candlestick_awesome-Copy3_WORKING-Copy1 (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:106: MatplotlibDeprecationWarning: The finance module has been deprecated in mpl 2.0 and will be removed in mpl 2.2. Please use the module mpl_finance instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "import re\n",
    "import pandas as pd\n",
    "import requests\n",
    "import argparse\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "from matplotlib.finance import candlestick_ohlc\n",
    "from matplotlib.finance import candlestick2_ohlc\n",
    "\n",
    "import matplotlib.dates as mdates\n",
    "import datetime as dt\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "period = 3600\n",
    "#cryptowat.ch supported periods\n",
    "#60\t1m\n",
    "#180\t3m\n",
    "#300\t5m\n",
    "#900\t15m\n",
    "#1800\t30m\n",
    "#3600\t1h\n",
    "#7200\t2h\n",
    "#14400\t4h\n",
    "#21600\t6h\n",
    "#43200\t12h\n",
    "#86400\t1d\n",
    "#259200\t3d\n",
    "#604800\t1w\n",
    "\n",
    "\n",
    "after = 1522119148\n",
    "\n",
    "resp = requests.get(\n",
    "  \"https://api.cryptowat.ch/markets/{}/{}/ohlc?after={}&periods={}\".format(\n",
    "    \"bitfinex\", \"btcusd\", after, period))\n",
    "\n",
    "resp = resp.json()\n",
    "#print(resp)\n",
    "\n",
    "resp = resp[\"result\"][str(period)]\n",
    "\n",
    "all_candles = []\n",
    "for c in resp:\n",
    "  all_candles.append(\n",
    "      {\"Date\": dt.datetime.fromtimestamp((c[0])),\n",
    "       \"Open\": float(c[1]), \"High\": float(c[2]), \"Low\": float(c[3]),\n",
    "       \"Close\": float(c[4])})\n",
    "\n",
    "df = pd.DataFrame(all_candles)\n",
    "df = df.set_index([\"Date\"])\n",
    "#df.head()\n",
    "\n",
    "# total ticks in df\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "#Reset the index to remove Date column from index\n",
    "df_ohlc = df.reset_index()\n",
    "\n",
    "#Naming columns\n",
    "df_ohlc.columns = [\"Date\",\"Open\",\"High\",'Low',\"Close\"]\n",
    "\n",
    "#Converting dates column to float values\n",
    "df_ohlc[\"Date\"] = df_ohlc[\"Date\"].map(mdates.date2num)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f4dd471a160>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4dd472bba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setup plot demensions etc.\n",
    "plt.figure(figsize=(28, 16), dpi= 160, facecolor='w', edgecolor='k')\n",
    "ax1 = plt.subplot2grid((60,1), (0,0), rowspan=60, colspan=1)\n",
    "\n",
    "#Converts raw mdate numbers to dates\n",
    "ax1.xaxis_date()\n",
    "\n",
    "#Making candlestick plot\n",
    "candlestick_ohlc(ax1,df_ohlc.values,width=0.03, colorup='r', colordown='g',alpha=0.75)\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4ddece2c50>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ddf717da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calc simple moving average and plot\n",
    "short_rolling = df['Close'].rolling(window=10).mean()\n",
    "short_rolling.head(50)\n",
    "plt.plot(short_rolling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/lib/python3.6/site-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4dd4519438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df.reset_index(inplace=True)\n",
    "madf = df[\"Close\"].rolling(5).mean()\n",
    "\n",
    "plt.figure(figsize=(28, 16), dpi= 160, facecolor='w', edgecolor='k')\n",
    "ax =  plt.subplot2grid((60,1), (0,0), rowspan=60, colspan=1)\n",
    "plt.xticks(rotation = 45)\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.title(\"EUR/USD\")\n",
    "\n",
    "\n",
    "ohlc = df[['Date','Open','High','Low','Close']]\n",
    "ohlc[\"Date\"] = ohlc[\"Date\"].map(mdates.date2num)\n",
    "\n",
    "candlestick2_ohlc(ax,ohlc.Open, ohlc.High, ohlc.Low, ohlc.Close, width=0.4, colorup='g', colordown='r',alpha=0.75)\n",
    "\n",
    "## plot the moving average lines\n",
    "ax.plot(df.index, madf, color = 'blue', label = 'ma50')\n",
    "\n",
    "# other parameters\n",
    "ax.grid(False)\n",
    "ax.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}