triple EMA35 Trading bot with logic and np.diff() for position entry and exit(3).ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ignoring warnings\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "#importing neccesary modules\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib.dates import DateFormatter\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import seaborn as sns\n",
    "from dateutil.parser import parse\n",
    "import sklearn\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "import joblib\n",
    "import time\n",
    "from datetime import datetime, timezone\n",
    "from dateutil import parser\n",
    "import oandapyV20.endpoints.instruments as instruments\n",
    "import configparser\n",
    "import json\n",
    "from oandapyV20 import API\n",
    "import oandapyV20.endpoints.positions as positions\n",
    "import oandapyV20.endpoints.trades as trades\n",
    "from oandapyV20.endpoints.pricing import PricingStream\n",
    "import oandapyV20.endpoints.orders as orders\n",
    "import oandapyV20.endpoints.accounts as accounts\n",
    "from oandapyV20.contrib.factories import InstrumentsCandlesFactory\n",
    "from oandapyV20.contrib.requests import MarketOrderRequest, TrailingStopLossDetails\n",
    "from oandapyV20.exceptions import V20Error, StreamTerminated\n",
    "import sqlite3\n",
    "from collections import OrderedDict, ChainMap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://oanda-api-v20.readthedocs.io/en/latest/index.html\n",
    "config = configparser.ConfigParser()\n",
    "config.read('oanda_new.cfg')\n",
    "\n",
    "client = API(access_token=config['oanda']['access_token'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(_from, _to, granularity, instrument, save_csv=False):\n",
    "    \"\"\"\n",
    "        Collect historical data for training\n",
    "        _from=\"2018-01-01 00:00\"    date and time to start collecting candles\n",
    "        _to=\"\"                      date and time to end the collection of candles, if \"\", then end will be now.\n",
    "        save_csv if True will save csv file in current folder\n",
    "    \"\"\"\n",
    "    if _to == \"\":\n",
    "        _to = datetime.strptime(datetime.now().isoformat()[0:19] + \"Z\", '%Y-%m-%dT%H:%M:%SZ').strftime('%Y-%m-%dT%H:%M:%SZ')\n",
    "    else:\n",
    "        _to = datetime.strptime(parser.parse(_to).isoformat() + \"Z\", '%Y-%m-%dT%H:%M:%SZ').strftime('%Y-%m-%dT%H:%M:%SZ')\n",
    "    params = {\n",
    "    \"granularity\": granularity,\n",
    "    \"from\": datetime.strptime(parser.parse(_from).isoformat() + \"Z\", '%Y-%m-%dT%H:%M:%SZ').strftime('%Y-%m-%dT%H:%M:%SZ'),\n",
    "    \"to\": _to\n",
    "    }\n",
    "    \n",
    "    df = pd.DataFrame(columns=[\"Date\", 'Open', 'High', 'Low', 'Close', \"Volume\"])\n",
    "    for r in InstrumentsCandlesFactory(instrument=instrument, params=params):\n",
    "        rv = client.request(r)\n",
    "\n",
    "        results = [{\"Date\":candle['time'][0:19], \"Open\":float(candle['mid']['o']),\n",
    "                    \"High\":float(candle['mid']['h']), \"Low\":float(candle['mid']['l']),\n",
    "                   \"Close\":float(candle['mid']['c']), \"Volume\": float(candle['volume'])\n",
    "                   } for candle in r.response.get('candles')]\n",
    "        df = df.append(pd.DataFrame(results), ignore_index=True)\n",
    "    \n",
    "    df.set_index(\"Date\", inplace=True)\n",
    "    if save_csv:\n",
    "        df.to_csv(instrument + \"_\" + granularity + \".csv\")\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = \"2021-07-16 00:00\"    #date and time to start collecting candles\n",
    "end = \"\"    #date and time to end the collection of candles, if \"\", then end will be now.\n",
    "granularity = \"M1\"\n",
    "instrument = \"IN50_USD\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-07-19T18:57:00</th>\n",
       "      <td>15647.8</td>\n",
       "      <td>15651.5</td>\n",
       "      <td>15643.9</td>\n",
       "      <td>15644.5</td>\n",
       "      <td>95.0</td>\n",
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       "      <th>2021-07-19T18:58:00</th>\n",
       "      <td>15644.3</td>\n",
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       "      <td>129.0</td>\n",
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       "      <th>2021-07-19T18:59:00</th>\n",
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       "      <td>86.0</td>\n",
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       "    <tr>\n",
       "      <th>2021-07-19T19:00:00</th>\n",
       "      <td>15634.1</td>\n",
       "      <td>15637.5</td>\n",
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       "      <td>99.0</td>\n",
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       "    <tr>\n",
       "      <th>2021-07-19T19:01:00</th>\n",
       "      <td>15634.9</td>\n",
       "      <td>15635.5</td>\n",
       "      <td>15633.8</td>\n",
       "      <td>15633.8</td>\n",
       "      <td>12.0</td>\n",
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      ],
      "text/plain": [
       "                        Open     High      Low    Close  Volume\n",
       "Date                                                           \n",
       "2021-07-19T18:57:00  15647.8  15651.5  15643.9  15644.5    95.0\n",
       "2021-07-19T18:58:00  15644.3  15644.5  15635.8  15636.0   129.0\n",
       "2021-07-19T18:59:00  15636.3  15638.7  15633.9  15633.9    86.0\n",
       "2021-07-19T19:00:00  15634.1  15637.5  15633.3  15635.1    99.0\n",
       "2021-07-19T19:01:00  15634.9  15635.5  15633.8  15633.8    12.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = get_data(_from=start, _to=end, granularity=granularity, instrument=instrument, save_csv=False)\n",
    "date_ori = pd.to_datetime(df.iloc[:, 0]).tolist() \n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import talib as ta\n",
    "from talib import MA_Type\n",
    "\n",
    "def generate_features(df):\n",
    "    \"\"\" Generate features for a stock/index/currency/commodity based on historical price and performance\n",
    "    Args:\n",
    "        df (dataframe with columns \"open\", \"close\", \"high\", \"low\", \"volume\")\n",
    "    Returns:\n",
    "        dataframe, data set with new features\n",
    "    \"\"\"\n",
    "    df_new = pd.DataFrame()\n",
    "    \n",
    "    # 6 original features\n",
    "    df_new['Open'] = df['Open']\n",
    "    df_new['High'] = df['High']     \n",
    "    df_new['Low'] = df['Low'] \n",
    "    df_new['Close'] = df['Close']  \n",
    "    df_new['Trend'] = ta.HT_TRENDLINE(df_new['Close'])\n",
    "    df_new['EMA'] = ta.EMA(df_new['Close'], timeperiod = 20)\n",
    "    df_new['EMA2'] = ta.EMA(df_new['EMA'], timeperiod = 20)\n",
    "    df_new['EMA3'] = ta.EMA(df_new['EMA2'], timeperiod = 20)\n",
    "    \n",
    "    \n",
    "        \n",
    "  \n",
    "    df_new = df_new.dropna(axis=0)\n",
    "    return df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Trend</th>\n",
       "      <th>EMA</th>\n",
       "      <th>EMA2</th>\n",
       "      <th>EMA3</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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-07-19T18:57:00</th>\n",
       "      <td>15647.8</td>\n",
       "      <td>15651.5</td>\n",
       "      <td>15643.9</td>\n",
       "      <td>15644.5</td>\n",
       "      <td>15651.339526</td>\n",
       "      <td>15645.132724</td>\n",
       "      <td>15648.965212</td>\n",
       "      <td>15652.934735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-19T18:58:00</th>\n",
       "      <td>15644.3</td>\n",
       "      <td>15644.5</td>\n",
       "      <td>15635.8</td>\n",
       "      <td>15636.0</td>\n",
       "      <td>15650.337561</td>\n",
       "      <td>15644.262941</td>\n",
       "      <td>15648.517377</td>\n",
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       "      <th>2021-07-19T18:59:00</th>\n",
       "      <td>15636.3</td>\n",
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       "      <td>15633.9</td>\n",
       "      <td>15649.224596</td>\n",
       "      <td>15643.275994</td>\n",
       "      <td>15648.018197</td>\n",
       "      <td>15652.085860</td>\n",
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       "    <tr>\n",
       "      <th>2021-07-19T19:00:00</th>\n",
       "      <td>15634.1</td>\n",
       "      <td>15637.5</td>\n",
       "      <td>15633.3</td>\n",
       "      <td>15635.1</td>\n",
       "      <td>15648.234060</td>\n",
       "      <td>15642.497328</td>\n",
       "      <td>15647.492400</td>\n",
       "      <td>15651.648387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-19T19:01:00</th>\n",
       "      <td>15634.9</td>\n",
       "      <td>15635.5</td>\n",
       "      <td>15633.8</td>\n",
       "      <td>15633.8</td>\n",
       "      <td>15647.511460</td>\n",
       "      <td>15641.669011</td>\n",
       "      <td>15646.937792</td>\n",
       "      <td>15651.199759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                        Open     High      Low    Close         Trend  \\\n",
       "Date                                                                    \n",
       "2021-07-19T18:57:00  15647.8  15651.5  15643.9  15644.5  15651.339526   \n",
       "2021-07-19T18:58:00  15644.3  15644.5  15635.8  15636.0  15650.337561   \n",
       "2021-07-19T18:59:00  15636.3  15638.7  15633.9  15633.9  15649.224596   \n",
       "2021-07-19T19:00:00  15634.1  15637.5  15633.3  15635.1  15648.234060   \n",
       "2021-07-19T19:01:00  15634.9  15635.5  15633.8  15633.8  15647.511460   \n",
       "\n",
       "                              EMA          EMA2          EMA3  \n",
       "Date                                                           \n",
       "2021-07-19T18:57:00  15645.132724  15648.965212  15652.934735  \n",
       "2021-07-19T18:58:00  15644.262941  15648.517377  15652.514035  \n",
       "2021-07-19T18:59:00  15643.275994  15648.018197  15652.085860  \n",
       "2021-07-19T19:00:00  15642.497328  15647.492400  15651.648387  \n",
       "2021-07-19T19:01:00  15641.669011  15646.937792  15651.199759  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = generate_features(df)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "current_palette = sns.color_palette('Paired', 12)\n",
    "fig = plt.figure(figsize = (15, 10))\n",
    "ax = plt.subplot(111)\n",
    "x_range_original = np.arange(df.shape[0])\n",
    "\n",
    "\n",
    "ax.plot(\n",
    "    x_range_original,\n",
    "    df['Close'][0:],\n",
    "    color = current_palette[1],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2509c2f9d60>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "current_palette = sns.color_palette('Paired', 12)\n",
    "fig = plt.figure(figsize = (15, 10))\n",
    "ax = plt.subplot(111)\n",
    "x_range_original = np.arange(df.shape[0])\n",
    "\n",
    "\n",
    "ax.plot(\n",
    "    x_range_original,\n",
    "    df['EMA'][0:],\n",
    "    color = current_palette[1],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2509c66e460>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "current_palette = sns.color_palette('Paired', 12)\n",
    "fig = plt.figure(figsize = (15, 10))\n",
    "ax = plt.subplot(111)\n",
    "x_range_original = np.arange(df.shape[0])\n",
    "\n",
    "\n",
    "ax.plot(\n",
    "    x_range_original,\n",
    "    df['EMA2'][0:],\n",
    "    color = current_palette[1],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.34405412,  0.380737  ,  0.37647299, ..., -0.4991793 ,\n",
       "       -0.52579709, -0.5546085 ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diff(df['EMA2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2509c33c1f0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "current_palette = sns.color_palette('Paired', 12)\n",
    "fig = plt.figure(figsize = (15, 10))\n",
    "ax = plt.subplot(111)\n",
    "x_range_original = np.arange(df.shape[0])\n",
    "\n",
    "\n",
    "ax.plot(\n",
    "    x_range_original,\n",
    "    df['EMA3'][0:],\n",
    "    color = current_palette[1],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.58101329,  0.56193936,  0.54427589, ..., -0.42817497,\n",
       "       -0.43747232, -0.44862815])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diff(df['EMA3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Trend</th>\n",
       "      <th>EMA</th>\n",
       "      <th>EMA2</th>\n",
       "      <th>EMA3</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-07-16T02:03:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-16T02:04:00</th>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.122697</td>\n",
       "      <td>0.490195</td>\n",
       "      <td>0.344054</td>\n",
       "      <td>0.581013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-16T02:05:00</th>\n",
       "      <td>2.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.193671</td>\n",
       "      <td>0.729224</td>\n",
       "      <td>0.380737</td>\n",
       "      <td>0.561939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-16T02:06:00</th>\n",
       "      <td>2.6</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>0.122190</td>\n",
       "      <td>0.335965</td>\n",
       "      <td>0.376473</td>\n",
       "      <td>0.544276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-16T02:07:00</th>\n",
       "      <td>-3.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.104872</td>\n",
       "      <td>0.303968</td>\n",
       "      <td>0.369568</td>\n",
       "      <td>0.527637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-19T18:57:00</th>\n",
       "      <td>1.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-0.578807</td>\n",
       "      <td>-0.066603</td>\n",
       "      <td>-0.403420</td>\n",
       "      <td>-0.417845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-19T18:58:00</th>\n",
       "      <td>-3.5</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-8.1</td>\n",
       "      <td>-8.5</td>\n",
       "      <td>-1.001965</td>\n",
       "      <td>-0.869783</td>\n",
       "      <td>-0.447835</td>\n",
       "      <td>-0.420701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-19T18:59:00</th>\n",
       "      <td>-8.0</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-1.112965</td>\n",
       "      <td>-0.986947</td>\n",
       "      <td>-0.499179</td>\n",
       "      <td>-0.428175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-19T19:00:00</th>\n",
       "      <td>-2.2</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>-0.990536</td>\n",
       "      <td>-0.778666</td>\n",
       "      <td>-0.525797</td>\n",
       "      <td>-0.437472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-19T19:01:00</th>\n",
       "      <td>0.8</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>-0.722600</td>\n",
       "      <td>-0.828317</td>\n",
       "      <td>-0.554609</td>\n",
       "      <td>-0.448628</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2140 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Open  High  Low  Close     Trend       EMA      EMA2  \\\n",
       "Date                                                                        \n",
       "2021-07-16T02:03:00   NaN   NaN  NaN    NaN       NaN       NaN       NaN   \n",
       "2021-07-16T02:04:00   2.3   4.0  2.5    2.5  0.122697  0.490195  0.344054   \n",
       "2021-07-16T02:05:00   2.9   1.3  2.3    3.0  0.193671  0.729224  0.380737   \n",
       "2021-07-16T02:06:00   2.6  -0.2 -1.0   -3.4  0.122190  0.335965  0.376473   \n",
       "2021-07-16T02:07:00  -3.0  -1.1  1.0    0.0  0.104872  0.303968  0.369568   \n",
       "...                   ...   ...  ...    ...       ...       ...       ...   \n",
       "2021-07-19T18:57:00   1.1   4.0 -0.1   -3.0 -0.578807 -0.066603 -0.403420   \n",
       "2021-07-19T18:58:00  -3.5  -7.0 -8.1   -8.5 -1.001965 -0.869783 -0.447835   \n",
       "2021-07-19T18:59:00  -8.0  -5.8 -1.9   -2.1 -1.112965 -0.986947 -0.499179   \n",
       "2021-07-19T19:00:00  -2.2  -1.2 -0.6    1.2 -0.990536 -0.778666 -0.525797   \n",
       "2021-07-19T19:01:00   0.8  -2.0  0.5   -1.3 -0.722600 -0.828317 -0.554609   \n",
       "\n",
       "                         EMA3  \n",
       "Date                           \n",
       "2021-07-16T02:03:00       NaN  \n",
       "2021-07-16T02:04:00  0.581013  \n",
       "2021-07-16T02:05:00  0.561939  \n",
       "2021-07-16T02:06:00  0.544276  \n",
       "2021-07-16T02:07:00  0.527637  \n",
       "...                       ...  \n",
       "2021-07-19T18:57:00 -0.417845  \n",
       "2021-07-19T18:58:00 -0.420701  \n",
       "2021-07-19T18:59:00 -0.428175  \n",
       "2021-07-19T19:00:00 -0.437472  \n",
       "2021-07-19T19:01:00 -0.448628  \n",
       "\n",
       "[2140 rows x 8 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.diff(periods=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_candles(count, granularity, price='B'):\n",
    "    params={\"count\": count, \"granularity\": granularity, 'price': price}\n",
    "    r = instruments.InstrumentsCandles(instrument=instrument,params=params)\n",
    "    data = client.request(r)\n",
    "    \n",
    "    if price == 'B':\n",
    "        p = 'bid'\n",
    "    elif price == 'A':\n",
    "        p = 'ask'\n",
    "    results= [{\"Date\":x['time'],\"Open\":float(x[p]['o']),\"High\":float(x[p]['h']),\n",
    "               \"Low\":float(x[p]['l']), \"Close\":float(x[p]['c']), \"Volume\": float(x['volume'])\n",
    "              } for x in data['candles'] ] #if x[\"complete\"]\n",
    "    df = pd.DataFrame(results).set_index('Date')\n",
    "    df.index = pd.DatetimeIndex(df.index)\n",
    "    \n",
    "    return df[[\"Open\", \"High\", \"Low\", \"Close\", \"Volume\"]]\n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_order(units, instrument, sld):\n",
    "    #sld = trailing stop loss distance\n",
    "    \n",
    "    dfOrder = pd.DataFrame()\n",
    "    order = orders.OrderCreate(accountID=config['oanda']['account_id'], \n",
    "                               data=MarketOrderRequest(instrument=instrument, units=units, \n",
    "                                                       trailingStopLossOnFill=TrailingStopLossDetails(distance=sld).data).data)\n",
    "    response = client.request(order)\n",
    "    \n",
    "    print(response[\"orderFillTransaction\"]['time'], \"--\",\n",
    "          \"Instrument = \", response['orderFillTransaction']['instrument'], \"--\",\n",
    "          \"Price = \", float(response['orderFillTransaction']['price']), \"--\",\n",
    "          \"PL = \", float(response['orderFillTransaction']['pl']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'positions': [], 'lastTransactionID': '1797'}\n",
      "                              Open     High      Low    Close         Trend  \\\n",
      "Date                                                                          \n",
      "2021-07-19T18:58:00        15644.3  15644.5  15635.8  15636.0  15650.337561   \n",
      "2021-07-19T18:59:00        15636.3  15638.7  15633.9  15633.9  15649.224596   \n",
      "2021-07-19T19:00:00        15634.1  15637.5  15633.3  15635.1  15648.234060   \n",
      "2021-07-19T19:01:00        15634.9  15635.5  15633.8  15633.8  15647.511460   \n",
      "2021-07-19 19:01:00+00:00  15633.4  15634.0  15632.3  15632.3           NaN   \n",
      "\n",
      "                                    EMA          EMA2          EMA3  Volume  \n",
      "Date                                                                         \n",
      "2021-07-19T18:58:00        15644.262941  15648.517377  15652.514035     NaN  \n",
      "2021-07-19T18:59:00        15643.275994  15648.018197  15652.085860     NaN  \n",
      "2021-07-19T19:00:00        15642.497328  15647.492400  15651.648387     NaN  \n",
      "2021-07-19T19:01:00        15641.669011  15646.937792  15651.199759     NaN  \n",
      "2021-07-19 19:01:00+00:00           NaN           NaN           NaN    18.0  \n",
      "2021-07-19T19:02:11.698334921Z -- Instrument =  IN50_USD -- Price =  15629.3 -- PL =  0.0\n",
      "SELL\n",
      "{'positions': [{'instrument': 'IN50_USD', 'long': {'units': '0.0', 'pl': '-62.0676', 'resettablePL': '-62.0676', 'financing': '-0.7389', 'dividendAdjustment': '1.6975', 'guaranteedExecutionFees': '0.0000', 'unrealizedPL': '0.0000'}, 'short': {'units': '-1.0', 'averagePrice': '15629.3', 'pl': '-63.9350', 'resettablePL': '-63.9350', 'financing': '0.0000', 'dividendAdjustment': '0.0000', 'guaranteedExecutionFees': '0.0000', 'tradeIDs': ['1799'], 'unrealizedPL': '-2.5564'}, 'pl': '-126.0026', 'resettablePL': '-126.0026', 'financing': '-0.7389', 'commission': '0.0000', 'dividendAdjustment': '1.6975', 'guaranteedExecutionFees': '0.0000', 'unrealizedPL': '-2.5564', 'marginUsed': '1325.3000'}], 'lastTransactionID': '1800'}\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-6b8ccd199b90>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     39\u001b[0m             \u001b[0mposition\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m     \u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     42\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Below is the while Loop that is used to append the the close data from the pricing stream over time. \n",
    "# Like this with every iteration the Renko chart can be built again and new Renko bars get shown. \n",
    "\n",
    "from time import sleep\n",
    "\n",
    "\n",
    "\n",
    "df3 =  df.iloc[-200:]\n",
    "\n",
    "units = 1\n",
    "sld = 27\n",
    "\n",
    "while True:\n",
    "    \n",
    "\n",
    "    \n",
    "    df2 = get_candles(count=1, granularity='M1')\n",
    "    df2.index.name = \"Date\"\n",
    "    \n",
    "    r = positions.OpenPositions(accountID='101-004-11521897-001')\n",
    "    client.request(r)\n",
    "    print(r.response) \n",
    "    \n",
    "    t = df.index[-1]\n",
    "    t_old = None\n",
    "    \n",
    "    if t_old is None or t != t_old:\n",
    "        t_old = df.index[-1]\n",
    "        \n",
    "\n",
    "        # Trade\n",
    "        #get position from broker\n",
    "        global position\n",
    "        r = trades.OpenTrades(accountID=config['oanda']['account_id'])\n",
    "        t = dict(ChainMap(*client.request(r)[\"trades\"]))\n",
    "        try:\n",
    "            position = float(t[\"currentUnits\"])\n",
    "        except:\n",
    "            position = 0\n",
    "\n",
    "    sleep(60)\n",
    "    \n",
    "\n",
    "    df3 = df3.append(df2)\n",
    "    \n",
    "\n",
    "    print(df3.tail())\n",
    "\n",
    "    # hier kommt noch das df mit den aktuellen Werten für EMA1 - 3 hinein. \n",
    "\n",
    "\n",
    "    df = generate_features(df3)\n",
    "    df.tail()\n",
    "\n",
    "\n",
    "    # Here I have built in the trading logic to show a working trading strategy \n",
    "\n",
    "    if np.diff(df['EMA3'])[-1] < 0 and position == 0:\n",
    "        create_order(units=-units, instrument=\"IN50_USD\", sld=sld)\n",
    "        print('SELL')\n",
    "\n",
    "    elif np.diff(df['EMA3'])[-1] > 0 and position == -units:\n",
    "        create_order(units= units, instrument=\"IN50_USD\", sld=sld)\n",
    "        print('Close Short Position')\n",
    "\n",
    "    if np.diff(df['EMA3'])[-1] > 0 and position == 0:\n",
    "        create_order(units=units, instrument=\"IN50_USD\", sld=sld)\n",
    "        print('Buy')\n",
    "\n",
    "    elif np.diff(df['EMA3'])[-1] < 0 and position == units:\n",
    "        create_order(units= -units, instrument='IN50_USD', sld=sld)\n",
    "        print('Close Long Position')\n",
    "        \n",
    "        \n",
    "    current_palette = sns.color_palette('Paired', 12)\n",
    "    fig = plt.figure(figsize = (15, 10))\n",
    "    ax = plt.subplot(111)\n",
    "    x_range_original = np.arange(df.shape[0])\n",
    "\n",
    "\n",
    "    ax.plot(\n",
    "        x_range_original,\n",
    "        df['EMA3'][0:],\n",
    "        color = current_palette[1],\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}