examples/scratch_pad/dev_scale_padding.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This allows multiple outputs from a single jupyter notebook cell:\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(252, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>UpperB</th>\n",
       "      <th>LowerB</th>\n",
       "      <th>PercentB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>132.089996</td>\n",
       "      <td>134.100006</td>\n",
       "      <td>131.779999</td>\n",
       "      <td>133.919998</td>\n",
       "      <td>117.161659</td>\n",
       "      <td>202385700</td>\n",
       "      <td>132.373927</td>\n",
       "      <td>125.316073</td>\n",
       "      <td>1.219057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-05</th>\n",
       "      <td>133.779999</td>\n",
       "      <td>134.080002</td>\n",
       "      <td>133.389999</td>\n",
       "      <td>133.809998</td>\n",
       "      <td>117.065437</td>\n",
       "      <td>165936000</td>\n",
       "      <td>133.254297</td>\n",
       "      <td>124.912703</td>\n",
       "      <td>1.066618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-06</th>\n",
       "      <td>133.490005</td>\n",
       "      <td>134.139999</td>\n",
       "      <td>133.110001</td>\n",
       "      <td>133.970001</td>\n",
       "      <td>117.205429</td>\n",
       "      <td>143331600</td>\n",
       "      <td>134.040915</td>\n",
       "      <td>124.627085</td>\n",
       "      <td>0.992467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2011-07-01  132.089996  134.100006  131.779999  133.919998  117.161659   \n",
       "2011-07-05  133.779999  134.080002  133.389999  133.809998  117.065437   \n",
       "2011-07-06  133.490005  134.139999  133.110001  133.970001  117.205429   \n",
       "\n",
       "               Volume      UpperB      LowerB  PercentB  \n",
       "Date                                                     \n",
       "2011-07-01  202385700  132.373927  125.316073  1.219057  \n",
       "2011-07-05  165936000  133.254297  124.912703  1.066618  \n",
       "2011-07-06  143331600  134.040915  124.627085  0.992467  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>UpperB</th>\n",
       "      <th>LowerB</th>\n",
       "      <th>PercentB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-06-27</th>\n",
       "      <td>132.419998</td>\n",
       "      <td>133.429993</td>\n",
       "      <td>131.970001</td>\n",
       "      <td>133.169998</td>\n",
       "      <td>118.980804</td>\n",
       "      <td>108088000</td>\n",
       "      <td>136.447962</td>\n",
       "      <td>128.140042</td>\n",
       "      <td>0.605441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-28</th>\n",
       "      <td>132.289993</td>\n",
       "      <td>132.990005</td>\n",
       "      <td>131.279999</td>\n",
       "      <td>132.789993</td>\n",
       "      <td>118.641281</td>\n",
       "      <td>169242100</td>\n",
       "      <td>136.500761</td>\n",
       "      <td>128.219241</td>\n",
       "      <td>0.551922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-29</th>\n",
       "      <td>135.199997</td>\n",
       "      <td>136.270004</td>\n",
       "      <td>134.850006</td>\n",
       "      <td>136.100006</td>\n",
       "      <td>121.598610</td>\n",
       "      <td>212250900</td>\n",
       "      <td>136.721010</td>\n",
       "      <td>128.792993</td>\n",
       "      <td>0.921670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2012-06-27  132.419998  133.429993  131.970001  133.169998  118.980804   \n",
       "2012-06-28  132.289993  132.990005  131.279999  132.789993  118.641281   \n",
       "2012-06-29  135.199997  136.270004  134.850006  136.100006  121.598610   \n",
       "\n",
       "               Volume      UpperB      LowerB  PercentB  \n",
       "Date                                                     \n",
       "2012-06-27  108088000  136.447962  128.140042  0.605441  \n",
       "2012-06-28  169242100  136.500761  128.219241  0.551922  \n",
       "2012-06-29  212250900  136.721010  128.792993  0.921670  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idf = pd.read_csv('../data/SPY_20110701_20120630_Bollinger.csv',index_col=0,parse_dates=True)\n",
    "idf.shape\n",
    "idf.head(3)\n",
    "idf.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = idf.loc['2011-07-01':'2011-12-30',:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'left': 0.7, 'right': 0.8, 'top': 1.2, 'bot': 0.9}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = dict(left=0.7,right=0.8,top=1.2,bot=0.9)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = {'left': 0.7, 'right': 0.8, 'top': 1.2, 'bot': 0.9, 'right': 0.9}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'left': 0.7, 'right': 0.9, 'top': 1.2, 'bot': 0.9}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'left' in d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'bottom' in d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9\n"
     ]
    }
   ],
   "source": [
    "keys=('left','right','top','bottom')\n",
    "for x in d:\n",
    "    x in keys\n",
    "    print(d[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 5\n",
    "isinstance(x,float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.12.6a2'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x575 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import mplfinance as mpf\n",
    "mpf.__version__\n",
    "#mpf.plot(df,volume=True,scale_padding=dict(left=0.1,right=0.05),tight_layout=True)\n",
    "mpf.plot(df,volume=True,tight_layout=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-13-a6774c8535dd>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-13-a6774c8535dd>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    STOP HERE\u001b[0m\n\u001b[0m            ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "STOP HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 1.5\n",
    "a = b = c = d = x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a\n",
    "b\n",
    "c\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a *= 0.4\n",
    "b *= 0.4\n",
    "c *= 0.4\n",
    "d *= 0.4\n",
    "a\n",
    "b\n",
    "c\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 1.1\n",
    "b = 1.2\n",
    "c = 1.3\n",
    "d = 1.4\n",
    "a\n",
    "b\n",
    "c\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### We can switch which panel is main and which contains volume:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpf.plot(df,type='candle',volume=True,main_panel=1,volume_panel=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "isinstance(ax,mpl.axes.Axes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "isinstance(fig,mpl.figure.Figure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "isinstance(fig,mpl.figure.Figure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = fig.add_axes([0.15,0.18,0.7,0.7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "isinstance(ax,mpl.axes.Axes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax2 = fig.add_axes([0.15,0.18,0.7,0.7],sharex=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "isinstance(ax2,mpl.axes.Axes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "axlist = fig.get_axes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "axlist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "axlist = fig.get_axes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "axlist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax == axlist[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [ax,ax,ax]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all([x is None for x in a])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all(isinstance(x,mpl.axes.Axes) for x in a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ap = [mpf.make_addplot(df['High'],ax=ax),\n",
    "      mpf.make_addplot(df['Low'],ax=ax)\n",
    "     ]\n",
    "#mpf.plot(df,volume=False,ax=None,addplot=ap)\n",
    "mpf.plot(df,volume=False,ax=ax,addplot=ap,fig=fig)\n",
    "mpf.plot(df,volume=False,fig=fig)\n",
    "#mpf.plot(df,volume=False)#,addplot=ap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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