{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transformar os dados RAW (Binance BTCUSDT, all trades) -> OHCL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_dataset_path = '../datasets/BTCUSDT-Trades/'\n",
    "output_path = '../output'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from loguru import logger\n",
    "import dask.dataframe as dd\n",
    "from dask.diagnostics import ProgressBar\n",
    "import pandas as pd\n",
    "\n",
    "logger.remove() \n",
    "logger.add(lambda msg: print(msg, end=\"\"), level=\"INFO\")\n",
    "\n",
    "from utils import create_dollar_bars\n",
    "\n",
    "def dataSampler(barType=\"standard\", samplerType=\"time\", samplerAmount=100, maxRecords=None):\n",
    "    barTypeDictionary = {\n",
    "        \"standard\": \"Padrão\",\n",
    "        \"imbalance\": \"de Desequilíbrio\",\n",
    "        \"run\": \"de Ordem Iceberg\"\n",
    "    }\n",
    "    samplerDictionary = {\n",
    "        \"time\": \"Temporal\",\n",
    "        \"ticks\": \"Ticks\",\n",
    "        \"volume\": \"Volume\",\n",
    "        \"dollar\": \"Dollar\"\n",
    "    }\n",
    "    \n",
    "    output_directory = os.path.join(output_path, f\"{samplerType}-bars-[{samplerAmount}]\")\n",
    "    if not os.path.exists(output_directory):\n",
    "        os.makedirs(output_directory)\n",
    "        logger.info(f\"Diretório criado: {output_directory}\")\n",
    "    \n",
    "    print(f\"Criando Barras {samplerDictionary[samplerType]} {barTypeDictionary[barType]} agrupadas a cada {samplerAmount}...\")       \n",
    "\n",
    "    # Verificar se já existem arquivos .parquet no diretório de saída\n",
    "    parquet_files_output = [f for f in os.listdir(output_directory) if f.endswith('.parquet')]\n",
    "    if parquet_files_output:\n",
    "        logger.info(f\"'{output_directory}' já existe e contém arquivos .parquet. Carregando de {output_directory}...\")\n",
    "        \n",
    "        # Carregar todos os arquivos .parquet usando Dask\n",
    "        try:\n",
    "            bars = dd.read_parquet(os.path.join(output_directory, '*.parquet')).compute()\n",
    "            logger.info(\"'dollar_bars' carregado com sucesso.\")\n",
    "            return bars\n",
    "        except Exception as e:\n",
    "            logger.error(f\"Erro ao carregar arquivos .parquet: {e}\")\n",
    "    \n",
    "    logger.info(\"Criando 'dollar_bars'...\")\n",
    "\n",
    "    dollar_bars_path = os.path.join(output_directory, 'dollar_bars.parquet')\n",
    "\n",
    "    # Obter a lista de todos os arquivos Parquet no raw_dataset_path\n",
    "    parquet_files = [os.path.join(raw_dataset_path, f) for f in os.listdir(raw_dataset_path) if f.endswith('.parquet')]\n",
    "    parquet_files.sort()\n",
    "    \n",
    "    if not parquet_files:\n",
    "        logger.warning(f\"Nenhum arquivo .parquet encontrado em '{raw_dataset_path}'.\")\n",
    "        return []\n",
    "    \n",
    "    logger.info(f\"Total de arquivos .parquet a serem processados: {len(parquet_files)}\")\n",
    "    \n",
    "    # Carregar todos os arquivos .parquet usando Dask diretamente\n",
    "    try:\n",
    "        df_dask = dd.read_parquet(os.path.join(raw_dataset_path, '*.parquet'))\n",
    "        logger.info(\"Todos os arquivos .parquet foram carregados com sucesso.\")\n",
    "    except Exception as e:\n",
    "        logger.error(f\"Erro ao carregar arquivos .parquet: {e}\")\n",
    "        return []\n",
    "    \n",
    "    # Se maxRecords estiver definido, limitar o DataFrame\n",
    "    if maxRecords is not None:\n",
    "        df_dask = df_dask.head(maxRecords, compute=False)\n",
    "        logger.info(f\"Limite de registros definido para {maxRecords}.\")\n",
    "    \n",
    "    # Criar e salvar 'dollar_bars'\n",
    "    try:\n",
    "        dollar_bars = create_dollar_bars(df_dask, samplerAmount, dollar_bars_path)\n",
    "        logger.info(\"'dollar_bars' criado e salvo com sucesso.\")\n",
    "        return dollar_bars\n",
    "    except Exception as e:\n",
    "        logger.error(f\"Erro ao criar 'dollar_bars': {e}\")\n",
    "        return []\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Realiza Amostragem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "serieBars = dataSampler(\n",
    "    barType=\"standard\",\n",
    "    samplerType=\"dollar\",\n",
    "    samplerAmount=10_000_000\n",
    ")\n",
    "\n",
    "sample_bars = serieBars.head()\n",
    "display(sample_bars)"
   ]
  }
 ],
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