I Predicted The Covid Market Crash!

In this podcast, Amit Dhamija shares how he built an investing and trading strategy by studying FII, DII, and participant-wise market data available on the stock exchanges. He begins by talking about his journey into the stock market, the mistakes he made as a retail investor, and how a simple discovery changed the way he looked at the markets. Amit explains how years of backtesting helped him identify patterns in institutional activity and why understanding where smart money is moving can give investors an important edge. A major part of the discussion focuses on who actually moves the market. Amit explains the difference between operators, smart money, FIIs, DIIs, proprietary traders, and retail investors, and how each participant influences market movements. He also clears up common misconceptions about operators and explains why retail investors often end up on the wrong side of a trade. The conversation explores how large institutions build positions, why market movements are rarely random, and how publicly available exchange data can reveal valuable insights when analysed correctly. The discussion then moves to Amit's data-driven approach, where he explains how he studies participant-wise positioning every day, performs backtesting, and uses the data to build a view on the next trading session. He also demonstrates how this data is presented, how traders can interpret it, and why understanding institutional positioning can sometimes be more useful than relying only on technical indicators, news, or market rumours. Towards the end, Amit shares practical lessons for retail investors on developing the right market mindset and avoiding common mistakes. He explains why following institutional money, understanding market structure, and using data instead of emotions can improve decision-making over the long term. If you want to learn about FII and DII data, smart money, operator activity, institutional trading, market prediction, and data-driven investing, this c