Out use this aggregate pricing series later on, in order to convert the exchange rates of scrypt cryptocurrencies to USD. Now we have a dictionary with out dataframes, each containing the scrypt daily average exchange prices between the bitcoin and Bitcoin. Strong enough to use as the sole basis for an investment? Bitcoin can we predict what will happen sync Hopefully, now you have the skills to do your own analysis and to think critically about any speculative cryptocurrency articles you might read in the future, especially those written without any data to back up the provided predictions. There is however a clever solution that might speed thing up, you can download a file to sync you import most of the blockchain locally much faster and then synchronize with the rest of the network in no time.
As in, is the goal to make synchronization faster so coins show up, or to get the coins out of the wallet that is synchronizing and into a different wallet? They're organized by the txids of what inputs are spent from, and thus inherently randomly distributed. April 9, Get the latest posts delivered to your inbox. So for me, the important information here is: Since the asker hasn't been here for more than 2.
Have separate options that you can set in the bitcoin options in the GUI for location of the database vs the raw blockchain Organize scrypt on-disk so that single large sequential reads can gather multiple hundreds of bitcoin pieces of data needed for validation all at once and then run through them sequentially from sync. So a huge speed up! Bitcoin scrypt wallet not syncing. Quick Plug - I'm a contributor to Outa very early-stage startup using Stellar with the aim of disrupting micro-remittances in Africa. It should be storing some of that db in there right?
Open up the unsynced Bitcoin-QT wallet or start the bitcoind daemon , and go to the Help menu, and select Debug Console. In the window that opens, replace the appropriate fields, and then type:.
Now we just have to sign it. This has to be done on the same daemon that generated the address, so that you have the private keys. Replace with your appropriate fields and then execute:. And enter the raw transaction hex. Select the "Bitcoin" network, hit the "Broadcast Transaction" button, and you will have successfully removed your coins from the unsynced wallet!
Questions Tags Users Badges Unanswered. Bitcoin Stack Exchange is a question and answer site for Bitcoin crypto-currency enthusiasts. Join them; it only takes a minute: Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top. Cannot access my bitcoins because my wallet is still synchronising. What can I do to spend them immediately? Alex Jackson 64 1 3. How can I export the private key for an address from the satoshi client?
My client stopped synchronizing, how can I access my wallet? Murch But what is the goal in this question? As in, is the goal to make synchronization faster so coins show up, or to get the coins out of the wallet that is synchronizing and into a different wallet? I think it would be helpful if the question specified this.
Since the asker hasn't been here for more than 2. What do you think about this version? I had to do this once for a Dogecoin wallet that was way out of sync, and I didn't feel like waiting for it to catch up. I'll write an answer tonight or tomorrow describing what I did and maybe even giving a script to help do it. Jan 1 3 AlexJackson instructions for exporting private keys from bitcoin-qt are here bitcoin.
To get the transaction ID, use the hex code at the top of the transaction box. To get the output index, count the transaction outputs from zero until you find your address. For example, in the transaction shown below, my 13x If it were in the second output, it would be index 1, etc. To get the amount, make sure you select "BTC" as the website units in the top right.
Then copy the amount from the output where you see your address receiving coins. So for me, the important information here is: Under your address, it will say something like: We will walk through a simple Python script to retrieve, analyze, and visualize data on different cryptocurrencies. In the process, we will uncover an interesting trend in how these volatile markets behave, and how they are evolving.
This is not a post explaining what cryptocurrencies are if you want one, I would recommend this great overview , nor is it an opinion piece on which specific currencies will rise and which will fall. Instead, all that we are concerned about in this tutorial is procuring the raw data and uncovering the stories hidden in the numbers.
The tutorial is intended to be accessible for enthusiasts, engineers, and data scientists at all skill levels. The only skills that you will need are a basic understanding of Python and enough knowledge of the command line to setup a project. A completed version of the notebook with all of the results is available here. The easiest way to install the dependencies for this project from scratch is to use Anaconda, a prepackaged Python data science ecosystem and dependency manager.
To setup Anaconda, I would recommend following the official installation instructions - https: If you're an advanced user, and you don't want to use Anaconda, that's totally fine; I'll assume you don't need help installing the required dependencies.
Feel free to skip to section 2. Once Anaconda is installed, we'll want to create a new environment to keep our dependencies organized. This could take a few minutes to complete. If you plan on developing multiple Python projects on your computer, it is helpful to keep the dependencies software libraries and packages separate in order to avoid conflicts.
Anaconda will create a special environment directory for the dependencies for each project to keep everything organized and separated. Once the environment and dependencies are all set up, run jupyter notebook to start the iPython kernel, and open your browser to http: Create a new Python notebook, making sure to use the Python [conda env: Once you've got a blank Jupyter notebook open, the first thing we'll do is import the required dependencies.
Now that everything is set up, we're ready to start retrieving data for analysis. To assist with this data retrieval we'll define a function to download and cache datasets from Quandl. We're using pickle to serialize and save the downloaded data as a file, which will prevent our script from re-downloading the same data each time we run the script. The function will return the data as a Pandas dataframe. If you're not familiar with dataframes, you can think of them as super-powered spreadsheets.
Let's first pull the historical Bitcoin exchange rate for the Kraken Bitcoin exchange. Here, we're using Plotly for generating our visualizations. This is a less traditional choice than some of the more established Python data visualization libraries such as Matplotlib , but I think Plotly is a great choice since it produces fully-interactive charts using D3. These charts have attractive visual defaults, are easy to explore, and are very simple to embed in web pages. As a quick sanity check, you should compare the generated chart with publicly available graphs on Bitcoin prices such as those on Coinbase , to verify that the downloaded data is legit.
You might have noticed a hitch in this dataset - there are a few notable down-spikes, particularly in late and early These spikes are specific to the Kraken dataset, and we obviously don't want them to be reflected in our overall pricing analysis. The nature of Bitcoin exchanges is that the pricing is determined by supply and demand, hence no single exchange contains a true "master price" of Bitcoin. To solve this issue, along with that of down-spikes which are likely the result of technical outages and data set glitches we will pull data from three more major Bitcoin exchanges to calculate an aggregate Bitcoin price index.
Next, we will define a simple function to merge a common column of each dataframe into a new combined dataframe. Finally, we can preview last five rows the result using the tail method, to make sure it looks ok.
The prices look to be as expected: The next logical step is to visualize how these pricing datasets compare. For this, we'll define a helper function to provide a single-line command to generate a graph from the dataframe.
In the interest of brevity, I won't go too far into how this helper function works. Check out the documentation for Pandas and Plotly if you would like to learn more. We can see that, although the four series follow roughly the same path, there are various irregularities in each that we'll want to get rid of. Let's remove all of the zero values from the dataframe, since we know that the price of Bitcoin has never been equal to zero in the timeframe that we are examining. We can now calculate a new column, containing the average daily Bitcoin price across all of the exchanges.
We'll use this aggregate pricing series later on, in order to convert the exchange rates of other cryptocurrencies to USD. Now that we have a solid time series dataset for the price of Bitcoin, let's pull in some data for non-Bitcoin cryptocurrencies, commonly referred to as altcoins. For retrieving data on cryptocurrencies we'll be using the Poloniex API.
Please follow the FAQ instructions under My coin is out of sync wearebeachhouse.com is a short video on setting up, backing up and restoring a Bitcoin wallet. AnonCoin-qt wallet will not sync to network through Tor or Clearnet. My bit coin wallet sync is stuck, if anything the blocks remaining is wearebeachhouse.comn BTC Scrypt Version has the. [ANN] GirlsToken - Mandatory Update - Sync issues fixed[ANN] The minute we have a wallet that works cleanly with pools, I will begin chatting with some other pool owners to try and help hash diversification. So far I have been .. If we remove scrypt then they would be cut out from mining. Get price, charts, news, exchanges and detailed analysis for Bitcoin Scrypt ( BTCS). All metrics are updated by minute to minute, as they happen.