1. Grab_ichi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "from time import sleep\n",
    "from datetime import datetime\n",
    "import requests, json\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "market = ['BTCUSDT','ETHUSDT','LTCUSDT','LINKUSDT','XRPUSDT','RENUSDT','ALGOUSDT','SUSHIUSDT','YFIUSDT','ETHBTC','LTCBTC','XRPBTC','LINKBTC','RENBTC','SUSHIBTC','YFIBTC']\n",
    "tick_interval = ['1h','4h','12h','1d']#,'3d','1w']\n",
    "path='data_ichi/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clear data folder\n",
    "def clean():\n",
    "    filelist = [ f for f in os.listdir(path) if f.endswith(\".csv\") ]\n",
    "    for f in filelist:\n",
    "        os.remove(os.path.join(path, f))\n",
    "\n",
    "    filelist = [ f for f in os.listdir(path) if f.endswith(\".png\") ]\n",
    "    for f in filelist:\n",
    "        os.remove(os.path.join(path, f))\n",
    "\n",
    "def countdown(t=60, step=1,msg='Sleeping'):  # in seconds\n",
    "    pad_str = ' ' * len('%d' % step)\n",
    "    for i in range(t, 0, -step):\n",
    "        sys.stdout.write('%s for %d seconds   %s\\r' % (msg, i, pad_str)),\n",
    "        sys.stdout.flush()\n",
    "        time.sleep(step)\n",
    "        \n",
    "def get_filename(market, tick_interval):\n",
    "    return '%s_%s.csv' %  (market, tick_interval)\n",
    "def get_filenamed(market, tick_interval):\n",
    "    return '%s_%s_add.csv' %  (market, tick_interval)\n",
    "    \n",
    "def getData(market, tick_interval):\n",
    "    url = 'https://api.binance.com/api/v3/klines?symbol='+market+'&interval='+tick_interval\n",
    "    response = requests.get(url).json()\n",
    "    data={\n",
    "            'OpenTime':[x[0]for x in response],\n",
    "            'Open':[x[1]for x in response],\n",
    "            'High':[x[2]for x in response],\n",
    "            'Low':[x[3]for x in response],\n",
    "            'Close':[x[4]for x in response],\n",
    "            'Volume':[x[5]for x in response]\n",
    "        }\n",
    "\n",
    "    df = pd.DataFrame(data)\n",
    "    df['Open']= df['Open'].astype(float)\n",
    "    df['High']= df['High'].astype(float)\n",
    "    df['Low']= df['Low'].astype(float)\n",
    "    df['Close']= df['Close'].astype(float)\n",
    "    val=3600000 #1h to ms\n",
    "    last_index = df.iloc[-1:].index[0]\n",
    "    last_date = df.iloc[-1,df.columns.get_loc(\"OpenTime\")]\n",
    "\n",
    "    if tick_interval == '1h':\n",
    "        interval = 1\n",
    "    elif tick_interval == '4h':\n",
    "        interval = 4\n",
    "    elif tick_interval == '12h':\n",
    "        interval = 12\n",
    "    elif tick_interval == '1d':\n",
    "        interval = 24\n",
    "    elif tick_interval == '3d':\n",
    "        interval = 3*24\n",
    "    elif tick_interval == '1w':\n",
    "        interval = 7*24\n",
    "\n",
    "    for i in range(1,31):\n",
    "        df.loc[last_index+1+i, 'OpenTime'] = last_date + i*val*interval\n",
    "    return df\n",
    "\n",
    "def ichimoku(df):\n",
    "    tenkan_window = 20\n",
    "    kijun_window = 60\n",
    "    senkou_span_b_window = 120\n",
    "    cloud_displacement = 30\n",
    "    chikou_shift = -30\n",
    "    # Tenkan \n",
    "    tenkan_sen_high = df['High'].rolling( window=tenkan_window ).max()\n",
    "    tenkan_sen_low = df['Low'].rolling( window=tenkan_window ).min()\n",
    "    tenkan = df['tenkan_sen'] = (tenkan_sen_high + tenkan_sen_low) /2\n",
    "    # Kijun \n",
    "    kijun_sen_high = df['High'].rolling( window=kijun_window ).max()\n",
    "    kijun_sen_low = df['Low'].rolling( window=kijun_window ).min()\n",
    "    kijun = df['kijun_sen'] = (kijun_sen_high + kijun_sen_low) / 2\n",
    "    # Senkou Span A \n",
    "    sen_a =df['senkou_span_a'] = ((df['tenkan_sen'] + df['kijun_sen']) / 2).shift(cloud_displacement)\n",
    "    # Senkou Span B \n",
    "    senkou_span_b_high = df['High'].rolling( window=senkou_span_b_window ).max()\n",
    "    senkou_span_b_low = df['Low'].rolling( window=senkou_span_b_window ).min()\n",
    "    sen_b =df['senkou_span_b'] = ((senkou_span_b_high + senkou_span_b_low) / 2).shift(cloud_displacement)\n",
    "    # Chikou\n",
    "    chikou=df['chikou_span'] = df['Close'].shift(chikou_shift)\n",
    "    return df\n",
    " \n",
    "def run():\n",
    "    df = getData(x, y)\n",
    "    df.to_csv(filename,index = None, header=True)\n",
    "    df.to_csv(filenamed)\n",
    "    df = ichimoku(df)\n",
    "    df['OpenTime'] = pd.to_datetime(df['OpenTime'], unit='ms')\n",
    "    #df = df.fillna(0)\n",
    "    df.to_csv(filenamed,index = None, header=True)\n",
    "    os.remove(filename)\n",
    "    print(\"Grabbed:\",x, y, filenamed) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Grabber started!\n",
      "Grabbed: BTCUSDT 1h data_ichi/BTCUSDT_1h_add.csv\n",
      "Grabbed: BTCUSDT 4h data_ichi/BTCUSDT_4h_add.csv\n",
      "Grabbed: BTCUSDT 12h data_ichi/BTCUSDT_12h_add.csv\n",
      "Grabbed: BTCUSDT 1d data_ichi/BTCUSDT_1d_add.csv\n",
      "Grabbed: ETHUSDT 1h data_ichi/ETHUSDT_1h_add.csv\n",
      "Grabbed: ETHUSDT 4h data_ichi/ETHUSDT_4h_add.csv\n",
      "Grabbed: ETHUSDT 12h data_ichi/ETHUSDT_12h_add.csv\n",
      "Grabbed: ETHUSDT 1d data_ichi/ETHUSDT_1d_add.csv\n",
      "Grabbed: LTCUSDT 1h data_ichi/LTCUSDT_1h_add.csv\n",
      "Grabbed: LTCUSDT 4h data_ichi/LTCUSDT_4h_add.csv\n",
      "Grabbed: LTCUSDT 12h data_ichi/LTCUSDT_12h_add.csv\n",
      "Grabbed: LTCUSDT 1d data_ichi/LTCUSDT_1d_add.csv\n",
      "Grabbed: LINKUSDT 1h data_ichi/LINKUSDT_1h_add.csv\n",
      "Grabbed: LINKUSDT 4h data_ichi/LINKUSDT_4h_add.csv\n",
      "Grabbed: LINKUSDT 12h data_ichi/LINKUSDT_12h_add.csv\n",
      "Grabbed: LINKUSDT 1d data_ichi/LINKUSDT_1d_add.csv\n",
      "Grabbed: XRPUSDT 1h data_ichi/XRPUSDT_1h_add.csv\n",
      "Grabbed: XRPUSDT 4h data_ichi/XRPUSDT_4h_add.csv\n",
      "Grabbed: XRPUSDT 12h data_ichi/XRPUSDT_12h_add.csv\n",
      "Grabbed: XRPUSDT 1d data_ichi/XRPUSDT_1d_add.csv\n",
      "Grabbed: RENUSDT 1h data_ichi/RENUSDT_1h_add.csv\n",
      "Grabbed: RENUSDT 4h data_ichi/RENUSDT_4h_add.csv\n",
      "Grabbed: RENUSDT 12h data_ichi/RENUSDT_12h_add.csv\n",
      "Grabbed: RENUSDT 1d data_ichi/RENUSDT_1d_add.csv\n",
      "Grabbed: ALGOUSDT 1h data_ichi/ALGOUSDT_1h_add.csv\n",
      "Grabbed: ALGOUSDT 4h data_ichi/ALGOUSDT_4h_add.csv\n",
      "Grabbed: ALGOUSDT 12h data_ichi/ALGOUSDT_12h_add.csv\n",
      "Grabbed: ALGOUSDT 1d data_ichi/ALGOUSDT_1d_add.csv\n",
      "Grabbed: SUSHIUSDT 1h data_ichi/SUSHIUSDT_1h_add.csv\n",
      "Grabbed: SUSHIUSDT 4h data_ichi/SUSHIUSDT_4h_add.csv\n",
      "Grabbed: SUSHIUSDT 12h data_ichi/SUSHIUSDT_12h_add.csv\n",
      "Grabbed: SUSHIUSDT 1d data_ichi/SUSHIUSDT_1d_add.csv\n",
      "Grabbed: YFIUSDT 1h data_ichi/YFIUSDT_1h_add.csv\n",
      "Grabbed: YFIUSDT 4h data_ichi/YFIUSDT_4h_add.csv\n",
      "Grabbed: YFIUSDT 12h data_ichi/YFIUSDT_12h_add.csv\n",
      "Grabbed: YFIUSDT 1d data_ichi/YFIUSDT_1d_add.csv\n",
      "Grabbed: ETHBTC 1h data_ichi/ETHBTC_1h_add.csv\n",
      "Grabbed: ETHBTC 4h data_ichi/ETHBTC_4h_add.csv\n",
      "Grabbed: ETHBTC 12h data_ichi/ETHBTC_12h_add.csv\n",
      "Grabbed: ETHBTC 1d data_ichi/ETHBTC_1d_add.csv\n",
      "Grabbed: LTCBTC 1h data_ichi/LTCBTC_1h_add.csv\n",
      "Grabbed: LTCBTC 4h data_ichi/LTCBTC_4h_add.csv\n",
      "Grabbed: LTCBTC 12h data_ichi/LTCBTC_12h_add.csv\n",
      "Grabbed: LTCBTC 1d data_ichi/LTCBTC_1d_add.csv\n",
      "Grabbed: XRPBTC 1h data_ichi/XRPBTC_1h_add.csv\n",
      "Grabbed: XRPBTC 4h data_ichi/XRPBTC_4h_add.csv\n",
      "Grabbed: XRPBTC 12h data_ichi/XRPBTC_12h_add.csv\n",
      "Grabbed: XRPBTC 1d data_ichi/XRPBTC_1d_add.csv\n",
      "Grabbed: LINKBTC 1h data_ichi/LINKBTC_1h_add.csv\n",
      "Grabbed: LINKBTC 4h data_ichi/LINKBTC_4h_add.csv\n",
      "Grabbed: LINKBTC 12h data_ichi/LINKBTC_12h_add.csv\n",
      "Grabbed: LINKBTC 1d data_ichi/LINKBTC_1d_add.csv\n",
      "Grabbed: RENBTC 1h data_ichi/RENBTC_1h_add.csv\n",
      "Grabbed: RENBTC 4h data_ichi/RENBTC_4h_add.csv\n",
      "Grabbed: RENBTC 12h data_ichi/RENBTC_12h_add.csv\n",
      "Grabbed: RENBTC 1d data_ichi/RENBTC_1d_add.csv\n",
      "Grabbed: SUSHIBTC 1h data_ichi/SUSHIBTC_1h_add.csv\n",
      "Grabbed: SUSHIBTC 4h data_ichi/SUSHIBTC_4h_add.csv\n",
      "Grabbed: SUSHIBTC 12h data_ichi/SUSHIBTC_12h_add.csv\n",
      "Grabbed: SUSHIBTC 1d data_ichi/SUSHIBTC_1d_add.csv\n",
      "Grabbed: YFIBTC 1h data_ichi/YFIBTC_1h_add.csv\n",
      "Grabbed: YFIBTC 4h data_ichi/YFIBTC_4h_add.csv\n",
      "Grabbed: YFIBTC 12h data_ichi/YFIBTC_12h_add.csv\n",
      "Grabbed: YFIBTC 1d data_ichi/YFIBTC_1d_add.csv\n",
      "### Grabber finished...reeeeeee!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"### Grabber started!\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    #while True:\n",
    "        clean()\n",
    "        for x in market:\n",
    "            for y in tick_interval:\n",
    "                filename = get_filename(x, y)\n",
    "                filename=os.path.join(path,filename)\n",
    "                filenamed = get_filenamed(x,y)\n",
    "                filenamed=os.path.join(path,filenamed)\n",
    "                run()\n",
    "        print(\"### Grabber finished...reeeeeee!\")\n",
    "        #countdown()\n",
    "        print(\"\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}