CHART PLOTTING.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mplfinance as mpf\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.dates as mdates\n",
    "import plotly\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "dff=pd.read_csv(\"F:\\\\Work\\\\Datasets\\\\bitmex_proj\\\\extra\\\\data_manu.csv\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dff.rename(columns={'open':'Open','high':'High','low':'Low','close':'Close'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "dff=dff[:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "dff.set_index('date',inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dff.index=pd.to_datetime(dff.index,format=\"%Y-%m-%d %H:%M:%S%z\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "dff=dff[-300:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x575 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "AttributeError",
     "evalue": "'dict' object has no attribute 'autofmt_xdate'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-211-9ac680775346>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'SPDR S&P 500 ETF Trust - Candlestick Chart'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_major_formatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmdates\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDateFormatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%b %d'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautofmt_xdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'dict' object has no attribute 'autofmt_xdate'"
     ]
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "\n",
    "mpf.plot(dff[-50:],type='candle')\n",
    "ax.plot(dff['h'][-50:],color = 'blue', linewidth = 2)\n",
    "ax.set_title('SPDR S&P 500 ETF Trust - Candlestick Chart')\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "INCREASING_COLOR = '#17BECF'\n",
    "DECREASING_COLOR = '#7F7F7F'\n",
    "data = [ dict(\n",
    "    type = 'candlestick',\n",
    "    open = dff.Open,\n",
    "    high = dff.High,\n",
    "    low = dff.Low,\n",
    "    close = dff.Close,\n",
    "    x = dff['Unnamed: 0'],\n",
    "    yaxis = 'y2',\n",
    "    name = 'GS',\n",
    "    increasing = dict( line = dict( color = INCREASING_COLOR ) ),\n",
    "    decreasing = dict( line = dict( color = DECREASING_COLOR ) ),\n",
    ") ]\n",
    "\n",
    "layout=dict()\n",
    "\n",
    "fig = dict( data=data, layout=layout )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig['layout'] = dict()\n",
    "# fig['layout']['plot_bgcolor'] = 'rgb(250, 250, 250)'\n",
    "# fig['layout']['xaxis'] = dict( rangeselector = dict( visible = True ) )\n",
    "# fig['layout']['yaxis'] = dict( domain = [0, 0.2], showticklabels = False )\n",
    "# fig['layout']['yaxis2'] = dict( domain = [0.2, 0.8] )\n",
    "# fig['layout']['legend'] = dict( orientation = 'h', y=0.9, x=0.3, yanchor='bottom' )\n",
    "# fig['layout']['margin'] = dict( t=40, b=40, r=40, l=40 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx=list(dff['Unnamed: 0'])\n",
    "fig['data'].append(dict( x=xx, y=dff['h'], type='scatter', mode='lines', \n",
    "                         line = dict( width = 1 ),\n",
    "                         marker = dict( color = '#E377C2' ),\n",
    "                         yaxis = 'y2', name='h' ) )\n",
    "fig['data'].append(dict( x=xx, y=dff['center'], type='scatter', mode='lines', \n",
    "                         line = dict( width = 1 ),\n",
    "                         marker = dict( color = '#CDCB6B' ),\n",
    "                         yaxis = 'y2', name='c' ) )\n",
    "fig['data'].append(dict( x=xx, y=dff['l'], type='scatter', mode='lines', \n",
    "                         line = dict( width = 1 ),\n",
    "                         marker = dict( color = '#601A2F' ),\n",
    "                         yaxis = 'y2', name='l' ) )\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"525px\"\n",
       "            src=\"https://plotly.com/~seemarana/7.embed\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0xfe6890>"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py.iplot( fig, filename = 'candlestick-test-3', validate = False,sharing='public' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "fig = go.Figure(data=[go.Candlestick(x=dff['Unnamed: 0'],\n",
    "                open=dff['open'], high=dff['high'],\n",
    "                low=dff['low'], close=dff['close'])\n",
    "                     ])\n",
    "\n",
    "fig.data.push()\n",
    "trace2 = px.line(dff, x='Unnamed: 0', y='h',title='h')\n",
    "\n",
    "trace3 = px.line(dff, x='Unnamed: 0', y='center',title='center')\n",
    "trace4 = px.line(dff, x='Unnamed: 0', y='l',title='l')\n",
    "\n",
    "# fig=make_subplots(specs=[[{\"secondary_y\": True}]], print_grid=True)\n",
    "\n",
    "\n",
    "fig.add_trace(trace2)\n",
    "\n",
    "\n",
    "# fig.update_layout(width=600, height=400, yaxis2= dict(fixedrange= True,range= [0, 1],visible= False))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}