Erika Andersen on Be Bad First, Part 6

In the book, Be Bad First: Get Good at Things Fast to Stay Ready for the Future, Erika Andersen shares her mindset and techniques for learning new things well and quickly.

These are some of my favorite concepts and takeaways from reading the book.

Chapter 6: Endless Curiosity: Not Just Kid Stuff

In this chapter, Erika Andersen discusses how being curious can help us take on new learning and mastery. She offers the following observations and recommendations for us to think about:

Erika defines curiosity as “I’m so fascinated by how things work and what might be possible that I’m completely willing to do what it takes to find out more and become more skilled.” Erika thinks genuine curiosity is a deep and abiding need to understand and master.

Even though our society largely socializes our curiosity out of us by the time we reach adulthood, all functional human beings are born curious. Erika believes we should reengage the endless curiosity we all had as children and apply it to becoming world-class learners.

To become endlessly curious again, we need to:

  • Find our own curiosity “sparks”: We all have at least one thing in our lives about which we are truly curious. Those are the places to look for our unextinguished sparks of curiosity.
  • Fan the flames with self-talk and action: Recognize our self-talk that impedes our curiosity and replace them with self-talk that supports our interest.
  • Feed the fire of curiosity daily: We will need to focus on making curiosity a daily habit to survive and thrive in a world that is changing faster than we ever thought possible.

We can ask some questions that help to encourage our curiosity:

  • How does that work?
  • I wonder if I could do that?
  • Why does that happen?
  • How can I find out more?
  • Why isn’t that like this?
  • I wonder what would happen if I tried this?

When we are genuinely curious about something, the “How,” “Why,” and “I wonder” questions we are asking demand answers. We should use leverage curiosity as momentum to act and find those answers. Do this every day and form a habit that can feed our curiosity daily.

變化是如何發生的

(從我一個尊敬的作家,賽斯·高汀

先慢慢地,然後一下子

對於那些不關注或不積極參與的人來說,文化變化似乎是突然的。一個接一個的大轉變。

事實上,文化變革總是相對來說緩慢地發生。一個人接一個人,一個接一個地交談,期望被建立,角色有定義,系統最後建立。

它是從基礎上來的。

媒體和講台上的人會得到了所有的關注,但他們只是一種症狀,而通常不是最終的原因。我們平常人不光只是在底層,他們是文化本身的根、基、源。我們就是文化,我們能改變它或是被它來改變。

從同行人到另一同行人。

變化也是橫向發生的。我們對別人有什麼期望?我們在談論什麼?我們效仿、追隨或支持誰?什麼變成常見的種類?

像我們這樣的人,會做這樣的事情。

一天又一天,一周又一周,一年又一年。

參加當天的抗議活動,表現出懈怠行為,從一個緊急狀態跳到另一個緊急狀態。這就是在我們的文化中進行日常交易的人被左右的方式。但是,一個始終如一地積極改變文化的人,是不會輕易地去分心。再多做一次動作,再多討論一次,再多建立一個標準。

互聯網希望我們專注於五分鐘前發生的事情。能持久的文化明白,五年內發生的事情才是最重要的。

專注、持久的社區行動是系統變化的方式。系統具體化和執行文化規範。

如果你在乎的話,請繼續討論。繼續做出動作。保持專注。並且不要容易的覺得無聊。

Algorithmic Trading Model with ML4T Chapter 1 Creating Dataset Examples

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This script aims to replicate the examples found in chapter one of the book Machine Learning for Algorithmic Trading by Stefan Jansen. The script seeks to validate further the Python environment and package requirements for running these code examples. The eventual goal is to integrate various example code segments into an end-to-end algorithmic trading system.

This notebook contains information on downloading the stock information and several other sources used throughout the book.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Various data services from Nasdaq, Stooq, Wikipedia, etc.

Source and Further Discussion of the Code Examples: https://www.ml4trading.io/chapter/1

The HTML formatted report can be found here on GitHub.

Algorithmic Trading Model with ML4T Chapter 1 Storage Benchmark Examples

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This script aims to replicate the examples found in chapter one of the book Machine Learning for Algorithmic Trading by Stefan Jansen. The script seeks to validate further the Python environment and package requirements for running these code examples. The eventual goal is to integrate various example code segments into an end-to-end algorithmic trading system.

This benchmarking exercise used the Google Colab environment as it has the prerequisite libraries and packages already built-in.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Programmatically generated DataFrame with random data

Source and Further Discussion of the Code Examples: https://www.ml4trading.io/chapter/1

The HTML formatted report can be found here on GitHub.

Algorithmic Trading Model with ML4T Chapter 1 SEC’s EDGAR Filing Examples

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This script aims to replicate the examples found in chapter one of the book Machine Learning for Algorithmic Trading by Stefan Jansen. The script seeks to validate further the Python environment and package requirements for running these code examples. The eventual goal is to integrate various example code segments into an end-to-end algorithmic trading system.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: US Public Company Filing Hosted by SEC’s EDGAR Service

Source and Further Discussion of the Code Examples: https://www.ml4trading.io/chapter/1

The HTML formatted report can be found here on GitHub.

Algorithmic Trading Model with ML4T Chapter 1 ‘yfinance’ Market and Fundamental Data Examples

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This script aims to replicate the examples found in chapter one of the book Machine Learning for Algorithmic Trading by Stefan Jansen. The script seeks to validate further the Python environment and package requirements for running these code examples. The eventual goal is to integrate various example code segments into an end-to-end algorithmic trading system.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: US Equities and Fund Prices Hosted by Yahoo! Finance

Source and Further Discussion of the Code Examples: https://www.ml4trading.io/chapter/1

The HTML formatted report can be found here on GitHub.

Algorithmic Trading Model with ML4T Chapter 1 Pandas Data Reader Examples

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This script aims to replicate the examples found in chapter one of the book Machine Learning for Algorithmic Trading by Stefan Jansen. The script seeks to validate further the Python environment and package requirements for running these code examples. The eventual goal is to integrate various example code segments into an end-to-end algorithmic trading system.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: US Equities and Fund Prices Hosted by Various Data Sources

Source and Further Discussion of the Code Examples: https://www.ml4trading.io/chapter/1

The HTML formatted report can be found here on GitHub.

Erika Andersen on Be Bad First, Part 5

In the book, Be Bad First: Get Good at Things Fast to Stay Ready for the Future, Erika Andersen shares her mindset and techniques for learning new things well and quickly.

These are some of my favorite concepts and takeaways from reading the book.

Chapter 5: Neutral Self-Awareness: The American Idol Syndrome

In this chapter, Erika Andersen discusses how a lack of neutral self-awareness can inhibit our ability to be open to learning. She offers the following observations and recommendations for us to think about:

Most of us have an inflated sense of our capabilities when assessing ourselves. We do not want to acknowledge our deficits because we secretly believe we cannot do anything about them.

There is a surprisingly simple way to become aware of how we see ourselves and shift our self-perception if it is inaccurate. We can become more neutrally self-aware by:

  • Managing our self-talk,
  • Becoming our fair witness, and
  • Inviting good “sources.”

People who see themselves objectively start by learning to manage how they talk about themselves. We all have a continuous mental monologue running at the back of our minds. But moreover, we change the content of that mental monologue.

Recognizing what we are saying inside our heads is the first step to having more control over it. Furthermore, having the ability to shift those inaccurate, unhelpful, unsupportive voices to be more accurate, neutral, and supportive is a powerful capability.

The steps involved in managing our self-talk include:

  • Recognize: The first step is to “hear” it.
  • Record: Write down our self-talk and narratives.
  • Rethink: Decide how to revise it to be more accurate and helpful.
  • Repeat: Managing our self-talk requires repetition.

Becoming a fair witness (FW) means being as objective and accurate as possible. When someone acts in the FW capacity, she speaks only from her direct experience. The FW role cannot indulge in speculation, cherry-pick the data, say what she hopes is true, or avoid looking at what she does not want to be true. The FW is proscribed from doing everything we generally do when thinking about ourselves.

The steps in becoming our own fair witness include:

  • Recognize and record our self-talk about our strengths and weaknesses in an area where we want to level up.
  • Ask ourselves, Is my self-talk accurate?
  • If we are unsure, ask, What facts do I have about myself in this area?
  • Rethink our self-talk to be more accurate and objective.

Incredibly self-aware people sometimes cannot see themselves entire clearly. For those occasions, we need feedback from external sources. A good “source” should include three equally essential elements:

  • See you clearly
  • Want the best for you
  • Are willing to be honest

Aspiration provides the fuel that will move us forward into new learning. Neutral self-awareness allows us to see where we are on the journey.