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As a trader worst thing one can do is anticipating trend all the time. When a trader anticipates a trend but rather market does a sideways action if often frustrates a trader and often leads to take wrong decisions throughout the sideways phase. This week lets analyze the weekly sentimental data points from Nifty and Bank Nifty Futures.
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Trading around the Psychological round numbers like 7000, 8000, 9000 are never been a easier task mentally. This is where many trading participants come up with weird forecasting stuff and this is the zone where frequent stop hunting happens and it is the process of removing weaker hands before market does its next move.
Too much of consolidation in Bank Nifty Futures weekly charts indicates the age of the trend is getting too old. Old trend doesnt mean that it will be come an end but from the investing stand point it many not be worth taking a risky investment bet especially if you are a medium term investor. Trade Longs in Bank Nifty are Absolutely Fine from Day trading or Short term investing perspective
We know that Open Interest is nothing but the total number of outstanding contracts. And for Options we have Open Interest for every individual strike price. It is a myth that 90% of the option expire worthless. Textbooks, Internet & even most of the option experts preach that option writing is smarter than buying options . But is that true?
If you are a professional future trader in the markets then transaction cost plays a major role while trading. Lower the transaction cost translates to better returns and also reduces the risk to greater extent in the long run. By constructing a Synthetic Futures (Long/Short) we can reduce the total transaction cost by two-third of the actual instead of trading the futures.
Here is a quick tutorial in python to compute Correlation Matrix between multiple stock instruments using python packages like NSEpy & Pandas. Generally Correlation Coefficient is a statistical measure that reflects the correlation between two stocks/financial instruments. Determining the relationship between two securities is useful for analyzing intermarket relationships, sector/stock relationships and sector/market relationships.