My PhD professors taught me MATLAB during my master's degree.

So I watched 200 YouTube videos to learn Python

96% of them were a complete waste of time.

But these 8 taught me more than all my PhD professors combined:

In 2012, my first options trade lost $9,000.

12 months later I was making $1,100 per week trading in my free time.

What changed?

I read 20 books on options and finished a master’s degree.

But what took my game to the next level was Python.

Here’s the code I still use today:

A quick primer on options in case you're not familiar:

• Over $450 billion in notional trades DAILY
• 39 million options contracts trade DAILY
• 25% of total options trading is from retail

So how do we make money?

Trade options with a simple, 3-part framework:

1. Design your risk
2. Value the position
3. Measure and monitor

Now pair this framework with Python and you get a potent combination for making money trading options.

Let's dig in:

To design our risk profile, we need the option payoff.

Let's start by defining the variables we need:

• Stock price
• Strike price
• Time to expiration
• Interest rate
• Volatility

We also define market prices for demonstrating trades.

Remember our payoff functions for calls and puts?

• call = max(S - K, 0)
• put = max(K - S, 0)

We can define them in Python in one line of code each.

And here's where it gets really interesting.

12 Python libraries for free market data everyone should know:

yfinance

Data for stocks (historic, intraday, fundamental), FX, crypto, and options. Uses Yahoo Finance so any data available through Yahoo is available through

pandas-datareader

pandas-datareader used to be part of the pandas project. Now an independent project. Includes data for stocks, FX, economic indicators, Fama-French factors, and many

IBApi

The official API for Interactive Brokers provides access to all the data available through IB. Replaces

Alpha Vantage

Alpha Vantage delivers a free API for real-time financial data and most used finance indicators in a simple JSON or CSV

The 7 must-have books everyone using Python for algorithmic trading needs to know:

Algorithmic Trading with Python: Quantitative Methods and Strategy Development

Algorithmic Trading with Python discusses modern quant trading methods in Python with a heavy focus on pandas, numpy, and

Algorithmic Trading with Interactive Brokers (Python and C++)

This book walks through the process of developing applications based on IB's Trader Workstation. Learn how to implement full-scale trading

Learn Algorithmic Trading: Build and deploy algorithmic trading systems and strategies using Python

Understand the fundamentals of algorithmic trading to apply algorithms to real market data and analyze the results of real-world trading

Python for Algorithmic Trading: From Idea to Cloud Deployment

You'll learn several ways to apply Python to different aspects of algorithmic trading, such as backtesting trading strategies and interacting with online trading

8 trading and backtesting libraries in Python that will help you find your edge:

QuantSoftware Toolkit

Python-based open source software framework designed to support portfolio construction and

quantitative

Quantitative finance, and backtesting

analyzer

Python framework for real-time financial and backtesting trading

pybacktest

Vectorized backtesting framework in Python / pandas, designed to make your backtesting

College completely failed to teach me data analysis.

So I spent over 10,000 hours learning Python.

Then, I picked the 13 best libraries for machine learning and data analysis.

But unlike college, these won't cost you $120,000.

Here they are for free:

AutoViz

AutoViz performs automatic visualization of any dataset with a single line of Python code. Give it any input file (CSV, txt or json) of any size and AutoViz will visualize

Numba

Numba translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. Numba-compiled numerical algorithms in Python can approach the speeds of C or

NetworkX

NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex

pandas

pandas is a fast, powerful, flexible, and easy-to-use open source data analysis and manipulation tool, built on top of the Python programming

NARHARI AND BHAGWAN PANDURANG/VITTHAL

Once a goldsmith by the name of Narhari lived in Pandharpur. He excelled in his craft. He had vowed not to look at Pandurang or visit his mandir ever, as he was a Shiva Bhakt.
One day, a wealthy merchant visited him.

He wanted a gold girdle studded with precious gems, to be made for Prabhu Vitthal. Narhari refused to make it as it was for Vitthal.
After many requests, he agreed to make the girdle but asked the merchant to bring the measurement from the mandir as he himself would not go there

The measurement was given n he made the girdle. When the girdle was put on Vitthal, it was loose. Narhari had to shorten it. Now the girdle became tight. So Narhari was forced to take measurement on his own. He had to go to the mandir but went blindfolded to avoid seeing Vitthal

In the Mandir, when blindfolded Narhari touched Vitthal, he felt as if he was touching Shiva with matted hair, the moon, snake in His neck and Trishul in His hand. Ecstatic with joy, he removed the blindfold to see Shiva but he found the murti of Vitthal with Rukmini there.

He realised the truth that there was no difference between Vitthal and Shiva. Narhari at once fell at the feet of Panduranga. He then took the measurement of the waist of Vitthal and this time the ornament fit the Murti perfectly.

Narayan Hari🙏🏻

Stories of Guruvayurappan
Shri Krishna Leela

This is a beautiful story of Guruvayurappan. Once there lived in Guruvayur a poor family. Kitta, his wife & kids were struggling for their daily needs. With only a coconut tree in house, he earned a living by selling coconuts.

Kitta was from a low caste family. He was a great devotee of Guruvayurappan. Everyday before going to work he always prayed infront of a picture of Guruvayurappan in his house. His only wish was to worship at Guruvayur & have annadanam in temple. But he was not allowed there

It was Utsavam time in Guruvayur. From far behind kitta and family worshipped Guruvayurappan. Annadanam was given daily and no one was left empty stomach. Only Kitta didn't receive. That night, it was only Kitta's family who was sleeping without food. After temple nada closed

Shri Krishna, dressed himself as a Brahmin went to temple Kitchen. He packed food and went directly to kitta's house. Reaching Kitta's house, Shri Krishna told he was sent by a cook of Guruvayur temple and gave them the food he packed. Kitta and family was very happy seeing food.

When Kitta finished his food, Brahmin asked it will be better if he get a tender coconut( karikku)to drink. Kitta climbed the tree and gave all coconuts to him. After having 1 tender coconut, the Brahmin asked to bring remaining coconuts to Guruvayur temple the next day.

@powerbeastt @buzz_chronicles @rattibha @SaveToBookmarks save as Movies

How will next 10-15 years be different for India?
In the next 10 years,India will undertake massive infrastructure projects which will propel India to a $5 Trillion economy

A thread🧵on how India is spending big on Infrastructure upgradation like never before

Lets go👇

(1/17)

How big is India's Infra push?

India spent $14 Trillion on infrastructure in the 75 years since its independence.

$7 trillion was in the last 9 years.

Another $7 Trillion will be spent in the next 9 years!

Big Big Spending is coming in!

(2/17)

So where is this money being spent?

1. Roads
There has been an increase in the construction of National Highways (NHs)/roads over
time, with 10,457 km of roads constructed in FY22 as compared to 6,061 km in FY16.

(3/17)

In FY23(until October 2022), 4,060 km of NHs/roads were constructed, which was around 91 per cent of the achievement in the corresponding period of the previous financial year

(4/17)

The ambition is to take the rate of daily highway construction in India to 100km per day

Dubbed India’s ‘highway revolution’,
Rapid highway construction is central to the country’s push to transform itself into a staggering US$5 trillion economy by 2025.

(5/17)