diff --git a/examples/2021-01-11-backtest-multicore.ipynb b/examples/2021-01-11-backtest-multicore.ipynb
new file mode 100644
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+++ b/examples/2021-01-11-backtest-multicore.ipynb
@@ -0,0 +1,2539 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# \"backtesting multiprocessing\"\n",
+    "> \"How to fetch and backtest crypto data using fastquant and use multiple cores\"\n",
+    "\n",
+    "- toc: true\n",
+    "- branch: master\n",
+    "- badges: true\n",
+    "- comments: true\n",
+    "- author: Mikee Jazmines\n",
+    "- categories: [crypto, backtest, grid search]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/enzoampil/fastquant/blob/master/examples/2020-05-20-backtest_crypto.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# uncomment to install in colab\n",
+    "# !pip3 install fastquant --update\n",
+    "# or pip install git+https://www.github.com/enzoampil/fastquant.git@history"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## fetch data from binance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastquant import get_crypto_data\n",
+    "\n",
+    "crypto = get_crypto_data(\"BTC/USDT\", \n",
+    "                         \"2018-12-01\", \n",
+    "                         \"2019-12-31\",\n",
+    "                         time_resolution='1d'\n",
+    "                        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>dt</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>2019-12-27</th>\n",
+       "      <td>7202.00</td>\n",
+       "      <td>7275.86</td>\n",
+       "      <td>7076.42</td>\n",
+       "      <td>7254.74</td>\n",
+       "      <td>33642.701861</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-28</th>\n",
+       "      <td>7254.77</td>\n",
+       "      <td>7365.01</td>\n",
+       "      <td>7238.67</td>\n",
+       "      <td>7316.14</td>\n",
+       "      <td>26848.982199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-29</th>\n",
+       "      <td>7315.36</td>\n",
+       "      <td>7528.45</td>\n",
+       "      <td>7288.00</td>\n",
+       "      <td>7388.24</td>\n",
+       "      <td>31387.106085</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-30</th>\n",
+       "      <td>7388.43</td>\n",
+       "      <td>7408.24</td>\n",
+       "      <td>7220.00</td>\n",
+       "      <td>7246.00</td>\n",
+       "      <td>29605.911782</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-31</th>\n",
+       "      <td>7246.00</td>\n",
+       "      <td>7320.00</td>\n",
+       "      <td>7145.01</td>\n",
+       "      <td>7195.23</td>\n",
+       "      <td>25954.453533</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               open     high      low    close        volume\n",
+       "dt                                                          \n",
+       "2019-12-27  7202.00  7275.86  7076.42  7254.74  33642.701861\n",
+       "2019-12-28  7254.77  7365.01  7238.67  7316.14  26848.982199\n",
+       "2019-12-29  7315.36  7528.45  7288.00  7388.24  31387.106085\n",
+       "2019-12-30  7388.43  7408.24  7220.00  7246.00  29605.911782\n",
+       "2019-12-31  7246.00  7320.00  7145.01  7195.23  25954.453533"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "crypto.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = crypto.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "an integer is required (got type Timestamp)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/js/_vkhp5md1h30xkynyqkbw15c0000gn/T/ipykernel_11316/4240321943.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dt'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutcfromtimestamp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/hawksight/fastquant/venv/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[1;32m   4211\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4212\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4213\u001b[0;31m                 \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4215\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32m/var/folders/js/_vkhp5md1h30xkynyqkbw15c0000gn/T/ipykernel_11316/4240321943.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(s)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dt'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutcfromtimestamp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: an integer is required (got type Timestamp)"
+     ]
+    }
+   ],
+   "source": [
+    "x['dt'].apply(lambda s: datetime.datetime.utcfromtimestamp(s))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Timestamp('2018-12-01 00:00:00')"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.iloc[0,0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## run backtest with a grid of values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "Can't pickle local object 'initalize_data.<locals>.CustomData'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/js/_vkhp5md1h30xkynyqkbw15c0000gn/T/ipykernel_11316/2990413032.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m                    \u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m                    \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m                    \u001b[0mmax_cpus\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m                   )\n",
+      "\u001b[0;32m~/hawksight/fastquant/venv/lib/python3.7/site-packages/fastquant/backtest/backtest.py\u001b[0m in \u001b[0;36mbacktest\u001b[0;34m(strategy, data, commission, init_cash, plot, fractional, slippage, single_position, verbose, sort_by, sentiments, strats, return_history, return_plot, channel, symbol, max_cpus, allow_short, short_max, figsize, multi_line_indicators, data_class, data_kwargs, plot_kwargs, fig, **kwargs)\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;31m# clock the start of the process\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m     \u001b[0mtstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m     \u001b[0mstratruns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcerebro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m     \u001b[0;31m# clock the end of the process\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/hawksight/fastquant/venv/lib/python3.7/site-packages/backtrader/cerebro.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m   1141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1142\u001b[0m             \u001b[0mpool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmultiprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaxcpus\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1143\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterstrats\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1144\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunstrats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1145\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptcbs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.7.7/lib/python3.7/multiprocessing/pool.py\u001b[0m in \u001b[0;36mnext\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    746\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msuccess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    747\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 748\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    749\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    750\u001b[0m     \u001b[0m__next__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m                    \u001b[0;31m# XXX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.7.7/lib/python3.7/multiprocessing/pool.py\u001b[0m in \u001b[0;36m_handle_tasks\u001b[0;34m(taskqueue, put, outqueue, pool, cache)\u001b[0m\n\u001b[1;32m    429\u001b[0m                         \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 431\u001b[0;31m                         \u001b[0mput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    432\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m                         \u001b[0mjob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.7.7/lib/python3.7/multiprocessing/connection.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    204\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_writable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ForkingPickler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrecv_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxlength\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.7.7/lib/python3.7/multiprocessing/reduction.py\u001b[0m in \u001b[0;36mdumps\u001b[0;34m(cls, obj, protocol)\u001b[0m\n\u001b[1;32m     49\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0mbuf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetbuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: Can't pickle local object 'initalize_data.<locals>.CustomData'"
+     ]
+    }
+   ],
+   "source": [
+    "from fastquant import backtest\n",
+    "\n",
+    "results = backtest('smac', \n",
+    "                   crypto, \n",
+    "                   fast_period=[7,14], \n",
+    "                   slow_period=[30,45],\n",
+    "                   plot=False,\n",
+    "                   verbose=False,\n",
+    "                   max_cpus=3\n",
+    "                  )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>strat_id</th>\n",
+       "      <th>init_cash</th>\n",
+       "      <th>buy_prop</th>\n",
+       "      <th>sell_prop</th>\n",
+       "      <th>commission</th>\n",
+       "      <th>execution_type</th>\n",
+       "      <th>channel</th>\n",
+       "      <th>symbol</th>\n",
+       "      <th>fast_period</th>\n",
+       "      <th>slow_period</th>\n",
+       "      <th>rtot</th>\n",
+       "      <th>ravg</th>\n",
+       "      <th>rnorm</th>\n",
+       "      <th>rnorm100</th>\n",
+       "      <th>sharperatio</th>\n",
+       "      <th>pnl</th>\n",
+       "      <th>final_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0075</td>\n",
+       "      <td>close</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>7</td>\n",
+       "      <td>75</td>\n",
+       "      <td>0.949014</td>\n",
+       "      <td>0.002397</td>\n",
+       "      <td>0.829272</td>\n",
+       "      <td>82.927229</td>\n",
+       "      <td>0.987367</td>\n",
+       "      <td>158316.23</td>\n",
+       "      <td>258316.234050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>14</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0075</td>\n",
+       "      <td>close</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>28</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0.931504</td>\n",
+       "      <td>0.002352</td>\n",
+       "      <td>0.809002</td>\n",
+       "      <td>80.900205</td>\n",
+       "      <td>0.986999</td>\n",
+       "      <td>153832.42</td>\n",
+       "      <td>253832.420400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0075</td>\n",
+       "      <td>close</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>14</td>\n",
+       "      <td>75</td>\n",
+       "      <td>0.830975</td>\n",
+       "      <td>0.002098</td>\n",
+       "      <td>0.696898</td>\n",
+       "      <td>69.689847</td>\n",
+       "      <td>0.984563</td>\n",
+       "      <td>129555.54</td>\n",
+       "      <td>229555.539175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0075</td>\n",
+       "      <td>close</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>21</td>\n",
+       "      <td>75</td>\n",
+       "      <td>0.782243</td>\n",
+       "      <td>0.001975</td>\n",
+       "      <td>0.645083</td>\n",
+       "      <td>64.508323</td>\n",
+       "      <td>0.983142</td>\n",
+       "      <td>118637.07</td>\n",
+       "      <td>218637.072700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>15</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0075</td>\n",
+       "      <td>close</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>28</td>\n",
+       "      <td>75</td>\n",
+       "      <td>0.769574</td>\n",
+       "      <td>0.001943</td>\n",
+       "      <td>0.631874</td>\n",
+       "      <td>63.187426</td>\n",
+       "      <td>0.982741</td>\n",
+       "      <td>115884.74</td>\n",
+       "      <td>215884.739100</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   strat_id  init_cash  buy_prop  sell_prop  commission execution_type  \\\n",
+       "0         3     100000         1          1      0.0075          close   \n",
+       "1        14     100000         1          1      0.0075          close   \n",
+       "2         7     100000         1          1      0.0075          close   \n",
+       "3        11     100000         1          1      0.0075          close   \n",
+       "4        15     100000         1          1      0.0075          close   \n",
+       "\n",
+       "  channel symbol  fast_period  slow_period      rtot      ravg     rnorm  \\\n",
+       "0    None   None            7           75  0.949014  0.002397  0.829272   \n",
+       "1    None   None           28           60  0.931504  0.002352  0.809002   \n",
+       "2    None   None           14           75  0.830975  0.002098  0.696898   \n",
+       "3    None   None           21           75  0.782243  0.001975  0.645083   \n",
+       "4    None   None           28           75  0.769574  0.001943  0.631874   \n",
+       "\n",
+       "    rnorm100  sharperatio        pnl    final_value  \n",
+       "0  82.927229     0.987367  158316.23  258316.234050  \n",
+       "1  80.900205     0.986999  153832.42  253832.420400  \n",
+       "2  69.689847     0.984563  129555.54  229555.539175  \n",
+       "3  64.508323     0.983142  118637.07  218637.072700  \n",
+       "4  63.187426     0.982741  115884.74  215884.739100  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's a 258% maximum profit using only SMAC because bitcoin was bullish all time long!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7, 75)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#get best parameters on top row \n",
+    "fast_best, slow_best = results.iloc[0][[\"fast_period\",\"slow_period\"]]\n",
+    "fast_best, slow_best"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## run backtest using optimum values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as pl\n",
+    "pl.style.use(\"default\")\n",
+    "pl.rcParams[\"figure.figsize\"] = (9,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 7\n",
+      "slow_period : 75\n",
+      "Final Portfolio Value: 258316.23405000006\n",
+      "Final PnL: 158316.23\n",
+      "Time used (seconds): 0.10248279571533203\n",
+      "Optimal parameters: {'init_cash': 100000, 'buy_prop': 1, 'sell_prop': 1, 'commission': 0.0075, 'execution_type': 'close', 'channel': None, 'symbol': None, 'fast_period': 7, 'slow_period': 75}\n",
+      "Optimal metrics: {'rtot': 0.9490143617322465, 'ravg': 0.002396500913465269, 'rnorm': 0.8292722866407841, 'rnorm100': 82.92722866407841, 'sharperatio': 0.9873670567519415, 'pnl': 158316.23, 'final_value': 258316.23405000006}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support. ' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>');\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option);\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>');\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\" width=\"900\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results = backtest('smac', \n",
+    "                   crypto, \n",
+    "                   fast_period=fast_best, \n",
+    "                   slow_period=slow_best,\n",
+    "                   plot=True,\n",
+    "                   verbose=False\n",
+    "                  )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## accessing history of transactions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is done by setting `return_history`=True."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### single strategy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 7\n",
+      "slow_period : 75\n",
+      "Final Portfolio Value: 258316.23405000006\n",
+      "Final PnL: 158316.23\n",
+      "Time used (seconds): 0.10444140434265137\n",
+      "Optimal parameters: {'init_cash': 100000, 'buy_prop': 1, 'sell_prop': 1, 'commission': 0.0075, 'execution_type': 'close', 'channel': None, 'symbol': None, 'fast_period': 7, 'slow_period': 75}\n",
+      "Optimal metrics: {'rtot': 0.9490143617322465, 'ravg': 0.002396500913465269, 'rnorm': 0.8292722866407841, 'rnorm100': 82.92722866407841, 'sharperatio': 0.9873670567519415, 'pnl': 158316.23, 'final_value': 258316.23405000006}\n"
+     ]
+    }
+   ],
+   "source": [
+    "results, history = backtest('smac', \n",
+    "                               crypto, \n",
+    "                               fast_period=fast_best, \n",
+    "                               slow_period=slow_best,\n",
+    "                               plot=False,\n",
+    "                               verbose=False,\n",
+    "                               return_history=True\n",
+    "                              )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>strat_id</th>\n",
+       "      <th>init_cash</th>\n",
+       "      <th>buy_prop</th>\n",
+       "      <th>sell_prop</th>\n",
+       "      <th>commission</th>\n",
+       "      <th>execution_type</th>\n",
+       "      <th>channel</th>\n",
+       "      <th>symbol</th>\n",
+       "      <th>fast_period</th>\n",
+       "      <th>slow_period</th>\n",
+       "      <th>rtot</th>\n",
+       "      <th>ravg</th>\n",
+       "      <th>rnorm</th>\n",
+       "      <th>rnorm100</th>\n",
+       "      <th>sharperatio</th>\n",
+       "      <th>pnl</th>\n",
+       "      <th>final_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0075</td>\n",
+       "      <td>close</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>7</td>\n",
+       "      <td>75</td>\n",
+       "      <td>0.949014</td>\n",
+       "      <td>0.002397</td>\n",
+       "      <td>0.829272</td>\n",
+       "      <td>82.927229</td>\n",
+       "      <td>0.987367</td>\n",
+       "      <td>158316.23</td>\n",
+       "      <td>258316.23405</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   strat_id  init_cash  buy_prop  sell_prop  commission execution_type  \\\n",
+       "0         0     100000         1          1      0.0075          close   \n",
+       "\n",
+       "  channel symbol  fast_period  slow_period      rtot      ravg     rnorm  \\\n",
+       "0    None   None            7           75  0.949014  0.002397  0.829272   \n",
+       "\n",
+       "    rnorm100  sharperatio        pnl   final_value  \n",
+       "0  82.927229     0.987367  158316.23  258316.23405  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['orders', 'periodic'])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "history.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>strat_id</th>\n",
+       "      <th>strat_name</th>\n",
+       "      <th>dt</th>\n",
+       "      <th>type</th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "      <th>value</th>\n",
+       "      <th>commission</th>\n",
+       "      <th>pnl</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>fast_period7_slow_period75</td>\n",
+       "      <td>2019-02-15</td>\n",
+       "      <td>buy</td>\n",
+       "      <td>3590.56</td>\n",
+       "      <td>27</td>\n",
+       "      <td>96945.12</td>\n",
+       "      <td>727.088400</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>fast_period7_slow_period75</td>\n",
+       "      <td>2019-08-21</td>\n",
+       "      <td>sell</td>\n",
+       "      <td>10142.57</td>\n",
+       "      <td>-27</td>\n",
+       "      <td>96945.12</td>\n",
+       "      <td>2053.870425</td>\n",
+       "      <td>176904.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>fast_period7_slow_period75</td>\n",
+       "      <td>2019-11-02</td>\n",
+       "      <td>buy</td>\n",
+       "      <td>9231.61</td>\n",
+       "      <td>29</td>\n",
+       "      <td>267716.69</td>\n",
+       "      <td>2007.875175</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>fast_period7_slow_period75</td>\n",
+       "      <td>2019-11-12</td>\n",
+       "      <td>sell</td>\n",
+       "      <td>8821.94</td>\n",
+       "      <td>-29</td>\n",
+       "      <td>267716.69</td>\n",
+       "      <td>1918.771950</td>\n",
+       "      <td>-11880.43</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   strat_id                  strat_name         dt  type     price  size  \\\n",
+       "0         0  fast_period7_slow_period75 2019-02-15   buy   3590.56    27   \n",
+       "1         0  fast_period7_slow_period75 2019-08-21  sell  10142.57   -27   \n",
+       "2         0  fast_period7_slow_period75 2019-11-02   buy   9231.61    29   \n",
+       "3         0  fast_period7_slow_period75 2019-11-12  sell   8821.94   -29   \n",
+       "\n",
+       "       value   commission        pnl  \n",
+       "0   96945.12   727.088400       0.00  \n",
+       "1   96945.12  2053.870425  176904.27  \n",
+       "2  267716.69  2007.875175       0.00  \n",
+       "3  267716.69  1918.771950  -11880.43  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "orders = history['orders']\n",
+    "orders"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final value in `results` can be calculated from the `commission` and `pnl` (profit & loss) of all the closed (bought and sold) transactions in history:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "258316.23405000006"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r = results.squeeze()\n",
+    "r.final_value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "258316.23405000003"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r.init_cash + orders.pnl.sum() - orders.commission.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### single signal strategy with grid search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 7\n",
+      "slow_period : 30\n",
+      "Final Portfolio Value: 167957.05730000004\n",
+      "Final PnL: 67957.06\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 7\n",
+      "slow_period : 45\n",
+      "Final Portfolio Value: 200109.894525\n",
+      "Final PnL: 100109.89\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 7\n",
+      "slow_period : 60\n",
+      "Final Portfolio Value: 189298.80590000006\n",
+      "Final PnL: 89298.81\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 14\n",
+      "slow_period : 30\n",
+      "Final Portfolio Value: 161429.22347500004\n",
+      "Final PnL: 61429.22\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 14\n",
+      "slow_period : 45\n",
+      "Final Portfolio Value: 166675.70495000004\n",
+      "Final PnL: 66675.7\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 14\n",
+      "slow_period : 60\n",
+      "Final Portfolio Value: 149527.12537499995\n",
+      "Final PnL: 49527.13\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 21\n",
+      "slow_period : 30\n",
+      "Final Portfolio Value: 119204.3985\n",
+      "Final PnL: 19204.4\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 21\n",
+      "slow_period : 45\n",
+      "Final Portfolio Value: 162617.28744999995\n",
+      "Final PnL: 62617.29\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 21\n",
+      "slow_period : 60\n",
+      "Final Portfolio Value: 185407.30802499995\n",
+      "Final PnL: 85407.31\n",
+      "Time used (seconds): 0.7071244716644287\n",
+      "Optimal parameters: {'init_cash': 100000, 'buy_prop': 1, 'sell_prop': 1, 'commission': 0.0075, 'execution_type': 'close', 'channel': None, 'symbol': None, 'fast_period': 7, 'slow_period': 45}\n",
+      "Optimal metrics: {'rtot': 0.6936965022801388, 'ravg': 0.0017517588441417647, 'rnorm': 0.5549497480208785, 'rnorm100': 55.494974802087846, 'sharperatio': 0.9800219547779011, 'pnl': 100109.89, 'final_value': 200109.894525}\n"
+     ]
+    }
+   ],
+   "source": [
+    "results, history = backtest('smac', \n",
+    "                               crypto, \n",
+    "                               fast_period=[7,14,21], \n",
+    "                               slow_period=[30,45,60],\n",
+    "                               plot=False,\n",
+    "                               verbose=False,\n",
+    "                               return_history=True\n",
+    "                              )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8]),\n",
+       " array(['fast_period7_slow_period30', 'fast_period7_slow_period45',\n",
+       "        'fast_period7_slow_period60', 'fast_period14_slow_period30',\n",
+       "        'fast_period14_slow_period45', 'fast_period14_slow_period60',\n",
+       "        'fast_period21_slow_period30', 'fast_period21_slow_period45',\n",
+       "        'fast_period21_slow_period60'], dtype=object))"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "orders = history['orders']\n",
+    "orders.strat_id.unique(), orders.strat_name.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fd996d8cd90>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "key = 'strat_id'\n",
+    "periodic = history['periodic']\n",
+    "g = periodic.set_index('dt').groupby(key)\n",
+    "axs = g.portfolio_value.plot(legend=key)\n",
+    "axs[0].set_ylabel('Returns')\n",
+    "axs[0].legend(g.groups, title=key, bbox_to_anchor=(1.01, 1), loc='upper left')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### multi signal strategy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 7\n",
+      "slow_period : 60\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "rsi_period : 14\n",
+      "rsi_upper : 70\n",
+      "rsi_lower : 30\n",
+      "Final Portfolio Value: 96154.84370000003\n",
+      "Final PnL: -3845.16\n",
+      "Final Portfolio Value: 96154.84370000003\n",
+      "Final PnL: -3845.16\n",
+      "Time used (seconds): 0.12853026390075684\n",
+      "Optimal parameters: {'init_cash': 100000, 'buy_prop': 1, 'sell_prop': 1, 'smac.commission': 0.0075, 'execution_type': 'close', 'smac.channel': None, 'smac.symbol': None, 'smac.fast_period': 7, 'smac.slow_period': 60, 'rsi.commission': 0.0075, 'rsi.channel': None, 'rsi.symbol': None, 'rsi.rsi_period': 14, 'rsi.rsi_upper': 70, 'rsi.rsi_lower': 30}\n",
+      "Optimal metrics: {'rtot': -0.039210338727095576, 'ravg': -9.901600688660499e-05, 'rnorm': -0.024643304876387637, 'rnorm100': -2.464330487638764, 'sharperatio': -0.12300657942849803, 'pnl': -3845.16, 'final_value': 96154.84370000003}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from fastquant import backtest\n",
+    "\n",
+    "strats= {\n",
+    "       'smac': {\n",
+    "           'fast_period': 7, \n",
+    "           'slow_period': 60\n",
+    "            },\n",
+    "        'rsi': {\n",
+    "            'rsi_upper': 70,\n",
+    "            'rsi_lower': 30 \n",
+    "        }\n",
+    "   }\n",
+    "results, history = backtest('multi', \n",
+    "                               crypto, \n",
+    "                               strats=strats,\n",
+    "                               plot=False,\n",
+    "                               verbose=False,\n",
+    "                               return_history=True\n",
+    "                              )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "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>strat_id</th>\n",
+       "      <th>init_cash</th>\n",
+       "      <th>buy_prop</th>\n",
+       "      <th>sell_prop</th>\n",
+       "      <th>smac.commission</th>\n",
+       "      <th>execution_type</th>\n",
+       "      <th>smac.channel</th>\n",
+       "      <th>smac.symbol</th>\n",
+       "      <th>smac.fast_period</th>\n",
+       "      <th>smac.slow_period</th>\n",
+       "      <th>...</th>\n",
+       "      <th>rsi.rsi_period</th>\n",
+       "      <th>rsi.rsi_upper</th>\n",
+       "      <th>rsi.rsi_lower</th>\n",
+       "      <th>rtot</th>\n",
+       "      <th>ravg</th>\n",
+       "      <th>rnorm</th>\n",
+       "      <th>rnorm100</th>\n",
+       "      <th>sharperatio</th>\n",
+       "      <th>pnl</th>\n",
+       "      <th>final_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0075</td>\n",
+       "      <td>close</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>7</td>\n",
+       "      <td>60</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14</td>\n",
+       "      <td>70</td>\n",
+       "      <td>30</td>\n",
+       "      <td>-0.03921</td>\n",
+       "      <td>-0.000099</td>\n",
+       "      <td>-0.024643</td>\n",
+       "      <td>-2.46433</td>\n",
+       "      <td>-0.123007</td>\n",
+       "      <td>-3845.16</td>\n",
+       "      <td>96154.8437</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   strat_id  init_cash  buy_prop  sell_prop  smac.commission execution_type  \\\n",
+       "0         0     100000         1          1           0.0075          close   \n",
+       "\n",
+       "  smac.channel smac.symbol  smac.fast_period  smac.slow_period  ...  \\\n",
+       "0         None        None                 7                60  ...   \n",
+       "\n",
+       "   rsi.rsi_period rsi.rsi_upper rsi.rsi_lower     rtot      ravg     rnorm  \\\n",
+       "0              14            70            30 -0.03921 -0.000099 -0.024643   \n",
+       "\n",
+       "   rnorm100  sharperatio      pnl  final_value  \n",
+       "0  -2.46433    -0.123007 -3845.16   96154.8437  \n",
+       "\n",
+       "[1 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0]),\n",
+       " array(['smac.fast_period7_slow_period60', 'rsi.rsi_upper70_rsi_lower30'],\n",
+       "       dtype=object))"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "orders = history['orders']\n",
+    "orders.strat_id.unique(), orders.strat_name.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 7\n",
+      "slow_period : 30\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "rsi_period : 14\n",
+      "rsi_upper : 70\n",
+      "rsi_lower : 20\n",
+      "Final Portfolio Value: 101479.475225\n",
+      "Final PnL: 1479.48\n",
+      "Final Portfolio Value: 101479.475225\n",
+      "Final PnL: 1479.48\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 7\n",
+      "slow_period : 30\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "rsi_period : 14\n",
+      "rsi_upper : 70\n",
+      "rsi_lower : 30\n",
+      "Final Portfolio Value: 92990.43297500002\n",
+      "Final PnL: -7009.57\n",
+      "Final Portfolio Value: 92990.43297500002\n",
+      "Final PnL: -7009.57\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 7\n",
+      "slow_period : 30\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "rsi_period : 14\n",
+      "rsi_upper : 80\n",
+      "rsi_lower : 20\n",
+      "Final Portfolio Value: 165538.20930000002\n",
+      "Final PnL: 65538.21\n",
+      "Final Portfolio Value: 165538.20930000002\n",
+      "Final PnL: 65538.21\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 7\n",
+      "slow_period : 30\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "rsi_period : 14\n",
+      "rsi_upper : 80\n",
+      "rsi_lower : 30\n",
+      "Final Portfolio Value: 151316.80185000005\n",
+      "Final PnL: 51316.8\n",
+      "Final Portfolio Value: 151316.80185000005\n",
+      "Final PnL: 51316.8\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 7\n",
+      "slow_period : 60\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "rsi_period : 14\n",
+      "rsi_upper : 70\n",
+      "rsi_lower : 20\n",
+      "Final Portfolio Value: 107714.30350000001\n",
+      "Final PnL: 7714.3\n",
+      "Final Portfolio Value: 107714.30350000001\n",
+      "Final PnL: 7714.3\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 7\n",
+      "slow_period : 60\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "rsi_period : 14\n",
+      "rsi_upper : 70\n",
+      "rsi_lower : 30\n",
+      "Final Portfolio Value: 96154.84370000003\n",
+      "Final PnL: -3845.16\n",
+      "Final Portfolio Value: 96154.84370000003\n",
+      "Final PnL: -3845.16\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 7\n",
+      "slow_period : 60\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "rsi_period : 14\n",
+      "rsi_upper : 80\n",
+      "rsi_lower : 20\n",
+      "Final Portfolio Value: 134756.84680000003\n",
+      "Final PnL: 34756.85\n",
+      "Final Portfolio Value: 134756.84680000003\n",
+      "Final PnL: 34756.85\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 7\n",
+      "slow_period : 60\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "rsi_period : 14\n",
+      "rsi_upper : 80\n",
+      "rsi_lower : 30\n",
+      "Final Portfolio Value: 120430.34112500004\n",
+      "Final PnL: 20430.34\n",
+      "Final Portfolio Value: 120430.34112500004\n",
+      "Final PnL: 20430.34\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 14\n",
+      "slow_period : 30\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "rsi_period : 14\n",
+      "rsi_upper : 70\n",
+      "rsi_lower : 20\n",
+      "Final Portfolio Value: 92345.06152500001\n",
+      "Final PnL: -7654.94\n",
+      "Final Portfolio Value: 92345.06152500001\n",
+      "Final PnL: -7654.94\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 14\n",
+      "slow_period : 30\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "rsi_period : 14\n",
+      "rsi_upper : 70\n",
+      "rsi_lower : 30\n",
+      "Final Portfolio Value: 83860.13070000001\n",
+      "Final PnL: -16139.87\n",
+      "Final Portfolio Value: 83860.13070000001\n",
+      "Final PnL: -16139.87\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 14\n",
+      "slow_period : 30\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "rsi_period : 14\n",
+      "rsi_upper : 80\n",
+      "rsi_lower : 20\n",
+      "Final Portfolio Value: 127624.16555000003\n",
+      "Final PnL: 27624.17\n",
+      "Final Portfolio Value: 127624.16555000003\n",
+      "Final PnL: 27624.17\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 14\n",
+      "slow_period : 30\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "rsi_period : 14\n",
+      "rsi_upper : 80\n",
+      "rsi_lower : 30\n",
+      "Final Portfolio Value: 115264.11515000004\n",
+      "Final PnL: 15264.12\n",
+      "Final Portfolio Value: 115264.11515000004\n",
+      "Final PnL: 15264.12\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 14\n",
+      "slow_period : 60\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "rsi_period : 14\n",
+      "rsi_upper : 70\n",
+      "rsi_lower : 20\n",
+      "Final Portfolio Value: 90231.50885000001\n",
+      "Final PnL: -9768.49\n",
+      "Final Portfolio Value: 90231.50885000001\n",
+      "Final PnL: -9768.49\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 14\n",
+      "slow_period : 60\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "rsi_period : 14\n",
+      "rsi_upper : 70\n",
+      "rsi_lower : 30\n",
+      "Final Portfolio Value: 84980.43325000003\n",
+      "Final PnL: -15019.57\n",
+      "Final Portfolio Value: 84980.43325000003\n",
+      "Final PnL: -15019.57\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 14\n",
+      "slow_period : 60\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "rsi_period : 14\n",
+      "rsi_upper : 80\n",
+      "rsi_lower : 20\n",
+      "Final Portfolio Value: 111348.934\n",
+      "Final PnL: 11348.93\n",
+      "Final Portfolio Value: 111348.934\n",
+      "Final PnL: 11348.93\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "fast_period : 14\n",
+      "slow_period : 60\n",
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "rsi_period : 14\n",
+      "rsi_upper : 80\n",
+      "rsi_lower : 30\n",
+      "Final Portfolio Value: 104417.4315\n",
+      "Final PnL: 4417.43\n",
+      "Final Portfolio Value: 104417.4315\n",
+      "Final PnL: 4417.43\n",
+      "Time used (seconds): 2.0397427082061768\n",
+      "Optimal parameters: {'init_cash': 100000, 'buy_prop': 1, 'sell_prop': 1, 'smac.commission': 0.0075, 'execution_type': 'close', 'smac.channel': None, 'smac.symbol': None, 'smac.fast_period': 7, 'smac.slow_period': 30, 'rsi.commission': 0.0075, 'rsi.channel': None, 'rsi.symbol': None, 'rsi.rsi_period': 14, 'rsi.rsi_upper': 80, 'rsi.rsi_lower': 20}\n",
+      "Optimal metrics: {'rtot': 0.5040318540855331, 'ravg': 0.0012728077123372048, 'rnorm': 0.37815761213727767, 'rnorm100': 37.81576121372777, 'sharperatio': 1.834688207984273, 'pnl': 65538.21, 'final_value': 165538.20930000002}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from fastquant import backtest\n",
+    "\n",
+    "strats= {\n",
+    "       'smac': {\n",
+    "           'fast_period': [7,14], \n",
+    "           'slow_period': [30,60]\n",
+    "            },\n",
+    "        'rsi': {\n",
+    "            'rsi_upper': [70,80],\n",
+    "            'rsi_lower': [20,30] \n",
+    "        }\n",
+    "   }\n",
+    "results, history = backtest('multi', \n",
+    "                               crypto, \n",
+    "                               strats=strats,\n",
+    "                               plot=False,\n",
+    "                               verbose=False,\n",
+    "                               return_history=True\n",
+    "                              )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15]),\n",
+       " array(['smac.fast_period7_slow_period30', 'rsi.rsi_upper70_rsi_lower20',\n",
+       "        'rsi.rsi_upper70_rsi_lower30', 'rsi.rsi_upper80_rsi_lower20',\n",
+       "        'rsi.rsi_upper80_rsi_lower30', 'smac.fast_period7_slow_period60',\n",
+       "        'smac.fast_period14_slow_period30',\n",
+       "        'smac.fast_period14_slow_period60'], dtype=object))"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "orders = history['orders']\n",
+    "orders.strat_id.unique(), orders.strat_name.unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### custom strategy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "#add a column which is a proxy buy/sell indicator for custom strategy\n",
+    "crypto[\"custom\"] = crypto.close.pct_change()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===Global level arguments===\n",
+      "init_cash : 100000\n",
+      "buy_prop : 1\n",
+      "sell_prop : 1\n",
+      "commission : 0.0075\n",
+      "===Strategy level arguments===\n",
+      "Upper limit:  0.05\n",
+      "Lower limit:  -0.05\n",
+      "Final Portfolio Value: 165576.88775000008\n",
+      "Final PnL: 65576.89\n",
+      "Time used (seconds): 0.12016487121582031\n",
+      "Optimal parameters: {'init_cash': 100000, 'buy_prop': 1, 'sell_prop': 1, 'commission': 0.0075, 'execution_type': 'close', 'channel': None, 'symbol': None, 'upper_limit': 0.05, 'lower_limit': -0.05, 'custom_column': 'custom'}\n",
+      "Optimal metrics: {'rtot': 0.5042654794956734, 'ravg': 0.0012733976754941247, 'rnorm': 0.3783625190470573, 'rnorm100': 37.83625190470573, 'sharperatio': 0.7416913074113402, 'pnl': 65576.89, 'final_value': 165576.88775000008}\n"
+     ]
+    }
+   ],
+   "source": [
+    "results, history = backtest('custom', \n",
+    "                                crypto, \n",
+    "                                upper_limit=0.05, \n",
+    "                                lower_limit=-0.05,\n",
+    "                                plot=False,\n",
+    "                                verbose=False,\n",
+    "                                return_history=True\n",
+    "                                )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0]), array(['upper_limit0.05_lower_limit-0.05'], dtype=object))"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "orders = history['orders']\n",
+    "orders.strat_id.unique(), orders.strat_name.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "fq-multi",
+   "language": "python",
+   "name": "fq-multi"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/fastquant/backtest/backtest.py b/python/fastquant/backtest/backtest.py
index ebbf7e8e..a6f82a7f 100644
--- a/python/fastquant/backtest/backtest.py
+++ b/python/fastquant/backtest/backtest.py
@@ -69,6 +69,7 @@ def backtest(
     return_plot=False,
     channel="",
     symbol="",
+    max_cpus=1,
     allow_short=False,
     short_max=1.5,
     figsize=(30, 15),
@@ -109,6 +110,8 @@ def backtest(
         Channel to be used for notifications - e.g. "slack" (default=None)
     symbol : str
         Symbol to be referenced in the channel notification if not None (default=None)
+    max_cpus : int
+        Determines how many cores will be used. Value of None means that the max cores will be used. This is 1 by default
     allow_short : bool
         Whether to allow short selling, with max set as `short_max` times the portfolio value (default=False)
     short_max : float
@@ -125,7 +128,7 @@ def backtest(
     """
     # Setting initial support for 1 cpu
     # Return the full strategy object to get all run information
-    cerebro = bt.Cerebro(stdstats=False, maxcpus=1, optreturn=False)
+    cerebro = bt.Cerebro(stdstats=False, maxcpus=max_cpus, optreturn=False)
     cerebro.addobserver(bt.observers.Broker)
     cerebro.addobserver(bt.observers.Trades)
     cerebro.addobserver(bt.observers.BuySell)
@@ -164,9 +167,7 @@ def backtest(
         # Allow instance of BaseStrategy or from the predefined mapping
         if not isinstance(strategy, str) and issubclass(strategy, bt.Strategy):
             strat_name = (
-                strategy.__name__
-                if hasattr(strategy, "__name__")
-                else str(strategy)
+                strategy.__name__ if hasattr(strategy, "__name__") else str(strategy)
             )
         else:
             strat_name = strategy
@@ -255,9 +256,7 @@ def backtest(
                 **optim_params,
             )
         else:
-            fig = plot_results(
-                cerebro, data_format_dict, figsize, **plot_kwargs
-            )
+            fig = plot_results(cerebro, data_format_dict, figsize, **plot_kwargs)
 
     if return_history and return_plot:
         return sorted_combined_df, history_dict, fig
diff --git a/python/fastquant/backtest/backtest_indicators.py b/python/fastquant/backtest/backtest_indicators.py
index 540b0d75..3b6af1f5 100644
--- a/python/fastquant/backtest/backtest_indicators.py
+++ b/python/fastquant/backtest/backtest_indicators.py
@@ -106,9 +106,7 @@ def get_indicators_as_dict(strat_run, multi_line_indicators):
     indicators_dict = dict()
     for i, ind in enumerate(indicators):
         indicator_name = (
-            ind.plotlabel()
-            if hasattr(ind, "plotlabel")
-            else "indicator{}".format(i)
+            ind.plotlabel() if hasattr(ind, "plotlabel") else "indicator{}".format(i)
         )
 
         # Check if indicator contains multiple lines
@@ -138,7 +136,5 @@ def rename_indicator(name, line_name=None):
     # Changes the name to <indicator>_<line>_<param1>_<param2>
     tokens = indicator_regex.findall(name)
     if line_name:
-        tokens = [tokens[0], line_name] + (
-            tokens[1:] if len(tokens) > 1 else []
-        )
+        tokens = [tokens[0], line_name] + (tokens[1:] if len(tokens) > 1 else [])
     return "_".join(tokens)
diff --git a/python/fastquant/backtest/data_prep.py b/python/fastquant/backtest/data_prep.py
index f37de36b..7c206507 100644
--- a/python/fastquant/backtest/data_prep.py
+++ b/python/fastquant/backtest/data_prep.py
@@ -107,9 +107,7 @@ def include_sentiment_score(data, sentiments):
     senti_series = pd.Series(sentiments, name="sentiment_score", dtype=float)
 
     # join and reset the index for dt to become the first column
-    data = data.merge(
-        senti_series, left_index=True, right_index=True, how="left"
-    )
+    data = data.merge(senti_series, left_index=True, right_index=True, how="left")
     data = data.reset_index()
 
     return data
diff --git a/python/fastquant/backtest/post_backtest.py b/python/fastquant/backtest/post_backtest.py
index 56cfdae7..2797fa0c 100644
--- a/python/fastquant/backtest/post_backtest.py
+++ b/python/fastquant/backtest/post_backtest.py
@@ -49,12 +49,9 @@ def analyze_strategies(
         for i, strat in enumerate(stratrun):
             # Get indicator history
             st_dtime = [
-                bt.utils.date.num2date(num)
-                for num in strat.lines.datetime.plot()
+                bt.utils.date.num2date(num) for num in strat.lines.datetime.plot()
             ]
-            indicators_dict = get_indicators_as_dict(
-                strat, multi_line_indicators
-            )
+            indicators_dict = get_indicators_as_dict(strat, multi_line_indicators)
             indicators_df = pd.DataFrame(indicators_dict)
             indicators_df.insert(0, "dt", st_dtime)
 
@@ -106,9 +103,7 @@ def analyze_strategies(
                 order_history_dfs.append(order_history_df)
 
                 periodic_history_df = strat.periodic_history_df
-                periodic_history_df["dt"] = pd.to_datetime(
-                    periodic_history_df.dt
-                )
+                periodic_history_df["dt"] = pd.to_datetime(periodic_history_df.dt)
                 periodic_history_df.insert(0, "strat_name", history_key)
                 periodic_history_df.insert(0, "strat_id", strat_idx)
                 periodic_history_df[
@@ -143,18 +138,12 @@ def analyze_strategies(
             total = np.nan
 
         if "won" in tradeanalyzer.keys():
-            win_rate = (
-                tradeanalyzer["won"]["total"] / tradeanalyzer["total"]["total"]
-            )
+            win_rate = tradeanalyzer["won"]["total"] / tradeanalyzer["total"]["total"]
             won = tradeanalyzer["won"]["total"]
             won_avg = tradeanalyzer["won"]["pnl"]["average"]
-            won_avg_prcnt = (
-                tradeanalyzer["won"]["pnl"]["average"] / init_cash * 100
-            )
+            won_avg_prcnt = tradeanalyzer["won"]["pnl"]["average"] / init_cash * 100
             won_max = tradeanalyzer["won"]["pnl"]["max"]
-            won_max_prcnt = (
-                tradeanalyzer["won"]["pnl"]["max"] / init_cash * 100
-            )
+            won_max_prcnt = tradeanalyzer["won"]["pnl"]["max"] / init_cash * 100
         else:
             win_rate = np.nan
             won = np.nan
@@ -166,13 +155,9 @@ def analyze_strategies(
         if "lost" in tradeanalyzer.keys():
             lost = tradeanalyzer["lost"]["total"]
             lost_avg = tradeanalyzer["lost"]["pnl"]["average"]
-            lost_avg_prcnt = (
-                tradeanalyzer["lost"]["pnl"]["average"] / init_cash * 100
-            )
+            lost_avg_prcnt = tradeanalyzer["lost"]["pnl"]["average"] / init_cash * 100
             lost_max = tradeanalyzer["lost"]["pnl"]["max"]
-            lost_max_prcnt = (
-                tradeanalyzer["lost"]["pnl"]["max"] / init_cash * 100
-            )
+            lost_max_prcnt = tradeanalyzer["lost"]["pnl"]["max"] / init_cash * 100
         else:
             lost = np.nan
             lost_avg = np.nan
@@ -233,9 +218,7 @@ def analyze_strategies(
     return sorted_combined_df, optim_params, history_dict
 
 
-def sort_metrics_params_and_strats(
-    metrics_df, params_df, strat_ids, sort_by, verbose
-):
+def sort_metrics_params_and_strats(metrics_df, params_df, strat_ids, sort_by, verbose):
 
     # Get indices based on `sort_by` metric
     optim_idxs = np.argsort(metrics_df[sort_by].values)[::-1]
@@ -251,11 +234,7 @@ def sort_metrics_params_and_strats(
     )
     # drop extra columns #248
     if (
-        len(
-            set(["channel" "symbol"]).intersection(
-                sorted_combined_df.columns.values
-            )
-        )
+        len(set(["channel" "symbol"]).intersection(sorted_combined_df.columns.values))
         == 2
     ):
         sorted_combined_df.drop(["channel", "symbol"], axis=1, inplace=True)
diff --git a/python/fastquant/data/crypto/crypto.py b/python/fastquant/data/crypto/crypto.py
index fdcab4b6..52f537de 100644
--- a/python/fastquant/data/crypto/crypto.py
+++ b/python/fastquant/data/crypto/crypto.py
@@ -21,11 +21,7 @@ def unix_time_millis(date):
     # epoch = datetime.utcfromtimestamp(0)
 
     # value will only have : if the date passed is intraday
-    dt_format = (
-        DATETIME_FORMAT["intraday"]
-        if ":" in date
-        else DATETIME_FORMAT["daily"]
-    )
+    dt_format = DATETIME_FORMAT["intraday"] if ":" in date else DATETIME_FORMAT["daily"]
     dt = datetime.strptime(date, dt_format)
     # return int((dt - epoch).total_seconds() * 1000)
     return int(dt.timestamp() * 1000)
@@ -88,9 +84,9 @@ def get_crypto_data(
                 )
                 # Make sure we're at the start of that day
                 request_start_date_epoch = unix_time_millis(
-                    pd.to_datetime(
-                        request_start_date_epoch, unit="ms"
-                    ).strftime(dt_format)
+                    pd.to_datetime(request_start_date_epoch, unit="ms").strftime(
+                        dt_format
+                    )
                 )
                 previous_request_end_date_epoch = request_start_date_epoch - 1
                 continue
@@ -107,10 +103,7 @@ def get_crypto_data(
             # Get the last entry timestamp after we've retrieved (or attempted to) additional records
             current_request_end_date_epoch = int(ohlcv_df.dt.max())
 
-            if (
-                current_request_end_date_epoch
-                <= previous_request_end_date_epoch
-            ):
+            if current_request_end_date_epoch <= previous_request_end_date_epoch:
                 # We haven't gained any additional records, so there's no point in further requests
                 # Let's mark this for the data end date, mostly so both end_date and end_date_epoch will be
                 # in sync in case someone in future uses them in code futher down and to ensure the loop bails
@@ -124,9 +117,7 @@ def get_crypto_data(
                 # The next request should start a millisecond after this one ended
                 request_start_date_epoch = current_request_end_date_epoch + 1
                 # This request's end date should now be set as current for the next loop
-                previous_request_end_date_epoch = (
-                    current_request_end_date_epoch
-                )
+                previous_request_end_date_epoch = current_request_end_date_epoch
 
         if ohlcv_df is not None:
             # Convert the unix timestampe to datetime
@@ -142,8 +133,6 @@ def get_crypto_data(
         return ohlcv_df
     else:
         raise NotImplementedError(
-            "The exchange "
-            + exchange
-            + " is not yet supported. Available exchanges: "
+            "The exchange " + exchange + " is not yet supported. Available exchanges: "
             ", ".join(CRYPTO_EXCHANGES)
         )
diff --git a/python/fastquant/data/stocks/phisix.py b/python/fastquant/data/stocks/phisix.py
index 16d0a1a4..8bc57bb2 100644
--- a/python/fastquant/data/stocks/phisix.py
+++ b/python/fastquant/data/stocks/phisix.py
@@ -83,9 +83,7 @@ def get_phisix_data_by_date(symbol, date):
                 return None
 
 
-def get_phisix_data(
-    symbol, start_date, end_date, save=False, max_straight_nones=10
-):
+def get_phisix_data(symbol, start_date, end_date, save=False, max_straight_nones=10):
     """Returns pricing data for a PHISIX stock symbol.
 
     Parameters
@@ -103,10 +101,7 @@ def get_phisix_data(
         Stock data (in CV format) for the specified company and date range
     """
     date_range = (
-        pd.period_range(start_date, end_date, freq="D")
-        .to_series()
-        .astype(str)
-        .values
+        pd.period_range(start_date, end_date, freq="D").to_series().astype(str).values
     )
 
     max_straight_nones = min(max_straight_nones, len(date_range))
diff --git a/python/fastquant/data/stocks/pse.py b/python/fastquant/data/stocks/pse.py
index f9f62506..14e0c9b3 100644
--- a/python/fastquant/data/stocks/pse.py
+++ b/python/fastquant/data/stocks/pse.py
@@ -73,9 +73,7 @@ def get_stock_table(stock_table_fp=None):
             pd.concat(
                 [
                     pd.read_html(r.text)[0],
-                    pd.DataFrame(
-                        {"attr": table.xpath("//tr/td/a/@onclick")[::2]}
-                    ),
+                    pd.DataFrame({"attr": table.xpath("//tr/td/a/@onclick")[::2]}),
                 ],
                 axis=1,
             )
@@ -112,9 +110,7 @@ def get_pse_all_stocks():
     return df
 
 
-def get_pse_data_old(
-    symbol, start_date, end_date, stock_table_fp=None, verbose=True
-):
+def get_pse_data_old(symbol, start_date, end_date, stock_table_fp=None, verbose=True):
     """Returns pricing data for a specified stock.
 
     Parameters
@@ -145,26 +141,18 @@ def get_pse_data_old(
 
     data = {
         "cmpy_id": int(
-            stock_table["company_id"][
-                stock_table["Stock Symbol"] == symbol
-            ].values[0]
+            stock_table["company_id"][stock_table["Stock Symbol"] == symbol].values[0]
         ),
         "security_id": int(
-            stock_table["security_id"][
-                stock_table["Stock Symbol"] == symbol
-            ].values[0]
+            stock_table["security_id"][stock_table["Stock Symbol"] == symbol].values[0]
         ),
         "startDate": datetime.strptime(start_date, CALENDAR_FORMAT).strftime(
             "%m-%d-%Y"
         ),
-        "endDate": datetime.strptime(end_date, CALENDAR_FORMAT).strftime(
-            "%m-%d-%Y"
-        ),
+        "endDate": datetime.strptime(end_date, CALENDAR_FORMAT).strftime("%m-%d-%Y"),
     }
 
-    r = requests.post(
-        url="https://edge.pse.com.ph/common/DisclosureCht.ax", json=data
-    )
+    r = requests.post(url="https://edge.pse.com.ph/common/DisclosureCht.ax", json=data)
     df = pd.DataFrame(r.json()["chartData"])
     rename_dict = {
         "CHART_DATE": "dt",
@@ -181,9 +169,7 @@ def get_pse_data_old(
     return df
 
 
-def get_pse_data_cache(
-    symbol=None, cache_fp=None, update=False, verbose=False
-):
+def get_pse_data_cache(symbol=None, cache_fp=None, update=False, verbose=False):
     """
     Loads cached historical data
     Returns all if symbol is None
@@ -198,22 +184,14 @@ def get_pse_data_cache(
         df.index = pd.to_datetime(df.index)
         if verbose:
             print("Loaded: ", cache_fp)
-        return (
-            df
-            if symbol is None
-            else df[symbol]
-            if symbol in df.columns
-            else None
-        )
+        return df if symbol is None else df[symbol] if symbol in df.columns else None
     else:
         errmsg = "Cache does not exist! Try update=True"
         print(errmsg)
         return None
 
 
-def update_pse_data_cache(
-    start_date="2010-01-01", verbose=True, cache_fp=None
-):
+def update_pse_data_cache(start_date="2010-01-01", verbose=True, cache_fp=None):
     """
     Downloads DOHLC data of all PSE comapnies using get_pse_old
     and saves as .zip in /data to be used as cache
@@ -229,9 +207,7 @@ def update_pse_data_cache(
     data, unavailable = {}, []
     for symbol in tqdm(names["Stock Symbol"].values):
         try:
-            df = get_pse_data_old(
-                symbol, start_date, date_today, verbose=False
-            )
+            df = get_pse_data_old(symbol, start_date, date_today, verbose=False)
             data[symbol] = df
         except Exception as e:
             unavailable.append(symbol)
@@ -281,9 +257,7 @@ def get_pse_data(
     start = datestring_to_datetime(start_date)
     end = datestring_to_datetime(end_date)
 
-    fp = Path(
-        DATA_PATH, "{}_stock_{}_{}.csv".format(symbol, start_date, end_date)
-    )
+    fp = Path(DATA_PATH, "{}_stock_{}_{}.csv".format(symbol, start_date, end_date))
 
     if "v" in format:
         if fp.exists():
@@ -309,9 +283,7 @@ def get_pse_data(
                 symbol, start_date, end_date, save=False, max_straight_nones=10
             )
             if not pse_data_df.empty:
-                pse_data_df = pd.concat(
-                    [cache, pse_data_df], ignore_index=True
-                )
+                pse_data_df = pd.concat([cache, pse_data_df], ignore_index=True)
         else:
             pse_data_df = cache.copy()
 
@@ -339,9 +311,7 @@ def pse_data_to_csv(symbol, start_date, end_date, pse_dir=DATA_PATH):
     pse = get_pse_data(symbol, start_date, end_date)
     fp = Path(
         pse_dir,
-        "{}_{}_{}_OHLCV.csCRYPTO_EXCHANGESv".format(
-            symbol, start_date, end_date
-        ),
+        "{}_{}_{}_OHLCV.csCRYPTO_EXCHANGESv".format(symbol, start_date, end_date),
     )
     if isinstance(pse, pd.DataFrame):
         pse.to_csv(fp)
@@ -349,12 +319,8 @@ def pse_data_to_csv(symbol, start_date, end_date, pse_dir=DATA_PATH):
         pse[0].to_csv(fp)
         performance_dict = pse[1]
         performance_dict["D"].to_csv(
-            Path(
-                pse_dir, "{}_{}_{}_D.csv".format(symbol, start_date, end_date)
-            )
+            Path(pse_dir, "{}_{}_{}_D.csv".format(symbol, start_date, end_date))
         )
         performance_dict["E"].to_csv(
-            Path(
-                pse_dir, "{}_{}_{}_E.csv".format(symbol, start_date, end_date)
-            )
+            Path(pse_dir, "{}_{}_{}_E.csv".format(symbol, start_date, end_date))
         )
diff --git a/python/fastquant/data/web/twitter.py b/python/fastquant/data/web/twitter.py
index fb211227..f22f3412 100644
--- a/python/fastquant/data/web/twitter.py
+++ b/python/fastquant/data/web/twitter.py
@@ -21,9 +21,7 @@ def tweepy_api(consumer_key, consumer_secret, access_token, access_secret):
     return api
 
 
-def get_twitter_sentiment(
-    stock_code, twitter_auth, start_date, twitter_accounts=None
-):
+def get_twitter_sentiment(stock_code, twitter_auth, start_date, twitter_accounts=None):
     """
     This function scrapes twitter data based on stock code and twitter accounts specified
     Parameters
@@ -105,9 +103,9 @@ def get_twitter_sentiment(
                 lambda tweet: sia.polarity_scores(tweet)["compound"]
             )
 
-            tweet_avg_df = tweet_df.groupby(
-                "tweet_created_at", as_index=False
-            ).agg({"sentiment_score": "mean"})
+            tweet_avg_df = tweet_df.groupby("tweet_created_at", as_index=False).agg(
+                {"sentiment_score": "mean"}
+            )
 
             date_sentiment = dict(
                 zip(
diff --git a/python/fastquant/disclosures/investagrams.py b/python/fastquant/disclosures/investagrams.py
index f68045b2..c160ef82 100644
--- a/python/fastquant/disclosures/investagrams.py
+++ b/python/fastquant/disclosures/investagrams.py
@@ -86,9 +86,7 @@ def get_disclosures_json(self):
             params=params,
         )
         if hasattr(response, "text"):
-            assert (
-                len(response.text) > 10
-            ), "Empty response from investagrams.com"
+            assert len(response.text) > 10, "Empty response from investagrams.com"
         return response.json()
 
     def disclosures_json_to_df(self):
@@ -109,9 +107,7 @@ def disclosures_json_to_df(self):
                     for ex in filtered_examples
                 ]
             )
-            main_df = pd.DataFrame(filtered_examples)[
-                ["id", "time", "color", "label"]
-            ]
+            main_df = pd.DataFrame(filtered_examples)[["id", "time", "color", "label"]]
             combined = pd.concat([main_df, additional_feats_df], axis=1)
             combined["time"] = pd.to_datetime(combined.time, unit="s")
             if "Total Revenue" in combined.columns.values:
@@ -135,9 +131,7 @@ def disclosures_json_to_df(self):
                     .apply(lambda x: x.split()[0])
                     .astype(float)
                 )
-                combined["Net Income YoY Growth (%)"] = combined[
-                    "Net Income"
-                ].apply(
+                combined["Net Income YoY Growth (%)"] = combined["Net Income"].apply(
                     lambda x: str(x)
                     .replace("(", "")
                     .replace(")", "")
diff --git a/python/fastquant/disclosures/pse.py b/python/fastquant/disclosures/pse.py
index dc8df35d..8cadd274 100644
--- a/python/fastquant/disclosures/pse.py
+++ b/python/fastquant/disclosures/pse.py
@@ -103,9 +103,7 @@ def __init__(
         )
         self.company_disclosures = self.get_company_disclosures()
         self.disclosure_types = (
-            self.company_disclosures["Template Name"]
-            .apply(_remove_amend)
-            .unique()
+            self.company_disclosures["Template Name"].apply(_remove_amend).unique()
         )
         if self.verbose:
             print(
@@ -201,9 +199,7 @@ def get_company_disclosures_page(self, page=1):
             data=data,
         )
         if hasattr(response, "text"):
-            assert (
-                len(response.text) > 10
-            ), "Empty response from edge.pse.com.ph"
+            assert len(response.text) > 10, "Empty response from edge.pse.com.ph"
 
         html = response.text
         # Indicating the parser (e.g.  lxml) removes the bs warning
@@ -228,13 +224,9 @@ def get_company_disclosures_page(self, page=1):
             td = tr.find_all("td")
             row = [tr.text for tr in td]
             onclicks_raw = [
-                tr.a["onclick"]
-                for tr in td
-                if tr.a and "onclick" in tr.a.attrs.keys()
-            ]
-            onclicks = [
-                s[s.find("('") + 2 : s.find("')")] for s in onclicks_raw
+                tr.a["onclick"] for tr in td if tr.a and "onclick" in tr.a.attrs.keys()
             ]
+            onclicks = [s[s.find("('") + 2 : s.find("')")] for s in onclicks_raw]
             lines.append(row)
             if onclicks:
                 edge_nos.append(onclicks[0])
@@ -251,12 +243,8 @@ def get_company_disclosures_page(self, page=1):
         # Filter to rows where not all columns are null
         df = df[df.isna().mean(axis=1) < 1]
         df["edge_no"] = edge_nos
-        df["url"] = (
-            "https://edge.pse.com.ph/openDiscViewer.do?edge_no=" + df.edge_no
-        )
-        df["Announce Date and Time"] = pd.to_datetime(
-            df["Announce Date and Time"]
-        )
+        df["url"] = "https://edge.pse.com.ph/openDiscViewer.do?edge_no=" + df.edge_no
+        df["Announce Date and Time"] = pd.to_datetime(df["Announce Date and Time"])
         # ensure index starts at 0
         return df.reset_index(drop=True)
 
@@ -364,9 +352,7 @@ def get_company_summary(self, edge_no):
 
         keys = []
         values = []
-        for dt, dd in zip(
-            parsed_html.find_all("dt"), parsed_html.find_all("dd")
-        ):
+        for dt, dd in zip(parsed_html.find_all("dt"), parsed_html.find_all("dd")):
             # Take out first token (number followed by a period)
             key = " ".join(dt.text.strip().split()[1:])
             value = dd.text.strip()
@@ -440,9 +426,7 @@ def load_disclosures(self):
         disclosure_details = {}
 
         # append older disclosures
-        older = (
-            oldest_date > self.company_disclosures["Announce Date and Time"]
-        )
+        older = oldest_date > self.company_disclosures["Announce Date and Time"]
         idxs1 = np.flatnonzero(older)
         if older.sum() > 0:
             for idx in tqdm(idxs1):
@@ -463,9 +447,7 @@ def load_disclosures(self):
                 print(e)
 
         # append newer disclosures
-        newer = (
-            newest_date < self.company_disclosures["Announce Date and Time"]
-        )
+        newer = newest_date < self.company_disclosures["Announce Date and Time"]
         idxs2 = np.flatnonzero(newer)
         # append newer disclosures
         if newer.sum() > 0:
@@ -510,9 +492,7 @@ def get_all_disclosure_tables_df(self):
             values.append(json.dumps(df_dict))
         return pd.DataFrame(values, columns=["disclosure_table"])
 
-    def get_disclosure_details(
-        self, key="Background/Description of the Disclosure"
-    ):
+    def get_disclosure_details(self, key="Background/Description of the Disclosure"):
         """
         Returns a dataframe of specific data from disclosure_tables
         """
@@ -555,9 +535,7 @@ def filter_disclosures(self, indicator="close", operation="max"):
         df = self.disclosures_combined.copy()
         df.dropna(subset=["Announce Date and Time"], inplace=True)
 
-        disclosure_dates = df["Announce Date and Time"].apply(
-            lambda x: x.date()
-        )
+        disclosure_dates = df["Announce Date and Time"].apply(lambda x: x.date())
 
         if self.stock_data is None:
             _ = self.get_stock_data()
@@ -589,9 +567,7 @@ def plot_disclosures(
         Returns a figure instance
         """
         disclosure_type = (
-            self.disclosure_type
-            if disclosure_type is None
-            else disclosure_type
+            self.disclosure_type if disclosure_type is None else disclosure_type
         )
 
         fig = pl.figure(figsize=(15, 10))
diff --git a/python/fastquant/disclosures/sentiment.py b/python/fastquant/disclosures/sentiment.py
index 627ecb79..1bfd5125 100644
--- a/python/fastquant/disclosures/sentiment.py
+++ b/python/fastquant/disclosures/sentiment.py
@@ -40,9 +40,7 @@ def get_sentiments(passage):
         return 0
 
 
-def get_disclosure_sentiment(
-    stock_code, start_date, end_date=None, source="pse"
-):
+def get_disclosure_sentiment(stock_code, start_date, end_date=None, source="pse"):
     """
     This function scrapes pse/investagram disclosure using fastquant and calculate the
     sentiment on each disclosure.
@@ -68,25 +66,19 @@ def get_disclosure_sentiment(
         The dictionary output of the data in form of {date: sentiment score}
 
     """
-    start_date = (
-        datetime.strptime(start_date, "%Y-%m-%d").date().strftime("%m-%d-%Y")
-    )
+    start_date = datetime.strptime(start_date, "%Y-%m-%d").date().strftime("%m-%d-%Y")
     if end_date is not None:
-        end_date = (
-            datetime.strptime(end_date, "%Y-%m-%d").date().strftime("%m-%d-%Y")
-        )
-    dpse = DisclosuresPSE(
-        symbol=stock_code, start_date=start_date, end_date=end_date
-    )
+        end_date = datetime.strptime(end_date, "%Y-%m-%d").date().strftime("%m-%d-%Y")
+    dpse = DisclosuresPSE(symbol=stock_code, start_date=start_date, end_date=end_date)
 
     df = dpse.get_combined_disclosures()[
         ["Announce Date and Time", "Background/Description of the Disclosure"]
     ]
     df.columns = ["date", "description"]
     df["sentiments"] = df["description"].apply(get_sentiments)
-    df["date"] = pd.to_datetime(
-        df["date"].apply(lambda x: str(x)[:11])
-    ).dt.strftime("%d %b %Y")
+    df["date"] = pd.to_datetime(df["date"].apply(lambda x: str(x)[:11])).dt.strftime(
+        "%d %b %Y"
+    )
     date_sentiment = {}
 
     for k, v in zip(df["date"], df["sentiments"]):
diff --git a/python/fastquant/indicators/sentiment.py b/python/fastquant/indicators/sentiment.py
index 17905486..f6f0d109 100644
--- a/python/fastquant/indicators/sentiment.py
+++ b/python/fastquant/indicators/sentiment.py
@@ -24,9 +24,7 @@ class Sentiment(bt.Indicator):
 
     lines = ("sentiment",)
 
-    plotinfo = dict(
-        plotymargin=0.15, plothlines=[0], plotyticks=[1.0, 0, -1.0]
-    )
+    plotinfo = dict(plotymargin=0.15, plothlines=[0], plotyticks=[1.0, 0, -1.0])
 
     plotlines = dict(
         sentiment=dict(
diff --git a/python/fastquant/network.py b/python/fastquant/network.py
index 5719051b..859afc68 100644
--- a/python/fastquant/network.py
+++ b/python/fastquant/network.py
@@ -72,9 +72,7 @@ def __init__(
         self.interpolation_method = interpolation_method
         self.n_companies = n_companies
         self.update_cache = update_cache
-        self.data_cache = get_pse_data_cache(
-            update=self.update_cache, verbose=False
-        )
+        self.data_cache = get_pse_data_cache(update=self.update_cache, verbose=False)
         self.data = self.data_cache.xs(indicator, level=1, axis=1)
         self.filtered_data = self.filter_data()
         self.company_table = self.load_company_table()
@@ -95,9 +93,7 @@ def load_company_table(self):
     def get_sector_of_symbol(self, symbol):
         """get sector or subsector where symbol belongs"""
         info = self.company_table.copy()
-        sector = info.loc[
-            info["Stock Symbol"].isin([symbol]), "Sector"
-        ].values[0]
+        sector = info.loc[info["Stock Symbol"].isin([symbol]), "Sector"].values[0]
         return sector
 
     def get_symbols_of_a_sector(self, sector, subsector=False, verbose=False):
@@ -108,9 +104,7 @@ def get_symbols_of_a_sector(self, sector, subsector=False, verbose=False):
 
         sector_indices = sectors_dict[sector]
         sector_symbols = info.loc[sector_indices, "Stock Symbol"]
-        data_availability_indices = self.filtered_data.columns.isin(
-            sector_symbols
-        )
+        data_availability_indices = self.filtered_data.columns.isin(sector_symbols)
         symbols_with_data = self.filtered_data.columns[
             data_availability_indices
         ].tolist()
@@ -119,9 +113,7 @@ def get_symbols_of_a_sector(self, sector, subsector=False, verbose=False):
         ].tolist()
         if verbose:
             print(
-                "Symbols without data in {}:\n{}".format(
-                    sector, symbols_without_data
-                )
+                "Symbols without data in {}:\n{}".format(sector, symbols_without_data)
             )
         return symbols_with_data
 
@@ -360,11 +352,7 @@ def plot_corr_company(
         if positive:
             # positively correlated
             if symbol2 is None:
-                symbol2 = (
-                    rank.index[-2]
-                    if rank.index[-1] == symbol
-                    else rank.index[-1]
-                )
+                symbol2 = rank.index[-2] if rank.index[-1] == symbol else rank.index[-1]
 
         else:
             # negatively correlated
diff --git a/python/fastquant/notification.py b/python/fastquant/notification.py
index 1b8beee9..822899a3 100644
--- a/python/fastquant/notification.py
+++ b/python/fastquant/notification.py
@@ -75,9 +75,7 @@ def email_notif(
     msg = MIMEMultipart()  # create a message
 
     date = date or datetime.utcnow().strftime("%Y-%m-%d")
-    message = (
-        message or "Today is " + date + ": " + action + " " + symbol or ""
-    )
+    message = message or "Today is " + date + ": " + action + " " + symbol or ""
 
     # setup the parameters of the message
     msg["From"] = my_address
diff --git a/python/fastquant/strategies/base.py b/python/fastquant/strategies/base.py
index cb48f0c8..6db9bf50 100644
--- a/python/fastquant/strategies/base.py
+++ b/python/fastquant/strategies/base.py
@@ -115,7 +115,7 @@ def __init__(self):
             self.strategy_position = 0
         else:
             self.strategy_position = None
-            
+
         # Longer term, we plan to add `freq` like notation, similar to pandas datetime
         # https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
         # M means add cash at the first day of each month
@@ -239,8 +239,7 @@ def notify_trade(self, trade):
             return
         if self.transaction_logging:
             self.log(
-                "OPERATION PROFIT, GROSS: %.2f, NET: %.2f"
-                % (trade.pnl, trade.pnlcomm)
+                "OPERATION PROFIT, GROSS: %.2f, NET: %.2f" % (trade.pnl, trade.pnlcomm)
             )
 
     def notify_cashvalue(self, cash, value):
@@ -254,9 +253,7 @@ def stop(self):
         # Saving to self so it's accessible later during optimization
         self.final_value = self.broker.getvalue()
         # Note that PnL is the final portfolio value minus the initial cash balance minus the total cash added
-        self.pnl = round(
-            self.final_value - self.init_cash - self.total_cash_added, 2
-        )
+        self.pnl = round(self.final_value - self.init_cash - self.total_cash_added, 2)
         if self.strategy_logging:
             self.log("Final Portfolio Value: {}".format(self.final_value))
             self.log("Final PnL: {}".format(self.pnl))
@@ -289,11 +286,7 @@ def next(self):
                 self.next_cash_datetime = self.cron.get_next(datetime.datetime)
 
                 if self.transaction_logging:
-                    self.log(
-                        "Start date: {}".format(
-                            start_date.strftime("%Y-%m-%d")
-                        )
-                    )
+                    self.log("Start date: {}".format(start_date.strftime("%Y-%m-%d")))
                     self.log(
                         "Next cash date: {}".format(
                             self.next_cash_datetime.strftime("%Y-%m-%d")
@@ -312,9 +305,7 @@ def next(self):
 
                 if self.transaction_logging:
                     self.log("Cash added: {}".format(self.add_cash_amount))
-                    self.log(
-                        "Total cash added: {}".format(self.total_cash_added)
-                    )
+                    self.log("Total cash added: {}".format(self.total_cash_added))
                     self.log(
                         "Next cash date: {}".format(
                             self.next_cash_datetime.strftime("%Y-%m-%d")
@@ -359,8 +350,7 @@ def next(self):
                 # Margin is required for buy commission
                 # Add allowance to commission per transaction (avoid margin)
                 afforded_size = self.cash / (
-                    (self.dataclose[0] * (1 + self.slippage))
-                    * (1 + self.commission)
+                    (self.dataclose[0] * (1 + self.slippage)) * (1 + self.commission)
                 )
                 buy_prop_size = afforded_size * self.buy_prop
 
@@ -384,9 +374,7 @@ def next(self):
 
                     # Implement stop loss at the purchase level (only this specific trade is closed)
                     if self.stop_loss:
-                        stop_price = self.data.close[0] * (
-                            1.0 - self.stop_loss
-                        )
+                        stop_price = self.data.close[0] * (1.0 - self.stop_loss)
                         if self.transaction_logging:
                             self.log("Stop price: {}".format(stop_price))
                         self.stoploss_order = self.sell(
@@ -399,9 +387,7 @@ def next(self):
                         # Create a stoploss trail order if None
                         if self.stoploss_trail_order is None:
                             if self.transaction_logging:
-                                self.log(
-                                    "Stop trail: {}".format(self.stop_trail)
-                                )
+                                self.log("Stop trail: {}".format(self.stop_trail))
                             self.stoploss_trail_order = self.sell(
                                 exectype=bt.Order.StopTrail,
                                 trailpercent=self.stop_trail,
@@ -415,8 +401,7 @@ def next(self):
                 else:
                     # Margin is required for buy commission
                     afforded_size = int(
-                        self.cash
-                        / (self.dataopen[1] * (1 + self.commission + 0.001))
+                        self.cash / (self.dataopen[1] * (1 + self.commission + 0.001))
                     )
                     final_size = min(buy_prop_size, afforded_size)
                     if self.transaction_logging:
@@ -427,9 +412,7 @@ def next(self):
 
                     # Implement stop loss at the purchase level (only this specific trade is closed)
                     if self.stop_loss:
-                        stop_price = self.data.close[0] * (
-                            1.0 - self.stop_loss
-                        )
+                        stop_price = self.data.close[0] * (1.0 - self.stop_loss)
                         if self.transaction_logging:
                             self.log("Stop price: {}".format(stop_price))
                         self.stoploss_order = self.sell(
@@ -463,11 +446,7 @@ def next(self):
                     # The max incremental short allowed is the short that would lead to a cumulative short position
                     # equal to the maximum short position (initial cash times the maximum short ratio, which is 1.5 by default)
                     max_position_size = max(
-                        int(
-                            self.broker.getvalue()
-                            * self.short_max
-                            / self.dataclose[1]
-                        )
+                        int(self.broker.getvalue() * self.short_max / self.dataclose[1])
                         + self.position.size,
                         0,
                     )
@@ -487,11 +466,7 @@ def next(self):
                     # The max incremental short allowed is the short that would lead to a cumulative short position
                     # equal to the maximum short position (initial cash times the maximum short ratio, which is 1.5 by default)
                     max_position_size = max(
-                        int(
-                            self.broker.getvalue()
-                            * self.short_max
-                            / self.dataopen[1]
-                        )
+                        int(self.broker.getvalue() * self.short_max / self.dataopen[1])
                         + self.position.size,
                         0,
                     )
@@ -515,17 +490,13 @@ def next(self):
                         # Sell based on the closing price of the previous closing day
                         self.order = self.sell(
                             size=int(
-                                (stock_value / (self.dataclose[1]))
-                                * self.sell_prop
+                                (stock_value / (self.dataclose[1])) * self.sell_prop
                             ),
                         )
                 else:
                     # Sell based on the opening price of the next closing day (only works "open" data exists in the dataset)
                     self.order = self.sell(
-                        size=int(
-                            (self.init_cash / self.dataopen[1])
-                            * self.sell_prop
-                        )
+                        size=int((self.init_cash / self.dataopen[1]) * self.sell_prop)
                     )
 
             # Explicitly cancel stoploss order
diff --git a/python/fastquant/strategies/ma_crossover.py b/python/fastquant/strategies/ma_crossover.py
index 7cc73b8d..c80862ea 100644
--- a/python/fastquant/strategies/ma_crossover.py
+++ b/python/fastquant/strategies/ma_crossover.py
@@ -46,9 +46,7 @@ def __init__(self):
             print("slow_period :", self.slow_period)
         sma_fast = bt.ind.SMA(period=self.fast_period)  # fast moving average
         sma_slow = bt.ind.SMA(period=self.slow_period)  # slow moving average
-        self.crossover = bt.ind.CrossOver(
-            sma_fast, sma_slow
-        )  # crossover signal
+        self.crossover = bt.ind.CrossOver(sma_fast, sma_slow)  # crossover signal
 
     def buy_signal(self):
         return self.crossover > 0
@@ -88,9 +86,7 @@ def __init__(self):
             print("slow_period :", self.slow_period)
         ema_fast = bt.ind.EMA(period=self.fast_period)  # fast moving average
         ema_slow = bt.ind.EMA(period=self.slow_period)  # slow moving average
-        self.crossover = bt.ind.CrossOver(
-            ema_fast, ema_slow
-        )  # crossover signal
+        self.crossover = bt.ind.CrossOver(ema_fast, ema_slow)  # crossover signal
 
     def buy_signal(self):
         return self.crossover > 0
diff --git a/python/tests/test_fastquant.py b/python/tests/test_fastquant.py
index 497bbfc4..62f55ba9 100644
--- a/python/tests/test_fastquant.py
+++ b/python/tests/test_fastquant.py
@@ -36,14 +36,10 @@ def test_get_yahoo_data_dividend():
 
 def test_get_stock_data():
     # Test w/ respective sources
-    stock_df = get_stock_data(
-        PHISIX_SYMBOL, DATE_START, DATE_END, source="phisix"
-    )
+    stock_df = get_stock_data(PHISIX_SYMBOL, DATE_START, DATE_END, source="phisix")
     assert isinstance(stock_df, pd.DataFrame)
 
-    stock_df = get_stock_data(
-        YAHOO_SYMBOL, DATE_START, DATE_END, source="yahoo"
-    )
+    stock_df = get_stock_data(YAHOO_SYMBOL, DATE_START, DATE_END, source="yahoo")
     assert isinstance(stock_df, pd.DataFrame)
 
     # Test getting yahoo when (default) phisix fails on a non PSE SYMBOL
diff --git a/python/tests/test_network.py b/python/tests/test_network.py
index ee96fda8..5841efd3 100644
--- a/python/tests/test_network.py
+++ b/python/tests/test_network.py
@@ -2,9 +2,7 @@
 import pandas as pd
 from fastquant import Network
 
-nw = Network(
-    symbol="JFC", start_date="2020-01-01", end_date="2020-04-01", metric="b"
-)
+nw = Network(symbol="JFC", start_date="2020-01-01", end_date="2020-04-01", metric="b")
 
 
 def test_network_init():
diff --git a/python/tests/test_strategies.py b/python/tests/test_strategies.py
index 3142e4d1..ce3a5fcf 100644
--- a/python/tests/test_strategies.py
+++ b/python/tests/test_strategies.py
@@ -32,9 +32,7 @@ def test_backtest():
 
     for strategy in STRATEGY_MAPPING.keys():
         if strategy == "sentiment":
-            data = get_yahoo_data(
-                "TSLA", "2020-01-01", "2020-07-04", dividends=True
-            )
+            data = get_yahoo_data("TSLA", "2020-01-01", "2020-07-04", dividends=True)
             # use cached data instead of scraping for tests purposes.
             # sentiments = get_bt_news_sentiment(keyword="tesla", page_nums=2)
             with open(SENTI_PKL, "rb") as handle:
@@ -42,9 +40,7 @@ def test_backtest():
             cerebro = backtest(
                 strategy, data, sentiments=sentiments, senti=0.4, plot=False
             )
-            errmsg = "Backtest encountered error for strategy '{}'!".format(
-                strategy
-            )
+            errmsg = "Backtest encountered error for strategy '{}'!".format(strategy)
             assert cerebro is not None, errmsg
 
             data_disclosures = get_stock_data(
@@ -70,18 +66,14 @@ def test_backtest():
                 senti=0.2,
                 plot=False,
             )
-            errmsg_disclosures = (
-                "Backtest encountered error for strategy '{}'!".format(
-                    strategy
-                )
+            errmsg_disclosures = "Backtest encountered error for strategy '{}'!".format(
+                strategy
             )
             assert cerebro_disclosures is not None, errmsg_disclosures
 
         else:
             cerebro = backtest(strategy, sample, plot=False)
-            errmsg = "Backtest encountered error for strategy '{}'!".format(
-                strategy
-            )
+            errmsg = "Backtest encountered error for strategy '{}'!".format(strategy)
             assert cerebro is not None, errmsg
 
 
@@ -91,9 +83,7 @@ def test_multi_backtest():
     """
     sample = pd.read_csv(SAMPLE_CSV, parse_dates=["dt"])
     cerebro = backtest("multi", sample, strats=SAMPLE_STRAT_DICT, plot=False)
-    assert (
-        cerebro is not None
-    ), "Backtest encountered error for strategy 'multi'!"
+    assert cerebro is not None, "Backtest encountered error for strategy 'multi'!"
 
 
 def test_grid_backtest():
@@ -108,6 +98,4 @@ def test_grid_backtest():
         slow_period=range(40, 55, 3),
         plot=False,
     )
-    assert (
-        cerebro is not None
-    ), "Backtest encountered error doing grid search on SMAC!"
+    assert cerebro is not None, "Backtest encountered error doing grid search on SMAC!"
diff --git a/python/tests/test_twitter.py b/python/tests/test_twitter.py
index 17b24b98..81d4b6da 100644
--- a/python/tests/test_twitter.py
+++ b/python/tests/test_twitter.py
@@ -15,9 +15,7 @@ def test_get_twitter_sentiment():
     access_token = ""
     access_secret = ""
 
-    api = tweepy_api(
-        consumer_key, consumer_secret, access_token, access_secret
-    )
+    api = tweepy_api(consumer_key, consumer_secret, access_token, access_secret)
     assert api is not None
     # account_list = ["2TradeAsia", "colfinancial"]
     # sentiment_dict = get_twitter_sentiment('$ALI', api, '2020-06-14', account_list)