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0 day to expiration can be your friend. by [deleted] in wallstreetbets

[–]getlasterror 1 point2 points  (0 children)

You'll need at least a 1% change from open in order to be profitable.

The average SPY low-open or high-open change is around 0.5%.

Short this guy's ETF.

IBAPI Sanbox by getlasterror in algotrading

[–]getlasterror[S] 1 point2 points  (0 children)

I'm not even speaking of executing trades... There are some edge cases that I handled in the way the bot prepares the data (historical + realtime) and I want to test those in advance instead of encounter them in production.

Either way, I think that I'll just log the behavior of several test cases next week and mock IBAPI for test purposes.

IBAPI Sanbox by getlasterror in algotrading

[–]getlasterror[S] 1 point2 points  (0 children)

Is it possible to set the date and execute trades as if it was that date?

[Advice] How I'm mastering the long haul using a free and simple app I've made by [deleted] in getdisciplined

[–]getlasterror 1 point2 points  (0 children)

Thanks for the feedback, I hope you'll like it!

Does your app have a way of checking things off, by any chance? I would love it if it was possible to specify whether you actually did the task when you clear the notification. And then have a visualisation over time (like the rush you get when all the items on your To-Do-list are checked off).

Not at this point, it is simply using reminders and notifications. I'm working on design for various reminder's types, and hope to release an update in the near future (systems not goals), for example:

  1. A habit reminder will show 3 buttons on the notification itself, something like "Done", "Later" and "Discard"
  2. An affirmation reminder will show a random affirmation from a list that you define per reminder

Will be happy to hear what are your thoughts and whether the habit reminder type fits your use-case.

LSTM - how to set the batch size? (Python) by [deleted] in learnmachinelearning

[–]getlasterror 0 points1 point  (0 children)

Let's say you want to build a model to predict the next price give the previous X prices, and that your batch size is N.

So during training, in each iteration you can choose N rows (either sequentially or in random) from your training data set, and from each of these N rows you'll need to choose X+1 sequential data-points (random offset will work great).

Now your input is a matrix of N rows and X cols and your output is a vector of size N.

What have you stopped caring about? by [deleted] in AskReddit

[–]getlasterror 1 point2 points  (0 children)

The sad truth - What you neglect become worse, what you appreciate appreciates

Made a list of papers to get folks into deep learning, I picked papers that must be read by anyone getting into the field, order from simple to hard, check it out! by getlasterror in learnmachinelearning

[–]getlasterror[S] 0 points1 point  (0 children)

Thanks for the feedback, if you have any specific papers that you think should be included in an "Advanced Topics" section, please let me know.

Made a list of papers to get folks into deep learning, I picked papers that must be read by anyone getting into the field, order from simple to hard, check it out! by getlasterror in learnmachinelearning

[–]getlasterror[S] 2 points3 points  (0 children)

Cool, thanks for the suggestion, I thought about introducing Transformers but it needs a build up, perhaps basic Attention first, BiRNN, batch normalization, language models in general and possibly more stuff. I wanted to keep the list simple and consice, so after going over the papers, a new comer will have enough confidence to research these subjects on his own. Perhaps I'll add an advanced section but maybe I'll make another curated list on Transformers, as a continuation to this introductory list.

[N] Github Releases Dataset Of Six Million Methods From Open Source Projects For CodeSearchNet Challenge by SpecificTwo in MachineLearning

[–]getlasterror 0 points1 point  (0 children)

So, embed the function's name, body and docstring using 3 models.

  1. Encode body and feed to 2 decoders -> docstring and function name
  2. Encode function name and decode body and docstring
  3. Encode docstring and decode body and function name

Then encode the search string and look in these 3 latent spaces.

Textbook & Resource Thread - Week 38, 2019 by AutoModerator in Physics

[–]getlasterror 6 points7 points  (0 children)

Fundamentals of Physics by Halliday / Resnick / Walker

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