diff --git a/examples/scratch_pad/issues/issue568.csv b/examples/scratch_pad/issues/issue568.csv
new file mode 100644
index 00000000..bb9f5856
--- /dev/null
+++ b/examples/scratch_pad/issues/issue568.csv
@@ -0,0 +1,206 @@
+Date,Open,High,Low,Close,Volume
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+27-Apr-22,17038.40,17038.40,17038.40,17038.40,25374
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+2-May-22,17069.10,17069.10,17069.10,17069.10,21223
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+28-Jul-22,16929.60,16929.60,16929.60,16929.60,26674
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+1-Aug-22,17340.05,17340.05,17340.05,17340.05,23221
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+5-Aug-22,17397.50,17397.50,17397.50,17397.50,20013
+8-Aug-22,17525.10,17525.10,17525.10,17525.10,18475
+10-Aug-22,17534.75,17534.75,17534.75,17534.75,21952
+11-Aug-22,17659.00,17659.00,17659.00,17659.00,20477
+12-Aug-22,17698.15,17698.15,17698.15,17698.15,18422
+16-Aug-22,17825.25,17825.25,17825.25,17825.25,18495
+17-Aug-22,17944.25,17944.25,17944.25,17944.25,21210
+18-Aug-22,17956.50,17956.50,17956.50,17956.50,18442
+19-Aug-22,17758.45,17758.45,17758.45,17758.45,21532
+22-Aug-22,17490.70,17490.70,17490.70,17490.70,19605
+23-Aug-22,17577.50,17577.50,17577.50,17577.50,20366
+24-Aug-22,17604.95,17604.95,17604.95,17604.95,17820
+25-Aug-22,17522.45,17522.45,17522.45,17522.45,17567
+26-Aug-22,17558.90,17558.90,17558.90,17558.90,17501
+29-Aug-22,17312.90,17312.90,17312.90,17312.90,19352
+30-Aug-22,17759.30,17759.30,17759.30,17759.30,26284
+1-Sep-22,17542.80,17542.80,17542.80,17542.80,26814
+2-Sep-22,17539.45,17539.45,17539.45,17539.45,19549
+5-Sep-22,17665.80,17665.80,17665.80,17665.80,17011
+6-Sep-22,17655.60,17655.60,17655.60,17655.60,17521
+7-Sep-22,17624.40,17624.40,17624.40,17624.40,25420
+8-Sep-22,17798.75,17798.75,17798.75,17798.75,19925
+9-Sep-22,17833.35,17833.35,17833.35,17833.35,20564
+12-Sep-22,17936.35,17936.35,17936.35,17936.35,17648
+13-Sep-22,18070.05,18070.05,18070.05,18070.05,21039
+14-Sep-22,18003.75,18003.75,18003.75,18003.75,26612
+15-Sep-22,17877.40,17877.40,17877.40,17877.40,22008
+16-Sep-22,17530.85,17530.85,17530.85,17530.85,36014
+19-Sep-22,17622.25,17622.25,17622.25,17622.25,19549
+20-Sep-22,17816.25,17816.25,17816.25,17816.25,20457
+21-Sep-22,17718.35,17718.35,17718.35,17718.35,19817
+22-Sep-22,17629.80,17629.80,17629.80,17629.80,21304
+23-Sep-22,17327.35,17327.35,17327.35,17327.35,23751
+26-Sep-22,17016.30,17016.30,17016.30,17016.30,27533
+27-Sep-22,17007.40,17007.40,17007.40,17007.40,22713
+28-Sep-22,16858.60,16858.60,16858.60,16858.60,21401
+29-Sep-22,16818.10,16818.10,16818.10,16818.10,24759
+30-Sep-22,17094.35,17094.35,17094.35,17094.35,26206
+3-Oct-22,16887.35,16887.35,16887.35,16887.35,21861
+4-Oct-22,17274.30,17274.30,17274.30,17274.30,18480
+6-Oct-22,17331.80,17331.80,17331.80,17331.80,20404
+7-Oct-22,17314.65,17314.65,17314.65,17314.65,17043
+10-Oct-22,17241.00,17241.00,17241.00,17241.00,16221
+11-Oct-22,16983.55,16983.55,16983.55,16983.55,22178
+12-Oct-22,17123.60,17123.60,17123.60,17123.60,18098
+13-Oct-22,17014.35,17014.35,17014.35,17014.35,18190
+14-Oct-22,17185.70,17185.70,17185.70,17185.70,17813
+17-Oct-22,17311.80,17311.80,17311.80,17311.80,16888
+18-Oct-22,17486.95,17486.95,17486.95,17486.95,18225
+19-Oct-22,17512.25,17512.25,17512.25,17512.25,17201
+20-Oct-22,17563.95,17563.95,17563.95,17563.95,18346
+21-Oct-22,17576.30,17576.30,17576.30,17576.30,23547
+24-Oct-22,17730.75,17730.75,17730.75,17730.75,3772
+25-Oct-22,17656.35,17656.35,17656.35,17656.35,20338
+27-Oct-22,17736.95,17736.95,17736.95,17736.95,24166
+28-Oct-22,17786.80,17786.80,17786.80,17786.80,19100
+31-Oct-22,18012.20,18012.20,18012.20,18012.20,19846
\ No newline at end of file
diff --git a/examples/scratch_pad/issues/issue568_pnf_calcs.ipynb b/examples/scratch_pad/issues/issue568_pnf_calcs.ipynb
new file mode 100644
index 00000000..7b8a16b6
--- /dev/null
+++ b/examples/scratch_pad/issues/issue568_pnf_calcs.ipynb
@@ -0,0 +1,705 @@
+{
+ "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": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.5.1'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'0.12.9b6'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import pandas as pd\n",
+    "pd.__version__  # for the record\n",
+    "\n",
+    "import mplfinance as mpf\n",
+    "mpf.__version__\n",
+    "\n",
+    "import numpy as np\n",
+    "from matplotlib import ticker\n",
+    "\n",
+    "import pprint\n",
+    "pp = pprint.PrettyPrinter(indent=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/dino/code/mplfinance/examples/scratch_pad/issues\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .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>Volume</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2022-01-03</th>\n",
+       "      <td>17625.70</td>\n",
+       "      <td>17625.70</td>\n",
+       "      <td>17625.70</td>\n",
+       "      <td>17625.70</td>\n",
+       "      <td>16181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-01-04</th>\n",
+       "      <td>17805.25</td>\n",
+       "      <td>17805.25</td>\n",
+       "      <td>17805.25</td>\n",
+       "      <td>17805.25</td>\n",
+       "      <td>18604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-01-05</th>\n",
+       "      <td>17925.25</td>\n",
+       "      <td>17925.25</td>\n",
+       "      <td>17925.25</td>\n",
+       "      <td>17925.25</td>\n",
+       "      <td>23737</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Open      High       Low     Close  Volume\n",
+       "Date                                                      \n",
+       "2022-01-03  17625.70  17625.70  17625.70  17625.70   16181\n",
+       "2022-01-04  17805.25  17805.25  17805.25  17805.25   18604\n",
+       "2022-01-05  17925.25  17925.25  17925.25  17925.25   23737"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .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>Volume</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2022-10-27</th>\n",
+       "      <td>17736.95</td>\n",
+       "      <td>17736.95</td>\n",
+       "      <td>17736.95</td>\n",
+       "      <td>17736.95</td>\n",
+       "      <td>24166</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-28</th>\n",
+       "      <td>17786.80</td>\n",
+       "      <td>17786.80</td>\n",
+       "      <td>17786.80</td>\n",
+       "      <td>17786.80</td>\n",
+       "      <td>19100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-31</th>\n",
+       "      <td>18012.20</td>\n",
+       "      <td>18012.20</td>\n",
+       "      <td>18012.20</td>\n",
+       "      <td>18012.20</td>\n",
+       "      <td>19846</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Open      High       Low     Close  Volume\n",
+       "Date                                                      \n",
+       "2022-10-27  17736.95  17736.95  17736.95  17736.95   24166\n",
+       "2022-10-28  17786.80  17786.80  17786.80  17786.80   19100\n",
+       "2022-10-31  18012.20  18012.20  18012.20  18012.20   19846"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('issue568.csv',index_col=0,parse_dates=True)\n",
+    "df.head(3)\n",
+    "df.tail(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Box_Size is = 180\n",
+      "Reversal is = 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "pnf_kwargs = dict(type='pnf', volume=True, figratio=(1, 1), figscale=5)\n",
+    "pnf_kwargs = dict(type='pnf', volume=False, figratio=(1, 1), figscale=1.5)\n",
+    "\n",
+    "close_last_value = int(df['Close'].iloc[-1])\n",
+    "pnf_box_size = round(close_last_value * 0.01)\n",
+    "print(\"Box_Size is = \" + str(pnf_box_size))\n",
+    "\n",
+    "pnf_reversal = 3\n",
+    "print(\"Reversal is = \" + str(pnf_reversal))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 862.5x862.5 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cv = {} # blank dict to hold return_calculated_values values\n",
+    "\n",
+    "mpf.plot(df, **pnf_kwargs, style='starsandstripes',\n",
+    "         pnf_params=dict(box_size=pnf_box_size, reversal=pnf_reversal), \n",
+    "         return_calculated_values=cv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#pp.pprint(cv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import yfinance as yf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[*********************100%***********************]  1 of 1 completed\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = yf.download('BRK-A',period='2y',interval='1d')#,auto_adjust=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1725x1150 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cv = {} # blank dict to hold return_calculated_values values\n",
+    "d1 = '2021-12-08'\n",
+    "#d1 = '2022-06-08'\n",
+    "d2 = '2022-12-08'\n",
+    "s  = 'nightclouds'\n",
+    "#s  = 'yahoo'\n",
+    "#s  = 'charles'\n",
+    "fig, axlist = mpf.plot(df.loc[d1:d2], type='pnf', style=s, figratio=(1.5,1), figscale=2,\n",
+    "         pnf_params=dict(box_size=10000, reversal=2), \n",
+    "         return_calculated_values=cv, returnfig=True)#, volume=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['pnf_dates', 'pnf_values', 'pnf_size', 'pnf_volumes', 'minx', 'maxx', 'miny', 'maxy'])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(-0.9411764705882353, 16.941176470588236)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2.0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1.8"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1.5"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#cv['pnf_dates']\n",
+    "len(cv['pnf_dates'])\n",
+    "cv['minx'], cv['maxx']\n",
+    "len(range(int(round(cv['minx'],0)),int(round(cv['maxx'],0))))\n",
+    "print()\n",
+    "round(len(cv['pnf_dates'])/6,0)\n",
+    "len(cv['pnf_dates'])/5\n",
+    "len(cv['pnf_dates'])/6\n",
+    "\n",
+    "round(29/10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-2,  0,  2,  4,  6,  8, 10, 12, 14, 16])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "axlist[-2].get_xticks()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.8333333333333333"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "11/6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (3031286301.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Cell \u001b[0;32mIn[15], line 1\u001b[0;36m\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": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infile = '../../data/SPY_20110701_20120630_Bollinger.csv'\n",
+    "testdf = pd.read_csv(infile,index_col=0,parse_dates=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "testdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "pcv={}\n",
+    "#mpf.plot(testdf,type='pnf',style='nightclouds',volume=True,figscale=1.5,mav=(2,3,4),return_calculated_values=pcv)#,volume=True)\n",
+    "mpf.plot(testdf,type='pnf',style='nightclouds',volume=True,figscale=1.5,return_calculated_values=pcv)#,volume=True)\n",
+    "rcv={}\n",
+    "mpf.plot(testdf,type='renko',style='nightclouds',mav=(2,3,4),volume=True,return_calculated_values=rcv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "#rcv\n",
+    "pcv.keys()\n",
+    "pcv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "mpf.plot(testdf,type='pnf',style='nightclouds',volume=False,figscale=1.5,\n",
+    "         pnf_params=dict(box_size=2.5,price_method='hilo'))\n",
+    "mpf.plot(testdf,type='pnf',style='nightclouds',volume=False,figscale=1.5,\n",
+    "         pnf_params=dict(box_size=2.5,price_method='close'))\n",
+    "mpf.plot(testdf,type='pnf',style='nightclouds',volume=False,figscale=1.5,\n",
+    "         pnf_params=dict(box_size=2.5,price_method='open'))\n",
+    "#mpf.plot(testdf,type='pnf',style='nightclouds',volume=False,figscale=1.5,\n",
+    "#         pnf_params=dict(box_size=2.5,price_method='high'))"
+   ]
+  },
+  {
+   "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": []
+  },
+  {
+   "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": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/mplfinance/_utils.py b/src/mplfinance/_utils.py
index be0e54ca..024b966e 100644
--- a/src/mplfinance/_utils.py
+++ b/src/mplfinance/_utils.py
@@ -105,8 +105,8 @@ def _construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volume
             dates, highs, lows, volumes, config['renko_params'], closes, marketcolors=style['marketcolors'])
 
     elif ptype == 'pnf':
-        collections = _construct_pointnfig_collections(
-            dates, highs, lows, volumes, config['pnf_params'], closes, marketcolors=style['marketcolors'])
+        raise ValueError('Plot type="pnf" should no longer come this way!')
+
     else:
         raise TypeError('Unknown ptype="',str(ptype),'"')
 
@@ -415,21 +415,51 @@ def _valid_pnf_kwargs():
                          a limited set of allowed values) may also validate that the
                          kwarg value is one of the allowed values.
     '''
+    def _box_size_validator(v):
+        if isinstance(v,(float,int)): return True
+        if v == 'atr': return True
+        if ( isinstance(v,str) and
+             v[-1:] == '%'     and
+             v[:-1].replace('.','',1).isdigit()
+           ) : return True
+        return False
+
     vkwargs = {
-        'box_size'    : { 'Default'     : 'atr',
-                          'Description' : 'size of each box on y-axis (typically price).'+
-                                          ' specify a number, or specify "atr" for average true range.',
-                          'Validator'   : lambda value: isinstance(value,(float,int))
-                                                        or value == 'atr' },
-        'atr_length'  : { 'Default'     : 14,
-                          'Description' : 'number of periods for atr calculation (if box size is "atr")',
-                          'Validator'   : lambda value: isinstance(value,int)
+        'box_size'     : { 'Default'     : 'atr',
+                           'Description' : 'size of each box on y-axis (typically price).'+
+                                           ' specify a number, or "atr" for average true range'+
+                                           ' or a string containing a number and "%" for'+
+                                           ' percent of the most recent close price.',
+                           'Validator'   : lambda value: _box_size_validator(value) },
+        'atr_length'   : { 'Default'     : 'total',
+                           'Description' : 'number of periods for atr calculation (if box size is "atr")',
+                           'Validator'   : lambda value: isinstance(value,int)
                                                         or value == 'total' },
 
-        'reversal'    : { 'Default'     : 1,
-                          'Description' : 'number of boxes, in opposite direction, needed to reverse'+
-                                          ' a trend (i.e. to start a new column).',
-                          'Validator'   : lambda value: isinstance(value,int) },
+        'reversal'     : { 'Default'     : 3,
+                           'Description' : 'number of boxes, in opposite direction, needed to reverse'+
+                                           ' a trend (i.e. to start a new column).',
+                           'Validator'   : lambda value: isinstance(value,int) },
+
+        'method'       : { 'Default'     : 'hilo',
+                           'Description' : 'pricing method:'+
+                                           ' specify "hilo" to use High for X and Low for O'+
+                                           ' or specify "open" or "close" to use only Open or only Close price.',
+                           'Validator'   : lambda value: value in ['hilo','open','close']},
+
+        'use_candle_colors': { 'Default' : False,
+                           'Description' : 'use same colors as candles for given style'+
+                                           ' (instead of PNF colors derived from candle colors).',
+                           'Validator'   : lambda value: isinstance(value,bool) },
+
+        'scale_markers': { 'Default'     : 1.0,
+                           'Description' : 'Scale PNF markers larger ( > 1.0) or smaller ( < 1.0)',
+                           'Validator'   : lambda value: isinstance(value,(int,float)) },
+
+        'scale_right_padding': {'Default': 1.0,
+                           'Description' : 'Scale the amount of padding on the right side'+
+                                           ' of the plot. (Padding helps the PnF remain square',
+                           'Validator'   : lambda value: isinstance(value,(int,float)) },
     }
 
     _validate_vkwargs_dict(vkwargs)
@@ -831,7 +861,6 @@ def _construct_renko_collections(dates, highs, lows, volumes, config_renko_param
     brick_size = renko_params['brick_size']
     atr_length = renko_params['atr_length']
 
-
     if brick_size == 'atr':
         if atr_length == 'total':
             brick_size = _calculate_atr(len(closes)-1, highs, lows, closes)
@@ -934,249 +963,6 @@ def _construct_renko_collections(dates, highs, lows, volumes, config_renko_param
                              values=brick_values,size=brick_size)
     return [rectCollection,], calculated_values
 
-
-def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnfig_params, closes, marketcolors=None):
-    r"""Represent the price change with Xs and Os
-
-    NOTE: this code assumes if any value open, low, high, close is
-    missing they all are missing
-
-    Algorithm Explanation
-    ---------------------
-    In the first part of the algorithm, we populate the boxes array
-    along with adjusting the dates and volumes arrays into the new_dates and
-    new_volumes arrays. A single date includes a range from no boxes to many
-    boxes, if a date has no boxes it shall not be included in new_dates,
-    and if it has n boxes then it will be included n times. Volumes use a
-    volume cache to save volume amounts for dates that do not have any boxes
-    before adding the cache to the next date that has at least one box.
-    We populate the boxes array with each close values difference from the
-    previously created brick divided by the box size.
-
-    The second part of the algorithm has a series of step. First we combine the
-    adjacent like signed values in the boxes array (ex. [-1, -2, 3, -4] -> [-3, 3, -4]).
-    Next we subtract 1 from the absolute value of each element in boxes except the
-    first to ensure every time there is a trend change (ex. previous box is
-    an X, current brick is a O) we draw one less box to account for the price
-    having to move the previous box's amount before creating a box in the
-    opposite direction. During this same step we also combine like signed elements
-    and associated volume/date data ignoring any zero values that are created by
-    subtracting 1 from the box value. Next we recreate the box array utilizing a
-    rolling_change and volume_cache to store and sum the changes that don't break
-    the reversal threshold.
-
-    Lastly, we enumerate through the boxes to populate the line_seg and circle_patches
-    arrays. line_seg holds the / and \ line segments that make up an X and
-    circle_patches holds matplotlib.patches Ellipse objects for each O. We start
-    by filling an x and y array each iteration which contain the x and y
-    coordinates for each box in the column. Then for each coordinate pair in
-    x, y we add to either the line_seg array or the circle_patches array
-    depending on the value of sign for the current column (1 indicates
-    line_seg, -1 indicates circle_patches). The height of the boxes take
-    into account padding which separates each box by a small margin in
-    order to increase readability.
-
-    Useful sources:
-    https://stackoverflow.com/questions/8750648/point-and-figure-chart-with-matplotlib
-    https://www.investopedia.com/articles/technical/03/081303.asp
-
-    Parameters
-    ----------
-    dates : sequence
-        sequence of dates
-    highs : sequence
-        sequence of high values
-    lows : sequence
-        sequence of low values
-    config_pointnfig_params : kwargs table (dictionary)
-        box_size : size of each box
-        atr_length : length of time used for calculating atr
-    closes : sequence
-        sequence of closing values
-    marketcolors : dict of colors: up, down, edge, wick, alpha
-
-    Returns
-    -------
-    ret : tuple
-        rectCollection
-    """
-    pointnfig_params = _process_kwargs(config_pointnfig_params, _valid_pnf_kwargs())
-    if marketcolors is None:
-        marketcolors = _get_mpfstyle('classic')['marketcolors']
-
-    box_size = pointnfig_params['box_size']
-    atr_length = pointnfig_params['atr_length']
-    reversal = pointnfig_params['reversal']
-
-    if box_size == 'atr':
-        if atr_length == 'total':
-            box_size = _calculate_atr(len(closes)-1, highs, lows, closes)
-        else:
-            box_size = _calculate_atr(atr_length, highs, lows, closes)
-    else: # is an integer or float
-        upper_limit = (max(closes) - min(closes)) / 2
-        lower_limit = 0.01 * _calculate_atr(len(closes)-1, highs, lows, closes)
-        if box_size > upper_limit:
-            raise ValueError("Specified box_size may not be larger than (50% of the close price range of the dataset) which has value: "+ str(upper_limit))
-        elif box_size < lower_limit:
-            raise ValueError("Specified box_size may not be smaller than (0.01* the Average True Value of the dataset) which has value: "+ str(lower_limit))
-
-    if reversal < 1 or reversal > 9:
-        raise ValueError("Specified reversal must be an integer in the range [1,9]")
-
-    alpha  = marketcolors['alpha']
-
-    uc     = mcolors.to_rgba(marketcolors['ohlc'][ 'up' ], alpha)
-    dc     = mcolors.to_rgba(marketcolors['ohlc']['down'], alpha)
-    tfc    = mcolors.to_rgba(marketcolors['edge']['down'], 0) # transparent face color
-
-    boxes = [] # each element in an integer representing the number of boxes to be drawn on that indexes column (negative numbers -> Os, positive numbers -> Xs)
-    prev_close_box = closes[0] # represents the value of the last box in the previous column
-    volume_cache = 0 # holds the volumes for the dates that were skipped
-    temp_volumes, temp_dates = [], [] # holds the temp adjusted volumes and dates respectively
-
-    for i in range(len(closes)-1):
-        box_diff = int((closes[i+1] - prev_close_box) / box_size)
-        if box_diff == 0:
-            if volumes is not None:
-                volume_cache += volumes[i]
-            continue
-
-        boxes.append(box_diff)
-        if volumes is not None:
-            temp_volumes.append(volumes[i] + volume_cache)
-            volume_cache = 0
-        temp_dates.append(dates[i])
-        prev_close_box += box_diff *box_size
-
-    # combine adjacent similarly signed differences
-    boxes, indexes = combine_adjacent(boxes)
-    new_volumes, new_dates = coalesce_volume_dates(temp_volumes, temp_dates, indexes)
-
-    adjusted_boxes = [boxes[0]]
-    temp_volumes, temp_dates = [new_volumes[0]], [new_dates[0]]
-    volume_cache = 0
-
-    # Clean data to subtract 1 from all box # not including the first boxes element and combine like signed adjacent values (after ignoring zeros)
-    for i in range(1, len(boxes)):
-        adjusted_value = boxes[i]- int((boxes[i]/abs(boxes[i])))
-
-        # not equal to 0 and different signs
-        if adjusted_value != 0 and adjusted_boxes[-1]*adjusted_value < 0:
-
-            # Append adjusted_value, volumes, and date to associated lists
-            adjusted_boxes.append(adjusted_value)
-            temp_volumes.append(new_volumes[i] + volume_cache)
-            temp_dates.append(new_dates[i])
-
-            # reset volume_cache once we use it
-            volume_cache = 0
-
-        # not equal to 0 and same signs
-        elif adjusted_value != 0 and adjusted_boxes[-1]*adjusted_value > 0:
-
-            # Add adjusted_value and volume values to last added elements
-            adjusted_boxes[-1] += adjusted_value
-            temp_volumes[-1] += new_volumes[i] + volume_cache
-
-            # reset volume_cache once we use it
-            volume_cache = 0
-
-        else: # adjusted_value == 0
-            volume_cache += new_volumes[i]
-
-    boxes = [adjusted_boxes[0]]
-    new_volumes = [temp_volumes[0]]
-    new_dates = [temp_dates[0]]
-
-    rolling_change = 0
-    volume_cache = 0
-    biggest_difference = 0 # only used for the last column
-
-    #Clean data to account for reversal size (added to allow overriding the default reversal of 1)
-    for i in range(1, len(adjusted_boxes)):
-
-        # Add to rolling_change and volume_cache which stores the box and volume values
-        rolling_change += adjusted_boxes[i]
-        volume_cache += temp_volumes[i]
-
-        # if rolling_change is the same sign as the previous box and the abs value is bigger than the
-        # abs value of biggest_difference then we should replace biggest_difference with rolling_change
-        if rolling_change*boxes[-1] > 0 and abs(rolling_change) > abs(biggest_difference):
-            biggest_difference = rolling_change
-
-        # Add to new list if the rolling change is >= the reversal
-        if abs(rolling_change) >= reversal:
-
-            # if rolling_change is the same sign as the previous # of boxes then combine
-            if rolling_change*boxes[-1] > 0:
-                boxes[-1] += rolling_change
-                new_volumes[-1] += volume_cache
-
-            # otherwise add new box
-            else: # < 0 (== 0 can't happen since neither rolling_change or boxes[-1] can be 0)
-                boxes.append(rolling_change)
-                new_volumes.append(volume_cache)
-                new_dates.append(temp_dates[i])
-
-            # reset rolling_change and volume_cache once we've used them
-            rolling_change = 0
-            volume_cache = 0
-
-            # reset biggest_difference as we start from the beginning every time there is a reversal
-            biggest_difference = 0
-
-    # Adjust the last box column if the left over rolling_change is the same sign as the column
-    boxes[-1] += biggest_difference
-    new_volumes[-1] += volume_cache
-
-    curr_price = closes[0]
-    box_values = [] # y values for the boxes
-    circle_patches = [] # list of circle patches to be used to create the cirCollection
-    line_seg = [] # line segments that make up the Xs
-
-    for index, difference in enumerate(boxes):
-        diff = abs(difference)
-
-        sign = (difference / abs(difference)) # -1 or 1
-        start_iteration = 0 if sign > 0 else 1
-
-        x = [index] * (diff)
-        y = [curr_price + (i * box_size * sign) for i in range(start_iteration, diff+start_iteration)]
-
-        curr_price += (box_size * sign * (diff))
-        box_values.append( y )
-
-        for i in range(len(x)): # x and y have the same length
-            height = box_size * 0.85
-            width = 0.6
-            if height < 0.5:
-                width = height
-
-            padding = (box_size * 0.075)
-            if sign == 1: # X
-                line_seg.append([(x[i]-width/2, y[i] + padding), (x[i]+width/2, y[i]+height + padding)]) # create / part of the X
-                line_seg.append([(x[i]-width/2, y[i]+height+padding), (x[i]+width/2, y[i]+padding)]) # create \ part of the X
-            else: # O
-                circle_patches.append(Ellipse((x[i], y[i]-(height/2) - padding), width, height))
-
-    useAA = 0,    # use tuple here
-    lw = 0.5
-
-    cirCollection = PatchCollection(circle_patches)
-    cirCollection.set_facecolor([tfc] * len(circle_patches))
-    cirCollection.set_edgecolor([dc] * len(circle_patches))
-
-    xCollection = LineCollection(line_seg,
-                                 colors=[uc] * len(line_seg),
-                                 linewidths=lw,
-                                 antialiaseds=useAA
-                                 )
-    calculated_values = dict(dates=new_dates,counts=boxes,values=box_values,
-                             volumes=new_volumes,size=box_size)
-    return [cirCollection, xCollection], calculated_values
-
-
 def _construct_aline_collections(alines, dtix=None):
     """construct arbitrary line collections
 
@@ -1513,3 +1299,292 @@ def _mscatter(x,y,ax=None, m=None, **kw):
             paths.append(path)
         sc.set_paths(paths)
     return sc
+
+
+def _pnf_calculator(indf,boxsize,reverse=3,method='hilo'):
+    '''Calculate Point and Figure Numbers
+
+    TODO: Support arbitrary column names of OHLC
+    '''
+    
+    def round_to(n, precision):
+        correction = 0.5 if n >= 0 else -0.5
+        return int( (n/precision)+correction ) * precision
+
+    # indf = df.copy()[df.columns.values]
+    
+    # suppliment data with the "box" that each row of data falls into:
+
+    if method == 'hilo':
+        Xprices = indf.High
+        Oprices = indf.Low
+    elif method == 'open':
+        Xprices = indf.Open
+        Oprices = indf.Open
+    elif method == 'close':
+        Xprices = indf.Close
+        Oprices = indf.Close
+    else:
+        raise ValueError('Bad value for method="'+str(method)+'"')
+    
+    # X Boxes: Round down, i.e. truncate, to boxsize:
+    indf.loc[:,'XBox'] = [(int(x/boxsize))*boxsize for x in Xprices]
+
+    # O Boxes: Round up to boxsize:
+    indf.loc[:,'OBox'] = [(int(round_to((x+(0.5*boxsize)),boxsize)/boxsize))*boxsize for x in Oprices]
+
+    # Initialize First Column:
+
+    # There are a number of ways to decide whether the first column is up (X) or down (O).
+    # Initially I tried something that I saw described online: 
+    # If the first day (period) is an up day (close>open) then make the first column up (X).
+    # The problem with this was sometimes the trend was really the opposite of what the first
+    # day (period) just happened to be.  Next I tried comparing the close of the first two
+    # days.  That had a better shot a being right but still often did not match what I saw
+    # with PnF charts on Bloomberg or Schwab.  Next I tried a "vote" among the first three
+    # days, seeing whether each day was an up day (Close>Open) or down day (Open>Close).
+    # Being an odd number of days the "vote" could never be a tie.  This worked quite well
+    # but sometimes did not match Bloomberg.  Finally, after close inspection of the cases
+    # that did not match, I decided to compare the Close of the 3rd day with the Open on the
+    # 1st day.  This technique matched Bloomberg perfectly for all of the approximately ten
+    # cases that I tested:
+
+    v = indf.iloc[2].Close - indf.iloc[0].Open
+    xo = 'X' if v > 0 else 'O' 
+
+    d0 = indf.index[0]
+    if xo == 'X':
+        column = [indf.OBox[d0]-boxsize]
+    else:
+        column = [indf.XBox[d0]+boxsize]
+    xo
+    pnf = {}
+    pnf[d0] = column
+    
+    # HERE STARTS THE MAIN LOOP:
+    
+    column_count   = 1
+    current_column = pnf[d0]
+    for d in indf.index[1:]:
+        current_level = current_column[-1]
+        new_column    = []
+        if xo == 'X':
+            box = indf.XBox[d]
+            reverse = current_level - 3*boxsize        
+            if box > current_level:
+               #print(xo,d,'curlev=',current_level,'box=',box,'num=',num)
+               #print(xo,d,'current_column.1=',current_column)
+                num = int(round((box-current_level)/boxsize))
+                for jj in range(1,num+1):
+                    current_column.append(current_level+(jj*boxsize))
+               #print(xo,d,'current_column.2=',current_column)
+            elif indf.OBox[d] <= reverse:
+                top = current_level - boxsize
+                box = indf.OBox[d]
+                num = int(round((top-box)/boxsize))
+                new_column = [top]
+                for jj in range(1,num+1):
+                    new_column.append(top-(jj*boxsize))
+                pnf[d] = new_column
+                xo = 'O'
+                current_column = new_column
+                column_count += 1
+        else: # xo = 'O'
+            box = indf.OBox[d]
+            #print('d=',d,'box=',box,'current_level=',current_level)
+            reverse = current_level + 3*boxsize
+            if round_to(box,1.0*boxsize) < current_level:
+               #print(xo,d,'curlev=',current_level,'box=',box,'num=',num)
+               #print(xo,d,'current_column.1=',current_column)
+                num = int(round((current_level-box)/boxsize))
+                for jj in range(1,num+1):
+                    current_column.append(current_level-(jj*boxsize))
+               #print(xo,d,'current_column.2=',current_column)
+            elif indf.XBox[d] >= reverse:
+                bot = current_level + boxsize
+                box = indf.XBox[d]
+                num = int(round((box-bot)/boxsize))
+                new_column = [bot]
+                for jj in range(1,num+1):
+                    new_column.append(bot+(jj*boxsize))
+                pnf[d] = new_column
+                xo = 'X'
+                current_column = new_column
+                column_count += 1
+        #print('d=',d,'reverse=',reverse)
+        #if column_count > 4:
+        #    break
+    return pnf
+
+def _construct_pnf_scatter(ax,ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style):
+    """Represent the price change with Xs and Os
+
+    NOTE: this code assumes if any value open, low, high, close is
+    missing they all are missing
+
+    Algorithm Explanation
+    ---------------------
+    In the first part of the algorithm ...
+
+    Useful sources:
+    https://...
+    https://...
+
+    Parameters
+    ----------
+    dates : sequence
+        sequence of dates
+    highs : sequence
+        sequence of high values
+    lows : sequence
+        sequence of low values
+    config_pointnfig_params : kwargs table (dictionary)
+        box_size : size of each box
+        atr_length : length of time used for calculating atr
+    closes : sequence
+        sequence of closing values
+    marketcolors : dict of colors: up, down, edge, wick, alpha
+
+    Returns
+    -------
+    calculate_values : dict of assorted point-and-figure box calculation results
+    """
+
+    # Put the data into a dataframe so easier to work with.
+    # Someday we may change mplfinance in general to keep the input data in a dataframe.
+    df = pd.DataFrame(dict(Open=opens,High=highs,Low=lows,Close=closes,Volume=volumes))
+    if config['tz_localize']:
+        df.index = pd.DatetimeIndex(mdates.num2date(dates)).tz_localize(None)
+    else:
+        df.index = pd.DatetimeIndex(mdates.num2date(dates))
+    df.index.name = 'Date'
+
+    marketcolors=style['marketcolors']
+    if marketcolors is None:
+        marketcolors = _get_mpfstyle('classic')['marketcolors']
+
+    pointnfig_params = _process_kwargs(config['pnf_params'], _valid_pnf_kwargs())
+
+    box_size   = pointnfig_params['box_size']
+    atr_length = pointnfig_params['atr_length']
+    reversal   = pointnfig_params['reversal']
+    method     = pointnfig_params['method']
+
+    if box_size == 'atr':
+        if atr_length == 'total':
+            box_size = _calculate_atr(len(closes)-1, df.High, df.Low, df.Close)
+        else:
+            box_size = _calculate_atr(atr_length, df.High, df.Low, df.Close)
+    elif isinstance(box_size,str) and box_size[-1] == '%':
+        percent  = float(box_size[:-1])
+        if not (percent > 0 and percent < 50) :
+            raise ValueError("Specified percent (for box_size) must be > 0. and < 50.")
+        box_size = (percent/100.) * df.Close.iloc[-1] # percent of last close
+    else: # is an integer or float
+        upper_limit = (max(df.Close) - min(df.Close)) / 2
+        lower_limit = 0.01 * _calculate_atr(len(df.Close)-1, df.High, df.Low, df.Close)
+        if box_size > upper_limit:
+            raise ValueError("Specified box_size may not be larger than [50% of the close"+
+                             " price range of the dataset] which has value: "+ str(upper_limit))
+        elif box_size < lower_limit:
+            raise ValueError("Specified box_size may not be smaller than [0.01* the Average"+
+                             " True Value of the dataset) which has value: "+ str(lower_limit))
+
+    if reversal < 1 or reversal > 9:
+        raise ValueError("Specified reversal must be an integer in the range [1,9]")
+
+
+    pnfd = _pnf_calculator(df,boxsize=box_size,reverse=reversal,method=method)
+
+    yvals = [y for key in pnfd.keys() for y in pnfd[key] ]
+
+    # yval is the bottom of the box:
+    ylim_top = max(yvals) + 0.5*box_size
+    ylim_bot = min(yvals) - 0.5*box_size
+
+    # Attempt to calculate the ideal marker size:
+    dpi = ax.figure.get_dpi()
+    wxt = ax.get_window_extent()
+
+    axis_height_inches = wxt.height / dpi
+    max_vertical_boxes = (ylim_top - ylim_bot) / box_size
+    inches_per_box     = axis_height_inches / max_vertical_boxes
+    ideal_marker_size  = (inches_per_box*72) ** 2  # 72 points per inch, square for area.
+    ideal_marker_size  *= 0.6 # kludgey adjustment (should have worked without??)
+    marker_size        = ideal_marker_size * pointnfig_params['scale_markers']
+
+    alpha  = marketcolors['alpha']
+
+    if pointnfig_params['use_candle_colors']:
+        uc = mcolors.to_rgba(marketcolors['candle'][ 'up' ], alpha)
+        ue = mcolors.to_rgba(marketcolors['edge']['up'])#, alpha) 
+        uw = 0.5
+        dc = mcolors.to_rgba(marketcolors['candle']['down'], alpha)
+        de = mcolors.to_rgba(marketcolors['edge']['up'])#, alpha)
+        dw = 0.5
+    else:
+        uc = mcolors.to_rgba(marketcolors['edge']['up'], alpha)
+        ue = mcolors.to_rgba(marketcolors['edge']['up'], alpha)
+        uw = 0.5
+        dc = mcolors.to_rgba(marketcolors['candle']['down'], 0.0)
+        de = mcolors.to_rgba(marketcolors['candle']['down'], alpha)
+        dw = 0.18*(marker_size**0.5) # empirical "guess"
+        #print('dw=',dw)
+
+    xvals = []
+    yvals = []
+    mvals = []
+    cvals = []
+    evals = []
+    lwids = []
+    jj = 0
+    for key in pnfd.keys():
+
+        m = 'X' if pnfd[key][0] < pnfd[key][-1] else 'o'  # marker
+        c = uc  if pnfd[key][0] < pnfd[key][-1] else dc   # color
+        e = ue  if pnfd[key][0] < pnfd[key][-1] else de   # edge color
+        w = uw  if pnfd[key][0] < pnfd[key][-1] else dw   # edge width
+
+        for v in pnfd[key]:
+            yvals.append(v)
+            xvals.append(jj)
+            mvals.append(m)
+            evals.append(e)
+            cvals.append(c)
+            lwids.append(w)
+        jj += 1
+
+    plot_yvals = [y+(0.5*box_size) for y in yvals] # adjust so marker is _in_ the box.
+
+    _ = _mscatter(xvals,plot_yvals,ax,mvals,s=marker_size,c=cvals,linewidths=lwids,edgecolors=evals)
+
+    if config['volume'] is not None:
+        pnf_volumes = []
+        d1list = [d for d in pnfd.keys()]
+        d2list = d1list[1:] + [df.index[-1],]
+        for d1,d2 in zip(d1list,d2list):
+            pnf_volumes.append(df.Volume.loc[d1:d2].sum())
+    else:
+        pnf_volumes = [0]*len(xvals)
+
+    # make the length of the x-axis approximately equal to the total 
+    # number of vertical boxes, so the boxes are approximately square:
+    hi   = max(yvals)
+    lo   = min(yvals)
+    xlen = int(round((hi-lo)/box_size,0)+2) # +2 empirical kludge
+    pad  = (xlen-xvals[-1]) * pointnfig_params['scale_right_padding']
+    pad  = max(0,pad) # less than zero not allowed
+    #print('hi,lo,xlen,xvals[-1],pad=',hi,lo,xlen,xvals[-1],pad)
+    #print('ylim_top,ylim_bot=',ylim_top,ylim_bot)
+
+    xdates = np.arange(len(pnfd)+int(pad))
+    pnf_volumes = pnf_volumes + [float('nan')]*int(pad)
+
+    pnf_results = dict(pnf_volumes=pnf_volumes,
+                       pnf_ylimits=(ylim_bot,ylim_top),
+                       pnf_values=pnfd,
+                       pnf_df=df,
+                       pnf_boxsize=box_size,
+                       pnf_xdates=xdates)
+
+    return pnf_results
diff --git a/src/mplfinance/_version.py b/src/mplfinance/_version.py
index d0083f63..7177e51c 100644
--- a/src/mplfinance/_version.py
+++ b/src/mplfinance/_version.py
@@ -1,4 +1,4 @@
-version_info = (0, 12, 9, 'beta', 6)
+version_info = (0, 12, 9, 'beta', 7)
 
 _specifier_ = {'alpha': 'a','beta': 'b','candidate': 'rc','final': ''}
 
diff --git a/src/mplfinance/plotting.py b/src/mplfinance/plotting.py
index 4e849f92..61a75a73 100644
--- a/src/mplfinance/plotting.py
+++ b/src/mplfinance/plotting.py
@@ -21,6 +21,7 @@
 from mplfinance._utils import _construct_vline_collections
 from mplfinance._utils import _construct_tline_collections
 from mplfinance._utils import _construct_mpf_collections
+from mplfinance._utils import _construct_pnf_scatter
 
 from mplfinance._widths import _determine_width_config
 
@@ -526,33 +527,45 @@ def plot( data, **kwargs ):
     if ptype == 'line':
         lw = config['_width_config']['line_width']
         axA1.plot(xdates, closes, color=config['linecolor'], linewidth=lw)
+    elif ptype == 'pnf':
+        pnf_results = _construct_pnf_scatter(axA1,ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style)
     else:
         collections =_construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style)
 
-    if ptype in VALID_PMOVE_TYPES:
-        collections, calculated_values = collections
-        volumes       = calculated_values['volumes']
-        pmove_dates   = calculated_values['dates']
-        pmove_values  = calculated_values['values']
-        if all([isinstance(v,(list,tuple)) for v in pmove_values]):
-            pmove_avgvals = [sum(v)/len(v) for v in pmove_values]
-        else:
-            pmove_avgvals = pmove_values
-        pmove_size    = calculated_values['size']
-        pmove_counts  = calculated_values['counts'] if 'counts' in calculated_values else None
-        formatter = IntegerIndexDateTimeFormatter(pmove_dates, fmtstring)
-        xdates = np.arange(len(pmove_dates))
+    if ptype == 'pnf':
+        volumes    = pnf_results['pnf_volumes']
+        pnf_values = pnf_results['pnf_values']
+        pnf_mdates = mdates.date2num(list(pnf_values.keys()))
+        formatter  = IntegerIndexDateTimeFormatter(pnf_mdates,fmtstring)
+        xdates     = pnf_results['pnf_xdates']
+    elif ptype == 'renko':
+        collections, renko_results = collections
+        volumes       = renko_results['volumes']
+        renko_dates   = renko_results['dates']
+        renko_values  = renko_results['values']
+        formatter = IntegerIndexDateTimeFormatter(renko_dates, fmtstring)
+        renko_avgvals = renko_values
+        renko_size    = renko_results['size']
+        xdates = np.arange(len(renko_dates))
 
     if collections is not None:
         for collection in collections:
             axA1.add_collection(collection)
 
-    if ptype in VALID_PMOVE_TYPES:
-        mavprices = _plot_mav(axA1,config,xdates,pmove_avgvals)
-        emaprices = _plot_ema(axA1, config, xdates, pmove_avgvals)
+    #formatter = IntegerIndexDateTimeFormatter(xdates, fmtstring)
+
+    if (ptype == 'pnf' and 
+        (config['mav'] is not None or config['ema'] is not None)):
+        warnings.warn('\n\n ================================================================ '+
+                      '\n\n   MOVING Averages IGNORED for POINT and FIGURE PLOTS!'+
+                      '\n\n ================================================================ ',
+                      category=UserWarning)
+    elif ptype == 'renko':
+        mavprices = _plot_mav(axA1,config,xdates,renko_avgvals)
+        emaprices = _plot_ema(axA1,config,xdates,renko_avgvals)
     else:
         mavprices = _plot_mav(axA1,config,xdates,closes)
-        emaprices = _plot_ema(axA1, config, xdates, closes)
+        emaprices = _plot_ema(axA1,config,xdates,closes)
 
     avg_dist_between_points = (xdates[-1] - xdates[0]) / float(len(xdates))
     if not config['tight_layout']:
@@ -565,15 +578,19 @@ def plot( data, **kwargs ):
     if len(xdates) == 1:  # kludge special case
         minx = minx - 0.75
         maxx = maxx + 0.75
-    if ptype not in VALID_PMOVE_TYPES:
+
+    if ptype == 'renko':
+        _lows  = renko_avgvals 
+        _highs = [value+renko_size for value in renko_avgvals]
+    else:
         _lows  = lows
         _highs = highs
-    else:
-        _lows  = pmove_avgvals
-        _highs = [value+pmove_size for value in pmove_avgvals]
 
-    miny = np.nanmin(_lows)
-    maxy = np.nanmax(_highs)
+    if ptype == 'pnf':
+       miny, maxy = pnf_results['pnf_ylimits']
+    else:
+        miny = np.nanmin(_lows)
+        maxy = np.nanmax(_highs)
 
     if config['ylim'] is not None:
         axA1.set_ylim(config['ylim'][0], config['ylim'][1])
@@ -600,25 +617,23 @@ def plot( data, **kwargs ):
     if config['return_calculated_values'] is not None:
         retdict = config['return_calculated_values']
         if ptype == 'renko':
-            retdict['renko_bricks' ] = pmove_values
-            retdict['renko_dates'  ] = mdates.num2date(pmove_dates)
-            retdict['renko_size'   ] = pmove_size
+            retdict['renko_bricks' ] = renko_values
+            retdict['renko_dates'  ] = mdates.num2date(renko_dates)
+            retdict['renko_size'   ] = renko_size
             retdict['renko_volumes'] = volumes if config['volume'] else None
         elif ptype == 'pnf':
-            retdict['pnf_dates'    ] = mdates.num2date(pmove_dates)
-            retdict['pnf_counts'   ] = pmove_counts
-            retdict['pnf_values'   ] = pmove_values
-            retdict['pnf_avgvals'  ] = pmove_avgvals
-            retdict['pnf_size'     ] = pmove_size
-            retdict['pnf_volumes'  ] = volumes if config['volume'] else None
-        if config['mav'] is not None:
+            retdict['pnf_dates'    ] = mdates.num2date(pnf_mdates)
+            retdict['pnf_values'   ] = pnf_values
+            retdict['pnf_size'     ] = pnf_results['pnf_boxsize']
+            retdict['pnf_volumes'  ] = volumes[:len(pnf_values)] if config['volume'] else None
+        if config['mav'] is not None and ptype != 'pnf':
             mav = config['mav']
             if len(mav) != len(mavprices):
                 warnings.warn('len(mav)='+str(len(mav))+' BUT len(mavprices)='+str(len(mavprices)))
             else:
                 for jj in range(0,len(mav)):     
                     retdict['mav' + str(mav[jj])] = mavprices[jj]
-        if config['ema'] is not None:
+        if config['ema'] is not None and ptype != 'pnf':
             ema = config['ema']
             if len(ema) != len(emaprices):
                 warnings.warn('len(ema)='+str(len(ema))+' BUT len(emaprices)='+str(len(emaprices)))
@@ -633,6 +648,7 @@ def plot( data, **kwargs ):
     # Note: these are NOT mutually exclusive, so the order of this
     #       if/elif is important: VALID_PMOVE_TYPES must be first.
     if ptype in VALID_PMOVE_TYPES:
+        pmove_dates = pnf_mdates if ptype == 'pnf' else renko_dates
         dtix = pd.DatetimeIndex([dt for dt in mdates.num2date(pmove_dates)])
     elif not config['show_nontrading']:
         dtix = data.index
@@ -686,6 +702,18 @@ def plot( data, **kwargs ):
         axA1.xaxis.set_major_formatter(formatter)
         axA1.set_xlabel(config['xlabel'])
 
+    if config['type'] == 'pnf':
+        pnf_xs = list(pnf_results['pnf_df'].XBox.values)
+        pnf_os = list(pnf_results['pnf_df'].OBox.values)
+        tick_vals = sorted( set(pnf_xs + pnf_os) )
+        axA1.set_yticks(tick_vals)
+        skip = int( round(len(xdates)/10.0, 0) )
+        skip = max(1,skip) # must be at least 1
+        tick_vals = [t for t in range(0-skip,len(xdates)+1,skip)]
+        #print('len(xdates)=',len(xdates),'len(pnf_mdates)=',len(pnf_mdates))
+        #print('skip=',skip,'\nxdates=',xdates,'\npnf_dates=',[str(d.date()) for d in mdates.num2date(pnf_mdates)])
+        axA1.set_xticks(tick_vals)
+
     ysd = config['yscale']
     if isinstance(ysd,dict):
         yscale = ysd['yscale']
diff --git a/tests/reference_images/pnf01.png b/tests/reference_images/pnf01.png
index 830ea71e..16986504 100644
Binary files a/tests/reference_images/pnf01.png and b/tests/reference_images/pnf01.png differ
diff --git a/tests/reference_images/pnf02.png b/tests/reference_images/pnf02.png
index 67629b2b..347da5e3 100644
Binary files a/tests/reference_images/pnf02.png and b/tests/reference_images/pnf02.png differ
diff --git a/tests/reference_images/pnf03.png b/tests/reference_images/pnf03.png
index cdfbd4c6..7ca2b5f4 100644
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