Inverted OIL

 

Is  BSE OIL ready to reverse?
What are the reasons?
What does inverted OIL suggest?
What are the targets for BSEOIL in the next few months?
Where is Reliance among BSE OIL components?
What are the targets for Reliance?
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Our Jiseki Time cycles are seasonal patterns of strength or weakness in assets. They are derived from percentile rankings from 1 to 100. The higher the percentile more the chance for an asset to weaken and worst the ranking, better the chance for the respective asset to outperform. 100 is top relative performance and 1 is worst performance. The idea is that performance is cyclical. A top performer will underperform in future and vice versa. A top relative performer is also the worst value pick and the top relative underperformer is the best value pick. Jiseki is another name for Performance cycles, time triads and time fractals. The signals are illustrated as a running portfolio and as Jiseki Indices. These signals can be used by fund managers for relative allocations, traders for leverage bets and high net worth clients for selective trades.
Jiseki Interpretation. Signals are interpreted as crossovers between various Jiseki Cycles. All three Jiseki cycles (Jiseki 1,2 and 3) depict different time frames. Example: An asset is ranked above 80 percentile and all the three Jiseki cycles are pointing lower, this suggests a running SHORT SIGNAL. Our Jiseki Indices use different kind of exits based on price and Jiseki Cycles. We have color coded the (Jiseki 1>Jiseki 2) SHORT zones with brown sandy (burlywood) and grey (Jiseki 1>Jiseki2) for LONG SIGNALS.
Coverage India: CNX100 traded stocks and Indian Indices.

Michesan Anna-Maria, discovered her interest of markets immediately after completing her graduate studies in Economics. She followed it up with post graduate studies in corporate finance. A host of research work in behavioral finance, option strategies and quantifying market sentiment followed. Anna covers Indian equity and combines Elliott, Time Fractals and Time Analytics to deliver accuracy across time frames.