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Abstract

o lower. One should always remember that this indicator is very lagging and therefore has to be used with extreme caution.Since it has been created by Wilder Wiles, also the creator of the Relative Strength Index, it uses Wilder’s own type of moving average, the smoothed kind. To simplify things, the smoothed moving average can be found through a simple transformation of the exponential moving average.<figure id="ef99"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/0*kcMsTOLJ3uJyUeN_.png"><figcaption></figcaption></figure>The above formula means that a 100 smoothed moving average is the same thing as (100 x 2) -1 = 199 exponential moving average. While we are on that, we can code the exponential moving average using this function:<div id="b794"><pre>def ma(Data, lookback, what, where):

<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-built_in">len</span>(Data)):
  <span class="hljs-keyword">try</span>:
    Data[i, where] = (Data[i - lookback + <span class="hljs-number">1</span>:i + <span class="hljs-number">1</span>, what].mean())
    
        <span class="hljs-keyword">except</span> IndexError:
            <span class="hljs-keyword">pass</span>
<span class="hljs-keyword">return</span> Data</pre></div><div id="7a41"><pre>def ema(Data, alpha, lookback, what, <span class="hljs-built_in">where</span>):

<span class="hljs-comment"># alpha is the smoothing factor</span>
<span class="hljs-comment"># window is the lookback period</span>
<span class="hljs-comment"># what is the column that needs to have its average calculated</span>
<span class="hljs-comment"># where is where to put the exponential moving average</span>

alpha = alpha / (lookback + 1.0)
beta  = 1 - alpha

<span class="hljs-comment"># First value is a simple SMA</span>
Data = ma(Data, lookback, what, <span class="hljs-built_in">where</span>)

<span class="hljs-comment"># Calculating first EMA</span>
Data[lookback + 1, <span class="hljs-built_in">where</span>] = (Data[lookback + 1, what] * alpha) + (Data[lookback, <span class="hljs-built_in">where</span>] * beta)</pre></div><div id="3da7"><pre><span class="hljs-comment"># Calculating the rest of EMA</span>
<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(lookback + <span class="hljs-number">2</span>, <span class="hljs-built_in">len</span>(Data)):
        <span class="hljs-keyword">try</span>:
            Data[i, where] = (Data[i, what] * alpha) + (Data[i - <span class="hljs-number">1</span>, where] * beta)
    
        <span class="hljs-keyword">except</span> IndexError:
            <span class="hljs-keyword">pass</span>
<span class="hljs-keyword">return</span> Data</pre></div><p id="8783">Below is the function code that calculates the ATR.</p><div id="b80a"><pre>def atr(<span class="hljs-keyword">data</span>, lookback, high, low, close, <span class="hljs-keyword">where</span>):

<span class="hljs-keyword">data</span> = adder(<span class="hljs-keyword">data</span>, <span class="hljs-number">1</span>)
  
<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> range(len(<span class="hljs-keyword">data</span>)):
    
    <span class="hljs-keyword">try</span>:
        
        <span class="hljs-keyword">data</span>[i, <span class="hljs-keyword">where</span>] = max(<span class="hljs-keyword">data</span>[i, high] - <span class="hljs-keyword">data</span>[i, low], abs(<span class="hljs-keyword">data</span>[i, high] - <span class="hljs-keyword">data</span>[i - <span class="hljs-number">1</span>, close]), abs(<span class="hljs-keyword">data</span>[i, low]  - <span class="hljs-keyword">data</span>[i - <span class="hljs-number">1</span>, close]))
        
    except ValueError:
        
        pass
    
<span class="hljs-keyword">data</span>[<span class="hljs-number">0</span>, <span class="hljs-keyword">where</span>] = <span class="hljs-number">0</span>   
  
<span class="hljs-keyword">data</span> = ema(<span class="hljs-keyword">data</span>, <span class="hljs-number">2</span>, (lookback * <span class="hljs-number">2</span>) - <span class="hljs-number">1</span>, <span class="hljs-keyword">where</span>, <span class="hljs-keyword">where</span> + <span class="hljs-number">1</span>)</pre></div><div id="e149"><pre><span class="hljs-keyword">data</span> = deleter(<span class="hljs-keyword">data</span>, <span class="hljs-keyword">where</span>, <span class="hljs-number">1</span>)

<span class="hljs-keyword">data</span> = jump(<span class="hljs-keyword">data</span>, lookback)

<span class="hljs-keyword">return</span> <span class="hljs-keyword">data</span></pre></div><p id="3950">Let us try applying the code on OHLC data and see the plot of a 14-period Average True Range:</p><div id="da61"><pre><span class="hljs-attr">my_data</span> = atr(my_data, <span class="hljs-number">14</span>, <span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">4</span>)</pre></div><figure id="8f6c"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/0*AI40oZyZ11lSAGcg.png"><figcaption><b>EURUSD daily values in the first panel with the 14-period Average True Range in the second panel.</b></figcaption></figure><p id="0934">Now we are ready to continue with the choppiness index. Let us consider that we will calculate a 20-period Average True Range on our OHLC historical data:</p><div id="0afd"><pre><span class="hljs-comment"># Adding a few columns</span>

my_data = adder(my_data, 10)</pre></div><div id="4e56"><pre># Calculating a 20-period ATR my_data = atr(my_data, 20, 1, 2, 3, 4)</pre></div>Now, the first step into the indicator is, to sum up, the values of the ATR together. This intuition can be coded in the following manner (Full function code provided below):<div id="d525"><pre># Calculating the Sum of ATR's (atr_col is the index where the ATR is stored, in our example, it is 4) for i in range(len(my_data)):</pre></div><div id="8d39"><pre>my_data[i, where] = my_data[i - lookback + 1:i + 1, atr_col].sum()</pre></div>Now, we have to calculate the range from the highest to lowest using the max() and min() built-in functions. The code should resemble the following:<div id="4e6e"><pre># Calculating the range for i in ran

Options

ge(len(my_data)): try: my_data[i, 5] = max(my_data[i - lookback + 1:i + 1, 1] - min(my_data[i - lookback + 1:i + 1, 2])) except: pass</pre></div>Next, we calculate the ratio between the two measures we have just derived. The code can be as simple as:<div id="6fde"><pre># Calculating the Ratio my_data[:, 6] = my_data[:, 4] / my_data[:, 5]</pre></div>And finally, we simply apply the function as presented above, using this code:<div id="2f0c"><pre># Calculate the Choppiness Index for i in range(len(Data)):

Data[i, <span class="hljs-number">7</span>] =</span> <span class="hljs-number">100</span> * np.<span class="hljs-built_in">log</span>(Data[i, <span class="hljs-number">6</span>]) * (<span class="hljs-number">1</span> / np.<span class="hljs-built_in">log</span>(<span class="hljs-number">20</span>))</pre></div><figure id="84eb"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/0*Pdavt5otr708my32.png"><figcaption><b>EURUSD in the first panel with the 20-period Choppiness Index in the second panel.</b></figcaption></figure><div id="a88b"><pre><span class="hljs-keyword">def</span> <span class="hljs-title function_">choppiness_index</span>(<span class="hljs-params">Data, lookback, high, low, where</span>):

<span class="hljs-comment"># Calculating the Sum of ATR's</span>
<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-built_in">len</span>(Data)):
    Data[i, where] = Data[i - lookback + <span class="hljs-number">1</span>:i + <span class="hljs-number">1</span>, <span class="hljs-number">4</span>].<span class="hljs-built_in">sum</span>()    

<span class="hljs-comment"># Calculating the range</span>
<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-built_in">len</span>(Data)):
    <span class="hljs-keyword">try</span>:
        Data[i, where + <span class="hljs-number">1</span>] = <span class="hljs-built_in">max</span>(Data[i - lookback + <span class="hljs-number">1</span>:i + <span class="hljs-number">1</span>, <span class="hljs-number">1</span>] - <span class="hljs-built_in">min</span>(Data[i - lookback + <span class="hljs-number">1</span>:i + <span class="hljs-number">1</span>, <span class="hljs-number">2</span>]))
    <span class="hljs-keyword">except</span>:
        <span class="hljs-keyword">pass</span></pre></div><div id="26e1"><pre><span class="hljs-comment"># Calculating the Ratio</span>
Data[:, <span class="hljs-built_in">where</span> + 2] = Data[:, <span class="hljs-built_in">where</span>] / Data[:, <span class="hljs-built_in">where</span> + 1]

<span class="hljs-comment"># Calculate the Choppiness Index</span>
<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> range(len(Data)):
    
    Data[i, <span class="hljs-built_in">where</span> + 3] = 100 * np.log(Data[i, <span class="hljs-built_in">where</span> + 2]) * (1 / np.log(lookback))</pre></div><div id="afbd"><pre><span class="hljs-comment"># Cleaning</span>
Data = deleter(Data, 5, 3)

<span class="hljs-built_in">return</span> Data</pre></div><figure id="0506"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/0*Pus4gyPeLlz_vLbr.png"><figcaption><b>USDCHF in the first panel with the 20-period Choppiness Index in the second panel.</b></figcaption></figure><p id="a466">The choppiness index function can therefore be called using the following code:</p><div id="c8a9"><pre><span class="hljs-attr">my_data</span> = choppiness_index(my_data, <span class="hljs-number">20</span>, <span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">4</span>)</pre></div><figure id="4ed9"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/0*_oe4B9oeIE-hr1Yz.png"><figcaption><b>GBPUSD in the first panel with the 20-period Choppiness Index in the second panel.</b></figcaption></figure><h1 id="f15a">Using the Choppiness Index</h1><p id="549d">The way to use the choppiness index is to place the barriers by default at 38.2% and 61.8%, then we interpret the readings as follow:</p><ul><li><b>Readings above 61.8% indicate a choppy market that is bound to breakout. We should be ready for some directional.</b></li><li><b>Readings below 38.2% indicate a strong trending market that is bound to stabilize. Hence, it may not be the best idea to follow the trend at the moment.</b></li></ul><figure id="7bc6"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/0*MA4E4UosNty1IRHD.png"><figcaption><b>EURCAD in the first panel with the 20-period Choppiness Index in the second panel.</b></figcaption></figure><p id="3618">If you want to see how to create all sorts of algorithms yourself, feel free to check out Lumiwealth. From algorithmic trading to blockchain and machine learning, they have <i>hands-on</i> detailed courses that I highly recommend.</p><div id="fead" class="link-block">
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    </div><h1 id="e420">Summary</h1><p id="05a4">To sum up, what I am trying to do is to simply contribute to the world of objective technical analysis which is promoting more transparent techniques and strategies that need to be back-tested before being implemented. This way, technical analysis will get rid of the bad reputation of being subjective and scientifically unfounded.</p><p id="5d41">I recommend you always follow the the below steps whenever you come across a trading technique or strategy:</p><ul><li><i>Have a critical mindset and get rid of any emotions.</i></li><li><i>Back-test it using real life simulation and conditions.</i></li><li><i>If you find potential, try optimizing it and running a forward test.</i></li><li><i>Always include transaction costs and any slippage simulation in your tests.</i></li><li><i>Always include risk management and position sizing in your tests.</i></li></ul><p id="beb3">Finally, even after making sure of the above, stay careful and monitor the strategy because market dynamics may shift and make the strategy unprofitable.</p></article></body>

How I Learnt About Cryptocurrencies and Built One in 2 Months

#9 — Ninth month of my challenge: 12 topics to learn in 12 months

This is actually how a running blockchain looks like!

Blockchain, Cryptocurrency, Bitcoin… those are 3 buzz words we keep hearing about 😬. In order to understand why are these words so trending, I decided to dig into them and learn about this topic.

I already knew the basics of the concepts of blockchain and cryptocurrencies. I even learnt about how Cryptocurrency trading works but I still had a lot of questions unanswered: how are cryptocurrencies decentralized? what is the purpose of mining? what are the alternatives? how does the mining puzzle work? how is the security ensured? how can you create a currency?

Creating a currency feels like creating a website … 15 years ago

In order to answer all these questions, I needed to take my time to learn about cryptocurrencies and blockchain 🚀.

This learning is part of my learning challenge, I started it the 25th of January right after learning about Cryptocurrency Trading.

Preparation

Finding a mentor I didn’t have a dedicated mentor to learn about cryptocurrencies. Nevertheless, a few people helped me solve some of the issues I encountered and I want to thanks them: Xaxuke (from the Pesetacoin community), Unmesh (a friend), the Peercoin community (via the forum), the Jelurida community ,and all the people who helped me compile in mac since I don’t have one (Patricia, Etienne, and Farbod).
Defining the scope of the topic Understanding how cryptocurrencies work, how mining work, what are the different consensus, and how to build one.
Choosing a learning resource I used many resources, here are the main ones: - Bitcoin and Cryptocurrency Technologies —Coursera MOOC from Princeton University — https://www.coursera.org/learn/cryptocurrency - Naivecoin Tutorial — https://lhartikk.github.io/ - Bitcoin Whitepaper —https://bitcoin.org/bitcoin.pdf - Peercoin Whitepaper — https://peercoin.net/whitepaper - Peershares Whitepaper — http://wiki.peercointalk.org/index.php?title=Peershares_whitepaper - IOTA Whitepaper — https://iota.org/IOTA_Whitepaper.pdf - ByteBall Whitepaper — https://byteball.org/Byteball.pdf - Nxt Whitepaper —https://nxtwiki.org/wiki/Whitepaper:Nxt
Defining a project My initial project was to build a Proof of Stake Cryptocurrency with its own blockchain, but after starting I decided to also create a Proof of Stake coin generator.

Because this article would have been too long, I decided to cut it into 6 smaller articles! You can read them in any order! I hope you like them 😃.

Why Cryptocurrencies Matter?

Let’s talk about the specifics of cryptocurrencies. What makes them special, and why it’s more than just buzzwords. If you don’t know what a cryptocurrency is you should definitely read it!

Why Cryptocurrencies Matter? - Sandoche ADITTANE - Medium

What are cryptocurrencies and what make them so special

medium.com

How Cryptocurrencies Work (Technical Guide)

Wonder what is happening when you transfer your cryptocurrency? Or why some cryptocurrencies need mining? how the mining works? or what are the current limitations of cryptocurrencies? Read this article, you will have all these questions answered and more!

How Cryptocurrencies Work (Technical Guide) - Sandoche ADITTANE - Medium

Blockchain, Cryptography and Concensuses explained.

medium.com

Proof of What? Understand the Distributed Consensuses in Blockchain

In order to runs cryptocurrencies need nodes to run. Since these nodes are distributed all around the world they need to agree on what transactions are valid and what are the next blocks. Therefore they need a consensus to do so. In this article I describe the different existing consensuses with their pros and cons. Also you can see that you don’t necersarly need to waste energy with mining!

Proof of What? Understand the Distributed Consensuses in Blockchain

Proof of Work? Proof of Stake? Delegated Proof of Stake? An overview of the different distributed consensuses…

medium.com

How to Make Money with Cryptocurrencies?

Cryptocurrency rhymes with money, see what are the different ways to make money with cryptocurrencies 💸🤑.

How to Make Money with Cryptocurrencies? - Sandoche ADITTANE - Medium

18 ways to make money with cryptocurrencies

medium.com

How I Integrated Blockchain to My Existing Productivity App (Kanbanote)

I wanted to experiment by creating my own cryptocurrency with its own blockchain and to implement it to an existing product. This is how I created Motive, a coin given as a reward for doing your own tasks in Kanbanote (a productivity web app).

How I Integrated Blockchain to My Existing Productivity App (Kanbanote)

How I created a cryptocurrency that rewards you for doing your own tasks

medium.com

How to build a Proof of Stake coin in a few minutes?

Want to create your own energy efficient cryptocurrency with its own blockchain? Follow this tutorial!

A step by step guide to build a Proof of Stake cryptocurrency with its own blockchain

How to Build Your Own Cryptocurrency (PoS) in Just a Few Minutes

Do you want to create the future of money? Do you want to build a local currency for your city?

medium.com

The final results

Here are the projects I built during this learning challenge about Cryptocurrencies & Blockchain 🙌🚀.

Motive Cryptocurrency

As you could read here, I built Motive, a coin given as a reward when you accomplish a task in Kanbanote.

I invite you to check out its website: https://motive.network Feel free to stake it or to go to https://www.kanbanote.com and set up your wallet to earn some MOTIVs.

CoinGenerator

As you could read here, I built CoinGenerator, a tool to generate a Proof of Stake Cryptocurrency with its own blockchain.

I invite you to check out its website: https://coingenerator.sh

Feedback from this learning month

What went well

Learning from Coursera was very good, and I validated my assignments (you can find them on my Github)
I understand how blockchain & bitcoin works
I understand how some of the Proof of Work alternatives work
I created Motive, a Proof of Stake Cryptocurrency with its own blockchain, integrated in Kanbanote
I created CoinGenerator, a Proof of Stake Cryptocurrency generator
I learnt a new style of design (cf. Motive’s website, and CoinGenerator’s website)
I learnt how to create an interactive CLI generator
I learnt how electron works (when I first tried to build a wallet for my cryptocurrency)
I combined some of my previous learning: colors (by using the color palette I did), Webpack (by using the Jekyll-Webpack-Boilerplate), and creativity (by using the process of finding inspiration)

What to improve

My Java skills, I was losing some time in the Coursera assignments with some Java syntax problems. I am a Web developer using Javascript, PHP, HTML, CSS, life is a lot easier with those languages
My C++ skills, I struggled a lot trying to compile some cryptocurrencies such as Peercoin, I just talk about compiling not even reading or editing the code
I finished my learning the 5th of April it took me 70 days!
I kept adding tasks for the projects: like the generator, then building a website for the generator,…

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