### Configure MT5 Credentials for ML-SuperTrend-MT5

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/QUICKSTART.md

This snippet demonstrates how to copy an example configuration file and then edit the JSON to include your MetaTrader 5 demo account login, password, and server details.

```bash
copy config\config.example.json config\config.json
```

```json
{
    "accounts": {
        "demo": {
            "login": YOUR_DEMO_LOGIN,
            "password": "YOUR_DEMO_PASSWORD",
            "server": "YOUR_BROKER_SERVER"
        }
    }
}
```

--------------------------------

### Setup Development Environment (Bash)

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/CONTRIBUTING.md

Commands to clone the repository, create and activate a virtual environment, and install project dependencies using pip.

```bash
git clone https://github.com/xPOURY4/ML-SuperTrend-MT5.git
cd ML-SuperTrend-MT5
python -m venv venv
venv\Scripts\activate
pip install -r requirements.txt
```

--------------------------------

### Run ML-SuperTrend-MT5 Bot

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/QUICKSTART.md

This snippet shows the basic command to run the ML-SuperTrend-MT5 bot. It also includes examples of running the bot with specific options like specifying a symbol, enabling dry-run mode, activating performance monitoring, or setting the log level to DEBUG.

```bash
python run_bot.py
python run_bot.py --symbol GBPUSD
python run_bot.py --dry-run
python run_bot.py --monitor
python run_bot.py --log-level DEBUG
```

--------------------------------

### Install ml-supertrend-mt5 from GitHub

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/README.md

This command sequence is for developers who have cloned the ml-supertrend-mt5 repository from GitHub. It clones the repository and then installs the package in editable mode, allowing for direct code modification.

```bash
git clone <repository_url>
cd ml-supertrend-mt5
pip install -e .
```

--------------------------------

### Install ml-supertrend-mt5 Package using pip

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/README.md

This command installs the ml-supertrend-mt5 package from the Python Package Index (PyPI). It is the recommended method for end-users who want to use the bot as-is.

```bash
pip install ml-supertrend-mt5
```

--------------------------------

### Run ml-supertrend Command Line Tool

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/README.md

These examples demonstrate how to run the ml-supertrend command-line interface (CLI) tool. You can specify the trading symbol, account type (demo or live), and other parameters like interval and dry-run mode.

```bash
# Run with default settings
ml-supertrend --symbol EURUSD --account demo

# Run with custom parameters
ml-supertrend --symbol GBPUSD --account live --interval 60 --dry-run
```

--------------------------------

### Test MetaTrader 5 Connection with Python

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/QUICKSTART.md

This Python snippet demonstrates how to initialize the MetaTrader 5 terminal using the 'MetaTrader5' library and print the version. This is useful for testing the connection and setup.

```python
import MetaTrader5 as mt5
mt5.initialize()
print(mt5.version())
```

--------------------------------

### Import Core Components from GitHub Installation

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/README.md

When using the GitHub installation method, developers import core components like `SuperTrendBot` and `Config` from the `core` module instead of the top-level package. This is typically for making modifications to the source code.

```python
# For developers using GitHub installation
from core.supertrend_bot import SuperTrendBot, Config
from core.performance_monitor import PerformanceMonitor

# Same usage as above
```

--------------------------------

### Install TA-Lib Python Package

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/FAQ.md

Instructions for installing the TA-Lib Python package, which is a dependency for the ML-SuperTrend-MT5 bot. This involves downloading a specific Windows wheel file and using pip to install it.

```bash
# Download the .whl file for your Python version from:
# https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
pip install TA_Lib‑0.4.24‑cp39‑cp39‑win_amd64.whl
```

--------------------------------

### Troubleshoot 'No module named MetaTrader5'

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/QUICKSTART.md

This bash snippet provides commands to uninstall and then reinstall the 'MetaTrader5' Python package, which can resolve issues where the module is not found.

```bash
pip uninstall MetaTrader5
pip install MetaTrader5 --no-cache-dir
```

--------------------------------

### Run SuperTrendBot using Python Script

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/README.md

This Python script demonstrates how to initialize and run the SuperTrendBot. It involves creating a configuration object, connecting to an MT5 account, and starting the bot's trading interval.

```python
from ml_supertrend_mt5 import SuperTrendBot, Config
import MetaTrader5 as mt5

# Initialize configuration
config = Config(
    symbol="EURUSD",
    timeframe=mt5.TIMEFRAME_M30,
    risk_percent=1.0,
    cluster_choice="Average"  # "Best", "Average", or "Worst"
)

# Create and run bot
bot = SuperTrendBot(config)
bot.connect(login=YOUR_LOGIN, password="YOUR_PASSWORD", server="YOUR_SERVER")
bot.run(interval_seconds=30)
```

--------------------------------

### Execute Bot via Command Line Interface

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

Provides commands to install and run the ml-supertrend-mt5 package. Users can execute the bot with default settings, custom parameters (symbol, account type, interval, log level), in dry-run mode for simulation, perform backtests, or specify a custom configuration file.

```bash
# Install the package
pip install ml-supertrend-mt5

# Run with default settings (demo account, EURUSD)
ml-supertrend --symbol EURUSD --account demo

# Run with custom parameters
ml-supertrend --symbol GBPUSD --account live --interval 60 --log-level DEBUG

# Run in dry-run mode (simulation, no real trades)
ml-supertrend --symbol EURUSD --account demo --dry-run

# Run backtest
ml-supertrend --backtest --symbol USDJPY --monitor

# Run with custom config file
ml-supertrend --symbol EURUSD --config /path/to/config.json --interval 30
```

--------------------------------

### Upgrade ml-supertrend-mt5 Package using pip

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/README.md

This command upgrades an existing installation of the ml-supertrend-mt5 package to the latest version available on PyPI.

```bash
pip install --upgrade ml-supertrend-mt5
```

--------------------------------

### Run ML-SuperTrend Bot on Multiple Symbols (Command Line)

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/README.md

Demonstrates how to run the ML-SuperTrend bot on multiple currency pairs simultaneously using separate terminal instances. This allows for parallel trading across different markets.

```bash
# Terminal 1
ml-supertrend --symbol EURUSD --account demo --interval 30

# Terminal 2  
ml-supertrend --symbol GBPUSD --account demo --interval 30

# Terminal 3
ml-supertrend --symbol USDJPY --account demo --interval 30
```

--------------------------------

### ml-supertrend Command Line Options

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/README.md

This details the available options and arguments for the `ml-supertrend` command-line tool. It covers account types, symbols, configuration files, intervals, simulation modes, and logging levels.

```bash
ml-supertrend [OPTIONS]

Options:
  --account [demo|live]     Account type to use (default: demo)
  --symbol TEXT            Trading symbol (default: EURUSD)
  --config TEXT            Configuration file path (default: config.json)
  --interval INTEGER       Update interval in seconds (default: 30)
  --dry-run               Run in simulation mode without placing real trades
  --backtest              Run backtest instead of live trading
  --monitor               Show performance monitor after running
  --log-level [DEBUG|INFO|WARNING|ERROR]  Logging level (default: INFO)
  --help                  Show this message and exit

Examples:
  ml-supertrend --symbol GBPUSD --account demo --interval 60
  ml-supertrend --symbol EURUSD --account live --dry-run --log-level DEBUG
  ml-supertrend --backtest --symbol USDJPY --monitor
```

--------------------------------

### Enable Performance Monitoring for ML-SuperTrend (Command Line)

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/README.md

Explains how to activate and view the performance monitoring feature of the ML-SuperTrend bot using command-line arguments. The '--monitor' flag enables logging and reporting.

```bash
# Run with performance monitoring
ml-supertrend --symbol EURUSD --account demo --monitor

# View performance after running
ml-supertrend --monitor
```

--------------------------------

### Run ML-Supertrend with Debug Logging (Bash)

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/README.md

Demonstrates how to execute the ML-Supertrend command-line interface with debug logging enabled. This helps in diagnosing issues by providing verbose output during execution. It requires the 'ml-supertrend' executable to be in the system's PATH.

```bash
# Run with debug logging
ml-supertrend --symbol EURUSD --account demo --log-level DEBUG

# Dry run mode for testing
ml-supertrend --symbol EURUSD --account demo --dry-run
```

--------------------------------

### Utilize Performance Monitoring in ML-SuperTrend (Python)

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/README.md

Demonstrates using the 'PerformanceMonitor' class in Python to generate reports and calculate statistics from trade data. It allows for analysis of recent performance over a specified number of days.

```python
from ml_supertrend_mt5 import PerformanceMonitor

# Generate performance report
monitor = PerformanceMonitor('trades.json')
monitor.generate_report(days=30)  # Last 30 days

# Get specific metrics
stats = bot.calculate_statistics()
print(f"Win Rate: {stats['win_rate']:.2f}%")
print(f"Profit Factor: {stats['profit_factor']:.2f}")
print(f"Total Trades: {stats['total_trades']}")
```

--------------------------------

### Configure Custom Risk Profiles for ML-SuperTrend (Command Line)

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/README.md

Shows how to adjust the trading risk profile via command-line arguments for the ML-SuperTrend bot. Options include 'dry-run' for simulation and specifying intervals for aggressive trading.

```bash
# Conservative approach
ml-supertrend --symbol EURUSD --account demo --dry-run

# Aggressive approach  
ml-supertrend --symbol EURUSD --account demo --interval 15
```

--------------------------------

### Configure ML-SuperTrend Bot Settings (Python)

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/README.md

Sets up advanced configuration parameters for the SuperTrend bot, including clustering options, volume filters, and position management. This configuration is crucial for fine-tuning the bot's behavior.

```python
config = Config(
    # Clustering
    cluster_choice="Best",        # Use best performing cluster
    perf_alpha=10.0,             # Performance EMA period
    
    # Volume Filter
    volume_ma_period=20,         # Volume MA period
    volume_multiplier=1.2,       # Volume threshold
    
    # Position Management
    max_positions=1,             # Max positions per symbol
    magic_number=123456          # Unique identifier
)
```

--------------------------------

### Read and Parse JSON Configuration in Python

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

Demonstrates how to read a JSON configuration file using Python's `json` and `pathlib` libraries. It shows how to load the data and access specific settings like account details, symbol configurations, and global trading parameters.

```python
import json
from pathlib import Path

# Load configuration
config_path = Path("config.json")
with open(config_path, 'r') as f:
    config_data = json.load(f)

# Access account settings
demo_account = config_data["accounts"]["demo"]
print(f"Login: {demo_account['login']}")
print(f"Server: {demo_account['server']}")

# Access symbol settings
eurusd_config = config_data["symbols"]["EURUSD"]
print(f"Timeframe: {eurusd_config['timeframe']}")
print(f"Risk: {eurusd_config['risk_percent']}%)"

# Access global settings
global_settings = config_data["global_settings"]
print(f"Max Daily Loss: {global_settings['max_daily_loss_percent']}%)"
```

--------------------------------

### Run ML-SuperTrend Bot on Multiple Symbols (Python)

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/README.md

Illustrates launching multiple SuperTrendBot instances in Python, each configured for a different symbol and timeframe. It uses threading to run bots concurrently for efficient multi-symbol trading.

```python
from ml_supertrend_mt5 import SuperTrendBot, Config
import MetaTrader5 as mt5

symbols = ["EURUSD", "GBPUSD", "USDJPY"]
bots = []

for symbol in symbols:
    config = Config(
        symbol=symbol,
        timeframe=mt5.TIMEFRAME_M30,
        risk_percent=0.5  # Lower risk for multiple pairs
    )
    bot = SuperTrendBot(config)
    bot.connect(login, password, server)
    bots.append(bot)

# Run all bots
import threading
for bot in bots:
    thread = threading.Thread(target=bot.run)
    thread.start()
```

--------------------------------

### Initialize and Connect SuperTrendBot to MetaTrader 5

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

Initializes the SuperTrendBot with a configuration object and establishes a connection to the MetaTrader 5 platform. It requires MT5 connection credentials (login, password, server) and a detailed configuration for trading parameters. Successful connection prints account balance; failure indicates an issue.

```python
import MetaTrader5 as mt5
from core.supertrend_bot import SuperTrendBot, Config

# Configure bot parameters
config = Config(
    symbol="EURUSD",
    timeframe=mt5.TIMEFRAME_M30,
    atr_period=10,
    min_factor=1.0,
    max_factor=5.0,
    factor_step=0.5,
    perf_alpha=10.0,
    cluster_choice="Best",  # "Best", "Average", or "Worst"
    volume_ma_period=20,
    volume_multiplier=1.2,
    sl_multiplier=2.0,
    tp_multiplier=3.0,
    use_trailing=True,
    trail_activation=1.5,
    risk_percent=1.0,
    max_positions=1,
    magic_number=123456
)

# Initialize bot
bot = SuperTrendBot(config)

# Connect to MT5
login = 12345678
password = "your_password"
server = "MetaQuotes-Demo"

if bot.connect(login, password, server):
    print("Connected successfully!")
    account_info = mt5.account_info()
    print(f"Balance: {account_info.balance} {account_info.currency}")
else:
    print("Connection failed")
```

--------------------------------

### Configure MT5 Connection using JSON

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/README.md

This JSON structure defines the configuration for connecting to MetaTrader 5 accounts and setting up trading symbols. It includes account credentials, symbol-specific parameters, and global trading settings.

```json
{
    "accounts": {
        "demo": {
            "login": YOUR_DEMO_LOGIN,
            "password": "YOUR_DEMO_PASSWORD",
            "server": "YOUR_BROKER_SERVER"
        },
        "live": {
            "login": YOUR_LIVE_LOGIN,
            "password": "YOUR_LIVE_PASSWORD",
            "server": "YOUR_BROKER_SERVER"
        }
    },
    "symbols": {
        "EURUSD": {
            "enabled": true,
            "timeframe": "M30",
            "min_factor": 1.0,
            "max_factor": 5.0,
            "factor_step": 0.5,
            "cluster_choice": "Best",
            "volume_multiplier": 1.2,
            "sl_multiplier": 2.0,
            "tp_multiplier": 3.0,
            "risk_percent": 1.0
        }
    },
    "global_settings": {
        "atr_period": 10,
        "performance_alpha": 10.0,
        "volume_ma_period": 20,
        "use_trailing_stop": true,
        "trail_activation_atr": 1.5,
        "max_positions_per_symbol": 1
    }
}
```

--------------------------------

### Initialize and Run Multiple SuperTrend Bots

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

This snippet demonstrates how to initialize multiple SuperTrendBot instances for different symbols with a common configuration and run them concurrently in separate threads. It requires the `SuperTrendBot`, `Config` classes and the `MetaTrader5` library.

```python
from core.supertrend_bot import SuperTrendBot, Config
import MetaTrader5 as mt5
import threading

symbols = ["EURUSD", "GBPUSD", "USDJPY"]
bots = []

# Create bot for each symbol
for symbol in symbols:
    config = Config(
        symbol=symbol,
        timeframe=mt5.TIMEFRAME_M30,
        risk_percent=0.5,  # Lower risk when trading multiple pairs
        cluster_choice="Average",
        max_positions=1
    )

    bot = SuperTrendBot(config)

    if bot.connect(12345678, "password", "MetaQuotes-Demo"):
        bots.append(bot)
        print(f"Bot initialized for {symbol}")

# Run all bots in separate threads
threads = []
for bot in bots:
    thread = threading.Thread(target=bot.run, args=(60,)) # Run with 60s interval
    thread.start()
    threads.append(thread)
    print(f"Started bot for {bot.config.symbol}")

# Wait for all threads
for thread in threads:
    thread.join()
```

--------------------------------

### Load Trading Configuration from JSON

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

Loads trading parameters from a JSON file to enable easy management of settings for different accounts, symbols, and global trading rules. This configuration is essential for initializing the trading bot with specific parameters.

```json
{
    "accounts": {
        "demo": {
            "login": 12345678,
            "password": "demo_password",
            "server": "MetaQuotes-Demo"
        },
        "live": {
            "login": 87654321,
            "password": "live_password",
            "server": "YourBroker-Live"
        }
    },
    "symbols": {
        "EURUSD": {
            "enabled": true,
            "timeframe": "M30",
            "min_factor": 1.0,
            "max_factor": 5.0,
            "factor_step": 0.5,
            "cluster_choice": "Best",
            "volume_multiplier": 1.2,
            "sl_multiplier": 2.0,
            "tp_multiplier": 3.0,
            "risk_percent": 1.0
        }
    },
    "global_settings": {
        "atr_period": 10,
        "performance_alpha": 10.0,
        "volume_ma_period": 20,
        "use_trailing_stop": true,
        "trail_activation_atr": 1.5,
        "max_positions_per_symbol": 1,
        "max_daily_loss_percent": 5.0
    }
}
```

--------------------------------

### Troubleshoot ML-SuperTrend Connection Issues (Python)

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/README.md

Provides Python code snippets and comments to help diagnose and resolve connection failures with the MetaTrader 5 terminal. It emphasizes checking terminal status, credentials, and Algo Trading enablement.

```python
# Check MT5 terminal is running
# Verify credentials
# Ensure "Algo Trading" is enabled in MT5
```

--------------------------------

### Enable Debug Mode in Python Script (Python)

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/README.md

Shows how to enable debug logging and run the ML-Supertrend bot in debug mode using a Python script. This involves configuring the 'logging' module and passing a 'debug=True' argument to the bot's run method. Useful for in-depth debugging within a Python environment.

```python
# Enable detailed logging
import logging
logging.basicConfig(level=logging.DEBUG)

# Run bot with debug
bot.run(interval_seconds=30, debug=True)
```

--------------------------------

### Run Tests (Bash)

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/CONTRIBUTING.md

Command to execute the test suite for the project using pytest, located in the 'tests/' directory.

```bash
pytest tests/
```

--------------------------------

### Configure Custom Risk Profiles for ML-SuperTrend (Python)

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/README.md

Defines distinct 'conservative' and 'aggressive' risk profiles using the Config object in Python. These profiles adjust parameters like cluster choice, risk percentage, stop-loss, and take-profit multipliers.

```python
from ml_supertrend_mt5 import SuperTrendBot, Config

# Conservative approach
conservative_config = Config(
    cluster_choice="Worst",      # Use worst performing cluster
    risk_percent=0.5,            # Lower risk
    sl_multiplier=3.0,           # Wider stop loss
    tp_multiplier=2.0            # Closer take profit
)

# Aggressive approach
aggressive_config = Config(
    cluster_choice="Best",       # Use best performing cluster
    risk_percent=2.0,            # Higher risk
    sl_multiplier=1.5,           # Tighter stop loss
    tp_multiplier=4.0            # Further take profit
)
```

--------------------------------

### Execute Backtest Engine

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

Runs historical backtests to evaluate strategy performance using the 'BacktestEngine' and 'SuperTrendBot'. Requires MetaTrader5 initialization and configuration of the bot's parameters. The backtest is performed over a specified date range and timeframe, returning results including equity curves and performance metrics.

```python
from backtest_engine import BacktestEngine
from core.supertrend_bot import SuperTrendBot, Config
import MetaTrader5 as mt5
from datetime import datetime, timedelta

# Initialize MT5
mt5.initialize()

# Configure strategy
config = Config(
    symbol="EURUSD",
    timeframe=mt5.TIMEFRAME_H1,
    risk_percent=1.0,
    cluster_choice="Best",
    sl_multiplier=2.0,
    tp_multiplier=3.0
)

bot = SuperTrendBot(config)

# Create backtest engine
backtest = BacktestEngine(
    strategy=bot,
    initial_balance=10000
)

# Define backtest period
start_date = datetime(2023, 1, 1)
end_date = datetime(2023, 12, 31)

# Run backtest
print(f"Running backtest: {start_date.date()} to {end_date.date()}")
results = backtest.run_backtest(
    symbol="EURUSD",
    start_date=start_date,
    end_date=end_date,
    timeframe=mt5.TIMEFRAME_H1
)

# Results include equity curve, drawdown analysis, and performance metrics
```

--------------------------------

### Set Up Multi-Symbol Trading

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

Configures and initializes multiple SuperTrendBot instances for simultaneous trading across different currency pairs. It defines a list of symbols and creates a bot for each, potentially for use with threading or multiprocessing.

```python
from core.supertrend_bot import SuperTrendBot, Config
import MetaTrader5 as mt5
import threading

# Define symbols to trade
symbols = ["EURUSD", "GBPUSD", "USDJPY"]
bots = []

# Example of how bots might be initialized (actual initialization loop omitted for brevity)
# for symbol in symbols:
#     config = Config(symbol=symbol, timeframe=mt5.TIMEFRAME_M30)
#     bot = SuperTrendBot(config)
#     bots.append(bot)

# Further logic would involve connecting bots and managing their execution, possibly using threading.
```

--------------------------------

### Push to GitHub (Bash)

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/CONTRIBUTING.md

Pushes the current feature branch to the remote 'origin' repository on GitHub.

```bash
git push origin feature/your-feature-name
```

--------------------------------

### Commit Changes (Bash)

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/CONTRIBUTING.md

Stages all changes and creates a commit with a descriptive message.

```bash
git add .
git commit -m "Add feature: description of your changes"
```

--------------------------------

### Place Order with Stop Loss and Take Profit (Python)

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

Executes a market buy order with automated stop loss and take profit levels calculated based on current price and ATR. It determines position size to manage risk relative to the stop loss distance. Requires SuperTrendBot and MetaTrader5 libraries.

```python
from core.supertrend_bot import SuperTrendBot, Config
import MetaTrader5 as mt5

config = Config(
    symbol="GBPUSD",
    timeframe=mt5.TIMEFRAME_M30,
    sl_multiplier=2.0,
    tp_multiplier=3.0
)

bot = SuperTrendBot(config)
bot.connect(12345678, "password", "server")

# Get market data
df = bot.get_data()
current_price = df['close'].iloc[-1]
current_atr = df['atr'].iloc[-1]

# Calculate levels
sl = current_price - (current_atr * config.sl_multiplier)
tp = current_price + (current_atr * config.tp_multiplier)

# Calculate position size
symbol_info = mt5.symbol_info(config.symbol)
sl_points = (current_price - sl) / symbol_info.point
volume = bot.calculate_position_size(sl_points)

# Place BUY order
ticket = bot.place_order(
    order_type=mt5.ORDER_TYPE_BUY,
    volume=volume,
    price=current_price,
    sl=sl,
    tp=tp
)

if ticket:
    print(f"Order placed successfully! Ticket: {ticket}")
    print(f"Entry: {current_price:.5f}, SL: {sl:.5f}, TP: {tp:.5f}")
else:
    print("Order failed")
```

--------------------------------

### Generate Performance Reports with Trade History

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

Generates comprehensive performance reports, including equity curves and statistical analysis, from a JSON file containing trade history. Requires the 'core.performance_monitor' module and a list of trades with profit, entry/exit times, and volume. Outputs visual charts and detailed metrics.

```python
from core.performance_monitor import PerformanceMonitor
import json
from datetime import datetime, timedelta

# Create sample trade history
trades = [
    {
        "symbol": "EURUSD",
        "profit": 145.50,
        "entry_time": "2024-01-15 09:30:00",
        "exit_time": "2024-01-15 14:45:00",
        "volume": 0.1
    },
    {
        "symbol": "EURUSD",
        "profit": -82.30,
        "entry_time": "2024-01-16 10:15:00",
        "exit_time": "2024-01-16 11:30:00",
        "volume": 0.1
    },
    {
        "symbol": "GBPUSD",
        "profit": 217.80,
        "entry_time": "2024-01-17 13:00:00",
        "exit_time": "2024-01-17 18:20:00",
        "volume": 0.15
    }
]

# Save trades to JSON
with open('trades.json', 'w') as f:
    json.dump(trades, f)

# Generate performance report
monitor = PerformanceMonitor('trades.json')
monitor.generate_report(days=30)

# Output includes:
# - Equity curve chart
# - Win rate by hour of day
# - Profit distribution histogram
# - Trade duration vs profit scatter plot
# - Detailed statistics (win rate, profit factor, avg win/loss, etc.)
```

--------------------------------

### ML-Based SuperTrend Clustering for Optimal Parameters

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

Calculates multiple SuperTrend indicators with varying ATR factors and utilizes K-means clustering to identify the optimal parameter set based on performance metrics. This function requires market data and returns the best performing ATR factor and its associated performance score. Ensure the bot is connected to MT5 and has retrieved market data.

```python
from core.supertrend_bot import SuperTrendBot, Config
import MetaTrader5 as mt5

config = Config(
    symbol="EURUSD",
    timeframe=mt5.TIMEFRAME_H1,
    min_factor=1.0,
    max_factor=5.0,
    factor_step=0.5,
    cluster_choice="Best"  # Select best performing cluster
)

bot = SuperTrendBot(config)
bot.connect(12345678, "password", "server")

# Get market data
df = bot.get_data(bars=1000)

# Calculate SuperTrend for all factors
supertrends = bot.calculate_supertrends(df)

# Perform ML clustering to find optimal factor
optimal_factor, performance_score = bot.perform_clustering(supertrends)

print(f"Optimal ATR Factor: {optimal_factor:.2f}")
print(f"Performance Score: {performance_score:.4f}")
```

--------------------------------

### Risk Manager Daily Loss Limit Check (Python)

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

Monitors the total trading losses within a single day and halts further trading if a predefined percentage of the account balance is lost. It simulates daily trades to demonstrate the check. Requires RiskManager and MetaTrader5 libraries.

```python
from core.risk_manager import RiskManager
from datetime import datetime
import MetaTrader5 as mt5

# Initialize risk manager
risk_manager = RiskManager(
    max_daily_loss_percent=5.0,  # Stop trading if 5% daily loss
    max_correlation=0.7
)

# Get account balance
mt5.initialize()
account_info = mt5.account_info()
account_balance = account_info.balance

# Simulate some trades
risk_manager.daily_trades = [
    {'profit': -150.50, 'time': datetime.now()},
    {'profit': 80.00, 'time': datetime.now()},
    {'profit': -220.00, 'time': datetime.now()}
]

# Check if we can continue trading
if risk_manager.check_daily_loss_limit(account_balance):
    print("Daily loss limit OK - can continue trading")
    daily_loss = sum(t['profit'] for t in risk_manager.daily_trades if t['profit'] < 0)
    print(f"Daily Loss: ${abs(daily_loss):.2f}")
else:
    print("Daily loss limit reached - STOP TRADING")
    max_loss = account_balance * 0.05
    print(f"Max Allowed Loss: ${max_loss:.2f}")
```

--------------------------------

### Run Trading Bot with Auto-Trading Loop

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

Executes the main trading loop of the SuperTrendBot, continuously analyzing market data, generating trading signals, and managing open positions. The bot runs at a specified interval, and can be stopped gracefully using a KeyboardInterrupt. Ensure successful connection to MT5 before running.

```python
from core.supertrend_bot import SuperTrendBot, Config
import MetaTrader5 as mt5

config = Config(
    symbol="EURUSD",
    timeframe=mt5.TIMEFRAME_M30,
    risk_percent=1.0,
    cluster_choice="Average"
)

bot = SuperTrendBot(config)

if bot.connect(12345678, "password", "MetaQuotes-Demo"):
    try:
        # Run bot with 30-second update interval
        bot.run(interval_seconds=30)
    except KeyboardInterrupt:
        print("Bot stopped by user")
        bot.shutdown()
```

--------------------------------

### Risk Manager Kelly Criterion Position Sizing (Python)

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

Calculates the optimal position size for a trade using the Kelly Criterion formula, aiming to maximize long-term growth rate. It uses win rate, average win, and average loss as inputs, with a fractional Kelly application for reduced risk. Requires RiskManager library.

```python
from core.risk_manager import RiskManager

risk_manager = RiskManager()

# Historical performance metrics
win_rate = 58.5  # 58.5% win rate
avg_win = 65.0   # Average win: 65 pips
avg_loss = 42.0  # Average loss: 42 pips

# Calculate Kelly Criterion position size
kelly_percent = risk_manager.calculate_kelly_criterion(win_rate, avg_win, avg_loss)

account_balance = 10000
kelly_risk_amount = account_balance * kelly_percent

print(f"Win Rate: {win_rate}%")
print(f"Avg Win/Loss Ratio: {avg_win/avg_loss:.2f}")
print(f"Kelly Criterion: {kelly_percent*100:.2f}% per trade")
print(f"Recommended Risk: ${kelly_risk_amount:.2f}")
print(f"Note: Using 25% fractional Kelly for safety")
```

--------------------------------

### Update Trailing Stop Loss (Python)

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

Automatically adjusts the stop loss for an existing open position to lock in profits as the market price moves favorably. It uses an activation threshold based on ATR and a multiplier for the stop loss distance. Requires SuperTrendBot and MetaTrader5 libraries.

```python
from core.supertrend_bot import SuperTrendBot, Config
import MetaTrader5 as mt5

config = Config(
    symbol="EURUSD",
    use_trailing=True,
    trail_activation=1.5,  # Activate after 1.5 ATR profit
    sl_multiplier=2.0
)

bot = SuperTrendBot(config)
bot.connect(12345678, "password", "server")

# Get current positions
positions = mt5.positions_get(symbol=config.symbol)

if positions:
    for position in positions:
        if position.magic == config.magic_number:
            # Get current market data
            df = bot.get_data()
            current_price = df['close'].iloc[-1]
            current_atr = df['atr'].iloc[-1]

            # Update trailing stop
            updated = bot.update_trailing_stop(position, current_price, current_atr)

            if updated:
                print(f"Trailing stop updated for position {position.ticket}")
            else:
                print("No trailing stop update needed")
```

--------------------------------

### Dynamic Position Sizing with Risk Management

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

Calculates the appropriate position size for a trade based on the account's balance, the defined risk percentage per trade, and the stop-loss distance derived from the Average True Range (ATR). This function ensures trades adhere to risk management rules by calculating volume in lots. It requires current market data and symbol information.

```python
from core.supertrend_bot import SuperTrendBot, Config
import MetaTrader5 as mt5

config = Config(
    symbol="EURUSD",
    timeframe=mt5.TIMEFRAME_M30,
    risk_percent=1.0  # Risk 1% of account per trade
)

bot = SuperTrendBot(config)
bot.connect(12345678, "password", "server")

# Get current market data
df = bot.get_data()
current_price = df['close'].iloc[-1]
current_atr = df['atr'].iloc[-1]

# Calculate stop loss distance
sl_distance = current_atr * config.sl_multiplier
stop_loss = current_price - sl_distance

# Calculate stop loss in points
symbol_info = mt5.symbol_info(config.symbol)
sl_points = sl_distance / symbol_info.point

# Calculate position size
volume = bot.calculate_position_size(sl_points)

print(f"Entry Price: {current_price:.5f}")
print(f"Stop Loss: {stop_loss:.5f}")
print(f"Position Size: {volume:.2f} lots")
print(f"Risk Amount: ${mt5.account_info().balance * 0.01:.2f}")
```

--------------------------------

### Calculate Bot Trading Statistics

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

Calculates real-time trading statistics for a SuperTrendBot, including total trades, wins, losses, win rate, profit factor, total profit/loss, and average win/loss. It takes a list of trade dictionaries (with 'profit' and 'symbol') as input and utilizes the 'core.supertrend_bot' module.

```python
from core.supertrend_bot import SuperTrendBot, Config
import MetaTrader5 as mt5

config = Config(symbol="EURUSD", timeframe=mt5.TIMEFRAME_M30)
bot = SuperTrendBot(config)
bot.connect(12345678, "password", "server")

# Simulate some trades in history
bot.trade_history = [
    {'profit': 45.50, 'symbol': 'EURUSD'},
    {'profit': -22.30, 'symbol': 'EURUSD'},
    {'profit': 67.80, 'symbol': 'EURUSD'},
    {'profit': -18.90, 'symbol': 'EURUSD'},
    {'profit': 102.40, 'symbol': 'EURUSD'},
    {'profit': -35.60, 'symbol': 'EURUSD'},
]

# Calculate statistics
stats = bot.calculate_statistics()

print("=== Trading Statistics ===")
print(f"Total Trades: {stats['total_trades']}")
print(f"Winning Trades: {stats['wins']}")
print(f"Losing Trades: {stats['losses']}")
print(f"Win Rate: {stats['win_rate']:.2f}%")
print(f"Profit Factor: {stats['profit_factor']:.2f}")
print(f"Total Profit: ${stats['total_profit']:.2f}")
print(f"Total Loss: ${stats['total_loss']:.2f}")
print(f"Average Win: ${stats['avg_win']:.2f}")
print(f"Average Loss: ${stats['avg_loss']:.2f}")
```

--------------------------------

### Configure Aggressive Trading Strategy for SuperTrend Bot

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

This Python snippet configures the SuperTrendBot for higher risk/reward with faster execution, using a lower timeframe, higher risk percentage, and aggressive stop-loss/take-profit settings. It requires `SuperTrendBot`, `Config` from `core` and `MetaTrader5`.

```python
from core.supertrend_bot import SuperTrendBot, Config
import MetaTrader5 as mt5

# Aggressive strategy configuration
config = Config(
    symbol="GBPUSD",
    timeframe=mt5.TIMEFRAME_M15,  # Lower timeframe for more trades
    risk_percent=2.0,             # Higher risk per trade
    cluster_choice="Best",        # Use best performing cluster
    sl_multiplier=1.5,            # Tighter stop loss
    tp_multiplier=4.0,            # Further take profit target
    volume_multiplier=1.0,        # Lower volume threshold (more signals)
    use_trailing=True,
    trail_activation=1.0,         # Aggressive trailing activation
    max_positions=1,
    perf_alpha=5.0                # Faster performance adaptation
)

bot = SuperTrendBot(config)

if bot.connect(12345678, "password", "server"):
    print("Running aggressive strategy...")
    print(f"Risk per trade: {config.risk_percent}%")
    print(f"Timeframe: M15")
    print(f"SL/TP Ratio: {config.sl_multiplier}:{config.tp_multiplier}")
    bot.run(interval_seconds=15)  # 15-second updates for fast execution
```

--------------------------------

### Create a Feature Branch (Bash)

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/CONTRIBUTING.md

Command to create a new Git branch for developing a new feature, branching off the 'main' branch.

```bash
git checkout -b feature/your-feature-name
```

--------------------------------

### Configure Conservative Trading Strategy for SuperTrend Bot

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

This Python snippet configures the SuperTrendBot with risk-averse parameters for stable returns, utilizing a higher timeframe, lower risk percentage, and conservative cluster/stop-loss/take-profit settings. It depends on `SuperTrendBot`, `Config` from `core` and `MetaTrader5`.

```python
from core.supertrend_bot import SuperTrendBot, Config
import MetaTrader5 as mt5

# Conservative strategy configuration
config = Config(
    symbol="EURUSD",
    timeframe=mt5.TIMEFRAME_H4,  # Higher timeframe for fewer trades
    risk_percent=0.5,            # Low risk per trade
    cluster_choice="Worst",      # Use worst cluster (most conservative)
    sl_multiplier=3.0,           # Wider stop loss
    tp_multiplier=2.0,           # Closer take profit (better R:R)
    volume_multiplier=1.5,       # Higher volume confirmation threshold
    use_trailing=True,
    trail_activation=2.0,        # Conservative trailing activation
    max_positions=1
)

bot = SuperTrendBot(config)

if bot.connect(12345678, "password", "server"):
    print("Running conservative strategy...")
    print(f"Risk per trade: {config.risk_percent}%")
    print(f"Timeframe: H4")
    print(f"SL/TP Ratio: {config.sl_multiplier}/{config.tp_multiplier}")
    bot.run(interval_seconds=300)  # 5-minute updates
```

--------------------------------

### Calculate Position Size (Python)

Source: https://github.com/xpoury4/ml-supertrend-mt5/blob/main/CONTRIBUTING.md

A Python function to calculate the appropriate position size for a trade based on account balance, risk percentage, and stop-loss in pips. It adheres to PEP 8 style guidelines and includes type hints and a docstring.

```python
def calculate_position_size(
    balance: float, 
    risk_percent: float, 
    stop_loss_pips: float
) -> float:
    """
    Calculate position size based on account risk.
    
    Args:
        balance: Account balance in base currency
        risk_percent: Risk percentage per trade
        stop_loss_pips: Stop loss distance in pips
        
    Returns:
        Position size in lots
    """
    risk_amount = balance * (risk_percent / 100)
    position_size = risk_amount / (stop_loss_pips * 10)
    return round(position_size, 2)
```

--------------------------------

### Risk Manager Correlation Check (Python)

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

Evaluates the correlation between currently open trading positions to prevent opening new positions that are highly correlated, thereby reducing portfolio risk. It checks against a predefined maximum correlation threshold. Requires RiskManager library.

```python
from core.risk_manager import RiskManager

risk_manager = RiskManager(max_correlation=0.7)

# Current open positions
symbols_in_trade = ["EURUSD", "GBPUSD"]

# Check if new position creates too much correlation
if risk_manager.check_correlation(symbols_in_trade):
    print("Correlation check passed - positions are sufficiently diversified")
else:
    print("Correlation too high - avoid opening correlated positions")
    print("EURUSD and GBPUSD are highly correlated")
```

--------------------------------

### Integrate News Filter with SuperTrend Bot

Source: https://context7.com/xpoury4/ml-supertrend-mt5/llms.txt

This Python snippet shows how to integrate a `NewsFilter` with the `SuperTrendBot` to prevent trading during high-impact news events. It checks for news proximity before initiating a trading cycle. Dependencies include `NewsFilter`, `SuperTrendBot`, `Config` from `core` and `MetaTrader5`.

```python
from core.news_filter import NewsFilter
from core.supertrend_bot import SuperTrendBot, Config
import MetaTrader5 as mt5

# Initialize news filter
news_filter = NewsFilter()

# Check if safe to trade
symbol = "EURUSD"
minutes_before = 30
minutes_after = 30

if news_filter.is_news_time(symbol, minutes_before, minutes_after):
    print(f"High impact news event near for {symbol}")
    print("Avoiding trade entry")
else:
    print(f"No major news events - safe to trade {symbol}")

    # Proceed with trading
    config = Config(symbol=symbol, timeframe=mt5.TIMEFRAME_M30)
    bot = SuperTrendBot(config)

    if bot.connect(12345678, "password", "server"):
        bot.run_cycle()  # Execute one trading cycle
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.