This session covers proprietary trading strategies of the Pack Trade Group.
Focus will be on applying the midnight opening price concept and pack trading models.
Difference between trading models and entry strategies; personal customization is key.
Core Concepts
Midnight Opening Price
Importance: High probability due to daily candle fluctuations.
Mechanism: Observing fluctuations from the opening price and making high-probability trading decisions based on these movements.
Applications: Helps in predicting whether the price will close bullish or bearish, or if it will retest the opening price.
Risk and Confirmation Entries
Risk Entry: Higher potential reward but also higher risk of loss.
Confirmation Entry: Lower potential reward but higher probability of success.
Strategy: Balance between the two is essential for effective trading.
Utilizing Midnight Open and Distributions
Objective: To trade high-probability movements from the midnight open or distribution ranges.
Execution: For the Pack Trade Group, this involves waiting for price to align with their distribution models before entering a trade.
Daily Candle Analysis: Used for identifying high-probability trades based on the opening and closing of daily candles.
NASDAQ-Specific Strategies
High Probability Zones: Identifying moments within the 3-3:30 AM European market opening, midnight open, and 1615 settlement price for high-probability trades.
Price Movements: Use fluctuations and statistical data to predict movements and set trades within these zones.
Objective Trading Zones
Creation of Zones: Use 3-3:30 AM, midnight, and 1615 settlement prices to create high-probability trading zones.
Application: Helps in creating trades that revert back to these high-probability zones.
Trading with Statistical Analysis
Data-Driven Decisions: Emphases on using statistical data to inform trades rather than subjective price movements.
Risk Management: Adjusting risk based on statistical probabilities and market data to improve efficiency and reduce losses.
Trading Distribution Models
AM Session Examples: Detailed observation of how specific days (e.g., Wednesday 3rd July) underwent specific price movements and distributions.
Objective Measures for Quality Trades: Utilize statistical distributions and critical price points to execute trades.
Steps to Successful Trading
Initial Setup: Create high probability zones using 3-3:30 AM, midnight, and 1615 prices.
Statistical Validation: Confirm the high-probability price movements through statistical measures and avoid subjective biases.
Practical Applications and Examples
Backtesting with Objective Data: Practical examples of how trades align with the stated models and statistical data.
Risk-Managed Trades: Use examples to explain how to manage risk while maximizing the reward to achieve consistent success.
Conclusion
Think Multi-Dimensionally: Trade like the minority (top 10%), leveraging detailed statistical models and avoiding common pitfalls.
Continuous Learning: Emphasis on continuously updating statistical data and learning from each trading session.
Customization is Key: Everyone's trading model should be specific to their own needs, times, and asset preferences.