Trading Lecture Notes

Introduction

• Recap of last week's trading activities
• Focus on using statistics and probabilities in trading
• Emphasis on simplicity: executing on a single timeframe
• Importance of data and black and white statistics

Subjectivity in Trading

• Different interpretations of trends on various timeframes
• Subjectivity in determining good vs. bad market structures
• Statistics eliminate subjectivity in trading decisions

Statistical Data in Trading

• Example: Hourly data showing 65-68% probability of retracement to midnight open
• In 954 days, 68.7% retraced to midnight open
• Data collection through Google Sheets or indicators
• Statistics survive various market conditions: elections, bull/bear markets, news events

Trading Strategy and Execution

• Use of data to determine trading direction
• Trading based on high probabilities rather than predictions
• Not necessary to focus on market conditions or fundamentals if leaning on data
• Example: Starting at 8 AM, price above midnight gives permission to go short

Trading Psychology

• Avoid predicting trend days
• Highlight on market ranging more than trending
• Importance of statistical probabilities over market predictions

Trading Techniques

• Midnight Snap and Opening Hours
  • Retracement statistics between 8-11:15 AM
  • Opening hour prices as key statistical points
• Market Open Statistics
  • Specific high probability retracement windows

Trade Entries

• Focus on statistically backed lines for trade decisions
• Use of "footprints" to identify sell pressure
• Proprietary trading signals: "red and green wolves"

Risk Management

• Emphasis on dollar-cost averaging (DCA) and risk budgets
• Comparison of single-entry trading vs. DCA
• Importance of managing trades with statistical backing

Conclusion

• Trading success lies in statistical consistency, not perfect predictions
• Encouragement to base trading on irrefutable statistical data
• Final thoughts on maintaining simplicity and stress-free trading

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Example Days

Monday

• Retracement of midnight snap with statistical confidence
• Example trades showing minimal drawdown and quick profit taking

Wednesday

• Asia range retracement statistics
• Specific high probability shorts based on overlapping data

Thursday

• Example of a day with less statistical confidence
• Use of risk management to navigate low statistical days

Friday

• Observation of weekly profiles and statistical highs/lows
• Use of weekly trends to forecast potential market direction

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Additional Insights

• Consistency over performance in trade strategies
• Adapting trading to high-probability setups rather than chasing trends
• Weekend analysis for better trading in the following week

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Key Takeaways

• Utilize statistical data for trading with confidence
• Simplify trading execution to reduce stress and improve focus
• Use past data for creating a reliable trading model and plan