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Forex-MT5-Bot/fxstudy.ipynb
Qoyyuum Kadir ef2d649240 ⚡️ Test Trading Bot v1
2021-10-25 19:11:47 +08:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Find out what model works best with the dataset from XM"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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" (1634169600, 1.15903, 1.16245, 1.15876, 1.15992, 33794, 0, 0)]\n"
]
}
],
"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 = \"EURUSD\"\n",
"rates = mt5.copy_rates_from_pos(pair, mt5.TIMEFRAME_D1, 0, 100)\n",
"print(rates)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Convert the `rates` into a Dataframe"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
" time open high low close tick_volume spread \\\n",
"0 2021-05-28 1.21932 1.22050 1.21327 1.21896 60110 0 \n",
"1 2021-05-31 1.21993 1.22314 1.21833 1.22250 36694 0 \n",
"2 2021-06-01 1.22254 1.22543 1.22118 1.22126 52448 0 \n",
"3 2021-06-02 1.22125 1.22265 1.21643 1.22100 53793 0 \n",
"4 2021-06-03 1.22097 1.22143 1.21183 1.21265 59299 0 \n",
"\n",
" real_volume \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n"
]
},
{
"data": {
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"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>tick_volume</th>\n",
" <th>spread</th>\n",
" <th>real_volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2021-05-28</td>\n",
" <td>1.21932</td>\n",
" <td>1.22050</td>\n",
" <td>1.21327</td>\n",
" <td>1.21896</td>\n",
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" <th>1</th>\n",
" <td>2021-05-31</td>\n",
" <td>1.21993</td>\n",
" <td>1.22314</td>\n",
" <td>1.21833</td>\n",
" <td>1.22250</td>\n",
" <td>36694</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>2021-06-01</td>\n",
" <td>1.22254</td>\n",
" <td>1.22543</td>\n",
" <td>1.22118</td>\n",
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" <th>3</th>\n",
" <td>2021-06-02</td>\n",
" <td>1.22125</td>\n",
" <td>1.22265</td>\n",
" <td>1.21643</td>\n",
" <td>1.22100</td>\n",
" <td>53793</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2021-06-03</td>\n",
" <td>1.22097</td>\n",
" <td>1.22143</td>\n",
" <td>1.21183</td>\n",
" <td>1.21265</td>\n",
" <td>59299</td>\n",
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" <tr>\n",
" <th>95</th>\n",
" <td>2021-10-08</td>\n",
" <td>1.15526</td>\n",
" <td>1.15861</td>\n",
" <td>1.15414</td>\n",
" <td>1.15736</td>\n",
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" <tr>\n",
" <th>96</th>\n",
" <td>2021-10-11</td>\n",
" <td>1.15659</td>\n",
" <td>1.15867</td>\n",
" <td>1.15487</td>\n",
" <td>1.15517</td>\n",
" <td>46485</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>2021-10-12</td>\n",
" <td>1.15522</td>\n",
" <td>1.15702</td>\n",
" <td>1.15242</td>\n",
" <td>1.15295</td>\n",
" <td>57798</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>2021-10-13</td>\n",
" <td>1.15294</td>\n",
" <td>1.15973</td>\n",
" <td>1.15250</td>\n",
" <td>1.15940</td>\n",
" <td>67602</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>2021-10-14</td>\n",
" <td>1.15903</td>\n",
" <td>1.16245</td>\n",
" <td>1.15876</td>\n",
" <td>1.15992</td>\n",
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"<p>100 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" time open high low close tick_volume spread \\\n",
"0 2021-05-28 1.21932 1.22050 1.21327 1.21896 60110 0 \n",
"1 2021-05-31 1.21993 1.22314 1.21833 1.22250 36694 0 \n",
"2 2021-06-01 1.22254 1.22543 1.22118 1.22126 52448 0 \n",
"3 2021-06-02 1.22125 1.22265 1.21643 1.22100 53793 0 \n",
"4 2021-06-03 1.22097 1.22143 1.21183 1.21265 59299 0 \n",
".. ... ... ... ... ... ... ... \n",
"95 2021-10-08 1.15526 1.15861 1.15414 1.15736 59970 0 \n",
"96 2021-10-11 1.15659 1.15867 1.15487 1.15517 46485 0 \n",
"97 2021-10-12 1.15522 1.15702 1.15242 1.15295 57798 0 \n",
"98 2021-10-13 1.15294 1.15973 1.15250 1.15940 67602 0 \n",
"99 2021-10-14 1.15903 1.16245 1.15876 1.15992 33794 0 \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]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"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"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<bound method NDFrame.describe of time open high low close tick_volume spread \\\n",
"0 2021-05-28 1.21932 1.22050 1.21327 1.21896 60110 0 \n",
"1 2021-05-31 1.21993 1.22314 1.21833 1.22250 36694 0 \n",
"2 2021-06-01 1.22254 1.22543 1.22118 1.22126 52448 0 \n",
"3 2021-06-02 1.22125 1.22265 1.21643 1.22100 53793 0 \n",
"4 2021-06-03 1.22097 1.22143 1.21183 1.21265 59299 0 \n",
".. ... ... ... ... ... ... ... \n",
"95 2021-10-08 1.15526 1.15861 1.15414 1.15736 59970 0 \n",
"96 2021-10-11 1.15659 1.15867 1.15487 1.15517 46485 0 \n",
"97 2021-10-12 1.15522 1.15702 1.15242 1.15295 57798 0 \n",
"98 2021-10-13 1.15294 1.15973 1.15250 1.15940 67602 0 \n",
"99 2021-10-14 1.15903 1.16245 1.15876 1.15992 33794 0 \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]>\n"
]
}
],
"source": [
"# Understand the dataset\n",
"print(data.describe)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll be using scikit learn's to train_test_split the dataset but we have to define a few things"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Need to define the X set and the y set from the data"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" time open high low tick_volume spread real_volume\n",
"0 2021-05-28 1.21932 1.22050 1.21327 60110 0 0\n",
"1 2021-05-31 1.21993 1.22314 1.21833 36694 0 0\n",
"2 2021-06-01 1.22254 1.22543 1.22118 52448 0 0\n",
"3 2021-06-02 1.22125 1.22265 1.21643 53793 0 0\n",
"4 2021-06-03 1.22097 1.22143 1.21183 59299 0 0\n",
"0 1.22100\n",
"1 1.21265\n",
"2 1.21662\n",
"3 1.21893\n",
"4 1.21726\n",
"Name: close, dtype: float64\n",
"95 1.15940\n",
"96 1.15992\n",
"97 0.00000\n",
"98 0.00000\n",
"99 0.00000\n",
"Name: close, dtype: float64\n",
"(100,)\n",
"(100,)\n",
"1 1.21265\n",
"2 1.21662\n",
"3 1.21893\n",
"4 1.21726\n",
"5 1.21787\n",
"Name: close, dtype: float64\n",
"96 1.15992\n",
"97 0.00000\n",
"98 0.00000\n",
"99 0.00000\n",
"100 0.00000\n",
"Name: close, dtype: float64\n"
]
}
],
"source": [
"X = data.drop(columns='close')\n",
"print(X.head())\n",
"y = data['close']\n",
"print(y.head())\n",
"print(y.tail())\n",
"print(y.shape)\n",
"# Shift the close prices by 1\n",
"y.drop(0,inplace=True)\n",
"y.loc[100] = 0\n",
"print(y.shape)\n",
"print(y.head())\n",
"print(y.tail())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X_train: time open high low tick_volume spread real_volume\n",
"65 2021-08-27 1.17514 1.18026 1.17349 54550 0 0\n",
"62 2021-08-24 1.17449 1.17650 1.17273 40186 0 0\n",
"27 2021-07-06 1.18619 1.18950 1.18067 79642 0 0\n",
"75 2021-09-10 1.18229 1.18510 1.18091 52650 0 0\n",
"61 2021-08-23 1.16897 1.17503 1.16897 42138 0 0\n",
".. ... ... ... ... ... ... ...\n",
"87 2021-09-28 1.16940 1.17032 1.16682 64793 0 0\n",
"1 2021-05-31 1.21993 1.22314 1.21833 36694 0 0\n",
"69 2021-09-02 1.18374 1.18759 1.18344 42004 0 0\n",
"55 2021-08-13 1.17290 1.18048 1.17281 35375 0 0\n",
"46 2021-08-02 1.18641 1.18969 1.18590 47011 0 0\n",
"\n",
"[70 rows x 7 columns]\n",
"X_test: time open high low tick_volume spread real_volume\n",
"20 2021-06-25 1.19315 1.19752 1.19265 59055 0 0\n",
"10 2021-06-11 1.21751 1.21931 1.20927 54807 0 0\n",
"96 2021-10-11 1.15659 1.15867 1.15487 46485 0 0\n",
"16 2021-06-21 1.18550 1.19212 1.18477 87967 0 0\n",
"63 2021-08-25 1.17537 1.17743 1.17260 45250 0 0\n",
"24 2021-07-01 1.18559 1.18842 1.18374 70316 0 0\n",
"53 2021-08-11 1.17184 1.17540 1.17061 45441 0 0\n",
"97 2021-10-12 1.15522 1.15702 1.15242 57798 0 0\n",
"41 2021-07-26 1.17677 1.18173 1.17635 53860 0 0\n",
"47 2021-08-03 1.18670 1.18933 1.18537 49688 0 0\n",
"43 2021-07-28 1.18159 1.18494 1.17721 64207 0 0\n",
"2 2021-06-01 1.22254 1.22543 1.22118 52448 0 0\n",
"95 2021-10-08 1.15526 1.15861 1.15414 59970 0 0\n",
"26 2021-07-05 1.18576 1.18805 1.18515 47260 0 0\n",
"13 2021-06-16 1.21256 1.21348 1.19940 66416 0 0\n",
"37 2021-07-20 1.17980 1.18025 1.17558 70566 0 0\n",
"14 2021-06-17 1.19939 1.20064 1.18918 90570 0 0\n",
"29 2021-07-08 1.17899 1.18679 1.17836 90999 0 0\n",
"35 2021-07-16 1.18118 1.18224 1.17921 70772 0 0\n",
"54 2021-08-12 1.17380 1.17482 1.17237 36888 0 0\n",
"80 2021-09-17 1.17675 1.17888 1.17242 56213 0 0\n",
"4 2021-06-03 1.22097 1.22143 1.21183 59299 0 0\n",
"81 2021-09-20 1.17272 1.17363 1.17004 58444 0 0\n",
"76 2021-09-13 1.18143 1.18171 1.17702 51815 0 0\n",
"85 2021-09-24 1.17379 1.17476 1.17007 51401 0 0\n",
"60 2021-08-20 1.16752 1.17045 1.16640 42854 0 0\n",
"5 2021-06-04 1.21261 1.21856 1.21041 56073 0 0\n",
"70 2021-09-03 1.18739 1.19091 1.18657 53226 0 0\n",
"71 2021-09-06 1.18768 1.18862 1.18557 29883 0 0\n",
"19 2021-06-24 1.19254 1.19563 1.19175 65027 0 0\n",
"y_train: 65 1.17955\n",
"62 1.17555\n",
"27 1.18231\n",
"75 1.18107\n",
"61 1.17457\n",
" ... \n",
"87 1.16826\n",
"1 1.22250\n",
"69 1.18744\n",
"55 1.17962\n",
"46 1.18688\n",
"Name: close, Length: 70, dtype: float64\n",
"y_test: 20 1.19370\n",
"10 1.21063\n",
"96 1.15517\n",
"16 1.19185\n",
"63 1.17716\n",
"24 1.18497\n",
"53 1.17388\n",
"97 1.15295\n",
"41 1.18029\n",
"47 1.18637\n",
"43 1.18441\n",
"2 1.22126\n",
"95 1.15736\n",
"26 1.18634\n",
"13 1.19943\n",
"37 1.17805\n",
"14 1.19069\n",
"29 1.18449\n",
"35 1.18055\n",
"54 1.17296\n",
"80 1.17250\n",
"4 1.21265\n",
"81 1.17257\n",
"76 1.18103\n",
"85 1.17206\n",
"60 1.16996\n",
"5 1.21662\n",
"70 1.18772\n",
"71 1.18701\n",
"19 1.19315\n",
"Name: close, dtype: float64\n"
]
}
],
"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}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using LazyRegressor to find out the models"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"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)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"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)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the Linear Regression model defined with fitting and predicting, let's map and plot the current dataset"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# import mplfinance as mpf\n",
"\n",
"# data = data.set_index('time')\n",
"# mpf.plot(data, type='line', title=pair)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Metatrader 5 seems to not be able to handle order management very well. This includes current order sent.\n",
"However, the broker that I'm using (XM) is giving me a high spread that the predicted price just happens to be in the middle of."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"------------USDCAD--------------\n",
"Points : 1e-05\n",
"Ask Price : 1.23816\n",
"Bid Price : 1.23816\n",
"Current Open Price : 1.2382900000000001\n",
"Predicted Price : 1.237242894242747\n",
"Stop Loss : 1.2381600104710575\n",
"Signal : Sell\n",
"Distance : 0.0010471057572531972\n",
"Can we place a trade? : True\n",
"Calculated Profit : 7.41\n",
"Current DataFrame before splitting close price\n",
" time open high low close tick_volume spread \\\n",
"0 1634223600 1.23829 1.23831 1.23725 1.23816 1520 0 \n",
"\n",
" real_volume \n",
"0 0 \n",
"---------------------------------------\n"
]
}
],
"source": [
"# Login\n",
"import config\n",
"client = mt5.login(config.ACCOUNT, password=config.PASSWORD, server=config.SERVER)\n",
"for p in config.PAIRS:\n",
"\tsymbol_info = mt5.symbol_info(p)\n",
"\tsymbol_tick = mt5.symbol_info_tick(p)\n",
"\tpoints = symbol_info.point\n",
"\task = symbol_tick.ask\n",
"\tbid = symbol_tick.bid\n",
"\tcurrent_rate = mt5.copy_rates_from_pos(p, config.TIMEFRAME, 0, 1)\n",
"\tcurrent_df = pd.DataFrame(current_rate)\n",
"\tcurrent_open_price = current_df.iloc[0][\"open\"]\n",
"\tto_predict = current_df.drop(columns=\"close\")\n",
"\tsymbol_predict = model.predict(to_predict)\n",
"\tif current_open_price > symbol_predict[0]:\n",
"\t\tsignal = \"Sell\"\n",
"\t\ttrade_feasible = symbol_predict[0] < bid\n",
"\t\tprofit=mt5.order_calc_profit(mt5.ORDER_TYPE_SELL, p, config.LOT_SIZE, bid, symbol_predict[0])\n",
"\t\tdistance = current_open_price - symbol_predict[0]\n",
"\t\tstop_loss = bid+distance*points\n",
"\telse:\n",
"\t\tsignal = \"Buy\"\n",
"\t\ttrade_feasible = symbol_predict[0] > ask\n",
"\t\tprofit=mt5.order_calc_profit(mt5.ORDER_TYPE_BUY, p, config.LOT_SIZE, ask, symbol_predict[0])\n",
"\t\tdistance = symbol_predict[0] - current_open_price\n",
"\t\tstop_loss = ask-distance*points\n",
"\tprint(f\"------------{p}--------------\")\n",
"\tprint(f\"Points : {points}\")\n",
"\tprint(f\"Ask Price : {ask}\")\n",
"\tprint(f\"Bid Price : {bid}\")\n",
"\tprint(f\"Current Open Price : {current_open_price}\")\n",
"\tprint(f\"Predicted Price : {symbol_predict[0]}\")\n",
"\tprint(f\"Stop Loss : {stop_loss}\")\n",
"\tprint(f\"Signal : {signal}\")\n",
"\tprint(f\"Distance : {distance}\")\n",
"\tprint(f\"Can we place a trade? : {trade_feasible}\")\n",
"\tprint(f\"Calculated Profit : {profit}\")\n",
"\tprint(\"Current DataFrame before splitting close price\")\n",
"\tprint(current_df)\n",
"\tprint(\"---------------------------------------\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Try forecasting 2 ticks ahead. This should solve the \"invalid stops\" issue."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(1634225580, 1.23825, 1.23863, 1.23818, 1.23863, 86, 0, 0)]\n",
" time open high low close tick_volume spread \\\n",
"0 1634225580 1.23825 1.23863 1.23818 1.23863 86 0 \n",
"\n",
" real_volume \n",
"0 0 \n",
"Index(['time', 'open', 'high', 'low', 'close', 'tick_volume', 'spread',\n",
" 'real_volume'],\n",
" dtype='object')\n",
"RangeIndex(start=0, stop=1, step=1)\n"
]
}
],
"source": [
"import config\n",
"import MetaTrader5 as mt5\n",
"import pandas as pd\n",
"if not mt5.initialize():\n",
"\tprint(\"Something went wrong\")\n",
"elif mt5.login(login=config.ACCOUNT, password=config.PASSWORD, server=config.SERVER):\n",
"\tcurrent_rate = mt5.copy_rates_from_pos(\"USDCAD\", mt5.TIMEFRAME_M1, 0, 1)\n",
"\tprint(current_rate)\n",
"\tcurrent_rate_df = pd.DataFrame(current_rate)\n",
"\tprint(current_rate_df)\n",
"\tfeatures = current_rate_df.columns\n",
"\tprint(features)\n",
"\tindex = current_rate_df.index\n",
"\tprint(index)"
]
}
],
"metadata": {
"interpreter": {
"hash": "398df2b33328d7497935b300d4507ade3456a01361fefa2e3b5959f5ae897d3f"
},
"kernelspec": {
"display_name": "Python 3.9.4 64-bit ('forex-zmq-GFmDeAiW': virtualenv)",
"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": "3.9.4"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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