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<h1>
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Computing the Mayer multiple
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</h1>
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<ol class="breadcrumb">
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<li>
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<time class="published" datetime="2018-12-16T08:51:00+01:00">
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16 décembre 2018
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</time>
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</li>
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<li>Cryptocurrencies</li>
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<li>Visualization</li>
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</ol>
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</header>
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<div class='article_content'>
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<p>I've learnt about the Mayer mutliple from <a href="https://www.theinvestorspodcast.com/bitcoin-mayer-multiple/">The Inverstor Podcast</a>. The Mayer multiple is the ratio of the bitcoin price divided by the 200-day moving average. It is designed to understand the price of bitcoin without taking in account the short term volatility. It helps investors filter out their emotions during a bull run.</p>
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<p>Let's see how to compute the Mayer mutliple in python.</p>
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<p>First, we need to import the data, we will use Quandl to download data from coinbase</p>
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<div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">quandl</span>
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<span class="n">btc_price_data</span> <span class="o">=</span> <span class="n">quandl</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"BCHARTS/COINBASEEUR"</span><span class="p">)</span>
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<span class="n">btc_price_data</span><span class="o">.</span><span class="n">tail</span><span class="p">()</span>
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</pre></div>
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<table border="1" class="dataframe">
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<thead>
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<tr style="text-align: right;">
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<th></th>
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<th>Open</th>
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<th>High</th>
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<th>Low</th>
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<th>Close</th>
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<th>Volume (BTC)</th>
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<th>Volume (Currency)</th>
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<th>Weighted Price</th>
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</tr>
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<tr>
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<th>Date</th>
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<th></th>
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<th></th>
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<th></th>
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<th></th>
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<th></th>
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<th></th>
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<th></th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th>2018-12-12</th>
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<td>2966.00</td>
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<td>3076.71</td>
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<td>2952.05</td>
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<td>3026.00</td>
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<td>1447.627465</td>
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<td>4.372890e+06</td>
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<td>3020.728514</td>
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</tr>
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<tr>
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<th>2018-12-13</th>
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<td>3025.19</td>
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<td>3028.06</td>
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<td>2861.15</td>
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<td>2886.91</td>
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<td>2125.242928</td>
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<td>6.261750e+06</td>
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<td>2946.369017</td>
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</tr>
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<tr>
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<th>2018-12-14</th>
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<td>2886.91</td>
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<td>2919.00</td>
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<td>2800.32</td>
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<td>2835.50</td>
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<td>2527.558347</td>
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<td>7.256959e+06</td>
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<td>2871.134083</td>
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</tr>
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<tr>
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<th>2018-12-15</th>
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<td>2835.49</td>
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<td>2865.00</td>
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<td>2781.47</td>
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<td>2830.45</td>
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<td>1267.004758</td>
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<td>3.568614e+06</td>
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<td>2816.575409</td>
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</tr>
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<tr>
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<th>2018-12-16</th>
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<td>2830.45</td>
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<td>2830.45</td>
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<td>2830.44</td>
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<td>2830.45</td>
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<td>0.144249</td>
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<td>4.082886e+02</td>
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<td>2830.447385</td>
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</tr>
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</tbody>
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</table>
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</div>
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<p>Next we need to compute the 200 days moving average of the price of bitcoin</p>
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<div class="highlight"><pre><span></span><span class="n">moving_averages</span> <span class="o">=</span> <span class="n">btc_price_data</span><span class="p">[[</span><span class="s2">"Open"</span> <span class="p">,</span><span class="s2">"High"</span> <span class="p">,</span><span class="s2">"Low"</span><span class="p">,</span><span class="s2">"Close"</span><span class="p">]]</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
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<span class="n">moving_averages</span><span class="o">.</span><span class="n">tail</span><span class="p">()</span>
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</pre></div>
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<table border="1" class="dataframe">
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<thead>
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<tr style="text-align: right;">
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<th></th>
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<th>Open</th>
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<th>High</th>
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<th>Low</th>
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<th>Close</th>
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</tr>
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<tr>
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<th>Date</th>
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<th></th>
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<th></th>
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<th></th>
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<th></th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th>2018-12-12</th>
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<td>5507.88295</td>
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<td>5611.72570</td>
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<td>5380.60150</td>
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<td>5491.43610</td>
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</tr>
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<tr>
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<th>2018-12-13</th>
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<td>5491.44155</td>
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<td>5595.14440</td>
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<td>5363.77840</td>
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<td>5474.38560</td>
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</tr>
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<tr>
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<th>2018-12-14</th>
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<td>5474.39910</td>
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<td>5577.89585</td>
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<td>5347.27500</td>
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<td>5457.99125</td>
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</tr>
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<tr>
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<th>2018-12-15</th>
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<td>5457.94930</td>
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<td>5559.49145</td>
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<td>5330.78235</td>
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<td>5439.75570</td>
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</tr>
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<tr>
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<th>2018-12-16</th>
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<td>5439.71660</td>
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<td>5540.89425</td>
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<td>5313.62955</td>
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<td>5422.17700</td>
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</tr>
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</tbody>
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</table>
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</div>
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<p>Finally, we can compute the ratio and plot it.</p>
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<div class="highlight"><pre><span></span><span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
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<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'savefig.dpi'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">300</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'figure.dpi'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">163</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'figure.autolayout'</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'figure.figsize'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">12</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'font.size'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">26</span>
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<span class="n">mayer_multiple</span> <span class="o">=</span> <span class="n">btc_price_data</span><span class="o">/</span><span class="n">moving_averages</span>
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<span class="n">mayer_multiple</span><span class="p">[</span><span class="s2">"High"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Mayer Mutliple over time"</span><span class="p">)</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Mayer Mutliple"</span><span class="p">)</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Time"</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Mayer multiple </span><span class="si">{</span><span class="n">mayer_multiple</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="s1">'High'</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Mayer multiple average </span><span class="si">{</span><span class="n">mayer_multiple</span><span class="o">.</span><span class="n">mean</span><span class="p">()[</span><span class="s1">'High'</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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</pre></div>
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<div class="highlight"><pre><span></span><span class="err">Mayer multiple 0.5108290958630005</span>
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<span class="err">Mayer multiple average 1.3789102045356179</span>
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</pre></div>
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<p><img alt="png" src="../images/mayer_multiple/output_5_1.png"></p>
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<p>Lastly, I wanted to plot the distribution of the Mayer multiple</p>
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<div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="n">x</span> <span class="o">=</span> <span class="n">mayer_multiple</span><span class="p">[</span><span class="s2">"High"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
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<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">x</span><span class="p">)]</span>
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<span class="n">n</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">patches</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">'green'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="mf">2.4</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"red"</span><span class="p">)</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="s1">'We are here today'</span><span class="p">,</span>
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<span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mayer_multiple</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="s2">"High"</span><span class="p">],</span>
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<span class="n">n</span><span class="p">[(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">bins</span><span class="o">-</span><span class="n">mayer_multiple</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="s2">"High"</span><span class="p">]))</span><span class="o">.</span><span class="n">argmin</span><span class="p">()]),</span>
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<span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="n">mayer_multiple</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="s2">"High"</span><span class="p">]</span><span class="o">*</span><span class="mi">3</span><span class="p">,</span><span class="n">n</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">/</span><span class="mi">2</span><span class="p">),</span>
|
|
<span class="n">arrowprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">facecolor</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">shrink</span><span class="o">=</span><span class="mf">0.05</span><span class="p">),</span>
|
|
<span class="p">)</span>
|
|
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Distribution of the Mayer mutliple"</span><span class="p">)</span>
|
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<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
|
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</pre></div>
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<div class="highlight"><pre><span></span><span class="err">[]</span>
|
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</pre></div>
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<p><img alt="png" src="../images/mayer_multiple/output_7_1.png"></p>
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<div class="bug-reporting__panel">
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<h3>Find an error or bug? Have a suggestion?</h3>
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<p>Everything on this site is avaliable on GitHub. Head on over and <a href='https://github.com/redoules/redoules.github.io/issues/new'>submit an issue.</a> You can also message me directly by <a href='mailto:guillaume.redoules@gadz.org'>email</a>.</p>
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Copyright © Guillaume Redoulès,
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<time datetime="2018">2018</time>.
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