{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad08f522",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import datetime\n",
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b9b18667",
   "metadata": {},
   "outputs": [],
   "source": [
    "class riskManager:\n",
    "    \n",
    "    def __init__(self,commision=0.04):\n",
    "        self.commision = commision\n",
    "        pass\n",
    "    def getDecision(self,signalDecision,probabilityDecision, price, deals=None) -> dict:\n",
    "        ans = {}\n",
    "        if probabilityDecision['trande'] == 'up':\n",
    "            ans['decision'] = 'buy'\n",
    "            ans['amount'] = 1\n",
    "        elif probabilityDecision['trande'] == 'none':\n",
    "            ans['decision'] = 'none'\n",
    "        elif probabilityDecision['trande'] == 'down': \n",
    "            for i in deals.shape[0]:\n",
    "                ans['decision'] = 'None'\n",
    "                ans['deals'] = []\n",
    "                row = deals.iloc[i]\n",
    "                if row.startPrice < price*pow(1+self.commission,2):\n",
    "                    ans['decision'] = 'sell'\n",
    "                    ans['deals'].append(row.name)\n",
    "        return ans\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f5dd64e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}