Does pinescript have any built in functions to check if a line has been crossed in the past? The line would be repainting as I am looking to check if a linear regression channel's deviation lines have been crossed previously or not, but more specifically the number of times crossed to attempt to validate it's strength.
This code will work, although the code execution time is very slow:
priceFound = 0
countAbove = 0
countBelow = 0
length = 100
slope = (y2-y1)/(x2-x1) //Calculates slope of using line coordinates
for i = 0 to length
priceFound := slope*((_y1/slope) + bar_index[i] - _x1) //Calculates price at y-coord
countAbove := high[i]>priceFound ? countAbove+1 : countAbove //Checks if above
countBelow := low[i]<priceFound ? countBelow+1 : countBelow //Checks if below
What your code is doing is checking on every single newly created bar 100 bars back if price was above/below your coordinate. Yes it's not really efficient, though I don't think simple arithmetic operations would challenge pine script. (Of course I can be wrong)
Instead you could try another direction with ta.crossunder() / ta.crossover() or ta.cross(). With these you wouldn't need slope calculation or whatsoever because you can check your plot value and the high/low real time (or rather on bar close). Of course this way you would only find out if your line did not hold and you would build up your strength tracking the opposite way.
Still, I think it's worth considering as a solution to your problem.
I am currently implementing an optimization problem with pyomo and since now some hours I get the message that my problem is unbounded. After searching for the issue, I came along one term which seems to be unbounded. I excluded this term from the objective function and it shows that it takes a very high negative value, which supports the assumption that it is unbounded to -Inf.
But I have checked the problem further and it is impossible that the term is unbounded, as following code and results show:
model.nominal_cap_storage = Var(model.STORAGE, bounds=(0,None)) #lower bound is 0
#I assumed very high CAPEX for each storage (see print)
dict_capex_storage = {'battery': capex_battery_storage,
'co2': capex_co2_storage,
'hydrogen': capex_hydrogen_storage,
'heat': capex_heat_storage,
'syncrude': capex_syncrude_storage}
print(dict_capex_storage)
>>> {'battery': 100000000000000000, 'co2': 100000000000000000,
'hydrogen': 1000000000000000000, 'heat': 1000000000000000, 'syncrude': 10000000000000000000}
From these assumptions I already assume that it is impossible that the one term can be unbounded towards -Inf as the capacity has the lower bound of 0 and the CAPEX is a positive fixed value. But now it gets crazy. The following term is has the issue of being unbounded:
model.total_investment_storage = Var()
def total_investment_storage_rule(model):
return model.total_investment_storage == sum(model.nominal_cap_storage[storage] * dict_capex_storage[storage] \
for storage in model.STORAGE)
model.total_investment_storage_con = Constraint(rule=total_investment_storage_rule)
If I exclude the term from the objective function, I get following value after the optimization. It seems, that it can take high negative values.
>>>>
Variable total_investment_storage
-1004724108.3426505
So I checked the term regarding the component model.nominal_cap_storage to see the value of the capacity:
model.total_cap_storage = Var()
def total_cap_storage_rule(model):
return model.total_cap_storage == sum(model.nominal_cap_storage[storage] for storage in model.STORAGE)
model.total_cap_storage_con = Constraint(rule=total_cap_storage_rule)
>>>>
Variable total_cap_storage
0.0
I did the same for the dictionary, but made a mistake: I forgot to delete the model.nominal_cap_storage. But the result is confusing:
model.total_capex_storage = Var()
def total_capex_storage_rule(model):
return model.total_capex_storage == sum(model.nominal_cap_storage[storage] * dict_capex_storage[storage] \
for storage in model.STORAGE)
model.total_capex_storage_con = Constraint(rule=total_capex_storage_rule)
>>>>
Variable total_capex_storage
0.0
So my question is why is the term unbounded and how is it possible that model.total_investment_storage and model.total_capex_storage have different solutions though both are calculated equally? Any help is highly appreciated.
I think you are misinterpreting "unbounded." When the solver says the problem is unbounded, that means the objective function value is unbounded based on the variables and constraints in the problem. It has nothing to do with bounds on variables, unless one of those variable bounds prevents the objective from being unbound.
If you want help on above problem, you need to edit and post the full problem, with the objective function, and (if possible) the error. What you have now is a collection of different snippets of different variations of a problem, which isn't really informative on the overall issue.
I solved the problem by setting a lower bound to the term, which takes a negative value:
model.total_investment_storage = Var(bounds=(0, None)
I am still not sure why this term can take negative values but this solved at least my problem
I am programming in Solidity and trying to write a formula to calculate a compounding amount, I can write the formula in something like Excel as:
1 * (1 + 0.0025) ^ 2212
However, this obviously doesn't work is Solidity, so I tried;
uint rate = 25;
return 1 * (1 + (rate / 10000)) ** 2212;
Which still doesn't work.. explanation in edit below.
Anyone have some guidance on how I can get this working?
Thanks!
EDIT: by doesn't work, I mean that Solidity does not support floating point numbers in any form, 25/10000 returns 0 (not 0.0025). Which then results in the whole equation returning 1 (the real answer is 250).
My assumption is that the only way I can really do this is by re-working this equation into something that doesn't involve decimals places (or very large numbers, because raising to-the-power-of quickly gets out of hand).
I am trying to solve a set of problems using AMPL and add their objective values. However, the sum operator does not seem to work and only keeps getting updated to the most recent value.
set CASES := {1,2,3,4,5,6};
model modelFile.mod;
option solver cplex;
option eexit -123456789;
var total;
let total := 0;
for {j in CASES}
{
reset data;
data ("data" & j & ".dat")
solve;
display total_Cost;
let total := total + total_Cost;
display total;
}
Sample Output:
CPLEX 12.6.3.0: optimal solution; objective 4.236067977
2 dual simplex iterations (0 in phase I)
total_Cost = 4.23607
total = 4.23607
CPLEX 12.6.3.0: optimal solution; objective 5.656854249
5 dual simplex iterations (0 in phase I)
total_Cost = 5.65685
total = 5.65685
where total_cost is the objective value from the optimization problem
Since AMPL is an algebraic modeling language rather than a general-purpose programming language, variables in it denote optimization variables which are determined during the solution process. So each time you call solve, optimization variable total is reset. What you need here is a parameter which, unlike variable, is not changed during the optimization:
param total;
I finally realized that this happened due to the new keyword "reset data" that AMPL has. By changing the keyword to "update", the code works.
I'm teaching/helping a student to program.
I remember the following process always helped me when I started; It looks pretty intuitive and I wonder if someone else have had a similar approach.
Read the problem and understand it ( of course ) .
Identify possible "functions" and variables.
Write how would I do it step by step ( algorithm )
Translate it into code, if there is something you cannot do, create a function that does it for you and keep moving.
With the time and practice I seem to have forgotten how hard it was to pass from problem description to a coding solution, but, by applying this method I managed to learn how to program.
So for a project description like:
A system has to calculate the price of an Item based on the following rules ( a description of the rules... client, discounts, availability etc.. etc.etc. )
I first step is to understand what the problem is.
Then identify the item, the rules the variables etc.
pseudo code something like:
function getPrice( itemPrice, quantity , clientAge, hourOfDay ) : int
if( hourOfDay > 18 ) then
discount = 5%
if( quantity > 10 ) then
discount = 5%
if( clientAge > 60 or < 18 ) then
discount = 5%
return item_price - discounts...
end
And then pass it to the programming language..
public class Problem1{
public int getPrice( int itemPrice, int quantity,hourOdDay ) {
int discount = 0;
if( hourOfDay > 10 ) {
// uh uh.. U don't know how to calculate percentage...
// create a function and move on.
discount += percentOf( 5, itemPriece );
.
.
.
you get the idea..
}
}
public int percentOf( int percent, int i ) {
// ....
}
}
Did you went on a similar approach?.. Did some one teach you a similar approach or did you discovered your self ( as I did :( )
I go via the test-driven approach.
1. I write down (on paper or plain text editor) a list of tests or specification that would satisfy the needs of the problem.
- simple calculations (no discounts and concessions) with:
- single item
- two items
- maximum number of items that doesn't have a discount
- calculate for discounts based on number of items
- buying 10 items gives you a 5% discount
- buying 15 items gives you a 7% discount
- etc.
- calculate based on hourly rates
- calculate morning rates
- calculate afternoon rates
- calculate evening rates
- calculate midnight rates
- calculate based on buyer's age
- children
- adults
- seniors
- calculate based on combinations
- buying 10 items in the afternoon
2. Look for the items that I think would be the easiest to implement and write a test for it. E.g single items looks easy
The sample using Nunit and C#.
[Test] public void SingleItems()
{
Assert.AreEqual(5, GetPrice(5, 1));
}
Implement that using:
public decimal GetPrice(decimal amount, int quantity)
{
return amount * quantity; // easy!
}
Then move on to the two items.
[Test]
public void TwoItemsItems()
{
Assert.AreEqual(10, GetPrice(5, 2));
}
The implementation still passes the test so move on to the next test.
3. Be always on the lookout for duplication and remove it. You are done when all the tests pass and you can no longer think of any test.
This doesn't guarantee that you will create the most efficient algorithm, but as long as you know what to test for and it all passes, it will guarantee that you are getting the right answers.
the old-school OO way:
write down a description of the problem and its solution
circle the nouns, these are candidate objects
draw boxes around the verbs, these are candidate messages
group the verbs with the nouns that would 'do' the action; list any other nouns that would be required to help
see if you can restate the solution using the form noun.verb(other nouns)
code it
[this method preceeds CRC cards, but its been so long (over 20 years) that I don't remember where i learned it]
when learning programming I don't think TDD is helpful. TDD is good later on when you have some concept of what programming is about, but for starters, having an environment where you write code and see the results in the quickest possible turn around time is the most important thing.
I'd go from problem statement to code instantly. Hack it around. Help the student see different ways of composing software / structuring algorithms. Teach the student to change their minds and rework the code. Try and teach a little bit about code aesthetics.
Once they can hack around code.... then introduce the idea of formal restructuring in terms of refactoring. Then introduce the idea of TDD as a way to make the process a bit more robust. But only once they are feeling comfortable in manipulating code to do what they want. Being able to specify tests is then somewhat easier at that stage. The reason is that TDD is about Design. When learning you don't really care so much about design but about what you can do, what toys do you have to play with, how do they work, how do you combine them together. Once you have a sense of that, then you want to think about design and thats when TDD really kicks in.
From there I'd start introducing micro patterns leading into design patterns
I did something similar.
Figure out the rules/logic.
Figure out the math.
Then try and code it.
After doing that for a couple of months it just gets internalized. You don't realize your doing it until you come up against a complex problem that requires you to break it down.
I start at the top and work my way down. Basically, I'll start by writing a high level procedure, sketch out the details inside of it, and then start filling in the details.
Say I had this problem (yoinked from project euler)
The sum of the squares of the first
ten natural numbers is, 1^2 + 2^2 +
... + 10^2 = 385
The square of the sum of the first ten
natural numbers is, (1 + 2 + ... +
10)^2 = 55^2 = 3025
Hence the difference between the sum
of the squares of the first ten
natural numbers and the square of the
sum is 3025 385 = 2640.
Find the difference between the sum of
the squares of the first one hundred
natural numbers and the square of the
sum.
So I start like this:
(display (- (sum-of-squares (list-to 10))
(square-of-sums (list-to 10))))
Now, in Scheme, there is no sum-of-squares, square-of-sums or list-to functions. So the next step would be to build each of those. In building each of those functions, I may find I need to abstract out more. I try to keep things simple so that each function only really does one thing. When I build some piece of functionality that is testable, I write a unit test for it. When I start noticing a logical grouping for some data, and the functions that act on them, I may push it into an object.
I've enjoyed TDD every since it was introduced to me. Helps me plan out my code, and it just puts me at ease having all my tests return with "success" every time I modify my code, letting me know I'm going home on time today!
Wishful thinking is probably the most important tool to solve complex problems. When in doubt, assume that a function exists to solve your problem (create a stub, at first). You'll come back to it later to expand it.
A good book for beginners looking for a process: Test Driven Development: By Example
My dad had a bunch of flow chart stencils that he used to make me use when he was first teaching me about programming. to this day I draw squares and diamonds to build out a logical process of how to analyze a problem.
I think there are about a dozen different heuristics I know of when it comes to programming and so I tend to go through the list at times with what I'm trying to do. At the start, it is important to know what is the desired end result and then try to work backwards to find it.
I remember an Algorithms class covering some of these ways like:
Reduce it to a known problem or trivial problem
Divide and conquer (MergeSort being a classic example here)
Use Data Structures that have the right functions (HeapSort being an example here)
Recursion (Knowing trivial solutions and being able to reduce to those)
Dynamic programming
Organizing a solution as well as testing it for odd situations, e.g. if someone thinks L should be a number, are what I'd usually use to test out the idea in pseudo code before writing it up.
Design patterns can be a handy set of tools to use for specific cases like where an Adapter is needed or organizing things into a state or strategy solution.
Yes.. well TDD did't existed ( or was not that popular ) when I began. Would be TDD the way to go to pass from problem description to code?... Is not that a little bit advanced? I mean, when a "future" developer hardly understand what a programming language is, wouldn't it be counterproductive?
What about hamcrest the make the transition from algorithm to code.
I think there's a better way to state your problem.
Instead of defining it as 'a system,' define what is expected in terms of user inputs and outputs.
"On a window, a user should select an item from a list, and a box should show him how much it costs."
Then, you can give him some of the factors determining the costs, including sample items and what their costs should end up being.
(this is also very much a TDD-like idea)
Keep in mind, if you get 5% off then another 5% off, you don't get 10% off. Rather, you pay 95% of 95%, which is 90.25%, or 9.75% off. So, you shouldn't add the percentage.