Not working Alpha model with fixed time

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Not working Alpha model with fixed time

Last comment by - September 2024

Not working Alpha model with fixed time

Alpha model issue: Unexpected sell at 10:01 am, only 4 orders. Need help with timing logic.

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Not working Alpha model with fixed time

Sukhvinder Pal Singh | Stahl | September 2024

Hello everyone,

can you please someone explain to me why there is a sell order at 10:01 am and why only 4 orders?

explanation of Alpha model:
1st symbol will hold a long position at market open and sell at market close everyday.

2ns symbol will buy at Monday morning and lisquidate at Friday before market close.

please let me know How can I do this, it will be helpful.

Author

Sukhvinder Pal Singh

September 2024

Upvote

Mia Alissi INVESTOR

September 2024

The code still has a return type mismatch in the update method of the Model1AlphaModel class. The method should return an IEnumerable[Insight] instead of an Iterable[Insight]. Ensure that you import IEnumerable from the appropriate module and use it in the return type.

Sources: SVM Wavelet Forecasting, Futures Fast Trend Following, with Trend Strength, Key Concepts - QuantConnect.com, Accumulative Insight Portfolio Regression Algorithm, Add Alpha Model Algorithm

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Upvote

Sukhvinder Pal Singh INVESTOR

Stahl | September 2024

Thank you Mia Alissi \

can you please explain me how can I rebalance my portfolio after every 3 months.
My idea suppose I allocated both the tickers 50% each at start, now I will calculate which asset has more profit then I will increase the allocation at that asset and decrease from losing asset.

Upvote

Mia Alissi INVESTOR

September 2024

To rebalance your portfolio every three months based on asset performance, you can follow these steps using QuantConnect's scheduled events and portfolio management features:

Step-by-Step Guide:

Initialize Variables: Set up your initial allocations and any necessary variables to track performance.
Schedule Rebalancing: Use QuantConnect's Schedule.On method to trigger a rebalance every three months.
Calculate Profit: Determine the profit or loss for each asset over the three-month period.
Determine Allocation Changes: Adjust allocations based on performance, increasing the allocation for the asset with higher profit and decreasing for the one with lower profit.
Adjust Portfolio: Use the SetHoldings method to adjust the portfolio allocations.
Log Changes: Record the changes for future reference.

Example Code:

class RebalancePortfolioAlgorithm(QCAlgorithm):
    def initialize(self):
        self.set_start_date(2020, 1, 1)
        self.set_cash(100000)
        self.symbol_1 = self.add_equity("AAPL", Resolution.DAILY).symbol
        self.symbol_2 = self.add_equity("SPY", Resolution.DAILY).symbol
        self.initial_allocation = 0.5
        self.portfolio_weights = {self.symbol_1: self.initial_allocation, self.symbol_2: self.initial_allocation}
        # Schedule rebalancing every three months
        self.schedule.on(self.date_rules.month_start(), self.time_rules.at(0, 0), self.rebalance_portfolio)
    def on_data(self, data):
        pass
    def rebalance_portfolio(self):
        # Calculate profits
        profit_1 = self.portfolio[self.symbol_1].unrealized_profit
        profit_2 = self.portfolio[self.symbol_2].unrealized_profit
        # Determine new allocations
        if profit_1 > profit_2:
            self.portfolio_weights[self.symbol_1] += 0.1
            self.portfolio_weights[self.symbol_2] -= 0.1
        else:
            self.portfolio_weights[self.symbol_1] -= 0.1
            self.portfolio_weights[self.symbol_2] += 0.1
        # Normalize weights to ensure they sum to 1
        total_weight = sum(self.portfolio_weights.values())
        for symbol in self.portfolio_weights:
            self.portfolio_weights[symbol] /= total_weight
        # Adjust portfolio
        for symbol, weight in self.portfolio_weights.items():
            self.set_holdings(symbol, weight)
        # Log changes
        self.log(f"Rebalanced Portfolio: {self.portfolio_weights}")
+ Expand
- Collapse

Explanation:

Scheduled Events: The Schedule.On method is used to trigger the rebalance_portfolio method at the start of every month. You can adjust this to every three months by using a counter or a different scheduling rule.
Profit Calculation: The unrealized profit for each asset is calculated to determine which asset performed better.
Portfolio Adjustment: The SetHoldings method is used to adjust the portfolio according to the new weights.
Logging: Changes in allocations are logged for tracking purposes.

This approach allows you to systematically rebalance your portfolio based on asset performance every three months. Adjust the allocation changes and scheduling as needed for your specific strategy.

Sources: Paired Switching, Combining Mean Reversion And Momentum In Forex Market, Sentiment And Style Rotation Effect In Stocks, Price And Earnings Momentum, Scheduled Events - QuantConnect.com

Upvote

Erik Bengtson INVESTOR

November 2017

Cool James. I will take look. I might migrate what I'm doing to yours baseline implementation.I was just about to create an implementation for turtle soup strategy, better to start from a good basis

Upvote

Erik Bengtson INVESTOR

November 2017

Thanks Jay, I will take a look at that option.

James, here my version of your baseline project. I will try to merge with the changes I've done on the previous project. The code looks cleaner.

GIT GenericTree updates

Upvote

Erik Bengtson INVESTOR

November 2017

and of course, the current BB strategy implementation there must be only for trading ranges

Upvote

Erik Bengtson INVESTOR

November 2017

James,

I updated some code with fixes and to make it compatible with QC

Upvote

James Smith INVESTOR

November 2017

Thanks Erik. I'm going to try to integrate your changes as soon as I can. I can work without a pull request but you can go that route if you prefer. I'm giving genetic programming using this setup a lot of attention so feel free to suggest improvements or report any issues. The number one thing that helps me out is getting a third-party opinion on things. I have made quite a lot of changes to this and the genetic optimizer project and am getting fairly pleasing results.

In terms of an optimization rig, I have an old 4 slot server capable of 24 cores that I obtained for basically peanuts. I don't know how the costs stack up over time against cloud compute. I imagine it would even out after a few weeks of running 24/7 as long as power costs aren't too high.

For live hosting of trading algorithms +1 for digitalocean.

Upvote

Erik Bengtson INVESTOR

November 2017

James, I will check how to do a pull request. Not really familiar with that.

Next steps for me are the integration of additional signals in order of creating a few strategies. The additional signals I'm looking at is the Autochartist, integration with rest based NN services.

I will provide further feedback as soon as I progress using this framework, but immeditelly I think the configuration is rather verbose, I will change it into 2 steps, to make it more human friendly.

Thanks for this.

Upvote

James Smith INVESTOR

November 2017

I've merged the Bollinger and Channel breakout from your fork. Seems like a great idea to allow a survival period for the approximate coincidence of signals. I'm wondering whether this could use QuantConnect.Indicators.RollingWindow?

You're right that the Optimizer configuration is unwieldy for this level of complexity. I may address that sometime soon. In the meantime I'm using a few scripts and tools to shift json around.

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