adding tools file

data/tools.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3ba753f3eddd59d0ef1d87ee0a0a51b24a34b1d40ffe73b217481b0bef59e811
-size 552698691
+oid sha256:3c76ee1cf2a3cd51f434b4d591f747c307d90f20f5a347fd3aab36a0b5eab08c
+size 592980860

notebooks/markets_analysis.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,6 +13,27 @@
     "sns.set_style(\"darkgrid\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'scripts'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscripts\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mget_mech_info\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m update_fpmmTrades_parquet\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'scripts'"
+     ]
+    }
+   ],
+   "source": [
+    "from scripts.get_mech_info import update_fpmmTrades_parquet"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -31,6 +52,187 @@
     "markets = pd.read_parquet('../data/fpmms.parquet')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trades_data = pd.read_parquet('../tmp/fpmmTrades.parquet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trades_filename = \"new_fpmmTrades.parquet\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_trades = pd.read_parquet(\"../tmp/new_fpmmTrades.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merge_df = pd.concat([trades_data, new_trades], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merge_df[\"fpmm.arbitrationOccurred\"] = merge_df[\"fpmm.arbitrationOccurred\"].astype(\n",
+    "    bool\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merge_df[\"fpmm.isPendingArbitration\"] = merge_df[\n",
+    "        \"fpmm.isPendingArbitration\"\n",
+    "    ].astype(bool)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial length before removing duplicates in fpmmTrades= 123556\n"
+     ]
+    }
+   ],
+   "source": [
+    " print(f\"Initial length before removing duplicates in fpmmTrades= {len(merge_df)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final length after removing duplicates in fpmmTrades= 117771\n"
+     ]
+    }
+   ],
+   "source": [
+    "merge_df.drop_duplicates(\"id\", inplace=True)\n",
+    "print(f\"Final length after removing duplicates in fpmmTrades= {len(merge_df)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merge_df.to_parquet(\"../tmp/fpmmTrades.parquet\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['collateralAmount', 'collateralAmountUSD', 'collateralToken',\n",
+       "       'creationTimestamp', 'trader_address', 'feeAmount', 'id',\n",
+       "       'oldOutcomeTokenMarginalPrice', 'outcomeIndex',\n",
+       "       'outcomeTokenMarginalPrice', 'outcomeTokensTraded', 'title',\n",
+       "       'transactionHash', 'type', 'market_creator',\n",
+       "       'fpmm.answerFinalizedTimestamp', 'fpmm.arbitrationOccurred',\n",
+       "       'fpmm.currentAnswer', 'fpmm.id', 'fpmm.isPendingArbitration',\n",
+       "       'fpmm.openingTimestamp', 'fpmm.outcomes', 'fpmm.title',\n",
+       "       'fpmm.condition.id'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trades_data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "102664"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(trades_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max(fpmmsTra)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -1356,7 +1558,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "market_creator",
+   "display_name": "hf_dashboards",
    "language": "python",
    "name": "python3"
   },

notebooks/tools_accuracy.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15,13 +15,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "tools = pd.read_parquet('../data/tools.parquet')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Timestamp('2024-12-10 07:50:55+0000', tz='UTC')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(tools.request_time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Timestamp('2024-10-13 00:00:30+0000', tz='UTC')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(tools.request_time)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,