Evaluation.ipynb

{
    "cells": [
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [],
            "source": [
                "import datetime\n",
                "import os\n",
                "\n",
                "import requests\n",
                "import pandas as pd\n",
                "import matplotlib.pyplot as pl\n",
                "import yaml\n",
                "import numpy as np",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": [
                "with open(os.path.expanduser('~/.config/vigilant-crypto-snatch.yml')) as f:\n",
                "    config = yaml.safe_load(f)\n",
                "api_key = config['cryptocompare']['api_key']",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "data": {"text/plain": ["1616249738"]},
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": [
                "timestamp = int(datetime.datetime.now().timestamp())\n",
                "timestamp",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [],
            "source": [
                "url = f'https://min-api.cryptocompare.com/data/histohour?api_key={api_key}&fsym=BTC&tsym=EUR&limit=2000&toTs={timestamp}'"
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [],
            "source": ["r = requests.get(url)"],
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {"text/plain": ["<Response [200]>"]},
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": ["r"],
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "metadata": {},
            "outputs": [],
            "source": [
                "data = r.json()['Data']\n",
                "df = pd.DataFrame(data)\n",
                "df['datetime'] = list(map(datetime.datetime.fromtimestamp, df['time']))",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 31,
            "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>time</th>\n",
                            "      <th>high</th>\n",
                            "      <th>low</th>\n",
                            "      <th>open</th>\n",
                            "      <th>volumefrom</th>\n",
                            "      <th>volumeto</th>\n",
                            "      <th>close</th>\n",
                            "      <th>conversionType</th>\n",
                            "      <th>conversionSymbol</th>\n",
                            "      <th>datetime</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>1609048800</td>\n",
                            "      <td>22659.23</td>\n",
                            "      <td>21970.89</td>\n",
                            "      <td>21975.21</td>\n",
                            "      <td>1817.48</td>\n",
                            "      <td>40692787.44</td>\n",
                            "      <td>22581.74</td>\n",
                            "      <td>direct</td>\n",
                            "      <td></td>\n",
                            "      <td>2020-12-27 07:00:00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>1609052400</td>\n",
                            "      <td>22680.31</td>\n",
                            "      <td>22452.10</td>\n",
                            "      <td>22581.74</td>\n",
                            "      <td>1072.69</td>\n",
                            "      <td>24216731.48</td>\n",
                            "      <td>22588.76</td>\n",
                            "      <td>direct</td>\n",
                            "      <td></td>\n",
                            "      <td>2020-12-27 08:00:00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>1609056000</td>\n",
                            "      <td>22843.48</td>\n",
                            "      <td>22377.87</td>\n",
                            "      <td>22588.76</td>\n",
                            "      <td>1983.46</td>\n",
                            "      <td>44993542.12</td>\n",
                            "      <td>22468.94</td>\n",
                            "      <td>direct</td>\n",
                            "      <td></td>\n",
                            "      <td>2020-12-27 09:00:00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>1609059600</td>\n",
                            "      <td>22754.38</td>\n",
                            "      <td>22321.87</td>\n",
                            "      <td>22468.94</td>\n",
                            "      <td>1329.94</td>\n",
                            "      <td>30020405.63</td>\n",
                            "      <td>22741.71</td>\n",
                            "      <td>direct</td>\n",
                            "      <td></td>\n",
                            "      <td>2020-12-27 10:00:00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>1609063200</td>\n",
                            "      <td>22790.44</td>\n",
                            "      <td>22648.87</td>\n",
                            "      <td>22741.71</td>\n",
                            "      <td>1014.26</td>\n",
                            "      <td>23045329.41</td>\n",
                            "      <td>22766.19</td>\n",
                            "      <td>direct</td>\n",
                            "      <td></td>\n",
                            "      <td>2020-12-27 11:00:00</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",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1996</th>\n",
                            "      <td>1616234400</td>\n",
                            "      <td>49564.58</td>\n",
                            "      <td>49352.23</td>\n",
                            "      <td>49483.03</td>\n",
                            "      <td>213.29</td>\n",
                            "      <td>10545408.62</td>\n",
                            "      <td>49394.21</td>\n",
                            "      <td>direct</td>\n",
                            "      <td></td>\n",
                            "      <td>2021-03-20 11:00:00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1997</th>\n",
                            "      <td>1616238000</td>\n",
                            "      <td>49836.89</td>\n",
                            "      <td>49359.62</td>\n",
                            "      <td>49394.21</td>\n",
                            "      <td>344.81</td>\n",
                            "      <td>17115707.94</td>\n",
                            "      <td>49802.05</td>\n",
                            "      <td>direct</td>\n",
                            "      <td></td>\n",
                            "      <td>2021-03-20 12:00:00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1998</th>\n",
                            "      <td>1616241600</td>\n",
                            "      <td>49851.39</td>\n",
                            "      <td>49443.01</td>\n",
                            "      <td>49802.05</td>\n",
                            "      <td>341.22</td>\n",
                            "      <td>16948646.71</td>\n",
                            "      <td>49764.77</td>\n",
                            "      <td>direct</td>\n",
                            "      <td></td>\n",
                            "      <td>2021-03-20 13:00:00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1999</th>\n",
                            "      <td>1616245200</td>\n",
                            "      <td>50203.07</td>\n",
                            "      <td>49293.62</td>\n",
                            "      <td>49764.77</td>\n",
                            "      <td>1271.67</td>\n",
                            "      <td>63456750.40</td>\n",
                            "      <td>49626.71</td>\n",
                            "      <td>direct</td>\n",
                            "      <td></td>\n",
                            "      <td>2021-03-20 14:00:00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2000</th>\n",
                            "      <td>1616248800</td>\n",
                            "      <td>49685.67</td>\n",
                            "      <td>49439.41</td>\n",
                            "      <td>49626.71</td>\n",
                            "      <td>121.46</td>\n",
                            "      <td>6017386.90</td>\n",
                            "      <td>49549.22</td>\n",
                            "      <td>direct</td>\n",
                            "      <td></td>\n",
                            "      <td>2021-03-20 15:00:00</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "<p>2001 rows × 10 columns</p>\n",
                            "</div>",
                        ],
                        "text/plain": [
                            "            time      high       low      open  volumefrom     volumeto  \\\n",
                            "0     1609048800  22659.23  21970.89  21975.21     1817.48  40692787.44   \n",
                            "1     1609052400  22680.31  22452.10  22581.74     1072.69  24216731.48   \n",
                            "2     1609056000  22843.48  22377.87  22588.76     1983.46  44993542.12   \n",
                            "3     1609059600  22754.38  22321.87  22468.94     1329.94  30020405.63   \n",
                            "4     1609063200  22790.44  22648.87  22741.71     1014.26  23045329.41   \n",
                            "...          ...       ...       ...       ...         ...          ...   \n",
                            "1996  1616234400  49564.58  49352.23  49483.03      213.29  10545408.62   \n",
                            "1997  1616238000  49836.89  49359.62  49394.21      344.81  17115707.94   \n",
                            "1998  1616241600  49851.39  49443.01  49802.05      341.22  16948646.71   \n",
                            "1999  1616245200  50203.07  49293.62  49764.77     1271.67  63456750.40   \n",
                            "2000  1616248800  49685.67  49439.41  49626.71      121.46   6017386.90   \n",
                            "\n",
                            "         close conversionType conversionSymbol            datetime  \n",
                            "0     22581.74         direct                  2020-12-27 07:00:00  \n",
                            "1     22588.76         direct                  2020-12-27 08:00:00  \n",
                            "2     22468.94         direct                  2020-12-27 09:00:00  \n",
                            "3     22741.71         direct                  2020-12-27 10:00:00  \n",
                            "4     22766.19         direct                  2020-12-27 11:00:00  \n",
                            "...        ...            ...              ...                 ...  \n",
                            "1996  49394.21         direct                  2021-03-20 11:00:00  \n",
                            "1997  49802.05         direct                  2021-03-20 12:00:00  \n",
                            "1998  49764.77         direct                  2021-03-20 13:00:00  \n",
                            "1999  49626.71         direct                  2021-03-20 14:00:00  \n",
                            "2000  49549.22         direct                  2021-03-20 15:00:00  \n",
                            "\n",
                            "[2001 rows x 10 columns]",
                        ],
                    },
                    "execution_count": 31,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": ["df"],
        },
        {
            "cell_type": "code",
            "execution_count": 33,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": ["<Figure size 432x288 with 1 Axes>"],
                    },
                    "metadata": {"needs_background": "light"},
                    "output_type": "display_data",
                }
            ],
            "source": [
                "pl.clf()\n",
                "pl.plot(df['datetime'], df['close'])\n",
                "pl.\n",
                "pl.show()",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "0            NaN\n",
                            "1       22581.74\n",
                            "2       22588.76\n",
                            "3       22468.94\n",
                            "4       22741.71\n",
                            "          ...   \n",
                            "1996    49483.03\n",
                            "1997    49394.21\n",
                            "1998    49802.05\n",
                            "1999    49764.77\n",
                            "2000    49626.71\n",
                            "Name: close, Length: 2001, dtype: float64",
                        ]
                    },
                    "execution_count": 11,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": ["df['close'].shift()"],
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": ["<Figure size 432x288 with 1 Axes>"],
                    },
                    "metadata": {"needs_background": "light"},
                    "output_type": "display_data",
                }
            ],
            "source": [
                "pl.clf()\n",
                "pl.hist(df['close'].shift() / df['close'], bins='sturges')\n",
                "pl.show()",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": ["<Figure size 432x288 with 1 Axes>"],
                    },
                    "metadata": {"needs_background": "light"},
                    "output_type": "display_data",
                }
            ],
            "source": [
                "pl.clf()\n",
                "pl.hist(df['close'].shift(2) / df['close'], bins='sturges')\n",
                "pl.show()",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": ["<Figure size 432x288 with 1 Axes>"],
                    },
                    "metadata": {"needs_background": "light"},
                    "output_type": "display_data",
                }
            ],
            "source": [
                "pl.clf()\n",
                "pl.hist(df['close'].shift(3) / df['close'], bins='sturges')\n",
                "pl.show()",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": ["<Figure size 432x288 with 1 Axes>"],
                    },
                    "metadata": {"needs_background": "light"},
                    "output_type": "display_data",
                }
            ],
            "source": [
                "pl.clf()\n",
                "pl.hist(df['close'].shift(24) / df['close'], bins='sturges')\n",
                "pl.show()",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": ["<Figure size 432x288 with 1 Axes>"],
                    },
                    "metadata": {"needs_background": "light"},
                    "output_type": "display_data",
                }
            ],
            "source": [
                "pl.clf()\n",
                "pl.hist(df['close'].shift(7 * 24) / df['close'], bins='sturges')\n",
                "pl.show()",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [],
            "source": [
                "def compute_gains(df: pd.DataFrame, hours: int, drop: float):\n",
                "    close_shift = df['close'].shift(hours)\n",
                "    ratio = df['close'] / close_shift\n",
                "    \n",
                "    btc = 0.0\n",
                "    eur = 0.0\n",
                "\n",
                "    last = -hours\n",
                "    \n",
                "    for i in range(len(df)):\n",
                "        if ratio[i] < (1-drop) and last + hours <= i:\n",
                "            last = i\n",
                "            btc += 1.0 / df['close'][i]\n",
                "            eur += 1.0\n",
                "    return btc, eur, btc / eur if eur > 0 else 0.0",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [],
            "source": [
                "def compute_dca(df: pd.DataFrame, hours: int):    \n",
                "    btc = 0.0\n",
                "    eur = 0.0\n",
                "    \n",
                "    last = -hours\n",
                "    \n",
                "    for i in range(len(df)):\n",
                "        if last + hours <= i:\n",
                "            last = i\n",
                "            btc += 1.0 / df['close'][i]\n",
                "            eur += 1.0\n",
                "    return btc, eur, btc / eur if eur > 0 else 0.0",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "(0.00018338533396577644, 5.0, 3.667706679315529e-05)"
                        ]
                    },
                    "execution_count": 19,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": ["compute_gains(df, 1, 0.05)"],
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "(0.00021539932193016136, 6.0, 3.5899886988360225e-05)"
                        ]
                    },
                    "execution_count": 20,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": ["compute_gains(df, 2, 0.05)"],
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "(0.06002080831882478, 2001.0, 2.99954064561843e-05)"
                        ]
                    },
                    "execution_count": 21,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": ["compute_dca(df, 1)"],
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "(0.03002975001043276, 1001.0, 2.9999750260172587e-05)"
                        ]
                    },
                    "execution_count": 22,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": ["compute_dca(df, 2)"],
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {},
            "outputs": [],
            "source": [
                "hours = np.arange(1, 25)\n",
                "drops = np.linspace(0.01, 0.25, 15)\n",
                "factor = np.zeros(hours.shape + drops.shape)",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "metadata": {},
            "outputs": [],
            "source": [
                "for i, hour in enumerate(hours):\n",
                "    for j, drop in enumerate(drops):\n",
                "        factor[i, j] = compute_gains(df, hour, drop)[2]",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "<ipython-input-25-0dc9374620ff>:2: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
                        "  pl.pcolormesh(hours, drops * 100, factor.T, cmap='turbo')\n",
                    ],
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": ["<Figure size 432x288 with 2 Axes>"],
                    },
                    "metadata": {"needs_background": "light"},
                    "output_type": "display_data",
                },
            ],
            "source": [
                "pl.clf()\n",
                "pl.pcolormesh(hours, drops * 100, factor.T, cmap='turbo')\n",
                "pl.colorbar()\n",
                "pl.title('BTC / EUR')\n",
                "pl.xlabel('Delay / h')\n",
                "pl.ylabel('Drop / %')\n",
                "pl.tight_layout()\n",
                "pl.savefig('gains.pdf')\n",
                "pl.show()",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [],
        },
        {
            "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": "3.9.1",
        },
    },
    "nbformat": 4,
    "nbformat_minor": 4,
}