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On this episode of the Words of Rizdom podcast we are joined by Brian Shannon. Brian is an american author and technical analyst. His acclaimed book entitled Technical Analysis Using Multiple Timeframes has made him "one of the best indie traders in the business". He's regarded as the adopted father of anchored VWAP, a tool that utilises volume, time and price to give an accurate reading of whether an asset is bullish or bearish and Brian swears by it. He also states that algorithms make up to 60% of the volume being traded in the stock market & specifically, VWAP trading. I hope you guys enjoy this incredible episode!
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Connect with Words of Rizdom:
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https://www.instagram.com/realriziqbal
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Timestamps:
00:00:00 Introduction To Brian Shannon
00:12:12 Utilizing Moving Averages in Trading Strategies
00:16:21 Effective Swing Trading Strategies
00:29:04 Understanding Market Reversals and Patterns
00:36:45 Ken Griffin's Insights on GameStop Volatility and Trading Algorithms
00:41:25 Understanding Market Moves and Profit Objectives
00:54:19 Early Inspiration for Trading
00:58:23 Risk Management Lessons from Early Trading Experiences
01:07:14 Day Trading vs. Swing Trading: Strategies and Time Frames
01:19:57 Understanding VWAP
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Disclaimer:
The content on Words of Rizdom is for informational and educational purposes only and should not be considered financial, investment or trading advice. I am not a licensed financial advisor, and the strategies, opinions, and commentary shared are based on personal experience of the respective guests. We are not personally affiliated with any of the guests featured in our episodes. The views expressed by our guests are their own and do not necessarily reflect the opinions or endorsements of Words of Rizdom.
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Trading and Investing involves risk, and you should always do your own research or consult with a qualified financial professional before making any investment decisions. You are solely responsible for your financial decisions. This channel and its creator are not liable for any loss or damage arising from the use of information presented in videos.
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