diff --git "a/notebooks/mech_calls_analysis.ipynb" "b/notebooks/mech_calls_analysis.ipynb"
new file mode 100644--- /dev/null
+++ "b/notebooks/mech_calls_analysis.ipynb"
@@ -0,0 +1,24946 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import gc\n",
+    "sns.set_style(\"darkgrid\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_trades = pd.read_parquet('../data/all_trades_profitability.parquet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>trader_address</th>\n",
+       "      <th>market_creator</th>\n",
+       "      <th>trade_id</th>\n",
+       "      <th>creation_timestamp</th>\n",
+       "      <th>title</th>\n",
+       "      <th>market_status</th>\n",
+       "      <th>collateral_amount</th>\n",
+       "      <th>outcome_index</th>\n",
+       "      <th>trade_fee_amount</th>\n",
+       "      <th>outcomes_tokens_traded</th>\n",
+       "      <th>...</th>\n",
+       "      <th>is_invalid</th>\n",
+       "      <th>winning_trade</th>\n",
+       "      <th>earnings</th>\n",
+       "      <th>redeemed</th>\n",
+       "      <th>redeemed_amount</th>\n",
+       "      <th>num_mech_calls</th>\n",
+       "      <th>mech_fee_amount</th>\n",
+       "      <th>net_earnings</th>\n",
+       "      <th>roi</th>\n",
+       "      <th>staking</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0x01274796ce41aa8e8312e05a427ffb4b0d2148f6</td>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>0x007068173910cf8719b6f2e66a18b6825c9dde820x01...</td>\n",
+       "      <td>2024-10-10 21:43:25+00:00</td>\n",
+       "      <td>Will the emergency public warning tests planne...</td>\n",
+       "      <td>CLOSED</td>\n",
+       "      <td>0.930597</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.009306</td>\n",
+       "      <td>1.574258</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.574258</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.574258</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.624356</td>\n",
+       "      <td>0.657284</td>\n",
+       "      <td>non_staking</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0x01274796ce41aa8e8312e05a427ffb4b0d2148f6</td>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>0x00d659d7749fda4f1c9402182ca5d7ce26cf5cd10x01...</td>\n",
+       "      <td>2024-10-18 00:36:50+00:00</td>\n",
+       "      <td>Will the Northern Lights be visible over UK sk...</td>\n",
+       "      <td>CLOSED</td>\n",
+       "      <td>1.375603</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.013756</td>\n",
+       "      <td>1.942215</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>-1.399359</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>non_staking</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0x01274796ce41aa8e8312e05a427ffb4b0d2148f6</td>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>0x02ccdf04646d9a55332e67a73e4ffdab2368d05f0x01...</td>\n",
+       "      <td>2024-10-23 22:37:35+00:00</td>\n",
+       "      <td>Will any Republican lawmakers introduce legisl...</td>\n",
+       "      <td>CLOSED</td>\n",
+       "      <td>0.471695</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.004717</td>\n",
+       "      <td>0.784784</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.784784</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.784784</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.298372</td>\n",
+       "      <td>0.613414</td>\n",
+       "      <td>non_staking</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0x01274796ce41aa8e8312e05a427ffb4b0d2148f6</td>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>0x09f47ce8995abf1d5b91f2cbfa940ede2fb954c30x01...</td>\n",
+       "      <td>2024-10-20 23:58:35+00:00</td>\n",
+       "      <td>Will any new major AI-driven fraud detection t...</td>\n",
+       "      <td>CLOSED</td>\n",
+       "      <td>0.289046</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.002890</td>\n",
+       "      <td>0.445590</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>-0.341936</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>non_staking</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0x01274796ce41aa8e8312e05a427ffb4b0d2148f6</td>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>0x0c86942c52740316bbdb70303c5aaee40876d8ce0x01...</td>\n",
+       "      <td>2024-10-24 22:42:00+00:00</td>\n",
+       "      <td>Will any new norovirus vaccine trial sites be ...</td>\n",
+       "      <td>CLOSED</td>\n",
+       "      <td>0.286552</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.002866</td>\n",
+       "      <td>0.470457</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.470457</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.470457</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.171040</td>\n",
+       "      <td>0.571242</td>\n",
+       "      <td>non_staking</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               trader_address market_creator  \\\n",
+       "0  0x01274796ce41aa8e8312e05a427ffb4b0d2148f6     quickstart   \n",
+       "1  0x01274796ce41aa8e8312e05a427ffb4b0d2148f6     quickstart   \n",
+       "2  0x01274796ce41aa8e8312e05a427ffb4b0d2148f6     quickstart   \n",
+       "3  0x01274796ce41aa8e8312e05a427ffb4b0d2148f6     quickstart   \n",
+       "4  0x01274796ce41aa8e8312e05a427ffb4b0d2148f6     quickstart   \n",
+       "\n",
+       "                                            trade_id  \\\n",
+       "0  0x007068173910cf8719b6f2e66a18b6825c9dde820x01...   \n",
+       "1  0x00d659d7749fda4f1c9402182ca5d7ce26cf5cd10x01...   \n",
+       "2  0x02ccdf04646d9a55332e67a73e4ffdab2368d05f0x01...   \n",
+       "3  0x09f47ce8995abf1d5b91f2cbfa940ede2fb954c30x01...   \n",
+       "4  0x0c86942c52740316bbdb70303c5aaee40876d8ce0x01...   \n",
+       "\n",
+       "         creation_timestamp  \\\n",
+       "0 2024-10-10 21:43:25+00:00   \n",
+       "1 2024-10-18 00:36:50+00:00   \n",
+       "2 2024-10-23 22:37:35+00:00   \n",
+       "3 2024-10-20 23:58:35+00:00   \n",
+       "4 2024-10-24 22:42:00+00:00   \n",
+       "\n",
+       "                                               title market_status  \\\n",
+       "0  Will the emergency public warning tests planne...        CLOSED   \n",
+       "1  Will the Northern Lights be visible over UK sk...        CLOSED   \n",
+       "2  Will any Republican lawmakers introduce legisl...        CLOSED   \n",
+       "3  Will any new major AI-driven fraud detection t...        CLOSED   \n",
+       "4  Will any new norovirus vaccine trial sites be ...        CLOSED   \n",
+       "\n",
+       "   collateral_amount outcome_index  trade_fee_amount  outcomes_tokens_traded  \\\n",
+       "0           0.930597             0          0.009306                1.574258   \n",
+       "1           1.375603             1          0.013756                1.942215   \n",
+       "2           0.471695             1          0.004717                0.784784   \n",
+       "3           0.289046             1          0.002890                0.445590   \n",
+       "4           0.286552             1          0.002866                0.470457   \n",
+       "\n",
+       "   ...  is_invalid  winning_trade  earnings  redeemed  redeemed_amount  \\\n",
+       "0  ...       False           True  1.574258      True         1.574258   \n",
+       "1  ...       False          False  0.000000     False         0.000000   \n",
+       "2  ...       False           True  0.784784      True         0.784784   \n",
+       "3  ...       False          False  0.000000     False         0.000000   \n",
+       "4  ...       False           True  0.470457      True         0.470457   \n",
+       "\n",
+       "   num_mech_calls  mech_fee_amount  net_earnings       roi      staking  \n",
+       "0               1             0.01      0.624356  0.657284  non_staking  \n",
+       "1               1             0.01     -1.399359 -1.000000  non_staking  \n",
+       "2               1             0.01      0.298372  0.613414  non_staking  \n",
+       "3               5             0.05     -0.341936 -1.000000  non_staking  \n",
+       "4               1             0.01      0.171040  0.571242  non_staking  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_trades.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "staking\n",
+       "quickstart     18996\n",
+       "non_staking     8360\n",
+       "non_agent       7249\n",
+       "pearl           2679\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_trades.staking.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    26797.000000\n",
+       "mean         4.126320\n",
+       "std          4.063486\n",
+       "min          0.000000\n",
+       "25%          2.000000\n",
+       "50%          4.000000\n",
+       "75%          6.000000\n",
+       "max         65.000000\n",
+       "Name: num_mech_calls, dtype: float64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_trades.num_mech_calls.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_trades['creation_timestamp'] = pd.to_datetime(all_trades['creation_timestamp'])\n",
+    "all_trades['creation_date'] = all_trades['creation_timestamp'].dt.date\n",
+    "all_trades['creation_time'] = all_trades['creation_timestamp'].dt.time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_mech_calls = all_trades.groupby([\"market_creator\", \"trader_address\",\"creation_date\"]).agg(mean=(\"num_mech_calls\", 'mean'), max=(\"num_mech_calls\", 'max'), min=(\"num_mech_calls\", 'min'), nr_trades=(\"num_mech_calls\", 'count')).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>market_creator</th>\n",
+       "      <th>trader_address</th>\n",
+       "      <th>creation_date</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>max</th>\n",
+       "      <th>min</th>\n",
+       "      <th>nr_trades</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2</td>\n",
+       "      <td>2024-09-16</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x01c72d0743a22b70d73c76c5e16ba7524e20e0c0</td>\n",
+       "      <td>2024-10-15</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x032533005f65026fa7f360ff9a211bc94315325d</td>\n",
+       "      <td>2024-08-23</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x032533005f65026fa7f360ff9a211bc94315325d</td>\n",
+       "      <td>2024-10-04</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x032533005f65026fa7f360ff9a211bc94315325d</td>\n",
+       "      <td>2024-10-06</td>\n",
+       "      <td>3.454545</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  market_creator                              trader_address creation_date  \\\n",
+       "0          pearl  0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2    2024-09-16   \n",
+       "1          pearl  0x01c72d0743a22b70d73c76c5e16ba7524e20e0c0    2024-10-15   \n",
+       "2          pearl  0x032533005f65026fa7f360ff9a211bc94315325d    2024-08-23   \n",
+       "3          pearl  0x032533005f65026fa7f360ff9a211bc94315325d    2024-10-04   \n",
+       "4          pearl  0x032533005f65026fa7f360ff9a211bc94315325d    2024-10-06   \n",
+       "\n",
+       "       mean  max  min  nr_trades  \n",
+       "0  2.000000    2    2          1  \n",
+       "1  2.000000    2    2          1  \n",
+       "2  2.000000    2    2          1  \n",
+       "3  2.000000    2    2          1  \n",
+       "4  3.454545    5    2         11  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "daily_mech_calls.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='creation_date', ylabel='mean'>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(daily_mech_calls, x=\"creation_date\", y=\"mean\", hue=\"market_creator\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x17e9932f0>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 859.375x250 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.FacetGrid(daily_mech_calls, col=\"market_creator\", hue=\"market_creator\", col_wrap=3, height=2.5, sharey=False)\n",
+    "g.map(sns.scatterplot, data= daily_mech_calls, x=\"creation_date\", y=\"mean\")\n",
+    "g.add_legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_ratio_mech_calls = all_trades.groupby([\"market_creator\", \"trader_address\",\"creation_date\"]).agg(total_mech_calls=(\"num_mech_calls\", 'sum'), total_trades=(\"num_mech_calls\", 'count')).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ratio_mech_calls = all_trades.groupby([\"market_creator\", \"trader_address\"]).agg(total_mech_calls=(\"num_mech_calls\", 'sum'), total_trades=(\"num_mech_calls\", 'count')).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_ratio_mech_calls[\"mech_calls_ratio\"] = daily_ratio_mech_calls[\"total_mech_calls\"]/daily_ratio_mech_calls[\"total_trades\"]\n",
+    "ratio_mech_calls[\"mech_calls_ratio\"] = ratio_mech_calls[\"total_mech_calls\"]/ratio_mech_calls[\"total_trades\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>market_creator</th>\n",
+       "      <th>trader_address</th>\n",
+       "      <th>creation_date</th>\n",
+       "      <th>total_mech_calls</th>\n",
+       "      <th>total_trades</th>\n",
+       "      <th>mech_calls_ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2</td>\n",
+       "      <td>2024-09-16</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x01c72d0743a22b70d73c76c5e16ba7524e20e0c0</td>\n",
+       "      <td>2024-10-15</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x032533005f65026fa7f360ff9a211bc94315325d</td>\n",
+       "      <td>2024-08-23</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x032533005f65026fa7f360ff9a211bc94315325d</td>\n",
+       "      <td>2024-10-04</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x032533005f65026fa7f360ff9a211bc94315325d</td>\n",
+       "      <td>2024-10-06</td>\n",
+       "      <td>38</td>\n",
+       "      <td>11</td>\n",
+       "      <td>3.454545</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  market_creator                              trader_address creation_date  \\\n",
+       "0          pearl  0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2    2024-09-16   \n",
+       "1          pearl  0x01c72d0743a22b70d73c76c5e16ba7524e20e0c0    2024-10-15   \n",
+       "2          pearl  0x032533005f65026fa7f360ff9a211bc94315325d    2024-08-23   \n",
+       "3          pearl  0x032533005f65026fa7f360ff9a211bc94315325d    2024-10-04   \n",
+       "4          pearl  0x032533005f65026fa7f360ff9a211bc94315325d    2024-10-06   \n",
+       "\n",
+       "   total_mech_calls  total_trades  mech_calls_ratio  \n",
+       "0                 2             1          2.000000  \n",
+       "1                 2             1          2.000000  \n",
+       "2                 2             1          2.000000  \n",
+       "3                 2             1          2.000000  \n",
+       "4                38            11          3.454545  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "daily_ratio_mech_calls.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>market_creator</th>\n",
+       "      <th>trader_address</th>\n",
+       "      <th>total_mech_calls</th>\n",
+       "      <th>total_trades</th>\n",
+       "      <th>mech_calls_ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x01c72d0743a22b70d73c76c5e16ba7524e20e0c0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x032533005f65026fa7f360ff9a211bc94315325d</td>\n",
+       "      <td>96</td>\n",
+       "      <td>30</td>\n",
+       "      <td>3.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x03429c1ab7d9d14baa42ded8a2dd7d684fc950ed</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x034c4ad84f7ac6638bf19300d5bbe7d9b981e736</td>\n",
+       "      <td>0</td>\n",
+       "      <td>114</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  market_creator                              trader_address  \\\n",
+       "0          pearl  0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2   \n",
+       "1          pearl  0x01c72d0743a22b70d73c76c5e16ba7524e20e0c0   \n",
+       "2          pearl  0x032533005f65026fa7f360ff9a211bc94315325d   \n",
+       "3          pearl  0x03429c1ab7d9d14baa42ded8a2dd7d684fc950ed   \n",
+       "4          pearl  0x034c4ad84f7ac6638bf19300d5bbe7d9b981e736   \n",
+       "\n",
+       "   total_mech_calls  total_trades  mech_calls_ratio  \n",
+       "0                 2             1               2.0  \n",
+       "1                 2             1               2.0  \n",
+       "2                96            30               3.2  \n",
+       "3                 2             2               1.0  \n",
+       "4                 0           114               0.0  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ratio_mech_calls.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Distribution of total ratio = total_nr_mech_calls/total_trades at the trader level')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.kdeplot(ratio_mech_calls, x=\"mech_calls_ratio\", hue=\"market_creator\")\n",
+    "plt.title('Distribution of total ratio = total_nr_mech_calls/total_trades at the trader level')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Distribution of daily ratio = total_daily_nr_mech_calls/total_daily_trades at the trader level')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.kdeplot(daily_ratio_mech_calls, x=\"mech_calls_ratio\", hue=\"market_creator\")\n",
+    "plt.title('Distribution of daily ratio = total_daily_nr_mech_calls/total_daily_trades at the trader level')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    311.000000\n",
+       "mean       2.601484\n",
+       "std        2.648168\n",
+       "min        0.000000\n",
+       "25%        1.000000\n",
+       "50%        1.666667\n",
+       "75%        3.899545\n",
+       "max       15.344262\n",
+       "Name: mech_calls_ratio, dtype: float64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ratio_mech_calls.mech_calls_ratio.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Histogram of total ratio = total_nr_mech_calls/total_trades at the trader level')"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(ratio_mech_calls, x=\"mech_calls_ratio\", hue=\"market_creator\")\n",
+    "plt.title('Histogram of total ratio = total_nr_mech_calls/total_trades at the trader level')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>market_creator</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>pearl</th>\n",
+       "      <td>154.0</td>\n",
+       "      <td>1.592610</td>\n",
+       "      <td>1.792455</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>13.202703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quickstart</th>\n",
+       "      <td>157.0</td>\n",
+       "      <td>3.591079</td>\n",
+       "      <td>2.965505</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.09375</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>5.117788</td>\n",
+       "      <td>15.344262</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                count      mean       std  min      25%  50%       75%  \\\n",
+       "market_creator                                                           \n",
+       "pearl           154.0  1.592610  1.792455  0.0  1.00000  1.0  2.000000   \n",
+       "quickstart      157.0  3.591079  2.965505  0.0  1.09375  3.5  5.117788   \n",
+       "\n",
+       "                      max  \n",
+       "market_creator             \n",
+       "pearl           13.202703  \n",
+       "quickstart      15.344262  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ratio_mech_calls.groupby('market_creator')['mech_calls_ratio'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>market_creator</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>pearl</th>\n",
+       "      <td>1295.0</td>\n",
+       "      <td>1.818533</td>\n",
+       "      <td>3.947135</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quickstart</th>\n",
+       "      <td>25502.0</td>\n",
+       "      <td>4.243510</td>\n",
+       "      <td>4.034311</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>65.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  count      mean       std  min  25%  50%  75%   max\n",
+       "market_creator                                                       \n",
+       "pearl            1295.0  1.818533  3.947135  0.0  0.0  0.0  1.0  27.0\n",
+       "quickstart      25502.0  4.243510  4.034311  0.0  2.0  4.0  6.0  65.0"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_trades.groupby('market_creator')['num_mech_calls'].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analysis taking into account all mech calls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = pd.read_parquet(\"../data/tools.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = pd.read_parquet(\"../data/tools.parquet\")\n",
+    "trades = pd.read_parquet(\"../data/fpmmTrades.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 29593 entries, 0 to 29592\n",
+      "Data columns (total 24 columns):\n",
+      " #   Column                         Non-Null Count  Dtype \n",
+      "---  ------                         --------------  ----- \n",
+      " 0   collateralAmount               29593 non-null  object\n",
+      " 1   collateralAmountUSD            29593 non-null  object\n",
+      " 2   collateralToken                29593 non-null  object\n",
+      " 3   creationTimestamp              29593 non-null  object\n",
+      " 4   trader_address                 29593 non-null  object\n",
+      " 5   feeAmount                      29593 non-null  object\n",
+      " 6   id                             29593 non-null  object\n",
+      " 7   oldOutcomeTokenMarginalPrice   29593 non-null  object\n",
+      " 8   outcomeIndex                   29593 non-null  object\n",
+      " 9   outcomeTokenMarginalPrice      29593 non-null  object\n",
+      " 10  outcomeTokensTraded            29593 non-null  object\n",
+      " 11  title                          29593 non-null  object\n",
+      " 12  transactionHash                29593 non-null  object\n",
+      " 13  type                           29593 non-null  object\n",
+      " 14  market_creator                 29593 non-null  object\n",
+      " 15  fpmm.answerFinalizedTimestamp  27798 non-null  object\n",
+      " 16  fpmm.arbitrationOccurred       29593 non-null  bool  \n",
+      " 17  fpmm.currentAnswer             27798 non-null  object\n",
+      " 18  fpmm.id                        29593 non-null  object\n",
+      " 19  fpmm.isPendingArbitration      29593 non-null  bool  \n",
+      " 20  fpmm.openingTimestamp          29593 non-null  object\n",
+      " 21  fpmm.outcomes                  29593 non-null  object\n",
+      " 22  fpmm.title                     29593 non-null  object\n",
+      " 23  fpmm.condition.id              29593 non-null  object\n",
+      "dtypes: bool(2), object(22)\n",
+      "memory usage: 5.0+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "trades.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trades_per_trader = trades.groupby([\"market_creator\", \"trader_address\"]).agg(total_trades=(\"title\",\"count\")).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>market_creator</th>\n",
+       "      <th>trader_address</th>\n",
+       "      <th>total_trades</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x01c72d0743a22b70d73c76c5e16ba7524e20e0c0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x032533005f65026fa7f360ff9a211bc94315325d</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x03429c1ab7d9d14baa42ded8a2dd7d684fc950ed</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x034c4ad84f7ac6638bf19300d5bbe7d9b981e736</td>\n",
+       "      <td>132</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  market_creator                              trader_address  total_trades\n",
+       "0          pearl  0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2             2\n",
+       "1          pearl  0x01c72d0743a22b70d73c76c5e16ba7524e20e0c0             1\n",
+       "2          pearl  0x032533005f65026fa7f360ff9a211bc94315325d            47\n",
+       "3          pearl  0x03429c1ab7d9d14baa42ded8a2dd7d684fc950ed             2\n",
+       "4          pearl  0x034c4ad84f7ac6638bf19300d5bbe7d9b981e736           132"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trades_per_trader.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>market_creator</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>pearl</th>\n",
+       "      <td>162.0</td>\n",
+       "      <td>9.222222</td>\n",
+       "      <td>25.013537</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>202.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quickstart</th>\n",
+       "      <td>173.0</td>\n",
+       "      <td>162.421965</td>\n",
+       "      <td>234.143345</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>213.0</td>\n",
+       "      <td>1095.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                count        mean         std  min   25%   50%    75%     max\n",
+       "market_creator                                                               \n",
+       "pearl           162.0    9.222222   25.013537  1.0   1.0   2.0    4.0   202.0\n",
+       "quickstart      173.0  162.421965  234.143345  1.0  18.0  56.0  213.0  1095.0"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trades_per_trader.groupby('market_creator')['total_trades'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>request_id</th>\n",
+       "      <th>request_block</th>\n",
+       "      <th>prompt_request</th>\n",
+       "      <th>tool</th>\n",
+       "      <th>nonce</th>\n",
+       "      <th>trader_address</th>\n",
+       "      <th>deliver_block</th>\n",
+       "      <th>error</th>\n",
+       "      <th>error_message</th>\n",
+       "      <th>prompt_response</th>\n",
+       "      <th>...</th>\n",
+       "      <th>confidence</th>\n",
+       "      <th>info_utility</th>\n",
+       "      <th>vote</th>\n",
+       "      <th>win_probability</th>\n",
+       "      <th>market_creator</th>\n",
+       "      <th>title</th>\n",
+       "      <th>currentAnswer</th>\n",
+       "      <th>request_time</th>\n",
+       "      <th>request_month_year</th>\n",
+       "      <th>request_month_year_week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2225618424541398419933635980124149566532513522...</td>\n",
+       "      <td>35791858</td>\n",
+       "      <td>Please take over the role of a Data Scientist ...</td>\n",
+       "      <td>prediction-offline</td>\n",
+       "      <td>ab641d61-9f86-4259-a6c9-29fb94dbbd5f</td>\n",
+       "      <td>0x9694c0fdb79a37d048ea19deb15e051482a690c4</td>\n",
+       "      <td>35791862</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>\\nYou are an LLM inside a multi-agent system t...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>Will Iceland's cucumber supply return to norma...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>2024-09-02 08:19:30</td>\n",
+       "      <td>2024-09</td>\n",
+       "      <td>2024-09-02/2024-09-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1148749493371728409514710538112897053025639675...</td>\n",
+       "      <td>36588666</td>\n",
+       "      <td>Please take over the role of a Data Scientist ...</td>\n",
+       "      <td>claude-prediction-online</td>\n",
+       "      <td>940bdc48-050a-41be-809c-0cc079c5a4e2</td>\n",
+       "      <td>0xbb9ee65ce6646a6b1d6a2511b72343a7e3d355af</td>\n",
+       "      <td>36588672</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>\\nYou are an LLM inside a multi-agent system t...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>Will Pakistan take a first-innings lead of ove...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2024-10-19 19:15:40</td>\n",
+       "      <td>2024-10</td>\n",
+       "      <td>2024-10-14/2024-10-20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1011880839868261161033272472370322925264448801...</td>\n",
+       "      <td>35667678</td>\n",
+       "      <td>Please take over the role of a Data Scientist ...</td>\n",
+       "      <td>prediction-request-reasoning</td>\n",
+       "      <td>8974731b-93bf-46eb-b7d7-7c3d6fae3a1b</td>\n",
+       "      <td>0x246f6787c409dd5139b7029cd63b7d6aad08cc61</td>\n",
+       "      <td>35667695</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>\\nHere is the user's question: Will Hurricane ...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>Will Hurricane Gilma hit land by 29 August 2024?</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2024-08-25 22:00:15</td>\n",
+       "      <td>2024-08</td>\n",
+       "      <td>2024-08-19/2024-08-25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1152862668912527760950055055523417275018202992...</td>\n",
+       "      <td>36086142</td>\n",
+       "      <td>Please take over the role of a Data Scientist ...</td>\n",
+       "      <td>prediction-offline-sme</td>\n",
+       "      <td>da8080f9-c92d-4cf9-9387-71974bef548d</td>\n",
+       "      <td>0x3badd0a1beb34fc1532f6c717fa857b3325da184</td>\n",
+       "      <td>36086168</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>\\nYou are an LLM inside a multi-agent system t...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>pearl</td>\n",
+       "      <td>Will Manchester City be found guilty of the 11...</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2024-09-19 20:00:45</td>\n",
+       "      <td>2024-09</td>\n",
+       "      <td>2024-09-16/2024-09-22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3074222847102674989172916916176718419667980583...</td>\n",
+       "      <td>35854248</td>\n",
+       "      <td>Please take over the role of a Data Scientist ...</td>\n",
+       "      <td>prediction-offline</td>\n",
+       "      <td>99749c5f-325d-4aca-b4ec-ed4227777020</td>\n",
+       "      <td>0x9d8337b10c7b820e44ae3273dab47220ea41bfc9</td>\n",
+       "      <td>35854264</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>\\nYou are an LLM inside a multi-agent system t...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>Will Lenovo release the new Copilot Plus PCs o...</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2024-09-06 01:36:35</td>\n",
+       "      <td>2024-09</td>\n",
+       "      <td>2024-09-02/2024-09-08</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          request_id request_block  \\\n",
+       "0  2225618424541398419933635980124149566532513522...      35791858   \n",
+       "1  1148749493371728409514710538112897053025639675...      36588666   \n",
+       "2  1011880839868261161033272472370322925264448801...      35667678   \n",
+       "3  1152862668912527760950055055523417275018202992...      36086142   \n",
+       "4  3074222847102674989172916916176718419667980583...      35854248   \n",
+       "\n",
+       "                                      prompt_request  \\\n",
+       "0  Please take over the role of a Data Scientist ...   \n",
+       "1  Please take over the role of a Data Scientist ...   \n",
+       "2  Please take over the role of a Data Scientist ...   \n",
+       "3  Please take over the role of a Data Scientist ...   \n",
+       "4  Please take over the role of a Data Scientist ...   \n",
+       "\n",
+       "                           tool                                 nonce  \\\n",
+       "0            prediction-offline  ab641d61-9f86-4259-a6c9-29fb94dbbd5f   \n",
+       "1      claude-prediction-online  940bdc48-050a-41be-809c-0cc079c5a4e2   \n",
+       "2  prediction-request-reasoning  8974731b-93bf-46eb-b7d7-7c3d6fae3a1b   \n",
+       "3        prediction-offline-sme  da8080f9-c92d-4cf9-9387-71974bef548d   \n",
+       "4            prediction-offline  99749c5f-325d-4aca-b4ec-ed4227777020   \n",
+       "\n",
+       "                               trader_address deliver_block  error  \\\n",
+       "0  0x9694c0fdb79a37d048ea19deb15e051482a690c4      35791862      0   \n",
+       "1  0xbb9ee65ce6646a6b1d6a2511b72343a7e3d355af      36588672      0   \n",
+       "2  0x246f6787c409dd5139b7029cd63b7d6aad08cc61      35667695      0   \n",
+       "3  0x3badd0a1beb34fc1532f6c717fa857b3325da184      36086168      0   \n",
+       "4  0x9d8337b10c7b820e44ae3273dab47220ea41bfc9      35854264      0   \n",
+       "\n",
+       "  error_message                                    prompt_response  ...  \\\n",
+       "0          None  \\nYou are an LLM inside a multi-agent system t...  ...   \n",
+       "1          None  \\nYou are an LLM inside a multi-agent system t...  ...   \n",
+       "2          None  \\nHere is the user's question: Will Hurricane ...  ...   \n",
+       "3          None  \\nYou are an LLM inside a multi-agent system t...  ...   \n",
+       "4          None  \\nYou are an LLM inside a multi-agent system t...  ...   \n",
+       "\n",
+       "  confidence  info_utility  vote  win_probability  market_creator  \\\n",
+       "0        0.1           0.0  None             0.50      quickstart   \n",
+       "1        0.2           0.1  None             0.50      quickstart   \n",
+       "2        0.6           0.7    No             0.60      quickstart   \n",
+       "3        0.6           0.0    No             0.55           pearl   \n",
+       "4        0.1           0.0  None             0.50      quickstart   \n",
+       "\n",
+       "                                               title  currentAnswer  \\\n",
+       "0  Will Iceland's cucumber supply return to norma...            Yes   \n",
+       "1  Will Pakistan take a first-innings lead of ove...           None   \n",
+       "2   Will Hurricane Gilma hit land by 29 August 2024?             No   \n",
+       "3  Will Manchester City be found guilty of the 11...             No   \n",
+       "4  Will Lenovo release the new Copilot Plus PCs o...             No   \n",
+       "\n",
+       "          request_time request_month_year request_month_year_week  \n",
+       "0  2024-09-02 08:19:30            2024-09   2024-09-02/2024-09-08  \n",
+       "1  2024-10-19 19:15:40            2024-10   2024-10-14/2024-10-20  \n",
+       "2  2024-08-25 22:00:15            2024-08   2024-08-19/2024-08-25  \n",
+       "3  2024-09-19 20:00:45            2024-09   2024-09-16/2024-09-22  \n",
+       "4  2024-09-06 01:36:35            2024-09   2024-09-02/2024-09-08  \n",
+       "\n",
+       "[5 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tools.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Please take over the role of a Data Scientist to evaluate the given question. With the given question \"Will Iceland\\'s cucumber supply return to normal by September 3, 2024?\" and the `yes` option represented by `Yes` and the `no` option represented by `No`, what are the respective probabilities of `p_yes` and `p_no` occurring?'"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tools.iloc[0].prompt_request"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "\n",
+    "def extract_title(text: str) -> str:\n",
+    "    question = re.search('\"([^\"]+)\"', text).group(1)\n",
+    "    return question"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools[\"title\"] = tools.apply(lambda x: extract_title(x.prompt_request), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Will Iceland's cucumber supply return to normal by September 3, 2024?\""
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tools.iloc[0].title"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_nr_mech_calls(prompt_request, title) -> int:\n",
+    "    print(\"prompt_request\")\n",
+    "    print(\"title\")\n",
+    "    count = prompt_request.lower().count(title.lower())\n",
+    "    return count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools[\"num_mech_calls\"] = tools.apply(lambda x: get_nr_mech_calls(x.prompt_request, x.title), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tools.iloc[0].num_mech_calls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    162131.0\n",
+       "mean          1.0\n",
+       "std           0.0\n",
+       "min           1.0\n",
+       "25%           1.0\n",
+       "50%           1.0\n",
+       "75%           1.0\n",
+       "max           1.0\n",
+       "Name: num_mech_calls, dtype: float64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tools.num_mech_calls.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So each row is a single mech request"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mech_calls_per_trader = tools.groupby([\"market_creator\", \"trader_address\"]).agg(total_mech_calls=(\"title\",\"count\")).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>market_creator</th>\n",
+       "      <th>trader_address</th>\n",
+       "      <th>total_mech_calls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2</td>\n",
+       "      <td>282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x01c72d0743a22b70d73c76c5e16ba7524e20e0c0</td>\n",
+       "      <td>142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x032533005f65026fa7f360ff9a211bc94315325d</td>\n",
+       "      <td>223</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x03429c1ab7d9d14baa42ded8a2dd7d684fc950ed</td>\n",
+       "      <td>140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x04430ebfb7d088960233b7353cb4cefb528dc31e</td>\n",
+       "      <td>305</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  market_creator                              trader_address  total_mech_calls\n",
+       "0          pearl  0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2               282\n",
+       "1          pearl  0x01c72d0743a22b70d73c76c5e16ba7524e20e0c0               142\n",
+       "2          pearl  0x032533005f65026fa7f360ff9a211bc94315325d               223\n",
+       "3          pearl  0x03429c1ab7d9d14baa42ded8a2dd7d684fc950ed               140\n",
+       "4          pearl  0x04430ebfb7d088960233b7353cb4cefb528dc31e               305"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mech_calls_per_trader.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>market_creator</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>pearl</th>\n",
+       "      <td>137.0</td>\n",
+       "      <td>229.386861</td>\n",
+       "      <td>167.584932</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>189.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>1037.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quickstart</th>\n",
+       "      <td>140.0</td>\n",
+       "      <td>933.607143</td>\n",
+       "      <td>926.353062</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>553.0</td>\n",
+       "      <td>1703.0</td>\n",
+       "      <td>4315.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                count        mean         std  min    25%    50%     75%  \\\n",
+       "market_creator                                                             \n",
+       "pearl           137.0  229.386861  167.584932  5.0  132.0  189.0   280.0   \n",
+       "quickstart      140.0  933.607143  926.353062  5.0  122.0  553.0  1703.0   \n",
+       "\n",
+       "                   max  \n",
+       "market_creator          \n",
+       "pearl           1037.0  \n",
+       "quickstart      4315.0  "
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mech_calls_per_trader.groupby('market_creator')['total_mech_calls'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>market_creator</th>\n",
+       "      <th>trader_address</th>\n",
+       "      <th>total_trades</th>\n",
+       "      <th>total_mech_calls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x01c72d0743a22b70d73c76c5e16ba7524e20e0c0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x032533005f65026fa7f360ff9a211bc94315325d</td>\n",
+       "      <td>47</td>\n",
+       "      <td>223</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x03429c1ab7d9d14baa42ded8a2dd7d684fc950ed</td>\n",
+       "      <td>2</td>\n",
+       "      <td>140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x04430ebfb7d088960233b7353cb4cefb528dc31e</td>\n",
+       "      <td>40</td>\n",
+       "      <td>305</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  market_creator                              trader_address  total_trades  \\\n",
+       "0          pearl  0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2             2   \n",
+       "1          pearl  0x01c72d0743a22b70d73c76c5e16ba7524e20e0c0             1   \n",
+       "2          pearl  0x032533005f65026fa7f360ff9a211bc94315325d            47   \n",
+       "3          pearl  0x03429c1ab7d9d14baa42ded8a2dd7d684fc950ed             2   \n",
+       "4          pearl  0x04430ebfb7d088960233b7353cb4cefb528dc31e            40   \n",
+       "\n",
+       "   total_mech_calls  \n",
+       "0               282  \n",
+       "1               142  \n",
+       "2               223  \n",
+       "3               140  \n",
+       "4               305  "
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_data = pd.merge(trades_per_trader, mech_calls_per_trader, on=['trader_address', 'market_creator'])\n",
+    "final_data.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_data[\"ratio_mech_calls\"] = final_data[\"total_mech_calls\"]/final_data[\"total_trades\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>market_creator</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>pearl</th>\n",
+       "      <td>137.0</td>\n",
+       "      <td>112.477637</td>\n",
+       "      <td>118.879586</td>\n",
+       "      <td>2.50000</td>\n",
+       "      <td>50.25000</td>\n",
+       "      <td>89.000000</td>\n",
+       "      <td>150.500000</td>\n",
+       "      <td>1037.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quickstart</th>\n",
+       "      <td>140.0</td>\n",
+       "      <td>13.269699</td>\n",
+       "      <td>52.727037</td>\n",
+       "      <td>1.83707</td>\n",
+       "      <td>3.81075</td>\n",
+       "      <td>5.598246</td>\n",
+       "      <td>9.452414</td>\n",
+       "      <td>618.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                count        mean         std      min       25%        50%  \\\n",
+       "market_creator                                                                \n",
+       "pearl           137.0  112.477637  118.879586  2.50000  50.25000  89.000000   \n",
+       "quickstart      140.0   13.269699   52.727037  1.83707   3.81075   5.598246   \n",
+       "\n",
+       "                       75%     max  \n",
+       "market_creator                      \n",
+       "pearl           150.500000  1037.0  \n",
+       "quickstart        9.452414   618.0  "
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_data.groupby('market_creator')['ratio_mech_calls'].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Time analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 29593 entries, 0 to 29592\n",
+      "Data columns (total 24 columns):\n",
+      " #   Column                         Non-Null Count  Dtype \n",
+      "---  ------                         --------------  ----- \n",
+      " 0   collateralAmount               29593 non-null  object\n",
+      " 1   collateralAmountUSD            29593 non-null  object\n",
+      " 2   collateralToken                29593 non-null  object\n",
+      " 3   creationTimestamp              29593 non-null  object\n",
+      " 4   trader_address                 29593 non-null  object\n",
+      " 5   feeAmount                      29593 non-null  object\n",
+      " 6   id                             29593 non-null  object\n",
+      " 7   oldOutcomeTokenMarginalPrice   29593 non-null  object\n",
+      " 8   outcomeIndex                   29593 non-null  object\n",
+      " 9   outcomeTokenMarginalPrice      29593 non-null  object\n",
+      " 10  outcomeTokensTraded            29593 non-null  object\n",
+      " 11  title                          29593 non-null  object\n",
+      " 12  transactionHash                29593 non-null  object\n",
+      " 13  type                           29593 non-null  object\n",
+      " 14  market_creator                 29593 non-null  object\n",
+      " 15  fpmm.answerFinalizedTimestamp  27798 non-null  object\n",
+      " 16  fpmm.arbitrationOccurred       29593 non-null  bool  \n",
+      " 17  fpmm.currentAnswer             27798 non-null  object\n",
+      " 18  fpmm.id                        29593 non-null  object\n",
+      " 19  fpmm.isPendingArbitration      29593 non-null  bool  \n",
+      " 20  fpmm.openingTimestamp          29593 non-null  object\n",
+      " 21  fpmm.outcomes                  29593 non-null  object\n",
+      " 22  fpmm.title                     29593 non-null  object\n",
+      " 23  fpmm.condition.id              29593 non-null  object\n",
+      "dtypes: bool(2), object(22)\n",
+      "memory usage: 5.0+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "trades.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime\n",
+    "trades[\"creation_timestamp\"] = trades.apply(lambda x: datetime.datetime.fromtimestamp(\n",
+    "                int(x[\"creationTimestamp\"]), tz=datetime.timezone.utc\n",
+    "            ), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trades['creation_timestamp'] = pd.to_datetime(trades['creation_timestamp'])\n",
+    "trades['creation_date'] = trades['creation_timestamp'].dt.date\n",
+    "trades['creation_time'] = trades['creation_timestamp'].dt.time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/gp/02mb1d514ng739czlxw1lhh00000gn/T/ipykernel_15601/3921345989.py:7: UserWarning:\n",
+      "\n",
+      "Converting to PeriodArray/Index representation will drop timezone information.\n",
+      "\n",
+      "/var/folders/gp/02mb1d514ng739czlxw1lhh00000gn/T/ipykernel_15601/3921345989.py:10: UserWarning:\n",
+      "\n",
+      "Converting to PeriodArray/Index representation will drop timezone information.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "trades[\"creation_timestamp\"] = pd.to_datetime(trades[\"creation_timestamp\"])\n",
+    "trades[\"creation_timestamp\"] = trades[\"creation_timestamp\"].dt.tz_convert(\n",
+    "        \"UTC\"\n",
+    "    )\n",
+    "trades = trades.sort_values(by=\"creation_timestamp\", ascending=True)\n",
+    "trades[\"month_year\"] = (\n",
+    "        trades[\"creation_timestamp\"].dt.to_period(\"M\").astype(str)\n",
+    "    )\n",
+    "trades[\"month_year_week\"] = (\n",
+    "        trades[\"creation_timestamp\"].dt.to_period(\"W\").dt.strftime(\"%b-%d\")\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_trades_per_trader = trades.groupby([\"market_creator\", \"trader_address\",\"creation_date\"]).agg(total_trades=(\"title\",\"count\")).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weekly_trades_per_trader = trades.groupby([\"market_creator\", \"trader_address\",\"month_year_week\"], sort=False).agg(total_trades=(\"title\",\"count\")).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weekly_total_trades_stats = weekly_trades_per_trader.groupby([\"market_creator\",\"month_year_week\"], sort=False).agg(mean=(\"total_trades\", 'mean'),median=(\"total_trades\", 'median'), max=(\"total_trades\", 'max'), min=(\"total_trades\", 'min'), count=(\"total_trades\", 'count')).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>market_creator</th>\n",
+       "      <th>month_year_week</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>median</th>\n",
+       "      <th>max</th>\n",
+       "      <th>min</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>Aug-25</td>\n",
+       "      <td>14.069767</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>Aug-25</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>Sep-01</td>\n",
+       "      <td>50.186667</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>193</td>\n",
+       "      <td>2</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>Sep-01</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>89</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>Sep-08</td>\n",
+       "      <td>45.714286</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>144</td>\n",
+       "      <td>1</td>\n",
+       "      <td>77</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  market_creator month_year_week       mean  median  max  min  count\n",
+       "0     quickstart          Aug-25  14.069767    11.0   34    1     43\n",
+       "1          pearl          Aug-25   3.000000     3.0    8    1      7\n",
+       "2     quickstart          Sep-01  50.186667    39.0  193    2     75\n",
+       "3          pearl          Sep-01  11.000000     4.0   89    1     25\n",
+       "4     quickstart          Sep-08  45.714286    37.0  144    1     77"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weekly_total_trades_stats.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>market_creator</th>\n",
+       "      <th>trader_address</th>\n",
+       "      <th>creation_date</th>\n",
+       "      <th>total_trades</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2</td>\n",
+       "      <td>2024-09-01</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2</td>\n",
+       "      <td>2024-09-16</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x01c72d0743a22b70d73c76c5e16ba7524e20e0c0</td>\n",
+       "      <td>2024-10-15</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x032533005f65026fa7f360ff9a211bc94315325d</td>\n",
+       "      <td>2024-08-23</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x032533005f65026fa7f360ff9a211bc94315325d</td>\n",
+       "      <td>2024-10-04</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  market_creator                              trader_address creation_date  \\\n",
+       "0          pearl  0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2    2024-09-01   \n",
+       "1          pearl  0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2    2024-09-16   \n",
+       "2          pearl  0x01c72d0743a22b70d73c76c5e16ba7524e20e0c0    2024-10-15   \n",
+       "3          pearl  0x032533005f65026fa7f360ff9a211bc94315325d    2024-08-23   \n",
+       "4          pearl  0x032533005f65026fa7f360ff9a211bc94315325d    2024-10-04   \n",
+       "\n",
+       "   total_trades  \n",
+       "0             1  \n",
+       "1             1  \n",
+       "2             1  \n",
+       "3             1  \n",
+       "4             1  "
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "daily_trades_per_trader.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 3262 entries, 0 to 3261\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column          Non-Null Count  Dtype \n",
+      "---  ------          --------------  ----- \n",
+      " 0   market_creator  3262 non-null   object\n",
+      " 1   trader_address  3262 non-null   object\n",
+      " 2   creation_date   3262 non-null   object\n",
+      " 3   total_trades    3262 non-null   int64 \n",
+      "dtypes: int64(1), object(3)\n",
+      "memory usage: 102.1+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "daily_trades_per_trader.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 3262 entries, 0 to 3261\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column          Non-Null Count  Dtype   \n",
+      "---  ------          --------------  -----   \n",
+      " 0   market_creator  3262 non-null   object  \n",
+      " 1   trader_address  3262 non-null   object  \n",
+      " 2   creation_date   3262 non-null   object  \n",
+      " 3   total_trades    3262 non-null   int64   \n",
+      " 4   day_id          3262 non-null   category\n",
+      "dtypes: category(1), int64(1), object(3)\n",
+      "memory usage: 107.8+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "daily_trades_per_trader[\"day_id\"] =  daily_trades_per_trader[\"creation_date\"].astype('category')\n",
+    "daily_trades_per_trader.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>market_creator</th>\n",
+       "      <th>trader_address</th>\n",
+       "      <th>creation_date</th>\n",
+       "      <th>total_trades</th>\n",
+       "      <th>day_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2</td>\n",
+       "      <td>2024-09-01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2024-09-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2</td>\n",
+       "      <td>2024-09-16</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2024-09-16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x01c72d0743a22b70d73c76c5e16ba7524e20e0c0</td>\n",
+       "      <td>2024-10-15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2024-10-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x032533005f65026fa7f360ff9a211bc94315325d</td>\n",
+       "      <td>2024-08-23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2024-08-23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x032533005f65026fa7f360ff9a211bc94315325d</td>\n",
+       "      <td>2024-10-04</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2024-10-04</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  market_creator                              trader_address creation_date  \\\n",
+       "0          pearl  0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2    2024-09-01   \n",
+       "1          pearl  0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2    2024-09-16   \n",
+       "2          pearl  0x01c72d0743a22b70d73c76c5e16ba7524e20e0c0    2024-10-15   \n",
+       "3          pearl  0x032533005f65026fa7f360ff9a211bc94315325d    2024-08-23   \n",
+       "4          pearl  0x032533005f65026fa7f360ff9a211bc94315325d    2024-10-04   \n",
+       "\n",
+       "   total_trades      day_id  \n",
+       "0             1  2024-09-01  \n",
+       "1             1  2024-09-16  \n",
+       "2             1  2024-10-15  \n",
+       "3             1  2024-08-23  \n",
+       "4             1  2024-10-04  "
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "daily_trades_per_trader.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plotly.express as px"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>market_creator</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>pearl</th>\n",
+       "      <td>623.0</td>\n",
+       "      <td>2.398074</td>\n",
+       "      <td>2.834468</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quickstart</th>\n",
+       "      <td>2639.0</td>\n",
+       "      <td>10.647594</td>\n",
+       "      <td>8.860392</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>15.0</td>\n",
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+       "    </tr>\n",
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+      "text/plain": [
+       "                 count       mean       std  min  25%  50%   75%   max\n",
+       "market_creator                                                        \n",
+       "pearl            623.0   2.398074  2.834468  1.0  1.0  1.0   3.0  33.0\n",
+       "quickstart      2639.0  10.647594  8.860392  1.0  4.0  8.0  15.0  74.0"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
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+   "source": [
+    "daily_trades_per_trader.groupby('market_creator')['total_trades'].describe()"
+   ]
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+        },
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+   ],
+   "source": [
+    "fig = px.scatter(daily_trades_per_trader.loc[daily_trades_per_trader[\"market_creator\"]==\"pearl\"], x=\"creation_date\", y=\"total_trades\", title='Daily total_trades by pearl markets')\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
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+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
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+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
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+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
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+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Daily total_trades by qs markets"
+        },
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "creation_date"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "total_trades"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = px.scatter(daily_trades_per_trader.loc[daily_trades_per_trader[\"market_creator\"]!=\"pearl\"], x=\"creation_date\", y=\"total_trades\", color=\"market_creator\", title='Daily total_trades by qs markets')\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>market_creator</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>pearl</th>\n",
+       "      <td>623.0</td>\n",
+       "      <td>2.398074</td>\n",
+       "      <td>2.834468</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quickstart</th>\n",
+       "      <td>2639.0</td>\n",
+       "      <td>10.647594</td>\n",
+       "      <td>8.860392</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>74.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 count       mean       std  min  25%  50%   75%   max\n",
+       "market_creator                                                        \n",
+       "pearl            623.0   2.398074  2.834468  1.0  1.0  1.0   3.0  33.0\n",
+       "quickstart      2639.0  10.647594  8.860392  1.0  4.0  8.0  15.0  74.0"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "daily_trades_per_trader.groupby('market_creator')['total_trades'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools['request_time'] = pd.to_datetime(tools['request_time'])\n",
+    "tools['request_date'] = tools['request_time'].dt.date"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = tools.sort_values(by=\"request_time\", ascending=True)\n",
+    "tools[\"month_year\"] = (\n",
+    "        tools[\"request_time\"].dt.to_period(\"M\").astype(str)\n",
+    "    )\n",
+    "tools[\"month_year_week\"] = (\n",
+    "        tools[\"request_time\"].dt.to_period(\"W\").dt.strftime(\"%b-%d\")\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_mech_calls_per_trader = tools.groupby([\"market_creator\", \"trader_address\",\"request_date\"]).agg(total_mech_calls=(\"title\",\"count\")).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weekly_mech_calls_per_trader = tools.groupby([\"market_creator\", \"trader_address\",\"month_year_week\"], sort=False).agg(total_mech_calls=(\"title\",\"count\")).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>market_creator</th>\n",
+       "      <th>trader_address</th>\n",
+       "      <th>creation_date</th>\n",
+       "      <th>total_trades</th>\n",
+       "      <th>day_id</th>\n",
+       "      <th>request_date</th>\n",
+       "      <th>total_mech_calls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2</td>\n",
+       "      <td>2024-09-01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2024-09-01</td>\n",
+       "      <td>2024-09-01</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2</td>\n",
+       "      <td>2024-09-16</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2024-09-16</td>\n",
+       "      <td>2024-09-16</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x01c72d0743a22b70d73c76c5e16ba7524e20e0c0</td>\n",
+       "      <td>2024-10-15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2024-10-15</td>\n",
+       "      <td>2024-10-15</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x032533005f65026fa7f360ff9a211bc94315325d</td>\n",
+       "      <td>2024-08-23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2024-08-23</td>\n",
+       "      <td>2024-08-23</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x032533005f65026fa7f360ff9a211bc94315325d</td>\n",
+       "      <td>2024-10-04</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2024-10-04</td>\n",
+       "      <td>2024-10-04</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  market_creator                              trader_address creation_date  \\\n",
+       "0          pearl  0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2    2024-09-01   \n",
+       "1          pearl  0x006f70b4e3c3a3648f31ec16b2e7106fc58166f2    2024-09-16   \n",
+       "2          pearl  0x01c72d0743a22b70d73c76c5e16ba7524e20e0c0    2024-10-15   \n",
+       "3          pearl  0x032533005f65026fa7f360ff9a211bc94315325d    2024-08-23   \n",
+       "4          pearl  0x032533005f65026fa7f360ff9a211bc94315325d    2024-10-04   \n",
+       "\n",
+       "   total_trades      day_id request_date  total_mech_calls  \n",
+       "0             1  2024-09-01   2024-09-01                 6  \n",
+       "1             1  2024-09-16   2024-09-16                12  \n",
+       "2             1  2024-10-15   2024-10-15                 5  \n",
+       "3             1  2024-08-23   2024-08-23                20  \n",
+       "4             1  2024-10-04   2024-10-04                11  "
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "daily_final_data = pd.merge(daily_trades_per_trader, daily_mech_calls_per_trader, left_on=['trader_address', 'market_creator', \"creation_date\"], right_on=['trader_address', 'market_creator', \"request_date\"])\n",
+    "daily_final_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_final_data[\"daily_ratio_mech_calls\"] = daily_final_data[\"total_mech_calls\"]/daily_final_data[\"total_trades\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
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+            "linecolor": "white",
+            "ticks": ""
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+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
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+          "title": {
+           "x": 0.05
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+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
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+           "zerolinecolor": "white",
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+        },
+        "title": {
+         "text": "Daily ratio of mech calls by pearl markets"
+        },
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
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+        "yaxis": {
+         "anchor": "x",
+         "domain": [
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+         "title": {
+          "text": "daily_ratio_mech_calls"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = px.scatter(daily_final_data.loc[daily_final_data[\"market_creator\"]==\"pearl\"], x=\"creation_date\", y=\"daily_ratio_mech_calls\", title='Daily ratio of mech calls by pearl markets')\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_final_data['c_date'] = pd.date_range(datetime.datetime.today(), periods=daily_final_data.shape[0]).astype('int64')"
+   ]
+  },
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+   "execution_count": 87,
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+            "linecolor": "white",
+            "ticks": ""
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+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
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+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
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+           "ticks": "",
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+         "text": "Daily ratio of mech calls by qs markets"
+        },
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
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+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = px.scatter(daily_final_data.loc[daily_final_data[\"market_creator\"]!=\"pearl\"], x=\"creation_date\", y=\"daily_ratio_mech_calls\", title='Daily ratio of mech calls by qs markets')\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  <thead>\n",
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+       "      <th>count</th>\n",
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+       "      <th>min</th>\n",
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+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
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+       "    <tr>\n",
+       "      <th>market_creator</th>\n",
+       "      <th></th>\n",
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+       "      <th></th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>pearl</th>\n",
+       "      <td>289.0</td>\n",
+       "      <td>9.970581</td>\n",
+       "      <td>15.065041</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>197.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quickstart</th>\n",
+       "      <td>2050.0</td>\n",
+       "      <td>8.468985</td>\n",
+       "      <td>14.026061</td>\n",
+       "      <td>0.238095</td>\n",
+       "      <td>2.316388</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>226.0</td>\n",
+       "    </tr>\n",
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+       "</div>"
+      ],
+      "text/plain": [
+       "                 count      mean        std       min       25%  50%   75%  \\\n",
+       "market_creator                                                               \n",
+       "pearl            289.0  9.970581  15.065041  1.000000  5.000000  6.0  10.0   \n",
+       "quickstart      2050.0  8.468985  14.026061  0.238095  2.316388  4.5   8.2   \n",
+       "\n",
+       "                  max  \n",
+       "market_creator         \n",
+       "pearl           197.0  \n",
+       "quickstart      226.0  "
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "daily_final_data.groupby('market_creator')['daily_ratio_mech_calls'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_ratio_stats = daily_final_data.groupby([\"market_creator\",\"creation_date\"]).agg(mean=(\"daily_ratio_mech_calls\", 'mean'),median=(\"daily_ratio_mech_calls\", 'median'), max=(\"daily_ratio_mech_calls\", 'max'), min=(\"daily_ratio_mech_calls\", 'min'), count=(\"daily_ratio_mech_calls\", 'count')).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pearl_daily_ratio_stats = daily_ratio_stats.loc[daily_ratio_stats[\"market_creator\"]==\"pearl\"]\n",
+    "qs_daily_ratio_stats = daily_ratio_stats.loc[daily_ratio_stats[\"market_creator\"]!=\"pearl\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
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+       "      <th>0</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>2024-08-23</td>\n",
+       "      <td>37.916667</td>\n",
+       "      <td>42.333333</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>2024-08-24</td>\n",
+       "      <td>33.660714</td>\n",
+       "      <td>20.071429</td>\n",
+       "      <td>89.5</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>2024-08-25</td>\n",
+       "      <td>44.666667</td>\n",
+       "      <td>44.666667</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>4.333333</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>2024-08-26</td>\n",
+       "      <td>11.962963</td>\n",
+       "      <td>9.888889</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>2024-08-27</td>\n",
+       "      <td>17.082143</td>\n",
+       "      <td>17.082143</td>\n",
+       "      <td>29.2</td>\n",
+       "      <td>4.964286</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  market_creator creation_date       mean     median   max        min  count\n",
+       "0          pearl    2024-08-23  37.916667  42.333333  47.0  20.000000      4\n",
+       "1          pearl    2024-08-24  33.660714  20.071429  89.5   5.000000      4\n",
+       "2          pearl    2024-08-25  44.666667  44.666667  85.0   4.333333      2\n",
+       "3          pearl    2024-08-26  11.962963   9.888889  21.0   5.000000      3\n",
+       "4          pearl    2024-08-27  17.082143  17.082143  29.2   4.964286      2"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pearl_daily_ratio_stats.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>market_creator</th>\n",
+       "      <th>trader_address</th>\n",
+       "      <th>month_year_week</th>\n",
+       "      <th>total_trades</th>\n",
+       "      <th>total_mech_calls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>0x74d2b585a46279b4fa9feeae001efc972726c709</td>\n",
+       "      <td>Aug-25</td>\n",
+       "      <td>30</td>\n",
+       "      <td>105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>0x5540b853357c2c04bf02896b028c1e5a8f6a114c</td>\n",
+       "      <td>Aug-25</td>\n",
+       "      <td>3</td>\n",
+       "      <td>267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>0x0822e82311bd2d7e381bd2c64cacd13b4c3fbe8a</td>\n",
+       "      <td>Aug-25</td>\n",
+       "      <td>11</td>\n",
+       "      <td>351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>0x022b36c50b85b8ae7addfb8a35d76c59d5814834</td>\n",
+       "      <td>Aug-25</td>\n",
+       "      <td>23</td>\n",
+       "      <td>123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>0x96fc9f4eb6b7c228aa018b7fea9b43d77023aa5a</td>\n",
+       "      <td>Aug-25</td>\n",
+       "      <td>8</td>\n",
+       "      <td>123</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  market_creator                              trader_address month_year_week  \\\n",
+       "0     quickstart  0x74d2b585a46279b4fa9feeae001efc972726c709          Aug-25   \n",
+       "1          pearl  0x5540b853357c2c04bf02896b028c1e5a8f6a114c          Aug-25   \n",
+       "2     quickstart  0x0822e82311bd2d7e381bd2c64cacd13b4c3fbe8a          Aug-25   \n",
+       "3     quickstart  0x022b36c50b85b8ae7addfb8a35d76c59d5814834          Aug-25   \n",
+       "4     quickstart  0x96fc9f4eb6b7c228aa018b7fea9b43d77023aa5a          Aug-25   \n",
+       "\n",
+       "   total_trades  total_mech_calls  \n",
+       "0            30               105  \n",
+       "1             3               267  \n",
+       "2            11               351  \n",
+       "3            23               123  \n",
+       "4             8               123  "
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weekly_final_data = pd.merge(weekly_trades_per_trader, weekly_mech_calls_per_trader, on=['trader_address', 'market_creator', \"month_year_week\"])\n",
+    "weekly_final_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weekly_final_data[\"weekly_ratio_mech_calls\"] = weekly_final_data[\"total_mech_calls\"]/weekly_final_data[\"total_trades\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weekly_ratio_stats = weekly_final_data.groupby([\"market_creator\",\"month_year_week\"]).agg(mean=(\"weekly_ratio_mech_calls\", 'mean'),median=(\"weekly_ratio_mech_calls\", 'median'), max=(\"weekly_ratio_mech_calls\", 'max'), min=(\"weekly_ratio_mech_calls\", 'min'), count=(\"weekly_ratio_mech_calls\", 'count')).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weekly_ratio_stats = weekly_final_data.groupby([\"market_creator\",\"month_year_week\"], sort=False).agg(mean=(\"weekly_ratio_mech_calls\", 'mean'),median=(\"weekly_ratio_mech_calls\", 'median'), max=(\"weekly_ratio_mech_calls\", 'max'), min=(\"weekly_ratio_mech_calls\", 'min'), count=(\"weekly_ratio_mech_calls\", 'count')).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>market_creator</th>\n",
+       "      <th>month_year_week</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>median</th>\n",
+       "      <th>max</th>\n",
+       "      <th>min</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>Aug-25</td>\n",
+       "      <td>16.900132</td>\n",
+       "      <td>11.928571</td>\n",
+       "      <td>97.000000</td>\n",
+       "      <td>2.645161</td>\n",
+       "      <td>42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>Aug-25</td>\n",
+       "      <td>57.220238</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>124.000000</td>\n",
+       "      <td>7.500000</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>Sep-01</td>\n",
+       "      <td>7.875711</td>\n",
+       "      <td>4.629032</td>\n",
+       "      <td>33.722222</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>pearl</td>\n",
+       "      <td>Sep-01</td>\n",
+       "      <td>19.276245</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>54.000000</td>\n",
+       "      <td>3.250000</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>quickstart</td>\n",
+       "      <td>Sep-08</td>\n",
+       "      <td>6.803005</td>\n",
+       "      <td>3.848101</td>\n",
+       "      <td>29.576923</td>\n",
+       "      <td>1.285714</td>\n",
+       "      <td>61</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  market_creator month_year_week       mean     median         max       min  \\\n",
+       "0     quickstart          Aug-25  16.900132  11.928571   97.000000  2.645161   \n",
+       "1          pearl          Aug-25  57.220238  50.000000  124.000000  7.500000   \n",
+       "2     quickstart          Sep-01   7.875711   4.629032   33.722222  1.000000   \n",
+       "3          pearl          Sep-01  19.276245  15.000000   54.000000  3.250000   \n",
+       "4     quickstart          Sep-08   6.803005   3.848101   29.576923  1.285714   \n",
+       "\n",
+       "   count  \n",
+       "0     42  \n",
+       "1      7  \n",
+       "2     59  \n",
+       "3     15  \n",
+       "4     61  "
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weekly_ratio_stats.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pearl_weekly_ratio_stats = weekly_ratio_stats.loc[weekly_ratio_stats[\"market_creator\"]==\"pearl\"]\n",
+    "qs_weekly_ratio_stats = weekly_ratio_stats.loc[weekly_ratio_stats[\"market_creator\"]!=\"pearl\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plotly.graph_objects as go"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "line": {
+          "color": "blue",
+          "dash": "dash"
+         },
+         "name": "Mean ratio",
+         "type": "scatter",
+         "x": [
+          "Aug-25",
+          "Sep-01",
+          "Sep-08",
+          "Sep-15",
+          "Sep-22",
+          "Sep-29",
+          "Oct-06",
+          "Oct-13",
+          "Oct-20",
+          "Oct-27"
+         ],
+         "y": [
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+          19.276244505292755,
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+          36.35555555555556,
+          33.6035373608903,
+          40.756449948400416,
+          82
+         ]
+        },
+        {
+         "name": "Median ratio",
+         "type": "scatter",
+         "x": [
+          "Aug-25",
+          "Sep-01",
+          "Sep-08",
+          "Sep-15",
+          "Sep-22",
+          "Sep-29",
+          "Oct-06",
+          "Oct-13",
+          "Oct-20",
+          "Oct-27"
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+         "y": [
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+          15,
+          29,
+          33.5,
+          31,
+          38,
+          33,
+          30,
+          32.5,
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+        {
+         "name": "Max ratio",
+         "type": "scatter",
+         "x": [
+          "Aug-25",
+          "Sep-01",
+          "Sep-08",
+          "Sep-15",
+          "Sep-22",
+          "Sep-29",
+          "Oct-06",
+          "Oct-13",
+          "Oct-20",
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+          54,
+          50,
+          252,
+          102,
+          96,
+          89,
+          128,
+          210,
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+         ]
+        },
+        {
+         "name": "Min ratio",
+         "type": "scatter",
+         "x": [
+          "Aug-25",
+          "Sep-01",
+          "Sep-08",
+          "Sep-15",
+          "Sep-22",
+          "Sep-29",
+          "Oct-06",
+          "Oct-13",
+          "Oct-20",
+          "Oct-27"
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+         "y": [
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+          3.25,
+          4.875,
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+          5,
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+          2.7058823529411766,
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+       "layout": {
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+          "ternary": {
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+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
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+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Weekly ratio of all_mech_calls(ending and not ending in trades)/all_trades in Pearl"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the figure\n",
+    "fig = go.Figure()\n",
+    "\n",
+    "# Add each line to the figure\n",
+    "fig.add_trace(go.Scatter(x=pearl_weekly_ratio_stats[\"month_year_week\"], y=pearl_weekly_ratio_stats[\"mean\"], name='Mean ratio',line=dict(color='blue', dash='dash')))\n",
+    "fig.add_trace(go.Scatter(x=pearl_weekly_ratio_stats[\"month_year_week\"], y=pearl_weekly_ratio_stats[\"median\"], name='Median ratio'))\n",
+    "fig.add_trace(go.Scatter(x=pearl_weekly_ratio_stats[\"month_year_week\"], y=pearl_weekly_ratio_stats[\"max\"], name='Max ratio'))\n",
+    "fig.add_trace(go.Scatter(x=pearl_weekly_ratio_stats[\"month_year_week\"], y=pearl_weekly_ratio_stats[\"min\"], name='Min ratio'))\n",
+    "fig.update_layout(\n",
+    "    title='Weekly ratio of all_mech_calls(ending and not ending in trades)/all_trades in Pearl')\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
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+         "name": "Mean ratio",
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+          "Aug-25",
+          "Sep-01",
+          "Sep-08",
+          "Sep-15",
+          "Sep-22",
+          "Sep-29",
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+          "Oct-13",
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+          "ternary": {
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+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
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+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Weekly ratio of all_mech_calls(ending and not ending in trades)/all_trades in Quickstart"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the figure\n",
+    "fig = go.Figure()\n",
+    "\n",
+    "# Add each line to the figure\n",
+    "fig.add_trace(go.Scatter(x=qs_weekly_ratio_stats[\"month_year_week\"], y=qs_weekly_ratio_stats[\"mean\"], name='Mean ratio',line=dict(color='blue', dash='dash')))\n",
+    "fig.add_trace(go.Scatter(x=qs_weekly_ratio_stats[\"month_year_week\"], y=qs_weekly_ratio_stats[\"median\"], name='Median ratio'))\n",
+    "fig.add_trace(go.Scatter(x=qs_weekly_ratio_stats[\"month_year_week\"], y=qs_weekly_ratio_stats[\"max\"], name='Max ratio'))\n",
+    "fig.add_trace(go.Scatter(x=qs_weekly_ratio_stats[\"month_year_week\"], y=qs_weekly_ratio_stats[\"min\"], name='Min ratio'))\n",
+    "fig.update_layout(\n",
+    "    title='Weekly ratio of all_mech_calls(ending and not ending in trades)/all_trades in Quickstart')\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+        "plotlyServerURL": "https://plot.ly"
+       },
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+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Daily ratio of all_mech_calls(ending and not ending in trades)/all_trades in Pearl agents"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the figure\n",
+    "fig = go.Figure()\n",
+    "\n",
+    "# Add each line to the figure\n",
+    "fig.add_trace(go.Scatter(x=pearl_daily_ratio_stats[\"creation_date\"], y=pearl_daily_ratio_stats[\"mean\"], name='Mean ratio',line=dict(color='blue', dash='dash')))\n",
+    "fig.add_trace(go.Scatter(x=pearl_daily_ratio_stats[\"creation_date\"], y=pearl_daily_ratio_stats[\"median\"], name='Median ratio'))\n",
+    "fig.add_trace(go.Scatter(x=pearl_daily_ratio_stats[\"creation_date\"], y=pearl_daily_ratio_stats[\"max\"], name='Max ratio'))\n",
+    "fig.add_trace(go.Scatter(x=pearl_daily_ratio_stats[\"creation_date\"], y=pearl_daily_ratio_stats[\"min\"], name='Min ratio'))\n",
+    "fig.update_layout(\n",
+    "    title='Daily ratio of all_mech_calls(ending and not ending in trades)/all_trades in Pearl agents')\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
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+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
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+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Daily ratio of all_mech_calls(ending and not ending in trades)/all_trades in Quickstart agents"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the figure\n",
+    "fig = go.Figure()\n",
+    "\n",
+    "# Add each line to the figure\n",
+    "fig.add_trace(go.Scatter(x=qs_daily_ratio_stats[\"creation_date\"], y=qs_daily_ratio_stats[\"mean\"], name='Mean ratio',line=dict(color='blue', dash='dash')))\n",
+    "fig.add_trace(go.Scatter(x=qs_daily_ratio_stats[\"creation_date\"], y=qs_daily_ratio_stats[\"median\"], name='Median ratio'))\n",
+    "fig.add_trace(go.Scatter(x=qs_daily_ratio_stats[\"creation_date\"], y=qs_daily_ratio_stats[\"max\"], name='Max ratio'))\n",
+    "fig.add_trace(go.Scatter(x=qs_daily_ratio_stats[\"creation_date\"], y=qs_daily_ratio_stats[\"min\"], name='Min ratio'))\n",
+    "fig.update_layout(\n",
+    "    title='Daily ratio of all_mech_calls(ending and not ending in trades)/all_trades in Quickstart agents')\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pearl_weekly_total_trades_stats = weekly_total_trades_stats.loc[weekly_total_trades_stats[\"market_creator\"]==\"pearl\"]\n",
+    "qs_weekly_total_trades_stats = weekly_total_trades_stats.loc[weekly_total_trades_stats[\"market_creator\"]!=\"pearl\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>market_creator</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>pearl</th>\n",
+       "      <td>341.0</td>\n",
+       "      <td>4.381232</td>\n",
+       "      <td>7.856515</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>89.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quickstart</th>\n",
+       "      <td>669.0</td>\n",
+       "      <td>42.001495</td>\n",
+       "      <td>42.564622</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>193.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                count       mean        std  min   25%   50%   75%    max\n",
+       "market_creator                                                           \n",
+       "pearl           341.0   4.381232   7.856515  1.0   1.0   1.0   3.0   89.0\n",
+       "quickstart      669.0  42.001495  42.564622  1.0  10.0  27.0  58.0  193.0"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weekly_trades_per_trader.groupby('market_creator')['total_trades'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
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+          "Sep-01",
+          "Sep-08",
+          "Sep-15",
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+          "ternary": {
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+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
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+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Weekly total_trades in Pearl agents"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the figure\n",
+    "fig = go.Figure()\n",
+    "\n",
+    "# Add each line to the figure\n",
+    "fig.add_trace(go.Scatter(x=pearl_weekly_total_trades_stats[\"month_year_week\"], y=pearl_weekly_total_trades_stats[\"mean\"], name='Mean nr trades',line=dict(color='blue', dash='dash')))\n",
+    "fig.add_trace(go.Scatter(x=pearl_weekly_total_trades_stats[\"month_year_week\"], y=pearl_weekly_total_trades_stats[\"median\"], name='Median nr trades'))\n",
+    "fig.add_trace(go.Scatter(x=pearl_weekly_total_trades_stats[\"month_year_week\"], y=pearl_weekly_total_trades_stats[\"max\"], name='Max nr trades'))\n",
+    "fig.add_trace(go.Scatter(x=pearl_weekly_total_trades_stats[\"month_year_week\"], y=pearl_weekly_total_trades_stats[\"min\"], name='Min nr trades'))\n",
+    "fig.update_layout(\n",
+    "    title='Weekly total_trades in Pearl agents')\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
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+         "line": {
+          "color": "blue",
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+         "name": "Mean ratio",
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+          "Aug-25",
+          "Sep-01",
+          "Sep-08",
+          "Sep-15",
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+            "ticks": "",
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+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
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+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Weekly total_trades in Quickstart agents"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the figure\n",
+    "fig = go.Figure()\n",
+    "\n",
+    "# Add each line to the figure\n",
+    "fig.add_trace(go.Scatter(x=qs_weekly_total_trades_stats[\"month_year_week\"], y=qs_weekly_total_trades_stats[\"mean\"], name='Mean ratio',line=dict(color='blue', dash='dash')))\n",
+    "fig.add_trace(go.Scatter(x=qs_weekly_total_trades_stats[\"month_year_week\"], y=qs_weekly_total_trades_stats[\"median\"], name='Median ratio'))\n",
+    "fig.add_trace(go.Scatter(x=qs_weekly_total_trades_stats[\"month_year_week\"], y=qs_weekly_total_trades_stats[\"max\"], name='Max ratio'))\n",
+    "fig.add_trace(go.Scatter(x=qs_weekly_total_trades_stats[\"month_year_week\"], y=qs_weekly_total_trades_stats[\"min\"], name='Min ratio'))\n",
+    "fig.update_layout(\n",
+    "    title='Weekly total_trades in Quickstart agents')\n",
+    "fig.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "hf_dashboards",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}