I'm working on a score strategy, based on several indicators. The script has no error, but when i add the score sistem i got "Study error: internal server study error"
score = 0
plot(score)
if ma_buy
score := score + 1
if st_buy
score := score + 1
if hs_buy
score := score + 1
if vortex_buy
score := score + 1
if vo_buy
score := score + 1
if ma_sell
score := score - 1
if st_sell
score := score - 1
if hs_sell
score := score - 1
if vortex_sell
score := score - 1
if vo_sell
score := score - 1
if score > 0
strategy.entry("Long", strategy.long)
if score < 0
strategy.entry("Short", strategy.short)
The problem was putting a plot before the calculations...
Although you already found a solution, I want to tell you, that your code can be written better by removing repetitive code:
score = 0
if ma_buy or st_buy or hs_buy or vortex_buy or vo_buy or cm_buy or vfi_buy or qqe_buy
score += 1
if ma_sell or st_sell or hs_sell or vortex_sell or vo_sell or cm_sell or vfi_sell or qqe_sell
score -= 1
Related
I am strugling with this loop. I want to get "6" in the second row of column "Newcolumn".I get the following error.
Error in if (mydata$type_name[i] == "a" && mydata$type_name[i - :
missing value where TRUE/FALSE needed.
The code that I created:
id type_name name score newcolumn
1 a Car 2 2
1 a van 2 6
1 b Car 2 2
1 b Car 2 2
mydata$newcolumn <-c(0)
for (i in 1:length(mydata$id)){
if ((mydata$type_name [i] == "a") && (mydata$type_name[i-1] == "a") && ((mydata$name[i]) != (mydata$name[i-1]))){
mydata$newcolumn[i]=mydata$score[i]*3 }
else {
mydata$newcolumn[i]=mydata$score[i]*1
}
}
Thank you very much in advance
List starts at index 1 in R but like you are doing a i-1 in your loop starting at 1, your list is out of range (i-1=0) so your code can not return a True or False.
I am looking to explore the GameTheory package from CRAN, but I would appreciate help in converting my data (in the form of a data frame of unique combinations and results) in to the required coalition object. The precursor to this I believe to be an ordered list of all coalition values (https://cran.r-project.org/web/packages/GameTheory/vignettes/GameTheory.pdf).
My real data has n ~ 30 'players', and unique combinations = large (say 1000 unique combinations), for which I have 1 and 0 identifiers to describe the combinations. This data is sparsely populated in that I do not have data for all combinations, but will assume combinations not described have zero value. I plan to have one specific 'player' who will appear in all combinations, and act as a baseline.
By way of example this is the data frame I am starting with:
require(GameTheory)
games <- read.csv('C:\\Users\\me\\Desktop\\SampleGames.csv', header = TRUE, row.names = 1)
games
n1 n2 n3 n4 Stakes Wins Success_Rate
1 1 1 0 0 800 60 7.50%
2 1 0 1 0 850 45 5.29%
3 1 0 0 1 150000 10 0.01%
4 1 1 1 0 300 25 8.33%
5 1 1 0 1 1800 65 3.61%
6 1 0 1 1 1900 55 2.89%
7 1 1 1 1 700 40 5.71%
8 1 0 0 0 3000000 10 0.00333%
where n1 is my universal player, and in this instance, I have described all combinations.
To calculate my 'base' coalition value from player {1} alone, I am looking to perform the calculation: 0.00333% (success rate) * all stakes, i.e.
0.00333% * (800 + 850 + 150000 + 300 + 1800 + 1900 + 700 + 3000000) = 105
I'll then have zero values for {2}, {3} and {4} as they never "play" alone in this example.
To calculate my first pair coalition value, I am looking to perform the calculation:
7.5%(800 + 300 + 1800 + 700) + 0.00333%(850 + 150000 + 1900 + 3000000) = 375
This is calculated as players {1,2} base win rate (7.5%) by the stakes they feature in, plus player {1} base win rate (0.00333%) by the combinations he features in that player {2} does not - i.e. exclusive sets.
This logic is repeated for the other unique combinations. For example row 4 would be the combination of {1,2,3} so the calculation is:
7.5%(800+1800) + 5.29%(850+1900) + 8.33%(300+700) + 0.00333%(3000000+150000) = 529 which descriptively is set {1,2} success rate% by Stakes for the combinations it appears in that {3} does not, {1,3} by where {2} does not feature, {1,2,3} by their occurrences, and the base player {1} by examples where neither {2} nor {3} occur.
My expected outcome therefore should look like this I believe:
c(105,0,0,0, 375,304,110,0,0,0, 529,283,246,0, 400)
where the first four numbers are the single player combinations {1} {2} {3} and {4}, the next six numbers are two player combinations {1,2} {1,3} {1,4} (and the null cases {2,3} {2,4} {3,4} which don't exist), then the next four are the three player combinations {1,2,3} {1,2,4} {1,3,4} and the null case {2,3,4}, and lastly the full combination set {1,2,3,4}.
I'd then feed this in to the DefineGame function of the package to create my coalitions object.
Appreciate any help: I have tried to be as descriptive as possible. I really don't know where to start on generating the necessary sets and set exclusions.
I want to make a new variable "churned" by taking into account five variables :
Include in churn
A-Churn
B-Churn
C-Churn
D-Churn
My condition is - If variable "Include in churn" has 1 and for all other variables , if any one of the variables has 1 than my new variable "Churned" should have 1 else 0. I am a newbie in using mutate function.
Please help me to create this new variable thru 'mutate' function.
If I understand your formulation logically, you want
mutate(data, Churned = Include.in.Churn == 1 & (A.Churn == 1 | B.Churn == 1 | C.Churn == 1 | D.Churn == 1))
This will make Churned a logical. If you really need an integer, as.integer will produce 1 for TRUE and 0 for FALSE.
If all mentioned Variables are either 1 or 0 you can also use the possibly faster
mutate(data, Churned = Include.in.Churn * (A.Churn + B.Churn + C.Churn + D.Churn) >= 1)
I'm trying to set a default value in a DropDown box depending on the current time. (substring-after(now(), "T"))
To be more specific, I would like my DropDown box to have as default value
"AM" if the current time is between 06:00:01 and 12:00:00.
"PM" if the current time is between 12:00:01 and 18:00:00.
"NIGHT" if the current time is between 18:00:01 and 06:00:00.
I tried with "Rules" and "Set default value" but can't figure out a solution.
Thanks for help!
go get the hours and seconds number and put it fields.
A - hours
B - seconds
A >= 6 and B > 0 OR A =< 12 and B = 0 ---> AM
if A >= 12 and B > 0 OR A =< 18 and B = 0 ---> PM
if A >= 18 and B > 0 OR A =< 6 and B = 0 ---> NIGHT
I found a way, I put hours in a field (I don't think it's absolutely necessary but it's more clear for the formula after) then I set the default value for the formula of the DropDown Box as :
concat(substring("AM", 1, (field4 < 12) * string-length("AM")), substring("PM", 1, (not(field4 < 12)) * string-length("PM")))
field4 is the field when I extract the current hour of the day with that formula:
substring-before((substring-after(now(), "T")), ":")
You will find usefull tips here for this application:
http://blogs.msdn.com/b/infopath/archive/2006/11/27/conditional-default-values.aspx
I'm trying to write some code to analyze my company's insurance plan offerings... but they're complicated! The PPO plan is straightforward, but the high deductible health plans are complicated, as they introduced a "split" deductible and out of pocket maximum (individual and total) for the family plans. It works like this:
Once the individual meets the individual deductible, he/she is covered at 90%
Once the remaining 1+ individuals on the plan meet the total deductible, the entire family is covered at 90%
The individual cannot satisfy the family deductible with only their medical expenses
I want to feed in a vector of expenses for my family members (there are four of them) and output the total cost for each plan. Below is a table of possible scenarios, with the following column codes:
ded_ind: did one individual meet the individual deductible?
ded_tot: was the total deductible reached?
oop_ind: was the individual out of pocket max reached
oop_tot: was the total out of pocket max reached?
exp_ind = the expenses of the highest spender
exp_rem = the expenses of the remaining /other/ family members (not the highest spender)
oop_max_ind = the level of expenses at which the individual has paid their out of pocket maximum (when ded_ind + 0.1 * exp_ind = out of pocket max for the individual
oop_max_fam = same as for individual, but for remaining family members
The table:
| ded_ind | oop_ind | ded_rem | oop_rem | formula
|---------+---------+---------+---------+---------------------------------------------------------------------------|
| 0 | 0 | 0 | 0 | exp_ind + exp_rem |
| 1 | 0 | 0 | 0 | ded_ind + 0.1 * (exp_ind - ded_ind) + exp_rem |
| 0 | 0 | 1 | 0 | exp_ind + ded_rem + 0.1 * (exp_rem - ded_rem) |
| 1 | 1 | 0 | 0 | oop_max_ind + exp_fam |
| 1 | 0 | 1 | 0 | ded_ind + 0.1 * (exp_ind - ded_ind) + ded_rem + 0.1 * (exp_rem - ded_rem) |
| 0 | 0 | 1 | 1 | oop_max_rem + exp_ind |
| 1 | 0 | 1 | 1 | ded_ind + 0.1 * (exp_ind - ded_ind) + oop_max_rem |
| 1 | 1 | 1 | 0 | oop_ind_max + ded_rem + 0.1 * (exp_rem - ded_rem) |
| 1 | 1 | 1 | 1 | oop_ind_max + oop_rem_max |
Omitted: 0 1 0 0, 0 0 0 1, 0 1 1 0, and 0 1 0 1 are not present, as oop_ind and oop_rem could not have been met if ded_ind and ded_rem, respectively, have not been met.
My current code is a somewhat massive ifelse loop like so (not the code, but what it does):
check if plan is ppo or hsa
if hsa plan
if exp_ind + exp_rem < ded_rem # didn't meet family deductible
if exp_ind < ded_ind # individual deductible also not met
cost = exp_ind + exp_rem
else is exp_ind > oop_ind_max # ded_ind met, is oop_ind?
ded_ind + 0.1 * (exp_ind - ded_ind) + exp_fam # didn't reach oop_max_ind
else oop_max_ind + exp_fam # reached oop_max_ind
else ...
After the else, the total is greater than the family deductible. I check to see if it was contributed by more than two people and just continue on like that.
My question, now that I've given some background to the problem: Is there a better way to manage conditional situations like this than ifelse loops to filter them down a bit at a time?
The code ends up seeming redundant, as one checks for some higher level conditions (consider the table where ded_rem is met or not met... one still has to check for ded_ind and oop_max_ind in both cases, and the code is the same... just positioned at two different places in the ifelse structure).
Could this be done with some sort of matrix operation? Are there other examples online of more clever ways to deal with filtering of conditions?
Many thanks for any suggestions.
P.S. I'm using R and will be creating an interactive with shiny so that other employees can input best and worst case scenarios for each of their family members and see which plan comes out ahead via a dot or bar chart.
The suggestion to convert to a binary value based on the result gave me an idea, which also helped me learn that one can do vectorized TRUE / FALSE checks (I guess that was probably obvious to many).
Here's my current idea:
expenses will be a vector of individual forecast medical expenses for the year (example of three people):
expenses <- c(1500, 100, 400)
We set exp_ind to the max value, and sum the rest for exp_rem
exp_ind <- max(expenses)
# [1] index of which() for cases with multiple max values
exp_rem <- sum(expenses[-which(expenses == exp_ind)[1]])
For any given plan, I can set up a vector with the cutoffs, for example:
individual deductible = 1000
individual out of pocket max = 2000 (need to incur 11k of expenses to get there)
family deductible = 2000
family out of pocket max = 4000 (need to incur 22k of expenses to get there)
Set those values:
ded_ind <- 1000
oop_max_ind <- 11000
ded_tot <- 2000
oop_max_tot <- 22000
cutoffs <- c(ded_ind, oop_max_ind, ded_tot, oop_max_tot)
Now we can check the input expense against the cutoffs:
result <- as.numeric(rep(c(exp_ind, exp_rem), each = 2) > cutoffs)
Last, convert to binary:
result_bin <- sum(2^(seq_along(result) - 1) * result)
Now I can set up functions for the possible outcomes based on the value in result_bin:
if(result_bin == 1) {cost <- ded_ind + 0.1 * (exp_ind - ded_ind) + exp_rem }
cost
[1] 1550
We can check this...
High spender would have paid his 1000 and then 10% of remaining 500 = 1050
Other members did not reach the family deductible and paid the full 400 + 100 = 500
Total: 1550
I still need to create a mapping of results_bin values to corresponding functions, but doing a vectorized check and converting a unique binary value is much, much better, in my opinion, than my ifelse nested mess.
I look at it like this: I'd have had to set the variables and write the functions anyway; this saves me 1) explicitly writing all the conditions, 2) the redundancy issue I was talking about in that one ends up writing identical "sibling" branches of parent splits in the ifelse structure, and lastly, 3) the code is far, far, far more easily followed.
Since this question is not very specific, here is a simpler example/answer:
# example data
test <- expand.grid(opt1=0:1,opt2=0:1)
# create a unique identifier to represent the binary variables
test$code <- with(allopts,paste(opt1,opt2,sep=""))
# create an input variable to be used in functions
test$var1 <- 1:4
# opt1 opt2 code var1
#1 0 0 00 1
#2 1 0 10 2
#3 0 1 01 3
#4 1 1 11 4
Respective functions to apply depending on binary conditions, along with intended results for each combo:
var1 + 10 #code 00 - intended result = 11
var1 + 100 #code 10 - intended result = 102
var1 + 1000 #code 01 - intended result = 1003
var1 + var1 #code 11 - intended result = 8
Use ifelse combinations to do the calculations:
test$result <- with(test,
ifelse(code == "00", var1 + 10,
ifelse(code == "10", var1 + 100,
ifelse(code == "01", var1 + 1000,
ifelse(code == "11", var1 + var1,
NA
)))))
Result:
opt1 opt2 code var1 result
1 0 0 00 1 11
2 1 0 10 2 102
3 0 1 01 3 1003
4 1 1 11 4 8