api.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# этот скрипт делает запрос к openai api."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%pip install numpy\n",
    "#%pip install pandas\n",
    "#%pip install python-dotenv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import base64\n",
    "import requests\n",
    "from dotenv import load_dotenv\n",
    "import os\n",
    "import json\n",
    "\n",
    "# Загрузка переменных из файла .env\n",
    "load_dotenv()\n",
    "\n",
    "# Получение значения переменной OPENAI_API_KEY\n",
    "api_key = os.getenv('OPENAI_API_KEY')\n",
    "\n",
    "headers = {\n",
    "  \"Content-Type\": \"application/json\",\n",
    "  \"Authorization\": f\"Bearer {api_key}\"\n",
    "}\n",
    "\n",
    "# Function to encode the image\n",
    "def encode_image(image_path):\n",
    "  with open(image_path, \"rb\") as image_file:\n",
    "    return base64.b64encode(image_file.read()).decode('utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to your image\n",
    "image_path = \"img/1T.png\"\n",
    "\n",
    "# Getting the base64 string\n",
    "base64_image = encode_image(image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "payload = {\n",
    "  \"model\": \"gpt-4o\",\n",
    "  \"messages\": [\n",
    "    {\n",
    "      \"role\": \"system\", \n",
    "      \"content\": \"You are a professional quantitative analyst specializing in financial market analysis and price\" \n",
    "              + \" forecasting. You always spend a few sentences explaining background context, assumptions, and\" \n",
    "              + \" step-by-step thinking BEFORE you try to answer a question to ensure accuracy. You conduct a\"\n",
    "              + \" thorough analysis of the overall market situation before providing forecasts.\"\n",
    "    },\n",
    "    {\n",
    "      \"role\": \"user\", \n",
    "      \"content\": [\n",
    "        {\n",
    "          \"type\": \"text\", \n",
    "          \"text\": \"Could you please analyze the attached minute candlestick chart and provide forecasts for the next\"\n",
    "                + \" 5 minutes. The main chart includes Bollinger Bands (period=20, color=black), VWAP (color=blue), ATR\"\n",
    "                + \" (period=10, color=red). The volume chart also includes RSI (period=14, color=black).\"\n",
    "                + \" Below the volume chart is a panel with MACD (fast=12, slow=26, signal=9). Please MAKE SURE TO\"\n",
    "                + \" INCLUDE the predicted CLOSING PRICE RANGE after the next 5 minutes as well as the PROBABILITIES of\"\n",
    "                + \" price increase and decrease.\"\n",
    "        },\n",
    "        {\n",
    "          \"type\": \"image_url\",\n",
    "          \"image_url\": {\n",
    "            \"url\": f\"data:image/jpeg;base64,{base64_image}\"\n",
    "          },\n",
    "        }\n",
    "      ],\n",
    "    }\n",
    "  ],\n",
    "  \"max_tokens\": 1000\n",
    "}\n",
    "\n",
    "# делаем первый запрос к API, в котором просим провести анализ графика.\n",
    "response = requests.post(\"https://api.openai.com/v1/chat/completions\", headers=headers, json=payload)\n",
    "j = response.json()\n",
    "j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# эта ячейка берет ответ из предыдущей ячейки и вставляет его в маркдаун ячейку ниже, чтобы было удобно читать.\n",
    "\n",
    "# Запустите следующий код в Jupyter Notebook, чтобы создать функцию, которая будет добавлять новую ячейку с содержимым:\n",
    "from IPython.display import display, Markdown, Javascript\n",
    "\n",
    "def create_markdown_cell(content):\n",
    "    display(Markdown(content))\n",
    "\n",
    "# Создайте функцию, которая удаляет следующую ячейку, если она имеет тип Markdown:\n",
    "def delete_next_markdown_cell():\n",
    "    display(Javascript(\"\"\"\n",
    "    var currentCell = Jupyter.notebook.get_selected_index();\n",
    "    var nextCell = Jupyter.notebook.get_cell(currentCell + 1);\n",
    "    if (nextCell && nextCell.cell_type === 'markdown') {\n",
    "        Jupyter.notebook.delete_cell(currentCell + 1);\n",
    "    }\n",
    "    \"\"\"))\n",
    "\n",
    "# Извлечение значения поля content\n",
    "content = j['choices'][0]['message']['content']\n",
    "\n",
    "# Удаление следующей Markdown ячейки, если она существует\n",
    "delete_next_markdown_cell()\n",
    "\n",
    "# Создание новой markdown ячейки с содержимым\n",
    "create_markdown_cell(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# в этой ячейке делаем второй запрос к API, в котором просим предыдущий ответ оформить в виде json.\n",
    "\n",
    "payload = {\n",
    "  \"model\": \"gpt-3.5-turbo\",# берем модель 3.5 турбо, т.к. она в 10 раз дешевле gpt-4o и с задачей справляется.\n",
    "  \"response_format\": { \"type\": \"json_object\" },\n",
    "  \"messages\": [\n",
    "    {\n",
    "      \"role\": \"system\", \n",
    "      \"content\": \"\"\"You are an LLM assistant specialized in converting financial analysis text into structured JSON format.\n",
    " Your task is to extract key numerical values and probabilities from the provided analysis and format\n",
    " them into a JSON object with the following fields: 'h' for the predicted highest price, 'l' for the\n",
    " predicted lowest price, 'p_up' for the probability of price increase, 'p_down' for the probability\n",
    " of price decrease, and 'error' for any potential issues or discrepancies noted during extraction.\n",
    " Ensure the JSON output accurately reflects the information in the analysis, adheres to the following\n",
    " JSON Schema:\n",
    "\n",
    "```\n",
    "{\n",
    "  \"$schema\": \"http://json-schema.org/draft-07/schema#\",\n",
    "  \"type\": \"object\",\n",
    "  \"properties\": {\n",
    "    \"h\": {\n",
    "      \"type\": \"number\",\n",
    "      \"description\": \"Predicted highest price\"\n",
    "    },\n",
    "    \"l\": {\n",
    "      \"type\": \"number\",\n",
    "      \"description\": \"Predicted lowest price\"\n",
    "    },\n",
    "    \"p_up\": {\n",
    "      \"type\": \"number\",\n",
    "      \"description\": \"Probability of price increase\",\n",
    "      \"minimum\": 0,\n",
    "      \"maximum\": 100\n",
    "    },\n",
    "    \"p_down\": {\n",
    "      \"type\": \"number\",\n",
    "      \"description\": \"Probability of price decrease\",\n",
    "      \"minimum\": 0,\n",
    "      \"maximum\": 100\n",
    "    },\n",
    "    \"error\": {\n",
    "      \"type\": \"string\",\n",
    "      \"description\": \"Error message if any issue is found\"\n",
    "    }\n",
    "  },\n",
    "  \"additionalProperties\": false\n",
    "}\n",
    "```\n",
    "\n",
    "Respond only in JSON format without any additional text, as your response will be processed automatically by a program.\"\"\"\n",
    "    },\n",
    "    {\n",
    "      \"role\": \"user\", \n",
    "      \"content\": [\n",
    "        {\n",
    "          \"type\": \"text\", \n",
    "          \"text\": content\n",
    "        }\n",
    "      ],\n",
    "    }\n",
    "  ],\n",
    "  \"max_tokens\": 1000\n",
    "}\n",
    "\n",
    "response = requests.post(\"https://api.openai.com/v1/chat/completions\", headers=headers, json=payload)\n",
    "j = response.json()\n",
    "j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "json_string = j['choices'][0]['message']['content']\n",
    "\n",
    "# Преобразование строки JSON в объект Python (словарь)\n",
    "data = json.loads(json_string)\n",
    "\n",
    "# Чтение значения 'h'\n",
    "data[\"h\"], data['l'], data['p_up'], data['p_down'], data['error']"
   ]
  }
 ],
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