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data-pipelines/notebooks/python-example-of-query-for...

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  28.        "      <th></th>\n",

  29.        "      <th>time</th>\n",

  30.        "      <th>amount</th>\n",

  31.        "      <th>exch</th>\n",

  32.        "      <th>price</th>\n",

  33.        "      <th>server_time</th>\n",

  34.        "      <th>side</th>\n",

  35.        "      <th>ticker</th>\n",

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  93.       "text/plain": [

  94.        "                  time  amount  exch     price  server_time side   ticker\n",

  95.        "0  1531094401700852527  0.0801  bits 6706.6001            0   na  btc_usd\n",

  96.        "1  1531094401780298519  0.0284  bits 6706.6099            0   na  btc_usd\n",

  97.        "2  1531094402305708472  0.0050  btfx 6707.0000            0   na  btc_usd\n",

  98.        "3  1531094403455657797  0.0050  btfx 6706.7002            0   na  btc_usd\n",

  99.        "4  1531094403592663872  0.0658  btfx 6705.8999            0   na  btc_usd"

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  107.    "source": [

  108.     "df = pd.read_csv('/xfs/trades.csv', nrows=1024 * 1024)\n",

  109.     "df.head()"

  110.    ]

  111.   },

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  113.    "cell_type": "code",

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  120.        "<div>\n",

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  135.        "  <thead>\n",

  136.        "    <tr style=\"text-align: right;\">\n",

  137.        "      <th></th>\n",

  138.        "      <th>time</th>\n",

  139.        "      <th>amount</th>\n",

  140.        "      <th>exch</th>\n",

  141.        "      <th>price</th>\n",

  142.        "      <th>server_time</th>\n",

  143.        "      <th>side</th>\n",

  144.        "      <th>ticker</th>\n",

  145.        "    </tr>\n",

  146.        "    <tr>\n",

  147.        "      <th>time</th>\n",

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  149.        "      <th></th>\n",

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  191.        "      <td>0.0050</td>\n",

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  204.        "      <td>0</td>\n",

  205.        "      <td>na</td>\n",

  206.        "      <td>btc_usd</td>\n",

  207.        "    </tr>\n",

  208.        "  </tbody>\n",

  209.        "</table>\n",

  210.        "</div>"

  211.       ],

  212.       "text/plain": [

  213.        "                                                    time  amount  exch  \\\n",

  214.        "time                                                                     \n",

  215.        "2018-07-09 00:00:01.700852527+00:00  1531094401700852527  0.0801  bits   \n",

  216.        "2018-07-09 00:00:01.780298519+00:00  1531094401780298519  0.0284  bits   \n",

  217.        "2018-07-09 00:00:02.305708472+00:00  1531094402305708472  0.0050  btfx   \n",

  218.        "2018-07-09 00:00:03.455657797+00:00  1531094403455657797  0.0050  btfx   \n",

  219.        "2018-07-09 00:00:03.592663872+00:00  1531094403592663872  0.0658  btfx   \n",

  220.        "\n",

  221.        "                                        price  server_time side   ticker  \n",

  222.        "time                                                                      \n",

  223.        "2018-07-09 00:00:01.700852527+00:00 6706.6001            0   na  btc_usd  \n",

  224.        "2018-07-09 00:00:01.780298519+00:00 6706.6099            0   na  btc_usd  \n",

  225.        "2018-07-09 00:00:02.305708472+00:00 6707.0000            0   na  btc_usd  \n",

  226.        "2018-07-09 00:00:03.455657797+00:00 6706.7002            0   na  btc_usd  \n",

  227.        "2018-07-09 00:00:03.592663872+00:00 6705.8999            0   na  btc_usd  "

  228.       ]

  229.      },

  230.      "execution_count": 2,

  231.      "metadata": {},

  232.      "output_type": "execute_result"

  233.     }

  234.    ],

  235.    "source": [

  236.     "df.index = pd.to_datetime(df['time'], utc=True)\n",

  237.     "df.head()"

  238.    ]

  239.   },

  240.   {

  241.    "cell_type": "code",

  242.    "execution_count": 3,

  243.    "metadata": {},

  244.    "outputs": [

  245.     {

  246.      "data": {

  247.       "text/plain": [

  248.        "(Timestamp('2018-07-09 00:00:01.700852527+0000', tz='UTC'),\n",

  249.        " Timestamp('2018-07-11 19:27:34.453569864+0000', tz='UTC'),\n",

  250.        " Timedelta('2 days 19:27:32.752717'))"

  251.       ]

  252.      },

  253.      "execution_count": 3,

  254.      "metadata": {},

  255.      "output_type": "execute_result"

  256.     }

  257.    ],

  258.    "source": [

  259.     "df.index[0], df.index[-1], df.index[-1] - df.index[0]"

  260.    ]

  261.   },

  262.   {

  263.    "cell_type": "code",

  264.    "execution_count": 4,

  265.    "metadata": {},

  266.    "outputs": [

  267.     {

  268.      "data": {

  269.       "text/plain": [

  270.        "1.000578250137079"

  271.       ]

  272.      },

  273.      "execution_count": 4,

  274.      "metadata": {},

  275.      "output_type": "execute_result"

  276.     }

  277.    ],

  278.    "source": [

  279.     "start = \"2018-07-10T04:00:00Z\" # <- randomly selected start/end \n",

  280.     "end   = \"2018-07-10T05:00:00Z\" #    important point is, 1h span\n",

  281.     "\n",

  282.     "target_hr = (df.index >= start) & (df.index < end)\n",

  283.     "assert target_hr.sum() > 0\n",

  284.     "\n",

  285.     "of_btcusd = df['ticker'] == 'btc_usd'\n",

  286.     "assert of_btcusd.sum() > 0\n",

  287.     "\n",

  288.     "of_bmex = df['exch'] == 'bmex'\n",

  289.     "of_gdax = df['exch'] == 'gdax'\n",

  290.     "\n",

  291.     "assert of_bmex.sum() > 0\n",

  292.     "assert of_gdax.sum() > 0\n",

  293.     "\n",

  294.     "bmex_hr = df.loc[target_hr & of_bmex & of_btcusd, ['price', 'amount']]\n",

  295.     "gdax_hr = df.loc[target_hr & of_gdax & of_btcusd, ['price', 'amount']]\n",

  296.     "\n",

  297.     "bmex_size_wt_price = (\n",

  298.     "    (bmex_hr['price'] * bmex_hr['amount']).sum()\n",

  299.     "    / bmex_hr['amount'].sum()\n",

  300.     ")\n",

  301.     "\n",

  302.     "gdax_size_wt_price = (\n",

  303.     "    (gdax_hr['price'] * gdax_hr['amount']).sum()\n",

  304.     "    / gdax_hr['amount'].sum()\n",

  305.     ")\n",

  306.     "\n",

  307.     "ratio = bmex_size_wt_price / gdax_size_wt_price # <- final answer (for start,end hr at least)\n",

  308.     "ratio"

  309.    ]

  310.   },

  311.   {

  312.    "cell_type": "code",

  313.    "execution_count": 5,

  314.    "metadata": {},

  315.    "outputs": [

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  317.      "data": {

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  319.        "((2714, 2), (1305, 2))"

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  323.      "metadata": {},

  324.      "output_type": "execute_result"

  325.     }

  326.    ],

  327.    "source": [

  328.     "bmex_hr.shape, gdax_hr.shape"

  329.    ]

  330.   },

  331.   {

  332.    "cell_type": "code",

  333.    "execution_count": 6,

  334.    "metadata": {},

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  338.       "text/plain": [

  339.        "(6642.42451802828, 6638.585754905495)"

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  345.     }

  346.    ],

  347.    "source": [

  348.     "bmex_size_wt_price, gdax_size_wt_price"

  349.    ]

  350.   },

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