{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# data handling" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "import ccxt" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['aax',\n", " 'ascendex',\n", " 'bequant',\n", " 'bibox',\n", " 'bigone',\n", " 'binance',\n", " 'binancecoinm',\n", " 'binanceus',\n", " 'binanceusdm',\n", " 'bit2c']" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ccxt.exchanges[:10]" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "#exchange = ccxt.gateio()\n", "exchange = ccxt.gateio()" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "markets = exchange.load_markets()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'id': 'BTC_USDT',\n", " 'base': 'BTC',\n", " 'quote': 'USDT',\n", " 'fee': '0.2',\n", " 'min_base_amount': '0.0001',\n", " 'min_quote_amount': '1',\n", " 'amount_precision': '4',\n", " 'precision': '2',\n", " 'trade_status': 'tradable',\n", " 'sell_start': '0',\n", " 'buy_start': '0'}" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "markets['BTC/USDT']['info']" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[1639922400000, 0.123798, 0.12492, 0.120898, 0.122276, 809464.8808856723],\n", " [1639926000000, 0.12235, 0.12356, 0.121666, 0.122294, 266477.612003161],\n", " [1639929600000, 0.122361, 0.124724, 0.109641, 0.111786, 2484544.688555346],\n", " [1639933200000, 0.111766, 0.114451, 0.111607, 0.112699, 487156.2636242045],\n", " [1639936800000, 0.112575, 0.113083, 0.10511, 0.110877, 760740.9529056046],\n", " [1639940400000, 0.111185, 0.112232, 0.108526, 0.110535, 367653.41106644075],\n", " [1639944000000, 0.110536, 0.114461, 0.11, 0.112868, 332452.2867395275],\n", " [1639947600000, 0.11341, 0.116272, 0.112588, 0.114826, 168831.37896901928],\n", " [1639951200000, 0.114828, 0.117365, 0.11361, 0.114, 269444.78434601205],\n", " [1639954800000, 0.11419, 0.114463, 0.110758, 0.112529, 366875.3387874912]]" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"\n", "[\n", " 1504541580000, // UTC timestamp in milliseconds, integer\n", " 4235.4, // (O)pen price, float\n", " 4240.6, // (H)ighest price, float\n", " 4230.0, // (L)owest price, float\n", " 4230.7, // (C)losing price, float\n", " 37.72941911 // (V)olume (in terms of the base currency), float\n", "],\n", "\"\"\"\n", "\n", "class ItemOfOHLCV:\n", " timestamp: int\n", " open: float\n", " high: float\n", " low: float\n", " close: float\n", " volume: float\n", "\n", "ohlcvData = exchange.fetch_ohlcv('PEOPLE/USDT', '1h', params={'price':'index'})\n", "ohlcvData[:10]" ] }, { "cell_type": "code", "execution_count": 179, "metadata": {}, "outputs": [], "source": [ "TOKEN = 'DOGE/USDT'\n", "startMonthTime = \"2019-10-01\"\n", "endMonthTime = \"2022-01-07\"" ] }, { "cell_type": "code", "execution_count": 180, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['2019-10-01',\n", " '2019-11-01',\n", " '2019-12-01',\n", " '2020-01-01',\n", " '2020-02-01',\n", " '2020-03-01',\n", " '2020-04-01',\n", " '2020-05-01',\n", " '2020-06-01',\n", " '2020-07-01',\n", " '2020-08-01',\n", " '2020-09-01',\n", " '2020-10-01',\n", " '2020-11-01',\n", " '2020-12-01',\n", " '2021-01-01',\n", " '2021-02-01',\n", " '2021-03-01',\n", " '2021-04-01',\n", " '2021-05-01',\n", " '2021-06-01',\n", " '2021-07-01',\n", " '2021-08-01',\n", " '2021-09-01',\n", " '2021-10-01',\n", " '2021-11-01',\n", " '2021-12-01',\n", " '2022-01-01']" ] }, "execution_count": 180, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def getEveryStartDayOfMonth(startDate: str, endDate: str):\n", " import datetime\n", " import calendar\n", " start = datetime.datetime.strptime(startDate, \"%Y-%m-%d\")\n", " end = datetime.datetime.strptime(endDate, \"%Y-%m-%d\")\n", " while start <= end:\n", " yield start.strftime(\"%Y-%m-%d\")\n", " start += datetime.timedelta(days=calendar.monthrange(start.year, start.month)[1])\n", "\n", "startPointOfMonths = list(getEveryStartDayOfMonth(startMonthTime, endMonthTime))\n", "startPointOfMonths" ] }, { "cell_type": "code", "execution_count": 181, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('2019-09-23', '2019-10-08'),\n", " ('2019-10-24', '2019-11-08'),\n", " ('2019-11-23', '2019-12-08'),\n", " ('2019-12-24', '2020-01-08'),\n", " ('2020-01-24', '2020-02-08'),\n", " ('2020-02-22', '2020-03-08'),\n", " ('2020-03-24', '2020-04-08'),\n", " ('2020-04-23', '2020-05-08'),\n", " ('2020-05-24', '2020-06-08'),\n", " ('2020-06-23', '2020-07-08'),\n", " ('2020-07-24', '2020-08-08'),\n", " ('2020-08-24', '2020-09-08'),\n", " ('2020-09-23', '2020-10-08'),\n", " ('2020-10-24', '2020-11-08'),\n", " ('2020-11-23', '2020-12-08'),\n", " ('2020-12-24', '2021-01-08'),\n", " ('2021-01-24', '2021-02-08'),\n", " ('2021-02-21', '2021-03-08'),\n", " ('2021-03-24', '2021-04-08'),\n", " ('2021-04-23', '2021-05-08'),\n", " ('2021-05-24', '2021-06-08'),\n", " ('2021-06-23', '2021-07-08'),\n", " ('2021-07-24', '2021-08-08'),\n", " ('2021-08-24', '2021-09-08'),\n", " ('2021-09-23', '2021-10-08'),\n", " ('2021-10-24', '2021-11-08'),\n", " ('2021-11-23', '2021-12-08'),\n", " ('2021-12-24', '2022-01-08')]" ] }, "execution_count": 181, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def getTimeRangeOfADateWithinXDays(date: str, x: int):\n", " import datetime\n", " halfRange = x/2\n", " start = datetime.datetime.strptime(date, \"%Y-%m-%d\") - datetime.timedelta(hours=halfRange*24)\n", " end = start + datetime.timedelta(hours=halfRange*24*2)\n", " return start.strftime(\"%Y-%m-%d\"), end.strftime(\"%Y-%m-%d\")\n", "\n", "dateRangeFor3DaysInEveryStartDayOfMonth = [getTimeRangeOfADateWithinXDays(startPointOfMonths[i], 15) for i,_ in enumerate(startPointOfMonths)]\n", "dateRangeFor3DaysInEveryStartDayOfMonth" ] }, { "cell_type": "code", "execution_count": 182, "metadata": {}, "outputs": [], "source": [ "def dateTimeToUnixTime(date: str):\n", " import datetime\n", " return int(datetime.datetime.strptime(date, \"%Y-%m-%d\").timestamp() * 1000)" ] }, { "cell_type": "code", "execution_count": 183, "metadata": {}, "outputs": [], "source": [ "ohlcvData = []\n", "for range in dateRangeFor3DaysInEveryStartDayOfMonth:\n", " ohlcvData += exchange.fetch_ohlcv(TOKEN, '1h', params={'price':'index'}, since=dateTimeToUnixTime(range[0]))" ] }, { "cell_type": "code", "execution_count": 184, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[1569168000000, 0.00254, 0.002544, 0.00254, 0.00254, 2363.05615391534],\n", " [1569171600000, 0.002542, 0.002553, 0.002537, 0.00254, 1831.4285314910496],\n", " [1569175200000, 0.00254, 0.00254, 0.00253, 0.002535, 1354.9719272751552],\n", " [1569178800000, 0.002529, 0.002545, 0.002528, 0.002538, 1860.8520037460162],\n", " [1569182400000, 0.002535, 0.00254, 0.002535, 0.00254, 2141.3644061059063],\n", " [1569186000000, 0.002537, 0.00259, 0.002521, 0.002589, 23803.360643332322],\n", " [1569189600000, 0.002589, 0.002597, 0.002583, 0.002583, 3623.772092035654],\n", " [1569193200000, 0.002571, 0.002571, 0.002567, 0.002567, 28.89564338033988],\n", " [1569196800000, 0.002571, 0.002572, 0.002546, 0.002547, 5280.5586329815405],\n", " [1569200400000, 0.002541, 0.00255, 0.002517, 0.002522, 6480.144910244407]]" ] }, "execution_count": 184, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ohlcvData[:10]" ] }, { "cell_type": "code", "execution_count": 185, "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", "class ItemOfOHLCV:\n", " timestamp: int\n", " open: float\n", " high: float\n", " low: float\n", " close: float\n", " volume: float\n", "\"\"\"\n", "def convertTimestampToString(timestamp: int):\n", " import datetime\n", " return datetime.datetime.fromtimestamp(timestamp/1000).strftime(\"%Y-%m-%d\")\n", "\n", "\n", "def checkIfTheItemOfOHLCVIsInTheDateRange(item: ItemOfOHLCV, start: str, end: str):\n", " import datetime\n", " itemDate = datetime.datetime.fromtimestamp(item.timestamp/1000)\n", " if itemDate >= datetime.datetime.strptime(start, \"%Y-%m-%d\") and itemDate <= datetime.datetime.strptime(end, \"%Y-%m-%d\"):\n", " return True\n", " return False\n", "\n", "itemsForEachRange =[[] for _ in dateRangeFor3DaysInEveryStartDayOfMonth]\n", "for one in ohlcvData:\n", " item = ItemOfOHLCV()\n", " item.timestamp, item.open, item.high, item.low, item.close, item.volume = one\n", " #print(convertTimestampToString(item.timestamp))\n", " for index, dateRange in enumerate(dateRangeFor3DaysInEveryStartDayOfMonth):\n", " if checkIfTheItemOfOHLCVIsInTheDateRange(item, dateRange[0], dateRange[1]):\n", " itemsForEachRange[index].append(item)" ] }, { "cell_type": "code", "execution_count": 186, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<__main__.ItemOfOHLCV at 0x12abdcfa0>,\n", " <__main__.ItemOfOHLCV at 0x12abdcf70>,\n", " <__main__.ItemOfOHLCV at 0x12abdcfd0>,\n", " <__main__.ItemOfOHLCV at 0x107ba7c70>,\n", " <__main__.ItemOfOHLCV at 0x107ba7fa0>,\n", " <__main__.ItemOfOHLCV at 0x12b89f310>,\n", " <__main__.ItemOfOHLCV at 0x12bb4cd00>,\n", " <__main__.ItemOfOHLCV at 0x107aa6040>,\n", " <__main__.ItemOfOHLCV at 0x107aa6070>,\n", " <__main__.ItemOfOHLCV at 0x107aa60a0>]" ] }, "execution_count": 186, "metadata": {}, "output_type": "execute_result" } ], "source": [ "itemsForEachRange[1][:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# graph" ] }, { "cell_type": "code", "execution_count": 187, "metadata": {}, "outputs": [], "source": [ "def convertTimestampToDetailString(timestamp: int):\n", " import datetime\n", " return datetime.datetime.fromtimestamp(timestamp/1000).strftime(\"%Y-%m-%d %H:%M:%S\")" ] }, { "cell_type": "code", "execution_count": 188, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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timestampopenhighlowclosevolume
02019-10-24 00:00:000.0024280.0024420.0023800.00242217378.149674
12019-10-24 01:00:000.0024250.0024430.0024250.0024433811.442302
22019-10-24 02:00:000.0024440.0024890.0024200.00243114802.574014
32019-10-24 03:00:000.0024290.0024400.0024220.0024333046.580145
42019-10-24 04:00:000.0024300.0024440.0024220.0024294386.969989
.....................
6122019-11-07 20:00:000.0026420.0027230.0026310.00272093298.523107
6132019-11-07 21:00:000.0027230.0027490.0026940.00271589357.069322
6142019-11-07 22:00:000.0027130.0027150.0026860.00269670736.931892
6152019-11-07 23:00:000.0026870.0026940.0026480.00265037707.319691
6162019-11-08 00:00:000.0026500.0026700.0026140.00266927467.471249
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617 rows × 6 columns

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" ], "text/plain": [ " timestamp open high low close volume\n", "0 2019-10-24 00:00:00 0.002428 0.002442 0.002380 0.002422 17378.149674\n", "1 2019-10-24 01:00:00 0.002425 0.002443 0.002425 0.002443 3811.442302\n", "2 2019-10-24 02:00:00 0.002444 0.002489 0.002420 0.002431 14802.574014\n", "3 2019-10-24 03:00:00 0.002429 0.002440 0.002422 0.002433 3046.580145\n", "4 2019-10-24 04:00:00 0.002430 0.002444 0.002422 0.002429 4386.969989\n", ".. ... ... ... ... ... ...\n", "612 2019-11-07 20:00:00 0.002642 0.002723 0.002631 0.002720 93298.523107\n", "613 2019-11-07 21:00:00 0.002723 0.002749 0.002694 0.002715 89357.069322\n", "614 2019-11-07 22:00:00 0.002713 0.002715 0.002686 0.002696 70736.931892\n", "615 2019-11-07 23:00:00 0.002687 0.002694 0.002648 0.002650 37707.319691\n", "616 2019-11-08 00:00:00 0.002650 0.002670 0.002614 0.002669 27467.471249\n", "\n", "[617 rows x 6 columns]" ] }, "execution_count": 188, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def itemDataToListOfDictionaries(itemData: list):\n", " return [{\n", " 'timestamp': convertTimestampToDetailString(item.timestamp),\n", " 'open': item.open,\n", " 'high': item.high,\n", " 'low': item.low,\n", " 'close': item.close,\n", " 'volume': item.volume\n", " } for item in itemData]\n", "\n", "def itemDataToPandaDataFrame(itemData: list):\n", " import pandas as pd\n", " return pd.DataFrame(itemDataToListOfDictionaries(itemData), columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])\n", "\n", "testData = itemDataToPandaDataFrame(itemsForEachRange[1])\n", "testData" ] }, { "cell_type": "code", "execution_count": 189, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<__main__.ItemOfOHLCV at 0x12abdcfa0>,\n", " <__main__.ItemOfOHLCV at 0x12abdcf70>,\n", " <__main__.ItemOfOHLCV at 0x12abdcfd0>,\n", " <__main__.ItemOfOHLCV at 0x107ba7c70>,\n", " <__main__.ItemOfOHLCV at 0x107ba7fa0>,\n", " <__main__.ItemOfOHLCV at 0x12b89f310>,\n", " <__main__.ItemOfOHLCV at 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0x12a23cb20>,\n", " <__main__.ItemOfOHLCV at 0x12a23cb50>,\n", " <__main__.ItemOfOHLCV at 0x12a23cb80>,\n", " <__main__.ItemOfOHLCV at 0x12a23cbb0>,\n", " <__main__.ItemOfOHLCV at 0x12a23cbe0>,\n", " <__main__.ItemOfOHLCV at 0x12a23cc10>,\n", " <__main__.ItemOfOHLCV at 0x12a23cc40>,\n", " <__main__.ItemOfOHLCV at 0x12a23cc70>,\n", " <__main__.ItemOfOHLCV at 0x12a23cca0>,\n", " <__main__.ItemOfOHLCV at 0x12a23ccd0>,\n", " <__main__.ItemOfOHLCV at 0x12a23cd00>,\n", " <__main__.ItemOfOHLCV at 0x12a23cd30>,\n", " <__main__.ItemOfOHLCV at 0x12a23cd60>,\n", " <__main__.ItemOfOHLCV at 0x12a23cd90>,\n", " <__main__.ItemOfOHLCV at 0x12a23cdc0>,\n", " <__main__.ItemOfOHLCV at 0x12a23cdf0>,\n", " <__main__.ItemOfOHLCV at 0x12a23ce20>]" ] }, "execution_count": 189, "metadata": {}, "output_type": "execute_result" } ], "source": [ "itemsForEachRange[1]" ] }, { "cell_type": "code", "execution_count": 190, "metadata": {}, "outputs": [], "source": [ "import plotly.graph_objects as go\n", "\n", "figureList = []\n", "\n", "for items in itemsForEachRange:\n", " if len(items) == 0:\n", " continue\n", "\n", " df = itemDataToPandaDataFrame(items)\n", "\n", " fig = go.Figure(data=go.Ohlc(x=df['timestamp'],\n", " open=df['open'],\n", " high=df['high'],\n", " low=df['low'],\n", " close=df['close']))\n", "\n", " fig.update_layout(height=600, width=600, title_text=f\"{TOKEN} on {convertTimestampToString(items[0].timestamp)}~{convertTimestampToString(items[-1].timestamp)}\")\n", " figureList.append(fig)" ] }, { "cell_type": "code", "execution_count": 191, "metadata": {}, "outputs": [], "source": [ "def figures_to_html(figs, filename=\"1.dogePrice.html\"):\n", " dashboard = open(filename, 'w')\n", " dashboard.write(\"\" + \"\\n\")\n", " for fig in figs:\n", " inner_html = fig.to_html().split('')[1].split('')[0]\n", " dashboard.write(inner_html)\n", " dashboard.write(\"\" + \"\\n\")" ] }, { "cell_type": "code", "execution_count": 192, "metadata": {}, "outputs": [], "source": [ "figures_to_html(figureList)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "interpreter": { "hash": "7b72adbd49a3b3355baa2266f96b23c2fc51277f72701eff913cb4147c8895c1" }, "kernelspec": { "display_name": "Python 3.9.7 64-bit ('.venv': poetry)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }