Claude_Investor.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mshumer/gpt-investor/blob/main/Claude_Investor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gQWLeJCFS6C4"
      },
      "source": [
        "## claude-investor\n",
        "By Matt Shumer (https://twitter.com/mattshumer_)\n",
        "\n",
        "Github repo: https://github.com/mshumer/gpt-investor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rlKQLdG8v23g"
      },
      "outputs": [],
      "source": [
        "!pip install yfinance requests beautifulsoup4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "50ZEVdt53Xkr"
      },
      "outputs": [],
      "source": [
        "ANTHROPIC_API_KEY = \"YOUR_API_KEY\"  # Replace with your Anthropic API key"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tg78Hup6xona"
      },
      "outputs": [],
      "source": [
        "import yfinance as yf\n",
        "from datetime import datetime, timedelta\n",
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "import ast\n",
        "import json\n",
        "\n",
        "\n",
        "def get_article_text(url):\n",
        "    try:\n",
        "        response = requests.get(url)\n",
        "        response.raise_for_status()  # Raise an exception for HTTP errors\n",
        "        soup = BeautifulSoup(response.content, 'html.parser')\n",
        "        article_text = ' '.join([p.get_text() for p in soup.find_all('p')])\n",
        "        return article_text\n",
        "    except Exception as e:\n",
        "        print(f\"Error retrieving article text: {e}\")\n",
        "        return None\n",
        "\n",
        "def get_stock_data(ticker, years):\n",
        "    try:\n",
        "        end_date = datetime.now().date()\n",
        "        start_date = end_date - timedelta(days=years*365)\n",
        "\n",
        "        stock = yf.Ticker(ticker)\n",
        "\n",
        "        # Retrieve historical price data\n",
        "        hist_data = stock.history(start=start_date, end=end_date)\n",
        "\n",
        "        # Retrieve balance sheet\n",
        "        balance_sheet = stock.balance_sheet\n",
        "\n",
        "        # Retrieve financial statements\n",
        "        financials = stock.financials\n",
        "\n",
        "        # Retrieve news articles\n",
        "        news = stock.news\n",
        "\n",
        "        return hist_data, balance_sheet, financials, news\n",
        "    except Exception as e:\n",
        "        print(f\"Error retrieving stock data for {ticker}: {e}\")\n",
        "        return None, None, None, None\n",
        "\n",
        "def get_claude_comps_analysis(ticker, hist_data, balance_sheet, financials, news):\n",
        "    try:\n",
        "        system_prompt = f\"You are a financial analyst assistant. Analyze the given data for {ticker} and suggest a few comparable companies to consider. Do so in a Python-parseable list.\"\n",
        "\n",
        "        news_content = \"\"\n",
        "\n",
        "        for article in news:\n",
        "            article_text = get_article_text(article['link'])\n",
        "            news_content = news_content + f\"\\n\\n---\\n\\nTitle: {article['title']}\\nText: {article_text}\"\n",
        "\n",
        "        messages = [\n",
        "            {\"role\": \"user\", \"content\": f\"Historical price data:\\n{hist_data.tail().to_string()}\\n\\nBalance Sheet:\\n{balance_sheet.to_string()}\\n\\nFinancial Statements:\\n{financials.to_string()}\\n\\nNews articles:\\n{news_content.strip()}\\n\\n----\\n\\nNow, suggest a few comparable companies to consider, in a Python-parseable list. Return nothing but the list. Make sure the companies are in the form of their tickers.\"},\n",
        "        ]\n",
        "\n",
        "        headers = {\n",
        "            \"x-api-key\": ANTHROPIC_API_KEY,\n",
        "            \"anthropic-version\": \"2023-06-01\",\n",
        "            \"content-type\": \"application/json\"\n",
        "        }\n",
        "        data = {\n",
        "            \"model\": 'claude-3-haiku-20240307',\n",
        "            \"max_tokens\": 2000,\n",
        "            \"temperature\": 0.5,\n",
        "            \"system\": system_prompt,\n",
        "            \"messages\": messages,\n",
        "        }\n",
        "        response = requests.post(\"https://api.anthropic.com/v1/messages\", headers=headers, json=data)\n",
        "        \n",
        "        # Check if the response was successful\n",
        "        response.raise_for_status()\n",
        "        \n",
        "        response_text = response.json()['content'][0]['text']\n",
        "        return ast.literal_eval(response_text)\n",
        "    except requests.exceptions.RequestException as req_error:\n",
        "        print(f\"Request error occurred for {ticker}: {req_error}\")\n",
        "        return None\n",
        "    except Exception as e:\n",
        "        print(f\"Error occurred for {ticker}: {e}\")\n",
        "        return None\n",
        "\n",
        "def compare_companies(main_ticker, main_data, comp_ticker, comp_data):\n",
        "    system_prompt = f\"You are a financial analyst assistant. Compare the data of {main_ticker} against {comp_ticker} and provide a detailed comparison, like a world-class analyst would. Be measured and discerning. Truly think about the positives and negatives of each company. Be sure of your analysis. You are a skeptical investor.\"\n",
        "\n",
        "    messages = [\n",
        "        {\"role\": \"user\", \"content\": f\"Data for {main_ticker}:\\n\\nHistorical price data:\\n{main_data['hist_data'].tail().to_string()}\\n\\nBalance Sheet:\\n{main_data['balance_sheet'].to_string()}\\n\\nFinancial Statements:\\n{main_data['financials'].to_string()}\\n\\n----\\n\\nData for {comp_ticker}:\\n\\nHistorical price data:\\n{comp_data['hist_data'].tail().to_string()}\\n\\nBalance Sheet:\\n{comp_data['balance_sheet'].to_string()}\\n\\nFinancial Statements:\\n{comp_data['financials'].to_string()}\\n\\n----\\n\\nNow, provide a detailed comparison of {main_ticker} against {comp_ticker}. Explain your thinking very clearly.\"},\n",
        "    ]\n",
        "\n",
        "    headers = {\n",
        "        \"x-api-key\": ANTHROPIC_API_KEY,\n",
        "        \"anthropic-version\": \"2023-06-01\",\n",
        "        \"content-type\": \"application/json\"\n",
        "    }\n",
        "    data = {\n",
        "        \"model\": 'claude-3-haiku-20240307',\n",
        "        \"max_tokens\": 3000,\n",
        "        \"temperature\": 0.5,\n",
        "        \"system\": system_prompt,\n",
        "        \"messages\": messages,\n",
        "    }\n",
        "    response = requests.post(\"https://api.anthropic.com/v1/messages\", headers=headers, json=data)\n",
        "    response_text = response.json()['content'][0]['text']\n",
        "\n",
        "    # return json.loads(response_text)\n",
        "    return response_text\n",
        "\n",
        "def get_sentiment_analysis(ticker, news):\n",
        "    system_prompt = f\"You are a sentiment analysis assistant. Analyze the sentiment of the given news articles for {ticker} and provide a summary of the overall sentiment and any notable changes over time. Be measured and discerning. You are a skeptical investor.\"\n",
        "\n",
        "    news_text = \"\"\n",
        "    for article in news:\n",
        "        article_text = get_article_text(article['link'])\n",
        "        timestamp = datetime.fromtimestamp(article['providerPublishTime']).strftime(\"%Y-%m-%d\")\n",
        "        news_text += f\"\\n\\n---\\n\\nDate: {timestamp}\\nTitle: {article['title']}\\nText: {article_text}\"\n",
        "\n",
        "    messages = [\n",
        "        {\"role\": \"user\", \"content\": f\"News articles for {ticker}:\\n{news_text}\\n\\n----\\n\\nProvide a summary of the overall sentiment and any notable changes over time.\"},\n",
        "    ]\n",
        "\n",
        "    headers = {\n",
        "        \"x-api-key\": ANTHROPIC_API_KEY,\n",
        "        \"anthropic-version\": \"2023-06-01\",\n",
        "        \"content-type\": \"application/json\"\n",
        "    }\n",
        "    data = {\n",
        "        \"model\": 'claude-3-haiku-20240307',\n",
        "        \"max_tokens\": 2000,\n",
        "        \"temperature\": 0.5,\n",
        "        \"system\": system_prompt,\n",
        "        \"messages\": messages,\n",
        "    }\n",
        "    response = requests.post(\"https://api.anthropic.com/v1/messages\", headers=headers, json=data)\n",
        "    response_text = response.json()['content'][0]['text']\n",
        "\n",
        "    return response_text\n",
        "\n",
        "def get_analyst_ratings(ticker):\n",
        "    stock = yf.Ticker(ticker)\n",
        "    recommendations = stock.recommendations\n",
        "    if recommendations is None or recommendations.empty:\n",
        "        return \"No analyst ratings available.\"\n",
        "\n",
        "    latest_rating = recommendations.iloc[-1]\n",
        "\n",
        "    firm = latest_rating.get('Firm', 'N/A')\n",
        "    to_grade = latest_rating.get('To Grade', 'N/A')\n",
        "    action = latest_rating.get('Action', 'N/A')\n",
        "\n",
        "    rating_summary = f\"Latest analyst rating for {ticker}:\\nFirm: {firm}\\nTo Grade: {to_grade}\\nAction: {action}\"\n",
        "\n",
        "    return rating_summary\n",
        "\n",
        "def get_industry_analysis(ticker):\n",
        "\n",
        "    ### update to use search to find recent data!!\n",
        "\n",
        "    stock = yf.Ticker(ticker)\n",
        "    industry = stock.info['industry']\n",
        "    sector = stock.info['sector']\n",
        "\n",
        "    system_prompt = f\"You are an industry analysis assistant. Provide an analysis of the {industry} industry and {sector} sector, including trends, growth prospects, regulatory changes, and competitive landscape. Be measured and discerning. Truly think about the positives and negatives of the stock. Be sure of your analysis. You are a skeptical investor.\"\n",
        "\n",
        "    messages = [\n",
        "        {\"role\": \"user\", \"content\": f\"Provide an analysis of the {industry} industry and {sector} sector.\"},\n",
        "    ]\n",
        "\n",
        "    headers = {\n",
        "        \"x-api-key\": ANTHROPIC_API_KEY,\n",
        "        \"anthropic-version\": \"2023-06-01\",\n",
        "        \"content-type\": \"application/json\"\n",
        "    }\n",
        "    data = {\n",
        "        \"model\": 'claude-3-haiku-20240307',\n",
        "        \"max_tokens\": 2000,\n",
        "        \"temperature\": 0.5,\n",
        "        \"system\": system_prompt,\n",
        "        \"messages\": messages,\n",
        "    }\n",
        "    response = requests.post(\"https://api.anthropic.com/v1/messages\", headers=headers, json=data)\n",
        "    response_text = response.json()['content'][0]['text']\n",
        "\n",
        "    return response_text\n",
        "\n",
        "\n",
        "def get_final_analysis(ticker, comparisons, sentiment_analysis, analyst_ratings, industry_analysis):\n",
        "    system_prompt = f\"You are a financial analyst providing a final investment recommendation for {ticker} based on the given data and analyses. Be measured and discerning. Truly think about the positives and negatives of the stock. Be sure of your analysis. You are a skeptical investor.\"\n",
        "\n",
        "    messages = [\n",
        "        {\"role\": \"user\", \"content\": f\"Ticker: {ticker}\\n\\nComparative Analysis:\\n{json.dumps(comparisons, indent=2)}\\n\\nSentiment Analysis:\\n{sentiment_analysis}\\n\\nAnalyst Ratings:\\n{analyst_ratings}\\n\\nIndustry Analysis:\\n{industry_analysis}\\n\\nBased on the provided data and analyses, please provide a comprehensive investment analysis and recommendation for {ticker}. Consider the company's financial strength, growth prospects, competitive position, and potential risks. Provide a clear and concise recommendation on whether to buy, hold, or sell the stock, along with supporting rationale.\"},\n",
        "    ]\n",
        "\n",
        "    headers = {\n",
        "        \"x-api-key\": ANTHROPIC_API_KEY,\n",
        "        \"anthropic-version\": \"2023-06-01\",\n",
        "        \"content-type\": \"application/json\"\n",
        "    }\n",
        "    data = {\n",
        "        \"model\": 'claude-3-opus-20240229',\n",
        "        \"max_tokens\": 3000,\n",
        "        \"temperature\": 0.5,\n",
        "        \"system\": system_prompt,\n",
        "        \"messages\": messages,\n",
        "    }\n",
        "    response = requests.post(\"https://api.anthropic.com/v1/messages\", headers=headers, json=data)\n",
        "    response_text = response.json()['content'][0]['text']\n",
        "\n",
        "    return response_text\n",
        "\n",
        "def generate_ticker_ideas(industry):\n",
        "    system_prompt = f\"You are a financial analyst assistant. Generate a list of 5 ticker symbols for major companies in the {industry} industry, as a Python-parseable list.\"\n",
        "\n",
        "    messages = [\n",
        "        {\"role\": \"user\", \"content\": f\"Please provide a list of 5 ticker symbols for major companies in the {industry} industry as a Python-parseable list. Only respond with the list, no other text.\"},\n",
        "    ]\n",
        "\n",
        "    headers = {\n",
        "        \"x-api-key\": ANTHROPIC_API_KEY,\n",
        "        \"anthropic-version\": \"2023-06-01\",\n",
        "        \"content-type\": \"application/json\"\n",
        "    }\n",
        "    data = {\n",
        "        \"model\": 'claude-3-haiku-20240307',\n",
        "        \"max_tokens\": 200,\n",
        "        \"temperature\": 0.5,\n",
        "        \"system\": system_prompt,\n",
        "        \"messages\": messages,\n",
        "    }\n",
        "    response = requests.post(\"https://api.anthropic.com/v1/messages\", headers=headers, json=data)\n",
        "    response_text = response.json()['content'][0]['text']\n",
        "\n",
        "    ticker_list = ast.literal_eval(response_text)\n",
        "    return [ticker.strip() for ticker in ticker_list]\n",
        "\n",
        "def get_current_price(ticker):\n",
        "    stock = yf.Ticker(ticker)\n",
        "    data = stock.history(period='1d', interval='1m')\n",
        "    return data['Close'][-1]\n",
        "\n",
        "def rank_companies(industry, analyses, prices):\n",
        "    system_prompt = f\"You are a financial analyst providing a ranking of companies in the {industry} industry based on their investment potential. Be discerning and sharp. Truly think about whether a stock is valuable or not. You are a skeptical investor.\"\n",
        "\n",
        "    analysis_text = \"\\n\\n\".join(\n",
        "        f\"Ticker: {ticker}\\nCurrent Price: {prices.get(ticker, 'N/A')}\\nAnalysis:\\n{analysis}\"\n",
        "        for ticker, analysis in analyses.items()\n",
        "    )\n",
        "\n",
        "    messages = [\n",
        "        {\"role\": \"user\", \"content\": f\"Industry: {industry}\\n\\nCompany Analyses:\\n{analysis_text}\\n\\nBased on the provided analyses, please rank the companies from most attractive to least attractive for investment. Provide a brief rationale for your ranking. In each rationale, include the current price (if available) and a price target.\"},\n",
        "    ]\n",
        "\n",
        "    headers = {\n",
        "        \"x-api-key\": ANTHROPIC_API_KEY,\n",
        "        \"anthropic-version\": \"2023-06-01\",\n",
        "        \"content-type\": \"application/json\"\n",
        "    }\n",
        "    data = {\n",
        "        \"model\": 'claude-3-opus-20240229',\n",
        "        \"max_tokens\": 3000,\n",
        "        \"temperature\": 0.5,\n",
        "        \"system\": system_prompt,\n",
        "        \"messages\": messages,\n",
        "    }\n",
        "    response = requests.post(\"https://api.anthropic.com/v1/messages\", headers=headers, json=data)\n",
        "    response_text = response.json()['content'][0]['text']\n",
        "\n",
        "    return response_text\n",
        "\n",
        "# User input\n",
        "industry = input(\"Enter the industry to analyze: \")\n",
        "years = 1 # int(input(\"Enter the number of years for analysis: \"))\n",
        "\n",
        "# Generate ticker ideas for the industry\n",
        "tickers = generate_ticker_ideas(industry)\n",
        "print(f\"\\nTicker Ideas for {industry} Industry:\")\n",
        "print(\", \".join(tickers))\n",
        "\n",
        "# Perform analysis for each company\n",
        "analyses = {}\n",
        "prices = {}\n",
        "for ticker in tickers:\n",
        "    try:\n",
        "        print(f\"\\nAnalyzing {ticker}...\")\n",
        "        hist_data, balance_sheet, financials, news = get_stock_data(ticker, years)\n",
        "        main_data = {\n",
        "            'hist_data': hist_data,\n",
        "            'balance_sheet': balance_sheet,\n",
        "            'financials': financials,\n",
        "            'news': news\n",
        "        }\n",
        "        sentiment_analysis = get_sentiment_analysis(ticker, news)\n",
        "        analyst_ratings = get_analyst_ratings(ticker)\n",
        "        industry_analysis = get_industry_analysis(ticker)\n",
        "        final_analysis = get_final_analysis(ticker, {}, sentiment_analysis, analyst_ratings, industry_analysis)\n",
        "        analyses[ticker] = final_analysis\n",
        "        prices[ticker] = get_current_price(ticker)\n",
        "    except:\n",
        "        pass\n",
        "\n",
        "# Rank the companies based on their analyses\n",
        "ranking = rank_companies(industry, analyses, prices)\n",
        "print(f\"\\nRanking of Companies in the {industry} Industry:\")\n",
        "print(ranking)"
      ]
    }
  ],
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