Sharing Most Advanced Options Trading Strategy Call Ratio Spread for Free that you can use based on the different market view

Worth 25,000 Course in Free ‼️

A thread 🧵

Call Ratio Spread Example:-

Buy x 1 = 40000 CE
Sell x 2 = 40500 CE
Let's decode this above position

Now look at this payoff graph max profit at selling position 40500

If the market expires at 40500 we will gain a max profit of Rs 16000

If the market keeps falling we are at a profit of 1.5-2%
Our breakeven level at 41180

If the market went up after 41180 our position will start showing losses

So if our view is market fall or sideways we can use the Call Ratio Spread
Now we can make different Ratios

For Example:-

40000 CE x 1 Buy
410000 CE x 3 Sell
Let's decode this above position

Now look at this payoff graph max profit at selling position 41000

If the market expires at 41000 we will gain a max profit of Rs 29700

If the market keeps falling we are at a profit of 1-1.5%
Our breakeven level is increased in this case 41600

If the market went up after 41600 our position will start showing losses

So if our view is market fall or sideways we can use the Call Ratio Spread
You can increase your break even by increase your far OTM Call Sell

If you Buy 1 call and sell 4 more OTM Call sell your breakeven increase more

By following risk management this setup can become a holy grail for beginner but follow Max Loss in this setup 2.5% of your capital
If you enjoyed this thread:

1. Follow me @Mohitsharma202
for more.

2. RT the tweet below to share this thread with your audience.

3. Have a great weekend.💙

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)

You May Also Like