diff --git "a/coret-coretan.ipynb" "b/coret-coretan.ipynb"
--- "a/coret-coretan.ipynb"
+++ "b/coret-coretan.ipynb"
@@ -73,7 +73,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "cdTcjhxS2FYc",
-        "outputId": "07fbccef-6ae3-4623-e965-6051df8db7c4"
+        "outputId": "4b013a76-e6a7-4756-e9e3-036d1488b46a"
       },
       "outputs": [
         {
@@ -81,265 +81,67 @@
           "name": "stdout",
           "text": [
             "Cloning into 'cryptocurrency_prediction'...\n",
-            "remote: Enumerating objects: 632, done.\u001b[K\n",
-            "remote: Counting objects: 100% (347/347), done.\u001b[K\n",
-            "remote: Compressing objects: 100% (192/192), done.\u001b[K\n",
-            "remote: Total 632 (delta 160), reused 155 (delta 155), pack-reused 285\u001b[K\n",
-            "Receiving objects: 100% (632/632), 10.33 MiB | 13.72 MiB/s, done.\n",
-            "Resolving deltas: 100% (315/315), done.\n",
-            "total 20720\n",
-            "drwxr-xr-x 2 root root  12288 Jun  1 07:08 .\n",
-            "drwxr-xr-x 1 root root   4096 Jun  1 07:08 ..\n",
-            "-rw-r--r-- 1 root root  11259 Jun  1 07:08 1000SATS-USD.csv\n",
-            "-rw-r--r-- 1 root root  81927 Jun  1 07:08 1INCH-USD.csv\n",
-            "-rw-r--r-- 1 root root  97627 Jun  1 07:08 AAVE-USD.csv\n",
-            "-rw-r--r-- 1 root root 146342 Jun  1 07:08 ABT-USD.csv\n",
-            "-rw-r--r-- 1 root root 158138 Jun  1 07:08 ADA-USD.csv\n",
-            "-rw-r--r-- 1 root root   7796 Jun  1 07:08 AERO29270-USD.csv\n",
-            "-rw-r--r-- 1 root root   5275 Jun  1 07:08 AEVO-USD.csv\n",
-            "-rw-r--r-- 1 root root 148725 Jun  1 07:08 AGIX-USD.csv\n",
-            "-rw-r--r-- 1 root root  73377 Jun  1 07:08 AIOZ-USD.csv\n",
-            "-rw-r--r-- 1 root root  84187 Jun  1 07:08 AKT-USD.csv\n",
-            "-rw-r--r-- 1 root root 118342 Jun  1 07:08 ALGO-USD.csv\n",
-            "-rw-r--r-- 1 root root   8434 Jun  1 07:08 ALT29073-USD.csv\n",
-            "-rw-r--r-- 1 root root 124096 Jun  1 07:08 ANKR-USD.csv\n",
-            "-rw-r--r-- 1 root root 153325 Jun  1 07:08 ANT-USD.csv\n",
-            "-rw-r--r-- 1 root root  53130 Jun  1 07:08 APE18876-USD.csv\n",
-            "-rw-r--r-- 1 root root  39708 Jun  1 07:08 APT21794-USD.csv\n",
-            "-rw-r--r-- 1 root root  28830 Jun  1 07:08 ARB11841-USD.csv\n",
-            "-rw-r--r-- 1 root root  20779 Jun  1 07:08 ARKM-USD.csv\n",
-            "-rw-r--r-- 1 root root  98543 Jun  1 07:08 AR-USD.csv\n",
-            "-rw-r--r-- 1 root root  55864 Jun  1 07:08 ASTR-USD.csv\n",
-            "-rw-r--r-- 1 root root 129779 Jun  1 07:08 ATOM-USD.csv\n",
-            "-rw-r--r-- 1 root root  98030 Jun  1 07:08 AVAX-USD.csv\n",
-            "-rw-r--r-- 1 root root  39389 Jun  1 07:08 AXL17799-USD.csv\n",
-            "-rw-r--r-- 1 root root  88498 Jun  1 07:08 AXS-USD.csv\n",
-            "-rw-r--r-- 1 root root 156103 Jun  1 07:08 BAT-USD.csv\n",
-            "-rw-r--r-- 1 root root 184199 Jun  1 07:08 BCH-USD.csv\n",
-            "-rw-r--r-- 1 root root  14193 Jun  1 07:08 BEAM28298-USD.csv\n",
-            "-rw-r--r-- 1 root root  96835 Jun  1 07:08 BETH-USD.csv\n",
-            "-rw-r--r-- 1 root root  66238 Jun  1 07:08 BGB-USD.csv\n",
-            "-rw-r--r-- 1 root root  58891 Jun  1 07:08 BICO-USD.csv\n",
-            "-rw-r--r-- 1 root root  30958 Jun  1 07:08 BLUR-USD.csv\n",
-            "-rw-r--r-- 1 root root 175266 Jun  1 07:08 BNB-USD.csv\n",
-            "-rw-r--r-- 1 root root  29918 Jun  1 07:08 BNX23635-USD.csv\n",
-            "-rw-r--r-- 1 root root   5266 Jun  1 07:08 BOME-USD.csv\n",
-            "-rw-r--r-- 1 root root  33661 Jun  1 07:08 BONK-USD.csv\n",
-            "-rw-r--r-- 1 root root   5358 Jun  1 07:08 BRETT29743-USD.csv\n",
-            "-rw-r--r-- 1 root root  11721 Jun  1 07:08 BSOL-USD.csv\n",
-            "-rw-r--r-- 1 root root 148560 Jun  1 07:08 BSV-USD.csv\n",
-            "-rw-r--r-- 1 root root 150193 Jun  1 07:08 BTCB-USD.csv\n",
-            "-rw-r--r-- 1 root root 294365 Jun  1 07:08 BTC-USD.csv\n",
-            "-rw-r--r-- 1 root root 165834 Jun  1 07:08 BTG-USD.csv\n",
-            "-rw-r--r-- 1 root root 126421 Jun  1 07:08 BTT-USD.csv\n",
-            "-rw-r--r-- 1 root root  89274 Jun  1 07:08 CAKE-USD.csv\n",
-            "-rw-r--r-- 1 root root  51338 Jun  1 07:08 CBETH-USD.csv\n",
-            "-rw-r--r-- 1 root root  95272 Jun  1 07:08 CELO-USD.csv\n",
-            "-rw-r--r-- 1 root root  66639 Jun  1 07:08 CFG-USD.csv\n",
-            "-rw-r--r-- 1 root root  84008 Jun  1 07:08 CFX-USD.csv\n",
-            "-rw-r--r-- 1 root root  31812 Jun  1 07:08 CHEEL-USD.csv\n",
-            "-rw-r--r-- 1 root root 117117 Jun  1 07:08 CHZ-USD.csv\n",
-            "-rw-r--r-- 1 root root 106649 Jun  1 07:08 CKB-USD.csv\n",
-            "-rw-r--r-- 1 root root 105142 Jun  1 07:08 COMP5692-USD.csv\n",
-            "-rw-r--r-- 1 root root  31101 Jun  1 07:08 CORE23254-USD.csv\n",
-            "-rw-r--r-- 1 root root 129369 Jun  1 07:08 CRO-USD.csv\n",
-            "-rw-r--r-- 1 root root  90889 Jun  1 07:08 CRV-USD.csv\n",
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-            "-rw-r--r-- 1 root root 108602 Jun  1 07:08 DAI-USD.csv\n",
-            "-rw-r--r-- 1 root root 174761 Jun  1 07:08 DASH-USD.csv\n",
-            "-rw-r--r-- 1 root root 167525 Jun  1 07:08 DCR-USD.csv\n",
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-            "-rw-r--r-- 1 root root   2365 Jun  1 07:08 DOG30933-USD.csv\n",
-            "-rw-r--r-- 1 root root 157635 Jun  1 07:08 DOGE-USD.csv\n",
-            "-rw-r--r-- 1 root root  94044 Jun  1 07:08 DOT-USD.csv\n",
-            "-rw-r--r-- 1 root root  64505 Jun  1 07:08 DYDX-USD.csv\n",
-            "-rw-r--r-- 1 root root   8087 Jun  1 07:08 DYM-USD.csv\n",
-            "-rw-r--r-- 1 root root   9360 Jun  1 07:08 EETH-USD.csv\n",
-            "-rw-r--r-- 1 root root  97567 Jun  1 07:08 EGLD-USD.csv\n",
-            "-rw-r--r-- 1 root root 152407 Jun  1 07:08 ELF-USD.csv\n",
-            "-rw-r--r-- 1 root root   4011 Jun  1 07:08 ENA-USD.csv\n",
-            "-rw-r--r-- 1 root root 155284 Jun  1 07:08 ENJ-USD.csv\n",
-            "-rw-r--r-- 1 root root  64384 Jun  1 07:08 ENS-USD.csv\n",
-            "-rw-r--r-- 1 root root 159563 Jun  1 07:08 EOS-USD.csv\n",
-            "-rw-r--r-- 1 root root 166427 Jun  1 07:08 ETC-USD.csv\n",
-            "-rw-r--r-- 1 root root  14368 Jun  1 07:08 ETHDYDX-USD.csv\n",
-            "-rw-r--r-- 1 root root   4991 Jun  1 07:08 ETHFI-USD.csv\n",
-            "-rw-r--r-- 1 root root 191972 Jun  1 07:08 ETH-USD.csv\n",
-            "-rw-r--r-- 1 root root  43122 Jun  1 07:08 ETHW-USD.csv\n",
-            "-rw-r--r-- 1 root root  24982 Jun  1 07:08 ETHX-USD.csv\n",
-            "-rw-r--r-- 1 root root   7977 Jun  1 07:08 EZETH-USD.csv\n",
-            "-rw-r--r-- 1 root root  20712 Jun  1 07:08 FDUSD-USD.csv\n",
-            "-rw-r--r-- 1 root root 124405 Jun  1 07:08 FET-USD.csv\n",
-            "-rw-r--r-- 1 root root 158255 Jun  1 07:08 FIL-USD.csv\n",
-            "-rw-r--r-- 1 root root  68354 Jun  1 07:08 FLOKI-USD.csv\n",
-            "-rw-r--r-- 1 root root  81203 Jun  1 07:08 FLOW-USD.csv\n",
-            "-rw-r--r-- 1 root root  32831 Jun  1 07:08 FLR-USD.csv\n",
-            "-rw-r--r-- 1 root root 135979 Jun  1 07:08 FLUX-USD.csv\n",
-            "-rw-r--r-- 1 root root  80860 Jun  1 07:08 FRAX-USD.csv\n",
-            "-rw-r--r-- 1 root root 132786 Jun  1 07:08 FTM-USD.csv\n",
-            "-rw-r--r-- 1 root root  31859 Jun  1 07:08 FTN-USD.csv\n",
-            "-rw-r--r-- 1 root root 118012 Jun  1 07:08 FTT-USD.csv\n",
-            "-rw-r--r-- 1 root root  82444 Jun  1 07:08 FXS-USD.csv\n",
-            "-rw-r--r-- 1 root root  49164 Jun  1 07:08 GAL11877-USD.csv\n",
-            "-rw-r--r-- 1 root root  87110 Jun  1 07:08 GALA-USD.csv\n",
-            "-rw-r--r-- 1 root root 156090 Jun  1 07:08 GAS-USD.csv\n",
-            "-rw-r--r-- 1 root root 154176 Jun  1 07:08 GLM-USD.csv\n",
-            "-rw-r--r-- 1 root root  53334 Jun  1 07:08 GMT18069-USD.csv\n",
-            "-rw-r--r-- 1 root root  68590 Jun  1 07:08 GMX11857-USD.csv\n",
-            "-rw-r--r-- 1 root root 170467 Jun  1 07:08 GNO-USD.csv\n",
-            "-rw-r--r-- 1 root root  82607 Jun  1 07:08 GRT6719-USD.csv\n",
-            "-rw-r--r-- 1 root root 111729 Jun  1 07:08 GT-USD.csv\n",
-            "-rw-r--r-- 1 root root 111949 Jun  1 07:08 HBAR-USD.csv\n",
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-            "-rw-r--r-- 1 root root  76035 Jun  1 07:08 ICP-USD.csv\n",
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-            "-rw-r--r-- 1 root root 142999 Jun  1 07:08 XDC-USD.csv\n",
-            "-rw-r--r-- 1 root root  68638 Jun  1 07:08 XEC-USD.csv\n",
-            "-rw-r--r-- 1 root root 155416 Jun  1 07:08 XEM-USD.csv\n",
-            "-rw-r--r-- 1 root root 157560 Jun  1 07:08 XLM-USD.csv\n",
-            "-rw-r--r-- 1 root root 177520 Jun  1 07:08 XMR-USD.csv\n",
-            "-rw-r--r-- 1 root root  62836 Jun  1 07:08 XRD-USD.csv\n",
-            "-rw-r--r-- 1 root root 159892 Jun  1 07:08 XRP-USD.csv\n",
-            "-rw-r--r-- 1 root root 155723 Jun  1 07:08 XTZ-USD.csv\n",
-            "-rw-r--r-- 1 root root  67554 Jun  1 07:08 YGG-USD.csv\n",
-            "-rw-r--r-- 1 root root  19073 Jun  1 07:08 ZBU-USD.csv\n",
-            "-rw-r--r-- 1 root root 173523 Jun  1 07:08 ZEC-USD.csv\n",
-            "-rw-r--r-- 1 root root  11762 Jun  1 07:08 ZETA-USD.csv\n",
-            "-rw-r--r-- 1 root root 150908 Jun  1 07:08 ZIL-USD.csv\n",
-            "-rw-r--r-- 1 root root 155786 Jun  1 07:08 ZRX-USD.csv\n"
+            "remote: Enumerating objects: 4725, done.\u001b[K\n",
+            "remote: Counting objects: 100% (4556/4556), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (1411/1411), done.\u001b[K\n",
+            "remote: Total 4725 (delta 3431), reused 4142 (delta 3120), pack-reused 169\u001b[K\n",
+            "Receiving objects: 100% (4725/4725), 316.49 MiB | 11.64 MiB/s, done.\n",
+            "Resolving deltas: 100% (3433/3433), done.\n",
+            "Updating files: 100% (296/296), done.\n",
+            "total 5420\n",
+            "drwxr-xr-x 2 root root   4096 Jun 23 10:42 .\n",
+            "drwxr-xr-x 1 root root   4096 Jun 23 10:42 ..\n",
+            "-rw-r--r-- 1 root root 159656 Jun 23 10:42 ADA-USD.csv\n",
+            "-rw-r--r-- 1 root root  41219 Jun 23 10:42 APT21794-USD.csv\n",
+            "-rw-r--r-- 1 root root  30347 Jun 23 10:42 ARB11841-USD.csv\n",
+            "-rw-r--r-- 1 root root 100157 Jun 23 10:42 AR-USD.csv\n",
+            "-rw-r--r-- 1 root root 131290 Jun 23 10:42 ATOM-USD.csv\n",
+            "-rw-r--r-- 1 root root  99663 Jun 23 10:42 AVAX-USD.csv\n",
+            "-rw-r--r-- 1 root root 185947 Jun 23 10:42 BCH-USD.csv\n",
+            "-rw-r--r-- 1 root root 177037 Jun 23 10:42 BNB-USD.csv\n",
+            "-rw-r--r-- 1 root root 152142 Jun 23 10:42 BTCB-USD.csv\n",
+            "-rw-r--r-- 1 root root 296387 Jun 23 10:42 BTC-USD.csv\n",
+            "-rw-r--r-- 1 root root 130852 Jun 23 10:42 CRO-USD.csv\n",
+            "-rw-r--r-- 1 root root 110120 Jun 23 10:42 DAI-USD.csv\n",
+            "-rw-r--r-- 1 root root 159157 Jun 23 10:42 DOGE-USD.csv\n",
+            "-rw-r--r-- 1 root root  95561 Jun 23 10:42 DOT-USD.csv\n",
+            "-rw-r--r-- 1 root root  11152 Jun 23 10:42 EETH-USD.csv\n",
+            "-rw-r--r-- 1 root root 168057 Jun 23 10:42 ETC-USD.csv\n",
+            "-rw-r--r-- 1 root root 193875 Jun 23 10:42 ETH-USD.csv\n",
+            "-rw-r--r-- 1 root root   9822 Jun 23 10:42 EZETH-USD.csv\n",
+            "-rw-r--r-- 1 root root 159769 Jun 23 10:42 FIL-USD.csv\n",
+            "-rw-r--r-- 1 root root      0 Jun 23 10:42 .gitkeep\n",
+            "-rw-r--r-- 1 root root 113446 Jun 23 10:42 HBAR-USD.csv\n",
+            "-rw-r--r-- 1 root root  77597 Jun 23 10:42 ICP-USD.csv\n",
+            "-rw-r--r-- 1 root root  62639 Jun 23 10:42 IMX10603-USD.csv\n",
+            "-rw-r--r-- 1 root root  48559 Jun 23 10:42 KAS-USD.csv\n",
+            "-rw-r--r-- 1 root root 119270 Jun 23 10:42 LEO-USD.csv\n",
+            "-rw-r--r-- 1 root root 163285 Jun 23 10:42 LINK-USD.csv\n",
+            "-rw-r--r-- 1 root root 251825 Jun 23 10:42 LTC-USD.csv\n",
+            "-rw-r--r-- 1 root root 123859 Jun 23 10:42 MATIC-USD.csv\n",
+            "-rw-r--r-- 1 root root  22354 Jun 23 10:42 MNT27075-USD.csv\n",
+            "-rw-r--r-- 1 root root  89375 Jun 23 10:42 NEAR-USD.csv\n",
+            "-rw-r--r-- 1 root root  28585 Jun 23 10:42 PEPE24478-USD.csv\n",
+            "-rw-r--r-- 1 root root  95368 Jun 23 10:42 RNDR-USD.csv\n",
+            "-rw-r--r-- 1 root root  92698 Jun 23 10:42 SHIB-USD.csv\n",
+            "-rw-r--r-- 1 root root 109405 Jun 23 10:42 SOL-USD.csv\n",
+            "-rw-r--r-- 1 root root 101283 Jun 23 10:42 STETH-USD.csv\n",
+            "-rw-r--r-- 1 root root  66723 Jun 23 10:42 TON11419-USD.csv\n",
+            "-rw-r--r-- 1 root root 160279 Jun 23 10:42 TRX-USD.csv\n",
+            "-rw-r--r-- 1 root root  92953 Jun 23 10:42 UNI7083-USD.csv\n",
+            "-rw-r--r-- 1 root root 138757 Jun 23 10:42 USDC-USD.csv\n",
+            "-rw-r--r-- 1 root root   8215 Jun 23 10:42 USDE29470-USD.csv\n",
+            "-rw-r--r-- 1 root root 164142 Jun 23 10:42 USDT-USD.csv\n",
+            "-rw-r--r-- 1 root root  33413 Jun 23 10:42 WBETH-USD.csv\n",
+            "-rw-r--r-- 1 root root 164970 Jun 23 10:42 WBTC-USD.csv\n",
+            "-rw-r--r-- 1 root root  15438 Jun 23 10:42 WEETH-USD.csv\n",
+            "-rw-r--r-- 1 root root 183054 Jun 23 10:42 WETH-USD.csv\n",
+            "-rw-r--r-- 1 root root  26057 Jun 23 10:42 WHBAR-USD.csv\n",
+            "-rw-r--r-- 1 root root  12398 Jun 23 10:42 WIF-USD.csv\n",
+            "-rw-r--r-- 1 root root  78410 Jun 23 10:42 WSTETH-USD.csv\n",
+            "-rw-r--r-- 1 root root  53592 Jun 23 10:42 WTRX-USD.csv\n",
+            "-rw-r--r-- 1 root root 159057 Jun 23 10:42 XLM-USD.csv\n",
+            "-rw-r--r-- 1 root root 161425 Jun 23 10:42 XRP-USD.csv\n"
           ]
         },
         {
@@ -390,7 +192,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "6Waj_50o7A5T",
-        "outputId": "ec62437c-cfd0-4863-933e-13c73885d86e"
+        "outputId": "782f1eb0-6247-4f01-cbb1-41072b34d128"
       },
       "outputs": [
         {
@@ -398,17 +200,17 @@
           "name": "stdout",
           "text": [
             "Datasets:\n",
-            "  - 1000SATS-USD.csv\n",
-            "  - 1INCH-USD.csv\n",
-            "  - AAVE-USD.csv\n",
-            "  - ABT-USD.csv\n",
             "  - ADA-USD.csv\n",
-            "  - AERO29270-USD.csv\n",
-            "  - AEVO-USD.csv\n",
-            "  - AGIX-USD.csv\n",
-            "  - AIOZ-USD.csv\n",
-            "  - AKT-USD.csv\n",
-            "  - and other (... 240)\n"
+            "  - APT21794-USD.csv\n",
+            "  - AR-USD.csv\n",
+            "  - ARB11841-USD.csv\n",
+            "  - ATOM-USD.csv\n",
+            "  - AVAX-USD.csv\n",
+            "  - BCH-USD.csv\n",
+            "  - BNB-USD.csv\n",
+            "  - BTC-USD.csv\n",
+            "  - BTCB-USD.csv\n",
+            "  - and other (... 40)\n"
           ]
         }
       ],
@@ -439,108 +241,143 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "5FFY-zfREV6N",
-        "outputId": "23f7c0bd-c862-4539-a178-9f9efbd2be38"
+        "outputId": "335cff2d-0800-487b-9268-ffa311c8c276"
       },
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Head of 1000SATS-USD.csv:\n",
-            "           Date      Open      High       Low     Close  Adj Close     Volume\n",
-            "167  2024-05-27  0.000296  0.000322  0.000289  0.000317   0.000317   50512670\n",
-            "168  2024-05-28  0.000317  0.000332  0.000300  0.000313   0.000313   58345517\n",
-            "169  2024-05-29  0.000313  0.000336  0.000304  0.000306   0.000306   45893011\n",
-            "170  2024-05-30  0.000306  0.000360  0.000302  0.000342   0.000342  105649208\n",
-            "171  2024-05-31  0.000350  0.000360  0.000322  0.000325   0.000325   64675212\n",
+            "Head of ADA-USD.csv:\n",
+            "            Date      Open      High       Low     Close  Adj Close     Volume\n",
+            "2414  2024-06-19  0.383565  0.397249  0.380962  0.383791   0.383791  295424813\n",
+            "2415  2024-06-20  0.383791  0.396790  0.379014  0.384244   0.384244  286206619\n",
+            "2416  2024-06-21  0.384248  0.390342  0.374608  0.376052   0.376052  272257203\n",
+            "2417  2024-06-22  0.376051  0.389592  0.371937  0.384812   0.384812  241766850\n",
+            "2418  2024-06-23  0.384794  0.390563  0.384018  0.390563   0.390563  228810128\n",
             "\n",
             "\n",
-            "Head of 1INCH-USD.csv:\n",
-            "            Date      Open      High       Low     Close  Adj Close     Volume\n",
-            "1249  2024-05-27  0.417205  0.428995  0.415036  0.425754   0.425754   25438849\n",
-            "1250  2024-05-28  0.425754  0.471344  0.410179  0.459180   0.459180  121329700\n",
-            "1251  2024-05-29  0.459180  0.509221  0.457314  0.492686   0.492686  218430988\n",
-            "1252  2024-05-30  0.492686  0.528235  0.463533  0.506827   0.506827   97779014\n",
-            "1253  2024-05-31  0.506976  0.531380  0.473836  0.477532   0.477532  120853560\n",
+            "Head of APT21794-USD.csv:\n",
+            "           Date      Open      High       Low     Close  Adj Close     Volume\n",
+            "609  2024-06-19  6.829489  7.109319  6.751914  6.891644   6.891644  153104339\n",
+            "610  2024-06-20  6.891644  7.158962  6.729643  6.907999   6.907999  168130290\n",
+            "611  2024-06-21  6.906493  7.029233  6.812755  6.937375   6.937375  144017997\n",
+            "612  2024-06-22  6.937681  6.938184  6.816058  6.863215   6.863215   72928164\n",
+            "613  2024-06-23  6.862212  6.980551  6.856907  6.966905   6.966905   67397728\n",
             "\n",
             "\n",
-            "Head of AAVE-USD.csv:\n",
-            "            Date        Open        High         Low       Close   Adj Close  \\\n",
-            "1333  2024-05-27  109.737770  114.262810  107.440498  108.638657  108.638657   \n",
-            "1334  2024-05-28  108.638657  108.787201  103.785156  106.612122  106.612122   \n",
-            "1335  2024-05-29  106.612122  107.396194  102.636078  103.601227  103.601227   \n",
-            "1336  2024-05-30  103.601257  107.090523  102.201813  102.948799  102.948799   \n",
-            "1337  2024-05-31  102.930801  104.463913  100.138252  102.952927  102.952927   \n",
+            "Head of AR-USD.csv:\n",
+            "            Date       Open       High        Low      Close  Adj Close  \\\n",
+            "1484  2024-06-19  25.905121  28.720102  25.653254  27.214239  27.214239   \n",
+            "1485  2024-06-20  27.214239  29.259483  25.848511  26.031660  26.031660   \n",
+            "1486  2024-06-21  26.032036  28.137741  25.237125  27.950066  27.950066   \n",
+            "1487  2024-06-22  27.947155  28.206366  27.162918  27.569958  27.569958   \n",
+            "1488  2024-06-23  27.572495  27.938768  27.286060  27.455824  27.455824   \n",
             "\n",
-            "         Volume  \n",
-            "1333  139573136  \n",
-            "1334  120893656  \n",
-            "1335  101582510  \n",
-            "1336   98789837  \n",
-            "1337   96990888  \n",
+            "        Volume  \n",
+            "1484  93779407  \n",
+            "1485  99510760  \n",
+            "1486  72331851  \n",
+            "1487  50006957  \n",
+            "1488  37841332  \n",
             "\n",
             "\n",
-            "Head of ABT-USD.csv:\n",
-            "            Date      Open      High       Low     Close  Adj Close   Volume\n",
-            "2282  2024-05-27  4.158429  4.259275  4.089497  4.204973   4.204973  5705498\n",
-            "2283  2024-05-28  4.204995  4.222257  3.972209  3.983082   3.983082  5723700\n",
-            "2284  2024-05-29  3.983082  4.252806  3.975017  4.054780   4.054780  4466002\n",
-            "2285  2024-05-30  4.055531  4.180861  4.016908  4.061492   4.061492  4519097\n",
-            "2286  2024-05-31  4.061519  4.096845  3.938419  3.989476   3.989476  4599103\n",
+            "Head of ARB11841-USD.csv:\n",
+            "           Date      Open      High       Low     Close  Adj Close     Volume\n",
+            "454  2024-06-19  0.795205  0.833718  0.788190  0.812209   0.812209  233180885\n",
+            "455  2024-06-20  0.812209  0.846925  0.800655  0.800927   0.800927  233003450\n",
+            "456  2024-06-21  0.800919  0.808209  0.783914  0.800980   0.800980  223405761\n",
+            "457  2024-06-22  0.801005  0.809611  0.792755  0.804520   0.804520  107189509\n",
+            "458  2024-06-23  0.804471  0.814209  0.803329  0.813469   0.813469   97651768\n",
             "\n",
             "\n",
-            "Head of ADA-USD.csv:\n",
+            "Head of ATOM-USD.csv:\n",
             "            Date      Open      High       Low     Close  Adj Close     Volume\n",
-            "2391  2024-05-27  0.458375  0.473999  0.457198  0.467963   0.467963  323304261\n",
-            "2392  2024-05-28  0.467963  0.468437  0.453115  0.456990   0.456990  418594476\n",
-            "2393  2024-05-29  0.456990  0.463107  0.450914  0.450995   0.450995  350482630\n",
-            "2394  2024-05-30  0.450995  0.454546  0.443807  0.446581   0.446581  356151973\n",
-            "2395  2024-05-31  0.446581  0.454929  0.444753  0.447624   0.447624  297679808\n",
+            "1924  2024-06-19  6.696579  6.948950  6.645863  6.886108   6.886108  115990687\n",
+            "1925  2024-06-20  6.886108  7.101937  6.825943  6.836057   6.836057  122051264\n",
+            "1926  2024-06-21  6.835967  6.917904  6.761434  6.786554   6.786554  108532734\n",
+            "1927  2024-06-22  6.785939  6.801940  6.716470  6.741094   6.741094   67174738\n",
+            "1928  2024-06-23  6.741003  6.831733  6.740849  6.814218   6.814218   59929412\n",
             "\n",
             "\n",
-            "Head of AERO29270-USD.csv:\n",
-            "           Date      Open      High       Low     Close  Adj Close    Volume\n",
-            "115  2024-05-27  1.180737  1.251915  1.180345  1.223966   1.223966  33164568\n",
-            "116  2024-05-28  1.223966  1.230146  1.193577  1.206009   1.206009  22426190\n",
-            "117  2024-05-29  1.206000  1.219929  1.173513  1.182047   1.182047  24998550\n",
-            "118  2024-05-30  1.179020  1.194829  1.169990  1.180606   1.180606  21318593\n",
-            "119  2024-05-31  1.181559  1.192797  1.167693  1.170986   1.170986  17242936\n",
+            "Head of AVAX-USD.csv:\n",
+            "            Date       Open       High        Low      Close  Adj Close  \\\n",
+            "1437  2024-06-19  26.617783  27.589130  26.420097  26.891941  26.891941   \n",
+            "1438  2024-06-20  26.891941  28.845137  26.717438  27.544409  27.544409   \n",
+            "1439  2024-06-21  27.543884  27.984089  26.976751  27.450439  27.450439   \n",
+            "1440  2024-06-22  27.449894  27.468800  24.671511  25.617315  25.617315   \n",
+            "1441  2024-06-23  25.616165  25.961617  25.594069  25.826704  25.826704   \n",
             "\n",
+            "           Volume  \n",
+            "1437  214897798.0  \n",
+            "1438  295674926.0  \n",
+            "1439  229113421.0  \n",
+            "1440  568500434.0  \n",
+            "1441  254679968.0  \n",
             "\n",
-            "Head of AEVO-USD.csv:\n",
-            "          Date      Open      High       Low     Close  Adj Close    Volume\n",
-            "75  2024-05-27  0.868152  0.933117  0.864895  0.928258   0.928258  83954913\n",
-            "76  2024-05-28  0.928258  0.960330  0.878598  0.912342   0.912342  97428040\n",
-            "77  2024-05-29  0.912342  0.935289  0.879129  0.892153   0.892153  67430063\n",
-            "78  2024-05-30  0.887652  0.911202  0.855200  0.864568   0.864568  57523981\n",
-            "79  2024-05-31  0.866365  0.903869  0.856600  0.888438   0.888438  54982584\n",
             "\n",
+            "Head of BCH-USD.csv:\n",
+            "            Date        Open        High         Low       Close   Adj Close  \\\n",
+            "2414  2024-06-19  389.297791  397.895386  385.556122  388.660278  388.660278   \n",
+            "2415  2024-06-20  388.660278  400.227478  385.939758  389.861938  389.861938   \n",
+            "2416  2024-06-21  389.827545  393.751251  377.373901  382.875336  382.875336   \n",
+            "2417  2024-06-22  382.868744  396.144928  380.335052  392.161041  392.161041   \n",
+            "2418  2024-06-23  392.153168  396.133057  389.011749  392.329559  392.329559   \n",
             "\n",
-            "Head of AGIX-USD.csv:\n",
-            "            Date      Open      High       Low     Close  Adj Close     Volume\n",
-            "2320  2024-05-27  0.935255  0.975797  0.928733  0.958144   0.958144   92105101\n",
-            "2321  2024-05-28  0.958144  0.959997  0.910985  0.935140   0.935140  102221300\n",
-            "2322  2024-05-29  0.935140  0.972330  0.914147  0.915936   0.915936  110466565\n",
-            "2323  2024-05-30  0.915937  0.956007  0.886250  0.913017   0.913017  110977704\n",
-            "2324  2024-05-31  0.912972  0.921924  0.877482  0.897971   0.897971   94826288\n",
+            "         Volume  \n",
+            "2414  212059873  \n",
+            "2415  227242865  \n",
+            "2416  241770183  \n",
+            "2417  177606883  \n",
+            "2418  185443392  \n",
+            "\n",
+            "\n",
+            "Head of BNB-USD.csv:\n",
+            "            Date        Open        High         Low       Close   Adj Close  \\\n",
+            "2414  2024-06-19  588.584717  605.731689  587.014160  598.771729  598.771729   \n",
+            "2415  2024-06-20  598.771729  607.400757  584.759949  587.107178  587.107178   \n",
+            "2416  2024-06-21  587.112610  588.705444  578.015869  585.911926  585.911926   \n",
+            "2417  2024-06-22  585.900940  590.939331  581.137634  590.763367  590.763367   \n",
+            "2418  2024-06-23  590.671570  592.581238  587.399963  591.205078  591.205078   \n",
+            "\n",
+            "          Volume  \n",
+            "2414  1788904109  \n",
+            "2415  1817442036  \n",
+            "2416  1672110625  \n",
+            "2417  1387634576  \n",
+            "2418  1372477440  \n",
+            "\n",
+            "\n",
+            "Head of BTC-USD.csv:\n",
+            "            Date          Open          High           Low         Close  \\\n",
+            "3563  2024-06-19  65146.660156  65695.351563  64693.300781  64960.296875   \n",
+            "3564  2024-06-20  64960.296875  66438.960938  64547.847656  64828.656250   \n",
+            "3565  2024-06-21  64837.988281  65007.546875  63378.894531  64096.199219   \n",
+            "3566  2024-06-22  64113.863281  64475.468750  63929.757813  64252.578125   \n",
+            "3567  2024-06-23  64248.964844  64491.703125  64248.667969  64380.187500   \n",
             "\n",
+            "         Adj Close       Volume  \n",
+            "3563  64960.296875  21103423504  \n",
+            "3564  64828.656250  25641109124  \n",
+            "3565  64096.199219  26188171739  \n",
+            "3566  64252.578125   9858198793  \n",
+            "3567  64380.187500   8030789632  \n",
             "\n",
-            "Head of AIOZ-USD.csv:\n",
-            "            Date      Open      High       Low     Close  Adj Close   Volume\n",
-            "1151  2024-05-27  0.796441  0.825174  0.774462  0.802574   0.802574  9483831\n",
-            "1152  2024-05-28  0.802576  0.806413  0.774489  0.774489   0.774489  8967281\n",
-            "1153  2024-05-29  0.774479  0.775057  0.736021  0.744582   0.744582  9334501\n",
-            "1154  2024-05-30  0.744582  0.762691  0.728228  0.738802   0.738802  7308381\n",
-            "1155  2024-05-31  0.738838  0.769013  0.736816  0.737131   0.737131  6045616\n",
             "\n",
+            "Head of BTCB-USD.csv:\n",
+            "            Date          Open          High           Low         Close  \\\n",
+            "1828  2024-06-19  65101.085938  65581.906250  64787.019531  64865.839844   \n",
+            "1829  2024-06-20  64865.839844  66308.250000  64619.820313  64922.941406   \n",
+            "1830  2024-06-21  64914.968750  64945.152344  63503.847656  64129.816406   \n",
+            "1831  2024-06-22  64122.351563  64394.425781  63935.769531  64318.062500   \n",
+            "1832  2024-06-23  64319.468750  64494.671875  64314.351563  64377.347656   \n",
             "\n",
-            "Head of AKT-USD.csv:\n",
-            "            Date      Open      High       Low     Close  Adj Close    Volume\n",
-            "1315  2024-05-27  5.246012  5.545616  5.049523  5.318102   5.318102  26365621\n",
-            "1316  2024-05-28  5.318102  5.446484  5.121305  5.220311   5.220311  18814068\n",
-            "1317  2024-05-29  5.220296  5.910374  5.074440  5.192272   5.192272  64562310\n",
-            "1318  2024-05-30  5.192272  5.383185  4.905253  4.919753   4.919753  34674388\n",
-            "1319  2024-05-31  4.919669  4.999711  4.616309  4.632178   4.632178  21811510\n",
+            "         Adj Close    Volume  \n",
+            "1828  64865.839844  26950021  \n",
+            "1829  64922.941406  24110562  \n",
+            "1830  64129.816406  18859219  \n",
+            "1831  64318.062500   6413044  \n",
+            "1832  64377.347656   4965773  \n",
             "\n",
             "\n"
           ]
@@ -575,7 +412,7 @@
           "height": 394
         },
         "id": "yqS2EZx6G4ha",
-        "outputId": "22313972-2832-4eb3-d640-0b3f59249df4"
+        "outputId": "03a82ecf-bcde-4037-ce46-7a3e92f755e9"
       },
       "outputs": [
         {
@@ -584,33 +421,33 @@
             "text/plain": [
               "                   Open         High          Low        Close    Adj Close  \\\n",
               "Date                                                                          \n",
-              "2024-05-22  3789.372803  3810.948486  3655.075195  3737.217773  3737.217773   \n",
-              "2024-05-23  3737.178467  3943.553955  3552.642578  3776.927246  3776.927246   \n",
-              "2024-05-24  3776.992432  3825.122559  3631.990234  3726.934570  3726.934570   \n",
-              "2024-05-25  3726.975586  3776.006592  3710.528320  3749.236572  3749.236572   \n",
-              "2024-05-26  3749.179932  3879.470703  3732.022949  3825.897461  3825.897461   \n",
-              "2024-05-27  3826.127197  3973.556396  3821.930420  3892.006836  3892.006836   \n",
-              "2024-05-28  3892.096924  3924.895752  3771.213867  3840.256348  3840.256348   \n",
-              "2024-05-29  3840.235107  3880.648438  3742.041260  3763.196533  3763.196533   \n",
-              "2024-05-30  3763.357666  3823.643311  3702.263672  3746.849609  3746.849609   \n",
-              "2024-05-31  3746.764893  3841.127441  3724.708252  3778.403320  3778.403320   \n",
+              "2024-06-14  3467.969971  3528.602295  3366.223877  3480.271973  3480.271973   \n",
+              "2024-06-15  3479.785645  3589.887451  3473.452637  3565.549561  3565.549561   \n",
+              "2024-06-16  3566.761963  3648.093018  3541.534424  3620.563477  3620.563477   \n",
+              "2024-06-17  3622.383545  3634.285400  3468.148926  3511.378906  3511.378906   \n",
+              "2024-06-18  3510.565186  3514.177246  3371.590332  3483.681396  3483.681396   \n",
+              "2024-06-19  3482.350830  3583.320068  3466.480957  3559.347412  3559.347412   \n",
+              "2024-06-20  3559.347412  3623.886475  3485.459961  3511.086914  3511.086914   \n",
+              "2024-06-21  3511.268311  3542.957275  3447.935547  3516.075684  3516.075684   \n",
+              "2024-06-22  3516.551270  3519.497559  3477.169922  3494.813477  3494.813477   \n",
+              "2024-06-23  3494.953857  3518.871094  3494.098633  3504.028076  3504.028076   \n",
               "\n",
               "                 Volume  \n",
               "Date                     \n",
-              "2024-05-22  25155809461  \n",
-              "2024-05-23  45623656317  \n",
-              "2024-05-24  22257061429  \n",
-              "2024-05-25  10000027764  \n",
-              "2024-05-26  14650794791  \n",
-              "2024-05-27  18949181813  \n",
-              "2024-05-28  19846044324  \n",
-              "2024-05-29  17411416736  \n",
-              "2024-05-30  15065849797  \n",
-              "2024-05-31  15432449024  "
+              "2024-06-14  15793876596  \n",
+              "2024-06-15  12733651076  \n",
+              "2024-06-16   9878388158  \n",
+              "2024-06-17  17838856988  \n",
+              "2024-06-18  21022514455  \n",
+              "2024-06-19  15275373778  \n",
+              "2024-06-20  16115123753  \n",
+              "2024-06-21  15933353456  \n",
+              "2024-06-22   7423703673  \n",
+              "2024-06-23   6673526784  "
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-fe3974a0-7c41-407a-adc2-6c7fc2d110d9\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-82de0430-a039-4b0a-a9aa-b045df7e74a7\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -648,94 +485,94 @@
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <th>2024-05-22</th>\n",
-              "      <td>3789.372803</td>\n",
-              "      <td>3810.948486</td>\n",
-              "      <td>3655.075195</td>\n",
-              "      <td>3737.217773</td>\n",
-              "      <td>3737.217773</td>\n",
-              "      <td>25155809461</td>\n",
+              "      <th>2024-06-14</th>\n",
+              "      <td>3467.969971</td>\n",
+              "      <td>3528.602295</td>\n",
+              "      <td>3366.223877</td>\n",
+              "      <td>3480.271973</td>\n",
+              "      <td>3480.271973</td>\n",
+              "      <td>15793876596</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-23</th>\n",
-              "      <td>3737.178467</td>\n",
-              "      <td>3943.553955</td>\n",
-              "      <td>3552.642578</td>\n",
-              "      <td>3776.927246</td>\n",
-              "      <td>3776.927246</td>\n",
-              "      <td>45623656317</td>\n",
+              "      <th>2024-06-15</th>\n",
+              "      <td>3479.785645</td>\n",
+              "      <td>3589.887451</td>\n",
+              "      <td>3473.452637</td>\n",
+              "      <td>3565.549561</td>\n",
+              "      <td>3565.549561</td>\n",
+              "      <td>12733651076</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-24</th>\n",
-              "      <td>3776.992432</td>\n",
-              "      <td>3825.122559</td>\n",
-              "      <td>3631.990234</td>\n",
-              "      <td>3726.934570</td>\n",
-              "      <td>3726.934570</td>\n",
-              "      <td>22257061429</td>\n",
+              "      <th>2024-06-16</th>\n",
+              "      <td>3566.761963</td>\n",
+              "      <td>3648.093018</td>\n",
+              "      <td>3541.534424</td>\n",
+              "      <td>3620.563477</td>\n",
+              "      <td>3620.563477</td>\n",
+              "      <td>9878388158</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-25</th>\n",
-              "      <td>3726.975586</td>\n",
-              "      <td>3776.006592</td>\n",
-              "      <td>3710.528320</td>\n",
-              "      <td>3749.236572</td>\n",
-              "      <td>3749.236572</td>\n",
-              "      <td>10000027764</td>\n",
+              "      <th>2024-06-17</th>\n",
+              "      <td>3622.383545</td>\n",
+              "      <td>3634.285400</td>\n",
+              "      <td>3468.148926</td>\n",
+              "      <td>3511.378906</td>\n",
+              "      <td>3511.378906</td>\n",
+              "      <td>17838856988</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-26</th>\n",
-              "      <td>3749.179932</td>\n",
-              "      <td>3879.470703</td>\n",
-              "      <td>3732.022949</td>\n",
-              "      <td>3825.897461</td>\n",
-              "      <td>3825.897461</td>\n",
-              "      <td>14650794791</td>\n",
+              "      <th>2024-06-18</th>\n",
+              "      <td>3510.565186</td>\n",
+              "      <td>3514.177246</td>\n",
+              "      <td>3371.590332</td>\n",
+              "      <td>3483.681396</td>\n",
+              "      <td>3483.681396</td>\n",
+              "      <td>21022514455</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-27</th>\n",
-              "      <td>3826.127197</td>\n",
-              "      <td>3973.556396</td>\n",
-              "      <td>3821.930420</td>\n",
-              "      <td>3892.006836</td>\n",
-              "      <td>3892.006836</td>\n",
-              "      <td>18949181813</td>\n",
+              "      <th>2024-06-19</th>\n",
+              "      <td>3482.350830</td>\n",
+              "      <td>3583.320068</td>\n",
+              "      <td>3466.480957</td>\n",
+              "      <td>3559.347412</td>\n",
+              "      <td>3559.347412</td>\n",
+              "      <td>15275373778</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-28</th>\n",
-              "      <td>3892.096924</td>\n",
-              "      <td>3924.895752</td>\n",
-              "      <td>3771.213867</td>\n",
-              "      <td>3840.256348</td>\n",
-              "      <td>3840.256348</td>\n",
-              "      <td>19846044324</td>\n",
+              "      <th>2024-06-20</th>\n",
+              "      <td>3559.347412</td>\n",
+              "      <td>3623.886475</td>\n",
+              "      <td>3485.459961</td>\n",
+              "      <td>3511.086914</td>\n",
+              "      <td>3511.086914</td>\n",
+              "      <td>16115123753</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-29</th>\n",
-              "      <td>3840.235107</td>\n",
-              "      <td>3880.648438</td>\n",
-              "      <td>3742.041260</td>\n",
-              "      <td>3763.196533</td>\n",
-              "      <td>3763.196533</td>\n",
-              "      <td>17411416736</td>\n",
+              "      <th>2024-06-21</th>\n",
+              "      <td>3511.268311</td>\n",
+              "      <td>3542.957275</td>\n",
+              "      <td>3447.935547</td>\n",
+              "      <td>3516.075684</td>\n",
+              "      <td>3516.075684</td>\n",
+              "      <td>15933353456</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-30</th>\n",
-              "      <td>3763.357666</td>\n",
-              "      <td>3823.643311</td>\n",
-              "      <td>3702.263672</td>\n",
-              "      <td>3746.849609</td>\n",
-              "      <td>3746.849609</td>\n",
-              "      <td>15065849797</td>\n",
+              "      <th>2024-06-22</th>\n",
+              "      <td>3516.551270</td>\n",
+              "      <td>3519.497559</td>\n",
+              "      <td>3477.169922</td>\n",
+              "      <td>3494.813477</td>\n",
+              "      <td>3494.813477</td>\n",
+              "      <td>7423703673</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-31</th>\n",
-              "      <td>3746.764893</td>\n",
-              "      <td>3841.127441</td>\n",
-              "      <td>3724.708252</td>\n",
-              "      <td>3778.403320</td>\n",
-              "      <td>3778.403320</td>\n",
-              "      <td>15432449024</td>\n",
+              "      <th>2024-06-23</th>\n",
+              "      <td>3494.953857</td>\n",
+              "      <td>3518.871094</td>\n",
+              "      <td>3494.098633</td>\n",
+              "      <td>3504.028076</td>\n",
+              "      <td>3504.028076</td>\n",
+              "      <td>6673526784</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -743,7 +580,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe3974a0-7c41-407a-adc2-6c7fc2d110d9')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82de0430-a039-4b0a-a9aa-b045df7e74a7')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -795,12 +632,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-fe3974a0-7c41-407a-adc2-6c7fc2d110d9 button.colab-df-convert');\n",
+              "        document.querySelector('#df-82de0430-a039-4b0a-a9aa-b045df7e74a7 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-fe3974a0-7c41-407a-adc2-6c7fc2d110d9');\n",
+              "        const element = document.querySelector('#df-82de0430-a039-4b0a-a9aa-b045df7e74a7');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -820,8 +657,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "<div id=\"df-a1be6a50-5f87-4ac9-958b-d16bf41c980a\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a1be6a50-5f87-4ac9-958b-d16bf41c980a')\"\n",
+              "<div id=\"df-8e944d30-4b4b-4b49-b0f3-ab60d4ae9983\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8e944d30-4b4b-4b49-b0f3-ab60d4ae9983')\"\n",
               "            title=\"Suggest charts\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -940,18 +777,19 @@
               "    }\n",
               "    (() => {\n",
               "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-a1be6a50-5f87-4ac9-958b-d16bf41c980a button');\n",
+              "        document.querySelector('#df-8e944d30-4b4b-4b49-b0f3-ab60d4ae9983 button');\n",
               "      quickchartButtonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "    })();\n",
               "  </script>\n",
               "</div>\n",
+              "\n",
               "    </div>\n",
               "  </div>\n"
             ],
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "dataframe",
-              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"2024-05-30\",\n          \"2024-05-23\",\n          \"2024-05-27\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.87530877724932,\n        \"min\": 3726.975586,\n        \"max\": 3892.096924,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3763.357666,\n          3737.178467,\n          3826.127197\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63.792129185899874,\n        \"min\": 3776.006592,\n        \"max\": 3973.556396,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3823.643311,\n          3943.553955,\n          3973.556396\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 75.73223141957936,\n        \"min\": 3552.642578,\n        \"max\": 3821.93042,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3702.263672,\n          3552.642578,\n          3821.93042\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.84413583123411,\n        \"min\": 3726.93457,\n        \"max\": 3892.006836,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3746.849609,\n          3776.927246,\n          3892.006836\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.84413583123411,\n        \"min\": 3726.93457,\n        \"max\": 3892.006836,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3746.849609,\n          3776.927246,\n          3892.006836\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9820441967,\n        \"min\": 10000027764,\n        \"max\": 45623656317,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          15065849797,\n          45623656317,\n          18949181813\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"2024-06-22\",\n          \"2024-06-15\",\n          \"2024-06-19\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48.01536575149276,\n        \"min\": 3467.969971,\n        \"max\": 3622.383545,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3516.55127,\n          3479.785645,\n          3482.35083\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.10295309560295,\n        \"min\": 3514.177246,\n        \"max\": 3648.093018,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3519.497559,\n          3589.887451,\n          3583.320068\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.5334882229941,\n        \"min\": 3366.223877,\n        \"max\": 3541.534424,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3477.169922,\n          3473.452637,\n          3466.480957\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 44.061808263651415,\n        \"min\": 3480.271973,\n        \"max\": 3620.563477,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3494.813477,\n          3565.549561,\n          3559.347412\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 44.061808263651415,\n        \"min\": 3480.271973,\n        \"max\": 3620.563477,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3494.813477,\n          3565.549561,\n          3559.347412\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4624246210,\n        \"min\": 6673526784,\n        \"max\": 21022514455,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          7423703673,\n          12733651076,\n          15275373778\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
             }
           },
           "metadata": {},
@@ -973,7 +811,7 @@
           "height": 526
         },
         "id": "KjBBUiY4JP1W",
-        "outputId": "538f292f-4102-45a4-9e09-c8fcbe6ab409"
+        "outputId": "540e9356-c20f-4b91-dd2a-f5cb978a52ed"
       },
       "outputs": [
         {
@@ -981,18 +819,18 @@
           "name": "stdout",
           "text": [
             "<class 'pandas.core.frame.DataFrame'>\n",
-            "Index: 2396 entries, 2017-11-09 to 2024-05-31\n",
+            "Index: 2419 entries, 2017-11-09 to 2024-06-23\n",
             "Data columns (total 6 columns):\n",
             " #   Column     Non-Null Count  Dtype  \n",
             "---  ------     --------------  -----  \n",
-            " 0   Open       2396 non-null   float64\n",
-            " 1   High       2396 non-null   float64\n",
-            " 2   Low        2396 non-null   float64\n",
-            " 3   Close      2396 non-null   float64\n",
-            " 4   Adj Close  2396 non-null   float64\n",
-            " 5   Volume     2396 non-null   int64  \n",
+            " 0   Open       2419 non-null   float64\n",
+            " 1   High       2419 non-null   float64\n",
+            " 2   Low        2419 non-null   float64\n",
+            " 3   Close      2419 non-null   float64\n",
+            " 4   Adj Close  2419 non-null   float64\n",
+            " 5   Volume     2419 non-null   int64  \n",
             "dtypes: float64(5), int64(1)\n",
-            "memory usage: 131.0+ KB\n"
+            "memory usage: 132.3+ KB\n"
           ]
         },
         {
@@ -1000,28 +838,28 @@
           "data": {
             "text/plain": [
               "              Open         High          Low        Close    Adj Close  \\\n",
-              "count  2396.000000  2396.000000  2396.000000  2396.000000  2396.000000   \n",
-              "mean   1352.665262  1390.927683  1310.927133  1353.921352  1353.921352   \n",
-              "std    1176.574368  1209.698191  1139.730911  1177.074974  1177.074974   \n",
+              "count  2419.000000  2419.000000  2419.000000  2419.000000  2419.000000   \n",
+              "mean   1374.367581  1412.706739  1332.386708  1375.499080  1375.499080   \n",
+              "std    1191.807181  1224.351161  1155.337026  1192.059729  1192.059729   \n",
               "min      84.279694    85.342743    82.829887    84.308296    84.308296   \n",
-              "25%     242.309002   247.320042   235.626839   242.292122   242.292122   \n",
-              "50%    1206.991821  1230.078553  1168.261536  1209.387512  1209.387512   \n",
-              "75%    2012.372711  2066.105041  1944.020234  2013.507904  2013.507904   \n",
+              "25%     244.145607   248.187210   236.747917   244.160736   244.160736   \n",
+              "50%    1218.079346  1257.770020  1185.702148  1218.426758  1218.426758   \n",
+              "75%    2058.074462  2110.714478  1998.625488  2061.309570  2061.309570   \n",
               "max    4810.071289  4891.704590  4718.039063  4812.087402  4812.087402   \n",
               "\n",
               "             Volume  \n",
-              "count  2.396000e+03  \n",
-              "mean   1.229681e+10  \n",
-              "std    1.007116e+10  \n",
+              "count  2.419000e+03  \n",
+              "mean   1.230856e+10  \n",
+              "std    1.003120e+10  \n",
               "min    6.217330e+08  \n",
-              "25%    5.084750e+09  \n",
-              "50%    9.831786e+09  \n",
-              "75%    1.681544e+10  \n",
+              "25%    5.126564e+09  \n",
+              "50%    9.908112e+09  \n",
+              "75%    1.679948e+10  \n",
               "max    8.448291e+10  "
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-4e4907eb-04d2-4517-811d-a75132ad0fac\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-d783d39c-967b-4add-bd15-cb33f3ec04fa\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -1051,30 +889,30 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>count</th>\n",
-              "      <td>2396.000000</td>\n",
-              "      <td>2396.000000</td>\n",
-              "      <td>2396.000000</td>\n",
-              "      <td>2396.000000</td>\n",
-              "      <td>2396.000000</td>\n",
-              "      <td>2.396000e+03</td>\n",
+              "      <td>2419.000000</td>\n",
+              "      <td>2419.000000</td>\n",
+              "      <td>2419.000000</td>\n",
+              "      <td>2419.000000</td>\n",
+              "      <td>2419.000000</td>\n",
+              "      <td>2.419000e+03</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>mean</th>\n",
-              "      <td>1352.665262</td>\n",
-              "      <td>1390.927683</td>\n",
-              "      <td>1310.927133</td>\n",
-              "      <td>1353.921352</td>\n",
-              "      <td>1353.921352</td>\n",
-              "      <td>1.229681e+10</td>\n",
+              "      <td>1374.367581</td>\n",
+              "      <td>1412.706739</td>\n",
+              "      <td>1332.386708</td>\n",
+              "      <td>1375.499080</td>\n",
+              "      <td>1375.499080</td>\n",
+              "      <td>1.230856e+10</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>std</th>\n",
-              "      <td>1176.574368</td>\n",
-              "      <td>1209.698191</td>\n",
-              "      <td>1139.730911</td>\n",
-              "      <td>1177.074974</td>\n",
-              "      <td>1177.074974</td>\n",
-              "      <td>1.007116e+10</td>\n",
+              "      <td>1191.807181</td>\n",
+              "      <td>1224.351161</td>\n",
+              "      <td>1155.337026</td>\n",
+              "      <td>1192.059729</td>\n",
+              "      <td>1192.059729</td>\n",
+              "      <td>1.003120e+10</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>min</th>\n",
@@ -1087,30 +925,30 @@
               "    </tr>\n",
               "    <tr>\n",
               "      <th>25%</th>\n",
-              "      <td>242.309002</td>\n",
-              "      <td>247.320042</td>\n",
-              "      <td>235.626839</td>\n",
-              "      <td>242.292122</td>\n",
-              "      <td>242.292122</td>\n",
-              "      <td>5.084750e+09</td>\n",
+              "      <td>244.145607</td>\n",
+              "      <td>248.187210</td>\n",
+              "      <td>236.747917</td>\n",
+              "      <td>244.160736</td>\n",
+              "      <td>244.160736</td>\n",
+              "      <td>5.126564e+09</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>50%</th>\n",
-              "      <td>1206.991821</td>\n",
-              "      <td>1230.078553</td>\n",
-              "      <td>1168.261536</td>\n",
-              "      <td>1209.387512</td>\n",
-              "      <td>1209.387512</td>\n",
-              "      <td>9.831786e+09</td>\n",
+              "      <td>1218.079346</td>\n",
+              "      <td>1257.770020</td>\n",
+              "      <td>1185.702148</td>\n",
+              "      <td>1218.426758</td>\n",
+              "      <td>1218.426758</td>\n",
+              "      <td>9.908112e+09</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>75%</th>\n",
-              "      <td>2012.372711</td>\n",
-              "      <td>2066.105041</td>\n",
-              "      <td>1944.020234</td>\n",
-              "      <td>2013.507904</td>\n",
-              "      <td>2013.507904</td>\n",
-              "      <td>1.681544e+10</td>\n",
+              "      <td>2058.074462</td>\n",
+              "      <td>2110.714478</td>\n",
+              "      <td>1998.625488</td>\n",
+              "      <td>2061.309570</td>\n",
+              "      <td>2061.309570</td>\n",
+              "      <td>1.679948e+10</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>max</th>\n",
@@ -1127,7 +965,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4e4907eb-04d2-4517-811d-a75132ad0fac')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d783d39c-967b-4add-bd15-cb33f3ec04fa')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -1179,12 +1017,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-4e4907eb-04d2-4517-811d-a75132ad0fac button.colab-df-convert');\n",
+              "        document.querySelector('#df-d783d39c-967b-4add-bd15-cb33f3ec04fa button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-4e4907eb-04d2-4517-811d-a75132ad0fac');\n",
+              "        const element = document.querySelector('#df-d783d39c-967b-4add-bd15-cb33f3ec04fa');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -1204,8 +1042,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "<div id=\"df-52ff010c-c7bd-47fa-aebd-bc3acbd02d42\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-52ff010c-c7bd-47fa-aebd-bc3acbd02d42')\"\n",
+              "<div id=\"df-d6bbc015-fc67-4bbd-a7df-f986ddd5f5b7\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d6bbc015-fc67-4bbd-a7df-f986ddd5f5b7')\"\n",
               "            title=\"Suggest charts\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -1324,18 +1162,19 @@
               "    }\n",
               "    (() => {\n",
               "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-52ff010c-c7bd-47fa-aebd-bc3acbd02d42 button');\n",
+              "        document.querySelector('#df-d6bbc015-fc67-4bbd-a7df-f986ddd5f5b7 button');\n",
               "      quickchartButtonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "    })();\n",
               "  </script>\n",
               "</div>\n",
+              "\n",
               "    </div>\n",
               "  </div>\n"
             ],
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "dataframe",
-              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1493.554011477898,\n        \"min\": 84.279694,\n        \"max\": 4810.071289,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1352.6652621352255,\n          1206.991821,\n          2396.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1515.5722389152984,\n        \"min\": 85.342743,\n        \"max\": 4891.70459,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1390.927682657763,\n          1230.0785525000001,\n          2396.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1469.4258971151999,\n        \"min\": 82.829887,\n        \"max\": 4718.039063,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1310.9271326736225,\n          1168.2615355,\n          2396.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1494.0339417872121,\n        \"min\": 84.308296,\n        \"max\": 4812.087402,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1353.921351550501,\n          1209.3875125,\n          2396.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1494.0339417872121,\n        \"min\": 84.308296,\n        \"max\": 4812.087402,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1353.921351550501,\n          1209.3875125,\n          2396.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27706179749.599216,\n        \"min\": 2396.0,\n        \"max\": 84482912776.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          12296811588.455341,\n          9831785501.0,\n          2396.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1494.720281925156,\n        \"min\": 84.279694,\n        \"max\": 4810.071289,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1374.3675809355104,\n          1218.079346,\n          2419.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1516.1766236639855,\n        \"min\": 85.342743,\n        \"max\": 4891.70459,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1412.7067394162877,\n          1257.77002,\n          2419.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1470.6448616802188,\n        \"min\": 82.829887,\n        \"max\": 4718.039063,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1332.3867076374534,\n          1185.702148,\n          2419.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1495.3773967904688,\n        \"min\": 84.308296,\n        \"max\": 4812.087402,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1375.4990803455976,\n          1218.426758,\n          2419.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1495.3773967904688,\n        \"min\": 84.308296,\n        \"max\": 4812.087402,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1375.4990803455976,\n          1218.426758,\n          2419.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27701817495.35504,\n        \"min\": 2419.0,\n        \"max\": 84482912776.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          12308557309.507235,\n          9908112156.0,\n          2419.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
             }
           },
           "metadata": {},
@@ -1366,7 +1205,7 @@
           "height": 507
         },
         "id": "ZyQukOjYJ4w9",
-        "outputId": "c5cde4f9-7016-4b49-96b2-2d44a46bfb02"
+        "outputId": "8f201eee-ad73-4150-edd6-7c5e04d6fb5f"
       },
       "outputs": [
         {
@@ -1385,7 +1224,7 @@
             "text/plain": [
               "<Figure size 500x500 with 2 Axes>"
             ],
-            "image/png": 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\n"
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1eu3QoUNmZpaUlGQVKlSw5s2b++/bs2dPi46ODtp4+EK4adMma9++vd1+++1WtmxZ69atm39Nfv78+VayZEl77rnngmLt/ExO3Q23fPlyi4mJsTFjxljNmjXtf//3fy0xMdFatGgR1B84vtcnPz/f7rjjDmvSpIndfPPNVrlyZfv444/998vMzLQRI0ZY+fLlbePGjYEa7jk7ceKExcbG2q233ur/f/rRRx9Zhw4drG/fvsUunJ999pnFxMTYo48+6p8Vu3DhQouNjbWuXbtaenq6TZs2zTp37vybE6OKI6IZYL4PnW+//dZiY2MtLi7OWrRoYV9//bXt27fPhg4dag0bNrQ///nP1rFjR2vSpIl/rT1Yw/nTTz9ZxYoVbfr06fbTTz/Z1KlTLT4+3rp27erfVTt79myLiYkJ2mianQzJzJkz7fDhw+b1ei0hIcGeeOIJy8jIsISEBLvrrrvM4/HY7NmzAz3Uc1JwT0CvXr38u++ysrLs6aeftjJlytg333xjeXl5NmPGDIuOjg7K76VPnDhhZmaHDh2ym2++2e68807/FueSJUuKTTi9Xm+h99uyZcssJibGBgwY4A/n8uXLrVGjRla7dm2rUaOGffPNN4EabsAQzQDyRS8tLc2qVq1q48ePt3feeccef/xxCwkJ8e+6++qrr2zq1Kn2/vvv+x8TDBMKzE5+qLz66quFrvvyyy8tLi7Of9nr9dq8efOsfv361rt3b384fcsarOHcsGGDlS1b1hISEuzNN9+0EydOWExMjC1YsMCOHz9uX331lXXs2NG2bt0a6KE627Fjh3Xp0sV/2ev12vHjxy0uLs7ef//9Qvft37+/NW3a1PLz8+2///2v/3UNJr4Vg7Vr19p9991nPXv2NI/HYw0bNvRvYRWHcBb8PMnNzfWvmH/55ZfWtGlTGzBggG3ZssXMTr7mGzdutH379gVkrIFGNAPs+++/t3HjxtmTTz5Z6PoxY8ZYhQoVbPv27ac9Jhi2MPPz8+3YsWP20ksv2aZNmwrdtmDBAgsLC7PDhw/7r/MFpV69epaQkGBHjx41s+AKZsHXxTfu3bt324QJEyw6OtruueceGz16tD3yyCN25MiRQA3znPiW5+eff7ZKlSpZ27Zt/bcdOXLE2rdv75/p7QvN+++/b/Hx8Rd/sH+wtLQ0u/HGG+3vf/+7ZWZmWmpqqkVFRdktt9ziD+fixYutU6dO9sgjj9i2bdsCPOLfV/B95ZsFnJ+fb507d7YOHTpYRESEvfnmm5aTk2Nr1qyxpk2b2uDBgy/JLctTEc2LLD8/3/Lz8/3fX/omvkRERPjfgL6JQJGRkbZ8+fLADvg8ZWZmmtnJ6ff/+Mc//Nffdddd1rZt20JruH379rWkpCSLj4+3pUuXXvSxng9fMPPz82348OHWu3dve/nll/3f36Wlpdl9991n1apVM4/Hc9pWWVFW8AN2586d9t1331l4eHihvQVjxoyxqlWr2meffeZ/zcePH29t2rSxrKysoFr5OVVKSorVr1+/0JbV0aNHrVatWtaqVStLS0szs5PhvO2222zevHmBGupZGzx4sH/ruHHjxvbAAw9YcnKyPfXUUxYdHW0vvviimZl9/vnnVq9ePRs2bNhph9pcaojmRZSZmWndu3e3+Ph4u/766+2ee+6x7777zhYsWGAej8dmzJhR6Huihg0b2sKFCwM44nPj+24kJibG7r33XsvMzLTx48dbZGSkTZkyxczM1q1bZ61atbKIiAibOHGi3X333RYdHW3Hjh2zJk2a2FNPPRXgpTh7+fn51rhxY7v//vvt8ccft7vvvttatmxpb7/9tv8+c+fOtc6dO/t3dRV1BWPXr18/a9eunf31r3+1rVu32o033mitWrXy3z5gwAALDw+31q1bW69evaxixYpBM+nnTHtvfO/FrVu3WmRkpH322WeFru/Tp495PB5LTEz0P6Zt27ZB9X+3adOm1rlzZ9u4caO1bt260G3/+te/LCIiwtatW2dmZitXrrRdu3YFYphFCtG8SDIzM61mzZrWq1cvW7p0qf3rX/+yvn37msfjsUWLFtns2bPN4/HYkCFD7O2337Y+ffrYLbfcEjTfXZ5JamqqXX311TZw4EDbtm2bjR492lq1amV///vfzezkISeDBw+2hx56yB5++GHLzs42M7MHH3zQRo0aFXRbJ7NmzSq09fXDDz/Ys88+a507dy50Hk7fxJJg0r59e4uNjbW0tDT/bN/du3db7dq1rUWLFv77zZ8/36ZMmWITJ04Mit2UBR09etSeffZZW7169Wm7zzt27GhRUVG2bds2/+s3cOBA+/zzz/3BzcnJsdjY2KCY7OR7b82fP99atWpl7733npUtW9YfSJ/mzZvb448/HoghFllE8yLIz8+3rl27Wq9evQpdn5uba5MmTTKPx2ObNm2yt956yzwej7Vo0cJefvllfzCDIZynBs43kWDLli1Wvnx5e+KJJ2zLli02YsQIa9mypU2bNu2Mf2fcuHFWoUKF007+HAzGjRvn/67PtzXyzTff2DXXXGPJycmBHNp5SUpKsqioqELX+ZZv9+7dduONN1rLli0DMbQ/1IgRI8zj8VhMTIw1aNDAFi9ebD/++KP/9mbNmllkZKS1a9fO4uPjrXbt2oWCaVZ0TzJy6vvTd/nAgQNWs2ZN+8tf/mIDBgywMWPG2C+//OK/30MPPWSvvPLKRR1rUcdPg10EOTk5OnjwoBISEvyXJSk0NFQDBw5UYmKinn/+eXXr1k2vvPKKvvjiC1WqVEmhoaHKz8/3/+p7UebxeJSbm6u0tDRJJ5ftxIkTioiI0PDhwzVp0iS99tpr6t69u1q0aKGFCxdq1KhR/senpaXpH//4h15//XUtXbpUderUCdSiOMnIyDjtupiYGKWmpmrRokX+c5JGRkaqUaNGys3NvdhD/MPs2rXL/xuYvuXw/axV9erVtXz5cu3du1dRUVGBHOZ5a926tUqUKKHmzZvr7rvvVmJiohISEjRq1Cjl5ORoxYoV+stf/qKYmBjdcccdSk1NVUhIiLxer/9cq2c6F21R4PF4JMn//vT9bFmFChU0ZswYJScny+v16tNPP9Xo0aM1Z84cTZkyRR988IHuuuuuQA69yCn6n8bFwIkTJ7R7927t379fkgqdzLhkyZKKiorS2LFjdfToUQ0YMEAnTpzQgw8+qOPHj/s/rIo6r9erBx98UFWqVNGAAQNUo0YNlSpVSvPmzVNSUpJeffVVDRs2TLm5uRoyZIgyMzN14MAB/8npK1eurDZt2qhTp05F/gdrR48erSNHjujRRx9V9erVJZ1c/oiICHXt2lUTJkxQenq6OnbsqFmzZiklJUU33nhjgEd9bnJycrRhwwb/CdUL/t/1BWLFihV67bXX9Pjjj2v37t2qUaNGQMZ6vpo0aaK+ffsqJSVF77//vuLi4vTDDz+oX79+WrhwoaKiotSvXz916dLF/5hgOvn68OHDtWzZMvXu3VtdunRR+fLlJUnh4eHKyclR+/btFRoaqvnz52vy5MmqXr26PvnkkyK/AnvRBXpT91KQnZ1t0dHR9vDDD/uv8x3fZnZy1t29997r38VjZjZq1CirWLFiocMyirrVq1dbTEyMjRw50vLz823RokUWFhZmixYtMrOT33GWL1/eevbsaXv27Cl0Nplgsnr1arv99ttt9OjRp/1m4KZNmywpKcnCwsIsPj7ewsPDg+pnr06Vm5trHTp0sJ49e/qv83q9hV6z7t2728qVK4PiUKjfM2PGDPuf//kf/6Fe48aNs2rVqtnTTz9t9evXt3vuuSfAIzx3mzdvtpEjR1r58uWtbdu2NnToUP+hXUlJSVa3bl3/6Q2zsrIu+Vmyv4ZoXiQff/yxhYSE2NSpU0+7rUuXLv4ZeAU/jILxYPC1a9f6z5VbpkwZ/4/xHjt2zMxOvnFbt27tX85gm+zjs27dOrv99ttt1KhR/nAWXJaJEyfa+vXr/adEDGYrVqyw0NBQ/8zngqZPn2716tUr9HuZwS4yMtIefvhhmzp1qtWoUaPQxJ5gW8E7kx07dtjkyZOtQYMGFh4ebkOGDLE333zT4uPjC536EGdGNC+iadOmWUhIiPXr189mz55tn3zyiXXp0sXq1q3r38oseCqrYA3K+vXrLTw83BISEgrFseCWtFnwfwCtXbvWH86CU/GTkpLM4/EE1Zl+fs8rr7xiISEhNmTIEFu+fLmtWrXKhg8fbmFhYUExW9SF7/02c+ZMu+KKK6xWrVq2atUqMyt8SEqw/78taMqUKZaQkGCXXXaZeTwe69u3b6CHVOQRzYts6dKl1rRpU6tTp461bdvWevXq5Y9JMMySdbV+/Xpr2rSpjRw50n766adAD+eC8YVz5MiRlpOTY5MnT7ZSpUoF7enUfsuHH35oERERVrt2bYuOjrb77rvP//NRxckvv/xi1113nQ0cONDMgnfl9becukwrVqywAQMG2ObNmwM0ouDhMTML9Peql5rjx48rJydHpUqVUqlSpSRJeXl5QTFL9mysX79eQ4YMUdOmTfX4448X+Qk+52rdunV64oknZGZas2aNkpOTFRkZGehhXRBHjhxRdna2Spcurcsuu0yXX355oId0QUybNk2TJ0/WsmXLdMMNNwR6OBeE/f9JeD7F8TPoQiia86OLudKlS6ts2bL+YJpZsfzP2qhRI40fP14ZGRmqVKlSoIdzwTRu3FgTJkzQ0aNH9Z///KfYBlOSypQpo8qVK6tcuXLFNpiS1LZtW9155526/vrrAz2UC6ZgMCUVy8+gC4EtTVxwvjXaU9dsi5sTJ074V4QQ/Hz/X71eb5E9/hIXH9HERVHcgwng0sDqEy4KggmgOCCaAAA4IpoAADgimgAAOAqKaG7fvl0PP/ywGjRooNDQUNWrVy/QQwIAXIKC4sCczZs3a/HixYqOjpbX65XX6w30kAAAl6CgOOSk4HFSiYmJWrt2rVJSUgI8KgDApSYods9yYDEAoCigRgAAOAqK7zT/CJH9/xnoIVxwpS8L1VfT+kiSYgbN0PGcvACP6MJgOYsXlrP4Wvtqvwv+HF3+Nk+79h7Sq0+0U+Pwqhf8+djSBADAEdEEAMAR0QQAwBHRBADAUVBMBMrOztaSJUskST/99JMyMzM1f/58SVKzZs2K9Q8cAwCKjqCI5r59+/SnP/2p0HW+y8uXL1fz5s0DMCoAwKUmKKJ5/fXXKwhOXAQAKOb4ThMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHRBMAAEdEEwAAR0QTAABHZxXNd999V+3bt1e1atV05ZVXqkGDBpoxY4bM7Iz3/+CDD+TxeFSvXr1C1+fk5GjYsGG68847deWVV8rj8ejAgQNn/BszZ85URESESpUqpRtvvFHTpk07myEDAPCHOatoTpo0SVdccYUmTpyohQsXKj4+Xg899JBGjBhx2n2PHTumxx57TJUrVz7ttuzsbL322msqXbq0YmNjf/X55s2bpz59+qht27ZatGiRunfvrscee0yvvPLK2QwbAFCE7dy5Uy1atNBNN92k+vXrKysry+lxa1L36L8Hj0iSxr71ldak7rngzxnq/AySFi5cqIoVK/ovt2zZUgcPHtSkSZP017/+VSVK/F+Dx4wZoxo1aqhmzZpau3Ztob9Tvnx5paeny+PxaNasWVq6dOkZn++FF15Qp06dNHnyZElS69atlZGRoeHDh6t///4qWbLk2QwfAFAEJSYmatSoUYqNjVV6erpKlSr1u48Z8e+V+nDVVv/lnf89pIFTlqh90zr6a69mF+Q5pbPc0iwYTJ+GDRsqMzOzUKV//PFHTZw4UVOnTv3Vv+XxeH7zubKzs7Vt2zbFxcUVur5NmzY6ePCgVq9efTZDBwAUQZs3b1bJkiX9ex2vvvpqhYb+9vbcmtQ9hYJZ0ILkrfrPlt/e4jyX5/Q574lAX331la677jqVKVPGf92QIUPUq1cv3Xrrref8d0+cOCEzO63+vstbtmw5578NACgafvjhB1111VW699571ahRIyUlJf3uY179cO1v3j59wW/ffi7P6XNWu2dP9dVXX2nu3LmaOHGi/7qFCxdq1apV2rZt2/n8aYWFhalChQr6z3/+o8TERP/1a9askSSlp6ef1d9b+2q/8xpPsPlqWp9AD+GiYDmLF5bz0pOXl6cvv/xSGzdu1DXXXKO2bdvqtttuU+vWrX/1MfsPZ//m3/y928/lOX3OOZp79uxR165d1aJFCw0ePFiSdPz4cQ0dOlQvvvjiGXflnq0BAwZo/PjxiomJUXx8vJKTkzVlyhRJv797FwBQ9F133XWKjIxU9erVJUl33323Nm7c+JsBWzSm+0V/Tp9z2j176NAhxcfHq0KFCnrvvff8E4AmT56sEiVKqFu3bjp06JAOHTqknJwceb1e/7/PxjPPPKNOnTqpR48euvrqq/XAAw/oxRdflCRVqVLlXIYOAChCbrvtNu3bt08ZGRnyer364osvVLdu3SL7nGe9pXns2DG1a9dOhw8f1urVq1WuXDn/bd9//722b9+uSpUqnfa4sLAwTZ8+XQ8//LDzc11++eWaM2eOJk+erL179+qGG25QamqqJKlJkyZnO3QAQBETGhqqpKQk3XnnnTIzxcXFqV27dkX2OT32a2cmOIO8vDx17NhRq1at0pdffqmbbrqp0O3ff/+99u7dW+i6sWPHauvWrZo5c6bCw8NVtWrVQrfPmjVLvXv31v79+5126SYkJGjnzp364osvXIcNAMAf4qy2NAcMGKBFixZp4sSJyszM9E/KkU4eehIREaGIiIhCj5k1a5b27Nmj5s2bF7r+o48+UlZWlv8YzoULF6pMmTK66aab/DH+6KOPtH37dt18881KT0/XnDlztHz5ciUnJ5/LsgIAcF7OKpqffPKJJOmJJ5447badO3fq+uuvd/5bjzzyiH766Sf/5T59Ts4m+9vf/qbhw4efHFxoqF5//XX98MMPKlmypJo3b67Vq1df8P3dAACcyVntngUA4FLGr5wAAOCIaAIA4IhoAgDgiGgCAOCIaAIA4IhoAgDgiGgCAOCIaAIA4IhoAgDgiGgCAOCIaAIA4Oj/AZOu9jIDPhQzAAAAAElFTkSuQmCC\n"
           },
           "metadata": {}
         }
@@ -1409,7 +1248,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "58wCGTtJKWSA",
-        "outputId": "12edff88-1a1e-453c-bb8a-fd182869c8d7"
+        "outputId": "3c34b21f-206d-4eab-c43c-72dd31c06893"
       },
       "outputs": [
         {
@@ -1435,13 +1274,31 @@
     },
     {
       "cell_type": "code",
+      "source": [
+        "for col in dataframe.columns:\n",
+        "  if dataframe[col].isnull().any():\n",
+        "    if dataframe[col].dtype == 'object':\n",
+        "      print(col)\n",
+        "      dataframe[col].fillna(dataframe[col].mode()[0], inplace = True)\n",
+        "    else:\n",
+        "      print(col)\n",
+        "      dataframe[col].fillna(dataframe[col].mean(), inplace = True)"
+      ],
+      "metadata": {
+        "id": "et7xEcRVvxla"
+      },
       "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "-qTBTOEaKyJt",
-        "outputId": "abe975c5-3f78-4567-cae1-c90787303afd"
+        "outputId": "e7d43277-2abd-4482-fb34-8e49deb517cd"
       },
       "outputs": [
         {
@@ -1473,14 +1330,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 394
         },
         "id": "TI_rGlzqQjnf",
-        "outputId": "2edcf649-14f6-417d-b9c8-3cbb48d132ef"
+        "outputId": "d0b2e66f-3fe5-4724-9cbf-6ac015def6c0"
       },
       "outputs": [
         {
@@ -1489,20 +1346,20 @@
             "text/plain": [
               "               Close\n",
               "Date                \n",
-              "2024-05-22  0.772648\n",
-              "2024-05-23  0.781047\n",
-              "2024-05-24  0.770473\n",
-              "2024-05-25  0.775190\n",
-              "2024-05-26  0.791405\n",
-              "2024-05-27  0.805388\n",
-              "2024-05-28  0.794442\n",
-              "2024-05-29  0.778143\n",
-              "2024-05-30  0.774685\n",
-              "2024-05-31  0.781359"
+              "2024-06-14  0.718300\n",
+              "2024-06-15  0.736338\n",
+              "2024-06-16  0.747974\n",
+              "2024-06-17  0.724880\n",
+              "2024-06-18  0.719021\n",
+              "2024-06-19  0.735026\n",
+              "2024-06-20  0.724818\n",
+              "2024-06-21  0.725873\n",
+              "2024-06-22  0.721376\n",
+              "2024-06-23  0.723325"
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-709f5375-9a8a-4b0c-b381-3f977e11704f\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-0c78f67e-117b-4b49-9095-34e197d5d10f\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -1530,44 +1387,44 @@
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <th>2024-05-22</th>\n",
-              "      <td>0.772648</td>\n",
+              "      <th>2024-06-14</th>\n",
+              "      <td>0.718300</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-23</th>\n",
-              "      <td>0.781047</td>\n",
+              "      <th>2024-06-15</th>\n",
+              "      <td>0.736338</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-24</th>\n",
-              "      <td>0.770473</td>\n",
+              "      <th>2024-06-16</th>\n",
+              "      <td>0.747974</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-25</th>\n",
-              "      <td>0.775190</td>\n",
+              "      <th>2024-06-17</th>\n",
+              "      <td>0.724880</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-26</th>\n",
-              "      <td>0.791405</td>\n",
+              "      <th>2024-06-18</th>\n",
+              "      <td>0.719021</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-27</th>\n",
-              "      <td>0.805388</td>\n",
+              "      <th>2024-06-19</th>\n",
+              "      <td>0.735026</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-28</th>\n",
-              "      <td>0.794442</td>\n",
+              "      <th>2024-06-20</th>\n",
+              "      <td>0.724818</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-29</th>\n",
-              "      <td>0.778143</td>\n",
+              "      <th>2024-06-21</th>\n",
+              "      <td>0.725873</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-30</th>\n",
-              "      <td>0.774685</td>\n",
+              "      <th>2024-06-22</th>\n",
+              "      <td>0.721376</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-31</th>\n",
-              "      <td>0.781359</td>\n",
+              "      <th>2024-06-23</th>\n",
+              "      <td>0.723325</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -1575,7 +1432,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-709f5375-9a8a-4b0c-b381-3f977e11704f')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0c78f67e-117b-4b49-9095-34e197d5d10f')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -1627,12 +1484,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-709f5375-9a8a-4b0c-b381-3f977e11704f button.colab-df-convert');\n",
+              "        document.querySelector('#df-0c78f67e-117b-4b49-9095-34e197d5d10f button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-709f5375-9a8a-4b0c-b381-3f977e11704f');\n",
+              "        const element = document.querySelector('#df-0c78f67e-117b-4b49-9095-34e197d5d10f');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -1652,8 +1509,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "<div id=\"df-ad0beff6-49f8-44ed-92a6-73a8f80552ee\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ad0beff6-49f8-44ed-92a6-73a8f80552ee')\"\n",
+              "<div id=\"df-9b1b6ab7-1ec1-4bb0-a896-6d9c1025f7bc\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9b1b6ab7-1ec1-4bb0-a896-6d9c1025f7bc')\"\n",
               "            title=\"Suggest charts\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -1772,22 +1629,23 @@
               "    }\n",
               "    (() => {\n",
               "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-ad0beff6-49f8-44ed-92a6-73a8f80552ee button');\n",
+              "        document.querySelector('#df-9b1b6ab7-1ec1-4bb0-a896-6d9c1025f7bc button');\n",
               "      quickchartButtonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "    })();\n",
               "  </script>\n",
               "</div>\n",
+              "\n",
               "    </div>\n",
               "  </div>\n"
             ],
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "dataframe",
-              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"2024-05-30\",\n          \"2024-05-23\",\n          \"2024-05-27\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011177369891112288,\n        \"min\": 0.770473025987437,\n        \"max\": 0.8053884190925227,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.7746853714785209,\n          0.7810472670590123,\n          0.8053884190925227\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"2024-06-22\",\n          \"2024-06-15\",\n          \"2024-06-19\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0093197688123231,\n        \"min\": 0.7182999884005156,\n        \"max\": 0.7479738587008342,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.7213757463143202,\n          0.7363375460122438,\n          0.7350256934783281\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
             }
           },
           "metadata": {},
-          "execution_count": 9
+          "execution_count": 10
         }
       ],
       "source": [
@@ -1803,6 +1661,7 @@
         "minmax_scaler = MinMaxScaler(feature_range=(0, 1))\n",
         "dataframe['Close'] = minmax_scaler.fit_transform(dataframe[['Close']])\n",
         "\n",
+        "\n",
         "dataframe.tail(10)"
       ]
     },
@@ -1816,18 +1675,193 @@
         "id": "PAyJUmwUaqBF"
       }
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.iloc[1:60+1].values"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sR8-NnvXGO2A",
+        "outputId": "8f862c24-43e6-48a9-8000-53ce4b5f8969"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([['2019-06-19', 9068.174805, 9277.677734, 9051.094727, 9271.459961,\n",
+              "        9271.459961, 131077],\n",
+              "       ['2019-06-20', 9271.567383, 9573.689453, 9209.416992, 9519.200195,\n",
+              "        9519.200195, 83052],\n",
+              "       ['2019-06-21', 9526.833984, 10130.935547, 9526.833984,\n",
+              "        10127.998047, 10127.998047, 76227],\n",
+              "       ['2019-06-22', 10151.890625, 11171.013672, 10083.189453,\n",
+              "        10719.981445, 10719.981445, 84485],\n",
+              "       ['2019-06-23', 10718.117188, 11293.773438, 10561.124023,\n",
+              "        11246.518555, 11246.518555, 52072],\n",
+              "       ['2019-06-24', 11246.518555, 11251.15625, 10659.144531,\n",
+              "        11010.517578, 11010.517578, 106205],\n",
+              "       ['2019-06-25', 10999.733398, 11676.888672, 10999.733398,\n",
+              "        11666.96582, 11666.96582, 95820],\n",
+              "       ['2019-06-26', 11666.96582, 13739.329102, 11631.251953,\n",
+              "        12827.663086, 12827.663086, 67574],\n",
+              "       ['2019-06-27', 12827.663086, 13022.981445, 10452.151367,\n",
+              "        11136.211914, 11136.211914, 117404],\n",
+              "       ['2019-06-28', 11152.90332, 12430.348633, 10863.209961,\n",
+              "        12398.671875, 12398.671875, 96399],\n",
+              "       ['2019-06-29', 12409.884766, 12409.884766, 11521.609375,\n",
+              "        11915.464844, 11915.464844, 44215],\n",
+              "       ['2019-06-30', 11905.128906, 12138.600586, 10757.251953,\n",
+              "        10757.251953, 10757.251953, 12704],\n",
+              "       ['2019-07-01', 10757.251953, 11199.141602, 10060.441406,\n",
+              "        10538.941406, 10538.941406, 18800],\n",
+              "       ['2019-07-02', 10546.290039, 10895.975586, 9631.026367,\n",
+              "        10840.748047, 10840.748047, 41470],\n",
+              "       ['2019-07-03', 10841.888672, 11951.766602, 10841.888672,\n",
+              "        11940.806641, 11940.806641, 54592],\n",
+              "       ['2019-07-04', 11940.260742, 12001.268555, 11174.304688,\n",
+              "        11199.027344, 11199.027344, 6803],\n",
+              "       ['2019-07-05', 11192.214844, 11417.396484, 10875.37793,\n",
+              "        10985.602539, 10985.602539, 32516],\n",
+              "       ['2019-07-06', 10985.306641, 11614.435547, 10985.306641,\n",
+              "        11230.970703, 11230.970703, 19273],\n",
+              "       ['2019-07-07', 11230.369141, 12058.25, 11162.37793, 11592.706055,\n",
+              "        11592.706055, 38151],\n",
+              "       ['2019-07-08', 11593.509766, 12309.231445, 11368.134766,\n",
+              "        12256.126953, 12256.126953, 60043],\n",
+              "       ['2019-07-09', 12259.253906, 12744.650391, 12235.339844,\n",
+              "        12521.632813, 12521.632813, 59686],\n",
+              "       ['2019-07-10', 12519.288086, 13111.345703, 11714.165039,\n",
+              "        12133.441406, 12133.441406, 56542],\n",
+              "       ['2019-07-11', 12134.422852, 12134.422852, 11194.456055,\n",
+              "        11660.28418, 11660.28418, 206567],\n",
+              "       ['2019-07-12', 11663.8125, 12320.452148, 11339.581055,\n",
+              "        11652.459961, 11652.459961, 437704],\n",
+              "       ['2019-07-13', 11651.46582, 12209.827148, 10979.837891,\n",
+              "        11515.664063, 11515.664063, 842523],\n",
+              "       ['2019-07-14', 11447.645508, 11641.489258, 10241.913086,\n",
+              "        10241.913086, 10241.913086, 414119],\n",
+              "       ['2019-07-15', 10143.97168, 11078.411133, 9725.791992,\n",
+              "        10926.464844, 10926.464844, 431893],\n",
+              "       ['2019-07-16', 10987.557617, 12246.306641, 9673.839844,\n",
+              "        9717.038086, 9717.038086, 485403],\n",
+              "       ['2019-07-17', 9833.953125, 10002.206055, 9138.09375, 9685.883789,\n",
+              "        9685.883789, 444337],\n",
+              "       ['2019-07-18', 9689.371094, 10660.923828, 9239.602539,\n",
+              "        10548.814453, 10548.814453, 321096],\n",
+              "       ['2019-07-19', 10548.814453, 10825.875, 10193.408203,\n",
+              "        10572.655273, 10572.655273, 122121],\n",
+              "       ['2019-07-20', 10597.679688, 11314.213867, 10265.417969,\n",
+              "        10913.607422, 10913.607422, 240565],\n",
+              "       ['2019-07-21', 10913.607422, 11229.353516, 10204.167969,\n",
+              "        10792.991211, 10792.991211, 145889],\n",
+              "       ['2019-07-22', 10793.253906, 10975.225586, 10195.461914,\n",
+              "        10556.007813, 10556.007813, 225410],\n",
+              "       ['2019-07-23', 10555.688477, 39504.628906, 8098.790039,\n",
+              "        9900.445313, 9900.445313, 367821],\n",
+              "       ['2019-07-24', 9900.445313, 10894.75, 9115.704102, 9882.260742,\n",
+              "        9882.260742, 81862],\n",
+              "       ['2019-07-25', 9856.210938, 10546.202148, 9635.571289,\n",
+              "        9840.331055, 9840.331055, 10967],\n",
+              "       ['2019-07-26', 9812.708008, 10003.717773, 9641.316406,\n",
+              "        9869.240234, 9869.240234, 11464],\n",
+              "       ['2019-07-27', 9869.240234, 10722.198242, 9854.723633,\n",
+              "        10295.592773, 10295.592773, 43797],\n",
+              "       ['2019-07-28', 10295.592773, 10814.21582, 9866.942383,\n",
+              "        10002.868164, 10002.868164, 209195],\n",
+              "       ['2019-07-29', 9998.833984, 10587.529297, 9647.981445,\n",
+              "        9950.451172, 9950.451172, 362786],\n",
+              "       ['2019-07-30', 9825.239258, 10064.118164, 8893.631836,\n",
+              "        9578.966797, 9578.966797, 204983],\n",
+              "       ['2019-07-31', 9567.248047, 10681.686523, 9548.852539,\n",
+              "        10132.297852, 10132.297852, 100753],\n",
+              "       ['2019-08-01', 10214.501953, 10549.6875, 9411.796875,\n",
+              "        10457.677734, 10457.677734, 112061],\n",
+              "       ['2019-08-02', 10519.310547, 13505.805664, 9548.776367,\n",
+              "        10626.240234, 10626.240234, 186035],\n",
+              "       ['2019-08-03', 10625.523438, 14391.37207, 9896.265625,\n",
+              "        10860.224609, 10860.224609, 99920],\n",
+              "       ['2019-08-04', 10781.024414, 11855.058594, 10075.219727,\n",
+              "        10979.398438, 10979.398438, 56532],\n",
+              "       ['2019-08-05', 10976.354492, 14664.880859, 10792.6875,\n",
+              "        11939.714844, 11939.714844, 193825],\n",
+              "       ['2019-08-06', 12116.700195, 12516.666016, 11311.829102,\n",
+              "        11503.787109, 11503.787109, 114939],\n",
+              "       ['2019-08-07', 11503.538086, 17742.109375, 11415.768555,\n",
+              "        15917.913086, 15917.913086, 84101],\n",
+              "       ['2019-08-08', 15917.913086, 21598.978516, 5282.191406,\n",
+              "        11788.261719, 11788.261719, 122648],\n",
+              "       ['2019-08-09', 11787.677734, 14115.886719, 10901.922852,\n",
+              "        12379.829102, 12379.829102, 84676],\n",
+              "       ['2019-08-10', 12379.829102, 12676.383789, 11068.366211,\n",
+              "        11418.65332, 11418.65332, 104937],\n",
+              "       ['2019-08-11', 11414.408203, 11917.924805, 10714.913086,\n",
+              "        11251.161133, 11251.161133, 75153],\n",
+              "       ['2019-08-12', 11062.529297, 13127.165039, 2389.728516,\n",
+              "        12075.470703, 12075.470703, 102306],\n",
+              "       ['2019-08-13', 12156.397461, 13020.619141, 3998.595703,\n",
+              "        10857.993164, 10857.993164, 99478],\n",
+              "       ['2019-08-14', 10882.438477, 11251.541016, 9841.882813,\n",
+              "        9952.604492, 9952.604492, 122763],\n",
+              "       ['2019-08-15', 9952.604492, 11244.59375, 9609.75, 10247.99707,\n",
+              "        10247.99707, 99753],\n",
+              "       ['2019-08-16', 10235.629883, 11305.080078, 9334.977539,\n",
+              "        10775.947266, 10775.947266, 131836],\n",
+              "       ['2019-08-17', 10778.519531, 11390.839844, 9853.967773,\n",
+              "        10009.873047, 10009.873047, 117695]], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.iloc[1].values[0]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "W0n-Wf_dGSyw",
+        "outputId": "30f3e2b1-bc52-4b42-d9e9-4e98f0fecb04"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'2019-06-19'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
         "import numpy as np\n",
         "\n",
-        "def create_sequences(df, sequence_length):\n",
-        "  labels = []\n",
+        "def create_sequences(df, seq_len) -> tuple:\n",
+        "  labels    = []\n",
         "  sequences = []\n",
         "\n",
-        "  for i in range(len(df) - sequence_length):\n",
-        "    seq   = df.iloc[i:i+sequence_length].values\n",
-        "    label = df.iloc[i+sequence_length].values[0]\n",
+        "  for i in range(len(df) - seq_len):\n",
+        "    seq   = df.iloc[i:(seq_len + i)].values\n",
+        "    label = df.iloc[seq_len + i].values[0]\n",
         "\n",
         "    sequences.append(seq)\n",
         "    labels.append(label)\n",
@@ -1836,27 +1870,24 @@
         "\n",
         "\n",
         "sequence_length   = 60\n",
-        "sequences, labels = create_sequences(dataframe, sequence_length)\n",
-        "sequences.shape, labels.shape"
+        "sequences, labels = create_sequences(df = dataframe, seq_len = sequence_length)\n",
+        "print(f'Shapes: {(sequences.shape, labels.shape)}')"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "3rcbbn5WavFz",
-        "outputId": "64479bb4-b0c3-4a24-9f42-e886577aac23"
+        "outputId": "0502df53-a7ee-424d-bc5f-edbcaf203135"
       },
-      "execution_count": 10,
+      "execution_count": 28,
       "outputs": [
         {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "((2336, 60, 1), (2336,))"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 10
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Shapes: ((2359, 60, 1), (2359,))\n"
+          ]
         }
       ]
     },
@@ -1911,9 +1942,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "olldmw1pbPYE",
-        "outputId": "4d0335e8-4893-4c70-efe1-4d10ae718671"
+        "outputId": "b538d231-3747-467b-a1b7-9c1fbd9939d9"
       },
-      "execution_count": 11,
+      "execution_count": 14,
       "outputs": [
         {
           "output_type": "stream",
@@ -1952,7 +1983,7 @@
         ")\n",
         "\n",
         "model_checkpoint = ModelCheckpoint(\n",
-        "  filepath       = 'eth.keras',\n",
+        "  filepath       = 'ETH-USD.keras',\n",
         "  save_best_only = True,\n",
         "  monitor        = 'val_loss',\n",
         "  mode           = 'min'\n",
@@ -1971,158 +2002,66 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "kjkalCASbm5H",
-        "outputId": "68b707b6-2667-4a76-aa40-27f11e1c1cfc"
+        "outputId": "ef6a81cf-b23c-4807-bc74-b56324e87bc6"
       },
-      "execution_count": 12,
+      "execution_count": 15,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
             "Epoch 1/200\n",
-            "59/59 [==============================] - 21s 130ms/step - loss: 0.0175 - val_loss: 0.0014\n",
+            "59/59 [==============================] - 12s 122ms/step - loss: 0.0107 - val_loss: 0.0017\n",
             "Epoch 2/200\n",
-            "59/59 [==============================] - 5s 81ms/step - loss: 0.0029 - val_loss: 0.0036\n",
+            "59/59 [==============================] - 4s 72ms/step - loss: 0.0028 - val_loss: 0.0012\n",
             "Epoch 3/200\n",
-            "59/59 [==============================] - 4s 76ms/step - loss: 0.0027 - val_loss: 0.0011\n",
+            "59/59 [==============================] - 5s 85ms/step - loss: 0.0025 - val_loss: 0.0010\n",
             "Epoch 4/200\n",
-            "59/59 [==============================] - 4s 61ms/step - loss: 0.0026 - val_loss: 0.0012\n",
+            "59/59 [==============================] - 5s 85ms/step - loss: 0.0023 - val_loss: 9.8405e-04\n",
             "Epoch 5/200\n",
-            "59/59 [==============================] - 4s 71ms/step - loss: 0.0021 - val_loss: 0.0010\n",
+            "59/59 [==============================] - 4s 72ms/step - loss: 0.0018 - val_loss: 0.0015\n",
             "Epoch 6/200\n",
-            "59/59 [==============================] - 5s 82ms/step - loss: 0.0022 - val_loss: 9.1701e-04\n",
+            "59/59 [==============================] - 5s 91ms/step - loss: 0.0022 - val_loss: 8.3344e-04\n",
             "Epoch 7/200\n",
-            "59/59 [==============================] - 4s 60ms/step - loss: 0.0020 - val_loss: 0.0011\n",
+            "59/59 [==============================] - 5s 79ms/step - loss: 0.0019 - val_loss: 7.8464e-04\n",
             "Epoch 8/200\n",
-            "59/59 [==============================] - 3s 58ms/step - loss: 0.0019 - val_loss: 9.5400e-04\n",
+            "59/59 [==============================] - 4s 70ms/step - loss: 0.0017 - val_loss: 7.6916e-04\n",
             "Epoch 9/200\n",
-            "59/59 [==============================] - 5s 91ms/step - loss: 0.0019 - val_loss: 8.8370e-04\n",
+            "59/59 [==============================] - 5s 89ms/step - loss: 0.0015 - val_loss: 9.1782e-04\n",
             "Epoch 10/200\n",
-            "59/59 [==============================] - 4s 61ms/step - loss: 0.0020 - val_loss: 7.8336e-04\n",
+            "59/59 [==============================] - 5s 81ms/step - loss: 0.0017 - val_loss: 6.4996e-04\n",
             "Epoch 11/200\n",
-            "59/59 [==============================] - 4s 60ms/step - loss: 0.0019 - val_loss: 0.0012\n",
+            "59/59 [==============================] - 4s 72ms/step - loss: 0.0016 - val_loss: 0.0024\n",
             "Epoch 12/200\n",
-            "59/59 [==============================] - 4s 67ms/step - loss: 0.0017 - val_loss: 8.1429e-04\n",
+            "59/59 [==============================] - 6s 98ms/step - loss: 0.0015 - val_loss: 7.1000e-04\n",
             "Epoch 13/200\n",
-            "59/59 [==============================] - 5s 82ms/step - loss: 0.0016 - val_loss: 0.0013\n",
+            "59/59 [==============================] - 4s 70ms/step - loss: 0.0015 - val_loss: 8.1860e-04\n",
             "Epoch 14/200\n",
-            "59/59 [==============================] - 4s 61ms/step - loss: 0.0017 - val_loss: 7.2635e-04\n",
+            "59/59 [==============================] - 4s 76ms/step - loss: 0.0017 - val_loss: 5.5193e-04\n",
             "Epoch 15/200\n",
-            "59/59 [==============================] - 5s 93ms/step - loss: 0.0018 - val_loss: 8.0339e-04\n",
+            "59/59 [==============================] - 6s 97ms/step - loss: 0.0014 - val_loss: 0.0013\n",
             "Epoch 16/200\n",
-            "59/59 [==============================] - 5s 85ms/step - loss: 0.0015 - val_loss: 8.5450e-04\n",
+            "59/59 [==============================] - 5s 84ms/step - loss: 0.0013 - val_loss: 0.0012\n",
             "Epoch 17/200\n",
-            "59/59 [==============================] - 4s 60ms/step - loss: 0.0016 - val_loss: 7.7747e-04\n",
+            "59/59 [==============================] - 5s 83ms/step - loss: 0.0013 - val_loss: 7.6420e-04\n",
             "Epoch 18/200\n",
-            "59/59 [==============================] - 4s 62ms/step - loss: 0.0015 - val_loss: 5.7924e-04\n",
+            "59/59 [==============================] - 5s 91ms/step - loss: 0.0017 - val_loss: 8.6200e-04\n",
             "Epoch 19/200\n",
-            "59/59 [==============================] - 5s 81ms/step - loss: 0.0014 - val_loss: 5.4883e-04\n",
+            "59/59 [==============================] - 4s 74ms/step - loss: 0.0013 - val_loss: 5.0253e-04\n",
             "Epoch 20/200\n",
-            "59/59 [==============================] - 4s 73ms/step - loss: 0.0015 - val_loss: 0.0010\n",
+            "59/59 [==============================] - 5s 84ms/step - loss: 0.0012 - val_loss: 8.1612e-04\n",
             "Epoch 21/200\n",
-            "59/59 [==============================] - 4s 60ms/step - loss: 0.0013 - val_loss: 5.3849e-04\n",
+            "59/59 [==============================] - 5s 88ms/step - loss: 0.0014 - val_loss: 4.7569e-04\n",
             "Epoch 22/200\n",
-            "59/59 [==============================] - 4s 72ms/step - loss: 0.0013 - val_loss: 5.0904e-04\n",
+            "59/59 [==============================] - 4s 76ms/step - loss: 0.0014 - val_loss: 6.3227e-04\n",
             "Epoch 23/200\n",
-            "59/59 [==============================] - 4s 76ms/step - loss: 0.0013 - val_loss: 6.6130e-04\n",
+            "59/59 [==============================] - 5s 89ms/step - loss: 0.0013 - val_loss: 0.0010\n",
             "Epoch 24/200\n",
-            "59/59 [==============================] - 4s 61ms/step - loss: 0.0012 - val_loss: 5.0616e-04\n",
+            "59/59 [==============================] - 5s 79ms/step - loss: 0.0013 - val_loss: 5.3331e-04\n",
             "Epoch 25/200\n",
-            "59/59 [==============================] - 3s 58ms/step - loss: 0.0013 - val_loss: 5.2636e-04\n",
+            "59/59 [==============================] - 4s 73ms/step - loss: 0.0012 - val_loss: 6.2773e-04\n",
             "Epoch 26/200\n",
-            "59/59 [==============================] - 5s 93ms/step - loss: 0.0012 - val_loss: 8.5814e-04\n",
-            "Epoch 27/200\n",
-            "59/59 [==============================] - 4s 61ms/step - loss: 0.0014 - val_loss: 9.5452e-04\n",
-            "Epoch 28/200\n",
-            "59/59 [==============================] - 4s 62ms/step - loss: 0.0012 - val_loss: 4.3667e-04\n",
-            "Epoch 29/200\n",
-            "59/59 [==============================] - 4s 73ms/step - loss: 0.0013 - val_loss: 8.5634e-04\n",
-            "Epoch 30/200\n",
-            "59/59 [==============================] - 5s 77ms/step - loss: 0.0011 - val_loss: 4.2766e-04\n",
-            "Epoch 31/200\n",
-            "59/59 [==============================] - 4s 63ms/step - loss: 0.0012 - val_loss: 4.2406e-04\n",
-            "Epoch 32/200\n",
-            "59/59 [==============================] - 4s 60ms/step - loss: 0.0011 - val_loss: 5.3112e-04\n",
-            "Epoch 33/200\n",
-            "59/59 [==============================] - 5s 90ms/step - loss: 0.0012 - val_loss: 4.0849e-04\n",
-            "Epoch 34/200\n",
-            "59/59 [==============================] - 4s 60ms/step - loss: 0.0011 - val_loss: 7.4729e-04\n",
-            "Epoch 35/200\n",
-            "59/59 [==============================] - 4s 60ms/step - loss: 0.0011 - val_loss: 3.8810e-04\n",
-            "Epoch 36/200\n",
-            "59/59 [==============================] - 4s 75ms/step - loss: 0.0012 - val_loss: 7.5411e-04\n",
-            "Epoch 37/200\n",
-            "59/59 [==============================] - 5s 76ms/step - loss: 0.0011 - val_loss: 5.6271e-04\n",
-            "Epoch 38/200\n",
-            "59/59 [==============================] - 4s 60ms/step - loss: 0.0010 - val_loss: 3.8006e-04\n",
-            "Epoch 39/200\n",
-            "59/59 [==============================] - 4s 60ms/step - loss: 9.9454e-04 - val_loss: 3.7607e-04\n",
-            "Epoch 40/200\n",
-            "59/59 [==============================] - 6s 95ms/step - loss: 0.0011 - val_loss: 8.1985e-04\n",
-            "Epoch 41/200\n",
-            "59/59 [==============================] - 4s 60ms/step - loss: 0.0011 - val_loss: 6.0866e-04\n",
-            "Epoch 42/200\n",
-            "59/59 [==============================] - 4s 59ms/step - loss: 9.6114e-04 - val_loss: 0.0011\n",
-            "Epoch 43/200\n",
-            "59/59 [==============================] - 4s 72ms/step - loss: 9.5140e-04 - val_loss: 3.6197e-04\n",
-            "Epoch 44/200\n",
-            "59/59 [==============================] - 4s 75ms/step - loss: 0.0011 - val_loss: 7.0947e-04\n",
-            "Epoch 45/200\n",
-            "59/59 [==============================] - 4s 60ms/step - loss: 0.0010 - val_loss: 3.6501e-04\n",
-            "Epoch 46/200\n",
-            "59/59 [==============================] - 4s 60ms/step - loss: 8.2942e-04 - val_loss: 3.5953e-04\n",
-            "Epoch 47/200\n",
-            "59/59 [==============================] - 6s 100ms/step - loss: 9.0532e-04 - val_loss: 4.5668e-04\n",
-            "Epoch 48/200\n",
-            "59/59 [==============================] - 4s 63ms/step - loss: 0.0010 - val_loss: 3.5209e-04\n",
-            "Epoch 49/200\n",
-            "59/59 [==============================] - 4s 60ms/step - loss: 0.0011 - val_loss: 5.2621e-04\n",
-            "Epoch 50/200\n",
-            "59/59 [==============================] - 5s 79ms/step - loss: 0.0011 - val_loss: 3.3283e-04\n",
-            "Epoch 51/200\n",
-            "59/59 [==============================] - 4s 74ms/step - loss: 9.2801e-04 - val_loss: 4.5561e-04\n",
-            "Epoch 52/200\n",
-            "59/59 [==============================] - 4s 60ms/step - loss: 0.0010 - val_loss: 3.1821e-04\n",
-            "Epoch 53/200\n",
-            "59/59 [==============================] - 4s 59ms/step - loss: 0.0010 - val_loss: 3.5321e-04\n",
-            "Epoch 54/200\n",
-            "59/59 [==============================] - 5s 92ms/step - loss: 9.0325e-04 - val_loss: 3.6632e-04\n",
-            "Epoch 55/200\n",
-            "59/59 [==============================] - 5s 89ms/step - loss: 9.4327e-04 - val_loss: 3.0977e-04\n",
-            "Epoch 56/200\n",
-            "59/59 [==============================] - 4s 60ms/step - loss: 8.7158e-04 - val_loss: 3.0950e-04\n",
-            "Epoch 57/200\n",
-            "59/59 [==============================] - 5s 88ms/step - loss: 0.0010 - val_loss: 4.4770e-04\n",
-            "Epoch 58/200\n",
-            "59/59 [==============================] - 4s 62ms/step - loss: 9.8695e-04 - val_loss: 4.3848e-04\n",
-            "Epoch 59/200\n",
-            "59/59 [==============================] - 4s 61ms/step - loss: 9.0576e-04 - val_loss: 3.0263e-04\n",
-            "Epoch 60/200\n",
-            "59/59 [==============================] - 4s 75ms/step - loss: 8.8868e-04 - val_loss: 3.5009e-04\n",
-            "Epoch 61/200\n",
-            "59/59 [==============================] - 5s 80ms/step - loss: 8.8596e-04 - val_loss: 5.8498e-04\n",
-            "Epoch 62/200\n",
-            "59/59 [==============================] - 4s 61ms/step - loss: 9.8473e-04 - val_loss: 3.6592e-04\n",
-            "Epoch 63/200\n",
-            "59/59 [==============================] - 4s 62ms/step - loss: 8.4708e-04 - val_loss: 8.4570e-04\n",
-            "Epoch 64/200\n",
-            "59/59 [==============================] - 5s 92ms/step - loss: 8.9016e-04 - val_loss: 2.9699e-04\n",
-            "Epoch 65/200\n",
-            "59/59 [==============================] - 4s 60ms/step - loss: 8.1578e-04 - val_loss: 3.3800e-04\n",
-            "Epoch 66/200\n",
-            "59/59 [==============================] - 4s 59ms/step - loss: 8.6326e-04 - val_loss: 3.1552e-04\n",
-            "Epoch 67/200\n",
-            "59/59 [==============================] - 4s 72ms/step - loss: 8.9034e-04 - val_loss: 2.8460e-04\n",
-            "Epoch 68/200\n",
-            "59/59 [==============================] - 5s 79ms/step - loss: 8.2446e-04 - val_loss: 3.4850e-04\n",
-            "Epoch 69/200\n",
-            "59/59 [==============================] - 4s 60ms/step - loss: 8.8816e-04 - val_loss: 4.5005e-04\n",
-            "Epoch 70/200\n",
-            "59/59 [==============================] - 4s 61ms/step - loss: 9.2996e-04 - val_loss: 2.8503e-04\n",
-            "Epoch 71/200\n",
-            "59/59 [==============================] - 6s 96ms/step - loss: 9.9439e-04 - val_loss: 4.2666e-04\n",
-            "Epoch 72/200\n",
-            "59/59 [==============================] - 4s 69ms/step - loss: 9.8471e-04 - val_loss: 2.8688e-04\n"
+            "59/59 [==============================] - 6s 95ms/step - loss: 0.0012 - val_loss: 5.2385e-04\n"
           ]
         }
       ]
@@ -2130,7 +2069,7 @@
     {
       "cell_type": "code",
       "source": [
-        "model.load_weights('eth.keras')\n",
+        "model.load_weights('ETH-USD.keras')\n",
         "loss = model.evaluate(X_test, y_test)\n",
         "predictions = model.predict(X_test)"
       ],
@@ -2139,20 +2078,36 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "23nPU1PYg7nZ",
-        "outputId": "84fbdcdf-0498-4d07-8d99-418d4a6dce48"
+        "outputId": "2d8725c7-18df-4601-e1a6-97e6aa9eb6d5"
       },
-      "execution_count": 13,
+      "execution_count": 16,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "15/15 [==============================] - 0s 24ms/step - loss: 2.8460e-04\n",
-            "15/15 [==============================] - 1s 21ms/step\n"
+            "15/15 [==============================] - 0s 21ms/step - loss: 4.7569e-04\n",
+            "15/15 [==============================] - 1s 20ms/step\n"
           ]
         }
       ]
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "import joblib\n",
+        "\n",
+        "joblib.dump(minmax_scaler, 'minmax_scaler.pickle')\n",
+        "joblib.dump(standard_scaler, 'standard_scaler.pickle')\n",
+        "\n",
+        "model.save('ETH-USD.keras')"
+      ],
+      "metadata": {
+        "id": "8bnjl03gBK-c"
+      },
+      "execution_count": 17,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "source": [
@@ -2173,9 +2128,9 @@
           "height": 430
         },
         "id": "GZdxZwfmr3aN",
-        "outputId": "8fb4eaa4-30a8-4bda-aec9-451575be6c72"
+        "outputId": "192b67c3-9f0a-4dae-9afd-6feaf6868f26"
       },
-      "execution_count": 14,
+      "execution_count": 18,
       "outputs": [
         {
           "output_type": "display_data",
@@ -2183,7 +2138,7 @@
             "text/plain": [
               "<Figure size 1400x500 with 1 Axes>"
             ],
-            "image/png": 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\n"
+            "image/png": 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\n"
           },
           "metadata": {}
         }
@@ -2202,6 +2157,12 @@
     {
       "cell_type": "code",
       "source": [
+        "from tensorflow.keras.models import load_model\n",
+        "\n",
+        "model = load_model('ETH-USD.keras')\n",
+        "minmax_scaler = joblib.load('minmax_scaler.pickle')\n",
+        "standard_scaler = joblib.load('standard_scaler.pickle')\n",
+        "\n",
         "days = 7\n",
         "\n",
         "lst_seq = dataframe[-sequence_length:].values\n",
@@ -2228,21 +2189,47 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "TyB5g_klixdP",
-        "outputId": "c5240f14-3e80-44c9-8a2a-d3d5afa6fa3a"
+        "outputId": "f506b0b5-6751-463d-ffdd-27cb6336a82f"
       },
-      "execution_count": 15,
+      "execution_count": 19,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "1/1 [==============================] - 0s 28ms/step\n",
-            "1/1 [==============================] - 0s 29ms/step\n",
-            "1/1 [==============================] - 0s 27ms/step\n",
-            "1/1 [==============================] - 0s 28ms/step\n",
-            "1/1 [==============================] - 0s 37ms/step\n",
+            "1/1 [==============================] - 1s 946ms/step\n",
+            "1/1 [==============================] - 0s 30ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 31ms/step\n",
             "1/1 [==============================] - 0s 32ms/step\n",
-            "1/1 [==============================] - 0s 28ms/step\n"
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 42ms/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print({\n",
+        "    'Date': dataframe[-sequence_length:].index.tolist(),\n",
+        "    'Close': [ _[0] for _ in eth_prices[-sequence_length:].tolist() ]\n",
+        "})"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4E3gLDLPUhQ2",
+        "outputId": "32223981-4086-4056-960a-96c7a8cb357f"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'Date': ['2024-04-25', '2024-04-26', '2024-04-27', '2024-04-28', '2024-04-29', '2024-04-30', '2024-05-01', '2024-05-02', '2024-05-03', '2024-05-04', '2024-05-05', '2024-05-06', '2024-05-07', '2024-05-08', '2024-05-09', '2024-05-10', '2024-05-11', '2024-05-12', '2024-05-13', '2024-05-14', '2024-05-15', '2024-05-16', '2024-05-17', '2024-05-18', '2024-05-19', '2024-05-20', '2024-05-21', '2024-05-22', '2024-05-23', '2024-05-24', '2024-05-25', '2024-05-26', '2024-05-27', '2024-05-28', '2024-05-29', '2024-05-30', '2024-05-31', '2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05', '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10', '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15', '2024-06-16', '2024-06-17', '2024-06-18', '2024-06-19', '2024-06-20', '2024-06-21', '2024-06-22', '2024-06-23'], 'Close': [3156.509521, 3130.164795, 3252.168213, 3262.7746579999994, 3215.4289549999994, 3012.286865, 2969.7846680000002, 2988.168457, 3103.5419919999995, 3117.576416, 3137.2490230000003, 3062.72876, 3006.577148, 2973.657227, 3036.0205079999996, 2909.79126, 2911.602051, 2928.7019039999996, 2949.359619, 2881.157959, 3037.056641, 2945.131104, 3094.118652, 3122.9489750000002, 3071.843018, 3663.855469, 3789.312744, 3737.217773, 3776.9272460000007, 3726.93457, 3749.2365720000007, 3825.8974610000005, 3892.0068360000005, 3840.256348, 3763.1965330000003, 3746.849609, 3760.0266110000002, 3813.1989750000002, 3780.895996000001, 3766.38916, 3812.5158689999994, 3864.2609860000002, 3811.605957, 3678.6291499999998, 3680.949951, 3705.9038090000004, 3666.717529, 3498.330566, 3559.617676, 3469.2812500000005, 3480.271973, 3565.549561, 3620.5634770000006, 3511.378906, 3483.681396, 3559.3474120000005, 3511.0869139999995, 3516.075684, 3494.8134769999997, 3504.0280759999996]}\n"
           ]
         }
       ]
@@ -2275,12 +2262,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 447
+          "height": 445
         },
         "id": "Y9zYnSUakgPZ",
-        "outputId": "52875e13-6c0b-4e2e-f777-5c37f9b4f79b"
+        "outputId": "69514f2d-8c6a-4490-d851-8d7a1923c0dc"
       },
-      "execution_count": 16,
+      "execution_count": 21,
       "outputs": [
         {
           "output_type": "display_data",
@@ -2288,19 +2275,29 @@
             "text/plain": [
               "<Figure size 1400x500 with 1 Axes>"
             ],
-            "image/png": 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\n"
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           },
           "metadata": {}
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<br />\n",
+        "<h3>Utility Development (to be consumed by REST API Service)</h3>"
+      ],
+      "metadata": {
+        "id": "ZmVTaqU1Ngwd"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
         "import json\n",
         "\n",
         "dataframe_date = dataframe.index.tolist()\n",
-        "dataframe_close = [idx[0] for idx in eth_prices.tolist()]\n",
+        "dataframe_close = dataframe['Close'].tolist()\n",
         "dataframe_json = {\n",
         "  'Date': dataframe_date,\n",
         "  'Close': dataframe_close\n",
@@ -2312,7 +2309,7 @@
       "metadata": {
         "id": "DQgzDWLXGOfp"
       },
-      "execution_count": 17,
+      "execution_count": 22,
       "outputs": []
     },
     {
@@ -2336,9 +2333,9 @@
           "height": 394
         },
         "id": "FyhjJbUTm1Cg",
-        "outputId": "ecfe685e-80e0-434d-8cb5-09d1c8acb990"
+        "outputId": "8e5d8045-546c-4e03-b95a-1f78d0521d68"
       },
-      "execution_count": 18,
+      "execution_count": 23,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -2346,20 +2343,20 @@
             "text/plain": [
               "                  Close\n",
               "Date                   \n",
-              "2024-05-29  3763.196533\n",
-              "2024-05-30  3746.849609\n",
-              "2024-05-31  3778.403320\n",
-              "2024-06-01  3724.088135\n",
-              "2024-06-02  3707.247070\n",
-              "2024-06-03  3688.963623\n",
-              "2024-06-04  3671.277588\n",
-              "2024-06-05  3654.622803\n",
-              "2024-06-06  3638.932861\n",
-              "2024-06-07  3624.011230"
+              "2024-06-21     0.725873\n",
+              "2024-06-22     0.721376\n",
+              "2024-06-23     0.723325\n",
+              "2024-06-24  3533.773682\n",
+              "2024-06-25  3533.459717\n",
+              "2024-06-26  3536.406738\n",
+              "2024-06-27  3541.040527\n",
+              "2024-06-28  3546.467773\n",
+              "2024-06-29  3552.183594\n",
+              "2024-06-30  3557.900879"
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-d28992a2-1fd4-4b2b-9c2d-ac8e82f92c82\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-16149e32-eab1-4fd2-ab56-4a1f731a05af\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -2387,44 +2384,44 @@
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <th>2024-05-29</th>\n",
-              "      <td>3763.196533</td>\n",
+              "      <th>2024-06-21</th>\n",
+              "      <td>0.725873</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-30</th>\n",
-              "      <td>3746.849609</td>\n",
+              "      <th>2024-06-22</th>\n",
+              "      <td>0.721376</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-05-31</th>\n",
-              "      <td>3778.403320</td>\n",
+              "      <th>2024-06-23</th>\n",
+              "      <td>0.723325</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-06-01</th>\n",
-              "      <td>3724.088135</td>\n",
+              "      <th>2024-06-24</th>\n",
+              "      <td>3533.773682</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-06-02</th>\n",
-              "      <td>3707.247070</td>\n",
+              "      <th>2024-06-25</th>\n",
+              "      <td>3533.459717</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-06-03</th>\n",
-              "      <td>3688.963623</td>\n",
+              "      <th>2024-06-26</th>\n",
+              "      <td>3536.406738</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-06-04</th>\n",
-              "      <td>3671.277588</td>\n",
+              "      <th>2024-06-27</th>\n",
+              "      <td>3541.040527</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-06-05</th>\n",
-              "      <td>3654.622803</td>\n",
+              "      <th>2024-06-28</th>\n",
+              "      <td>3546.467773</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-06-06</th>\n",
-              "      <td>3638.932861</td>\n",
+              "      <th>2024-06-29</th>\n",
+              "      <td>3552.183594</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-06-07</th>\n",
-              "      <td>3624.011230</td>\n",
+              "      <th>2024-06-30</th>\n",
+              "      <td>3557.900879</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -2432,7 +2429,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d28992a2-1fd4-4b2b-9c2d-ac8e82f92c82')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-16149e32-eab1-4fd2-ab56-4a1f731a05af')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -2484,12 +2481,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-d28992a2-1fd4-4b2b-9c2d-ac8e82f92c82 button.colab-df-convert');\n",
+              "        document.querySelector('#df-16149e32-eab1-4fd2-ab56-4a1f731a05af button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-d28992a2-1fd4-4b2b-9c2d-ac8e82f92c82');\n",
+              "        const element = document.querySelector('#df-16149e32-eab1-4fd2-ab56-4a1f731a05af');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -2509,8 +2506,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "<div id=\"df-b6c8e971-116f-4b9c-ad82-3bb75c43c381\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b6c8e971-116f-4b9c-ad82-3bb75c43c381')\"\n",
+              "<div id=\"df-1be80b3b-65f4-4809-9495-d9cc86a66850\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1be80b3b-65f4-4809-9495-d9cc86a66850')\"\n",
               "            title=\"Suggest charts\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -2629,22 +2626,175 @@
               "    }\n",
               "    (() => {\n",
               "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-b6c8e971-116f-4b9c-ad82-3bb75c43c381 button');\n",
+              "        document.querySelector('#df-1be80b3b-65f4-4809-9495-d9cc86a66850 button');\n",
               "      quickchartButtonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "    })();\n",
               "  </script>\n",
               "</div>\n",
+              "\n",
               "    </div>\n",
               "  </div>\n"
             ],
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "dataframe",
-              "summary": "{\n  \"name\": \"df_loaded\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"2024-06-06\",\n          \"2024-05-30\",\n          \"2024-06-03\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.20537164783098,\n        \"min\": 3624.01123046875,\n        \"max\": 3778.4033199999994,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3638.932861328125,\n          3746.849608999999,\n          3688.963623046875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+              "summary": "{\n  \"name\": \"df_loaded\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"2024-06-29\",\n          \"2024-06-22\",\n          \"2024-06-26\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1711.1157629141298,\n        \"min\": 0.7213757463143202,\n        \"max\": 3557.90087890625,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3552.18359375,\n          0.7213757463143202,\n          3536.40673828125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
             }
           },
           "metadata": {},
-          "execution_count": 18
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dataframe[-10:].index.values.tolist()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AzIQ5ATfT4Jm",
+        "outputId": "d443056e-e79b-4471-dc01-f084dc4efa5b"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['2024-06-14',\n",
+              " '2024-06-15',\n",
+              " '2024-06-16',\n",
+              " '2024-06-17',\n",
+              " '2024-06-18',\n",
+              " '2024-06-19',\n",
+              " '2024-06-20',\n",
+              " '2024-06-21',\n",
+              " '2024-06-22',\n",
+              " '2024-06-23']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from tensorflow.keras.models import load_model\n",
+        "\n",
+        "class Utilities:\n",
+        "    @staticmethod\n",
+        "    def cryptocurrency_prediction(days: int, sequence_length: int, model_name: str) -> dict:\n",
+        "        model_path = f'./{model_name}.keras'\n",
+        "        model = load_model(model_path)\n",
+        "\n",
+        "        dataframe = pd.read_json(f'./{model_name}-posttrained.json')\n",
+        "        dataframe.set_index('Date', inplace=True)\n",
+        "\n",
+        "        minmax_scaler = joblib.load('minmax_scaler.pickle')\n",
+        "        standard_scaler = joblib.load('standard_scaler.pickle')\n",
+        "\n",
+        "        lst_seq = dataframe[-sequence_length:].values\n",
+        "        lst_seq = np.expand_dims(lst_seq, axis = 0)\n",
+        "\n",
+        "        # Predicted\n",
+        "        predicted_prices = {}\n",
+        "        last_date = pd.to_datetime(dataframe.index[-1])\n",
+        "\n",
+        "        for _ in range(days):\n",
+        "          predicted_price = model.predict(lst_seq)\n",
+        "          last_date       = last_date + pd.Timedelta(days = 1)\n",
+        "\n",
+        "          predicted_prices[last_date] = minmax_scaler.inverse_transform(predicted_price)\n",
+        "          predicted_prices[last_date] = standard_scaler.inverse_transform(predicted_prices[last_date])\n",
+        "\n",
+        "          lst_seq = np.append(lst_seq[:, 1:, :], [predicted_price], axis = 1)\n",
+        "\n",
+        "        predicted = [\n",
+        "            {'date': date.strftime('%Y-%m-%d'), 'price': float(price)} \\\n",
+        "            for date, price in predicted_prices.items()\n",
+        "        ]\n",
+        "\n",
+        "        # Actual\n",
+        "        df_date = dataframe.index[-sequence_length:].values\n",
+        "        df_date = [pd.to_datetime(date) for date in df_date]\n",
+        "\n",
+        "        dataframe[['Close']] = minmax_scaler.inverse_transform(dataframe)\n",
+        "        dataframe[['Close']] = standard_scaler.inverse_transform(dataframe)\n",
+        "        df_close = dataframe.iloc[-sequence_length:]['Close'].values\n",
+        "        actual = [\n",
+        "            {'date': date.strftime('%Y-%m-%d'), 'price': close} \\\n",
+        "            for date, close in zip(df_date, df_close)\n",
+        "        ]\n",
+        "\n",
+        "        return actual, predicted"
+      ],
+      "metadata": {
+        "id": "_xi20jtTGsTE"
+      },
+      "execution_count": 25,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "util = Utilities()\n",
+        "actual, predicted = util.cryptocurrency_prediction(\n",
+        "    days            = 7,\n",
+        "    model_name      = 'ETH-USD',\n",
+        "    sequence_length = 60\n",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-A62yAAFl_aX",
+        "outputId": "fe063e28-fceb-4b04-c8d7-d6e50d61961d"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1/1 [==============================] - 1s 1s/step\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "1/1 [==============================] - 0s 34ms/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print({ 'actual': actual, 'predicted': predicted })"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ytJxMi2xmHIi",
+        "outputId": "41e13553-9dd7-42a9-f9bf-dd6b254329c2"
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'actual': [{'date': '2024-04-25', 'price': 3156.509520999997}, {'date': '2024-04-26', 'price': 3130.1647949999965}, {'date': '2024-04-27', 'price': 3252.1682129999995}, {'date': '2024-04-28', 'price': 3262.7746579999966}, {'date': '2024-04-29', 'price': 3215.4289549999985}, {'date': '2024-04-30', 'price': 3012.286864999999}, {'date': '2024-05-01', 'price': 2969.7846679999984}, {'date': '2024-05-02', 'price': 2988.168456999999}, {'date': '2024-05-03', 'price': 3103.541991999996}, {'date': '2024-05-04', 'price': 3117.576416}, {'date': '2024-05-05', 'price': 3137.2490229999994}, {'date': '2024-05-06', 'price': 3062.72876}, {'date': '2024-05-07', 'price': 3006.577148}, {'date': '2024-05-08', 'price': 2973.657227}, {'date': '2024-05-09', 'price': 3036.020507999998}, {'date': '2024-05-10', 'price': 2909.791259999998}, {'date': '2024-05-11', 'price': 2911.602051}, {'date': '2024-05-12', 'price': 2928.7019039999996}, {'date': '2024-05-13', 'price': 2949.359618999998}, {'date': '2024-05-14', 'price': 2881.1579589999983}, {'date': '2024-05-15', 'price': 3037.0566409999974}, {'date': '2024-05-16', 'price': 2945.1311039999973}, {'date': '2024-05-17', 'price': 3094.118652}, {'date': '2024-05-18', 'price': 3122.948974999998}, {'date': '2024-05-19', 'price': 3071.8430179999973}, {'date': '2024-05-20', 'price': 3663.855468999998}, {'date': '2024-05-21', 'price': 3789.3127439999985}, {'date': '2024-05-22', 'price': 3737.217772999996}, {'date': '2024-05-23', 'price': 3776.9272459999997}, {'date': '2024-05-24', 'price': 3726.93457}, {'date': '2024-05-25', 'price': 3749.2365719999984}, {'date': '2024-05-26', 'price': 3825.897460999999}, {'date': '2024-05-27', 'price': 3892.0068359999973}, {'date': '2024-05-28', 'price': 3840.256348}, {'date': '2024-05-29', 'price': 3763.196532999998}, {'date': '2024-05-30', 'price': 3746.8496089999953}, {'date': '2024-05-31', 'price': 3760.0266109999984}, {'date': '2024-06-01', 'price': 3813.1989749999993}, {'date': '2024-06-02', 'price': 3780.895996000001}, {'date': '2024-06-03', 'price': 3766.389159999998}, {'date': '2024-06-04', 'price': 3812.515868999996}, {'date': '2024-06-05', 'price': 3864.260985999997}, {'date': '2024-06-06', 'price': 3811.6059569999993}, {'date': '2024-06-07', 'price': 3678.6291499999984}, {'date': '2024-06-08', 'price': 3680.9499509999955}, {'date': '2024-06-09', 'price': 3705.903808999999}, {'date': '2024-06-10', 'price': 3666.7175289999973}, {'date': '2024-06-11', 'price': 3498.3305659999996}, {'date': '2024-06-12', 'price': 3559.6176759999985}, {'date': '2024-06-13', 'price': 3469.2812499999995}, {'date': '2024-06-14', 'price': 3480.271972999997}, {'date': '2024-06-15', 'price': 3565.5495609999966}, {'date': '2024-06-16', 'price': 3620.5634769999997}, {'date': '2024-06-17', 'price': 3511.378906}, {'date': '2024-06-18', 'price': 3483.6813959999986}, {'date': '2024-06-19', 'price': 3559.3474119999996}, {'date': '2024-06-20', 'price': 3511.086913999997}, {'date': '2024-06-21', 'price': 3516.075683999999}, {'date': '2024-06-22', 'price': 3494.8134769999992}, {'date': '2024-06-23', 'price': 3504.0280759999973}], 'predicted': [{'date': '2024-06-24', 'price': 3533.773681640625}, {'date': '2024-06-25', 'price': 3533.459716796875}, {'date': '2024-06-26', 'price': 3536.40673828125}, {'date': '2024-06-27', 'price': 3541.04052734375}, {'date': '2024-06-28', 'price': 3546.4677734375}, {'date': '2024-06-29', 'price': 3552.18359375}, {'date': '2024-06-30', 'price': 3557.90087890625}]}\n"
+          ]
         }
       ]
     }