├── Pontos de pivo para Daytrade - simulação resultados .ipynb ├── README.md ├── Robô Pivot point-Mt5-Python.ipynb └── ilustrac.png /Pontos de pivo para Daytrade - simulação resultados .ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### Pontos de pivo para Daytrade\n", 8 | "Uma estrategia basica para trading\n", 9 | "\n", 10 | "OBS: Esse modelo Só é funcional para ativos com volatilidade superior a do IBOV, ativos de Beta baixo deverar consultar se compensa com os custo operacionais." 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 2, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "import pandas as pd\n", 20 | "from pandas_datareader import data as pdr\n", 21 | "import numpy as np\n", 22 | "import matplotlib.pyplot as plt\n", 23 | "import yfinance\n", 24 | "import warnings\n", 25 | "%matplotlib inline\n", 26 | "warnings.filterwarnings('ignore')" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 89, 32 | "metadata": {}, 33 | "outputs": [], 34 | "source": [ 35 | "ativo = yfinance.Ticker(\"csna3.SA\")\n", 36 | "df = ativo.history(interval=\"5m\", period = \"1d\")\n", 37 | "\n", 38 | "# buscando series historias outro jeito mais simples\n", 39 | "#df = pdr.DataReader('itub4.SA',data_source='yahoo', start='2020-02-28', end = '2020-04-02');\n", 40 | "#df.tail()" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 90, 46 | "metadata": {}, 47 | "outputs": [ 48 | { 49 | "data": { 50 | "text/plain": [ 51 | "Open 33.730000\n", 52 | "High 33.900002\n", 53 | "Low 33.700001\n", 54 | "Close 33.740002\n", 55 | "Volume 226300.000000\n", 56 | "Dividends 0.000000\n", 57 | "Stock Splits 0.000000\n", 58 | "Name: 2021-01-22 15:55:00-03:00, dtype: float64" 59 | ] 60 | }, 61 | "execution_count": 90, 62 | "metadata": {}, 63 | "output_type": "execute_result" 64 | } 65 | ], 66 | "source": [ 67 | "df.iloc[-1].copy()" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": 91, 73 | "metadata": {}, 74 | "outputs": [ 75 | { 76 | "data": { 77 | "text/html": [ 78 | "

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	Open	High	Low	Close	Volume	Dividends	Stock Splits
Datetime
2021-01-21 10:05:00-03:00	33.660000	34.150002	33.660000	34.150002	0	0	0
2021-01-21 10:10:00-03:00	34.110001	34.599998	34.070000	34.150002	701900	0	0
2021-01-21 10:15:00-03:00	34.180000	34.490002	34.150002	34.490002	494200	0	0
2021-01-21 10:20:00-03:00	34.490002	34.930000	34.410000	34.860001	721500	0	0
2021-01-21 10:25:00-03:00	34.830002	34.900002	34.330002	34.490002	835800	0	0
...	...	...	...	...	...	...	...
2021-01-22 15:35:00-03:00	33.680000	33.840000	33.669998	33.799999	121100	0	0
2021-01-22 15:40:00-03:00	33.810001	33.869999	33.740002	33.830002	80100	0	0
2021-01-22 15:45:00-03:00	33.849998	33.860001	33.650002	33.650002	98400	0	0
2021-01-22 15:50:00-03:00	33.650002	33.779999	33.650002	33.720001	61300	0	0
2021-01-22 15:55:00-03:00	33.730000	33.900002	33.700001	33.740002	226300	0	0

\n", 228 | "

141 rows × 7 columns

\n", 229 | "

" 230 | ], 231 | "text/plain": [ 232 | " Open High Low Close Volume \\\n", 233 | "Datetime \n", 234 | "2021-01-21 10:05:00-03:00 33.660000 34.150002 33.660000 34.150002 0 \n", 235 | "2021-01-21 10:10:00-03:00 34.110001 34.599998 34.070000 34.150002 701900 \n", 236 | "2021-01-21 10:15:00-03:00 34.180000 34.490002 34.150002 34.490002 494200 \n", 237 | "2021-01-21 10:20:00-03:00 34.490002 34.930000 34.410000 34.860001 721500 \n", 238 | "2021-01-21 10:25:00-03:00 34.830002 34.900002 34.330002 34.490002 835800 \n", 239 | "... ... ... ... ... ... \n", 240 | "2021-01-22 15:35:00-03:00 33.680000 33.840000 33.669998 33.799999 121100 \n", 241 | "2021-01-22 15:40:00-03:00 33.810001 33.869999 33.740002 33.830002 80100 \n", 242 | "2021-01-22 15:45:00-03:00 33.849998 33.860001 33.650002 33.650002 98400 \n", 243 | "2021-01-22 15:50:00-03:00 33.650002 33.779999 33.650002 33.720001 61300 \n", 244 | "2021-01-22 15:55:00-03:00 33.730000 33.900002 33.700001 33.740002 226300 \n", 245 | "\n", 246 | " Dividends Stock Splits \n", 247 | "Datetime \n", 248 | "2021-01-21 10:05:00-03:00 0 0 \n", 249 | "2021-01-21 10:10:00-03:00 0 0 \n", 250 | "2021-01-21 10:15:00-03:00 0 0 \n", 251 | "2021-01-21 10:20:00-03:00 0 0 \n", 252 | "2021-01-21 10:25:00-03:00 0 0 \n", 253 | "... ... ... \n", 254 | "2021-01-22 15:35:00-03:00 0 0 \n", 255 | "2021-01-22 15:40:00-03:00 0 0 \n", 256 | "2021-01-22 15:45:00-03:00 0 0 \n", 257 | "2021-01-22 15:50:00-03:00 0 0 \n", 258 | "2021-01-22 15:55:00-03:00 0 0 \n", 259 | "\n", 260 | "[141 rows x 7 columns]" 261 | ] 262 | }, 263 | "execution_count": 91, 264 | "metadata": {}, 265 | "output_type": "execute_result" 266 | } 267 | ], 268 | "source": [ 269 | "df" 270 | ] 271 | }, 272 | { 273 | "cell_type": "markdown", 274 | "metadata": {}, 275 | "source": [ 276 | "### Agora vamos para a formula do pivot point\n", 277 | "Fonte:\n", 278 | "https://www.mql5.com/pt/code/95\n", 279 | "\n", 280 | "Resistência 3 = High + 2*(Pivot - Low)\n", 281 | "\n", 282 | "Resistência 2 = Pivot + (R1 - S1)\n", 283 | "\n", 284 | "Resistência 1 = 2 * Pivot - Low\n", 285 | "\n", 286 | "Pontos Pivô = ( High + Close + Low )/3\n", 287 | "\n", 288 | "Suporte 1 = 2 * Pivot - High\n", 289 | "\n", 290 | "Suporte 2 = Pivot - (R1 - S1)\n", 291 | "\n", 292 | "Suporte 3 = Low - 2*(High - Pivot)\n" 293 | ] 294 | }, 295 | { 296 | "cell_type": "code", 297 | "execution_count": 92, 298 | "metadata": {}, 299 | "outputs": [], 300 | "source": [ 301 | "df['Pivot'] = (df['High'] + df['Low'] + df['Close'])/3\n", 302 | "df['R1'] = 2*df['Pivot'] - df['Low']\n", 303 | "df['S1'] = 2*df['Pivot'] - df['High']\n", 304 | "df['R2'] = df['Pivot'] + (df['High'] - df['Low'])\n", 305 | "df['S2'] = df['Pivot'] - (df['High'] - df['Low'])\n", 306 | "df['R3'] = df['Pivot'] + 2*(df['High'] - df['Low'])\n", 307 | "df['S3'] = df['Pivot'] - 2*(df['High'] - df['Low'])\n" 308 | ] 309 | }, 310 | { 311 | "cell_type": "code", 312 | "execution_count": 93, 313 | "metadata": {}, 314 | "outputs": [], 315 | "source": [ 316 | "#exportar data frame\n", 317 | "#df.to_excel('csna3.xlsx')" 318 | ] 319 | }, 320 | { 321 | "cell_type": "markdown", 322 | "metadata": {}, 323 | "source": [ 324 | "### Generalizando para todos os daods\n" 325 | ] 326 | }, 327 | { 328 | "cell_type": "code", 329 | "execution_count": 94, 330 | "metadata": {}, 331 | "outputs": [], 332 | "source": [ 333 | "#Codigo para exclução de colunas \n", 334 | "#df =df.drop(columns=[('')])" 335 | ] 336 | }, 337 | { 338 | "cell_type": "code", 339 | "execution_count": 95, 340 | "metadata": {}, 341 | "outputs": [ 342 | { 343 | "data": { 344 | "text/html": [ 345 | "

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	Open	High	Low	Close	Volume	Dividends	Stock Splits	Pivot	R1	S1	R2	S2	R3	S3
Datetime
2021-01-22 15:35:00-03:00	33.680000	33.840000	33.669998	33.799999	121100	0	0	33.769999	33.870000	33.699998	33.940001	33.599997	34.110003	33.429995
2021-01-22 15:40:00-03:00	33.810001	33.869999	33.740002	33.830002	80100	0	0	33.813334	33.886667	33.756669	33.943331	33.683337	34.073329	33.553340
2021-01-22 15:45:00-03:00	33.849998	33.860001	33.650002	33.650002	98400	0	0	33.720001	33.790001	33.580002	33.930000	33.510002	34.139999	33.300003
2021-01-22 15:50:00-03:00	33.650002	33.779999	33.650002	33.720001	61300	0	0	33.716667	33.783333	33.653336	33.846664	33.586670	33.976662	33.456673
2021-01-22 15:55:00-03:00	33.730000	33.900002	33.700001	33.740002	226300	0	0	33.780001	33.860002	33.660001	33.980002	33.580001	34.180003	33.380000

\n", 484 | "

" 485 | ], 486 | "text/plain": [ 487 | " Open High Low Close Volume \\\n", 488 | "Datetime \n", 489 | "2021-01-22 15:35:00-03:00 33.680000 33.840000 33.669998 33.799999 121100 \n", 490 | "2021-01-22 15:40:00-03:00 33.810001 33.869999 33.740002 33.830002 80100 \n", 491 | "2021-01-22 15:45:00-03:00 33.849998 33.860001 33.650002 33.650002 98400 \n", 492 | "2021-01-22 15:50:00-03:00 33.650002 33.779999 33.650002 33.720001 61300 \n", 493 | "2021-01-22 15:55:00-03:00 33.730000 33.900002 33.700001 33.740002 226300 \n", 494 | "\n", 495 | " Dividends Stock Splits Pivot R1 \\\n", 496 | "Datetime \n", 497 | "2021-01-22 15:35:00-03:00 0 0 33.769999 33.870000 \n", 498 | "2021-01-22 15:40:00-03:00 0 0 33.813334 33.886667 \n", 499 | "2021-01-22 15:45:00-03:00 0 0 33.720001 33.790001 \n", 500 | "2021-01-22 15:50:00-03:00 0 0 33.716667 33.783333 \n", 501 | "2021-01-22 15:55:00-03:00 0 0 33.780001 33.860002 \n", 502 | "\n", 503 | " S1 R2 S2 R3 \\\n", 504 | "Datetime \n", 505 | "2021-01-22 15:35:00-03:00 33.699998 33.940001 33.599997 34.110003 \n", 506 | "2021-01-22 15:40:00-03:00 33.756669 33.943331 33.683337 34.073329 \n", 507 | "2021-01-22 15:45:00-03:00 33.580002 33.930000 33.510002 34.139999 \n", 508 | "2021-01-22 15:50:00-03:00 33.653336 33.846664 33.586670 33.976662 \n", 509 | "2021-01-22 15:55:00-03:00 33.660001 33.980002 33.580001 34.180003 \n", 510 | "\n", 511 | " S3 \n", 512 | "Datetime \n", 513 | "2021-01-22 15:35:00-03:00 33.429995 \n", 514 | "2021-01-22 15:40:00-03:00 33.553340 \n", 515 | "2021-01-22 15:45:00-03:00 33.300003 \n", 516 | "2021-01-22 15:50:00-03:00 33.456673 \n", 517 | "2021-01-22 15:55:00-03:00 33.380000 " 518 | ] 519 | }, 520 | "execution_count": 95, 521 | "metadata": {}, 522 | "output_type": "execute_result" 523 | } 524 | ], 525 | "source": [ 526 | "#visualizando DF\n", 527 | "df.tail()" 528 | ] 529 | }, 530 | { 531 | "cell_type": "code", 532 | "execution_count": 96, 533 | "metadata": {}, 534 | "outputs": [], 535 | "source": [ 536 | "#criando colulas vazias no data frema\n", 537 | "df['Compra pivot'],df['Venda S1'],df['Acumulado'] = 'NaN','NaN','NaN'" 538 | ] 539 | }, 540 | { 541 | "cell_type": "code", 542 | "execution_count": 97, 543 | "metadata": {}, 544 | "outputs": [], 545 | "source": [ 546 | "#copiando dataframe para poder fazer os calculos\n", 547 | "#dfcalc = df.copy()" 548 | ] 549 | }, 550 | { 551 | "cell_type": "code", 552 | "execution_count": 98, 553 | "metadata": {}, 554 | "outputs": [], 555 | "source": [ 556 | "#excluindo primeira linha do df para poder usar como inicio o comparador do outro df calc vou excluir apenas com um filtro\n", 557 | "#df = df.loc[df.index > '2020-01-02']" 558 | ] 559 | }, 560 | { 561 | "cell_type": "code", 562 | "execution_count": 99, 563 | "metadata": {}, 564 | "outputs": [ 565 | { 566 | "data": { 567 | "text/html": [ 568 | "

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	Open	High	Low	Close	Volume	Dividends	Stock Splits	Pivot	R1	S1	R2	S2	R3	S3	Compra pivot	Venda S1	Acumulado
Datetime
2021-01-21 10:05:00-03:00	33.660000	34.150002	33.660000	34.150002	0	0	0	33.986668	34.313335	33.823334	34.476669	33.496666	34.966671	33.006664	NaN	NaN	NaN
2021-01-21 10:10:00-03:00	34.110001	34.599998	34.070000	34.150002	701900	0	0	34.273333	34.476667	33.946668	34.803332	33.743334	35.333331	33.213336	NaN	NaN	NaN
2021-01-21 10:15:00-03:00	34.180000	34.490002	34.150002	34.490002	494200	0	0	34.376668	34.603335	34.263335	34.716668	34.036668	35.056669	33.696668	NaN	NaN	NaN
2021-01-21 10:20:00-03:00	34.490002	34.930000	34.410000	34.860001	721500	0	0	34.733334	35.056667	34.536667	35.253334	34.213333	35.773335	33.693333	NaN	NaN	NaN
2021-01-21 10:25:00-03:00	34.830002	34.900002	34.330002	34.490002	835800	0	0	34.573335	34.816668	34.246668	35.143335	34.003335	35.713334	33.433336	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2021-01-22 15:35:00-03:00	33.680000	33.840000	33.669998	33.799999	121100	0	0	33.769999	33.870000	33.699998	33.940001	33.599997	34.110003	33.429995	NaN	NaN	NaN
2021-01-22 15:40:00-03:00	33.810001	33.869999	33.740002	33.830002	80100	0	0	33.813334	33.886667	33.756669	33.943331	33.683337	34.073329	33.553340	NaN	NaN	NaN
2021-01-22 15:45:00-03:00	33.849998	33.860001	33.650002	33.650002	98400	0	0	33.720001	33.790001	33.580002	33.930000	33.510002	34.139999	33.300003	NaN	NaN	NaN
2021-01-22 15:50:00-03:00	33.650002	33.779999	33.650002	33.720001	61300	0	0	33.716667	33.783333	33.653336	33.846664	33.586670	33.976662	33.456673	NaN	NaN	NaN
2021-01-22 15:55:00-03:00	33.730000	33.900002	33.700001	33.740002	226300	0	0	33.780001	33.860002	33.660001	33.980002	33.580001	34.180003	33.380000	NaN	NaN	NaN

\n", 848 | "

141 rows × 17 columns

\n", 849 | "

" 850 | ], 851 | "text/plain": [ 852 | " Open High Low Close Volume \\\n", 853 | "Datetime \n", 854 | "2021-01-21 10:05:00-03:00 33.660000 34.150002 33.660000 34.150002 0 \n", 855 | "2021-01-21 10:10:00-03:00 34.110001 34.599998 34.070000 34.150002 701900 \n", 856 | "2021-01-21 10:15:00-03:00 34.180000 34.490002 34.150002 34.490002 494200 \n", 857 | "2021-01-21 10:20:00-03:00 34.490002 34.930000 34.410000 34.860001 721500 \n", 858 | "2021-01-21 10:25:00-03:00 34.830002 34.900002 34.330002 34.490002 835800 \n", 859 | "... ... ... ... ... ... \n", 860 | "2021-01-22 15:35:00-03:00 33.680000 33.840000 33.669998 33.799999 121100 \n", 861 | "2021-01-22 15:40:00-03:00 33.810001 33.869999 33.740002 33.830002 80100 \n", 862 | "2021-01-22 15:45:00-03:00 33.849998 33.860001 33.650002 33.650002 98400 \n", 863 | "2021-01-22 15:50:00-03:00 33.650002 33.779999 33.650002 33.720001 61300 \n", 864 | "2021-01-22 15:55:00-03:00 33.730000 33.900002 33.700001 33.740002 226300 \n", 865 | "\n", 866 | " Dividends Stock Splits Pivot R1 \\\n", 867 | "Datetime \n", 868 | "2021-01-21 10:05:00-03:00 0 0 33.986668 34.313335 \n", 869 | "2021-01-21 10:10:00-03:00 0 0 34.273333 34.476667 \n", 870 | "2021-01-21 10:15:00-03:00 0 0 34.376668 34.603335 \n", 871 | "2021-01-21 10:20:00-03:00 0 0 34.733334 35.056667 \n", 872 | "2021-01-21 10:25:00-03:00 0 0 34.573335 34.816668 \n", 873 | "... ... ... ... ... \n", 874 | "2021-01-22 15:35:00-03:00 0 0 33.769999 33.870000 \n", 875 | "2021-01-22 15:40:00-03:00 0 0 33.813334 33.886667 \n", 876 | "2021-01-22 15:45:00-03:00 0 0 33.720001 33.790001 \n", 877 | "2021-01-22 15:50:00-03:00 0 0 33.716667 33.783333 \n", 878 | "2021-01-22 15:55:00-03:00 0 0 33.780001 33.860002 \n", 879 | "\n", 880 | " S1 R2 S2 R3 \\\n", 881 | "Datetime \n", 882 | "2021-01-21 10:05:00-03:00 33.823334 34.476669 33.496666 34.966671 \n", 883 | "2021-01-21 10:10:00-03:00 33.946668 34.803332 33.743334 35.333331 \n", 884 | "2021-01-21 10:15:00-03:00 34.263335 34.716668 34.036668 35.056669 \n", 885 | "2021-01-21 10:20:00-03:00 34.536667 35.253334 34.213333 35.773335 \n", 886 | "2021-01-21 10:25:00-03:00 34.246668 35.143335 34.003335 35.713334 \n", 887 | "... ... ... ... ... \n", 888 | "2021-01-22 15:35:00-03:00 33.699998 33.940001 33.599997 34.110003 \n", 889 | "2021-01-22 15:40:00-03:00 33.756669 33.943331 33.683337 34.073329 \n", 890 | "2021-01-22 15:45:00-03:00 33.580002 33.930000 33.510002 34.139999 \n", 891 | "2021-01-22 15:50:00-03:00 33.653336 33.846664 33.586670 33.976662 \n", 892 | "2021-01-22 15:55:00-03:00 33.660001 33.980002 33.580001 34.180003 \n", 893 | "\n", 894 | " S3 Compra pivot Venda S1 Acumulado \n", 895 | "Datetime \n", 896 | "2021-01-21 10:05:00-03:00 33.006664 NaN NaN NaN \n", 897 | "2021-01-21 10:10:00-03:00 33.213336 NaN NaN NaN \n", 898 | "2021-01-21 10:15:00-03:00 33.696668 NaN NaN NaN \n", 899 | "2021-01-21 10:20:00-03:00 33.693333 NaN NaN NaN \n", 900 | "2021-01-21 10:25:00-03:00 33.433336 NaN NaN NaN \n", 901 | "... ... ... ... ... \n", 902 | "2021-01-22 15:35:00-03:00 33.429995 NaN NaN NaN \n", 903 | "2021-01-22 15:40:00-03:00 33.553340 NaN NaN NaN \n", 904 | "2021-01-22 15:45:00-03:00 33.300003 NaN NaN NaN \n", 905 | "2021-01-22 15:50:00-03:00 33.456673 NaN NaN NaN \n", 906 | "2021-01-22 15:55:00-03:00 33.380000 NaN NaN NaN \n", 907 | "\n", 908 | "[141 rows x 17 columns]" 909 | ] 910 | }, 911 | "execution_count": 99, 912 | "metadata": {}, 913 | "output_type": "execute_result" 914 | } 915 | ], 916 | "source": [ 917 | "df" 918 | ] 919 | }, 920 | { 921 | "cell_type": "code", 922 | "execution_count": 100, 923 | "metadata": {}, 924 | "outputs": [], 925 | "source": [ 926 | "#renomedo colunas codigo comentado para nao execultaR\n", 927 | "##dfcalc = dfcalc.rename(columns={'Pivot': 'P-Pivot','S1':'S-S1'})" 928 | ] 929 | }, 930 | { 931 | "cell_type": "code", 932 | "execution_count": 101, 933 | "metadata": {}, 934 | "outputs": [], 935 | "source": [ 936 | "#Criando nova coluna inutil kkkkkkkk\n", 937 | "###df['P-Pivot'] = 'NaN'" 938 | ] 939 | }, 940 | { 941 | "cell_type": "code", 942 | "execution_count": null, 943 | "metadata": {}, 944 | "outputs": [], 945 | "source": [] 946 | }, 947 | { 948 | "cell_type": "markdown", 949 | "metadata": {}, 950 | "source": [ 951 | "## Inicio de teste de loops\n", 952 | "Não execultar linhas abaixos" 953 | ] 954 | }, 955 | { 956 | "cell_type": "code", 957 | "execution_count": 102, 958 | "metadata": {}, 959 | "outputs": [], 960 | "source": [ 961 | "#testes = df['Pivot'][n]/df['Pivot'][n -1]" 962 | ] 963 | }, 964 | { 965 | "cell_type": "code", 966 | "execution_count": 103, 967 | "metadata": {}, 968 | "outputs": [], 969 | "source": [ 970 | "#testes" 971 | ] 972 | }, 973 | { 974 | "cell_type": "code", 975 | "execution_count": 104, 976 | "metadata": {}, 977 | "outputs": [], 978 | "source": [ 979 | "# testa dor de entrada de valor está correto!\n", 980 | "\n", 981 | "#posicao = 4\n", 982 | "#if df['High'][posicao]>df['Pivot'][posicao - 1]:\n", 983 | "# df['Compra pivot'][posicao] = df['Close'][posicao] - df['Pivot'][posicao - 1]\n", 984 | "#else:\n", 985 | "# df['Compra pivot'][posicao] = 0" 986 | ] 987 | }, 988 | { 989 | "cell_type": "code", 990 | "execution_count": 105, 991 | "metadata": {}, 992 | "outputs": [], 993 | "source": [ 994 | "#n = 0\n", 995 | "#while n<7:\n", 996 | "# df['Compra pivot'] = 'NaN';\n", 997 | " # n = n +1;" 998 | ] 999 | }, 1000 | { 1001 | "cell_type": "markdown", 1002 | "metadata": {}, 1003 | "source": [ 1004 | "## Fim de teste de loops\n", 1005 | "tudo abaixo pode ser execultado " 1006 | ] 1007 | }, 1008 | { 1009 | "cell_type": "code", 1010 | "execution_count": 106, 1011 | "metadata": {}, 1012 | "outputs": [], 1013 | "source": [ 1014 | "#criando valores de lucro na entrada comprando no Pivot\n", 1015 | "posicao = 0\n", 1016 | "while posicaodf['Pivot'][posicao - 1]:\n", 1018 | " df['Compra pivot'][posicao] = df['Close'][posicao] - df['Pivot'][posicao - 1]\n", 1019 | " else:\n", 1020 | " df['Compra pivot'][posicao] = 0\n", 1021 | " posicao = posicao +1;\n" 1022 | ] 1023 | }, 1024 | { 1025 | "cell_type": "code", 1026 | "execution_count": 107, 1027 | "metadata": {}, 1028 | "outputs": [], 1029 | "source": [ 1030 | "#criando valores de lucro na entrada vendido na Primeiro Suporte\n", 1031 | "posicao = 0\n", 1032 | "while posicao\n", 1060 | "\n", 1073 | "\n", 1074 | " \n", 1075 | " \n", 1076 | " \n", 1077 | " \n", 1078 | " \n", 1079 | " \n", 1080 | " \n", 1081 | " \n", 1082 | " \n", 1083 | " \n", 1084 | " \n", 1085 | " \n", 1086 | " \n", 1087 | " \n", 1088 | " \n", 1089 | " \n", 1090 | " \n", 1091 | " \n", 1092 | " \n", 1093 | " \n", 1094 | " \n", 1095 | " \n", 1096 | " \n", 1097 | " \n", 1098 | " \n", 1099 | " \n", 1100 | " \n", 1101 | " \n", 1102 | " \n", 1103 | " \n", 1104 | " \n", 1105 | " \n", 1106 | " \n", 1107 | " \n", 1108 | " \n", 1109 | " \n", 1110 | " \n", 1111 | " \n", 1112 | " \n", 1113 | " \n", 1114 | " \n", 1115 | " \n", 1116 | " \n", 1117 | " \n", 1118 | " \n", 1119 | " \n", 1120 | " \n", 1121 | " \n", 1122 | " \n", 1123 | " \n", 1124 | " \n", 1125 | " \n", 1126 | " \n", 1127 | " \n", 1128 | " \n", 1129 | " \n", 1130 | " \n", 1131 | " \n", 1132 | " \n", 1133 | " \n", 1134 | " \n", 1135 | " \n", 1136 | " \n", 1137 | " \n", 1138 | " \n", 1139 | " \n", 1140 | " \n", 1141 | " \n", 1142 | " \n", 1143 | " \n", 1144 | " \n", 1145 | " \n", 1146 | " \n", 1147 | " \n", 1148 | " \n", 1149 | " \n", 1150 | " \n", 1151 | " \n", 1152 | " \n", 1153 | " \n", 1154 | " \n", 1155 | " \n", 1156 | " \n", 1157 | " \n", 1158 | " \n", 1159 | " \n", 1160 | " \n", 1161 | " \n", 1162 | " \n", 1163 | " \n", 1164 | " \n", 1165 | " \n", 1166 | " \n", 1167 | " \n", 1168 | " \n", 1169 | " \n", 1170 | " \n", 1171 | " \n", 1172 | " \n", 1173 | " \n", 1174 | " \n", 1175 | " \n", 1176 | " \n", 1177 | " \n", 1178 | " \n", 1179 | " \n", 1180 | " \n", 1181 | " \n", 1182 | " \n", 1183 | " \n", 1184 | " \n", 1185 | " \n", 1186 | " \n", 1187 | " \n", 1188 | " \n", 1189 | " \n", 1190 | " \n", 1191 | " \n", 1192 | " \n", 1193 | " \n", 1194 | " \n", 1195 | " \n", 1196 | " \n", 1197 | " \n", 1198 | " \n", 1199 | " \n", 1200 | " \n", 1201 | " \n", 1202 | " \n", 1203 | " \n", 1204 | " \n", 1205 | " \n", 1206 | " \n", 1207 | " \n", 1208 | " \n", 1209 | " \n", 1210 | " \n", 1211 | " \n", 1212 | " \n", 1213 | " \n", 1214 | " \n", 1215 | " \n", 1216 | " \n", 1217 | " \n", 1218 | "

	Open	High	Low	Close	Volume	Dividends	Stock Splits	Pivot	R1	S1	R2	S2	R3	S3	Compra pivot	Venda S1	Acumulado
Datetime
2021-01-22 15:35:00-03:00	33.680000	33.840000	33.669998	33.799999	121100	0	0	33.769999	33.870000	33.699998	33.940001	33.599997	34.110003	33.429995	0.146666	0	14.6666
2021-01-22 15:40:00-03:00	33.810001	33.869999	33.740002	33.830002	80100	0	0	33.813334	33.886667	33.756669	33.943331	33.683337	34.073329	33.553340	0.0600026	0	6.00026
2021-01-22 15:45:00-03:00	33.849998	33.860001	33.650002	33.650002	98400	0	0	33.720001	33.790001	33.580002	33.930000	33.510002	34.139999	33.300003	-0.163333	0.106668	-5.66648
2021-01-22 15:50:00-03:00	33.650002	33.779999	33.650002	33.720001	61300	0	0	33.716667	33.783333	33.653336	33.846664	33.586670	33.976662	33.456673	0	0	0
2021-01-22 15:55:00-03:00	33.730000	33.900002	33.700001	33.740002	226300	0	0	33.780001	33.860002	33.660001	33.980002	33.580001	34.180003	33.380000	0.0233345	0	2.33345

\n", 1219 | "" 1220 | ], 1221 | "text/plain": [ 1222 | " Open High Low Close Volume \\\n", 1223 | "Datetime \n", 1224 | "2021-01-22 15:35:00-03:00 33.680000 33.840000 33.669998 33.799999 121100 \n", 1225 | "2021-01-22 15:40:00-03:00 33.810001 33.869999 33.740002 33.830002 80100 \n", 1226 | "2021-01-22 15:45:00-03:00 33.849998 33.860001 33.650002 33.650002 98400 \n", 1227 | "2021-01-22 15:50:00-03:00 33.650002 33.779999 33.650002 33.720001 61300 \n", 1228 | "2021-01-22 15:55:00-03:00 33.730000 33.900002 33.700001 33.740002 226300 \n", 1229 | "\n", 1230 | " Dividends Stock Splits Pivot R1 \\\n", 1231 | "Datetime \n", 1232 | "2021-01-22 15:35:00-03:00 0 0 33.769999 33.870000 \n", 1233 | "2021-01-22 15:40:00-03:00 0 0 33.813334 33.886667 \n", 1234 | "2021-01-22 15:45:00-03:00 0 0 33.720001 33.790001 \n", 1235 | "2021-01-22 15:50:00-03:00 0 0 33.716667 33.783333 \n", 1236 | "2021-01-22 15:55:00-03:00 0 0 33.780001 33.860002 \n", 1237 | "\n", 1238 | " S1 R2 S2 R3 \\\n", 1239 | "Datetime \n", 1240 | "2021-01-22 15:35:00-03:00 33.699998 33.940001 33.599997 34.110003 \n", 1241 | "2021-01-22 15:40:00-03:00 33.756669 33.943331 33.683337 34.073329 \n", 1242 | "2021-01-22 15:45:00-03:00 33.580002 33.930000 33.510002 34.139999 \n", 1243 | "2021-01-22 15:50:00-03:00 33.653336 33.846664 33.586670 33.976662 \n", 1244 | "2021-01-22 15:55:00-03:00 33.660001 33.980002 33.580001 34.180003 \n", 1245 | "\n", 1246 | " S3 Compra pivot Venda S1 Acumulado \n", 1247 | "Datetime \n", 1248 | "2021-01-22 15:35:00-03:00 33.429995 0.146666 0 14.6666 \n", 1249 | "2021-01-22 15:40:00-03:00 33.553340 0.0600026 0 6.00026 \n", 1250 | "2021-01-22 15:45:00-03:00 33.300003 -0.163333 0.106668 -5.66648 \n", 1251 | "2021-01-22 15:50:00-03:00 33.456673 0 0 0 \n", 1252 | "2021-01-22 15:55:00-03:00 33.380000 0.0233345 0 2.33345 " 1253 | ] 1254 | }, 1255 | "execution_count": 109, 1256 | "metadata": {}, 1257 | "output_type": "execute_result" 1258 | } 1259 | ], 1260 | "source": [ 1261 | "df.tail()" 1262 | ] 1263 | }, 1264 | { 1265 | "cell_type": "code", 1266 | "execution_count": 110, 1267 | "metadata": {}, 1268 | "outputs": [], 1269 | "source": [ 1270 | "#excluindo primeira linha do df usar como inicio do comparador - SE NAO VC TEM UM ENTRADA COM O PARAMENTRO DO ULTIMO DIA\n", 1271 | "# E ISSO IRIA MUDAR O VALOR NA SOMATORIA TOTAL\n", 1272 | "df = df.loc[df.index > '2020-10-20 10:10:00']" 1273 | ] 1274 | }, 1275 | { 1276 | "cell_type": "code", 1277 | "execution_count": 111, 1278 | "metadata": {}, 1279 | "outputs": [ 1280 | { 1281 | "data": { 1282 | "text/html": [ 1283 | "

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	Open	High	Low	Close	Volume	Dividends	Stock Splits	Pivot	R1	S1	R2	S2	R3	S3	Compra pivot	Venda S1	Acumulado
Datetime
2021-01-21 10:05:00-03:00	33.660000	34.150002	33.660000	34.150002	0	0	0	33.986668	34.313335	33.823334	34.476669	33.496666	34.966671	33.006664	0.37	-0.49	-12
2021-01-21 10:10:00-03:00	34.110001	34.599998	34.070000	34.150002	701900	0	0	34.273333	34.476667	33.946668	34.803332	33.743334	35.333331	33.213336	0.163334	0	16.3334
2021-01-21 10:15:00-03:00	34.180000	34.490002	34.150002	34.490002	494200	0	0	34.376668	34.603335	34.263335	34.716668	34.036668	35.056669	33.696668	0.216668	0	21.6668
2021-01-21 10:20:00-03:00	34.490002	34.930000	34.410000	34.860001	721500	0	0	34.733334	35.056667	34.536667	35.253334	34.213333	35.773335	33.693333	0.483332	0	48.3332
2021-01-21 10:25:00-03:00	34.830002	34.900002	34.330002	34.490002	835800	0	0	34.573335	34.816668	34.246668	35.143335	34.003335	35.713334	33.433336	-0.243332	0.0466652	-19.6667
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2021-01-22 15:35:00-03:00	33.680000	33.840000	33.669998	33.799999	121100	0	0	33.769999	33.870000	33.699998	33.940001	33.599997	34.110003	33.429995	0.146666	0	14.6666
2021-01-22 15:40:00-03:00	33.810001	33.869999	33.740002	33.830002	80100	0	0	33.813334	33.886667	33.756669	33.943331	33.683337	34.073329	33.553340	0.0600026	0	6.00026
2021-01-22 15:45:00-03:00	33.849998	33.860001	33.650002	33.650002	98400	0	0	33.720001	33.790001	33.580002	33.930000	33.510002	34.139999	33.300003	-0.163333	0.106668	-5.66648
2021-01-22 15:50:00-03:00	33.650002	33.779999	33.650002	33.720001	61300	0	0	33.716667	33.783333	33.653336	33.846664	33.586670	33.976662	33.456673	0	0	0
2021-01-22 15:55:00-03:00	33.730000	33.900002	33.700001	33.740002	226300	0	0	33.780001	33.860002	33.660001	33.980002	33.580001	34.180003	33.380000	0.0233345	0	2.33345

\n", 1563 | "

141 rows × 17 columns

\n", 1564 | "

" 1565 | ], 1566 | "text/plain": [ 1567 | " Open High Low Close Volume \\\n", 1568 | "Datetime \n", 1569 | "2021-01-21 10:05:00-03:00 33.660000 34.150002 33.660000 34.150002 0 \n", 1570 | "2021-01-21 10:10:00-03:00 34.110001 34.599998 34.070000 34.150002 701900 \n", 1571 | "2021-01-21 10:15:00-03:00 34.180000 34.490002 34.150002 34.490002 494200 \n", 1572 | "2021-01-21 10:20:00-03:00 34.490002 34.930000 34.410000 34.860001 721500 \n", 1573 | "2021-01-21 10:25:00-03:00 34.830002 34.900002 34.330002 34.490002 835800 \n", 1574 | "... ... ... ... ... ... \n", 1575 | "2021-01-22 15:35:00-03:00 33.680000 33.840000 33.669998 33.799999 121100 \n", 1576 | "2021-01-22 15:40:00-03:00 33.810001 33.869999 33.740002 33.830002 80100 \n", 1577 | "2021-01-22 15:45:00-03:00 33.849998 33.860001 33.650002 33.650002 98400 \n", 1578 | "2021-01-22 15:50:00-03:00 33.650002 33.779999 33.650002 33.720001 61300 \n", 1579 | "2021-01-22 15:55:00-03:00 33.730000 33.900002 33.700001 33.740002 226300 \n", 1580 | "\n", 1581 | " Dividends Stock Splits Pivot R1 \\\n", 1582 | "Datetime \n", 1583 | "2021-01-21 10:05:00-03:00 0 0 33.986668 34.313335 \n", 1584 | "2021-01-21 10:10:00-03:00 0 0 34.273333 34.476667 \n", 1585 | "2021-01-21 10:15:00-03:00 0 0 34.376668 34.603335 \n", 1586 | "2021-01-21 10:20:00-03:00 0 0 34.733334 35.056667 \n", 1587 | "2021-01-21 10:25:00-03:00 0 0 34.573335 34.816668 \n", 1588 | "... ... ... ... ... \n", 1589 | "2021-01-22 15:35:00-03:00 0 0 33.769999 33.870000 \n", 1590 | "2021-01-22 15:40:00-03:00 0 0 33.813334 33.886667 \n", 1591 | "2021-01-22 15:45:00-03:00 0 0 33.720001 33.790001 \n", 1592 | "2021-01-22 15:50:00-03:00 0 0 33.716667 33.783333 \n", 1593 | "2021-01-22 15:55:00-03:00 0 0 33.780001 33.860002 \n", 1594 | "\n", 1595 | " S1 R2 S2 R3 \\\n", 1596 | "Datetime \n", 1597 | "2021-01-21 10:05:00-03:00 33.823334 34.476669 33.496666 34.966671 \n", 1598 | "2021-01-21 10:10:00-03:00 33.946668 34.803332 33.743334 35.333331 \n", 1599 | "2021-01-21 10:15:00-03:00 34.263335 34.716668 34.036668 35.056669 \n", 1600 | "2021-01-21 10:20:00-03:00 34.536667 35.253334 34.213333 35.773335 \n", 1601 | "2021-01-21 10:25:00-03:00 34.246668 35.143335 34.003335 35.713334 \n", 1602 | "... ... ... ... ... \n", 1603 | "2021-01-22 15:35:00-03:00 33.699998 33.940001 33.599997 34.110003 \n", 1604 | "2021-01-22 15:40:00-03:00 33.756669 33.943331 33.683337 34.073329 \n", 1605 | "2021-01-22 15:45:00-03:00 33.580002 33.930000 33.510002 34.139999 \n", 1606 | "2021-01-22 15:50:00-03:00 33.653336 33.846664 33.586670 33.976662 \n", 1607 | "2021-01-22 15:55:00-03:00 33.660001 33.980002 33.580001 34.180003 \n", 1608 | "\n", 1609 | " S3 Compra pivot Venda S1 Acumulado \n", 1610 | "Datetime \n", 1611 | "2021-01-21 10:05:00-03:00 33.006664 0.37 -0.49 -12 \n", 1612 | "2021-01-21 10:10:00-03:00 33.213336 0.163334 0 16.3334 \n", 1613 | "2021-01-21 10:15:00-03:00 33.696668 0.216668 0 21.6668 \n", 1614 | "2021-01-21 10:20:00-03:00 33.693333 0.483332 0 48.3332 \n", 1615 | "2021-01-21 10:25:00-03:00 33.433336 -0.243332 0.0466652 -19.6667 \n", 1616 | "... ... ... ... ... \n", 1617 | "2021-01-22 15:35:00-03:00 33.429995 0.146666 0 14.6666 \n", 1618 | "2021-01-22 15:40:00-03:00 33.553340 0.0600026 0 6.00026 \n", 1619 | "2021-01-22 15:45:00-03:00 33.300003 -0.163333 0.106668 -5.66648 \n", 1620 | "2021-01-22 15:50:00-03:00 33.456673 0 0 0 \n", 1621 | "2021-01-22 15:55:00-03:00 33.380000 0.0233345 0 2.33345 \n", 1622 | "\n", 1623 | "[141 rows x 17 columns]" 1624 | ] 1625 | }, 1626 | "execution_count": 111, 1627 | "metadata": {}, 1628 | "output_type": "execute_result" 1629 | } 1630 | ], 1631 | "source": [ 1632 | "df" 1633 | ] 1634 | }, 1635 | { 1636 | "cell_type": "code", 1637 | "execution_count": 112, 1638 | "metadata": {}, 1639 | "outputs": [ 1640 | { 1641 | "data": { 1642 | "text/plain": [ 1643 | "" 1644 | ] 1645 | }, 1646 | "execution_count": 112, 1647 | "metadata": {}, 1648 | "output_type": "execute_result" 1649 | }, 1650 | { 1651 | "data": { 1652 | "image/png": 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\n", 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" 1655 | ] 1656 | }, 1657 | "metadata": { 1658 | "needs_background": "light" 1659 | }, 1660 | "output_type": "display_data" 1661 | } 1662 | ], 1663 | "source": [ 1664 | "np.cumsum(df['Acumulado']).plot(figsize = (16,8))" 1665 | ] 1666 | }, 1667 | { 1668 | "cell_type": "code", 1669 | "execution_count": null, 1670 | "metadata": {}, 1671 | "outputs": [], 1672 | "source": [] 1673 | } 1674 | ], 1675 | "metadata": { 1676 | "kernelspec": { 1677 | "display_name": "Python 3", 1678 | "language": "python", 1679 | "name": "python3" 1680 | }, 1681 | "language_info": { 1682 | "codemirror_mode": { 1683 | "name": "ipython", 1684 | "version": 3 1685 | }, 1686 | "file_extension": ".py", 1687 | "mimetype": "text/x-python", 1688 | "name": "python", 1689 | "nbconvert_exporter": "python", 1690 | "pygments_lexer": "ipython3", 1691 | "version": "3.8.3" 1692 | } 1693 | }, 1694 | "nbformat": 4, 1695 | "nbformat_minor": 2 1696 | } 1697 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Robô-Pivot-Point-Mt5-com-Python 2 | 3 | * Vídeo do código: https://youtu.be/mD7_zojami4 4 | 5 | 6 | Robôs Investidores para Metatrader 5, usando a estratégia de Pivot Point feito em python. 7 | Para que esse robô funcione você precisa ter algumas bibliotecas instaladas em seu python: 8 | 9 | pip install metatrader 5; pip install date time;pip install pandas, pip install numpy 10 | 11 | Ponts Pivot são sempre muito úteis para as operações, esta é uma maneira simples para se ter uma idéia de onde o mercado está indo durante o dia. 12 | 13 | O indicador também fornece as três primeiros suportes e resistências. 14 | 15 | As fórmulas que eu usei são: 16 | 17 | Resistência 3 = High + 2*(Pivot - Low) 18 | 19 | Resistência 2 = Pivot + (R1 - S1) 20 | 21 | Resistência 1 = 2 * Pivot - Low 22 | 23 | Pontos Pivô = ( High + Close + Low )/3 24 | 25 | Suporte 1 = 2 * Pivot - High 26 | 27 | Suporte 2 = Pivot - (R1 - S1) 28 | 29 | Suporte 3 = Low - 2*(High - Pivot) 30 | 31 | ![Resultado](https://github.com/alissonf216/Robo-Pivot-Point-Mt5-com-Python/blob/main/ilustrac.png) 32 | 33 | Obs: Isso nao é uma indicação que essa é a melhor estrategia para negocioação, é a penas um estudo. 34 | 35 | -------------------------------------------------------------------------------- /Robô Pivot point-Mt5-Python.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Criando Robô Pivot point " 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "import MetaTrader5 as mt5\n", 17 | "from datetime import datetime\n", 18 | "import time\n", 19 | "import pandas as pd\n", 20 | "from pandas_datareader import data as pdr\n", 21 | "import numpy as np\n", 22 | "import matplotlib.pyplot as plt\n", 23 | "%matplotlib inline\n" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": 49, 29 | "metadata": {}, 30 | "outputs": [], 31 | "source": [ 32 | "# estabelecemos a conexão ao MetaTrader 5\n", 33 | "# conecte-se ao MetaTrader 5\n", 34 | "if not mt5.initialize():\n", 35 | " print(\"initialize() Falha ao Iniciar seu metra Trade 5\")\n", 36 | " mt5.shutdown()" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": 50, 42 | "metadata": {}, 43 | "outputs": [], 44 | "source": [ 45 | "### Obtendo valores de ativos\n", 46 | "#tenho que fazer uma estrutura de repecticao para ficar gemrado os pontos de entrar e saida\n", 47 | "def get_ohlc(ativo, timeframe, n=5):\n", 48 | " ativo = mt5.copy_rates_from_pos(ativo,timeframe, 0, n)\n", 49 | " ativo = pd.DataFrame(ativo)\n", 50 | " ativo['time'] = pd.to_datetime(ativo['time'], unit='s')\n", 51 | " ativo['Pivot'] = (ativo['high'] + ativo['low'] + ativo['close'])/3\n", 52 | " ativo['R1'] = 2*ativo['Pivot'] - ativo['low']\n", 53 | " ativo['S1'] = 2*ativo['Pivot'] - ativo['high']\n", 54 | " ativo['R2'] = ativo['Pivot'] + (ativo['high'] - ativo['low'])\n", 55 | " ativo['S2'] = ativo['Pivot'] - (ativo['high'] - ativo['low'])\n", 56 | " ativo['R3'] = ativo['Pivot'] + 2*(ativo['high'] - ativo['low'])\n", 57 | " ativo['S3'] = ativo['Pivot'] - 2*(ativo['high'] - ativo['low'])\n", 58 | "\n", 59 | " \n", 60 | " ativo.set_index('time', inplace = True)\n", 61 | " return ativo" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 51, 67 | "metadata": {}, 68 | "outputs": [], 69 | "source": [ 70 | "ativo = (get_ohlc('ITUB4', mt5.TIMEFRAME_M5))" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": 52, 76 | "metadata": {}, 77 | "outputs": [ 78 | { 79 | "data": { 80 | "text/html": [ 81 | "

\n", 82 | "\n", 95 | "\n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | "

	open	high	low	close	tick_volume	spread	real_volume	Pivot	R1	S1	R2	S2	R3	S3
time
2020-10-26 12:35:00	25.73	25.74	25.70	25.70	122	1	292200	25.713333	25.726667	25.686667	25.753333	25.673333	25.793333	25.633333
2020-10-26 12:40:00	25.70	25.71	25.65	25.68	200	1	218900	25.680000	25.710000	25.650000	25.740000	25.620000	25.800000	25.560000
2020-10-26 12:45:00	25.70	25.71	25.65	25.65	114	1	126900	25.670000	25.690000	25.630000	25.730000	25.610000	25.790000	25.550000
2020-10-26 12:50:00	25.65	25.75	25.65	25.75	368	1	229400	25.716667	25.783333	25.683333	25.816667	25.616667	25.916667	25.516667
2020-10-26 12:55:00	25.75	25.75	25.73	25.74	6	1	2700	25.740000	25.750000	25.730000	25.760000	25.720000	25.780000	25.700000

\n", 220 | "

" 221 | ], 222 | "text/plain": [ 223 | " open high low close tick_volume spread \\\n", 224 | "time \n", 225 | "2020-10-26 12:35:00 25.73 25.74 25.70 25.70 122 1 \n", 226 | "2020-10-26 12:40:00 25.70 25.71 25.65 25.68 200 1 \n", 227 | "2020-10-26 12:45:00 25.70 25.71 25.65 25.65 114 1 \n", 228 | "2020-10-26 12:50:00 25.65 25.75 25.65 25.75 368 1 \n", 229 | "2020-10-26 12:55:00 25.75 25.75 25.73 25.74 6 1 \n", 230 | "\n", 231 | " real_volume Pivot R1 S1 R2 \\\n", 232 | "time \n", 233 | "2020-10-26 12:35:00 292200 25.713333 25.726667 25.686667 25.753333 \n", 234 | "2020-10-26 12:40:00 218900 25.680000 25.710000 25.650000 25.740000 \n", 235 | "2020-10-26 12:45:00 126900 25.670000 25.690000 25.630000 25.730000 \n", 236 | "2020-10-26 12:50:00 229400 25.716667 25.783333 25.683333 25.816667 \n", 237 | "2020-10-26 12:55:00 2700 25.740000 25.750000 25.730000 25.760000 \n", 238 | "\n", 239 | " S2 R3 S3 \n", 240 | "time \n", 241 | "2020-10-26 12:35:00 25.673333 25.793333 25.633333 \n", 242 | "2020-10-26 12:40:00 25.620000 25.800000 25.560000 \n", 243 | "2020-10-26 12:45:00 25.610000 25.790000 25.550000 \n", 244 | "2020-10-26 12:50:00 25.616667 25.916667 25.516667 \n", 245 | "2020-10-26 12:55:00 25.720000 25.780000 25.700000 " 246 | ] 247 | }, 248 | "execution_count": 52, 249 | "metadata": {}, 250 | "output_type": "execute_result" 251 | } 252 | ], 253 | "source": [ 254 | "#verifica se está calculando \n", 255 | "ativo" 256 | ] 257 | }, 258 | { 259 | "cell_type": "markdown", 260 | "metadata": {}, 261 | "source": [ 262 | "### preparando a orden" 263 | ] 264 | }, 265 | { 266 | "cell_type": "code", 267 | "execution_count": 53, 268 | "metadata": {}, 269 | "outputs": [], 270 | "source": [ 271 | "#testando se o ativo é valido \n", 272 | "symbol = 'ITUB4'\n", 273 | "symbol_info = mt5.symbol_info(symbol)\n", 274 | "if symbol_info is None:\n", 275 | " print(symbol, \"Não encontrato\")\n", 276 | " mt5.shutdown()\n", 277 | " quit()" 278 | ] 279 | }, 280 | { 281 | "cell_type": "code", 282 | "execution_count": 54, 283 | "metadata": {}, 284 | "outputs": [], 285 | "source": [ 286 | "#adicionado symbol se nao existir\n", 287 | "if not symbol_info.visible:\n", 288 | " print('Symbol Não visivel, tentnado adicionar')\n", 289 | " if not mt5.symbol_select(symbol,True):\n", 290 | " print('symbol_select({{}})failed, exit', symbol)\n", 291 | " mt5.shutdown()\n", 292 | " quit()" 293 | ] 294 | }, 295 | { 296 | "cell_type": "code", 297 | "execution_count": 55, 298 | "metadata": {}, 299 | "outputs": [], 300 | "source": [ 301 | "#preparando a ordem compra request e ordem de venda\n", 302 | "\n", 303 | "#########################################################\n", 304 | "#preparando a ordem \n", 305 | "lot = 100.0\n", 306 | "point = mt5.symbol_info(symbol).point\n", 307 | "price = mt5.symbol_info_tick(symbol).ask\n", 308 | "desviation = 1\n", 309 | "request = {\n", 310 | " \"action\": mt5.TRADE_ACTION_DEAL,\n", 311 | " \"symbol\": symbol,\n", 312 | " \"volume\": lot,\n", 313 | " \"type\": mt5.ORDER_TYPE_BUY,\n", 314 | " \"price\": price,\n", 315 | " \"magic\": 234000,\n", 316 | " \"desviation\": desviation,\n", 317 | " \"comment\": \"prython script open\",\n", 318 | " \"type_time\":mt5.ORDER_TIME_GTC,\n", 319 | " 'type_filling':mt5.ORDER_FILLING_RETURN,\n", 320 | " \n", 321 | " }" 322 | ] 323 | }, 324 | { 325 | "cell_type": "code", 326 | "execution_count": 56, 327 | "metadata": {}, 328 | "outputs": [], 329 | "source": [ 330 | "#preparando a ordem de venda \n", 331 | "lot = 100.0\n", 332 | "point = mt5.symbol_info(symbol).point\n", 333 | "price=mt5.symbol_info_tick(symbol).bid\n", 334 | "desviation = 1\n", 335 | "request2={\n", 336 | " \"action\": mt5.TRADE_ACTION_DEAL,\n", 337 | " \"symbol\": symbol,\n", 338 | " \"volume\": lot,\n", 339 | " \"type\": mt5.ORDER_TYPE_SELL,\n", 340 | " \"price\": price,\n", 341 | " \n", 342 | " \"deviation\": desviation,\n", 343 | " \"magic\": 234000,\n", 344 | " \"comment\": \"python script close\",\n", 345 | " \"type_time\": mt5.ORDER_TIME_GTC,\n", 346 | " \"type_filling\": mt5.ORDER_FILLING_RETURN,\n", 347 | "}" 348 | ] 349 | }, 350 | { 351 | "cell_type": "code", 352 | "execution_count": null, 353 | "metadata": {}, 354 | "outputs": [], 355 | "source": [ 356 | "#ENVIO de Ordem buy, APENAS UM TESTE PARA SABER SE A COMUNICAÇÃO ENTRE PYTHON E MT5 ESTÁ CORRETA...\n", 357 | "result = mt5.order_send(request)\n", 358 | "#verificando a resultado da execulção \n", 359 | "print(f'1. Ordem enviada:{lot} de {symbol} ao preço de {price} com desvio de {desviation}',end = '\\r')" 360 | ] 361 | }, 362 | { 363 | "cell_type": "code", 364 | "execution_count": null, 365 | "metadata": {}, 366 | "outputs": [ 367 | { 368 | "name": "stdout", 369 | "output_type": "stream", 370 | "text": [ 371 | "ITUB4 - ultimo valor: 25.48, Topo do Book C: 25.48,Topo do Book V: 25.49 False\r" 372 | ] 373 | } 374 | ], 375 | "source": [ 376 | "# EXECULÇÃO DO BOT PIVOTPOINT \n", 377 | "# ANTES DE EXECUTAR ESSA CELULA CERTIFIQUE QUE NENHUMA ORDEM ESTEJA ABERTA.\n", 378 | "\n", 379 | "tempo = time.time() + 18000\n", 380 | "while time.time() < tempo:\n", 381 | " ativo = (get_ohlc('ITUB4', mt5.TIMEFRAME_M5))\n", 382 | " tick = mt5.symbol_info_tick('ITUB4')\n", 383 | " print (f'ITUB4 - ultimo valor: {tick.last}, Topo do Book C: {tick.bid},Topo do Book V: {tick.ask}', tick.last>ativo['Pivot'][-1 -1], end = '\\r')\n", 384 | " if tick.last> ativo['Pivot'][-1 -1]:\n", 385 | " if mt5.positions_get(symbol=\"ITUB4\") == () or mt5.positions_get(symbol=\"ITUB4\")[0][5] == 1:\n", 386 | " #enviadno ordem de compra \n", 387 | " result = mt5.order_send(request)\n", 388 | " print(f'1. Ordem COMPRA enviada:{lot} de {symbol} ao preço de {price} com desvio de {desviation}',end = '\\r')\n", 389 | "\n", 390 | " if tick.last