Trading with Statistics and Probabilities

Introduction

• Focus on understanding the use of statistics in trading.
• Simplifying trading by using a single time frame and statistical data.
• Avoid subjective trading decisions by relying on black-and-white statistical data.

Importance of Statistical Data

• Trending can be subjective; different time frames show different trends.
• Statistical data removes subjectivity by providing clear probabilities.
• Example: Using an hourly timeframe to analyze data and probabilities.
• Probabilities are based on historical data, e.g., 954 days of historical insights.

Key Concepts in Statistical Trading

• Midnight Snap: A specific timeframe and probability (65-68%) of price retracing to the midnight open.
• Opening Hourly Prices: High probabilities associated with retracing to hourly openings.
• Market Conditions: Statistical data remains consistent across various market conditions (elections, news events, etc.).
• Statistical Direction: Trading decisions based solely on statistical probabilities, not market predictions.

Trading Strategies

• Risk Management: Tailoring risk based on statistical data rather than market predictions.
• Profit Targets: Focus on statistical profit targets, e.g., 0.13%, for consistent results.

Execution on the One Minute Chart

• Footprints: Identifying where significant entries should occur based on historical "footprints" in the chart.
• Distributions: Using distribution patterns to identify potential trade entries.

High Probability Trading

• Opening Hours: Analyzing data from specific opening hours for high probability trading windows.
• Market Open Specific: High probability of retracing to opening prices during specific times of day (e.g., 9:30-10:00 AM).
• Asia Range Statistics: Statistical analysis of moves in the Asian session as indicators.

Practical Application

• Live Analysis: Using statistical data to guide real-time trading decisions.
• Participation: Engaging with an audience to reinforce understanding of statistical trading.

Conclusion

• Emphasizing the importance of statistical data in removing emotional and subjective biases in trading.
• Continuous learning and back-testing to refine trading strategies based on statistical insights.