I use these four features(primarily)
☑️ Option Chain
☑️ Total PE-CE Data
☑️ Option OI breakup
☑️ Strangle Charts(Combined Premium)
A tool every Option trader should use : @iChartsIndia 🧵
I use these four features(primarily)
☑️ Option Chain
☑️ Total PE-CE Data
☑️ Option OI breakup
☑️ Strangle Charts(Combined Premium)
Option Chain data is reliable when the trading week begins(i.e Friday). Even, Monday's data is not very accurate as traders are still building their positions.
Looking at this week's expiry data(i.e 23rd June 2022),
We can observe
Put writing at 15300, Call writing at 16000
Puts are still more pricey than calls, implying traders have been factoring a down move more than an up-move.
How I interpret this data and use it to my benefit.
The difference indicates whether there is more put writing or more call writing.
If (PE writing > CE writing)
{
Column Color : Green
}
Else
{
Column Color : Red
}
And if the market trades below VWAP, color is pink.
If VWAP = Green, Diff = Red : Conflicting Signal(Market can remain rangebound)
If VWAP = Green, Diff = Green : Market is trending up and we can look for long trades
If VWAP = Pink, Diff = Green : Conflicting Signal(Market can remain rangebound)
If you are trading non-directionally, only way to make money is if you are able to either avoid spikes/ get out with a minor injury.
Low premiums + Market trending down(accelerated move) + Spike in put premiums + Calls not decaying
Combined premium charts are of great use by just plotting VWAP and 20 EMA.
If you trade non-directionally by shorting options, the chart should be trending down.
Am still exploring the tool.
Thought of sharing it for the benefit of everyone.
https://t.co/UL259fMIg3
8 Powerful Features of @iChartsIndia Option Data users should be using:\U0001f9f5
— Aditya Todmal (@AdityaTodmal) June 3, 2022
Collaborated with @niki_poojary
Retweet the first tweet to spread it across.
Feedback appreciated.
Happy Trading.
Until next time, bye 👋
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Viruses and other pathogens are often studied as stand-alone entities, despite that, in nature, they mostly live in multispecies associations called biofilms—both externally and within the host.
https://t.co/FBfXhUrH5d
Microorganisms in biofilms are enclosed by an extracellular matrix that confers protection and improves survival. Previous studies have shown that viruses can secondarily colonize preexisting biofilms, and viral biofilms have also been described.
...we raise the perspective that CoVs can persistently infect bats due to their association with biofilm structures. This phenomenon potentially provides an optimal environment for nonpathogenic & well-adapted viruses to interact with the host, as well as for viral recombination.
Biofilms can also enhance virion viability in extracellular environments, such as on fomites and in aquatic sediments, allowing viral persistence and dissemination.
Viruses and other pathogens are often studied as stand-alone entities, despite that, in nature, they mostly live in multispecies associations called biofilms—both externally and within the host.
https://t.co/FBfXhUrH5d
Microorganisms in biofilms are enclosed by an extracellular matrix that confers protection and improves survival. Previous studies have shown that viruses can secondarily colonize preexisting biofilms, and viral biofilms have also been described.
...we raise the perspective that CoVs can persistently infect bats due to their association with biofilm structures. This phenomenon potentially provides an optimal environment for nonpathogenic & well-adapted viruses to interact with the host, as well as for viral recombination.
Biofilms can also enhance virion viability in extracellular environments, such as on fomites and in aquatic sediments, allowing viral persistence and dissemination.
How can we use language supervision to learn better visual representations for robotics?
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
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