historical data of AltCoin prices

[altsheets]

Could anyone please provide me with historical data of

* HZ / BTC
* NXT / BTC

Plus (even thought I can probably find that elsewhere)
* USD / BTC
* EUR / USD or EUR / BTC

... back to early 2015.  Daily precision is fine.


I need to calculate 2015 income positions, to pay dividends.

And to look up tons of old prices manually ... sounds really boring.

Thanks a lot!

:-)

EDIT: Of course, to avoid the manual ordeal,  I want a table of data or an API access. Anything that I can feed a dozens of DATEs into, and get PRICEs out of.  

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[alyssa85]

Try the following site:

http://www.cryptocoincharts.info/

I think their charts go back to at least 2013

[altsheets]

Try the following site:

http://www.cryptocoincharts.info/

I think their charts go back to at least 2013

Yes, I can get current data there, but

I do not see historical data access anywhere .

Thanks anyways.

[Nxtblg]

Try the following site:

http://www.cryptocoincharts.info/

I think their charts go back to at least 2013

Yes, I can get current data there, but

I do not see historical data access anywhere .

Thanks anyways.

I'd like to see some historical data too: preferably, raw trade records. Those records do exist for bitcoin trades.

[gjhiggins]

Could anyone please provide me with historical data of

* HZ / BTC
* NXT / BTC

... back to early 2015.  Daily precision is fine.

https://coinmarketcap.com/historical/

Weekly precision.

It's pages of HTML tables, so you'll need to roll your own processing which is trivially easy to do with pandas. The defaults returns prices in USD, from which BTC price can be derived by using df.iloc[0, 4] (assuming Bitcoin occupies the first row).

Code:

$ python
Python 3.4.3 (default, Jul 28 2015, 18:20:59) 
[GCC 4.8.4] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from pandas.io.html import read_html
>>> df = read_html('https://coinmarketcap.com/historical/20151101/')[0]
>>> btcusd = float(df.iloc[0, 4].split(' ')[1])
>>> for il in range(0, len(df)):
...     if df.iloc[il, 2] in ['NXT', 'HZ']:
...         print("{name} {symbol} {btcprice:02.8f}".format(
...             name=df.iloc[il, 1],
...             symbol=df.iloc[il, 2],
...             btcprice=(float(df.iloc[il, 4].split(' ')[1]) / btcusd)))
... 
Nxt NXT 0.00002371
Horizon HZ 0.00000041

Close enough for government work, I'd say.

Cheers

Graham

Edit: added pandas url

[alyssa85]

Try the following site:

http://www.cryptocoincharts.info/

I think their charts go back to at least 2013

Yes, I can get current data there, but

I do not see historical data access anywhere .

Thanks anyways.

Some exchanges have graphs - you could try logging into one and seeing if they go back to 2013. Bitstamp definitely existed in 2013 so they should have the info on BTC/USD as should BTC-E. 

[altsheets]

Try the following site:

http://www.cryptocoincharts.info/

I think their charts go back to at least 2013

Yes, I can get current data there, but

I do not see historical data access anywhere .

Thanks anyways.

Some exchanges have graphs - you could try logging into one and seeing if they go back to 2013. Bitstamp definitely existed in 2013 so they should have the info on BTC/USD as should BTC-E.  

Thanks a lot, alyssa85. But what I meant is not manually extracting 2 times 49 datapoints for all the dates I have, but automatic extraction. So if an exchange offers a graph of past data (they all do), that is not what I am looking for.

Thanks anyways.

[altsheets]

WOW!

Grrrrreat Graham Gjhiggins

Brute force approach, just downloading all 49 pages - but hey, as long as it gets there ... very cool!

Weekly precision - fine, then just needs a preparational and a final step, to quantize the wanted dates to that grid.


Even though I had now already solved it myself (actually paying a 3rd party for the HZ data - which is fine he is providing a great website), and actually also using panda ...

... your new solution is much much appreciated. Might help a lot, in the future. Thanks!

I have just tried out your script ...

Code:

pip install html5lib pandas

then it works like a charm.

Could anyone please provide me with historical data of
* HZ / BTC
* NXT / BTC
... back to early 2015.  Daily precision is fine.

https://coinmarketcap.com/historical/

Weekly precision.

It's pages of HTML tables, so you'll need to roll your own processing which is trivially easy to do with pandas. The defaults returns prices in USD, from which BTC price can be derived by using df.iloc[0, 4] (assuming Bitcoin occupies the first row).

Code:

$ python
Python 3.4.3 (default, Jul 28 2015, 18:20:59) 
[GCC 4.8.4] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from pandas.io.html import read_html
>>> df = read_html('https://coinmarketcap.com/historical/20151101/')[0]
>>> btcusd = float(df.iloc[0, 4].split(' ')[1])
>>> for il in range(0, len(df)):
...     if df.iloc[il, 2] in ['NXT', 'HZ']:
...         print("{name} {symbol} {btcprice:02.8f}".format(
...             name=df.iloc[il, 1],
...             symbol=df.iloc[il, 2],
...             btcprice=(float(df.iloc[il, 4].split(' ')[1]) / btcusd)))
... 
Nxt NXT 0.00002371
Horizon HZ 0.00000041

Close enough for government work, I'd say.

Cheers

Graham

Edit: added pandas url

Great solution.

Thank you gjhiggins - happy Easter!

[alyssa85]

OK try the following chart:

http://bitcoincharts.com/charts/bitstampUSD#tgSzm1g10zm2g25zv

[altsheets]

OK try the following chart:

http://bitcoincharts.com/charts/bitstampUSD#tgSzm1g10zm2g25zv

YES. That's it, the "Load raw data" below the chart would have helped.


The BTC/fiat is the easy part, there is an abundance of sources.
I had actually used http://www.investing.com/currencies/btc-eur-historical-data now.


Thanks a lot for your help!

[altsheets]

Ready.

49 times 3 prices



Now I can calculate the dividends, finally

 

[altsheets]

P.S.: PM me your HZ addresses, both of you - then I can say 'thank you' in the form of my asset.  

[altsheets]

On the basis of all those insights ...

--> Paying dividends - done.

Thanks a bunch for your help.