ranton-mean-reversion-study.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ranton Mean Reversion: Study 1\n",
    "\n",
    "Use the \"Run\" button to execute the code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Importing libraries & packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "!pip install jovian ccxt backtesting pyti dateparser pandas_ta plotly --upgrade --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import ccxt\n",
    "import jovian\n",
    "import requests\n",
    "import math\n",
    "import datetime\n",
    "import concurrent\n",
    "import os\n",
    "import glob\n",
    "import time\n",
    "import dateparser\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_ta as ta\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.tsa.stattools as ts\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "import scipy.optimize as spop\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "from pyti import bollinger_bands\n",
    "from pyti.average_true_range import average_true_range as average_true_range\n",
    "from pyti import exponential_moving_average"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enter Symbols:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1= ['DASHUSDT', 'ICXUSDT', 'DASHUSDT',\n",
    "     'DASHUSDT','VETUSDT', 'SOLUSDT', 'DASHUSDT',\n",
    "     'SOLUSDT', 'OMGUSDT', 'DASHUSDT', 'DASHUSDT']\n",
    "\n",
    "s2 = ['VETUSDT', 'IOTAUSDT', 'IOSTUSDT',\n",
    "     'DOTUSDT', 'ICXUSDT', 'DOTUSDT', 'HBARUSDT',\n",
    "     'VETUSDT', 'QTUMUSDT', 'SOLUSDT', 'LTCUSDT']\n",
    "\n",
    "coins = ['DASHUSDT','SOLUSDT']\n",
    "timeinterval = '4h'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DASHUSDTPERP - VETUSDTPERP\n",
      "ICXUSDTPERP - IOTAUSDTPERP\n",
      "DASHUSDTPERP - IOSTUSDTPERP\n",
      "DASHUSDTPERP - DOTUSDTPERP\n",
      "VETUSDTPERP - ICXUSDTPERP\n",
      "SOLUSDTPERP - DOTUSDTPERP\n",
      "DASHUSDTPERP - HBARUSDTPERP\n",
      "SOLUSDTPERP - VETUSDTPERP\n",
      "OMGUSDTPERP - QTUMUSDTPERP\n",
      "DASHUSDTPERP - SOLUSDTPERP\n",
      "DASHUSDTPERP - LTCUSDTPERP\n"
     ]
    }
   ],
   "source": [
    "for i,j in zip(s1,s2):\n",
    "        print(f'{i}PERP - {j}PERP')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Data collection & cleaning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "code_folding": [
     3
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "binance = ccxt.binanceusdm({'options': {'enableRateLimit': True}})\n",
    "\n",
    "def getdata(symbol, interval):\n",
    "    '''Function to fetch historical closing prices of futures ticker\n",
    "        symbol: futures ticker symbol as string\n",
    "        interval: timeframe as string '5m', '1h','1d'\n",
    "    '''\n",
    "    \n",
    "    data = pd.DataFrame(binance.fetch_ohlcv( symbol, interval, limit=1500))\n",
    "    data.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume']\n",
    "    data['Date'] = pd.to_datetime(data['Date'], unit='ms') + pd.Timedelta(hours=2)\n",
    "    data = data.set_index('Date')\n",
    "    data = data.drop(['Open', 'High', 'Low', 'Volume'], axis=1)\n",
    "    data.rename(columns={'Close': f'{symbol}'}, inplace=True)\n",
    "    \n",
    "    return data\n",
    "\n",
    "dfs=[]\n",
    "interval = timeinterval\n",
    "\n",
    "\n",
    "for coin in coins:\n",
    "    dfs.append(getdata(coin, interval))\n",
    "    \n",
    "data = pd.concat(dfs, axis=1)\n",
    "returns = np.log(data.pct_change()+1)\n",
    "returns = returns.iloc[1:,:]\n",
    "data = data.iloc[1:,:]\n",
    "#data = data.loc[year:]\n",
    "data = data.dropna()\n",
    "\n",
    "gross_returns = np.array([])\n",
    "net_returns = np.array([])\n",
    "t_s = np.array([])\n",
    "coin1 = coins[0]\n",
    "coin2 =  coins[1]\n",
    "\n",
    "data['close'] = data[coin1]- data[coin2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Bollinger Bands dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DASHUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "      <th>close</th>\n",
       "      <th>upper</th>\n",
       "      <th>lower</th>\n",
       "      <th>above_upper_bb</th>\n",
       "      <th>below_lower_bb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-08-23 02:00:00</th>\n",
       "      <td>47.01</td>\n",
       "      <td>35.20</td>\n",
       "      <td>11.81</td>\n",
       "      <td>11.994950</td>\n",
       "      <td>8.618050</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 06:00:00</th>\n",
       "      <td>46.90</td>\n",
       "      <td>34.94</td>\n",
       "      <td>11.96</td>\n",
       "      <td>12.172069</td>\n",
       "      <td>8.738931</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 10:00:00</th>\n",
       "      <td>47.98</td>\n",
       "      <td>35.66</td>\n",
       "      <td>12.32</td>\n",
       "      <td>12.348383</td>\n",
       "      <td>8.918617</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 14:00:00</th>\n",
       "      <td>47.92</td>\n",
       "      <td>35.69</td>\n",
       "      <td>12.23</td>\n",
       "      <td>12.451200</td>\n",
       "      <td>9.154800</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 18:00:00</th>\n",
       "      <td>47.98</td>\n",
       "      <td>35.30</td>\n",
       "      <td>12.68</td>\n",
       "      <td>12.658998</td>\n",
       "      <td>9.284002</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     DASHUSDT  SOLUSDT  close      upper     lower  \\\n",
       "Date                                                                 \n",
       "2022-08-23 02:00:00     47.01    35.20  11.81  11.994950  8.618050   \n",
       "2022-08-23 06:00:00     46.90    34.94  11.96  12.172069  8.738931   \n",
       "2022-08-23 10:00:00     47.98    35.66  12.32  12.348383  8.918617   \n",
       "2022-08-23 14:00:00     47.92    35.69  12.23  12.451200  9.154800   \n",
       "2022-08-23 18:00:00     47.98    35.30  12.68  12.658998  9.284002   \n",
       "\n",
       "                     above_upper_bb  below_lower_bb  \n",
       "Date                                                 \n",
       "2022-08-23 02:00:00           False           False  \n",
       "2022-08-23 06:00:00           False           False  \n",
       "2022-08-23 10:00:00           False           False  \n",
       "2022-08-23 14:00:00           False           False  \n",
       "2022-08-23 18:00:00            True           False  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bb  = data.copy()\n",
    "df_bb['upper'] = bollinger_bands.upper_bollinger_band(df_bb.close, period=20, std_mult=1.9)\n",
    "df_bb['lower'] = bollinger_bands.lower_bollinger_band(df_bb.close, period=20, std=1.9)\n",
    "df_bb['above_upper_bb'] = df_bb.close > df_bb.upper\n",
    "df_bb['below_lower_bb'] = df_bb.close < df_bb.lower\n",
    "df_bb.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    ">\n",
    "\n",
    "Price closes above the upper bollinger band:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DASHUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "      <th>close</th>\n",
       "      <th>upper</th>\n",
       "      <th>lower</th>\n",
       "      <th>above_upper_bb</th>\n",
       "      <th>below_lower_bb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-08-17 18:00:00</th>\n",
       "      <td>53.15</td>\n",
       "      <td>40.72</td>\n",
       "      <td>12.43</td>\n",
       "      <td>11.423100</td>\n",
       "      <td>8.530900</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-17 22:00:00</th>\n",
       "      <td>53.04</td>\n",
       "      <td>40.51</td>\n",
       "      <td>12.53</td>\n",
       "      <td>11.862065</td>\n",
       "      <td>8.294935</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-18 02:00:00</th>\n",
       "      <td>53.46</td>\n",
       "      <td>40.80</td>\n",
       "      <td>12.66</td>\n",
       "      <td>12.285692</td>\n",
       "      <td>8.126308</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-22 14:00:00</th>\n",
       "      <td>46.43</td>\n",
       "      <td>34.78</td>\n",
       "      <td>11.65</td>\n",
       "      <td>11.381344</td>\n",
       "      <td>8.549656</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 18:00:00</th>\n",
       "      <td>47.98</td>\n",
       "      <td>35.30</td>\n",
       "      <td>12.68</td>\n",
       "      <td>12.658998</td>\n",
       "      <td>9.284002</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     DASHUSDT  SOLUSDT  close      upper     lower  \\\n",
       "Date                                                                 \n",
       "2022-08-17 18:00:00     53.15    40.72  12.43  11.423100  8.530900   \n",
       "2022-08-17 22:00:00     53.04    40.51  12.53  11.862065  8.294935   \n",
       "2022-08-18 02:00:00     53.46    40.80  12.66  12.285692  8.126308   \n",
       "2022-08-22 14:00:00     46.43    34.78  11.65  11.381344  8.549656   \n",
       "2022-08-23 18:00:00     47.98    35.30  12.68  12.658998  9.284002   \n",
       "\n",
       "                     above_upper_bb  below_lower_bb  \n",
       "Date                                                 \n",
       "2022-08-17 18:00:00            True           False  \n",
       "2022-08-17 22:00:00            True           False  \n",
       "2022-08-18 02:00:00            True           False  \n",
       "2022-08-22 14:00:00            True           False  \n",
       "2022-08-23 18:00:00            True           False  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bb[(df_bb.above_upper_bb == True)].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    ">\n",
    "\n",
    "Price closes below the lower bollinger band:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DASHUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "      <th>close</th>\n",
       "      <th>upper</th>\n",
       "      <th>lower</th>\n",
       "      <th>above_upper_bb</th>\n",
       "      <th>below_lower_bb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-07-30 18:00:00</th>\n",
       "      <td>51.75</td>\n",
       "      <td>45.07</td>\n",
       "      <td>6.68</td>\n",
       "      <td>11.150247</td>\n",
       "      <td>6.896753</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-12 22:00:00</th>\n",
       "      <td>56.09</td>\n",
       "      <td>45.46</td>\n",
       "      <td>10.63</td>\n",
       "      <td>13.016932</td>\n",
       "      <td>11.098068</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-13 02:00:00</th>\n",
       "      <td>56.78</td>\n",
       "      <td>47.40</td>\n",
       "      <td>9.38</td>\n",
       "      <td>13.375555</td>\n",
       "      <td>10.453445</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-13 06:00:00</th>\n",
       "      <td>56.44</td>\n",
       "      <td>47.17</td>\n",
       "      <td>9.27</td>\n",
       "      <td>13.548182</td>\n",
       "      <td>9.953818</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-13 10:00:00</th>\n",
       "      <td>56.12</td>\n",
       "      <td>46.73</td>\n",
       "      <td>9.39</td>\n",
       "      <td>13.654852</td>\n",
       "      <td>9.579148</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     DASHUSDT  SOLUSDT  close      upper      lower  \\\n",
       "Date                                                                  \n",
       "2022-07-30 18:00:00     51.75    45.07   6.68  11.150247   6.896753   \n",
       "2022-08-12 22:00:00     56.09    45.46  10.63  13.016932  11.098068   \n",
       "2022-08-13 02:00:00     56.78    47.40   9.38  13.375555  10.453445   \n",
       "2022-08-13 06:00:00     56.44    47.17   9.27  13.548182   9.953818   \n",
       "2022-08-13 10:00:00     56.12    46.73   9.39  13.654852   9.579148   \n",
       "\n",
       "                     above_upper_bb  below_lower_bb  \n",
       "Date                                                 \n",
       "2022-07-30 18:00:00           False            True  \n",
       "2022-08-12 22:00:00           False            True  \n",
       "2022-08-13 02:00:00           False            True  \n",
       "2022-08-13 06:00:00           False            True  \n",
       "2022-08-13 10:00:00           False            True  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bb[(df_bb.below_lower_bb == True)].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### ATR Bands dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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       "    .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>DASHUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "      <th>close</th>\n",
       "      <th>atr</th>\n",
       "      <th>sell_stop_loss</th>\n",
       "      <th>buy_stop_loss</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-08-23 02:00:00</th>\n",
       "      <td>47.01</td>\n",
       "      <td>35.20</td>\n",
       "      <td>11.81</td>\n",
       "      <td>2.597411</td>\n",
       "      <td>15.706116</td>\n",
       "      <td>7.913884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 06:00:00</th>\n",
       "      <td>46.90</td>\n",
       "      <td>34.94</td>\n",
       "      <td>11.96</td>\n",
       "      <td>2.558917</td>\n",
       "      <td>15.798375</td>\n",
       "      <td>8.121625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 10:00:00</th>\n",
       "      <td>47.98</td>\n",
       "      <td>35.66</td>\n",
       "      <td>12.32</td>\n",
       "      <td>2.546989</td>\n",
       "      <td>16.140484</td>\n",
       "      <td>8.499516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 14:00:00</th>\n",
       "      <td>47.92</td>\n",
       "      <td>35.69</td>\n",
       "      <td>12.23</td>\n",
       "      <td>2.535856</td>\n",
       "      <td>16.033785</td>\n",
       "      <td>8.426215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 18:00:00</th>\n",
       "      <td>47.98</td>\n",
       "      <td>35.30</td>\n",
       "      <td>12.68</td>\n",
       "      <td>2.549466</td>\n",
       "      <td>16.504199</td>\n",
       "      <td>8.855801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     DASHUSDT  SOLUSDT  close       atr  sell_stop_loss  \\\n",
       "Date                                                                      \n",
       "2022-08-23 02:00:00     47.01    35.20  11.81  2.597411       15.706116   \n",
       "2022-08-23 06:00:00     46.90    34.94  11.96  2.558917       15.798375   \n",
       "2022-08-23 10:00:00     47.98    35.66  12.32  2.546989       16.140484   \n",
       "2022-08-23 14:00:00     47.92    35.69  12.23  2.535856       16.033785   \n",
       "2022-08-23 18:00:00     47.98    35.30  12.68  2.549466       16.504199   \n",
       "\n",
       "                     buy_stop_loss  \n",
       "Date                                \n",
       "2022-08-23 02:00:00       7.913884  \n",
       "2022-08-23 06:00:00       8.121625  \n",
       "2022-08-23 10:00:00       8.499516  \n",
       "2022-08-23 14:00:00       8.426215  \n",
       "2022-08-23 18:00:00       8.855801  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_atr = data.copy()\n",
    "atr_multiplier = 1.5\n",
    "df_atr['atr'] = average_true_range(df_atr.close,15)\n",
    "df_atr['sell_stop_loss'] = (df_atr.close + df_atr['atr'] * atr_multiplier) #atr_upper_band\n",
    "df_atr['buy_stop_loss'] = (df_atr.close - df_atr['atr'] * atr_multiplier) #atr_lower_band\n",
    "df_atr.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 200 Period EMA dataframe: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>DASHUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "      <th>close</th>\n",
       "      <th>ema_200</th>\n",
       "      <th>price_above_ema_200</th>\n",
       "      <th>price_below_ema_200</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-08-23 02:00:00</th>\n",
       "      <td>47.01</td>\n",
       "      <td>35.20</td>\n",
       "      <td>11.81</td>\n",
       "      <td>10.384687</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 06:00:00</th>\n",
       "      <td>46.90</td>\n",
       "      <td>34.94</td>\n",
       "      <td>11.96</td>\n",
       "      <td>10.411092</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 10:00:00</th>\n",
       "      <td>47.98</td>\n",
       "      <td>35.66</td>\n",
       "      <td>12.32</td>\n",
       "      <td>10.440396</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 14:00:00</th>\n",
       "      <td>47.92</td>\n",
       "      <td>35.69</td>\n",
       "      <td>12.23</td>\n",
       "      <td>10.468186</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 18:00:00</th>\n",
       "      <td>47.98</td>\n",
       "      <td>35.30</td>\n",
       "      <td>12.68</td>\n",
       "      <td>10.501983</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     DASHUSDT  SOLUSDT  close    ema_200  price_above_ema_200  \\\n",
       "Date                                                                            \n",
       "2022-08-23 02:00:00     47.01    35.20  11.81  10.384687                 True   \n",
       "2022-08-23 06:00:00     46.90    34.94  11.96  10.411092                 True   \n",
       "2022-08-23 10:00:00     47.98    35.66  12.32  10.440396                 True   \n",
       "2022-08-23 14:00:00     47.92    35.69  12.23  10.468186                 True   \n",
       "2022-08-23 18:00:00     47.98    35.30  12.68  10.501983                 True   \n",
       "\n",
       "                     price_below_ema_200  \n",
       "Date                                      \n",
       "2022-08-23 02:00:00                False  \n",
       "2022-08-23 06:00:00                False  \n",
       "2022-08-23 10:00:00                False  \n",
       "2022-08-23 14:00:00                False  \n",
       "2022-08-23 18:00:00                False  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ema = data.copy()\n",
    "df_ema['ema_200'] = exponential_moving_average.exponential_moving_average(df_ema.close, period=200)\n",
    "df_ema['price_above_ema_200'] = df_ema.ema_200 < df_ema.close\n",
    "df_ema['price_below_ema_200'] = df_ema.ema_200 > df_ema.close\n",
    "\n",
    "df_ema.tail()#.iloc[1324:1330]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    ">\n",
    "\n",
    "Price closes above the ema line:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DASHUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "      <th>close</th>\n",
       "      <th>ema_200</th>\n",
       "      <th>price_above_ema_200</th>\n",
       "      <th>price_below_ema_200</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-08-23 02:00:00</th>\n",
       "      <td>47.01</td>\n",
       "      <td>35.20</td>\n",
       "      <td>11.81</td>\n",
       "      <td>10.384687</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 06:00:00</th>\n",
       "      <td>46.90</td>\n",
       "      <td>34.94</td>\n",
       "      <td>11.96</td>\n",
       "      <td>10.411092</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 10:00:00</th>\n",
       "      <td>47.98</td>\n",
       "      <td>35.66</td>\n",
       "      <td>12.32</td>\n",
       "      <td>10.440396</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 14:00:00</th>\n",
       "      <td>47.92</td>\n",
       "      <td>35.69</td>\n",
       "      <td>12.23</td>\n",
       "      <td>10.468186</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 18:00:00</th>\n",
       "      <td>47.98</td>\n",
       "      <td>35.30</td>\n",
       "      <td>12.68</td>\n",
       "      <td>10.501983</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     DASHUSDT  SOLUSDT  close    ema_200  price_above_ema_200  \\\n",
       "Date                                                                            \n",
       "2022-08-23 02:00:00     47.01    35.20  11.81  10.384687                 True   \n",
       "2022-08-23 06:00:00     46.90    34.94  11.96  10.411092                 True   \n",
       "2022-08-23 10:00:00     47.98    35.66  12.32  10.440396                 True   \n",
       "2022-08-23 14:00:00     47.92    35.69  12.23  10.468186                 True   \n",
       "2022-08-23 18:00:00     47.98    35.30  12.68  10.501983                 True   \n",
       "\n",
       "                     price_below_ema_200  \n",
       "Date                                      \n",
       "2022-08-23 02:00:00                False  \n",
       "2022-08-23 06:00:00                False  \n",
       "2022-08-23 10:00:00                False  \n",
       "2022-08-23 14:00:00                False  \n",
       "2022-08-23 18:00:00                False  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ema[(df_ema.price_above_ema_200 == True)].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    ">\n",
    "\n",
    "Price closes below the ema line:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hidden": 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>DASHUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "      <th>close</th>\n",
       "      <th>ema_200</th>\n",
       "      <th>price_above_ema_200</th>\n",
       "      <th>price_below_ema_200</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-08-20 10:00:00</th>\n",
       "      <td>45.88</td>\n",
       "      <td>36.57</td>\n",
       "      <td>9.31</td>\n",
       "      <td>10.183788</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-20 14:00:00</th>\n",
       "      <td>46.12</td>\n",
       "      <td>36.48</td>\n",
       "      <td>9.64</td>\n",
       "      <td>10.182333</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-21 14:00:00</th>\n",
       "      <td>46.22</td>\n",
       "      <td>35.99</td>\n",
       "      <td>10.23</td>\n",
       "      <td>10.241952</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-22 02:00:00</th>\n",
       "      <td>46.14</td>\n",
       "      <td>36.08</td>\n",
       "      <td>10.06</td>\n",
       "      <td>10.274293</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-22 06:00:00</th>\n",
       "      <td>44.83</td>\n",
       "      <td>34.89</td>\n",
       "      <td>9.94</td>\n",
       "      <td>10.278317</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     DASHUSDT  SOLUSDT  close    ema_200  price_above_ema_200  \\\n",
       "Date                                                                            \n",
       "2022-08-20 10:00:00     45.88    36.57   9.31  10.183788                False   \n",
       "2022-08-20 14:00:00     46.12    36.48   9.64  10.182333                False   \n",
       "2022-08-21 14:00:00     46.22    35.99  10.23  10.241952                False   \n",
       "2022-08-22 02:00:00     46.14    36.08  10.06  10.274293                False   \n",
       "2022-08-22 06:00:00     44.83    34.89   9.94  10.278317                False   \n",
       "\n",
       "                     price_below_ema_200  \n",
       "Date                                      \n",
       "2022-08-20 10:00:00                 True  \n",
       "2022-08-20 14:00:00                 True  \n",
       "2022-08-21 14:00:00                 True  \n",
       "2022-08-22 02:00:00                 True  \n",
       "2022-08-22 06:00:00                 True  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ema[(df_ema.price_below_ema_200 == True)].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### EOT indicator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "code_folding": [
     3,
     18,
     34,
     53,
     64
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#Boom-Pro Indicator\n",
    "\n",
    "#Input Close values to return HP\n",
    "def high_pass_filter(data):\n",
    "    PI = np.pi\n",
    "    HP = []\n",
    "\n",
    "    for i, _ in enumerate(data):\n",
    "        if i < 2:\n",
    "            HP.append(0)\n",
    "        else:\n",
    "            angle = 0.707 * 2 * PI/100\n",
    "            alpha1 = (math.cos(angle) + math.sin(angle)-1)/ math.cos(angle)\n",
    "            HP.append(math.pow(1.0-alpha1/2.0, 2)*(data[i]-2*data[i-1]+data[i-2]) + 2*(1-alpha1)*HP[i-1] - math.pow(1-alpha1, 2)*HP[i-2])\n",
    "\n",
    "    return HP\n",
    "\n",
    "#Input HP to smoothen and return Filt \n",
    "def super_smoother(data, LPPeriod):\n",
    "    Filt = []\n",
    "    for i, _ in enumerate(data):\n",
    "        if i < 2:\n",
    "            Filt.append(0)\n",
    "        else:\n",
    "            arg = 1.414 * 3.14159 / LPPeriod\n",
    "            a_1 = math.exp(-arg)\n",
    "            b_1 = 2 * a_1 * math.cos(4.44/float(LPPeriod))\n",
    "            c_2 = b_1\n",
    "            c_3 = -a_1 * a_1\n",
    "            c_1 = 1 - c_2 - c_3\n",
    "            Filt.append(c_1 * (data[i] + data[i-1]) / 2 + c_2 * Filt[i-1] + c_3 * Filt[i-2])\n",
    "    return Filt\n",
    "\n",
    "#Input Filt values to return X\n",
    "def agc(data):\n",
    "    X = []\n",
    "    Peak = []\n",
    "    for i, _ in enumerate(data):\n",
    "        if i < 1:\n",
    "            X.append(0)\n",
    "            Peak.append(0)\n",
    "            #Peak.append(.0000001)\n",
    "        else:\n",
    "            Peak.append(0.991 * Peak[i - 1])\n",
    "            if abs(data[i]) > Peak[i]:\n",
    "                Peak[i] = abs(data[i])\n",
    "\n",
    "            if Peak[i] != 0:\n",
    "                X.append(data[i] / Peak[i])\n",
    "\n",
    "    return X\n",
    "\n",
    "#Input X to return final Quotient \n",
    "def quotient(data, K_val):\n",
    "    K = K_val\n",
    "    Q = []\n",
    "    for i, _ in enumerate(data):\n",
    "        if i<1:\n",
    "            Q.append(0)\n",
    "        else:\n",
    "            Q.append((data[i]+ K) / (K*data[i]+1))\n",
    "    return Q\n",
    "\n",
    "#Input Quotient to return smoothen the quotient\n",
    "def sma(data, length):\n",
    "    trigger = []\n",
    "    for i, _ in reversed(list(enumerate(data))):\n",
    "        sum = 0\n",
    "        for t in range(i - length + 1, i + 1):\n",
    "            sum = sum + data[t] / length\n",
    "        trigger.insert(0, sum)\n",
    "    return trigger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DASHUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "      <th>close</th>\n",
       "      <th>trig</th>\n",
       "      <th>white_line</th>\n",
       "      <th>red_line</th>\n",
       "      <th>prev_white_line</th>\n",
       "      <th>prev_red_line</th>\n",
       "      <th>crossover</th>\n",
       "      <th>top_level_eot</th>\n",
       "      <th>bottom_level_eot</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-08-23 02:00:00</th>\n",
       "      <td>47.01</td>\n",
       "      <td>35.20</td>\n",
       "      <td>11.81</td>\n",
       "      <td>0.787907</td>\n",
       "      <td>88.790738</td>\n",
       "      <td>89.695698</td>\n",
       "      <td>85.025340</td>\n",
       "      <td>81.772969</td>\n",
       "      <td>bearish crossover</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 06:00:00</th>\n",
       "      <td>46.90</td>\n",
       "      <td>34.94</td>\n",
       "      <td>11.96</td>\n",
       "      <td>0.809204</td>\n",
       "      <td>90.920390</td>\n",
       "      <td>97.024559</td>\n",
       "      <td>88.790738</td>\n",
       "      <td>89.695698</td>\n",
       "      <td>bearish crossover</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 10:00:00</th>\n",
       "      <td>47.98</td>\n",
       "      <td>35.66</td>\n",
       "      <td>12.32</td>\n",
       "      <td>0.840041</td>\n",
       "      <td>94.004106</td>\n",
       "      <td>103.553014</td>\n",
       "      <td>90.920390</td>\n",
       "      <td>97.024559</td>\n",
       "      <td>none</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 14:00:00</th>\n",
       "      <td>47.92</td>\n",
       "      <td>35.69</td>\n",
       "      <td>12.23</td>\n",
       "      <td>0.868231</td>\n",
       "      <td>96.823142</td>\n",
       "      <td>109.299294</td>\n",
       "      <td>94.004106</td>\n",
       "      <td>103.553014</td>\n",
       "      <td>none</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 18:00:00</th>\n",
       "      <td>47.98</td>\n",
       "      <td>35.30</td>\n",
       "      <td>12.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.823142</td>\n",
       "      <td>109.299294</td>\n",
       "      <td>none</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     DASHUSDT  SOLUSDT  close      trig  white_line  \\\n",
       "Date                                                                  \n",
       "2022-08-23 02:00:00     47.01    35.20  11.81  0.787907   88.790738   \n",
       "2022-08-23 06:00:00     46.90    34.94  11.96  0.809204   90.920390   \n",
       "2022-08-23 10:00:00     47.98    35.66  12.32  0.840041   94.004106   \n",
       "2022-08-23 14:00:00     47.92    35.69  12.23  0.868231   96.823142   \n",
       "2022-08-23 18:00:00     47.98    35.30  12.68       NaN         NaN   \n",
       "\n",
       "                       red_line  prev_white_line  prev_red_line  \\\n",
       "Date                                                              \n",
       "2022-08-23 02:00:00   89.695698        85.025340      81.772969   \n",
       "2022-08-23 06:00:00   97.024559        88.790738      89.695698   \n",
       "2022-08-23 10:00:00  103.553014        90.920390      97.024559   \n",
       "2022-08-23 14:00:00  109.299294        94.004106     103.553014   \n",
       "2022-08-23 18:00:00         NaN        96.823142     109.299294   \n",
       "\n",
       "                             crossover  top_level_eot  bottom_level_eot  \n",
       "Date                                                                     \n",
       "2022-08-23 02:00:00  bearish crossover           True             False  \n",
       "2022-08-23 06:00:00  bearish crossover           True             False  \n",
       "2022-08-23 10:00:00               none           True             False  \n",
       "2022-08-23 14:00:00               none           True             False  \n",
       "2022-08-23 18:00:00               none          False             False  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_eot = data.copy()\n",
    "\n",
    "K1 = 0\n",
    "K2 = 0.4\n",
    "LPPeriod_1 = 6\n",
    "LPPeriod_2 = 27\n",
    "\n",
    "HP = high_pass_filter(df_eot.close)\n",
    "\n",
    "#quotient 1 params\n",
    "Filt_1 = super_smoother(HP, LPPeriod_1)\n",
    "X_1 = agc(Filt_1)\n",
    "\n",
    "#quotient 2 params\n",
    "Filt_2 = super_smoother(HP, LPPeriod_2)\n",
    "X_2 = agc(Filt_2)\n",
    "\n",
    "df_eot = df_eot.reset_index()\n",
    "q1 = quotient(X_1, K1)\n",
    "q2 = quotient(X_2, K2)\n",
    "\n",
    "trig = sma(q1,length=2)\n",
    "df_eot['trig'] = pd.Series(trig)\n",
    "\n",
    "df_eot['white_line'] = df_eot['trig']\n",
    "df_eot['red_line'] = pd.Series(q2)\n",
    "\n",
    "df_eot['white_line'] = (df_eot['white_line']*100)+10\n",
    "df_eot['red_line'] = (df_eot['red_line']*100)+10\n",
    "\n",
    "\n",
    "df_eot['prev_white_line'] = df_eot['white_line']\n",
    "df_eot['prev_white_line'] = df_eot['prev_white_line'].shift(1)\n",
    "\n",
    "df_eot['prev_red_line'] = df_eot['red_line']\n",
    "df_eot['prev_red_line'] = df_eot['prev_red_line'].shift(1)\n",
    "\n",
    "#df_eot = df_eot.fillna(method='ffill')\n",
    "#df_eot.dropna(inplace=True)\n",
    "\n",
    "df_eot['bullish_cross'] = (df_eot['prev_white_line'] < df_eot['red_line']) & (df_eot['white_line'] > df_eot['red_line']) & (df_eot['white_line']<85)\n",
    "\n",
    "df_eot['bearish_cross'] = (df_eot['prev_red_line'] < df_eot['white_line']) & (df_eot['red_line'] > df_eot['white_line']) & (df_eot['red_line']>15)\n",
    "df_eot['crossover'] = df_eot.apply(lambda x: 'bullish crossover' if x.bullish_cross == True\n",
    "                      else ('bearish crossover' if x.bearish_cross == True else 'none'), axis=1)\n",
    "df_eot = df_eot.drop(['bullish_cross','bearish_cross'], axis=1)\n",
    "df_eot = df_eot[df_eot.columns.drop(list(df_eot.filter(regex='index')))]\n",
    "\n",
    "df_eot['top_level_eot'] = (df_eot.prev_white_line >55) & (df_eot.red_line>55)\n",
    "df_eot['bottom_level_eot'] = (df_eot.prev_white_line <45) & (df_eot.red_line<45)\n",
    "\n",
    "df_eot = df_eot.set_index('Date')\n",
    "df_eot.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Six Candle Close dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hidden": true,
    "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>DASHUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "      <th>close</th>\n",
       "      <th>six_greater</th>\n",
       "      <th>six_lower</th>\n",
       "      <th>change_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-08-23 02:00:00</th>\n",
       "      <td>47.01</td>\n",
       "      <td>35.20</td>\n",
       "      <td>11.81</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.812877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 06:00:00</th>\n",
       "      <td>46.90</td>\n",
       "      <td>34.94</td>\n",
       "      <td>11.96</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.688213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 10:00:00</th>\n",
       "      <td>47.98</td>\n",
       "      <td>35.66</td>\n",
       "      <td>12.32</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.751073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 14:00:00</th>\n",
       "      <td>47.92</td>\n",
       "      <td>35.69</td>\n",
       "      <td>12.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.563764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 18:00:00</th>\n",
       "      <td>47.98</td>\n",
       "      <td>35.30</td>\n",
       "      <td>12.68</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.457627</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     DASHUSDT  SOLUSDT  close  six_greater  six_lower  \\\n",
       "Date                                                                    \n",
       "2022-08-23 02:00:00     47.01    35.20  11.81          1.0        0.0   \n",
       "2022-08-23 06:00:00     46.90    34.94  11.96          1.0        0.0   \n",
       "2022-08-23 10:00:00     47.98    35.66  12.32          1.0        0.0   \n",
       "2022-08-23 14:00:00     47.92    35.69  12.23          0.0        0.0   \n",
       "2022-08-23 18:00:00     47.98    35.30  12.68          1.0        0.0   \n",
       "\n",
       "                     change_pct  \n",
       "Date                             \n",
       "2022-08-23 02:00:00   18.812877  \n",
       "2022-08-23 06:00:00   13.688213  \n",
       "2022-08-23 10:00:00    5.751073  \n",
       "2022-08-23 14:00:00    7.563764  \n",
       "2022-08-23 18:00:00    7.457627  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_6 = data.copy()\n",
    "df_6['six_greater'] = (df_6['close'].rolling(window=7, min_periods=1)\n",
    "             .apply(lambda w: w.iloc[:-1].lt(w.iloc[-1]).all()))\n",
    "\n",
    "df_6['six_lower'] = (df_6['close'].rolling(window=7, min_periods=1)\n",
    "             .apply(lambda w: w.iloc[:-1].gt(w.iloc[-1]).all()))\n",
    "\n",
    "\n",
    "df_6['close_shifted'] = df_6['close'].shift(5)\n",
    "\n",
    "df_6['change_pct'] = (df_6['close'] - df_6['close_shifted'])/df_6['close_shifted']*100\n",
    "df_6 = df_6.drop(['close_shifted'], axis=1)\n",
    "\n",
    "#df_6[(df_6['greater']== 1) & (df_6['change_pct']> 100)] #filter condition\n",
    "df_6.tail() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combined dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DASHUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "      <th>close</th>\n",
       "      <th>upper</th>\n",
       "      <th>lower</th>\n",
       "      <th>above_upper_bb</th>\n",
       "      <th>below_lower_bb</th>\n",
       "      <th>atr</th>\n",
       "      <th>sell_stop_loss</th>\n",
       "      <th>buy_stop_loss</th>\n",
       "      <th>...</th>\n",
       "      <th>white_line</th>\n",
       "      <th>red_line</th>\n",
       "      <th>prev_white_line</th>\n",
       "      <th>prev_red_line</th>\n",
       "      <th>crossover</th>\n",
       "      <th>top_level_eot</th>\n",
       "      <th>bottom_level_eot</th>\n",
       "      <th>six_greater</th>\n",
       "      <th>six_lower</th>\n",
       "      <th>change_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-08-23 02:00:00</th>\n",
       "      <td>47.01</td>\n",
       "      <td>35.20</td>\n",
       "      <td>11.81</td>\n",
       "      <td>11.994950</td>\n",
       "      <td>8.618050</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.597411</td>\n",
       "      <td>15.706116</td>\n",
       "      <td>7.913884</td>\n",
       "      <td>...</td>\n",
       "      <td>88.790738</td>\n",
       "      <td>89.695698</td>\n",
       "      <td>85.025340</td>\n",
       "      <td>81.772969</td>\n",
       "      <td>bearish crossover</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.812877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 06:00:00</th>\n",
       "      <td>46.90</td>\n",
       "      <td>34.94</td>\n",
       "      <td>11.96</td>\n",
       "      <td>12.172069</td>\n",
       "      <td>8.738931</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.558917</td>\n",
       "      <td>15.798375</td>\n",
       "      <td>8.121625</td>\n",
       "      <td>...</td>\n",
       "      <td>90.920390</td>\n",
       "      <td>97.024559</td>\n",
       "      <td>88.790738</td>\n",
       "      <td>89.695698</td>\n",
       "      <td>bearish crossover</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.688213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 10:00:00</th>\n",
       "      <td>47.98</td>\n",
       "      <td>35.66</td>\n",
       "      <td>12.32</td>\n",
       "      <td>12.348383</td>\n",
       "      <td>8.918617</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.546989</td>\n",
       "      <td>16.140484</td>\n",
       "      <td>8.499516</td>\n",
       "      <td>...</td>\n",
       "      <td>94.004106</td>\n",
       "      <td>103.553014</td>\n",
       "      <td>90.920390</td>\n",
       "      <td>97.024559</td>\n",
       "      <td>none</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.751073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 14:00:00</th>\n",
       "      <td>47.92</td>\n",
       "      <td>35.69</td>\n",
       "      <td>12.23</td>\n",
       "      <td>12.451200</td>\n",
       "      <td>9.154800</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.535856</td>\n",
       "      <td>16.033785</td>\n",
       "      <td>8.426215</td>\n",
       "      <td>...</td>\n",
       "      <td>96.823142</td>\n",
       "      <td>109.299294</td>\n",
       "      <td>94.004106</td>\n",
       "      <td>103.553014</td>\n",
       "      <td>none</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.563764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23 18:00:00</th>\n",
       "      <td>47.98</td>\n",
       "      <td>35.30</td>\n",
       "      <td>12.68</td>\n",
       "      <td>12.658998</td>\n",
       "      <td>9.284002</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2.549466</td>\n",
       "      <td>16.504199</td>\n",
       "      <td>8.855801</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.823142</td>\n",
       "      <td>109.299294</td>\n",
       "      <td>none</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.457627</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     DASHUSDT  SOLUSDT  close      upper     lower  \\\n",
       "Date                                                                 \n",
       "2022-08-23 02:00:00     47.01    35.20  11.81  11.994950  8.618050   \n",
       "2022-08-23 06:00:00     46.90    34.94  11.96  12.172069  8.738931   \n",
       "2022-08-23 10:00:00     47.98    35.66  12.32  12.348383  8.918617   \n",
       "2022-08-23 14:00:00     47.92    35.69  12.23  12.451200  9.154800   \n",
       "2022-08-23 18:00:00     47.98    35.30  12.68  12.658998  9.284002   \n",
       "\n",
       "                     above_upper_bb  below_lower_bb       atr  sell_stop_loss  \\\n",
       "Date                                                                            \n",
       "2022-08-23 02:00:00           False           False  2.597411       15.706116   \n",
       "2022-08-23 06:00:00           False           False  2.558917       15.798375   \n",
       "2022-08-23 10:00:00           False           False  2.546989       16.140484   \n",
       "2022-08-23 14:00:00           False           False  2.535856       16.033785   \n",
       "2022-08-23 18:00:00            True           False  2.549466       16.504199   \n",
       "\n",
       "                     buy_stop_loss  ...  white_line    red_line  \\\n",
       "Date                                ...                           \n",
       "2022-08-23 02:00:00       7.913884  ...   88.790738   89.695698   \n",
       "2022-08-23 06:00:00       8.121625  ...   90.920390   97.024559   \n",
       "2022-08-23 10:00:00       8.499516  ...   94.004106  103.553014   \n",
       "2022-08-23 14:00:00       8.426215  ...   96.823142  109.299294   \n",
       "2022-08-23 18:00:00       8.855801  ...         NaN         NaN   \n",
       "\n",
       "                     prev_white_line  prev_red_line          crossover  \\\n",
       "Date                                                                     \n",
       "2022-08-23 02:00:00        85.025340      81.772969  bearish crossover   \n",
       "2022-08-23 06:00:00        88.790738      89.695698  bearish crossover   \n",
       "2022-08-23 10:00:00        90.920390      97.024559               none   \n",
       "2022-08-23 14:00:00        94.004106     103.553014               none   \n",
       "2022-08-23 18:00:00        96.823142     109.299294               none   \n",
       "\n",
       "                     top_level_eot  bottom_level_eot  six_greater six_lower  \\\n",
       "Date                                                                          \n",
       "2022-08-23 02:00:00           True             False          1.0       0.0   \n",
       "2022-08-23 06:00:00           True             False          1.0       0.0   \n",
       "2022-08-23 10:00:00           True             False          1.0       0.0   \n",
       "2022-08-23 14:00:00           True             False          0.0       0.0   \n",
       "2022-08-23 18:00:00          False             False          1.0       0.0   \n",
       "\n",
       "                     change_pct  \n",
       "Date                             \n",
       "2022-08-23 02:00:00   18.812877  \n",
       "2022-08-23 06:00:00   13.688213  \n",
       "2022-08-23 10:00:00    5.751073  \n",
       "2022-08-23 14:00:00    7.563764  \n",
       "2022-08-23 18:00:00    7.457627  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs= [data, df_bb,df_atr,df_ema,df_eot,df_6]\n",
    "df = pd.concat(dfs, join='outer', axis=1)#.fillna(0)\n",
    "df = df.loc[:,~df.columns.duplicated()].copy()\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DASHUSDT', 'SOLUSDT', 'close', 'upper', 'lower', 'above_upper_bb',\n",
       "       'below_lower_bb', 'atr', 'sell_stop_loss', 'buy_stop_loss', 'ema_200',\n",
       "       'price_above_ema_200', 'price_below_ema_200', 'trig', 'white_line',\n",
       "       'red_line', 'prev_white_line', 'prev_red_line', 'crossover',\n",
       "       'top_level_eot', 'bottom_level_eot', 'six_greater', 'six_lower',\n",
       "       'change_pct'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sell Signals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DASHUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "      <th>close</th>\n",
       "      <th>upper</th>\n",
       "      <th>lower</th>\n",
       "      <th>above_upper_bb</th>\n",
       "      <th>below_lower_bb</th>\n",
       "      <th>atr</th>\n",
       "      <th>sell_stop_loss</th>\n",
       "      <th>buy_stop_loss</th>\n",
       "      <th>...</th>\n",
       "      <th>white_line</th>\n",
       "      <th>red_line</th>\n",
       "      <th>prev_white_line</th>\n",
       "      <th>prev_red_line</th>\n",
       "      <th>crossover</th>\n",
       "      <th>top_level_eot</th>\n",
       "      <th>bottom_level_eot</th>\n",
       "      <th>six_greater</th>\n",
       "      <th>six_lower</th>\n",
       "      <th>change_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-07-22 18:00:00</th>\n",
       "      <td>48.02</td>\n",
       "      <td>40.19</td>\n",
       "      <td>7.83</td>\n",
       "      <td>7.388467</td>\n",
       "      <td>3.284533</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3.167040</td>\n",
       "      <td>12.580561</td>\n",
       "      <td>3.079439</td>\n",
       "      <td>...</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>102.606447</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>none</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.357143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-22 22:00:00</th>\n",
       "      <td>48.33</td>\n",
       "      <td>40.49</td>\n",
       "      <td>7.84</td>\n",
       "      <td>7.555320</td>\n",
       "      <td>3.631680</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3.181904</td>\n",
       "      <td>12.612857</td>\n",
       "      <td>3.067143</td>\n",
       "      <td>...</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>none</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.405872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-23 02:00:00</th>\n",
       "      <td>49.09</td>\n",
       "      <td>41.07</td>\n",
       "      <td>8.02</td>\n",
       "      <td>7.906902</td>\n",
       "      <td>3.638098</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3.207777</td>\n",
       "      <td>12.831666</td>\n",
       "      <td>3.208334</td>\n",
       "      <td>...</td>\n",
       "      <td>109.948347</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>none</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.765432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-28 18:00:00</th>\n",
       "      <td>52.51</td>\n",
       "      <td>42.13</td>\n",
       "      <td>10.38</td>\n",
       "      <td>9.839248</td>\n",
       "      <td>6.730752</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.866188</td>\n",
       "      <td>13.179283</td>\n",
       "      <td>7.580717</td>\n",
       "      <td>...</td>\n",
       "      <td>91.764054</td>\n",
       "      <td>70.694224</td>\n",
       "      <td>84.880802</td>\n",
       "      <td>62.455452</td>\n",
       "      <td>none</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.736573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-29 02:00:00</th>\n",
       "      <td>54.35</td>\n",
       "      <td>43.46</td>\n",
       "      <td>10.89</td>\n",
       "      <td>10.413795</td>\n",
       "      <td>6.513205</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2.078546</td>\n",
       "      <td>14.007820</td>\n",
       "      <td>7.772180</td>\n",
       "      <td>...</td>\n",
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       "      <td>85.895548</td>\n",
       "      <td>92.502391</td>\n",
       "      <td>78.542780</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.635135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-17 22:00:00</th>\n",
       "      <td>53.04</td>\n",
       "      <td>40.51</td>\n",
       "      <td>12.53</td>\n",
       "      <td>11.862065</td>\n",
       "      <td>8.294935</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2.203096</td>\n",
       "      <td>15.834643</td>\n",
       "      <td>9.225357</td>\n",
       "      <td>...</td>\n",
       "      <td>101.278355</td>\n",
       "      <td>69.941555</td>\n",
       "      <td>82.347144</td>\n",
       "      <td>54.365486</td>\n",
       "      <td>none</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.565657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-18 02:00:00</th>\n",
       "      <td>53.46</td>\n",
       "      <td>40.80</td>\n",
       "      <td>12.66</td>\n",
       "      <td>12.285692</td>\n",
       "      <td>8.126308</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2.298889</td>\n",
       "      <td>16.108334</td>\n",
       "      <td>9.211666</td>\n",
       "      <td>...</td>\n",
       "      <td>106.871661</td>\n",
       "      <td>83.351179</td>\n",
       "      <td>101.278355</td>\n",
       "      <td>69.941555</td>\n",
       "      <td>none</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.852071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     DASHUSDT  SOLUSDT  close      upper     lower  \\\n",
       "Date                                                                 \n",
       "2022-07-22 18:00:00     48.02    40.19   7.83   7.388467  3.284533   \n",
       "2022-07-22 22:00:00     48.33    40.49   7.84   7.555320  3.631680   \n",
       "2022-07-23 02:00:00     49.09    41.07   8.02   7.906902  3.638098   \n",
       "2022-07-28 18:00:00     52.51    42.13  10.38   9.839248  6.730752   \n",
       "2022-07-29 02:00:00     54.35    43.46  10.89  10.413795  6.513205   \n",
       "2022-08-17 22:00:00     53.04    40.51  12.53  11.862065  8.294935   \n",
       "2022-08-18 02:00:00     53.46    40.80  12.66  12.285692  8.126308   \n",
       "\n",
       "                     above_upper_bb  below_lower_bb       atr  sell_stop_loss  \\\n",
       "Date                                                                            \n",
       "2022-07-22 18:00:00            True           False  3.167040       12.580561   \n",
       "2022-07-22 22:00:00            True           False  3.181904       12.612857   \n",
       "2022-07-23 02:00:00            True           False  3.207777       12.831666   \n",
       "2022-07-28 18:00:00            True           False  1.866188       13.179283   \n",
       "2022-07-29 02:00:00            True           False  2.078546       14.007820   \n",
       "2022-08-17 22:00:00            True           False  2.203096       15.834643   \n",
       "2022-08-18 02:00:00            True           False  2.298889       16.108334   \n",
       "\n",
       "                     buy_stop_loss  ...  white_line    red_line  \\\n",
       "Date                                ...                           \n",
       "2022-07-22 18:00:00       3.079439  ...  110.000000  110.000000   \n",
       "2022-07-22 22:00:00       3.067143  ...  110.000000  110.000000   \n",
       "2022-07-23 02:00:00       3.208334  ...  109.948347  110.000000   \n",
       "2022-07-28 18:00:00       7.580717  ...   91.764054   70.694224   \n",
       "2022-07-29 02:00:00       7.772180  ...   94.916796   85.895548   \n",
       "2022-08-17 22:00:00       9.225357  ...  101.278355   69.941555   \n",
       "2022-08-18 02:00:00       9.211666  ...  106.871661   83.351179   \n",
       "\n",
       "                     prev_white_line  prev_red_line  crossover  top_level_eot  \\\n",
       "Date                                                                            \n",
       "2022-07-22 18:00:00       102.606447     110.000000       none           True   \n",
       "2022-07-22 22:00:00       110.000000     110.000000       none           True   \n",
       "2022-07-23 02:00:00       110.000000     110.000000       none           True   \n",
       "2022-07-28 18:00:00        84.880802      62.455452       none           True   \n",
       "2022-07-29 02:00:00        92.502391      78.542780       none           True   \n",
       "2022-08-17 22:00:00        82.347144      54.365486       none           True   \n",
       "2022-08-18 02:00:00       101.278355      69.941555       none           True   \n",
       "\n",
       "                     bottom_level_eot  six_greater six_lower  change_pct  \n",
       "Date                                                                      \n",
       "2022-07-22 18:00:00             False          1.0       0.0   55.357143  \n",
       "2022-07-22 22:00:00             False          1.0       0.0   35.405872  \n",
       "2022-07-23 02:00:00             False          1.0       0.0   23.765432  \n",
       "2022-07-28 18:00:00             False          1.0       0.0   32.736573  \n",
       "2022-07-29 02:00:00             False          1.0       0.0   22.635135  \n",
       "2022-08-17 22:00:00             False          1.0       0.0   26.565657  \n",
       "2022-08-18 02:00:00             False          1.0       0.0   24.852071  \n",
       "\n",
       "[7 rows x 24 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sell_df = df[ (df.above_upper_bb == True) & (df.price_above_ema_200 == True) & (df.top_level_eot == True) & (df.six_greater==1) & (df.change_pct>15) ].tail(10) \n",
    "sell_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Buy Signals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DASHUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "      <th>close</th>\n",
       "      <th>upper</th>\n",
       "      <th>lower</th>\n",
       "      <th>above_upper_bb</th>\n",
       "      <th>below_lower_bb</th>\n",
       "      <th>atr</th>\n",
       "      <th>sell_stop_loss</th>\n",
       "      <th>buy_stop_loss</th>\n",
       "      <th>...</th>\n",
       "      <th>white_line</th>\n",
       "      <th>red_line</th>\n",
       "      <th>prev_white_line</th>\n",
       "      <th>prev_red_line</th>\n",
       "      <th>crossover</th>\n",
       "      <th>top_level_eot</th>\n",
       "      <th>bottom_level_eot</th>\n",
       "      <th>six_greater</th>\n",
       "      <th>six_lower</th>\n",
       "      <th>change_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-04-20 02:00:00</th>\n",
       "      <td>107.76</td>\n",
       "      <td>108.23</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>8.453937</td>\n",
       "      <td>1.223063</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4.855335</td>\n",
       "      <td>6.813003</td>\n",
       "      <td>-7.753003</td>\n",
       "      <td>...</td>\n",
       "      <td>-71.328875</td>\n",
       "      <td>16.595000</td>\n",
       "      <td>-56.661690</td>\n",
       "      <td>27.329610</td>\n",
       "      <td>none</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-109.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-20 10:00:00</th>\n",
       "      <td>109.00</td>\n",
       "      <td>110.33</td>\n",
       "      <td>-1.33</td>\n",
       "      <td>8.670030</td>\n",
       "      <td>-0.314030</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>5.213581</td>\n",
       "      <td>6.490372</td>\n",
       "      <td>-9.150372</td>\n",
       "      <td>...</td>\n",
       "      <td>-77.648582</td>\n",
       "      <td>-8.968412</td>\n",
       "      <td>-77.201320</td>\n",
       "      <td>4.269266</td>\n",
       "      <td>none</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-148.540146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-25 02:00:00</th>\n",
       "      <td>97.61</td>\n",
       "      <td>97.97</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>3.142305</td>\n",
       "      <td>-0.344305</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4.201564</td>\n",
       "      <td>5.942346</td>\n",
       "      <td>-6.662346</td>\n",
       "      <td>...</td>\n",
       "      <td>-14.100924</td>\n",
       "      <td>36.166404</td>\n",
       "      <td>-5.209245</td>\n",
       "      <td>39.474895</td>\n",
       "      <td>none</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-209.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-25 06:00:00</th>\n",
       "      <td>93.77</td>\n",
       "      <td>95.09</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>3.351483</td>\n",
       "      <td>-0.717483</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4.215460</td>\n",
       "      <td>5.003190</td>\n",
       "      <td>-7.643190</td>\n",
       "      <td>...</td>\n",
       "      <td>-25.157174</td>\n",
       "      <td>32.337392</td>\n",
       "      <td>-14.100924</td>\n",
       "      <td>36.166404</td>\n",
       "      <td>none</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1750.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-25 18:00:00</th>\n",
       "      <td>99.17</td>\n",
       "      <td>100.59</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>3.263861</td>\n",
       "      <td>-1.366861</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4.102513</td>\n",
       "      <td>4.733770</td>\n",
       "      <td>-7.573770</td>\n",
       "      <td>...</td>\n",
       "      <td>-20.702414</td>\n",
       "      <td>23.358666</td>\n",
       "      <td>-17.111097</td>\n",
       "      <td>26.784631</td>\n",
       "      <td>none</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-282.051282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-27 06:00:00</th>\n",
       "      <td>96.06</td>\n",
       "      <td>99.45</td>\n",
       "      <td>-3.39</td>\n",
       "      <td>1.679135</td>\n",
       "      <td>-3.315135</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3.846331</td>\n",
       "      <td>2.379496</td>\n",
       "      <td>-9.159496</td>\n",
       "      <td>...</td>\n",
       "      <td>-48.902009</td>\n",
       "      <td>3.569222</td>\n",
       "      <td>-39.446893</td>\n",
       "      <td>8.190843</td>\n",
       "      <td>none</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1795.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-15 22:00:00</th>\n",
       "      <td>63.04</td>\n",
       "      <td>58.80</td>\n",
       "      <td>4.24</td>\n",
       "      <td>11.201519</td>\n",
       "      <td>5.474481</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4.416562</td>\n",
       "      <td>10.864844</td>\n",
       "      <td>-2.384844</td>\n",
       "      <td>...</td>\n",
       "      <td>-90.000000</td>\n",
       "      <td>-89.510126</td>\n",
       "      <td>-90.000000</td>\n",
       "      <td>-25.007460</td>\n",
       "      <td>none</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-56.423433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-16 06:00:00</th>\n",
       "      <td>57.41</td>\n",
       "      <td>53.78</td>\n",
       "      <td>3.63</td>\n",
       "      <td>11.393101</td>\n",
       "      <td>4.197899</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4.625228</td>\n",
       "      <td>10.567842</td>\n",
       "      <td>-3.307842</td>\n",
       "      <td>...</td>\n",
       "      <td>-90.000000</td>\n",
       "      <td>-90.000000</td>\n",
       "      <td>-90.000000</td>\n",
       "      <td>-90.000000</td>\n",
       "      <td>none</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-58.983051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-16 10:00:00</th>\n",
       "      <td>56.97</td>\n",
       "      <td>53.78</td>\n",
       "      <td>3.19</td>\n",
       "      <td>11.439640</td>\n",
       "      <td>3.502360</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4.768213</td>\n",
       "      <td>10.342319</td>\n",
       "      <td>-3.962319</td>\n",
       "      <td>...</td>\n",
       "      <td>-88.855605</td>\n",
       "      <td>-90.000000</td>\n",
       "      <td>-90.000000</td>\n",
       "      <td>-90.000000</td>\n",
       "      <td>none</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-59.974906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-19 14:00:00</th>\n",
       "      <td>49.24</td>\n",
       "      <td>46.54</td>\n",
       "      <td>2.70</td>\n",
       "      <td>8.177263</td>\n",
       "      <td>3.271737</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2.613543</td>\n",
       "      <td>6.620314</td>\n",
       "      <td>-1.220314</td>\n",
       "      <td>...</td>\n",
       "      <td>-86.202331</td>\n",
       "      <td>8.250075</td>\n",
       "      <td>-90.000000</td>\n",
       "      <td>43.547199</td>\n",
       "      <td>none</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-59.641256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     DASHUSDT  SOLUSDT  close      upper     lower  \\\n",
       "Date                                                                 \n",
       "2022-04-20 02:00:00    107.76   108.23  -0.47   8.453937  1.223063   \n",
       "2022-04-20 10:00:00    109.00   110.33  -1.33   8.670030 -0.314030   \n",
       "2022-04-25 02:00:00     97.61    97.97  -0.36   3.142305 -0.344305   \n",
       "2022-04-25 06:00:00     93.77    95.09  -1.32   3.351483 -0.717483   \n",
       "2022-04-25 18:00:00     99.17   100.59  -1.42   3.263861 -1.366861   \n",
       "2022-04-27 06:00:00     96.06    99.45  -3.39   1.679135 -3.315135   \n",
       "2022-05-15 22:00:00     63.04    58.80   4.24  11.201519  5.474481   \n",
       "2022-05-16 06:00:00     57.41    53.78   3.63  11.393101  4.197899   \n",
       "2022-05-16 10:00:00     56.97    53.78   3.19  11.439640  3.502360   \n",
       "2022-07-19 14:00:00     49.24    46.54   2.70   8.177263  3.271737   \n",
       "\n",
       "                     above_upper_bb  below_lower_bb       atr  sell_stop_loss  \\\n",
       "Date                                                                            \n",
       "2022-04-20 02:00:00           False            True  4.855335        6.813003   \n",
       "2022-04-20 10:00:00           False            True  5.213581        6.490372   \n",
       "2022-04-25 02:00:00           False            True  4.201564        5.942346   \n",
       "2022-04-25 06:00:00           False            True  4.215460        5.003190   \n",
       "2022-04-25 18:00:00           False            True  4.102513        4.733770   \n",
       "2022-04-27 06:00:00           False            True  3.846331        2.379496   \n",
       "2022-05-15 22:00:00           False            True  4.416562       10.864844   \n",
       "2022-05-16 06:00:00           False            True  4.625228       10.567842   \n",
       "2022-05-16 10:00:00           False            True  4.768213       10.342319   \n",
       "2022-07-19 14:00:00           False            True  2.613543        6.620314   \n",
       "\n",
       "                     buy_stop_loss  ...  white_line   red_line  \\\n",
       "Date                                ...                          \n",
       "2022-04-20 02:00:00      -7.753003  ...  -71.328875  16.595000   \n",
       "2022-04-20 10:00:00      -9.150372  ...  -77.648582  -8.968412   \n",
       "2022-04-25 02:00:00      -6.662346  ...  -14.100924  36.166404   \n",
       "2022-04-25 06:00:00      -7.643190  ...  -25.157174  32.337392   \n",
       "2022-04-25 18:00:00      -7.573770  ...  -20.702414  23.358666   \n",
       "2022-04-27 06:00:00      -9.159496  ...  -48.902009   3.569222   \n",
       "2022-05-15 22:00:00      -2.384844  ...  -90.000000 -89.510126   \n",
       "2022-05-16 06:00:00      -3.307842  ...  -90.000000 -90.000000   \n",
       "2022-05-16 10:00:00      -3.962319  ...  -88.855605 -90.000000   \n",
       "2022-07-19 14:00:00      -1.220314  ...  -86.202331   8.250075   \n",
       "\n",
       "                     prev_white_line  prev_red_line  crossover  top_level_eot  \\\n",
       "Date                                                                            \n",
       "2022-04-20 02:00:00       -56.661690      27.329610       none          False   \n",
       "2022-04-20 10:00:00       -77.201320       4.269266       none          False   \n",
       "2022-04-25 02:00:00        -5.209245      39.474895       none          False   \n",
       "2022-04-25 06:00:00       -14.100924      36.166404       none          False   \n",
       "2022-04-25 18:00:00       -17.111097      26.784631       none          False   \n",
       "2022-04-27 06:00:00       -39.446893       8.190843       none          False   \n",
       "2022-05-15 22:00:00       -90.000000     -25.007460       none          False   \n",
       "2022-05-16 06:00:00       -90.000000     -90.000000       none          False   \n",
       "2022-05-16 10:00:00       -90.000000     -90.000000       none          False   \n",
       "2022-07-19 14:00:00       -90.000000      43.547199       none          False   \n",
       "\n",
       "                     bottom_level_eot  six_greater six_lower   change_pct  \n",
       "Date                                                                       \n",
       "2022-04-20 02:00:00              True          0.0       1.0  -109.090909  \n",
       "2022-04-20 10:00:00              True          0.0       1.0  -148.540146  \n",
       "2022-04-25 02:00:00              True          0.0       1.0  -209.090909  \n",
       "2022-04-25 06:00:00              True          0.0       1.0 -1750.000000  \n",
       "2022-04-25 18:00:00              True          0.0       1.0  -282.051282  \n",
       "2022-04-27 06:00:00              True          0.0       1.0 -1795.000000  \n",
       "2022-05-15 22:00:00              True          0.0       1.0   -56.423433  \n",
       "2022-05-16 06:00:00              True          0.0       1.0   -58.983051  \n",
       "2022-05-16 10:00:00              True          0.0       1.0   -59.974906  \n",
       "2022-07-19 14:00:00              True          0.0       1.0   -59.641256  \n",
       "\n",
       "[10 rows x 24 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buy_df = df[(df.below_lower_bb == True) & (df.price_below_ema_200 == True) & (df.bottom_level_eot == True) & (df.six_lower==1) & (df.change_pct<-50) ].tail(10) \n",
    "#(df.below_lower_bb == True) & (df.price_below_ema_200 == True) & (df.bottom_level_eot == True) & (df.six_lower==1) & (df.change_pct<80) \n",
    "buy_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8e9d4c9280>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(12,6), dpi=200)\n",
    "df= df.tail(300)\n",
    "buy_df = buy_df.tail(1)\n",
    "sell_df = sell_df.tail(1)\n",
    "plt.plot(df.close, label=f'Price pair ({coin1}-{coin2})')\n",
    "plt.plot(df.upper, label=f'BB-Upper')\n",
    "plt.plot(df.lower, label=f'BB-Lower')\n",
    "plt.plot(df.ema_200, label=f'EMA-200')\n",
    "plt.scatter(buy_df.index, buy_df.close.to_numpy(), marker ='^', s=150, c='g')\n",
    "plt.scatter(sell_df.index, sell_df.close.to_numpy(), marker ='v', s=150, c='r')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Commit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Execute this to save new versions of the notebook\n",
    "jovian.commit(project=\"ranton-mean-reversion-study-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}