remove copy

tabs/error.py

@@ -16,7 +16,7 @@ def set_error(row: pd.Series) -> bool:
 
 def get_error_data(tools_df: pd.DataFrame, inc_tools: List[str]) -> pd.DataFrame:
     """Gets the error data for the given tools and calculates the error percentage."""
-    tools_inc = tools_df[tools_df['tool'].isin(inc_tools)].copy()
+    tools_inc = tools_df[tools_df['tool'].isin(inc_tools)]
     tools_inc['error'] = tools_inc.apply(set_error, axis=1)
     error = tools_inc.groupby(['tool', 'request_month_year_week', 'error']).size().unstack().fillna(0).reset_index()
     error['error_perc'] = (error[True] / (error[False] + error[True])) * 100
@@ -50,7 +50,7 @@ def plot_error_data(error_all_df: pd.DataFrame) -> gr.BarPlot:
 
 def plot_tool_error_data(error_df: pd.DataFrame, tool: str) -> gr.BarPlot:
     """Plots the error data for the given tool."""
-    error_tool = error_df[error_df['tool'] == tool].copy()
+    error_tool = error_df[error_df['tool'] == tool]
     error_tool.columns = error_tool.columns.astype(str)
     error_tool['error_perc'] = error_tool['error_perc'].apply(lambda x: round(x, 4))
                 
@@ -71,7 +71,7 @@ def plot_tool_error_data(error_df: pd.DataFrame, tool: str) -> gr.BarPlot:
 
 def plot_week_error_data(error_df: pd.DataFrame, week: str) -> gr.BarPlot:
     """Plots the error data for the given week."""
-    error_week = error_df[error_df['request_month_year_week'] == week].copy()
+    error_week = error_df[error_df['request_month_year_week'] == week]
     error_week.columns = error_week.columns.astype(str)
     error_week['error_perc'] = error_week['error_perc'].apply(lambda x: round(x, 4))
     return gr.BarPlot(

tabs/tool_win.py

@@ -18,7 +18,7 @@ def set_error(row: pd.Series) -> bool:
 
 def get_tool_winning_rate(tools_df: pd.DataFrame, inc_tools: List[str]) -> pd.DataFrame:
     """Gets the tool winning rate data for the given tools and calculates the winning percentage."""
-    tools_inc = tools_df[tools_df['tool'].isin(inc_tools)].copy()
+    tools_inc = tools_df[tools_df['tool'].isin(inc_tools)]
     tools_inc['error'] = tools_inc.apply(set_error, axis=1)
     tools_non_error = tools_inc[tools_inc['error'] != True]
     tools_non_error.loc[:, 'currentAnswer'] = tools_non_error['currentAnswer'].replace({'no': 'No', 'yes': 'Yes'})

test.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,584 +25,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tools = pd.read_parquet('/Users/arshath/play/openautonomy/olas-prediction-live-dashboard/data/tools.parquet')\n",
-    "tools['trader_address'] = tools['trader_address'].str.lower()\n",
-    "fpmmTrades = pd.read_parquet('/Users/arshath/play/openautonomy/olas-prediction-live-dashboard/data/fpmmTrades.parquet')\n",
-    "# trades = pd.read_parquet('/Users/arshath/play/openautonomy/olas-prediction-live-dashboard/data/all_trades_profitability.parquet')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "IRRELEVANT_TOOLS = [\n",
-    "    \"openai-text-davinci-002\",\n",
-    "    \"openai-text-davinci-003\",\n",
-    "    \"openai-gpt-3.5-turbo\",\n",
-    "    \"openai-gpt-4\",\n",
-    "    \"stabilityai-stable-diffusion-v1-5\",\n",
-    "    \"stabilityai-stable-diffusion-xl-beta-v2-2-2\",\n",
-    "    \"stabilityai-stable-diffusion-512-v2-1\",\n",
-    "    \"stabilityai-stable-diffusion-768-v2-1\",\n",
-    "    \"deepmind-optimization-strong\",\n",
-    "    \"deepmind-optimization\",\n",
-    "]\n",
-    "QUERY_BATCH_SIZE = 1000\n",
-    "DUST_THRESHOLD = 10000000000000\n",
-    "INVALID_ANSWER_HEX = (\n",
-    "    \"0xffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff\"\n",
-    ")\n",
-    "INVALID_ANSWER = -1\n",
-    "FPMM_CREATOR = \"0x89c5cc945dd550bcffb72fe42bff002429f46fec\"\n",
-    "DEFAULT_FROM_DATE = \"1970-01-01T00:00:00\"\n",
-    "DEFAULT_TO_DATE = \"2038-01-19T03:14:07\"\n",
-    "DEFAULT_FROM_TIMESTAMP = 0\n",
-    "DEFAULT_TO_TIMESTAMP = 2147483647\n",
-    "WXDAI_CONTRACT_ADDRESS = \"0xe91D153E0b41518A2Ce8Dd3D7944Fa863463a97d\"\n",
-    "DEFAULT_MECH_FEE = 0.01\n",
-    "DUST_THRESHOLD = 10000000000000\n",
-    "SCRIPTS_DIR = Path('/Users/arshath/play/openautonomy/olas-prediction-live-dashboard/scripts')\n",
-    "ROOT_DIR = SCRIPTS_DIR.parent\n",
-    "DATA_DIR = ROOT_DIR / \"data\"\n",
-    "\n",
-    "class MarketState(Enum):\n",
-    "    \"\"\"Market state\"\"\"\n",
-    "\n",
-    "    OPEN = 1\n",
-    "    PENDING = 2\n",
-    "    FINALIZING = 3\n",
-    "    ARBITRATING = 4\n",
-    "    CLOSED = 5\n",
-    "\n",
-    "    def __str__(self) -> str:\n",
-    "        \"\"\"Prints the market status.\"\"\"\n",
-    "        return self.name.capitalize()\n",
-    "\n",
-    "\n",
-    "class MarketAttribute(Enum):\n",
-    "    \"\"\"Attribute\"\"\"\n",
-    "\n",
-    "    NUM_TRADES = \"Num_trades\"\n",
-    "    WINNER_TRADES = \"Winner_trades\"\n",
-    "    NUM_REDEEMED = \"Num_redeemed\"\n",
-    "    INVESTMENT = \"Investment\"\n",
-    "    FEES = \"Fees\"\n",
-    "    MECH_CALLS = \"Mech_calls\"\n",
-    "    MECH_FEES = \"Mech_fees\"\n",
-    "    EARNINGS = \"Earnings\"\n",
-    "    NET_EARNINGS = \"Net_earnings\"\n",
-    "    REDEMPTIONS = \"Redemptions\"\n",
-    "    ROI = \"ROI\"\n",
-    "\n",
-    "    def __str__(self) -> str:\n",
-    "        \"\"\"Prints the attribute.\"\"\"\n",
-    "        return self.value\n",
-    "\n",
-    "    def __repr__(self) -> str:\n",
-    "        \"\"\"Prints the attribute representation.\"\"\"\n",
-    "        return self.name\n",
-    "\n",
-    "    @staticmethod\n",
-    "    def argparse(s: str) -> \"MarketAttribute\":\n",
-    "        \"\"\"Performs string conversion to MarketAttribute.\"\"\"\n",
-    "        try:\n",
-    "            return MarketAttribute[s.upper()]\n",
-    "        except KeyError as e:\n",
-    "            raise ValueError(f\"Invalid MarketAttribute: {s}\") from e\n",
-    "\n",
-    "\n",
-    "ALL_TRADES_STATS_DF_COLS = [\n",
-    "    \"trader_address\",\n",
-    "    \"trade_id\",\n",
-    "    \"creation_timestamp\",\n",
-    "    \"title\",\n",
-    "    \"market_status\",\n",
-    "    \"collateral_amount\",\n",
-    "    \"outcome_index\",\n",
-    "    \"trade_fee_amount\",\n",
-    "    \"outcomes_tokens_traded\",\n",
-    "    \"current_answer\",\n",
-    "    \"is_invalid\",\n",
-    "    \"winning_trade\",\n",
-    "    \"earnings\",\n",
-    "    \"redeemed\",\n",
-    "    \"redeemed_amount\",\n",
-    "    \"num_mech_calls\",\n",
-    "    \"mech_fee_amount\",\n",
-    "    \"net_earnings\",\n",
-    "    \"roi\",\n",
-    "]\n",
-    "\n",
-    "SUMMARY_STATS_DF_COLS = [\n",
-    "    \"trader_address\",\n",
-    "    \"num_trades\",\n",
-    "    \"num_winning_trades\",\n",
-    "    \"num_redeemed\",\n",
-    "    \"total_investment\",\n",
-    "    \"total_trade_fees\",\n",
-    "    \"num_mech_calls\",\n",
-    "    \"total_mech_fees\",\n",
-    "    \"total_earnings\",\n",
-    "    \"total_redeemed_amount\",\n",
-    "    \"total_net_earnings\",\n",
-    "    \"total_net_earnings_wo_mech_fees\",\n",
-    "    \"total_roi\",\n",
-    "    \"total_roi_wo_mech_fees\",\n",
-    "    \"mean_mech_calls_per_trade\",\n",
-    "    \"mean_mech_fee_amount_per_trade\",\n",
-    "]\n",
-    "headers = {\n",
-    "    \"Accept\": \"application/json, multipart/mixed\",\n",
-    "    \"Content-Type\": \"application/json\",\n",
-    "}\n",
-    "\n",
-    "\n",
-    "omen_xdai_trades_query = Template(\n",
-    "    \"\"\"\n",
-    "    {\n",
-    "        fpmmTrades(\n",
-    "            where: {\n",
-    "                type: Buy,\n",
-    "                fpmm_: {\n",
-    "                    creator: \"${fpmm_creator}\"\n",
-    "                    creationTimestamp_gte: \"${fpmm_creationTimestamp_gte}\",\n",
-    "                    creationTimestamp_lt: \"${fpmm_creationTimestamp_lte}\"\n",
-    "                },\n",
-    "                creationTimestamp_gte: \"${creationTimestamp_gte}\",\n",
-    "                creationTimestamp_lte: \"${creationTimestamp_lte}\"\n",
-    "                id_gt: \"${id_gt}\"\n",
-    "            }\n",
-    "            first: ${first}\n",
-    "            orderBy: id\n",
-    "            orderDirection: asc\n",
-    "        ) {\n",
-    "            id\n",
-    "            title\n",
-    "            collateralToken\n",
-    "            outcomeTokenMarginalPrice\n",
-    "            oldOutcomeTokenMarginalPrice\n",
-    "            type\n",
-    "            creator {\n",
-    "                id\n",
-    "            }\n",
-    "            creationTimestamp\n",
-    "            collateralAmount\n",
-    "            collateralAmountUSD\n",
-    "            feeAmount\n",
-    "            outcomeIndex\n",
-    "            outcomeTokensTraded\n",
-    "            transactionHash\n",
-    "            fpmm {\n",
-    "                id\n",
-    "                outcomes\n",
-    "                title\n",
-    "                answerFinalizedTimestamp\n",
-    "                currentAnswer\n",
-    "                isPendingArbitration\n",
-    "                arbitrationOccurred\n",
-    "                openingTimestamp\n",
-    "                condition {\n",
-    "                    id\n",
-    "                }\n",
-    "            }\n",
-    "        }\n",
-    "    }\n",
-    "    \"\"\"\n",
-    ")\n",
-    "\n",
-    "\n",
-    "conditional_tokens_gc_user_query = Template(\n",
-    "    \"\"\"\n",
-    "    {\n",
-    "        user(id: \"${id}\") {\n",
-    "            userPositions(\n",
-    "                first: ${first}\n",
-    "                where: {\n",
-    "                    id_gt: \"${userPositions_id_gt}\"\n",
-    "                }\n",
-    "                orderBy: id\n",
-    "            ) {\n",
-    "                balance\n",
-    "                id\n",
-    "                position {\n",
-    "                    id\n",
-    "                    conditionIds\n",
-    "                }\n",
-    "                totalBalance\n",
-    "                wrappedBalance\n",
-    "            }\n",
-    "        }\n",
-    "    }\n",
-    "    \"\"\"\n",
-    ")\n",
-    "\n",
-    "\n",
-    "def _to_content(q: str) -> dict[str, Any]:\n",
-    "    \"\"\"Convert the given query string to payload content, i.e., add it under a `queries` key and convert it to bytes.\"\"\"\n",
-    "    finalized_query = {\n",
-    "        \"query\": q,\n",
-    "        \"variables\": None,\n",
-    "        \"extensions\": {\"headers\": None},\n",
-    "    }\n",
-    "    return finalized_query\n",
-    "\n",
-    "\n",
-    "def _query_omen_xdai_subgraph(\n",
-    "    from_timestamp: float,\n",
-    "    to_timestamp: float,\n",
-    "    fpmm_from_timestamp: float,\n",
-    "    fpmm_to_timestamp: float,\n",
-    ") -> dict[str, Any]:\n",
-    "    \"\"\"Query the subgraph.\"\"\"\n",
-    "    url = \"https://api.thegraph.com/subgraphs/name/protofire/omen-xdai\"\n",
-    "\n",
-    "    grouped_results = defaultdict(list)\n",
-    "    id_gt = \"\"\n",
-    "\n",
-    "    while True:\n",
-    "        query = omen_xdai_trades_query.substitute(\n",
-    "            fpmm_creator=FPMM_CREATOR.lower(),\n",
-    "            creationTimestamp_gte=int(from_timestamp),\n",
-    "            creationTimestamp_lte=int(to_timestamp),\n",
-    "            fpmm_creationTimestamp_gte=int(fpmm_from_timestamp),\n",
-    "            fpmm_creationTimestamp_lte=int(fpmm_to_timestamp),\n",
-    "            first=QUERY_BATCH_SIZE,\n",
-    "            id_gt=id_gt,\n",
-    "        )\n",
-    "        content_json = _to_content(query)\n",
-    "        res = requests.post(url, headers=headers, json=content_json)\n",
-    "        result_json = res.json()\n",
-    "        user_trades = result_json.get(\"data\", {}).get(\"fpmmTrades\", [])\n",
-    "\n",
-    "        if not user_trades:\n",
-    "            break\n",
-    "\n",
-    "        for trade in user_trades:\n",
-    "            fpmm_id = trade.get(\"fpmm\", {}).get(\"id\")\n",
-    "            grouped_results[fpmm_id].append(trade)\n",
-    "\n",
-    "        id_gt = user_trades[len(user_trades) - 1][\"id\"]\n",
-    "\n",
-    "    all_results = {\n",
-    "        \"data\": {\n",
-    "            \"fpmmTrades\": [\n",
-    "                trade\n",
-    "                for trades_list in grouped_results.values()\n",
-    "                for trade in trades_list\n",
-    "            ]\n",
-    "        }\n",
-    "    }\n",
-    "\n",
-    "    return all_results\n",
-    "\n",
-    "\n",
-    "def _query_conditional_tokens_gc_subgraph(creator: str) -> dict[str, Any]:\n",
-    "    \"\"\"Query the subgraph.\"\"\"\n",
-    "    url = \"https://api.thegraph.com/subgraphs/name/gnosis/conditional-tokens-gc\"\n",
-    "\n",
-    "    all_results: dict[str, Any] = {\"data\": {\"user\": {\"userPositions\": []}}}\n",
-    "    userPositions_id_gt = \"\"\n",
-    "    while True:\n",
-    "        query = conditional_tokens_gc_user_query.substitute(\n",
-    "            id=creator.lower(),\n",
-    "            first=QUERY_BATCH_SIZE,\n",
-    "            userPositions_id_gt=userPositions_id_gt,\n",
-    "        )\n",
-    "        content_json = {\"query\": query}\n",
-    "        res = requests.post(url, headers=headers, json=content_json)\n",
-    "        result_json = res.json()\n",
-    "        user_data = result_json.get(\"data\", {}).get(\"user\", {})\n",
-    "\n",
-    "        if not user_data:\n",
-    "            break\n",
-    "\n",
-    "        user_positions = user_data.get(\"userPositions\", [])\n",
-    "\n",
-    "        if user_positions:\n",
-    "            all_results[\"data\"][\"user\"][\"userPositions\"].extend(user_positions)\n",
-    "            userPositions_id_gt = user_positions[len(user_positions) - 1][\"id\"]\n",
-    "        else:\n",
-    "            break\n",
-    "\n",
-    "    if len(all_results[\"data\"][\"user\"][\"userPositions\"]) == 0:\n",
-    "        return {\"data\": {\"user\": None}}\n",
-    "\n",
-    "    return all_results\n",
-    "\n",
-    "\n",
-    "def convert_hex_to_int(x: Union[str, float]) -> Union[int, float]:\n",
-    "    \"\"\"Convert hex to int\"\"\"\n",
-    "    if isinstance(x, float):\n",
-    "        return np.nan\n",
-    "    elif isinstance(x, str):\n",
-    "        if x == INVALID_ANSWER_HEX:\n",
-    "            return -1\n",
-    "        else:\n",
-    "            return int(x, 16)\n",
-    "\n",
-    "\n",
-    "def wei_to_unit(wei: int) -> float:\n",
-    "    \"\"\"Converts wei to currency unit.\"\"\"\n",
-    "    return wei / 10**18\n",
-    "\n",
-    "\n",
-    "def _is_redeemed(user_json: dict[str, Any], fpmmTrade: dict[str, Any]) -> bool:\n",
-    "    \"\"\"Returns whether the user has redeemed the position.\"\"\"\n",
-    "    user_positions = user_json[\"data\"][\"user\"][\"userPositions\"]\n",
-    "    outcomes_tokens_traded = int(fpmmTrade[\"outcomeTokensTraded\"])\n",
-    "    condition_id = fpmmTrade[\"fpmm.condition.id\"]\n",
-    "\n",
-    "    for position in user_positions:\n",
-    "        position_condition_ids = position[\"position\"][\"conditionIds\"]\n",
-    "        balance = int(position[\"balance\"])\n",
-    "\n",
-    "        if condition_id in position_condition_ids:\n",
-    "            if balance == 0:\n",
-    "                return True\n",
-    "            # return early\n",
-    "            return False\n",
-    "    return False\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determine_market_status(trade, current_answer):\n",
-    "    \"\"\"Determine the market status of a trade.\"\"\"\n",
-    "    if current_answer is np.nan and time.time() >= int(trade[\"fpmm.openingTimestamp\"]):\n",
-    "        return MarketState.PENDING\n",
-    "    elif current_answer == np.nan:\n",
-    "        return MarketState.OPEN\n",
-    "    elif trade[\"fpmm.isPendingArbitration\"]:\n",
-    "        return MarketState.ARBITRATING\n",
-    "    elif time.time() < int(trade[\"fpmm.answerFinalizedTimestamp\"]):\n",
-    "        return MarketState.FINALIZING\n",
-    "    return MarketState.CLOSED"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_traders = []\n",
-    "\n",
-    "for trader_address in tqdm(\n",
-    "    fpmmTrades[\"trader_address\"].unique(),\n",
-    "    total=len(fpmmTrades[\"trader_address\"].unique()),\n",
-    "    desc=\"Analysing creators\"\n",
-    "):\n",
-    "    trades = fpmmTrades[fpmmTrades[\"trader_address\"] == trader_address]\n",
-    "    tools_usage = tools[tools[\"trader_address\"].str.lower() == trader_address]\n",
-    "\n",
-    "    # Prepare the DataFrame\n",
-    "    trades_df = pd.DataFrame(columns=ALL_TRADES_STATS_DF_COLS)\n",
-    "\n",
-    "    if trades.empty:\n",
-    "        continue\n",
-    "\n",
-    "    # Fetch user's conditional tokens gc graph\n",
-    "    try:\n",
-    "        user_json = _query_conditional_tokens_gc_subgraph(trader_address)\n",
-    "    except Exception as e:\n",
-    "        print(f\"Error fetching user data: {e}\")\n",
-    "        raise e\n",
-    "    \n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i, trade in tqdm(trades.iterrows(), total=len(trades), desc=\"Analysing trades\"):\n",
-    "    if not trade['fpmm.currentAnswer']:\n",
-    "        print(f\"Skipping trade {i} because currentAnswer is NaN\")\n",
-    "        continue\n",
-    "\n",
-    "    creation_timestamp_utc = datetime.datetime.fromtimestamp(\n",
-    "        int(trade[\"creationTimestamp\"]), tz=datetime.timezone.utc\n",
-    "    )\n",
-    "    collateral_amount = wei_to_unit(float(trade[\"collateralAmount\"]))\n",
-    "    fee_amount = wei_to_unit(float(trade[\"feeAmount\"]))\n",
-    "    outcome_tokens_traded = wei_to_unit(float(trade[\"outcomeTokensTraded\"]))\n",
-    "    earnings, winner_trade = (0, False)\n",
-    "    redemption = _is_redeemed(user_json, trade)\n",
-    "    current_answer = trade[\"fpmm.currentAnswer\"]\n",
-    "    # Determine market status\n",
-    "    market_status = determine_market_status(trade, current_answer)\n",
-    "\n",
-    "    # Skip non-closed markets\n",
-    "    if market_status != MarketState.CLOSED:\n",
-    "        print(\n",
-    "            f\"Skipping trade {i} because market is not closed. Market Status: {market_status}\"\n",
-    "        )\n",
-    "        continue\n",
-    "    current_answer = convert_hex_to_int(current_answer)\n",
-    "\n",
-    "    # Compute invalidity\n",
-    "    is_invalid = current_answer == INVALID_ANSWER\n",
-    "\n",
-    "    # Compute earnings and winner trade status\n",
-    "    if is_invalid:\n",
-    "        earnings = collateral_amount\n",
-    "        winner_trade = False\n",
-    "    elif int(trade[\"outcomeIndex\"]) == current_answer:\n",
-    "        earnings = outcome_tokens_traded\n",
-    "        winner_trade = True\n",
-    "\n",
-    "    # Compute mech calls\n",
-    "    num_mech_calls = (\n",
-    "        tools_usage[\"prompt_request\"].apply(lambda x: trade[\"title\"] in x).sum()\n",
-    "    )\n",
-    "    net_earnings = (\n",
-    "        earnings\n",
-    "        - fee_amount\n",
-    "        - (num_mech_calls * DEFAULT_MECH_FEE)\n",
-    "        - collateral_amount\n",
-    "    )\n",
-    "\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
+      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
+      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
    "source": [
-    "# all_traders = []\n",
-    "# for trader_address in tqdm(\n",
-    "#     fpmmTrades[\"trader_address\"].unique(),\n",
-    "#     total=len(fpmmTrades[\"trader_address\"].unique()),\n",
-    "#     desc=\"Analysing creators\",\n",
-    "# ):\n",
-    "\n",
-    "#     trades = fpmmTrades[fpmmTrades[\"trader_address\"] == trader_address]\n",
-    "#     tools_usage = tools[tools[\"trader_address\"] == trader_address]\n",
+    "tools_df = pd.read_parquet(\"./data/tools.parquet\")\n",
+    "trades_df = pd.read_parquet(\"./data/all_trades_profitability.parquet\")\n",
     "\n",
-    "#     # Prepare the DataFrame\n",
-    "#     trades_df = pd.DataFrame(columns=ALL_TRADES_STATS_DF_COLS)\n",
-    "#     if trades.empty:\n",
-    "#         continue\n",
+    "tools_df['request_time'] = pd.to_datetime(tools_df['request_time'])\n",
+    "tools_df = tools_df[tools_df['request_time'].dt.year == 2024]\n",
     "\n",
-    "#     # Fetch user's conditional tokens gc graph\n",
-    "#     try:\n",
-    "#         user_json = _query_conditional_tokens_gc_subgraph(trader_address)\n",
-    "#     except Exception as e:\n",
-    "#         print(f\"Error fetching user data: {e}\")\n",
-    "#         raise e\n",
-    "\n",
-    "#     # Iterate over the trades\n",
-    "#     for i, trade in tqdm(trades.iterrows(), total=len(trades), desc=\"Analysing trades\"):\n",
-    "#         try:\n",
-    "#             if not trade['fpmm.currentAnswer']:\n",
-    "#                 print(f\"Skipping trade {i} because currentAnswer is NaN\")\n",
-    "#                 continue\n",
-    "#             # Parsing and computing shared values\n",
-    "#             creation_timestamp_utc = datetime.datetime.fromtimestamp(\n",
-    "#                 int(trade[\"creationTimestamp\"]), tz=datetime.timezone.utc\n",
-    "#             )\n",
-    "#             collateral_amount = wei_to_unit(float(trade[\"collateralAmount\"]))\n",
-    "#             fee_amount = wei_to_unit(float(trade[\"feeAmount\"]))\n",
-    "#             outcome_tokens_traded = wei_to_unit(float(trade[\"outcomeTokensTraded\"]))\n",
-    "#             earnings, winner_trade = (0, False)\n",
-    "#             redemption = _is_redeemed(user_json, trade)\n",
-    "#             current_answer = trade[\"fpmm.currentAnswer\"]\n",
-    "#             # Determine market status\n",
-    "#             market_status = determine_market_status(trade, current_answer)\n",
-    "\n",
-    "#             # Skip non-closed markets\n",
-    "#             if market_status != MarketState.CLOSED:\n",
-    "#                 print(\n",
-    "#                     f\"Skipping trade {i} because market is not closed. Market Status: {market_status}\"\n",
-    "#                 )\n",
-    "#                 continue\n",
-    "#             current_answer = convert_hex_to_int(current_answer)\n",
-    "\n",
-    "#             # Compute invalidity\n",
-    "#             is_invalid = current_answer == INVALID_ANSWER\n",
-    "\n",
-    "#             # Compute earnings and winner trade status\n",
-    "#             if is_invalid:\n",
-    "#                 earnings = collateral_amount\n",
-    "#                 winner_trade = False\n",
-    "#             elif trade[\"outcomeIndex\"] == current_answer:\n",
-    "#                 earnings = outcome_tokens_traded\n",
-    "#                 winner_trade = True\n",
-    "\n",
-    "#             # Compute mech calls\n",
-    "#             num_mech_calls = (\n",
-    "#                 tools_usage[\"prompt_request\"].apply(lambda x: trade[\"title\"] in x).sum()\n",
-    "#             )\n",
-    "#             net_earnings = (\n",
-    "#                 earnings\n",
-    "#                 - fee_amount\n",
-    "#                 - (num_mech_calls * DEFAULT_MECH_FEE)\n",
-    "#                 - collateral_amount\n",
-    "#             )\n",
-    "\n",
-    "#             # Assign values to DataFrame\n",
-    "#             trades_df.loc[i] = {\n",
-    "#                 \"trader_address\": trader_address,\n",
-    "#                 \"trade_id\": trade[\"id\"],\n",
-    "#                 \"market_status\": market_status.name,\n",
-    "#                 \"creation_timestamp\": creation_timestamp_utc,\n",
-    "#                 \"title\": trade[\"title\"],\n",
-    "#                 \"collateral_amount\": collateral_amount,\n",
-    "#                 \"outcome_index\": trade[\"outcomeIndex\"],\n",
-    "#                 \"trade_fee_amount\": fee_amount,\n",
-    "#                 \"outcomes_tokens_traded\": outcome_tokens_traded,\n",
-    "#                 \"current_answer\": current_answer,\n",
-    "#                 \"is_invalid\": is_invalid,\n",
-    "#                 \"winning_trade\": winner_trade,\n",
-    "#                 \"earnings\": earnings,\n",
-    "#                 \"redeemed\": redemption,\n",
-    "#                 \"redeemed_amount\": earnings if redemption else 0,\n",
-    "#                 \"num_mech_calls\": num_mech_calls,\n",
-    "#                 \"mech_fee_amount\": num_mech_calls * DEFAULT_MECH_FEE,\n",
-    "#                 \"net_earnings\": net_earnings,\n",
-    "#                 \"roi\": net_earnings / (collateral_amount + fee_amount + num_mech_calls * DEFAULT_MECH_FEE),\n",
-    "#             }\n",
-    "#         except Exception as e:\n",
-    "#             print(f\"Error processing trade {i}: {e}\")\n",
-    "#             raise e"
+    "trades_df['creation_timestamp'] = pd.to_datetime(trades_df['creation_timestamp'])\n",
+    "trades_df = trades_df[trades_df['creation_timestamp'].dt.year == 2024]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trades = pd.read_parquet('/Users/arshath/play/openautonomy/olas-prediction-live-dashboard/data/all_trades_profitability.parquet')\n",
-    "tools = pd.read_parquet('/Users/arshath/play/openautonomy/olas-prediction-live-dashboard/data/tools.parquet')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -617,112 +68,13 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "trades.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trades = pd.read_parquet('/Users/arshath/play/openautonomy/olas-prediction-live-dashboard/data/all_trades_profitability.parquet')\n",
-    "trades['creation_timestamp'] = pd.to_datetime(trades['creation_timestamp'], unit='s')\n",
-    "trades = trades[trades['creation_timestamp'].dt.year == 2024]\n",
-    "trades_winning = trades.groupby(['title','winning_trade']).size().unstack().fillna(0)\n",
-    "trades_winning_perc = trades_winning[True] / (trades_winning[True] + trades_winning[False])\n",
-    "trades_winning_perc = trades_winning_perc.reset_index()\n",
-    "trades_winning_perc.columns = ['title', 'winning_trade_perc']\n",
-    "def bucket_winning_trade_perc(x):\n",
-    "    if x < 0.1:\n",
-    "        return 0.1\n",
-    "    elif x < 0.2:\n",
-    "        return 0.2\n",
-    "    elif x < 0.3:\n",
-    "        return 0.3\n",
-    "    elif x < 0.4:\n",
-    "        return 0.4\n",
-    "    elif x < 0.5:\n",
-    "        return 0.5\n",
-    "    elif x < 0.6:\n",
-    "        return 0.6\n",
-    "    elif x < 0.7:\n",
-    "        return 0.7\n",
-    "    elif x < 0.8:\n",
-    "        return 0.8\n",
-    "    elif x < 0.9:\n",
-    "        return 0.9\n",
-    "    else:\n",
-    "        return 1\n",
-    "\n",
-    "trades_winning_perc['winning_trade_perc_bucket'] = trades_winning_perc['winning_trade_perc'].apply(bucket_winning_trade_perc)\n",
-    "trades_winning_perc['winning_trade_perc_bucket'].plot(kind='hist', bins=10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: ylabel='Frequency'>"
-      ]
-     },
-     "execution_count": 21,
-     "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": [
-    "\n"
+    "trades_df.columns\n"
    ]
   },
   {