7-summary-backtest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "307a7e23",
   "metadata": {},
   "source": [
    "We will define strategies and do backtests for all selected pairs for '1D' & '1H' resolutions.\n",
    "A summary pdf report will be printed in the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "0df4934c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:26:43.416529Z",
     "start_time": "2022-11-01T17:26:43.402528Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "import dataframe_image as dfi\n",
    "import math\n",
    "\n",
    "# define report path\n",
    "Path(\"report\").mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f21bd6f7",
   "metadata": {},
   "source": [
    "### SETUP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "53736a13",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:26:43.431528Z",
     "start_time": "2022-11-01T17:26:43.421531Z"
    }
   },
   "outputs": [],
   "source": [
    "resolution_list = ['1D','1H']\n",
    "\n",
    "# report_heading = \"Utilities 1Y\"  # change for the report heading\n",
    "report_heading = \"UTILITIES\"   # change for the report heading\n",
    "\n",
    "pos_value = 7500 # x2 for pair buy & sell\n",
    "mar_req = 0.3 # margin requirement\n",
    "cash_req = mar_req * pos_value * 2 # required cash\n",
    "avg_slip = 0.001 # per order per position value\n",
    "avg_commission = 0.007 # per contract\n",
    "\n",
    "avg_fin_cost_dic = {'1D':(0.07/365),'1H':(0.07/365/7)} # per borrowed cash per day&hour"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9f4dba5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-24T23:11:05.837372Z",
     "start_time": "2022-10-24T23:11:05.826330Z"
    }
   },
   "source": [
    "### GET PAIR INFORMATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "9e0e18ae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:26:43.462526Z",
     "start_time": "2022-11-01T17:26:43.435529Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('AEP', 'VST'): 0.25,\n",
       " ('OGE', 'XEL'): 1.79,\n",
       " ('CMS', 'DUK'): 1.6,\n",
       " ('ED', 'VST'): 0.25,\n",
       " ('CNP', 'VST'): 0.78}"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_items = 24\n",
    "pairs_dic_filtered = {}\n",
    "\n",
    "with open('pairs_final.pkl', 'rb') as f:\n",
    "    pairs_dic = pickle.load(f)\n",
    "\n",
    "i = 0\n",
    "for key,item in pairs_dic.items():\n",
    "    if i<max_items:\n",
    "        i = i+1\n",
    "        pairs_dic_filtered[key] = item\n",
    "        \n",
    "pairs_dic = pairs_dic_filtered\n",
    "pairs_dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "0e53b3a4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:26:43.493524Z",
     "start_time": "2022-11-01T17:26:43.467526Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('VST', 'WEC'): 4.17, ('RUN', 'WTRG'): 1.58, ('AEE', 'DUK'): 1.17}"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_drop_items = 10\n",
    "drop_dic_filtered = {}\n",
    "\n",
    "with open('pairs_dropped.pkl', 'rb') as f:\n",
    "    drop_dic = pickle.load(f)  \n",
    "drop_dic\n",
    "\n",
    "i = 0\n",
    "for key,item in drop_dic.items():\n",
    "    if i<max_drop_items:\n",
    "        i = i+1\n",
    "        drop_dic_filtered[key] = item\n",
    "        \n",
    "drop_dic = drop_dic_filtered\n",
    "drop_dic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "979f8f8c",
   "metadata": {},
   "source": [
    "### DEFINE STRATEGIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "id": "1658f6fe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:26:43.556529Z",
     "start_time": "2022-11-01T17:26:43.496541Z"
    }
   },
   "outputs": [],
   "source": [
    "# buy and sell at upper and lower bollinger band, exit at sma\n",
    "def exit_sma(data, lower_bb, upper_bb, sma_20, _):\n",
    "    buy_price = []\n",
    "    sell_price = []\n",
    "    bb_signal = []\n",
    "    position = []\n",
    "    signal = 0\n",
    "      \n",
    "    position = [0] * len(data)\n",
    "      \n",
    "    for i in range(len(data)):\n",
    "        # define band crossings\n",
    "        if data[i - 1] > lower_bb[i - 1] and data[i] < lower_bb[i]:\n",
    "            if position[i-1] == 0:\n",
    "                buy_price.append(data[i])\n",
    "                sell_price.append(np.nan)\n",
    "                signal = 1\n",
    "                bb_signal.append(signal)\n",
    "                position[i] = 1\n",
    "            else:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(np.nan)\n",
    "                bb_signal.append(0)\n",
    "                position[i] = position[i-1]\n",
    "        elif data[i - 1] < upper_bb[i - 1] and data[i] > upper_bb[i]:\n",
    "            if position[i-1] == 0:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(data[i])\n",
    "                signal = -1\n",
    "                bb_signal.append(signal)\n",
    "                position[i] = -1\n",
    "            else:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(np.nan)\n",
    "                bb_signal.append(0)\n",
    "                position[i] = position[i-1]\n",
    "\n",
    "        # define sma crossings\n",
    "        elif data[i - 1] > sma_20[i - 1] and data[i] < sma_20[i]:\n",
    "            if position[i-1] != 0:\n",
    "                buy_price.append(data[i])\n",
    "                sell_price.append(np.nan)\n",
    "                signal = 1\n",
    "                bb_signal.append(signal)\n",
    "                position[i] = 0\n",
    "            else:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(np.nan)\n",
    "                bb_signal.append(0)\n",
    "                position[i] = position[i-1]\n",
    "        elif data[i - 1] < sma_20[i - 1] and data[i] > sma_20[i]:\n",
    "            if position[i-1] != 0:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(data[i])\n",
    "                signal = -1\n",
    "                bb_signal.append(signal)\n",
    "                position[i] = 0\n",
    "            else:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(np.nan)\n",
    "                bb_signal.append(0)\n",
    "                position[i] = position[i-1]\n",
    "\n",
    "        else:\n",
    "            buy_price.append(np.nan)\n",
    "            sell_price.append(np.nan)\n",
    "            bb_signal.append(0)\n",
    "            position[i] = position[i-1]\n",
    "\n",
    "    return buy_price, sell_price, bb_signal, position\n",
    "\n",
    "\n",
    "# buy and sell at upper and lower bollinger band, keep position, no exit\n",
    "def no_exit(data, lower_bb, upper_bb, *_):\n",
    "\n",
    "    buy_price = []\n",
    "    sell_price = []\n",
    "    bb_signal = []\n",
    "    position = []\n",
    "    signal = 0\n",
    "           \n",
    "    position = [0] * len(data)\n",
    "    \n",
    "    for i in range(len(data)):\n",
    "        if data[i - 1] > lower_bb[i - 1] and data[i] < lower_bb[i]:\n",
    "            if signal != 1:\n",
    "                buy_price.append(data[i])\n",
    "                sell_price.append(np.nan)\n",
    "                signal = 1\n",
    "                bb_signal.append(signal)\n",
    "                position[i] = 1\n",
    "            else:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(np.nan)\n",
    "                bb_signal.append(0)\n",
    "                position[i] = position[i-1]\n",
    "        elif data[i - 1] < upper_bb[i - 1] and data[i] > upper_bb[i]:\n",
    "            if signal != -1:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(data[i])\n",
    "                signal = -1\n",
    "                bb_signal.append(signal)\n",
    "                position[i] = -1\n",
    "            else:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(np.nan)\n",
    "                bb_signal.append(0)\n",
    "                position[i] = position[i-1]\n",
    "        else:\n",
    "            buy_price.append(np.nan)\n",
    "            sell_price.append(np.nan)\n",
    "            bb_signal.append(0)\n",
    "            position[i] = position[i-1]\n",
    "\n",
    "    return buy_price, sell_price, bb_signal, position\n",
    "\n",
    "\n",
    "# buy and sell at upper and lower bollinger band, exit at sma, no exit if moving towards n days-sma\n",
    "def skp_sma(data, lower_bb, upper_bb, sma_20, sma_nd):\n",
    "    buy_price = []\n",
    "    sell_price = []\n",
    "    bb_signal = []\n",
    "    position = []\n",
    "    signal = 0\n",
    "      \n",
    "    position = [0] * len(data)\n",
    "      \n",
    "    for i in range(len(data)):\n",
    "        # define band crossings\n",
    "        if data[i - 1] > lower_bb[i - 1] and data[i] < lower_bb[i]:\n",
    "            if position[i-1] != 1:\n",
    "                buy_price.append(data[i])\n",
    "                sell_price.append(np.nan)\n",
    "                signal = 1\n",
    "                bb_signal.append(signal)\n",
    "                position[i] = 1\n",
    "            else:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(np.nan)\n",
    "                bb_signal.append(0)\n",
    "                position[i] = position[i-1]\n",
    "        elif data[i - 1] < upper_bb[i - 1] and data[i] > upper_bb[i]:\n",
    "            if position[i-1] != -1:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(data[i])\n",
    "                signal = -1\n",
    "                bb_signal.append(signal)\n",
    "                position[i] = -1\n",
    "            else:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(np.nan)\n",
    "                bb_signal.append(0)\n",
    "                position[i] = position[i-1]\n",
    "\n",
    "        # define sma crossings\n",
    "        elif data[i - 1] > sma_20[i - 1] and data[i] < sma_20[i]:\n",
    "            if data[i] < sma_nd[i]: # check 20d-sma\n",
    "                if position[i-1] == -1:\n",
    "                    buy_price.append(data[i])\n",
    "                    sell_price.append(np.nan)\n",
    "                    signal = 1\n",
    "                    bb_signal.append(signal)\n",
    "                    position[i] = 0\n",
    "                else:\n",
    "                    buy_price.append(np.nan)\n",
    "                    sell_price.append(np.nan)\n",
    "                    bb_signal.append(0)\n",
    "                    position[i] = position[i-1]\n",
    "                    \n",
    "            else: # moving towards 20d-sma               \n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(np.nan)\n",
    "                bb_signal.append(0)\n",
    "                position[i] = position[i-1]\n",
    "                \n",
    "                \n",
    "        elif data[i - 1] < sma_20[i - 1] and data[i] > sma_20[i]:\n",
    "            if data[i] > sma_nd[i]: # check 20d-sma\n",
    "                if position[i-1] == 1:\n",
    "                    buy_price.append(np.nan)\n",
    "                    sell_price.append(data[i])\n",
    "                    signal = -1\n",
    "                    bb_signal.append(signal)\n",
    "                    position[i] = 0\n",
    "                else:\n",
    "                    buy_price.append(np.nan)\n",
    "                    sell_price.append(np.nan)\n",
    "                    bb_signal.append(0)\n",
    "                    position[i] = position[i-1]\n",
    "                    \n",
    "            else: # moving towards 20d-sma               \n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(np.nan)\n",
    "                bb_signal.append(0)\n",
    "                position[i] = position[i-1]\n",
    "\n",
    "        else:\n",
    "            buy_price.append(np.nan)\n",
    "            sell_price.append(np.nan)\n",
    "            bb_signal.append(0)\n",
    "            position[i] = position[i-1]\n",
    "\n",
    "    return buy_price, sell_price, bb_signal, position"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fffc18d",
   "metadata": {},
   "source": [
    "### DEFINE SMA & BOLLINGER FUNCTIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "09eeb5b7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:26:43.572528Z",
     "start_time": "2022-11-01T17:26:43.561527Z"
    }
   },
   "outputs": [],
   "source": [
    "def sma(values, n):\n",
    "    return pd.Series(values).rolling(n).mean()\n",
    "\n",
    "def bb(data, sma, sd=2.0, n=20):\n",
    "    std = data.rolling(n).std(ddof=1) # default ddof=1, sample standard deviation, divide by (n-1)\n",
    "    bb_up = sma + std * sd\n",
    "    bb_low = sma - std * sd\n",
    "    return bb_up, bb_low"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "609e4422",
   "metadata": {},
   "source": [
    "### CALCULATE STRATEGY RETURNS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3620462",
   "metadata": {},
   "source": [
    "Define function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "id": "f0b2c331",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:26:43.620526Z",
     "start_time": "2022-11-01T17:26:43.575526Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def calculate_returns(data_dic, str_list, b=2):  # b is 1.0, 1.5, 2.0, 2.5 or 3 std\n",
    "\n",
    "    # list of strategies to apply\n",
    "    strategy_list = str_list\n",
    "\n",
    "    # create a dataframe to fill with strategy return values\n",
    "    df = pd.DataFrame.from_dict(data_dic, orient=\"index\")\n",
    "    returns_df = df.iloc[:, 1:]  # drop the first column\n",
    "    returns_with_costs_df = df.iloc[:, 1:]\n",
    "    # create a dataframe to fill order details\n",
    "    orders_df = df.iloc[:, 1:]\n",
    "\n",
    "    for res in resolution_list:\n",
    "\n",
    "        for st in range(len(strategy_list)):\n",
    "            apply_strategy = globals()[strategy_list[st]]\n",
    "\n",
    "            returns_list = []\n",
    "            costs_list = []\n",
    "            orders_list = []\n",
    "\n",
    "            for key in data_dic:\n",
    "                ticker1 = key[0]\n",
    "                ticker2 = key[1]\n",
    "                hedge = data_dic[key]\n",
    "                # print(f\"EQUATION: {ticker2}={hedge}*{ticker1}\")\n",
    "\n",
    "                # read data from csv\n",
    "                alltickersfile = \"data/tickers_\" + res + \".csv\"\n",
    "                df = pd.read_csv(alltickersfile)\n",
    "                df_sorted = df.set_index([\"ticker\", \"time\"]).sort_index()  # set indexes\n",
    "                df1 = df_sorted.xs(ticker1)  # the first ticker\n",
    "                df2 = df_sorted.xs(ticker2)  # the second ticker\n",
    "\n",
    "                # calculate spread\n",
    "                df1_1 = hedge * df1\n",
    "                df_spread = df2.subtract(df1_1).round(5)\n",
    "\n",
    "                # add sma & bollinger bands\n",
    "                df_spread[\"sma_20\"] = sma(df_spread.Close, 20)\n",
    "                \n",
    "                if res == '1D':                 \n",
    "                    df_spread['sma_50d']=sma(df_spread.Close, 50) # add 50d sma for 1D resolution\n",
    "                \n",
    "                else:\n",
    "                    df_spread['sma_20d']=sma(df_spread.Close, 20*7) # add 20d sma for 1H resolution\n",
    "                \n",
    "                for i in range(2, 7):\n",
    "                    step = 0.5\n",
    "                    b_band = round(step*i,1)\n",
    "                    (\n",
    "                        df_spread[\"bb_up_\" + str(b_band)],\n",
    "                        df_spread[\"bb_low_\" + str(b_band)],\n",
    "                    ) = bb(df_spread[\"Close\"], df_spread[\"sma_20\"], b_band, 20)\n",
    "\n",
    "                df_spread_bb = df_spread.copy()\n",
    "                df_spread_bb = df_spread_bb.dropna()\n",
    "\n",
    "                # apply strategy & positions\n",
    "                \n",
    "                if res == '1D':               \n",
    "                    buy_price, sell_price, signal, position = apply_strategy(\n",
    "                        df_spread_bb[\"Close\"],\n",
    "                        df_spread_bb[\"bb_low_\" + str(round(b,1))],\n",
    "                        df_spread_bb[\"bb_up_\" + str(round(b,1))],\n",
    "                        df_spread_bb[\"sma_20\"],\n",
    "                        df_spread_bb[\"sma_50d\"]\n",
    "                    )\n",
    "                else:                       \n",
    "                    buy_price, sell_price, signal, position = apply_strategy(\n",
    "                        df_spread_bb[\"Close\"],\n",
    "                        df_spread_bb[\"bb_low_\" + str(round(b,1))],\n",
    "                        df_spread_bb[\"bb_up_\" + str(round(b,1))],\n",
    "                        df_spread_bb[\"sma_20\"],\n",
    "                        df_spread_bb[\"sma_20d\"]\n",
    "                    )\n",
    "                        \n",
    "                signal_df = (\n",
    "                    pd.DataFrame(signal)\n",
    "                    .rename(columns={0: \"signal\"})\n",
    "                    .set_index(df_spread_bb.index)\n",
    "                )\n",
    "                position_df = (\n",
    "                    pd.DataFrame(position)\n",
    "                    .rename(columns={0: \"bb_position\"})\n",
    "                    .set_index(df_spread_bb.index)\n",
    "                )\n",
    "\n",
    "                # calculate price difference per bar\n",
    "                df_spread_bb_ret = pd.DataFrame(np.diff(df_spread_bb[\"Close\"])).rename(\n",
    "                    columns={0: \"returns\"}\n",
    "                )\n",
    "\n",
    "                # calculate return per bar\n",
    "                bb_strategy_ret = []\n",
    "                for i in range(len(df_spread_bb_ret)):\n",
    "                    try:\n",
    "                        returns = (\n",
    "                            df_spread_bb_ret[\"returns\"][i]\n",
    "                            * position_df[\"bb_position\"][i]\n",
    "                        )\n",
    "                        bb_strategy_ret.append(returns)\n",
    "                    except:\n",
    "                        pass\n",
    "\n",
    "                bb_strategy_ret_df = pd.DataFrame(bb_strategy_ret).rename(\n",
    "                    columns={0: \"bb_returns\"}\n",
    "                )\n",
    "\n",
    "                # calculate investment return\n",
    "                contract_price = round(df2.iloc[-1,df2.columns.get_loc('Close')],0) # get the latest price\n",
    "                number_of_stocks = math.floor(pos_value / contract_price)\n",
    "                bb_investment_ret = []\n",
    "\n",
    "                for i in range(len(bb_strategy_ret_df[\"bb_returns\"])):\n",
    "                    returns = number_of_stocks * bb_strategy_ret_df[\"bb_returns\"][i]\n",
    "                    bb_investment_ret.append(returns)\n",
    "\n",
    "                bb_investment_ret_df = pd.DataFrame(bb_investment_ret).rename(\n",
    "                    columns={0: \"investment_returns\"}\n",
    "                )\n",
    "                total_investment_ret = round(\n",
    "                    sum(bb_investment_ret_df[\"investment_returns\"]), 2\n",
    "                )\n",
    "                profit_percentage = math.floor((total_investment_ret / cash_req) * 100)\n",
    "\n",
    "                returns_list.append(profit_percentage)\n",
    "                                               \n",
    "                # get orders & order returns\n",
    "                cum_inv_ret = bb_investment_ret_df.cumsum()\n",
    "                o_df = signal_df[['signal']].reset_index()\n",
    "                o_df['cum_return'] = cum_inv_ret\n",
    "                mask = o_df.signal!=0\n",
    "                o_df = o_df[mask][['signal','cum_return']]\n",
    "                o_df = o_df.reset_index().dropna()\n",
    "                o_df['prof_loss'] = o_df.cum_return.diff()\n",
    "\n",
    "                if not o_df.empty:\n",
    "                    o_df.at[0, 'prof_loss'] = o_df['cum_return'][0]\n",
    "                    o_df.at[len(o_df), 'prof_loss'] = total_investment_ret - o_df.loc[o_df.index[-1],'cum_return']\n",
    "                    o_df.at[len(o_df)-1, 'cum_return'] = total_investment_ret\n",
    "                    orders_list.append(o_df.prof_loss)\n",
    "                else:\n",
    "                    orders_list.append([])\n",
    "                    \n",
    "                # get costs\n",
    "                slip = signal_df.signal.abs() * avg_slip * pos_value\n",
    "                commission = signal_df.signal.abs() * avg_commission * number_of_stocks * 2\n",
    "                fincost = (\n",
    "                    position_df.bb_position.abs()\n",
    "                    * avg_fin_cost_dic[res]\n",
    "                    * (2 * pos_value - cash_req)\n",
    "                )\n",
    "\n",
    "                total_cost = (\n",
    "                    slip.sum().round(1)\n",
    "                    + commission.sum().round(1)\n",
    "                    + fincost.sum().round(1)\n",
    "                )\n",
    "\n",
    "                total_investment_ret_cost = (\n",
    "                    round(sum(bb_investment_ret_df[\"investment_returns\"]), 2)\n",
    "                    - total_cost\n",
    "                )\n",
    "                profit_percentage_cost = math.floor(\n",
    "                    (total_investment_ret_cost / cash_req) * 100\n",
    "                )\n",
    "\n",
    "                costs_list.append(profit_percentage_cost)\n",
    "\n",
    "            returns_df[res + \"_\" + strategy_list[st]] = returns_list\n",
    "            returns_with_costs_df[res + \"_\" + strategy_list[st]] = costs_list\n",
    "            orders_df[res + \"_\" + strategy_list[st]] = orders_list\n",
    "\n",
    "    return returns_df, returns_with_costs_df, orders_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3ae89c6",
   "metadata": {},
   "source": [
    "Run and display results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "id": "c691e650",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:26:47.719722Z",
     "start_time": "2022-11-01T17:26:43.623529Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average return - 1D_exit_sma : 39.0%\n",
      "average return - 1D_no_exit : 44.0%\n",
      "average return - 1H_exit_sma : 24.0%\n",
      "average return - 1H_no_exit : 42.2%\n",
      "average return (costs included) - 1D_exit_sma : 31.6%\n",
      "average return (costs included) - 1D_no_exit : 34.2%\n",
      "average return (costs included) - 1H_exit_sma : -7.0%\n",
      "average return (costs included) - 1H_no_exit : 23.2%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_afee3_\" style='display:inline; margin-right:20px;'>\n",
       "  <caption>2DEV Return%</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >1D_exit_sma</th>\n",
       "      <th class=\"col_heading level0 col1\" >1D_no_exit</th>\n",
       "      <th class=\"col_heading level0 col2\" >1H_exit_sma</th>\n",
       "      <th class=\"col_heading level0 col3\" >1H_no_exit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_afee3_level0_row0\" class=\"row_heading level0 row0\" >('AEP', 'VST')</th>\n",
       "      <td id=\"T_afee3_row0_col0\" class=\"data row0 col0\" >58</td>\n",
       "      <td id=\"T_afee3_row0_col1\" class=\"data row0 col1\" >76</td>\n",
       "      <td id=\"T_afee3_row0_col2\" class=\"data row0 col2\" >39</td>\n",
       "      <td id=\"T_afee3_row0_col3\" class=\"data row0 col3\" >102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_afee3_level0_row1\" class=\"row_heading level0 row1\" >('CNP', 'VST')</th>\n",
       "      <td id=\"T_afee3_row1_col0\" class=\"data row1 col0\" >63</td>\n",
       "      <td id=\"T_afee3_row1_col1\" class=\"data row1 col1\" >76</td>\n",
       "      <td id=\"T_afee3_row1_col2\" class=\"data row1 col2\" >14</td>\n",
       "      <td id=\"T_afee3_row1_col3\" class=\"data row1 col3\" >32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_afee3_level0_row2\" class=\"row_heading level0 row2\" >('OGE', 'XEL')</th>\n",
       "      <td id=\"T_afee3_row2_col0\" class=\"data row2 col0\" >19</td>\n",
       "      <td id=\"T_afee3_row2_col1\" class=\"data row2 col1\" >29</td>\n",
       "      <td id=\"T_afee3_row2_col2\" class=\"data row2 col2\" >34</td>\n",
       "      <td id=\"T_afee3_row2_col3\" class=\"data row2 col3\" >18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_afee3_level0_row3\" class=\"row_heading level0 row3\" >('CMS', 'DUK')</th>\n",
       "      <td id=\"T_afee3_row3_col0\" class=\"data row3 col0\" >19</td>\n",
       "      <td id=\"T_afee3_row3_col1\" class=\"data row3 col1\" >16</td>\n",
       "      <td id=\"T_afee3_row3_col2\" class=\"data row3 col2\" >28</td>\n",
       "      <td id=\"T_afee3_row3_col3\" class=\"data row3 col3\" >37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_afee3_level0_row4\" class=\"row_heading level0 row4\" >('ED', 'VST')</th>\n",
       "      <td id=\"T_afee3_row4_col0\" class=\"data row4 col0\" >36</td>\n",
       "      <td id=\"T_afee3_row4_col1\" class=\"data row4 col1\" >23</td>\n",
       "      <td id=\"T_afee3_row4_col2\" class=\"data row4 col2\" >5</td>\n",
       "      <td id=\"T_afee3_row4_col3\" class=\"data row4 col3\" >22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_6a036_\" style='display:inline'>\n",
       "  <caption> 2DEV Return% (costs inc.)</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >1D_exit_sma</th>\n",
       "      <th class=\"col_heading level0 col1\" >1D_no_exit</th>\n",
       "      <th class=\"col_heading level0 col2\" >1H_exit_sma</th>\n",
       "      <th class=\"col_heading level0 col3\" >1H_no_exit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6a036_level0_row0\" class=\"row_heading level0 row0\" >('AEP', 'VST')</th>\n",
       "      <td id=\"T_6a036_row0_col0\" class=\"data row0 col0\" >49</td>\n",
       "      <td id=\"T_6a036_row0_col1\" class=\"data row0 col1\" >65</td>\n",
       "      <td id=\"T_6a036_row0_col2\" class=\"data row0 col2\" >5</td>\n",
       "      <td id=\"T_6a036_row0_col3\" class=\"data row0 col3\" >81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a036_level0_row1\" class=\"row_heading level0 row1\" >('CNP', 'VST')</th>\n",
       "      <td id=\"T_6a036_row1_col0\" class=\"data row1 col0\" >54</td>\n",
       "      <td id=\"T_6a036_row1_col1\" class=\"data row1 col1\" >66</td>\n",
       "      <td id=\"T_6a036_row1_col2\" class=\"data row1 col2\" >-22</td>\n",
       "      <td id=\"T_6a036_row1_col3\" class=\"data row1 col3\" >11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a036_level0_row2\" class=\"row_heading level0 row2\" >('OGE', 'XEL')</th>\n",
       "      <td id=\"T_6a036_row2_col0\" class=\"data row2 col0\" >12</td>\n",
       "      <td id=\"T_6a036_row2_col1\" class=\"data row2 col1\" >20</td>\n",
       "      <td id=\"T_6a036_row2_col2\" class=\"data row2 col2\" >7</td>\n",
       "      <td id=\"T_6a036_row2_col3\" class=\"data row2 col3\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a036_level0_row3\" class=\"row_heading level0 row3\" >('CMS', 'DUK')</th>\n",
       "      <td id=\"T_6a036_row3_col0\" class=\"data row3 col0\" >12</td>\n",
       "      <td id=\"T_6a036_row3_col1\" class=\"data row3 col1\" >6</td>\n",
       "      <td id=\"T_6a036_row3_col2\" class=\"data row3 col2\" >2</td>\n",
       "      <td id=\"T_6a036_row3_col3\" class=\"data row3 col3\" >20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a036_level0_row4\" class=\"row_heading level0 row4\" >('ED', 'VST')</th>\n",
       "      <td id=\"T_6a036_row4_col0\" class=\"data row4 col0\" >31</td>\n",
       "      <td id=\"T_6a036_row4_col1\" class=\"data row4 col1\" >14</td>\n",
       "      <td id=\"T_6a036_row4_col2\" class=\"data row4 col2\" >-27</td>\n",
       "      <td id=\"T_6a036_row4_col3\" class=\"data row4 col3\" >3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display_html \n",
    "\n",
    "#strategy_list = [\"exit_sma\", \"no_exit\", \"skp_sma\"]\n",
    "str_list = [\"exit_sma\", \"no_exit\"]\n",
    "\n",
    "df1_2_0std, df2_2_0std, orders_2_0std = calculate_returns(pairs_dic, str_list, b=2.0) # b is 1.0, 1.5, 2.0, 2.5 or 3 std\n",
    "\n",
    "for col in df1_2_0std.columns:\n",
    "    print(f\"average return - {col} : {round(df1_2_0std[col].mean(),2)}%\")\n",
    "\n",
    "for col in df2_2_0std.columns:\n",
    "    print(f\"average return (costs included) - {col} : {round(df2_2_0std[col].mean(),2)}%\")\n",
    "    \n",
    "df1_sort = df1_2_0std.copy()\n",
    "df2_sort = df2_2_0std.copy()\n",
    "df1_sort['mean'] = df1_2_0std.mean(axis=1)\n",
    "df1_sort = df1_sort.sort_values('mean', ascending=False)\n",
    "df2_sort['mean'] = df2_2_0std.mean(axis=1)\n",
    "df2_sort = df2_sort.sort_values('mean', ascending=False)\n",
    "df1_sort = df1_sort.drop(columns=['mean'])\n",
    "df2_sort = df2_sort.drop(columns=['mean'])\n",
    "\n",
    "df1_style = df1_sort.style.set_table_attributes(\"style='display:inline; margin-right:20px;'\").set_caption(\"2DEV Return%\")\n",
    "df2_style = df2_sort.style.set_table_attributes(\"style='display:inline'\").set_caption(\" 2DEV Return% (costs inc.)\")\n",
    "\n",
    "display_html(df1_style._repr_html_() + df2_style._repr_html_(), raw=True)\n",
    "\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(14, 7))\n",
    "# fig.tight_layout()\n",
    "seaborn.heatmap(\n",
    "    df1_sort,\n",
    "    xticklabels=df1_sort.columns,\n",
    "    yticklabels=df1_sort.index,\n",
    "    cmap=\"Greens\",\n",
    "    square=True,\n",
    "    vmin=0,\n",
    "    ax=ax1\n",
    ")\n",
    "\n",
    "seaborn.heatmap(\n",
    "    df2_sort,\n",
    "    xticklabels=df2_sort.columns,\n",
    "    yticklabels=df2_sort.index,\n",
    "    cmap=\"Greens\",\n",
    "    square=True,\n",
    "    vmin=0,\n",
    "    ax=ax2\n",
    ")\n",
    "\n",
    "dpi_set=100\n",
    "dfi.export(df1_style, 'summary1.png', dpi=dpi_set)\n",
    "dfi.export(df2_style, 'summary2.png', dpi=dpi_set)\n",
    "plt.savefig('summary_plot1', dpi=dpi_set, bbox_inches='tight', pad_inches=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c10f93a",
   "metadata": {},
   "source": [
    "Visualize order details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "id": "8e0a31e2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:26:49.530296Z",
     "start_time": "2022-11-01T17:26:47.723724Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_pairs = 5\n",
    "\n",
    "col_len = len(orders_2_0std.columns)\n",
    "\n",
    "fig, axs = plt.subplots(1,col_len, figsize=(4*col_len, 20))\n",
    "# _ = plt.figure(figsize=(5,25))\n",
    "\n",
    "for x in range(col_len):\n",
    "    vip =seaborn.violinplot(data=orders_2_0std[orders_2_0std.columns[x]][:max_pairs], inner=None, orient='h', ax=axs[x]);\n",
    "    swp =seaborn.swarmplot(data=orders_2_0std[orders_2_0std.columns[x]][:max_pairs], size=7, color=\"k\", alpha=0.5, orient='h', ax=axs[x]);\n",
    "    vip.set(yticklabels=[])\n",
    "    swp.set(yticklabels=[])\n",
    "    \n",
    "    if x ==0:\n",
    "        vip.set_yticklabels(list(orders_2_0std.index.values)[:max_pairs])\n",
    "    \n",
    "for ax in range(len(axs)):\n",
    "    axs[ax].set_title(orders_2_0std.columns[ax], fontstyle='italic')\n",
    "\n",
    "fig.text(0.5, 0.10, 'PROFIT/LOSS ($ per trade ●)', ha='center', va='center')\n",
    "\n",
    "dpi_set=100\n",
    "fig.savefig('summary_plot2', dpi=dpi_set, bbox_inches='tight', pad_inches=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc68b8a0",
   "metadata": {},
   "source": [
    "Exercise: Dropped Pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "id": "a4ed9791",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:26:51.969318Z",
     "start_time": "2022-11-01T17:26:49.538298Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average return - 1D_exit_sma : 53.67%\n",
      "average return - 1D_no_exit : 42.0%\n",
      "average return - 1H_exit_sma : 36.33%\n",
      "average return - 1H_no_exit : 62.0%\n",
      "average return (costs included) - 1D_exit_sma : 47.33%\n",
      "average return (costs included) - 1D_no_exit : 32.0%\n",
      "average return (costs included) - 1H_exit_sma : 10.0%\n",
      "average return (costs included) - 1H_no_exit : 44.33%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_5f84b_\" style='display:inline; margin-right:20px;'>\n",
       "  <caption>Return%</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >1D_exit_sma</th>\n",
       "      <th class=\"col_heading level0 col1\" >1D_no_exit</th>\n",
       "      <th class=\"col_heading level0 col2\" >1H_exit_sma</th>\n",
       "      <th class=\"col_heading level0 col3\" >1H_no_exit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_5f84b_level0_row0\" class=\"row_heading level0 row0\" >('RUN', 'WTRG')</th>\n",
       "      <td id=\"T_5f84b_row0_col0\" class=\"data row0 col0\" >93</td>\n",
       "      <td id=\"T_5f84b_row0_col1\" class=\"data row0 col1\" >64</td>\n",
       "      <td id=\"T_5f84b_row0_col2\" class=\"data row0 col2\" >90</td>\n",
       "      <td id=\"T_5f84b_row0_col3\" class=\"data row0 col3\" >125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5f84b_level0_row1\" class=\"row_heading level0 row1\" >('VST', 'WEC')</th>\n",
       "      <td id=\"T_5f84b_row1_col0\" class=\"data row1 col0\" >50</td>\n",
       "      <td id=\"T_5f84b_row1_col1\" class=\"data row1 col1\" >41</td>\n",
       "      <td id=\"T_5f84b_row1_col2\" class=\"data row1 col2\" >8</td>\n",
       "      <td id=\"T_5f84b_row1_col3\" class=\"data row1 col3\" >37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5f84b_level0_row2\" class=\"row_heading level0 row2\" >('AEE', 'DUK')</th>\n",
       "      <td id=\"T_5f84b_row2_col0\" class=\"data row2 col0\" >18</td>\n",
       "      <td id=\"T_5f84b_row2_col1\" class=\"data row2 col1\" >21</td>\n",
       "      <td id=\"T_5f84b_row2_col2\" class=\"data row2 col2\" >11</td>\n",
       "      <td id=\"T_5f84b_row2_col3\" class=\"data row2 col3\" >24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_e3d96_\" style='display:inline'>\n",
       "  <caption>Return% (costs inc.)</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >1D_exit_sma</th>\n",
       "      <th class=\"col_heading level0 col1\" >1D_no_exit</th>\n",
       "      <th class=\"col_heading level0 col2\" >1H_exit_sma</th>\n",
       "      <th class=\"col_heading level0 col3\" >1H_no_exit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e3d96_level0_row0\" class=\"row_heading level0 row0\" >('RUN', 'WTRG')</th>\n",
       "      <td id=\"T_e3d96_row0_col0\" class=\"data row0 col0\" >86</td>\n",
       "      <td id=\"T_e3d96_row0_col1\" class=\"data row0 col1\" >54</td>\n",
       "      <td id=\"T_e3d96_row0_col2\" class=\"data row0 col2\" >62</td>\n",
       "      <td id=\"T_e3d96_row0_col3\" class=\"data row0 col3\" >107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3d96_level0_row1\" class=\"row_heading level0 row1\" >('VST', 'WEC')</th>\n",
       "      <td id=\"T_e3d96_row1_col0\" class=\"data row1 col0\" >44</td>\n",
       "      <td id=\"T_e3d96_row1_col1\" class=\"data row1 col1\" >31</td>\n",
       "      <td id=\"T_e3d96_row1_col2\" class=\"data row1 col2\" >-16</td>\n",
       "      <td id=\"T_e3d96_row1_col3\" class=\"data row1 col3\" >20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3d96_level0_row2\" class=\"row_heading level0 row2\" >('AEE', 'DUK')</th>\n",
       "      <td id=\"T_e3d96_row2_col0\" class=\"data row2 col0\" >12</td>\n",
       "      <td id=\"T_e3d96_row2_col1\" class=\"data row2 col1\" >11</td>\n",
       "      <td id=\"T_e3d96_row2_col2\" class=\"data row2 col2\" >-16</td>\n",
       "      <td id=\"T_e3d96_row2_col3\" class=\"data row2 col3\" >6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if bool(drop_dic):\n",
    "    \n",
    "    df1_drop, df2_drop, _ = calculate_returns(drop_dic, str_list, b=2.0) # b is 1,2 or 3 std\n",
    "\n",
    "    for col in df1_drop.columns:\n",
    "        print(f\"average return - {col} : {round(df1_drop[col].mean(),2)}%\")\n",
    "\n",
    "    for col in df2_drop.columns:\n",
    "        print(f\"average return (costs included) - {col} : {round(df2_drop[col].mean(),2)}%\")\n",
    "\n",
    "    df1_drop_sort = df1_drop.copy()\n",
    "    df2_drop_sort = df2_drop.copy()\n",
    "    df1_drop_sort['mean'] = df1_drop.mean(axis=1)\n",
    "    df1_drop_sort = df1_drop_sort.sort_values('mean', ascending=False)\n",
    "    df2_drop_sort['mean'] = df2_drop.mean(axis=1)\n",
    "    df2_drop_sort = df2_drop_sort.sort_values('mean', ascending=False)\n",
    "    df1_drop_sort = df1_drop_sort.drop(columns=['mean'])\n",
    "    df2_drop_sort = df2_drop_sort.drop(columns=['mean'])\n",
    "\n",
    "    df1_style_drop = df1_drop_sort.style.set_table_attributes(\"style='display:inline; margin-right:20px;'\").set_caption(\"Return%\")\n",
    "    df2_style_drop = df2_drop_sort.style.set_table_attributes(\"style='display:inline'\").set_caption(\"Return% (costs inc.)\")\n",
    "\n",
    "\n",
    "    display_html(df1_style_drop._repr_html_() + df2_style_drop._repr_html_(), raw=True)\n",
    "\n",
    "    fig, (ax1, ax2) = plt.subplots(1,2, figsize=(14, 7))\n",
    "    seaborn.heatmap(\n",
    "        df1_drop_sort,\n",
    "        xticklabels=df1_drop_sort.columns,\n",
    "        yticklabels=df1_drop_sort.index,\n",
    "        cmap=\"Greens\",\n",
    "        square=True,\n",
    "        vmin=0,\n",
    "        ax=ax1\n",
    "    )\n",
    "    seaborn.heatmap(\n",
    "        df2_drop_sort,\n",
    "        xticklabels=df2_drop_sort.columns,\n",
    "        yticklabels=df2_drop_sort.index,\n",
    "        cmap=\"Greens\",\n",
    "        square=True,\n",
    "        vmin=0,\n",
    "        ax=ax2\n",
    "    )\n",
    "\n",
    "    # dpi_set=100\n",
    "    # dfi.export(df1_style_drop, 'summary1_drop.png', dpi=dpi_set)\n",
    "    # dfi.export(df2_style_drop, 'summary2_drop.png', dpi=dpi_set)\n",
    "    # plt.savefig('summary_plot1_drop', dpi=dpi_set, bbox_inches='tight', pad_inches=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "957e73c4",
   "metadata": {},
   "source": [
    "### EXERCISE : Calculate all strategies from 1 std to 3 std dev"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ccd9d2d",
   "metadata": {},
   "source": [
    "Calculate returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "id": "0df01d1f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:27:17.526589Z",
     "start_time": "2022-11-01T17:26:51.972320Z"
    },
    "scrolled": false
   },
   "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>1D_exit_sma</th>\n",
       "      <th>1D_no_exit</th>\n",
       "      <th>1D_skp_sma</th>\n",
       "      <th>1H_exit_sma</th>\n",
       "      <th>1H_no_exit</th>\n",
       "      <th>1H_skp_sma</th>\n",
       "      <th>bb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(AEP, VST)</th>\n",
       "      <td>37</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>-9</td>\n",
       "      <td>-7</td>\n",
       "      <td>-27</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(OGE, XEL)</th>\n",
       "      <td>2</td>\n",
       "      <td>-9</td>\n",
       "      <td>4</td>\n",
       "      <td>-20</td>\n",
       "      <td>32</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(CMS, DUK)</th>\n",
       "      <td>13</td>\n",
       "      <td>-4</td>\n",
       "      <td>12</td>\n",
       "      <td>-21</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(ED, VST)</th>\n",
       "      <td>105</td>\n",
       "      <td>133</td>\n",
       "      <td>116</td>\n",
       "      <td>-79</td>\n",
       "      <td>-28</td>\n",
       "      <td>-42</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(CNP, VST)</th>\n",
       "      <td>46</td>\n",
       "      <td>53</td>\n",
       "      <td>55</td>\n",
       "      <td>-73</td>\n",
       "      <td>-3</td>\n",
       "      <td>-5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            1D_exit_sma  1D_no_exit  1D_skp_sma  1H_exit_sma  1H_no_exit  \\\n",
       "(AEP, VST)           37          57          45           -9          -7   \n",
       "(OGE, XEL)            2          -9           4          -20          32   \n",
       "(CMS, DUK)           13          -4          12          -21          18   \n",
       "(ED, VST)           105         133         116          -79         -28   \n",
       "(CNP, VST)           46          53          55          -73          -3   \n",
       "\n",
       "            1H_skp_sma   bb  \n",
       "(AEP, VST)         -27  1.0  \n",
       "(OGE, XEL)          27  1.0  \n",
       "(CMS, DUK)           4  1.0  \n",
       "(ED, VST)          -42  1.0  \n",
       "(CNP, VST)          -5  1.0  "
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str_list = [\"exit_sma\", \"no_exit\", \"skp_sma\"]\n",
    "\n",
    "df1_1_0std, df2_1_0std, _ = calculate_returns(pairs_dic,str_list,b=1.0)\n",
    "df1_1_5std, df2_1_5std, _ = calculate_returns(pairs_dic,str_list,b=1.5)\n",
    "df1_2_0std, df2_2_0std, _ = calculate_returns(pairs_dic,str_list,b=2.0)\n",
    "df1_2_5std, df2_2_5std, _ = calculate_returns(pairs_dic,str_list,b=2.5)\n",
    "df1_3_0std, df2_3_0std, _ = calculate_returns(pairs_dic,str_list,b=3.0)\n",
    "\n",
    "df2_1_0std['bb'] = 1.0\n",
    "df2_1_5std['bb'] = 1.5\n",
    "df2_2_0std['bb'] = 2.0\n",
    "df2_2_5std['bb'] = 2.5\n",
    "df2_3_0std['bb'] = 3.0\n",
    "df2_all = pd.concat([df2_1_0std,df2_1_5std,df2_2_0std,df2_2_5std,df2_3_0std])\n",
    "\n",
    "df2_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f697bc9a",
   "metadata": {},
   "source": [
    "Group by bb band"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "id": "e657d3e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:27:17.762568Z",
     "start_time": "2022-11-01T17:27:17.529611Z"
    },
    "scrolled": false
   },
   "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></th>\n",
       "      <th>1D_exit_sma</th>\n",
       "      <th>1D_no_exit</th>\n",
       "      <th>1D_skp_sma</th>\n",
       "      <th>1H_exit_sma</th>\n",
       "      <th>1H_no_exit</th>\n",
       "      <th>1H_skp_sma</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bb</th>\n",
       "      <th>pair</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">3.0</th>\n",
       "      <th>(AEP, VST)</th>\n",
       "      <td>8</td>\n",
       "      <td>-10</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>97</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(CMS, DUK)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(CNP, VST)</th>\n",
       "      <td>6</td>\n",
       "      <td>-6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(ED, VST)</th>\n",
       "      <td>7</td>\n",
       "      <td>-2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(OGE, XEL)</th>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>-4</td>\n",
       "      <td>-6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                1D_exit_sma  1D_no_exit  1D_skp_sma  1H_exit_sma  1H_no_exit  \\\n",
       "bb  pair                                                                       \n",
       "3.0 (AEP, VST)            8         -10           2           22          97   \n",
       "    (CMS, DUK)            0           0           0            8          12   \n",
       "    (CNP, VST)            6          -6           6            3          -1   \n",
       "    (ED, VST)             7          -2           4            7           7   \n",
       "    (OGE, XEL)           -1           3           2           -4          -6   \n",
       "\n",
       "                1H_skp_sma  \n",
       "bb  pair                    \n",
       "3.0 (AEP, VST)          56  \n",
       "    (CMS, DUK)          20  \n",
       "    (CNP, VST)          26  \n",
       "    (ED, VST)           17  \n",
       "    (OGE, XEL)           9  "
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_all1 = df2_all.copy()\n",
    "df2_all1 = df2_all1.reset_index()\n",
    "df2_all1 = df2_all1.rename(columns={\"index\": \"pair\"})\n",
    "df2_all1 = df2_all1.set_index(['bb','pair']).sort_index()\n",
    "df2_all1.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a2ecac9",
   "metadata": {},
   "source": [
    "Slice & Add level by std. dev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "id": "ca74c9e6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:27:17.963906Z",
     "start_time": "2022-11-01T17:27:17.773569Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df2_list =[]\n",
    "df2_sort =[]\n",
    "df2_sort_leveled =[]\n",
    "\n",
    "for i in range(2,7):\n",
    "    step = 0.5\n",
    "    b_band = round(step*i,1)\n",
    "    df2_list.append(df2_all1.xs((b_band,))) \n",
    "    \n",
    "for i in range(len(df2_list)):\n",
    "    df2_sort.append(df2_list[i])\n",
    "    df2_sort[i]['mean'] = df2_list[i].mean(axis=1).round(1)\n",
    "    df2_sort[i] = df2_sort[i].sort_values('mean', ascending=False)\n",
    "    mean_cols = df2_list[i].mean(axis=0).round(1)\n",
    "    df2_sort[i].loc[\"avg\"] = mean_cols\n",
    "    df2_nomean= df2_sort[i].loc[ : , df2_sort[i].columns != 'mean']\n",
    "    df2_nomean = df2_nomean.sort_values('avg', axis=1, ascending=False)\n",
    "    df2_nomean['mean'] = df2_sort[i]['mean']\n",
    "    df2_sort[i] = df2_nomean\n",
    "    df2_sort_leveled.append(pd.concat([df2_sort[i]], keys=[str(i/2+1) + ' DEV RETURN%'], axis=1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c05193",
   "metadata": {},
   "source": [
    "Show dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "76a452c6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:27:18.367078Z",
     "start_time": "2022-11-01T17:27:17.965419Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c9100_row0_col0, #T_c9100_row0_col1, #T_c9100_row0_col2, #T_c9100_row0_col6, #T_c9100_row1_col5, #T_c9100_row3_col3, #T_c9100_row3_col4 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "#T_c9100_row0_col3, #T_c9100_row0_col4, #T_c9100_row0_col5, #T_c9100_row3_col0, #T_c9100_row3_col1, #T_c9100_row3_col2, #T_c9100_row4_col6 {\n",
       "  background-color: lightsalmon;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c9100_\" style='display:inline; margin-right:20px;'>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" colspan=\"7\">1.0 DEV RETURN%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level1 col0\" >1D_skp_sma</th>\n",
       "      <th class=\"col_heading level1 col1\" >1D_no_exit</th>\n",
       "      <th class=\"col_heading level1 col2\" >1D_exit_sma</th>\n",
       "      <th class=\"col_heading level1 col3\" >1H_no_exit</th>\n",
       "      <th class=\"col_heading level1 col4\" >1H_skp_sma</th>\n",
       "      <th class=\"col_heading level1 col5\" >1H_exit_sma</th>\n",
       "      <th class=\"col_heading level1 col6\" >mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >pair</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c9100_level0_row0\" class=\"row_heading level0 row0\" >('ED', 'VST')</th>\n",
       "      <td id=\"T_c9100_row0_col0\" class=\"data row0 col0\" >116.0</td>\n",
       "      <td id=\"T_c9100_row0_col1\" class=\"data row0 col1\" >133.0</td>\n",
       "      <td id=\"T_c9100_row0_col2\" class=\"data row0 col2\" >105.0</td>\n",
       "      <td id=\"T_c9100_row0_col3\" class=\"data row0 col3\" >-28.0</td>\n",
       "      <td id=\"T_c9100_row0_col4\" class=\"data row0 col4\" >-42.0</td>\n",
       "      <td id=\"T_c9100_row0_col5\" class=\"data row0 col5\" >-79.0</td>\n",
       "      <td id=\"T_c9100_row0_col6\" class=\"data row0 col6\" >34.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9100_level0_row1\" class=\"row_heading level0 row1\" >('AEP', 'VST')</th>\n",
       "      <td id=\"T_c9100_row1_col0\" class=\"data row1 col0\" >45.0</td>\n",
       "      <td id=\"T_c9100_row1_col1\" class=\"data row1 col1\" >57.0</td>\n",
       "      <td id=\"T_c9100_row1_col2\" class=\"data row1 col2\" >37.0</td>\n",
       "      <td id=\"T_c9100_row1_col3\" class=\"data row1 col3\" >-7.0</td>\n",
       "      <td id=\"T_c9100_row1_col4\" class=\"data row1 col4\" >-27.0</td>\n",
       "      <td id=\"T_c9100_row1_col5\" class=\"data row1 col5\" >-9.0</td>\n",
       "      <td id=\"T_c9100_row1_col6\" class=\"data row1 col6\" >16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9100_level0_row2\" class=\"row_heading level0 row2\" >('CNP', 'VST')</th>\n",
       "      <td id=\"T_c9100_row2_col0\" class=\"data row2 col0\" >55.0</td>\n",
       "      <td id=\"T_c9100_row2_col1\" class=\"data row2 col1\" >53.0</td>\n",
       "      <td id=\"T_c9100_row2_col2\" class=\"data row2 col2\" >46.0</td>\n",
       "      <td id=\"T_c9100_row2_col3\" class=\"data row2 col3\" >-3.0</td>\n",
       "      <td id=\"T_c9100_row2_col4\" class=\"data row2 col4\" >-5.0</td>\n",
       "      <td id=\"T_c9100_row2_col5\" class=\"data row2 col5\" >-73.0</td>\n",
       "      <td id=\"T_c9100_row2_col6\" class=\"data row2 col6\" >12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9100_level0_row3\" class=\"row_heading level0 row3\" >('OGE', 'XEL')</th>\n",
       "      <td id=\"T_c9100_row3_col0\" class=\"data row3 col0\" >4.0</td>\n",
       "      <td id=\"T_c9100_row3_col1\" class=\"data row3 col1\" >-9.0</td>\n",
       "      <td id=\"T_c9100_row3_col2\" class=\"data row3 col2\" >2.0</td>\n",
       "      <td id=\"T_c9100_row3_col3\" class=\"data row3 col3\" >32.0</td>\n",
       "      <td id=\"T_c9100_row3_col4\" class=\"data row3 col4\" >27.0</td>\n",
       "      <td id=\"T_c9100_row3_col5\" class=\"data row3 col5\" >-20.0</td>\n",
       "      <td id=\"T_c9100_row3_col6\" class=\"data row3 col6\" >6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9100_level0_row4\" class=\"row_heading level0 row4\" >('CMS', 'DUK')</th>\n",
       "      <td id=\"T_c9100_row4_col0\" class=\"data row4 col0\" >12.0</td>\n",
       "      <td id=\"T_c9100_row4_col1\" class=\"data row4 col1\" >-4.0</td>\n",
       "      <td id=\"T_c9100_row4_col2\" class=\"data row4 col2\" >13.0</td>\n",
       "      <td id=\"T_c9100_row4_col3\" class=\"data row4 col3\" >18.0</td>\n",
       "      <td id=\"T_c9100_row4_col4\" class=\"data row4 col4\" >4.0</td>\n",
       "      <td id=\"T_c9100_row4_col5\" class=\"data row4 col5\" >-21.0</td>\n",
       "      <td id=\"T_c9100_row4_col6\" class=\"data row4 col6\" >3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9100_level0_row5\" class=\"row_heading level0 row5\" >avg</th>\n",
       "      <td id=\"T_c9100_row5_col0\" class=\"data row5 col0\" >46.4</td>\n",
       "      <td id=\"T_c9100_row5_col1\" class=\"data row5 col1\" >46.0</td>\n",
       "      <td id=\"T_c9100_row5_col2\" class=\"data row5 col2\" >40.6</td>\n",
       "      <td id=\"T_c9100_row5_col3\" class=\"data row5 col3\" >2.4</td>\n",
       "      <td id=\"T_c9100_row5_col4\" class=\"data row5 col4\" >-8.6</td>\n",
       "      <td id=\"T_c9100_row5_col5\" class=\"data row5 col5\" >-40.4</td>\n",
       "      <td id=\"T_c9100_row5_col6\" class=\"data row5 col6\" >14.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<style type=\"text/css\">\n",
       "#T_a2898_row0_col0, #T_a2898_row0_col1, #T_a2898_row0_col2, #T_a2898_row0_col6, #T_a2898_row1_col4, #T_a2898_row4_col3, #T_a2898_row4_col5 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "#T_a2898_row0_col3, #T_a2898_row0_col4, #T_a2898_row2_col5, #T_a2898_row4_col0, #T_a2898_row4_col1, #T_a2898_row4_col2, #T_a2898_row4_col6 {\n",
       "  background-color: lightsalmon;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_a2898_\" style='display:inline'>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" colspan=\"7\">1.5 DEV RETURN%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level1 col0\" >1D_skp_sma</th>\n",
       "      <th class=\"col_heading level1 col1\" >1D_exit_sma</th>\n",
       "      <th class=\"col_heading level1 col2\" >1D_no_exit</th>\n",
       "      <th class=\"col_heading level1 col3\" >1H_skp_sma</th>\n",
       "      <th class=\"col_heading level1 col4\" >1H_no_exit</th>\n",
       "      <th class=\"col_heading level1 col5\" >1H_exit_sma</th>\n",
       "      <th class=\"col_heading level1 col6\" >mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >pair</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a2898_level0_row0\" class=\"row_heading level0 row0\" >('ED', 'VST')</th>\n",
       "      <td id=\"T_a2898_row0_col0\" class=\"data row0 col0\" >110.0</td>\n",
       "      <td id=\"T_a2898_row0_col1\" class=\"data row0 col1\" >86.0</td>\n",
       "      <td id=\"T_a2898_row0_col2\" class=\"data row0 col2\" >105.0</td>\n",
       "      <td id=\"T_a2898_row0_col3\" class=\"data row0 col3\" >-33.0</td>\n",
       "      <td id=\"T_a2898_row0_col4\" class=\"data row0 col4\" >-36.0</td>\n",
       "      <td id=\"T_a2898_row0_col5\" class=\"data row0 col5\" >-32.0</td>\n",
       "      <td id=\"T_a2898_row0_col6\" class=\"data row0 col6\" >33.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a2898_level0_row1\" class=\"row_heading level0 row1\" >('AEP', 'VST')</th>\n",
       "      <td id=\"T_a2898_row1_col0\" class=\"data row1 col0\" >64.0</td>\n",
       "      <td id=\"T_a2898_row1_col1\" class=\"data row1 col1\" >67.0</td>\n",
       "      <td id=\"T_a2898_row1_col2\" class=\"data row1 col2\" >72.0</td>\n",
       "      <td id=\"T_a2898_row1_col3\" class=\"data row1 col3\" >-9.0</td>\n",
       "      <td id=\"T_a2898_row1_col4\" class=\"data row1 col4\" >17.0</td>\n",
       "      <td id=\"T_a2898_row1_col5\" class=\"data row1 col5\" >-27.0</td>\n",
       "      <td id=\"T_a2898_row1_col6\" class=\"data row1 col6\" >30.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a2898_level0_row2\" class=\"row_heading level0 row2\" >('CNP', 'VST')</th>\n",
       "      <td id=\"T_a2898_row2_col0\" class=\"data row2 col0\" >53.0</td>\n",
       "      <td id=\"T_a2898_row2_col1\" class=\"data row2 col1\" >52.0</td>\n",
       "      <td id=\"T_a2898_row2_col2\" class=\"data row2 col2\" >30.0</td>\n",
       "      <td id=\"T_a2898_row2_col3\" class=\"data row2 col3\" >10.0</td>\n",
       "      <td id=\"T_a2898_row2_col4\" class=\"data row2 col4\" >4.0</td>\n",
       "      <td id=\"T_a2898_row2_col5\" class=\"data row2 col5\" >-39.0</td>\n",
       "      <td id=\"T_a2898_row2_col6\" class=\"data row2 col6\" >18.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a2898_level0_row3\" class=\"row_heading level0 row3\" >('CMS', 'DUK')</th>\n",
       "      <td id=\"T_a2898_row3_col0\" class=\"data row3 col0\" >31.0</td>\n",
       "      <td id=\"T_a2898_row3_col1\" class=\"data row3 col1\" >23.0</td>\n",
       "      <td id=\"T_a2898_row3_col2\" class=\"data row3 col2\" >15.0</td>\n",
       "      <td id=\"T_a2898_row3_col3\" class=\"data row3 col3\" >1.0</td>\n",
       "      <td id=\"T_a2898_row3_col4\" class=\"data row3 col4\" >1.0</td>\n",
       "      <td id=\"T_a2898_row3_col5\" class=\"data row3 col5\" >-12.0</td>\n",
       "      <td id=\"T_a2898_row3_col6\" class=\"data row3 col6\" >9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a2898_level0_row4\" class=\"row_heading level0 row4\" >('OGE', 'XEL')</th>\n",
       "      <td id=\"T_a2898_row4_col0\" class=\"data row4 col0\" >9.0</td>\n",
       "      <td id=\"T_a2898_row4_col1\" class=\"data row4 col1\" >0.0</td>\n",
       "      <td id=\"T_a2898_row4_col2\" class=\"data row4 col2\" >-1.0</td>\n",
       "      <td id=\"T_a2898_row4_col3\" class=\"data row4 col3\" >27.0</td>\n",
       "      <td id=\"T_a2898_row4_col4\" class=\"data row4 col4\" >9.0</td>\n",
       "      <td id=\"T_a2898_row4_col5\" class=\"data row4 col5\" >-4.0</td>\n",
       "      <td id=\"T_a2898_row4_col6\" class=\"data row4 col6\" >6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a2898_level0_row5\" class=\"row_heading level0 row5\" >avg</th>\n",
       "      <td id=\"T_a2898_row5_col0\" class=\"data row5 col0\" >53.4</td>\n",
       "      <td id=\"T_a2898_row5_col1\" class=\"data row5 col1\" >45.6</td>\n",
       "      <td id=\"T_a2898_row5_col2\" class=\"data row5 col2\" >44.2</td>\n",
       "      <td id=\"T_a2898_row5_col3\" class=\"data row5 col3\" >-0.8</td>\n",
       "      <td id=\"T_a2898_row5_col4\" class=\"data row5 col4\" >-1.0</td>\n",
       "      <td id=\"T_a2898_row5_col5\" class=\"data row5 col5\" >-22.8</td>\n",
       "      <td id=\"T_a2898_row5_col6\" class=\"data row5 col6\" >19.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<style type=\"text/css\">\n",
       "#T_52da1_row0_col2, #T_52da1_row0_col4, #T_52da1_row0_col6, #T_52da1_row1_col0, #T_52da1_row1_col1, #T_52da1_row1_col3, #T_52da1_row2_col5 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "#T_52da1_row2_col2, #T_52da1_row2_col3, #T_52da1_row2_col4, #T_52da1_row3_col5, #T_52da1_row4_col0, #T_52da1_row4_col1, #T_52da1_row4_col3, #T_52da1_row4_col6 {\n",
       "  background-color: lightsalmon;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_52da1_\" style='display:inline'>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" colspan=\"7\">2.0 DEV RETURN%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level1 col0\" >1D_no_exit</th>\n",
       "      <th class=\"col_heading level1 col1\" >1D_skp_sma</th>\n",
       "      <th class=\"col_heading level1 col2\" >1H_skp_sma</th>\n",
       "      <th class=\"col_heading level1 col3\" >1D_exit_sma</th>\n",
       "      <th class=\"col_heading level1 col4\" >1H_no_exit</th>\n",
       "      <th class=\"col_heading level1 col5\" >1H_exit_sma</th>\n",
       "      <th class=\"col_heading level1 col6\" >mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >pair</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_52da1_level0_row0\" class=\"row_heading level0 row0\" >('AEP', 'VST')</th>\n",
       "      <td id=\"T_52da1_row0_col0\" class=\"data row0 col0\" >65.0</td>\n",
       "      <td id=\"T_52da1_row0_col1\" class=\"data row0 col1\" >53.0</td>\n",
       "      <td id=\"T_52da1_row0_col2\" class=\"data row0 col2\" >67.0</td>\n",
       "      <td id=\"T_52da1_row0_col3\" class=\"data row0 col3\" >49.0</td>\n",
       "      <td id=\"T_52da1_row0_col4\" class=\"data row0 col4\" >81.0</td>\n",
       "      <td id=\"T_52da1_row0_col5\" class=\"data row0 col5\" >5.0</td>\n",
       "      <td id=\"T_52da1_row0_col6\" class=\"data row0 col6\" >53.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52da1_level0_row1\" class=\"row_heading level0 row1\" >('CNP', 'VST')</th>\n",
       "      <td id=\"T_52da1_row1_col0\" class=\"data row1 col0\" >66.0</td>\n",
       "      <td id=\"T_52da1_row1_col1\" class=\"data row1 col1\" >67.0</td>\n",
       "      <td id=\"T_52da1_row1_col2\" class=\"data row1 col2\" >21.0</td>\n",
       "      <td id=\"T_52da1_row1_col3\" class=\"data row1 col3\" >54.0</td>\n",
       "      <td id=\"T_52da1_row1_col4\" class=\"data row1 col4\" >11.0</td>\n",
       "      <td id=\"T_52da1_row1_col5\" class=\"data row1 col5\" >-22.0</td>\n",
       "      <td id=\"T_52da1_row1_col6\" class=\"data row1 col6\" >32.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52da1_level0_row2\" class=\"row_heading level0 row2\" >('OGE', 'XEL')</th>\n",
       "      <td id=\"T_52da1_row2_col0\" class=\"data row2 col0\" >20.0</td>\n",
       "      <td id=\"T_52da1_row2_col1\" class=\"data row2 col1\" >18.0</td>\n",
       "      <td id=\"T_52da1_row2_col2\" class=\"data row2 col2\" >16.0</td>\n",
       "      <td id=\"T_52da1_row2_col3\" class=\"data row2 col3\" >12.0</td>\n",
       "      <td id=\"T_52da1_row2_col4\" class=\"data row2 col4\" >1.0</td>\n",
       "      <td id=\"T_52da1_row2_col5\" class=\"data row2 col5\" >7.0</td>\n",
       "      <td id=\"T_52da1_row2_col6\" class=\"data row2 col6\" >12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52da1_level0_row3\" class=\"row_heading level0 row3\" >('ED', 'VST')</th>\n",
       "      <td id=\"T_52da1_row3_col0\" class=\"data row3 col0\" >14.0</td>\n",
       "      <td id=\"T_52da1_row3_col1\" class=\"data row3 col1\" >15.0</td>\n",
       "      <td id=\"T_52da1_row3_col2\" class=\"data row3 col2\" >36.0</td>\n",
       "      <td id=\"T_52da1_row3_col3\" class=\"data row3 col3\" >31.0</td>\n",
       "      <td id=\"T_52da1_row3_col4\" class=\"data row3 col4\" >3.0</td>\n",
       "      <td id=\"T_52da1_row3_col5\" class=\"data row3 col5\" >-27.0</td>\n",
       "      <td id=\"T_52da1_row3_col6\" class=\"data row3 col6\" >12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52da1_level0_row4\" class=\"row_heading level0 row4\" >('CMS', 'DUK')</th>\n",
       "      <td id=\"T_52da1_row4_col0\" class=\"data row4 col0\" >6.0</td>\n",
       "      <td id=\"T_52da1_row4_col1\" class=\"data row4 col1\" >10.0</td>\n",
       "      <td id=\"T_52da1_row4_col2\" class=\"data row4 col2\" >20.0</td>\n",
       "      <td id=\"T_52da1_row4_col3\" class=\"data row4 col3\" >12.0</td>\n",
       "      <td id=\"T_52da1_row4_col4\" class=\"data row4 col4\" >20.0</td>\n",
       "      <td id=\"T_52da1_row4_col5\" class=\"data row4 col5\" >2.0</td>\n",
       "      <td id=\"T_52da1_row4_col6\" class=\"data row4 col6\" >11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52da1_level0_row5\" class=\"row_heading level0 row5\" >avg</th>\n",
       "      <td id=\"T_52da1_row5_col0\" class=\"data row5 col0\" >34.2</td>\n",
       "      <td id=\"T_52da1_row5_col1\" class=\"data row5 col1\" >32.6</td>\n",
       "      <td id=\"T_52da1_row5_col2\" class=\"data row5 col2\" >32.0</td>\n",
       "      <td id=\"T_52da1_row5_col3\" class=\"data row5 col3\" >31.6</td>\n",
       "      <td id=\"T_52da1_row5_col4\" class=\"data row5 col4\" >23.2</td>\n",
       "      <td id=\"T_52da1_row5_col5\" class=\"data row5 col5\" >-7.0</td>\n",
       "      <td id=\"T_52da1_row5_col6\" class=\"data row5 col6\" >24.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<style type=\"text/css\">\n",
       "#T_230cf_row0_col0, #T_230cf_row0_col1, #T_230cf_row0_col2, #T_230cf_row0_col6, #T_230cf_row1_col3, #T_230cf_row1_col4, #T_230cf_row1_col5 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "#T_230cf_row0_col5, #T_230cf_row3_col2, #T_230cf_row3_col3, #T_230cf_row3_col4, #T_230cf_row4_col0, #T_230cf_row4_col1, #T_230cf_row4_col6 {\n",
       "  background-color: lightsalmon;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_230cf_\" style='display:inline'>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" colspan=\"7\">2.5 DEV RETURN%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level1 col0\" >1H_skp_sma</th>\n",
       "      <th class=\"col_heading level1 col1\" >1H_no_exit</th>\n",
       "      <th class=\"col_heading level1 col2\" >1H_exit_sma</th>\n",
       "      <th class=\"col_heading level1 col3\" >1D_exit_sma</th>\n",
       "      <th class=\"col_heading level1 col4\" >1D_skp_sma</th>\n",
       "      <th class=\"col_heading level1 col5\" >1D_no_exit</th>\n",
       "      <th class=\"col_heading level1 col6\" >mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >pair</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_230cf_level0_row0\" class=\"row_heading level0 row0\" >('AEP', 'VST')</th>\n",
       "      <td id=\"T_230cf_row0_col0\" class=\"data row0 col0\" >74.0</td>\n",
       "      <td id=\"T_230cf_row0_col1\" class=\"data row0 col1\" >82.0</td>\n",
       "      <td id=\"T_230cf_row0_col2\" class=\"data row0 col2\" >28.0</td>\n",
       "      <td id=\"T_230cf_row0_col3\" class=\"data row0 col3\" >0.0</td>\n",
       "      <td id=\"T_230cf_row0_col4\" class=\"data row0 col4\" >-2.0</td>\n",
       "      <td id=\"T_230cf_row0_col5\" class=\"data row0 col5\" >-13.0</td>\n",
       "      <td id=\"T_230cf_row0_col6\" class=\"data row0 col6\" >28.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_230cf_level0_row1\" class=\"row_heading level0 row1\" >('OGE', 'XEL')</th>\n",
       "      <td id=\"T_230cf_row1_col0\" class=\"data row1 col0\" >23.0</td>\n",
       "      <td id=\"T_230cf_row1_col1\" class=\"data row1 col1\" >1.0</td>\n",
       "      <td id=\"T_230cf_row1_col2\" class=\"data row1 col2\" >8.0</td>\n",
       "      <td id=\"T_230cf_row1_col3\" class=\"data row1 col3\" >12.0</td>\n",
       "      <td id=\"T_230cf_row1_col4\" class=\"data row1 col4\" >15.0</td>\n",
       "      <td id=\"T_230cf_row1_col5\" class=\"data row1 col5\" >29.0</td>\n",
       "      <td id=\"T_230cf_row1_col6\" class=\"data row1 col6\" >14.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_230cf_level0_row2\" class=\"row_heading level0 row2\" >('CMS', 'DUK')</th>\n",
       "      <td id=\"T_230cf_row2_col0\" class=\"data row2 col0\" >26.0</td>\n",
       "      <td id=\"T_230cf_row2_col1\" class=\"data row2 col1\" >26.0</td>\n",
       "      <td id=\"T_230cf_row2_col2\" class=\"data row2 col2\" >11.0</td>\n",
       "      <td id=\"T_230cf_row2_col3\" class=\"data row2 col3\" >1.0</td>\n",
       "      <td id=\"T_230cf_row2_col4\" class=\"data row2 col4\" >2.0</td>\n",
       "      <td id=\"T_230cf_row2_col5\" class=\"data row2 col5\" >-4.0</td>\n",
       "      <td id=\"T_230cf_row2_col6\" class=\"data row2 col6\" >10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_230cf_level0_row3\" class=\"row_heading level0 row3\" >('ED', 'VST')</th>\n",
       "      <td id=\"T_230cf_row3_col0\" class=\"data row3 col0\" >36.0</td>\n",
       "      <td id=\"T_230cf_row3_col1\" class=\"data row3 col1\" >0.0</td>\n",
       "      <td id=\"T_230cf_row3_col2\" class=\"data row3 col2\" >3.0</td>\n",
       "      <td id=\"T_230cf_row3_col3\" class=\"data row3 col3\" >-1.0</td>\n",
       "      <td id=\"T_230cf_row3_col4\" class=\"data row3 col4\" >-4.0</td>\n",
       "      <td id=\"T_230cf_row3_col5\" class=\"data row3 col5\" >-10.0</td>\n",
       "      <td id=\"T_230cf_row3_col6\" class=\"data row3 col6\" >4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_230cf_level0_row4\" class=\"row_heading level0 row4\" >('CNP', 'VST')</th>\n",
       "      <td id=\"T_230cf_row4_col0\" class=\"data row4 col0\" >21.0</td>\n",
       "      <td id=\"T_230cf_row4_col1\" class=\"data row4 col1\" >-31.0</td>\n",
       "      <td id=\"T_230cf_row4_col2\" class=\"data row4 col2\" >10.0</td>\n",
       "      <td id=\"T_230cf_row4_col3\" class=\"data row4 col3\" >6.0</td>\n",
       "      <td id=\"T_230cf_row4_col4\" class=\"data row4 col4\" >6.0</td>\n",
       "      <td id=\"T_230cf_row4_col5\" class=\"data row4 col5\" >-6.0</td>\n",
       "      <td id=\"T_230cf_row4_col6\" class=\"data row4 col6\" >1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_230cf_level0_row5\" class=\"row_heading level0 row5\" >avg</th>\n",
       "      <td id=\"T_230cf_row5_col0\" class=\"data row5 col0\" >36.0</td>\n",
       "      <td id=\"T_230cf_row5_col1\" class=\"data row5 col1\" >15.6</td>\n",
       "      <td id=\"T_230cf_row5_col2\" class=\"data row5 col2\" >12.0</td>\n",
       "      <td id=\"T_230cf_row5_col3\" class=\"data row5 col3\" >3.6</td>\n",
       "      <td id=\"T_230cf_row5_col4\" class=\"data row5 col4\" >3.4</td>\n",
       "      <td id=\"T_230cf_row5_col5\" class=\"data row5 col5\" >-0.8</td>\n",
       "      <td id=\"T_230cf_row5_col6\" class=\"data row5 col6\" >11.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<style type=\"text/css\">\n",
       "#T_9b051_row0_col0, #T_9b051_row0_col1, #T_9b051_row0_col2, #T_9b051_row0_col3, #T_9b051_row0_col6, #T_9b051_row3_col4, #T_9b051_row4_col5 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "#T_9b051_row0_col5, #T_9b051_row1_col4, #T_9b051_row4_col0, #T_9b051_row4_col1, #T_9b051_row4_col2, #T_9b051_row4_col3, #T_9b051_row4_col6 {\n",
       "  background-color: lightsalmon;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_9b051_\" style='display:inline'>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" colspan=\"7\">3.0 DEV RETURN%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level1 col0\" >1H_skp_sma</th>\n",
       "      <th class=\"col_heading level1 col1\" >1H_no_exit</th>\n",
       "      <th class=\"col_heading level1 col2\" >1H_exit_sma</th>\n",
       "      <th class=\"col_heading level1 col3\" >1D_exit_sma</th>\n",
       "      <th class=\"col_heading level1 col4\" >1D_skp_sma</th>\n",
       "      <th class=\"col_heading level1 col5\" >1D_no_exit</th>\n",
       "      <th class=\"col_heading level1 col6\" >mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >pair</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9b051_level0_row0\" class=\"row_heading level0 row0\" >('AEP', 'VST')</th>\n",
       "      <td id=\"T_9b051_row0_col0\" class=\"data row0 col0\" >56.0</td>\n",
       "      <td id=\"T_9b051_row0_col1\" class=\"data row0 col1\" >97.0</td>\n",
       "      <td id=\"T_9b051_row0_col2\" class=\"data row0 col2\" >22.0</td>\n",
       "      <td id=\"T_9b051_row0_col3\" class=\"data row0 col3\" >8.0</td>\n",
       "      <td id=\"T_9b051_row0_col4\" class=\"data row0 col4\" >2.0</td>\n",
       "      <td id=\"T_9b051_row0_col5\" class=\"data row0 col5\" >-10.0</td>\n",
       "      <td id=\"T_9b051_row0_col6\" class=\"data row0 col6\" >29.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b051_level0_row1\" class=\"row_heading level0 row1\" >('CMS', 'DUK')</th>\n",
       "      <td id=\"T_9b051_row1_col0\" class=\"data row1 col0\" >20.0</td>\n",
       "      <td id=\"T_9b051_row1_col1\" class=\"data row1 col1\" >12.0</td>\n",
       "      <td id=\"T_9b051_row1_col2\" class=\"data row1 col2\" >8.0</td>\n",
       "      <td id=\"T_9b051_row1_col3\" class=\"data row1 col3\" >0.0</td>\n",
       "      <td id=\"T_9b051_row1_col4\" class=\"data row1 col4\" >0.0</td>\n",
       "      <td id=\"T_9b051_row1_col5\" class=\"data row1 col5\" >0.0</td>\n",
       "      <td id=\"T_9b051_row1_col6\" class=\"data row1 col6\" >6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b051_level0_row2\" class=\"row_heading level0 row2\" >('ED', 'VST')</th>\n",
       "      <td id=\"T_9b051_row2_col0\" class=\"data row2 col0\" >17.0</td>\n",
       "      <td id=\"T_9b051_row2_col1\" class=\"data row2 col1\" >7.0</td>\n",
       "      <td id=\"T_9b051_row2_col2\" class=\"data row2 col2\" >7.0</td>\n",
       "      <td id=\"T_9b051_row2_col3\" class=\"data row2 col3\" >7.0</td>\n",
       "      <td id=\"T_9b051_row2_col4\" class=\"data row2 col4\" >4.0</td>\n",
       "      <td id=\"T_9b051_row2_col5\" class=\"data row2 col5\" >-2.0</td>\n",
       "      <td id=\"T_9b051_row2_col6\" class=\"data row2 col6\" >6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b051_level0_row3\" class=\"row_heading level0 row3\" >('CNP', 'VST')</th>\n",
       "      <td id=\"T_9b051_row3_col0\" class=\"data row3 col0\" >26.0</td>\n",
       "      <td id=\"T_9b051_row3_col1\" class=\"data row3 col1\" >-1.0</td>\n",
       "      <td id=\"T_9b051_row3_col2\" class=\"data row3 col2\" >3.0</td>\n",
       "      <td id=\"T_9b051_row3_col3\" class=\"data row3 col3\" >6.0</td>\n",
       "      <td id=\"T_9b051_row3_col4\" class=\"data row3 col4\" >6.0</td>\n",
       "      <td id=\"T_9b051_row3_col5\" class=\"data row3 col5\" >-6.0</td>\n",
       "      <td id=\"T_9b051_row3_col6\" class=\"data row3 col6\" >5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b051_level0_row4\" class=\"row_heading level0 row4\" >('OGE', 'XEL')</th>\n",
       "      <td id=\"T_9b051_row4_col0\" class=\"data row4 col0\" >9.0</td>\n",
       "      <td id=\"T_9b051_row4_col1\" class=\"data row4 col1\" >-6.0</td>\n",
       "      <td id=\"T_9b051_row4_col2\" class=\"data row4 col2\" >-4.0</td>\n",
       "      <td id=\"T_9b051_row4_col3\" class=\"data row4 col3\" >-1.0</td>\n",
       "      <td id=\"T_9b051_row4_col4\" class=\"data row4 col4\" >2.0</td>\n",
       "      <td id=\"T_9b051_row4_col5\" class=\"data row4 col5\" >3.0</td>\n",
       "      <td id=\"T_9b051_row4_col6\" class=\"data row4 col6\" >0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b051_level0_row5\" class=\"row_heading level0 row5\" >avg</th>\n",
       "      <td id=\"T_9b051_row5_col0\" class=\"data row5 col0\" >25.6</td>\n",
       "      <td id=\"T_9b051_row5_col1\" class=\"data row5 col1\" >21.8</td>\n",
       "      <td id=\"T_9b051_row5_col2\" class=\"data row5 col2\" >7.2</td>\n",
       "      <td id=\"T_9b051_row5_col3\" class=\"data row5 col3\" >4.0</td>\n",
       "      <td id=\"T_9b051_row5_col4\" class=\"data row5 col4\" >2.8</td>\n",
       "      <td id=\"T_9b051_row5_col5\" class=\"data row5 col5\" >-3.0</td>\n",
       "      <td id=\"T_9b051_row5_col6\" class=\"data row5 col6\" >9.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2_std1_0leveled = df2_sort_leveled[0]\n",
    "df2_std1_5leveled = df2_sort_leveled[1]\n",
    "df2_std2_0leveled = df2_sort_leveled[2]\n",
    "df2_std2_5leveled = df2_sort_leveled[3]\n",
    "df2_std3_0leveled = df2_sort_leveled[4]\n",
    "\n",
    "\n",
    "std1_0style = df2_std1_0leveled.style.set_table_attributes(\"style='display:inline; margin-right:20px;'\")\\\n",
    "        .highlight_max(color = 'lightgreen', axis = 0)\\\n",
    "        .highlight_min(color = 'lightsalmon', axis = 0)\\\n",
    "        .format('{:,}')\n",
    "\n",
    "std1_5style = df2_std1_5leveled.style.set_table_attributes(\"style='display:inline'\")\\\n",
    "        .highlight_max(color = 'lightgreen', axis = 0)\\\n",
    "        .highlight_min(color = 'lightsalmon', axis = 0)\\\n",
    "        .format('{:,}')\n",
    "\n",
    "std2_0style = df2_std2_0leveled.style.set_table_attributes(\"style='display:inline'\")\\\n",
    "        .highlight_max(color = 'lightgreen', axis = 0)\\\n",
    "        .highlight_min(color = 'lightsalmon', axis = 0)\\\n",
    "        .format('{:,}')\n",
    "\n",
    "std2_5style = df2_std2_5leveled.style.set_table_attributes(\"style='display:inline'\")\\\n",
    "        .highlight_max(color = 'lightgreen', axis = 0)\\\n",
    "        .highlight_min(color = 'lightsalmon', axis = 0)\\\n",
    "        .format('{:,}')\n",
    "\n",
    "std3_0style = df2_std3_0leveled.style.set_table_attributes(\"style='display:inline'\")\\\n",
    "        .highlight_max(color = 'lightgreen', axis = 0)\\\n",
    "        .highlight_min(color = 'lightsalmon', axis = 0)\\\n",
    "        .format('{:,}')\n",
    "\n",
    "display_html(std1_0style._repr_html_() + std1_5style._repr_html_() + std2_0style._repr_html_() + std2_5style._repr_html_() + std3_0style._repr_html_(), raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "id": "558b67c6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:27:23.288156Z",
     "start_time": "2022-11-01T17:27:18.370063Z"
    }
   },
   "outputs": [],
   "source": [
    "dpi_set=100\n",
    "dfi.export(std1_0style, 'table1.png', dpi=dpi_set)\n",
    "dfi.export(std1_5style, 'table2.png', dpi=dpi_set)\n",
    "dfi.export(std2_0style, 'table3.png', dpi=dpi_set)\n",
    "dfi.export(std2_5style, 'table4.png', dpi=dpi_set)\n",
    "dfi.export(std3_0style, 'table5.png', dpi=dpi_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fb2af9b",
   "metadata": {},
   "source": [
    "Get col. values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "id": "b8ee49e2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:27:23.305158Z",
     "start_time": "2022-11-01T17:27:23.292157Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# df2_sort_leveled[2][('2.0 DEV RETURN%', '1H_exit_sma')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "297509da",
   "metadata": {},
   "source": [
    "Get index values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "id": "7d0ccf34",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:27:23.335157Z",
     "start_time": "2022-11-01T17:27:23.311163Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# df2_all1.xs((1,('LNT', 'NEE')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6ea7d1e",
   "metadata": {},
   "source": [
    "Average returns by selected standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "3a5b1b83",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:27:23.367157Z",
     "start_time": "2022-11-01T17:27:23.339157Z"
    },
    "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>1D_exit_sma</th>\n",
       "      <th>1D_no_exit</th>\n",
       "      <th>1D_skp_sma</th>\n",
       "      <th>1H_exit_sma</th>\n",
       "      <th>1H_no_exit</th>\n",
       "      <th>1H_skp_sma</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bb</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>40.6</td>\n",
       "      <td>46.0</td>\n",
       "      <td>46.4</td>\n",
       "      <td>-40.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>-8.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>45.6</td>\n",
       "      <td>44.2</td>\n",
       "      <td>53.4</td>\n",
       "      <td>-22.8</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>31.6</td>\n",
       "      <td>34.2</td>\n",
       "      <td>32.6</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5</th>\n",
       "      <td>3.6</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>7.2</td>\n",
       "      <td>21.8</td>\n",
       "      <td>25.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     1D_exit_sma  1D_no_exit  1D_skp_sma  1H_exit_sma  1H_no_exit  1H_skp_sma\n",
       "bb                                                                           \n",
       "1.0         40.6        46.0        46.4        -40.4         2.4        -8.6\n",
       "1.5         45.6        44.2        53.4        -22.8        -1.0        -0.8\n",
       "2.0         31.6        34.2        32.6         -7.0        23.2        32.0\n",
       "2.5          3.6        -0.8         3.4         12.0        15.6        36.0\n",
       "3.0          4.0        -3.0         2.8          7.2        21.8        25.6"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_all1.groupby([\"bb\"]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af5a8c68",
   "metadata": {},
   "source": [
    "Mean,Max,Min by selected standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "b30fa00b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:27:23.398159Z",
     "start_time": "2022-11-01T17:27:23.374160Z"
    },
    "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">1H_no_exit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bb</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>2.4</td>\n",
       "      <td>32</td>\n",
       "      <td>-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>17</td>\n",
       "      <td>-36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>23.2</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5</th>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>21.8</td>\n",
       "      <td>97</td>\n",
       "      <td>-6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    1H_no_exit        \n",
       "          mean max min\n",
       "bb                    \n",
       "1.0        2.4  32 -28\n",
       "1.5       -1.0  17 -36\n",
       "2.0       23.2  81   1\n",
       "2.5       15.6  82 -31\n",
       "3.0       21.8  97  -6"
      ]
     },
     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_all1.groupby([\"bb\"]).agg({\"1H_no_exit\": [\"mean\", \"max\", \"min\"]})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2342fa60",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-26T20:47:58.654687Z",
     "start_time": "2022-10-26T20:47:58.641680Z"
    }
   },
   "source": [
    "### EXERCISE : PRINT TO PDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "id": "970ed781",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:27:37.180308Z",
     "start_time": "2022-11-01T17:27:23.403160Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import fpdf\n",
    "from fpdf import FPDF\n",
    "import time\n",
    "from PIL import Image\n",
    "\n",
    "#find the table size\n",
    "im = Image.open(\"summary1.png\")\n",
    "\n",
    "#image size\n",
    "width_table = im.size[0]*(25.4/dpi_set) #converted to approx mm\n",
    "height_table = im.size[1]*(25.4/dpi_set) #converted to approx mm\n",
    "\n",
    "def write_big(pdf, words):\n",
    "    \n",
    "    # Set text colour, font size, and font type\n",
    "    pdf.set_text_color(r=0,g=0,b=0)\n",
    "    pdf.set_font('Helvetica', '', 12)\n",
    "    \n",
    "    pdf.write(5, words)\n",
    "    \n",
    "def write_small(pdf, words):\n",
    "    \n",
    "    # Set text colour, font size, and font type\n",
    "    pdf.set_text_color(r=0,g=0,b=0)\n",
    "    pdf.set_font('Helvetica', 'b', 8)\n",
    "    \n",
    "    pdf.write(5, words)\n",
    "\n",
    "title = \"Summary Report (\" +report_heading+ \")\"\n",
    "# A4 (210 by 297 mm)\n",
    "WIDTH = 210\n",
    "HEIGHT = 297\n",
    "\n",
    "# Create PDF\n",
    "pdf = FPDF() \n",
    "\n",
    "# Add Page\n",
    "pdf.add_page()\n",
    "\n",
    "# Add main title\n",
    "pdf.set_font('Helvetica', 'b', 20)  \n",
    "pdf.write(5, title)\n",
    "pdf.ln(8)\n",
    "\n",
    "# Add date of report\n",
    "pdf.set_font('Helvetica', '', 14)\n",
    "pdf.set_text_color(r=128,g=128,b=128)\n",
    "today = time.strftime(\"%d/%m/%Y\")\n",
    "pdf.write(4, f'{today}')\n",
    "pdf.ln(10)\n",
    "    \n",
    "# Add explanation\n",
    "write_big(pdf, \"Selected Pairs:\")\n",
    "pdf.ln(5)\n",
    "\n",
    "# Add pairs\n",
    "for ind in df2_2_0std.index:\n",
    "    ticker1 = ind[0]\n",
    "    ticker2 = ind[1]\n",
    "    hedge = pairs_dic[ind]\n",
    "    pairtext = f\"| {ticker2}={hedge}*{ticker1} \"\n",
    "    write_small(pdf, pairtext)\n",
    "write_small(pdf, \"|\")\n",
    "pdf.ln(5)\n",
    "# Write avg returns\n",
    "# write_small(pdf, \"Average Returns:\")\n",
    "# for col in df2_2_0std.columns:\n",
    "#     returntext = f\" {col} : {round(df2_2_0std[col].mean(),2)}% \"\n",
    "#     write_small(pdf, returntext)\n",
    "\n",
    "    # Add tables\n",
    "pos_y = 50\n",
    "pdf.image(\"summary1.png\", x=5, y=pos_y, w=WIDTH/2.2)\n",
    "pdf.image(\"summary2.png\", x=WIDTH/2, y=pos_y, w=WIDTH/2.2)\n",
    "\n",
    "if len(df2_2_0std.index)<=16:\n",
    "    # Add the generated visualisations to the PDF\n",
    "    plot_y = pos_y + int(height_table/1.2)\n",
    "    pdf.image(\"summary_plot1.png\", x=8, y=plot_y, w=190)\n",
    "    \n",
    "elif len(df2_2_0std.index)<=24:\n",
    "    # Add Another Page\n",
    "    pdf.add_page()\n",
    "    pdf.image(\"summary_plot1.png\", w=190)\n",
    "else:\n",
    "    pass\n",
    "\n",
    "# Add Swarm Plot\n",
    "pdf.add_page()\n",
    "pdf.set_font('Helvetica', 'b', 14)  \n",
    "pdf.write(5, \"Trade & Profit/Loss Distribution:\")\n",
    "pdf.ln(5)\n",
    "pdf.set_font('Helvetica', 'b', 10)\n",
    "pdf.write(3, \"(2 STD DEV)\")\n",
    "pdf.ln(8)\n",
    "pdf.image(\"summary_plot2.png\", w=190)\n",
    "\n",
    "\n",
    "# Add Another Page\n",
    "#pdf.add_page(orientation=\"landscape\")\n",
    "pdf.add_page()\n",
    "\n",
    "# Add main title\n",
    "pdf.set_font('Helvetica', 'b', 14)  \n",
    "pdf.write(5, \"Detailed Tables(costs included.):\")\n",
    "pdf.ln(8)\n",
    "\n",
    "# Add tables landscape 2 x 3\n",
    "# pos_y = 20\n",
    "# pos_x = 4\n",
    "# pdf.image(\"table1.png\", x=pos_x, y=pos_y, w=HEIGHT/3.2)\n",
    "# pdf.image(\"table2.png\", x=pos_x+HEIGHT/3, y=pos_y, w=HEIGHT/3.2)\n",
    "# pdf.image(\"table3.png\", x=pos_x+2*(HEIGHT/3), y=pos_y, w=HEIGHT/3.2)\n",
    "# pos_y = pos_y + WIDTH/2 -len(df2_2_0std.index)\n",
    "# pdf.image(\"table4.png\", x=pos_x, y=pos_y, w=HEIGHT/3.2)\n",
    "# pdf.image(\"table5.png\", x=pos_x+HEIGHT/3, y=pos_y, w=HEIGHT/3.2)\n",
    "\n",
    "# Add tables 1 x 1\n",
    "pdf.ln(2)\n",
    "pdf.image(\"table1.png\", w=WIDTH/1.4)\n",
    "pdf.ln(4)\n",
    "pdf.image(\"table2.png\", w=WIDTH/1.4)\n",
    "pdf.ln(4)\n",
    "pdf.image(\"table3.png\", w=WIDTH/1.4)\n",
    "pdf.ln(4)\n",
    "pdf.image(\"table4.png\", w=WIDTH/1.4)\n",
    "pdf.ln(4)\n",
    "pdf.image(\"table5.png\", w=WIDTH/1.4)\n",
    "\n",
    "\n",
    "# Generate the PDF\n",
    "today = time.strftime(\"%d_%m_%Y\")\n",
    "filename = f\"report/summary_{today}_{report_heading}.pdf\"\n",
    "pdf.output(filename, 'F')"
   ]
  }
 ],
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