_ Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top up vote 7 down vote favorite 1 I'm trying to so some maths calculations with Excel but I need to get the current btc value from any website that can provide it in any format but simple as possible.Do you have any idea to get the current btc value (usd) and update it in real time in an excel sheet?value btce live-data up vote 7 down vote This more of a question about excel than it is actually about Bitcoin.But the answer is actually pretty easy.The first thing is that the data that you want to import from the web may not be easily isolated, so you might have to import much more data than you want into a second excel sheet, then link the cell with the data you are interested to a cell in your actual sheet.Follow these steps: Find a site that has the dynamic data you are looking for.Copy the data that you want and paste it into excel.

You can't miss the current price right at the top.After you paste it into excel the little paste options icon will popup next to it.Click it then select Refreshable Web Query.A new window will popup that will load the whole page.You might get some script errors; you can ignore those.The window directs you to click the arrow icon next to the table that you want to import.Unfortunately, with BitStamp, the only option is the whole page.Click the arrow then click import.Excel will now import a load of data from the webpage.The second cell is the one we are looking for; that is the current Bitcoin price in USD.This is where have a separate sheet for this import data is convenient.Go to the sheet where you want to use that data and link the cell you want to the other by first clicking = then clicking the cell in the sheet with all the data from bitstamp.You will see something like =Sheet1!A2 in the formula bar.That's all there really is to it.Microsoft has an extensive article on the topic complete with all the technical details.

up vote 1 down vote Here is the link.Just insert this into your excel file =/currencies/bitcoin/","//span[@id='quote_price']") up vote 0 down vote Start here: /neo/groups/smf_addin/info While it is a stock market add-in, it works with Yahoo Finanace and since Yahoo now lists Bitcoin (Symbol: BTCUSD=X) Yes, that is the symbol you use to get the price, it will update your spreadsheet every time there is a change made to the spreadsheet or when you force the sheet to recalculate.This fits the bill for what you need.up vote 0 down vote Here's a simplified macro that works as far back as Excel 2007, original credit Samir Khan: Sub GetBTCUSD() Range("BTCUSD").Select Selection.ClearContents With ActiveSheet.QueryTables.Add(Connection:= _ "/ticker/USD/last", Destination:=Range("BTCUSD")) .Name = "BTCUSD field that will pollute your Name Manager but I don't know how to get rid of" .AdjustColumnWidth = False .PreserveFormatting = True .RefreshOnFileOpen = True .BackgroundQuery = True .RefreshStyle = xlOverwriteCells .SaveData = False .RefreshPeriod = 0 .WebSingleBlockTextImport = False .WebDisableRedirections = False .Refresh BackgroundQuery:=False End With End Sub To use the macro: Name a column in your spreadsheet "BTCUSD" (just click in the Name Box in the top left and type "BTCUSD" Add a button to the sheet (you can name it "Get BTCUSD") Create a new Macro and paste the code above Right click the button, choose Assign macro, and pick the GetBTCUSD macro.

up vote 0 down vote I tried this with Excel 2016: /v1/public/yql?q= /currencies/bitcoin/' and xpath='//span[@id="quote_price"]' /alltableswithkeys Insert the following into the Cell where you want the actual price: =XMLFILTERN(WEBDIENST($J$2&URLCODIEREN($J$3)&$J$4);"//span") Your Answer Sign up or log in Sign up using Google Sign up using Email and Password Post as a guest Name Email discard By posting your answer, you agree to the privacy policy and terms of service.bitcoin lompakko varmuuskopioBrowse other questions tagged value btce live-data or ask your own question.martin lorton bitcoinRecently there has been a lot of attention around Google DeepMinds victory over Lee Sedol in the board game Go.bitcoin lunatics

This is a remarkable achievement since Go has for long been considered a game unbreakable by artificial intelligence, mainly because the game was thought to rely to a large part upon human intuition in order to handle the extremely large amount of possible states during gameplay.The technique DeepMind used was a combination of deep neural networks and reinforcement learning.In machine learning deep neural networks has for the past few years been shown to achieve remarkable results in a number of different fields (such as image recognition, speech recognition, language processing and so on, for some cool examples see The Unreasonable Effectiveness of Recurrent Neural Networks).Reinforcement learning has not had the same amount of academic and public attention and has often been used to solve various toy problems.But, recently the combination of deep neural nets and reinforcement learning has proven to be very powerful and before DeepMind put their attention to Go they showed that a combination of these techniques could be used to achieve better than human results in a number of Atari computer games, using only the input and output that a regular human player would have access to.The reason for combining a neural net with reinforcement learning is that a neural net will be able to handle a large amount of possible states.

In plain reinforcement learning you often use a lookup table, and as long as the amount of possible states are finite and not too large this is fine.But when the number of possible states grows or continuous inputs are used then something that can handle a large state space is needed.To go beyond the toy examples, video games and board games this post is a tutorial for combining (deep) neural nets and self reinforcement learning and some real data and see if it is be possible to create a simple self learning quant (or algorithmic financial trader).A serious warning for anyone thinking about copy/pasting this to make a live algorithmic trading robot: Whatever the result will be in the end, a real algorithmic trader will be a very different beast to implement as there are numerous other factors that must be handled in live trading with real assets.I’m doing this in Python (2.7) with a few different imported libraries.Again, my goal is to explain and show the concept of self reinforcement learning combined with a neural network.

If you think you understand the basic concepts, then just search the internet for better and more mathematical correct explanations.In reinforcement learning there are a few basic notations and concepts:Now, I will go through a few different cases where the complexity of data and the algorithm gradually increase.Some key parts of the code is copied, but to keep the post readable and at a reasonable length the entire code for each example is not explained or copied here./danielzak/sl-quant.In the first example I will see if I can learn the system to recognise an asset with a linearly increasing price.In terms of a quant trader this means that the trader should always buy (or go long).The data is simply created by a function that returns a straight line:So what is this self reinforcement thing then?What we have here is a pretty basic system with a set of states, some actions that can be taken and some way of measuring rewards based to these actions.We also have a Q function that should learn to approximate the reward.

In a simple world we could just let Q be a table of all possible states and then find a way to explore all possible states, actions and rewards, save these to the table and then look up the best action for a given state when needed.In a more complex world we need a way to generalise our knowledge and to be able to handle a very large number of different states.The self learning comes from a concept of looping through a number of different states and actions many times, and each time update the Q function a little bit.So in each loop the Q function will know a little bit more about the world around it and should be able to approximate the real reward a little bit better for each possible action.Also, one very important thing in the learning process is to add a bit of randomness in order to explore as much as possible of the world.In our case we do this by adding a chance of selecting a random action instead of the action suggested by the Q function, this is the epsilon value in the code below.In my code this main loop looks like this:There is also a Q function, in this case the neural net.

This is a simple three layer neural network with just 4 neurons in each layer that should be sufficient to learn what a straight line looks like.Ok, that are the basic components of the system.Let’s run the code and see what happens.After one epoch (one training loop in the main loop above) I ask the system to suggest trades for each time step.This is the result:Clearly our self learning quant has no clue what it is doing.Let’s run the code for 10 epochs and see what the output is:Wow!Without modelling anything or giving any prior knowledge we have a system that has learnt what a straight line looks like :-) Good stuff.Now we can make things a little more complicated by replacing the straight line with a sine wave shaped line.In example 1 and 2 there is no separate training and testing set of data, that is of course outrageous for anyone interested in machine learning but is done only to keep things simple.A train/test split will come in example 3.Let’s also also introduce another concept in self reinforcement learning — the gamma parameter.

Remember that we evaluate each action based on its reward.By default this means that on each time step the system will learn what is the best choice of action to maximize its reward the next time step.Gamma is chosen between 0 and 1 and by setting a large gamma we will value a high long term reward as well, so the system can learn to value a path of choices that will give a high reward several time steps into the future.One thing that hasn’t been covered yet is the reward function, I will just copy it here to give an example of what it looks like.The reward function will give a reward if the action (or signal) is in the same direction as the price movement, and it will also give a small extra reward if the action is the same as the last action.So what does this look like after one epoch?Again, the system has no clue what is going on.What it has learned in one epoch is that it gets a reward if the action is the same as the previous action.Ahh, it can learn a what a smooth wavy line looks like.

With this simple neural network the result will not be much better even if we increase the number of epochs far beyond this.Example 3: Bitcoin price dataNow let’s try to dip our toes a little deeper into the water.Daily Bitcoin price data is used as input data (source Kraken via Quandl)2.I have made a train/test split of the data (600 data points for training, 200 for testing).I have added some common technical indicators to the input data as well (Simple Moving Average (15 and 60 periods), Relative Strength Index, Average True Range)4.The neural network is now a two layer recurrent neural network (LSTM) with 64 neurons in each layer.5.The self reinforcement learning loop is using a trick called experience replay that greatly improve the speed of learning by making each update batch bigger which is computationally efficient when updating a neural network.Ok, so in the end the best result for this system using this input data was to buy and hold rather than to do shorter trades during this time frame.