#Astro #fintwit
A thread on how to look at a day
#Basics

Basically planets are moving around the Sun
The angle they make between them is called a aspect
Here Sun and moon are 90° apart
They are making a square aspect,
and so on
In financial astrology
The major aspects are
0° Conjuct
30° semisrxtile
45° semisquare
60° sextile
90° square
120° trine
180° opposite
Here
Conj and Opposition are trend changing aspects
Mostly when Sun Moon are Conj(Amavasya/ New Moon next day) or at Opposition(Purnima/Full Moon) we see trend changes around this dates
See #sensex recently
It may not happen exactly on same +– 3 days is the range mostly
Trine 120° is considered a positive aspect
Similary sextile and semisextile are also considered positive

The hard aspect is Square 90° and is associated with bearishness

These things happen mostly
But it needs to be seen as trend change event rather then some hard rules
Now lets see the planets

Mercury Venus Jupite are considered positive or bulls planets
Wheresas Sun Mars Saturn Rahu(North Node) are considered negative or bears planets

This happens mostly not always
So when some aspect is formed by these planets one should look for reversals
Now lets see the movement of planets around the sun
Earth completes one circle around the sun in 365 days or 12 months
This motion of 360° rotation is divided into 12 signs from Aries to Pisces each of 30°
So earth moves in 1 sign(rashi) for around 30 days
Similarly other planets cycles are
Mercury 88 days
Venus 225 days
Earth 365 days
Mars 1.88 years
Jupiter 11.88 years
Saturn 29.42 years
Uranus 83.75 years
Neptune 163.74 years
Pluto 245.33 years
Note
Jupiter stays in one sign
for approximately 1 year
and Saturn for approximately 2.5 years
And Uranus for approximately 7 years
This is not constant because of elliptical nature of planets rotation around the sun
Hence approximately
The normal motion of the planets is direct - from the point of view of someone on earth, But because the earth is not the center of the solar system, there are times when the planets appear to change direction and reverse themselves. This is backward (retrograde) motion
So planets have two motions
Direct and Retrograde
The Energy of a planets gets amplified during #Retrograde motion
Now look at the planeta and their associated signs
Sun > Leo
Moon > Cancer
Mercury > Gemini & Virgo
Venus > Taurus & Libra
Mars > Aries & Scorpio
Jupiter > Sagittarius
Saturn > Capirorn
Uranus >Aquarius
Neptune > Piscea
Pluto >Scorpio
The 12 signs(Rashis) are
1 Aries Fire(1️⃣) Cardinal
2 Taurus Earth(2️⃣) Fixed
3 Gemini Air(3️⃣) Mutable
4 Cancer Water(4️⃣) C
5 Leo 1️⃣ F
6 Virgo 2️⃣ M
7 Libra 3️⃣ C
8 Scorpio 4️⃣ F
9 Sagittarius 1️⃣ M
10 Capricorn 2️⃣ C
11 Aquarius 3️⃣ F
12 Pisces 4️⃣ M
Lets see the strength of the houses
Strength in descending order
1
10
7
4
11
8
9
12
2
3
5
6
Planets are strong in the middle of a sign
A sign is of 30°
So it is strong at 15° in a sign(rashi)

And If a planet is exalted then the strength triples whether positive or neither of the planet
And if planet is debliated then the strength reduces by half
Planets are exalted at _° in sign
Sun is exalted @ 19° of Aries
Moon 3° Taurus
Mercury 15° Virgo
Venus 27° Pisces
Mars 28° Capricorn
Jupiter 15° Cancer
Saturn 25° Libra
Planets are Debilitated at the opposite side of the exaltation
i.e. 180° apart

These calculations may very from person to person by some degrees
But are always in those signs only
A thread to understand the Nodes
https://t.co/dk8IfZ7Mgd
A thread on SUN Conjunctions
https://t.co/J0sYqC6aJG
A thread on Planets
And trading them https://t.co/1ycWH5AeiF
A thread in zodiac signs and theor associated Traits
https://t.co/iPBvBxFD8Z
A thread on differend zodiac calculations
https://t.co/CedTK2O8ai

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)

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