Hi Max,

I took a look at your code and here is what I found. One problem is you're nesting your stop-loss loops inside of your ordering loops.

Here's how your algo runs: Handle_data gets called once per minute. After 10AM, if there are no positions, it will place an order to buy the stock according to your MA logic. However the order is filled in the next minute bar and is subject to the slippage model. Since the order is placed in one bar and filled in the next bar, code on line 60 never gets triggered. It will never be true that

if number_of_shares == 0 then if (context.portfolio.positions[stock].amount != 0):

I updated your code and have attached the backtest. The logic now says:

• Enter position at or after 10AM according to the MA logic
  
• Once we have a position, set a stop-loss at today's low. If triggered, this will exit the entire position
  
• If have a profit that equals or exceeds $400, sell half of the portfolio

To note, it's more robust to use order_target_percent, which is more flexible with changes in your portfolio cash. Let me know if this makes sense and if its the behavior that you expected.

Cheers,
Alisa

great thanks a lot. the orders were not triggered when trying to sell half the shares but it's working by moving

elif (current_price - stock_avg_cost)*number_of_shares >= 400:  

inside the previous loop
elif context.portfolio.positions[stock].amount != 0: Now the problem is that as I sell some shares the value of stock_avg_cost goes down and as a result my profit condition current_price - stock_avg_cost)*number_of_shares >= 400: is true many times and the algo keeps on selling half my positions.

I also don't understand why after I have 0 shares left I keep getting log saying sold 0 shares: how could that log happen when my condition current_price - stock_avg_cost)*number_of_shares >= 400 should be false since number_of_shares = 0

Also once I sell half my shares how do I make sure that the original stop order is now for just half of the shares? And how can I move it to stock_avg_cost from todays_low?

thanks so much for your help and sorry for my ignorance!

Max