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<!DOCTYPE html>
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<title>Noeyislearning · Data Science - Global Stock Price Archive</title>
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<h2 id="Global-Stock-Price">Global Stock Price Archive<a class="anchor-link" href="#Global-Stock-Price">¶</a></h2><p>Conducts a comprehensive analysis of a dataset containing global stock price information. The dataset includes data related to various stock indices, companies, and their stock price details such as opening, highest, lowest, closing prices, adjusted closing prices, and traded shares. The dataset is structured around key attributes, including company name, stock ticker symbols, currency, and exchange markets.</p>
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<h3 id="Import-Libraries">Import Libraries<a class="anchor-link" href="#Import-Libraries">¶</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="kn">from</span> <span class="nn">statsmodels.tsa.seasonal</span> <span class="kn">import</span> <span class="n">seasonal_decompose</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">pearsonr</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
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<h2 id="Global-Options">Global Options<a class="anchor-link" href="#Global-Options">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">style</span><span class="o">=</span><span class="s1">'darkgrid'</span><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="s1">'Set2'</span><span class="p">)</span>
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<h2 id="Data-Exploration">Data Exploration<a class="anchor-link" href="#Data-Exploration">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Import the dataset</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'assets/data/All_Stock_Prices.csv'</span><span class="p">)</span>
<span class="c1"># Explore the dataset</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">200</span><span class="p">)</span>
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<style scoped>
.dataframe tbody tr th:only-of-type {
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.dataframe tbody tr th {
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.dataframe thead th {
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>company_public</th>
<th>stock_ticker</th>
<th>yahoo_finance_ticker</th>
<th>currency</th>
<th>exchange</th>
<th>opening_price</th>
<th>highest_price</th>
<th>lowest_price</th>
<th>closing_price</th>
<th>adjusted_closing_price</th>
<th>traded_shares</th>
<th>company</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>AEX-Index</td>
<td>^AEX</td>
<td>^AEX</td>
<td>EUR</td>
<td>AMS</td>
<td>730.65</td>
<td>731.91</td>
<td>721.29</td>
<td>724.18</td>
<td>724.18</td>
<td>43000</td>
<td>NaN</td>
</tr>
<tr>
<th>1</th>
<td>Austrian Traded Index in EUR</td>
<td>^ATX</td>
<td>^ATX</td>
<td>EUR</td>
<td>VIE</td>
<td>3170.08</td>
<td>3191.83</td>
<td>3118.64</td>
<td>3128.03</td>
<td>3128.03</td>
<td>3145900</td>
<td>NaN</td>
</tr>
<tr>
<th>2</th>
<td>S&P/ASX ALL AUSTRALIAN 50</td>
<td>^AXAF</td>
<td>^AXAF</td>
<td>AUD</td>
<td>ASX</td>
<td>7160.40</td>
<td>7160.40</td>
<td>7118.60</td>
<td>7129.60</td>
<td>7129.60</td>
<td>0</td>
<td>NaN</td>
</tr>
<tr>
<th>3</th>
<td>S&P/ASX ALL AUSTRALIAN 200</td>
<td>^AXAT</td>
<td>^AXAT</td>
<td>AUD</td>
<td>ASX</td>
<td>7046.60</td>
<td>7049.90</td>
<td>7013.10</td>
<td>7032.00</td>
<td>7032.00</td>
<td>483100</td>
<td>NaN</td>
</tr>
<tr>
<th>4</th>
<td>S&P/ASX 200 Cons Disc</td>
<td>^AXDJ</td>
<td>^AXDJ</td>
<td>AUD</td>
<td>ASX</td>
<td>3062.90</td>
<td>3068.40</td>
<td>3034.40</td>
<td>3035.20</td>
<td>3035.20</td>
<td>27580900</td>
<td>NaN</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>195</th>
<td>Livzon Pharmaceutical Group</td>
<td>000513</td>
<td>000513.SZ</td>
<td>CNY</td>
<td>SHZ</td>
<td>36.85</td>
<td>37.37</td>
<td>36.70</td>
<td>36.94</td>
<td>36.94</td>
<td>4454452</td>
<td>NaN</td>
</tr>
<tr>
<th>196</th>
<td>Chongqing Yukaifa</td>
<td>000514</td>
<td>000514.SZ</td>
<td>CNY</td>
<td>SHZ</td>
<td>4.49</td>
<td>4.58</td>
<td>4.46</td>
<td>4.55</td>
<td>4.55</td>
<td>18185312</td>
<td>NaN</td>
</tr>
<tr>
<th>197</th>
<td>Xi'an International Medical Investment Company</td>
<td>000516</td>
<td>000516.SZ</td>
<td>CNY</td>
<td>SHZ</td>
<td>8.01</td>
<td>8.18</td>
<td>7.95</td>
<td>7.99</td>
<td>7.99</td>
<td>29403871</td>
<td>NaN</td>
</tr>
<tr>
<th>198</th>
<td>Rongan Property</td>
<td>000517</td>
<td>000517.SZ</td>
<td>CNY</td>
<td>SHZ</td>
<td>2.86</td>
<td>2.88</td>
<td>2.85</td>
<td>2.86</td>
<td>2.86</td>
<td>11079300</td>
<td>NaN</td>
</tr>
<tr>
<th>199</th>
<td>Jiangsu Sihuan Bioengineering</td>
<td>000518</td>
<td>000518.SZ</td>
<td>CNY</td>
<td>SHZ</td>
<td>2.89</td>
<td>2.90</td>
<td>2.85</td>
<td>2.86</td>
<td>2.86</td>
<td>9264100</td>
<td>NaN</td>
</tr>
</tbody>
</table>
<p>200 rows × 12 columns</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Check for missing values</span>
<span class="n">df</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
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<pre>company_public 7
stock_ticker 1
yahoo_finance_ticker 0
currency 7
exchange 7
opening_price 0
highest_price 0
lowest_price 0
closing_price 0
adjusted_closing_price 128
traded_shares 0
company 43497
dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Check for duplicates</span>
<span class="n">df</span><span class="o">.</span><span class="n">duplicated</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
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<pre>0</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Check for outliers</span>
<span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
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<table border="1" class="dataframe">
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<tr style="text-align: right;">
<th></th>
<th>opening_price</th>
<th>highest_price</th>
<th>lowest_price</th>
<th>closing_price</th>
<th>adjusted_closing_price</th>
<th>traded_shares</th>
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<tr>
<th>count</th>
<td>4.572900e+04</td>
<td>4.572900e+04</td>
<td>4.572900e+04</td>
<td>4.572900e+04</td>
<td>4.560100e+04</td>
<td>4.572900e+04</td>
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<th>mean</th>
<td>2.890110e+06</td>
<td>2.891975e+06</td>
<td>2.877317e+06</td>
<td>2.877339e+06</td>
<td>2.885415e+06</td>
<td>2.379324e+06</td>
</tr>
<tr>
<th>std</th>
<td>3.875226e+08</td>
<td>3.877759e+08</td>
<td>3.856975e+08</td>
<td>3.856975e+08</td>
<td>3.862384e+08</td>
<td>2.992968e+07</td>
</tr>
<tr>
<th>min</th>
<td>0.000000e+00</td>
<td>0.000000e+00</td>
<td>0.000000e+00</td>
<td>0.000000e+00</td>
<td>0.000000e+00</td>
<td>0.000000e+00</td>
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<tr>
<th>25%</th>
<td>1.320000e+00</td>
<td>1.340000e+00</td>
<td>1.300000e+00</td>
<td>1.590000e+00</td>
<td>1.600000e+00</td>
<td>0.000000e+00</td>
</tr>
<tr>
<th>50%</th>
<td>1.416000e+01</td>
<td>1.427000e+01</td>
<td>1.400000e+01</td>
<td>1.481000e+01</td>
<td>1.481000e+01</td>
<td>1.292400e+04</td>
</tr>
<tr>
<th>75%</th>
<td>8.066000e+01</td>
<td>8.110000e+01</td>
<td>7.970000e+01</td>
<td>8.289000e+01</td>
<td>8.265000e+01</td>
<td>3.101090e+05</td>
</tr>
<tr>
<th>max</th>
<td>5.972360e+10</td>
<td>5.972360e+10</td>
<td>5.972360e+10</td>
<td>5.972360e+10</td>
<td>5.972360e+10</td>
<td>4.002354e+09</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Check for data types</span>
<span class="n">df</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
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<pre><class 'pandas.core.frame.DataFrame'>
RangeIndex: 45729 entries, 0 to 45728
Data columns (total 12 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 company_public 45722 non-null object
1 stock_ticker 45728 non-null object
2 yahoo_finance_ticker 45729 non-null object
3 currency 45722 non-null object
4 exchange 45722 non-null object
5 opening_price 45729 non-null float64
6 highest_price 45729 non-null float64
7 lowest_price 45729 non-null float64
8 closing_price 45729 non-null float64
9 adjusted_closing_price 45601 non-null float64
10 traded_shares 45729 non-null int64
11 company 2232 non-null object
dtypes: float64(5), int64(1), object(6)
memory usage: 4.2+ MB
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<h2 id="Data-Cleaning">Data Cleaning<a class="anchor-link" href="#Data-Cleaning">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Drop duplicates</span>
<span class="n">df</span><span class="o">.</span><span class="n">drop_duplicates</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># Drop missing values</span>
<span class="n">df</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># Display the cleaned dataset</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">200</span><span class="p">)</span>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>company_public</th>
<th>stock_ticker</th>
<th>yahoo_finance_ticker</th>
<th>currency</th>
<th>exchange</th>
<th>opening_price</th>
<th>highest_price</th>
<th>lowest_price</th>
<th>closing_price</th>
<th>adjusted_closing_price</th>
<th>traded_shares</th>
<th>company</th>
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<th>14893</th>
<td>Argo Blockchain</td>
<td>ARB</td>
<td>ARB.L</td>
<td>GBP</td>
<td>LSE</td>
<td>8.1250</td>
<td>8.6700</td>
<td>8.0000</td>
<td>8.6700</td>
<td>8.6700</td>
<td>1836825</td>
<td>Argo Blockchain</td>
</tr>
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<th>14895</th>
<td>Arbuthnot Banking Group</td>
<td>ARBB</td>
<td>ARBB.L</td>
<td>GBP</td>
<td>LSE</td>
<td>964.7000</td>
<td>964.7000</td>
<td>964.7000</td>
<td>965.0000</td>
<td>965.0000</td>
<td>1000</td>
<td>Argo Blockchain</td>
</tr>
<tr>
<th>14903</th>
<td>Arcontech Group</td>
<td>ARC</td>
<td>ARC.L</td>
<td>GBP</td>
<td>LSE</td>
<td>98.0000</td>
<td>98.9000</td>
<td>96.0000</td>
<td>97.5000</td>
<td>97.5000</td>
<td>4450</td>
<td>Argo Blockchain</td>
</tr>
<tr>
<th>14916</th>
<td>Arc Minerals</td>
<td>ARCM</td>
<td>ARCM.L</td>
<td>GBP</td>
<td>LSE</td>
<td>2.6250</td>
<td>2.6800</td>
<td>2.3000</td>
<td>2.6500</td>
<td>2.6500</td>
<td>7347334</td>
<td>Argo Blockchain</td>
</tr>
<tr>
<th>14946</th>
<td>Argos Resources</td>
<td>ARG</td>
<td>ARG.L</td>
<td>GBP</td>
<td>LSE</td>
<td>0.0035</td>
<td>0.0035</td>
<td>0.0035</td>
<td>0.0035</td>
<td>0.0035</td>
<td>0</td>
<td>Argo Blockchain</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
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<tr>
<th>19266</th>
<td>Chamberlin</td>
<td>CMH</td>
<td>CMH.L</td>
<td>GBP</td>
<td>LSE</td>
<td>2.9000</td>
<td>2.8680</td>
<td>2.8500</td>
<td>2.9000</td>
<td>2.9000</td>
<td>13626</td>
<td>Capital Metals</td>
</tr>
<tr>
<th>19269</th>
<td>Invesco Bond Income Plus</td>
<td>CMHY</td>
<td>CMHY.L</td>
<td>GBP</td>
<td>LSE</td>
<td>165.5000</td>
<td>167.5000</td>
<td>165.5000</td>
<td>166.0000</td>
<td>166.0000</td>
<td>183537</td>
<td>Capital Metals</td>
</tr>
<tr>
<th>19277</th>
<td>CML Microsystems</td>
<td>CML</td>
<td>CML.L</td>
<td>GBP</td>
<td>LSE</td>
<td>435.0000</td>
<td>426.7000</td>
<td>425.8750</td>
<td>430.0000</td>
<td>430.0000</td>
<td>8292</td>
<td>Capital Metals</td>
</tr>
<tr>
<th>19292</th>
<td>Invesco Bloomberg Commodity UCITS ETF</td>
<td>CMOD</td>
<td>CMOD.L</td>
<td>USD</td>
<td>LSE</td>
<td>23.0750</td>
<td>23.0775</td>
<td>22.8200</td>
<td>22.8113</td>
<td>22.8113</td>
<td>7394</td>
<td>Capital Metals</td>
</tr>
<tr>
<th>19293</th>
<td>Invesco Bloomberg Commodity UCITS ETF</td>
<td>CMOP</td>
<td>CMOP.L</td>
<td>GBP</td>
<td>LSE</td>
<td>1887.5000</td>
<td>1892.7500</td>
<td>1880.5000</td>
<td>1880.5000</td>
<td>1880.5000</td>
<td>26341</td>
<td>Capital Metals</td>
</tr>
</tbody>
</table>
<p>200 rows × 12 columns</p>
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<h2 id="Descriptive-Statistics">Descriptive Statistics<a class="anchor-link" href="#Descriptive-Statistics">¶</a></h2><p>Present summary statistics for numeric columns (opening_price, highest_price, lowest_price, closing_price, adjusted_closing_price, traded_shares):</p>
<ul>
<li>Mean</li>
<li>Standard Deviation</li>
<li>Minimum</li>
<li>Maximum</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Calculate statistics</span>
<span class="n">numeric_columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'opening_price'</span><span class="p">,</span> <span class="s1">'highest_price'</span><span class="p">,</span> <span class="s1">'lowest_price'</span><span class="p">,</span> <span class="s1">'closing_price'</span><span class="p">,</span> <span class="s1">'adjusted_closing_price'</span><span class="p">,</span> <span class="s1">'traded_shares'</span><span class="p">]</span>
<span class="c1"># Calculate and display summary statistics</span>
<span class="n">summary_statistics</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">numeric_columns</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
<span class="c1"># Extract specific statistics </span>
<span class="n">summary_statistics</span><span class="o">.</span><span class="n">loc</span><span class="p">[[</span><span class="s1">'mean'</span><span class="p">,</span> <span class="s1">'std'</span><span class="p">,</span> <span class="s1">'min'</span><span class="p">,</span> <span class="s1">'max'</span><span class="p">]]</span>
<span class="c1"># Display the summary statistics</span>
<span class="n">summary_statistics</span>
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