More on Leadership
Alison Randel
3 years ago
Raising the Bar on Your 1:1s
Managers spend much time in 1:1s. Most team members meet with supervisors regularly. 1:1s can help create relationships and tackle tough topics. Few appreciate the 1:1 format's potential. Most of the time, that potential is spent on small talk, surface-level updates, and ranting (Ugh, the marketing team isn’t stepping up the way I want them to).
What if you used that time to have deeper conversations and important insights? What if change was easy?
This post introduces a new 1:1 format to help you dive deeper, faster, and develop genuine relationships without losing impact.
A 1:1 is a chat, you would assume. Why use structure to talk to a coworker? Go! I know how to talk to people. I can write. I've always written. Also, This article was edited by Zoe.
Before you discard something, ask yourself if there's a good reason not to try anything new. Is the 1:1 only a talk, or do you want extra benefits? Try the steps below to discover more.
I. Reflection (5 minutes)
Context-free, broad comments waste time and are useless. Instead, give team members 5 minutes to write these 3 prompts.
What's effective?
What is decent but could be improved?
What is broken or missing?
Why these? They encourage people to be honest about all their experiences. Answering these questions helps people realize something isn't working. These prompts let people consider what's working.
Why take notes? Because you get more in less time. Will you feel awkward sitting quietly while your coworker writes? Probably. Persevere. Multi-task. Take a break from your afternoon meeting marathon. Any awkwardness will pay off.
What happens? After a few minutes of light conversation, create a template like the one given here and have team members fill in their replies. You can pre-share the template (with the caveat that this isn’t meant to take much prep time). Do this with your coworker: Answer the prompts. Everyone can benefit from pondering and obtaining guidance.
This step's output.
Part II: Talk (10-20 minutes)
Most individuals can explain what they see but not what's behind an answer. You don't like a meeting. Why not? Marketing partnership is difficult. What makes working with them difficult? I don't recommend slandering coworkers. Consider how your meetings, decisions, and priorities make work harder. The excellent stuff too. You want to know what's humming so you can reproduce the magic.
First, recognize some facts.
Real power dynamics exist. To encourage individuals to be honest, you must provide a safe environment and extend clear invites. Even then, it may take a few 1:1s for someone to feel secure enough to go there in person. It is part of your responsibility to admit that it is normal.
Curiosity and self-disclosure are crucial. Most leaders have received training to present themselves as the authorities. However, you will both benefit more from the dialogue if you can be open and honest about your personal experience, ask questions out of real curiosity, and acknowledge the pertinent sacrifices you're making as a leader.
Honesty without bias is difficult and important. Due to concern for the feelings of others, people frequently hold back. Or if they do point anything out, they do so in a critical manner. The key is to be open and unapologetic about what you observe while not presuming that your viewpoint is correct and that of the other person is incorrect.
Let's go into some prompts (based on genuine conversations):
“What do you notice across your answers?”
“What about the way you/we/they do X, Y, or Z is working well?”
“ Will you say more about item X in ‘What’s not working?’”
“I’m surprised there isn’t anything about Z. Why is that?”
“All of us tend to play some role in maintaining certain patterns. How might you/we be playing a role in this pattern persisting?”
“How might the way we meet, make decisions, or collaborate play a role in what’s currently happening?”
Consider the preceding example. What about the Monday meeting isn't working? Why? or What about the way we work with marketing makes collaboration harder? Remember to share your honest observations!
Third section: observe patterns (10-15 minutes)
Leaders desire to empower their people but don't know how. We also have many preconceptions about what empowerment means to us and how it works. The next phase in this 1:1 format will assist you and your team member comprehend team power and empowerment. This understanding can help you support and shift your team member's behavior, especially where you disagree.
How to? After discussing the stated responses, ask each team member what they can control, influence, and not control. Mark their replies. You can do the same, adding colors where you disagree.
This step's output.
Next, consider the color constellation. Discuss these questions:
Is one color much more prevalent than the other? Why, if so?
Are the colors for the "what's working," "what's fine," and "what's not working" categories clearly distinct? Why, if so?
Do you have any disagreements? If yes, specifically where does your viewpoint differ? What activities do you object to? (Remember, there is no right or wrong in this. Give explicit details and ask questions with curiosity.)
Example: Based on the colors, you can ask, Is the marketing meeting's quality beyond your control? Were our marketing partners consulted? Are there any parts of team decisions we can control? We can't control people, but have we explored another decision-making method? How can we collaborate and generate governance-related information to reduce work, even if the requirement for prep can't be eliminated?
Consider the top one or two topics for this conversation. No 1:1 can cover everything, and that's OK. Focus on the present.
Part IV: Determine the next step (5 minutes)
Last, examine what this conversation means for you and your team member. It's easy to think we know the next moves when we don't.
Like what? You and your teammate answer these questions.
What does this signify moving ahead for me? What can I do to change this? Make requests, for instance, and see how people respond before thinking they won't be responsive.
What demands do I have on other people or my partners? What should I do first? E.g. Make a suggestion to marketing that we hold a monthly retrospective so we can address problems and exchange input more frequently. Include it on the meeting's agenda for next Monday.
Close the 1:1 by sharing what you noticed about the chat. Observations? Learn anything?
Yourself, you, and the 1:1
As a leader, you either reinforce or disrupt habits. Try this template if you desire greater ownership, empowerment, or creativity. Consider how you affect surrounding dynamics. How can you expect others to try something new in high-stakes scenarios, like meetings with cross-functional partners or senior stakeholders, if you won't? How can you expect deep thought and relationship if you don't encourage it in 1:1s? What pattern could this new format disrupt or reinforce?
Fight reluctance. First attempts won't be ideal, and that's OK. You'll only learn by trying.
Al Anany
2 years ago
Because of this covert investment that Bezos made, Amazon became what it is today.
He kept it under wraps for years until he legally couldn’t.
His shirt is incomplete. I can’t stop thinking about this…
Actually, ignore the article. Look at it. JUST LOOK at it… It’s quite disturbing, isn’t it?
Ughh…
Me: “Hey, what up?” Friend: “All good, watching lord of the rings on amazon prime video.” Me: “Oh, do you know how Amazon grew and became famous?” Friend: “Geek alert…Can I just watch in peace?” Me: “But… Bezos?” Friend: “Let it go, just let it go…”
I can question you, the reader, and start answering instantly without his consent. This far.
Reader, how did Amazon succeed? You'll say, Of course, it was an internet bookstore, then it sold everything.
Mistaken. They moved from zero to one because of this. How did they get from one to thousand? AWS-some. Understand? It's geeky and lame. If not, I'll explain my geekiness.
Over an extended period of time, Amazon was not profitable.
Business basics. You want customers if you own a bakery, right?
Well, 100 clients per day order $5 cheesecakes (because cheesecakes are awesome.)
$5 x 100 consumers x 30 days Equals $15,000 monthly revenue. You proudly work here.
Now you have to pay the barista (unless ChatGPT is doing it haha? Nope..)
The barista is requesting $5000 a month.
Each cheesecake costs the cheesecake maker $2.5 ($2.5 × 100 x 30 = $7500).
The monthly cost of running your bakery, including power, is about $5000.
Assume no extra charges. Your operating costs are $17,500.
Just $15,000? You have income but no profit. You might make money selling coffee with your cheesecake next month.
Is losing money bad? You're broke. Losing money. It's bad for financial statements.
It's almost a business ultimatum. Most startups fail. Amazon took nine years.
I'm reading Amazon Unbound: Jeff Bezos and the Creation of a Global Empire to comprehend how a company has a $1 trillion market cap.
Many things made Amazon big. The book claims that Bezos and Amazon kept a specific product secret for a long period.
Clouds above the bald head.
In 2006, Bezos started a cloud computing initiative. They believed many firms like Snapchat would pay for reliable servers.
In 2006, cloud computing was not what it is today. I'll simplify. 2006 had no iPhone.
Bezos invested in Amazon Web Services (AWS) without disclosing its revenue. That's permitted till a certain degree.
Google and Microsoft would realize Amazon is heavily investing in this market and worry.
Bezos anticipated high demand for this product. Microsoft built its cloud in 2010, and Google in 2008.
If you managed Google or Microsoft, you wouldn't know how much Amazon makes from their cloud computing service. It's enough. Yet, Amazon is an internet store, so they'll focus on that.
All but Bezos were wrong.
Time to come clean now.
They revealed AWS revenue in 2015. Two things were apparent:
Bezos made the proper decision to bet on the cloud and keep it a secret.
In this race, Amazon is in the lead.
They continued. Let me list some AWS users today.
Netflix
Airbnb
Twitch
More. Amazon was unprofitable for nine years, remember? This article's main graph.
AWS accounted for 74% of Amazon's profit in 2021. This 74% might not exist if they hadn't invested in AWS.
Bring this with you home.
Amazon predated AWS. Yet, it helped the giant reach $1 trillion. Bezos' secrecy? Perhaps, until a time machine is invented (they might host the time machine software on AWS, though.)
Without AWS, Amazon would have been profitable but unimpressive. They may have invested in anything else that would have returned more (like crypto? No? Ok.)
Bezos has business flaws. His success. His failures include:
introducing the Fire Phone and suffering a $170 million loss.
Amazon's failure in China In 2011, Amazon had a about 15% market share in China. 2019 saw a decrease of about 1%.
not offering a higher price to persuade the creator of Netflix to sell the company to him. He offered a rather reasonable $15 million in his proposal. But what if he had offered $30 million instead (Amazon had over $100 million in revenue at the time)? He might have owned Netflix, which has a $156 billion market valuation (and saved billions rather than invest in Amazon Prime Video).
Some he could control. Some were uncontrollable. Nonetheless, every action he made in the foregoing circumstances led him to invest in AWS.
Solomon Ayanlakin
3 years ago
Metrics for product management and being a good leader
Never design a product without explicit metrics and tracking tools.
Imagine driving cross-country without a dashboard. How do you know your school zone speed? Low gas? Without a dashboard, you can't monitor your car. You can't improve what you don't measure, as Peter Drucker said. Product managers must constantly enhance their understanding of their users, how they use their product, and how to improve it for optimum value. Customers will only pay if they consistently acquire value from your product.
I’m Solomon Ayanlakin. I’m a product manager at CredPal, a financial business that offers credit cards and Buy Now Pay Later services. Before falling into product management (like most PMs lol), I self-trained as a data analyst, using Alex the Analyst's YouTube playlists and DannyMas' virtual data internship. This article aims to help product managers, owners, and CXOs understand product metrics, give a methodology for creating them, and execute product experiments to enhance them.
☝🏽Introduction
Product metrics assist companies track product performance from the user's perspective. Metrics help firms decide what to construct (feature priority), how to build it, and the outcome's success or failure. To give the best value to new and existing users, track product metrics.
Why should a product manager monitor metrics?
to assist your users in having a "aha" moment
To inform you of which features are frequently used by users and which are not
To assess the effectiveness of a product feature
To aid in enhancing client onboarding and retention
To assist you in identifying areas throughout the user journey where customers are satisfied or dissatisfied
to determine the percentage of returning users and determine the reasons for their return
📈 What Metrics Ought a Product Manager to Monitor?
What indicators should a product manager watch to monitor product health? The metrics to follow change based on the industry, business stage (early, growth, late), consumer needs, and company goals. A startup should focus more on conversion, activation, and active user engagement than revenue growth and retention. The company hasn't found product-market fit or discovered what features drive customer value.
Depending on your use case, company goals, or business stage, here are some important product metric buckets:
All measurements shouldn't be used simultaneously. It depends on your business goals and what value means for your users, then selecting what metrics to track to see if they get it.
Some KPIs are more beneficial to track, independent of industry or customer type. To prevent recording vanity metrics, product managers must clearly specify the types of metrics they should track. Here's how to segment metrics:
The North Star Metric, also known as the Focus Metric, is the indicator and aid in keeping track of the top value you provide to users.
Primary/Level 1 Metrics: These metrics should either add to the north star metric or be used to determine whether it is moving in the appropriate direction. They are metrics that support the north star metric.
These measures serve as leading indications for your north star and Level 2 metrics. You ought to have been aware of certain problems with your L2 measurements prior to the North star metric modifications.
North Star Metric
This is the key metric. A good north star metric measures customer value. It emphasizes your product's longevity. Many organizations fail to grow because they confuse north star measures with other indicators. A good focus metric should touch all company teams and be tracked forever. If a company gives its customers outstanding value, growth and success are inevitable. How do we measure this value?
A north star metric has these benefits:
Customer Obsession: It promotes a culture of customer value throughout the entire organization.
Consensus: Everyone can quickly understand where the business is at and can promptly make improvements, according to consensus.
Growth: It provides a tool to measure the company's long-term success. Do you think your company will last for a long time?
How can I pick a reliable North Star Metric?
Some fear a single metric. Ensure product leaders can objectively determine a north star metric. Your company's focus metric should meet certain conditions. Here are a few:
A good focus metric should reflect value and, as such, should be closely related to the point at which customers obtain the desired value from your product. For instance, the quick delivery to your home is a value proposition of UberEats. The value received from a delivery would be a suitable focal metric to use. While counting orders is alluring, the quantity of successfully completed positive review orders would make a superior north star statistic. This is due to the fact that a client who placed an order but received a defective or erratic delivery is not benefiting from Uber Eats. By tracking core value gain, which is the number of purchases that resulted in satisfied customers, we are able to track not only the total number of orders placed during a specific time period but also the core value proposition.
Focus metrics need to be quantifiable; they shouldn't only be feelings or states; they need to be actionable. A smart place to start is by counting how many times an activity has been completed.
A great focus metric is one that can be measured within predetermined time limits; otherwise, you are not measuring at all. The company can improve that measure more quickly by having time-bound focus metrics. Measuring and accounting for progress over set time periods is the only method to determine whether or not you are moving in the right path. You can then evaluate your metrics for today and yesterday. It's generally not a good idea to use a year as a time frame. Ideally, depending on the nature of your organization and the measure you are focusing on, you want to take into account on a daily, weekly, or monthly basis.
Everyone in the firm has the potential to affect it: A short glance at the well-known AAARRR funnel, also known as the Pirate Metrics, reveals that various teams inside the organization have an impact on the funnel. Ideally, the NSM should be impacted if changes are made to one portion of the funnel. Consider how the growth team in your firm is enhancing customer retention. This would have a good effect on the north star indicator because at this stage, a repeat client is probably being satisfied on a regular basis. Additionally, if the opposite were true and a client churned, it would have a negative effect on the focus metric.
It ought to be connected to the business's long-term success: The direction of sustainability would be indicated by a good north star metric. A company's lifeblood is product demand and revenue, so it's critical that your NSM points in the direction of sustainability. If UberEats can effectively increase the monthly total of happy client orders, it will remain in operation indefinitely.
Many product teams make the mistake of focusing on revenue. When the bottom line is emphasized, a company's goal moves from giving value to extracting money from customers. A happy consumer will stay and pay for your service. Customer lifetime value always exceeds initial daily, monthly, or weekly revenue.
Great North Star Metrics Examples
🥇 Basic/L1 Metrics:
The NSM is broad and focuses on providing value for users, while the primary metric is product/feature focused and utilized to drive the focus metric or signal its health. The primary statistic is team-specific, whereas the north star metric is company-wide. For UberEats' NSM, the marketing team may measure the amount of quality food vendors who sign up using email marketing. With quality vendors, more orders will be satisfied. Shorter feedback loops and unambiguous team assignments make L1 metrics more actionable and significant in the immediate term.
🥈 Supporting L2 metrics:
These are supporting metrics to the L1 and focus metrics. Location, demographics, or features are examples of L1 metrics. UberEats' supporting metrics might be the number of sales emails sent to food vendors, the number of opens, and the click-through rate. Secondary metrics are low-level and evident, and they relate into primary and north star measurements. UberEats needs a high email open rate to attract high-quality food vendors. L2 is a leading sign for L1.
Where can I find product metrics?
How can I measure in-app usage and activity now that I know what metrics to track? Enter product analytics. Product analytics tools evaluate and improve product management parameters that indicate a product's health from a user's perspective.
Various analytics tools on the market supply product insight. From page views and user flows through A/B testing, in-app walkthroughs, and surveys. Depending on your use case and necessity, you may combine tools to see how users engage with your product. Gainsight, MixPanel, Amplitude, Google Analytics, FullStory, Heap, and Pendo are product tools.
This article isn't sponsored and doesn't market product analytics tools. When choosing an analytics tool, consider the following:
Tools for tracking your Focus, L1, and L2 measurements
Pricing
Adaptations to include external data sources and other products
Usability and the interface
Scalability
Security
An investment in the appropriate tool pays off. To choose the correct metrics to track, you must first understand your business need and what value means to your users. Metrics and analytics are crucial for any tech product's growth. It shows how your business is doing and how to best serve users.
You might also like
Amelia Winger-Bearskin
3 years ago
Reasons Why AI-Generated Images Remind Me of Nightmares
AI images are like funhouse mirrors.
Google's AI Blog introduced the puppy-slug in the summer of 2015.
Puppy-slug isn't a single image or character. "Puppy-slug" refers to Google's DeepDream's unsettling psychedelia. This tool uses convolutional neural networks to train models to recognize dataset entities. If researchers feed the model millions of dog pictures, the network will learn to recognize a dog.
DeepDream used neural networks to analyze and classify image data as well as generate its own images. DeepDream's early examples were created by training a convolutional network on dog images and asking it to add "dog-ness" to other images. The models analyzed images to find dog-like pixels and modified surrounding pixels to highlight them.
Puppy-slugs and other DeepDream images are ugly. Even when they don't trigger my trypophobia, they give me vertigo when my mind tries to reconcile familiar features and forms in unnatural, physically impossible arrangements. I feel like I've been poisoned by a forbidden mushroom or a noxious toad. I'm a Lovecraft character going mad from extradimensional exposure. They're gross!
Is this really how AIs see the world? This is possibly an even more unsettling topic that DeepDream raises than the blatant abjection of the images.
When these photographs originally circulated online, many friends were startled and scandalized. People imagined a computer's imagination would be literal, accurate, and boring. We didn't expect vivid hallucinations and organic-looking formations.
DeepDream's images didn't really show the machines' imaginations, at least not in the way that scared some people. DeepDream displays data visualizations. DeepDream reveals the "black box" of convolutional network training.
Some of these images look scary because the models don't "know" anything, at least not in the way we do.
These images are the result of advanced algorithms and calculators that compare pixel values. They can spot and reproduce trends from training data, but can't interpret it. If so, they'd know dogs have two eyes and one face per head. If machines can think creatively, they're keeping it quiet.
You could be forgiven for thinking otherwise, given OpenAI's Dall-impressive E's results. From a technological perspective, it's incredible.
Arthur C. Clarke once said, "Any sufficiently advanced technology is indistinguishable from magic." Dall-magic E's requires a lot of math, computer science, processing power, and research. OpenAI did a great job, and we should applaud them.
Dall-E and similar tools match words and phrases to image data to train generative models. Matching text to images requires sorting and defining the images. Untold millions of low-wage data entry workers, content creators optimizing images for SEO, and anyone who has used a Captcha to access a website make these decisions. These people could live and die without receiving credit for their work, even though the project wouldn't exist without them.
This technique produces images that are less like paintings and more like mirrors that reflect our own beliefs and ideals back at us, albeit via a very complex prism. Due to the limitations and biases that these models portray, we must exercise caution when viewing these images.
The issue was succinctly articulated by artist Mimi Onuoha in her piece "On Algorithmic Violence":
As we continue to see the rise of algorithms being used for civic, social, and cultural decision-making, it becomes that much more important that we name the reality that we are seeing. Not because it is exceptional, but because it is ubiquitous. Not because it creates new inequities, but because it has the power to cloak and amplify existing ones. Not because it is on the horizon, but because it is already here.
Victoria Kurichenko
3 years ago
Here's what happened after I launched my second product on Gumroad.
One-hour ebook sales, affiliate relationships, and more.
If you follow me, you may know I started a new ebook in August 2022.
Despite publishing on this platform, my website, and Quora, I'm not a writer.
My writing speed is slow, 2,000 words a day, and I struggle to communicate cohesively.
In April 2022, I wrote a successful guide on How to Write Google-Friendly Blog Posts.
I had no email list or social media presence. I've made $1,600+ selling ebooks.
Evidence:
My first digital offering isn't a book.
It's an actionable guide with my tried-and-true process for writing Google-friendly content.
I'm not bragging.
Established authors like Tim Denning make more from my ebook sales with one newsletter.
This experience taught me writing isn't a privilege.
Writing a book and making money online doesn't require expertise.
Many don't consult experts. They want someone approachable.
Two years passed before I realized my own limits.
I have a brain, two hands, and Internet to spread my message.
I wrote and published a second ebook after the first's success.
On Gumroad, I released my second digital product.
Here's my complete Gumroad evaluation.
Gumroad is a marketplace for content providers to develop and sell sales pages.
Gumroad handles payments and client requests. It's helpful when someone sends a bogus payment receipt requesting an ebook (actual story!).
You'll forget administrative concerns after your first ebook sale.
After my first ebook sale, I did this: I made additional cash!
After every sale, I tell myself, "I built a new semi-passive revenue source."
This thinking shift helps me become less busy while increasing my income and quality of life.
Besides helping others, folks sell evergreen digital things to earn passive money.
It's in my second ebook.
I explain how I built and sold 50+ copies of my SEO writing ebook without being an influencer.
I show how anyone can sell ebooks on Gumroad and automate their sales process.
This is my ebook.
After publicizing the ebook release, I sold three copies within an hour.
Wow, or meh?
I don’t know.
The answer is different for everyone.
These three sales came from a small email list of 40 motivated fans waiting for my ebook release.
I had bigger plans.
I'll market my ebook on Medium, my website, Quora, and email.
I'm testing affiliate partnerships this time.
One of my ebook buyers is now promoting it for 40% commission.
Become my affiliate if you think your readers would like my ebook.
My ebook is a few days old, but I'm interested to see where it goes.
My SEO writing book started without an email list, affiliates, or 4,000 website visitors. I've made four figures.
I'm slowly expanding my communication avenues to have more impact.
Even a small project can open doors you never knew existed.
So began my writing career.
In summary
If you dare, every concept can become a profitable trip.
Before, I couldn't conceive of creating an ebook.
How to Sell eBooks on Gumroad is my second digital product.
Marketing and writing taught me that anything can be sold online.
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.