knn crypto.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f3bc377",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ccxt\n",
    "import pandas as pd\n",
    "import config\n",
    "import yfinance as yf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "b62dc484",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# Plotting graphs\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Machine learning libraries\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Data fetching\n",
    "from pandas_datareader import data as pdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "ee526eca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ohlcv(acct=ccxt.gemini({'apiKey': config.apiKey, 'secret': config.apiSecret}), security='ETH/USD',time_period='1m'):\n",
    "    ohlcv = acct.fetch_ohlcv('ETH/USD','1m')\n",
    "    df = pd.DataFrame(ohlcv, columns=['Date', 'Open', 'High', 'Low', 'Close', 'Volume'])\n",
    "    df['Date'] = pd.to_datetime(df['Date'], unit='ms')\n",
    "    df.set_index('Date', inplace=True)\n",
    "    df = df.sort_index(ascending=True)\n",
    "    \n",
    "    df['Open-Close'] = df.Open - df.Close\n",
    "    df['High-Low'] = df.High - df.Low\n",
    "    df = df.dropna()\n",
    "    \n",
    "    return df\n",
    "def buy_signal(df):\n",
    "    return np.where(df['Close'].shift(-1)>df['Close'],1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "36167ff6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "d = get_ohlcv()\n",
    "Y= buy_signal(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "78870c0f",
   "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",
       "      <th>Open-Close</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-07-02 02:31:00</th>\n",
       "      <td>1919.05</td>\n",
       "      <td>1919.59</td>\n",
       "      <td>1919.05</td>\n",
       "      <td>1919.59</td>\n",
       "      <td>1.188984</td>\n",
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       "      <th>2023-07-02 02:32:00</th>\n",
       "      <td>1919.59</td>\n",
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       "      <td>1919.59</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>2023-07-02 02:33:00</th>\n",
       "      <td>1919.59</td>\n",
       "      <td>1919.59</td>\n",
       "      <td>1919.59</td>\n",
       "      <td>1919.59</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>2023-07-02 02:34:00</th>\n",
       "      <td>1919.59</td>\n",
       "      <td>1920.63</td>\n",
       "      <td>1919.59</td>\n",
       "      <td>1920.62</td>\n",
       "      <td>0.028492</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-02 02:35:00</th>\n",
       "      <td>1920.62</td>\n",
       "      <td>1920.62</td>\n",
       "      <td>1920.62</td>\n",
       "      <td>1920.62</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2023-07-03 02:26:00</th>\n",
       "      <td>1948.33</td>\n",
       "      <td>1948.33</td>\n",
       "      <td>1948.33</td>\n",
       "      <td>1948.33</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-03 02:27:00</th>\n",
       "      <td>1948.33</td>\n",
       "      <td>1948.33</td>\n",
       "      <td>1948.33</td>\n",
       "      <td>1948.33</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>2023-07-03 02:28:00</th>\n",
       "      <td>1948.33</td>\n",
       "      <td>1949.62</td>\n",
       "      <td>1948.33</td>\n",
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       "      <td>0.002626</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>1.29</td>\n",
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       "    <tr>\n",
       "      <th>2023-07-03 02:29:00</th>\n",
       "      <td>1949.62</td>\n",
       "      <td>1949.62</td>\n",
       "      <td>1949.62</td>\n",
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       "      <td>0.00</td>\n",
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       "      <th>2023-07-03 02:30:00</th>\n",
       "      <td>1949.62</td>\n",
       "      <td>1950.55</td>\n",
       "      <td>1949.62</td>\n",
       "      <td>1950.55</td>\n",
       "      <td>0.391547</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1440 rows × 7 columns</p>\n",
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       "                        open     high      low    close    volume  Open-Close   \n",
       "date                                                                            \n",
       "2023-07-02 02:31:00  1919.05  1919.59  1919.05  1919.59  1.188984       -0.54  \\\n",
       "2023-07-02 02:32:00  1919.59  1919.59  1919.59  1919.59  0.000000        0.00   \n",
       "2023-07-02 02:33:00  1919.59  1919.59  1919.59  1919.59  0.000000        0.00   \n",
       "2023-07-02 02:34:00  1919.59  1920.63  1919.59  1920.62  0.028492       -1.03   \n",
       "2023-07-02 02:35:00  1920.62  1920.62  1920.62  1920.62  0.000000        0.00   \n",
       "...                      ...      ...      ...      ...       ...         ...   \n",
       "2023-07-03 02:26:00  1948.33  1948.33  1948.33  1948.33  0.000000        0.00   \n",
       "2023-07-03 02:27:00  1948.33  1948.33  1948.33  1948.33  0.000000        0.00   \n",
       "2023-07-03 02:28:00  1948.33  1949.62  1948.33  1949.62  0.002626       -1.29   \n",
       "2023-07-03 02:29:00  1949.62  1949.62  1949.62  1949.62  0.000000        0.00   \n",
       "2023-07-03 02:30:00  1949.62  1950.55  1949.62  1950.55  0.391547       -0.93   \n",
       "\n",
       "                     High-Low  \n",
       "date                           \n",
       "2023-07-02 02:31:00      0.54  \n",
       "2023-07-02 02:32:00      0.00  \n",
       "2023-07-02 02:33:00      0.00  \n",
       "2023-07-02 02:34:00      1.04  \n",
       "2023-07-02 02:35:00      0.00  \n",
       "...                       ...  \n",
       "2023-07-03 02:26:00      0.00  \n",
       "2023-07-03 02:27:00      0.00  \n",
       "2023-07-03 02:28:00      1.29  \n",
       "2023-07-03 02:29:00      0.00  \n",
       "2023-07-03 02:30:00      0.93  \n",
       "\n",
       "[1440 rows x 7 columns]"
      ]
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     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "45ece237",
   "metadata": {},
   "outputs": [],
   "source": [
    "X= d[['Open-Close', 'High-Low']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9fb1d2be",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th>2023-07-02 02:34:00</th>\n",
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       "                     Open-Close  High-Low\n",
       "date                                     \n",
       "2023-07-02 02:31:00       -0.54      0.54\n",
       "2023-07-02 02:32:00        0.00      0.00\n",
       "2023-07-02 02:33:00        0.00      0.00\n",
       "2023-07-02 02:34:00       -1.03      1.04\n",
       "2023-07-02 02:35:00        0.00      0.00\n",
       "...                         ...       ...\n",
       "2023-07-03 02:26:00        0.00      0.00\n",
       "2023-07-03 02:27:00        0.00      0.00\n",
       "2023-07-03 02:28:00       -1.29      1.29\n",
       "2023-07-03 02:29:00        0.00      0.00\n",
       "2023-07-03 02:30:00       -0.93      0.93\n",
       "\n",
       "[1440 rows x 2 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "X"
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  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "682336cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, -1,  1, ..., -1, -1, -1])"
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     "execution_count": 18,
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    "Y"
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  },
  {
   "cell_type": "markdown",
   "id": "02f938a3",
   "metadata": {},
   "source": [
    "# RSI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e18c0a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_ta as ta\n",
    "import pandas as pd\n",
    "import ccxt\n",
    "\n",
    "def rsi(exchange= ccxt.gemini({'apiKey': config.apiKey, 'secret': config.apiSecret}), symbol='ETH/USD'):\n",
    "    symbol = 'ETH/USD'\n",
    "    timeframe = '1m'\n",
    "    limit = 50\n",
    "    try:\n",
    "        ohlcv = exchange.fetch_ohlcv(symbol, timeframe)\n",
    "        print('--------------------------------------------------------------')\n",
    "        if len(ohlcv):\n",
    "            df = pd.DataFrame(ohlcv, columns=['time', 'open', 'high', 'low', 'close', 'volume'])\n",
    "            df['time'] = pd.to_datetime(df['time'], unit='ms')\n",
    "            df = pd.concat([df, df.ta.rsi()], axis=1)\n",
    "            return df[-20:]\n",
    "            print(exchange.iso8601 (exchange.milliseconds()))\n",
    "    except Exception as e:\n",
    "        print(type(e).__name__, str(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a47df7ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n"
     ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
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       "      <th>RSI_14</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1420</th>\n",
       "      <td>2023-07-03 23:13:00</td>\n",
       "      <td>1954.33</td>\n",
       "      <td>1954.47</td>\n",
       "      <td>1954.33</td>\n",
       "      <td>1954.47</td>\n",
       "      <td>0.024441</td>\n",
       "      <td>37.851441</td>\n",
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       "    <tr>\n",
       "      <th>1421</th>\n",
       "      <td>2023-07-03 23:14:00</td>\n",
       "      <td>1954.47</td>\n",
       "      <td>1954.47</td>\n",
       "      <td>1952.98</td>\n",
       "      <td>1952.98</td>\n",
       "      <td>1.190000</td>\n",
       "      <td>25.140636</td>\n",
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       "    <tr>\n",
       "      <th>1422</th>\n",
       "      <td>2023-07-03 23:15:00</td>\n",
       "      <td>1952.98</td>\n",
       "      <td>1953.06</td>\n",
       "      <td>1952.98</td>\n",
       "      <td>1953.06</td>\n",
       "      <td>0.004590</td>\n",
       "      <td>26.566485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1423</th>\n",
       "      <td>2023-07-03 23:16:00</td>\n",
       "      <td>1953.06</td>\n",
       "      <td>1953.06</td>\n",
       "      <td>1953.06</td>\n",
       "      <td>1953.06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.566485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1424</th>\n",
       "      <td>2023-07-03 23:17:00</td>\n",
       "      <td>1953.06</td>\n",
       "      <td>1953.06</td>\n",
       "      <td>1951.23</td>\n",
       "      <td>1951.86</td>\n",
       "      <td>0.054805</td>\n",
       "      <td>19.954536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1425</th>\n",
       "      <td>2023-07-03 23:18:00</td>\n",
       "      <td>1951.86</td>\n",
       "      <td>1951.86</td>\n",
       "      <td>1951.86</td>\n",
       "      <td>1951.86</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.954536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1426</th>\n",
       "      <td>2023-07-03 23:19:00</td>\n",
       "      <td>1951.86</td>\n",
       "      <td>1953.37</td>\n",
       "      <td>1951.86</td>\n",
       "      <td>1953.37</td>\n",
       "      <td>0.550608</td>\n",
       "      <td>41.281729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1427</th>\n",
       "      <td>2023-07-03 23:20:00</td>\n",
       "      <td>1953.37</td>\n",
       "      <td>1953.37</td>\n",
       "      <td>1952.89</td>\n",
       "      <td>1952.89</td>\n",
       "      <td>0.011978</td>\n",
       "      <td>37.831125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1428</th>\n",
       "      <td>2023-07-03 23:21:00</td>\n",
       "      <td>1952.89</td>\n",
       "      <td>1952.89</td>\n",
       "      <td>1952.40</td>\n",
       "      <td>1952.40</td>\n",
       "      <td>0.049440</td>\n",
       "      <td>34.647319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1429</th>\n",
       "      <td>2023-07-03 23:22:00</td>\n",
       "      <td>1952.40</td>\n",
       "      <td>1952.40</td>\n",
       "      <td>1952.27</td>\n",
       "      <td>1952.27</td>\n",
       "      <td>0.004592</td>\n",
       "      <td>33.833778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1430</th>\n",
       "      <td>2023-07-03 23:23:00</td>\n",
       "      <td>1952.27</td>\n",
       "      <td>1952.69</td>\n",
       "      <td>1952.27</td>\n",
       "      <td>1952.69</td>\n",
       "      <td>0.011979</td>\n",
       "      <td>38.831036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1431</th>\n",
       "      <td>2023-07-03 23:24:00</td>\n",
       "      <td>1952.69</td>\n",
       "      <td>1953.01</td>\n",
       "      <td>1952.69</td>\n",
       "      <td>1953.01</td>\n",
       "      <td>0.023956</td>\n",
       "      <td>42.400473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432</th>\n",
       "      <td>2023-07-03 23:25:00</td>\n",
       "      <td>1953.01</td>\n",
       "      <td>1953.01</td>\n",
       "      <td>1953.01</td>\n",
       "      <td>1953.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.400473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>2023-07-03 23:26:00</td>\n",
       "      <td>1953.01</td>\n",
       "      <td>1953.50</td>\n",
       "      <td>1952.98</td>\n",
       "      <td>1953.50</td>\n",
       "      <td>0.061388</td>\n",
       "      <td>47.809008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1434</th>\n",
       "      <td>2023-07-03 23:27:00</td>\n",
       "      <td>1953.50</td>\n",
       "      <td>1953.59</td>\n",
       "      <td>1953.50</td>\n",
       "      <td>1953.59</td>\n",
       "      <td>0.024452</td>\n",
       "      <td>48.760697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1435</th>\n",
       "      <td>2023-07-03 23:28:00</td>\n",
       "      <td>1953.59</td>\n",
       "      <td>1953.59</td>\n",
       "      <td>1953.31</td>\n",
       "      <td>1953.31</td>\n",
       "      <td>0.658978</td>\n",
       "      <td>45.953224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1436</th>\n",
       "      <td>2023-07-03 23:29:00</td>\n",
       "      <td>1953.31</td>\n",
       "      <td>1953.77</td>\n",
       "      <td>1953.31</td>\n",
       "      <td>1953.77</td>\n",
       "      <td>0.011973</td>\n",
       "      <td>50.949784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1437</th>\n",
       "      <td>2023-07-03 23:30:00</td>\n",
       "      <td>1953.77</td>\n",
       "      <td>1953.77</td>\n",
       "      <td>1953.77</td>\n",
       "      <td>1953.77</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.949784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1438</th>\n",
       "      <td>2023-07-03 23:31:00</td>\n",
       "      <td>1953.77</td>\n",
       "      <td>1953.77</td>\n",
       "      <td>1953.77</td>\n",
       "      <td>1953.77</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.949784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1439</th>\n",
       "      <td>2023-07-03 23:32:00</td>\n",
       "      <td>1953.77</td>\n",
       "      <td>1953.77</td>\n",
       "      <td>1953.77</td>\n",
       "      <td>1953.77</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.949784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    time     open     high      low    close    volume   \n",
       "1420 2023-07-03 23:13:00  1954.33  1954.47  1954.33  1954.47  0.024441  \\\n",
       "1421 2023-07-03 23:14:00  1954.47  1954.47  1952.98  1952.98  1.190000   \n",
       "1422 2023-07-03 23:15:00  1952.98  1953.06  1952.98  1953.06  0.004590   \n",
       "1423 2023-07-03 23:16:00  1953.06  1953.06  1953.06  1953.06  0.000000   \n",
       "1424 2023-07-03 23:17:00  1953.06  1953.06  1951.23  1951.86  0.054805   \n",
       "1425 2023-07-03 23:18:00  1951.86  1951.86  1951.86  1951.86  0.000000   \n",
       "1426 2023-07-03 23:19:00  1951.86  1953.37  1951.86  1953.37  0.550608   \n",
       "1427 2023-07-03 23:20:00  1953.37  1953.37  1952.89  1952.89  0.011978   \n",
       "1428 2023-07-03 23:21:00  1952.89  1952.89  1952.40  1952.40  0.049440   \n",
       "1429 2023-07-03 23:22:00  1952.40  1952.40  1952.27  1952.27  0.004592   \n",
       "1430 2023-07-03 23:23:00  1952.27  1952.69  1952.27  1952.69  0.011979   \n",
       "1431 2023-07-03 23:24:00  1952.69  1953.01  1952.69  1953.01  0.023956   \n",
       "1432 2023-07-03 23:25:00  1953.01  1953.01  1953.01  1953.01  0.000000   \n",
       "1433 2023-07-03 23:26:00  1953.01  1953.50  1952.98  1953.50  0.061388   \n",
       "1434 2023-07-03 23:27:00  1953.50  1953.59  1953.50  1953.59  0.024452   \n",
       "1435 2023-07-03 23:28:00  1953.59  1953.59  1953.31  1953.31  0.658978   \n",
       "1436 2023-07-03 23:29:00  1953.31  1953.77  1953.31  1953.77  0.011973   \n",
       "1437 2023-07-03 23:30:00  1953.77  1953.77  1953.77  1953.77  0.000000   \n",
       "1438 2023-07-03 23:31:00  1953.77  1953.77  1953.77  1953.77  0.000000   \n",
       "1439 2023-07-03 23:32:00  1953.77  1953.77  1953.77  1953.77  0.000000   \n",
       "\n",
       "         RSI_14  \n",
       "1420  37.851441  \n",
       "1421  25.140636  \n",
       "1422  26.566485  \n",
       "1423  26.566485  \n",
       "1424  19.954536  \n",
       "1425  19.954536  \n",
       "1426  41.281729  \n",
       "1427  37.831125  \n",
       "1428  34.647319  \n",
       "1429  33.833778  \n",
       "1430  38.831036  \n",
       "1431  42.400473  \n",
       "1432  42.400473  \n",
       "1433  47.809008  \n",
       "1434  48.760697  \n",
       "1435  45.953224  \n",
       "1436  50.949784  \n",
       "1437  50.949784  \n",
       "1438  50.949784  \n",
       "1439  50.949784  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsi()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0d6c21da",
   "metadata": {},
   "outputs": [],
   "source": [
    "split_percentage = 0.7\n",
    "split = int(split_percentage*len(d))\n",
    "\n",
    "X_train = X[:split]\n",
    "Y_train = Y[:split]\n",
    "\n",
    "X_test = X[split:]\n",
    "Y_test = Y[split:] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb93f4d9",
   "metadata": {},
   "source": [
    "# instantiate knn model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f3865666",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train_data Accuracy: 0.77\n",
      "Test_data Accuracy: 0.67\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=10)\n",
    "\n",
    "# fit the model\n",
    "knn.fit(X_train, Y_train)\n",
    "\n",
    "# Accuracy Score\n",
    "accuracy_train = accuracy_score(Y_train, knn.predict(X_train))\n",
    "accuracy_test = accuracy_score(Y_test, knn.predict(X_test))\n",
    "\n",
    "print ('Train_data Accuracy: %.2f' %accuracy_train)\n",
    "print ('Test_data Accuracy: %.2f' %accuracy_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9038228f",
   "metadata": {},
   "source": [
    "# create trading strat using model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c8be2212",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d['Predicted_Signal'] = knn.predict(X)\n",
    "print(list(d['Predicted_Signal'].values))\n",
    "# SPY Cumulative Returns\n",
    "d['ETH_RETURNS'] = np.log(d['close']/d['close'].shift(1))\n",
    "cumu_Eth_returns = d[split:]['SPY_returns'].cumsum()*100\n",
    "\n",
    "# Cumulative Strategy Returns \n",
    "d['Startegy_returns'] = d['ETH_RETURNS']* d['Predicted_Signal'].shift(1)\n",
    "Cumulative_Strategy_returns = d[split:]['Startegy_returns'].cumsum()*100\n",
    "\n",
    "# Plot the results to visualize the performance\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(cumu_Eth_returns, color='r',label = 'ETH RETURNS')\n",
    "plt.plot(Cumulative_Strategy_returns, color='g', label = 'Strategy Returns')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "8ae5588e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
     "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>Adj Close</th>\n",
       "      <th>Volume</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>2023-01-03</th>\n",
       "      <td>384.369995</td>\n",
       "      <td>386.429993</td>\n",
       "      <td>377.829987</td>\n",
       "      <td>380.820007</td>\n",
       "      <td>377.968140</td>\n",
       "      <td>74850700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-04</th>\n",
       "      <td>383.179993</td>\n",
       "      <td>385.880005</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>383.760010</td>\n",
       "      <td>380.886139</td>\n",
       "      <td>85934100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-05</th>\n",
       "      <td>381.720001</td>\n",
       "      <td>381.839996</td>\n",
       "      <td>378.760010</td>\n",
       "      <td>379.380005</td>\n",
       "      <td>376.538910</td>\n",
       "      <td>76970500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-06</th>\n",
       "      <td>382.609985</td>\n",
       "      <td>389.250000</td>\n",
       "      <td>379.410004</td>\n",
       "      <td>388.079987</td>\n",
       "      <td>385.173737</td>\n",
       "      <td>104189600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-09</th>\n",
       "      <td>390.369995</td>\n",
       "      <td>393.700012</td>\n",
       "      <td>387.670013</td>\n",
       "      <td>387.859985</td>\n",
       "      <td>384.955383</td>\n",
       "      <td>73978100</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-26</th>\n",
       "      <td>432.619995</td>\n",
       "      <td>434.609985</td>\n",
       "      <td>431.190002</td>\n",
       "      <td>431.440002</td>\n",
       "      <td>431.440002</td>\n",
       "      <td>72823600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-27</th>\n",
       "      <td>432.350006</td>\n",
       "      <td>436.809998</td>\n",
       "      <td>431.880005</td>\n",
       "      <td>436.170013</td>\n",
       "      <td>436.170013</td>\n",
       "      <td>72813700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-28</th>\n",
       "      <td>435.049988</td>\n",
       "      <td>437.440002</td>\n",
       "      <td>434.410004</td>\n",
       "      <td>436.390015</td>\n",
       "      <td>436.390015</td>\n",
       "      <td>75636000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-29</th>\n",
       "      <td>435.959991</td>\n",
       "      <td>438.279999</td>\n",
       "      <td>435.540009</td>\n",
       "      <td>438.109985</td>\n",
       "      <td>438.109985</td>\n",
       "      <td>67882300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-30</th>\n",
       "      <td>441.440002</td>\n",
       "      <td>444.299988</td>\n",
       "      <td>441.109985</td>\n",
       "      <td>443.279999</td>\n",
       "      <td>443.279999</td>\n",
       "      <td>104921500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close   \n",
       "Date                                                                     \n",
       "2023-01-03  384.369995  386.429993  377.829987  380.820007  377.968140  \\\n",
       "2023-01-04  383.179993  385.880005  380.000000  383.760010  380.886139   \n",
       "2023-01-05  381.720001  381.839996  378.760010  379.380005  376.538910   \n",
       "2023-01-06  382.609985  389.250000  379.410004  388.079987  385.173737   \n",
       "2023-01-09  390.369995  393.700012  387.670013  387.859985  384.955383   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2023-06-26  432.619995  434.609985  431.190002  431.440002  431.440002   \n",
       "2023-06-27  432.350006  436.809998  431.880005  436.170013  436.170013   \n",
       "2023-06-28  435.049988  437.440002  434.410004  436.390015  436.390015   \n",
       "2023-06-29  435.959991  438.279999  435.540009  438.109985  438.109985   \n",
       "2023-06-30  441.440002  444.299988  441.109985  443.279999  443.279999   \n",
       "\n",
       "               Volume  \n",
       "Date                   \n",
       "2023-01-03   74850700  \n",
       "2023-01-04   85934100  \n",
       "2023-01-05   76970500  \n",
       "2023-01-06  104189600  \n",
       "2023-01-09   73978100  \n",
       "...               ...  \n",
       "2023-06-26   72823600  \n",
       "2023-06-27   72813700  \n",
       "2023-06-28   75636000  \n",
       "2023-06-29   67882300  \n",
       "2023-06-30  104921500  \n",
       "\n",
       "[124 rows x 6 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Created on Fri Jul  5 10:31:29 2019\n",
    "\n",
    "@author: Vibhu\n",
    "\"\"\"\n",
    "# Data Manupulation\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Plotting graphs\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Machine learning libraries\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Data fetching\n",
    "from pandas_datareader import data as pdr\n",
    "import yfinance as yf\n",
    "\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "\n",
    "# Read the data from Yahoo\n",
    "ticker='SPY'\n",
    "df = yf.download(ticker,period='6mo')\n",
    "# df= pdr.get_data_yahoo('SPY', '2012-01-01', '2017-01-01')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "2460deb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = get_ohlcv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "496de454",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Adj Close</th>\n",
       "      <th>Volume</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>2023-04-03</th>\n",
       "      <td>408.850006</td>\n",
       "      <td>411.369995</td>\n",
       "      <td>408.440002</td>\n",
       "      <td>410.950012</td>\n",
       "      <td>409.429138</td>\n",
       "      <td>67391100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-04</th>\n",
       "      <td>411.619995</td>\n",
       "      <td>411.920013</td>\n",
       "      <td>407.239990</td>\n",
       "      <td>408.670013</td>\n",
       "      <td>407.157593</td>\n",
       "      <td>66601500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-05</th>\n",
       "      <td>407.910004</td>\n",
       "      <td>408.700012</td>\n",
       "      <td>405.880005</td>\n",
       "      <td>407.600006</td>\n",
       "      <td>406.091553</td>\n",
       "      <td>65200200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-06</th>\n",
       "      <td>406.769989</td>\n",
       "      <td>409.480011</td>\n",
       "      <td>405.679993</td>\n",
       "      <td>409.190002</td>\n",
       "      <td>407.675659</td>\n",
       "      <td>63743300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-10</th>\n",
       "      <td>406.609985</td>\n",
       "      <td>409.690002</td>\n",
       "      <td>405.970001</td>\n",
       "      <td>409.609985</td>\n",
       "      <td>408.094086</td>\n",
       "      <td>63681000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-26</th>\n",
       "      <td>432.619995</td>\n",
       "      <td>434.609985</td>\n",
       "      <td>431.190002</td>\n",
       "      <td>431.440002</td>\n",
       "      <td>431.440002</td>\n",
       "      <td>72823600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-27</th>\n",
       "      <td>432.350006</td>\n",
       "      <td>436.809998</td>\n",
       "      <td>431.880005</td>\n",
       "      <td>436.170013</td>\n",
       "      <td>436.170013</td>\n",
       "      <td>72813700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-28</th>\n",
       "      <td>435.049988</td>\n",
       "      <td>437.440002</td>\n",
       "      <td>434.410004</td>\n",
       "      <td>436.390015</td>\n",
       "      <td>436.390015</td>\n",
       "      <td>75636000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-29</th>\n",
       "      <td>435.959991</td>\n",
       "      <td>438.279999</td>\n",
       "      <td>435.540009</td>\n",
       "      <td>438.109985</td>\n",
       "      <td>438.109985</td>\n",
       "      <td>67882300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-30</th>\n",
       "      <td>441.440002</td>\n",
       "      <td>444.299988</td>\n",
       "      <td>441.109985</td>\n",
       "      <td>443.279999</td>\n",
       "      <td>443.279999</td>\n",
       "      <td>104921500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close   \n",
       "Date                                                                     \n",
       "2023-04-03  408.850006  411.369995  408.440002  410.950012  409.429138  \\\n",
       "2023-04-04  411.619995  411.920013  407.239990  408.670013  407.157593   \n",
       "2023-04-05  407.910004  408.700012  405.880005  407.600006  406.091553   \n",
       "2023-04-06  406.769989  409.480011  405.679993  409.190002  407.675659   \n",
       "2023-04-10  406.609985  409.690002  405.970001  409.609985  408.094086   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2023-06-26  432.619995  434.609985  431.190002  431.440002  431.440002   \n",
       "2023-06-27  432.350006  436.809998  431.880005  436.170013  436.170013   \n",
       "2023-06-28  435.049988  437.440002  434.410004  436.390015  436.390015   \n",
       "2023-06-29  435.959991  438.279999  435.540009  438.109985  438.109985   \n",
       "2023-06-30  441.440002  444.299988  441.109985  443.279999  443.279999   \n",
       "\n",
       "               Volume  \n",
       "Date                   \n",
       "2023-04-03   67391100  \n",
       "2023-04-04   66601500  \n",
       "2023-04-05   65200200  \n",
       "2023-04-06   63743300  \n",
       "2023-04-10   63681000  \n",
       "...               ...  \n",
       "2023-06-26   72823600  \n",
       "2023-06-27   72813700  \n",
       "2023-06-28   75636000  \n",
       "2023-06-29   67882300  \n",
       "2023-06-30  104921500  \n",
       "\n",
       "[62 rows x 6 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "6df08291",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Open-Close</th>\n",
       "      <th>High-Low</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-01-03</th>\n",
       "      <td>3.549988</td>\n",
       "      <td>8.600006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-04</th>\n",
       "      <td>-0.580017</td>\n",
       "      <td>5.880005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-05</th>\n",
       "      <td>2.339996</td>\n",
       "      <td>3.079987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-06</th>\n",
       "      <td>-5.470001</td>\n",
       "      <td>9.839996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-09</th>\n",
       "      <td>2.510010</td>\n",
       "      <td>6.029999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-26</th>\n",
       "      <td>1.179993</td>\n",
       "      <td>3.419983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-27</th>\n",
       "      <td>-3.820007</td>\n",
       "      <td>4.929993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-28</th>\n",
       "      <td>-1.340027</td>\n",
       "      <td>3.029999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-29</th>\n",
       "      <td>-2.149994</td>\n",
       "      <td>2.739990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-30</th>\n",
       "      <td>-1.839996</td>\n",
       "      <td>3.190002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Open-Close  High-Low\n",
       "Date                            \n",
       "2023-01-03    3.549988  8.600006\n",
       "2023-01-04   -0.580017  5.880005\n",
       "2023-01-05    2.339996  3.079987\n",
       "2023-01-06   -5.470001  9.839996\n",
       "2023-01-09    2.510010  6.029999\n",
       "...                ...       ...\n",
       "2023-06-26    1.179993  3.419983\n",
       "2023-06-27   -3.820007  4.929993\n",
       "2023-06-28   -1.340027  3.029999\n",
       "2023-06-29   -2.149994  2.739990\n",
       "2023-06-30   -1.839996  3.190002\n",
       "\n",
       "[124 rows x 2 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "df = df[['Open', 'High', 'Low','Close']]\n",
    "df.head()\n",
    "\n",
    "# Predictor variables\n",
    "df['Open-Close']= df.Open -df.Close\n",
    "df['High-Low']  = df.High - df.Low\n",
    "df =df.dropna()\n",
    "X= df[['Open-Close', 'High-Low']]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "9e49d2dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, -1, 1, -1, 1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, -1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, 1, 1, -1]\n"
     ]
    }
   ],
   "source": [
    "# Target variable\n",
    "Y= np.where(df['Close'].shift(-1)>df['Close'],1,-1)\n",
    "print(list(Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "6804a147",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train_data Accuracy: 0.49\n",
      "Test_data Accuracy: 0.63\n"
     ]
    }
   ],
   "source": [
    "# Splitting the dataset\n",
    "split_percentage = 0.7\n",
    "split = int(split_percentage*len(df))\n",
    "\n",
    "X_train = X[:split]\n",
    "Y_train = Y[:split]\n",
    "\n",
    "X_test = X[split:]\n",
    "Y_test = Y[split:]\n",
    "\n",
    "# Instantiate KNN learning model(k=15)\n",
    "knn = KNeighborsClassifier(n_neighbors=15)\n",
    "\n",
    "# fit the model\n",
    "knn.fit(X_train, Y_train)\n",
    "\n",
    "# Accuracy Score\n",
    "accuracy_train = accuracy_score(Y_train, knn.predict(X_train))\n",
    "accuracy_test = accuracy_score(Y_test, knn.predict(X_test))\n",
    "\n",
    "print ('Train_data Accuracy: %.2f' %accuracy_train)\n",
    "print ('Test_data Accuracy: %.2f' %accuracy_test)\n",
    "\n",
    "# Predicted Signal\n",
    "df['Predicted_Signal'] = knn.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "eb9a34df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, -1, -1, 1, -1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, -1, 1, 1, 1, 1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, -1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "print(list(df['Predicted_Signal'].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "f367dc7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sharpe ratio: -2.25\n"
     ]
    }
   ],
   "source": [
    "# SPY Cumulative Returns\n",
    "df['SPY_returns'] = np.log(df['Close']/df['Close'].shift(1))\n",
    "Cumulative_SPY_returns = df[split:]['SPY_returns'].cumsum()*100\n",
    "\n",
    "# Cumulative Strategy Returns \n",
    "df['Startegy_returns'] = df['SPY_returns']* df['Predicted_Signal'].shift(1)\n",
    "Cumulative_Strategy_returns = df[split:]['Startegy_returns'].cumsum()*100 * -1\n",
    "\n",
    "# Plot the results to visualize the performance\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(Cumulative_SPY_returns, color='r',label = 'SPY Returns')\n",
    "plt.plot(Cumulative_Strategy_returns, color='g', label = 'Strategy Returns')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Calculate Sharpe reatio\n",
    "Std = Cumulative_Strategy_returns.std()\n",
    "Sharpe = (Cumulative_Strategy_returns-Cumulative_SPY_returns)/Std\n",
    "Sharpe = Sharpe.mean()\n",
    "print('Sharpe ratio: %.2f'%Sharpe )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "97450b0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'info': [{'type': 'exchange',\n",
       "   'currency': 'ETH',\n",
       "   'amount': '0.004456',\n",
       "   'available': '0.004456',\n",
       "   'availableForWithdrawal': '0.004456'},\n",
       "  {'type': 'exchange',\n",
       "   'currency': 'USD',\n",
       "   'amount': '-9.75',\n",
       "   'available': '0.00',\n",
       "   'availableForWithdrawal': '0.00'}],\n",
       " 'ETH': {'free': 0.004456, 'used': 0.0, 'total': 0.004456},\n",
       " 'USD': {'free': 0.0, 'used': -9.75, 'total': -9.75},\n",
       " 'free': {'ETH': 0.004456, 'USD': 0.0},\n",
       " 'used': {'ETH': 0.0, 'USD': -9.75},\n",
       " 'total': {'ETH': 0.004456, 'USD': -9.75}}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exchange.fetch_balance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1f8e7e24",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_ta as ta\n",
    "import pandas as pd\n",
    "import ccxt\n",
    "\n",
    "def getBalanceUSD(exchange= ccxt.gemini({'apiKey': config.apiKey, 'secret': config.apiSecret})):\n",
    "    balances = exchange.fetch_balance()['free']\n",
    "    balance = 0\n",
    "\n",
    "    for token, value in balances.items():\n",
    "        if float(value)!=0:\n",
    "            if token != 'USDT':\n",
    "                request = token+'/USDT'\n",
    "                try:\n",
    "                    price = float(exchange.fetchTicker(request)['last'])\n",
    "                except:\n",
    "                    try:\n",
    "                        request = token+'/BTC'\n",
    "                        price = float(exchange.fetchTicker(request)['last'])\n",
    "                        val_btc = (float(value))*price\n",
    "                        price = float(exchange.fetchTicker('BTC/USDT')['last'])\n",
    "                        balance += val_btc*price\n",
    "                    except:\n",
    "                        try:\n",
    "                            request = token+'/ETH'\n",
    "                            price = float(exchange.fetchTicker(request)['last'])\n",
    "                            val_btc = (float(value))*price\n",
    "                            price = float(exchange.fetchTicker('ETH/USDT')['last'])\n",
    "                            balance += val_btc*price\n",
    "                        except:\n",
    "                            print(token)\n",
    "\n",
    "                balance += (float(value))*price\n",
    "            else:\n",
    "                balance += float(value)\n",
    "    return balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e1acbee7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'info': [{'type': 'exchange',\n",
       "   'currency': 'USD',\n",
       "   'amount': '-8.79273',\n",
       "   'available': '0.95727',\n",
       "   'availableForWithdrawal': '0.00'},\n",
       "  {'type': 'exchange',\n",
       "   'currency': 'ETH',\n",
       "   'amount': '0.003456',\n",
       "   'available': '0.003456',\n",
       "   'availableForWithdrawal': '0.003456'}],\n",
       " 'USD': {'free': 0.95727, 'used': -9.75, 'total': -8.79273},\n",
       " 'ETH': {'free': 0.003456, 'used': 0.0, 'total': 0.003456},\n",
       " 'free': {'USD': 0.95727, 'ETH': 0.003456},\n",
       " 'used': {'USD': -9.75, 'ETH': 0.0},\n",
       " 'total': {'USD': -8.79273, 'ETH': 0.003456}}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exchange.fetch_balance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c01671e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getBalanceSymbol(exchange= ccxt.gemini({'apiKey': config.apiKey, 'secret': config.apiSecret}), symbol='ETH'):\n",
    "    balances = exchange.fetch_balance()['free']\n",
    "    balance = 0\n",
    "\n",
    "    for token, value in balances.items():\n",
    "        if float(value)!=0:\n",
    "            if token == symbol:\n",
    "                request = token+'/USDT'\n",
    "                try:\n",
    "                    price = float(exchange.fetchTicker(request)['last'])\n",
    "                except:\n",
    "                    try:\n",
    "                        request = token+'/BTC'\n",
    "                        price = float(exchange.fetchTicker(request)['last'])\n",
    "                        val_btc = (float(value))*price\n",
    "                        price = float(exchange.fetchTicker('BTC/USDT')['last'])\n",
    "                        balance += val_btc*price\n",
    "                    except:\n",
    "                        try:\n",
    "                            request = token+'/ETH'\n",
    "                            price = float(exchange.fetchTicker(request)['last'])\n",
    "                            val_btc = (float(value))*price\n",
    "                            price = float(exchange.fetchTicker('ETH/USDT')['last'])\n",
    "                            balance += val_btc*price\n",
    "                        except:\n",
    "                            print(token)\n",
    "\n",
    "                balance += (float(value))*price\n",
    "                return balance, float(exchange.fetchTicker(request)['last'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4d6251f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6.76432512, 1957.27)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getBalanceSymbol()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c063d616",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'id': '185635742144', 'order': '185635742125', 'info': {'price': '1965.88', 'amount': '0.004456', 'timestamp': '1688429303', 'timestampms': '1688429303317', 'type': 'Buy', 'aggressor': True, 'fee_currency': 'USD', 'fee_amount': '0.99003872', 'tid': '185635742144', 'order_id': '185635742125', 'exchange': 'gemini', 'is_auction_fill': False, 'is_clearing_fill': False, 'symbol': 'ETHUSD'}, 'timestamp': 1688429303317, 'datetime': '2023-07-04T00:08:23.317Z', 'symbol': 'ETH/USD', 'type': None, 'side': 'buy', 'takerOrMaker': None, 'price': 1965.88, 'cost': 8.75996128, 'amount': 0.004456, 'fee': {'cost': 0.99003872, 'currency': 'USD'}, 'fees': [{'cost': 0.99003872, 'currency': 'USD'}]}\n",
      "\n",
      "{'id': '185640487423', 'order': '185640487412', 'info': {'price': '1947.27', 'amount': '0.001', 'timestamp': '1688431522', 'timestampms': '1688431522743', 'type': 'Sell', 'aggressor': True, 'fee_currency': 'USD', 'fee_amount': '0.99', 'tid': '185640487423', 'order_id': '185640487412', 'exchange': 'gemini', 'is_auction_fill': False, 'is_clearing_fill': False, 'symbol': 'ETHUSD'}, 'timestamp': 1688431522743, 'datetime': '2023-07-04T00:45:22.743Z', 'symbol': 'ETH/USD', 'type': None, 'side': 'sell', 'takerOrMaker': None, 'price': 1947.27, 'cost': 1.94727, 'amount': 0.001, 'fee': {'cost': 0.99, 'currency': 'USD'}, 'fees': [{'cost': 0.99, 'currency': 'USD'}]}\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1971.2648379629634"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exchange= ccxt.gemini({'apiKey': config.apiKey, 'secret': config.apiSecret})\n",
    "\n",
    "def average_purchase_price(symbol= 'ETH/USD'):\n",
    "    associated_trades = exchange.fetchMyTrades(symbol='ETH/USD')\n",
    "    avg_sum = 0\n",
    "    shares_owned = 0\n",
    "    for trade in associated_trades:\n",
    "        print(trade)\n",
    "        print()\n",
    "        if trade['side'] == 'buy':\n",
    "            avg_sum += trade['amount'] * trade['price']\n",
    "            shares_owned += trade['amount']  \n",
    "        elif trade['side'] == 'sell':\n",
    "            avg_sum -= trade['amount'] * trade['price']\n",
    "            shares_owned -= trade['amount']  \n",
    "    return avg_sum / shares_owned\n",
    "average_purchase_price()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "82878285",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'id': 'ethbtc', 'symbol': 'ETH/BTC', 'base': 'ETH', 'quote': 'BTC', 'settle': None, 'baseId': 'eth', 'quoteId': 'btc', 'settleId': None, 'type': 'spot', 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'active': None, 'contract': False, 'linear': None, 'inverse': None, 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': {'amount': 1e-06, 'price': 1e-05}, 'limits': {'leverage': {'min': None, 'max': None}, 'amount': {'min': 0.001, 'max': None}, 'price': {'min': None, 'max': None}, 'cost': {'min': None, 'max': None}}, 'info': '<td>ethbtc</td>\\n<td>0.001 ETH (1e-3)</td>\\n<td>0.000001 ETH (1e-6)</td>\\n<td>0.00001 BTC (1e-5)</td>\\n</tr>'}\n",
      "{'id': 'ethusd', 'symbol': 'ETH/USD', 'base': 'ETH', 'quote': 'USD', 'settle': None, 'baseId': 'eth', 'quoteId': 'usd', 'settleId': None, 'type': 'spot', 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'active': None, 'contract': False, 'linear': None, 'inverse': None, 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': {'amount': 1e-06, 'price': 0.01}, 'limits': {'leverage': {'min': None, 'max': None}, 'amount': {'min': 0.001, 'max': None}, 'price': {'min': None, 'max': None}, 'cost': {'min': None, 'max': None}}, 'info': '<td>ethusd</td>\\n<td>0.001 ETH (1e-3)</td>\\n<td>0.000001 ETH (1e-6)</td>\\n<td>0.01 USD</td>\\n</tr>'}\n",
      "{'id': 'etheur', 'symbol': 'ETH/EUR', 'base': 'ETH', 'quote': 'EUR', 'settle': None, 'baseId': 'eth', 'quoteId': 'eur', 'settleId': None, 'type': 'spot', 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'active': None, 'contract': False, 'linear': None, 'inverse': None, 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': {'amount': 1e-06, 'price': 0.01}, 'limits': {'leverage': {'min': None, 'max': None}, 'amount': {'min': 0.001, 'max': None}, 'price': {'min': None, 'max': None}, 'cost': {'min': None, 'max': None}}, 'info': '<td>etheur</td>\\n<td>0.001 ETH (1e-3)</td>\\n<td>0.000001 ETH (1e-6)</td>\\n<td>0.01 EUR</td>\\n</tr>'}\n",
      "{'id': 'ethgbp', 'symbol': 'ETH/GBP', 'base': 'ETH', 'quote': 'GBP', 'settle': None, 'baseId': 'eth', 'quoteId': 'gbp', 'settleId': None, 'type': 'spot', 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'active': None, 'contract': False, 'linear': None, 'inverse': None, 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': {'amount': 1e-06, 'price': 0.01}, 'limits': {'leverage': {'min': None, 'max': None}, 'amount': {'min': 0.001, 'max': None}, 'price': {'min': None, 'max': None}, 'cost': {'min': None, 'max': None}}, 'info': '<td>ethgbp</td>\\n<td>0.001 ETH (1e-3)</td>\\n<td>0.000001 ETH (1e-6)</td>\\n<td>0.01 GBP</td>\\n</tr>'}\n",
      "{'id': 'ethsgd', 'symbol': 'ETH/SGD', 'base': 'ETH', 'quote': 'SGD', 'settle': None, 'baseId': 'eth', 'quoteId': 'sgd', 'settleId': None, 'type': 'spot', 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'active': None, 'contract': False, 'linear': None, 'inverse': None, 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': {'amount': 1e-06, 'price': 0.01}, 'limits': {'leverage': {'min': None, 'max': None}, 'amount': {'min': 0.001, 'max': None}, 'price': {'min': None, 'max': None}, 'cost': {'min': None, 'max': None}}, 'info': '<td>ethsgd</td>\\n<td>0.001 ETH (1e-3)</td>\\n<td>0.000001 ETH (1e-6)</td>\\n<td>0.01 SGD</td>\\n</tr>'}\n",
      "{'id': 'zeceth', 'symbol': 'ZEC/ETH', 'base': 'ZEC', 'quote': 'ETH', 'settle': None, 'baseId': 'zec', 'quoteId': 'eth', 'settleId': None, 'type': 'spot', 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'active': None, 'contract': False, 'linear': None, 'inverse': None, 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': {'amount': 1e-06, 'price': 1e-05}, 'limits': {'leverage': {'min': None, 'max': None}, 'amount': {'min': 0.001, 'max': None}, 'price': {'min': None, 'max': None}, 'cost': {'min': None, 'max': None}}, 'info': '<td>zeceth</td>\\n<td>0.001 ZEC (1e-3)</td>\\n<td>0.000001 ZEC (1e-6)</td>\\n<td>0.00001 ETH (1e-5)</td>\\n</tr>'}\n",
      "{'id': 'bcheth', 'symbol': 'BCH/ETH', 'base': 'BCH', 'quote': 'ETH', 'settle': None, 'baseId': 'bch', 'quoteId': 'eth', 'settleId': None, 'type': 'spot', 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'active': None, 'contract': False, 'linear': None, 'inverse': None, 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': {'amount': 1e-06, 'price': 0.0001}, 'limits': {'leverage': {'min': None, 'max': None}, 'amount': {'min': 0.001, 'max': None}, 'price': {'min': None, 'max': None}, 'cost': {'min': None, 'max': None}}, 'info': '<td>bcheth</td>\\n<td>0.001 BCH (1e-3)</td>\\n<td>0.000001 BCH (1e-6)</td>\\n<td>0.0001 ETH (1e-4)</td>\\n</tr>'}\n",
      "{'id': 'ltceth', 'symbol': 'LTC/ETH', 'base': 'LTC', 'quote': 'ETH', 'settle': None, 'baseId': 'ltc', 'quoteId': 'eth', 'settleId': None, 'type': 'spot', 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'active': None, 'contract': False, 'linear': None, 'inverse': None, 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': {'amount': 1e-05, 'price': 1e-05}, 'limits': {'leverage': {'min': None, 'max': None}, 'amount': {'min': 0.01, 'max': None}, 'price': {'min': None, 'max': None}, 'cost': {'min': None, 'max': None}}, 'info': '<td>ltceth</td>\\n<td>0.01 LTC (1e-2)</td>\\n<td>0.00001 LTC (1e-5)</td>\\n<td>0.00001 ETH (1e-5)</td>\\n</tr>'}\n",
      "{'id': 'bateth', 'symbol': 'BAT/ETH', 'base': 'BAT', 'quote': 'ETH', 'settle': None, 'baseId': 'bat', 'quoteId': 'eth', 'settleId': None, 'type': 'spot', 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'active': None, 'contract': False, 'linear': None, 'inverse': None, 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': {'amount': 1e-06, 'price': 1e-07}, 'limits': {'leverage': {'min': None, 'max': None}, 'amount': {'min': 1.0, 'max': None}, 'price': {'min': None, 'max': None}, 'cost': {'min': None, 'max': None}}, 'info': '<td>bateth</td>\\n<td>1.0 BAT (1e0)</td>\\n<td>0.000001 BAT (1e-6)</td>\\n<td>0.0000001 ETH (1e-7)</td>\\n</tr>'}\n",
      "{'id': 'linketh', 'symbol': 'LINK/ETH', 'base': 'LINK', 'quote': 'ETH', 'settle': None, 'baseId': 'link', 'quoteId': 'eth', 'settleId': None, 'type': 'spot', 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'active': None, 'contract': False, 'linear': None, 'inverse': None, 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': {'amount': 1e-06, 'price': 1e-07}, 'limits': {'leverage': {'min': None, 'max': None}, 'amount': {'min': 0.1, 'max': None}, 'price': {'min': None, 'max': None}, 'cost': {'min': None, 'max': None}}, 'info': '<td>linketh</td>\\n<td>0.1 LINK (1e-1)</td>\\n<td>0.000001 LINK (1e-6)</td>\\n<td>0.0000001 ETH (1e-7)</td>\\n</tr>'}\n",
      "{'id': 'oxteth', 'symbol': 'OXT/ETH', 'base': 'OXT', 'quote': 'ETH', 'settle': None, 'baseId': 'oxt', 'quoteId': 'eth', 'settleId': None, 'type': 'spot', 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'active': None, 'contract': False, 'linear': None, 'inverse': None, 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': {'amount': 1e-06, 'price': 1e-07}, 'limits': {'leverage': {'min': None, 'max': None}, 'amount': {'min': 1.0, 'max': None}, 'price': {'min': None, 'max': None}, 'cost': {'min': None, 'max': None}}, 'info': '<td>oxteth</td>\\n<td>1.0 OXT (1e0)</td>\\n<td>0.000001 OXT (1e-6)</td>\\n<td>0.0000001 ETH (1e-7)</td>\\n</tr>'}\n",
      "{'id': 'ethdai', 'symbol': 'ETH/DAI', 'base': 'ETH', 'quote': 'DAI', 'settle': None, 'baseId': 'eth', 'quoteId': 'dai', 'settleId': None, 'type': 'spot', 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'active': None, 'contract': False, 'linear': None, 'inverse': None, 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': {'amount': 1e-06, 'price': 0.01}, 'limits': {'leverage': {'min': None, 'max': None}, 'amount': {'min': 0.001, 'max': None}, 'price': {'min': None, 'max': None}, 'cost': {'min': None, 'max': None}}, 'info': '<td>ethdai</td>\\n<td>0.001 ETH (1e-3)</td>\\n<td>0.000001 ETH (1e-6)</td>\\n<td>0.01 DAI</td>\\n</tr>'}\n",
      "{'id': 'ethgusd', 'symbol': 'ETH/GUSD', 'base': 'ETH', 'quote': 'GUSD', 'settle': None, 'baseId': 'eth', 'quoteId': 'gusd', 'settleId': None, 'type': 'spot', 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'active': None, 'contract': False, 'linear': None, 'inverse': None, 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': {'amount': 1e-06, 'price': 0.01}, 'limits': {'leverage': {'min': None, 'max': None}, 'amount': {'min': 0.001, 'max': None}, 'price': {'min': None, 'max': None}, 'cost': {'min': None, 'max': None}}, 'info': '<td>ethgusd</td>\\n<td>0.001 ETH (1e-3)</td>\\n<td>0.000001 ETH (1e-6)</td>\\n<td>0.01 GUSD</td>\\n</tr>'}\n",
      "{'id': 'dogeeth', 'symbol': 'DOGE/ETH', 'base': 'DOGE', 'quote': 'ETH', 'settle': None, 'baseId': 'doge', 'quoteId': 'eth', 'settleId': None, 'type': 'spot', 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'active': None, 'contract': False, 'linear': None, 'inverse': None, 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': {'amount': 1e-08, 'price': 1e-08}, 'limits': {'leverage': {'min': None, 'max': None}, 'amount': {'min': 1.0, 'max': None}, 'price': {'min': None, 'max': None}, 'cost': {'min': None, 'max': None}}, 'info': '<td>dogeeth</td>\\n<td>1.0 DOGE (1e0)</td>\\n<td>0.00000001 DOGE (1e-8)</td>\\n<td>0.00000001 ETH (1e-8)</td>\\n</tr>'}\n",
      "{'id': 'ethusdt', 'symbol': 'ETH/USDT', 'base': 'ETH', 'quote': 'USDT', 'settle': None, 'baseId': 'eth', 'quoteId': 'usdt', 'settleId': None, 'type': 'spot', 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'active': None, 'contract': False, 'linear': None, 'inverse': None, 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': {'amount': 1e-06, 'price': 0.01}, 'limits': {'leverage': {'min': None, 'max': None}, 'amount': {'min': 0.001, 'max': None}, 'price': {'min': None, 'max': None}, 'cost': {'min': None, 'max': None}}, 'info': '<td>ethusdt</td>\\n<td>0.001 ETH (1e-3)</td>\\n<td>0.000001 ETH (1e-6)</td>\\n<td>0.01 USDT (1e-2)</td>\\n</tr>'}\n"
     ]
    }
   ],
   "source": [
    "exchange= ccxt.gemini({'apiKey': config.apiKey, 'secret': config.apiSecret})\n",
    "for mk in exchange.fetchMarkets():\n",
    "    if \"ETH\" in mk.values():\n",
    "        print(mk)"
   ]
  }
 ],
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