding the historical data handling and condition checks. This example will still be somewhat simplified but will aim to provide a clearer direction.

from AlgorithmImports import *

class MyAlgorithm(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2016, 1, 1)  # Adjust as needed
        self.SetEndDate(2027, 12, 31)  # Adjust as needed
        self.xom = self.AddEquity("XOM").Symbol
        self.ibb = self.AddEquity("IBB").Symbol

        self.lookbackPeriod = 252 * 4  # Approximation of 4 years of trading days
        self.Schedule.On(self.DateRules.EveryDay(self.ibb), self.TimeRules.AfterMarketOpen(self.ibb, 10), self.CheckConditionsAndTrade)

    def CheckConditionsAndTrade(self):
        # Historical data request for XOM and IBB to check for new highs
        historyXOM = self.History(self.xom, self.lookbackPeriod, Resolution.Daily)
        historyIBB = self.History(self.ibb, self.lookbackPeriod, Resolution.Daily)

        if not (historyXOM.empty or historyIBB.empty):
            # Find previous highs
            previousHighXOM = historyXOM['high'].max()
            previousHighIBB = historyIBB['high'].max()

            # Current price
            currentPriceXOM = self.Securities[self.xom].Price
            currentPriceIBB = self.Securities[self.ibb].Price

            # Check conditions
            xomNewHigh = currentPriceXOM > previousHighXOM
            ibbNewHigh = currentPriceIBB > previousHighIBB
            ibbUptrend = self.CheckIBBUptrend()  # This method needs to be implemented

            if xomNewHigh and ibbNewHigh and ibbUptrend:
                self.SetHoldings(self.ibb, 1)  # Adjust position size as needed

    def CheckIBBUptrend(self):
        # Implement the logic to check for IBB's uptrend over the specified periods
        # This is a placeholder for the uptrend checking logic
        return True  # Placeholder return value

    def OnData(self, data):
        # Implement any additional logic needed for each new data point
        pass

    # Additional methods for handling targets, drawdown, and performance metrics would be added here

Key Points to Note and Implement:

1. Historical Data: The CheckConditionsAndTrade method requests historical data for XOM and IBB to find previous highs. Adjust the lookbackPeriod as needed to match your specific time frames.
2. Condition Checks: The conditions for entering a trade are checked using current prices and historical highs. The CheckIBBUptrend method is a placeholder where you should implement the logic to verify IBB's uptrend condition.
3. Trading Logic: The example uses SetHoldings for simplicity. You might want to use MarketOrder or other order types depending on your strategy's specifics, including handling the exit based on a 10% target or other criteria.
4. Performance Metrics: Calculating drawdown and the Sharpe ratio would typically be part of your analysis after running the backtest. QuantConnect provides some built-in statistics, but you may need custom calculations for specific metrics.

This example aims to provide

thank you mia but it doesn't work someone else please ?

I understand the previous examples might not have fully met your needs or contained complexities that made them difficult to directly apply. Let's simplify the approach to focus on the core elements of your strategy, breaking it down into more manageable steps. This will not be a complete solution but should offer a clearer starting point for developing your strategy on QuantConnect.

Step 1: Define Your Universe and Initialize Variables

First, you need to set up your algorithm's initial configuration, including setting the start and end dates for the backtest, adding the securities you're interested in (XOM and IBB), and initializing any variables you'll need.

class MyAlgorithm(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2016, 1, 1)  # Start date for the backtest
        self.SetEndDate(2027, 12, 31)  # End date for the backtest

        self.xom = self.AddEquity("XOM").Symbol
        self.ibb = self.AddEquity("IBB").Symbol

        # Variables to track the previous highs and uptrend status
        self.previousHighXOM = 0
        self.previousHighIBB = 0
        self.ibbUptrend = False

Step 2: Implement the Logic to Check Conditions

You'll need to periodically check the conditions you've outlined for entering a trade. This involves comparing the current price to previous highs and assessing the uptrend condition for IBB.

    def OnData(self, data):
        # Ensure we have data for both securities
        if not data.ContainsKey(self.xom) or not data.ContainsKey(self.ibb):
            return

        # Example of how to check for a new high and set the variable
        # You'll need to implement the logic to update these based on your specific conditions
        currentHighXOM = data[self.xom].High
        if currentHighXOM > self.previousHighXOM:
            self.previousHighXOM = currentHighXOM

        # Similar for IBB
        currentHighIBB = data[self.ibb].High
        if currentHighIBB > self.previousHighIBB:
            self.previousHighIBB = currentHighIBB

        # Implement the logic to check for IBB's uptrend condition
        # This is a placeholder and needs your specific logic
        self.ibbUptrend = True  # Placeholder, implement your logic

        # If all conditions are met, enter a buy position
        if self.previousHighXOM > 0 and self.previousHighIBB > 0 and self.ibbUptrend:
            self.SetHoldings(self.ibb, 1)  # Adjust the position size as needed

Step 3: Implement Exit Strategy and Performance Metrics

You mentioned a 10% target, drawdown calculation, and Sharpe ratio calculation. These will require additional logic to implement. For the 10% target, you could set a limit order at 10% above your buy price. Drawdown and Sharpe ratio are typically analyzed post-backtest using the backtest results.

Additional Considerations

• Historical Data: To assess the uptrend condition and previous highs accurately, you might need to request historical data at the start of your algorithm. This can be done using the History method.
• Scheduling: Consider using the Schedule function to periodically check your conditions instead of doing it every day, depending on your strategy's needs.
• Performance Metrics: QuantConnect provides some built-in statistics, but you may need to implement custom calculations for specific metrics like drawdown and the Sharpe ratio.

This simplified approach should

Hi

Not quite sure about what do you want but this code is running. Please check carefully that the trading logic matches your wishes.

Brgds

SL