Erika Andersen on Be Bad First, Part 12

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 9: Do It Now: Making ANEW Your Own

In this chapter, Erika Andersen discusses the techniques we can practice to build our ANEW skills. She offers the following observations and recommendations for us to think about:

Steps for Building Aspiration:

Envision a “hoped-for-future” where we are reaping the benefits of my learning.

Pick a time frame (when we will be much more skilled or knowledgeable in this area).

Imagine ourselves in that future, and then describe what success looks like and feels like (how we think and what we are doing, having gained the benefits from this learning).

Select the key elements (two or three sentences best capture the benefits we are experiencing in this future world of successful learning).

Steps for Building Neutral Self-Awareness:

Make two lists: current strengths/assets and current weaknesses/gaps.

Review what we have written and ask: Is my self-talk accurate?

If we are unsure about some things, note them and ask: What facts do I have to support my point of view?

Use our answers to revise our lists, to make them as “fair witness” as possible.

Note any self-talk we recognize that reflects strong feelings about our strengths or weaknesses – it is also essential to be accurate about those.

Finally, if we make pessimistic self-talk predictions based on our current weaknesses, revise them using the “self-talk of self-belief.”

Steps for Re-engaging Endless Curiosity:

Create two or three “How,” “Why,” or “I wonder” questions about this new area of learning, questions to which we want to find the answers.

Decide an easy-for-us action that we could take to pursue the answer to each question above.

Steps for Willing to be Bad First:

Go through the list of “unsupportive self-talk” items and create a list of our “accepting not-good” self-talk for learning our chosen skill.

Go through the list of supportive (and more accurate) alternatives and create a list of our “self-belief self-talk” for learning the skill we want.

Pick a skill or capability we already have that we believe may be related to our chosen topic. Ask ourselves: How is this skill or ability similar to and different from what might be required in the new situation?

從協議來開始

(從我一個尊敬的作家,賽斯·高汀

有彈性的系統會比脆弱的系統要好。

離開營地的狀況會比你到來之前要更好。

乾淨的空氣會比骯髒的空氣要好。

投資於隨著時間的推移,來產生積極影響的事物會更可靠。

當數字加起來是對的時,去相信他們。

能展示自己工作的人,更有可能是對的。

重要的工作最好是現在來完成,而不是等到以後。

談論我們的問題,會使解決方案更加強大。

最好在看到數據後才下定決心,而不是之前。

如果我們從我們同意的內容開始,就更容易向前推進。

當然,總是有這樣的選擇

Quantitative Finance Model using Donadio and Ghosh Learn Algorithmic Trading Chapter 3 Ridge Example

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 Ridge example found in chapter three of the book Learn Algorithmic Trading by Sebastien Donadio and Sourav Ghosh. The script seeks to validate the Python environment and package requirements for running these code examples successfully. The eventual goal is to integrate various example code segments from the book into an end-to-end algorithmic trading system.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Sharadar US Equities and Fund Prices from Quandl/Nasdaq Data Link

Source and Further Discussion of the Code Examples: https://github.com/PacktPublishing/Learn-Algorithmic-Trading

The HTML formatted report can be found here on GitHub.

Quantitative Finance Model using Donadio and Ghosh Learn Algorithmic Trading Chapter 3 LASSO Example

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 LASSO example found in chapter three of the book Learn Algorithmic Trading by Sebastien Donadio and Sourav Ghosh. The script seeks to validate the Python environment and package requirements for running these code examples successfully. The eventual goal is to integrate various example code segments from the book into an end-to-end algorithmic trading system.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Sharadar US Equities and Fund Prices from Quandl/Nasdaq Data Link

Source and Further Discussion of the Code Examples: https://github.com/PacktPublishing/Learn-Algorithmic-Trading

The HTML formatted report can be found here on GitHub.

Quantitative Finance Model using Donadio and Ghosh Learn Algorithmic Trading Chapter 3 OLS Example

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 Ordinary Least Squares (OLS) example found in chapter three of the book Learn Algorithmic Trading by Sebastien Donadio and Sourav Ghosh. The script seeks to validate the Python environment and package requirements for running these code examples successfully. The eventual goal is to integrate various example code segments from the book into an end-to-end algorithmic trading system.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Sharadar US Equities and Fund Prices from Quandl/Nasdaq Data Link

Source and Further Discussion of the Code Examples: https://github.com/PacktPublishing/Learn-Algorithmic-Trading

The HTML formatted report can be found here on GitHub.

Quantitative Finance Model using Donadio and Ghosh Learn Algorithmic Trading Chapter 2 Seasonality Example

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 seasonality example found in chapter two of the book Learn Algorithmic Trading by Sebastien Donadio and Sourav Ghosh. The script seeks to validate the Python environment and package requirements for running these code examples successfully. The eventual goal is to integrate various example code segments from the book into an end-to-end algorithmic trading system.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Sharadar US Equities and Fund Prices from Quandl/Nasdaq Data Link

Source and Further Discussion of the Code Examples: https://github.com/PacktPublishing/Learn-Algorithmic-Trading

The HTML formatted report can be found here on GitHub.

Quantitative Finance Model using Donadio and Ghosh Learn Algorithmic Trading Chapter 2 STDEV & MOM 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 STDEV and MOM examples found in chapter two of the book Learn Algorithmic Trading by Sebastien Donadio and Sourav Ghosh. The script seeks to validate the Python environment and package requirements for running these code examples successfully. The eventual goal is to integrate various example code segments from the book into an end-to-end algorithmic trading system.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Sharadar US Equities and Fund Prices from Quandl/Nasdaq Data Link

Source and Further Discussion of the Code Examples: https://github.com/PacktPublishing/Learn-Algorithmic-Trading

The HTML formatted report can be found here on GitHub.

Erika Andersen on Be Bad First, Part 11

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 8: Slaying Your Personal Dragons: One the Road to Mastery

In this chapter, Erika Andersen discusses some of the personal challenges we will face when we try to become high-payoff, Michelangelo-style learners. She offers the following observations and recommendations for us to think about:

For questions on Willingness to Be Bad First:

Q: Come on. You’re telling me it’s okay to be bad at my job?

A: It is okay to be bad at those parts of our job that we have not yet had the opportunity to learn. When we enter a new situation or environment, we will not know everything we need to know about the environment right at the beginning. However, if we pretend to be already “good” at that and do not ask many curiosity-based questions initially, people will be suspicious.

Q: The idea of “being bad” at something in front of people (especially the people I work for and the ones who work for me) makes me very nervous. What can I do to make it easier?

A: Just as in every other area of learning, practice makes perfect. The more we practice “being bad” in public, the easier it gets. More practices in public also create opportunities for displaying openness and building trust with others.

Q: Isn’t this just about “failing fast”?

A: Recognizing that we will experience failure when learning new things and accepting that is part of being willing to be bad first. More importantly, the ANEW model offers other essential tools – the thing we need for mastering much more than just the “failure” part of learning.

With ANEW, we can practice being better at reducing failures, speeding up the cycle time from novice to expert, and positioning us well for our next round of new learning in the same area.

Q: Shouldn’t we play to our strengths? Why would I try to do things that I’m bad at?

A: We should play to our strengths. That is why neutral self-awareness is such an essential part of real learning. But there is a big difference between being bad at something because we are physically unable to improve and being bad at something. After all, we have not learned to do it yet.

The good news is that the sweet spot of learning is being able to play to our strengths to get good at things we are now bad at doing. Rather than using our strengths as a limitation on our future learning, we should use them as a lever for learning even more and better.

Q: Do you ever get done being bad?

A: Even in the areas where we are most experienced and expert, we can always keep trying new things by returning to being bad to get better. People who are most truly masters of mastery level feel as though they are always learning. They always find areas where they are “bad” (even if only relative to their existing expertise) and get better.