TTR | Technical analysis and other functions to construct | Cryptocurrency library

here is a reproducible sample:

to put it simply, I have a list of DFMs created by quanteda package(LD1). each DFM has different texts of different lengths.

now, I want to calculate and compare lexical diversity for each text within DFMs and among DFMs.

I am using the following code to find the RSI (Relative Strength Index) and DEMA (double exponential moving average) of a stock.

I'm writing code to pull out a TTR (Time to Repair) metric for our components. The data is given as below as a list of the location name, datetime, and status codes. The code is meant to log the time elapsed from when a component fails to when it's repaired, as long as it's been over 24 hours (to filter out planned maintenance).

Here is the code I have below. The subquery part seems to be working, and it seems to work if I just have the (seqnum - seqnum_2) in the groupby. The problem is that it returns nothing if I group by Sitename as well, which I need to do to pull location names into the data. What am I doing wrong in getting the location names in?

I uploaded my data. https://filebin.net/a29fn87b8wpfnos0/Plume_2.csv?t=iouc5vg7

It looks like this in a csv file format

I tried to look for a proper answer that suits my data. I couldn't find it, it took me about a month trying by myself to solve it.

First I need to do a moving average for:

• 30 min
• 1 hour
• 1 day
• 1 week

for each PM2.5, PM10, NO2

However, I can't do that manually using this type of code:

I am undertaking a new project encompassing large time-series datasets from which dependent calculations are fed into a shiny application. As such, efficiency is of interest to me. The operations are typically restricted to elementary period conversions and subsequent summary statistics for risk metrics.

I am investigating which library/approach to build the calculation scripts with. At present, I am OK with xts and data.table. Although I can resort to libraries as quantmod and TTR, I am hesitant to deploy blackbox functions in production and would prefer to maintain full traceability.

Thus far, I have carried out the following benchmarking exercise in which a data.frame of daily prices is converted into monthly returns. The packages used thus far are xts, data.table and quantmod (as reference). The code is pasted below but can also be found on GitHub.

Benchmark Code

I'm trying to optimize the parameters of a simple strategy as the code below by maximizing the sharpe ratio. The output results are clearly wrong. Can you please provide some help?

I am trying to find running correlation by group using runCor. Since the last Team has just one element, the runCor function is throwing the error.

I have a data.table as follows -