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 data

Make 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.

This season's three best money moves.

“Millionaires are made in recessions.” — Time Capital

We're in a serious downturn, whether or not we're in a recession.

97% of business owners are decreasing costs by more than 10%, and all markets are down 30%.

If you know what you're doing and analyze the markets correctly, this is your chance to become a millionaire.

In any recession, there are always excellent possibilities to seize. Real estate, crypto, stocks, enterprises, etc.

What you do with your money could influence your future riches.

This article analyzes the three key markets, their circumstances for 2023, and how to profit from them.

Ways to make money on the stock market.

If you're conservative like me, you should invest in an index fund. Most of these funds are down 10-30% of ATH:

In earlier recessions, most money index funds lost 20%. After this downturn, they grew and passed the ATH in subsequent months.

Now is the greatest moment to invest in index funds to grow your money in a low-risk approach and make 20%.

If you want to be risky but wise, pick companies that will get better next year but are struggling now.

Even while we can't be 100% confident of a company's future performance, we know some are strong and will have a fantastic year.

Microsoft (down 22%), JPMorgan Chase (15.6%), Amazon (45%), and Disney (33.8%).

These firms give dividends, so you can earn passively while you wait.

So I consider that a good strategy to make wealth in the current stock market is to create two portfolios: one based on index funds to earn 10% to 20% profit when the corrections end, and the other based on individual stocks of popular and strong companies to earn 20%-30% return and dividends while you wait.

How to profit from the downturn in the real estate industry.

With rising mortgage rates, it's the worst moment to buy a home if you don't want to be eaten by banks. In the U.S., interest rates are double what they were three years ago, so buying now looks foolish.

Due to these rates, property prices are falling, but that won't last long since individuals will take advantage.

According to historical data, now is the ideal moment to buy a house for the next five years and perhaps forever.

If you can buy a house, do it. You can refinance the interest at a lower rate with acceptable credit, but not the house price.

Take advantage of the housing market prices now because you won't find a decent deal when rates normalize.

How to profit from the cryptocurrency market.

This is the riskiest market to tackle right now, but it could offer the most opportunities if done appropriately.

The most powerful cryptocurrencies are down more than 60% from last year: $68,990 for BTC and $4,865 for ETH.

If you focus on those two coins, you can make 30%-60% without waiting for them to return to their ATH, and they're low enough to be a solid investment.

I don't encourage trying other altcoins because the crypto market is in crisis and you can lose everything if you're greedy.

Still, the main Cryptos are a good investment provided you store them in an external wallet and follow financial gurus' security advice.

Last thoughts

We can't anticipate a recession until it ends. We can't forecast a market or asset's lowest point, therefore waiting makes little sense.

If you want to develop your wealth, assess the money prospects on all the marketplaces and initiate long-term trades.

Many millionaires are made during recessions because they don't fear negative figures and use them to scale their money.

Don't know how to start an online business? Here's a guide. By following these recommendations, you can build a lucrative and profitable online business.

What Are Online Businesses Used For?

Most online businesses are websites. A self-created, self-managed website. You may sell things and services online.

To establish an internet business, you must locate a host and set up accounts with numerous companies. Once your accounts are set up, you may start publishing content and selling products or services.

How to Make Money from Your Online Business

Advertising and marketing are the best ways to make money online. You must develop strategies to contact new customers and generate leads. Make sure your website is search engine optimized so people can find you online.

Top 5 Online Business Tips for Startups:

1. Know your target audience's needs.

2. Make your website as appealing as possible.

3. Generate leads and sales with marketing.

4. Track your progress and learn from your mistakes to improve.

5. Be prepared to expand into new markets or regions.

How to Launch a Successful Online Business Without Putting in a Lot of Work

Build with a solid business model to start a profitable online business. By using these tips, you can start your online business without paying much.

First, develop a user-friendly website. You can use an internet marketing platform or create your own website. Once your website is live, optimize it for search engines and add relevant content.

Second, sell online. This can be done through ads or direct sales to website visitors. Finally, use social media to advertise your internet business. By accomplishing these things, you'll draw visitors to your website and make money.

When launching a business, invest long-term. This involves knowing your goals and how you'll pay for them. Volatility can have several effects on your business. If you offer things online, you may need to examine if the market is ready for them.

Invest wisely

Investing all your money in one endeavor can lead to too much risk and little ROI. Diversify your investments to take advantage of all available chances. So, your investments won't encounter unexpected price swings and you'll be immune to economic upheavals.

Financial news updates

When launching or running a thriving online business, financial news is crucial. By knowing current trends and upcoming developments, you can keep your business lucrative.

Keeping up with financial news can also help you avoid potential traps that could harm your bottom line. If you don't know about new legislation that could affect your industry, potential customers may choose another store when they learn about your business's problems.

Volatility ahead

You should expect volatility in the financial sector. Without a plan for coping with volatility, you could run into difficulty. If your organization relies on client input, you may not be able to exploit customer behavior shifts.

Your company could go bankrupt if you don't understand how fickle the stock market can be. By preparing for volatility, you can ensure your organization survives difficult times and market crashes.

Conclusion

Many internet businesses can be profitable. Start quickly with a few straightforward steps. Diversify your investments, follow financial news, and be prepared for volatility to develop a successful business.

Thanks for reading!

Ali's $1,054,053.74 YouTube Adsense haul.

YouTuber, entrepreneur, and former doctor Ali Abdaal. He began filming productivity and financial videos in 2017. Ali Abdaal has 3 million YouTube subscribers and has crossed $1 million in AdSense revenue. Crazy, no?

Ali will share the revenue of his top 5 youtube videos, things he's learned that you can apply to your side hustle, and how many views it takes to make a livelihood off youtube.

First, "The Long Game."

All good things take time to bear fruit. Compounding improves everything. Long-term work yields better returns. Ali made his first dollar after nine months and 85 videos.

Second, "One piece of content can transform your life, but you never know which one."

Had he abandoned YouTube at 84 videos without making any money, he wouldn't have filmed the 85th video that altered everything.

Third Lesson: Your Industry Choice Can Multiply.

The industry or niche you target as a business owner or side hustler can have a major impact on how much money you make.

Here are the top 5 videos.

1) 9.8m views: $191,258.16 for 9 passive income ideas

Ali made 2 points.

We should consider YouTube videos digital assets. They're investments, which make us money. His investments are yielding passive income.

Investing extra time and effort in your films can pay off.

2) How to Invest for Beginners — 5.2m Views: $87,200.08.

This video did poorly in the first several weeks after it was published; it was his tenth poorest performer. Don't worry about things you can't control. This applies to life, not just YouTube videos.

He stated we constantly have anxieties, fears, and concerns about things outside our control, but if we can find that line, life is easier and more pleasurable.

3) How to Build a Website in 2022— 866.3k views: $42,132.72.

The RPM was $48.86 per thousand views, making it his highest-earning video. Squarespace, Wix, and other website builders are trying to put ads on it and competing against one other, so ad rates go up.

Because it was beyond his niche, Ali almost didn't make the video. He made the video because he wanted to help at least one person.

4) How I take notes on my iPad in medical school — 5.9m views: $24,479.80

85th video. It's the video that affected Ali's YouTube channel and his life the most. The video's success wasn't certain.

5) How I Type Fast 156 Words Per Minute — 8.2M views: $25,143.17

Ali didn't know this video would perform well; he made it because he can type fast and has been practicing for 10 years. So he made a video with his best advice.

How many views to different wealth levels?

It depends on geography, niche, and other monetization sources. To keep things simple, he would solely utilize AdSense.

How many views to generate money?

To generate money on Youtube, you need 1,000 subscribers and 4,000 hours of view time. How much work do you need to make pocket money?

Ali's first 1,000 subscribers took 52 videos and 6 months. The typical channel with 1,000 subscribers contains 152 videos, according to Tubebuddy. It's time-consuming.

After monetizing, you'll need 15,000 views/month to make $5-$10/day.

How many views to go part-time?

Say you make $35,000/year at your day job. If you work 5 days/week, you make $7,000/year each day. If you want to drop down from 5 days to 4 days/week, you need to make an extra $7,000/year from YouTube, or $600/month.

What's the quit-your-job budget?

Silicon Valley Girl is in a highly successful niche targeting tech-focused folks in the west. When her channel had 500k views/month, she made roughly $3,000/month or $47,000/year, enough to quit your work.

Marina has another 1.5m subscriber channel in Russia, which has a lower rpm because fewer corporations advertise there than in the west. 2.3 million views/month is $4,000/month or $50,000/year, enough to quit your employment.

Marina is an intriguing example because she has three YouTube channels with the same skills, but one is 16x more profitable due to the niche she chose.

In Ali's case, he made 100+ videos when his channel was producing enough money to quit his job, roughly $4,000/month.

How many views make you rich?

Depending on how you define rich. Ali felt prosperous with over $100,000/year and 3–5m views/month.

Conclusion

YouTubers and artists don't treat their work like a company, which is a mistake. Businesses have been attempting to figure this out for decades, if not centuries.

We can learn from the business world how to monetize YouTube, Instagram, and Tiktok and make them into sustainable enterprises where we can hire people and delegate tasks.

Bonus

Watch Ali's video explaining all this:

This post is a summary. Read the full article here

It's ok and even desired to be unprofitable if you're gaining revenue at a reasonable cost and have 100%+ net dollar retention, meaning you never lose customers and expand them. To estimate the acceptable cost of new SaaS revenue, we compare new revenue to operating loss and payback period. If you pay back the customer acquisition cost in 1.5 years and never lose them (100%+ NDR), you're doing well.

To evaluate payback period, we compared new revenue to net operating loss for the last 73 SaaS companies to IPO since October 2017. (55 out of 73). Here's the data. 1/(new revenue/operating loss) equals payback period. New revenue/operating loss equals cost of new revenue.

Payback averages a year. 55 SaaS companies that weren't profitable at IPO got a 1-year payback. Outstanding. If you pay for a customer in a year and never lose them (100%+ NDR), you're establishing a valuable business. The average was 1.3 years, which is within the 1.5-year range.

New revenue costs $0.96 on average. These SaaS companies lost $0.96 every $1 of new revenue last year. Again, impressive. Average new revenue per operating loss was $1.59.

Loss-in-operations definition. Operating loss revenue COGS S&M R&D G&A (technical point: be sure to use the absolute value of operating loss). It's wrong to only consider S&M costs and ignore other business costs. Operating loss and new revenue are measured over one year to eliminate seasonality.

Operating losses are desirable if you never lose a customer and have a quick payback period, especially when SaaS enterprises are valued on ARR. The payback period should be under 1.5 years, the cost of new income < $1, and net dollar retention 100%.