The biggest change that has been made since this algo was written is the move to pipelines to fetch data instead of get_fundamentals. Creating a pipeline definition to match the original query and filter logic is the first step.
@Tyler Trumbo could you verify that the following is the basic strategy
Choose stocks on the first trading day of the month and store in context.stocks. Total quantity determined by constant 'context.max_positions'.
Every day set hedge True if 10 day SMA < 100 day SMA of IWM (Russel 2000 ETF)
Every day 20 min after market open sell everything if hedge is True. If it's False sell everything except TLT
Every day 30 min after market open buy 100% TLT if hedge is True. If it's False place orders for stocks in context.stocks where 10 day SMA of stock > 100 day SMA of stock. Equally weight stocks.
Record, context.account.leverage, context.portfolio.cash/context.portfolio.portfolio_value, number of positions, and max cost basis)
One thing this algo does is not buy stocks where 10 day SMA of stock < 100 day SMA. This leaves that portion of the portfolio, which that stock would normally occupy, in cash. Is that intended? Would you rather always be 100% invested? This means selecting other stocks which meet the original criteria but also meet the SMA requirement. Also, this algo sells everything 20 minutes after open but then potentially buys the same stocks back 10 minutes later. Would you rather just rebalance once a day and adjust the weights as needed?
Is that close to what you intended? Anything I missed?
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