Best Nifty Option Strategy for Working People: Monthly Income + Protection
Автор: Profit Breakout
Загружено: 2025-07-01
Просмотров: 21775
Описание:
📈 Yearly Nifty Option Strategy for Steady Income + Downside Protection 💰
In this video, we explore a powerful yearly options strategy designed for traders looking for monthly income with long-term downside protection.
🔹 Strategy Setup:
✅ Buy Nifty Future (2-month expiry)
✅ Sell 2 Lots of Call Options (Monthly Expiry)
✅ Buy 1 Lot Put Option (0.25 Delta, 1-Year Expiry)
🔁 Each month, roll over the 2 sold Call Options, roll over the Nifty Future when it expires after 2 months. The protective Put stays active throughout the year.
💡 This approach helps you:
Generate monthly income from the call writing
Maintain bullish exposure via Nifty Futures
Limit downside risk with a long-term protective Put
Whether you're a conservative trader or want to diversify your core portfolio with an options overlay, this strategy offers a smart balance of income and safety.
👉 Perfect for working professionals or long-term investors who don’t want to monitor the market every day.
📊 Don’t forget to like, share, and subscribe for more such advanced option strategies!
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Disclaimer:
The views and opinions expressed in this YouTube channel are for informational and educational purposes only. I am not a SEBI-registered investment advisor, and this content should not be considered financial or investment advice. Any mention of specific numbers, historical averages, or percentage benchmarks (such as annual or monthly targets) are strictly theoretical business analogies used for opportunity-cost comparisons against passive mutual funds, and must NOT be interpreted as expected, targeted, or projected returns. Investing in the stock market involves risk, including the potential loss of principal. Always conduct your own research and consult with a SEBI-registered financial advisor before making any investment decisions. The creator and affiliated parties are not responsible for any financial losses or decisions made based on the content provided. Past performance is not indicative of future results.
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