analise_crash.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import hashlib\n",
    "import time\n",
    "import hmac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "salt = \"0xd2867566759e9158bda9bf93b343bbd9aa02ce1e0c5bc2b37a2d70d391b04f14\"\n",
    "e = 2**52"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "hash_partida = 'bfb99dd5642e6e21733cccb66272e603117760e0d9cffa1eb3a5c19a0520e46f'  #refenreica de 3.08\n",
    "\n",
    "\n",
    "#fonte de dados sobre como a honestidade dos resultados rola \n",
    "#https://roobet.com/fair\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resultado(hash_partida):\n",
    "    hm = hmac.new(str.encode(hash_partida), b'', hashlib.sha256)\n",
    "    hm.update(salt.encode(\"utf-8\"))\n",
    "    h = hm.hexdigest()\n",
    "    if (int(h, 16) % 33 == 0):\n",
    "        return 1\n",
    "    h = int(h[:13], 16)\n",
    "    e = 2**52\n",
    "    return (((100 * e - h) / (e-h)) // 1) / 100.0\n",
    "\n",
    "def partida_anterior(hash_code):\n",
    "    m = hashlib.sha256()\n",
    "    m.update(hash_code.encode(\"utf-8\"))\n",
    "    return m.hexdigest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.08"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultado(hash_partida)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "ult_partida = '5ec9caae6ebf166e0ea5220846eb1b1b83421e4b1a20dc561b17e84f111259fa' # Update to latest game's hash for more results\n",
    "prim_partida = \"77b271fe12fca03c618f63dfb79d4105726ba9d4a25bb3f1964e435ccf9cb209\"\n",
    "\n",
    "resultados = []\n",
    "contador = 0\n",
    "while ult_partida != prim_partida:\n",
    "    contador += 1\n",
    "    resultados.append(resultado(ult_partida))\n",
    "    ult_partida = partida_anterior(ult_partida)\n",
    "    if contador == 500000:\n",
    "        break\n",
    "    \n",
    "resultados = np.array(resultados)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.951592"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiplicador = 20\n",
    "#probabilidea de erro\n",
    "(resultados <= multiplicador).mean()\n",
    "\n",
    "\n",
    "\n",
    "# entendesse que eu tenho 52% de chance de perder $ ganho aposto com 2 de multiplicador, usando caso real "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Martin Gale \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hipote de balnaço de 1000 reias \n",
    "valor_aposta = 0.01\n",
    "conta = 1000\n",
    "alvo = 2\n",
    "\n",
    "def Martingale(resultados,alvo,valor_aposta,conta):\n",
    "    \n",
    "    balanco = [conta]\n",
    "    valores_apostados = [0]\n",
    "    erro = 0\n",
    "    for i in resultados:\n",
    "        valores_apostados.append(valor_aposta*2**erro)\n",
    "\n",
    "        if alvo >= i:\n",
    "            \n",
    "            balanco.append(conta + valor_aposta*2**erro)\n",
    "            erro = 0\n",
    "        else:\n",
    "            balanco.append(conta - valor_aposta*2**erro)\n",
    "            erro += 1 \n",
    "        conta = balanco[-1]\n",
    "        \n",
    "    balanco = [i - balanco[0] for i in balanco]   \n",
    "    \n",
    "    return balanco, valores_apostados\n",
    "\n",
    "def plot_strategy(y_balanco,y_aposta,estrategia,alvo,valor_aposta):\n",
    "    x = list(range(len(y_balanco)))\n",
    "    plt.plot(x, y_balanco, label='Lucro Acumulado')\n",
    "    plt.bar(x, y_aposta, label='Valor da aposta', color='orange', alpha=0.7)\n",
    "    plt.xlabel('Numero de apostas')\n",
    "    plt.ylabel('$')\n",
    "    plt.title(f'Cenário hipotetico apostando no alvo de  {alvo}x, e usando {valor_aposta} como primeira aposta - {estrategia}')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1310.72\n",
      "2595.7900000010573\n"
     ]
    }
   ],
   "source": [
    "balanco_m, apostas_m = Martingale(resultados,alvo,valor_aposta,conta)\n",
    "print(max(apostas_m))\n",
    "print(balanco_m[-1])\n",
    "#plot_strategy(balanco_m,apostas_m,\"Martingale\",alvo,valor_aposta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analise de Valor Esperado\n",
    "a ideia é ver a longo prazo quando cada tipo de aposta simple irá me dar de retorno \n",
    "https://en.wikipedia.org/wiki/Expected_value\n",
    "\n",
    "\n",
    "E[X]=x \n",
    "1\n",
    "​\n",
    " ⋅p \n",
    "1\n",
    "​\n",
    " +x \n",
    "2\n",
    "​\n",
    " ⋅p \n",
    "2\n",
    "​\n",
    " +…+x \n",
    "k\n",
    "​\n",
    " ⋅p \n",
    "k\n",
    "​\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#p erro = (resultados < multiplicador).mean()\n",
    "#p acerto = (resultados >= multiplicador).mean()\n",
    "esperanca,alvo = [],[]\n",
    "\n",
    "alvo = np.linspace(101, 1001, 901) / 100\n",
    "esperanca = [(resultados < x).mean() * -1 + (x - 1)*(resultados >= x).mean() for x in alvo]\n",
    "\n",
    "plt.plot(alvo, esperanca, linewidth=3, color='red')\n",
    "\n",
    "plt.xlabel('Multiplicador Alvo')\n",
    "plt.ylabel('Valor Esperado ($)')\n",
    "#plt.title(f'Cenário hipotetico apostando no alvo de  {alvo}x, e usando {valor_aposta} como primeira aposta - {estrategia}')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}