Resources mentioned in this video:

The Money Show: https://orlando.moneyshow.com/?scode=068013

Historical Gamma Indicator: https://alphacrunching.sensamarket.com

Alpha Crunching Trading Community, Research, Tools: https://www.alphacrunching.com

In this episode of the Stock Market Options Trading Podcast, I break down another 0DTE XSP trade that I was able to convert into a risk-free position—but this time, I spend more time on the market analysis behind the entries.

I’ll show how I used Historical Net Gamma Exposure (GEX) to identify an important SPX gamma level around 7,600, look for potential mean reversion, and time multiple credit spread entries. We’ll walk through how the trade evolved from an in-the-money call credit spread, into a reverse iron condor, and eventually into a position with multiple potential profit zones and no remaining downside risk beyond commissions.

We’ll also discuss why I’m experimenting with XSP for these trades, how gamma levels can change throughout the trading day, and why I view GEX as another indicator—not something that should be traded blindly.

In this episode: Trading 0DTE XSP before the regular market open Using Historical GEX to identify potential SPX levels Turning a profitable credit spread into a reverse iron condor Adding an iron butterfly around a major gamma level Creating wider profit zones instead of simply closing a winning trade Why gamma exposure is context—not a standalone trading signal Managing a volatile 0DTE trading day while reducing risk

I’ll also be speaking about SPX 0DTE options trading at the Traders Expo / Orlando MoneyShow, October 5–7. If you’re attending, I’d love to meet you in person. Options involve risk and are not suitable for all investors.

This content is for educational purposes only and is not financial advice.

In this week’s Stock Market Options Trading Podcast, I break down an XSP 0DTE trade I entered at the market open and how I was able to turn the position into a risk-free trade after the market moved in my favor.

We’ll walk through the trade structure, why I started with an in-the-money call credit spread, and how adding the opposing spread converted the position into an Iron Condor with no remaining downside risk.

I also take a first look at CBOE’s newer Magnificent 10 Index (MGTN), an equal-weighted index featuring the Magnificent 7 plus Broadcom, Palantir, and AMD. We’ll look at why cash-settled index options are interesting for traders, along with the biggest issue with MGTN right now: liquidity.

Plus, this week’s episode covers:

• The current SPX market setup and important gamma levels

• What I’m watching as the market trades below 7700

• This week’s major economic and employment reports

• Tuesday’s SPX Opening Range Breakout statistics

• The return of the X Files segment

The goal is always the same: use data, backtesting, and market statistics to find practical ways to trade the S&P 500 and index options.

📊 Learn more about Alpha Crunching and our SPX trading tools, research, trade setups, and community at AlphaCrunching.com.

New episodes of the Stock Market Options Trading Podcast are posted every Monday after the market close.

#SPX #0DTE #OptionsTrading #XSP #StockMarket #SP500 #IronCondor

📊 Trade These SPX Setups With Us

The strategies discussed in this episode are part of the Alpha Crunching Trade Setups, where we use backtesting and market data to build a portfolio of mechanical SPX strategies.

Members get access to:

• Weekly rules-based SPX Trade Setups
• Live trade alerts in the Alpha Crunching Discord
• SPX trading tools and market statistics
• Strategy discussion and live trading chat
• Automation options for select strategies

The goal is simple: data-driven SPX trading with repeatable rules and less time watching charts.

👉 Learn more and join at AlphaCrunching.com

What happens when you stop looking for the “best” SPX trading strategy and start thinking in terms of a portfolio of strategies?

In Episode 196, I break down four mechanical SPX strategies based purely on the math — without focusing on the actual strategy or setup.

We compare:

• Trade frequency
• Win rate
• Average winner
• Average loser
• Expectancy
• Risk/reward

The differences are significant. One strategy wins just 35% of the time, while others win more than 70%. But higher win rates come with their own trade-offs, including larger average losses.

The interesting part comes when we combine all four.

Instead of relying on one strategy that might go weeks without a trade or experience a losing streak, the different mathematical profiles can complement each other and produce a much smoother overall P&L curve.

The goal isn't necessarily to find the highest win rate or the perfect strategy. It's to build a collection of positive-expectancy trades that can work together over time.

Would you trade a strategy with only a 35% win rate if it improved the overall portfolio?