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Error Handling #11

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163 changes: 89 additions & 74 deletions Claude_Investor.ipynb
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@@ -1,42 +1,26 @@
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyPuI82WtCRP7mS26mCp9B+j",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
"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"
],
"metadata": {
"id": "gQWLeJCFS6C4"
}
]
},
{
"cell_type": "code",
Expand All @@ -51,17 +35,22 @@
},
{
"cell_type": "code",
"source": [
"ANTHROPIC_API_KEY = \"YOUR_API_KEY\" # Replace with your Anthropic API key"
],
"execution_count": null,
"metadata": {
"id": "50ZEVdt53Xkr"
},
"execution_count": null,
"outputs": []
"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",
Expand All @@ -74,62 +63,77 @@
"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:\n",
" return \"Error retrieving 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",
" end_date = datetime.now().date()\n",
" start_date = end_date - timedelta(days=years*365)\n",
" try:\n",
" end_date = datetime.now().date()\n",
" start_date = end_date - timedelta(days=years*365)\n",
"\n",
" stock = yf.Ticker(ticker)\n",
" stock = yf.Ticker(ticker)\n",
"\n",
" # Retrieve historical price data\n",
" hist_data = stock.history(start=start_date, end=end_date)\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",
" # Retrieve balance sheet\n",
" balance_sheet = stock.balance_sheet\n",
"\n",
" # Retrieve financial statements\n",
" financials = stock.financials\n",
" # Retrieve financial statements\n",
" financials = stock.financials\n",
"\n",
" # Retrieve news articles\n",
" news = stock.news\n",
" # Retrieve news articles\n",
" news = stock.news\n",
"\n",
" return hist_data, balance_sheet, financials, news\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",
" 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 = \"\"\n",
"\n",
" for article in news:\n",
" article_text = get_article_text(article['link'])\n",
" news = news + f\"\\n\\n---\\n\\nTitle: {article['title']}\\nText: {article_text}\"\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",
" 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.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",
" 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",
" 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",
" 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",
" return ast.literal_eval(response_text)\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",
Expand Down Expand Up @@ -352,12 +356,23 @@
"ranking = rank_companies(industry, analyses, prices)\n",
"print(f\"\\nRanking of Companies in the {industry} Industry:\")\n",
"print(ranking)"
],
"metadata": {
"id": "tg78Hup6xona"
},
"execution_count": null,
"outputs": []
]
}
],
"metadata": {
"colab": {
"authorship_tag": "ABX9TyPuI82WtCRP7mS26mCp9B+j",
"include_colab_link": true,
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
]
}
},
"nbformat": 4,
"nbformat_minor": 0
}