tbot-v0-9-9.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Version 0.9.9 2021 03 08\n",
    "\n",
    "### Tbot - Trade Bot\n",
    "\n",
    " Tbot is a Robot of trade developed to trade cryptourrencies or s&p500 stocks, based on https/json queries. With few adjustments it may help traders to improve their workflow. In this file we shall compile three steps followed at the \"Algorithimic Trading Using Python\" course created by the api developer and completed by the developer of this api. (\"https://freecodecamp.org\")\n",
    " The code is also hosted at https://github.com/n4rus/tbot and is licensed under creative commons.\n",
    " \n",
    "### Library Imports\n",
    "The first thing we need to do is import the open-source software libraries that we'll be using in this api. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "import secrets\n",
    "import xlsxwriter\n",
    "import math\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### API token, secret key and acess keys\n",
    "API tokens (and other sensitive information) shall be stored in a secrets.py file that doesn't get pushed to your local Git repository. The AcessKey and SecretKey are provided by the server admin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from secrets import SecretKey\n",
    "from secrets import AccessKey\n",
    "from novadax import RequestClient as NovaClient\n",
    "from datetime import datetime\n",
    "\n",
    "nova_client = NovaClient(AccessKey, SecretKey)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing List of currencies, making the API Call and the table Dataframe\n",
    "List of crypto-currencies, get balance, and table creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "balance = nova_client.get_account_balance()\n",
    "bdata = balance['data']\n",
    "\n",
    "bcolumns = ['Currency', 'Balance']\n",
    "bdataframe = pd.DataFrame(columns = bcolumns,)\n",
    "bs = 0\n",
    "bcr = {}\n",
    "\n",
    "for bsymbol in bdata:\n",
    "    pdis = bdata[bs]\n",
    "    bis = pdis['balance']\n",
    "    bic = pdis['currency']\n",
    "    bcr[f\"{bic}\"] = f\"{bis}\"   \n",
    "    bdataframe.append(\n",
    "        pd.Series([\n",
    "            bic,\n",
    "            bis,\n",
    "        ], index = bcolumns),\n",
    "        ignore_index = True)\n",
    "    bdataframe = bdataframe.append(\n",
    "    pd.Series([\n",
    "        bic,\n",
    "        bis,\n",
    "    ], index=bcolumns),\n",
    "        ignore_index=True)\n",
    "    bs = bs + 1  \n",
    "\n",
    "sublcq = nova_client.subs()\n",
    "sublcd = sublcq['data']\n",
    "\n",
    "blccolumns = ['Currency', 'Sub-Balance']\n",
    "blcdataframe = pd.DataFrame(columns = bcolumns,)\n",
    "\n",
    "p = 0\n",
    "subids = {}\n",
    "sublcs = {}\n",
    "\n",
    "for i in sublcd:\n",
    "    sublcd = sublcq['data']\n",
    "    sub = sublcd[p]\n",
    "    sublci = sub['subId']\n",
    "    sublcy = sub['subIdentify']\n",
    "    subids[f\"{sublcy}\"] = f\"{sublci}\"\n",
    "    subId = sublci\n",
    "    sublcid = nova_client.subs_balance(subId)\n",
    "    subd = sublcid['data']\n",
    "    sd = subd[p]\n",
    "    subcy = sd['currency']\n",
    "    sublc = sd['balance']\n",
    "    sublcs[f\"{subcy}\"] = f\"{sublc}\"\n",
    "    \n",
    "    blcdataframe.append(\n",
    "        pd.Series([\n",
    "            subcy,\n",
    "            sublc,\n",
    "        ], index = blccolumns),\n",
    "        ignore_index = True)\n",
    "    blcdataframe = blcdataframe.append(\n",
    "    pd.Series([\n",
    "        subcy,\n",
    "        sublc,\n",
    "    ], index=blccolumns),\n",
    "        ignore_index=True)\n",
    " \n",
    "    p = p + 1\n",
    "\n",
    "blcdataframe.sort_values('Balance', ascending = False, inplace = True)\n",
    "blcdataframe.reset_index(drop = True, inplace = True)\n",
    "\n",
    "bdataframe.sort_values('Balance', ascending = False, inplace = True)\n",
    "bdataframe.reset_index(drop = True, inplace = True)\n",
    "#bdataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "blcdataframe.sort_values('Sub-Balance', ascending = False, inplace = True)\n",
    "blcdataframe.reset_index(drop = True, inplace = True)\n",
    "#blcdataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Symbol position, Dolar price and timestamp\n",
    "For the execution to be done independently for each symbol, the below cell is needed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'price'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-34-eff85c7f45be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mfdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspld\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0mfdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mprice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfdt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'price'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0mamount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfdt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'amount'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mcprice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamount\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'price'"
     ]
    }
   ],
   "source": [
    "symbol = 'ETH_USDT'\n",
    "symbols = ['ETH_USDT']\n",
    "currency = 'R$'\n",
    "my_columns = ['Symbol', 'Currency Price', 'Number of Shares to Buy']\n",
    "e_dataframe = pd.DataFrame(columns = my_columns)\n",
    "\n",
    "spld = nova_client.list_tickers()\n",
    "\n",
    "for symbol in symbols:\n",
    "    t = 0\n",
    "    for i in spld['data']:  \n",
    "        fdata = spld['data']\n",
    "        fdt = fdata[t]\n",
    "        price = fdt['price']\n",
    "        amount = fdt['amount']\n",
    "        cprice = float(amount) * float(price)\n",
    "        edolar = bcr['USDT']\n",
    "        eth_wal = float(bcr['ETH']) * float(price)\n",
    "        edolarwal = float(bcr['USDT']) + float(eth_wal)\n",
    "        shares_to_buy = (float(edolarwal)/float(price))\n",
    "        etsp = (fdt['timestamp'])\n",
    "        r = r + 1\n",
    "        e_dataframe.append(\n",
    "                pd.Series(\n",
    "                    [\n",
    "                        symbol,\n",
    "                        price,\n",
    "                        shares_to_buy\n",
    "                    ]\n",
    "                    ,index = my_columns),\n",
    "                ignore_index=True)\n",
    "    \n",
    "    \n",
    "        e_dataframe = e_dataframe.append(\n",
    "            pd.Series([\n",
    "                        symbol, \n",
    "                        price,\n",
    "                        shares_to_buy\n",
    "                        ],\n",
    "                    index = my_columns\n",
    "                    ),\n",
    "                    ignore_index = True)\n",
    "\n",
    "e_dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the momentum table\n",
    "For mommentum we use the last order minus the order from an amount x of time. The place from wich the data comes is main importance on the formula. As stated in the comment section on the first versions, data must come from the common filled orders or the last trades from the users history, here the second case is uded, as well from this version on, but the first ones used common filled orders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Number of Shares to Buy</th>\n",
       "      <th>Last Orders Price</th>\n",
       "      <th>B/S</th>\n",
       "      <th>Momentum Price</th>\n",
       "      <th>momentum-raw-data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ETH_USDT</td>\n",
       "      <td>0.0697</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>1804.33</td>\n",
       "      <td>BUY</td>\n",
       "      <td>1796.29</td>\n",
       "      <td>8.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ETH_USDT</td>\n",
       "      <td>0.1413</td>\n",
       "      <td>0.071659</td>\n",
       "      <td>1816.89</td>\n",
       "      <td>SELL</td>\n",
       "      <td>1796.29</td>\n",
       "      <td>20.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ETH_USDT</td>\n",
       "      <td>0.1406</td>\n",
       "      <td>0.070959</td>\n",
       "      <td>1825.00</td>\n",
       "      <td>BUY</td>\n",
       "      <td>1796.29</td>\n",
       "      <td>28.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETH_USDT</td>\n",
       "      <td>0.1413</td>\n",
       "      <td>0.071659</td>\n",
       "      <td>1827.14</td>\n",
       "      <td>BUY</td>\n",
       "      <td>1796.29</td>\n",
       "      <td>30.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ETH_USDT</td>\n",
       "      <td>0.0391</td>\n",
       "      <td>-0.030540</td>\n",
       "      <td>1830.85</td>\n",
       "      <td>SELL</td>\n",
       "      <td>1796.29</td>\n",
       "      <td>34.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ETH_USDT</td>\n",
       "      <td>0.1395</td>\n",
       "      <td>0.069860</td>\n",
       "      <td>1838.47</td>\n",
       "      <td>BUY</td>\n",
       "      <td>1796.29</td>\n",
       "      <td>42.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ETH_USDT</td>\n",
       "      <td>0.1118</td>\n",
       "      <td>0.042160</td>\n",
       "      <td>1839.31</td>\n",
       "      <td>SELL</td>\n",
       "      <td>1796.29</td>\n",
       "      <td>43.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ETH_USDT</td>\n",
       "      <td>0.1405</td>\n",
       "      <td>0.070860</td>\n",
       "      <td>1840.00</td>\n",
       "      <td>SELL</td>\n",
       "      <td>1796.29</td>\n",
       "      <td>43.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ETH_USDT</td>\n",
       "      <td>0.0391</td>\n",
       "      <td>-0.030540</td>\n",
       "      <td>1850.77</td>\n",
       "      <td>SELL</td>\n",
       "      <td>1796.29</td>\n",
       "      <td>54.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Symbol  Amount  Number of Shares to Buy  Last Orders Price   B/S  \\\n",
       "0  ETH_USDT  0.0697                 0.000058            1804.33   BUY   \n",
       "1  ETH_USDT  0.1413                 0.071659            1816.89  SELL   \n",
       "2  ETH_USDT  0.1406                 0.070959            1825.00   BUY   \n",
       "3  ETH_USDT  0.1413                 0.071659            1827.14   BUY   \n",
       "4  ETH_USDT  0.0391                -0.030540            1830.85  SELL   \n",
       "5  ETH_USDT  0.1395                 0.069860            1838.47   BUY   \n",
       "6  ETH_USDT  0.1118                 0.042160            1839.31  SELL   \n",
       "7  ETH_USDT  0.1405                 0.070860            1840.00  SELL   \n",
       "8  ETH_USDT  0.0391                -0.030540            1850.77  SELL   \n",
       "\n",
       "  Momentum Price  momentum-raw-data  \n",
       "0        1796.29               8.04  \n",
       "1        1796.29              20.60  \n",
       "2        1796.29              28.71  \n",
       "3        1796.29              30.85  \n",
       "4        1796.29              34.56  \n",
       "5        1796.29              42.18  \n",
       "6        1796.29              43.02  \n",
       "7        1796.29              43.71  \n",
       "8        1796.29              54.48  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listtrades = nova_client.list_orders(symbol, 'FILLED', limit=5)\n",
    "ldata = listtrades['data']\n",
    "lindice= ldata[0]\n",
    "amount = lindice['amount']\n",
    "my_columns = ['Symbol', 'Amount', 'Number of Shares to Buy','Last Orders Price', 'B/S', 'Momentum Price',  'momentum-raw-data']\n",
    "m_dataframe = pd.DataFrame(columns = my_columns)\n",
    "\n",
    "for symbol in symbols:\n",
    "    listtrades = nova_client.list_orders(symbol, 'FINISHED')\n",
    "    s = 0\n",
    "    for i in listtrades['data'][:9]:\n",
    "        ldata = listtrades['data']\n",
    "        ldc = ldata[s]\n",
    "        amount = ldc['amount']\n",
    "        price = float(ldc['price'])\n",
    "        \n",
    "        amount = float(ldc['amount'])\n",
    "        cprice = float(amount) * float(price)\n",
    "        edolar = bcr['USDT']\n",
    "        eth_wal = float(bcr['ETH']) * float(price)\n",
    "        edolarwal = float(bcr['USDT']) + float(eth_wal)\n",
    "        sharestobuy = float(amount) - (float(edolarwal))/float(price)\n",
    "        bs = ldc['side']\n",
    "        order = float(amount) - float(sharestobuy)\n",
    "        srto = float(order)\n",
    "        \n",
    "        #M = V - Vx ; M representa o indicador de momentum, V o valor de fechamento atual e V(x_diasatras) o valor anterior\n",
    "        #nao é deste modo que se utiliza o simbolo x na multiplicação. Vx seriam multiplos valores de V em x dias. Para este caso, Vi\n",
    "        #veja que por conotaçao convencional parece nao fazer diferença, porém, na matemática, tais convenções são necessárias e de certo modo indispensáveis no tempo t\n",
    "        #h = \n",
    "        vxdata = e_dataframe['Currency Price']\n",
    "        vxi = vxdata[s]\n",
    "        vx = vxi\n",
    "        momentum = float(price) - float(vx)\n",
    "        tsp = (ldc['timestamp'])\n",
    "        s = s + 1\n",
    "        m_dataframe.append(\n",
    "                    pd.Series(\n",
    "                    [\n",
    "                        symbol,\n",
    "                        amount,\n",
    "                        sharestobuy,\n",
    "                        price,\n",
    "                        bs,\n",
    "                        vx,\n",
    "                        momentum,\n",
    "                    ]\n",
    "                    ,index = my_columns),\n",
    "                ignore_index=True)\n",
    "\n",
    "    \n",
    "        m_dataframe = m_dataframe.append(\n",
    "                pd.Series([\n",
    "                        symbol,\n",
    "                        amount,\n",
    "                        sharestobuy,\n",
    "                        price,\n",
    "                        bs,\n",
    "                        vx,\n",
    "                        momentum,\n",
    "                    \n",
    "                      ]\n",
    "                ,index = my_columns), \n",
    "                ignore_index = True)\n",
    "\n",
    "m_dataframe.sort_values('Last Orders Price', ascending = True, inplace = True)\n",
    "m_dataframe.reset_index(drop = True, inplace = True)\n",
    "m_dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sending the requisition to the web\n",
    "The content in the order requisition should be sent to the server following the instructions given by the company or individual that holds the coin. Here as columns the last used titles shall be all used in one go to give a more complete dataframe in our xlsx database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Last Orders Price</th>\n",
       "      <th>B/S</th>\n",
       "      <th>Momentum Price</th>\n",
       "      <th>momentum-raw-data</th>\n",
       "      <th>Status</th>\n",
       "      <th>Timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ETH_USDT</td>\n",
       "      <td>0.0697</td>\n",
       "      <td>1804.33</td>\n",
       "      <td>BUY</td>\n",
       "      <td>1792.85</td>\n",
       "      <td>11.48</td>\n",
       "      <td>FILLED</td>\n",
       "      <td>20210310 17:46:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ETH_USDT</td>\n",
       "      <td>0.0391</td>\n",
       "      <td>1850.77</td>\n",
       "      <td>SELL</td>\n",
       "      <td>1777.94</td>\n",
       "      <td>72.83</td>\n",
       "      <td>FILLED</td>\n",
       "      <td>20210310 16:17:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ETH_USDT</td>\n",
       "      <td>0.0391</td>\n",
       "      <td>1830.85</td>\n",
       "      <td>SELL</td>\n",
       "      <td>1811.39</td>\n",
       "      <td>19.46</td>\n",
       "      <td>CANCELED</td>\n",
       "      <td>20210310 13:22:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETH_USDT</td>\n",
       "      <td>0.1118</td>\n",
       "      <td>1839.31</td>\n",
       "      <td>SELL</td>\n",
       "      <td>1810.84</td>\n",
       "      <td>28.47</td>\n",
       "      <td>CANCELED</td>\n",
       "      <td>20210310 13:15:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ETH_USDT</td>\n",
       "      <td>0.1413</td>\n",
       "      <td>1827.14</td>\n",
       "      <td>BUY</td>\n",
       "      <td>1810.84</td>\n",
       "      <td>16.30</td>\n",
       "      <td>PARTIAL_CANCELED</td>\n",
       "      <td>20210310 12:53:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ETH_USDT</td>\n",
       "      <td>0.1405</td>\n",
       "      <td>1840.00</td>\n",
       "      <td>SELL</td>\n",
       "      <td>1804.66</td>\n",
       "      <td>35.34</td>\n",
       "      <td>FILLED</td>\n",
       "      <td>20210309 11:50:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ETH_USDT</td>\n",
       "      <td>0.1406</td>\n",
       "      <td>1825.00</td>\n",
       "      <td>BUY</td>\n",
       "      <td>1800.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>FILLED</td>\n",
       "      <td>20210308 23:10:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ETH_USDT</td>\n",
       "      <td>0.1395</td>\n",
       "      <td>1838.47</td>\n",
       "      <td>BUY</td>\n",
       "      <td>1825.09</td>\n",
       "      <td>13.38</td>\n",
       "      <td>CANCELED</td>\n",
       "      <td>20210308 22:51:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ETH_USDT</td>\n",
       "      <td>0.1413</td>\n",
       "      <td>1816.89</td>\n",
       "      <td>SELL</td>\n",
       "      <td>1857.82</td>\n",
       "      <td>-40.93</td>\n",
       "      <td>FILLED</td>\n",
       "      <td>20210308 21:30:09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Symbol  Amount  Last Orders Price   B/S  Momentum Price  \\\n",
       "0  ETH_USDT  0.0697            1804.33   BUY         1792.85   \n",
       "1  ETH_USDT  0.0391            1850.77  SELL         1777.94   \n",
       "2  ETH_USDT  0.0391            1830.85  SELL         1811.39   \n",
       "3  ETH_USDT  0.1118            1839.31  SELL         1810.84   \n",
       "4  ETH_USDT  0.1413            1827.14   BUY         1810.84   \n",
       "5  ETH_USDT  0.1405            1840.00  SELL         1804.66   \n",
       "6  ETH_USDT  0.1406            1825.00   BUY         1800.00   \n",
       "7  ETH_USDT  0.1395            1838.47   BUY         1825.09   \n",
       "8  ETH_USDT  0.1413            1816.89  SELL         1857.82   \n",
       "\n",
       "   momentum-raw-data            Status          Timestamp  \n",
       "0              11.48            FILLED  20210310 17:46:17  \n",
       "1              72.83            FILLED  20210310 16:17:31  \n",
       "2              19.46          CANCELED  20210310 13:22:47  \n",
       "3              28.47          CANCELED  20210310 13:15:18  \n",
       "4              16.30  PARTIAL_CANCELED  20210310 12:53:09  \n",
       "5              35.34            FILLED  20210309 11:50:06  \n",
       "6              25.00            FILLED  20210308 23:10:58  \n",
       "7              13.38          CANCELED  20210308 22:51:25  \n",
       "8             -40.93            FILLED  20210308 21:30:09  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#orders dataframe\n",
    "ttrades = nova_client.list_trades(symbol, limit=9)\n",
    "tdata = ttrades['data']\n",
    "my_columns = ['Symbol', 'Amount', 'Last Orders Price', 'B/S', 'Momentum Price',  'momentum-raw-data', 'Status', 'Timestamp']\n",
    "o_dataframe = pd.DataFrame(columns = my_columns)\n",
    "\n",
    "for symbol in symbols:\n",
    "    listorders = nova_client.list_orders(symbol, 'FINISHED')\n",
    "    s = 0\n",
    "    for i in listorders['data'][:9]:\n",
    "        ldata = listorders['data']\n",
    "        ldc = ldata[s]\n",
    "        amount = ldc['amount']\n",
    "        price = float(ldc['price'])\n",
    "        \n",
    "        amount = float(ldc['amount'])\n",
    "        cprice = float(amount) * float(price)\n",
    "        edolar = bcr['USDT']\n",
    "        eth_wal = float(bcr['ETH']) * float(price)\n",
    "        edolarwal = float(bcr['USDT']) + float(eth_wal)\n",
    "        bs = ldc['side']\n",
    "        \n",
    "        #rework into a precise formula with momentum\n",
    "        #This must free the mommentum table cell\n",
    "        vxdata = tdata[s]\n",
    "        vxi = vxdata['price']\n",
    "        momentumprice = float(vxi)\n",
    "        momentumraw = float(price) - float(vxi)\n",
    "\n",
    "        tspi = float(ldc['timestamp']/1000)\n",
    "        tsp = datetime.fromtimestamp(tspi).strftime(\"%Y%m%d %H:%M:%S\")\n",
    "        status = ldc['status']\n",
    "        \n",
    "        s = s + 1\n",
    "        \n",
    "        o_dataframe.append(\n",
    "                    pd.Series(\n",
    "                    [\n",
    "                        symbol,\n",
    "                        amount,\n",
    "                        price,\n",
    "                        bs,\n",
    "                        momentumprice,\n",
    "                        momentumraw,\n",
    "                        status,\n",
    "                        tsp\n",
    "                    ]\n",
    "                    ,index = my_columns),\n",
    "                ignore_index=True)\n",
    "\n",
    "    \n",
    "        o_dataframe = o_dataframe.append(\n",
    "                pd.Series([\n",
    "                        symbol,\n",
    "                        amount,\n",
    "                        price,\n",
    "                        bs,\n",
    "                        momentumprice,\n",
    "                        momentumraw,\n",
    "                        status,\n",
    "                        tsp\n",
    "                    \n",
    "                      ]\n",
    "                ,index = my_columns), \n",
    "                ignore_index = True)\n",
    "\n",
    "o_dataframe.sort_values('Timestamp', ascending = False, inplace = True)\n",
    "o_dataframe.reset_index(drop = True, inplace = True)\n",
    "o_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'BUY': 'CANCELED, 1838.47', 'SELL': 'FILLED, 1816.89'}\n"
     ]
    }
   ],
   "source": [
    "#sort price [ok]\n",
    "#wallet_amount [ok]\n",
    "#dataframe momentum[ok]pt2[ok]\n",
    "#buy lower(wallet_amount - amount)  [ok]   \n",
    "#sell + profit(constant*market momentum)[ok]\n",
    "\n",
    "\n",
    "#if sell = true; [buy lower < sell][(sell - (contant*momentum)/2 or (buy + constant/momentum)/2\n",
    "#P = S - B; SUM(Pi to Pn)  = Pi{(S - D) or (S - B)}\n",
    "\n",
    "#in the if statement one more contidional is needed at the \"or\" statment. If or else(momentum>=x or else)\n",
    "#20210222#v0-9#import all the list itens from the last dataframes and send them to the xlsx file. \n",
    "#20200225#v0-9-1# the data must come from the common filled orders or the last trades from the users history, \n",
    "# here the second case is uded, as well from this version on, first ones used common filled orders\n",
    "\n",
    "#loop time count and do momentum profit auto cicle\n",
    "#publish\n",
    "\n",
    "listorders = nova_client.list_orders(symbol, 'FINISHED')\n",
    "for i in o_dataframe['Last Orders Price']:\n",
    "#define booleans\n",
    "#boolean dataframe\n",
    "    a = 9 \n",
    "    b = 2\n",
    "    gama = 0\n",
    "    z = 0\n",
    "    sti = 0\n",
    "    filled = 'FILLED'\n",
    "    statushd = {}\n",
    "    stdata = o_dataframe['Status']\n",
    "    bsdata = o_dataframe['B/S']\n",
    "    lopdta = o_dataframe['Last Orders Price']\n",
    "\n",
    "for i in bsdata:\n",
    "        bsdaty = bsdata[z]\n",
    "        stdaty = stdata[z] \n",
    "        lopdty = lopdta[z]\n",
    "        statushd[f\"{bsdaty}\"] = f\"{stdaty}, {lopdty}\"\n",
    "        z = z + 1\n",
    "print(statushd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "RequestException",
     "evalue": "('A30012', \"Price can't be less than 0.9 of current price\")",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRequestException\u001b[0m                          Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-9e40f4742f8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmomentumprice\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mprice\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mamount\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0.02\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0msellprofit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprice\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m1.00\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnova_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_order\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msymbol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'LIMIT'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'SELL'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprofit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mamount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mamount\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[0;31m#first block to be solved\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/narusb/Empresa/dev/novadax-1.1.2/novadax/request_client.py\u001b[0m in \u001b[0;36mcreate_order\u001b[0;34m(self, symbol, _type, side, price, amount, value, account_id)\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_order\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msymbol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mside\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mamount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccount_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m         return self._client.post_with_auth('/v1/orders/create', {}, {\n\u001b[0m\u001b[1;32m     69\u001b[0m             \u001b[0;34m'accountId'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0maccount_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m             \u001b[0;34m'symbol'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0msymbol\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/narusb/Empresa/dev/novadax-1.1.2/novadax/kernel/http/base_http_client.py\u001b[0m in \u001b[0;36mpost_with_auth\u001b[0;34m(self, path, params, json)\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpost_with_auth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'post'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mauth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/narusb/Empresa/dev/novadax-1.1.2/novadax/kernel/http/base_http_client.py\u001b[0m in \u001b[0;36m_request\u001b[0;34m(self, auth, method, path, **kwargs)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m400\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRequestException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'code'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'message'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRequestException\u001b[0m: ('A30012', \"Price can't be less than 0.9 of current price\")"
     ]
    }
   ],
   "source": [
    "\n",
    "my_columns = ['B/S', 'Status','Last Orders Price', 'Timestamp']\n",
    "boolean_dataframe = pd.DataFrame(columns = my_columns)\n",
    "\n",
    "\n",
    "listorders = nova_client.list_orders(symbol, 'FINISHED')\n",
    "for i in o_dataframe['Last Orders Price']:\n",
    "#define booleans\n",
    "#boolean dataframe\n",
    "    a = 9 \n",
    "    b = 2\n",
    "    gama = 0\n",
    "    z = 0\n",
    "    sti = 0\n",
    "    filled = 'FILLED'\n",
    "    statushd = {}\n",
    "    stdata = o_dataframe['Status']\n",
    "    bsdata = o_dataframe['B/S']\n",
    "    lopdta = o_dataframe['Last Orders Price']\n",
    "    for i in stdata:\n",
    "        bsdaty = bsdata[z]\n",
    "        stdaty = stdata[z] \n",
    "        lopdty = lopdta[z]\n",
    "        ldata = listorders['data']\n",
    "        ldc = ldata[z]\n",
    "        tspi = float(ldc['timestamp']/1000)\n",
    "        tsp = datetime.fromtimestamp(tspi).strftime(\"%Y%m%d %H:%M:%S\")\n",
    "        sell = float(lopdty) - float(lopdty *(0.03))    \n",
    "        buy = float(lopdty) - float(lopdty *(0.03))\n",
    "        profit = sell - buy\n",
    "        bm = buy - momentumprice\n",
    "        if stdaty == filled:\n",
    "            statushd[f\"{bsdaty}\"] = f\"{stdaty}, {lopdty}\"\n",
    "            boolean_dataframe.append(\n",
    "                    pd.Series(\n",
    "                    [\n",
    "                        bsdaty,\n",
    "                        stdaty,\n",
    "                        lopdty,\n",
    "                        tsp\n",
    "                    ]\n",
    "                    ,index = my_columns),\n",
    "                ignore_index=True)\n",
    "\n",
    "    \n",
    "        boolean_dataframe = boolean_dataframe.append(\n",
    "                pd.Series([\n",
    "                        bsdaty,\n",
    "                        stdaty,\n",
    "                        lopdty,\n",
    "                        tsp\n",
    "                      ]\n",
    "                ,index = my_columns), \n",
    "                ignore_index = True)\n",
    "\n",
    "boolean_dataframe\n",
    "if momentumprice > price and amount > 0.02 :\n",
    "    sellprofit = math.floor(price*2/100)/1.00\n",
    "    result = nova_client.create_order(symbol, 'LIMIT', 'SELL', price = profit, amount = amount)  \n",
    "\n",
    "#first block to be solved\n",
    "if momentumprice < price and amount < 0.02:\n",
    "    print(\"Market is not ready to sell\")\n",
    "\n",
    "if momentumprice < price and amount > 0.02:\n",
    "    result = nova_client.create_order(symbol, 'LIMIT', 'BUY', price = bmprice, amount = amount)\n",
    "    print(\"The quantity bought was ETH: \", bm)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1852.89\n"
     ]
    }
   ],
   "source": [
    "sellprofit = math.floor(price*2/100)/1.00\n",
    "spe = price + sellprofit\n",
    "print(spe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time = d.ctime()\n",
    "print(datetime.fromtimestamp(time.time()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Format and export xlsx file\n",
    "In the last section of the tbot a xlsx file is saved with the order table for later reading purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "writer = pd.ExcelWriter('orders.xlsx', engine = 'xlsxwriter')\n",
    "o_dataframe.to_excel(writer, sheet_name = 'orderlist', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "background_color = '#0a0a23'\n",
    "font_color = '#ffffff'\n",
    "\n",
    "string_template = writer.book.add_format(\n",
    "        {\n",
    "            'font_color': font_color,\n",
    "            'bg_color': background_color,\n",
    "            'border': 1\n",
    "        }\n",
    "    )\n",
    "\n",
    "dollar_template = writer.book.add_format(\n",
    "        {\n",
    "            'num_format':'$0.00',\n",
    "            'font_color': font_color,\n",
    "            'bg_color': background_color,\n",
    "            'border': 1\n",
    "        }\n",
    "    )\n",
    "\n",
    "integer_template = writer.book.add_format(\n",
    "        {\n",
    "            'num_format':'0',\n",
    "            'font_color': font_color,\n",
    "            'bg_color': background_color,\n",
    "            'border': 1\n",
    "        }\n",
    "    )\n",
    "\n",
    "float_template = writer.book.add_format(\n",
    "        {\n",
    "            'num_format':'0',\n",
    "            'font_color': font_color,\n",
    "            'bg_color': background_color,\n",
    "            'border': 1\n",
    "        }\n",
    "    )\n",
    "\n",
    "percent_template = writer.book.add_format(\n",
    "        {\n",
    "            'num_format':'0.0%',\n",
    "            'font_color': font_color,\n",
    "            'bg_color': background_color,\n",
    "            'border': 1\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_formats = {\n",
    "    'A':['Symbol', string_template],\n",
    "    'B':['Amount', float_template],\n",
    "    'C':['Price', dollar_template],\n",
    "}\n",
    "\n",
    "for column in column_formats.keys():\n",
    "    writer.sheets['orderlist'].set_column(f'{column}:{column}', 25, column_formats[column][1])\n",
    "    writer.sheets['orderlist'].write(f'{column}1', column_formats[column][0], column_formats[column][1])\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "writer.save()"
   ]
  }
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