Choix_option_impot_liberatoire.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aide à la décision pour le mode d'imposition en tant que micro-entrepreneur au régime micro-fiscal\n",
    "Choix entre les régimes :\n",
    "- barème progressif de l'impôt sur le revenu avec abattement\n",
    "- impôt libératoire sur le revenu sans abattement\n",
    "\n",
    "Dans chaque cas, l'impôt est directement calculé sur le chiffre d'affaire (CA), et non sur les bénéfices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Paramètres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mon entreprise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Type d'activité de la micro entreprise\n",
    "# 'BNC', 'BIC_AUTRES', 'BIC_ACHAT_REVENTE'\n",
    "REGIME = 'BNC'\n",
    "\n",
    "# CA total : ceci est le CA avant abattement\n",
    "CA_PERSO_BRUT = 39000\n",
    "\n",
    "# Nombre de parts de quotient familial\n",
    "N_PARTS_Q_FAMIL = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seuils et taux réglementaires au 01-09-2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impôt libératoire\n",
    "IMP_LIB = 0.022\n",
    "\n",
    "# Abattements sur CA\n",
    "DIC_ABATTEMENTS = {'BNC':0.34,\n",
    "                   'BIC_AUTRES':0.5,\n",
    "                   'BIC_ACHAT_REVENTE':0.71}\n",
    "\n",
    "# Abattement plancher\n",
    "ABATTEMENT_MINIMUM = 305\n",
    "\n",
    "# Barème impôt sur le revenu 01-09-2023\n",
    "LIST_DICS_TAUX_TRANCHE = [\n",
    "    {'borne_inf': 10778,\n",
    "     'borne_sup': 27478,\n",
    "     'taux': 0.11},\n",
    "    {'borne_inf': 27479,\n",
    "     'borne_sup': 78570,\n",
    "     'taux': 0.3},\n",
    "    {'borne_inf': 78571,\n",
    "     'borne_sup': 168994,\n",
    "     'taux': 0.41},\n",
    "    {'borne_inf': 168995,\n",
    "     'taux': 0.45}\n",
    "]\n",
    "\n",
    "# Seuils au-delà desquels l'option de l'impot\n",
    "# libératoire sur le revenu n'est plus possible\n",
    "\n",
    "# Revenu fiscal de référence de 2022, pour une application\n",
    "# au 1er janvier 2024, en fonction de la part de quotient familial.\n",
    "# ex : 1 part de quotient familial (personne seule) => seuil 27519€\n",
    "DIC_SEUILS_OPTION_LIB = {1: 25710,\n",
    "                         2: 51420,\n",
    "                         2.5: 64275,\n",
    "                         3: 77130}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ensemble des CA à visualiser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "LISTE_CA = range(10, int(CA_PERSO_BRUT*1.2), 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Graphs et couche design\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fonctions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calcul_impots_bareme_progressif(ca, list_dics_taux_tranche):\n",
    "    \"\"\" \n",
    "    Calcule le montant d'impôt en appliquant le barème progressif\n",
    "    sur le chiffre d'affaires (CA) après abattement.\n",
    "    \"\"\"\n",
    "    impots = 0\n",
    "    \n",
    "    # Calcule l'impôt en appliquant le barème des tranches ayant\n",
    "    # une borne supérieure sur le CA compris dans cette tranche\n",
    "    for tranche in list_dics_taux_tranche[:-1]:\n",
    "        \n",
    "        # Renvoie le montant d'impôts déjà calculé si le CA \n",
    "        # n'atteint pas la borne inférieure de la tranche\n",
    "        if ca < tranche['borne_inf']:\n",
    "            return impots\n",
    "        \n",
    "        # Si borne_inf < CA < borne_sup, applique le \n",
    "        # taux de la tranche sur le montant excédentaire\n",
    "        # de CA uniquement\n",
    "        elif ca <= tranche['borne_sup']:\n",
    "            impots += (ca - tranche['borne_inf']) * tranche['taux']\n",
    "        \n",
    "        else :\n",
    "            # Si CA > borne_sup, applique le taux sur toute la tranche\n",
    "            impots += (tranche['borne_sup'] - tranche['borne_inf']) \\\n",
    "                    * tranche['taux']\n",
    "\n",
    "    # Calcule le CA excédant la dernière tranche\n",
    "    ca_exces = ca - list_dics_taux_tranche[-1]['borne_inf']\n",
    "\n",
    "    if ca_exces > 0:\n",
    "        # Calcule l'impôt de la dernière tranche en appliquant\n",
    "        # son taux sur le CA excédentaire\n",
    "        impots += ca_exces * list_dics_taux_tranche[-1]['taux']\n",
    "\n",
    "    return impots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calcul_impots_liberatoire(ca, imp_lib):\n",
    "    \"\"\"\n",
    "    Calcule le montant d'impôt sur le chiffre d'affaires (CA),\n",
    "    en appliquant le taux défini par l'option libératoire de\n",
    "    l'impôt sur le revenu\n",
    "    \"\"\"\n",
    "    return ca * imp_lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calcul_ca_apres_abattement(ca, dic_abattements, abattement_min, regime):\n",
    "    \"\"\"\n",
    "    Calcule le CA après avoir appliqué l'abattement, dépendant du\n",
    "    type d'activité 'BNC', 'BIC_AUTRES', 'BIC_ACHAT_REVENTE'\n",
    "\n",
    "    Ne s'applique que pour le régime standard d'imposition \n",
    "    avec le barème progressif (et non avec l'option libératoire)\n",
    "    \"\"\"\n",
    "    # Récupération du taux d'abattement correspondant à\n",
    "    # l'activité de l'entreprise\n",
    "    taux_abattement = dic_abattements[regime]\n",
    "\n",
    "    # Calcul du montant d'abattement\n",
    "    montant_abattement = ca * taux_abattement\n",
    "\n",
    "    # Application du seuil minimum d'abattement\n",
    "    if montant_abattement < abattement_min:\n",
    "        montant_abattement = abattement_min\n",
    "\n",
    "    # Calcul du CA après abattement\n",
    "    ca_apres_abattement = ca - montant_abattement\n",
    "\n",
    "    return ca_apres_abattement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calcul_intersection(liste_1, liste_2):\n",
    "    \"\"\"\n",
    "    Calcule le point le plus proche de l'intersection\n",
    "    entre deux lignes droites (définies par des listes)\n",
    "    Ps: Il n'y a donc pas d'interpolation linéaire\n",
    "\n",
    "    Méthode 1 [non implémentée] :\n",
    "      Recherche de la valeur dans liste_1 qui\n",
    "      soit encadrée par deux valeurs de liste_2\n",
    "\n",
    "    Méthode 2 [implémentée] :\n",
    "      Calcule de l'ensemble des écarts entre\n",
    "      chaque valeur de liste_1 et de liste_2\n",
    "    \n",
    "      Recherche de l'écart minimum, puis \n",
    "      récupèration de l'indice, et\n",
    "      récupération de la valeur correspondante\n",
    "    \"\"\"\n",
    "    # Calcul des écarts entre liste_1 et liste_2\n",
    "    liste_ecarts_x = [abs(x1 - x2) \\\n",
    "                       for x1, x2 in zip(liste_1, liste_2)]\n",
    "    \n",
    "    # Récupération de l'indice correspondant à l'écart min\n",
    "    idx_min = liste_ecarts_x.index(min(liste_ecarts_x))\n",
    "    \n",
    "    # Déduction de la valeur d'intersection\n",
    "    val_1_intersec = liste_1[idx_min]\n",
    "    val_2_intersec = liste_2[idx_min]\n",
    "    \n",
    "    return idx_min, (val_1_intersec, val_2_intersec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Montants impots en libératoire\n",
    "liste_impots_lib = [calcul_impots_liberatoire(ca, IMP_LIB) for ca in LISTE_CA]\n",
    "\n",
    "# CAs après abattement\n",
    "liste_ca_apres_abattement = [calcul_ca_apres_abattement(ca, DIC_ABATTEMENTS, ABATTEMENT_MINIMUM, REGIME) \\\n",
    "    for ca in LISTE_CA]\n",
    "\n",
    "# Montants impots en progressif basés\n",
    "# sur CAs après abattement\n",
    "liste_impots_progress = [calcul_impots_bareme_progressif(ca_aps_abattement, LIST_DICS_TAUX_TRANCHE) \\\n",
    "    for ca_aps_abattement in liste_ca_apres_abattement]\n",
    "\n",
    "# Limite de droit à l'option en fonction\n",
    "# du quotient familial\n",
    "limite_ca_option = DIC_SEUILS_OPTION_LIB[N_PARTS_Q_FAMIL]\n",
    "\n",
    "# Récupération de l'indice et des deux valeurs d'impôts\n",
    "# auxquelles les deux régimes se valent~ (intersection)\n",
    "point_intersec, imps_intersec = \\\n",
    "    calcul_intersection(liste_impots_lib, liste_impots_progress)\n",
    "\n",
    "# Déduction du CA d'intersection\n",
    "ca_intersec = LISTE_CA[point_intersec]\n",
    "\n",
    "# 'Interpolation' de l'impôt d'intersection\n",
    "imp_intersec = (imps_intersec[0]+imps_intersec[1])/2\n",
    "\n",
    "# Coordonnées de l'intersection combinées sous\n",
    "# format texte\n",
    "intersec_coords = \"CA: {:.0f}€\\nImpôts: {:.0f}€\".format(ca_intersec, imp_intersec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nb : La taille du graph et le positionnement des coordonnées de l'intersection ont été définies arbitrairement et ne sont donc pas dynamiques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Définition de la taille du graph\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "# Trace les lignes correspondant aux deux régimes\n",
    "plt.plot(LISTE_CA, liste_impots_lib, \\\n",
    "         label='Impôt régime libératoire')\n",
    "plt.plot(LISTE_CA, liste_impots_progress, \\\n",
    "         label='Impôt régime progressif')\n",
    "\n",
    "# Ajout d'un marker et des coordonnées sur le point d'intersection\n",
    "plt.plot(ca_intersec, imp_intersec, marker='o')\n",
    "plt.annotate(text=intersec_coords, xy=(ca_intersec, imp_intersec), \\\n",
    "             xytext =(-80,10),\n",
    "             textcoords=\"offset points\",\n",
    "             color='g')\n",
    "\n",
    "# Ajout du CA limite pour lequel l'option libératoire est possible\n",
    "plt.axvline(limite_ca_option,\n",
    "            label=\"CA maximum pour option libératoire\",\n",
    "            linewidth=.4, color='r')\n",
    "\n",
    "# Ajout du CA d'intersection\n",
    "plt.axvline(ca_intersec,\n",
    "            label=\"CA d'indifférence\",\n",
    "            linewidth=.4, color='g')\n",
    "\n",
    "# Ajout du CA perso de l'entreprise\n",
    "plt.axvline(CA_PERSO_BRUT,\n",
    "            label=\"CA de l'entreprise\",\n",
    "            linewidth=1, color='k')\n",
    "\n",
    "# Labels\n",
    "plt.xlabel(\"Chiffre d'affaires (CA)\")\n",
    "plt.ylabel(\"Montant d'imposition\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion :\n",
    "- Il faut avoir un CA > 23430€ pour que ça devienne intéressant de\n",
    "bénéficier de l'option libératoire de l'impôt sur le revenu.\n",
    "- Il est possible d'en bénéficier uniquement si le revenu fiscal\n",
    "de référence N-2 est inférieur à 25710 (si quot. familial = 1).\n",
    "- Donc impot libératoire intéressant entre 23430€ et 25710€."
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "eda",
   "language": "python",
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   "pygments_lexer": "ipython3",
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 },
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