Now bank nifty closing Spot is :- 40990
Buy 1 call 200 points above the spot and sell 3 calls 700 points above the spot.
Buy 1 put 200 points below the spot and sell 3 puts 700 points below the spot.
** Batman Strategy for Intraday and Positional with adjustments**
-> Many successful traders are using this strategy . They call different name but strategy is same. We can use this strategy for intraday and positional also.
-> This Strategy will work excellent on expiry day .
Now bank nifty closing Spot is :- 40990
Buy 1 call 200 points above the spot and sell 3 calls 700 points above the spot.
Buy 1 put 200 points below the spot and sell 3 puts 700 points below the spot.
41700 ce 3 lots sell
40800 pe 1 lot buy
40300 pe 3 lots sell
If your carrying for positional buy hedges and exit hedges next day morning it is depends on individuals.
If market is trading between both call buy and put buy range i.e40800 to 41200range don't do any adjustment. You will end up with profits.
Case 1:- If market reach to call buy position 41200 by that time you will be in profit in Put buy and sell legs.
More from All
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)
