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"\n",
"Table 4 – Augmented Dickey-Fuller\n"
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" <tbody>\n",
" <tr>\n",
" <th>Building construction copper</th>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>US copper production</th>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
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" <th>Copper mining cost</th>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Commodity speculation</th>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chile copper production</th>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COVID</th>\n",
" <td>0.7920</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Terrorist attack</th>\n",
" <td>0.8097</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Electronics manufacturing</th>\n",
" <td>0.8776</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China US trade tensions</th>\n",
" <td>0.9989</td>\n",
" <td>0.0002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Copper tariff</th>\n",
" <td>1.0000</td>\n",
" <td>0.0000</td>\n",
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"text/plain": [
" Level p Diff p\n",
"Building construction copper 0.0000 0.0000\n",
"US copper production 0.0000 0.0000\n",
"Copper mining cost 0.0000 0.0000\n",
"Commodity speculation 0.0000 0.0000\n",
"Chile copper production 0.0000 0.0000\n",
"... ... ...\n",
"COVID 0.7920 0.0000\n",
"Terrorist attack 0.8097 0.0000\n",
"Electronics manufacturing 0.8776 0.0000\n",
"China US trade tensions 0.9989 0.0002\n",
"Copper tariff 1.0000 0.0000\n",
"\n",
"[98 rows x 2 columns]"
]
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"\n",
"Table 5 – Cointegration\n"
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" <th></th>\n",
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" <tr>\n",
" <th>Iran Iraq War</th>\n",
" <td>-2.8943</td>\n",
" <td>0.1375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5G technology</th>\n",
" <td>-2.8166</td>\n",
" <td>0.1605</td>\n",
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" <th>Electronics manufacturing</th>\n",
" <td>-2.7911</td>\n",
" <td>0.1685</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Copper ore reserves</th>\n",
" <td>-2.7675</td>\n",
" <td>0.1762</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Currency exchange rates</th>\n",
" <td>-2.7040</td>\n",
" <td>0.1981</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Copper concentrate</th>\n",
" <td>-2.6545</td>\n",
" <td>0.2164</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Middle East conflicts</th>\n",
" <td>-2.6240</td>\n",
" <td>0.2281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mining sanctions</th>\n",
" <td>-2.5874</td>\n",
" <td>0.2420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yahoo finance</th>\n",
" <td>-2.5775</td>\n",
" <td>0.2460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RMB exchange rate</th>\n",
" <td>-2.5758</td>\n",
" <td>0.2467</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Energy crisis</th>\n",
" <td>-2.5618</td>\n",
" <td>0.2525</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Scrap copper prices</th>\n",
" <td>-2.5581</td>\n",
" <td>0.2541</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Manufacturing PMI</th>\n",
" <td>-2.5548</td>\n",
" <td>0.2554</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COVID</th>\n",
" <td>-2.5403</td>\n",
" <td>0.2616</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Extreme weather</th>\n",
" <td>-2.5326</td>\n",
" <td>0.2649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Euro exchange rate</th>\n",
" <td>-2.5289</td>\n",
" <td>0.2665</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Copper tariff</th>\n",
" <td>-2.5263</td>\n",
" <td>0.2676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China US trade tensions</th>\n",
" <td>-2.5121</td>\n",
" <td>0.2738</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COVID copper</th>\n",
" <td>-2.5116</td>\n",
" <td>0.2740</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Electric vehicle demand</th>\n",
" <td>-2.4723</td>\n",
" <td>0.2914</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bloomberg</th>\n",
" <td>-2.3920</td>\n",
" <td>0.3286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Copper price forecast</th>\n",
" <td>-2.3662</td>\n",
" <td>0.3410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Terrorist attack</th>\n",
" <td>-2.3257</td>\n",
" <td>0.3608</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Trade war</th>\n",
" <td>-2.3010</td>\n",
" <td>0.3731</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Global GDP growth</th>\n",
" <td>-2.2684</td>\n",
" <td>0.3894</td>\n",
" </tr>\n",
" <tr>\n",
" <th>World GDP by country</th>\n",
" <td>-2.2533</td>\n",
" <td>0.3970</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Inflation expectations</th>\n",
" <td>-2.2304</td>\n",
" <td>0.4087</td>\n",
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"text/plain": [
" t p\n",
"Series \n",
"Iran Iraq War -2.8943 0.1375\n",
"5G technology -2.8166 0.1605\n",
"Electronics manufacturing -2.7911 0.1685\n",
"Copper ore reserves -2.7675 0.1762\n",
"Currency exchange rates -2.7040 0.1981\n",
"Copper concentrate -2.6545 0.2164\n",
"Middle East conflicts -2.6240 0.2281\n",
"Mining sanctions -2.5874 0.2420\n",
"Yahoo finance -2.5775 0.2460\n",
"RMB exchange rate -2.5758 0.2467\n",
"Energy crisis -2.5618 0.2525\n",
"Scrap copper prices -2.5581 0.2541\n",
"Manufacturing PMI -2.5548 0.2554\n",
"COVID -2.5403 0.2616\n",
"Extreme weather -2.5326 0.2649\n",
"Euro exchange rate -2.5289 0.2665\n",
"Copper tariff -2.5263 0.2676\n",
"China US trade tensions -2.5121 0.2738\n",
"COVID copper -2.5116 0.2740\n",
"Electric vehicle demand -2.4723 0.2914\n",
"Bloomberg -2.3920 0.3286\n",
"Copper price forecast -2.3662 0.3410\n",
"Terrorist attack -2.3257 0.3608\n",
"Trade war -2.3010 0.3731\n",
"Global GDP growth -2.2684 0.3894\n",
"World GDP by country -2.2533 0.3970\n",
"Inflation expectations -2.2304 0.4087"
]
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"text": [
"\n",
"Table 6 – Granger causality (all lags)\n"
]
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" <th>24</th>\n",
" <td>5G technology</td>\n",
" <td>1</td>\n",
" <td>0.9159</td>\n",
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" <th>25</th>\n",
" <td>5G technology</td>\n",
" <td>2</td>\n",
" <td>0.4953</td>\n",
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" <th>26</th>\n",
" <td>5G technology</td>\n",
" <td>3</td>\n",
" <td>0.4765</td>\n",
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" <tr>\n",
" <th>27</th>\n",
" <td>5G technology</td>\n",
" <td>4</td>\n",
" <td>0.5444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>5G technology</td>\n",
" <td>5</td>\n",
" <td>0.3709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <th>211</th>\n",
" <td>Yahoo finance</td>\n",
" <td>20</td>\n",
" <td>0.1242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>212</th>\n",
" <td>Yahoo finance</td>\n",
" <td>21</td>\n",
" <td>0.1664</td>\n",
" </tr>\n",
" <tr>\n",
" <th>213</th>\n",
" <td>Yahoo finance</td>\n",
" <td>22</td>\n",
" <td>0.2101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>214</th>\n",
" <td>Yahoo finance</td>\n",
" <td>23</td>\n",
" <td>0.2764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>215</th>\n",
" <td>Yahoo finance</td>\n",
" <td>24</td>\n",
" <td>0.3186</td>\n",
" </tr>\n",
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"</table>\n",
"<p>648 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" Series Lag p\n",
"24 5G technology 1 0.9159\n",
"25 5G technology 2 0.4953\n",
"26 5G technology 3 0.4765\n",
"27 5G technology 4 0.5444\n",
"28 5G technology 5 0.3709\n",
".. ... ... ...\n",
"211 Yahoo finance 20 0.1242\n",
"212 Yahoo finance 21 0.1664\n",
"213 Yahoo finance 22 0.2101\n",
"214 Yahoo finance 23 0.2764\n",
"215 Yahoo finance 24 0.3186\n",
"\n",
"[648 rows x 3 columns]"
]
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"text": [
"\n",
"Feature Selection Results:\n",
"\n",
"p-value < 0.01: 6 series\n",
"['Copper ore reserves', 'Mining sanctions', 'Extreme weather', 'Euro exchange rate', 'Copper tariff', 'Terrorist attack']\n",
"\n",
"p-value < 0.05: 12 series\n",
"['Iran Iraq War', 'Electronics manufacturing', 'Copper ore reserves', 'Mining sanctions', 'Yahoo finance', 'Scrap copper prices', 'Extreme weather', 'Euro exchange rate', 'Copper tariff', 'China US trade tensions', 'Terrorist attack', 'Trade war']\n",
"\n",
"p-value < 0.10: 13 series\n",
"['Iran Iraq War', 'Electronics manufacturing', 'Copper ore reserves', 'Middle East conflicts', 'Mining sanctions', 'Yahoo finance', 'Scrap copper prices', 'Extreme weather', 'Euro exchange rate', 'Copper tariff', 'China US trade tensions', 'Terrorist attack', 'Trade war']\n",
"\n",
"Table 7 – Granger-causal summary\n"
]
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" <th>Extreme weather</th>\n",
" <td>0.0007</td>\n",
" <td>7</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Terrorist attack</th>\n",
" <td>0.0008</td>\n",
" <td>15</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Euro exchange rate</th>\n",
" <td>0.0024</td>\n",
" <td>9</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Copper tariff</th>\n",
" <td>0.0040</td>\n",
" <td>6</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Copper ore reserves</th>\n",
" <td>0.0044</td>\n",
" <td>21</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mining sanctions</th>\n",
" <td>0.0057</td>\n",
" <td>24</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Trade war</th>\n",
" <td>0.0121</td>\n",
" <td>9</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Scrap copper prices</th>\n",
" <td>0.0196</td>\n",
" <td>21</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China US trade tensions</th>\n",
" <td>0.0263</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yahoo finance</th>\n",
" <td>0.0356</td>\n",
" <td>15</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Electronics manufacturing</th>\n",
" <td>0.0371</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iran Iraq War</th>\n",
" <td>0.0394</td>\n",
" <td>6</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Middle East conflicts</th>\n",
" <td>0.0580</td>\n",
" <td>8</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Copper concentrate</th>\n",
" <td>0.1174</td>\n",
" <td>15</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COVID</th>\n",
" <td>0.1325</td>\n",
" <td>23</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Currency exchange rates</th>\n",
" <td>0.1420</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Global GDP growth</th>\n",
" <td>0.1694</td>\n",
" <td>16</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COVID copper</th>\n",
" <td>0.1707</td>\n",
" <td>23</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Inflation expectations</th>\n",
" <td>0.2726</td>\n",
" <td>6</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RMB exchange rate</th>\n",
" <td>0.2776</td>\n",
" <td>15</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Manufacturing PMI</th>\n",
" <td>0.3599</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bloomberg</th>\n",
" <td>0.3698</td>\n",
" <td>17</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5G technology</th>\n",
" <td>0.3709</td>\n",
" <td>5</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>World GDP by country</th>\n",
" <td>0.4347</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Energy crisis</th>\n",
" <td>0.5680</td>\n",
" <td>5</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Copper price forecast</th>\n",
" <td>0.7506</td>\n",
" <td>6</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Electric vehicle demand</th>\n",
" <td>0.7906</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Min p Best Lag p<0.01 p<0.05 p<0.10\n",
"Series \n",
"Extreme weather 0.0007 7 True True True\n",
"Terrorist attack 0.0008 15 True True True\n",
"Euro exchange rate 0.0024 9 True True True\n",
"Copper tariff 0.0040 6 True True True\n",
"Copper ore reserves 0.0044 21 True True True\n",
"Mining sanctions 0.0057 24 True True True\n",
"Trade war 0.0121 9 False True True\n",
"Scrap copper prices 0.0196 21 False True True\n",
"China US trade tensions 0.0263 1 False True True\n",
"Yahoo finance 0.0356 15 False True True\n",
"Electronics manufacturing 0.0371 2 False True True\n",
"Iran Iraq War 0.0394 6 False True True\n",
"Middle East conflicts 0.0580 8 False False True\n",
"Copper concentrate 0.1174 15 False False False\n",
"COVID 0.1325 23 False False False\n",
"Currency exchange rates 0.1420 1 False False False\n",
"Global GDP growth 0.1694 16 False False False\n",
"COVID copper 0.1707 23 False False False\n",
"Inflation expectations 0.2726 6 False False False\n",
"RMB exchange rate 0.2776 15 False False False\n",
"Manufacturing PMI 0.3599 1 False False False\n",
"Bloomberg 0.3698 17 False False False\n",
"5G technology 0.3709 5 False False False\n",
"World GDP by country 0.4347 3 False False False\n",
"Energy crisis 0.5680 5 False False False\n",
"Copper price forecast 0.7506 6 False False False\n",
"Electric vehicle demand 0.7906 2 False False False"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Accuracy comparison – direction prediction\n"
]
},
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Scenario</th>\n",
" <th>0.05</th>\n",
" <th>0.10</th>\n",
" <th>without</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Model</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>DecTree</th>\n",
" <td>46.67%</td>\n",
" <td>57.78%</td>\n",
" <td>46.67%</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HMM</th>\n",
" <td>55.56%</td>\n",
" <td>55.56%</td>\n",
" <td>55.56%</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LogReg</th>\n",
" <td>44.44%</td>\n",
" <td>48.89%</td>\n",
" <td>51.11%</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RandForest</th>\n",
" <td>46.67%</td>\n",
" <td>44.44%</td>\n",
" <td>46.67%</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SVM</th>\n",
" <td>51.11%</td>\n",
" <td>48.89%</td>\n",
" <td>62.22%</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Stack</th>\n",
" <td>44.44%</td>\n",
" <td>40.00%</td>\n",
" <td>55.56%</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Voting</th>\n",
" <td>48.89%</td>\n",
" <td>48.89%</td>\n",
" <td>46.67%</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Scenario 0.05 0.10 without\n",
"Model \n",
"DecTree 46.67% 57.78% 46.67%\n",
"HMM 55.56% 55.56% 55.56%\n",
"LogReg 44.44% 48.89% 51.11%\n",
"RandForest 46.67% 44.44% 46.67%\n",
"SVM 51.11% 48.89% 62.22%\n",
"Stack 44.44% 40.00% 55.56%\n",
"Voting 48.89% 48.89% 46.67%"
]
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"source": [
"# ================================================================\n",
"# Copper-Price Direction-of-Move Classification\n",
"# • Baseline (“without”), tier 0·05, tier 0·10\n",
"# • Models: LogReg, DecisionTree, RandomForest, SVM, HMM\n",
"# + Ensembles: Voting, Stacking\n",
"# ================================================================\n",
"import pathlib, warnings, numpy as np, pandas as pd\n",
"from statsmodels.tsa.stattools import adfuller, coint, grangercausalitytests\n",
"from sklearn.model_selection import TimeSeriesSplit, KFold\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import (RandomForestClassifier, VotingClassifier,\n",
" StackingClassifier)\n",
"from sklearn.svm import SVC\n",
"from hmmlearn.hmm import GaussianHMM\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"warnings.filterwarnings(\"ignore\")\n",
"pd.set_option(\"display.float_format\", \"{:,.4f}\".format)\n",
"np.random.seed(42)\n",
"\n",
"# ── 1. Load copper price and Google-Trends data ------------------\n",
"ROOT = pathlib.Path(\".\")\n",
"def load_copper(fp=ROOT / \"Copper Prices.csv\"):\n",
" df = (pd.read_csv(fp)\n",
" .assign(Date=lambda d: pd.to_datetime(d[\"Date\"]))\n",
" .set_index(\"Date\")\n",
" .asfreq(\"W-SUN\"))\n",
" return df[\"Price\"].rename(\"Copper_Price\")\n",
"\n",
"def load_trends():\n",
" def one_folder(folder):\n",
" frames = []\n",
" for fp in (ROOT / folder).glob(\"*.csv\"):\n",
" key = fp.stem.replace(\",\", \"\")\n",
" t = pd.read_csv(fp); t.columns = [c.strip() for c in t.columns]\n",
" val = t.columns[1]\n",
" frames.append(t.assign(Date=lambda d: pd.to_datetime(d[t.columns[0]]))\n",
" .set_index(\"Date\")\n",
" .asfreq(\"W-SUN\")\n",
" .rename(columns={val: key}))\n",
" return pd.concat(frames, axis=1) if frames else pd.DataFrame()\n",
" cats = [\"Supply Factors\", \"Demand Factors\",\n",
" \"Speculative Factors\", \"Sudden Factors\"]\n",
" return pd.concat([one_folder(c) for c in cats], axis=1).sort_index()\n",
"\n",
"copper, trends = load_copper(), load_trends()\n",
"data_raw = pd.concat([copper, trends], axis=1).dropna()\n",
"\n",
"# ── 2. ADF to find I(1) series -----------------------------------\n",
"ALPHA_ADF, ALPHA_COINT, MAX_LAG_GC = 0.01, 0.50, 24\n",
"adf_p = lambda s: adfuller(s.dropna(), autolag=\"AIC\")[1]\n",
"i1 = [c for c in data_raw.columns\n",
" if adf_p(data_raw[c]) > ALPHA_ADF and\n",
" adf_p(data_raw[c].diff()) < ALPHA_ADF and\n",
" c != \"Copper_Price\"]\n",
"\n",
"# ── 3. Cointegration filter --------------------------------------\n",
"cands = [s for s in i1\n",
" if coint(data_raw[\"Copper_Price\"], data_raw[s])[1] < ALPHA_COINT]\n",
"\n",
"# ── 4. Granger causality tiers -----------------------------------\n",
"gc_rows = []\n",
"for s in cands:\n",
" if data_raw[s].nunique() <= 1: continue\n",
" for lag in range(1, MAX_LAG_GC + 1):\n",
" try:\n",
" p = grangercausalitytests(\n",
" data_raw[[\"Copper_Price\", s]].dropna().values,\n",
" maxlag=lag, verbose=False)[lag][0][\"ssr_ftest\"][1]\n",
" gc_rows.append({\"Series\": s, \"Lag\": lag, \"p\": p})\n",
" except Exception:\n",
" continue\n",
"df_gc = pd.DataFrame(gc_rows)\n",
"\n",
"TIERS = {0.05: [s for s in cands if df_gc.loc[df_gc[\"Series\"] == s, \"p\"].min() < 0.05],\n",
" 0.10: [s for s in cands if df_gc.loc[df_gc[\"Series\"] == s, \"p\"].min() < 0.10]}\n",
"\n",
"# ────────────────────────────────────────────────────────────────\n",
"# Tables 4 → 7 + Feature Selection Results (no CSV, no .style)\n",
"# ────────────────────────────────────────────────────────────────\n",
"from IPython.display import display\n",
"from statsmodels.tools.sm_exceptions import InfeasibleTestError\n",
"\n",
"# ── Table 4 – Augmented Dickey-Fuller ----------------------------\n",
"tab4 = pd.DataFrame(\n",
" {\n",
" \"Level p\": [adf_p(data_raw[c]) for c in data_raw.columns],\n",
" \"Diff p\": [adf_p(data_raw[c].diff()) for c in data_raw.columns],\n",
" },\n",
" index=data_raw.columns,\n",
").sort_values(\"Level p\")\n",
"\n",
"print(\"\\nTable 4 – Augmented Dickey-Fuller\")\n",
"display(tab4)\n",
"\n",
"# Identify I(1) candidates\n",
"i1 = tab4.index[\n",
" (tab4[\"Level p\"] > ALPHA_ADF) & (tab4[\"Diff p\"] < ALPHA_ADF)\n",
"]\n",
"\n",
"# ── Table 5 – Engle-Granger cointegration ------------------------\n",
"rows = []\n",
"for s in i1:\n",
" if s == \"Copper_Price\":\n",
" continue\n",
" t_stat, p_val, *_ = coint(data_raw[\"Copper_Price\"], data_raw[s])\n",
" rows.append({\"Series\": s, \"t\": t_stat, \"p\": p_val})\n",
"\n",
"df_coint = pd.DataFrame(rows).set_index(\"Series\").sort_values(\"p\")\n",
"\n",
"print(\"\\nTable 5 – Cointegration\")\n",
"display(df_coint)\n",
"\n",
"cointegrated = df_coint.index[df_coint[\"p\"] < ALPHA_COINT]\n",
"\n",
"# ── Table 6 – full Granger matrix --------------------------------\n",
"gc_records = []\n",
"for s in cointegrated:\n",
" if data_raw[s].nunique() <= 1:\n",
" continue\n",
" for lag in range(1, MAX_LAG_GC + 1):\n",
" try:\n",
" p = grangercausalitytests(\n",
" data_raw[[\"Copper_Price\", s]].dropna().values,\n",
" maxlag=lag,\n",
" verbose=False,\n",
" )[lag][0][\"ssr_ftest\"][1]\n",
" gc_records.append({\"Series\": s, \"Lag\": lag, \"p\": p})\n",
" except InfeasibleTestError:\n",
" continue\n",
"\n",
"df_gc = pd.DataFrame(gc_records).sort_values([\"Series\", \"Lag\"])\n",
"\n",
"print(\"\\nTable 6 – Granger causality (all lags)\")\n",
"display(df_gc)\n",
"\n",
"# ── Feature Selection Results ------------------------------------\n",
"tiers = {\n",
" a: [\n",
" s\n",
" for s in cointegrated\n",
" if df_gc.loc[df_gc[\"Series\"] == s, \"p\"].min() < a\n",
" ]\n",
" for a in TIERS_TO_BUILD\n",
"}\n",
"\n",
"print(\"\\nFeature Selection Results:\")\n",
"for a in TIERS_TO_BUILD:\n",
" print(f\"\\np-value < {a:.2f}: {len(tiers[a])} series\")\n",
" print(tiers[a])\n",
"\n",
"# ── Table 7 – Granger-causal summary -----------------------------\n",
"summary_rows = []\n",
"for series in cointegrated:\n",
" subset = df_gc.loc[df_gc[\"Series\"] == series, [\"Lag\", \"p\"]]\n",
" best = subset.loc[subset[\"p\"].idxmin()]\n",
" summary_rows.append(\n",
" {\n",
" \"Series\": series,\n",
" \"Min p\": float(best[\"p\"]),\n",
" \"Best Lag\":int(best[\"Lag\"]),\n",
" \"p<0.01\": best[\"p\"] < 0.01,\n",
" \"p<0.05\": best[\"p\"] < 0.05,\n",
" \"p<0.10\": best[\"p\"] < 0.10,\n",
" }\n",
" )\n",
"\n",
"df_summary = pd.DataFrame(summary_rows).set_index(\"Series\").sort_values(\"Min p\")\n",
"\n",
"print(\"\\nTable 7 – Granger-causal summary\")\n",
"display(df_summary)\n",
"\n",
"\n",
"# ── 5. Helper: build lag-1 feature frame --------------------------\n",
"def lag_df(feats, lag=1):\n",
" base = {\"Copper_Price\": data_raw[\"Copper_Price\"]}\n",
" base.update({f\"{f}_lag{lag}\": data_raw[f].shift(lag) for f in feats})\n",
" return pd.DataFrame(base).dropna()\n",
"\n",
"# ── 6. Scenarios --------------------------------------------------\n",
"SCENARIOS = {\n",
" \"without\": [], # copper-only baseline\n",
" \"0.05\" : TIERS[0.05],\n",
" \"0.10\" : TIERS[0.10],\n",
"}\n",
"\n",
"# ── 7. Model factories -------------------------------------------\n",
"MODEL_FNS = {\n",
" \"LogReg\" : lambda: make_pipeline(StandardScaler(),\n",
" LogisticRegression(max_iter=1000,\n",
" random_state=42)),\n",
" \"DecTree\" : lambda: DecisionTreeClassifier(random_state=42),\n",
" \"RandForest\":lambda: RandomForestClassifier(n_estimators=500,\n",
" random_state=42),\n",
" \"SVM\" : lambda: make_pipeline(StandardScaler(),\n",
" SVC(kernel=\"rbf\",\n",
" probability=True,\n",
" random_state=42)),\n",
"}\n",
"\n",
"records = []\n",
"\n",
"# ── 8. Run classification for each scenario ----------------------\n",
"for scen, feats in SCENARIOS.items():\n",
" # A. dataset\n",
" if scen == \"without\":\n",
" df = (pd.concat([data_raw[\"Copper_Price\"],\n",
" data_raw[\"Copper_Price\"].shift(1)\n",
" .rename(\"Copper_Price_lag1\")],\n",
" axis=1)\n",
" .dropna())\n",
" else:\n",
" df = lag_df(feats, lag=1)\n",
" # label\n",
" df[\"y\"] = (df[\"Copper_Price\"].diff().shift(-1) > 0).astype(int)\n",
" df.dropna(inplace=True)\n",
"\n",
" X, y = df.drop(columns=[\"Copper_Price\", \"y\"]), df[\"y\"]\n",
" cut = int(len(X) * 0.83)\n",
" X_tr, X_te, y_tr, y_te = X.iloc[:cut], X.iloc[cut:], y.iloc[:cut], y.iloc[cut:]\n",
"\n",
" # B. supervised models\n",
" fitted = {n: fn() for n, fn in MODEL_FNS.items()}\n",
" for n, clf in fitted.items():\n",
" clf.fit(X_tr, y_tr)\n",
"\n",
" # C. HMM\n",
" rets = df[\"Copper_Price\"].pct_change().dropna().values.reshape(-1, 1)\n",
" split_pt = cut - 1\n",
" hmm = GaussianHMM(n_components=2, covariance_type=\"full\",\n",
" n_iter=100, random_state=42).fit(rets[:split_pt])\n",
" states = hmm.predict(rets[split_pt:]) # length == len(y_te)\n",
" state_map = {s: int(mu > 0) for s, mu in enumerate(hmm.means_.flatten())}\n",
" y_pred_hmm = pd.Series([state_map[s] for s in states[:len(y_te)]],\n",
" index=y_te.index)\n",
"\n",
" # D. ensembles (use KFold to satisfy cross_val_predict)\n",
" kf = KFold(n_splits=5, shuffle=False)\n",
" voting = VotingClassifier([(n, c) for n, c in fitted.items()],\n",
" voting=\"hard\").fit(X_tr, y_tr)\n",
" stack = StackingClassifier([(n, c) for n, c in fitted.items()],\n",
" final_estimator=LogisticRegression(max_iter=1000,\n",
" random_state=42),\n",
" cv=kf, n_jobs=-1).fit(X_tr, y_tr)\n",
"\n",
" # E. accuracy scores\n",
" scores = {n: accuracy_score(y_te, c.predict(X_te)) for n, c in fitted.items()}\n",
" scores[\"HMM\"] = accuracy_score(y_te, y_pred_hmm)\n",
" scores[\"Voting\"] = accuracy_score(y_te, voting.predict(X_te))\n",
" scores[\"Stack\"] = accuracy_score(y_te, stack.predict(X_te))\n",
"\n",
" for mdl, acc in scores.items():\n",
" records.append({\"Model\": mdl, \"Scenario\": scen, \"Accuracy\": acc})\n",
"\n",
"# ── 9. Show comparison table -------------------------------------\n",
"acc_df = (pd.DataFrame(records)\n",
" .pivot_table(index=\"Model\", columns=\"Scenario\", values=\"Accuracy\")\n",
" .applymap(\"{:.2%}\".format)\n",
" .sort_index())\n",
"\n",
"print(\"\\nAccuracy comparison – direction prediction\")\n",
"display(acc_df)\n"
]
}
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