cci | A modern C compiler written in C20 | Compiler library

From the documentation:

The CCI (commodity channel index) is calculated as the difference between the typical price of a commodity and its simple moving average, divided by the mean absolute deviation of the typical price. The index is scaled by an inverse factor of 0.015 to provide more readable numbers

The "High" and "Low" values are not used for calculations CCI. I assume that you have confused the terms typical price and true range.

[Addition]

The pine script allows you to choose which series of calculations will be performed. Choose HLС3 in your case.

Linked List is a data structure that is easy to add and delete nodes from the list, and these operations should be considered as intended usage.

The main reason it may seem confusing is because the Linked list above lacks implementation to make the structure actually useful, and those are what may cause problems.

When the author says problematic when "changing the head", she may be considering some of the following scenarios:

Where we add a new node before head and make it the new head. In cases that we need to iterate through the list, having a reference to the old head means that anything before the current head may not be considered.

Where we add new nodes and discard the old head for whatever reason it is no longer useable. A reference to the old head may read data that shouldn't exist in the moment.

There may be more scenarios, but the point is you have to be careful what references the linked list. I often maintain a null value head node that always stays in front of everything.

Happy coding!

The equivalent for thinkScript's BarNumber() is Pine-Script's bar_index.

thinkScript and Pine-Script both use a loop that represents the trading period range in effect. The BarNumber/bar_index value represents each measurement period that is being calculated through the loop.

To compare this to other types of coding, you might consider something like for bar_index in 0 to 10 for a trading period of 10 days, where bar_index would count from 0 through 9 (ie, it acts like the stereotypical i in a for loop).

The period the index represents could be a day, minute, tick, etc - whatever you set for your chart or calculations. And, note: *a "day" represents a "trading day", not a "calendar day".

A confusing issue is: where do the bars start counting? The BarNumber() or bar_index starts at the earliest period of your time frame and counts up toward the most recent trading period of your time frame. Eg, the bar_index of 0 might represent 10 days ago, while the bar_index of 9 might represent today.

Here's some example code from the Kodify site (which provides Pine-Script tutorials):

Your data is in a good form to get into a dataframe with the right indices.

Converting series integer to integer in pinescript to cannot be done. Therefore, it is necessary to look for workarounds. In your case, you can use the following script.

Found the issue. It was, of course, a configuration issue. There is a property in the angular.json file to specify a custom karmaConfig path.

I am not sure how it happened, but it had an incorrect path of src/karma.config.js Karma still ran when I ran ng test because (as per Angualr.io)

The karma.conf.js file is a partial Karma configuration file. The CLI constructs the full runtime configuration in memory, based on application structure specified in the angular.json file, supplemented by karma.conf.js.

The error I was getting about my customLoader being missing is technically correct since Karma was not loading my config. So if the Karma config file specified in the angular.json file is not found, the CLI continues and makes a "default" config for Karma to run.

My two cents: there should be some error outputted to the console that the specified karma config could not be found, but alas, there is not. Check your paths people!

I did find the trick !

Just add to your /etc/hosts the dns used by fabric sample like so:

1. According to this document, we can see Clustered columnstore indexes is one of the in-memory technologies and it can reduce our storage footprint (up to 10 times) and improve performance for reporting and analytics queries. So that it will increase performance without increasing our service tier.

2. As we know rowstore indexes perform best on queries that seek into the data, when searching for a particular value, or for queries on a small range of values. According to this document, we can combine rowstore and columnstore on the same table in Azure sql.

In summary:
So you can add any specific column index , if it is necessary.

It looks like you have a time variable with np.datetime64 type. You can use ds.swap_dims({"sounding_dim": "time"}) to make time the coordinate variable. See https://xarray.pydata.org/en/stable/generated/xarray.Dataset.swap_dims.html