FII_BI.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime,timedelta\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compras_grafs(fii, data_ini, data_fim):\n",
    "    \n",
    "# ------------- SUBSET -------------\n",
    "\n",
    "    # Monta filtro e verifica se é válido\n",
    "    filtro = (df['Ativo']==fii) & (df['Data']>=data_ini) & (df['Data']<=data_fim) & (df['Operação'].isin(['C','SE']))\n",
    "    if df[filtro].empty:\n",
    "        print(f'Não há operações de compra de {fii} neste período.')\n",
    "        return\n",
    "\n",
    "    # Filtra DataFrame original\n",
    "    df_filtrado = df[filtro].copy()\n",
    "\n",
    "# ------------- CORES -------------\n",
    "\n",
    "    color_back = 'Gainsboro'\n",
    "    color_graf = 'firebrick'\n",
    "    color_grid = 'DarkGray'\n",
    "\n",
    "# ------------- CANVAS -------------\n",
    "\n",
    "    # Inicia figura e define tamanho\n",
    "    fig = plt.figure(figsize=(26, 13))\n",
    "    # Título\n",
    "    fig.suptitle(fii, fontsize=20, weight='bold')\n",
    "    # Cria subplots\n",
    "    ax1 = plt.subplot(221)  #121: 2 Linhas, 2 Colunas, Subplot 1\n",
    "    ax2 = plt.subplot(222)\n",
    "    ax3 = plt.subplot(223)\n",
    "    #ax4 = plt.subplot(224)\n",
    "    fig.subplots_adjust(top=0.93) #Afastamento dos subplots do topo da figura\n",
    "                                 #Permite o ajuste fino do espaço entre o título e os subplots \n",
    "    \n",
    "# ------------- GRÁFICO COTAS POR FAIXA -------------\n",
    "    \n",
    "    # Cria lista de valores por faixa\n",
    "    df_val = list(map(lambda i: int(i), df_filtrado['Valor']))\n",
    "\n",
    "    # Define escala auto-ajustável no eixo X\n",
    "    mi = min(df_val)\n",
    "    ma = max(df_val)\n",
    "    barras = ma - mi + 1\n",
    "    max_bars = 15\n",
    "    passo_x = (barras // max_bars) + bool(barras % max_bars)  #Soma 1 se houver resto da divisão.\n",
    "    eixo_x = np.arange(mi, ma+1+passo_x, passo_x)  #Soma 1 no max por causa do \"exclusive\".\n",
    "    \n",
    "    # Configura gráfico\n",
    "    ax2.hist(df_val, weights=df_filtrado['Quantidade'], rwidth=0.7, bins=eixo_x, color=color_graf, alpha=0.9)\n",
    "    ax2.rwidth=0.7\n",
    "    ax2.set_facecolor(color_back)\n",
    "    ax2.set_axisbelow(True)\n",
    "    ax2.grid(axis='y', color=color_grid)\n",
    "    ax2.tick_params(labelright=True)\n",
    "    ax2.set_ylabel('Cotas', fontsize=14, weight='bold', labelpad=10)\n",
    "    ax2.set_xlabel('Valor', fontsize=14, weight='bold', labelpad=10)\n",
    "\n",
    "    # Configura ticks do eixo X\n",
    "    ax2.set_xticks(eixo_x)\n",
    "    \n",
    "    # Define escala auto-ajustável no eixo Y e configura ticks\n",
    "    tick = int(max(ax2.get_yticks()))  #Lê maior y_tick\n",
    "    max_yticks = 20\n",
    "    passo_y = (tick // max_yticks) + bool(tick % max_yticks)\n",
    "    eixo_y = np.arange(0, tick+1, passo_y)  #Configura a faixa em inteiros\n",
    "    ax2.set_yticks(eixo_y)\n",
    "\n",
    "# ------------- GRÁFICO SCATTER -------------\n",
    "    \n",
    "    # Dados\n",
    "    x = df_filtrado['Data']\n",
    "    y = df_filtrado['Valor']\n",
    "    markers = list(map(lambda i: i*80, df_filtrado['Quantidade']))\n",
    "\n",
    "    # Configura gráfico\n",
    "    ax1.scatter(x, y, s=markers, color=color_graf, edgecolors='white', linewidth=1.2, alpha=0.75)\n",
    "    ax1.set_facecolor(color_back)\n",
    "    ax1.set_axisbelow(True)\n",
    "    ax1.grid(color=color_grid)\n",
    "    ax1.tick_params(labelright=True)\n",
    "    ax1.set_ylabel('Valor', fontsize=14, weight='bold', labelpad=10)\n",
    "    \n",
    "    # Escala do eixo X (o automático expande demais)\n",
    "    folga = 2 \n",
    "    data_min = min(df_filtrado['Data']) - timedelta(days=folga)\n",
    "    data_max = max(df_filtrado['Data']) + timedelta(days=folga)\n",
    "    ax1.set_xlim(data_min, data_max)\n",
    "\n",
    "# ------------- GRÁFICO PREÇO MÉDIO -------------    \n",
    "\n",
    "    # Filtro do Dataframe\n",
    "    filtro = (df['Ativo']==fii) & (df['Data']>=data_ini) & (df['Data']<=data_fim) & (df['Operação'].isin(['C','V','SE']))\n",
    "\n",
    "    # Configura gráfico\n",
    "    ax3.plot(df[filtro]['Data'], df[filtro]['PM'], color=color_graf, alpha=0.8, marker='o', markersize=9, markeredgecolor='white', linestyle='dotted', linewidth=2)\n",
    "    ax3.set_facecolor(color_back)\n",
    "    ax3.set_axisbelow(True)\n",
    "    ax3.grid(color=color_grid)\n",
    "    ax3.tick_params(labelright=True)\n",
    "    ax3.set_ylabel('Preço Médio', fontsize=14, weight='bold', labelpad=10)\n",
    "    ax3.set_xlim(data_min, data_max)\n",
    "\n",
    "# ------------- MOSTRAR -------------\n",
    "    \n",
    "    # Mostra gráfico\n",
    "    plt.show()"
   ]
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    "def compras_clicked(b):\n",
    "    with output:\n",
    "        # Limpa gráfico anterior antes de mostrar o novo, esperando o novo aparecer\n",
    "        clear_output(wait=True)\n",
    "        \n",
    "        # Lê dados dos widgets\n",
    "        fii = fii_dropdown.value\n",
    "        data_ini = datetime.combine(data_ini_picker.value, datetime.min.time()) # Data vem sem \"time\" do widget\n",
    "        data_fim = datetime.combine(data_fim_picker.value, datetime.min.time()) # Data vem sem \"time\" do widget\n",
    "        \n",
    "        # Verifica se data inicial não é maior que final\n",
    "        if data_ini > data_fim:\n",
    "            print('ERRO: Data inicial maior que a final!!!')\n",
    "        # Chama função pra mostrar gráfico\n",
    "        else:\n",
    "            compras_grafs(fii, data_ini, data_fim)"
   ]
  },
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   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def atualiza_lista_fiis(ck):\n",
    "    lista_fiis = sorted(df[df['Ativo'].str.endswith('11')]['Ativo'].unique())\n",
    "    if ck:\n",
    "        lista_fiis = df[(df['Ativo'].str.endswith('11')) & (df['Operação'].isin(['C','V','SE']))].copy()\n",
    "        lista_fiis = lista_fiis.groupby('Ativo')['Quantidade'].sum()\n",
    "        lista_fiis = list(lista_fiis[lista_fiis>0].index)\n",
    "    fii_dropdown.options = lista_fiis"
   ]
  },
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    "# Importa dataset\n",
    "df = pd.read_csv('FII_database.csv', encoding=\"ISO-8859-1\")\n",
    "\n",
    "# Converte coluna 'Data' para formato \"datetime\"\n",
    "df['Data'] = pd.to_datetime(df['Data'], format='%d/%m/%Y')\n",
    "\n",
    "# Converte quantidade das vendas para negativo\n",
    "df.loc[df['Operação']=='V','Quantidade'] *= -1\n",
    "\n",
    "# Ajusta agrupamentos e desdobramentos\n",
    "for fii in df[df['Operação']=='X']['Ativo'].unique(): #Varre FIIs com 'X'\n",
    "    for data_X in list(df[(df['Ativo']==fii) & (df['Operação']=='X')]['Data']): #Recursivo em todos os 'X' de um mesmo FII\n",
    "        fator = df[(df['Ativo']==fii) & (df['Operação']=='X') & (df['Data']==data_X)]['Quantidade'].values\n",
    "        df.loc[(df['Ativo']==fii) & (df['Data']<data_X),'Quantidade'] *= fator\n",
    "        df.loc[(df['Ativo']==fii) & (df['Data']<data_X),'Valor'] /= fator\n",
    "        \n",
    "# Cria coluna com o histórico do preço médio\n",
    "df['Total'] = df['Quantidade'] * df['Valor'] + df['Taxas']  #Cria coluna com o valor total da operação\n",
    "for fii in df[df['Operação'].isin(['C','V','SE'])]['Ativo'].unique():\n",
    "    filtro = (df['Ativo']==fii) & (df['Operação'].isin(['C','V','SE']))\n",
    "    df.loc[filtro,'Cotas'] = df[filtro]['Quantidade'].cumsum()  #Cria coluna com o saldo de cotas\n",
    "    saldo = 0\n",
    "    medio = []\n",
    "    for quant,total,cotas in zip(df[filtro]['Quantidade'],df[filtro]['Total'],df[filtro]['Cotas']):\n",
    "        if quant > 0:  #Compra\n",
    "            saldo += total\n",
    "            medio.append(saldo / cotas)\n",
    "        elif cotas == 0:  #Zera saldo e PM se vender tudo\n",
    "            saldo = 0\n",
    "            medio.append(0)\n",
    "        else:  #Venda\n",
    "            saldo += quant * medio[-1]  #Soma \"saldo\" porque \"quant\" vem negativo nas vendas\n",
    "            medio.append(medio[-1])     #Venda não altera PM (pega o último)\n",
    "    df.loc[filtro,'PM'] = np.around(np.array(medio),2)"
   ]
  },
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       "HBox(children=(VBox(children=(DatePicker(value=Timestamp('2018-04-05 00:00:00'), description='DATA INICIAL', l…"
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       "Output()"
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    "# Configura widgets\n",
    "# Datas\n",
    "data_ini_picker = widgets.DatePicker(value=df['Data'].min(), description='DATA INICIAL', style={'description_width':'82px'}, layout=widgets.Layout(width='230px'))\n",
    "data_fim_picker = widgets.DatePicker(value=df['Data'].max(), description='DATA FINAL', style={'description_width':'82px'}, layout=widgets.Layout(width='230px'))\n",
    "# Combo FIIs\n",
    "fii_dropdown = widgets.Dropdown(layout=widgets.Layout(width='148px'))\n",
    "carteira_checkbox = widgets.Checkbox(value=True, description='CARTEIRA ATUAL', indent=False, layout=widgets.Layout(width='95%', justify_content='center'))\n",
    "atualiza_lista_fiis(carteira_checkbox) #Primeira carga da lista de FIIs\n",
    "# Botão\n",
    "botao_compras = widgets.Button(description='MOSTRAR DADOS', style=widgets.ButtonStyle(button_color='LightGray'), layout=widgets.Layout(height='95%'))\n",
    "# Saída de resultados\n",
    "output = widgets.Output()\n",
    "\n",
    "# Chama função na alteração do checkbox\n",
    "widgets.interactive(atualiza_lista_fiis, ck=carteira_checkbox)\n",
    "# Chama função no click do botão\n",
    "botao_compras.on_click(compras_clicked)\n",
    "\n",
    "# Mostra widgets\n",
    "vBox1 = widgets.VBox([data_ini_picker, data_fim_picker])\n",
    "vBox2 = widgets.VBox([fii_dropdown, carteira_checkbox])\n",
    "vBox3 = widgets.VBox([botao_compras])\n",
    "menu = widgets.HBox([vBox1,vBox2,vBox3])\n",
    "display(menu,output)"
   ]
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