diff --git a/trade_flow/environments/gym-mtsim/CITATION.cff b/trade_flow/environments/gym-mtsim/CITATION.cff
new file mode 100644
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+++ b/trade_flow/environments/gym-mtsim/CITATION.cff
@@ -0,0 +1,8 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+  - family-names: Haghpanah
+    given-names: Mohammad Amin
+title: "gym-mtsim"
+version: 1.1.0
+date-released: 2021-09-09
diff --git a/trade_flow/environments/gym-mtsim/LICENSE b/trade_flow/environments/gym-mtsim/LICENSE
new file mode 100644
index 0000000..1c2d1cf
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+++ b/trade_flow/environments/gym-mtsim/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 Mohammad Amin Haghpanah
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/trade_flow/environments/gym-mtsim/README.ipynb b/trade_flow/environments/gym-mtsim/README.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# gym-mtsim: OpenAI Gym - MetaTrader 5 Simulator\n",
+    "\n",
+    "`MtSim` is a simulator for the [MetaTrader 5](https://www.metatrader5.com) trading platform alongside an [OpenAI Gym](https://github.com/openai/gym) environment for reinforcement learning-based trading algorithms. `MetaTrader 5` is a **multi-asset** platform that allows trading **Forex**, **Stocks**, **Crypto**, and Futures. It is one of the most popular trading platforms and supports numerous useful features, such as opening demo accounts on various brokers.\n",
+    "\n",
+    "The simulator is separated from the Gym environment and can work independently. Although the Gym environment is designed to be suitable for RL frameworks, it is also proper for backtesting and classic analysis.\n",
+    "\n",
+    "The goal of this project was to provide a *general-purpose*, *flexible*, and *easy-to-use* library with a focus on *code readability* that enables users to do all parts of the trading process through it from 0 to 100. So, `gym-mtsim` is not just a testing tool or a Gym environment. It is a combination of a **real-world** simulator, a **backtesting** tool with *high detail visualization*, and a **Gym environment** appropriate for RL/classic algorithms.\n",
+    "\n",
+    "**Note:** For beginners, it is recommended to check out the [gym-anytrading](https://github.com/AminHP/gym-anytrading) project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "### Install MetaTrader 5\n",
+    "Download and install MetaTrader 5 software from [here](https://www.metatrader5.com/en/download).\n",
+    "\n",
+    "Open a demo account on any broker. By default, the software opens a demo account automatically after installation.\n",
+    "\n",
+    "Explore the software and try to get familiar with it by trading different symbols in both **hedged** and **unhedged** accounts.\n",
+    "\n",
+    "### Install gym-mtsim\n",
+    "\n",
+    "#### Via PIP\n",
+    "```bash\n",
+    "pip install gym-mtsim\n",
+    "```\n",
+    "\n",
+    "#### From Repository\n",
+    "```bash\n",
+    "git clone https://github.com/AminHP/gym-mtsim\n",
+    "cd gym-mtsim\n",
+    "pip install -e .\n",
+    "\n",
+    "## or\n",
+    "\n",
+    "pip install --upgrade --no-deps --force-reinstall https://github.com/AminHP/gym-mtsim/archive/main.zip\n",
+    "```\n",
+    "\n",
+    "### Install stable-baselines3\n",
+    "This package is required to run some examples. Install it from [here](https://github.com/DLR-RM/stable-baselines3#installation)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Components"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. SymbolInfo\n",
+    "\n",
+    "This is a data class that contains the essential properties of a symbol. Try to get fully acquainted with [these properties](https://github.com/AminHP/gym-mtsim/blob/main/gym_mtsim/metatrader/symbol.py) in case they are unfamiliar. There are plenty of resources that provide good explanations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Order\n",
+    "\n",
+    "This is another data class that consists of information of an order. Each order has the following properties:\n",
+    "\n",
+    "> `id`: A unique number that helps with tracking orders.\n",
+    ">\n",
+    "> `type`: An enum that specifies the type of the order. It can be either **Buy** or **Sell**.\n",
+    ">\n",
+    "> `symbol`: The symbol selected for the order.\n",
+    ">\n",
+    "> `volume`: The volume chose for the order. It can be a multiple of *volume_step* between *volume_min* and *volume_max*. \n",
+    ">\n",
+    "> `fee`: It is a tricky property. In MetaTrader, there is *no* such concept called fee. Each symbol has bid and ask prices, the difference between which represents the **fee**. Although MetaTrader API provides these bid/ask prices for the recent past, it is not possible to access them for the distant past. Therefore, the **fee** property helps to manage the mentioned difference.\n",
+    ">\n",
+    "> `entry_time`: The time when the order was placed.\n",
+    ">\n",
+    "> `entry_price`: The **close** price when the order was placed.\n",
+    ">\n",
+    "> `exit_time`: The time when the order was closed.\n",
+    ">\n",
+    "> `exit_price`: The **close** price when the order was closed.\n",
+    ">\n",
+    "> `profit`: The amount of profit earned by this order so far.\n",
+    ">\n",
+    "> `margin`: The required amount of margin for this order.\n",
+    ">\n",
+    "> `closed`: A boolean that specifies whether this order is closed or not."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. MtSimulator\n",
+    "\n",
+    "This is the core class that simulates the main parts of MetaTrader. Most of its public properties and methods are explained here. But feel free to take a look at the complete [source code](https://github.com/AminHP/gym-mtsim/blob/main/gym_mtsim/simulator/mt_simulator.py).\n",
+    "\n",
+    "* Properties:\n",
+    "\n",
+    "    > `unit`: The unit currency. It is usually *USD*, but it can be anything the broker allows, such as *EUR*.\n",
+    "    >\n",
+    "    > `balance`: The amount of money before taking into account any open positions.\n",
+    "    >\n",
+    "    > `equity`: The amount of money, including the value of any open positions.\n",
+    "    >\n",
+    "    > `margin`: The amount of money which is required for having positions opened.\n",
+    "    >\n",
+    "    > `leverage`: The leverage ratio.\n",
+    "    >\n",
+    "    > `free_margin`: The amount of money that is available to open new positions.\n",
+    "    >\n",
+    "    > `margin_level`: The ratio between **equity** and **margin**.\n",
+    "    >\n",
+    "    > `stop_out_level`: If the **margin_level** drops below **stop_out_level**, the most unprofitable position will be closed automatically by the broker.\n",
+    "    >\n",
+    "    > `hedge`: A boolean that specifies whether hedging is enabled or not.\n",
+    "    >\n",
+    "    > `symbols_info`: A dictionary that contains symbols' information.\n",
+    "    >\n",
+    "    > `symbols_data`: A dictionary that contains symbols' OHLCV data.\n",
+    "    >\n",
+    "    > `orders`: The list of open orders.\n",
+    "    >\n",
+    "    > `closed_orders`: The list of closed orders.\n",
+    "    >\n",
+    "    > `current_time`: The current time of the system.\n",
+    "\n",
+    "* Methods:\n",
+    "\n",
+    "    > `download_data`: Downloads required data from MetaTrader for a list of symbols in a time range. This method can be overridden in order to download data from servers other than MetaTrader. *Note that this method only works on Windows, as the MetaTrader5 Python package is not available on other platforms.*\n",
+    "    >\n",
+    "    > `save_symbols`: Saves the downloaded symbols' data to a file.\n",
+    "    >\n",
+    "    > `load_symbols`: Loads the symbols' data from a file.\n",
+    "    >\n",
+    "    > `tick`: Moves forward in time (by a delta time) and updates orders and other related properties.\n",
+    "    >\n",
+    "    > `create_order`: Creates a **Buy** or **Sell** order and updates related properties.\n",
+    "    >\n",
+    "    > `close_order`: Closes an order and updates related properties.\n",
+    "    >\n",
+    "    > `get_state`: Returns the state of the system. The result is similar to the *Trading tab* and *History tab* of the *Toolbox window* in MetaTrader software."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. MtEnv\n",
+    "\n",
+    "This is the Gym environment that works on top of the *MtSim*. Most of its public properties and methods are explained here. But feel free to take a look at the complete [source code](https://github.com/AminHP/gym-mtsim/blob/main/gym_mtsim/envs/mt_env.py).\n",
+    "\n",
+    "* Properties:\n",
+    "\n",
+    "    > `original_simulator`: An instance of **MtSim** class as a baseline for simulating the system.\n",
+    "    >\n",
+    "    > `simulator`: The current simulator in use. It is a copy of the **original_simulator**.\n",
+    "    >\n",
+    "    > `trading_symbols`: The list of symbols to trade.\n",
+    "    >\n",
+    "    > `time_points`: A list of time points based on which the simulator moves time. The default value is taken from the *pandas DataFrame.Index* of the first symbol in the **trading_symbols** list.\n",
+    "    >\n",
+    "    > `hold_threshold`: A probability threshold that controls holding or placing a new order.\n",
+    "    >\n",
+    "    > `close_threshold`: A probability threshold that controls closing an order.\n",
+    "    >\n",
+    "    > `fee`: A constant number or a callable that takes a *symbol* as input and returns the **fee** based on that.\n",
+    "    >\n",
+    "    > `symbol_max_orders`: Specifies the maximum number of open positions per symbol in hedge trading. \n",
+    "    >\n",
+    "    > `multiprocessing_processes`: Specifies the maximum number of processes used for parallel processing.\n",
+    "    >\n",
+    "    > `prices`: The symbol prices over time. It is used to calculate signal features and render the environment.\n",
+    "    >\n",
+    "    > `signal_features`: The extracted features over time. It is used to generate *Gym observations*.\n",
+    "    >\n",
+    "    > `window_size`: The number of time points (current and previous points) as the length of each observation's features. \n",
+    "    >\n",
+    "    > `features_shape`: The shape of a single observation's features.\n",
+    "    >\n",
+    "    > `action_space`: The *Gym action_space* property. It has a complex structure since **stable-baselines** does not support *Dict* or *2D Box* action spaces. The action space is a 1D vector of size `count(trading_symbols) * (symbol_max_orders + 2)`. For each symbol, two types of actions can be performed, closing previous orders and placing a new order. The former is controlled by the first *symbol_max_orders* elements and the latter is controlled by the last two elements. Therefore, the action for each symbol is ***[probability of closing order 1, probability of closing order 2, ..., probability of closing order symbol_max_orders, probability of holding or creating a new order, volume of the new order]***. The last two elements specify whether to hold or place a new order and the volume of the new order (positive volume indicates buy and negative volume indicates sell). These elements are a number in range (-∞, ∞), but the probability values must be in the range [0, 1]. This is a problem with **stable-baselines** as mentioned earlier. To overcome this problem, it is assumed that the probability values belong to the [logit](https://en.wikipedia.org/wiki/Logit) function. So, applying the [expit](https://en.wikipedia.org/wiki/Expit) function on them gives the desired probability values in the range [0, 1]. This function is applied in the **step** method of the environment.\n",
+    "    >\n",
+    "    > `observation_space`: The *Gym observation_space* property. Each observation contains information about *balance*, *equity*, *margin*, *features*, and *orders*. The **features** is a window on the *signal_features* from index *current_tick - window_size + 1* to *current_tick*. The **orders** is a 3D array. Its first dimension specifies the symbol index in the *trading_symbols* list. The second dimension specifies the order number (each symbol can have more than one open order at the same time in hedge trading). The last dimension has three elements, *entry_price*, *volume*, and *profit* of corresponding order.\n",
+    "    >\n",
+    "    > `history`: Stores the information of all steps.\n",
+    "\n",
+    "* Methods:\n",
+    "\n",
+    "    > `seed`: The typical *Gym seed* method.\n",
+    "    >\n",
+    "    > `reset`: The typical *Gym reset* method.\n",
+    "    >\n",
+    "    > `step`: The typical *Gym step* method.\n",
+    "    >\n",
+    "    > `render`: The typical *Gym render* method. It can render in three modes, **human**, **simple_figure**, and **advanced_figure**.\n",
+    "    >\n",
+    "    > `close`: The typical *Gym close* method.\n",
+    "\n",
+    "* Virtual Methods:\n",
+    "\n",
+    "    > `_get_prices`: It is called in the constructor and calculates symbol **prices**.\n",
+    "    >\n",
+    "    > `_process_data`: It is called in the constructor and calculates **signal_features**.\n",
+    "    >\n",
+    "    > `_calculate_reward`: The reward function for the RL agent."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A Simple Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### MtSim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create a simulator with custom parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pytz\n",
+    "from datetime import datetime, timedelta\n",
+    "from gym_mtsim import MtSimulator, OrderType, Timeframe, FOREX_DATA_PATH\n",
+    "\n",
+    "\n",
+    "sim = MtSimulator(\n",
+    "    unit='USD',\n",
+    "    balance=10000.,\n",
+    "    leverage=100.,\n",
+    "    stop_out_level=0.2,\n",
+    "    hedge=False,\n",
+    ")\n",
+    "\n",
+    "if not sim.load_symbols(FOREX_DATA_PATH):\n",
+    "    sim.download_data(\n",
+    "        symbols=['EURUSD', 'GBPCAD', 'GBPUSD', 'USDCAD', 'USDCHF', 'GBPJPY', 'USDJPY'],\n",
+    "        time_range=(\n",
+    "            datetime(2021, 5, 5, tzinfo=pytz.UTC),\n",
+    "            datetime(2021, 9, 5, tzinfo=pytz.UTC)\n",
+    "        ),\n",
+    "        timeframe=Timeframe.D1\n",
+    "    )\n",
+    "    sim.save_symbols(FOREX_DATA_PATH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Place some orders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "balance: 10000.0, equity: 10717.58118589908, margin: 3375.480933228619\n",
+      "free_margin: 7342.1002526704615, margin_level: 3.1751271592500743\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Id</th>\n",
+       "      <th>Symbol</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>Entry Time</th>\n",
+       "      <th>Entry Price</th>\n",
+       "      <th>Exit Time</th>\n",
+       "      <th>Exit Price</th>\n",
+       "      <th>Exit Balance</th>\n",
+       "      <th>Exit Equity</th>\n",
+       "      <th>Profit</th>\n",
+       "      <th>Margin</th>\n",
+       "      <th>Fee</th>\n",
+       "      <th>Closed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>USDJPY</td>\n",
+       "      <td>Sell</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2021-09-01 00:17:52+00:00</td>\n",
+       "      <td>110.02500</td>\n",
+       "      <td>2021-09-06 00:17:52+00:00</td>\n",
+       "      <td>109.71200</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>552.355257</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>0.0100</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>GBPCAD</td>\n",
+       "      <td>Buy</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2021-08-30 00:17:52+00:00</td>\n",
+       "      <td>1.73389</td>\n",
+       "      <td>2021-09-06 00:17:52+00:00</td>\n",
+       "      <td>1.73626</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.225928</td>\n",
+       "      <td>1375.480933</td>\n",
+       "      <td>0.0003</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Id  Symbol  Type  Volume                Entry Time  Entry Price  \\\n",
+       "0   2  USDJPY  Sell     2.0 2021-09-01 00:17:52+00:00    110.02500   \n",
+       "1   1  GBPCAD   Buy     1.0 2021-08-30 00:17:52+00:00      1.73389   \n",
+       "\n",
+       "                  Exit Time  Exit Price  Exit Balance  Exit Equity  \\\n",
+       "0 2021-09-06 00:17:52+00:00   109.71200           NaN          NaN   \n",
+       "1 2021-09-06 00:17:52+00:00     1.73626           NaN          NaN   \n",
+       "\n",
+       "       Profit       Margin     Fee  Closed  \n",
+       "0  552.355257  2000.000000  0.0100   False  \n",
+       "1  165.225928  1375.480933  0.0003   False  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sim.current_time = datetime(2021, 8, 30, 0, 17, 52, tzinfo=pytz.UTC)\n",
+    "\n",
+    "order1 = sim.create_order(\n",
+    "    order_type=OrderType.Buy,\n",
+    "    symbol='GBPCAD',\n",
+    "    volume=1.,\n",
+    "    fee=0.0003,\n",
+    ")\n",
+    "\n",
+    "sim.tick(timedelta(days=2))\n",
+    "\n",
+    "order2 = sim.create_order(\n",
+    "    order_type=OrderType.Sell,\n",
+    "    symbol='USDJPY',\n",
+    "    volume=2.,\n",
+    "    fee=0.01,\n",
+    ")\n",
+    "\n",
+    "sim.tick(timedelta(days=5))\n",
+    "\n",
+    "state = sim.get_state()\n",
+    "\n",
+    "print(\n",
+    "    f\"balance: {state['balance']}, equity: {state['equity']}, margin: {state['margin']}\\n\"\n",
+    "    f\"free_margin: {state['free_margin']}, margin_level: {state['margin_level']}\\n\"\n",
+    ")\n",
+    "state['orders']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Close all orders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "balance: 10717.58118589908, equity: 10717.58118589908, margin: 0.0\n",
+      "free_margin: 10717.58118589908, margin_level: inf\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Id</th>\n",
+       "      <th>Symbol</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>Entry Time</th>\n",
+       "      <th>Entry Price</th>\n",
+       "      <th>Exit Time</th>\n",
+       "      <th>Exit Price</th>\n",
+       "      <th>Exit Balance</th>\n",
+       "      <th>Exit Equity</th>\n",
+       "      <th>Profit</th>\n",
+       "      <th>Margin</th>\n",
+       "      <th>Fee</th>\n",
+       "      <th>Closed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>USDJPY</td>\n",
+       "      <td>Sell</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2021-09-01 00:17:52+00:00</td>\n",
+       "      <td>110.02500</td>\n",
+       "      <td>2021-09-06 00:17:52+00:00</td>\n",
+       "      <td>109.71200</td>\n",
+       "      <td>10717.581186</td>\n",
+       "      <td>10717.581186</td>\n",
+       "      <td>552.355257</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>0.0100</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>GBPCAD</td>\n",
+       "      <td>Buy</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2021-08-30 00:17:52+00:00</td>\n",
+       "      <td>1.73389</td>\n",
+       "      <td>2021-09-06 00:17:52+00:00</td>\n",
+       "      <td>1.73626</td>\n",
+       "      <td>10165.225928</td>\n",
+       "      <td>10717.581186</td>\n",
+       "      <td>165.225928</td>\n",
+       "      <td>1375.480933</td>\n",
+       "      <td>0.0003</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Id  Symbol  Type  Volume                Entry Time  Entry Price  \\\n",
+       "0   2  USDJPY  Sell     2.0 2021-09-01 00:17:52+00:00    110.02500   \n",
+       "1   1  GBPCAD   Buy     1.0 2021-08-30 00:17:52+00:00      1.73389   \n",
+       "\n",
+       "                  Exit Time  Exit Price  Exit Balance   Exit Equity  \\\n",
+       "0 2021-09-06 00:17:52+00:00   109.71200  10717.581186  10717.581186   \n",
+       "1 2021-09-06 00:17:52+00:00     1.73626  10165.225928  10717.581186   \n",
+       "\n",
+       "       Profit       Margin     Fee  Closed  \n",
+       "0  552.355257  2000.000000  0.0100    True  \n",
+       "1  165.225928  1375.480933  0.0003    True  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "order1_profit = sim.close_order(order1)\n",
+    "order2_profit = sim.close_order(order2)\n",
+    "\n",
+    "# alternatively:\n",
+    "# for order in sim.orders:\n",
+    "#     sim.close_order(order)\n",
+    "\n",
+    "state = sim.get_state()\n",
+    "\n",
+    "print(\n",
+    "    f\"balance: {state['balance']}, equity: {state['equity']}, margin: {state['margin']}\\n\"\n",
+    "    f\"free_margin: {state['free_margin']}, margin_level: {state['margin_level']}\\n\"\n",
+    ")\n",
+    "state['orders']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### MtEnv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create an environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gymnasium as gym\n",
+    "import gym_mtsim\n",
+    "\n",
+    "env = gym.make('forex-hedge-v0')\n",
+    "# env = gym.make('stocks-hedge-v0')\n",
+    "# env = gym.make('crypto-hedge-v0')\n",
+    "# env = gym.make('mixed-hedge-v0')\n",
+    "\n",
+    "# env = gym.make('forex-unhedge-v0')\n",
+    "# env = gym.make('stocks-unhedge-v0')\n",
+    "# env = gym.make('crypto-unhedge-v0')\n",
+    "# env = gym.make('mixed-unhedge-v0')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* This will create a default environment. There are eight default environments, but it is also possible to create environments with custom parameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create an environment with custom parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pytz\n",
+    "from datetime import datetime, timedelta\n",
+    "import numpy as np\n",
+    "from gym_mtsim import MtEnv, MtSimulator, FOREX_DATA_PATH\n",
+    "\n",
+    "\n",
+    "sim = MtSimulator(\n",
+    "    unit='USD',\n",
+    "    balance=10000.,\n",
+    "    leverage=100.,\n",
+    "    stop_out_level=0.2,\n",
+    "    hedge=True,\n",
+    "    symbols_filename=FOREX_DATA_PATH\n",
+    ")\n",
+    "\n",
+    "env = MtEnv(\n",
+    "    original_simulator=sim,\n",
+    "    trading_symbols=['GBPCAD', 'EURUSD', 'USDJPY'],\n",
+    "    window_size=10,\n",
+    "    # time_points=[desired time points ...],\n",
+    "    hold_threshold=0.5,\n",
+    "    close_threshold=0.5,\n",
+    "    fee=lambda symbol: {\n",
+    "        'GBPCAD': max(0., np.random.normal(0.0007, 0.00005)),\n",
+    "        'EURUSD': max(0., np.random.normal(0.0002, 0.00003)),\n",
+    "        'USDJPY': max(0., np.random.normal(0.02, 0.003)),\n",
+    "    }[symbol],\n",
+    "    symbol_max_orders=2,\n",
+    "    multiprocessing_processes=2\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Print some information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env information:\n",
+      "> prices[GBPCAD].shape: (88, 2)\n",
+      "> prices[EURUSD].shape: (88, 2)\n",
+      "> prices[USDJPY].shape: (88, 2)\n",
+      "> signal_features.shape: (88, 6)\n",
+      "> features_shape: (10, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"env information:\")\n",
+    "\n",
+    "for symbol in env.prices:\n",
+    "    print(f\"> prices[{symbol}].shape:\", env.prices[symbol].shape)\n",
+    "\n",
+    "print(\"> signal_features.shape:\", env.signal_features.shape)\n",
+    "print(\"> features_shape:\", env.features_shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Trade randomly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "balance: 18179.65219519348, equity: 18179.65219519348, margin: 0.0\n",
+      "free_margin: 18179.65219519348, margin_level: inf\n",
+      "step_reward: 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "observation = env.reset()\n",
+    "\n",
+    "while True:\n",
+    "    action = env.action_space.sample()\n",
+    "    observation, reward, terminated, truncated, info = env.step(action)\n",
+    "    done = terminated or truncated\n",
+    "\n",
+    "    if done:\n",
+    "        # print(info)\n",
+    "        print(\n",
+    "            f\"balance: {info['balance']}, equity: {info['equity']}, margin: {info['margin']}\\n\"\n",
+    "            f\"free_margin: {info['free_margin']}, margin_level: {info['margin_level']}\\n\"\n",
+    "            f\"step_reward: {info['step_reward']}\"\n",
+    "        )\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Render in *human* mode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "balance: 18179.65219519348, equity: 18179.65219519348, margin: 0.0\n",
+      "free_margin: 18179.65219519348, margin_level: inf\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Id</th>\n",
+       "      <th>Symbol</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>Entry Time</th>\n",
+       "      <th>Entry Price</th>\n",
+       "      <th>Exit Time</th>\n",
+       "      <th>Exit Price</th>\n",
+       "      <th>Exit Balance</th>\n",
+       "      <th>Exit Equity</th>\n",
+       "      <th>Profit</th>\n",
+       "      <th>Margin</th>\n",
+       "      <th>Fee</th>\n",
+       "      <th>Closed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14</td>\n",
+       "      <td>EURUSD</td>\n",
+       "      <td>Buy</td>\n",
+       "      <td>9.95</td>\n",
+       "      <td>2021-08-27 00:00:00+00:00</td>\n",
+       "      <td>1.17955</td>\n",
+       "      <td>2021-08-31 00:00:00+00:00</td>\n",
+       "      <td>1.18083</td>\n",
+       "      <td>18179.652195</td>\n",
+       "      <td>18179.652195</td>\n",
+       "      <td>1052.554631</td>\n",
+       "      <td>11736.522500</td>\n",
+       "      <td>0.000222</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13</td>\n",
+       "      <td>EURUSD</td>\n",
+       "      <td>Buy</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>2021-08-26 00:00:00+00:00</td>\n",
+       "      <td>1.17515</td>\n",
+       "      <td>2021-08-31 00:00:00+00:00</td>\n",
+       "      <td>1.18083</td>\n",
+       "      <td>17127.097565</td>\n",
+       "      <td>18179.652195</td>\n",
+       "      <td>120.009649</td>\n",
+       "      <td>258.533000</td>\n",
+       "      <td>0.000225</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>12</td>\n",
+       "      <td>GBPCAD</td>\n",
+       "      <td>Buy</td>\n",
+       "      <td>7.10</td>\n",
+       "      <td>2021-08-24 00:00:00+00:00</td>\n",
+       "      <td>1.72784</td>\n",
+       "      <td>2021-08-26 00:00:00+00:00</td>\n",
+       "      <td>1.73770</td>\n",
+       "      <td>17007.087916</td>\n",
+       "      <td>17007.087916</td>\n",
+       "      <td>5140.996853</td>\n",
+       "      <td>9746.529273</td>\n",
+       "      <td>0.000675</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11</td>\n",
+       "      <td>EURUSD</td>\n",
+       "      <td>Sell</td>\n",
+       "      <td>3.33</td>\n",
+       "      <td>2021-08-20 00:00:00+00:00</td>\n",
+       "      <td>1.16996</td>\n",
+       "      <td>2021-08-23 00:00:00+00:00</td>\n",
+       "      <td>1.17457</td>\n",
+       "      <td>11866.091062</td>\n",
+       "      <td>11866.091062</td>\n",
+       "      <td>-1610.650324</td>\n",
+       "      <td>3895.966800</td>\n",
+       "      <td>0.000227</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10</td>\n",
+       "      <td>GBPCAD</td>\n",
+       "      <td>Buy</td>\n",
+       "      <td>6.65</td>\n",
+       "      <td>2021-07-30 00:00:00+00:00</td>\n",
+       "      <td>1.73335</td>\n",
+       "      <td>2021-08-02 00:00:00+00:00</td>\n",
+       "      <td>1.73577</td>\n",
+       "      <td>13476.741387</td>\n",
+       "      <td>13476.741387</td>\n",
+       "      <td>868.941338</td>\n",
+       "      <td>9248.130601</td>\n",
+       "      <td>0.000786</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>9</td>\n",
+       "      <td>EURUSD</td>\n",
+       "      <td>Sell</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>2021-07-21 00:00:00+00:00</td>\n",
+       "      <td>1.17946</td>\n",
+       "      <td>2021-07-22 00:00:00+00:00</td>\n",
+       "      <td>1.17707</td>\n",
+       "      <td>12607.800048</td>\n",
+       "      <td>12607.800048</td>\n",
+       "      <td>56.809064</td>\n",
+       "      <td>306.659600</td>\n",
+       "      <td>0.000205</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>8</td>\n",
+       "      <td>USDJPY</td>\n",
+       "      <td>Buy</td>\n",
+       "      <td>7.11</td>\n",
+       "      <td>2021-07-12 00:00:00+00:00</td>\n",
+       "      <td>110.34900</td>\n",
+       "      <td>2021-07-16 00:00:00+00:00</td>\n",
+       "      <td>110.08100</td>\n",
+       "      <td>12550.990984</td>\n",
+       "      <td>12550.990984</td>\n",
+       "      <td>-1850.301309</td>\n",
+       "      <td>7110.000000</td>\n",
+       "      <td>0.018474</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>EURUSD</td>\n",
+       "      <td>Buy</td>\n",
+       "      <td>4.23</td>\n",
+       "      <td>2021-07-07 00:00:00+00:00</td>\n",
+       "      <td>1.17903</td>\n",
+       "      <td>2021-07-09 00:00:00+00:00</td>\n",
+       "      <td>1.18774</td>\n",
+       "      <td>14401.292293</td>\n",
+       "      <td>14401.292293</td>\n",
+       "      <td>3618.699910</td>\n",
+       "      <td>4987.296900</td>\n",
+       "      <td>0.000155</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>6</td>\n",
+       "      <td>GBPCAD</td>\n",
+       "      <td>Sell</td>\n",
+       "      <td>2.77</td>\n",
+       "      <td>2021-07-02 00:00:00+00:00</td>\n",
+       "      <td>1.70511</td>\n",
+       "      <td>2021-07-05 00:00:00+00:00</td>\n",
+       "      <td>1.70716</td>\n",
+       "      <td>10782.592383</td>\n",
+       "      <td>10782.592383</td>\n",
+       "      <td>-612.337927</td>\n",
+       "      <td>3831.428119</td>\n",
+       "      <td>0.000678</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>5</td>\n",
+       "      <td>EURUSD</td>\n",
+       "      <td>Sell</td>\n",
+       "      <td>6.07</td>\n",
+       "      <td>2021-06-21 00:00:00+00:00</td>\n",
+       "      <td>1.19185</td>\n",
+       "      <td>2021-06-22 00:00:00+00:00</td>\n",
+       "      <td>1.19413</td>\n",
+       "      <td>11394.930310</td>\n",
+       "      <td>11394.930310</td>\n",
+       "      <td>-1512.813611</td>\n",
+       "      <td>7234.529500</td>\n",
+       "      <td>0.000212</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>4</td>\n",
+       "      <td>USDJPY</td>\n",
+       "      <td>Buy</td>\n",
+       "      <td>4.18</td>\n",
+       "      <td>2021-06-11 00:00:00+00:00</td>\n",
+       "      <td>109.68200</td>\n",
+       "      <td>2021-06-17 00:00:00+00:00</td>\n",
+       "      <td>110.22100</td>\n",
+       "      <td>12907.743921</td>\n",
+       "      <td>12907.743921</td>\n",
+       "      <td>1980.439673</td>\n",
+       "      <td>4180.000000</td>\n",
+       "      <td>0.016785</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>3</td>\n",
+       "      <td>GBPCAD</td>\n",
+       "      <td>Buy</td>\n",
+       "      <td>5.58</td>\n",
+       "      <td>2021-06-01 00:00:00+00:00</td>\n",
+       "      <td>1.70755</td>\n",
+       "      <td>2021-06-02 00:00:00+00:00</td>\n",
+       "      <td>1.70462</td>\n",
+       "      <td>10927.304248</td>\n",
+       "      <td>10927.304248</td>\n",
+       "      <td>-1678.531017</td>\n",
+       "      <td>7894.516666</td>\n",
+       "      <td>0.000689</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2</td>\n",
+       "      <td>EURUSD</td>\n",
+       "      <td>Buy</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>2021-05-26 00:00:00+00:00</td>\n",
+       "      <td>1.21922</td>\n",
+       "      <td>2021-05-28 00:00:00+00:00</td>\n",
+       "      <td>1.21896</td>\n",
+       "      <td>12605.835265</td>\n",
+       "      <td>12605.835265</td>\n",
+       "      <td>-130.546444</td>\n",
+       "      <td>3230.933000</td>\n",
+       "      <td>0.000233</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1</td>\n",
+       "      <td>USDJPY</td>\n",
+       "      <td>Sell</td>\n",
+       "      <td>6.73</td>\n",
+       "      <td>2021-05-19 00:00:00+00:00</td>\n",
+       "      <td>109.22700</td>\n",
+       "      <td>2021-05-20 00:00:00+00:00</td>\n",
+       "      <td>108.76700</td>\n",
+       "      <td>12736.381709</td>\n",
+       "      <td>12736.381709</td>\n",
+       "      <td>2736.381709</td>\n",
+       "      <td>6730.000000</td>\n",
+       "      <td>0.017759</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Id  Symbol  Type  Volume                Entry Time  Entry Price  \\\n",
+       "0   14  EURUSD   Buy    9.95 2021-08-27 00:00:00+00:00      1.17955   \n",
+       "1   13  EURUSD   Buy    0.22 2021-08-26 00:00:00+00:00      1.17515   \n",
+       "2   12  GBPCAD   Buy    7.10 2021-08-24 00:00:00+00:00      1.72784   \n",
+       "3   11  EURUSD  Sell    3.33 2021-08-20 00:00:00+00:00      1.16996   \n",
+       "4   10  GBPCAD   Buy    6.65 2021-07-30 00:00:00+00:00      1.73335   \n",
+       "5    9  EURUSD  Sell    0.26 2021-07-21 00:00:00+00:00      1.17946   \n",
+       "6    8  USDJPY   Buy    7.11 2021-07-12 00:00:00+00:00    110.34900   \n",
+       "7    7  EURUSD   Buy    4.23 2021-07-07 00:00:00+00:00      1.17903   \n",
+       "8    6  GBPCAD  Sell    2.77 2021-07-02 00:00:00+00:00      1.70511   \n",
+       "9    5  EURUSD  Sell    6.07 2021-06-21 00:00:00+00:00      1.19185   \n",
+       "10   4  USDJPY   Buy    4.18 2021-06-11 00:00:00+00:00    109.68200   \n",
+       "11   3  GBPCAD   Buy    5.58 2021-06-01 00:00:00+00:00      1.70755   \n",
+       "12   2  EURUSD   Buy    2.65 2021-05-26 00:00:00+00:00      1.21922   \n",
+       "13   1  USDJPY  Sell    6.73 2021-05-19 00:00:00+00:00    109.22700   \n",
+       "\n",
+       "                   Exit Time  Exit Price  Exit Balance   Exit Equity  \\\n",
+       "0  2021-08-31 00:00:00+00:00     1.18083  18179.652195  18179.652195   \n",
+       "1  2021-08-31 00:00:00+00:00     1.18083  17127.097565  18179.652195   \n",
+       "2  2021-08-26 00:00:00+00:00     1.73770  17007.087916  17007.087916   \n",
+       "3  2021-08-23 00:00:00+00:00     1.17457  11866.091062  11866.091062   \n",
+       "4  2021-08-02 00:00:00+00:00     1.73577  13476.741387  13476.741387   \n",
+       "5  2021-07-22 00:00:00+00:00     1.17707  12607.800048  12607.800048   \n",
+       "6  2021-07-16 00:00:00+00:00   110.08100  12550.990984  12550.990984   \n",
+       "7  2021-07-09 00:00:00+00:00     1.18774  14401.292293  14401.292293   \n",
+       "8  2021-07-05 00:00:00+00:00     1.70716  10782.592383  10782.592383   \n",
+       "9  2021-06-22 00:00:00+00:00     1.19413  11394.930310  11394.930310   \n",
+       "10 2021-06-17 00:00:00+00:00   110.22100  12907.743921  12907.743921   \n",
+       "11 2021-06-02 00:00:00+00:00     1.70462  10927.304248  10927.304248   \n",
+       "12 2021-05-28 00:00:00+00:00     1.21896  12605.835265  12605.835265   \n",
+       "13 2021-05-20 00:00:00+00:00   108.76700  12736.381709  12736.381709   \n",
+       "\n",
+       "         Profit        Margin       Fee  Closed  \n",
+       "0   1052.554631  11736.522500  0.000222    True  \n",
+       "1    120.009649    258.533000  0.000225    True  \n",
+       "2   5140.996853   9746.529273  0.000675    True  \n",
+       "3  -1610.650324   3895.966800  0.000227    True  \n",
+       "4    868.941338   9248.130601  0.000786    True  \n",
+       "5     56.809064    306.659600  0.000205    True  \n",
+       "6  -1850.301309   7110.000000  0.018474    True  \n",
+       "7   3618.699910   4987.296900  0.000155    True  \n",
+       "8   -612.337927   3831.428119  0.000678    True  \n",
+       "9  -1512.813611   7234.529500  0.000212    True  \n",
+       "10  1980.439673   4180.000000  0.016785    True  \n",
+       "11 -1678.531017   7894.516666  0.000689    True  \n",
+       "12  -130.546444   3230.933000  0.000233    True  \n",
+       "13  2736.381709   6730.000000  0.017759    True  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state = env.render()\n",
+    "\n",
+    "print(\n",
+    "    f\"balance: {state['balance']}, equity: {state['equity']}, margin: {state['margin']}\\n\"\n",
+    "    f\"free_margin: {state['free_margin']}, margin_level: {state['margin_level']}\\n\"\n",
+    ")\n",
+    "state['orders']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Render in *simple_figure* mode\n",
+    "\n",
+    "* Each *symbol* is illustrated with a separate color.\n",
+    "* The **green**/**red** triangles show successful **buy**/**sell** actions.\n",
+    "* The **gray** triangles indicate that the **buy**/**sell** action has encountered an **error**.\n",
+    "* The **black** vertical bars specify **close** actions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "env.render('simple_figure')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Render in *advanced_figure* mode\n",
+    "\n",
+    "* Clicking on a symbol name will hide/show its plot.\n",
+    "* Hovering over points and markers will display their detail.\n",
+    "* The size of triangles indicates their relative volume."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "hovertext": [
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+          "balance: 10000.000000 USD<br>equity: 10000.000000<br>margin: 0.000000<br>free margin: 10000.000000<br>margin level: inf",
+          "balance: 10000.000000 USD<br>equity: 10000.000000<br>margin: 0.000000<br>free margin: 10000.000000<br>margin level: inf",
+          "balance: 10000.000000 USD<br>equity: 10000.000000<br>margin: 0.000000<br>free margin: 10000.000000<br>margin level: inf",
+          "balance: 10000.000000 USD<br>equity: 10000.000000<br>margin: 0.000000<br>free margin: 10000.000000<br>margin level: inf",
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+        "title": {
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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "env.render('advanced_figure', time_format=\"%Y-%m-%d\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### A Complete Example using `stable-baselines`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "hovertext": [
+          "balance: 10000.000000 USD<br>equity: 10000.000000<br>margin: 0.000000<br>free margin: 10000.000000<br>margin level: inf",
+          "balance: 10000.000000 USD<br>equity: 10000.000000<br>margin: 0.000000<br>free margin: 10000.000000<br>margin level: inf",
+          "balance: 10000.000000 USD<br>equity: 10000.000000<br>margin: 0.000000<br>free margin: 10000.000000<br>margin level: inf",
+          "balance: 10000.000000 USD<br>equity: 10000.000000<br>margin: 0.000000<br>free margin: 10000.000000<br>margin level: inf",
+          "balance: 10000.000000 USD<br>equity: 10000.000000<br>margin: 0.000000<br>free margin: 10000.000000<br>margin level: inf",
+          "balance: 10000.000000 USD<br>equity: 10000.000000<br>margin: 0.000000<br>free margin: 10000.000000<br>margin level: inf",
+          "balance: 10000.000000 USD<br>equity: 10000.000000<br>margin: 0.000000<br>free margin: 10000.000000<br>margin level: inf",
+          "balance: 10000.000000 USD<br>equity: 10000.000000<br>margin: 0.000000<br>free margin: 10000.000000<br>margin level: inf",
+          "balance: 10000.000000 USD<br>equity: 10000.000000<br>margin: 0.000000<br>free margin: 10000.000000<br>margin level: inf",
+          "balance: 10000.000000 USD<br>equity: 10000.000000<br>margin: 0.000000<br>free margin: 10000.000000<br>margin level: inf",
+          "balance: 10000.000000 USD<br>equity: 10427.993279<br>margin: 1693.331400<br>free margin: 8734.661879<br>margin level: 6.158271",
+          "balance: 10458.293279 USD<br>equity: 9693.437185<br>margin: 2904.659027<br>free margin: 6788.778158<br>margin level: 3.337203",
+          "balance: 10059.737185 USD<br>equity: 10915.543626<br>margin: 4544.226100<br>free margin: 6371.317526<br>margin level: 2.402069",
+          "balance: 10253.053626 USD<br>equity: 10013.189048<br>margin: 3610.715700<br>free margin: 6402.473348<br>margin level: 2.773187",
+          "balance: 10013.189048 USD<br>equity: 9983.029048<br>margin: 97.720000<br>free margin: 9885.309048<br>margin level: 102.159528",
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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import gymnasium as gym\n",
+    "from gym_mtsim import (\n",
+    "    Timeframe, SymbolInfo,\n",
+    "    MtSimulator, OrderType, Order, SymbolNotFound, OrderNotFound,\n",
+    "    MtEnv,\n",
+    "    FOREX_DATA_PATH, STOCKS_DATA_PATH, CRYPTO_DATA_PATH, MIXED_DATA_PATH,\n",
+    ")\n",
+    "from stable_baselines3 import A2C\n",
+    "from stable_baselines3.common.vec_env import DummyVecEnv\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "\n",
+    "env_name = 'forex-hedge-v0'\n",
+    "\n",
+    "# reproduce training and test\n",
+    "seed = 2024\n",
+    "random.seed(seed)\n",
+    "np.random.seed(seed)\n",
+    "torch.manual_seed(seed)\n",
+    "\n",
+    "env = gym.make(env_name)\n",
+    "model = A2C('MultiInputPolicy', env, verbose=0)\n",
+    "model.learn(total_timesteps=1000)\n",
+    "\n",
+    "observation, info = env.reset(seed=seed)\n",
+    "\n",
+    "while True:\n",
+    "    action, _states = model.predict(observation)\n",
+    "    observation, reward, terminated, truncated, info = env.step(action)\n",
+    "    done = terminated or truncated\n",
+    "\n",
+    "    if done:\n",
+    "        break\n",
+    "\n",
+    "env.unwrapped.render('advanced_figure', time_format='%Y-%m-%d')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "* [https://www.mql5.com/en/docs/python_metatrader5](https://www.mql5.com/en/docs/python_metatrader5)\n",
+    "* [https://www.metatrader5.com/en/terminal/help/trading_advanced/margin_forex](https://www.metatrader5.com/en/terminal/help/trading_advanced/margin_forex)\n",
+    "* [https://admiralmarkets.com/education/articles/forex-basics/margin-in-forex-trading-margin-level-vs-margin-call](https://admiralmarkets.com/education/articles/forex-basics/margin-in-forex-trading-margin-level-vs-margin-call)\n",
+    "* [https://www.investopedia.com/articles/forex/12/calculating-profits-and-losses-of-forex-trades.asp](https://www.investopedia.com/articles/forex/12/calculating-profits-and-losses-of-forex-trades.asp)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "0abee77d591a174194b91b850e12395de882ac6d36de3e6e63e8904e4cff1216"
+  },
+  "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": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/trade_flow/environments/gym-mtsim/README.md b/trade_flow/environments/gym-mtsim/README.md
new file mode 100644
index 0000000..2876d39
--- /dev/null
+++ b/trade_flow/environments/gym-mtsim/README.md
@@ -0,0 +1,929 @@
+# gym-mtsim: OpenAI Gym - MetaTrader 5 Simulator
+
+`MtSim` is a simulator for the [MetaTrader 5](https://www.metatrader5.com) trading platform alongside an [OpenAI Gym](https://github.com/openai/gym) environment for reinforcement learning-based trading algorithms. `MetaTrader 5` is a **multi-asset** platform that allows trading **Forex**, **Stocks**, **Crypto**, and Futures. It is one of the most popular trading platforms and supports numerous useful features, such as opening demo accounts on various brokers.
+
+The simulator is separated from the Gym environment and can work independently. Although the Gym environment is designed to be suitable for RL frameworks, it is also proper for backtesting and classic analysis.
+
+The goal of this project was to provide a *general-purpose*, *flexible*, and *easy-to-use* library with a focus on *code readability* that enables users to do all parts of the trading process through it from 0 to 100. So, `gym-mtsim` is not just a testing tool or a Gym environment. It is a combination of a **real-world** simulator, a **backtesting** tool with *high detail visualization*, and a **Gym environment** appropriate for RL/classic algorithms.
+
+**Note:** For beginners, it is recommended to check out the [gym-anytrading](https://github.com/AminHP/gym-anytrading) project.
+
+## Prerequisites
+
+### Install MetaTrader 5
+Download and install MetaTrader 5 software from [here](https://www.metatrader5.com/en/download).
+
+Open a demo account on any broker. By default, the software opens a demo account automatically after installation.
+
+Explore the software and try to get familiar with it by trading different symbols in both **hedged** and **unhedged** accounts.
+
+### Install gym-mtsim
+
+#### Via PIP
+```bash
+pip install gym-mtsim
+```
+
+#### From Repository
+```bash
+git clone https://github.com/AminHP/gym-mtsim
+cd gym-mtsim
+pip install -e .
+
+## or
+
+pip install --upgrade --no-deps --force-reinstall https://github.com/AminHP/gym-mtsim/archive/main.zip
+```
+
+### Install stable-baselines3
+This package is required to run some examples. Install it from [here](https://github.com/DLR-RM/stable-baselines3#installation).
+
+## Components
+
+### 1. SymbolInfo
+
+This is a data class that contains the essential properties of a symbol. Try to get fully acquainted with [these properties](https://github.com/AminHP/gym-mtsim/blob/main/gym_mtsim/metatrader/symbol.py) in case they are unfamiliar. There are plenty of resources that provide good explanations.
+
+### 2. Order
+
+This is another data class that consists of information of an order. Each order has the following properties:
+
+> `id`: A unique number that helps with tracking orders.
+>
+> `type`: An enum that specifies the type of the order. It can be either **Buy** or **Sell**.
+>
+> `symbol`: The symbol selected for the order.
+>
+> `volume`: The volume chose for the order. It can be a multiple of *volume_step* between *volume_min* and *volume_max*. 
+>
+> `fee`: It is a tricky property. In MetaTrader, there is *no* such concept called fee. Each symbol has bid and ask prices, the difference between which represents the **fee**. Although MetaTrader API provides these bid/ask prices for the recent past, it is not possible to access them for the distant past. Therefore, the **fee** property helps to manage the mentioned difference.
+>
+> `entry_time`: The time when the order was placed.
+>
+> `entry_price`: The **close** price when the order was placed.
+>
+> `exit_time`: The time when the order was closed.
+>
+> `exit_price`: The **close** price when the order was closed.
+>
+> `profit`: The amount of profit earned by this order so far.
+>
+> `margin`: The required amount of margin for this order.
+>
+> `closed`: A boolean that specifies whether this order is closed or not.
+
+### 3. MtSimulator
+
+This is the core class that simulates the main parts of MetaTrader. Most of its public properties and methods are explained here. But feel free to take a look at the complete [source code](https://github.com/AminHP/gym-mtsim/blob/main/gym_mtsim/simulator/mt_simulator.py).
+
+* Properties:
+
+    > `unit`: The unit currency. It is usually *USD*, but it can be anything the broker allows, such as *EUR*.
+    >
+    > `balance`: The amount of money before taking into account any open positions.
+    >
+    > `equity`: The amount of money, including the value of any open positions.
+    >
+    > `margin`: The amount of money which is required for having positions opened.
+    >
+    > `leverage`: The leverage ratio.
+    >
+    > `free_margin`: The amount of money that is available to open new positions.
+    >
+    > `margin_level`: The ratio between **equity** and **margin**.
+    >
+    > `stop_out_level`: If the **margin_level** drops below **stop_out_level**, the most unprofitable position will be closed automatically by the broker.
+    >
+    > `hedge`: A boolean that specifies whether hedging is enabled or not.
+    >
+    > `symbols_info`: A dictionary that contains symbols' information.
+    >
+    > `symbols_data`: A dictionary that contains symbols' OHLCV data.
+    >
+    > `orders`: The list of open orders.
+    >
+    > `closed_orders`: The list of closed orders.
+    >
+    > `current_time`: The current time of the system.
+
+* Methods:
+
+    > `download_data`: Downloads required data from MetaTrader for a list of symbols in a time range. This method can be overridden in order to download data from servers other than MetaTrader. *Note that this method only works on Windows, as the MetaTrader5 Python package is not available on other platforms.*
+    >
+    > `save_symbols`: Saves the downloaded symbols' data to a file.
+    >
+    > `load_symbols`: Loads the symbols' data from a file.
+    >
+    > `tick`: Moves forward in time (by a delta time) and updates orders and other related properties.
+    >
+    > `create_order`: Creates a **Buy** or **Sell** order and updates related properties.
+    >
+    > `close_order`: Closes an order and updates related properties.
+    >
+    > `get_state`: Returns the state of the system. The result is similar to the *Trading tab* and *History tab* of the *Toolbox window* in MetaTrader software.
+
+### 4. MtEnv
+
+This is the Gym environment that works on top of the *MtSim*. Most of its public properties and methods are explained here. But feel free to take a look at the complete [source code](https://github.com/AminHP/gym-mtsim/blob/main/gym_mtsim/envs/mt_env.py).
+
+* Properties:
+
+    > `original_simulator`: An instance of **MtSim** class as a baseline for simulating the system.
+    >
+    > `simulator`: The current simulator in use. It is a copy of the **original_simulator**.
+    >
+    > `trading_symbols`: The list of symbols to trade.
+    >
+    > `time_points`: A list of time points based on which the simulator moves time. The default value is taken from the *pandas DataFrame.Index* of the first symbol in the **trading_symbols** list.
+    >
+    > `hold_threshold`: A probability threshold that controls holding or placing a new order.
+    >
+    > `close_threshold`: A probability threshold that controls closing an order.
+    >
+    > `fee`: A constant number or a callable that takes a *symbol* as input and returns the **fee** based on that.
+    >
+    > `symbol_max_orders`: Specifies the maximum number of open positions per symbol in hedge trading. 
+    >
+    > `multiprocessing_processes`: Specifies the maximum number of processes used for parallel processing.
+    >
+    > `prices`: The symbol prices over time. It is used to calculate signal features and render the environment.
+    >
+    > `signal_features`: The extracted features over time. It is used to generate *Gym observations*.
+    >
+    > `window_size`: The number of time points (current and previous points) as the length of each observation's features. 
+    >
+    > `features_shape`: The shape of a single observation's features.
+    >
+    > `action_space`: The *Gym action_space* property. It has a complex structure since **stable-baselines** does not support *Dict* or *2D Box* action spaces. The action space is a 1D vector of size `count(trading_symbols) * (symbol_max_orders + 2)`. For each symbol, two types of actions can be performed, closing previous orders and placing a new order. The former is controlled by the first *symbol_max_orders* elements and the latter is controlled by the last two elements. Therefore, the action for each symbol is ***[probability of closing order 1, probability of closing order 2, ..., probability of closing order symbol_max_orders, probability of holding or creating a new order, volume of the new order]***. The last two elements specify whether to hold or place a new order and the volume of the new order (positive volume indicates buy and negative volume indicates sell). These elements are a number in range (-∞, ∞), but the probability values must be in the range [0, 1]. This is a problem with **stable-baselines** as mentioned earlier. To overcome this problem, it is assumed that the probability values belong to the [logit](https://en.wikipedia.org/wiki/Logit) function. So, applying the [expit](https://en.wikipedia.org/wiki/Expit) function on them gives the desired probability values in the range [0, 1]. This function is applied in the **step** method of the environment.
+    >
+    > `observation_space`: The *Gym observation_space* property. Each observation contains information about *balance*, *equity*, *margin*, *features*, and *orders*. The **features** is a window on the *signal_features* from index *current_tick - window_size + 1* to *current_tick*. The **orders** is a 3D array. Its first dimension specifies the symbol index in the *trading_symbols* list. The second dimension specifies the order number (each symbol can have more than one open order at the same time in hedge trading). The last dimension has three elements, *entry_price*, *volume*, and *profit* of corresponding order.
+    >
+    > `history`: Stores the information of all steps.
+
+* Methods:
+
+    > `seed`: The typical *Gym seed* method.
+    >
+    > `reset`: The typical *Gym reset* method.
+    >
+    > `step`: The typical *Gym step* method.
+    >
+    > `render`: The typical *Gym render* method. It can render in three modes, **human**, **simple_figure**, and **advanced_figure**.
+    >
+    > `close`: The typical *Gym close* method.
+
+* Virtual Methods:
+
+    > `_get_prices`: It is called in the constructor and calculates symbol **prices**.
+    >
+    > `_process_data`: It is called in the constructor and calculates **signal_features**.
+    >
+    > `_calculate_reward`: The reward function for the RL agent.
+
+## A Simple Example
+
+### MtSim
+
+#### Create a simulator with custom parameters
+
+
+```python
+import pytz
+from datetime import datetime, timedelta
+from gym_mtsim import MtSimulator, OrderType, Timeframe, FOREX_DATA_PATH
+
+
+sim = MtSimulator(
+    unit='USD',
+    balance=10000.,
+    leverage=100.,
+    stop_out_level=0.2,
+    hedge=False,
+)
+
+if not sim.load_symbols(FOREX_DATA_PATH):
+    sim.download_data(
+        symbols=['EURUSD', 'GBPCAD', 'GBPUSD', 'USDCAD', 'USDCHF', 'GBPJPY', 'USDJPY'],
+        time_range=(
+            datetime(2021, 5, 5, tzinfo=pytz.UTC),
+            datetime(2021, 9, 5, tzinfo=pytz.UTC)
+        ),
+        timeframe=Timeframe.D1
+    )
+    sim.save_symbols(FOREX_DATA_PATH)
+```
+
+#### Place some orders
+
+
+```python
+sim.current_time = datetime(2021, 8, 30, 0, 17, 52, tzinfo=pytz.UTC)
+
+order1 = sim.create_order(
+    order_type=OrderType.Buy,
+    symbol='GBPCAD',
+    volume=1.,
+    fee=0.0003,
+)
+
+sim.tick(timedelta(days=2))
+
+order2 = sim.create_order(
+    order_type=OrderType.Sell,
+    symbol='USDJPY',
+    volume=2.,
+    fee=0.01,
+)
+
+sim.tick(timedelta(days=5))
+
+state = sim.get_state()
+
+print(
+    f"balance: {state['balance']}, equity: {state['equity']}, margin: {state['margin']}\n"
+    f"free_margin: {state['free_margin']}, margin_level: {state['margin_level']}\n"
+)
+state['orders']
+```
+
+    balance: 10000.0, equity: 10717.58118589908, margin: 3375.480933228619
+    free_margin: 7342.1002526704615, margin_level: 3.1751271592500743
+    
+    
+
+
+
+
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Id</th>
+      <th>Symbol</th>
+      <th>Type</th>
+      <th>Volume</th>
+      <th>Entry Time</th>
+      <th>Entry Price</th>
+      <th>Exit Time</th>
+      <th>Exit Price</th>
+      <th>Exit Balance</th>
+      <th>Exit Equity</th>
+      <th>Profit</th>
+      <th>Margin</th>
+      <th>Fee</th>
+      <th>Closed</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>2</td>
+      <td>USDJPY</td>
+      <td>Sell</td>
+      <td>2.0</td>
+      <td>2021-09-01 00:17:52+00:00</td>
+      <td>110.02500</td>
+      <td>2021-09-06 00:17:52+00:00</td>
+      <td>109.71200</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>552.355257</td>
+      <td>2000.000000</td>
+      <td>0.0100</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>GBPCAD</td>
+      <td>Buy</td>
+      <td>1.0</td>
+      <td>2021-08-30 00:17:52+00:00</td>
+      <td>1.73389</td>
+      <td>2021-09-06 00:17:52+00:00</td>
+      <td>1.73626</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>165.225928</td>
+      <td>1375.480933</td>
+      <td>0.0003</td>
+      <td>False</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+#### Close all orders
+
+
+```python
+order1_profit = sim.close_order(order1)
+order2_profit = sim.close_order(order2)
+
+# alternatively:
+# for order in sim.orders:
+#     sim.close_order(order)
+
+state = sim.get_state()
+
+print(
+    f"balance: {state['balance']}, equity: {state['equity']}, margin: {state['margin']}\n"
+    f"free_margin: {state['free_margin']}, margin_level: {state['margin_level']}\n"
+)
+state['orders']
+```
+
+    balance: 10717.58118589908, equity: 10717.58118589908, margin: 0.0
+    free_margin: 10717.58118589908, margin_level: inf
+    
+    
+
+
+
+
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Id</th>
+      <th>Symbol</th>
+      <th>Type</th>
+      <th>Volume</th>
+      <th>Entry Time</th>
+      <th>Entry Price</th>
+      <th>Exit Time</th>
+      <th>Exit Price</th>
+      <th>Exit Balance</th>
+      <th>Exit Equity</th>
+      <th>Profit</th>
+      <th>Margin</th>
+      <th>Fee</th>
+      <th>Closed</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>2</td>
+      <td>USDJPY</td>
+      <td>Sell</td>
+      <td>2.0</td>
+      <td>2021-09-01 00:17:52+00:00</td>
+      <td>110.02500</td>
+      <td>2021-09-06 00:17:52+00:00</td>
+      <td>109.71200</td>
+      <td>10717.581186</td>
+      <td>10717.581186</td>
+      <td>552.355257</td>
+      <td>2000.000000</td>
+      <td>0.0100</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>GBPCAD</td>
+      <td>Buy</td>
+      <td>1.0</td>
+      <td>2021-08-30 00:17:52+00:00</td>
+      <td>1.73389</td>
+      <td>2021-09-06 00:17:52+00:00</td>
+      <td>1.73626</td>
+      <td>10165.225928</td>
+      <td>10717.581186</td>
+      <td>165.225928</td>
+      <td>1375.480933</td>
+      <td>0.0003</td>
+      <td>True</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+### MtEnv
+
+#### Create an environment
+
+
+```python
+import gymnasium as gym
+import gym_mtsim
+
+env = gym.make('forex-hedge-v0')
+# env = gym.make('stocks-hedge-v0')
+# env = gym.make('crypto-hedge-v0')
+# env = gym.make('mixed-hedge-v0')
+
+# env = gym.make('forex-unhedge-v0')
+# env = gym.make('stocks-unhedge-v0')
+# env = gym.make('crypto-unhedge-v0')
+# env = gym.make('mixed-unhedge-v0')
+```
+
+* This will create a default environment. There are eight default environments, but it is also possible to create environments with custom parameters.
+
+#### Create an environment with custom parameters
+
+
+```python
+import pytz
+from datetime import datetime, timedelta
+import numpy as np
+from gym_mtsim import MtEnv, MtSimulator, FOREX_DATA_PATH
+
+
+sim = MtSimulator(
+    unit='USD',
+    balance=10000.,
+    leverage=100.,
+    stop_out_level=0.2,
+    hedge=True,
+    symbols_filename=FOREX_DATA_PATH
+)
+
+env = MtEnv(
+    original_simulator=sim,
+    trading_symbols=['GBPCAD', 'EURUSD', 'USDJPY'],
+    window_size=10,
+    # time_points=[desired time points ...],
+    hold_threshold=0.5,
+    close_threshold=0.5,
+    fee=lambda symbol: {
+        'GBPCAD': max(0., np.random.normal(0.0007, 0.00005)),
+        'EURUSD': max(0., np.random.normal(0.0002, 0.00003)),
+        'USDJPY': max(0., np.random.normal(0.02, 0.003)),
+    }[symbol],
+    symbol_max_orders=2,
+    multiprocessing_processes=2
+)
+```
+
+#### Print some information
+
+
+```python
+print("env information:")
+
+for symbol in env.prices:
+    print(f"> prices[{symbol}].shape:", env.prices[symbol].shape)
+
+print("> signal_features.shape:", env.signal_features.shape)
+print("> features_shape:", env.features_shape)
+```
+
+    env information:
+    > prices[GBPCAD].shape: (88, 2)
+    > prices[EURUSD].shape: (88, 2)
+    > prices[USDJPY].shape: (88, 2)
+    > signal_features.shape: (88, 6)
+    > features_shape: (10, 6)
+    
+
+#### Trade randomly
+
+
+```python
+observation = env.reset()
+
+while True:
+    action = env.action_space.sample()
+    observation, reward, terminated, truncated, info = env.step(action)
+    done = terminated or truncated
+
+    if done:
+        # print(info)
+        print(
+            f"balance: {info['balance']}, equity: {info['equity']}, margin: {info['margin']}\n"
+            f"free_margin: {info['free_margin']}, margin_level: {info['margin_level']}\n"
+            f"step_reward: {info['step_reward']}"
+        )
+        break
+```
+
+    balance: 18179.65219519348, equity: 18179.65219519348, margin: 0.0
+    free_margin: 18179.65219519348, margin_level: inf
+    step_reward: 0.0
+    
+
+#### Render in *human* mode
+
+
+```python
+state = env.render()
+
+print(
+    f"balance: {state['balance']}, equity: {state['equity']}, margin: {state['margin']}\n"
+    f"free_margin: {state['free_margin']}, margin_level: {state['margin_level']}\n"
+)
+state['orders']
+```
+
+    balance: 18179.65219519348, equity: 18179.65219519348, margin: 0.0
+    free_margin: 18179.65219519348, margin_level: inf
+    
+    
+
+
+
+
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Id</th>
+      <th>Symbol</th>
+      <th>Type</th>
+      <th>Volume</th>
+      <th>Entry Time</th>
+      <th>Entry Price</th>
+      <th>Exit Time</th>
+      <th>Exit Price</th>
+      <th>Exit Balance</th>
+      <th>Exit Equity</th>
+      <th>Profit</th>
+      <th>Margin</th>
+      <th>Fee</th>
+      <th>Closed</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>14</td>
+      <td>EURUSD</td>
+      <td>Buy</td>
+      <td>9.95</td>
+      <td>2021-08-27 00:00:00+00:00</td>
+      <td>1.17955</td>
+      <td>2021-08-31 00:00:00+00:00</td>
+      <td>1.18083</td>
+      <td>18179.652195</td>
+      <td>18179.652195</td>
+      <td>1052.554631</td>
+      <td>11736.522500</td>
+      <td>0.000222</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>13</td>
+      <td>EURUSD</td>
+      <td>Buy</td>
+      <td>0.22</td>
+      <td>2021-08-26 00:00:00+00:00</td>
+      <td>1.17515</td>
+      <td>2021-08-31 00:00:00+00:00</td>
+      <td>1.18083</td>
+      <td>17127.097565</td>
+      <td>18179.652195</td>
+      <td>120.009649</td>
+      <td>258.533000</td>
+      <td>0.000225</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>12</td>
+      <td>GBPCAD</td>
+      <td>Buy</td>
+      <td>7.10</td>
+      <td>2021-08-24 00:00:00+00:00</td>
+      <td>1.72784</td>
+      <td>2021-08-26 00:00:00+00:00</td>
+      <td>1.73770</td>
+      <td>17007.087916</td>
+      <td>17007.087916</td>
+      <td>5140.996853</td>
+      <td>9746.529273</td>
+      <td>0.000675</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>11</td>
+      <td>EURUSD</td>
+      <td>Sell</td>
+      <td>3.33</td>
+      <td>2021-08-20 00:00:00+00:00</td>
+      <td>1.16996</td>
+      <td>2021-08-23 00:00:00+00:00</td>
+      <td>1.17457</td>
+      <td>11866.091062</td>
+      <td>11866.091062</td>
+      <td>-1610.650324</td>
+      <td>3895.966800</td>
+      <td>0.000227</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>10</td>
+      <td>GBPCAD</td>
+      <td>Buy</td>
+      <td>6.65</td>
+      <td>2021-07-30 00:00:00+00:00</td>
+      <td>1.73335</td>
+      <td>2021-08-02 00:00:00+00:00</td>
+      <td>1.73577</td>
+      <td>13476.741387</td>
+      <td>13476.741387</td>
+      <td>868.941338</td>
+      <td>9248.130601</td>
+      <td>0.000786</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>9</td>
+      <td>EURUSD</td>
+      <td>Sell</td>
+      <td>0.26</td>
+      <td>2021-07-21 00:00:00+00:00</td>
+      <td>1.17946</td>
+      <td>2021-07-22 00:00:00+00:00</td>
+      <td>1.17707</td>
+      <td>12607.800048</td>
+      <td>12607.800048</td>
+      <td>56.809064</td>
+      <td>306.659600</td>
+      <td>0.000205</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>8</td>
+      <td>USDJPY</td>
+      <td>Buy</td>
+      <td>7.11</td>
+      <td>2021-07-12 00:00:00+00:00</td>
+      <td>110.34900</td>
+      <td>2021-07-16 00:00:00+00:00</td>
+      <td>110.08100</td>
+      <td>12550.990984</td>
+      <td>12550.990984</td>
+      <td>-1850.301309</td>
+      <td>7110.000000</td>
+      <td>0.018474</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>7</td>
+      <td>EURUSD</td>
+      <td>Buy</td>
+      <td>4.23</td>
+      <td>2021-07-07 00:00:00+00:00</td>
+      <td>1.17903</td>
+      <td>2021-07-09 00:00:00+00:00</td>
+      <td>1.18774</td>
+      <td>14401.292293</td>
+      <td>14401.292293</td>
+      <td>3618.699910</td>
+      <td>4987.296900</td>
+      <td>0.000155</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>6</td>
+      <td>GBPCAD</td>
+      <td>Sell</td>
+      <td>2.77</td>
+      <td>2021-07-02 00:00:00+00:00</td>
+      <td>1.70511</td>
+      <td>2021-07-05 00:00:00+00:00</td>
+      <td>1.70716</td>
+      <td>10782.592383</td>
+      <td>10782.592383</td>
+      <td>-612.337927</td>
+      <td>3831.428119</td>
+      <td>0.000678</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>5</td>
+      <td>EURUSD</td>
+      <td>Sell</td>
+      <td>6.07</td>
+      <td>2021-06-21 00:00:00+00:00</td>
+      <td>1.19185</td>
+      <td>2021-06-22 00:00:00+00:00</td>
+      <td>1.19413</td>
+      <td>11394.930310</td>
+      <td>11394.930310</td>
+      <td>-1512.813611</td>
+      <td>7234.529500</td>
+      <td>0.000212</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>10</th>
+      <td>4</td>
+      <td>USDJPY</td>
+      <td>Buy</td>
+      <td>4.18</td>
+      <td>2021-06-11 00:00:00+00:00</td>
+      <td>109.68200</td>
+      <td>2021-06-17 00:00:00+00:00</td>
+      <td>110.22100</td>
+      <td>12907.743921</td>
+      <td>12907.743921</td>
+      <td>1980.439673</td>
+      <td>4180.000000</td>
+      <td>0.016785</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>11</th>
+      <td>3</td>
+      <td>GBPCAD</td>
+      <td>Buy</td>
+      <td>5.58</td>
+      <td>2021-06-01 00:00:00+00:00</td>
+      <td>1.70755</td>
+      <td>2021-06-02 00:00:00+00:00</td>
+      <td>1.70462</td>
+      <td>10927.304248</td>
+      <td>10927.304248</td>
+      <td>-1678.531017</td>
+      <td>7894.516666</td>
+      <td>0.000689</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>12</th>
+      <td>2</td>
+      <td>EURUSD</td>
+      <td>Buy</td>
+      <td>2.65</td>
+      <td>2021-05-26 00:00:00+00:00</td>
+      <td>1.21922</td>
+      <td>2021-05-28 00:00:00+00:00</td>
+      <td>1.21896</td>
+      <td>12605.835265</td>
+      <td>12605.835265</td>
+      <td>-130.546444</td>
+      <td>3230.933000</td>
+      <td>0.000233</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>13</th>
+      <td>1</td>
+      <td>USDJPY</td>
+      <td>Sell</td>
+      <td>6.73</td>
+      <td>2021-05-19 00:00:00+00:00</td>
+      <td>109.22700</td>
+      <td>2021-05-20 00:00:00+00:00</td>
+      <td>108.76700</td>
+      <td>12736.381709</td>
+      <td>12736.381709</td>
+      <td>2736.381709</td>
+      <td>6730.000000</td>
+      <td>0.017759</td>
+      <td>True</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+#### Render in *simple_figure* mode
+
+* Each *symbol* is illustrated with a separate color.
+* The **green**/**red** triangles show successful **buy**/**sell** actions.
+* The **gray** triangles indicate that the **buy**/**sell** action has encountered an **error**.
+* The **black** vertical bars specify **close** actions.
+
+
+```python
+env.render('simple_figure')
+```
+
+
+    
+![png](doc/output_28_0.png)
+    
+
+
+#### Render in *advanced_figure* mode
+
+* Clicking on a symbol name will hide/show its plot.
+* Hovering over points and markers will display their detail.
+* The size of triangles indicates their relative volume.
+
+
+```python
+env.render('advanced_figure', time_format="%Y-%m-%d")
+```
+
+
+    
+![png](doc/output_30_0.png)
+    
+
+
+### A Complete Example using `stable-baselines`
+
+
+```python
+import gymnasium as gym
+from gym_mtsim import (
+    Timeframe, SymbolInfo,
+    MtSimulator, OrderType, Order, SymbolNotFound, OrderNotFound,
+    MtEnv,
+    FOREX_DATA_PATH, STOCKS_DATA_PATH, CRYPTO_DATA_PATH, MIXED_DATA_PATH,
+)
+from stable_baselines3 import A2C
+from stable_baselines3.common.vec_env import DummyVecEnv
+import random
+import numpy as np
+import torch
+
+env_name = 'forex-hedge-v0'
+
+# reproduce training and test
+seed = 2024
+random.seed(seed)
+np.random.seed(seed)
+torch.manual_seed(seed)
+
+env = gym.make(env_name)
+model = A2C('MultiInputPolicy', env, verbose=0)
+model.learn(total_timesteps=1000)
+
+observation, info = env.reset(seed=seed)
+
+while True:
+    action, _states = model.predict(observation)
+    observation, reward, terminated, truncated, info = env.step(action)
+    done = terminated or truncated
+
+    if done:
+        break
+
+env.unwrapped.render('advanced_figure', time_format='%Y-%m-%d')
+```
+
+
+    
+![png](doc/output_32_0.png)
+    
+
+
+## References
+
+* [https://www.mql5.com/en/docs/python_metatrader5](https://www.mql5.com/en/docs/python_metatrader5)
+* [https://www.metatrader5.com/en/terminal/help/trading_advanced/margin_forex](https://www.metatrader5.com/en/terminal/help/trading_advanced/margin_forex)
+* [https://admiralmarkets.com/education/articles/forex-basics/margin-in-forex-trading-margin-level-vs-margin-call](https://admiralmarkets.com/education/articles/forex-basics/margin-in-forex-trading-margin-level-vs-margin-call)
+* [https://www.investopedia.com/articles/forex/12/calculating-profits-and-losses-of-forex-trades.asp](https://www.investopedia.com/articles/forex/12/calculating-profits-and-losses-of-forex-trades.asp)
+
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diff --git a/trade_flow/environments/gym-mtsim/examples/SB3_a2c_ppo.ipynb b/trade_flow/environments/gym-mtsim/examples/SB3_a2c_ppo.ipynb
new file mode 100644
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+++ b/trade_flow/environments/gym-mtsim/examples/SB3_a2c_ppo.ipynb
@@ -0,0 +1,392 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Imports"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "from tqdm import tqdm\n",
+        "import random\n",
+        "\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "import gymnasium as gym\n",
+        "import gym_mtsim\n",
+        "\n",
+        "from stable_baselines3 import A2C, PPO\n",
+        "from stable_baselines3.common.callbacks import BaseCallback\n",
+        "\n",
+        "import torch"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create Env"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# env_name = 'forex-hedge-v0'\n",
+        "env_name = 'stocks-hedge-v0'\n",
+        "# env_name = 'crypto-hedge-v0'\n",
+        "# env_name = 'mixed-hedge-v0'\n",
+        "\n",
+        "# env_name = 'forex-unhedge-v0'\n",
+        "# env_name = 'stocks-unhedge-v0'\n",
+        "# env_name = 'crypto-unhedge-v0'\n",
+        "# env_name = 'mixed-unhedge-v0'\n",
+        "\n",
+        "env = gym.make(env_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Define Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def print_stats(reward_over_episodes):\n",
+        "    \"\"\"  Print Reward  \"\"\"\n",
+        "\n",
+        "    avg = np.mean(reward_over_episodes)\n",
+        "    min = np.min(reward_over_episodes)\n",
+        "    max = np.max(reward_over_episodes)\n",
+        "\n",
+        "    print (f'Min. Reward          : {min:>10.3f}')\n",
+        "    print (f'Avg. Reward          : {avg:>10.3f}')\n",
+        "    print (f'Max. Reward          : {max:>10.3f}')\n",
+        "\n",
+        "    return min, avg, max\n",
+        "\n",
+        "\n",
+        "# ProgressBarCallback for model.learn()\n",
+        "class ProgressBarCallback(BaseCallback):\n",
+        "\n",
+        "    def __init__(self, check_freq: int, verbose: int = 1):\n",
+        "        super().__init__(verbose)\n",
+        "        self.check_freq = check_freq\n",
+        "\n",
+        "    def _on_training_start(self) -> None:\n",
+        "        \"\"\"\n",
+        "        This method is called before the first rollout starts.\n",
+        "        \"\"\"\n",
+        "        self.progress_bar = tqdm(total=self.model._total_timesteps, desc=\"model.learn()\")\n",
+        "\n",
+        "    def _on_step(self) -> bool:\n",
+        "        if self.n_calls % self.check_freq == 0:\n",
+        "            self.progress_bar.update(self.check_freq)\n",
+        "        return True\n",
+        "    \n",
+        "    def _on_training_end(self) -> None:\n",
+        "        \"\"\"\n",
+        "        This event is triggered before exiting the `learn()` method.\n",
+        "        \"\"\"\n",
+        "        self.progress_bar.close()\n",
+        "\n",
+        "\n",
+        "# TRAINING + TEST\n",
+        "def train_test_model(model, env, seed, total_num_episodes, total_learning_timesteps=10_000):\n",
+        "    \"\"\" if model=None then execute 'Random actions' \"\"\"\n",
+        "\n",
+        "    # reproduce training and test\n",
+        "    print('-' * 80)\n",
+        "    obs = env.reset(seed=seed)\n",
+        "    torch.manual_seed(seed)\n",
+        "    random.seed(seed)\n",
+        "    np.random.seed(seed)\n",
+        "\n",
+        "    vec_env = None\n",
+        "\n",
+        "    if model is not None:\n",
+        "        print(f'model {type(model)}')\n",
+        "        print(f'policy {type(model.policy)}')\n",
+        "        # print(f'model.learn(): {total_learning_timesteps} timesteps ...')\n",
+        "\n",
+        "        # custom callback for 'progress_bar'\n",
+        "        model.learn(total_timesteps=total_learning_timesteps, callback=ProgressBarCallback(100))\n",
+        "        # model.learn(total_timesteps=total_learning_timesteps, progress_bar=True)\n",
+        "        # ImportError: You must install tqdm and rich in order to use the progress bar callback. \n",
+        "        # It is included if you install stable-baselines with the extra packages: `pip install stable-baselines3[extra]`\n",
+        "\n",
+        "        vec_env = model.get_env()\n",
+        "        obs = vec_env.reset()\n",
+        "    else:\n",
+        "        print (\"RANDOM actions\")\n",
+        "\n",
+        "    reward_over_episodes = []\n",
+        "\n",
+        "    tbar = tqdm(range(total_num_episodes))\n",
+        "\n",
+        "    for episode in tbar:\n",
+        "        \n",
+        "        if vec_env: \n",
+        "            obs = vec_env.reset()\n",
+        "        else:\n",
+        "            obs, info = env.reset()\n",
+        "\n",
+        "        total_reward = 0\n",
+        "        done = False\n",
+        "\n",
+        "        while not done:\n",
+        "            if model is not None:\n",
+        "                action, _states = model.predict(obs)\n",
+        "                obs, reward, done, info = vec_env.step(action)\n",
+        "            else: # random\n",
+        "                action = env.action_space.sample()\n",
+        "                obs, reward, terminated, truncated, info = env.step(action)\n",
+        "                done = terminated or truncated\n",
+        "\n",
+        "            total_reward += reward\n",
+        "            if done:\n",
+        "                break\n",
+        "\n",
+        "        reward_over_episodes.append(total_reward)\n",
+        "\n",
+        "        if episode % 10 == 0:\n",
+        "            avg_reward = np.mean(reward_over_episodes)\n",
+        "            tbar.set_description(f'Episode: {episode}, Avg. Reward: {avg_reward:.3f}')\n",
+        "            tbar.update()\n",
+        "\n",
+        "    tbar.close()\n",
+        "    avg_reward = np.mean(reward_over_episodes)\n",
+        "\n",
+        "    return reward_over_episodes"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Train + Test Env"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "env_name                 : stocks-hedge-v0\n",
+            "seed                     : 2024\n",
+            "--------------------------------------------------------------------------------\n",
+            "RANDOM actions\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Episode: 40, Avg. Reward: -807.611: 100%|██████████| 50/50 [00:02<00:00, 18.58it/s] \n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Min. Reward          : -10000.000\n",
+            "Avg. Reward          :   -351.235\n",
+            "Max. Reward          :  21032.950\n",
+            "--------------------------------------------------------------------------------\n",
+            "model <class 'stable_baselines3.a2c.a2c.A2C'>\n",
+            "policy <class 'stable_baselines3.common.policies.MultiInputActorCriticPolicy'>\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "model.learn(): 100%|██████████| 25000/25000 [00:55<00:00, 446.53it/s]\n",
+            "Episode: 40, Avg. Reward: 157.205: 100%|██████████| 50/50 [00:05<00:00,  9.20it/s]\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Min. Reward          :   -231.670\n",
+            "Avg. Reward          :    170.335\n",
+            "Max. Reward          :    534.650\n",
+            "--------------------------------------------------------------------------------\n",
+            "model <class 'stable_baselines3.ppo.ppo.PPO'>\n",
+            "policy <class 'stable_baselines3.common.policies.MultiInputActorCriticPolicy'>\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "model.learn(): 26600it [00:46, 566.55it/s]                           \n",
+            "Episode: 40, Avg. Reward: 142.713: 100%|██████████| 50/50 [00:04<00:00, 10.18it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Min. Reward          :   -172.870\n",
+            "Avg. Reward          :    141.092\n",
+            "Max. Reward          :    600.040\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "seed = 2024  # random seed\n",
+        "total_num_episodes = 50\n",
+        "\n",
+        "print (\"env_name                 :\", env_name)\n",
+        "print (\"seed                     :\", seed)\n",
+        "\n",
+        "# INIT matplotlib\n",
+        "plot_settings = {}\n",
+        "plot_data = {'x': [i for i in range(1, total_num_episodes + 1)]}\n",
+        "\n",
+        "# Random actions\n",
+        "model = None \n",
+        "total_learning_timesteps = 0\n",
+        "rewards = train_test_model(model, env, seed, total_num_episodes, total_learning_timesteps)\n",
+        "min, avg, max = print_stats(rewards)\n",
+        "class_name = f'Random actions'\n",
+        "label = f'Avg. {avg:>7.2f} : {class_name}'\n",
+        "plot_data['rnd_rewards'] = rewards\n",
+        "plot_settings['rnd_rewards'] = {'label': label}\n",
+        "\n",
+        "learning_timesteps_list_in_K = [25]\n",
+        "# learning_timesteps_list_in_K = [50, 250, 500]\n",
+        "# learning_timesteps_list_in_K = [500, 1000, 3000, 5000]\n",
+        "\n",
+        "# RL Algorithms: https://stable-baselines3.readthedocs.io/en/master/guide/algos.html\n",
+        "model_class_list = [A2C, PPO]\n",
+        "\n",
+        "for timesteps in learning_timesteps_list_in_K:\n",
+        "    total_learning_timesteps = timesteps * 1000\n",
+        "    step_key = f'{timesteps}K'\n",
+        "\n",
+        "    for model_class in model_class_list:\n",
+        "        policy_dict = model_class.policy_aliases\n",
+        "        # https://stable-baselines3.readthedocs.io/en/master/guide/custom_policy.html\n",
+        "        policy = policy_dict.get('MultiInputPolicy')\n",
+        "\n",
+        "        try:\n",
+        "            model = model_class(policy, env, verbose=0)\n",
+        "            class_name = type(model).__qualname__\n",
+        "            plot_key = f'{class_name}_rewards_'+step_key\n",
+        "            rewards = train_test_model(model, env, seed, total_num_episodes, total_learning_timesteps)\n",
+        "            min, avg, max, = print_stats(rewards)\n",
+        "            label = f'Avg. {avg:>7.2f} : {class_name} - {step_key}'\n",
+        "            plot_data[plot_key] = rewards\n",
+        "            plot_settings[plot_key] = {'label': label}     \n",
+        "\n",
+        "        except Exception as e:\n",
+        "            print(f\"ERROR: {str(e)}\")\n",
+        "            continue"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Plot Results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "data = pd.DataFrame(plot_data)\n",
+        "\n",
+        "sns.set_style('whitegrid')\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "\n",
+        "for key in plot_data:\n",
+        "    if key == 'x':\n",
+        "        continue\n",
+        "    label = plot_settings[key]['label']\n",
+        "    line = plt.plot('x', key, data=data, linewidth=1, label=label)\n",
+        "\n",
+        "plt.xlabel('episode')\n",
+        "plt.ylabel('reward')\n",
+        "plt.title('Random vs. SB3 Agents')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [],
+      "name": "p3.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "algo_trading",
+      "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": "3.11.10"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/trade_flow/environments/gym-mtsim/gym_mtsim/__init__.py b/trade_flow/environments/gym-mtsim/gym_mtsim/__init__.py
new file mode 100644
index 0000000..1fa6fdb
--- /dev/null
+++ b/trade_flow/environments/gym-mtsim/gym_mtsim/__init__.py
@@ -0,0 +1,121 @@
+from gymnasium.envs.registration import register
+
+from .metatrader import Timeframe, SymbolInfo
+from .simulator import MtSimulator, OrderType, Order, SymbolNotFound, OrderNotFound
+from .envs import MtEnv
+from .data import FOREX_DATA_PATH, STOCKS_DATA_PATH, CRYPTO_DATA_PATH, MIXED_DATA_PATH
+
+
+register(
+    id='forex-hedge-v0',
+    entry_point='gym_mtsim.envs:MtEnv',
+    kwargs={
+        'original_simulator': MtSimulator(symbols_filename=FOREX_DATA_PATH, hedge=True),
+        'trading_symbols': ['EURUSD', 'GBPCAD', 'USDJPY'],
+        'window_size': 10,
+        'symbol_max_orders': 2,
+        'fee': lambda symbol: 0.03 if 'JPY' in symbol else 0.0003
+    }
+)
+
+register(
+    id='forex-unhedge-v0',
+    entry_point='gym_mtsim.envs:MtEnv',
+    kwargs={
+        'original_simulator': MtSimulator(symbols_filename=FOREX_DATA_PATH, hedge=False),
+        'trading_symbols': ['EURUSD', 'GBPCAD', 'USDJPY'],
+        'window_size': 10,
+        'fee': lambda symbol: 0.03 if 'JPY' in symbol else 0.0003
+    }
+)
+
+register(
+    id='stocks-hedge-v0',
+    entry_point='gym_mtsim.envs:MtEnv',
+    kwargs={
+        'original_simulator': MtSimulator(symbols_filename=STOCKS_DATA_PATH, hedge=True),
+        'trading_symbols': ['GOGL', 'AAPL', 'TSLA', 'MSFT'],
+        'window_size': 10,
+        'symbol_max_orders': 2,
+        'fee': 0.2
+    }
+)
+
+register(
+    id='stocks-unhedge-v0',
+    entry_point='gym_mtsim.envs:MtEnv',
+    kwargs={
+        'original_simulator': MtSimulator(symbols_filename=STOCKS_DATA_PATH, hedge=False),
+        'trading_symbols': ['GOGL', 'AAPL', 'TSLA', 'MSFT'],
+        'window_size': 10,
+        'fee': 0.2
+    }
+)
+
+register(
+    id='crypto-hedge-v0',
+    entry_point='gym_mtsim.envs:MtEnv',
+    kwargs={
+        'original_simulator': MtSimulator(symbols_filename=CRYPTO_DATA_PATH, hedge=True),
+        'trading_symbols': ['BTCUSD', 'ETHUSD', 'BCHUSD'],
+        'window_size': 10,
+        'symbol_max_orders': 2,
+        'fee': lambda symbol: {
+            'BTCUSD': 50.0,
+            'ETHUSD': 3.0,
+            'BCHUSD': 0.5,
+        }[symbol]
+    }
+)
+
+register(
+    id='crypto-unhedge-v0',
+    entry_point='gym_mtsim.envs:MtEnv',
+    kwargs={
+        'original_simulator': MtSimulator(symbols_filename=CRYPTO_DATA_PATH, hedge=False),
+        'trading_symbols': ['BTCUSD', 'ETHUSD', 'BCHUSD'],
+        'window_size': 10,
+        'fee': lambda symbol: {
+            'BTCUSD': 50.0,
+            'ETHUSD': 3.0,
+            'BCHUSD': 0.5,
+        }[symbol]
+    }
+)
+
+register(
+    id='mixed-hedge-v0',
+    entry_point='gym_mtsim.envs:MtEnv',
+    kwargs={
+        'original_simulator': MtSimulator(symbols_filename=MIXED_DATA_PATH, hedge=True),
+        'trading_symbols': ['EURUSD', 'USDCAD', 'GOGL', 'AAPL', 'BTCUSD', 'ETHUSD'],
+        'window_size': 10,
+        'symbol_max_orders': 2,
+        'fee': lambda symbol: {
+            'EURUSD': 0.0002,
+            'USDCAD': 0.0005,
+            'GOGL': 0.15,
+            'AAPL': 0.01,
+            'BTCUSD': 50.0,
+            'ETHUSD': 3.0,
+        }[symbol]
+    }
+)
+
+register(
+    id='mixed-unhedge-v0',
+    entry_point='gym_mtsim.envs:MtEnv',
+    kwargs={
+        'original_simulator': MtSimulator(symbols_filename=MIXED_DATA_PATH, hedge=False),
+        'trading_symbols': ['EURUSD', 'USDCAD', 'GOGL', 'AAPL', 'BTCUSD', 'ETHUSD'],
+        'window_size': 10,
+        'fee': lambda symbol: {
+            'EURUSD': 0.0002,
+            'USDCAD': 0.0005,
+            'GOGL': 0.15,
+            'AAPL': 0.01,
+            'BTCUSD': 50.0,
+            'ETHUSD': 3.0,
+        }[symbol]
+    }
+)
diff --git a/trade_flow/environments/gym-mtsim/gym_mtsim/data/__init__.py b/trade_flow/environments/gym-mtsim/gym_mtsim/data/__init__.py
new file mode 100644
index 0000000..e7c8113
--- /dev/null
+++ b/trade_flow/environments/gym-mtsim/gym_mtsim/data/__init__.py
@@ -0,0 +1,9 @@
+import os
+
+
+DATA_DIR = os.path.dirname(os.path.abspath(__file__))
+
+FOREX_DATA_PATH = os.path.join(DATA_DIR, 'symbols_forex.pkl')
+STOCKS_DATA_PATH = os.path.join(DATA_DIR, 'symbols_stocks.pkl')
+CRYPTO_DATA_PATH = os.path.join(DATA_DIR, 'symbols_crypto.pkl')
+MIXED_DATA_PATH = os.path.join(DATA_DIR, 'symbols_mixed.pkl')
diff --git a/trade_flow/environments/gym-mtsim/gym_mtsim/data/symbols_crypto.pkl b/trade_flow/environments/gym-mtsim/gym_mtsim/data/symbols_crypto.pkl
new file mode 100644
index 0000000..799d171
Binary files /dev/null and b/trade_flow/environments/gym-mtsim/gym_mtsim/data/symbols_crypto.pkl differ
diff --git a/trade_flow/environments/gym-mtsim/gym_mtsim/data/symbols_forex.pkl b/trade_flow/environments/gym-mtsim/gym_mtsim/data/symbols_forex.pkl
new file mode 100644
index 0000000..f52a0de
Binary files /dev/null and b/trade_flow/environments/gym-mtsim/gym_mtsim/data/symbols_forex.pkl differ
diff --git a/trade_flow/environments/gym-mtsim/gym_mtsim/data/symbols_mixed.pkl b/trade_flow/environments/gym-mtsim/gym_mtsim/data/symbols_mixed.pkl
new file mode 100644
index 0000000..c045976
Binary files /dev/null and b/trade_flow/environments/gym-mtsim/gym_mtsim/data/symbols_mixed.pkl differ
diff --git a/trade_flow/environments/gym-mtsim/gym_mtsim/data/symbols_stocks.pkl b/trade_flow/environments/gym-mtsim/gym_mtsim/data/symbols_stocks.pkl
new file mode 100644
index 0000000..fe48d1b
Binary files /dev/null and b/trade_flow/environments/gym-mtsim/gym_mtsim/data/symbols_stocks.pkl differ
diff --git a/trade_flow/environments/gym-mtsim/gym_mtsim/envs/__init__.py b/trade_flow/environments/gym-mtsim/gym_mtsim/envs/__init__.py
new file mode 100644
index 0000000..eb3f686
--- /dev/null
+++ b/trade_flow/environments/gym-mtsim/gym_mtsim/envs/__init__.py
@@ -0,0 +1 @@
+from .mt_env import MtEnv
diff --git a/trade_flow/environments/gym-mtsim/gym_mtsim/envs/mt_env.py b/trade_flow/environments/gym-mtsim/gym_mtsim/envs/mt_env.py
new file mode 100644
index 0000000..edd632d
--- /dev/null
+++ b/trade_flow/environments/gym-mtsim/gym_mtsim/envs/mt_env.py
@@ -0,0 +1,493 @@
+from typing import List, Tuple, Dict, Any, Optional, Union, Callable
+
+import copy
+from datetime import datetime
+from pathos.multiprocessing import ProcessingPool as Pool
+
+import numpy as np
+from scipy.special import expit
+
+import matplotlib.pyplot as plt
+import matplotlib.cm as plt_cm
+import matplotlib.colors as plt_colors
+import plotly.graph_objects as go
+
+import gymnasium as gym
+from gymnasium import spaces
+
+from ..simulator import MtSimulator, OrderType
+
+
+class MtEnv(gym.Env):
+
+    metadata = {'render_modes': ['human', 'simple_figure', 'advanced_figure']}
+
+    def __init__(
+        self,
+        original_simulator: MtSimulator,
+        trading_symbols: List[str],
+        window_size: int,
+        time_points: Optional[List[datetime]] = None,
+        hold_threshold: float = 0.5,
+        close_threshold: float = 0.5,
+        fee: Union[float, Callable[[str], float]] = 0.0005,
+        symbol_max_orders: int = 1,
+        multiprocessing_processes: Optional[int] = None,
+        render_mode: Optional[str] = None,
+    ) -> None:
+        # validations
+        assert len(original_simulator.symbols_data) > 0, "no data available"
+        assert len(original_simulator.symbols_info) > 0, "no data available"
+        assert len(trading_symbols) > 0, "no trading symbols provided"
+        assert 0. <= hold_threshold <= 1., "'hold_threshold' must be in range [0., 1.]"
+
+        if not original_simulator.hedge:
+            symbol_max_orders = 1
+
+        for symbol in trading_symbols:
+            assert symbol in original_simulator.symbols_info, f"symbol '{symbol}' not found"
+            currency_profit = original_simulator.symbols_info[symbol].currency_profit
+            assert original_simulator._get_unit_symbol_info(currency_profit) is not None, \
+                   f"unit symbol for '{currency_profit}' not found"
+
+        if time_points is None:
+            time_points = original_simulator.symbols_data[trading_symbols[0]].index.to_pydatetime().tolist()
+        assert len(time_points) > window_size, "not enough time points provided"
+
+        self.render_mode = render_mode
+
+        # attributes
+        self.original_simulator = original_simulator
+        self.trading_symbols = trading_symbols
+        self.window_size = window_size
+        self.time_points = time_points
+        self.hold_threshold = hold_threshold
+        self.close_threshold = close_threshold
+        self.fee = fee
+        self.symbol_max_orders = symbol_max_orders
+        self.multiprocessing_pool = Pool(multiprocessing_processes) if multiprocessing_processes else None
+
+        self.prices = self._get_prices()
+        self.signal_features = self._process_data()
+        self.features_shape = (window_size, self.signal_features.shape[1])
+
+        # spaces
+        self.action_space = spaces.Box(
+            low=-1e2, high=1e2, dtype=np.float64,
+            shape=(len(self.trading_symbols) * (self.symbol_max_orders + 2),)
+        )  # symbol -> [close_order_i(logit), hold(logit), volume]
+
+        INF = 1e10
+        self.observation_space = spaces.Dict({
+            'balance': spaces.Box(low=-INF, high=INF, shape=(1,), dtype=np.float64),
+            'equity': spaces.Box(low=-INF, high=INF, shape=(1,), dtype=np.float64),
+            'margin': spaces.Box(low=-INF, high=INF, shape=(1,), dtype=np.float64),
+            'features': spaces.Box(low=-INF, high=INF, shape=self.features_shape, dtype=np.float64),
+            'orders': spaces.Box(
+                low=-INF, high=INF, dtype=np.float64,
+                shape=(len(self.trading_symbols), self.symbol_max_orders, 3)
+            )  # symbol, order_i -> [entry_price, volume, profit]
+        })
+
+        # episode
+        self._start_tick = self.window_size - 1
+        self._end_tick = len(self.time_points) - 1
+        self._truncated: bool = NotImplemented
+        self._current_tick: int = NotImplemented
+        self.simulator: MtSimulator = NotImplemented
+        self.history: List[Dict[str, Any]] = NotImplemented
+
+    def reset(self, seed=None, options=None) -> Dict[str, np.ndarray]:
+        super().reset(seed=seed, options=options)
+
+        self._truncated = False
+        self._current_tick = self._start_tick
+        self.simulator = copy.deepcopy(self.original_simulator)
+        self.simulator.current_time = self.time_points[self._current_tick]
+        self.history = [self._create_info()]
+
+        observation = self._get_observation()
+        info = self._create_info()
+
+        return observation, info
+
+    def step(self, action: np.ndarray) -> Tuple[Dict[str, np.ndarray], float, bool, Dict[str, Any]]:
+        orders_info, closed_orders_info = self._apply_action(action)
+
+        self._current_tick += 1
+        if self._current_tick == self._end_tick:
+            self._truncated = True
+
+        dt = self.time_points[self._current_tick] - self.time_points[self._current_tick - 1]
+        self.simulator.tick(dt)
+
+        step_reward = self._calculate_reward()
+
+        info = self._create_info(
+            orders=orders_info, closed_orders=closed_orders_info, step_reward=step_reward
+        )
+        observation = self._get_observation()
+        self.history.append(info)
+
+        return observation, step_reward, False, self._truncated, info
+
+    def _apply_action(self, action: np.ndarray) -> Tuple[Dict, Dict]:
+        orders_info = {}
+        closed_orders_info = {symbol: [] for symbol in self.trading_symbols}
+
+        k = self.symbol_max_orders + 2
+
+        for i, symbol in enumerate(self.trading_symbols):
+            symbol_action = action[k*i:k*(i+1)]
+            close_orders_logit = symbol_action[:-2]
+            hold_logit = symbol_action[-2]
+            volume = symbol_action[-1]
+
+            close_orders_probability = expit(close_orders_logit)
+            hold_probability = expit(hold_logit)
+            hold = bool(hold_probability > self.hold_threshold)
+            modified_volume = self._get_modified_volume(symbol, volume)
+
+            symbol_orders = self.simulator.symbol_orders(symbol)
+            orders_to_close_index = np.where(
+                close_orders_probability[:len(symbol_orders)] > self.close_threshold
+            )[0]
+            orders_to_close = np.array(symbol_orders)[orders_to_close_index]
+
+            for j, order in enumerate(orders_to_close):
+                self.simulator.close_order(order)
+                closed_orders_info[symbol].append(dict(
+                    order_id=order.id, symbol=order.symbol, order_type=order.type,
+                    volume=order.volume, fee=order.fee,
+                    margin=order.margin, profit=order.profit,
+                    close_probability=close_orders_probability[orders_to_close_index][j],
+                ))
+
+            orders_capacity = self.symbol_max_orders - (len(symbol_orders) - len(orders_to_close))
+            orders_info[symbol] = dict(
+                order_id=None, symbol=symbol, hold_probability=hold_probability,
+                hold=hold, volume=volume, capacity=orders_capacity, order_type=None,
+                modified_volume=modified_volume, fee=float('nan'), margin=float('nan'),
+                error='',
+            )
+
+            if self.simulator.hedge and orders_capacity == 0:
+                orders_info[symbol].update(dict(
+                    error="cannot add more orders"
+                ))
+            elif not hold:
+                order_type = OrderType.Buy if volume > 0. else OrderType.Sell
+                fee = self.fee if type(self.fee) is float else self.fee(symbol)
+
+                try:
+                    order = self.simulator.create_order(order_type, symbol, modified_volume, fee)
+                    new_info = dict(
+                        order_id=order.id, order_type=order_type,
+                        fee=fee, margin=order.margin,
+                    )
+                except ValueError as e:
+                    new_info = dict(error=str(e))
+
+                orders_info[symbol].update(new_info)
+
+        return orders_info, closed_orders_info
+
+    def _get_prices(self, keys: List[str]=['Close', 'Open']) -> Dict[str, np.ndarray]:
+        prices = {}
+
+        for symbol in self.trading_symbols:
+            get_price_at = lambda time: \
+                self.original_simulator.price_at(symbol, time)[keys]
+
+            if self.multiprocessing_pool is None:
+                p = list(map(get_price_at, self.time_points))
+            else:
+                p = self.multiprocessing_pool.map(get_price_at, self.time_points)
+
+            prices[symbol] = np.array(p)
+
+        return prices
+
+    def _process_data(self) -> np.ndarray:
+        data = self.prices
+        signal_features = np.column_stack(list(data.values()))
+        return signal_features
+
+    def _get_observation(self) -> Dict[str, np.ndarray]:
+        features = self.signal_features[(self._current_tick-self.window_size+1):(self._current_tick+1)]
+
+        orders = np.zeros(self.observation_space['orders'].shape)
+        for i, symbol in enumerate(self.trading_symbols):
+            symbol_orders = self.simulator.symbol_orders(symbol)
+            for j, order in enumerate(symbol_orders):
+                orders[i, j] = [order.entry_price, order.volume, order.profit]
+
+        observation = {
+            'balance': np.array([self.simulator.balance]),
+            'equity': np.array([self.simulator.equity]),
+            'margin': np.array([self.simulator.margin]),
+            'features': features,
+            'orders': orders,
+        }
+        return observation
+
+    def _calculate_reward(self) -> float:
+        prev_equity = self.history[-1]['equity']
+        current_equity = self.simulator.equity
+        step_reward = current_equity - prev_equity
+        return step_reward
+
+    def _create_info(self, **kwargs: Any) -> Dict[str, Any]:
+        info = {k: v for k, v in kwargs.items()}
+        info['balance'] = self.simulator.balance
+        info['equity'] = self.simulator.equity
+        info['margin'] = self.simulator.margin
+        info['free_margin'] = self.simulator.free_margin
+        info['margin_level'] = self.simulator.margin_level
+        return info
+
+    def _get_modified_volume(self, symbol: str, volume: float) -> float:
+        si = self.simulator.symbols_info[symbol]
+        v = abs(volume)
+        v = np.clip(v, si.volume_min, si.volume_max)
+        v = round(v / si.volume_step) * si.volume_step
+        return v
+
+    def render(self, mode: str='human', **kwargs: Any) -> Any:
+        if mode == 'simple_figure':
+            return self._render_simple_figure(**kwargs)
+        if mode == 'advanced_figure':
+            return self._render_advanced_figure(**kwargs)
+        return self.simulator.get_state(**kwargs)
+
+    def _render_simple_figure(
+        self, figsize: Tuple[float, float]=(14, 6), return_figure: bool=False
+    ) -> Any:
+        fig, ax = plt.subplots(figsize=figsize, facecolor='white')
+
+        cmap_colors = np.array(plt_cm.tab10.colors)[[0, 1, 4, 5, 6, 8]]
+        cmap = plt_colors.LinearSegmentedColormap.from_list('mtsim', cmap_colors)
+        symbol_colors = cmap(np.linspace(0, 1, len(self.trading_symbols)))
+
+        for j, symbol in enumerate(self.trading_symbols):
+            close_price = self.prices[symbol][:, 0]
+            symbol_color = symbol_colors[j]
+
+            ax.plot(self.time_points, close_price, c=symbol_color, marker='.', label=symbol)
+
+            buy_ticks = []
+            buy_error_ticks = []
+            sell_ticks = []
+            sell_error_ticks = []
+            close_ticks = []
+
+            for i in range(1, len(self.history)):
+                tick = self._start_tick + i - 1
+
+                order = self.history[i]['orders'].get(symbol, {})
+                if order and not order['hold']:
+                    if order['order_type'] == OrderType.Buy:
+                        if order['error']:
+                            buy_error_ticks.append(tick)
+                        else:
+                            buy_ticks.append(tick)
+                    else:
+                        if order['error']:
+                            sell_error_ticks.append(tick)
+                        else:
+                            sell_ticks.append(tick)
+
+                closed_orders = self.history[i]['closed_orders'].get(symbol, [])
+                if len(closed_orders) > 0:
+                    close_ticks.append(tick)
+
+            tp = np.array(self.time_points)
+            ax.plot(tp[buy_ticks], close_price[buy_ticks], '^', color='green')
+            ax.plot(tp[buy_error_ticks], close_price[buy_error_ticks], '^', color='gray')
+            ax.plot(tp[sell_ticks], close_price[sell_ticks], 'v', color='red')
+            ax.plot(tp[sell_error_ticks], close_price[sell_error_ticks], 'v', color='gray')
+            ax.plot(tp[close_ticks], close_price[close_ticks], '|', color='black')
+
+            ax.tick_params(axis='y', labelcolor=symbol_color)
+            ax.yaxis.tick_left()
+            if j < len(self.trading_symbols) - 1:
+                ax = ax.twinx()
+
+        fig.suptitle(
+            f"Balance: {self.simulator.balance:.6f} {self.simulator.unit} ~ "
+            f"Equity: {self.simulator.equity:.6f} ~ "
+            f"Margin: {self.simulator.margin:.6f} ~ "
+            f"Free Margin: {self.simulator.free_margin:.6f} ~ "
+            f"Margin Level: {self.simulator.margin_level:.6f}"
+        )
+        fig.legend(loc='right')
+
+        if return_figure:
+            return fig
+
+        plt.show()
+
+    def _render_advanced_figure(
+        self,
+        figsize: Tuple[float, float] = (1400, 600),
+        time_format: str = "%Y-%m-%d %H:%m",
+        return_figure: bool = False,
+    ) -> Any:
+        fig = go.Figure()
+
+        cmap_colors = np.array(plt_cm.tab10.colors)[[0, 1, 4, 5, 6, 8]]
+        cmap = plt_colors.LinearSegmentedColormap.from_list('mtsim', cmap_colors)
+        symbol_colors = cmap(np.linspace(0, 1, len(self.trading_symbols)))
+        get_color_string = lambda color: "rgba(%s, %s, %s, %s)" % tuple(color)
+
+        extra_info = [
+            f"balance: {h['balance']:.6f} {self.simulator.unit}<br>"
+            f"equity: {h['equity']:.6f}<br>"
+            f"margin: {h['margin']:.6f}<br>"
+            f"free margin: {h['free_margin']:.6f}<br>"
+            f"margin level: {h['margin_level']:.6f}"
+            for h in self.history
+        ]
+        extra_info = [extra_info[0]] * (self.window_size - 1) + extra_info
+
+        for j, symbol in enumerate(self.trading_symbols):
+            close_price = self.prices[symbol][:, 0]
+            symbol_color = symbol_colors[j]
+
+            fig.add_trace(
+                go.Scatter(
+                    x=self.time_points,
+                    y=close_price,
+                    mode='lines+markers',
+                    line_color=get_color_string(symbol_color),
+                    opacity=1.0,
+                    hovertext=extra_info,
+                    name=symbol,
+                    yaxis=f'y{j+1}',
+                    legendgroup=f'g{j+1}',
+                ),
+            )
+
+            fig.update_layout(**{
+                f'yaxis{j+1}': dict(
+                    tickfont=dict(color=get_color_string(symbol_color * [1, 1, 1, 0.8])),
+                    overlaying='y' if j > 0 else None,
+                    # position=0.035*j
+                ),
+            })
+
+            trade_ticks = []
+            trade_markers = []
+            trade_colors = []
+            trade_sizes = []
+            trade_extra_info = []
+            trade_max_volume = max([
+                h.get('orders', {}).get(symbol, {}).get('modified_volume') or 0
+                for h in self.history
+            ])
+            close_ticks = []
+            close_extra_info = []
+
+            for i in range(1, len(self.history)):
+                tick = self._start_tick + i - 1
+
+                order = self.history[i]['orders'].get(symbol)
+                if order and not order['hold']:
+                    marker = None
+                    color = None
+                    size = 8 + 22 * (order['modified_volume'] / trade_max_volume)
+                    info = (
+                        f"order id: {order['order_id'] or ''}<br>"
+                        f"hold probability: {order['hold_probability']:.4f}<br>"
+                        f"hold: {order['hold']}<br>"
+                        f"volume: {order['volume']:.6f}<br>"
+                        f"modified volume: {order['modified_volume']:.4f}<br>"
+                        f"fee: {order['fee']:.6f}<br>"
+                        f"margin: {order['margin']:.6f}<br>"
+                        f"error: {order['error']}"
+                    )
+
+                    if order['order_type'] == OrderType.Buy:
+                        marker = 'triangle-up'
+                        color = 'gray' if order['error'] else 'green'
+                    else:
+                        marker = 'triangle-down'
+                        color = 'gray' if order['error'] else 'red'
+
+                    trade_ticks.append(tick)
+                    trade_markers.append(marker)
+                    trade_colors.append(color)
+                    trade_sizes.append(size)
+                    trade_extra_info.append(info)
+
+                closed_orders = self.history[i]['closed_orders'].get(symbol, [])
+                if len(closed_orders) > 0:
+                    info = []
+                    for order in closed_orders:
+                        info_i = (
+                            f"order id: {order['order_id']}<br>"
+                            f"order type: {order['order_type'].name}<br>"
+                            f"close probability: {order['close_probability']:.4f}<br>"
+                            f"margin: {order['margin']:.6f}<br>"
+                            f"profit: {order['profit']:.6f}"
+                        )
+                        info.append(info_i)
+                    info = '<br>---------------------------------<br>'.join(info)
+
+                    close_ticks.append(tick)
+                    close_extra_info.append(info)
+
+            fig.add_trace(
+                go.Scatter(
+                    x=np.array(self.time_points)[trade_ticks],
+                    y=close_price[trade_ticks],
+                    mode='markers',
+                    hovertext=trade_extra_info,
+                    marker_symbol=trade_markers,
+                    marker_color=trade_colors,
+                    marker_size=trade_sizes,
+                    name=symbol,
+                    yaxis=f'y{j+1}',
+                    showlegend=False,
+                    legendgroup=f'g{j+1}',
+                ),
+            )
+
+            fig.add_trace(
+                go.Scatter(
+                    x=np.array(self.time_points)[close_ticks],
+                    y=close_price[close_ticks],
+                    mode='markers',
+                    hovertext=close_extra_info,
+                    marker_symbol='line-ns',
+                    marker_color='black',
+                    marker_size=7,
+                    marker_line_width=1.5,
+                    name=symbol,
+                    yaxis=f'y{j+1}',
+                    showlegend=False,
+                    legendgroup=f'g{j+1}',
+                ),
+            )
+
+        title = (
+            f"Balance: {self.simulator.balance:.6f} {self.simulator.unit} ~ "
+            f"Equity: {self.simulator.equity:.6f} ~ "
+            f"Margin: {self.simulator.margin:.6f} ~ "
+            f"Free Margin: {self.simulator.free_margin:.6f} ~ "
+            f"Margin Level: {self.simulator.margin_level:.6f}"
+        )
+        fig.update_layout(
+            title=title,
+            xaxis_tickformat=time_format,
+            width=figsize[0],
+            height=figsize[1],
+        )
+
+        if return_figure:
+            return fig
+
+        fig.show()
+
+    def close(self) -> None:
+        plt.close()
diff --git a/trade_flow/environments/gym-mtsim/gym_mtsim/metatrader/__init__.py b/trade_flow/environments/gym-mtsim/gym_mtsim/metatrader/__init__.py
new file mode 100644
index 0000000..4e5edb4
--- /dev/null
+++ b/trade_flow/environments/gym-mtsim/gym_mtsim/metatrader/__init__.py
@@ -0,0 +1,3 @@
+from .interface import Timeframe
+from .symbol import SymbolInfo
+from .api import retrieve_data
diff --git a/trade_flow/environments/gym-mtsim/gym_mtsim/metatrader/api.py b/trade_flow/environments/gym-mtsim/gym_mtsim/metatrader/api.py
new file mode 100644
index 0000000..013fe7d
--- /dev/null
+++ b/trade_flow/environments/gym-mtsim/gym_mtsim/metatrader/api.py
@@ -0,0 +1,71 @@
+from typing import Tuple
+
+import pytz
+import calendar
+from datetime import datetime
+
+import pandas as pd
+
+from . import interface as mt
+from .symbol import SymbolInfo
+
+
+def retrieve_data(
+        symbol: str, from_dt: datetime, to_dt: datetime, timeframe: mt.Timeframe
+    ) -> Tuple[SymbolInfo, pd.DataFrame]:
+
+    if not mt.initialize():
+        raise ConnectionError(f"MetaTrader cannot be initialized")
+
+    symbol_info = _get_symbol_info(symbol)
+
+    utc_from = _local2utc(from_dt)
+    utc_to = _local2utc(to_dt)
+    all_rates = []
+
+    partial_from = utc_from
+    partial_to = _add_months(partial_from, 1)
+
+    while partial_from < utc_to:
+        rates = mt.copy_rates_range(symbol, timeframe, partial_from, partial_to)
+        all_rates.extend(rates)
+        partial_from = _add_months(partial_from, 1)
+        partial_to = min(_add_months(partial_to, 1), utc_to)
+
+    all_rates = [list(r) for r in all_rates]
+
+    rates_frame = pd.DataFrame(
+        all_rates,
+        columns=['Time', 'Open', 'High', 'Low', 'Close', 'Volume', '_', '_'],
+    )
+    rates_frame['Time'] = pd.to_datetime(rates_frame['Time'], unit='s', utc=True)
+
+    data = rates_frame[['Time', 'Open', 'Close', 'Low', 'High', 'Volume']].set_index('Time')
+    data = data.loc[~data.index.duplicated(keep='first')]
+
+    mt.shutdown()
+
+    return symbol_info, data
+
+
+def _get_symbol_info(symbol: str) -> SymbolInfo:
+    info = mt.symbol_info(symbol)
+    symbol_info = SymbolInfo(info)
+    return symbol_info
+
+
+def _local2utc(dt: datetime) -> datetime:
+    return dt.astimezone(pytz.timezone('Etc/UTC'))
+
+
+def _add_months(sourcedate: datetime, months: int) -> datetime:
+    month = sourcedate.month - 1 + months
+    year = sourcedate.year + month // 12
+    month = month % 12 + 1
+    day = min(sourcedate.day, calendar.monthrange(year, month)[1])
+
+    return datetime(
+        year, month, day,
+        sourcedate.hour, sourcedate.minute, sourcedate.second,
+        tzinfo=sourcedate.tzinfo
+    )
diff --git a/trade_flow/environments/gym-mtsim/gym_mtsim/metatrader/interface.py b/trade_flow/environments/gym-mtsim/gym_mtsim/metatrader/interface.py
new file mode 100644
index 0000000..058e8c2
--- /dev/null
+++ b/trade_flow/environments/gym-mtsim/gym_mtsim/metatrader/interface.py
@@ -0,0 +1,61 @@
+from enum import Enum
+from datetime import datetime
+
+import numpy as np
+
+try:
+    import MetaTrader5 as mt5
+    from MetaTrader5 import SymbolInfo as MtSymbolInfo
+    MT5_AVAILABLE = True
+except ImportError:
+    MtSymbolInfo = object
+    MT5_AVAILABLE = False
+
+
+class Timeframe(Enum):
+    M1 = 1  # mt5.TIMEFRAME_M1
+    M2 = 2  # mt5.TIMEFRAME_M2
+    M3 = 3  # mt5.TIMEFRAME_M3
+    M4 = 4  # mt5.TIMEFRAME_M4
+    M5 = 5  # mt5.TIMEFRAME_M5
+    M6 = 6  # mt5.TIMEFRAME_M6
+    M10 = 10  # mt5.TIMEFRAME_M10
+    M12 = 12  # mt5.TIMEFRAME_M12
+    M15 = 15  # mt5.TIMEFRAME_M15
+    M20 = 20  # mt5.TIMEFRAME_M20
+    M30 = 30  # mt5.TIMEFRAME_M30
+    H1 = 1  | 0x4000  # mt5.TIMEFRAME_H1
+    H2 = 2  | 0x4000  # mt5.TIMEFRAME_H2
+    H4 = 4  | 0x4000  # mt5.TIMEFRAME_H4
+    H3 = 3  | 0x4000  # mt5.TIMEFRAME_H3
+    H6 = 6  | 0x4000  # mt5.TIMEFRAME_H6
+    H8 = 8  | 0x4000  # mt5.TIMEFRAME_H8
+    H12 = 12 | 0x4000  # mt5.TIMEFRAME_H12
+    D1 = 24 | 0x4000  # mt5.TIMEFRAME_D1
+    W1 = 1  | 0x8000  # mt5.TIMEFRAME_W1
+    MN1 = 1  | 0xC000  # mt5.TIMEFRAME_MN1
+
+
+def initialize() -> bool:
+    _check_mt5_available()
+    return mt5.initialize()
+
+
+def shutdown() -> None:
+    _check_mt5_available()
+    mt5.shutdown()
+
+
+def copy_rates_range(symbol: str, timeframe: Timeframe, date_from: datetime, date_to: datetime) -> np.ndarray:
+    _check_mt5_available()
+    return mt5.copy_rates_range(symbol, timeframe.value, date_from, date_to)
+
+
+def symbol_info(symbol: str) -> MtSymbolInfo:
+    _check_mt5_available()
+    return mt5.symbol_info(symbol)
+
+
+def _check_mt5_available() -> None:
+    if not MT5_AVAILABLE:
+        raise OSError("MetaTrader5 is not available on your platform.")
diff --git a/trade_flow/environments/gym-mtsim/gym_mtsim/metatrader/symbol.py b/trade_flow/environments/gym-mtsim/gym_mtsim/metatrader/symbol.py
new file mode 100644
index 0000000..b93d5a2
--- /dev/null
+++ b/trade_flow/environments/gym-mtsim/gym_mtsim/metatrader/symbol.py
@@ -0,0 +1,38 @@
+from typing import Tuple
+
+from .interface import MtSymbolInfo
+
+
+class SymbolInfo:
+
+    def __init__(self, info: MtSymbolInfo) -> None:
+        self.name: str = info.name
+        self.market: str = self._get_market(info)
+
+        self.currency_margin: str = info.currency_margin
+        self.currency_profit: str = info.currency_profit
+        self.currencies: Tuple[str, ...] = tuple(set([self.currency_margin, self.currency_profit]))
+
+        self.trade_contract_size: float = info.trade_contract_size
+        self.margin_rate: float = 1.0  # MetaTrader info does not contain this value!
+
+        self.volume_min: float = info.volume_min
+        self.volume_max: float = info.volume_max
+        self.volume_step: float = info.volume_step
+
+    def __str__(self) -> str:
+        return f'{self.market}/{self.name}'
+
+    def _get_market(self, info: MtSymbolInfo) -> str:
+        mapping = {
+            'forex': 'Forex',
+            'crypto': 'Crypto',
+            'stock': 'Stock',
+        }
+
+        root = info.path.split('\\')[0]
+        for k, v in mapping.items():
+            if root.lower().startswith(k):
+                return v
+
+        return root
diff --git a/trade_flow/environments/gym-mtsim/gym_mtsim/simulator/__init__.py b/trade_flow/environments/gym-mtsim/gym_mtsim/simulator/__init__.py
new file mode 100644
index 0000000..8368376
--- /dev/null
+++ b/trade_flow/environments/gym-mtsim/gym_mtsim/simulator/__init__.py
@@ -0,0 +1,3 @@
+from .order import OrderType, Order
+from .exceptions import SymbolNotFound, OrderNotFound
+from .mt_simulator import MtSimulator
diff --git a/trade_flow/environments/gym-mtsim/gym_mtsim/simulator/exceptions.py b/trade_flow/environments/gym-mtsim/gym_mtsim/simulator/exceptions.py
new file mode 100644
index 0000000..b29af73
--- /dev/null
+++ b/trade_flow/environments/gym-mtsim/gym_mtsim/simulator/exceptions.py
@@ -0,0 +1,6 @@
+class SymbolNotFound(Exception):
+    pass
+
+
+class OrderNotFound(Exception):
+    pass
diff --git a/trade_flow/environments/gym-mtsim/gym_mtsim/simulator/mt_simulator.py b/trade_flow/environments/gym-mtsim/gym_mtsim/simulator/mt_simulator.py
new file mode 100644
index 0000000..4f6a987
--- /dev/null
+++ b/trade_flow/environments/gym-mtsim/gym_mtsim/simulator/mt_simulator.py
@@ -0,0 +1,308 @@
+from typing import List, Tuple, Dict, Any, Optional
+
+import os
+import pickle
+from datetime import datetime, timedelta
+
+import numpy as np
+import pandas as pd
+
+from ..metatrader import Timeframe, SymbolInfo, retrieve_data
+from .order import OrderType, Order
+from .exceptions import SymbolNotFound, OrderNotFound
+
+
+class MtSimulator:
+
+    def __init__(
+        self,
+        unit: str = 'USD',
+        balance: float = 10000.,
+        leverage: float = 100.,
+        stop_out_level: float = 0.2,
+        hedge: bool = True,
+        symbols_filename: Optional[str] = None,
+    ) -> None:
+        self.unit = unit
+        self.balance = balance
+        self.equity = balance
+        self.leverage = leverage
+        self.stop_out_level = stop_out_level
+        self.hedge = hedge
+        self.symbols_filename = symbols_filename
+        self.margin = 0.
+
+        self.symbols_info: Dict[str, SymbolInfo] = {}
+        self.symbols_data: Dict[str, pd.DataFrame] = {}
+        self.orders: List[Order] = []
+        self.closed_orders: List[Order] = []
+        self.current_time: datetime = NotImplemented
+
+        if symbols_filename:
+            if not self.load_symbols(symbols_filename):
+                raise FileNotFoundError(f"file '{symbols_filename}' not found")
+
+    @property
+    def free_margin(self) -> float:
+        return self.equity - self.margin
+
+    @property
+    def margin_level(self) -> float:
+        margin = round(self.margin, 6)
+        if margin == 0.:
+            return float('inf')
+        return self.equity / margin
+
+    def download_data(
+        self, symbols: List[str], time_range: Tuple[datetime, datetime], timeframe: Timeframe
+    ) -> None:
+        from_dt, to_dt = time_range
+        for symbol in symbols:
+            si, df = retrieve_data(symbol, from_dt, to_dt, timeframe)
+            self.symbols_info[symbol] = si
+            self.symbols_data[symbol] = df
+
+    def save_symbols(self, filename: str) -> None:
+        with open(filename, 'wb') as file:
+            pickle.dump((self.symbols_info, self.symbols_data), file)
+
+    def load_symbols(self, filename: str) -> bool:
+        if not os.path.exists(filename):
+            return False
+        with open(filename, 'rb') as file:
+            self.symbols_info, self.symbols_data = pickle.load(file)
+        return True
+
+    def tick(self, delta_time: timedelta=timedelta()) -> None:
+        self._check_current_time()
+
+        self.current_time += delta_time
+        self.equity = self.balance
+
+        for order in self.orders:
+            order.exit_time = self.current_time
+            order.exit_price = self.price_at(order.symbol, order.exit_time)['Close']
+            self._update_order_profit(order)
+            self.equity += order.profit
+
+        while self.margin_level < self.stop_out_level and len(self.orders) > 0:
+            most_unprofitable_order = min(self.orders, key=lambda order: order.profit)
+            self.close_order(most_unprofitable_order)
+
+        if self.balance < 0.:
+            self.balance = 0.
+            self.equity = self.balance
+
+    def nearest_time(self, symbol: str, time: datetime) -> datetime:
+        df = self.symbols_data[symbol]
+        if time in df.index:
+            return time
+        try:
+            i, = df.index.get_indexer([time], method='ffill')
+        except KeyError:
+            i, = df.index.get_indexer([time], method='bfill')
+        return df.index[i]
+
+    def price_at(self, symbol: str, time: datetime) -> pd.Series:
+        df = self.symbols_data[symbol]
+        time = self.nearest_time(symbol, time)
+        return df.loc[time]
+
+    def symbol_orders(self, symbol: str) -> List[Order]:
+        symbol_orders = list(filter(
+            lambda order: order.symbol == symbol, self.orders
+        ))
+        return symbol_orders
+
+    def create_order(
+        self, order_type: OrderType, symbol: str, volume: float, fee: float=0.0005,
+        raise_exception: bool = True
+    ) -> Optional[Order]:
+        self._check_current_time()
+        self._check_volume(symbol, volume)
+        if fee < 0.:
+            raise ValueError(f"negative fee '{fee}'")
+
+        if self.hedge:
+            return self._create_hedged_order(order_type, symbol, volume, fee, raise_exception)
+        return self._create_unhedged_order(order_type, symbol, volume, fee, raise_exception)
+
+    def _create_hedged_order(
+        self, order_type: OrderType, symbol: str, volume: float, fee: float,
+        raise_exception: bool
+    ) -> Optional[Order]:
+        order_id = len(self.closed_orders) + len(self.orders) + 1
+        entry_time = self.current_time
+        entry_price = self.price_at(symbol, entry_time)['Close']
+        exit_time = entry_time
+        exit_price = entry_price
+
+        order = Order(
+            order_id, order_type, symbol, volume, fee,
+            entry_time, entry_price, exit_time, exit_price
+        )
+        self._update_order_profit(order)
+        self._update_order_margin(order)
+
+        if order.margin > self.free_margin + order.profit:
+            if raise_exception:
+                raise ValueError(
+                    f"low free margin (order margin={order.margin}, order profit={order.profit}, "
+                    f"free margin={self.free_margin})"
+                )
+            return None
+
+        self.equity += order.profit
+        self.margin += order.margin
+        self.orders.append(order)
+        return order
+
+    def _create_unhedged_order(
+        self, order_type: OrderType, symbol: str, volume: float, fee: float,
+        raise_exception: bool
+    ) -> Optional[Order]:
+        if symbol not in map(lambda order: order.symbol, self.orders):
+            return self._create_hedged_order(order_type, symbol, volume, fee, raise_exception)
+
+        old_order: Order = self.symbol_orders(symbol)[0]
+
+        if old_order.type == order_type:
+            new_order = self._create_hedged_order(order_type, symbol, volume, fee, raise_exception)
+            if new_order is None:
+                return None
+            self.orders.remove(new_order)
+
+            entry_price_weighted_average = np.average(
+                [old_order.entry_price, new_order.entry_price],
+                weights=[old_order.volume, new_order.volume]
+            )
+
+            old_order.volume += new_order.volume
+            old_order.profit += new_order.profit
+            old_order.margin += new_order.margin
+            old_order.entry_price = entry_price_weighted_average
+            old_order.fee = max(old_order.fee, new_order.fee)
+
+            return old_order
+
+        if volume >= old_order.volume:
+             self.close_order(old_order)
+             if volume > old_order.volume:
+                 return self._create_hedged_order(order_type, symbol, volume - old_order.volume, fee)
+             return old_order
+
+        partial_profit = (volume / old_order.volume) * old_order.profit
+        partial_margin = (volume / old_order.volume) * old_order.margin
+
+        old_order.volume -= volume
+        old_order.profit -= partial_profit
+        old_order.margin -= partial_margin
+
+        self.balance += partial_profit
+        self.margin -= partial_margin
+
+        return old_order
+
+    def close_order(self, order: Order) -> float:
+        self._check_current_time()
+        if order not in self.orders:
+            raise OrderNotFound("order not found in the order list")
+
+        order.exit_time = self.current_time
+        order.exit_price = self.price_at(order.symbol, order.exit_time)['Close']
+        self._update_order_profit(order)
+
+        self.balance += order.profit
+        self.margin -= order.margin
+
+        order.exit_balance = self.balance
+        order.exit_equity = self.equity
+
+        order.closed = True
+        self.orders.remove(order)
+        self.closed_orders.append(order)
+
+        return order.profit
+
+    def get_state(self) -> Dict[str, Any]:
+        orders = []
+        for order in reversed(self.closed_orders + self.orders):
+            orders.append({
+                'Id': order.id,
+                'Symbol': order.symbol,
+                'Type': order.type.name,
+                'Volume': order.volume,
+                'Entry Time': order.entry_time,
+                'Entry Price': order.entry_price,
+                'Exit Time': order.exit_time,
+                'Exit Price': order.exit_price,
+                'Exit Balance': order.exit_balance,
+                'Exit Equity': order.exit_equity,
+                'Profit': order.profit,
+                'Margin': order.margin,
+                'Fee': order.fee,
+                'Closed': order.closed,
+            })
+        orders_df = pd.DataFrame(orders)
+
+        return {
+            'current_time': self.current_time,
+            'balance': self.balance,
+            'equity': self.equity,
+            'margin': self.margin,
+            'free_margin': self.free_margin,
+            'margin_level': self.margin_level,
+            'orders': orders_df,
+        }
+
+    def _update_order_profit(self, order: Order) -> None:
+        diff = order.exit_price - order.entry_price
+        v = order.volume * self.symbols_info[order.symbol].trade_contract_size
+        local_profit = v * (order.type.sign * diff - order.fee)
+        order.profit = local_profit * self._get_unit_ratio(order.symbol, order.exit_time)
+
+    def _update_order_margin(self, order: Order) -> None:
+        v = order.volume * self.symbols_info[order.symbol].trade_contract_size
+        local_margin = (v * order.entry_price) / self.leverage
+        local_margin *= self.symbols_info[order.symbol].margin_rate
+        order.margin = local_margin * self._get_unit_ratio(order.symbol, order.entry_time)
+
+    def _get_unit_ratio(self, symbol: str, time: datetime) -> float:
+        symbol_info = self.symbols_info[symbol]
+        if self.unit == symbol_info.currency_profit:
+            return 1.
+
+        if self.unit == symbol_info.currency_margin:
+            return 1 / self.price_at(symbol, time)['Close']
+
+        currency = symbol_info.currency_profit
+        unit_symbol_info = self._get_unit_symbol_info(currency)
+        if unit_symbol_info is None:
+            raise SymbolNotFound(f"unit symbol for '{currency}' not found")
+
+        unit_price = self.price_at(unit_symbol_info.name, time)['Close']
+        if unit_symbol_info.currency_margin == self.unit:
+            unit_price = 1. / unit_price
+
+        return unit_price
+
+    def _get_unit_symbol_info(self, currency: str) -> Optional[SymbolInfo]:  # Unit/Currency or Currency/Unit
+        for info in self.symbols_info.values():
+            if currency in info.currencies and self.unit in info.currencies:
+                return info
+        return None
+
+    def _check_current_time(self) -> None:
+        if self.current_time is NotImplemented:
+            raise ValueError("'current_time' must have a value")
+
+    def _check_volume(self, symbol: str, volume: float) -> None:
+        symbol_info = self.symbols_info[symbol]
+
+        if not (symbol_info.volume_min <= volume <= symbol_info.volume_max):
+            raise ValueError(
+                f"'volume' must be in range [{symbol_info.volume_min}, {symbol_info.volume_max}]"
+            )
+
+        if not round(volume / symbol_info.volume_step, 6).is_integer():
+            raise ValueError(f"'volume' must be a multiple of {symbol_info.volume_step}")
diff --git a/trade_flow/environments/gym-mtsim/gym_mtsim/simulator/order.py b/trade_flow/environments/gym-mtsim/gym_mtsim/simulator/order.py
new file mode 100644
index 0000000..0a3dd57
--- /dev/null
+++ b/trade_flow/environments/gym-mtsim/gym_mtsim/simulator/order.py
@@ -0,0 +1,48 @@
+from enum import IntEnum
+from datetime import datetime
+
+
+class OrderType(IntEnum):
+    Sell = 0
+    Buy = 1
+
+    @property
+    def sign(self) -> float:
+        return 1. if self == OrderType.Buy else -1.
+
+    @property
+    def opposite(self) -> 'OrderType':
+        if self == OrderType.Sell:
+            return OrderType.Buy
+        return OrderType.Sell
+
+
+class Order:
+
+    def __init__(
+        self,
+        id: int,
+        type: OrderType,
+        symbol: str,
+        volume: float,
+        fee: float,
+        entry_time: datetime,
+        entry_price: float,
+        exit_time: datetime,
+        exit_price: float,
+    ) -> None:
+
+        self.id = id
+        self.type = type
+        self.symbol = symbol
+        self.volume = volume
+        self.fee = fee
+        self.entry_time = entry_time
+        self.entry_price = entry_price
+        self.exit_time = exit_time
+        self.exit_price = exit_price
+        self.exit_balance = float('nan')
+        self.exit_equity = float('nan')
+        self.profit = 0.
+        self.margin = 0.
+        self.closed: bool = False
diff --git a/trade_flow/environments/gym-mtsim/setup.py b/trade_flow/environments/gym-mtsim/setup.py
new file mode 100644
index 0000000..7f6917e
--- /dev/null
+++ b/trade_flow/environments/gym-mtsim/setup.py
@@ -0,0 +1,26 @@
+from setuptools import setup, find_packages
+
+setup(
+    name='gym_mtsim',
+    version='2.0.0',
+    packages=find_packages(),
+
+    author='AminHP',
+    author_email='mdan.hagh@gmail.com',
+
+    install_requires=[
+        'gymnasium>=0.29.1',
+        'numpy>=1.25.2',
+        'scipy>=1.11.2',
+        'pandas>=2.0.3',
+        'matplotlib>=3.8.2',
+        'plotly>=5.16.1',
+        'nbformat>=5.9.2',
+        'pathos>=0.3.1',
+        'MetaTrader5>=5.0.45; platform_system == "Windows"',
+    ],
+
+    package_data={
+        'gym_mtsim': ['data/*.pkl']
+    }
+)