diff --git a/hydradx/notebooks/Omnipool/LP_fees_analysis.ipynb b/hydradx/notebooks/Omnipool/LP_fees_analysis.ipynb
index 0bd4ca16..89a3180f 100644
--- a/hydradx/notebooks/Omnipool/LP_fees_analysis.ipynb
+++ b/hydradx/notebooks/Omnipool/LP_fees_analysis.ipynb
@@ -22,7 +22,7 @@
    },
    "source": [
     "<H3>Effects of trade volume.</H3>\n",
-    "Trade volume generates fees, which are the primary way of offsetting IL. We'll assume prices do no change, so there is no impermanent loss. Trade volume goes from 0% to 5% of TVL per day. (So far we have observed ~1% on average.) We also assume 0.3% total fees. We fully simulate one month and then extrapolate the results to one year. This should still be fairly accurate, because of the linear nature of the correlation between time and profit, but it does introduce a small amount of uncertainty."
+    "Trade volume generates fees, which are the primary way of offsetting IL. We'll assume prices do no change, so there is no impermanent loss. Trade volume goes from 0% to 5% of TVL per day. (So far we have observed ~1-5% on average.) We also assume 0.3% total fees. We fully simulate one month and then extrapolate the results to one year. This should still be fairly accurate, because of the linear nature of the correlation between time and profit."
    ]
   },
   {
@@ -110,16 +110,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "b3f08712-922b-465f-b04b-5f3057a78ab5",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-05-24T15:14:34.207704241Z",
      "start_time": "2023-05-24T15:01:10.779629649Z"
     },
-    "jupyter": {
-     "source_hidden": true
-    },
     "tags": []
    },
    "outputs": [
@@ -127,24 +124,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting simulation...\n",
-      "Execution time: 38.873 seconds.\n",
       "Trade volume per day as a fraction of TVL: {'HDX': 0.0, 'USD': 0.0, 'BTC': 0.0, 'ETH': 0.0, 'DOT': 0.0, 'TKN': 0.0}\n",
-      "Starting simulation...\n",
-      "Execution time: 88.07 seconds.\n",
-      "Trade volume per day as a fraction of TVL: {'HDX': 0.010000012495836826, 'USD': 0.010000012502803262, 'BTC': 0.010000012502803355, 'ETH': 0.010000012502801739, 'DOT': 0.010000012502803362, 'TKN': 0.010000011029834038}\n",
-      "Starting simulation...\n",
-      "Execution time: 88.528 seconds.\n",
-      "Trade volume per day as a fraction of TVL: {'HDX': 0.02000002490813006, 'USD': 0.02000002493598734, 'BTC': 0.02000002493598931, 'ETH': 0.020000024935990127, 'DOT': 0.020000024935989045, 'TKN': 0.020000021990042104}\n",
-      "Starting simulation...\n",
-      "Execution time: 87.708 seconds.\n",
-      "Trade volume per day as a fraction of TVL: {'HDX': 0.030000037236865996, 'USD': 0.030000037299553438, 'BTC': 0.030000037299554354, 'ETH': 0.030000037299554885, 'DOT': 0.030000037299555186, 'TKN': 0.030000032880643755}\n",
-      "Starting simulation...\n",
-      "Execution time: 89.159 seconds.\n",
-      "Trade volume per day as a fraction of TVL: {'HDX': 0.040000049482057536, 'USD': 0.040000049593503596, 'BTC': 0.040000049593505595, 'ETH': 0.04000004959350389, 'DOT': 0.040000049593502444, 'TKN': 0.04000004370160887}\n",
-      "Starting simulation...\n",
-      "Execution time: 87.901 seconds.\n",
-      "Trade volume per day as a fraction of TVL: {'HDX': 0.05000006164369554, 'USD': 0.05000006181782769, 'BTC': 0.050000061817830586, 'ETH': 0.0500000618178282, 'DOT': 0.05000006181782925, 'TKN': 0.05000005445297287}\n"
+      "Starting simulation...\n"
+     ]
+    },
+    {
+     "ename": "KeyError",
+     "evalue": "'LRNA'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [3]\u001b[0m, in \u001b[0;36m<cell line: 8>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m6\u001b[39m):\n\u001b[0;32m      9\u001b[0m     initial_state\u001b[38;5;241m.\u001b[39magents[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTrader\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mtrade_strategy\u001b[38;5;241m=\u001b[39mback_and_forth(\n\u001b[0;32m     10\u001b[0m         pool_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124momnipool\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m     11\u001b[0m         percentage\u001b[38;5;241m=\u001b[39mi \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m360000\u001b[39m \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m asset_fee \u001b[38;5;241m-\u001b[39m lrna_fee)\n\u001b[0;32m     12\u001b[0m     ) \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m---> 13\u001b[0m     events \u001b[38;5;241m=\u001b[39m \u001b[43mrun\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtime_steps\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m [initial_state\u001b[38;5;241m.\u001b[39marchive()] \u001b[38;5;241m*\u001b[39m time_steps\n\u001b[0;32m     15\u001b[0m     trade_volume\u001b[38;5;241m.\u001b[39mappend({tkn: (\u001b[38;5;28msum\u001b[39m([event\u001b[38;5;241m.\u001b[39mpools[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124momnipool\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvolume_out[tkn] \n\u001b[0;32m     16\u001b[0m                                     \u001b[38;5;241m/\u001b[39m event\u001b[38;5;241m.\u001b[39mpools[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124momnipool\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mliquidity[tkn] \u001b[38;5;28;01mfor\u001b[39;00m event \u001b[38;5;129;01min\u001b[39;00m events])\n\u001b[0;32m     17\u001b[0m         \u001b[38;5;241m/\u001b[39m time_steps \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m7200\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m tkn \u001b[38;5;129;01min\u001b[39;00m assets}\n\u001b[0;32m     18\u001b[0m     )\n\u001b[0;32m     19\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTrade volume per day as a fraction of TVL:\u001b[39m\u001b[38;5;124m'\u001b[39m, trade_volume[i])\n",
+      "File \u001b[1;32m~\\PycharmProjects\\HydraDX-simulations\\hydradx\\notebooks\\Omnipool\\../..\\model\\run.py:28\u001b[0m, in \u001b[0;36mrun\u001b[1;34m(initial_state, time_steps, silent)\u001b[0m\n\u001b[0;32m     26\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m agent_id, agent \u001b[38;5;129;01min\u001b[39;00m agents\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m     27\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m agent\u001b[38;5;241m.\u001b[39mtrade_strategy:\n\u001b[1;32m---> 28\u001b[0m             new_global_state \u001b[38;5;241m=\u001b[39m \u001b[43magent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrade_strategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnew_global_state\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43magent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munique_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     30\u001b[0m     events\u001b[38;5;241m.\u001b[39mappend(new_global_state\u001b[38;5;241m.\u001b[39marchive())\n\u001b[0;32m     32\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m silent:\n",
+      "File \u001b[1;32m~\\PycharmProjects\\HydraDX-simulations\\hydradx\\notebooks\\Omnipool\\../..\\model\\amm\\trade_strategies.py:28\u001b[0m, in \u001b[0;36mTradeStrategy.execute\u001b[1;34m(self, state, agent_id)\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_once:\n\u001b[0;32m     27\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdone \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m---> 28\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43magent_id\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\PycharmProjects\\HydraDX-simulations\\hydradx\\notebooks\\Omnipool\\../..\\model\\amm\\trade_strategies.py:135\u001b[0m, in \u001b[0;36mback_and_forth.<locals>.strategy\u001b[1;34m(state, agent_id)\u001b[0m\n\u001b[0;32m    132\u001b[0m assets \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mdict\u001b[39m\u001b[38;5;241m.\u001b[39mfromkeys(agent\u001b[38;5;241m.\u001b[39masset_list \u001b[38;5;241m+\u001b[39m omnipool\u001b[38;5;241m.\u001b[39masset_list))\n\u001b[0;32m    133\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m asset \u001b[38;5;129;01min\u001b[39;00m assets:\n\u001b[0;32m    134\u001b[0m     \u001b[38;5;66;03m# asset = agent.asset_list[i]\u001b[39;00m\n\u001b[1;32m--> 135\u001b[0m     dr \u001b[38;5;241m=\u001b[39m percentage \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[43momnipool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mliquidity\u001b[49m\u001b[43m[\u001b[49m\u001b[43masset\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m    136\u001b[0m     lrna_init \u001b[38;5;241m=\u001b[39m state\u001b[38;5;241m.\u001b[39magents[agent_id]\u001b[38;5;241m.\u001b[39mholdings[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLRNA\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m    137\u001b[0m     oamm\u001b[38;5;241m.\u001b[39mexecute_swap(omnipool, agent, tkn_sell\u001b[38;5;241m=\u001b[39masset, tkn_buy\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLRNA\u001b[39m\u001b[38;5;124m'\u001b[39m, sell_quantity\u001b[38;5;241m=\u001b[39mdr, modify_imbalance\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'LRNA'"
      ]
     }
    ],
@@ -154,13 +149,14 @@
     "trade_volume = []\n",
     "semi_final_state = []\n",
     "final_state = []\n",
+    "graph_step = 200\n",
     "# initial_state._evolve_function = historical_prices(price_list)\n",
     "for i in range(6):\n",
     "    initial_state.agents['Trader'].trade_strategy=back_and_forth(\n",
     "        pool_id='omnipool',\n",
     "        percentage=i / 360000 / (1 - asset_fee - lrna_fee)\n",
     "    ) if i > 0 else None\n",
-    "    events = run.run(initial_state, time_steps)\n",
+    "    events = run.run(initial_state, time_steps) if i > 0 else [initial_state.archive()] * time_steps\n",
     "    \n",
     "    trade_volume.append({tkn: (sum([event.pools['omnipool'].volume_out[tkn] \n",
     "                                    / event.pools['omnipool'].liquidity[tkn] for event in events])\n",
@@ -168,7 +164,7 @@
     "    )\n",
     "    print('Trade volume per day as a fraction of TVL:', trade_volume[i])\n",
     "\n",
-    "    volume_events.append(events[::200])\n",
+    "    volume_events.append(events[::graph_step])\n",
     "    volume_events[-1].append(events[-1])\n",
     "    \n",
     "    del events"
@@ -176,42 +172,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "b5d6dadf-f4e3-4498-b99c-93b726fca2c1",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-05-24T15:14:34.207704241Z",
      "start_time": "2023-05-24T15:01:10.779629649Z"
     },
-    "jupyter": {
-     "source_hidden": true
-    },
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x1d4d7aadb50>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(20, 5))\n",
     "plt.title('LP fees earned after one month:')\n",
@@ -220,8 +190,8 @@
     "    [0, graph_length / 3, graph_length / 1.5, graph_length],\n",
     "    ['start', '10 days', '20 days', '30 days']\n",
     ")\n",
-    "colors = pu.color_gradient(length=6, color1=(255, 0, 0), color2=(0, 255, 0))\n",
-    "baseline =[value_assets(event.external_market, initial_state.agents['LP'].holdings) for event in volume_events[0]]\n",
+    "colors = pu.color_gradient(length=5, color1=(255, 0, 0), color2=(0, 255, 0))\n",
+    "baseline =[value_assets(initial_state.external_market, initial_state.agents['LP'].holdings)] * time_steps\n",
     "# [0] * len(volume_events[0])\n",
     "# [cash_out_omnipool(event.pools['omnipool'], event.agents['LP'], event.external_market) for event in volume_events[0]]\n",
     "volume_returns = [\n",
@@ -233,7 +203,7 @@
     "    for events in volume_events\n",
     "]\n",
     "\n",
-    "for i, scenario in enumerate(volume_returns):\n",
+    "for i, scenario in enumerate(volume_returns[1:]):\n",
     "    plt.plot(range(len(scenario)), scenario, color=colors[i])\n",
     "    plt.annotate(\n",
     "        f'${round(scenario[-1], 2)} ({round(scenario[-1] / 100, 2)}%)', \n",
@@ -242,44 +212,18 @@
     "\n",
     "plt.legend([\n",
     "    f'{round(volume[\"TKN\"] * 100, 1)}% {(\"trade volume per day\") if volume[\"TKN\"] == 0 else \"\"}' \n",
-    "    for volume in trade_volume\n",
+    "    for volume in trade_volume[1:]\n",
     "])\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "40b300ed-24b1-410e-be52-d64477ab2e2d",
    "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    },
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1d4f167e100>]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(20, 5))\n",
     "plt.title('extrapolated results for one year')\n",
@@ -288,7 +232,7 @@
     "    [f'{round(volume[\"TKN\"] * 100, 2)} %'  for volume in trade_volume]\n",
     ")\n",
     "points = [(trade_volume[i]['TKN'], volume_returns[i][-1] * 12) for i in range(len(trade_volume))]\n",
-    "for i, coors in enumerate(points):\n",
+    "for i, coors in enumerate(points[1:]):\n",
     "    plt.annotate(\n",
     "        text=f'${round(coors[1], 2)} ({round(coors[1] / 100, 2)}%)',\n",
     "        xy=coors,\n",
@@ -304,31 +248,10 @@
     "plt.plot([volume[\"TKN\"] for volume in trade_volume], [scenario[-1] * 12 for scenario in volume_returns])"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "812d1113-a211-4dc0-8fff-8779e3cb10bc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['TKN']"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "initial_state.agents['LP'].asset_list"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1f628309-8152-4804-a146-36501376b7b1",
+   "id": "799fda6c-4ada-4536-9d6d-3fe0f7eafd37",
    "metadata": {},
    "outputs": [],
    "source": []