Live trading + results. An easy strategy that actually works.

Key Concepts

• 60-Minute Opening Range Breakout (ORB): A strategy that identifies the high and low of the first hour of market trading to determine directional momentum.
• Credit Spreads: Options strategies (Put Credit Spreads for bullish moves, Call Credit Spreads for bearish moves) used to collect premium.
• Automated Trading Bots: Software used to execute trades based on predefined technical triggers.
• Data-Driven Optimization: Using historical performance metrics to refine trading frequency and position sizing.
• Sample Size: The number of trade occurrences required to draw statistically significant conclusions about strategy performance.

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1. The 60-Minute ORB Strategy

The trader employs a mechanical strategy based on the first 60 minutes of market activity (10:40 AM ET).

• The Setup: The bot monitors the high and low of the first hour.
• Execution Logic:
  • If the high is breached, the bot enters a Put Credit Spread (betting on continued upward momentum).
  • If the low is breached, the bot enters a Call Credit Spread (betting on continued downward momentum).
• Constraint: The trade only triggers if there is at least $0.70 of credit available in a 10-wide spread at the moment of the breakout.

2. Real-World Execution and Performance

The trader demonstrated the bot's performance across two platforms: TradeYear and Thinkorswim.

• TradeYear Fill: Filled at $1.30; reached profit target in 2 minutes.
• Thinkorswim Fill: Filled at $1.35; reached profit target in 3 minutes.
• Observation: The trader noted that Thinkorswim often provides slightly better fills, highlighting the impact of broker execution quality on strategy profitability.

3. Data Analysis and Strategy Refinement

The trader emphasizes that while they are not a "data analyst" by nature, the OptionAlpha platform provides automated metrics that allow for easy optimization after reaching a sample size of 60–70+ occurrences.

• Key Finding: Performance data revealed that Wednesdays and Fridays are significantly more profitable than other weekdays.
• Actionable Change:
  • Increased Position Sizing: Increasing contract sizes specifically for Wednesdays.
  • Frequency Adjustment: Continuing to trade Fridays while eliminating the strategy on Mondays, Tuesdays, and Thursdays to prevent "dragging down" overall profitability.
• Comparative Analysis: By cloning a template from another user ("Jack"), the trader identified that the strategy has a 100% win rate on Wednesdays, providing a high-conviction data point for scaling.

4. Methodology and Workflow

The trader’s approach to day trading is characterized by:

1. Manual vs. Automated: The trader manually trades a 15-minute ORB and a 10:30 AM strategy, using the 60-minute ORB bot to "sandwich" these trades for efficiency.
2. Post-Trade Review: Deep-diving into losing trades to identify outliers and commonalities.
3. Log-Based Analysis: Maintaining a trading log to track occurrences, which eventually feeds into the broader data analysis performed via OptionAlpha.

5. Notable Quotes

• "I don't really have a strategy problem. I do have a data problem." — Reflecting on the transition from simple spreadsheets to automated analytics.
• "I definitely deep dive my losers, find outliers, commonalities, obviously try and avoid future losers." — Describing the iterative process of risk management.

Synthesis and Conclusion

The video illustrates a transition from manual, intuition-based trading to a data-backed, automated framework. By utilizing the OptionAlpha platform, the trader moved beyond simple profit/loss tracking to identify specific temporal edges (Wednesdays/Fridays). The core takeaway is that sample size is essential for optimization; once a trader has enough occurrences, they can make surgical adjustments—such as increasing size on high-probability days and cutting low-performing days—to significantly improve the expectancy of their trading system.