This project is a modular framework for developing, backtesting, and deploying quantitative trading strategies using Python. It is designed to simulate trading logic as realistically as possible, with a focus on market-generated information and advanced risk management techniques. The platform has evolved from a basic concept to a robust engine capable of running complex, multi-day backtests.

The core backtesting engine is fully functional and stable. The primary focus has been the development and iterative refinement of the OpenRejectionReverseStrategy, a sophisticated day-trading strategy inspired by the principles in James Dalton's "Mind Over Markets".

• Event-Driven Backtesting Engine: The engine operates on a bar-by-bar basis, preventing lookahead bias and closely simulating a live trading environment. It now uses a pre-calculated trading calendar to reliably handle weekends and holidays.
• Realistic Cash Settlement: The BacktestLedger fully simulates a T+1 cash settlement environment, where proceeds from sales are not available for trading until the next session. This is critical for accurately testing strategies in a cash account.
• Configurable Strategies: All strategy parameters are externalized to config.yaml, allowing for easy tuning and experimentation without altering the source code.
• Advanced Analytics: The platform integrates VolumeProfiler and MarketProfiler to provide strategies with market-generated context, such as Point of Control (POC) and Value Area (VA).
• Sophisticated Risk Management: The active strategy employs a multi-layered position sizing model that considers:
  1. A percentage of total equity to define the maximum risk per trade.
  2. A maximum allocation cap to prevent over-concentration in a single position.
  3. A final check against available settled cash, scaling the position down if necessary.
• Dynamic Trade Management: The strategy includes advanced logic for managing open positions, such as a dynamic trailing stop-loss that uses the migrating Point of Control (POC) of the intraday trend.
• Detailed Performance Reporting: At the end of each run, the BacktestResults module calculates and displays a full suite of performance metrics (PnL, Win Rate, Profit Factor, Max Drawdown, etc.) and plots the equity curve.
• Extensive Logging: The engine and strategies produce detailed logs, which have been instrumental in diagnosing and fixing complex logical issues.

The platform maintains a modular design to separate concerns:

• /analytics: Contains reusable tools for market analysis (VolumeProfiler, MarketProfiler, VWAP).
• /backtest: Houses the core backtesting engine (engine.py) and results analysis tools (results.py).
• /core: Contains essential components like the BacktestLedger and BaseFeeModel.
• /strategies: Location for all trading strategy logic. Each strategy inherits from BaseStrategy.
• /logs: Output directory for the detailed strategy and engine logs.
• config.yaml: A central configuration file for managing all backtest, strategy, and fee parameters.

The primary strategy developed on this platform is a long-only, day-trading strategy that operates in three phases:

1. The Scan (Pre-Market): Analyzes the previous day's data to find stocks that closed with strong volume and momentum, creating a daily watchlist.
2. The Setup (Market Open): Confirms that a candidate has opened above the prior day's value area, indicating a continuation of bullish sentiment.
3. The Trigger (Intraday): Waits for a specific pattern where the price dips below the open and then rallies back through the opening bar's high. This pattern serves as the entry trigger, with a stop-loss placed at the low of the dip.

1. Install Dependencies:

   pip install -r requirements.txt

2. Add Data: Ensure your 1-minute historical data files are in the directory specified in config.yaml (default: /data/historical/).
3. Configure the Test: Edit config.yaml to select the active strategy (name) and set its corresponding parameters.
4. Run the Engine: Execute the main script from the project root. You will be prompted for a start and end date for the backtest.

   python main.py

• Strategy Refinement: Continue to test and refine the OpenRejectionReverseStrategy based on backtest results.
• New Strategies: Implement other strategies based on "Mind Over Markets" or different trading concepts.
• Live Trading Integration: Build out the components necessary to connect the strategy logic to a live paper or real money account via the Interactive Brokers API.
• Portfolio-Level Analysis: Enhance the results module to aggregate statistics across multiple backtest runs and provide portfolio-level insights.