{ "cells": [ { "cell_type": "code", "execution_count": 5, "id": "f4500c8d-0e58-4d3f-8dd3-06a4896f397f", "metadata": {}, "outputs": [], "source": [ "# import shelve\n", "# import pickle\n", "# import zlib\n", "# import lzma\n", "# import pytz\n", "from datetime import datetime, timedelta\n", "from aiomql import MetaTrader, TimeFrame, AccountInfo, TimeFrame, CopyTicks, Account, Symbol\n", "from MetaTrader5 import SymbolInfo\n", "import pandas as pd\n", "from pandas import DataFrame\n", "import pytz" ] }, { "cell_type": "code", "execution_count": 2, "id": "f2ef126c-6edc-4651-8a81-06bb97eed6f9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "res = await Account().sign_in()\n", "print(res)" ] }, { "cell_type": "code", "execution_count": 19, "id": "2c598a85-1e90-49b0-abf1-87329597feae", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tz = pytz.timezone('Etc/UTC')\n", "sym = Symbol(name='EURUSD')\n", "start = datetime(day=22, month=8, year=2024, tzinfo=tz)\n", "end = datetime(day=26, month=8, year=2024, hour=12, tzinfo=tz)\n", "# rates = await sym.mt5.copy_rates_from(symbol='EURUSD', date_from=end, count=5, timeframe=TimeFrame.H12)\n", "rates = await sym.mt5.copy_rates_from_pos(symbol='EURUSD', start_pos=0, count=5, timeframe=TimeFrame.H12)\n", "df = DataFrame(rates)\n", "df['time'] = pd.to_datetime(df['time'], unit='s')\n", "df.index[-1]" ] }, { "cell_type": "code", "execution_count": 30, "id": "0a7d319c-5760-4058-994f-27e8d10df108", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
timeopenhighlowclosetick_volumespreadreal_volume
22024-08-25 12:00:001.118691.119471.118491.11894457010
32024-08-26 00:00:001.118941.120161.116281.116524358300
42024-08-26 12:00:001.116521.117901.115011.116184503400
\n", "
" ], "text/plain": [ " time open high low close tick_volume \\\n", "2 2024-08-25 12:00:00 1.11869 1.11947 1.11849 1.11894 4570 \n", "3 2024-08-26 00:00:00 1.11894 1.12016 1.11628 1.11652 43583 \n", "4 2024-08-26 12:00:00 1.11652 1.11790 1.11501 1.11618 45034 \n", "\n", " spread real_volume \n", "2 1 0 \n", "3 0 0 \n", "4 0 0 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[2:6]" ] }, { "cell_type": "code", "execution_count": null, "id": "9307e3df-a5f3-42cf-9d3d-e65e5254c0e5", "metadata": {}, "outputs": [], "source": [ "now = datetime.now()\n", "st = now.replace(hour=0, minute=0, second=0, day=1, month=1, year=2023)\n", "et = now.replace(hour=9, minute=0, second=0)\n", "diff = et - st\n", "secs = int(diff.total_seconds())\n", "# st = now.replace(hour=0, day=16)\n", "# et = now.replace(hour=9)\n", "# start = now.replace(day=now.day-3, tzinfo=tz)\n", "# end = now.replace(day=now.day-1, tzinfo=tz)" ] }, { "cell_type": "code", "execution_count": null, "id": "63050d62-443a-4d30-b2a6-4ec6d4f00362", "metadata": {}, "outputs": [], "source": [ "symbols = {'Volatility 10 Index', 'Volatility 100 (1s) Index', 'Volatility 25 Index'}\n", "timeframes = {TimeFrame.M5, TimeFrame.H1, TimeFrame.M1, TimeFrame.H4, TimeFrame.M30, TimeFrame.M15}\n", "gd = GetData(st, et, timeframes, symbols, name='data')\n", "await gd.fail()" ] }, { "cell_type": "code", "execution_count": null, "id": "49d86767-d5cc-40a8-894e-c8f5b920f8cf", "metadata": {}, "outputs": [], "source": [ "symbols = {'Volatility 10 Index', 'Volatility 100 (1s) Index', 'Volatility 25 Index'}\n", "timeframes = {TimeFrame.M5, TimeFrame.H1, TimeFrame.M1, TimeFrame.H4, TimeFrame.M30, TimeFrame.M15}\n", "async with MetaTester(st, et, timeframes, symbols, name='data') as mt:\n", " # res = await mt.copy_ticks_range('Volatility 100 Index', st, et, CopyTicks.ALL)\n", " # print(res)\n", " await mt.get_and_save_data()" ] }, { "cell_type": "code", "execution_count": null, "id": "c4f8c64b-dcba-40cf-8342-53bb4b3fa6e6", "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(res)" ] }, { "cell_type": "code", "execution_count": null, "id": "970126ba-a143-4b45-a82e-8cf6b885763c", "metadata": {}, "outputs": [], "source": [ "# df.set_index(list(range(secs)))\n", "df.drop_duplicates(subset=['time'], keep='last', )" ] }, { "cell_type": "code", "execution_count": null, "id": "bf6d4786-4ee1-4bd4-81e2-fa43bdc527a4", "metadata": {}, "outputs": [], "source": [ "df = df.set_index('time', drop=False, verify_integrity=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "e788c43f-42d3-4c01-9bd9-5224e2175c1f", "metadata": {}, "outputs": [], "source": [ "bg = int(st.timestamp())\n", "en = int(secs) + bg\n", "index = range(bg, en)" ] }, { "cell_type": "code", "execution_count": null, "id": "fd9cffe6-c550-4f54-be8a-5e55b23b81a9", "metadata": {}, "outputs": [], "source": [ "bf = len(df.index)\n", "df = df.reindex(index=index, method='nearest')" ] }, { "cell_type": "code", "execution_count": null, "id": "c0b60fb9-0c27-4435-900d-898ea6979e11", "metadata": {}, "outputs": [], "source": [ "last = df.iloc[-1].time\n", "print(datetime.fromtimestamp(last, tz=tz), et)" ] }, { "cell_type": "code", "execution_count": null, "id": "60afd149-9600-4f0e-b3ff-1e8ad10247f3", "metadata": {}, "outputs": [], "source": [ "acc = AccountInfo(balance=500)\n", "data = MetaTester(start, end).load_data('data')\n", "td = TestData(acc, data)" ] }, { "cell_type": "code", "execution_count": null, "id": "be5704d5-4bff-4ec1-8cbe-2e96bfaf4f11", "metadata": {}, "outputs": [], "source": [ "ticks = data['ticks']['Volatility 10 Index']" ] }, { "cell_type": "code", "execution_count": null, "id": "12b3d5cb-0d31-4b70-b583-1f2becb74a0f", "metadata": {}, "outputs": [], "source": [ "ticks[-1]" ] }, { "cell_type": "code", "execution_count": null, "id": "ee0c4ccb-0e7c-434e-a655-fe89b09e606c", "metadata": {}, "outputs": [], "source": [ "y = TimeFrame.M5\n", "y.time" ] }, { "cell_type": "code", "execution_count": null, "id": "7a28ce72-0409-4231-92f7-f4e61fc0be94", "metadata": {}, "outputs": [], "source": [ "end.timestamp()" ] }, { "cell_type": "code", "execution_count": null, "id": "4aeeb64b-d3d2-4053-9808-2539e2755d09", "metadata": {}, "outputs": [], "source": [ "len(ticks)" ] }, { "cell_type": "code", "execution_count": null, "id": "29647ef4-01ad-46a0-afb2-f43a4988a529", "metadata": {}, "outputs": [], "source": [ "type(ticks[0])" ] }, { "cell_type": "code", "execution_count": null, "id": "bc593a4b-53d6-4203-a986-33b5267b9244", "metadata": {}, "outputs": [], "source": [ "ticks.reshape(8, 86160)" ] }, { "cell_type": "code", "execution_count": null, "id": "dc6440a3-4333-4363-b462-edee4facdd0f", "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "id": "0a246c6d-d35c-4878-a128-52ed1abb0108", "metadata": {}, "outputs": [], "source": [ "res = np.reshape(ticks, (-1, 8))" ] }, { "cell_type": "code", "execution_count": null, "id": "6e9de365-0f13-4d88-8302-f5e3418d489b", "metadata": {}, "outputs": [], "source": [ "ticks.shape = (86160, 8)" ] }, { "cell_type": "code", "execution_count": null, "id": "451b8e09-d17c-4662-a25d-39f00ff01788", "metadata": {}, "outputs": [], "source": [ "res = np.hstack(ticks)" ] }, { "cell_type": "code", "execution_count": null, "id": "395a1b7b-309d-4e38-b579-1b6b4854b19d", "metadata": {}, "outputs": [], "source": [ "v = np.array((*ticks[0]))" ] }, { "cell_type": "code", "execution_count": null, "id": "1bb16ff4-d92e-4474-a235-d8be87d094d6", "metadata": {}, "outputs": [], "source": [ "r = next(iter(ticks))" ] }, { "cell_type": "code", "execution_count": null, "id": "f2241c9c-68eb-4371-b3e3-202ebe25f7cd", "metadata": {}, "outputs": [], "source": [ "v.shape" ] }, { "cell_type": "code", "execution_count": null, "id": "ee0d3713-8578-46e2-969c-aebc295fae98", "metadata": {}, "outputs": [], "source": [ "ar = list(range(4))\n", "t = np.array(ar)\n", "t.shape = (1, 4)\n", "t" ] }, { "cell_type": "code", "execution_count": null, "id": "8502f7da-793e-4bfe-b469-bc109f051507", "metadata": {}, "outputs": [], "source": [ "mt.config.login" ] }, { "cell_type": "code", "execution_count": null, "id": "4d818ddd-f78c-4a50-b4a7-dc266321091c", "metadata": {}, "outputs": [], "source": [ "acc = Account()\n", "await acc.sign_in()" ] }, { "cell_type": "code", "execution_count": null, "id": "4c4c32d9-9c45-4ee7-afee-e04d1f5dca4a", "metadata": {}, "outputs": [], "source": [ "mt5 = MetaTrader()\n", "sym = await mt5.symbol_info('Volatility 100 (1s) Index')\n", "print(sym._asdict)" ] }, { "cell_type": "code", "execution_count": null, "id": "e47bd638-42fa-40d9-b573-474a39884dbc", "metadata": {}, "outputs": [], "source": [ "n = datetime.now()\n", "start = n.replace(hour=0, day=1, year=2020, month=1)\n", "end = n.replace(hour=15)\n", "res = await mt.copy_rates_range('Volatility 25 Index', TimeFrame.M1, start, end)" ] }, { "cell_type": "code", "execution_count": null, "id": "a4bc7b28-6dac-4863-b257-64d2c5c62575", "metadata": {}, "outputs": [], "source": [ "res = pd.DataFrame(res)\n", "len(res.index)" ] }, { "cell_type": "code", "execution_count": null, "id": "90c17d01-4ed0-468d-85c4-64988338b8a9", "metadata": {}, "outputs": [], "source": [ "print(start)" ] }, { "cell_type": "code", "execution_count": null, "id": "9a9141a1-7049-48e5-a96f-0b637b817d40", "metadata": {}, "outputs": [], "source": [ "data = shelve.open('./data/01-08-24_17-08-24', writeback=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "e2d4d4d7-5bfa-43e3-a455-012cf754a106", "metadata": {}, "outputs": [], "source": [ "data = dict(data)" ] }, { "cell_type": "code", "execution_count": null, "id": "95931276-fb8c-4a97-b440-330efc5e4d93", "metadata": {}, "outputs": [], "source": [ "data = pickle.dumps(data)" ] }, { "cell_type": "code", "execution_count": null, "id": "57c620ad-3fa5-41b9-a372-9a090e18ff85", "metadata": {}, "outputs": [], "source": [ "data = zlib.compress(data, level=9)" ] }, { "cell_type": "code", "execution_count": null, "id": "2d85bcdb-a86a-43f0-9b93-7d8c0ce7cf9e", "metadata": {}, "outputs": [], "source": [ "_data = lzma.compress(data)" ] }, { "cell_type": "code", "execution_count": null, "id": "eec34d71-7455-4064-9367-52156407335e", "metadata": {}, "outputs": [], "source": [ "fh = lzma.open('./data/ldata.xz', 'w')\n", "fh.write(_data)\n", "fh.close()" ] }, { "cell_type": "code", "execution_count": null, "id": "6f38b857-ed2d-4819-ba7f-4f70dc2fb3f9", "metadata": {}, "outputs": [], "source": [ "rb = lzma.open('./data/ldata.xz')\n", "rbb = rb.read()\n", "rb.close()" ] }, { "cell_type": "code", "execution_count": null, "id": "28220ba7-46af-409b-826d-2979ceb19f65", "metadata": {}, "outputs": [], "source": [ "rbd = lzma.decompress(rbb)" ] }, { "cell_type": "code", "execution_count": null, "id": "b3275fd6-70ff-4596-b4ba-9d9e4b6e52fe", "metadata": {}, "outputs": [], "source": [ "rdata = pickle.loads(rbd)" ] }, { "cell_type": "code", "execution_count": null, "id": "5bbcbc06-f353-4255-9792-89263793ba0f", "metadata": {}, "outputs": [], "source": [ "data.keys()" ] }, { "cell_type": "code", "execution_count": null, "id": "f9830b77-5e73-45c9-bc6a-40c19c3816c5", "metadata": {}, "outputs": [], "source": [ "fh = open('./data/pdata', 'wb')\n", "pickle.dump(rdata, fh)" ] }, { "cell_type": "code", "execution_count": null, "id": "064b8628-1331-4fa3-abf3-66f88c75da33", "metadata": {}, "outputs": [], "source": [ "fh.close()" ] }, { "cell_type": "code", "execution_count": null, "id": "fe60fc41-8844-4e00-b254-bb95d28528f2", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.6" } }, "nbformat": 4, "nbformat_minor": 5 }