What age range can my wallpaper app satisfy me in terms of AdMob revenues and downloads?
If I choose the whole age range, will my AdMob income decrease due to the conditions of the family policy (5-13 age range)?
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I can get bid and ask data from my market data provider but I want to convert this in OHLC values.
What is the good calculation using bid/ask? I saw in a post that for a specific period:
Open = (first bid + first ask) / 2.
High = Highest bid
Low = Lower ask
Close = (last bid + last ask) / 2
Is it true?
You are getting confused with terminology. In forex:
Ask is the price that you, the trader, can currently buy at.
Bid is the price that you, the trader, can currently sell at.
OHLC are historical prices for a predetermined period of time (common time periods at 1 min, 5 min, 15 min, 30 min, 1 hour, 4 hour, daily and weekly) and are usually used to plot candle stick charts (and tend to be based on the Bid price only).
Open - This is the bid price at the commencement of the time period.
High - This is the highest bid price that was quoted during the time period.
Low - This is the lowest bid price that was quoted during the time period
Close - This is the last bid price at the end of the time period.
Conversion between the two is not always straightforward or even possible. What many beginners (including myself) stumble upon:
Ohlc data represents trades that did actually happen. Bid and ask represent requests for trades that might never happen.
Simplified example:
Let's say investor A wants to sell 100 shares of a specific company for 20$ each, so he places ask(100,20) on the market. Investor B wants to buy 100 shares of the same company, but only wants to pay 18$ each, so he places bid(100,18).
If both are not willing to change their price, no trade will happen and no ohlc data will be generated (if no other trades occur in this timeframe).
Of course, one can assume that if trades happen in a specific time frame, h will be the highest price someone is willing to pay (highest bid) and l will be the lowest price someone is willing to sell for (lowest ask), as those orders have the highest chance of being met. But I think o and c values really depend on which bids/asks actually turned into a trade.
I have an issue right now and I believe many people might have this issue I am wondering if someone has found a work around so my issue is I have products that I buy stock for on a daily basis due to excahnge rate and other things the same product sometimes costs me different every time so my issue is,
If I have a Samsung watch I have 5 units in stock that cost me £35 Then I see a good deal on them and buy another 10 units these cost me £30 but I still have the 5 units in stock that cost me £35 so how can I upload/update the 10 units making 15 units in total but have a seperate cost price for the 10 units compared to the 5 units as I need this for reporting profit and loss reports.
So Ideally I want to be able to update my stock qty each time I do so I need to set a new cost price for that same product for selected amount of qtys,
Right now I am currently making the same product/SKU Multiple times because the product is the same but has cost me multiple by prices and I do not want to not have it listed for sale until the other stock sells and I do not want to keep making multiple products as customers think the products are different..
Any help will be strongly appriciated!
Have you check Tire Price concept in magento ?
I think this will full fill your requirement.
Let me know if you have any query.
I want to create a stock exchange simulation using C# programming language. But I couldn't decide on how to specify the price of an asset.
For example, the following table is an order book for an asset:
Buy Sell
----------------------------- ----------------------------
ID Time Size Price ID Price Size Time
4 8:00:04 250 100 1 101 750 8:00:01
6 8:00:10 500 100 5 101 500 8:00:05
2 8:00:01 750 97 8 101 750 8:00:30
7 8:00:10 150 96 3 102 250 8:00:02
The simplest order book matching algorithm is a price-time-priority algorithm. That means that the matching priority firstly is price and then time. The participants are rewarded for offering the best price and coming early.
Every asset has a current price in stock exchanges. But how can I calculate the price of this asset? Is there any algorithm for this?
An exchange will usually show the 'top of the book', showing best bid (the highest number someone is willing to buy at) and ask (the lowest price someone is willing to sell at).
Where you see an exchange offering a single price, it is derived in one of two ways:
if there have been recent (valid) trades, then it is the last traded price
otherwise, it is the reference price
What is a Reference Price?
Most equity and derivatives exchanges maintain a reference price for each book. This is used to prevent acceptance of orders which would be too far from the reference price - aka 'extreme trading range'.
Usually the reference price is set to the last traded price during the day, but how is it set in the first place before any trading happens?
The reference price is usually determined after each trading reset (e.g. start of day, start of week or start of a new book) as one of the following in order of precedence:
price discovered during an initial auction period (usually only in equity markets)
if no auction, then last traded (or settled, depending on the market) price
use a price from another market operator running the same book
or the market operator can use it's own 'reasonable' method to determine a reference price, e.g. for an initial listing of a new security
How to apply this?
So if you want to set a new 'current price' in BTC but you don't yet have any trades on your book, then because BTC is already widely traded you can:
use the last price traded on binance for the pair you're running
take a mean or median of last prices from multiple BTC books run by others
manually set some other price you think will attract both buyers and sellers
I have a list of people that live in various countries. Each person has fields for their name, address, and country they live in (Italy, France, USA, etc). Is it possible to limit the number of people for each country in Elasticsearch? I know I have options in my programming language but would be really helpful if I could, say, limit the number of people in Italy to 5,000 entries, France to 7,000 and the USA to 10,000. Each country is a field. So, if I have 5,000 people that live in Italy and I try to enter a record for Mary that lives in Italy, then it will not be able to. Is that possible in Elasticsearch? My problem is that I have to retrieve the count everytime I want to insert a record and that is taking a lot of time and slowing my program down when all I really need is to set a maximum in elasticsearch to take care of it.
You could have a look to Alerting but as soon as I understand its just alerting.
On the other hand this will slow down ES too, if it's possible to do what you need, because each time before index you need to count.
I'm creating system for a company renting apartments. All pricing setup is based on some periods. For example an apartment in category 'Junior Studio' there are price periods:
30.05.2016 - 31.01.2017: 3000 EUR
01.02.2017 - Infinity: 4000 EUR
There are also additional periods like: taxes, seasonal price(plus/minus some percent value), and fees based on other periods. So prices can vary often, for example:
31.05.2016 - 30.06.2016 (3500EUR because of some seasonal price period)
01.07-31.08.2016 (5000EUR other seasonal price period)
01.09.2016 - 31.01.2017 (3000 EUR)
01.02.2017 - 4000 EUR.
Also, if someone wants to rent an apartment, for example less than 15 days, there is additional fee, let's say 15% - all this is set up dynamically.
Now the problem is on our page we should let user find apartments based on their price. For example some users want to find only apartments where the price varies between 3000 - 4000 EUR and rent an apartment for 6 months. As I said price can change for example 5 times on those periods so I'm looking to calculate an average price.
Have you any idea how implement this algorithm to incorporate all the specified periods? We assume there can be for example 500 possible records so computing this dynamically could probably cause performance issues.
UPDATE
Here is some code to take periods related to one apartment category for one building:
private RentPriceAggregatedPeriodsDto prepareRentPriceAggregator(Long buildingId, Long categoryId, LocalDate dateFrom, LocalDate dateTo, Integer duration) {
List<CategoryPricePeriod> pricePeriods = categoryPricePeriodRepository.findCategoryPricePeriods(buildingId, categoryId, dateFrom, dateTo);
List<SeasonalPricePeriod> seasonalPricePeriods = seasonalPricePeriodRepository.findSeasonalPricePeriods(buildingId, categoryId, dateFrom, dateTo);
List<LastMinuteRatePeriod> lastMinuteRatePeriods = lastMinuteRatePeriodRepository.findLastMinuteRatePeriods(buildingId, categoryId, dateFrom, dateTo);
List<TaxesDefinitionPeriodDto> taxesDefinition = taxesDefinitionService.findTaxPeriodsForBuildingAndCategory(buildingId, categoryId, TaxTypeCode.VAT,
dateFrom, dateTo);
Optional<SurchargePolicy> surcharge = surchargePolicyRepository.findForDurationAndRentalObjectCategoryIds(categoryId, buildingId, duration);
return new RentPriceAggregatedPeriodsDto(pricePeriods, seasonalPricePeriods, lastMinuteRatePeriods, taxesDefinition, surcharge);
}
Based on all those periods I prepare list of unique price periods: dateFrom, dateTo, currency, value. After those steps I have list of unique prices for one category. Then I need to compute how many days of booking is in each of those unique price periods and multiply it, maybe round + multiply by tax and sum it to have final price for booking. Now re-run those steps, let's say, 500 times (multiple categories in multiple buildings).
As mentioned in the comments, averaging 6 numbers 500 times on the fly should not cause any performance issues.
Even then, if you'd want O(1) performance on computation of price (i.e. the calculation should not depend on the number of price switches in the mentioned period), you could preprocess by defining a date as day 0, and computing the amount of total rent that would be required for all days beyond that. When a user requests the average rent between a period, subtract the total rent till day zero from the two days, giving you the rent for the period in between. Dividing this by the number of days will give you the average rent. You can also add suitable multipliers depending on duration of stay (to add the 15% charge), etc. This is similar to finding the sum of values between two indices in an array in O(1). This is not a memory friendly suggestion, although one can modify it to use less memory.
The advantage is that the computation to give results will not depend on the number of price switches. However, every additional change in apartment rents will cause some amount of preprocessing.
I think you actually need two algorithms. One for representing and querying the object price at any given time. And another one for computing the price for renting an object for a given time period.
As for the representation of the object price, you should make a decision about the temporal granularity you want to support, e.g., days or months. Then create a lookup table or a decision tree, a neural network or anything to lookup the price at the given day or month for the given object or object class. You can factor in all the variables you'd like to have in there. If you want to support special prices for renting full calendar months, have another data structure for this different granularity, which you query with months instead of dates.
Then, given a period of time, you need to generate the corresponding series of dates or months, query for the individual daily or monthly prices and then compute the sum to get the total price. If you want to, you can then compute an average daily/monthly price.
I don't think performance will be an issue here. At least no issue you should address before coming up with an actual solution (because, premature optimization). If it is, consider scaling up your database.