Quant ATC Trading System

A modular trading system supporting multiple strategies and brokers.

Getting Started

Setting up the Development Environment

1. Create a Python virtual environment:

python -m venv venv

2. Activate the virtual environment:

   • On Windows (PowerShell):

   Set-ExecutionPolicy RemoteSigned -Scope Process
   .\venv\Scripts\activate

   • On Windows (Command Prompt):

   venv\Scripts\activate.bat

   • On Unix or MacOS:

   source venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

4. Install the package in development mode:

pip install -e .

Setup

1. Install dependencies:

pip install -r requirements.txt

2. Create a .env file with your broker credentials:

# FTMO
mt_login_id3=your_login
mt_password3=your_password
mt_server_name3=your_server
path3=path_to_mt5

# Oanda
mt_login_idOANDA=your_login
mt_passwordOANDA=your_password
mt_server_nameOANDA=your_server
pathOANDA=path_to_mt5

# Exness
mt_login_id5=your_login
mt_password5=your_password
mt_server_name5=your_server
path5=path_to_mt5

Available Strategies

Mean Reversion

• Uses Z-score to identify overbought/oversold conditions
• ATR-based position sizing and stop loss
• Configurable parameters in core/config.py

Momentum

• Uses dual moving average crossover
• Trend-following with ATR-based position sizing
• Configurable parameters in core/config.py

Scalping

• Uses RSI for entry signals
• Tighter stops and targets
• Volatility filtering with ATR
• Configurable parameters in core/config.py

Usage

You can start a strategy directly from Python:

from main import run_strategy

# Run scalping strategy on forex pairs with FTMO
run_strategy('scalping', 'FTMO', 'forex')

# Run momentum strategy on indices with Oanda
run_strategy('momentum', 'Oanda', 'indices')

# Run mean reversion on crypto with Exness
run_strategy('mean_reversion', 'Exness', 'crypto')

To stop a running strategy, use Ctrl+C in the terminal.

Adding New Strategies

1. Create a new strategy class in core/strategies/
2. Inherit from BaseTrader
3. Implement define_strategy() and execute_trades()
4. Add configuration to STRATEGY_CONFIGS in core/config.py

Risk Management

Each strategy includes:

• Position sizing based on account risk percentage
• ATR-based stop losses
• Risk:reward ratio management
• Maximum position checks

Logging

The system logs:

• Trade execution
• Position updates
• Error handling
• Strategy signals

Logs are formatted with timestamp, level, and message.

Directory Structure

quant-atc-mushini/
├── core/
│   ├── base_trader.py     # Base trading functionality
│   ├── config.py          # Configuration settings
│   ├── utils.py           # Technical indicators and helpers
│   └── strategies/        # Strategy implementations
│       ├── mean_reversion.py
│       ├── momentum.py
│       └── scalping.py
├── main.py                # Main execution script
└── requirements.txt       # Dependencies