More on Productivity
Leonardo Castorina
3 years ago
How to Use Obsidian to Boost Research Productivity
Tools for managing your PhD projects, reading lists, notes, and inspiration.
As a researcher, you have to know everything. But knowledge is useless if it cannot be accessed quickly. An easy-to-use method of archiving information makes taking notes effortless and enjoyable.
As a PhD student in Artificial Intelligence, I use Obsidian (https://obsidian.md) to manage my knowledge.
The article has three parts:
- What is a note, how to organize notes, tags, folders, and links? This section is tool-agnostic, so you can use most of these ideas with any note-taking app.
- Instructions for using Obsidian, managing notes, reading lists, and useful plugins. This section demonstrates how I use Obsidian, my preferred knowledge management tool.
- Workflows: How to use Zotero to take notes from papers, manage multiple projects' notes, create MOCs with Dataview, and more. This section explains how to use Obsidian to solve common scientific problems and manage/maintain your knowledge effectively.
This list is not perfect or complete, but it is my current solution to problems I've encountered during my PhD. Please leave additional comments or contact me if you have any feedback. I'll try to update this article.
Throughout the article, I'll refer to your digital library as your "Obsidian Vault" or "Zettelkasten".
Other useful resources are listed at the end of the article.
1. Philosophy: Taking and organizing notes
Carl Sagan: “To make an apple pie from scratch, you must first create the universe.”
Before diving into Obsidian, let's establish a Personal Knowledge Management System and a Zettelkasten. You can skip to Section 2 if you already know these terms.
Niklas Luhmann, a prolific sociologist who wrote 400 papers and 70 books, inspired this section and much of Zettelkasten. Zettelkasten means “slip box” (or library in this article). His Zettlekasten had around 90000 physical notes, which can be found here.
There are now many tools available to help with this process. Obsidian's website has a good introduction section: https://publish.obsidian.md/hub/
Notes
We'll start with "What is a note?" Although it may seem trivial, the answer depends on the topic or your note-taking style. The idea is that a note is as “atomic” (i.e. You should read the note and get the idea right away.
The resolution of your notes depends on their detail. Deep Learning, for example, could be a general description of Neural Networks, with a few notes on the various architectures (eg. Recurrent Neural Networks, Convolutional Neural Networks etc..).
Limiting length and detail is a good rule of thumb. If you need more detail in a specific section of this note, break it up into smaller notes. Deep Learning now has three notes:
- Deep Learning
- Recurrent Neural Networks
- Convolutional Neural Networks
Repeat this step as needed until you achieve the desired granularity. You might want to put these notes in a “Neural Networks” folder because they are all about the same thing. But there's a better way:
#Tags and [[Links]] over /Folders/
The main issue with folders is that they are not flexible and assume that all notes in the folder belong to a single category. This makes it difficult to make connections between topics.
Deep Learning has been used to predict protein structure (AlphaFold) and classify images (ImageNet). Imagine a folder structure like this:
- /Proteins/
- Protein Folding
- /Deep Learning/
- /Proteins/
Your notes about Protein Folding and Convolutional Neural Networks will be separate, and you won't be able to find them in the same folder.
This can be solved in several ways. The most common one is to use tags rather than folders. A note can be grouped with multiple topics this way. Obsidian tags can also be nested (have subtags).
You can also link two notes together. You can build your “Knowledge Graph” in Obsidian and other note-taking apps like Obsidian.
My Knowledge Graph. Green: Biology, Red: Machine Learning, Yellow: Autoencoders, Blue: Graphs, Brown: Tags.
My Knowledge Graph and the note “Backrpropagation” and its links.
Backpropagation note and all its links
Why use Folders?
Folders help organize your vault as it grows. The main suggestion is to have few folders that "weakly" collect groups of notes or better yet, notes from different sources.
Among my Zettelkasten folders are:
My Zettelkasten's 5 folders
They usually gather data from various sources:
MOC: Map of Contents for the Zettelkasten.
Projects: Contains one note for each side-project of my PhD where I log my progress and ideas. Notes are linked to these.
Bio and ML: These two are the main content of my Zettelkasten and could theoretically be combined.
Papers: All my scientific paper notes go here. A bibliography links the notes. Zotero .bib file
Books: I make a note for each book I read, which I then split into multiple notes.
Keeping images separate from other files can help keep your main folders clean.
I will elaborate on these in the Workflow Section.
My general recommendation is to use tags and links instead of folders.
Maps of Content (MOC)
Making Tables of Contents is a good solution (MOCs).
These are notes that "signposts" your Zettelkasten library, directing you to the right type of notes. It can link to other notes based on common tags. This is usually done with a title, then your notes related to that title. As an example:
An example of a Machine Learning MOC generated with Dataview.
As shown above, my Machine Learning MOC begins with the basics. Then it's on to Variational Auto-Encoders. Not only does this save time, but it also saves scrolling through the tag search section.
So I keep MOCs at the top of my library so I can quickly find information and see my library. These MOCs are generated automatically using an Obsidian Plugin called Dataview (https://github.com/blacksmithgu/obsidian-dataview).
Ideally, MOCs could be expanded to include more information about the notes, their status, and what's left to do. In the absence of this, Dataview does a fantastic job at creating a good structure for your notes.
In the absence of this, Dataview does a fantastic job at creating a good structure for your notes.
2. Tools: Knowing Obsidian
Obsidian is my preferred tool because it is free, all notes are stored in Markdown format, and each panel can be dragged and dropped. You can get it here: https://obsidian.md/
Obsidian interface.
Obsidian is highly customizable, so here is my preferred interface:
The theme is customized from https://github.com/colineckert/obsidian-things
Alternatively, each panel can be collapsed, moved, or removed as desired. To open a panel later, click on the vertical "..." (bottom left of the note panel).
My interface is organized as follows:
How my Obsidian Interface is organized.
Folders/Search:
This is where I keep all relevant folders. I usually use the MOC note to navigate, but sometimes I use the search button to find a note.
Tags:
I use nested tags and look into each one to find specific notes to link.
cMenu:
Easy-to-use menu plugin cMenu (https://github.com/chetachiezikeuzor/cMenu-Plugin)
Global Graph:
The global graph shows all your notes (linked and unlinked). Linked notes will appear closer together. Zoom in to read each note's title. It's a bit overwhelming at first, but as your library grows, you get used to the positions and start thinking of new connections between notes.
Local Graph:
Your current note will be shown in relation to other linked notes in your library. When needed, you can quickly jump to another link and back to the current note.
Links:
Finally, an outline panel and the plugin Obsidian Power Search (https://github.com/aviral-batra/obsidian-power-search) allow me to search my vault by highlighting text.
Start using the tool and worry about panel positioning later. I encourage you to find the best use-case for your library.
Plugins
An additional benefit of using Obsidian is the large plugin library. I use several (Calendar, Citations, Dataview, Templater, Admonition):
Obsidian Calendar Plugin: https://github.com/liamcain
It organizes your notes on a calendar. This is ideal for meeting notes or keeping a journal.
Calendar addon from hans/obsidian-citation-plugin
Obsidian Citation Plugin: https://github.com/hans/
Allows you to cite papers from a.bib file. You can also customize your notes (eg. Title, Authors, Abstract etc..)
Plugin citation from hans/obsidian-citation-plugin
Obsidian Dataview: https://github.com/blacksmithgu/
A powerful plugin that allows you to query your library as a database and generate content automatically. See the MOC section for an example.
Allows you to create notes with specific templates like dates, tags, and headings.
Templater. Obsidian Admonition: https://github.com/valentine195/obsidian-admonition
Blocks allow you to organize your notes.
Plugin warning. Obsidian Admonition (valentine195)
There are many more, but this list should get you started.
3. Workflows: Cool stuff
Here are a few of my workflows for using obsidian for scientific research. This is a list of resources I've found useful for my use-cases. I'll outline and describe them briefly so you can skim them quickly.
3.1 Using Templates to Structure Notes
3.2 Free Note Syncing (Laptop, Phone, Tablet)
3.3 Zotero/Mendeley/JabRef -> Obsidian — Managing Reading Lists
3.4 Projects and Lab Books
3.5 Private Encrypted Diary
3.1 Using Templates to Structure Notes
Plugins: Templater and Dataview (optional).
To take effective notes, you must first make adding new notes as easy as possible. Templates can save you time and give your notes a consistent structure. As an example:
An example of a note using a template.
### [[YOUR MOC]]
# Note Title of your note
**Tags**::
**Links**::
The top line links to your knowledge base's Map of Content (MOC) (see previous sections). After the title, I add tags (and a link between the note and the tag) and links to related notes.
To quickly identify all notes that need to be expanded, I add the tag “#todo”. In the “TODO:” section, I list the tasks within the note.
The rest are notes on the topic.
Templater can help you create these templates. For new books, I use the following template:
### [[Books MOC]]
# Title
**Author**::
**Date::
**Tags::
**Links::
A book template example.
Using a simple query, I can hook Dataview to it.
dataview
table author as Author, date as “Date Finished”, tags as “Tags”, grade as “Grade”
from “4. Books”
SORT grade DESCENDING
using Dataview to query templates.
3.2 Free Note Syncing (Laptop, Phone, Tablet)
No plugins used.
One of my favorite features of Obsidian is the library's self-contained and portable format. Your folder contains everything (plugins included).
Ordinary folders and documents are available as well. There is also a “.obsidian” folder. This contains all your plugins and settings, so you can use it on other devices.
So you can use Google Drive, iCloud, or Dropbox for free as long as you sync your folder (note: your folder should be in your Cloud Folder).
For my iOS and macOS work, I prefer iCloud. You can also use the paid service Obsidian Sync.
3.3 Obsidian — Managing Reading Lists and Notes in Zotero/Mendeley/JabRef
Plugins: Quotes (required).
3.3 Zotero/Mendeley/JabRef -> Obsidian — Taking Notes and Managing Reading Lists of Scientific Papers
My preferred reference manager is Zotero, but this workflow should work with any reference manager that produces a .bib file. This file is exported to my cloud folder so I can access it from any platform.
My Zotero library is tagged as follows:
My reference manager's tags
For readings, I usually search for the tags “!!!” and “To-Read” and select a paper. Annotate the paper next (either on PDF using GoodNotes or on physical paper).
Then I make a paper page using a template in the Citations plugin settings:
An example of my citations template.
Create a new note, open the command list with CMD/CTRL + P, and find the Citations “Insert literature note content in the current pane” to see this lovely view.
Citation generated by the article https://doi.org/10.1101/2022.01.24.22269144
You can then convert your notes to digital. I found that transcribing helped me retain information better.
3.4 Projects and Lab Books
Plugins: Tweaker (required).
PhD students offering advice on thesis writing are common (read as regret). I started asking them what they would have done differently or earlier.
“Deep stuff Leo,” one person said. So my main issue is basic organization, losing track of my tasks and the reasons for them.
As a result, I'd go on other experiments that didn't make sense, and have to reverse engineer my logic for thesis writing. - PhD student now wise Postdoc
Time management requires planning. Keeping track of multiple projects and lab books is difficult during a PhD. How I deal with it:
- One folder for all my projects
- One file for each project
I use a template to create each project
### [[Projects MOC]]
# <% tp.file.title %>
**Tags**::
**Links**::
**URL**::
**Project Description**::## Notes:
### <% tp.file.last_modified_date(“dddd Do MMMM YYYY”) %>
#### Done:
#### TODO:
#### Notes
You can insert a template into a new note with CMD + P and looking for the Templater option.
I then keep adding new days with another template:
### <% tp.file.last_modified_date("dddd Do MMMM YYYY") %>
#### Done:
#### TODO:
#### Notes:
This way you can keep adding days to your project and update with reasonings and things you still have to do and have done. An example below:
Example of project note with timestamped notes.
3.5 Private Encrypted Diary
This is one of my favorite Obsidian uses.
Mini Diary's interface has long frustrated me. After the author archived the project, I looked for a replacement. I had two demands:
- It had to be private, and nobody had to be able to read the entries.
- Cloud syncing was required for editing on multiple devices.
Then I learned about encrypting the Obsidian folder. Then decrypt and open the folder with Obsidian. Sync the folder as usual.
Use CryptoMator (https://cryptomator.org/). Create an encrypted folder in Cryptomator for your Obsidian vault, set a password, and let it do the rest.
If you need a step-by-step video guide, here it is:
Conclusion
So, I hope this was helpful!
In the first section of the article, we discussed notes and note-taking techniques. We discussed when to use tags and links over folders and when to break up larger notes.
Then we learned about Obsidian, its interface, and some useful plugins like Citations for citing papers and Templater for creating note templates.
Finally, we discussed workflows and how to use Zotero to take notes from scientific papers, as well as managing Lab Books and Private Encrypted Diaries.
Thanks for reading and commenting :)
Read original post here
Maria Stepanova
3 years ago
How Elon Musk Picks Things Up Quicker Than Anyone Else
Adopt Elon Musk's learning strategy to succeed.
Medium writers rank first and second when you Google “Elon Musk's learning approach”.
My article idea seems unoriginal. Lol
Musk is brilliant.
No doubt here.
His name connotes success and intelligence.
He knows rocket science, engineering, AI, and solar power.
Musk is a Unicorn, but his skills aren't special.
How does he manage it?
Elon Musk has two learning rules that anyone may use.
You can apply these rules and become anyone you want.
You can become a rocket scientist or a surgeon. If you want, of course.
The learning process is key.
Make sure you are creating a Tree of Knowledge according to Rule #1.
Musk told Reddit how he learns:
“It is important to view knowledge as sort of a semantic tree — make sure you understand the fundamental principles, i.e. the trunk and big branches, before you get into the leaves/details or there is nothing for them to hang onto.”
Musk understands the essential ideas and mental models of each of his business sectors.
He starts with the tree's trunk, making sure he learns the basics before going on to branches and leaves.
We often act otherwise. We memorize small details without understanding how they relate to the whole. Our minds are stuffed with useless data.
Cramming isn't learning.
Start with the basics to learn faster. Before diving into minutiae, grasp the big picture.
Rule #2: You can't connect what you can't remember.
Elon Musk transformed industries this way. As his expertise grew, he connected branches and leaves from different trees.
Musk read two books a day as a child. He didn't specialize like most people. He gained from his multidisciplinary education. It helped him stand out and develop billion-dollar firms.
He gained skills in several domains and began connecting them. World-class performances resulted.
Most of us never learn the basics and only collect knowledge. We never really comprehend information, thus it's hard to apply it.
Learn the basics initially to maximize your chances of success. Then start learning.
Learn across fields and connect them.
This method enabled Elon Musk to enter and revolutionize a century-old industry.
Jano le Roux
3 years ago
Never Heard Of: The Apple Of Email Marketing Tools
Unlimited everything for $19 monthly!?
Even with pretty words, no one wants to read an ugly email.
Not Gen Z
Not Millennials
Not Gen X
Not Boomers
I am a minimalist.
I like Mozart. I like avos. I love Apple.
When I hear seamlessly, effortlessly, or Apple's new adverb fluidly, my toes curl.
No email marketing tool gave me that feeling.
As a marketing consultant helping high-growth brands create marketing that doesn't feel like marketing, I've worked with every email marketing platform imaginable, including that naughty monkey and the expensive platform whose sales teams don't stop calling.
Most email marketing platforms are flawed.
They are overpriced.
They use dreadful templates.
They employ a poor visual designer.
The user experience there is awful.
Too many useless buttons are present. (Similar to the TV remote!)
I may have finally found the perfect email marketing tool. It creates strong flows. It helps me focus on storytelling.
It’s called Flodesk.
It’s effortless. It’s seamless. It’s fluid.
Here’s why it excites me.
Unlimited everything for $19 per month
Sends unlimited. Emails unlimited. Signups unlimited.
Most email platforms penalize success.
Pay for performance?
$87 for 10k contacts
$605 for 100K contacts
$1,300+ for 200K contacts
In the 1990s, this made sense, but not now. It reminds me of when ISPs capped internet usage at 5 GB per month.
Flodesk made unlimited email for a low price a reality. Affordable, attractive email marketing isn't just for big companies.
Flodesk doesn't penalize you for growing your list. Price stays the same as lists grow.
Flodesk plans cost $38 per month, but I'll give you a 30-day trial for $19.
Amazingly strong flows
Foster different people's flows.
Email marketing isn't one-size-fits-all.
Different times require different emails.
People don't open emails because they're irrelevant, in my experience. A colder audience needs a nurturing sequence.
Flodesk automates your email funnels so top-funnel prospects fall in love with your brand and values before mid- and bottom-funnel email flows nudge them to take action.
I wish I could save more custom audience fields to further customize the experience.
Dynamic editor
Easy. Effortless.
Flodesk's editor is Apple-like.
You understand how it works almost instantly.
Like many Apple products, it's intentionally limited. No distractions. You can focus on emotional email writing.
Flodesk's inability to add inline HTML to emails is my biggest issue with larger projects. I wish I could upload HTML emails.
Simple sign-up procedures
Dream up joining.
I like how easy it is to create conversion-focused landing pages. Linkly lets you easily create 5 landing pages and A/B test messaging.
I like that you can use signup forms to ask people what they're interested in so they get relevant emails instead of mindless mass emails nobody opens.
I love how easy it is to embed in-line on a website.
Wonderful designer templates
Beautiful, connecting emails.
Flodesk has calm email templates. My designer's eye felt at rest when I received plain text emails with big impacts.
As a typography nerd, I love Flodesk's handpicked designer fonts. It gives emails a designer feel that is hard to replicate on other platforms without coding and custom font licenses.
Small adjustments can have a big impact
Details matter.
Flodesk remembers your brand colors. Flodesk automatically adds your logo and social handles to emails after signup.
Flodesk uses Zapier. This lets you send emails based on a user's action.
A bad live chat can trigger a series of emails to win back a customer.
Flodesk isn't for everyone.
Flodesk is great for Apple users like me.
You might also like
Vitalik
3 years ago
An approximate introduction to how zk-SNARKs are possible (part 1)
You can make a proof for the statement "I know a secret number such that if you take the word ‘cow', add the number to the end, and SHA256 hash it 100 million times, the output starts with 0x57d00485aa". The verifier can verify the proof far more quickly than it would take for them to run 100 million hashes themselves, and the proof would also not reveal what the secret number is.
In the context of blockchains, this has 2 very powerful applications: Perhaps the most powerful cryptographic technology to come out of the last decade is general-purpose succinct zero knowledge proofs, usually called zk-SNARKs ("zero knowledge succinct arguments of knowledge"). A zk-SNARK allows you to generate a proof that some computation has some particular output, in such a way that the proof can be verified extremely quickly even if the underlying computation takes a very long time to run. The "ZK" part adds an additional feature: the proof can keep some of the inputs to the computation hidden.
You can make a proof for the statement "I know a secret number such that if you take the word ‘cow', add the number to the end, and SHA256 hash it 100 million times, the output starts with 0x57d00485aa". The verifier can verify the proof far more quickly than it would take for them to run 100 million hashes themselves, and the proof would also not reveal what the secret number is.
In the context of blockchains, this has two very powerful applications:
- Scalability: if a block takes a long time to verify, one person can verify it and generate a proof, and everyone else can just quickly verify the proof instead
- Privacy: you can prove that you have the right to transfer some asset (you received it, and you didn't already transfer it) without revealing the link to which asset you received. This ensures security without unduly leaking information about who is transacting with whom to the public.
But zk-SNARKs are quite complex; indeed, as recently as in 2014-17 they were still frequently called "moon math". The good news is that since then, the protocols have become simpler and our understanding of them has become much better. This post will try to explain how ZK-SNARKs work, in a way that should be understandable to someone with a medium level of understanding of mathematics.
Why ZK-SNARKs "should" be hard
Let us take the example that we started with: we have a number (we can encode "cow" followed by the secret input as an integer), we take the SHA256 hash of that number, then we do that again another 99,999,999 times, we get the output, and we check what its starting digits are. This is a huge computation.
A "succinct" proof is one where both the size of the proof and the time required to verify it grow much more slowly than the computation to be verified. If we want a "succinct" proof, we cannot require the verifier to do some work per round of hashing (because then the verification time would be proportional to the computation). Instead, the verifier must somehow check the whole computation without peeking into each individual piece of the computation.
One natural technique is random sampling: how about we just have the verifier peek into the computation in 500 different places, check that those parts are correct, and if all 500 checks pass then assume that the rest of the computation must with high probability be fine, too?
Such a procedure could even be turned into a non-interactive proof using the Fiat-Shamir heuristic: the prover computes a Merkle root of the computation, uses the Merkle root to pseudorandomly choose 500 indices, and provides the 500 corresponding Merkle branches of the data. The key idea is that the prover does not know which branches they will need to reveal until they have already "committed to" the data. If a malicious prover tries to fudge the data after learning which indices are going to be checked, that would change the Merkle root, which would result in a new set of random indices, which would require fudging the data again... trapping the malicious prover in an endless cycle.
But unfortunately there is a fatal flaw in naively applying random sampling to spot-check a computation in this way: computation is inherently fragile. If a malicious prover flips one bit somewhere in the middle of a computation, they can make it give a completely different result, and a random sampling verifier would almost never find out.
It only takes one deliberately inserted error, that a random check would almost never catch, to make a computation give a completely incorrect result.
If tasked with the problem of coming up with a zk-SNARK protocol, many people would make their way to this point and then get stuck and give up. How can a verifier possibly check every single piece of the computation, without looking at each piece of the computation individually? There is a clever solution.
see part 2
Will Lockett
3 years ago
Tesla recently disclosed its greatest secret.
The VP has revealed a secret that should frighten the rest of the EV world.
Tesla led the EV revolution. Elon Musk's invention offers a viable alternative to gas-guzzlers. Tesla has lost ground in recent years. VW, BMW, Mercedes, and Ford offer EVs with similar ranges, charging speeds, performance, and cost. Tesla's next-generation 4680 battery pack, Roadster, Cybertruck, and Semi were all delayed. CATL offers superior batteries than the 4680. Martin Viecha, Tesla's Vice President, recently told Business Insider something that startled the EV world and will establish Tesla as the EV king.
Viecha mentioned that Tesla's production costs have dropped 57% since 2017. This isn't due to cheaper batteries or devices like Model 3. No, this is due to amazing factory efficiency gains.
Musk wasn't crazy to want a nearly 100% automated production line, and Tesla's strategy of sticking with one model and improving it has paid off. Others change models every several years. This implies they must spend on new R&D, set up factories, and modernize service and parts systems. All of this costs a ton of money and prevents them from refining production to cut expenses.
Meanwhile, Tesla updates its vehicles progressively. Everything from the backseats to the screen has been enhanced in a 2022 Model 3. Tesla can refine, standardize, and cheaply produce every part without changing the production line.
In 2017, Tesla's automobile production averaged $84,000. In 2022, it'll be $36,000.
Mr. Viecha also claimed that new factories in Shanghai and Berlin will be significantly cheaper to operate once fully operating.
Tesla's hand is visible. Tesla selling $36,000 cars for $60,000 This barely beats the competition. Model Y long-range costs just over $60,000. Tesla makes $24,000+ every sale, giving it a 40% profit margin, one of the best in the auto business.
VW I.D4 costs about the same but makes no profit. Tesla's rivals face similar challenges. Their EVs make little or no profit.
Tesla costs the same as other EVs, but they're in a different league.
But don't forget that the battery pack accounts for 40% of an EV's cost. Tesla may soon fully utilize its 4680 battery pack.
The 4680 battery pack has larger cells and a unique internal design. This means fewer cells are needed for a car, making it cheaper to assemble and produce (per kWh). Energy density and charge speeds increase slightly.
Tesla underestimated the difficulty of making this revolutionary new cell. Each time they try to scale up production, quality drops and rejected cells rise.
Tesla recently installed this battery pack in Model Ys and is scaling production. If they succeed, Tesla battery prices will plummet.
Tesla's Model Ys 2170 battery costs $11,000. The same size pack with 4680 cells costs $3,400 less. Once scaled, it could be $5,500 (50%) less. The 4680 battery pack could reduce Tesla production costs by 20%.
With these cost savings, Tesla could sell Model Ys for $40,000 while still making a profit. They could offer a $25,000 car.
Even with new battery technology, it seems like other manufacturers will struggle to make EVs profitable.
Teslas cost about the same as competitors, so don't be fooled. Behind the scenes, they're still years ahead, and the 4680 battery pack and new factories will only increase that lead. Musk faces a first. He could sell Teslas at current prices and make billions while other manufacturers struggle. Or, he could massively undercut everyone and crush the competition once and for all. Tesla and Elon win.
Sofien Kaabar, CFA
3 years ago
How to Make a Trading Heatmap
Python Heatmap Technical Indicator
Heatmaps provide an instant overview. They can be used with correlations or to predict reactions or confirm the trend in trading. This article covers RSI heatmap creation.
The Market System
Market regime:
Bullish trend: The market tends to make higher highs, which indicates that the overall trend is upward.
Sideways: The market tends to fluctuate while staying within predetermined zones.
Bearish trend: The market has the propensity to make lower lows, indicating that the overall trend is downward.
Most tools detect the trend, but we cannot predict the next state. The best way to solve this problem is to assume the current state will continue and trade any reactions, preferably in the trend.
If the EURUSD is above its moving average and making higher highs, a trend-following strategy would be to wait for dips before buying and assuming the bullish trend will continue.
Indicator of Relative Strength
J. Welles Wilder Jr. introduced the RSI, a popular and versatile technical indicator. Used as a contrarian indicator to exploit extreme reactions. Calculating the default RSI usually involves these steps:
Determine the difference between the closing prices from the prior ones.
Distinguish between the positive and negative net changes.
Create a smoothed moving average for both the absolute values of the positive net changes and the negative net changes.
Take the difference between the smoothed positive and negative changes. The Relative Strength RS will be the name we use to describe this calculation.
To obtain the RSI, use the normalization formula shown below for each time step.
The 13-period RSI and black GBPUSD hourly values are shown above. RSI bounces near 25 and pauses around 75. Python requires a four-column OHLC array for RSI coding.
import numpy as np
def add_column(data, times):
for i in range(1, times + 1):
new = np.zeros((len(data), 1), dtype = float)
data = np.append(data, new, axis = 1)
return data
def delete_column(data, index, times):
for i in range(1, times + 1):
data = np.delete(data, index, axis = 1)
return data
def delete_row(data, number):
data = data[number:, ]
return data
def ma(data, lookback, close, position):
data = add_column(data, 1)
for i in range(len(data)):
try:
data[i, position] = (data[i - lookback + 1:i + 1, close].mean())
except IndexError:
pass
data = delete_row(data, lookback)
return data
def smoothed_ma(data, alpha, lookback, close, position):
lookback = (2 * lookback) - 1
alpha = alpha / (lookback + 1.0)
beta = 1 - alpha
data = ma(data, lookback, close, position)
data[lookback + 1, position] = (data[lookback + 1, close] * alpha) + (data[lookback, position] * beta)
for i in range(lookback + 2, len(data)):
try:
data[i, position] = (data[i, close] * alpha) + (data[i - 1, position] * beta)
except IndexError:
pass
return data
def rsi(data, lookback, close, position):
data = add_column(data, 5)
for i in range(len(data)):
data[i, position] = data[i, close] - data[i - 1, close]
for i in range(len(data)):
if data[i, position] > 0:
data[i, position + 1] = data[i, position]
elif data[i, position] < 0:
data[i, position + 2] = abs(data[i, position])
data = smoothed_ma(data, 2, lookback, position + 1, position + 3)
data = smoothed_ma(data, 2, lookback, position + 2, position + 4)
data[:, position + 5] = data[:, position + 3] / data[:, position + 4]
data[:, position + 6] = (100 - (100 / (1 + data[:, position + 5])))
data = delete_column(data, position, 6)
data = delete_row(data, lookback)
return dataMake sure to focus on the concepts and not the code. You can find the codes of most of my strategies in my books. The most important thing is to comprehend the techniques and strategies.
My weekly market sentiment report uses complex and simple models to understand the current positioning and predict the future direction of several major markets. Check out the report here:
Using the Heatmap to Find the Trend
RSI trend detection is easy but useless. Bullish and bearish regimes are in effect when the RSI is above or below 50, respectively. Tracing a vertical colored line creates the conditions below. How:
When the RSI is higher than 50, a green vertical line is drawn.
When the RSI is lower than 50, a red vertical line is drawn.
Zooming out yields a basic heatmap, as shown below.
Plot code:
def indicator_plot(data, second_panel, window = 250):
fig, ax = plt.subplots(2, figsize = (10, 5))
sample = data[-window:, ]
for i in range(len(sample)):
ax[0].vlines(x = i, ymin = sample[i, 2], ymax = sample[i, 1], color = 'black', linewidth = 1)
if sample[i, 3] > sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 0], ymax = sample[i, 3], color = 'black', linewidth = 1.5)
if sample[i, 3] < sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
if sample[i, 3] == sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
ax[0].grid()
for i in range(len(sample)):
if sample[i, second_panel] > 50:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'green', linewidth = 1.5)
if sample[i, second_panel] < 50:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'red', linewidth = 1.5)
ax[1].grid()
indicator_plot(my_data, 4, window = 500)
Call RSI on your OHLC array's fifth column. 4. Adjusting lookback parameters reduces lag and false signals. Other indicators and conditions are possible.
Another suggestion is to develop an RSI Heatmap for Extreme Conditions.
Contrarian indicator RSI. The following rules apply:
Whenever the RSI is approaching the upper values, the color approaches red.
The color tends toward green whenever the RSI is getting close to the lower values.
Zooming out yields a basic heatmap, as shown below.
Plot code:
import matplotlib.pyplot as plt
def indicator_plot(data, second_panel, window = 250):
fig, ax = plt.subplots(2, figsize = (10, 5))
sample = data[-window:, ]
for i in range(len(sample)):
ax[0].vlines(x = i, ymin = sample[i, 2], ymax = sample[i, 1], color = 'black', linewidth = 1)
if sample[i, 3] > sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 0], ymax = sample[i, 3], color = 'black', linewidth = 1.5)
if sample[i, 3] < sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
if sample[i, 3] == sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
ax[0].grid()
for i in range(len(sample)):
if sample[i, second_panel] > 90:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'red', linewidth = 1.5)
if sample[i, second_panel] > 80 and sample[i, second_panel] < 90:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'darkred', linewidth = 1.5)
if sample[i, second_panel] > 70 and sample[i, second_panel] < 80:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'maroon', linewidth = 1.5)
if sample[i, second_panel] > 60 and sample[i, second_panel] < 70:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'firebrick', linewidth = 1.5)
if sample[i, second_panel] > 50 and sample[i, second_panel] < 60:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'grey', linewidth = 1.5)
if sample[i, second_panel] > 40 and sample[i, second_panel] < 50:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'grey', linewidth = 1.5)
if sample[i, second_panel] > 30 and sample[i, second_panel] < 40:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'lightgreen', linewidth = 1.5)
if sample[i, second_panel] > 20 and sample[i, second_panel] < 30:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'limegreen', linewidth = 1.5)
if sample[i, second_panel] > 10 and sample[i, second_panel] < 20:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'seagreen', linewidth = 1.5)
if sample[i, second_panel] > 0 and sample[i, second_panel] < 10:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'green', linewidth = 1.5)
ax[1].grid()
indicator_plot(my_data, 4, window = 500)
Dark green and red areas indicate imminent bullish and bearish reactions, respectively. RSI around 50 is grey.
Summary
To conclude, my goal is to contribute to objective technical analysis, which promotes more transparent methods and strategies that must be back-tested before implementation.
Technical analysis will lose its reputation as subjective and unscientific.
When you find a trading strategy or technique, follow these steps:
Put emotions aside and adopt a critical mindset.
Test it in the past under conditions and simulations taken from real life.
Try optimizing it and performing a forward test if you find any potential.
Transaction costs and any slippage simulation should always be included in your tests.
Risk management and position sizing should always be considered in your tests.
After checking the above, monitor the strategy because market dynamics may change and make it unprofitable.