diff --git a/data/0. load poliniex data 30m multindex.ipynb b/data/0. load poliniex data 30m multindex.ipynb index b586ff5..18891eb 100644 --- a/data/0. load poliniex data 30m multindex.ipynb +++ b/data/0. load poliniex data 30m multindex.ipynb @@ -12,8 +12,8 @@ "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2017-10-29T11:47:14.596706Z", - "start_time": "2017-10-29T11:47:14.588605Z" + "end_time": "2017-11-11T07:28:18.603311Z", + "start_time": "2017-11-11T07:28:18.597894Z" } }, "outputs": [ @@ -34,9 +34,10 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2017-10-29T11:47:15.217642Z", - "start_time": "2017-10-29T11:47:14.601046Z" - } + "end_time": "2017-11-11T07:28:19.435869Z", + "start_time": "2017-11-11T07:28:18.739412Z" + }, + "collapsed": true }, "outputs": [], "source": [ @@ -61,11 +62,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 32, "metadata": { "ExecuteTime": { - "end_time": "2017-10-29T11:47:15.866889Z", - "start_time": "2017-10-29T11:47:15.219386Z" + "end_time": "2017-11-11T07:31:09.873608Z", + "start_time": "2017-11-11T07:31:09.186834Z" }, "collapsed": true }, @@ -79,7 +80,7 @@ " df.index=pd.to_datetime(df.date*1e9)\n", " del df['date']\n", " # just the cols from jiang 2017\n", - " df = df[['close','high','low','open']]\n", + " df = df[['close','high','low','open','volume','quoteVolume']]\n", " df=df.resample('30T').first()\n", " \n", " # name cols\n", @@ -92,45 +93,83 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2017-10-29T11:47:15.871262Z", - "start_time": "2017-10-29T11:47:15.868556Z" + "end_time": "2017-11-11T06:17:19.415705Z", + "start_time": "2017-11-11T06:17:19.410331Z" + }, + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "ExecuteTime": { + "end_time": "2017-11-11T07:31:09.881190Z", + "start_time": "2017-11-11T07:31:09.875134Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('LTCBTC', Timestamp('2014-01-18 04:00:00', freq='30T')),\n", + " ('DOGEBTC', Timestamp('2014-01-21 22:30:00', freq='30T')),\n", + " ('DASHBTC', Timestamp('2014-02-07 20:30:00', freq='30T')),\n", + " ('XMRBTC', Timestamp('2014-05-19 05:30:00', freq='30T')),\n", + " ('XRPBTC', Timestamp('2014-08-14 03:30:00', freq='30T')),\n", + " ('BTCUSDT', Timestamp('2015-02-19 19:00:00', freq='30T')),\n", + " ('ETHBTC', Timestamp('2015-08-08 05:00:00', freq='30T')),\n", + " ('ETHUSDT', Timestamp('2015-08-08 06:00:00', freq='30T')),\n", + " ('ETCBTC', Timestamp('2016-07-24 04:00:00', freq='30T')),\n", + " ('ETCETH', Timestamp('2016-07-24 04:30:00', freq='30T')),\n", + " ('REPETH', Timestamp('2016-10-04 18:30:00', freq='30T')),\n", + " ('REPBTC', Timestamp('2016-10-04 18:30:00', freq='30T')),\n", + " ('GNTBTC', Timestamp('2017-02-18 03:30:00', freq='30T')),\n", + " ('GNTETH', Timestamp('2017-02-18 03:30:00', freq='30T'))]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# sort by time lengths\n", + "dfs.sort(key=lambda x:len(x), reverse=True)\n", + "[(df.name,df.index[0]) for df in dfs]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "ExecuteTime": { + "end_time": "2017-11-11T07:31:09.891018Z", + "start_time": "2017-11-11T07:31:09.887625Z" }, "collapsed": true }, "outputs": [], "source": [ - "# sort by time lengths\n", - "dfs.sort(key=lambda x:len(x), reverse=True)" + "# # insert a fake one, the price of one bitcoin in bitcoin = 1\n", + "# df=dfs[0].copy()\n", + "# df[:]=1\n", + "# df.name='BTCBTC'\n", + "# dfs.insert(0,df)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "metadata": { "ExecuteTime": { - "end_time": "2017-10-29T11:47:15.881075Z", - "start_time": "2017-10-29T11:47:15.872904Z" - } - }, - "outputs": [], - "source": [ - "# insert a fake one, the price of one bitcoin in bitcoin = 1\n", - "df=dfs[0].copy()\n", - "df[:]=1\n", - "df.name='BTCBTC'\n", - "dfs.insert(0,df)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-29T11:47:16.232938Z", - "start_time": "2017-10-29T11:47:15.883338Z" + "end_time": "2017-11-11T07:31:10.313285Z", + "start_time": "2017-11-11T07:31:09.953511Z" } }, "outputs": [ @@ -138,13 +177,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "['2014-01-18 04:00:00', '2014-01-18 04:00:00', '2014-01-21 22:30:00', '2014-02-07 20:30:00', '2014-05-19 05:30:00']\n" + "['2014-01-18 04:00:00', '2014-01-21 22:30:00', '2014-02-07 20:30:00', '2014-05-19 05:30:00']\n" ] } ], "source": [ "# crop to ones with more data\n", "dfs1= [df for df in dfs if df.index.min()\n", " \n", " Pair\n", - " BTCBTC\n", - " LTCBTC\n", - " DASHBTC\n", - " XMRBTC\n", + " LTCBTC\n", + " DASHBTC\n", + " XMRBTC\n", " \n", " \n", " Price\n", @@ -265,18 +292,20 @@ " high\n", " low\n", " open\n", + " volume\n", + " quoteVolume\n", " close\n", " high\n", " low\n", " open\n", + " volume\n", + " quoteVolume\n", " close\n", " high\n", " low\n", " open\n", - " close\n", - " high\n", - " low\n", - " open\n", + " volume\n", + " quoteVolume\n", " \n", " \n", " date\n", @@ -296,19 +325,23 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " 2014-01-18 04:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028000\n", " 0.028000\n", " 0.028000\n", " 0.028000\n", + " 0.020590\n", + " 0.735400\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -320,14 +353,16 @@ " \n", " \n", " 2014-01-18 04:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.029000\n", " 0.028000\n", " 0.029000\n", + " 0.003106\n", + " 0.109700\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -339,14 +374,16 @@ " \n", " \n", " 2014-01-18 05:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -358,14 +395,16 @@ " \n", " \n", " 2014-01-18 05:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -377,14 +416,16 @@ " \n", " \n", " 2014-01-18 06:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -396,14 +437,16 @@ " \n", " \n", " 2014-01-18 06:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -415,14 +458,16 @@ " \n", " \n", " 2014-01-18 07:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -434,14 +479,16 @@ " \n", " \n", " 2014-01-18 07:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -453,14 +500,16 @@ " \n", " \n", " 2014-01-18 08:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -472,14 +521,16 @@ " \n", " \n", " 2014-01-18 08:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -491,14 +542,16 @@ " \n", " \n", " 2014-01-18 09:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -510,14 +563,16 @@ " \n", " \n", " 2014-01-18 09:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -529,14 +584,16 @@ " \n", " \n", " 2014-01-18 10:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -548,14 +605,16 @@ " \n", " \n", " 2014-01-18 10:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -567,14 +626,16 @@ " \n", " \n", " 2014-01-18 11:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -586,14 +647,16 @@ " \n", " \n", " 2014-01-18 11:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -605,14 +668,16 @@ " \n", " \n", " 2014-01-18 12:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -624,14 +689,16 @@ " \n", " \n", " 2014-01-18 12:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -643,14 +710,16 @@ " \n", " \n", " 2014-01-18 13:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -662,14 +731,16 @@ " \n", " \n", " 2014-01-18 13:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -681,14 +752,16 @@ " \n", " \n", " 2014-01-18 14:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -700,14 +773,16 @@ " \n", " \n", " 2014-01-18 14:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -719,14 +794,16 @@ " \n", " \n", " 2014-01-18 15:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -738,14 +815,16 @@ " \n", " \n", " 2014-01-18 15:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -757,14 +836,16 @@ " \n", " \n", " 2014-01-18 16:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -776,14 +857,16 @@ " \n", " \n", " 2014-01-18 16:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -795,14 +878,16 @@ " \n", " \n", " 2014-01-18 17:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -814,14 +899,16 @@ " \n", " \n", " 2014-01-18 17:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -833,14 +920,16 @@ " \n", " \n", " 2014-01-18 18:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -852,14 +941,16 @@ " \n", " \n", " 2014-01-18 18:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", " 0.028500\n", + " 0.000000\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -887,782 +978,909 @@ " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", " \n", " \n", " 2017-07-13 09:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.019103\n", " 0.019238\n", " 0.019043\n", " 0.019215\n", + " 102.001835\n", + " 5328.738151\n", " 0.072300\n", " 0.072809\n", " 0.072012\n", " 0.072809\n", + " 22.333296\n", + " 308.698352\n", " 0.016200\n", " 0.016260\n", " 0.016151\n", " 0.016220\n", + " 16.447470\n", + " 1014.826043\n", " \n", " \n", " 2017-07-13 09:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.019013\n", " 0.019128\n", " 0.019005\n", " 0.019103\n", + " 115.373214\n", + " 6051.922924\n", " 0.072481\n", " 0.072619\n", " 0.072300\n", " 0.072425\n", + " 14.041970\n", + " 194.101572\n", " 0.016214\n", " 0.016396\n", " 0.016200\n", " 0.016200\n", + " 16.381883\n", + " 1006.211103\n", " \n", " \n", " 2017-07-13 10:00:00\n", - 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61096 rows × 16 columns

\n", + "

61096 rows × 18 columns

\n", "" ], "text/plain": [ - "Pair BTCBTC LTCBTC \\\n", - "Price close high low open close high low \n", + "Pair LTCBTC \\\n", + "Price close high low open volume \n", "date \n", - "2014-01-18 04:00:00 1.0 1.0 1.0 1.0 0.028000 0.028000 0.028000 \n", - "2014-01-18 04:30:00 1.0 1.0 1.0 1.0 0.028500 0.029000 0.028000 \n", - "2014-01-18 05:00:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 05:30:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 06:00:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 06:30:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 07:00:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 07:30:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 08:00:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 08:30:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 09:00:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 09:30:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 10:00:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 10:30:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 11:00:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 11:30:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 12:00:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 12:30:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 13:00:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 13:30:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 14:00:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 14:30:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 15:00:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 15:30:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 16:00:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 16:30:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 17:00:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 17:30:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 18:00:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "2014-01-18 18:30:00 1.0 1.0 1.0 1.0 0.028500 0.028500 0.028500 \n", - "... ... ... ... ... ... ... ... \n", - "2017-07-13 09:00:00 1.0 1.0 1.0 1.0 0.019103 0.019238 0.019043 \n", - "2017-07-13 09:30:00 1.0 1.0 1.0 1.0 0.019013 0.019128 0.019005 \n", - "2017-07-13 10:00:00 1.0 1.0 1.0 1.0 0.019098 0.019100 0.019000 \n", - "2017-07-13 10:30:00 1.0 1.0 1.0 1.0 0.019193 0.019200 0.019087 \n", - "2017-07-13 11:00:00 1.0 1.0 1.0 1.0 0.019300 0.019500 0.019193 \n", - "2017-07-13 11:30:00 1.0 1.0 1.0 1.0 0.019386 0.019455 0.019300 \n", - "2017-07-13 12:00:00 1.0 1.0 1.0 1.0 0.019363 0.019492 0.019345 \n", - "2017-07-13 12:30:00 1.0 1.0 1.0 1.0 0.019385 0.019388 0.019257 \n", - "2017-07-13 13:00:00 1.0 1.0 1.0 1.0 0.019227 0.019388 0.019086 \n", - "2017-07-13 13:30:00 1.0 1.0 1.0 1.0 0.019200 0.019258 0.019135 \n", - "2017-07-13 14:00:00 1.0 1.0 1.0 1.0 0.019073 0.019202 0.019037 \n", - "2017-07-13 14:30:00 1.0 1.0 1.0 1.0 0.019210 0.019210 0.019036 \n", - "2017-07-13 15:00:00 1.0 1.0 1.0 1.0 0.019214 0.019268 0.019150 \n", - "2017-07-13 15:30:00 1.0 1.0 1.0 1.0 0.019110 0.019275 0.019100 \n", - "2017-07-13 16:00:00 1.0 1.0 1.0 1.0 0.019087 0.019186 0.018950 \n", - "2017-07-13 16:30:00 1.0 1.0 1.0 1.0 0.019220 0.019220 0.019085 \n", - "2017-07-13 17:00:00 1.0 1.0 1.0 1.0 0.019203 0.019245 0.019101 \n", - "2017-07-13 17:30:00 1.0 1.0 1.0 1.0 0.019021 0.019194 0.018983 \n", - "2017-07-13 18:00:00 1.0 1.0 1.0 1.0 0.019112 0.019112 0.018986 \n", - "2017-07-13 18:30:00 1.0 1.0 1.0 1.0 0.019124 0.019174 0.019040 \n", - "2017-07-13 19:00:00 1.0 1.0 1.0 1.0 0.019095 0.019200 0.019070 \n", - "2017-07-13 19:30:00 1.0 1.0 1.0 1.0 0.019040 0.019125 0.019040 \n", - "2017-07-13 20:00:00 1.0 1.0 1.0 1.0 0.019100 0.019130 0.019000 \n", - "2017-07-13 20:30:00 1.0 1.0 1.0 1.0 0.019266 0.019280 0.019100 \n", - "2017-07-13 21:00:00 1.0 1.0 1.0 1.0 0.019344 0.019382 0.019224 \n", - "2017-07-13 21:30:00 1.0 1.0 1.0 1.0 0.019210 0.019365 0.019110 \n", - "2017-07-13 22:00:00 1.0 1.0 1.0 1.0 0.019271 0.019309 0.019162 \n", - "2017-07-13 22:30:00 1.0 1.0 1.0 1.0 0.019263 0.019353 0.019143 \n", - "2017-07-13 23:00:00 1.0 1.0 1.0 1.0 0.019340 0.019380 0.019250 \n", - "2017-07-13 23:30:00 1.0 1.0 1.0 1.0 0.019462 0.019462 0.019339 \n", + "2014-01-18 04:00:00 0.028000 0.028000 0.028000 0.028000 0.020590 \n", + "2014-01-18 04:30:00 0.028500 0.029000 0.028000 0.029000 0.003106 \n", + "2014-01-18 05:00:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 05:30:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 06:00:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 06:30:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 07:00:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 07:30:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 08:00:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 08:30:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 09:00:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 09:30:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 10:00:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 10:30:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 11:00:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 11:30:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 12:00:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 12:30:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 13:00:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 13:30:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 14:00:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 14:30:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 15:00:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 15:30:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 16:00:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 16:30:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 17:00:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 17:30:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 18:00:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "2014-01-18 18:30:00 0.028500 0.028500 0.028500 0.028500 0.000000 \n", + "... ... ... ... ... ... \n", + "2017-07-13 09:00:00 0.019103 0.019238 0.019043 0.019215 102.001835 \n", + "2017-07-13 09:30:00 0.019013 0.019128 0.019005 0.019103 115.373214 \n", + "2017-07-13 10:00:00 0.019098 0.019100 0.019000 0.019032 50.748214 \n", + "2017-07-13 10:30:00 0.019193 0.019200 0.019087 0.019100 35.057789 \n", + "2017-07-13 11:00:00 0.019300 0.019500 0.019193 0.019200 181.517873 \n", + "2017-07-13 11:30:00 0.019386 0.019455 0.019300 0.019300 110.352974 \n", + "2017-07-13 12:00:00 0.019363 0.019492 0.019345 0.019386 87.880708 \n", + "2017-07-13 12:30:00 0.019385 0.019388 0.019257 0.019345 66.806285 \n", + "2017-07-13 13:00:00 0.019227 0.019388 0.019086 0.019385 176.470996 \n", + "2017-07-13 13:30:00 0.019200 0.019258 0.019135 0.019227 53.844416 \n", + "2017-07-13 14:00:00 0.019073 0.019202 0.019037 0.019202 120.209105 \n", + "2017-07-13 14:30:00 0.019210 0.019210 0.019036 0.019079 117.144992 \n", + "2017-07-13 15:00:00 0.019214 0.019268 0.019150 0.019187 60.409080 \n", + "2017-07-13 15:30:00 0.019110 0.019275 0.019100 0.019216 131.437257 \n", + "2017-07-13 16:00:00 0.019087 0.019186 0.018950 0.019110 352.480444 \n", + "2017-07-13 16:30:00 0.019220 0.019220 0.019085 0.019087 71.181496 \n", + "2017-07-13 17:00:00 0.019203 0.019245 0.019101 0.019220 104.015164 \n", + "2017-07-13 17:30:00 0.019021 0.019194 0.018983 0.019180 114.901113 \n", + "2017-07-13 18:00:00 0.019112 0.019112 0.018986 0.019045 61.162994 \n", + "2017-07-13 18:30:00 0.019124 0.019174 0.019040 0.019090 103.732512 \n", + "2017-07-13 19:00:00 0.019095 0.019200 0.019070 0.019124 50.057990 \n", + "2017-07-13 19:30:00 0.019040 0.019125 0.019040 0.019087 62.455953 \n", + "2017-07-13 20:00:00 0.019100 0.019130 0.019000 0.019040 57.416039 \n", + "2017-07-13 20:30:00 0.019266 0.019280 0.019100 0.019130 54.247496 \n", + "2017-07-13 21:00:00 0.019344 0.019382 0.019224 0.019280 131.677025 \n", + "2017-07-13 21:30:00 0.019210 0.019365 0.019110 0.019365 157.675558 \n", + "2017-07-13 22:00:00 0.019271 0.019309 0.019162 0.019204 89.599189 \n", + "2017-07-13 22:30:00 0.019263 0.019353 0.019143 0.019271 102.195033 \n", + "2017-07-13 23:00:00 0.019340 0.019380 0.019250 0.019263 51.805206 \n", + "2017-07-13 23:30:00 0.019462 0.019462 0.019339 0.019350 61.594625 \n", "\n", - "Pair DASHBTC \\\n", - "Price open close high low open \n", - "date \n", - "2014-01-18 04:00:00 0.028000 NaN NaN NaN NaN \n", - "2014-01-18 04:30:00 0.029000 NaN NaN NaN NaN \n", - "2014-01-18 05:00:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 05:30:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 06:00:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 06:30:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 07:00:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 07:30:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 08:00:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 08:30:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 09:00:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 09:30:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 10:00:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 10:30:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 11:00:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 11:30:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 12:00:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 12:30:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 13:00:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 13:30:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 14:00:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 14:30:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 15:00:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 15:30:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 16:00:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 16:30:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 17:00:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 17:30:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 18:00:00 0.028500 NaN NaN NaN NaN \n", - "2014-01-18 18:30:00 0.028500 NaN NaN NaN NaN \n", - "... ... ... ... ... ... \n", - "2017-07-13 09:00:00 0.019215 0.072300 0.072809 0.072012 0.072809 \n", - "2017-07-13 09:30:00 0.019103 0.072481 0.072619 0.072300 0.072425 \n", - "2017-07-13 10:00:00 0.019032 0.071730 0.072584 0.071729 0.072481 \n", - "2017-07-13 10:30:00 0.019100 0.071670 0.072127 0.071585 0.071730 \n", - "2017-07-13 11:00:00 0.019200 0.072920 0.072920 0.071670 0.071771 \n", - "2017-07-13 11:30:00 0.019300 0.073171 0.073316 0.072640 0.072640 \n", - "2017-07-13 12:00:00 0.019386 0.073020 0.073433 0.072867 0.073170 \n", - "2017-07-13 12:30:00 0.019345 0.073400 0.073400 0.073020 0.073070 \n", - "2017-07-13 13:00:00 0.019385 0.073230 0.073500 0.073221 0.073400 \n", - "2017-07-13 13:30:00 0.019227 0.073168 0.073500 0.072830 0.073230 \n", - "2017-07-13 14:00:00 0.019202 0.072918 0.073487 0.072800 0.073193 \n", - "2017-07-13 14:30:00 0.019079 0.072642 0.073489 0.072639 0.073000 \n", - "2017-07-13 15:00:00 0.019187 0.072485 0.073480 0.072056 0.072642 \n", - "2017-07-13 15:30:00 0.019216 0.071605 0.072485 0.071605 0.072298 \n", - "2017-07-13 16:00:00 0.019110 0.072035 0.072104 0.071100 0.071605 \n", - "2017-07-13 16:30:00 0.019087 0.072100 0.072486 0.071455 0.072033 \n", - "2017-07-13 17:00:00 0.019220 0.072656 0.072656 0.071630 0.072100 \n", - "2017-07-13 17:30:00 0.019180 0.072889 0.073760 0.072511 0.072635 \n", - "2017-07-13 18:00:00 0.019045 0.072943 0.073016 0.072492 0.072610 \n", - "2017-07-13 18:30:00 0.019090 0.072984 0.073311 0.072563 0.072563 \n", - "2017-07-13 19:00:00 0.019124 0.072545 0.073138 0.072381 0.072984 \n", - "2017-07-13 19:30:00 0.019087 0.071898 0.072545 0.071570 0.072545 \n", - "2017-07-13 20:00:00 0.019040 0.071810 0.072320 0.071586 0.071586 \n", - "2017-07-13 20:30:00 0.019130 0.072315 0.072750 0.071727 0.072091 \n", - "2017-07-13 21:00:00 0.019280 0.072148 0.072690 0.072067 0.072315 \n", - "2017-07-13 21:30:00 0.019365 0.072790 0.072791 0.072067 0.072148 \n", - "2017-07-13 22:00:00 0.019204 0.072360 0.072790 0.072068 0.072500 \n", - "2017-07-13 22:30:00 0.019271 0.072500 0.072740 0.072169 0.072169 \n", - "2017-07-13 23:00:00 0.019263 0.072770 0.072826 0.072430 0.072500 \n", - "2017-07-13 23:30:00 0.019350 0.072494 0.072800 0.072450 0.072770 \n", + "Pair DASHBTC \\\n", + "Price quoteVolume close high low open \n", + "date \n", + "2014-01-18 04:00:00 0.735400 NaN NaN NaN NaN \n", + "2014-01-18 04:30:00 0.109700 NaN NaN NaN NaN \n", + "2014-01-18 05:00:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 05:30:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 06:00:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 06:30:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 07:00:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 07:30:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 08:00:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 08:30:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 09:00:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 09:30:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 10:00:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 10:30:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 11:00:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 11:30:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 12:00:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 12:30:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 13:00:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 13:30:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 14:00:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 14:30:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 15:00:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 15:30:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 16:00:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 16:30:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 17:00:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 17:30:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 18:00:00 0.000000 NaN NaN NaN NaN \n", + "2014-01-18 18:30:00 0.000000 NaN NaN NaN NaN \n", + "... ... ... ... ... ... \n", + "2017-07-13 09:00:00 5328.738151 0.072300 0.072809 0.072012 0.072809 \n", + "2017-07-13 09:30:00 6051.922924 0.072481 0.072619 0.072300 0.072425 \n", + "2017-07-13 10:00:00 2664.117231 0.071730 0.072584 0.071729 0.072481 \n", + "2017-07-13 10:30:00 1829.450652 0.071670 0.072127 0.071585 0.071730 \n", + "2017-07-13 11:00:00 9369.638447 0.072920 0.072920 0.071670 0.071771 \n", + "2017-07-13 11:30:00 5691.409801 0.073171 0.073316 0.072640 0.072640 \n", + "2017-07-13 12:00:00 4528.467404 0.073020 0.073433 0.072867 0.073170 \n", + "2017-07-13 12:30:00 3454.094631 0.073400 0.073400 0.073020 0.073070 \n", + "2017-07-13 13:00:00 9187.940454 0.073230 0.073500 0.073221 0.073400 \n", + "2017-07-13 13:30:00 2805.453736 0.073168 0.073500 0.072830 0.073230 \n", + "2017-07-13 14:00:00 6299.759114 0.072918 0.073487 0.072800 0.073193 \n", + "2017-07-13 14:30:00 6130.597254 0.072642 0.073489 0.072639 0.073000 \n", + "2017-07-13 15:00:00 3144.541587 0.072485 0.073480 0.072056 0.072642 \n", + "2017-07-13 15:30:00 6844.490092 0.071605 0.072485 0.071605 0.072298 \n", + "2017-07-13 16:00:00 18463.961383 0.072035 0.072104 0.071100 0.071605 \n", + "2017-07-13 16:30:00 3718.401983 0.072100 0.072486 0.071455 0.072033 \n", + "2017-07-13 17:00:00 5418.887416 0.072656 0.072656 0.071630 0.072100 \n", + "2017-07-13 17:30:00 6023.751955 0.072889 0.073760 0.072511 0.072635 \n", + "2017-07-13 18:00:00 3211.881872 0.072943 0.073016 0.072492 0.072610 \n", + "2017-07-13 18:30:00 5425.850165 0.072984 0.073311 0.072563 0.072563 \n", + "2017-07-13 19:00:00 2615.336480 0.072545 0.073138 0.072381 0.072984 \n", + "2017-07-13 19:30:00 3276.442735 0.071898 0.072545 0.071570 0.072545 \n", + "2017-07-13 20:00:00 3013.924077 0.071810 0.072320 0.071586 0.071586 \n", + "2017-07-13 20:30:00 2826.468409 0.072315 0.072750 0.071727 0.072091 \n", + "2017-07-13 21:00:00 6816.243889 0.072148 0.072690 0.072067 0.072315 \n", + "2017-07-13 21:30:00 8198.708763 0.072790 0.072791 0.072067 0.072148 \n", + "2017-07-13 22:00:00 4649.761884 0.072360 0.072790 0.072068 0.072500 \n", + "2017-07-13 22:30:00 5304.391273 0.072500 0.072740 0.072169 0.072169 \n", + "2017-07-13 23:00:00 2683.849665 0.072770 0.072826 0.072430 0.072500 \n", + "2017-07-13 23:30:00 3174.332047 0.072494 0.072800 0.072450 0.072770 \n", "\n", - "Pair XMRBTC \n", - "Price close high low open \n", - "date \n", - "2014-01-18 04:00:00 NaN NaN NaN NaN \n", - "2014-01-18 04:30:00 NaN NaN NaN NaN \n", - "2014-01-18 05:00:00 NaN NaN NaN NaN \n", - "2014-01-18 05:30:00 NaN NaN NaN NaN \n", - "2014-01-18 06:00:00 NaN NaN NaN NaN \n", - "2014-01-18 06:30:00 NaN NaN NaN NaN \n", - "2014-01-18 07:00:00 NaN NaN NaN NaN \n", - "2014-01-18 07:30:00 NaN NaN NaN NaN \n", - "2014-01-18 08:00:00 NaN NaN NaN NaN \n", - "2014-01-18 08:30:00 NaN NaN NaN NaN \n", - "2014-01-18 09:00:00 NaN NaN NaN NaN \n", - "2014-01-18 09:30:00 NaN NaN NaN NaN \n", - "2014-01-18 10:00:00 NaN NaN NaN NaN \n", - "2014-01-18 10:30:00 NaN NaN NaN NaN \n", - "2014-01-18 11:00:00 NaN NaN NaN NaN \n", - "2014-01-18 11:30:00 NaN NaN NaN NaN \n", - "2014-01-18 12:00:00 NaN NaN NaN NaN \n", - "2014-01-18 12:30:00 NaN NaN NaN NaN \n", - "2014-01-18 13:00:00 NaN NaN NaN NaN \n", - "2014-01-18 13:30:00 NaN NaN NaN NaN \n", - "2014-01-18 14:00:00 NaN NaN NaN NaN \n", - "2014-01-18 14:30:00 NaN NaN NaN NaN \n", - "2014-01-18 15:00:00 NaN NaN NaN NaN \n", - "2014-01-18 15:30:00 NaN NaN NaN NaN \n", - "2014-01-18 16:00:00 NaN NaN NaN NaN \n", - "2014-01-18 16:30:00 NaN NaN NaN NaN \n", - "2014-01-18 17:00:00 NaN NaN NaN NaN \n", - "2014-01-18 17:30:00 NaN NaN NaN NaN \n", - "2014-01-18 18:00:00 NaN NaN NaN NaN \n", - "2014-01-18 18:30:00 NaN NaN NaN NaN \n", - "... ... ... ... ... \n", - "2017-07-13 09:00:00 0.016200 0.016260 0.016151 0.016220 \n", - "2017-07-13 09:30:00 0.016214 0.016396 0.016200 0.016200 \n", - "2017-07-13 10:00:00 0.016263 0.016281 0.016211 0.016214 \n", - "2017-07-13 10:30:00 0.016304 0.016367 0.016263 0.016279 \n", - "2017-07-13 11:00:00 0.016382 0.016408 0.016304 0.016304 \n", - "2017-07-13 11:30:00 0.016490 0.016490 0.016382 0.016404 \n", - "2017-07-13 12:00:00 0.016433 0.016499 0.016365 0.016490 \n", - "2017-07-13 12:30:00 0.016441 0.016499 0.016353 0.016499 \n", - "2017-07-13 13:00:00 0.016410 0.016437 0.016337 0.016437 \n", - "2017-07-13 13:30:00 0.016351 0.016425 0.016334 0.016410 \n", - "2017-07-13 14:00:00 0.016306 0.016400 0.016306 0.016400 \n", - "2017-07-13 14:30:00 0.016306 0.016377 0.016306 0.016306 \n", - "2017-07-13 15:00:00 0.016120 0.016392 0.016046 0.016306 \n", - "2017-07-13 15:30:00 0.016027 0.016200 0.015992 0.016200 \n", - "2017-07-13 16:00:00 0.016020 0.016080 0.015960 0.016010 \n", - "2017-07-13 16:30:00 0.016030 0.016100 0.016002 0.016070 \n", - "2017-07-13 17:00:00 0.016229 0.016232 0.016115 0.016115 \n", - "2017-07-13 17:30:00 0.016057 0.016232 0.016030 0.016218 \n", - "2017-07-13 18:00:00 0.016128 0.016157 0.016000 0.016038 \n", - "2017-07-13 18:30:00 0.016389 0.016399 0.016100 0.016110 \n", - "2017-07-13 19:00:00 0.016366 0.016488 0.016300 0.016300 \n", - "2017-07-13 19:30:00 0.016305 0.016488 0.016305 0.016362 \n", - "2017-07-13 20:00:00 0.016180 0.016382 0.016175 0.016341 \n", - "2017-07-13 20:30:00 0.016281 0.016380 0.016220 0.016318 \n", - "2017-07-13 21:00:00 0.016360 0.016425 0.016281 0.016379 \n", - "2017-07-13 21:30:00 0.016348 0.016422 0.016227 0.016360 \n", - "2017-07-13 22:00:00 0.016348 0.016401 0.016323 0.016348 \n", - "2017-07-13 22:30:00 0.016269 0.016400 0.016262 0.016348 \n", - "2017-07-13 23:00:00 0.016260 0.016365 0.016260 0.016269 \n", - "2017-07-13 23:30:00 0.016289 0.016420 0.016260 0.016261 \n", + "Pair XMRBTC \\\n", + "Price volume quoteVolume close high low \n", + "date \n", + "2014-01-18 04:00:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 04:30:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 05:00:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 05:30:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 06:00:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 06:30:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 07:00:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 07:30:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 08:00:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 08:30:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 09:00:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 09:30:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 10:00:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 10:30:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 11:00:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 11:30:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 12:00:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 12:30:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 13:00:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 13:30:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 14:00:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 14:30:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 15:00:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 15:30:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 16:00:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 16:30:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 17:00:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 17:30:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 18:00:00 NaN NaN NaN NaN NaN \n", + "2014-01-18 18:30:00 NaN NaN NaN NaN NaN \n", + "... ... ... ... ... ... \n", + "2017-07-13 09:00:00 22.333296 308.698352 0.016200 0.016260 0.016151 \n", + "2017-07-13 09:30:00 14.041970 194.101572 0.016214 0.016396 0.016200 \n", + "2017-07-13 10:00:00 23.715417 328.675688 0.016263 0.016281 0.016211 \n", + "2017-07-13 10:30:00 33.350912 465.074741 0.016304 0.016367 0.016263 \n", + "2017-07-13 11:00:00 26.761725 369.510973 0.016382 0.016408 0.016304 \n", + "2017-07-13 11:30:00 18.257227 249.933945 0.016490 0.016490 0.016382 \n", + "2017-07-13 12:00:00 12.723900 173.851297 0.016433 0.016499 0.016365 \n", + "2017-07-13 12:30:00 3.529383 48.274718 0.016441 0.016499 0.016353 \n", + "2017-07-13 13:00:00 14.243275 194.257627 0.016410 0.016437 0.016337 \n", + "2017-07-13 13:30:00 15.433198 210.906816 0.016351 0.016425 0.016334 \n", + "2017-07-13 14:00:00 5.574882 76.360152 0.016306 0.016400 0.016306 \n", + "2017-07-13 14:30:00 3.633284 49.801619 0.016306 0.016377 0.016306 \n", + "2017-07-13 15:00:00 26.060940 358.873489 0.016120 0.016392 0.016046 \n", + "2017-07-13 15:30:00 66.840261 929.381259 0.016027 0.016200 0.015992 \n", + "2017-07-13 16:00:00 45.988777 644.390298 0.016020 0.016080 0.015960 \n", + "2017-07-13 16:30:00 19.054083 264.591534 0.016030 0.016100 0.016002 \n", + "2017-07-13 17:00:00 16.594045 229.660914 0.016229 0.016232 0.016115 \n", + "2017-07-13 17:30:00 26.669919 364.394455 0.016057 0.016232 0.016030 \n", + "2017-07-13 18:00:00 24.627536 338.062082 0.016128 0.016157 0.016000 \n", + "2017-07-13 18:30:00 18.240710 250.051087 0.016389 0.016399 0.016100 \n", + "2017-07-13 19:00:00 18.109848 249.073822 0.016366 0.016488 0.016300 \n", + "2017-07-13 19:30:00 25.484479 354.450981 0.016305 0.016488 0.016305 \n", + "2017-07-13 20:00:00 5.415996 75.298057 0.016180 0.016382 0.016175 \n", + "2017-07-13 20:30:00 17.284449 238.635277 0.016281 0.016380 0.016220 \n", + "2017-07-13 21:00:00 8.701756 120.286405 0.016360 0.016425 0.016281 \n", + "2017-07-13 21:30:00 18.284087 252.250628 0.016348 0.016422 0.016227 \n", + "2017-07-13 22:00:00 31.410805 435.147544 0.016348 0.016401 0.016323 \n", + "2017-07-13 22:30:00 2.176476 29.992342 0.016269 0.016400 0.016262 \n", + "2017-07-13 23:00:00 11.451511 157.605870 0.016260 0.016365 0.016260 \n", + "2017-07-13 23:30:00 5.277151 72.698265 0.016289 0.016420 0.016260 \n", "\n", - "[61096 rows x 16 columns]" + "Pair \n", + "Price open volume quoteVolume \n", + "date \n", + "2014-01-18 04:00:00 NaN NaN NaN \n", + "2014-01-18 04:30:00 NaN NaN NaN \n", + "2014-01-18 05:00:00 NaN NaN NaN \n", + "2014-01-18 05:30:00 NaN NaN NaN \n", + "2014-01-18 06:00:00 NaN NaN NaN \n", + "2014-01-18 06:30:00 NaN NaN NaN \n", + "2014-01-18 07:00:00 NaN NaN NaN \n", + "2014-01-18 07:30:00 NaN NaN NaN \n", + "2014-01-18 08:00:00 NaN NaN NaN \n", + "2014-01-18 08:30:00 NaN NaN NaN \n", + "2014-01-18 09:00:00 NaN NaN NaN \n", + "2014-01-18 09:30:00 NaN NaN NaN \n", + "2014-01-18 10:00:00 NaN NaN NaN \n", + "2014-01-18 10:30:00 NaN NaN NaN \n", + "2014-01-18 11:00:00 NaN NaN NaN \n", + "2014-01-18 11:30:00 NaN NaN NaN \n", + "2014-01-18 12:00:00 NaN NaN NaN \n", + "2014-01-18 12:30:00 NaN NaN NaN \n", + "2014-01-18 13:00:00 NaN NaN NaN \n", + "2014-01-18 13:30:00 NaN NaN NaN \n", + "2014-01-18 14:00:00 NaN NaN NaN \n", + "2014-01-18 14:30:00 NaN NaN NaN \n", + "2014-01-18 15:00:00 NaN NaN NaN \n", + "2014-01-18 15:30:00 NaN NaN NaN \n", + "2014-01-18 16:00:00 NaN NaN NaN \n", + "2014-01-18 16:30:00 NaN NaN NaN \n", + "2014-01-18 17:00:00 NaN NaN NaN \n", + "2014-01-18 17:30:00 NaN NaN NaN \n", + "2014-01-18 18:00:00 NaN NaN NaN \n", + "2014-01-18 18:30:00 NaN NaN NaN \n", + "... ... ... ... \n", + "2017-07-13 09:00:00 0.016220 16.447470 1014.826043 \n", + "2017-07-13 09:30:00 0.016200 16.381883 1006.211103 \n", + "2017-07-13 10:00:00 0.016214 5.879561 362.057572 \n", + "2017-07-13 10:30:00 0.016279 6.862038 421.023648 \n", + "2017-07-13 11:00:00 0.016304 8.927679 544.949394 \n", + "2017-07-13 11:30:00 0.016404 8.967660 545.666655 \n", + "2017-07-13 12:00:00 0.016490 11.650610 709.466293 \n", + "2017-07-13 12:30:00 0.016499 12.990767 792.119330 \n", + "2017-07-13 13:00:00 0.016437 5.833544 356.266861 \n", + "2017-07-13 13:30:00 0.016410 5.270448 321.593298 \n", + "2017-07-13 14:00:00 0.016400 2.315221 141.803909 \n", + "2017-07-13 14:30:00 0.016306 1.577577 96.546480 \n", + "2017-07-13 15:00:00 0.016306 69.611434 4300.748677 \n", + "2017-07-13 15:30:00 0.016200 40.700323 2537.211215 \n", + "2017-07-13 16:00:00 0.016010 20.781892 1296.247685 \n", + "2017-07-13 16:30:00 0.016070 22.931610 1427.026698 \n", + "2017-07-13 17:00:00 0.016115 10.652046 658.748194 \n", + "2017-07-13 17:30:00 0.016218 17.885425 1106.663222 \n", + "2017-07-13 18:00:00 0.016038 13.959773 868.051721 \n", + "2017-07-13 18:30:00 0.016110 35.339157 2182.164699 \n", + "2017-07-13 19:00:00 0.016300 30.539915 1862.691421 \n", + "2017-07-13 19:30:00 0.016362 31.107351 1895.337919 \n", + "2017-07-13 20:00:00 0.016341 24.475405 1504.283095 \n", + "2017-07-13 20:30:00 0.016318 10.557378 646.264376 \n", + "2017-07-13 21:00:00 0.016379 2.052379 125.407612 \n", + "2017-07-13 21:30:00 0.016360 17.000980 1042.824300 \n", + "2017-07-13 22:00:00 0.016348 1.050736 64.258248 \n", + "2017-07-13 22:30:00 0.016348 4.130065 252.910135 \n", + "2017-07-13 23:00:00 0.016269 5.774802 354.476663 \n", + "2017-07-13 23:30:00 0.016261 5.284462 322.402887 \n", + "\n", + "[61096 rows x 18 columns]" ] }, - "execution_count": 10, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1675,11 +1893,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 40, "metadata": { "ExecuteTime": { - "end_time": "2017-10-29T11:47:16.675038Z", - "start_time": "2017-10-29T11:47:16.363440Z" + "end_time": "2017-11-11T07:31:11.024414Z", + "start_time": "2017-11-11T07:31:10.826771Z" } }, "outputs": [ @@ -1687,35 +1905,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "61096\n" + "cropped from 61096\n", + "to 55284\n" ] - }, - { - "data": { - "text/plain": [ - "55284" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ "# crop to when they all exist\n", - "print(len(df))\n", + "print('cropped from', len(df))\n", "t=max([min(df1.index) for df1 in dfs1])\n", "df=df[df.index>t]\n", - "len(df)" + "print('to',len(df))" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 41, "metadata": { "ExecuteTime": { - "end_time": "2017-10-29T11:47:16.746643Z", - "start_time": "2017-10-29T11:47:16.676614Z" + "end_time": "2017-11-11T07:31:11.113473Z", + "start_time": "2017-11-11T07:31:11.026303Z" } }, "outputs": [ @@ -1740,10 +1949,9 @@ " \n", " \n", " Pair\n", - " BTCBTC\n", - " LTCBTC\n", - " DASHBTC\n", - " XMRBTC\n", + " LTCBTC\n", + " DASHBTC\n", + " XMRBTC\n", " \n", " \n", " Price\n", @@ -1751,18 +1959,20 @@ " high\n", " low\n", " open\n", + " volume\n", + " quoteVolume\n", " close\n", " high\n", " low\n", " open\n", + " volume\n", + " quoteVolume\n", " close\n", " high\n", " low\n", " open\n", - " close\n", - " high\n", - " low\n", - " open\n", + " volume\n", + " quoteVolume\n", " \n", " \n", " date\n", @@ -1782,578 +1992,640 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " 2014-05-19 06:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.023234\n", " 0.023234\n", " 0.023234\n", " 0.023234\n", + " 0.538670\n", + " 23.184900\n", " 0.015199\n", " 0.015199\n", " 0.014502\n", " 0.014970\n", + " 2.343284\n", + " 156.369797\n", " 0.001110\n", " 0.011110\n", " 0.001110\n", " 0.011110\n", + " 1.995904\n", + " 1404.974609\n", " \n", " \n", " 2014-05-19 06:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.023421\n", " 0.023421\n", " 0.023421\n", " 0.023421\n", + " 0.022663\n", + " 0.967600\n", " 0.014520\n", " 0.015342\n", " 0.014510\n", " 0.015199\n", + " 0.321949\n", + " 21.883301\n", " 0.001125\n", " 0.001500\n", " 0.001125\n", " 0.001200\n", + " 0.619334\n", + " 441.371613\n", " \n", " \n", " 2014-05-19 07:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.023038\n", " 0.023100\n", " 0.023038\n", " 0.023100\n", + " 0.005663\n", + " 0.245200\n", " 0.015350\n", " 0.015400\n", " 0.014510\n", " 0.015000\n", + " 5.496416\n", + " 369.994202\n", " 0.001190\n", " 0.001410\n", " 0.001080\n", " 0.001410\n", + " 2.049713\n", + " 1798.664062\n", " \n", " \n", " 2014-05-19 07:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.023029\n", " 0.023029\n", " 0.023029\n", " 0.023029\n", + " 0.005128\n", + " 0.222700\n", " 0.015600\n", " 0.015600\n", " 0.015050\n", " 0.015200\n", + " 1.160088\n", + " 75.875801\n", " 0.001320\n", " 0.001867\n", " 0.001040\n", " 0.001040\n", + " 3.425346\n", + " 2460.367920\n", " \n", " \n", " 2014-05-19 08:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.023026\n", " 0.023420\n", " 0.023026\n", " 0.023029\n", + " 0.353329\n", + " 15.309300\n", " 0.015600\n", " 0.015600\n", " 0.015103\n", " 0.015149\n", + " 1.449590\n", + " 93.675102\n", " 0.001700\n", " 0.001800\n", " 0.001320\n", " 0.001700\n", + " 2.395254\n", + " 1450.168701\n", " \n", " \n", " 2014-05-19 08:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.023025\n", " 0.023420\n", " 0.023025\n", " 0.023025\n", + " 0.031986\n", + " 1.386400\n", " 0.016000\n", " 0.016000\n", " 0.015111\n", " 0.015700\n", + " 1.483967\n", + " 93.716499\n", " 0.001930\n", " 0.001940\n", " 0.001600\n", " 0.001600\n", + " 5.102005\n", + " 2842.072021\n", " \n", " \n", " 2014-05-19 09:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.023025\n", " 0.023025\n", " 0.023025\n", " 0.023025\n", + " 0.014403\n", + " 0.625500\n", " 0.016000\n", " 0.016000\n", " 0.015327\n", " 0.016000\n", + " 0.808186\n", + " 51.574699\n", " 0.001800\n", " 0.002700\n", " 0.001760\n", " 0.001930\n", + " 9.023416\n", + " 4452.659668\n", " \n", " \n", " 2014-05-19 09:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.023025\n", " 0.023200\n", " 0.023025\n", " 0.023200\n", + " 0.022038\n", + " 0.950200\n", " 0.015700\n", " 0.016000\n", " 0.015700\n", " 0.016000\n", + " 0.611756\n", + " 38.797901\n", " 0.001252\n", " 0.001780\n", " 0.001252\n", " 0.001780\n", + " 2.684633\n", + " 1781.344360\n", " \n", " \n", " 2014-05-19 10:00:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.023025\n", " 0.023200\n", " 0.023025\n", " 0.023200\n", + " 0.037687\n", + " 1.624600\n", " 0.015700\n", " 0.015980\n", " 0.015658\n", " 0.015980\n", + " 1.305152\n", + " 83.099098\n", " 0.001500\n", " 0.001800\n", " 0.001370\n", " 0.001465\n", + " 0.582902\n", + " 393.504395\n", " \n", " \n", " 2014-05-19 10:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.023025\n", " 0.023200\n", " 0.023025\n", " 0.023200\n", + " 0.007686\n", + " 0.331400\n", " 0.016000\n", " 0.016000\n", " 0.015396\n", " 0.015650\n", + " 1.035374\n", + " 64.767998\n", " 0.002000\n", " 0.002000\n", " 0.001700\n", " 0.001700\n", + " 3.449471\n", + " 1806.058594\n", " \n", " \n", " 2014-05-19 11:00:00\n", - 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" 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.019340\n", " 0.019380\n", " 0.019250\n", " 0.019263\n", + " 51.805206\n", + " 2683.849609\n", " 0.072770\n", " 0.072826\n", " 0.072430\n", " 0.072500\n", + " 11.451510\n", + " 157.605865\n", " 0.016260\n", " 0.016365\n", " 0.016260\n", " 0.016269\n", + " 5.774802\n", + " 354.476654\n", " \n", " \n", " 2017-07-13 23:30:00\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", " 0.019462\n", " 0.019462\n", " 0.019339\n", " 0.019350\n", + " 61.594624\n", + " 3174.332031\n", " 0.072494\n", " 0.072800\n", " 0.072450\n", " 0.072770\n", + " 5.277152\n", + " 72.698265\n", " 0.016289\n", " 0.016420\n", " 0.016260\n", " 0.016261\n", + " 5.284461\n", + " 322.402893\n", " \n", " \n", "\n", - "

55284 rows × 16 columns

\n", + "

55284 rows × 18 columns

\n", "" ], "text/plain": [ - "Pair BTCBTC LTCBTC \\\n", - "Price close high low open close high low \n", + "Pair LTCBTC \\\n", + "Price close high low open volume \n", "date \n", - "2014-05-19 06:00:00 1.0 1.0 1.0 1.0 0.023234 0.023234 0.023234 \n", - "2014-05-19 06:30:00 1.0 1.0 1.0 1.0 0.023421 0.023421 0.023421 \n", - "2014-05-19 07:00:00 1.0 1.0 1.0 1.0 0.023038 0.023100 0.023038 \n", - "2014-05-19 07:30:00 1.0 1.0 1.0 1.0 0.023029 0.023029 0.023029 \n", - "2014-05-19 08:00:00 1.0 1.0 1.0 1.0 0.023026 0.023420 0.023026 \n", - "2014-05-19 08:30:00 1.0 1.0 1.0 1.0 0.023025 0.023420 0.023025 \n", - "2014-05-19 09:00:00 1.0 1.0 1.0 1.0 0.023025 0.023025 0.023025 \n", - "2014-05-19 09:30:00 1.0 1.0 1.0 1.0 0.023025 0.023200 0.023025 \n", - "2014-05-19 10:00:00 1.0 1.0 1.0 1.0 0.023025 0.023200 0.023025 \n", - "2014-05-19 10:30:00 1.0 1.0 1.0 1.0 0.023025 0.023200 0.023025 \n", - "2014-05-19 11:00:00 1.0 1.0 1.0 1.0 0.023418 0.023418 0.023418 \n", - "2014-05-19 11:30:00 1.0 1.0 1.0 1.0 0.023025 0.023025 0.023025 \n", - "2014-05-19 12:00:00 1.0 1.0 1.0 1.0 0.023414 0.023414 0.023414 \n", - "2014-05-19 12:30:00 1.0 1.0 1.0 1.0 0.023409 0.023409 0.023409 \n", - "2014-05-19 13:00:00 1.0 1.0 1.0 1.0 0.023025 0.023408 0.023025 \n", - "2014-05-19 13:30:00 1.0 1.0 1.0 1.0 0.023025 0.023400 0.023025 \n", - "2014-05-19 14:00:00 1.0 1.0 1.0 1.0 0.023408 0.023408 0.023025 \n", - "2014-05-19 14:30:00 1.0 1.0 1.0 1.0 0.023408 0.023408 0.023408 \n", - "2014-05-19 15:00:00 1.0 1.0 1.0 1.0 0.023160 0.023408 0.023160 \n", - "2014-05-19 15:30:00 1.0 1.0 1.0 1.0 0.023407 0.023407 0.023001 \n", - "2014-05-19 16:00:00 1.0 1.0 1.0 1.0 0.023001 0.023407 0.023001 \n", - "2014-05-19 16:30:00 1.0 1.0 1.0 1.0 0.023001 0.023200 0.023001 \n", - "2014-05-19 17:00:00 1.0 1.0 1.0 1.0 0.023001 0.023200 0.023001 \n", - "2014-05-19 17:30:00 1.0 1.0 1.0 1.0 0.023001 0.023191 0.023001 \n", - "2014-05-19 18:00:00 1.0 1.0 1.0 1.0 0.023200 0.023200 0.023191 \n", - "2014-05-19 18:30:00 1.0 1.0 1.0 1.0 0.023001 0.023200 0.023001 \n", - "2014-05-19 19:00:00 1.0 1.0 1.0 1.0 0.023402 0.023402 0.023001 \n", - "2014-05-19 19:30:00 1.0 1.0 1.0 1.0 0.023402 0.023402 0.023402 \n", - "2014-05-19 20:00:00 1.0 1.0 1.0 1.0 0.023401 0.023402 0.023001 \n", - "2014-05-19 20:30:00 1.0 1.0 1.0 1.0 0.023401 0.023402 0.023401 \n", - "... ... ... ... ... ... ... ... \n", - "2017-07-13 09:00:00 1.0 1.0 1.0 1.0 0.019103 0.019238 0.019043 \n", - "2017-07-13 09:30:00 1.0 1.0 1.0 1.0 0.019013 0.019128 0.019005 \n", - "2017-07-13 10:00:00 1.0 1.0 1.0 1.0 0.019098 0.019100 0.019000 \n", - "2017-07-13 10:30:00 1.0 1.0 1.0 1.0 0.019193 0.019200 0.019087 \n", - "2017-07-13 11:00:00 1.0 1.0 1.0 1.0 0.019300 0.019500 0.019193 \n", - "2017-07-13 11:30:00 1.0 1.0 1.0 1.0 0.019386 0.019455 0.019300 \n", - "2017-07-13 12:00:00 1.0 1.0 1.0 1.0 0.019363 0.019492 0.019345 \n", - "2017-07-13 12:30:00 1.0 1.0 1.0 1.0 0.019385 0.019388 0.019257 \n", - "2017-07-13 13:00:00 1.0 1.0 1.0 1.0 0.019227 0.019388 0.019086 \n", - 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\n", + "2014-05-19 09:30:00 0.023025 0.023200 0.023025 0.023200 0.022038 \n", + "2014-05-19 10:00:00 0.023025 0.023200 0.023025 0.023200 0.037687 \n", + "2014-05-19 10:30:00 0.023025 0.023200 0.023025 0.023200 0.007686 \n", + "2014-05-19 11:00:00 0.023418 0.023418 0.023418 0.023418 0.023418 \n", + "2014-05-19 11:30:00 0.023025 0.023025 0.023025 0.023025 0.059156 \n", + "2014-05-19 12:00:00 0.023414 0.023414 0.023414 0.023414 0.068545 \n", + "2014-05-19 12:30:00 0.023409 0.023409 0.023409 0.023409 0.001400 \n", + "2014-05-19 13:00:00 0.023025 0.023408 0.023025 0.023408 0.094094 \n", + "2014-05-19 13:30:00 0.023025 0.023400 0.023025 0.023400 0.030565 \n", + "2014-05-19 14:00:00 0.023408 0.023408 0.023025 0.023050 0.073974 \n", + "2014-05-19 14:30:00 0.023408 0.023408 0.023408 0.023408 0.000000 \n", + "2014-05-19 15:00:00 0.023160 0.023408 0.023160 0.023408 0.019881 \n", + "2014-05-19 15:30:00 0.023408 0.023408 0.023001 0.023160 0.934453 \n", + "2014-05-19 16:00:00 0.023001 0.023407 0.023001 0.023407 0.363492 \n", + "2014-05-19 16:30:00 0.023001 0.023200 0.023001 0.023001 0.271593 \n", + "2014-05-19 17:00:00 0.023001 0.023200 0.023001 0.023001 0.055575 \n", + "2014-05-19 17:30:00 0.023001 0.023191 0.023001 0.023001 0.679097 \n", + "2014-05-19 18:00:00 0.023200 0.023200 0.023191 0.023191 0.115721 \n", + "2014-05-19 18:30:00 0.023001 0.023200 0.023001 0.023200 0.014856 \n", + "2014-05-19 19:00:00 0.023402 0.023402 0.023001 0.023001 0.047459 \n", + "2014-05-19 19:30:00 0.023402 0.023402 0.023402 0.023402 0.107424 \n", + "2014-05-19 20:00:00 0.023402 0.023402 0.023001 0.023402 0.054595 \n", + "2014-05-19 20:30:00 0.023402 0.023402 0.023402 0.023402 0.599039 \n", + "... ... ... ... ... ... \n", + "2017-07-13 09:00:00 0.019103 0.019238 0.019043 0.019215 102.001839 \n", + "2017-07-13 09:30:00 0.019012 0.019128 0.019005 0.019103 115.373215 \n", + "2017-07-13 10:00:00 0.019098 0.019100 0.019000 0.019032 50.748215 \n", + "2017-07-13 10:30:00 0.019193 0.019200 0.019087 0.019100 35.057789 \n", + "2017-07-13 11:00:00 0.019300 0.019500 0.019193 0.019200 181.517868 \n", + "2017-07-13 11:30:00 0.019386 0.019455 0.019300 0.019300 110.352974 \n", + "2017-07-13 12:00:00 0.019363 0.019492 0.019345 0.019386 87.880707 \n", + "2017-07-13 12:30:00 0.019385 0.019388 0.019257 0.019345 66.806282 \n", + "2017-07-13 13:00:00 0.019227 0.019388 0.019086 0.019385 176.470993 \n", + "2017-07-13 13:30:00 0.019200 0.019258 0.019135 0.019227 53.844418 \n", + "2017-07-13 14:00:00 0.019073 0.019202 0.019037 0.019202 120.209106 \n", + "2017-07-13 14:30:00 0.019210 0.019210 0.019036 0.019079 117.144989 \n", + "2017-07-13 15:00:00 0.019214 0.019268 0.019150 0.019187 60.409081 \n", + "2017-07-13 15:30:00 0.019110 0.019275 0.019100 0.019216 131.437256 \n", + "2017-07-13 16:00:00 0.019087 0.019186 0.018950 0.019110 352.480438 \n", + "2017-07-13 16:30:00 0.019220 0.019220 0.019085 0.019087 71.181496 \n", + "2017-07-13 17:00:00 0.019203 0.019245 0.019101 0.019220 104.015167 \n", + "2017-07-13 17:30:00 0.019021 0.019194 0.018983 0.019180 114.901115 \n", + "2017-07-13 18:00:00 0.019112 0.019112 0.018986 0.019045 61.162994 \n", + "2017-07-13 18:30:00 0.019124 0.019174 0.019040 0.019090 103.732513 \n", + "2017-07-13 19:00:00 0.019095 0.019200 0.019070 0.019124 50.057991 \n", + "2017-07-13 19:30:00 0.019040 0.019125 0.019040 0.019087 62.455952 \n", + "2017-07-13 20:00:00 0.019100 0.019130 0.019000 0.019040 57.416039 \n", + "2017-07-13 20:30:00 0.019266 0.019281 0.019100 0.019130 54.247498 \n", + "2017-07-13 21:00:00 0.019344 0.019382 0.019224 0.019280 131.677032 \n", + "2017-07-13 21:30:00 0.019210 0.019365 0.019110 0.019365 157.675552 \n", + "2017-07-13 22:00:00 0.019271 0.019309 0.019162 0.019204 89.599190 \n", + "2017-07-13 22:30:00 0.019263 0.019353 0.019143 0.019271 102.195030 \n", + "2017-07-13 23:00:00 0.019340 0.019380 0.019250 0.019263 51.805206 \n", + "2017-07-13 23:30:00 0.019462 0.019462 0.019339 0.019350 61.594624 \n", "\n", - "Pair DASHBTC \\\n", - "Price open close high low open \n", - "date \n", - "2014-05-19 06:00:00 0.023234 0.015199 0.015199 0.014502 0.014970 \n", - "2014-05-19 06:30:00 0.023421 0.014520 0.015342 0.014510 0.015199 \n", - "2014-05-19 07:00:00 0.023100 0.015350 0.015400 0.014510 0.015000 \n", - "2014-05-19 07:30:00 0.023029 0.015600 0.015600 0.015050 0.015200 \n", - "2014-05-19 08:00:00 0.023029 0.015600 0.015600 0.015103 0.015149 \n", - "2014-05-19 08:30:00 0.023025 0.016000 0.016000 0.015111 0.015700 \n", - "2014-05-19 09:00:00 0.023025 0.016000 0.016000 0.015327 0.016000 \n", - "2014-05-19 09:30:00 0.023200 0.015700 0.016000 0.015700 0.016000 \n", - "2014-05-19 10:00:00 0.023200 0.015700 0.015980 0.015658 0.015980 \n", - "2014-05-19 10:30:00 0.023200 0.016000 0.016000 0.015396 0.015650 \n", - "2014-05-19 11:00:00 0.023418 0.015119 0.016000 0.015111 0.015980 \n", - "2014-05-19 11:30:00 0.023025 0.015450 0.015450 0.015111 0.015119 \n", - "2014-05-19 12:00:00 0.023414 0.015200 0.015450 0.015200 0.015450 \n", - "2014-05-19 12:30:00 0.023409 0.015400 0.015400 0.015200 0.015200 \n", - "2014-05-19 13:00:00 0.023408 0.016000 0.016000 0.015500 0.015670 \n", - "2014-05-19 13:30:00 0.023400 0.015812 0.016000 0.015812 0.016000 \n", - "2014-05-19 14:00:00 0.023050 0.015446 0.015990 0.015446 0.015446 \n", - "2014-05-19 14:30:00 0.023408 0.015868 0.016000 0.014288 0.015889 \n", - "2014-05-19 15:00:00 0.023408 0.015960 0.015979 0.015000 0.015000 \n", - "2014-05-19 15:30:00 0.023160 0.015396 0.015838 0.015396 0.015600 \n", - "2014-05-19 16:00:00 0.023407 0.015450 0.015450 0.015400 0.015400 \n", - "2014-05-19 16:30:00 0.023001 0.015000 0.015450 0.014700 0.015450 \n", - "2014-05-19 17:00:00 0.023001 0.015000 0.015390 0.014951 0.015000 \n", - "2014-05-19 17:30:00 0.023001 0.015000 0.015250 0.015000 0.015250 \n", - "2014-05-19 18:00:00 0.023191 0.015150 0.015250 0.014951 0.015000 \n", - "2014-05-19 18:30:00 0.023200 0.015352 0.015352 0.015000 0.015000 \n", - "2014-05-19 19:00:00 0.023001 0.015005 0.015480 0.015005 0.015200 \n", - "2014-05-19 19:30:00 0.023402 0.014951 0.015100 0.014951 0.015005 \n", - "2014-05-19 20:00:00 0.023402 0.015199 0.015400 0.014951 0.015400 \n", - "2014-05-19 20:30:00 0.023401 0.015150 0.015150 0.015150 0.015150 \n", - "... ... ... ... ... ... \n", - "2017-07-13 09:00:00 0.019215 0.072300 0.072809 0.072012 0.072809 \n", - "2017-07-13 09:30:00 0.019103 0.072481 0.072619 0.072300 0.072425 \n", - "2017-07-13 10:00:00 0.019032 0.071730 0.072584 0.071729 0.072481 \n", - "2017-07-13 10:30:00 0.019100 0.071670 0.072127 0.071585 0.071730 \n", - "2017-07-13 11:00:00 0.019200 0.072920 0.072920 0.071670 0.071771 \n", - "2017-07-13 11:30:00 0.019300 0.073171 0.073316 0.072640 0.072640 \n", - "2017-07-13 12:00:00 0.019386 0.073020 0.073433 0.072867 0.073170 \n", - "2017-07-13 12:30:00 0.019345 0.073400 0.073400 0.073020 0.073070 \n", - "2017-07-13 13:00:00 0.019385 0.073230 0.073500 0.073221 0.073400 \n", - "2017-07-13 13:30:00 0.019227 0.073168 0.073500 0.072830 0.073230 \n", - "2017-07-13 14:00:00 0.019202 0.072918 0.073487 0.072800 0.073193 \n", - "2017-07-13 14:30:00 0.019079 0.072642 0.073489 0.072639 0.073000 \n", - "2017-07-13 15:00:00 0.019187 0.072485 0.073480 0.072056 0.072642 \n", - "2017-07-13 15:30:00 0.019216 0.071605 0.072485 0.071605 0.072298 \n", - "2017-07-13 16:00:00 0.019110 0.072035 0.072104 0.071100 0.071605 \n", - "2017-07-13 16:30:00 0.019087 0.072100 0.072486 0.071455 0.072033 \n", - "2017-07-13 17:00:00 0.019220 0.072656 0.072656 0.071630 0.072100 \n", - "2017-07-13 17:30:00 0.019180 0.072889 0.073760 0.072511 0.072635 \n", - "2017-07-13 18:00:00 0.019045 0.072943 0.073016 0.072492 0.072610 \n", - "2017-07-13 18:30:00 0.019090 0.072984 0.073311 0.072563 0.072563 \n", - "2017-07-13 19:00:00 0.019124 0.072545 0.073138 0.072381 0.072984 \n", - "2017-07-13 19:30:00 0.019087 0.071898 0.072545 0.071570 0.072545 \n", - "2017-07-13 20:00:00 0.019040 0.071810 0.072320 0.071586 0.071586 \n", - "2017-07-13 20:30:00 0.019130 0.072315 0.072750 0.071727 0.072091 \n", - "2017-07-13 21:00:00 0.019280 0.072148 0.072690 0.072067 0.072315 \n", - "2017-07-13 21:30:00 0.019365 0.072790 0.072791 0.072067 0.072148 \n", - "2017-07-13 22:00:00 0.019204 0.072360 0.072790 0.072068 0.072500 \n", - "2017-07-13 22:30:00 0.019271 0.072500 0.072740 0.072169 0.072169 \n", - "2017-07-13 23:00:00 0.019263 0.072770 0.072826 0.072430 0.072500 \n", - "2017-07-13 23:30:00 0.019350 0.072494 0.072800 0.072450 0.072770 \n", + "Pair DASHBTC \\\n", + "Price quoteVolume close high low open \n", + "date \n", + "2014-05-19 06:00:00 23.184900 0.015199 0.015199 0.014502 0.014970 \n", + "2014-05-19 06:30:00 0.967600 0.014520 0.015342 0.014510 0.015199 \n", + "2014-05-19 07:00:00 0.245200 0.015350 0.015400 0.014510 0.015000 \n", + "2014-05-19 07:30:00 0.222700 0.015600 0.015600 0.015050 0.015200 \n", + "2014-05-19 08:00:00 15.309300 0.015600 0.015600 0.015103 0.015149 \n", + "2014-05-19 08:30:00 1.386400 0.016000 0.016000 0.015111 0.015700 \n", + "2014-05-19 09:00:00 0.625500 0.016000 0.016000 0.015327 0.016000 \n", + "2014-05-19 09:30:00 0.950200 0.015700 0.016000 0.015700 0.016000 \n", + "2014-05-19 10:00:00 1.624600 0.015700 0.015980 0.015658 0.015980 \n", + "2014-05-19 10:30:00 0.331400 0.016000 0.016000 0.015396 0.015650 \n", + "2014-05-19 11:00:00 1.000000 0.015119 0.016000 0.015111 0.015980 \n", + "2014-05-19 11:30:00 2.569100 0.015450 0.015450 0.015111 0.015119 \n", + "2014-05-19 12:00:00 2.927500 0.015200 0.015450 0.015200 0.015450 \n", + "2014-05-19 12:30:00 0.059800 0.015400 0.015400 0.015200 0.015200 \n", + "2014-05-19 13:00:00 4.054200 0.016000 0.016000 0.015500 0.015670 \n", + "2014-05-19 13:30:00 1.318200 0.015812 0.016000 0.015812 0.016000 \n", + "2014-05-19 14:00:00 3.183400 0.015446 0.015990 0.015446 0.015446 \n", + "2014-05-19 14:30:00 0.000000 0.015868 0.016000 0.014288 0.015889 \n", + "2014-05-19 15:00:00 0.850200 0.015960 0.015979 0.015000 0.015000 \n", + "2014-05-19 15:30:00 40.586800 0.015396 0.015838 0.015396 0.015600 \n", + "2014-05-19 16:00:00 15.799400 0.015450 0.015450 0.015400 0.015400 \n", + "2014-05-19 16:30:00 11.807300 0.015000 0.015450 0.014700 0.015450 \n", + "2014-05-19 17:00:00 2.409300 0.015000 0.015390 0.014950 0.015000 \n", + "2014-05-19 17:30:00 29.458000 0.015000 0.015250 0.015000 0.015250 \n", + "2014-05-19 18:00:00 4.988000 0.015150 0.015250 0.014950 0.015000 \n", + "2014-05-19 18:30:00 0.645700 0.015352 0.015352 0.015000 0.015000 \n", + "2014-05-19 19:00:00 2.038400 0.015005 0.015480 0.015005 0.015200 \n", + "2014-05-19 19:30:00 4.590500 0.014950 0.015100 0.014950 0.015005 \n", + "2014-05-19 20:00:00 2.334000 0.015199 0.015400 0.014951 0.015400 \n", + "2014-05-19 20:30:00 25.598301 0.015150 0.015150 0.015150 0.015150 \n", + "... ... ... ... ... ... \n", + "2017-07-13 09:00:00 5328.738281 0.072300 0.072809 0.072012 0.072809 \n", + "2017-07-13 09:30:00 6051.922852 0.072481 0.072619 0.072300 0.072425 \n", + "2017-07-13 10:00:00 2664.117188 0.071730 0.072584 0.071729 0.072481 \n", + "2017-07-13 10:30:00 1829.450684 0.071670 0.072127 0.071585 0.071730 \n", + "2017-07-13 11:00:00 9369.638672 0.072920 0.072920 0.071670 0.071771 \n", + "2017-07-13 11:30:00 5691.409668 0.073171 0.073316 0.072640 0.072640 \n", + "2017-07-13 12:00:00 4528.467285 0.073020 0.073434 0.072867 0.073170 \n", + "2017-07-13 12:30:00 3454.094727 0.073400 0.073400 0.073020 0.073070 \n", + "2017-07-13 13:00:00 9187.940430 0.073230 0.073500 0.073221 0.073400 \n", + "2017-07-13 13:30:00 2805.453857 0.073168 0.073500 0.072830 0.073230 \n", + "2017-07-13 14:00:00 6299.759277 0.072918 0.073487 0.072800 0.073193 \n", + "2017-07-13 14:30:00 6130.597168 0.072642 0.073489 0.072639 0.073000 \n", + "2017-07-13 15:00:00 3144.541504 0.072485 0.073480 0.072056 0.072642 \n", + "2017-07-13 15:30:00 6844.490234 0.071605 0.072485 0.071605 0.072298 \n", + "2017-07-13 16:00:00 18463.960938 0.072035 0.072104 0.071100 0.071605 \n", + "2017-07-13 16:30:00 3718.402100 0.072100 0.072486 0.071455 0.072033 \n", + "2017-07-13 17:00:00 5418.887207 0.072656 0.072656 0.071630 0.072100 \n", + "2017-07-13 17:30:00 6023.751953 0.072889 0.073760 0.072511 0.072635 \n", + "2017-07-13 18:00:00 3211.881836 0.072943 0.073016 0.072492 0.072610 \n", + "2017-07-13 18:30:00 5425.850098 0.072984 0.073311 0.072563 0.072563 \n", + "2017-07-13 19:00:00 2615.336426 0.072545 0.073138 0.072381 0.072984 \n", + "2017-07-13 19:30:00 3276.442627 0.071898 0.072545 0.071570 0.072545 \n", + "2017-07-13 20:00:00 3013.924072 0.071810 0.072320 0.071586 0.071586 \n", + "2017-07-13 20:30:00 2826.468506 0.072315 0.072750 0.071727 0.072091 \n", + "2017-07-13 21:00:00 6816.243652 0.072148 0.072690 0.072067 0.072315 \n", + "2017-07-13 21:30:00 8198.708984 0.072790 0.072791 0.072067 0.072148 \n", + "2017-07-13 22:00:00 4649.761719 0.072360 0.072790 0.072068 0.072500 \n", + "2017-07-13 22:30:00 5304.391113 0.072500 0.072740 0.072169 0.072169 \n", + "2017-07-13 23:00:00 2683.849609 0.072770 0.072826 0.072430 0.072500 \n", + "2017-07-13 23:30:00 3174.332031 0.072494 0.072800 0.072450 0.072770 \n", "\n", - "Pair XMRBTC \n", - "Price close high low open \n", - "date \n", - "2014-05-19 06:00:00 0.001110 0.011110 0.001110 0.011110 \n", - "2014-05-19 06:30:00 0.001125 0.001500 0.001125 0.001200 \n", - "2014-05-19 07:00:00 0.001190 0.001410 0.001080 0.001410 \n", - "2014-05-19 07:30:00 0.001320 0.001867 0.001040 0.001040 \n", - "2014-05-19 08:00:00 0.001700 0.001800 0.001320 0.001700 \n", - "2014-05-19 08:30:00 0.001930 0.001940 0.001600 0.001600 \n", - "2014-05-19 09:00:00 0.001800 0.002700 0.001760 0.001930 \n", - "2014-05-19 09:30:00 0.001252 0.001780 0.001252 0.001780 \n", - "2014-05-19 10:00:00 0.001500 0.001800 0.001370 0.001465 \n", - "2014-05-19 10:30:00 0.002000 0.002000 0.001700 0.001700 \n", - "2014-05-19 11:00:00 0.002000 0.002000 0.001790 0.001999 \n", - "2014-05-19 11:30:00 0.001469 0.001850 0.001469 0.001850 \n", - "2014-05-19 12:00:00 0.002180 0.002180 0.001594 0.001594 \n", - "2014-05-19 12:30:00 0.002000 0.002000 0.002000 0.002000 \n", - "2014-05-19 13:00:00 0.002000 0.002189 0.001824 0.002000 \n", - "2014-05-19 13:30:00 0.002002 0.002200 0.002000 0.002189 \n", - "2014-05-19 14:00:00 0.002000 0.002300 0.002000 0.002002 \n", - "2014-05-19 14:30:00 0.002500 0.002800 0.002070 0.002070 \n", - "2014-05-19 15:00:00 0.003000 0.003000 0.002200 0.002670 \n", - "2014-05-19 15:30:00 0.002800 0.003270 0.002500 0.002900 \n", - "2014-05-19 16:00:00 0.003290 0.003300 0.002700 0.002720 \n", - "2014-05-19 16:30:00 0.004690 0.006000 0.003000 0.003300 \n", - "2014-05-19 17:00:00 0.003340 0.004500 0.003031 0.004500 \n", - "2014-05-19 17:30:00 0.003950 0.003950 0.002216 0.003310 \n", - "2014-05-19 18:00:00 0.003500 0.003990 0.003000 0.003950 \n", - "2014-05-19 18:30:00 0.003700 0.003907 0.003465 0.003465 \n", - "2014-05-19 19:00:00 0.003700 0.003899 0.003650 0.003825 \n", - "2014-05-19 19:30:00 0.003740 0.003779 0.003697 0.003700 \n", - "2014-05-19 20:00:00 0.003810 0.004179 0.003700 0.003740 \n", - "2014-05-19 20:30:00 0.004450 0.004450 0.003960 0.004000 \n", - "... ... ... ... ... \n", - "2017-07-13 09:00:00 0.016200 0.016260 0.016151 0.016220 \n", - "2017-07-13 09:30:00 0.016214 0.016396 0.016200 0.016200 \n", - "2017-07-13 10:00:00 0.016263 0.016281 0.016211 0.016214 \n", - "2017-07-13 10:30:00 0.016304 0.016367 0.016263 0.016279 \n", - "2017-07-13 11:00:00 0.016382 0.016408 0.016304 0.016304 \n", - "2017-07-13 11:30:00 0.016490 0.016490 0.016382 0.016404 \n", - "2017-07-13 12:00:00 0.016433 0.016499 0.016365 0.016490 \n", - "2017-07-13 12:30:00 0.016441 0.016499 0.016353 0.016499 \n", - "2017-07-13 13:00:00 0.016410 0.016437 0.016337 0.016437 \n", - "2017-07-13 13:30:00 0.016351 0.016425 0.016334 0.016410 \n", - "2017-07-13 14:00:00 0.016306 0.016400 0.016306 0.016400 \n", - "2017-07-13 14:30:00 0.016306 0.016377 0.016306 0.016306 \n", - "2017-07-13 15:00:00 0.016120 0.016392 0.016046 0.016306 \n", - "2017-07-13 15:30:00 0.016027 0.016200 0.015992 0.016200 \n", - "2017-07-13 16:00:00 0.016020 0.016080 0.015960 0.016010 \n", - "2017-07-13 16:30:00 0.016030 0.016100 0.016002 0.016070 \n", - "2017-07-13 17:00:00 0.016229 0.016232 0.016115 0.016115 \n", - "2017-07-13 17:30:00 0.016057 0.016232 0.016030 0.016218 \n", - "2017-07-13 18:00:00 0.016128 0.016157 0.016000 0.016038 \n", - "2017-07-13 18:30:00 0.016389 0.016399 0.016100 0.016110 \n", - "2017-07-13 19:00:00 0.016366 0.016488 0.016300 0.016300 \n", - "2017-07-13 19:30:00 0.016305 0.016488 0.016305 0.016362 \n", - "2017-07-13 20:00:00 0.016180 0.016382 0.016175 0.016341 \n", - "2017-07-13 20:30:00 0.016281 0.016380 0.016220 0.016318 \n", - "2017-07-13 21:00:00 0.016360 0.016425 0.016281 0.016379 \n", - "2017-07-13 21:30:00 0.016348 0.016422 0.016227 0.016360 \n", - "2017-07-13 22:00:00 0.016348 0.016401 0.016323 0.016348 \n", - "2017-07-13 22:30:00 0.016269 0.016400 0.016262 0.016348 \n", - "2017-07-13 23:00:00 0.016260 0.016365 0.016260 0.016269 \n", - "2017-07-13 23:30:00 0.016289 0.016420 0.016260 0.016261 \n", + "Pair XMRBTC \\\n", + "Price volume quoteVolume close high low \n", + "date \n", + "2014-05-19 06:00:00 2.343284 156.369797 0.001110 0.011110 0.001110 \n", + "2014-05-19 06:30:00 0.321949 21.883301 0.001125 0.001500 0.001125 \n", + "2014-05-19 07:00:00 5.496416 369.994202 0.001190 0.001410 0.001080 \n", + "2014-05-19 07:30:00 1.160088 75.875801 0.001320 0.001867 0.001040 \n", + "2014-05-19 08:00:00 1.449590 93.675102 0.001700 0.001800 0.001320 \n", + "2014-05-19 08:30:00 1.483967 93.716499 0.001930 0.001940 0.001600 \n", + "2014-05-19 09:00:00 0.808186 51.574699 0.001800 0.002700 0.001760 \n", + "2014-05-19 09:30:00 0.611756 38.797901 0.001252 0.001780 0.001252 \n", + "2014-05-19 10:00:00 1.305152 83.099098 0.001500 0.001800 0.001370 \n", + "2014-05-19 10:30:00 1.035374 64.767998 0.002000 0.002000 0.001700 \n", + "2014-05-19 11:00:00 2.999820 190.303802 0.002000 0.002000 0.001790 \n", + "2014-05-19 11:30:00 0.518863 34.062599 0.001469 0.001850 0.001469 \n", + "2014-05-19 12:00:00 1.277838 83.992401 0.002180 0.002180 0.001594 \n", + "2014-05-19 12:30:00 7.734338 507.866211 0.002000 0.002000 0.002000 \n", + "2014-05-19 13:00:00 2.108728 135.026093 0.002000 0.002189 0.001824 \n", + "2014-05-19 13:30:00 7.215445 450.966400 0.002002 0.002200 0.002000 \n", + "2014-05-19 14:00:00 2.004976 128.225800 0.002000 0.002300 0.002000 \n", + "2014-05-19 14:30:00 4.151054 277.807190 0.002500 0.002800 0.002070 \n", + "2014-05-19 15:00:00 3.191634 202.929794 0.003000 0.003000 0.002200 \n", + "2014-05-19 15:30:00 2.078391 133.140106 0.002800 0.003270 0.002500 \n", + "2014-05-19 16:00:00 0.329114 21.308300 0.003290 0.003300 0.002700 \n", + "2014-05-19 16:30:00 2.284134 151.404007 0.004690 0.006000 0.003000 \n", + "2014-05-19 17:00:00 1.321608 87.975800 0.003340 0.004500 0.003031 \n", + "2014-05-19 17:30:00 0.003808 0.250200 0.003950 0.003950 0.002216 \n", + "2014-05-19 18:00:00 1.449422 96.481102 0.003500 0.003990 0.003000 \n", + "2014-05-19 18:30:00 1.084782 71.904198 0.003700 0.003907 0.003465 \n", + "2014-05-19 19:00:00 1.144843 76.125000 0.003700 0.003899 0.003650 \n", + "2014-05-19 19:30:00 0.764598 50.961102 0.003740 0.003779 0.003697 \n", + "2014-05-19 20:00:00 0.307216 20.242901 0.003810 0.004179 0.003700 \n", + "2014-05-19 20:30:00 0.039417 2.601800 0.004450 0.004450 0.003960 \n", + "... ... ... ... ... ... \n", + "2017-07-13 09:00:00 22.333296 308.698364 0.016200 0.016260 0.016151 \n", + "2017-07-13 09:30:00 14.041969 194.101578 0.016214 0.016396 0.016200 \n", + "2017-07-13 10:00:00 23.715418 328.675690 0.016263 0.016281 0.016211 \n", + "2017-07-13 10:30:00 33.350910 465.074738 0.016304 0.016367 0.016263 \n", + "2017-07-13 11:00:00 26.761724 369.510986 0.016382 0.016408 0.016304 \n", + "2017-07-13 11:30:00 18.257227 249.933945 0.016490 0.016490 0.016382 \n", + "2017-07-13 12:00:00 12.723900 173.851303 0.016433 0.016499 0.016365 \n", + "2017-07-13 12:30:00 3.529383 48.274719 0.016441 0.016499 0.016353 \n", + "2017-07-13 13:00:00 14.243275 194.257629 0.016410 0.016437 0.016337 \n", + "2017-07-13 13:30:00 15.433198 210.906815 0.016351 0.016425 0.016334 \n", + "2017-07-13 14:00:00 5.574882 76.360153 0.016306 0.016400 0.016306 \n", + "2017-07-13 14:30:00 3.633284 49.801620 0.016306 0.016377 0.016306 \n", + "2017-07-13 15:00:00 26.060940 358.873474 0.016120 0.016392 0.016046 \n", + "2017-07-13 15:30:00 66.840263 929.381287 0.016027 0.016200 0.015992 \n", + "2017-07-13 16:00:00 45.988777 644.390320 0.016020 0.016080 0.015960 \n", + "2017-07-13 16:30:00 19.054083 264.591522 0.016030 0.016100 0.016002 \n", + "2017-07-13 17:00:00 16.594046 229.660919 0.016229 0.016232 0.016115 \n", + "2017-07-13 17:30:00 26.669918 364.394470 0.016057 0.016232 0.016030 \n", + "2017-07-13 18:00:00 24.627537 338.062073 0.016128 0.016157 0.016000 \n", + "2017-07-13 18:30:00 18.240709 250.051086 0.016389 0.016399 0.016100 \n", + "2017-07-13 19:00:00 18.109848 249.073822 0.016366 0.016488 0.016300 \n", + "2017-07-13 19:30:00 25.484478 354.450989 0.016305 0.016488 0.016305 \n", + "2017-07-13 20:00:00 5.415996 75.298058 0.016180 0.016382 0.016175 \n", + "2017-07-13 20:30:00 17.284449 238.635284 0.016281 0.016380 0.016220 \n", + "2017-07-13 21:00:00 8.701756 120.286407 0.016360 0.016425 0.016281 \n", + "2017-07-13 21:30:00 18.284086 252.250626 0.016348 0.016422 0.016227 \n", + "2017-07-13 22:00:00 31.410805 435.147552 0.016348 0.016401 0.016323 \n", + "2017-07-13 22:30:00 2.176476 29.992342 0.016269 0.016400 0.016262 \n", + "2017-07-13 23:00:00 11.451510 157.605865 0.016260 0.016365 0.016260 \n", + "2017-07-13 23:30:00 5.277152 72.698265 0.016289 0.016420 0.016260 \n", "\n", - "[55284 rows x 16 columns]" + "Pair \n", + "Price open volume quoteVolume \n", + "date \n", + "2014-05-19 06:00:00 0.011110 1.995904 1404.974609 \n", + "2014-05-19 06:30:00 0.001200 0.619334 441.371613 \n", + "2014-05-19 07:00:00 0.001410 2.049713 1798.664062 \n", + "2014-05-19 07:30:00 0.001040 3.425346 2460.367920 \n", + "2014-05-19 08:00:00 0.001700 2.395254 1450.168701 \n", + "2014-05-19 08:30:00 0.001600 5.102005 2842.072021 \n", + "2014-05-19 09:00:00 0.001930 9.023416 4452.659668 \n", + "2014-05-19 09:30:00 0.001780 2.684633 1781.344360 \n", + "2014-05-19 10:00:00 0.001465 0.582902 393.504395 \n", + "2014-05-19 10:30:00 0.001700 3.449471 1806.058594 \n", + "2014-05-19 11:00:00 0.001999 2.536090 1270.478882 \n", + "2014-05-19 11:30:00 0.001850 2.144056 1318.990356 \n", + "2014-05-19 12:00:00 0.001594 0.891866 445.721802 \n", + "2014-05-19 12:30:00 0.002000 0.040000 20.000000 \n", + "2014-05-19 13:00:00 0.002000 2.457004 1207.615845 \n", + "2014-05-19 13:30:00 0.002189 1.278166 592.451721 \n", + "2014-05-19 14:00:00 0.002002 3.455345 1588.773804 \n", + "2014-05-19 14:30:00 0.002070 12.403766 5021.875977 \n", + "2014-05-19 15:00:00 0.002670 16.717419 5765.500000 \n", + "2014-05-19 15:30:00 0.002900 31.239510 10526.804688 \n", + "2014-05-19 16:00:00 0.002720 17.100437 5458.317383 \n", + "2014-05-19 16:30:00 0.003300 70.831444 17873.626953 \n", + "2014-05-19 17:00:00 0.004500 20.828947 5533.049316 \n", + "2014-05-19 17:30:00 0.003310 18.371737 6569.975098 \n", + "2014-05-19 18:00:00 0.003950 19.149166 5459.770508 \n", + "2014-05-19 18:30:00 0.003465 6.955287 1840.305908 \n", + "2014-05-19 19:00:00 0.003825 4.688448 1235.570190 \n", + "2014-05-19 19:30:00 0.003700 4.765787 1278.033813 \n", + "2014-05-19 20:00:00 0.003740 9.082610 2326.187744 \n", + "2014-05-19 20:30:00 0.004000 5.771838 1374.612915 \n", + "... ... ... ... \n", + "2017-07-13 09:00:00 0.016220 16.447470 1014.826050 \n", + "2017-07-13 09:30:00 0.016200 16.381884 1006.211121 \n", + "2017-07-13 10:00:00 0.016214 5.879561 362.057587 \n", + "2017-07-13 10:30:00 0.016279 6.862038 421.023651 \n", + "2017-07-13 11:00:00 0.016304 8.927679 544.949402 \n", + "2017-07-13 11:30:00 0.016404 8.967661 545.666626 \n", + "2017-07-13 12:00:00 0.016490 11.650610 709.466309 \n", + "2017-07-13 12:30:00 0.016499 12.990767 792.119324 \n", + "2017-07-13 13:00:00 0.016437 5.833544 356.266846 \n", + "2017-07-13 13:30:00 0.016410 5.270448 321.593292 \n", + "2017-07-13 14:00:00 0.016400 2.315221 141.803909 \n", + "2017-07-13 14:30:00 0.016306 1.577577 96.546478 \n", + "2017-07-13 15:00:00 0.016306 69.611435 4300.748535 \n", + "2017-07-13 15:30:00 0.016200 40.700321 2537.211182 \n", + "2017-07-13 16:00:00 0.016010 20.781893 1296.247681 \n", + "2017-07-13 16:30:00 0.016070 22.931610 1427.026733 \n", + "2017-07-13 17:00:00 0.016115 10.652046 658.748169 \n", + "2017-07-13 17:30:00 0.016218 17.885426 1106.663208 \n", + "2017-07-13 18:00:00 0.016038 13.959773 868.051697 \n", + "2017-07-13 18:30:00 0.016110 35.339157 2182.164795 \n", + "2017-07-13 19:00:00 0.016300 30.539915 1862.691406 \n", + "2017-07-13 19:30:00 0.016362 31.107351 1895.337891 \n", + "2017-07-13 20:00:00 0.016341 24.475405 1504.283081 \n", + "2017-07-13 20:30:00 0.016318 10.557378 646.264404 \n", + "2017-07-13 21:00:00 0.016379 2.052379 125.407608 \n", + "2017-07-13 21:30:00 0.016360 17.000980 1042.824341 \n", + "2017-07-13 22:00:00 0.016348 1.050736 64.258247 \n", + "2017-07-13 22:30:00 0.016348 4.130065 252.910141 \n", + "2017-07-13 23:00:00 0.016269 5.774802 354.476654 \n", + "2017-07-13 23:30:00 0.016261 5.284461 322.402893 \n", + "\n", + "[55284 rows x 18 columns]" ] }, - "execution_count": 12, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -3160,28 +3559,318 @@ "# replace rest\n", "df.replace(np.nan, 0, inplace=True)\n", "# df.unstack()\n", + "df=df.astype(np.float32)\n", "df" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": { "ExecuteTime": { - "end_time": "2017-10-29T11:40:32.428742Z", - "start_time": "2017-10-29T11:40:32.418792Z" + "end_time": "2017-11-11T07:31:11.273934Z", + "start_time": "2017-11-11T07:31:11.132551Z" } }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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PairLTCBTCDASHBTCXMRBTC
PriceclosehighlowopenvolumequoteVolumeclosehighlowopenvolumequoteVolumeclosehighlowopenvolumequoteVolume
count55284.00000055284.00000055284.00000055284.00000055284.00000055284.00000055284.00000055284.00000055284.00000055284.00000055284.00000055284.00000055284.00000055284.00000055284.00000055284.00000055284.00000055284.000000
mean0.0092190.0092570.0091800.00921940.7194143502.8391110.0178160.0179410.0176840.01781422.125402593.7857670.0057190.0057680.0056670.00571841.8475494253.086914
std0.0037940.0038240.0037650.003794214.72932416516.4570310.0175990.0177620.0174190.017594103.3306201891.3287350.0058480.0058940.0058020.005848192.16912813965.763672
min0.0030400.0030560.0020000.0030400.0000000.0000000.0030100.0031730.0028600.0030100.0000000.0000000.0009290.0009400.0009100.0009290.0000000.000000
25%0.0063730.0063900.0063510.0063710.0150891.5498000.0091250.0091850.0090740.0091300.0380113.9690000.0018290.0018440.0018080.0018270.587202277.602768
50%0.0081600.0081820.0081250.0081590.38248245.9779000.0120900.0121420.0120130.0120861.06564091.0773010.0026870.0027110.0026500.0026833.2001621070.625854
75%0.0113250.0113810.0112930.0113203.824190562.4739840.0166930.0167880.0165910.0166907.986436493.7504650.0099990.0100550.0099160.01000019.8497893208.200928
max0.0317000.0322000.0316000.03179010390.376953731837.1875000.1207700.1241570.1176200.1209004456.25878961347.7148440.0252460.0265000.0241500.0252508192.482422570634.062500
\n", + "
" + ], + "text/plain": [ + "Pair LTCBTC \\\n", + "Price close high low open volume \n", + "count 55284.000000 55284.000000 55284.000000 55284.000000 55284.000000 \n", + "mean 0.009219 0.009257 0.009180 0.009219 40.719414 \n", + "std 0.003794 0.003824 0.003765 0.003794 214.729324 \n", + "min 0.003040 0.003056 0.002000 0.003040 0.000000 \n", + "25% 0.006373 0.006390 0.006351 0.006371 0.015089 \n", + "50% 0.008160 0.008182 0.008125 0.008159 0.382482 \n", + "75% 0.011325 0.011381 0.011293 0.011320 3.824190 \n", + "max 0.031700 0.032200 0.031600 0.031790 10390.376953 \n", + "\n", + "Pair DASHBTC \\\n", + "Price quoteVolume close high low open \n", + "count 55284.000000 55284.000000 55284.000000 55284.000000 55284.000000 \n", + "mean 3502.839111 0.017816 0.017941 0.017684 0.017814 \n", + "std 16516.457031 0.017599 0.017762 0.017419 0.017594 \n", + "min 0.000000 0.003010 0.003173 0.002860 0.003010 \n", + "25% 1.549800 0.009125 0.009185 0.009074 0.009130 \n", + "50% 45.977900 0.012090 0.012142 0.012013 0.012086 \n", + "75% 562.473984 0.016693 0.016788 0.016591 0.016690 \n", + "max 731837.187500 0.120770 0.124157 0.117620 0.120900 \n", + "\n", + "Pair XMRBTC \\\n", + "Price volume quoteVolume close high low \n", + "count 55284.000000 55284.000000 55284.000000 55284.000000 55284.000000 \n", + "mean 22.125402 593.785767 0.005719 0.005768 0.005667 \n", + "std 103.330620 1891.328735 0.005848 0.005894 0.005802 \n", + "min 0.000000 0.000000 0.000929 0.000940 0.000910 \n", + "25% 0.038011 3.969000 0.001829 0.001844 0.001808 \n", + "50% 1.065640 91.077301 0.002687 0.002711 0.002650 \n", + "75% 7.986436 493.750465 0.009999 0.010055 0.009916 \n", + "max 4456.258789 61347.714844 0.025246 0.026500 0.024150 \n", + "\n", + "Pair \n", + "Price open volume quoteVolume \n", + "count 55284.000000 55284.000000 55284.000000 \n", + "mean 0.005718 41.847549 4253.086914 \n", + "std 0.005848 192.169128 13965.763672 \n", + "min 0.000929 0.000000 0.000000 \n", + "25% 0.001827 0.587202 277.602768 \n", + "50% 0.002683 3.200162 1070.625854 \n", + "75% 0.010000 19.849789 3208.200928 \n", + "max 0.025250 8192.482422 570634.062500 " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check stats\n", + "df.describe()" + ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 43, "metadata": { "ExecuteTime": { - "end_time": "2017-10-29T11:47:16.759766Z", - "start_time": "2017-10-29T11:47:16.748236Z" + "end_time": "2017-11-11T07:31:11.302762Z", + "start_time": "2017-11-11T07:31:11.299763Z" + }, + "collapsed": true + }, + "outputs": [], + "source": [ + "assert np.isfinite(df.as_matrix()).all()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "ExecuteTime": { + "end_time": "2017-11-11T07:31:11.493632Z", + "start_time": "2017-11-11T07:31:11.482379Z" } }, "outputs": [ @@ -3207,11 +3896,24 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2017-10-29T11:47:16.880279Z", - "start_time": "2017-10-29T11:47:16.761231Z" + "end_time": "2017-11-11T06:19:43.502921Z", + "start_time": "2017-11-11T06:19:43.415446Z" + }, + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "ExecuteTime": { + "end_time": "2017-11-11T07:31:11.950491Z", + "start_time": "2017-11-11T07:31:11.842884Z" } }, "outputs": [ @@ -3236,10 +3938,9 @@ " \n", " \n", " Pair\n", - " BTCBTC\n", - " LTCBTC\n", - " DASHBTC\n", - " XMRBTC\n", + " LTCBTC\n", + " DASHBTC\n", + " XMRBTC\n", " \n", " \n", " Price\n", @@ -3247,6 +3948,1643 @@ " high\n", " low\n", " open\n", + " volume\n", + " quoteVolume\n", + " close\n", + " high\n", + " low\n", + " open\n", + " volume\n", + " quoteVolume\n", + " close\n", + " high\n", + " low\n", + " open\n", + " volume\n", + " quoteVolume\n", + " \n", + " \n", + " date\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 2014-05-19 06:00:00\n", + " 0.023234\n", + " 0.023234\n", + " 0.023234\n", + " 0.023234\n", + " 0.538670\n", + " 23.184900\n", + " 0.015199\n", + " 0.015199\n", + " 0.014502\n", + " 0.014970\n", + " 2.343284\n", + " 156.369797\n", + " 0.001110\n", + " 0.011110\n", + " 0.001110\n", + " 0.011110\n", + " 1.995904\n", + " 1404.974609\n", + " \n", + " \n", + " 2014-05-19 06:30:00\n", + " 0.023421\n", + " 0.023421\n", + " 0.023421\n", + " 0.023421\n", + " 0.022663\n", + " 0.967600\n", + " 0.014520\n", + " 0.015342\n", + " 0.014510\n", + " 0.015199\n", + " 0.321949\n", + " 21.883301\n", + " 0.001125\n", + " 0.001500\n", + " 0.001125\n", + " 0.001200\n", + " 0.619334\n", + " 441.371613\n", + " \n", + " \n", + " 2014-05-19 07:00:00\n", + " 0.023038\n", + " 0.023100\n", + " 0.023038\n", + " 0.023100\n", + " 0.005663\n", + " 0.245200\n", + " 0.015350\n", + " 0.015400\n", + " 0.014510\n", + " 0.015000\n", + " 5.496416\n", + " 369.994202\n", + " 0.001190\n", + " 0.001410\n", + " 0.001080\n", + " 0.001410\n", + " 2.049713\n", + " 1798.664062\n", + " \n", + " \n", + " 2014-05-19 07:30:00\n", + " 0.023029\n", + " 0.023029\n", + " 0.023029\n", + " 0.023029\n", + " 0.005128\n", + " 0.222700\n", + " 0.015600\n", + " 0.015600\n", + " 0.015050\n", + " 0.015200\n", + " 1.160088\n", + " 75.875801\n", + " 0.001320\n", + " 0.001867\n", + " 0.001040\n", + " 0.001040\n", + " 3.425346\n", + " 2460.367920\n", + " \n", + " \n", + " 2014-05-19 08:00:00\n", + " 0.023026\n", + " 0.023420\n", + " 0.023026\n", + " 0.023029\n", + " 0.353329\n", + " 15.309300\n", + " 0.015600\n", + " 0.015600\n", + " 0.015103\n", + " 0.015149\n", + " 1.449590\n", + " 93.675102\n", + " 0.001700\n", + " 0.001800\n", + " 0.001320\n", + " 0.001700\n", + " 2.395254\n", + " 1450.168701\n", + " \n", + " \n", + " 2014-05-19 08:30:00\n", + " 0.023025\n", + " 0.023420\n", + " 0.023025\n", + " 0.023025\n", + " 0.031986\n", + " 1.386400\n", + " 0.016000\n", + " 0.016000\n", + " 0.015111\n", + " 0.015700\n", + " 1.483967\n", + " 93.716499\n", + " 0.001930\n", + " 0.001940\n", + " 0.001600\n", + " 0.001600\n", + " 5.102005\n", + " 2842.072021\n", + " \n", + " \n", + " 2014-05-19 09:00:00\n", + " 0.023025\n", + " 0.023025\n", + " 0.023025\n", + " 0.023025\n", + " 0.014403\n", + " 0.625500\n", + " 0.016000\n", + " 0.016000\n", + " 0.015327\n", + " 0.016000\n", + " 0.808186\n", + " 51.574699\n", + " 0.001800\n", + " 0.002700\n", + " 0.001760\n", + " 0.001930\n", + " 9.023416\n", + " 4452.659668\n", + " \n", + " \n", + " 2014-05-19 09:30:00\n", + " 0.023025\n", + " 0.023200\n", + " 0.023025\n", + " 0.023200\n", + " 0.022038\n", + " 0.950200\n", + " 0.015700\n", + " 0.016000\n", + " 0.015700\n", + " 0.016000\n", + " 0.611756\n", + " 38.797901\n", + " 0.001252\n", + " 0.001780\n", + " 0.001252\n", + " 0.001780\n", + " 2.684633\n", + " 1781.344360\n", + " \n", + " \n", + " 2014-05-19 10:00:00\n", + " 0.023025\n", + " 0.023200\n", + " 0.023025\n", + " 0.023200\n", + " 0.037687\n", + " 1.624600\n", + " 0.015700\n", + " 0.015980\n", + " 0.015658\n", + " 0.015980\n", + " 1.305152\n", + " 83.099098\n", + " 0.001500\n", + " 0.001800\n", + " 0.001370\n", + " 0.001465\n", + " 0.582902\n", + " 393.504395\n", + " \n", + " \n", + " 2014-05-19 10:30:00\n", + " 0.023025\n", + " 0.023200\n", + " 0.023025\n", + " 0.023200\n", + " 0.007686\n", + " 0.331400\n", + " 0.016000\n", + " 0.016000\n", + " 0.015396\n", + " 0.015650\n", + " 1.035374\n", + " 64.767998\n", + " 0.002000\n", + " 0.002000\n", + " 0.001700\n", + " 0.001700\n", + " 3.449471\n", + " 1806.058594\n", + " \n", + " \n", + " 2014-05-19 11:00:00\n", + " 0.023418\n", + " 0.023418\n", + " 0.023418\n", + " 0.023418\n", + " 0.023418\n", + " 1.000000\n", + " 0.015119\n", + " 0.016000\n", + " 0.015111\n", + " 0.015980\n", + " 2.999820\n", + " 190.303802\n", + " 0.002000\n", + " 0.002000\n", + " 0.001790\n", + " 0.001999\n", + " 2.536090\n", + " 1270.478882\n", + " \n", + " \n", + " 2014-05-19 11:30:00\n", + " 0.023025\n", + " 0.023025\n", + " 0.023025\n", + " 0.023025\n", + " 0.059156\n", + " 2.569100\n", + " 0.015450\n", + " 0.015450\n", + " 0.015111\n", + " 0.015119\n", + " 0.518863\n", + " 34.062599\n", + " 0.001469\n", + " 0.001850\n", + " 0.001469\n", + " 0.001850\n", + " 2.144056\n", + " 1318.990356\n", + " \n", + " \n", + " 2014-05-19 12:00:00\n", + " 0.023414\n", + " 0.023414\n", + " 0.023414\n", + " 0.023414\n", + " 0.068545\n", + " 2.927500\n", + " 0.015200\n", + " 0.015450\n", + " 0.015200\n", + " 0.015450\n", + " 1.277838\n", + " 83.992401\n", + " 0.002180\n", + " 0.002180\n", + " 0.001594\n", + " 0.001594\n", + " 0.891866\n", + " 445.721802\n", + " \n", + " \n", + " 2014-05-19 12:30:00\n", + " 0.023409\n", + " 0.023409\n", + " 0.023409\n", + " 0.023409\n", + " 0.001400\n", + " 0.059800\n", + " 0.015400\n", + " 0.015400\n", + " 0.015200\n", + " 0.015200\n", + " 7.734338\n", + " 507.866211\n", + " 0.002000\n", + " 0.002000\n", + " 0.002000\n", + " 0.002000\n", + " 0.040000\n", + " 20.000000\n", + " \n", + " \n", + " 2014-05-19 13:00:00\n", + " 0.023025\n", + " 0.023408\n", + " 0.023025\n", + " 0.023408\n", + " 0.094094\n", + " 4.054200\n", + " 0.016000\n", + " 0.016000\n", + " 0.015500\n", + " 0.015670\n", + " 2.108728\n", + " 135.026093\n", + " 0.002000\n", + " 0.002189\n", + " 0.001824\n", + " 0.002000\n", + " 2.457004\n", + " 1207.615845\n", + " \n", + " \n", + " 2014-05-19 13:30:00\n", + " 0.023025\n", + " 0.023400\n", + " 0.023025\n", + " 0.023400\n", + " 0.030565\n", + " 1.318200\n", + " 0.015812\n", + " 0.016000\n", + " 0.015812\n", + " 0.016000\n", + " 7.215445\n", + " 450.966400\n", + " 0.002002\n", + " 0.002200\n", + " 0.002000\n", + " 0.002189\n", + " 1.278166\n", + " 592.451721\n", + " \n", + " \n", + " 2014-05-19 14:00:00\n", + " 0.023408\n", + " 0.023408\n", + " 0.023025\n", + " 0.023050\n", + " 0.073974\n", + " 3.183400\n", + " 0.015446\n", + " 0.015990\n", + " 0.015446\n", + " 0.015446\n", + " 2.004976\n", + " 128.225800\n", + " 0.002000\n", + " 0.002300\n", + " 0.002000\n", + " 0.002002\n", + " 3.455345\n", + " 1588.773804\n", + " \n", + " \n", + " 2014-05-19 14:30:00\n", + " 0.023408\n", + " 0.023408\n", + " 0.023408\n", + " 0.023408\n", + " 0.000000\n", + " 0.000000\n", + " 0.015868\n", + " 0.016000\n", + " 0.014288\n", + " 0.015889\n", + " 4.151054\n", + " 277.807190\n", + " 0.002500\n", + " 0.002800\n", + " 0.002070\n", + " 0.002070\n", + " 12.403766\n", + " 5021.875977\n", + " \n", + " \n", + " 2014-05-19 15:00:00\n", + " 0.023160\n", + " 0.023408\n", + " 0.023160\n", + " 0.023408\n", + " 0.019881\n", + " 0.850200\n", + " 0.015960\n", + " 0.015979\n", + " 0.015000\n", + " 0.015000\n", + " 3.191634\n", + " 202.929794\n", + " 0.003000\n", + " 0.003000\n", + " 0.002200\n", + " 0.002670\n", + " 16.717419\n", + " 5765.500000\n", + " \n", + " \n", + " 2014-05-19 15:30:00\n", + " 0.023408\n", + " 0.023408\n", + " 0.023001\n", + " 0.023160\n", + " 0.934453\n", + " 40.586800\n", + " 0.015396\n", + " 0.015838\n", + " 0.015396\n", + " 0.015600\n", + " 2.078391\n", + " 133.140106\n", + " 0.002800\n", + " 0.003270\n", + " 0.002500\n", + " 0.002900\n", + " 31.239510\n", + " 10526.804688\n", + " \n", + " \n", + " 2014-05-19 16:00:00\n", + " 0.023001\n", + " 0.023407\n", + " 0.023001\n", + " 0.023407\n", + " 0.363492\n", + " 15.799400\n", + " 0.015450\n", + " 0.015450\n", + " 0.015400\n", + " 0.015400\n", + " 0.329114\n", + " 21.308300\n", + " 0.003290\n", + " 0.003300\n", + " 0.002700\n", + " 0.002720\n", + " 17.100437\n", + " 5458.317383\n", + " \n", + " \n", + " 2014-05-19 16:30:00\n", + " 0.023001\n", + " 0.023200\n", + " 0.023001\n", + " 0.023001\n", + " 0.271593\n", + " 11.807300\n", + " 0.015000\n", + " 0.015450\n", + " 0.014700\n", + " 0.015450\n", + " 2.284134\n", + " 151.404007\n", + " 0.004690\n", + " 0.006000\n", + " 0.003000\n", + " 0.003300\n", + " 70.831444\n", + " 17873.626953\n", + " \n", + " \n", + " 2014-05-19 17:00:00\n", + " 0.023001\n", + " 0.023200\n", + " 0.023001\n", + " 0.023001\n", + " 0.055575\n", + " 2.409300\n", + " 0.015000\n", + " 0.015390\n", + " 0.014950\n", + " 0.015000\n", + " 1.321608\n", + " 87.975800\n", + " 0.003340\n", + " 0.004500\n", + " 0.003031\n", + " 0.004500\n", + " 20.828947\n", + " 5533.049316\n", + " \n", + " \n", + " 2014-05-19 17:30:00\n", + " 0.023001\n", + " 0.023191\n", + " 0.023001\n", + " 0.023001\n", + " 0.679097\n", + " 29.458000\n", + " 0.015000\n", + " 0.015250\n", + " 0.015000\n", + " 0.015250\n", + " 0.003808\n", + " 0.250200\n", + " 0.003950\n", + " 0.003950\n", + " 0.002216\n", + " 0.003310\n", + " 18.371737\n", + " 6569.975098\n", + " \n", + " \n", + " 2014-05-19 18:00:00\n", + " 0.023200\n", + " 0.023200\n", + " 0.023191\n", + " 0.023191\n", + " 0.115721\n", + " 4.988000\n", + " 0.015150\n", + " 0.015250\n", + " 0.014950\n", + " 0.015000\n", + " 1.449422\n", + " 96.481102\n", + " 0.003500\n", + " 0.003990\n", + " 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46993 rows × 18 columns

\n", + "" + ], + "text/plain": [ + "Pair LTCBTC \\\n", + "Price close high low open volume \n", + "date \n", + "2014-05-19 06:00:00 0.023234 0.023234 0.023234 0.023234 0.538670 \n", + "2014-05-19 06:30:00 0.023421 0.023421 0.023421 0.023421 0.022663 \n", + "2014-05-19 07:00:00 0.023038 0.023100 0.023038 0.023100 0.005663 \n", + "2014-05-19 07:30:00 0.023029 0.023029 0.023029 0.023029 0.005128 \n", + "2014-05-19 08:00:00 0.023026 0.023420 0.023026 0.023029 0.353329 \n", + "2014-05-19 08:30:00 0.023025 0.023420 0.023025 0.023025 0.031986 \n", + "2014-05-19 09:00:00 0.023025 0.023025 0.023025 0.023025 0.014403 \n", + "2014-05-19 09:30:00 0.023025 0.023200 0.023025 0.023200 0.022038 \n", + "2014-05-19 10:00:00 0.023025 0.023200 0.023025 0.023200 0.037687 \n", + "2014-05-19 10:30:00 0.023025 0.023200 0.023025 0.023200 0.007686 \n", + "2014-05-19 11:00:00 0.023418 0.023418 0.023418 0.023418 0.023418 \n", + "2014-05-19 11:30:00 0.023025 0.023025 0.023025 0.023025 0.059156 \n", + "2014-05-19 12:00:00 0.023414 0.023414 0.023414 0.023414 0.068545 \n", + "2014-05-19 12:30:00 0.023409 0.023409 0.023409 0.023409 0.001400 \n", + "2014-05-19 13:00:00 0.023025 0.023408 0.023025 0.023408 0.094094 \n", + "2014-05-19 13:30:00 0.023025 0.023400 0.023025 0.023400 0.030565 \n", + "2014-05-19 14:00:00 0.023408 0.023408 0.023025 0.023050 0.073974 \n", + "2014-05-19 14:30:00 0.023408 0.023408 0.023408 0.023408 0.000000 \n", + "2014-05-19 15:00:00 0.023160 0.023408 0.023160 0.023408 0.019881 \n", + "2014-05-19 15:30:00 0.023408 0.023408 0.023001 0.023160 0.934453 \n", + "2014-05-19 16:00:00 0.023001 0.023407 0.023001 0.023407 0.363492 \n", + "2014-05-19 16:30:00 0.023001 0.023200 0.023001 0.023001 0.271593 \n", + "2014-05-19 17:00:00 0.023001 0.023200 0.023001 0.023001 0.055575 \n", + "2014-05-19 17:30:00 0.023001 0.023191 0.023001 0.023001 0.679097 \n", + "2014-05-19 18:00:00 0.023200 0.023200 0.023191 0.023191 0.115721 \n", + "2014-05-19 18:30:00 0.023001 0.023200 0.023001 0.023200 0.014856 \n", + "2014-05-19 19:00:00 0.023402 0.023402 0.023001 0.023001 0.047459 \n", + "2014-05-19 19:30:00 0.023402 0.023402 0.023402 0.023402 0.107424 \n", + "2014-05-19 20:00:00 0.023402 0.023402 0.023001 0.023402 0.054595 \n", + "2014-05-19 20:30:00 0.023402 0.023402 0.023402 0.023402 0.599039 \n", + "... ... ... ... ... ... \n", + "2017-01-21 15:30:00 0.004240 0.004242 0.004223 0.004223 0.902628 \n", + "2017-01-21 16:00:00 0.004247 0.004247 0.004230 0.004240 3.981343 \n", + "2017-01-21 16:30:00 0.004239 0.004248 0.004233 0.004246 0.518569 \n", + "2017-01-21 17:00:00 0.004230 0.004254 0.004230 0.004248 2.802318 \n", + "2017-01-21 17:30:00 0.004222 0.004248 0.004219 0.004227 0.709948 \n", + "2017-01-21 18:00:00 0.004234 0.004238 0.004222 0.004230 0.396342 \n", + "2017-01-21 18:30:00 0.004234 0.004244 0.004228 0.004234 2.782868 \n", + "2017-01-21 19:00:00 0.004234 0.004244 0.004234 0.004234 1.762961 \n", + "2017-01-21 19:30:00 0.004219 0.004243 0.004215 0.004234 9.524260 \n", + "2017-01-21 20:00:00 0.004242 0.004242 0.004220 0.004220 2.662878 \n", + "2017-01-21 20:30:00 0.004239 0.004242 0.004223 0.004242 7.040715 \n", + "2017-01-21 21:00:00 0.004238 0.004240 0.004225 0.004239 1.381644 \n", + "2017-01-21 21:30:00 0.004222 0.004238 0.004222 0.004225 2.797171 \n", + "2017-01-21 22:00:00 0.004245 0.004245 0.004222 0.004224 5.168664 \n", + "2017-01-21 22:30:00 0.004231 0.004245 0.004227 0.004240 0.427217 \n", + "2017-01-21 23:00:00 0.004240 0.004311 0.004231 0.004231 24.080179 \n", + "2017-01-21 23:30:00 0.004236 0.004245 0.004220 0.004240 3.613784 \n", + "2017-01-22 00:00:00 0.004210 0.004268 0.004208 0.004235 10.927131 \n", + "2017-01-22 00:30:00 0.004198 0.004223 0.004185 0.004210 12.178608 \n", + "2017-01-22 01:00:00 0.004180 0.004201 0.004180 0.004185 4.309223 \n", + "2017-01-22 01:30:00 0.004193 0.004193 0.004177 0.004182 2.423690 \n", + "2017-01-22 02:00:00 0.004180 0.004194 0.004176 0.004177 11.360030 \n", + "2017-01-22 02:30:00 0.004165 0.004180 0.004152 0.004180 22.758385 \n", + "2017-01-22 03:00:00 0.004156 0.004195 0.004155 0.004166 7.154735 \n", + "2017-01-22 03:30:00 0.004159 0.004168 0.004150 0.004168 12.094599 \n", + "2017-01-22 04:00:00 0.004145 0.004159 0.004137 0.004150 12.359952 \n", + "2017-01-22 04:30:00 0.004136 0.004159 0.004135 0.004159 3.929579 \n", + "2017-01-22 05:00:00 0.004160 0.004160 0.004136 0.004136 1.836689 \n", + "2017-01-22 05:30:00 0.004179 0.004179 0.004150 0.004152 1.224472 \n", + "2017-01-22 06:00:00 0.004169 0.004183 0.004163 0.004180 2.599358 \n", + "\n", + "Pair DASHBTC \\\n", + "Price quoteVolume close high low open \n", + "date \n", + "2014-05-19 06:00:00 23.184900 0.015199 0.015199 0.014502 0.014970 \n", + "2014-05-19 06:30:00 0.967600 0.014520 0.015342 0.014510 0.015199 \n", + "2014-05-19 07:00:00 0.245200 0.015350 0.015400 0.014510 0.015000 \n", + "2014-05-19 07:30:00 0.222700 0.015600 0.015600 0.015050 0.015200 \n", + "2014-05-19 08:00:00 15.309300 0.015600 0.015600 0.015103 0.015149 \n", + "2014-05-19 08:30:00 1.386400 0.016000 0.016000 0.015111 0.015700 \n", + "2014-05-19 09:00:00 0.625500 0.016000 0.016000 0.015327 0.016000 \n", + "2014-05-19 09:30:00 0.950200 0.015700 0.016000 0.015700 0.016000 \n", + "2014-05-19 10:00:00 1.624600 0.015700 0.015980 0.015658 0.015980 \n", + "2014-05-19 10:30:00 0.331400 0.016000 0.016000 0.015396 0.015650 \n", + "2014-05-19 11:00:00 1.000000 0.015119 0.016000 0.015111 0.015980 \n", + "2014-05-19 11:30:00 2.569100 0.015450 0.015450 0.015111 0.015119 \n", + "2014-05-19 12:00:00 2.927500 0.015200 0.015450 0.015200 0.015450 \n", + "2014-05-19 12:30:00 0.059800 0.015400 0.015400 0.015200 0.015200 \n", + "2014-05-19 13:00:00 4.054200 0.016000 0.016000 0.015500 0.015670 \n", + "2014-05-19 13:30:00 1.318200 0.015812 0.016000 0.015812 0.016000 \n", + "2014-05-19 14:00:00 3.183400 0.015446 0.015990 0.015446 0.015446 \n", + "2014-05-19 14:30:00 0.000000 0.015868 0.016000 0.014288 0.015889 \n", + "2014-05-19 15:00:00 0.850200 0.015960 0.015979 0.015000 0.015000 \n", + "2014-05-19 15:30:00 40.586800 0.015396 0.015838 0.015396 0.015600 \n", + "2014-05-19 16:00:00 15.799400 0.015450 0.015450 0.015400 0.015400 \n", + "2014-05-19 16:30:00 11.807300 0.015000 0.015450 0.014700 0.015450 \n", + "2014-05-19 17:00:00 2.409300 0.015000 0.015390 0.014950 0.015000 \n", + "2014-05-19 17:30:00 29.458000 0.015000 0.015250 0.015000 0.015250 \n", + "2014-05-19 18:00:00 4.988000 0.015150 0.015250 0.014950 0.015000 \n", + "2014-05-19 18:30:00 0.645700 0.015352 0.015352 0.015000 0.015000 \n", + "2014-05-19 19:00:00 2.038400 0.015005 0.015480 0.015005 0.015200 \n", + "2014-05-19 19:30:00 4.590500 0.014950 0.015100 0.014950 0.015005 \n", + "2014-05-19 20:00:00 2.334000 0.015199 0.015400 0.014951 0.015400 \n", + "2014-05-19 20:30:00 25.598301 0.015150 0.015150 0.015150 0.015150 \n", + "... ... ... ... ... ... \n", + "2017-01-21 15:30:00 213.296402 0.016217 0.016240 0.016104 0.016240 \n", + "2017-01-21 16:00:00 937.566711 0.016170 0.016300 0.016071 0.016217 \n", + "2017-01-21 16:30:00 122.233398 0.016218 0.016279 0.016120 0.016196 \n", + "2017-01-21 17:00:00 660.370300 0.016158 0.016250 0.016120 0.016218 \n", + "2017-01-21 17:30:00 167.902893 0.016250 0.016250 0.016158 0.016158 \n", + "2017-01-21 18:00:00 93.864601 0.016250 0.016250 0.016162 0.016250 \n", + "2017-01-21 18:30:00 656.930176 0.016328 0.016328 0.016162 0.016250 \n", + "2017-01-21 19:00:00 415.573395 0.016298 0.016328 0.016298 0.016300 \n", + "2017-01-21 19:30:00 2252.598633 0.016267 0.016327 0.016250 0.016298 \n", + "2017-01-21 20:00:00 630.122009 0.016267 0.016307 0.016250 0.016250 \n", + "2017-01-21 20:30:00 1664.227295 0.016310 0.016339 0.016267 0.016267 \n", + "2017-01-21 21:00:00 326.710693 0.016339 0.016339 0.016270 0.016270 \n", + "2017-01-21 21:30:00 661.310730 0.016290 0.016339 0.016290 0.016290 \n", + "2017-01-21 22:00:00 1221.083984 0.016140 0.016339 0.016126 0.016317 \n", + "2017-01-21 22:30:00 100.868500 0.016140 0.016234 0.016140 0.016140 \n", + "2017-01-21 23:00:00 5632.762695 0.016230 0.016233 0.016127 0.016140 \n", + "2017-01-21 23:30:00 854.670105 0.016220 0.016230 0.016131 0.016131 \n", + "2017-01-22 00:00:00 2586.035400 0.016250 0.016259 0.016156 0.016220 \n", + "2017-01-22 00:30:00 2901.425293 0.016130 0.016250 0.016130 0.016250 \n", + "2017-01-22 01:00:00 1028.932373 0.016150 0.016213 0.016130 0.016130 \n", + "2017-01-22 01:30:00 579.752319 0.016180 0.016240 0.016150 0.016150 \n", + "2017-01-22 02:00:00 2716.172607 0.016236 0.016240 0.016159 0.016180 \n", + "2017-01-22 02:30:00 5465.259766 0.016259 0.016259 0.016169 0.016220 \n", + "2017-01-22 03:00:00 1713.548706 0.016231 0.016267 0.016188 0.016259 \n", + "2017-01-22 03:30:00 2912.451172 0.016294 0.016339 0.016200 0.016231 \n", + "2017-01-22 04:00:00 2981.219482 0.016331 0.016339 0.016150 0.016245 \n", + "2017-01-22 04:30:00 947.898682 0.016339 0.016339 0.016181 0.016232 \n", + "2017-01-22 05:00:00 442.009186 0.016250 0.016339 0.016189 0.016339 \n", + "2017-01-22 05:30:00 294.140198 0.016262 0.016339 0.016248 0.016250 \n", + "2017-01-22 06:00:00 622.501526 0.016339 0.016339 0.016248 0.016263 \n", + "\n", + "Pair XMRBTC \\\n", + "Price volume quoteVolume close high low \n", + "date \n", + "2014-05-19 06:00:00 2.343284 156.369797 0.001110 0.011110 0.001110 \n", + "2014-05-19 06:30:00 0.321949 21.883301 0.001125 0.001500 0.001125 \n", + "2014-05-19 07:00:00 5.496416 369.994202 0.001190 0.001410 0.001080 \n", + "2014-05-19 07:30:00 1.160088 75.875801 0.001320 0.001867 0.001040 \n", + "2014-05-19 08:00:00 1.449590 93.675102 0.001700 0.001800 0.001320 \n", + "2014-05-19 08:30:00 1.483967 93.716499 0.001930 0.001940 0.001600 \n", + "2014-05-19 09:00:00 0.808186 51.574699 0.001800 0.002700 0.001760 \n", + "2014-05-19 09:30:00 0.611756 38.797901 0.001252 0.001780 0.001252 \n", + "2014-05-19 10:00:00 1.305152 83.099098 0.001500 0.001800 0.001370 \n", + "2014-05-19 10:30:00 1.035374 64.767998 0.002000 0.002000 0.001700 \n", + "2014-05-19 11:00:00 2.999820 190.303802 0.002000 0.002000 0.001790 \n", + "2014-05-19 11:30:00 0.518863 34.062599 0.001469 0.001850 0.001469 \n", + "2014-05-19 12:00:00 1.277838 83.992401 0.002180 0.002180 0.001594 \n", + "2014-05-19 12:30:00 7.734338 507.866211 0.002000 0.002000 0.002000 \n", + "2014-05-19 13:00:00 2.108728 135.026093 0.002000 0.002189 0.001824 \n", + "2014-05-19 13:30:00 7.215445 450.966400 0.002002 0.002200 0.002000 \n", + "2014-05-19 14:00:00 2.004976 128.225800 0.002000 0.002300 0.002000 \n", + "2014-05-19 14:30:00 4.151054 277.807190 0.002500 0.002800 0.002070 \n", + "2014-05-19 15:00:00 3.191634 202.929794 0.003000 0.003000 0.002200 \n", + "2014-05-19 15:30:00 2.078391 133.140106 0.002800 0.003270 0.002500 \n", + "2014-05-19 16:00:00 0.329114 21.308300 0.003290 0.003300 0.002700 \n", + "2014-05-19 16:30:00 2.284134 151.404007 0.004690 0.006000 0.003000 \n", + "2014-05-19 17:00:00 1.321608 87.975800 0.003340 0.004500 0.003031 \n", + "2014-05-19 17:30:00 0.003808 0.250200 0.003950 0.003950 0.002216 \n", + "2014-05-19 18:00:00 1.449422 96.481102 0.003500 0.003990 0.003000 \n", + "2014-05-19 18:30:00 1.084782 71.904198 0.003700 0.003907 0.003465 \n", + "2014-05-19 19:00:00 1.144843 76.125000 0.003700 0.003899 0.003650 \n", + "2014-05-19 19:30:00 0.764598 50.961102 0.003740 0.003779 0.003697 \n", + "2014-05-19 20:00:00 0.307216 20.242901 0.003810 0.004179 0.003700 \n", + "2014-05-19 20:30:00 0.039417 2.601800 0.004450 0.004450 0.003960 \n", + "... ... ... ... ... ... \n", + "2017-01-21 15:30:00 3.368059 208.258698 0.012999 0.013080 0.012990 \n", + "2017-01-21 16:00:00 14.081318 871.229675 0.012960 0.013014 0.012922 \n", + "2017-01-21 16:30:00 5.120440 316.448303 0.013190 0.013200 0.012910 \n", + "2017-01-21 17:00:00 5.331674 329.266815 0.013100 0.013199 0.013100 \n", + "2017-01-21 17:30:00 1.672528 103.314301 0.013097 0.013159 0.012981 \n", + "2017-01-21 18:00:00 3.470650 214.131897 0.013151 0.013210 0.013060 \n", + "2017-01-21 18:30:00 20.836437 1277.687866 0.013200 0.013210 0.013151 \n", + "2017-01-21 19:00:00 1.224432 75.113998 0.013092 0.013200 0.013092 \n", + "2017-01-21 19:30:00 5.123699 314.760315 0.013121 0.013126 0.013081 \n", + "2017-01-21 20:00:00 1.287997 79.217003 0.013087 0.013121 0.013085 \n", + "2017-01-21 20:30:00 4.781244 292.842896 0.013014 0.013114 0.013014 \n", + "2017-01-21 21:00:00 4.923599 301.542786 0.013043 0.013058 0.013014 \n", + "2017-01-21 21:30:00 2.351681 144.343506 0.013024 0.013064 0.012973 \n", + "2017-01-21 22:00:00 18.847517 1160.558716 0.013049 0.013082 0.013019 \n", + "2017-01-21 22:30:00 1.586739 98.242401 0.013100 0.013100 0.013013 \n", + "2017-01-21 23:00:00 6.267624 386.617310 0.013066 0.013100 0.013056 \n", + "2017-01-21 23:30:00 0.767921 47.429699 0.013037 0.013085 0.013013 \n", + "2017-01-22 00:00:00 3.170950 195.215607 0.012981 0.013037 0.012900 \n", + "2017-01-22 00:30:00 2.599715 160.755905 0.012988 0.012992 0.012953 \n", + "2017-01-22 01:00:00 1.946634 120.242401 0.013018 0.013018 0.012984 \n", + "2017-01-22 01:30:00 5.277963 325.512909 0.012900 0.013018 0.012900 \n", + "2017-01-22 02:00:00 0.890858 54.929001 0.012965 0.013005 0.012900 \n", + "2017-01-22 02:30:00 4.026737 247.832504 0.013006 0.013008 0.012955 \n", + "2017-01-22 03:00:00 4.915900 302.988800 0.013000 0.013038 0.012975 \n", + "2017-01-22 03:30:00 12.083299 741.877686 0.012979 0.013062 0.012965 \n", + "2017-01-22 04:00:00 9.846511 604.524292 0.012976 0.013070 0.012971 \n", + "2017-01-22 04:30:00 2.600681 159.870499 0.012987 0.013000 0.012971 \n", + "2017-01-22 05:00:00 3.066367 188.080704 0.012980 0.013019 0.012971 \n", + "2017-01-22 05:30:00 0.992135 60.806702 0.012980 0.013040 0.012980 \n", + "2017-01-22 06:00:00 0.331264 20.321301 0.012969 0.013005 0.012934 \n", + "\n", + "Pair \n", + "Price open volume quoteVolume \n", + "date \n", + "2014-05-19 06:00:00 0.011110 1.995904 1404.974609 \n", + "2014-05-19 06:30:00 0.001200 0.619334 441.371613 \n", + "2014-05-19 07:00:00 0.001410 2.049713 1798.664062 \n", + "2014-05-19 07:30:00 0.001040 3.425346 2460.367920 \n", + "2014-05-19 08:00:00 0.001700 2.395254 1450.168701 \n", + "2014-05-19 08:30:00 0.001600 5.102005 2842.072021 \n", + "2014-05-19 09:00:00 0.001930 9.023416 4452.659668 \n", + "2014-05-19 09:30:00 0.001780 2.684633 1781.344360 \n", + "2014-05-19 10:00:00 0.001465 0.582902 393.504395 \n", + "2014-05-19 10:30:00 0.001700 3.449471 1806.058594 \n", + "2014-05-19 11:00:00 0.001999 2.536090 1270.478882 \n", + "2014-05-19 11:30:00 0.001850 2.144056 1318.990356 \n", + "2014-05-19 12:00:00 0.001594 0.891866 445.721802 \n", + "2014-05-19 12:30:00 0.002000 0.040000 20.000000 \n", + "2014-05-19 13:00:00 0.002000 2.457004 1207.615845 \n", + "2014-05-19 13:30:00 0.002189 1.278166 592.451721 \n", + "2014-05-19 14:00:00 0.002002 3.455345 1588.773804 \n", + "2014-05-19 14:30:00 0.002070 12.403766 5021.875977 \n", + "2014-05-19 15:00:00 0.002670 16.717419 5765.500000 \n", + "2014-05-19 15:30:00 0.002900 31.239510 10526.804688 \n", + "2014-05-19 16:00:00 0.002720 17.100437 5458.317383 \n", + "2014-05-19 16:30:00 0.003300 70.831444 17873.626953 \n", + "2014-05-19 17:00:00 0.004500 20.828947 5533.049316 \n", + "2014-05-19 17:30:00 0.003310 18.371737 6569.975098 \n", + "2014-05-19 18:00:00 0.003950 19.149166 5459.770508 \n", + "2014-05-19 18:30:00 0.003465 6.955287 1840.305908 \n", + "2014-05-19 19:00:00 0.003825 4.688448 1235.570190 \n", + "2014-05-19 19:30:00 0.003700 4.765787 1278.033813 \n", + "2014-05-19 20:00:00 0.003740 9.082610 2326.187744 \n", + "2014-05-19 20:30:00 0.004000 5.771838 1374.612915 \n", + "... ... ... ... \n", + "2017-01-21 15:30:00 0.013080 13.764748 1056.132446 \n", + "2017-01-21 16:00:00 0.012990 19.773813 1525.487915 \n", + "2017-01-21 16:30:00 0.012948 175.732056 13410.916992 \n", + "2017-01-21 17:00:00 0.013194 15.294087 1164.887207 \n", + "2017-01-21 17:30:00 0.013100 38.860748 2973.272705 \n", + "2017-01-21 18:00:00 0.013091 65.602135 4976.354980 \n", + "2017-01-21 18:30:00 0.013170 7.057232 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PairDASHBTCLTCBTCXMRBTC
Priceclosehighlow
2014-05-19 06:00:001.01.01.01.00.0232340.0232340.0232340.0232340.0151990.0151990.0145020.0149700.0011100.0111100.0011100.0111102017-01-22 06:30:000.0163390.0163390.0162550.0162550.0041570.0041770.0041570.0041760.0129630.0130050.0129300.012974
2014-05-19 06:30:001.01.01.01.00.0234210.0234210.0234210.0234210.0145200.0153420.0145100.0151990.0011250.0015000.0011250.0012002017-01-22 07:00:000.0162510.0163400.0162510.0163390.0041720.0041720.0041570.0041710.0129730.0130050.0129300.012931
2014-05-19 07:00:001.01.01.01.00.0230380.0231000.0230380.0231000.0153500.0154000.0145100.0150000.0011900.0014100.0010800.0014102017-01-22 07:30:000.0163080.0163080.0162510.0162510.0041570.0041710.0041570.0041610.0129950.0130050.0129680.012973
2014-05-19 07:30:001.01.01.01.00.0230290.0230290.0230290.0230290.0156000.0156000.0150500.0152000.0013200.0018670.0010400.0010402017-01-22 08:00:000.0163100.0163250.0162150.0163080.0041660.0041680.0041570.0041680.0129500.0130050.0129380.012995
2014-05-19 08:00:001.01.01.01.00.0230260.0234200.0230260.0230290.0156000.0156000.0151030.0151490.0017000.0018000.0013200.0017002017-01-22 08:30:000.0162700.0163400.0162210.0162210.0041700.0041710.0041570.0041570.0129570.0129650.0129030.012950
2014-05-19 08:30:001.01.01.01.00.0230250.0234200.0230250.0230250.0160000.0160000.0151110.0157000.0019300.0019400.0016000.0016002017-01-22 09:00:000.0162500.0163400.0162210.0163400.0041510.0041620.0041460.0041620.0129000.0130000.0128800.012958
2014-05-19 09:00:001.01.01.01.00.0230250.0230250.0230250.0230250.0160000.0160000.0153270.0160000.0018000.0027000.0017600.0019302017-01-22 09:30:000.0163980.0164600.0162400.0162500.0041760.0041760.0041470.0041470.0128470.0129130.0128200.012900
2014-05-19 09:30:001.01.01.01.00.0230250.0232000.0230250.0232000.0157000.0160000.0157000.0160000.0012520.0017800.0012520.0017802017-01-22 10:00:000.0164220.0164700.0163250.0163250.0041920.0041920.0041530.0041530.0130000.0130050.0128630.012863
2014-05-19 10:00:001.01.01.01.00.0230250.0232000.0230250.0232000.0157000.0159800.0156580.0159800.0015000.0018000.0013700.0014652017-01-22 10:30:000.0166000.0166000.0164000.0164220.0041820.0041940.0041760.0041920.0129600.0130050.0128830.012982
2014-05-19 10:30:001.01.01.01.00.0230250.0232000.0230250.0232000.0160000.0160000.0153960.0156500.0020000.0020000.0017000.0017002017-01-22 11:00:000.0166160.0166480.0165460.0165720.0041920.0042010.0041760.0041900.0128930.0129530.0128470.012901
2014-05-19 11:00:001.01.01.01.00.0234180.0234180.0234180.0234180.0151190.0160000.0151110.0159800.0020000.0020000.0017900.0019992017-01-22 11:30:000.0166580.0166780.0165690.0166160.0041930.0041980.0041760.0041760.0129730.0129750.0128610.012893
2014-05-19 11:30:001.01.01.01.00.0230250.0230250.0230250.0230250.0154500.0154500.0151110.0151190.0014690.0018500.0014690.0018502017-01-22 12:00:000.0168500.0168500.0166000.0166610.0042200.0042400.0041760.0041780.0131300.0131300.0129400.012973
2014-05-19 12:00:001.01.01.01.00.0234140.0234140.0234140.0234140.0152000.0154500.0152000.0154500.0021800.0021800.0015940.0015942017-01-22 12:30:000.0168150.0168520.0167270.0168490.0042380.0042440.0042030.0042200.0131250.0131950.0130810.013130
2014-05-19 12:30:001.01.01.01.00.0234090.0234090.0234090.0234090.0154000.0154000.0152000.0152000.0020000.0020000.0020000.0020002017-01-22 13:00:000.0167330.0168490.0167160.0168150.0042150.0042360.0042110.0042140.0131000.0131280.0130830.013125
2014-05-19 13:00:001.01.01.01.00.0230250.0234080.0230250.0234080.0160000.0160000.0155000.0156700.0020000.0021890.0018240.0020002017-01-22 13:30:000.0167300.0168640.0167000.0167330.0042420.0042430.0042150.0042250.0131000.0131190.0130780.013100
2014-05-19 13:30:001.01.01.01.00.0230250.0234000.0230250.0234000.0158120.0160000.0158120.0160000.0020020.0022000.0020000.0021892017-01-22 14:00:000.0167780.0168400.0167080.0167080.0042730.0042920.0042420.0042420.0131700.0132000.0130780.013100
2014-05-19 14:00:001.01.01.01.00.0234080.0234080.0230250.0230500.0154460.0159900.0154460.0154460.0020000.0023000.0020000.0020022017-01-22 14:30:000.0168500.0168870.0167190.0167780.0042410.0042800.0042400.0042770.0131400.0132000.0131200.013200
2014-05-19 14:30:001.01.01.01.00.0234080.0234080.0234080.0234080.0158680.0160000.0142880.0158890.0025000.0028000.0020700.0020702017-01-22 15:00:000.0168000.0168440.0167260.0167320.0042210.0042600.0042160.0042570.0131330.0131420.0130740.013140
2014-05-19 15:00:001.01.01.01.00.0231600.0234080.0231600.0234080.0159600.0159790.0150000.0150000.0030000.0030000.0022000.0026702017-01-22 15:30:000.0167540.0167550.0167260.0167550.0042200.0042510.0042160.0042210.0130600.0131600.0130590.013116
2014-05-19 15:30:001.01.01.01.00.0234070.0234070.0230010.0231600.0153960.0158380.0153960.0156000.0028000.0032700.0025000.0029002017-01-22 16:00:000.0167580.0168220.0166990.0167500.0041940.0042220.0041940.0042220.0131600.0131600.0130570.013059
2014-05-19 16:00:001.01.01.01.00.0230010.0234070.0230010.0234070.0154500.0154500.0154000.0154000.0032900.0033000.0027000.0027202017-01-22 16:30:000.0168270.0168270.0167100.0167100.0042280.0042290.0041940.0041940.0131620.0131650.0131070.013140
2014-05-19 16:30:001.01.01.01.00.0230010.0232000.0230010.0230010.0150000.0154500.0147000.0154500.0046900.0060000.0030000.0033002017-01-22 17:00:000.0168440.0168440.0168000.0168270.0042190.0042280.0042050.0042280.0131720.0131920.0131620.013162
2014-05-19 17:00:001.01.01.01.00.0230010.0232000.0230010.0230010.0150000.0153900.0149510.0150000.0033400.0045000.0030310.0045002017-01-22 17:30:000.0168310.0168700.0168310.0168440.0041950.0042220.0041950.0042060.0131620.0132000.0131620.013172
2014-05-19 17:30:001.01.01.01.00.0230010.0231910.0230010.0230010.0150000.0152500.0150000.0152500.0039500.0039500.0022160.0033102017-01-22 18:00:000.0165000.0168700.0164810.0168310.0041970.0042110.0041950.0041990.0131560.0131950.0131290.013162
2014-05-19 18:00:001.01.01.01.00.0232000.0232000.0231910.0231910.0151500.0152500.0149510.0150000.0035000.0039900.0030000.0039502017-01-22 18:30:000.0163700.0164960.0163500.0164960.0042110.0042110.0041950.0042110.0131350.0131980.0131300.013156
2014-05-19 18:30:001.01.01.01.00.0230010.0232000.0230010.0232000.0153520.0153520.0150000.0150000.0037000.0039070.0034650.0034652017-01-22 19:00:000.0164370.0165790.0162500.0163600.0042050.0042240.0041980.0041980.0131000.0131630.0130860.013135
2014-05-19 19:00:001.01.01.01.00.0234020.0234020.0230010.0230010.0150050.0154800.0150050.0152000.0037000.0038990.0036500.0038252017-01-22 19:30:000.0165450.0165740.0164410.0164410.0042210.0042220.0042000.0042050.0130910.0131250.0130500.013100
2014-05-19 19:30:001.01.01.01.00.0234020.0234020.0234020.0234020.0149510.0151000.0149510.0150050.0037400.0037790.0036970.0037002017-01-22 20:00:000.0164710.0165450.0164000.0165000.0041880.0042260.0041850.0042210.0130650.0131410.0130580.013091
2014-05-19 20:00:001.01.01.01.00.0234010.0234020.0230010.0234020.0151990.0154000.0149510.0154000.0038100.0041790.0037000.0037402017-01-22 20:30:000.0165050.0165380.0164500.0165000.0041740.0042020.0041740.0042020.0130710.0131100.0130500.013065
2014-05-19 20:30:001.01.01.01.00.0234010.0234020.0234010.0234010.0151500.0151500.0151500.0151500.0044500.0044500.0039600.0040002017-01-22 21:00:000.0166000.0166020.0165000.0165050.0041820.0041830.0041730.0041740.0130510.0130920.0130330.013050
........................
2017-01-21 15:30:001.01.01.01.00.0042400.0042420.0042230.0042230.0162180.0162400.0161040.0162400.0129990.0130800.0129900.013080
2017-01-21 16:00:001.01.01.01.00.0042470.0042470.0042300.0042400.0161700.0163000.0160710.0162180.0129600.0130140.0129220.012990
2017-01-21 16:30:001.01.01.01.00.0042390.0042480.0042330.0042460.0162180.0162790.0161200.0161960.0131900.0132000.0129100.012948
2017-01-21 17:00:001.01.01.01.00.0042300.0042540.0042300.0042480.0161580.0162500.0161200.0162180.0131000.0131990.0131000.013194
2017-01-21 17:30:001.01.01.01.00.0042220.0042480.0042190.0042270.0162500.0162500.0161580.0161580.0130970.0131590.0129810.013100
2017-01-21 18:00:001.01.01.01.00.0042340.0042380.0042220.0042300.0162500.0162500.0161620.0162500.0131510.0132100.0130600.013091
2017-01-21 18:30:001.01.01.01.00.0042340.0042440.0042280.0042340.0163280.0163280.0161620.0162500.0132000.0132100.0131510.013170
2017-01-21 19:00:001.01.01.01.00.0042340.0042440.0042340.0042340.0162980.0163280.0162980.0163000.0130920.0132000.0130920.013191
2017-01-21 19:30:001.01.01.01.00.0042200.0042430.0042150.0042340.0162670.0163270.0162500.0162980.0131210.0131260.0130810.013092
2017-01-21 20:00:001.01.01.01.00.0042420.0042420.0042200.0042200.0162670.0163070.0162500.0162500.0130870.0131210.0130850.013094
2017-01-21 20:30:001.01.01.01.00.0042390.0042420.0042230.0042420.0163100.0163390.0162670.0162670.0130140.0131140.0130140.013087
2017-01-21 21:00:001.01.01.01.00.0042380.0042400.0042250.0042390.0163390.0163390.0162700.0162700.0130430.0130580.0130140.013014
2017-01-21 21:30:001.01.01.01.00.0042220.0042380.0042220.0042250.0162900.0163390.0162900.0162900.0130240.0130640.0129730.013064
2017-01-21 22:00:001.01.01.01.00.0042450.0042450.0042220.0042240.0161400.0163390.0161260.0163170.0130490.0130820.0130190.013033
2017-01-21 22:30:001.01.01.01.00.0042310.0042450.0042270.0042400.0161400.0162340.0161400.0161400.0131000.0131000.0130130.013049
2017-01-21 23:00:001.01.01.01.00.0042400.0043110.0042320.0042320.0162300.0162330.0161270.0161400.0130660.0131000.0130560.013099
2017-01-21 23:30:001.01.01.01.00.0042360.0042450.0042200.0042400.0162200.0162300.0161310.0161310.0130370.0130850.0130130.013075
2017-01-22 00:00:001.01.01.01.00.0042100.0042680.0042080.0042350.0162500.0162590.0161560.0162200.0129810.0130370.0129000.013037
2017-01-22 00:30:001.01.01.01.00.0041980.0042230.0041850.0042100.0161300.0162500.0161300.0162500.0129880.0129920.0129530.012970
2017-01-22 01:00:001.01.01.01.00.0041800.0042010.0041800.0041850.0161500.0162130.0161300.0161300.0130180.0130180.0129840.012984
2017-01-22 01:30:001.01.01.01.00.0041930.0041930.0041770.0041820.0161800.0162400.0161500.0161500.0129000.0130180.0129000.013017
2017-01-22 02:00:001.01.01.01.00.0041800.0041940.0041760.0041770.0162360.0162400.0161590.0161800.0129650.0130050.0129000.012900
2017-01-22 02:30:001.01.01.01.00.0041650.0041800.0041520.0041800.0162590.0162590.0161690.0162200.0130060.0130080.0129550.012955
2017-01-22 03:00:001.01.01.01.00.0041560.0041950.0041560.0041660.0162310.0162670.0161880.0162590.0130000.0130380.0129750.013006
2017-01-22 03:30:001.01.01.01.00.0041590.0041680.0041500.0041680.0162940.0163392017-07-13 09:00:000.0723000.0728090.0720120.0728090.0191030.0192380.0190430.0192150.0162000.0162310.0129790.0130620.0129650.0130000.0162600.0161510.016220
2017-01-22 04:00:001.01.01.01.00.0041450.0041590.0041370.0041500.0163310.0163390.0161500.0162450.0129760.0130700.0129710.0129792017-07-13 09:30:000.0724810.0726190.0723000.0724250.0190120.0191280.0190050.0191030.0162140.0163960.0162000.016200
2017-01-22 04:30:001.01.01.01.00.0041360.0041590.0041350.0041590.0163390.0163390.0161810.0162320.0129870.0130000.0129710.012976
2017-01-22 05:00:001.01.01.01.00.0041600.0041600.0041360.0041360.0162500.0163390.0161890.0163390.0129800.0130190.0129710.012979
2017-01-22 05:30:001.01.01.01.00.0041790.0041790.0041500.0041520.0162620.0163390.0162480.0162500.0129800.0130400.0129800.012980
2017-01-22 06:00:001.01.01.01.00.0041690.0041830.0041630.0041800.0163390.0163390.0162482017-07-13 10:00:000.0717300.0725840.0717290.0724810.0190980.0191000.0190000.0190320.0162630.0129690.0130050.0129340.0129800.0162810.0162110.016214
2017-07-13 10:30:000.0716700.0721270.0715850.0717300.0191930.0192000.0190870.0191000.0163040.0163670.0162630.016279
2017-07-13 11:00:000.0729200.0729200.0716700.0717710.0193000.0195000.0191930.0192000.0163820.0164080.0163040.016304
2017-07-13 11:30:000.0731710.0733160.0726400.0726400.0193860.0194550.0193000.0193000.0164900.0164900.0163820.016404
2017-07-13 12:00:000.0730200.0734340.0728670.0731700.0193630.0194920.0193450.0193860.0164330.0164990.0163650.016490
2017-07-13 12:30:000.0734000.0734000.0730200.0730700.0193850.0193880.0192570.0193450.0164410.0164990.0163530.016499
2017-07-13 13:00:000.0732300.0735000.0732210.0734000.0192270.0193880.0190860.0193850.0164100.0164370.0163370.016437
2017-07-13 13:30:000.0731680.0735000.0728300.0732300.0192000.0192580.0191350.0192270.0163510.0164250.0163340.016410
2017-07-13 14:00:000.0729180.0734870.0728000.0731930.0190730.0192020.0190370.0192020.0163060.0164000.0163060.016400
2017-07-13 14:30:000.0726420.0734890.0726390.0730000.0192100.0192100.0190360.0190790.0163060.0163770.0163060.016306
2017-07-13 15:00:000.0724850.0734800.0720560.0726420.0192140.0192680.0191500.0191870.0161200.0163920.0160460.016306
2017-07-13 15:30:000.0716050.0724850.0716050.0722980.0191100.0192750.0191000.0192160.0160270.0162000.0159920.016200
2017-07-13 16:00:000.0720350.0721040.0711000.0716050.0190870.0191860.0189500.0191100.0160200.0160800.0159600.016010
2017-07-13 16:30:000.0721000.0724860.0714550.0720330.0192200.0192200.0190850.0190870.0160300.0161000.0160020.016070
2017-07-13 17:00:000.0726560.0726560.0716300.0721000.0192030.0192450.0191010.0192200.0162290.0162320.0161150.016115
2017-07-13 17:30:000.0728890.0737600.0725110.0726350.0190210.0191940.0189830.0191800.0160570.0162320.0160300.016218
2017-07-13 18:00:000.0729430.0730160.0724920.0726100.0191120.0191120.0189860.0190450.0161280.0161570.0160000.016038
2017-07-13 18:30:000.0729840.0733110.0725630.0725630.0191240.0191740.0190400.0190900.0163890.0163990.0161000.016110
2017-07-13 19:00:000.0725450.0731380.0723810.0729840.0190950.0192000.0190700.0191240.0163660.0164880.0163000.016300
2017-07-13 19:30:000.0718980.0725450.0715700.0725450.0190400.0191250.0190400.0190870.0163050.0164880.0163050.016362
2017-07-13 20:00:000.0718100.0723200.0715860.0715860.0191000.0191300.0190000.0190400.0161800.0163820.0161750.016341
2017-07-13 20:30:000.0723150.0727500.0717270.0720910.0192660.0192810.0191000.0191300.0162810.0163800.0162200.016318
2017-07-13 21:00:000.0721480.0726900.0720670.0723150.0193440.0193820.0192240.0192800.0163600.0164250.0162810.016379
2017-07-13 21:30:000.0727900.0727910.0720670.0721480.0192100.0193650.0191100.0193650.0163480.0164220.0162270.016360
2017-07-13 22:00:000.0723600.0727900.0720680.0725000.0192710.0193090.0191620.0192040.0163480.0164010.0163230.016348
2017-07-13 22:30:000.0725000.0727400.0721690.0721690.0192630.0193530.0191430.0192710.0162690.0164000.0162620.016348
2017-07-13 23:00:000.0727700.0728260.0724300.0725000.0193400.0193800.0192500.0192630.0162600.0163650.0162600.016269
2017-07-13 23:30:000.0724940.0728000.0724500.0727700.0194620.0194620.0193390.0193500.0162890.0164200.0162600.016261
\n", - "

46993 rows × 16 columns

\n", + "

8291 rows × 12 columns

\n", "
" ], "text/plain": [ - "Pair BTCBTC LTCBTC \\\n", - "Price close high low open close high low \n", - "date \n", - "2014-05-19 06:00:00 1.0 1.0 1.0 1.0 0.023234 0.023234 0.023234 \n", - "2014-05-19 06:30:00 1.0 1.0 1.0 1.0 0.023421 0.023421 0.023421 \n", - "2014-05-19 07:00:00 1.0 1.0 1.0 1.0 0.023038 0.023100 0.023038 \n", - "2014-05-19 07:30:00 1.0 1.0 1.0 1.0 0.023029 0.023029 0.023029 \n", - "2014-05-19 08:00:00 1.0 1.0 1.0 1.0 0.023026 0.023420 0.023026 \n", - "2014-05-19 08:30:00 1.0 1.0 1.0 1.0 0.023025 0.023420 0.023025 \n", - "2014-05-19 09:00:00 1.0 1.0 1.0 1.0 0.023025 0.023025 0.023025 \n", - "2014-05-19 09:30:00 1.0 1.0 1.0 1.0 0.023025 0.023200 0.023025 \n", - "2014-05-19 10:00:00 1.0 1.0 1.0 1.0 0.023025 0.023200 0.023025 \n", - "2014-05-19 10:30:00 1.0 1.0 1.0 1.0 0.023025 0.023200 0.023025 \n", - "2014-05-19 11:00:00 1.0 1.0 1.0 1.0 0.023418 0.023418 0.023418 \n", - "2014-05-19 11:30:00 1.0 1.0 1.0 1.0 0.023025 0.023025 0.023025 \n", - "2014-05-19 12:00:00 1.0 1.0 1.0 1.0 0.023414 0.023414 0.023414 \n", - "2014-05-19 12:30:00 1.0 1.0 1.0 1.0 0.023409 0.023409 0.023409 \n", - "2014-05-19 13:00:00 1.0 1.0 1.0 1.0 0.023025 0.023408 0.023025 \n", - "2014-05-19 13:30:00 1.0 1.0 1.0 1.0 0.023025 0.023400 0.023025 \n", - "2014-05-19 14:00:00 1.0 1.0 1.0 1.0 0.023408 0.023408 0.023025 \n", - "2014-05-19 14:30:00 1.0 1.0 1.0 1.0 0.023408 0.023408 0.023408 \n", - "2014-05-19 15:00:00 1.0 1.0 1.0 1.0 0.023160 0.023408 0.023160 \n", - "2014-05-19 15:30:00 1.0 1.0 1.0 1.0 0.023407 0.023407 0.023001 \n", - "2014-05-19 16:00:00 1.0 1.0 1.0 1.0 0.023001 0.023407 0.023001 \n", - "2014-05-19 16:30:00 1.0 1.0 1.0 1.0 0.023001 0.023200 0.023001 \n", - "2014-05-19 17:00:00 1.0 1.0 1.0 1.0 0.023001 0.023200 0.023001 \n", - "2014-05-19 17:30:00 1.0 1.0 1.0 1.0 0.023001 0.023191 0.023001 \n", - "2014-05-19 18:00:00 1.0 1.0 1.0 1.0 0.023200 0.023200 0.023191 \n", - "2014-05-19 18:30:00 1.0 1.0 1.0 1.0 0.023001 0.023200 0.023001 \n", - "2014-05-19 19:00:00 1.0 1.0 1.0 1.0 0.023402 0.023402 0.023001 \n", - "2014-05-19 19:30:00 1.0 1.0 1.0 1.0 0.023402 0.023402 0.023402 \n", - "2014-05-19 20:00:00 1.0 1.0 1.0 1.0 0.023401 0.023402 0.023001 \n", - "2014-05-19 20:30:00 1.0 1.0 1.0 1.0 0.023401 0.023402 0.023401 \n", - "... ... ... ... ... ... ... ... \n", - "2017-01-21 15:30:00 1.0 1.0 1.0 1.0 0.004240 0.004242 0.004223 \n", - "2017-01-21 16:00:00 1.0 1.0 1.0 1.0 0.004247 0.004247 0.004230 \n", - "2017-01-21 16:30:00 1.0 1.0 1.0 1.0 0.004239 0.004248 0.004233 \n", - "2017-01-21 17:00:00 1.0 1.0 1.0 1.0 0.004230 0.004254 0.004230 \n", - "2017-01-21 17:30:00 1.0 1.0 1.0 1.0 0.004222 0.004248 0.004219 \n", - "2017-01-21 18:00:00 1.0 1.0 1.0 1.0 0.004234 0.004238 0.004222 \n", - "2017-01-21 18:30:00 1.0 1.0 1.0 1.0 0.004234 0.004244 0.004228 \n", - "2017-01-21 19:00:00 1.0 1.0 1.0 1.0 0.004234 0.004244 0.004234 \n", - "2017-01-21 19:30:00 1.0 1.0 1.0 1.0 0.004220 0.004243 0.004215 \n", - "2017-01-21 20:00:00 1.0 1.0 1.0 1.0 0.004242 0.004242 0.004220 \n", - "2017-01-21 20:30:00 1.0 1.0 1.0 1.0 0.004239 0.004242 0.004223 \n", - "2017-01-21 21:00:00 1.0 1.0 1.0 1.0 0.004238 0.004240 0.004225 \n", - "2017-01-21 21:30:00 1.0 1.0 1.0 1.0 0.004222 0.004238 0.004222 \n", - "2017-01-21 22:00:00 1.0 1.0 1.0 1.0 0.004245 0.004245 0.004222 \n", - "2017-01-21 22:30:00 1.0 1.0 1.0 1.0 0.004231 0.004245 0.004227 \n", - "2017-01-21 23:00:00 1.0 1.0 1.0 1.0 0.004240 0.004311 0.004232 \n", - "2017-01-21 23:30:00 1.0 1.0 1.0 1.0 0.004236 0.004245 0.004220 \n", - "2017-01-22 00:00:00 1.0 1.0 1.0 1.0 0.004210 0.004268 0.004208 \n", - "2017-01-22 00:30:00 1.0 1.0 1.0 1.0 0.004198 0.004223 0.004185 \n", - "2017-01-22 01:00:00 1.0 1.0 1.0 1.0 0.004180 0.004201 0.004180 \n", - "2017-01-22 01:30:00 1.0 1.0 1.0 1.0 0.004193 0.004193 0.004177 \n", - "2017-01-22 02:00:00 1.0 1.0 1.0 1.0 0.004180 0.004194 0.004176 \n", - "2017-01-22 02:30:00 1.0 1.0 1.0 1.0 0.004165 0.004180 0.004152 \n", - "2017-01-22 03:00:00 1.0 1.0 1.0 1.0 0.004156 0.004195 0.004156 \n", - "2017-01-22 03:30:00 1.0 1.0 1.0 1.0 0.004159 0.004168 0.004150 \n", - "2017-01-22 04:00:00 1.0 1.0 1.0 1.0 0.004145 0.004159 0.004137 \n", - "2017-01-22 04:30:00 1.0 1.0 1.0 1.0 0.004136 0.004159 0.004135 \n", - "2017-01-22 05:00:00 1.0 1.0 1.0 1.0 0.004160 0.004160 0.004136 \n", - "2017-01-22 05:30:00 1.0 1.0 1.0 1.0 0.004179 0.004179 0.004150 \n", - "2017-01-22 06:00:00 1.0 1.0 1.0 1.0 0.004169 0.004183 0.004163 \n", - "\n", - "Pair DASHBTC \\\n", - "Price open close high low open \n", + "Pair DASHBTC LTCBTC \\\n", + "Price close high low open close \n", "date \n", - "2014-05-19 06:00:00 0.023234 0.015199 0.015199 0.014502 0.014970 \n", - "2014-05-19 06:30:00 0.023421 0.014520 0.015342 0.014510 0.015199 \n", - "2014-05-19 07:00:00 0.023100 0.015350 0.015400 0.014510 0.015000 \n", - "2014-05-19 07:30:00 0.023029 0.015600 0.015600 0.015050 0.015200 \n", - "2014-05-19 08:00:00 0.023029 0.015600 0.015600 0.015103 0.015149 \n", - "2014-05-19 08:30:00 0.023025 0.016000 0.016000 0.015111 0.015700 \n", - "2014-05-19 09:00:00 0.023025 0.016000 0.016000 0.015327 0.016000 \n", - "2014-05-19 09:30:00 0.023200 0.015700 0.016000 0.015700 0.016000 \n", - "2014-05-19 10:00:00 0.023200 0.015700 0.015980 0.015658 0.015980 \n", - "2014-05-19 10:30:00 0.023200 0.016000 0.016000 0.015396 0.015650 \n", - "2014-05-19 11:00:00 0.023418 0.015119 0.016000 0.015111 0.015980 \n", - "2014-05-19 11:30:00 0.023025 0.015450 0.015450 0.015111 0.015119 \n", - "2014-05-19 12:00:00 0.023414 0.015200 0.015450 0.015200 0.015450 \n", - "2014-05-19 12:30:00 0.023409 0.015400 0.015400 0.015200 0.015200 \n", - "2014-05-19 13:00:00 0.023408 0.016000 0.016000 0.015500 0.015670 \n", - "2014-05-19 13:30:00 0.023400 0.015812 0.016000 0.015812 0.016000 \n", - "2014-05-19 14:00:00 0.023050 0.015446 0.015990 0.015446 0.015446 \n", - "2014-05-19 14:30:00 0.023408 0.015868 0.016000 0.014288 0.015889 \n", - "2014-05-19 15:00:00 0.023408 0.015960 0.015979 0.015000 0.015000 \n", - "2014-05-19 15:30:00 0.023160 0.015396 0.015838 0.015396 0.015600 \n", - "2014-05-19 16:00:00 0.023407 0.015450 0.015450 0.015400 0.015400 \n", - "2014-05-19 16:30:00 0.023001 0.015000 0.015450 0.014700 0.015450 \n", - "2014-05-19 17:00:00 0.023001 0.015000 0.015390 0.014951 0.015000 \n", - "2014-05-19 17:30:00 0.023001 0.015000 0.015250 0.015000 0.015250 \n", - "2014-05-19 18:00:00 0.023191 0.015150 0.015250 0.014951 0.015000 \n", - "2014-05-19 18:30:00 0.023200 0.015352 0.015352 0.015000 0.015000 \n", - "2014-05-19 19:00:00 0.023001 0.015005 0.015480 0.015005 0.015200 \n", - "2014-05-19 19:30:00 0.023402 0.014951 0.015100 0.014951 0.015005 \n", - "2014-05-19 20:00:00 0.023402 0.015199 0.015400 0.014951 0.015400 \n", - "2014-05-19 20:30:00 0.023401 0.015150 0.015150 0.015150 0.015150 \n", + "2017-01-22 06:30:00 0.016339 0.016339 0.016255 0.016255 0.004157 \n", + "2017-01-22 07:00:00 0.016251 0.016340 0.016251 0.016339 0.004172 \n", + "2017-01-22 07:30:00 0.016308 0.016308 0.016251 0.016251 0.004157 \n", + "2017-01-22 08:00:00 0.016310 0.016325 0.016215 0.016308 0.004166 \n", + "2017-01-22 08:30:00 0.016270 0.016340 0.016221 0.016221 0.004170 \n", + "2017-01-22 09:00:00 0.016250 0.016340 0.016221 0.016340 0.004151 \n", + "2017-01-22 09:30:00 0.016398 0.016460 0.016240 0.016250 0.004176 \n", + "2017-01-22 10:00:00 0.016422 0.016470 0.016325 0.016325 0.004192 \n", + "2017-01-22 10:30:00 0.016600 0.016600 0.016400 0.016422 0.004182 \n", + "2017-01-22 11:00:00 0.016616 0.016648 0.016546 0.016572 0.004192 \n", + "2017-01-22 11:30:00 0.016658 0.016678 0.016569 0.016616 0.004193 \n", + "2017-01-22 12:00:00 0.016850 0.016850 0.016600 0.016661 0.004220 \n", + "2017-01-22 12:30:00 0.016815 0.016852 0.016727 0.016849 0.004238 \n", + "2017-01-22 13:00:00 0.016733 0.016849 0.016716 0.016815 0.004215 \n", + "2017-01-22 13:30:00 0.016730 0.016864 0.016700 0.016733 0.004242 \n", + "2017-01-22 14:00:00 0.016778 0.016840 0.016708 0.016708 0.004273 \n", + "2017-01-22 14:30:00 0.016850 0.016887 0.016719 0.016778 0.004241 \n", + "2017-01-22 15:00:00 0.016800 0.016844 0.016726 0.016732 0.004221 \n", + "2017-01-22 15:30:00 0.016754 0.016755 0.016726 0.016755 0.004220 \n", + "2017-01-22 16:00:00 0.016758 0.016822 0.016699 0.016750 0.004194 \n", + "2017-01-22 16:30:00 0.016827 0.016827 0.016710 0.016710 0.004228 \n", + "2017-01-22 17:00:00 0.016844 0.016844 0.016800 0.016827 0.004219 \n", + "2017-01-22 17:30:00 0.016831 0.016870 0.016831 0.016844 0.004195 \n", + "2017-01-22 18:00:00 0.016500 0.016870 0.016481 0.016831 0.004197 \n", + "2017-01-22 18:30:00 0.016370 0.016496 0.016350 0.016496 0.004211 \n", + "2017-01-22 19:00:00 0.016437 0.016579 0.016250 0.016360 0.004205 \n", + "2017-01-22 19:30:00 0.016545 0.016574 0.016441 0.016441 0.004221 \n", + "2017-01-22 20:00:00 0.016471 0.016545 0.016400 0.016500 0.004188 \n", + "2017-01-22 20:30:00 0.016505 0.016538 0.016450 0.016500 0.004174 \n", + "2017-01-22 21:00:00 0.016600 0.016602 0.016500 0.016505 0.004182 \n", "... ... ... ... ... ... \n", - "2017-01-21 15:30:00 0.004223 0.016218 0.016240 0.016104 0.016240 \n", - "2017-01-21 16:00:00 0.004240 0.016170 0.016300 0.016071 0.016218 \n", - "2017-01-21 16:30:00 0.004246 0.016218 0.016279 0.016120 0.016196 \n", - "2017-01-21 17:00:00 0.004248 0.016158 0.016250 0.016120 0.016218 \n", - "2017-01-21 17:30:00 0.004227 0.016250 0.016250 0.016158 0.016158 \n", - "2017-01-21 18:00:00 0.004230 0.016250 0.016250 0.016162 0.016250 \n", - "2017-01-21 18:30:00 0.004234 0.016328 0.016328 0.016162 0.016250 \n", - "2017-01-21 19:00:00 0.004234 0.016298 0.016328 0.016298 0.016300 \n", - "2017-01-21 19:30:00 0.004234 0.016267 0.016327 0.016250 0.016298 \n", - "2017-01-21 20:00:00 0.004220 0.016267 0.016307 0.016250 0.016250 \n", - "2017-01-21 20:30:00 0.004242 0.016310 0.016339 0.016267 0.016267 \n", - "2017-01-21 21:00:00 0.004239 0.016339 0.016339 0.016270 0.016270 \n", - "2017-01-21 21:30:00 0.004225 0.016290 0.016339 0.016290 0.016290 \n", - "2017-01-21 22:00:00 0.004224 0.016140 0.016339 0.016126 0.016317 \n", - "2017-01-21 22:30:00 0.004240 0.016140 0.016234 0.016140 0.016140 \n", - "2017-01-21 23:00:00 0.004232 0.016230 0.016233 0.016127 0.016140 \n", - "2017-01-21 23:30:00 0.004240 0.016220 0.016230 0.016131 0.016131 \n", - "2017-01-22 00:00:00 0.004235 0.016250 0.016259 0.016156 0.016220 \n", - "2017-01-22 00:30:00 0.004210 0.016130 0.016250 0.016130 0.016250 \n", - "2017-01-22 01:00:00 0.004185 0.016150 0.016213 0.016130 0.016130 \n", - "2017-01-22 01:30:00 0.004182 0.016180 0.016240 0.016150 0.016150 \n", - "2017-01-22 02:00:00 0.004177 0.016236 0.016240 0.016159 0.016180 \n", - "2017-01-22 02:30:00 0.004180 0.016259 0.016259 0.016169 0.016220 \n", - "2017-01-22 03:00:00 0.004166 0.016231 0.016267 0.016188 0.016259 \n", - "2017-01-22 03:30:00 0.004168 0.016294 0.016339 0.016200 0.016231 \n", - "2017-01-22 04:00:00 0.004150 0.016331 0.016339 0.016150 0.016245 \n", - "2017-01-22 04:30:00 0.004159 0.016339 0.016339 0.016181 0.016232 \n", - "2017-01-22 05:00:00 0.004136 0.016250 0.016339 0.016189 0.016339 \n", - "2017-01-22 05:30:00 0.004152 0.016262 0.016339 0.016248 0.016250 \n", - "2017-01-22 06:00:00 0.004180 0.016339 0.016339 0.016248 0.016263 \n", + "2017-07-13 09:00:00 0.072300 0.072809 0.072012 0.072809 0.019103 \n", + "2017-07-13 09:30:00 0.072481 0.072619 0.072300 0.072425 0.019012 \n", + "2017-07-13 10:00:00 0.071730 0.072584 0.071729 0.072481 0.019098 \n", + "2017-07-13 10:30:00 0.071670 0.072127 0.071585 0.071730 0.019193 \n", + "2017-07-13 11:00:00 0.072920 0.072920 0.071670 0.071771 0.019300 \n", + "2017-07-13 11:30:00 0.073171 0.073316 0.072640 0.072640 0.019386 \n", + "2017-07-13 12:00:00 0.073020 0.073434 0.072867 0.073170 0.019363 \n", + "2017-07-13 12:30:00 0.073400 0.073400 0.073020 0.073070 0.019385 \n", + "2017-07-13 13:00:00 0.073230 0.073500 0.073221 0.073400 0.019227 \n", + "2017-07-13 13:30:00 0.073168 0.073500 0.072830 0.073230 0.019200 \n", + "2017-07-13 14:00:00 0.072918 0.073487 0.072800 0.073193 0.019073 \n", + "2017-07-13 14:30:00 0.072642 0.073489 0.072639 0.073000 0.019210 \n", + "2017-07-13 15:00:00 0.072485 0.073480 0.072056 0.072642 0.019214 \n", + "2017-07-13 15:30:00 0.071605 0.072485 0.071605 0.072298 0.019110 \n", + "2017-07-13 16:00:00 0.072035 0.072104 0.071100 0.071605 0.019087 \n", + "2017-07-13 16:30:00 0.072100 0.072486 0.071455 0.072033 0.019220 \n", + "2017-07-13 17:00:00 0.072656 0.072656 0.071630 0.072100 0.019203 \n", + "2017-07-13 17:30:00 0.072889 0.073760 0.072511 0.072635 0.019021 \n", + "2017-07-13 18:00:00 0.072943 0.073016 0.072492 0.072610 0.019112 \n", + "2017-07-13 18:30:00 0.072984 0.073311 0.072563 0.072563 0.019124 \n", + "2017-07-13 19:00:00 0.072545 0.073138 0.072381 0.072984 0.019095 \n", + "2017-07-13 19:30:00 0.071898 0.072545 0.071570 0.072545 0.019040 \n", + "2017-07-13 20:00:00 0.071810 0.072320 0.071586 0.071586 0.019100 \n", + "2017-07-13 20:30:00 0.072315 0.072750 0.071727 0.072091 0.019266 \n", + "2017-07-13 21:00:00 0.072148 0.072690 0.072067 0.072315 0.019344 \n", + "2017-07-13 21:30:00 0.072790 0.072791 0.072067 0.072148 0.019210 \n", + "2017-07-13 22:00:00 0.072360 0.072790 0.072068 0.072500 0.019271 \n", + "2017-07-13 22:30:00 0.072500 0.072740 0.072169 0.072169 0.019263 \n", + "2017-07-13 23:00:00 0.072770 0.072826 0.072430 0.072500 0.019340 \n", + "2017-07-13 23:30:00 0.072494 0.072800 0.072450 0.072770 0.019462 \n", "\n", - "Pair XMRBTC \n", - "Price close high low open \n", - "date \n", - "2014-05-19 06:00:00 0.001110 0.011110 0.001110 0.011110 \n", - "2014-05-19 06:30:00 0.001125 0.001500 0.001125 0.001200 \n", - "2014-05-19 07:00:00 0.001190 0.001410 0.001080 0.001410 \n", - "2014-05-19 07:30:00 0.001320 0.001867 0.001040 0.001040 \n", - "2014-05-19 08:00:00 0.001700 0.001800 0.001320 0.001700 \n", - "2014-05-19 08:30:00 0.001930 0.001940 0.001600 0.001600 \n", - "2014-05-19 09:00:00 0.001800 0.002700 0.001760 0.001930 \n", - "2014-05-19 09:30:00 0.001252 0.001780 0.001252 0.001780 \n", - "2014-05-19 10:00:00 0.001500 0.001800 0.001370 0.001465 \n", - "2014-05-19 10:30:00 0.002000 0.002000 0.001700 0.001700 \n", - "2014-05-19 11:00:00 0.002000 0.002000 0.001790 0.001999 \n", - "2014-05-19 11:30:00 0.001469 0.001850 0.001469 0.001850 \n", - "2014-05-19 12:00:00 0.002180 0.002180 0.001594 0.001594 \n", - "2014-05-19 12:30:00 0.002000 0.002000 0.002000 0.002000 \n", - "2014-05-19 13:00:00 0.002000 0.002189 0.001824 0.002000 \n", - "2014-05-19 13:30:00 0.002002 0.002200 0.002000 0.002189 \n", - "2014-05-19 14:00:00 0.002000 0.002300 0.002000 0.002002 \n", - "2014-05-19 14:30:00 0.002500 0.002800 0.002070 0.002070 \n", - "2014-05-19 15:00:00 0.003000 0.003000 0.002200 0.002670 \n", - "2014-05-19 15:30:00 0.002800 0.003270 0.002500 0.002900 \n", - "2014-05-19 16:00:00 0.003290 0.003300 0.002700 0.002720 \n", - "2014-05-19 16:30:00 0.004690 0.006000 0.003000 0.003300 \n", - "2014-05-19 17:00:00 0.003340 0.004500 0.003031 0.004500 \n", - "2014-05-19 17:30:00 0.003950 0.003950 0.002216 0.003310 \n", - "2014-05-19 18:00:00 0.003500 0.003990 0.003000 0.003950 \n", - "2014-05-19 18:30:00 0.003700 0.003907 0.003465 0.003465 \n", - "2014-05-19 19:00:00 0.003700 0.003899 0.003650 0.003825 \n", - "2014-05-19 19:30:00 0.003740 0.003779 0.003697 0.003700 \n", - "2014-05-19 20:00:00 0.003810 0.004179 0.003700 0.003740 \n", - "2014-05-19 20:30:00 0.004450 0.004450 0.003960 0.004000 \n", - "... ... ... ... ... \n", - "2017-01-21 15:30:00 0.012999 0.013080 0.012990 0.013080 \n", - "2017-01-21 16:00:00 0.012960 0.013014 0.012922 0.012990 \n", - "2017-01-21 16:30:00 0.013190 0.013200 0.012910 0.012948 \n", - "2017-01-21 17:00:00 0.013100 0.013199 0.013100 0.013194 \n", - "2017-01-21 17:30:00 0.013097 0.013159 0.012981 0.013100 \n", - "2017-01-21 18:00:00 0.013151 0.013210 0.013060 0.013091 \n", - "2017-01-21 18:30:00 0.013200 0.013210 0.013151 0.013170 \n", - "2017-01-21 19:00:00 0.013092 0.013200 0.013092 0.013191 \n", - "2017-01-21 19:30:00 0.013121 0.013126 0.013081 0.013092 \n", - "2017-01-21 20:00:00 0.013087 0.013121 0.013085 0.013094 \n", - "2017-01-21 20:30:00 0.013014 0.013114 0.013014 0.013087 \n", - "2017-01-21 21:00:00 0.013043 0.013058 0.013014 0.013014 \n", - "2017-01-21 21:30:00 0.013024 0.013064 0.012973 0.013064 \n", - "2017-01-21 22:00:00 0.013049 0.013082 0.013019 0.013033 \n", - "2017-01-21 22:30:00 0.013100 0.013100 0.013013 0.013049 \n", - "2017-01-21 23:00:00 0.013066 0.013100 0.013056 0.013099 \n", - "2017-01-21 23:30:00 0.013037 0.013085 0.013013 0.013075 \n", - "2017-01-22 00:00:00 0.012981 0.013037 0.012900 0.013037 \n", - "2017-01-22 00:30:00 0.012988 0.012992 0.012953 0.012970 \n", - "2017-01-22 01:00:00 0.013018 0.013018 0.012984 0.012984 \n", - "2017-01-22 01:30:00 0.012900 0.013018 0.012900 0.013017 \n", - "2017-01-22 02:00:00 0.012965 0.013005 0.012900 0.012900 \n", - "2017-01-22 02:30:00 0.013006 0.013008 0.012955 0.012955 \n", - "2017-01-22 03:00:00 0.013000 0.013038 0.012975 0.013006 \n", - "2017-01-22 03:30:00 0.012979 0.013062 0.012965 0.013000 \n", - "2017-01-22 04:00:00 0.012976 0.013070 0.012971 0.012979 \n", - "2017-01-22 04:30:00 0.012987 0.013000 0.012971 0.012976 \n", - "2017-01-22 05:00:00 0.012980 0.013019 0.012971 0.012979 \n", - "2017-01-22 05:30:00 0.012980 0.013040 0.012980 0.012980 \n", - "2017-01-22 06:00:00 0.012969 0.013005 0.012934 0.012980 \n", + "Pair XMRBTC \\\n", + "Price high low open close high \n", + "date \n", + "2017-01-22 06:30:00 0.004177 0.004157 0.004176 0.012963 0.013005 \n", + "2017-01-22 07:00:00 0.004172 0.004157 0.004171 0.012973 0.013005 \n", + "2017-01-22 07:30:00 0.004171 0.004157 0.004161 0.012995 0.013005 \n", + "2017-01-22 08:00:00 0.004168 0.004157 0.004168 0.012950 0.013005 \n", + "2017-01-22 08:30:00 0.004171 0.004157 0.004157 0.012957 0.012965 \n", + "2017-01-22 09:00:00 0.004162 0.004146 0.004162 0.012900 0.013000 \n", + "2017-01-22 09:30:00 0.004176 0.004147 0.004147 0.012847 0.012913 \n", + "2017-01-22 10:00:00 0.004192 0.004153 0.004153 0.013000 0.013005 \n", + "2017-01-22 10:30:00 0.004194 0.004176 0.004192 0.012960 0.013005 \n", + "2017-01-22 11:00:00 0.004201 0.004176 0.004190 0.012893 0.012953 \n", + "2017-01-22 11:30:00 0.004198 0.004176 0.004176 0.012973 0.012975 \n", + "2017-01-22 12:00:00 0.004240 0.004176 0.004178 0.013130 0.013130 \n", + "2017-01-22 12:30:00 0.004244 0.004203 0.004220 0.013125 0.013195 \n", + "2017-01-22 13:00:00 0.004236 0.004211 0.004214 0.013100 0.013128 \n", + "2017-01-22 13:30:00 0.004243 0.004215 0.004225 0.013100 0.013119 \n", + "2017-01-22 14:00:00 0.004292 0.004242 0.004242 0.013170 0.013200 \n", + "2017-01-22 14:30:00 0.004280 0.004240 0.004277 0.013140 0.013200 \n", + "2017-01-22 15:00:00 0.004260 0.004216 0.004257 0.013133 0.013142 \n", + "2017-01-22 15:30:00 0.004251 0.004216 0.004221 0.013060 0.013160 \n", + "2017-01-22 16:00:00 0.004222 0.004194 0.004222 0.013160 0.013160 \n", + "2017-01-22 16:30:00 0.004229 0.004194 0.004194 0.013162 0.013165 \n", + "2017-01-22 17:00:00 0.004228 0.004205 0.004228 0.013172 0.013192 \n", + "2017-01-22 17:30:00 0.004222 0.004195 0.004206 0.013162 0.013200 \n", + "2017-01-22 18:00:00 0.004211 0.004195 0.004199 0.013156 0.013195 \n", + "2017-01-22 18:30:00 0.004211 0.004195 0.004211 0.013135 0.013198 \n", + "2017-01-22 19:00:00 0.004224 0.004198 0.004198 0.013100 0.013163 \n", + "2017-01-22 19:30:00 0.004222 0.004200 0.004205 0.013091 0.013125 \n", + "2017-01-22 20:00:00 0.004226 0.004185 0.004221 0.013065 0.013141 \n", + "2017-01-22 20:30:00 0.004202 0.004174 0.004202 0.013071 0.013110 \n", + "2017-01-22 21:00:00 0.004183 0.004173 0.004174 0.013051 0.013092 \n", + "... ... ... ... ... ... \n", + "2017-07-13 09:00:00 0.019238 0.019043 0.019215 0.016200 0.016260 \n", + "2017-07-13 09:30:00 0.019128 0.019005 0.019103 0.016214 0.016396 \n", + "2017-07-13 10:00:00 0.019100 0.019000 0.019032 0.016263 0.016281 \n", + "2017-07-13 10:30:00 0.019200 0.019087 0.019100 0.016304 0.016367 \n", + "2017-07-13 11:00:00 0.019500 0.019193 0.019200 0.016382 0.016408 \n", + "2017-07-13 11:30:00 0.019455 0.019300 0.019300 0.016490 0.016490 \n", + "2017-07-13 12:00:00 0.019492 0.019345 0.019386 0.016433 0.016499 \n", + "2017-07-13 12:30:00 0.019388 0.019257 0.019345 0.016441 0.016499 \n", + "2017-07-13 13:00:00 0.019388 0.019086 0.019385 0.016410 0.016437 \n", + "2017-07-13 13:30:00 0.019258 0.019135 0.019227 0.016351 0.016425 \n", + "2017-07-13 14:00:00 0.019202 0.019037 0.019202 0.016306 0.016400 \n", + "2017-07-13 14:30:00 0.019210 0.019036 0.019079 0.016306 0.016377 \n", + "2017-07-13 15:00:00 0.019268 0.019150 0.019187 0.016120 0.016392 \n", + "2017-07-13 15:30:00 0.019275 0.019100 0.019216 0.016027 0.016200 \n", + "2017-07-13 16:00:00 0.019186 0.018950 0.019110 0.016020 0.016080 \n", + "2017-07-13 16:30:00 0.019220 0.019085 0.019087 0.016030 0.016100 \n", + "2017-07-13 17:00:00 0.019245 0.019101 0.019220 0.016229 0.016232 \n", + "2017-07-13 17:30:00 0.019194 0.018983 0.019180 0.016057 0.016232 \n", + "2017-07-13 18:00:00 0.019112 0.018986 0.019045 0.016128 0.016157 \n", + "2017-07-13 18:30:00 0.019174 0.019040 0.019090 0.016389 0.016399 \n", + "2017-07-13 19:00:00 0.019200 0.019070 0.019124 0.016366 0.016488 \n", + "2017-07-13 19:30:00 0.019125 0.019040 0.019087 0.016305 0.016488 \n", + "2017-07-13 20:00:00 0.019130 0.019000 0.019040 0.016180 0.016382 \n", + "2017-07-13 20:30:00 0.019281 0.019100 0.019130 0.016281 0.016380 \n", + "2017-07-13 21:00:00 0.019382 0.019224 0.019280 0.016360 0.016425 \n", + "2017-07-13 21:30:00 0.019365 0.019110 0.019365 0.016348 0.016422 \n", + "2017-07-13 22:00:00 0.019309 0.019162 0.019204 0.016348 0.016401 \n", + "2017-07-13 22:30:00 0.019353 0.019143 0.019271 0.016269 0.016400 \n", + "2017-07-13 23:00:00 0.019380 0.019250 0.019263 0.016260 0.016365 \n", + "2017-07-13 23:30:00 0.019462 0.019339 0.019350 0.016289 0.016420 \n", "\n", - "[46993 rows x 16 columns]" + "Pair \n", + "Price low open \n", + "date \n", + "2017-01-22 06:30:00 0.012930 0.012974 \n", + "2017-01-22 07:00:00 0.012930 0.012931 \n", + "2017-01-22 07:30:00 0.012968 0.012973 \n", + "2017-01-22 08:00:00 0.012938 0.012995 \n", + "2017-01-22 08:30:00 0.012903 0.012950 \n", + "2017-01-22 09:00:00 0.012880 0.012958 \n", + "2017-01-22 09:30:00 0.012820 0.012900 \n", + "2017-01-22 10:00:00 0.012863 0.012863 \n", + "2017-01-22 10:30:00 0.012883 0.012982 \n", + "2017-01-22 11:00:00 0.012847 0.012901 \n", + "2017-01-22 11:30:00 0.012861 0.012893 \n", + "2017-01-22 12:00:00 0.012940 0.012973 \n", + "2017-01-22 12:30:00 0.013081 0.013130 \n", + "2017-01-22 13:00:00 0.013083 0.013125 \n", + "2017-01-22 13:30:00 0.013078 0.013100 \n", + "2017-01-22 14:00:00 0.013078 0.013100 \n", + "2017-01-22 14:30:00 0.013120 0.013200 \n", + "2017-01-22 15:00:00 0.013074 0.013140 \n", + "2017-01-22 15:30:00 0.013059 0.013116 \n", + "2017-01-22 16:00:00 0.013057 0.013059 \n", + "2017-01-22 16:30:00 0.013107 0.013140 \n", + "2017-01-22 17:00:00 0.013162 0.013162 \n", + "2017-01-22 17:30:00 0.013162 0.013172 \n", + "2017-01-22 18:00:00 0.013129 0.013162 \n", + "2017-01-22 18:30:00 0.013130 0.013156 \n", + "2017-01-22 19:00:00 0.013086 0.013135 \n", + "2017-01-22 19:30:00 0.013050 0.013100 \n", + "2017-01-22 20:00:00 0.013058 0.013091 \n", + "2017-01-22 20:30:00 0.013050 0.013065 \n", + "2017-01-22 21:00:00 0.013033 0.013050 \n", + "... ... ... \n", + "2017-07-13 09:00:00 0.016151 0.016220 \n", + "2017-07-13 09:30:00 0.016200 0.016200 \n", + "2017-07-13 10:00:00 0.016211 0.016214 \n", + "2017-07-13 10:30:00 0.016263 0.016279 \n", + "2017-07-13 11:00:00 0.016304 0.016304 \n", + "2017-07-13 11:30:00 0.016382 0.016404 \n", + "2017-07-13 12:00:00 0.016365 0.016490 \n", + "2017-07-13 12:30:00 0.016353 0.016499 \n", + "2017-07-13 13:00:00 0.016337 0.016437 \n", + "2017-07-13 13:30:00 0.016334 0.016410 \n", + "2017-07-13 14:00:00 0.016306 0.016400 \n", + "2017-07-13 14:30:00 0.016306 0.016306 \n", + "2017-07-13 15:00:00 0.016046 0.016306 \n", + "2017-07-13 15:30:00 0.015992 0.016200 \n", + "2017-07-13 16:00:00 0.015960 0.016010 \n", + "2017-07-13 16:30:00 0.016002 0.016070 \n", + "2017-07-13 17:00:00 0.016115 0.016115 \n", + "2017-07-13 17:30:00 0.016030 0.016218 \n", + "2017-07-13 18:00:00 0.016000 0.016038 \n", + "2017-07-13 18:30:00 0.016100 0.016110 \n", + "2017-07-13 19:00:00 0.016300 0.016300 \n", + "2017-07-13 19:30:00 0.016305 0.016362 \n", + "2017-07-13 20:00:00 0.016175 0.016341 \n", + "2017-07-13 20:30:00 0.016220 0.016318 \n", + "2017-07-13 21:00:00 0.016281 0.016379 \n", + "2017-07-13 21:30:00 0.016227 0.016360 \n", + "2017-07-13 22:00:00 0.016323 0.016348 \n", + "2017-07-13 22:30:00 0.016262 0.016348 \n", + "2017-07-13 23:00:00 0.016260 0.016269 \n", + "2017-07-13 23:30:00 0.016260 0.016261 \n", + "\n", + "[8291 rows x 12 columns]" ] }, - "execution_count": 14, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# save\n", - "df_train.to_hdf('./data/poloniex_30m.hf',key='train')\n", - "df_test.to_hdf('./data/poloniex_30m.hf',key='test')\n", - "df_train" + "# drop cols and update multiindex\n", + "df_train1 = df_train.drop(['volume','quoteVolume'],axis=1,level='Price')\n", + "df_train1.columns = pd.MultiIndex.from_tuples(df_train1.columns.tolist(), names=df_train1.columns.names) # update index to remove dropped cols\n", + "df_train1 = df_train1.sort_index(axis=1)\n", + "\n", + "df_test1 = df_test.drop(['volume','quoteVolume'],axis=1,level='Price')\n", + "df_test1.columns = pd.MultiIndex.from_tuples(df_test1.columns.tolist(), names=df_test1.columns.names)\n", + "df_test1 = df_test1.sort_index(axis=1)\n", + "df_test1" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 61, "metadata": { "ExecuteTime": { - "end_time": "2017-10-29T11:47:21.880468Z", - "start_time": "2017-10-29T11:47:16.882130Z" + "end_time": "2017-11-11T07:33:30.488887Z", + "start_time": "2017-11-11T07:33:30.480534Z" + } + }, + "outputs": [], + "source": [ + "df_train1 = df_train1.sort_index(axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "ExecuteTime": { + "end_time": "2017-11-11T07:33:31.497075Z", + "start_time": "2017-11-11T07:33:31.447367Z" + } + }, + "outputs": [], + "source": [ + "# save\n", + "df_train1.to_hdf('./data/poloniex_30m.hf',key='train', mode='w', append=False)\n", + "df_test1.to_hdf('./data/poloniex_30m.hf',key='test', mode='a', append=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "ExecuteTime": { + "end_time": "2017-11-11T07:31:18.322300Z", + "start_time": "2017-11-11T07:31:14.447227Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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dAABAccIOAACgOGEHAABQnLADAAAoTtgBAAAUJ+wAAACKE3YAAADFCTsAAIDihB0AAEBx\nwg4AAKA4YQcAAFCcsAMAAChO2AEAABQn7AAAAIoTdgAAAMUJOwAAgOKEHQAAQHHCDgAAoDhhBwAA\nUJywAwAAKE7YAQAAFCfsAAAAihN2AAAAxQk7AACA4oQdAABAccIOAACgOGEHAABQnLADAAAoTtgB\nAAAUJ+wAAACKE3YAAADFCTsAAIDihB0AAEBxwg4AAKA4YQcAAFCcsAMAAChO2AEAABQn7AAAAIoT\ndgAAAMUJOwAAgOKEHQAAQHHCDgAAoDhhBwAAUJywAwAAKE7YAQAAFCfsAAAAihN2AAAAxQk7AACA\n4oQdAABAccIOAACgOGEHAABQnLADAAAoTtgBAAAUJ+wAAACKE3YAAADFCTsAAIDihB0AAEBxwg4A\nAKA4YQcAAFCcsAMAAChO2AEAABQn7AAAAIoTdgAAAMUJOwAAgOKEHQAAQHHCDgAAoDhhBwAAUJyw\nAwAAKE7YAQAAFCfsAAAAihN2AAAAxQk7AACA4oQdAABAccIOAACgOGEHAABQnLADAAAoTtgBAAAU\nJ+wAAACKE3YAAADFCTsAAIDihB0AAEBxwg4AAKA4YQcAAFCcsAMAAChO2AEAABQn7AAAAIoTdgAA\nAMUJOwAAgOKEHQAAQHHCDgAAoDhhBwAAUJywAwAAKE7YAQAAFCfsAAAAihN2AAAAxQk7AACA4oQd\nAABAccIOAACgOGEHAABQnLADAAAoTtgBAAAUJ+wAAACKE3YAAADFCTsAAIDihB0AAEBxwg4AAKC4\nlkZ++PHHH8/999+fF154Iddff306Ozt3uu6SSy7J2LFjM2rUqDQ3N6e7u7uRbQEAAHidhsKuo6Mj\ns2fPzu23377btfPmzcuBBx7YyHYAAADsRENhd/jhhw/XOQAAAHibGgq7t+K6665Lkpx99tnp6ura\nU9sCAAC86+027BYsWJANGzbscP+MGTMyderUIW2yYMGCtLW1ZePGjVm4cGEmTpyYyZMn73RtT09P\nenp6kiTd3d1pb28f0h7DpaWlZY/vydtnXvWYWS3mVZO51WJeNZlbHfvKrHYbdnPnzm14k7a2tiTJ\nQQcdlKlTp2bNmjW7DLuurq43PKPX29vb8P5vRXt7+x7fk7fPvOoxs1rMqyZzq8W8ajK3OqrPauLE\niUNaN+Jfd7Bly5Zs3rx58N9PP/10jjjiiJHeFgAAYJ/R0HvsnnzyyXz5y1/Oz3/+83R3d+fII4/M\nX/7lX6avry+33XZb5syZk40bN+bmm29Okrz22ms59dRTc8IJJwzL4QEAAEiaBgYGBvb2Id7M2rVr\n9+h+1Z+q3deYVz1mVot51WRutZhXTeZWR/VZvWNeigkAAMDIEnYAAADFCTsAAIDihB0AAEBxwg4A\nAKA4YQcAAFCcsAMAAChO2AEAABQn7AAAAIoTdgAAAMUJOwAAgOKEHQAAQHHCDgAAoDhhBwAAUJyw\nAwAAKE7YAQAAFCfsAAAAihN2AAAAxQk7AACA4oQdAABAccIOAACgOGEHAABQnLADAAAoTtgBAAAU\nJ+wAAACKE3YAAADFCTsAAIDihB0AAEBxwg4AAKA4YQcAAFCcsAMAAChO2AEAABQn7AAAAIoTdgAA\nAMUJOwAAgOKEHQAAQHHCDgAAoDhhBwAAUJywAwAAKE7YAQAAFCfsAAAAihN2AAAAxQk7AACA4oQd\nAABAccIOAACgOGEHAABQnLADAAAoTtgBAAAUJ+wAAACKE3YAAADFCTsAAIDihB0AAEBxwg4AAKA4\nYQcAAFCcsAMAAChO2AEAABQn7AAAAIoTdgAAAMUJOwAAgOKEHQAAQHHCDgAAoDhhBwAAUJywAwAA\nKE7YAQAAFCfsAAAAihN2AAAAxQk7AACA4oQdAABAccIOAACgOGEHAABQnLADAAAoTtgBAAAUJ+wA\nAACKE3YAAADFCTsAAIDihB0AAEBxwg4AAKA4YQcAAFCcsAMAAChO2AEAABQn7AAAAIoTdgAAAMUJ\nOwAAgOKEHQAAQHHCDgAAoDhhBwAAUJywAwAAKE7YAQAAFCfsAAAAihN2AAAAxQk7AACA4oQdAABA\nccIOAACgOGEHAABQnLADAAAoTtgBAAAUJ+wAAACKE3YAAADFCTsAAIDihB0AAEBxwg4AAKA4YQcA\nAFCcsAMAAChO2AEAABQn7AAAAIoTdgAAAMUJOwAAgOKEHQAAQHHCDgAAoDhhBwAAUJywAwAAKE7Y\nAQAAFCfsAAAAihN2AAAAxQk7AACA4oQdAABAccIOAACgOGEHAABQnLADAAAoTtgBAAAUJ+wAAACK\nE3YAAADFCTsAAIDiWhr54bvvvjsrV65MS0tLJkyYkM9+9rM54IADdli3atWq3HHHHenv789ZZ52V\n6dOnN7ItAAAAr9PQM3bHHXdcbrnlltx888057LDD8tBDD+2wpr+/P4sXL87VV1+dRYsW5bHHHsvz\nzz/fyLYAAAC8TkNhd/zxx6e5uTlJctRRR6Wvr2+HNWvWrMmhhx6aCRMmpKWlJdOmTcuKFSsa2RYA\nAIDXaeilmK+3dOnSTJs2bYf7+/r6Mn78+MHb48ePz+rVq4dr2xJefnlDnli6t0/xbrVhbx+At8zM\najGvmsytFvOqydzq2P2s2g5PTjnlvXvgLCNnt2G3YMGCbNiw4x9jxowZmTp1apLkwQcfTHNzcz7y\nkY80fKCenp709PQkSbq7u9Pe3t7wNd+KlpaWYd/zf93nPz4AALxT9T2fPd4dw223YTd37tw3fXzZ\nsmVZuXJlrrnmmjQ1Ne3weFtbW9avXz94e/369Wlra9vl9bq6utLV1TV4u7e3d3dHHFbt7e3DvueH\nz4xn7AAA4B2q7fA93x1DNXHixCGta+ilmKtWrcojjzySv/qrv8qYMWN2uqazszMvvvhi1q1bl7a2\ntixfvjyzZs1qZNty3ve+9+a/X7i3T/HuNBIhzsgys1rMqyZzq8W8ajK3OvaVWTUUdosXL8727duz\nYMGCJMmkSZMyc+bM9PX15bbbbsucOXPS3Nyciy66KNddd136+/tzxhlnpKOjY1gODwAAQINhd+ut\nt+70/ra2tsyZM2fw9pQpUzJlypRGtgIAAGAXGvq6AwAAAPY+YQcAAFCcsAMAAChO2AEAABQn7AAA\nAIoTdgAAAMUJOwAAgOKEHQAAQHHCDgAAoDhhBwAAUJywAwAAKE7YAQAAFCfsAAAAihN2AAAAxQk7\nAACA4oQdAABAccIOAACgOGEHAABQnLADAAAoTtgBAAAUJ+wAAACKE3YAAADFCTsAAIDihB0AAEBx\nwg4AAKA4YQcAAFCcsAMAAChO2AEAABQn7AAAAIoTdgAAAMUJOwAAgOKEHQAAQHHCDgAAoDhhBwAA\nUJywAwAAKE7YAQAAFCfsAAAAihN2AAAAxQk7AACA4oQdAABAccIOAACgOGEHAABQnLADAAAoTtgB\nAAAUJ+wAAACKE3YAAADFCTsAAIDihB0AAEBxwg4AAKA4YQcAAFCcsAMAAChO2AEAABQn7AAAAIoT\ndgAAAMUJOwAAgOKEHQAAQHHCDgAAoDhhBwAAUJywAwAAKE7YAQAAFCfsAAAAihN2AAAAxQk7AACA\n4oQdAABAccIOAACgOGEHAABQnLADAAAoTtgBAAAUJ+wAAACKE3YAAADFCTsAAIDihB0AAEBxwg4A\nAKA4YQcAAFCcsAMAAChO2AEAABQn7AAAAIoTdgAAAMUJOwAAgOKEHQAAQHHCDgAAoDhhBwAAUJyw\nAwAAKE7YAQAAFCfsAAAAihN2AAAAxQk7AACA4oQdAABAccIOAACgOGEHAABQnLADAAAoTtgBAAAU\nJ+wAAACKE3YAAADFCTsAAIDihB0AAEBxwg4AAKA4YQcAAFCcsAMAAChO2AEAABQn7AAAAIoTdgAA\nAMUJOwAAgOKEHQAAQHHCDgAAoDhhBwAAUJywAwAAKE7YAQAAFCfsAAAAihN2AAAAxQk7AACA4oQd\nAABAccIOAACgOGEHAABQnLADAAAoTtgBAAAUJ+wAAACKE3YAAADFCTsAAIDihB0AAEBxwg4AAKA4\nYQcAAFCcsAMAACiupZEfvvvuu7Ny5cq0tLRkwoQJ+exnP5sDDjhgh3WXXHJJxo4dm1GjRqW5uTnd\n3d2NbAsAAMDrNBR2xx13XD7+8Y+nubk599xzTx566KF84hOf2OnaefPm5cADD2xkOwAAAHaioZdi\nHn/88Wlubk6SHHXUUenr6xuWQwEAADB0TQMDAwPDcaHu7u5MmzYtH/3oR3d47JJLLklra2uS5Oyz\nz05XV9cur9PT05Oenp7Ba/7yl78cjuMNWUtLS7Zv375H9+TtM696zKwW86rJ3Goxr5rMrY7qsxo9\nevSQ1u027BYsWJANGzbscP+MGTMyderUJMmDDz6YZ599NrNnz05TU9MOa/v6+tLW1paNGzdm4cKF\n+cM//MNMnjx5SAdcu3btkNYNl/b29vT29u7RPXn7zKseM6vFvGoyt1rMqyZzq6P6rCZOnDikdbt9\nj93cuXPf9PFly5Zl5cqVueaaa3YadUnS1taWJDnooIMyderUrFmzZshhBwAAwJtr6D12q1atyiOP\nPJIrr7wyY8aM2emaLVu2ZPPmzYP/fvrpp3PEEUc0si0AAACv09CnYi5evDjbt2/PggULkiSTJk3K\nzJkz09fXl9tuuy1z5szJxo0bc/PNNydJXnvttZx66qk54YQTGj85AAAASYbxw1NGivfY8WbMqx4z\nq8W8ajK3WsyrJnOro/qshvoeu4ZeigkAAMDeJ+wAAACKE3YAAADFCTsAAIDihB0AAEBxwg4AAKA4\nYQcAAFCcsAMAAChO2AEAABQn7AAAAIoTdgAAAMUJOwAAgOKEHQAAQHHCDgAAoDhhBwAAUJywAwAA\nKE7YAQAAFCfsAAAAihN2AAAAxQk7AACA4oQdAABAccIOAACgOGEHAABQnLADAAAoTtgBAAAUJ+wA\nAACKE3YAAADFCTsAAIDihB0AAEBxwg4AAKA4YQcAAFCcsAMAAChO2AEAABQn7AAAAIoTdgAAAMUJ\nOwAAgOKEHQAAQHHCDgAAoDhhBwAAUJywAwAAKE7YAQAAFCfsAAAAihN2AAAAxQk7AACA4oQdAABA\nccIOAACgOGEHAABQnLADAAAoTtgBAAAUJ+wAAACKE3YAAADFCTsAAIDihB0AAEBxwg4AAKA4YQcA\nAFCcsAMAAChO2AEAABQn7AAAAIoTdgAAAMUJOwAAgOKEHQAAQHHCDgAAoDhhBwAAUJywAwAAKE7Y\nAQAAFCfsAAAAihN2AAAAxQk7AACA4oQdAABAccIOAACgOGEHAABQnLADAAAoTtgBAAAUJ+wAAACK\nE3YAAADFCTsAAIDihB0AAEBxwg4AAKA4YQcAAFCcsAMAAChO2AEAABQn7AAAAIoTdgAAAMUJOwAA\ngOKEHQAAQHHCDgAAoDhhBwAAUJywAwAAKE7YAQAAFCfsAAAAihN2AAAAxQk7AACA4oQdAABAccIO\nAACgOGEHAABQnLADAAAoTtgBAAAUJ+wAAACKa9nbB3irBgYGsmXLlvT396epqWnYr//SSy9l69at\nw37dd5qBgYGMGjUqY8eOHZG/IwAAsOeUC7stW7bkPe95T1paRuboLS0taW5uHpFrv9Ns3749W7Zs\nyX777be3jwIAADSg3Esx+/v7Ryzq9jUtLS3p7+/f28cAAAAaVC7svGxwePl7AgBAfZ76ehs6Ojpy\nzDHHZGBgIM3NzVm4cGFaW1sza9asJMnatWszbty4jBs3Lm1tbbnvvvvy7LPPZv78+XnuuefS2tqa\nI488MgsXLszq1atz0UUXpaOjIwMDAxk/fnz+9m//Nt/+9rfz93//90mS1atXp7OzM6NGjcoZZ5yR\nq6++OkuXLs1NN92UzZs3Z/To0TnllFMyb968vflnAQAA9hJh9zaMHTs2S5YsSZIsW7Ys3d3d+cd/\n/MfB+y677LJ0dXXl3HPPTfKf7wv85Cc/mXnz5uWcc85Jkixfvjzr169Pkpx88sm56667kiQ33HBD\n7rzzzsyePTsXXnhhkuQ3fuM3cv/996etrS1J8swzz+Tzn/987rrrrnzwgx/Ma6+9lnvuuWfP/QEA\nAIB3lHJ9v9LPAAAYPElEQVQvxXyn+cUvfpGDDjroTdc8/PDDOfHEEwejLkmmTZuWY4455g3rBgYG\nsmnTpt1e70tf+lJmzZqVD37wg0mS5ubmfOpTn3qbvwEAAFCdZ+zehi1btuTss8/O1q1bs27dunzt\na1970/XPPPNMjjvuuF0+/uSTT+bss8/OK6+8kv333z9XXXXVm17vhz/8YT7zmc+8rbMDAADvPvvE\nM3YDAwMZ+OmzGRgYGJbr/eqlmI8++mjuueee/Nmf/VlD1z755JOzZMmSfO9738uFF16YhQsXDss5\nAQCAfcM+EXb52XPp774y+dlzw37pk046KX19fYPvl9uZo48+Ok8//fSQrnfOOefku9/97puuOeqo\no/Lv//7vb+mcAADAu9e+EXYdv5ZRV92YdPzasF96zZo1ee2113LwwQfvcs306dOzcuXK9PT0DN73\nxBNP5Jlnntlh7ZNPPpn3v//9b7rnn/7pn+bWW2/Ns88+m+Q/v9vvVx++AgAA7Hv2iffYNTU1JUd0\nDtv1fvUeu+Q/X+b513/912lubt7l+v322y//8A//kHnz5mXevHl5z3vekw996EO59tpr09fXN/ge\nu4GBgRx44IG56aab3nT/yZMnZ/78+bnkkkuyefPmNDU1paura9h+PwAAoJamgeF649kIWbt27Rtu\nv/rqq9l///1HbL+WlpZs3759xK7/TjPSf8+R1t7ent7e3r19DN4CM6vFvGoyt1rMqyZzq6P6rCZO\nnDikdfvGSzEBAADexYQdAABAccIOAACgOGEHAABQnLADAAAoTtgBAAAUt098j91wmzRpUlavXj14\ne9GiRfnmN7+ZJHnmmWdyzDHHJEn+4A/+IJ/+9Kdz33335bbbbktTU1NaWlpy/vnnZ+bMmbn00kuz\nYsWKjBs3Llu3bs3v/u7v5rLLLsunP/3pvPDCC3n11Vezfv36dHR0JEluvPHGHHvssbnxxhvzrW99\nK62trRkzZkyuuOKKnH766Xv87wAAALwzCLthcPnll+fyyy/P9u3bc+yxx2bJkiWDjy1ZsiR33nln\nvvrVr+aQQw7Jli1b8uCDDw4+Pn/+/PzWb/1WNm/enNNOOy2/93u/lzvvvDNJ8uijj+bOO+/Ml7/8\n5cH11157bTZs2JB/+Zd/yejRo7Nu3bo8+eSTe+x3BQAA3nmE3Qi79dZbM2/evBxyyCFJkrFjx+bj\nH//4Duu2bNmSpqam7Lfffru81qZNm/K1r30t3/3udzN69OgkySGHHJJzzz13ZA4PAACU4D12I+xH\nP/pRjjvuuF0+Pn/+/Jx99tmZOnVqLrjggrS1te1y7Y9//OMcccQROeCAA0biqAAAQFH7RNgNDAzk\nub4tGRgY2NtH2cH8+fOzZMmSrFq1KkuXLs2//du/7e0jAQAAxewTYffjV7bmyv/9f/LjV7bu8b0n\nTZqUp59+erfrWltb8+EPf/hN3y/3gQ98ID/96U/zH//xH8N5RAAAoLiGw+6rX/1qZs+enb/4i7/I\nwoUL09fXt9N1y5Yty6xZszJr1qwsW7as0W3fkg8cPCY3nvP+fODgMXt03yS59NJLs2DBgrz88stJ\nkq1bt+YrX/nKDuu2bduWVatW5cgjj9zltVpbW3PBBRdk3rx52bZtW5Kkt7c3//RP/zQiZwcAAGpo\n+MNTPvaxj2XGjBlJkm9+85t54IEHMnPmzDes2bRpUx544IF0d3cnSa666qqcdNJJaW1tbXT7IWlq\nasqvtY0dtutt3rw5J5544uDtmTNn5jOf+cxO155zzjlZv359fv/3f3/wLK//8JT58+fnlltuyS9/\n+cucdtppOeecc95076uvvjrd3d05/fTTM3bs2Oy333753Oc+Nwy/FQAAUFXTwDC+8eyhhx5Kb29v\n/uRP/uQN9//rv/5rfvCDHwwG3+23357Jkyfn1FNP3e01165d+4bbr776avbff//hOvIOWlpasn37\n9hG7/jvNSPw9V69enW9961vDek0AABgpJ554Yk455ZS9fYydmjhx4pDWDcvXHXzlK1/Jo48+mv33\n3z/z5s3b4fG+vr6MHz9+8HZbW9suX7LZ09OTnp6eJEl3d3fa29vf8PhLL72UlpaR/ZaGkb7+O8mY\nMWN2+Bs36m/+5m+G9XoAADCSVq5cmfPOO29vH6MhQ3rGbsGCBdmwYcMO98+YMSNTp04dvP3QQw9l\n27Ztgy87/JWvf/3r2bZtW84///wkyQMPPJDRo0fnYx/72G4P6Bm7keUZOwAA9nX7zDN2c+fOHdLF\nPvKRj+SGG27YIeza2trygx/8YPB2X19fJk+ePKRrUs+kSZMyadKkPbJXe3t7ent798heDA8zq8W8\najK3WsyrJnOrY1+ZVcOfivniiy8O/nvFihU7LcoTTjghTz31VDZt2pRNmzblqaeeygknnNDo1gAA\nAGQY3mN377335sUXX0xTU1Pa29sHPyDl2WefzZIlS3LxxRentbU1559/fubMmZMkueCCC/bYJ2IC\nAAC82zUcdrNnz97p/Z2dnens7By8feaZZ+bMM89sdDsAAAD+i33n4x+HUUdHR4455phs3749zc3N\nueCCCzJz5syMGvX/X9l6zTXX5Bvf+EZWrFgxeP/LL7+cP//zP8/atWuzffv2dHR05O67787Pfvaz\nfOpTn8rSpUsHf/6WW27JAQcckIsvvjiXXXZZnnjiiYwbNy5bt27N9OnTc8UVV+SP/uiP8tOf/jSv\nvvpq1q9fn46OjiTJ9ddfnxNOOCE33XRTvvGNb6S1tTWjR4/O5ZdfLq4BAOBdSNi9DWPHjs2SJUuS\nJL29vbnkkkuyadOmwWcv+/v788///M857LDD8vjjjw9+ws5NN92Uj370o/njP/7jJHnDB8rszuc/\n//mce+652bJlS84444xccMEFWbx4cZJk+fLl+bu/+7vcddddg+uvv/76vPTSS1m6dGnGjBmTl19+\nOY8//viw/P4AAMA7S8MfnrKva29vzxe+8IXccccd+dU3RyxfvjxHH310PvnJT+aRRx4ZXLtu3boc\ndthhg7ffzieDbt26NUne9CsKNm/enHvvvTcLFy7MmDFjkiTve9/7hvT1EgAAQD3Cbhi8//3vT39/\n/+DHqD7yyCM577zz8tu//dv59re/nW3b/m979x4UVRXHAfy7iMIqyhsdt9EwMWsa0tLERAVxNQXN\nHIZCLOnh5GucnGEIjWFsJHxLZjkkNT3I/KMmH0xNRRGSELYpmFk6ksgQJK/ltbErLJz+YNxxRRHh\nrt7Dfj8zzAj33nPvuV/Psj/O3nvbAQDx8fFISEhAdHQ09u7diytXrtjaKC8vh16vt31lZWXZ7SM1\nNRV6vR5TpkzB4sWLe3yoeFlZGXQ6HYYPH+6A3hIRERERkdo4RWEnhEBTgxW9eBZ7v7W1tSE3NxdP\nPfUUhg8fjsmTJyMvLw8AEBYWhsLCQsTFxaG0tBTz589HfX09gK7iMCcnx/b1/PPP27WbnJyMnJwc\nlJSUoKCgAAaDweF9ISIiIiIiOThFYdfc2IETP5rQ3NjhkPbLy8vh4uICPz8/5OXloampCREREZg2\nbRp+/fVXu49jent745lnnsG+ffvw6KOPoqio6I72NWzYMEyfPr3Hwi4wMBCVlZVoaWnpc5+IiIiI\niEgeTlHYjfAahNAID4zwGqR42/X19UhKSsKLL74IjUaDo0ePYteuXTh58iROnjyJoqIi5Ofnw2w2\n48SJEzCbzQAAk8mE8vJy6HS6O9qf1WpFcXExxo4de8t1tFotYmNjkZKSgra2NttxZmdn972jRERE\nRESkWk5xV0yNRgNPb+W6arFYoNfruz3uwGw2Iy8vD9u2bbOtO3ToUDzxxBP4/vvvUVVVheTkZLi6\nuqKzsxOxsbGYNGkSKioqbrvP1NRU7N27F+3t7QgNDcXChQt7XD8xMRE7duxAeHg43NzcMHTo0Fs+\nc5CIiIiIiOSmEXfjwrN+qKqqsvu+tbW1xztC9perqyusVqvD2lcbR59PR/Pz87PdtIbkwMzkwrzk\nxNzkwrzkxNzkIXtWo0eP7tV6TvFRTCIiIiIiooGMhR0REREREZHkWNgRERERERFJjoUdERERERGR\n5FjYERERERERSY6FHRERERERkeRY2N2hyspKhISEoKGhAQDQ2NiIkJAQVFRUQKfTYfv27bZ1jUYj\nxo4dizfeeAMAsHv3bjz++OPQ6/UICwvDkSNHbOu+9tprCAkJgV6vx6xZs7Bnzx4AwMsvvwy9Xo8Z\nM2Zg4sSJ0Ov10Ov1MBgMaG9vR1paGmbMmIH58+dj0aJFyM3NvYtng4iIiIiI1MApHlCuJJ1Ohxde\neAFbt27Fjh07kJaWhri4OADAmDFj8OOPP+L1118HAGRnZ2PChAl2269cuRKrVq3CpUuXsGDBAkRG\nRmLw4MEAgOTkZERFRcFisSA8PBzR0dH48MMPAQCFhYXIyMjAp59+amsrLS0N1dXVyM3NhZubG2pr\na/HLL7/cjdNAREREREQqwhm7Pli5ciVOnz6NzMxMGAwGrFq1CgCg1WoRFBSEM2fOAOgq7BYtWnTT\nNsaNGwetVoumpqZuy65evQoAPT443Gw24+DBg0hNTYWbmxsAwN/fH4sXL+5X34iIiIiISD6cseuD\nwYMHIzk5GXFxcTh06JBtxg0Ann76aRw9ehR+fn5wcXHByJEjUV1d3a2Ns2fPIjAwEH5+frafpaam\nYu/evbh8+TJeeuklu2U3Kisrg06nw/Dhw5XtHBERERERSccpZuyEEKipqYEQQrE2c3NzMXLkSJw/\nf97u52FhYcjPz8exY8duOnuWmZmJ8PBwREVFYf369XbLkpOTkZOTg5KSEhQUFMBgMCh2vERERERE\nNHA5RWFXW1uLL7/8ErW1tYq098cff+Dnn39GdnY2MjMz7WbkhgwZguDgYLz//vuIjIzstu3KlSvx\n008/ITMzEwkJCbBYLN3WGTZsGKZPn95jYRcYGIjKykq0tLQo0iciIiIiIpKXUxR2/v7+iI6Ohr+/\nf7/bEkJg48aNePPNN6HT6bB69Wps2bLFbp1XX30VmzZtgre39y3bmTdvHoKDg/HFF190W2a1WlFc\nXIyxY8fecnutVovY2FikpKSgra0NAFBfX4/s7Ow+9oyIiIiIiGTlFIWdRqNBQEAANBpNv9s6ePAg\ndDodZs2aBQBYsWIFLl68iH/++ce2zoMPPoiYmJjbtrVhwwYcOHAAnZ2dALqusdPr9Zg7dy4mTpyI\nhQsX9rh9YmIifH19ER4ejjlz5mDFihW85o6IiIiIyAlphJIXnjlAVVWV3fetra093i2yv1xdXWG1\nWh3Wvto4+nw6mp+fH+rq6u71YdAdYGZyYV5yYm5yYV5yYm7ykD2r0aNH92o9p5ixIyIiIiIiGshY\n2BEREREREUmOhR0REREREZHkpCvsVH5JoHR4PomIiIiI5CddYefi4uJUNzdxJKvVChcX6f4LEBER\nERHRDVzv9QHcKXd3d1gsFly9elWRxxfcyM3NDVevXlW8XbURQsDFxQXu7u73+lCIiIiIiKifpCvs\nNBoNtFqtw9qX/XaoRERERETkfPg5PCIiIiIiIsmxsCMiIiIiIpIcCzsiIiIiIiLJaQTvd09ERERE\nRCQ1ztjdICkp6V4fAt0B5iUfZiYX5iUn5iYX5iUn5iYPZ8mKhR0REREREZHkWNgRERERERFJbtDm\nzZs33+uDUJtx48bd60OgO8C85MPM5MK85MTc5MK85MTc5OEMWfHmKURERERERJLjRzGJiIiIiIgk\n53qvD6C/6urq8N5776GxsREajQZz587FwoULYTKZkJ6ejtraWvj7+2PDhg3w8PBAZWUl9u/fj7Ky\nMjz33HNYvHixXXudnZ1ISkqCj4/PLe+gk5eXh6+++goAsHTpUoSFhQEADh06hPz8fJhMJmRlZTm0\n37JSS15msxkpKSm2dYxGI2bOnIn4+HiH9V1WSma2du1auLu7w8XFBYMGDcK2bdtuus+SkhJ89NFH\n6OzsREREBJYsWQIA+Pbbb/H111+juroaH3zwAUaMGHFXzoFM1JRXSkoKzGYzAKC5uRkPPPAAEhMT\nHX8SJKRkbv/99x8yMjJQUVEBjUaD1atXY8KECd32yXHWd2rKi+Os95TKraqqCunp6bZ2a2pqEBMT\ng8jIyG775DjrGzVlJdUYE5IzGo3i77//FkII0draKtavXy8qKipEVlaWOHz4sBBCiMOHD4usrCwh\nhBCNjY3i4sWL4vPPPxdHjx7t1l52drZ4++23xdatW2+6v5aWFrF27VrR0tJi928hhLhw4YIwGo1i\n+fLljujqgKCmvK6XmJgozp07p1Q3BxQlM1uzZo1oamrqcX8dHR1i3bp14sqVK6K9vV0kJCSIiooK\nIYQQly5dEtXV1b1qx1mpKa/r7dy5U+Tl5SnRxQFJydz27dsnfvjhByGEEO3t7cJkMnXbH8dZ/6gp\nr+txnPVM6fcgQnRl88orr4iampqbLuM46xs1ZXU9tY8x6T+K6e3tbbsYUqvVQqfTwWg0wmAwYPbs\n2QCA2bNnw2AwAAA8PT0xfvx4DBo0qFtb9fX1OH36NCIiIm65v5KSEgQHB8PDwwMeHh4IDg5GSUkJ\nAGDChAnw9vZWuosDipryuqaqqgrNzc146KGHlOrmgKJkZr1RWlqKUaNGYeTIkXB1dcWTTz5pazsw\nMBABAQEK9GrgUlNe17S2tuLcuXOYOnVqP3o2sCmVW2trK/766y/MmTMHAODq6ophw4Z12x/HWf+o\nKa/r2+I465kjXh/Pnj2LUaNGwd/fv9syjrO+U1NW18gwxqT/KOb1ampqUFZWhvHjx6OpqclWZHl5\neaGpqem223/88cdYvny5bbr1ZoxGI3x9fW3f+/j4wGg09v/gnZBa8iosLMT06dOh0Wj62BPn0d/M\nAOCtt94CAOj1esydO7fb8hsz8/X1xcWLFxU4euejlrwMBgMeeeQRDB06tK9dcSr9ya2mpgYjRozA\n/v37UV5ejnHjxiE+Ph7u7u5263GcKUcteXGc3RklXh8BoKCgADNmzLjpMo4zZaglKxnGmPQzdtdY\nLBbs3r0b8fHx3U64RqO57Zv2U6dOwdPT0yluhaoGasqroKAAoaGh/W5noOtvZgCwZcsWbN++HZs2\nbcJ3332HP//801GH6/TUlFdPv0zJXn9z6+joQFlZGebNm4cdO3bAzc0NR44cceQhOzU15cVx1ntK\nvD4CgNVqxalTpxASEuKIwySoKysZxtiAmLGzWq3YvXs3Zs6ciWnTpgHompJtaGiAt7c3GhoabntR\n6oULF/Dbb7+huLgYbW1tMJvNeOedd7BgwQIcOHAAAPDss8/Cx8fH7s2N0WjEww8/7LjODUBqyuvy\n5cvo7OxkQX8bSmQGdM2YXtt26tSpKC0tRUBAALZv3w6ga1bo/vvvR319vW2b+vp623bUO2rKq7m5\nGaWlpUhISFCyiwOSErn5+vrC19cXQUFBAICQkBAcOXIEdXV1HGcKU1NeHGe9p9TrIwAUFxcjMDAQ\nXl5eAMBxpjA1ZSXLGJO+sBNCICMjAzqdDlFRUbafT5kyBcePH8eSJUtw/Pjx234edtmyZVi2bBkA\n4Ny5c8jOzsb69esBADt37rStZzKZcOjQIZhMJgDAmTNnbNvR7aktLxn++nKvKZWZxWKBEAJarRYW\niwW///47oqOj4efnZ5dZR0cH/v33X9TU1MDHxweFhYW2bOn21JZXUVERHnvsMQwZMkT5zg4gSuXm\n5eUFX19fVFVVYfTo0Th79izuu+8+jjOFqS0vjrPeUSq3a258D8Fxphy1ZSXLGJP+AeXnz59HSkoK\nxowZY5uOjY2NRVBQENLT01FXV2d3O9TGxkYkJSXBbDZDo9HA3d0de/bssZvevVYo3Or2+bm5uTh8\n+DCArtvnh4eHAwA+++wznDhxwvaXhDlz5iAmJsbBZ0AuasoLANatW4eNGzdCp9M5sNdyUyqzlpYW\n7Nq1C0DXC2hoaCiWLl16032ePn0an3zyCTo7OxEeHm5b75tvvsGxY8fQ2NgIT09PTJ48GatWrbo7\nJ0ISasoLADZv3owlS5Zg0qRJju+8xJR8bbx8+TIyMjJgtVoREBCANWvWwMPDo9s+Oc76Tk15ARxn\nvaVkbhaLBWvWrMG7777b4zVXHGd9o6asAHnGmPSFHRERERERkbMbMDdPISIiIiIiclYs7IiIiIiI\niCTHwo6IiIiIiEhyLOyIiIiIiIgkx8KOiIiIiIhIcizsiIiIiIiIJMfCjoiIiIiISHIs7IiIiIiI\niCT3P3uUE9wKdTtAAAAAAElFTkSuQmCC\n", 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IAAAAJGkbGRkZOdSH2B/r168f1/2afH/v4ci8msncmsOsmsncmsW8msncmqXJ8xrrZ/ha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\nL+n8qtraWiorK7n55ptpbGz0FF/9+/e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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -4700,29 +6825,69 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "metadata": { "ExecuteTime": { - "end_time": "2017-10-29T11:47:32.924749Z", - "start_time": "2017-10-29T11:47:21.882154Z" + "end_time": "2017-11-11T07:10:43.943684Z", + "start_time": "2017-11-11T07:10:43.600846Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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ScwAAAAWJOQAAgILEHAAAQEFiDgAAoCAxBwAAUJCYAwAAKEjMAQAAFCTmAAAAChJzAAAA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\noUMH2Wy2Otu4zprHk/J1kRmuMdNNs7zU8ePHdejQId14440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o1qp9tf6v62YtrrW6GCdxEeXGAJaIiEqi9biCRMwoeg3sYKyJWQAwfZYbCxf7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MAAAg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\ny5Yti59vOua0VOOmS3djp2NO6+m8B3JO6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"text/plain": [ - "" + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# View one day, normalized\n", + "data_window = df[1000:2000].copy()\n", + "open = data_window.xs('open', axis=1, level='Price')\n", + "data_window = data_window.divide(open.iloc[-1], level='Pair')\n", + "data_window = data_window.drop('open', axis=1, level='Price')\n", + "data_window.xs('close', axis=1, level='Price').plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "ExecuteTime": { + "end_time": "2017-11-11T07:10:54.468148Z", + "start_time": "2017-11-11T07:10:43.945502Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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W5uXKHAAAQEFiDgAAoCAxBwAAUJCYAwAAKEjMAQAAFCTmAAAAChJzAAAABYk5AACAgsQc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\nP35mbsSIEUpLS7upWDabTYsWLdKTTz6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+ "text/plain": [ + "" ] }, "metadata": {}, @@ -4742,52 +6907,18 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { - "ExecuteTime": { - "end_time": "2017-10-29T11:47:33.286565Z", - "start_time": "2017-10-29T11:47:32.926377Z" - } + "collapsed": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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WIysrC+np6bBarUhKSpJuky9l1RHkoZUBlppz99134+6775Y+V6lU+Pe//w3A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\nYnOIbNpuR4cC94Lq6mqCwSCppMYbL4dJpDMTczkJgHHe5jInLy013E/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ItiO/lJ3W5VL0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\nFxfzP//zPxw7dkwtl9XQ0EBHR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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# View one day\n", - "data_window = df[1000:2000].copy()\n", - "open = data_window.xs('open', axis=1, level='Price')\n", - "data_window = data_window.divide(open.iloc[-1], level='Pair')\n", - "data_window = data_window.drop('open', axis=1, level='Price')\n", - "data_window.xs('close', axis=1, level='Price').plot()" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { - "ExecuteTime": { - "end_time": "2017-10-29T11:40:53.103545Z", - "start_time": "2017-10-29T11:40:53.050102Z" - } + "collapsed": true }, "outputs": [], "source": [] diff --git a/data/poloniex_30m.hf b/data/poloniex_30m.hf index 7495361..aaee0c0 100644 Binary files a/data/poloniex_30m.hf and b/data/poloniex_30m.hf differ diff --git a/data/poloniex_30m_vol.hf b/data/poloniex_30m_vol.hf new file mode 100644 index 0000000..7fb1f0a Binary files /dev/null and b/data/poloniex_30m_vol.hf differ diff --git a/rl_portfolio_management/environments/portfolio.py b/rl_portfolio_management/environments/portfolio.py index 348fea3..8a64df3 100644 --- a/rl_portfolio_management/environments/portfolio.py +++ b/rl_portfolio_management/environments/portfolio.py @@ -38,13 +38,13 @@ class DataSrc(object): self.scale_extra_cols = scale_extra_cols self.window_length = window_length - df = df.copy() # get rid of NaN's + df = df.copy() df.replace(np.nan, 0, inplace=True) df = df.fillna(method="pad") - self._data = df.copy() + self._data = df self.asset_names = self._data.columns.levels[0].tolist() self.price_columns = ['close', 'high', 'low', 'open'] @@ -54,7 +54,7 @@ class DataSrc(object): if scale_extra_cols: self.stats = dict() for column in self.non_price_columns: - x = df.xs(key=column, axis=1, level='Price').as_matrix()[:, 1:] # [1:] to ignore cash columns + x = df.xs(key=column, axis=1, level='Price').as_matrix()[:, :] self.stats[column] = dict(mean=x.mean(), std=x.std()) self.reset() @@ -67,7 +67,6 @@ class DataSrc(object): # (eq 18) prices are divided by open price # While the paper says open/close, it only makes sense with close/open if self.scale: - # scale prices by dividing price columns by the last open price open_price = data_window.xs('open', axis=1, level='Price') last_open_price = open_price.iloc[-1] @@ -80,7 +79,7 @@ class DataSrc(object): for column, stat in self.stats.items(): x = data_window.loc[:, (pair, column)] # normalize by mean and std, then clip to 10 standard deviations - x = (x - stat["mean"]) / (stat["std"] + 1e-5) + x = (x - stat["mean"]) / (stat["std"] + 1e-10) x = np.clip(x, stat["mean"] - stat["std"] * 10, stat["mean"] + stat["std"] * 10) data_window.loc[:, (pair, column)] = x @@ -183,7 +182,7 @@ class PortfolioSim(object): def reset(self): self.infos = [] - self.w0 = np.array([1.0] + [0.0] * (len(self.asset_names) - 1)) + self.w0 = np.array([1.0] + [0.0] * len(self.asset_names)) self.p0 = 1.0 @@ -242,26 +241,26 @@ class PortfolioEnv(gym.Env): self.log_dir = log_dir # openai gym attributes - # action will be the portfolio weights from 0 to 1 for each asset + # action will be the portfolio weights [cash_bias,w1,w2...] where wn are [0, 1] for each asset nb_assets = len(self.src.asset_names) self.action_space = gym.spaces.Box( - 0.0, 1.0, shape=nb_assets) + 0.0, 1.0, shape=nb_assets + 1) # get the history space from the data min and max if output_mode == 'EIIE': obs_shape = ( - nb_assets - 1, # don't observe cash column + nb_assets, window_length, len(self.src._data.columns.levels[1]) - 1 ) elif output_mode == 'atari': obs_shape = ( - window_length, # don't observe cash column + window_length, window_length, len(self.src._data.columns.levels[1]) - 1 ) elif output_mode == 'mlp': - obs_shape = (nb_assets - 1) * window_length * \ + obs_shape = (nb_assets) * window_length * \ (len(self.src._data.columns.levels[1]) - 1) else: raise Exception('Invalid value for output_mode: %s' % @@ -269,8 +268,8 @@ class PortfolioEnv(gym.Env): self.observation_space = gym.spaces.Dict({ 'history': gym.spaces.Box( - 0, - 2 if scale else 1, # if scale=True observed price changes return could be large fractions + -10, + 20 if scale else 1, # if scale=True observed price changes return could be large fractions obs_shape ), 'weights': self.action_space @@ -282,7 +281,7 @@ class PortfolioEnv(gym.Env): Step the env. Actions should be portfolio [w0...] - - Where wn is a portfolio weight from 0 to 1. The first is cash_bias + - Where wn is a portfolio weight between 0 and 1. The first (w0) is cash_bias - cn is the portfolio conversion weights see PortioSim._step for description """ logger.debug('action: %s', action) @@ -291,24 +290,16 @@ class PortfolioEnv(gym.Env): weights /= weights.sum() + eps # Sanity checks - np.testing.assert_almost_equal( - action.shape, - (len(self.sim.asset_names),), - err_msg='Action should contain %s floats, not %s' % (len(self.sim.asset_names), action.shape) - ) - assert ((action >= 0) * (action <= 1) - ).all(), 'all action values should be between 0 and 1. Not %s' % action + assert self.action_space.contains(action), 'action should be within %r but is %r' % (self.action_space, action) np.testing.assert_almost_equal( np.sum(weights), 1.0, 3, err_msg='weights should sum to 1. action="%s"' % weights) history, done1 = self.src._step() y1 = history[:, -1, 0] # relative price vector (open/close) + y1 = np.concatenate([[1.0], y1]) # add cash price reward, info, done2 = self.sim._step(weights, y1) - # remove cash columns, they are just meaningless values - history = history[1:, :, :] - # calculate return for buy and hold a bit of each asset info['market_value'] = np.cumprod( [inf["market_return"] for inf in self.infos + [info]])[-1] diff --git a/test/test_env.py b/test/test_env.py index c66e7f7..769284c 100644 --- a/test/test_env.py +++ b/test/test_env.py @@ -82,23 +82,24 @@ def test_portfolio_env_hold(spec_id): assert df.portfolio_value.iloc[-1] > 0.9999, 'portfolio should retain value if holding bitcoin' assert df.portfolio_value.iloc[-1] < 1.01, 'portfolio should retain value if holding bitcoin' + def test_scaled_non_price_cols(): """Test env with scaled option.""" df = pd.read_hdf('./data/poloniex_30m_vol.hf', key='train') - env1 = PortfolioEnv(df=df, scale=True, window_length=40000) + env1 = PortfolioEnv(df=df, scale=True, window_length=len(df) - 300) env1.seed(0) obs1 = env1.reset() - nb_cols = len(env1.src._data.columns.levels[1]) - 1 # minus cash... - means = obs1["history"][:, :, :].reshape((-1, nb_cols)).mean(0) - stds = obs1["history"][:, :, :].reshape((-1, nb_cols)).std(0) + nb_cols = len(env1.src._data.columns.levels[1]) - 1 # minus open... + means = obs1["history"].reshape((-1, nb_cols)).mean(0) + stds = obs1["history"].reshape((-1, nb_cols)).std(0) non_price_means = means[3:] # if normalized: for a large window, mean non_prices should be near mean=0, std=1 non_price_std = stds[3:] np.testing.assert_almost_equal(non_price_means, [0, 0], decimal=1, err_msg='non price columns should be normalized to be close to one') - np.testing.assert_almost_equal(non_price_std, [1, 1], decimal=1, err_msg='non price columns should be normalized to be close to one') + np.testing.assert_allclose(non_price_std, [1, 1], rtol=0.1, err_msg='non price columns should be normalized to be close to one') def test_scaled(): @@ -117,7 +118,7 @@ def test_scaled(): # if scaled by last opening price: for a small window, mean prices should be near 1 nb_cols = len(env1.src._data.columns.levels[1]) - 1 - means = obs1["history"][:, :, :].reshape((-1, nb_cols)).mean(0) + means = obs1["history"].reshape((-1, nb_cols)).mean(0) price_means = means[:3] np.testing.assert_almost_equal(price_means, [1, 1, 1], decimal=1, err_msg='prices should be normalized to be close to one')