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Introduction
On a recent episode of Coffee With Q, hosted by entrepreneur and podcaster Qamar Zaman, guest John Kaplan shared his rollercoaster journey from entrepreneurship to active trading. The conversation explored John’s experiences with various trading strategies, his challenges with consistency, and ultimately, how the Q Algo reshaped his approach to SPX zero DTE options trading.
From Sales and Marketing to Options Trading
Before entering the trading world, Kaplan built a successful career in sales and marketing, even spending eight years in Bangkok working in commercial real estate while becoming fluent in Thai. But in 2017, he turned his focus to financial markets.
What began as buy-and-hold investing quickly evolved into an obsession with options trading. Kaplan joined trading rooms, sought out mentors, and experimented with strategies ranging from iron condors to calendars to spreads.
The Search for Consistency
Kaplan described his early trading journey as “a cycle of excitement, success, and then disappointment.”
- His first mentor focused on SPY scalps with modest daily profit targets. The method worked—until it didn’t.
- He then dove deep into earnings plays, executing over 1,000 trades in a single year, meticulously documenting each one. Early success was followed by “volatility crush” that left him breaking even.
- Later, he moved into range-bound strategies, futures backtesting, and opening range breaks. Despite hundreds of hours of research, none of these systems held up over time.
Kaplan even personalized his passion by changing his car’s license plate to SP0 DTE, celebrating the Chicago Board Options Exchange’s introduction of daily expirations for SPX options in 2022. Yet, despite the buzz around zero-day trading, he still lacked a reliable system.
Meeting Qamar Zaman and Discovering the Q Algo
That changed when Kaplan joined a trading room where he crossed paths with host Qamar Zaman.
Zaman, leveraging his background in coding and algorithmic strategy, had been developing a tool to solve a common problem traders face: emotional entries and exits. The result was the Q Algo, a multi-indicator ecosystem designed to identify pullbacks, pivots, and directional momentum for scalping SPX zero DTE options.
Kaplan quickly noticed the difference:
“Most indicators are lagging. The Q Algo gave me confidence because I could see multiple confirmations lining up before I entered,” he said.
Early Results: Five Trades, Five Wins
Within his first week using the Q Algo, Kaplan documented five consecutive winning trades in a single session. Importantly, he was trading only one contract at a time.
Why does that matter? Because it demonstrates that the Algo isn’t just for high-volume professionals—it works for traders who want to start small, limit their risk, and build confidence. Even beginners can benefit from using it conservatively.
His first trades included:
- Quick scalps lasting less than a minute
- Put and call entries validated by the Algo’s “pancakes” (moving average compression signals), throttle momentum, and river trend lines
- Avoiding “dogs”—trades where premiums decay and trap traders in losses
By starting with one contract per trade, Kaplan was able to test the system, learn how to spot entries, and develop trust in the process—without exposing himself to oversized losses.
Key Lessons From Kaplan’s SPX Zero DTE Journey
In reflecting on his experience, Kaplan highlighted several lessons for traders:
- Patience over impulse – Wait for confluence and confirmation, not just a trending chart.
- Respect pullbacks – Often the best entries come after the market pauses, not during breakouts.
- Cut losses quickly – Small losses preserve capital and confidence.
- Focus on the opening session – The first 30–60 minutes after market open offer the best volume, volatility, and momentum.
- Start small – As Kaplan proved, even one-contract trades can be consistently profitable with discipline.
- Avoid overtrading – Once you’ve hit your target, step away.
Why the Q Algo Stands Out
Kaplan had previously experimented with numerous platforms and indicators on Thinkorswim and TradingView. Most were rigid, delivering delayed buy/sell signals.
The Q Algo stood out because of its adaptability and confluence-based approach:
- Multiple confirmations across indicators
- Precision for SPX zero DTE scalps
- Beginner-friendly for small accounts—even one contract per trade can deliver results
- Focus on discipline over impulse
Kaplan explained:
“What I was trying to build was a lazy man’s robot—just tell me ‘buy now, sell now.’ But trading will never be that simple. The Q Algo forces you to see confluences and trade smarter.”
Conclusion
John Kaplan’s story is a familiar one for many traders: endless searching for the “perfect” strategy, early wins followed by crushing losses, and the constant challenge of managing emotions in high-risk markets like SPX zero DTE options.
For Kaplan, the Q Algo didn’t just provide a tool—it provided a framework for discipline, consistency, and confidence. And importantly, his success starting with just one contract per trade shows that it’s accessible to new traders building small accounts as well as experienced ones looking for precision.
As he summarized:
“If you become obsessed with watching the market all day, you’ll end up overtrading. Trade the morning, take your wins, and walk away.”
✅ Transparency Note
John Kaplan was not paid to test the Q Algo, nor did he pay to use it. He participated as an independent beta tester. His experiences reflect his personal journey and should not be interpreted as financial advice or guarantees of performance.
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Coffee With Q Podcast News
Disclaimers
This content is for educational purposes only. Trading involves risk, and past performance does not guarantee future results. Always consult with qualified financial professionals before making investment decisions.
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Final Note
This report is generated by Q Factor proprietary algorithms for informational purposes only. All analysis represents algorithmic processing of market data and should not be construed as investment advice. Users are responsible for their own trading decisions and risk management. Information may contain errors or omissions; users must independently verify all data and assume full responsibility for trading decisions.