{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "code", "execution_count": null, "source": [ "# Find out what model works best with the dataset from any FX Broker" ], "outputs": [], "metadata": { "id": "YpdFBEdNVI_k" } }, { "cell_type": "code", "execution_count": null, "source": [ "import pandas as pd\n", "\n", "from datetime import datetime\n", "import MetaTrader5 as mt5\n", "\n", "if not mt5.initialize():\n", "\tprint(f\"MT5 Init failed, error code {mt5.last_error()}\")\n", "\tquit()\n", "\n", "pair = \"GBPUSD\"\n", "rates = mt5.copy_rates_from_pos(pair, mt5.TIMEFRAME_D1, 0, 100)\n", "print(rates)" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[(1620604800, 1.39994, 1.41574, 1.39929, 1.41148, 28358, 19, 0)\n", " (1620691200, 1.41148, 1.41653, 1.41033, 1.41359, 32194, 19, 0)\n", " (1620777600, 1.41359, 1.41518, 1.40488, 1.40495, 40832, 19, 0)\n", " (1620864000, 1.40495, 1.40771, 1.40047, 1.40465, 37476, 19, 0)\n", " (1620950400, 1.40465, 1.41098, 1.40351, 1.40944, 28589, 19, 0)\n", " (1621209600, 1.41011, 1.4146 , 1.40762, 1.41258, 28368, 19, 0)\n", " (1621296000, 1.41258, 1.42193, 1.41258, 1.41827, 31182, 19, 0)\n", " (1621382400, 1.41829, 1.41998, 1.40993, 1.41097, 44625, 19, 0)\n", " (1621468800, 1.41097, 1.41915, 1.41002, 1.41825, 33713, 19, 0)\n", " (1621555200, 1.41825, 1.4233 , 1.41395, 1.4146 , 33552, 19, 0)\n", " (1621814400, 1.41531, 1.41708, 1.41102, 1.41481, 29336, 19, 0)\n", " (1621900800, 1.41481, 1.42101, 1.41148, 1.41451, 33269, 19, 0)\n", " (1621987200, 1.41451, 1.4175 , 1.41104, 1.41124, 31269, 19, 0)\n", " (1622073600, 1.41124, 1.42183, 1.40905, 1.41988, 33789, 19, 0)\n", " (1622160000, 1.41988, 1.42073, 1.41352, 1.41871, 44691, 19, 0)\n", " (1622419200, 1.41771, 1.42169, 1.41629, 1.42042, 26769, 19, 0)\n", " (1622505600, 1.42042, 1.42483, 1.41448, 1.41448, 42181, 19, 0)\n", " (1622592000, 1.41448, 1.41824, 1.41112, 1.4163 , 36998, 19, 0)\n", " (1622678400, 1.4163 , 1.4202 , 1.40855, 1.40987, 43213, 19, 0)\n", " (1622764800, 1.40987, 1.41993, 1.40823, 1.41551, 41735, 19, 0)\n", " (1623024000, 1.41454, 1.41891, 1.41105, 1.41731, 36046, 19, 0)\n", " (1623110400, 1.41747, 1.41834, 1.412 , 1.41505, 30964, 19, 0)\n", " (1623196800, 1.41505, 1.41879, 1.41092, 1.41125, 29958, 19, 0)\n", " (1623283200, 1.41125, 1.41772, 1.40725, 1.41721, 41583, 19, 0)\n", " (1623369600, 1.41721, 1.41845, 1.40946, 1.41071, 31313, 19, 0)\n", " (1623628800, 1.41041, 1.41227, 1.40692, 1.41039, 22491, 19, 0)\n", " (1623715200, 1.41032, 1.41276, 1.40332, 1.40762, 32252, 19, 0)\n", " (1623801600, 1.40752, 1.41316, 1.39808, 1.39817, 39772, 19, 0)\n", " (1623888000, 1.39817, 1.40075, 1.38946, 1.39138, 45190, 19, 0)\n", " (1623974400, 1.39138, 1.39433, 1.37908, 1.37964, 47299, 19, 0)\n", " (1624233600, 1.38064, 1.39358, 1.37854, 1.3929 , 46126, 19, 0)\n", " (1624320000, 1.3929 , 1.39625, 1.38593, 1.39414, 34394, 19, 0)\n", " (1624406400, 1.39414, 1.40002, 1.3923 , 1.39565, 35253, 19, 0)\n", " (1624492800, 1.39565, 1.39855, 1.38886, 1.39127, 38538, 19, 0)\n", " (1624579200, 1.39127, 1.39346, 1.38702, 1.38762, 32352, 19, 0)\n", " (1624838400, 1.38915, 1.39386, 1.38699, 1.38772, 30848, 19, 0)\n", " (1624924800, 1.38772, 1.38818, 1.38133, 1.38303, 30670, 19, 0)\n", " (1625011200, 1.38303, 1.38718, 1.37974, 1.38226, 34827, 19, 0)\n", " (1625097600, 1.38226, 1.38331, 1.3752 , 1.37597, 35844, 19, 0)\n", " (1625184000, 1.37597, 1.38435, 1.37302, 1.38297, 38104, 19, 0)\n", " (1625443200, 1.38214, 1.38607, 1.38163, 1.38362, 24428, 19, 0)\n", " (1625529600, 1.38362, 1.38966, 1.37719, 1.37936, 41510, 19, 0)\n", " (1625616000, 1.37936, 1.38406, 1.37527, 1.37938, 46461, 19, 0)\n", " (1625702400, 1.37938, 1.38038, 1.3741 , 1.37795, 52161, 19, 0)\n", " (1625788800, 1.37788, 1.39 , 1.37548, 1.38964, 46135, 19, 0)\n", " (1626048000, 1.38917, 1.39091, 1.38381, 1.38747, 37683, 19, 0)\n", " (1626134400, 1.38747, 1.39043, 1.37986, 1.38066, 37188, 19, 0)\n", " (1626220800, 1.38066, 1.3891 , 1.38005, 1.38557, 39483, 19, 0)\n", " (1626307200, 1.38557, 1.38977, 1.3804 , 1.38242, 43484, 19, 0)\n", " (1626393600, 1.38242, 1.38611, 1.37595, 1.37615, 35530, 19, 0)\n", " (1626652800, 1.37715, 1.37735, 1.36541, 1.36724, 54735, 19, 0)\n", " (1626739200, 1.36724, 1.36883, 1.35708, 1.36211, 47256, 19, 0)\n", " (1626825600, 1.36211, 1.37219, 1.35904, 1.37119, 37403, 19, 0)\n", " (1626912000, 1.37119, 1.3786 , 1.36895, 1.37602, 37734, 19, 0)\n", " (1626998400, 1.37602, 1.37789, 1.3719 , 1.37466, 30737, 19, 0)\n", " (1627257600, 1.37504, 1.38321, 1.3736 , 1.38098, 35242, 19, 0)\n", " (1627344000, 1.38096, 1.3893 , 1.37656, 1.38725, 35966, 19, 0)\n", " (1627430400, 1.38721, 1.391 , 1.38422, 1.3893 , 33732, 19, 0)\n", " (1627516800, 1.38951, 1.39806, 1.38913, 1.39519, 26251, 19, 0)\n", " (1627603200, 1.39506, 1.39822, 1.38871, 1.39006, 25422, 19, 0)\n", " (1627862400, 1.38996, 1.39316, 1.38748, 1.38777, 23673, 19, 0)\n", " (1627948800, 1.38777, 1.39374, 1.38736, 1.39106, 25014, 19, 0)\n", " (1628035200, 1.39096, 1.39566, 1.38826, 1.38862, 23921, 19, 0)\n", " (1628121600, 1.38859, 1.39478, 1.38713, 1.39272, 27727, 19, 0)\n", " (1628208000, 1.39272, 1.39317, 1.38602, 1.38723, 22461, 19, 0)\n", " (1628467200, 1.38677, 1.38931, 1.38385, 1.38385, 24500, 19, 0)\n", " (1628553600, 1.3844 , 1.38714, 1.38264, 1.38359, 20029, 19, 0)\n", " (1628640000, 1.38364, 1.38869, 1.38017, 1.38611, 21696, 19, 0)\n", " (1628726400, 1.38611, 1.38773, 1.37936, 1.37977, 20612, 19, 0)\n", " (1628812800, 1.37977, 1.38741, 1.37894, 1.38658, 17201, 19, 0)\n", " (1629072000, 1.38641, 1.38769, 1.38268, 1.38421, 19562, 19, 0)\n", " (1629158400, 1.38421, 1.38426, 1.37249, 1.37321, 23801, 19, 0)\n", " (1629244800, 1.37321, 1.3785 , 1.3719 , 1.375 , 21758, 19, 0)\n", " (1629331200, 1.375 , 1.37586, 1.36304, 1.36304, 27007, 19, 0)\n", " (1629417600, 1.36306, 1.36398, 1.36011, 1.36241, 22204, 19, 0)\n", " (1629676800, 1.36226, 1.37308, 1.36051, 1.37111, 21328, 19, 0)\n", " (1629763200, 1.37111, 1.37466, 1.36927, 1.37252, 19377, 19, 0)\n", " (1629849600, 1.3726 , 1.37659, 1.36955, 1.37559, 20814, 19, 0)\n", " (1629936000, 1.3754 , 1.37668, 1.36886, 1.3695 , 23813, 19, 0)\n", " (1630022400, 1.36945, 1.378 , 1.3679 , 1.37549, 25338, 19, 0)\n", " (1630281600, 1.37522, 1.37743, 1.37327, 1.37559, 18588, 19, 0)\n", " (1630368000, 1.37559, 1.38068, 1.3742 , 1.37533, 31027, 19, 0)\n", " (1630454400, 1.37519, 1.37972, 1.37302, 1.37663, 27741, 19, 0)\n", " (1630540800, 1.3766 , 1.3839 , 1.37654, 1.383 , 20250, 19, 0)\n", " (1630627200, 1.38323, 1.3891 , 1.38169, 1.38543, 27989, 19, 0)\n", " (1630886400, 1.38527, 1.38674, 1.38178, 1.3831 , 20769, 19, 0)\n", " (1630972800, 1.38323, 1.38554, 1.37666, 1.37813, 34685, 19, 0)\n", " (1631059200, 1.37812, 1.37894, 1.37254, 1.37684, 32355, 19, 0)\n", " (1631145600, 1.37688, 1.38617, 1.37526, 1.3834 , 35609, 19, 0)\n", " (1631232000, 1.38329, 1.38873, 1.38255, 1.3829 , 27899, 19, 0)\n", " (1631491200, 1.38375, 1.38503, 1.3796 , 1.38311, 30923, 19, 0)\n", " (1631577600, 1.38335, 1.3912 , 1.38022, 1.38038, 33876, 19, 0)\n", " (1631664000, 1.38058, 1.38527, 1.37917, 1.38344, 33271, 19, 0)\n", " (1631750400, 1.38358, 1.3852 , 1.37633, 1.37894, 30453, 19, 0)\n", " (1631836800, 1.37918, 1.38118, 1.37259, 1.37261, 35121, 19, 0)\n", " (1632096000, 1.37444, 1.37486, 1.364 , 1.3652 , 36677, 19, 0)\n", " (1632182400, 1.36497, 1.36921, 1.36402, 1.36552, 41087, 19, 0)\n", " (1632268800, 1.36553, 1.36886, 1.36083, 1.36136, 41217, 19, 0)\n", " (1632355200, 1.36126, 1.37499, 1.36107, 1.37149, 43973, 19, 0)\n", " (1632441600, 1.37183, 1.37352, 1.37091, 1.37181, 3993, 19, 0)]\n" ] } ], "metadata": { "id": "_lOkcd3cVI_m", "outputId": "ce6ec041-48eb-4605-a917-d43467444ac5" } }, { "cell_type": "markdown", "source": [ "# Convert the `rates` into a Dataframe" ], "metadata": { "id": "j9Mg9WEtVI_p" } }, { "cell_type": "code", "execution_count": null, "source": [ "data = pd.DataFrame(rates)\n", "data['time'] = pd.to_datetime(data['time'], unit='s')\n", "print(type(data))\n", "print(data.head())\n", "data" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", " time open high low close tick_volume spread \\\n", "0 2021-05-10 1.39994 1.41574 1.39929 1.41148 28358 19 \n", "1 2021-05-11 1.41148 1.41653 1.41033 1.41359 32194 19 \n", "2 2021-05-12 1.41359 1.41518 1.40488 1.40495 40832 19 \n", "3 2021-05-13 1.40495 1.40771 1.40047 1.40465 37476 19 \n", "4 2021-05-14 1.40465 1.41098 1.40351 1.40944 28589 19 \n", "\n", " real_volume \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n" ] }, { "output_type": "execute_result", "data": { "text/html": [ "
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timeopenhighlowclosetick_volumespreadreal_volume
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100 rows × 8 columns

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" ], "text/plain": [ " time open high low close tick_volume spread \\\n", "0 2021-05-10 1.39994 1.41574 1.39929 1.41148 28358 19 \n", "1 2021-05-11 1.41148 1.41653 1.41033 1.41359 32194 19 \n", "2 2021-05-12 1.41359 1.41518 1.40488 1.40495 40832 19 \n", "3 2021-05-13 1.40495 1.40771 1.40047 1.40465 37476 19 \n", "4 2021-05-14 1.40465 1.41098 1.40351 1.40944 28589 19 \n", ".. ... ... ... ... ... ... ... \n", "95 2021-09-20 1.37444 1.37486 1.36400 1.36520 36677 19 \n", "96 2021-09-21 1.36497 1.36921 1.36402 1.36552 41087 19 \n", "97 2021-09-22 1.36553 1.36886 1.36083 1.36136 41217 19 \n", "98 2021-09-23 1.36126 1.37499 1.36107 1.37149 43973 19 \n", "99 2021-09-24 1.37183 1.37352 1.37091 1.37181 3993 19 \n", "\n", " real_volume \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n", ".. ... \n", "95 0 \n", "96 0 \n", "97 0 \n", "98 0 \n", "99 0 \n", "\n", "[100 rows x 8 columns]" ] }, "metadata": {}, "execution_count": 3 } ], "metadata": { "id": "qfZLpqNdVI_q", "outputId": "b8bf50e9-331f-4dae-e639-bed94c1b8c8b" } }, { "cell_type": "code", "execution_count": null, "source": [ "# Understand the dataset\n", "print(data.describe)" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n" ] } ], "metadata": { "id": "90IVfmESVI_r", "outputId": "6ba04f60-343c-4396-e517-811a9696c3d3" } }, { "cell_type": "markdown", "source": [ "We'll be using scikit learn's to train_test_split the dataset but we have to define a few things" ], "metadata": { "id": "YcrbKuhNVI_s" } }, { "cell_type": "markdown", "source": [ "Need to define the X set and the y set from the data" ], "metadata": { "id": "0U7qmNKTVI_s" } }, { "cell_type": "code", "execution_count": null, "source": [ "X = data.drop(columns='close')\n", "print(X.head())\n", "y = data['close']\n", "print(y.head())" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " time open high low tick_volume spread real_volume\n", "0 2021-05-10 1.39994 1.41574 1.39929 28358 19 0\n", "1 2021-05-11 1.41148 1.41653 1.41033 32194 19 0\n", "2 2021-05-12 1.41359 1.41518 1.40488 40832 19 0\n", "3 2021-05-13 1.40495 1.40771 1.40047 37476 19 0\n", "4 2021-05-14 1.40465 1.41098 1.40351 28589 19 0\n", "0 1.41148\n", "1 1.41359\n", "2 1.40495\n", "3 1.40465\n", "4 1.40944\n", "Name: close, dtype: float64\n" ] } ], "metadata": { "id": "Ri5_AZycVI_t", "outputId": "38c216c9-cdba-43e8-d906-1f43e397139b" } }, { "cell_type": "code", "execution_count": null, "source": [ "# Split the data set into training and test set\n", "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=4)\n", "print(f\"X_train: {X_train}\")\n", "print(f\"X_test: {X_test}\")\n", "print(f\"y_train: {y_train}\")\n", "print(f\"y_test: {y_test}\")" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "X_train: time open high low tick_volume spread real_volume\n", "65 2021-08-09 1.38677 1.38931 1.38385 24500 19 0\n", "62 2021-08-04 1.39096 1.39566 1.38826 23921 19 0\n", "27 2021-06-16 1.40752 1.41316 1.39808 39772 19 0\n", "75 2021-08-23 1.36226 1.37308 1.36051 21328 19 0\n", "61 2021-08-03 1.38777 1.39374 1.38736 25014 19 0\n", ".. ... ... ... ... ... ... ...\n", "87 2021-09-08 1.37812 1.37894 1.37254 32355 19 0\n", "1 2021-05-11 1.41148 1.41653 1.41033 32194 19 0\n", "69 2021-08-13 1.37977 1.38741 1.37894 17201 19 0\n", "55 2021-07-26 1.37504 1.38321 1.37360 35242 19 0\n", "46 2021-07-13 1.38747 1.39043 1.37986 37188 19 0\n", "\n", "[70 rows x 7 columns]\n", "X_test: time open high low tick_volume spread real_volume\n", "20 2021-06-07 1.41454 1.41891 1.41105 36046 19 0\n", "10 2021-05-24 1.41531 1.41708 1.41102 29336 19 0\n", "96 2021-09-21 1.36497 1.36921 1.36402 41087 19 0\n", "16 2021-06-01 1.42042 1.42483 1.41448 42181 19 0\n", "63 2021-08-05 1.38859 1.39478 1.38713 27727 19 0\n", "24 2021-06-11 1.41721 1.41845 1.40946 31313 19 0\n", "53 2021-07-22 1.37119 1.37860 1.36895 37734 19 0\n", "97 2021-09-22 1.36553 1.36886 1.36083 41217 19 0\n", "41 2021-07-06 1.38362 1.38966 1.37719 41510 19 0\n", "47 2021-07-14 1.38066 1.38910 1.38005 39483 19 0\n", "43 2021-07-08 1.37938 1.38038 1.37410 52161 19 0\n", "2 2021-05-12 1.41359 1.41518 1.40488 40832 19 0\n", "95 2021-09-20 1.37444 1.37486 1.36400 36677 19 0\n", "26 2021-06-15 1.41032 1.41276 1.40332 32252 19 0\n", "13 2021-05-27 1.41124 1.42183 1.40905 33789 19 0\n", "37 2021-06-30 1.38303 1.38718 1.37974 34827 19 0\n", "14 2021-05-28 1.41988 1.42073 1.41352 44691 19 0\n", "29 2021-06-18 1.39138 1.39433 1.37908 47299 19 0\n", "35 2021-06-28 1.38915 1.39386 1.38699 30848 19 0\n", "54 2021-07-23 1.37602 1.37789 1.37190 30737 19 0\n", "80 2021-08-30 1.37522 1.37743 1.37327 18588 19 0\n", "4 2021-05-14 1.40465 1.41098 1.40351 28589 19 0\n", "81 2021-08-31 1.37559 1.38068 1.37420 31027 19 0\n", "76 2021-08-24 1.37111 1.37466 1.36927 19377 19 0\n", "85 2021-09-06 1.38527 1.38674 1.38178 20769 19 0\n", "60 2021-08-02 1.38996 1.39316 1.38748 23673 19 0\n", "5 2021-05-17 1.41011 1.41460 1.40762 28368 19 0\n", "70 2021-08-16 1.38641 1.38769 1.38268 19562 19 0\n", "71 2021-08-17 1.38421 1.38426 1.37249 23801 19 0\n", "19 2021-06-04 1.40987 1.41993 1.40823 41735 19 0\n", "y_train: 65 1.38385\n", "62 1.38862\n", "27 1.39817\n", "75 1.37111\n", "61 1.39106\n", " ... \n", "87 1.37684\n", "1 1.41359\n", "69 1.38658\n", "55 1.38098\n", "46 1.38066\n", "Name: close, Length: 70, dtype: float64\n", "y_test: 20 1.41731\n", "10 1.41481\n", "96 1.36552\n", "16 1.41448\n", "63 1.39272\n", "24 1.41071\n", "53 1.37602\n", "97 1.36136\n", "41 1.37936\n", "47 1.38557\n", "43 1.37795\n", "2 1.40495\n", "95 1.36520\n", "26 1.40762\n", "13 1.41988\n", "37 1.38226\n", "14 1.41871\n", "29 1.37964\n", "35 1.38772\n", "54 1.37466\n", "80 1.37559\n", "4 1.40944\n", "81 1.37533\n", "76 1.37252\n", "85 1.38310\n", "60 1.38777\n", "5 1.41258\n", "70 1.38421\n", "71 1.37321\n", "19 1.41551\n", "Name: close, dtype: float64\n" ] } ], "metadata": { "id": "PQk6rJr_VI_t", "outputId": "beff45f0-51f1-43b8-9cf8-611cd8a48347" } }, { "cell_type": "markdown", "source": [ "Using LazyRegressor to find out the models" ], "metadata": { "id": "cNWyvLiSVI_u" } }, { "cell_type": "code", "execution_count": null, "source": [ "from lazypredict.Supervised import LazyRegressor\n", "reg = LazyRegressor(verbose=0, ignore_warnings=False, custom_metric=None)\n", "models, predictions = reg.fit(X_train, X_test, y_train, y_test)\n", "\n", "print(models)" ], "outputs": [ { "output_type": "error", "ename": "ModuleNotFoundError", "evalue": "No module named 'lazypredict'", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mlazypredict\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSupervised\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mLazyRegressor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mreg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLazyRegressor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mignore_warnings\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcustom_metric\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mmodels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'lazypredict'" ] } ], "metadata": { "id": "5iBJPu5qVI_v", "outputId": "8a4981cc-a78a-40a8-fdb0-e7faa4204c70" } }, { "cell_type": "markdown", "source": [ "# Pick a Model to study\n", "\n", "So according to the `LazyRegressor`, there are a bunch of models are unknown to me at this time of writing this. The only model I am familiar with is the LinearRegression. I may start with that and if it doesn't perform as much, I will continue up the models. RidgeCV being the top." ], "metadata": { "id": "BxXBa8hUVI_w" } }, { "cell_type": "code", "execution_count": null, "source": [ "import torch\n", "import math\n", "\n", "class LinearRegression:\n", " def fit(self, X, y, method, learning_rate=0.01, iterations=500, batch_size=32):\n", " X, y = torch.from_numpy(X), torch.from_numpy(y)\n", " X = torch.cat([(X), torch.ones_like(y)], dim=1)\n", " rows, cols = X.size()\n", " if method == 'solve':\n", " if rows >= cols == torch.matrix_rank(X):\n", " self.weights = torch.matmul(\n", " torch.matmul(\n", " torch.inverse(\n", " torch.matmul(\n", " torch.transpose(X, 0, 1),\n", " X)),\n", " torch.transpose(X, 0, 1)),\n", " y)\n", " else:\n", " print('X has not full column rank. method=\\'solve\\' cannot be used.')\n", " elif method == 'sgd':\n", " self.weights = torch.normal(mean=0, std=1/cols, size=(cols, 1), dtype=torch.float64)\n", " for i in range(iterations):\n", " Xy = torch.cat([X, y], dim=1)\n", " Xy = Xy[torch.randperm(Xy.size()[0])]\n", " X, y = torch.split(Xy, [Xy.size()[1]-1, 1], dim=1)\n", " for j in range(int(math.ceil(rows/batch_size))):\n", " start, end = batch_size*j, min(batch_size*(j+1), rows)\n", " Xb = torch.index_select(X, 0, torch.arange(start, end))\n", " yb = torch.index_select(y, 0, torch.arange(start, end))\n", "\n", " self.weights.requires_grad_(True)\n", " diff = torch.matmul(Xb, self.weights) - yb\n", " loss = torch.matmul(torch.transpose(diff, 0, 1), diff)\n", " loss.backward()\n", "\n", " self.weights = (self.weights - learning_rate*self.weights.grad).detach()\n", " else:\n", " print(f'Unknown method: \\'{method}\\'')\n", "\n", " return self\n", "\n", " def predict(self, X):\n", " X = torch.from_numpy(X)\n", " if not hasattr(self, 'weights'):\n", " print('Cannot predict. You should call the .fit() method first.')\n", " return\n", "\n", " X = torch.cat([X, torch.ones((X.size()[0], 1))], dim=1)\n", "\n", " if X.size()[1] != self.weights.size()[0]:\n", " print(f'Shapes do not match. {X.size()[1]} != {self.weights.size()[0]}')\n", " return\n", "\n", " return torch.matmul(X, self.weights)\n", "\n", " def rmse(self, X, y):\n", " y = torch.from_numpy(y)\n", " y_hat = self.predict(X)\n", "\n", " if y_hat is None:\n", " return\n", "\n", " return torch.sqrt(torch.mean(torch.square(y_hat - y)))" ], "outputs": [], "metadata": { "id": "kFQZAyC3VI_w" } }, { "cell_type": "markdown", "source": [ "With the Linear Regression model defined with fitting and predicting, let's map and plot the current dataset" ], "metadata": { "id": "t-KvWJbxVI_y" } }, { "cell_type": "code", "execution_count": null, "source": [ "import mplfinance as mpf\n", "\n", "data = data.set_index('time')\n", "mpf.plot(data, type='line', title=pair)" ], "outputs": [ { "output_type": "error", "ename": "ModuleNotFoundError", "evalue": "No module named 'mplfinance'", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mmplfinance\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmpf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'time'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mmpf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'line'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpair\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'mplfinance'" ] } ], "metadata": { "id": "rg7jxvRBVI_y", "outputId": "8d47cba7-028b-4b3f-8da6-17c474b2f270" } }, { "cell_type": "code", "execution_count": null, "source": [ "from sklearn.linear_model import LinearRegression\n", "\n", "df_data = pd.DataFrame(rates)\n", "df_x = df_data.drop(columns='close')\n", "df_y = df_data['close']\n", "x_train, x_test, y_train, y_test = train_test_split(df_x, df_y, test_size=0.2, random_state=0)\n", "model = LinearRegression()\n", "model.fit(x_train, y_train)\n", "y_pre = model.predict(x_test)\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "plt.figure(num = 3, figsize=(10, 5))\n", "y_test_array = np.array(y_test)\n", "plt.plot(y_test_array)\n", "plt.plot(y_pre)\n", "plt.show()" ], "outputs": [], "metadata": { "id": "dK6MkRTwVI_y" } }, { "cell_type": "markdown", "source": [ "The Metatrader 5 seems to not be able to handle order management very well. This includes current order sent." ], "metadata": { "id": "cY-msNDsVI_z" } }, { "cell_type": "code", "execution_count": null, "source": [ "# Login\n", "import config\n", "client = mt5.login(config.ACCOUNT, password=config.PASSWORD, server=config.SERVER)\n", "positions = mt5.positions_get(symbol=\"GBPUSD\")\n", "positions" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(TradePosition(ticket=226046089, time=1632416475, time_msc=1632416475155, time_update=1632416475, time_update_msc=1632416475155, type=0, magic=20210922, identifier=226046089, reason=3, volume=0.01, price_open=1.37374, sl=0.0, tp=1.37533, price_current=1.3719000000000001, swap=-0.04, profit=-1.8399999999999999, symbol='GBPUSD', comment='my last shot', external_id=''),\n", " TradePosition(ticket=226046148, time=1632416502, time_msc=1632416502449, time_update=1632416502, time_update_msc=1632416502449, type=0, magic=20210922, identifier=226046148, reason=3, volume=0.01, price_open=1.3738299999999999, sl=0.0, tp=1.37541, price_current=1.3719000000000001, swap=-0.04, profit=-1.9300000000000002, symbol='GBPUSD', comment='my last shot', external_id=''))" ] }, "metadata": {}, "execution_count": 12 } ], "metadata": { "id": "uBoUnvQIVI_z", "outputId": "42b4d12b-0c8c-40fa-8bee-aa1e2699931e" } } ], "metadata": { "orig_nbformat": 4, "language_info": { "name": "python", "version": "3.9.4", "mimetype": "text/x-python", "codemirror_mode": { "name": "ipython", "version": 3 }, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py" }, "kernelspec": { "name": "python3", "display_name": "Python 3.9.4 64-bit" }, "interpreter": { "hash": "63fd5069d213b44bf678585dea6b12cceca9941eaf7f819626cde1f2670de90d" }, "colab": { "provenance": [], "include_colab_link": true } }, "nbformat": 4, "nbformat_minor": 0 }