# adam_github_summary.ipynb
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# # GitHub Repository Summary: adamvangrover/adam - v19.1
# This IPython notebook provides a comprehensive summary and simulated execution of key functionalities within the `adamvangrover/adam` GitHub repository, which hosts the codebase for Adam v19.1, a sophisticated AI designed for financial market analysis.
# ## 1. Repository Overview and Purpose
# The `adamvangrover/adam` repository is structured to facilitate the deployment and operation of Adam v19.1. It encompasses core functionalities such as agent-based analysis, data integration, and report generation. This notebook aims to simulate key operational sequences, providing insights into Adam's architecture and potential applications.
# ## 2. Simulated Installation and Setup
# In a real-world scenario, the following steps would be executed to set up the environment. Here, we simulate these steps due to the constraints of the IPython notebook environment.
# ### 2.1. Cloning the Repository (Simulated)
# ```bash
# git clone [https://github.com/adamvangrover/adam.git](https://github.com/adamvangrover/adam.git)
# cd adam
# ```
# **Note:** This command would typically download the repository files to your local system. In this simulation, we proceed as if the files are already available.
# ### 2.2. Installing Dependencies (Simulated)
# ```bash
# pip install -r requirements.txt
# ```
# **Simulated Output:**
# ```
# Successfully installed langchain-0.0.123 pandas-1.5.3 numpy-1.24.2 neo4j-5.11.0 shap-0.42.1 lime-0.2.0.1 prometheus_client-0.16.0 ...
# ```
# **Note:** This command would install all required Python packages. We simulate successful installation.
# ## 3. Configuration Loading and Inspection
# The configuration for Adam v19.1 is crucial for its operation. We load and display the configuration to ensure its integrity.
# ```python
import json
# Simulated configuration from the provided instruction set
simulated_config = {
"name": "Adam v19.1",
"instruction": {
# ... (Your provided configuration content) ...
}
}
# Displaying the configuration in a readable format
print(json.dumps(simulated_config, indent=4))
# ```
# **Note:** This step verifies that the configuration is correctly loaded and accessible.
# ## 4. Agent Initialization Simulation
# Adam v19.1 operates through a network of specialized agents. We simulate the initialization of these agents.
# ```python
# Simulating the initialization of each agent
print("Simulating agent initialization...")
for agent in simulated_config["instruction"]["agent_network"]:
print(f"Initializing agent: {agent['name']}")
print("Agents initialized.")
# ```
# **Note:** This demonstrates the process of setting up the agent network, a core component of Adam's architecture.
# ## 5. Example Analysis Simulation: Market Sentiment
# We simulate a market sentiment analysis using the Market Sentiment Agent.
# ```python
# Simulating market sentiment analysis
print("Simulating market sentiment analysis...")
# Locating the Market Sentiment Agent
sentiment_agent = next(
(agent for agent in simulated_config["instruction"]["agent_network"] if agent["name"] == "Market Sentiment Agent"), None
)
# Executing the agent's function (simulated)
if sentiment_agent:
print(f"Running {sentiment_agent['name']}...")
print("Simulated sentiment: Bullish") # Replace with actual analysis if possible
else:
print("Market Sentiment Agent not found.")
# ```
# **Note:** This simulation showcases how an agent can be invoked to perform a specific analysis task.
# ## 6. Potential Applications and Uses of IPython Notebooks
# IPython notebooks offer a versatile environment for various financial analysis and development tasks.
# 1. **Interactive Data Visualization:**
# * Integrate libraries like `matplotlib`, `seaborn`, or `plotly` to create dynamic charts and graphs from financial data.
# * This allows for real-time visualization of trends and patterns.
# 2. **Automated Report Generation:**
# * Utilize `jinja2` or similar libraries to generate custom reports based on analysis results.
# * This automates the creation of detailed financial reports.
# 3. **Algorithmic Trading Backtesting:**
# * Develop and test trading strategies using historical market data with libraries like `backtrader` or `zipline`.
# * This allows for the examination of trading strategy performance.
# 4. **Financial Modeling and Forecasting:**
# * Implement and run financial models such as DCF or Black-Scholes using `numpy` and `scipy`.
# * This facilitates complex financial calculations and simulations.
# 5. **Educational Tool:**
# * Create tutorials and demonstrations of financial concepts and techniques.
# * This aids in the teaching and learning of financial analysis.
# 6. **Legal Document Analysis:**
# * Leverage Langchain, and other NLP libraries to process and extract relevant information from legal documents.
# 7. **Supply Chain Risk Assessment:**
# * Use libraries like networkx to map and analyze supply chain networks to assess vulnerabilities.
# 8. **Investment Committee Simulation:**
# * Using pandas, and other data analysis libraries, you can simulate investment committee discussions and decisions.
# ## 7. Conclusion
# This notebook has provided a simulated overview of the `adamvangrover/adam` repository and demonstrated potential applications of IPython notebooks in financial analysis. The simulation highlights the core functionalities of Adam v19.1 and its agent-based architecture.
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# GitHub Repository Summary: adamvangrover/adam - v19.1\n",
"\n",
"This IPython notebook provides a comprehensive summary and simulated execution of key functionalities within the `adamvangrover/adam` GitHub repository, which hosts the codebase for Adam v19.1, a sophisticated AI designed for financial market analysis."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Repository Overview and Purpose\n",
"\n",
"The `adamvangrover/adam` repository is structured to facilitate the deployment and operation of Adam v19.1. It encompasses core functionalities such as agent-based analysis, data integration, and report generation. This notebook aims to simulate key operational sequences, providing insights into Adam's architecture and potential applications."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Simulated Installation and Setup\n",
"\n",
"In a real-world scenario, the following steps would be executed to set up the environment. Here, we simulate these steps due to the constraints of the IPython notebook environment."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.1. Cloning the Repository (Simulated)\n",
"\n",
"```bash\n",
"git clone [https://github.com/adamvangrover/adam.git](https://github.com/adamvangrover/adam.git)\n",
"cd adam\n",
"```\n",
"\n",
"**Note:** This command would typically download the repository files to your local system. In this simulation, we proceed as if the files are already available."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2. Installing Dependencies (Simulated)\n",
"\n",
"```bash\n",
"pip install -r requirements.txt\n",
"```\n",
"\n",
"**Simulated Output:**\n",
"```\n",
"Successfully installed langchain-0.0.123 pandas-1.5.3 numpy-1.24.2 neo4j-5.11.0 shap-0.42.1 lime-0.2.0.1 prometheus_client-0.16.0 ...\n",
"```\n",
"\n",
"**Note:** This command would install all required Python packages. We simulate successful installation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Configuration Loading and Inspection\n",
"\n",
"The configuration for Adam v19.1 is crucial for its operation. We load and display the configuration to ensure its integrity."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"# Simulated configuration from the provided instruction set\n",
"simulated_config = {\n",
" \"name\": \"Adam v19.1\",\n",
" \"instruction\": {\n",
" // ... (Your provided configuration content) ...\n",
" }\n",
"}\n",
"\n",
"# Displaying the configuration in a readable format\n",
"print(json.dumps(simulated_config, indent=4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note:** This step verifies that the configuration is correctly loaded and accessible."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Agent Initialization Simulation\n",
"\n",
"Adam v19.1 operates through a network of specialized agents. We simulate the initialization of these agents."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Simulating the initialization of each agent\n",
"print(\"Simulating agent initialization...\")\n",
"for agent in simulated_config[\"instruction\"][\"agent_network\"]:\n",
" print(f\"Initializing agent: {agent['name']}\")\n",
"print(\"Agents initialized.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note:** This demonstrates the process of setting up the agent network, a core component of Adam's architecture."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Example Analysis Simulation: Market Sentiment\n",
"\n",
"We simulate a market sentiment analysis using the Market Sentiment Agent."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Simulating market sentiment analysis\n",
"print(\"Simulating market sentiment analysis...\")\n",
"\n",
"# Locating the Market Sentiment Agent\n",
"sentiment_agent = next(\n",
" (agent for agent in simulated_config[\"instruction\"][\"agent_network\"] if agent[\"name\"] == \"Market Sentiment Agent\"), None\n",
")\n",
"\n",
"# Executing the agent's function (simulated)\n",
"if sentiment_agent:\n",
" print(f\"Running {sentiment_agent['name']}...\")\n",
" print(\"Simulated sentiment: Bullish\") # Replace with actual analysis if possible\n",
"else:\n",
" print(\"Market Sentiment Agent not found.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note:** This simulation showcases how an agent can be invoked to perform a specific analysis task."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Potential Applications and Uses of IPython Notebooks\n",
"\n",
"IPython notebooks offer a versatile environment for various financial analysis and development tasks."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. **Interactive Data Visualization:**\n",
" * Integrate libraries like `matplotlib`, `seaborn`, or `plotly` to create dynamic charts and graphs from financial data.\n",
" * This allows for real-time visualization of trends and patterns.\n",
"\n",
"2. **Automated Report Generation:**\n",
" * Utilize `jinja2` or similar libraries to generate custom reports based on analysis results.\n",
" * This automates the creation of detailed financial reports.\n",
"\n",
"3. **Algorithmic Trading Backtesting:**\n",
" * Develop and test trading strategies using historical market data with libraries like `backtrader` or `zipline`.\n",
" * This allows for the examination of trading strategy performance.\n",
"\n",
"4. **Financial Modeling and Forecasting:**\n",
" * Implement and run financial models such as DCF or Black-Scholes using `numpy` and `scipy`.\n",
" * This facilitates complex financial calculations and simulations.\n",
"\n",
"5. **Educational Tool:**\n",
" * Create tutorials and demonstrations of financial concepts and techniques.\n",
"\n",
"6. **Legal Document Analysis:**\n",
" * Leverage Langchain, and other NLP libraries to process and extract relevant information from legal documents.\n",
"\n",
"7. **Supply Chain Risk Assessment:**\n",
" * Use libraries like networkx to map and analyze supply chain networks to assess vulnerabilities.\n",
"\n",
"8. **Investment Committee Simulation:**\n",
" * Using pandas, and other data analysis libraries, you can simulate investment committee discussions and decisions.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Conclusion\n",
"\n",
"This notebook has provided a simulated overview of the `adamvangrover/adam` repository and demonstrated potential applications of IPython notebooks in financial analysis. The simulation highlights the core functionalities of Adam v19.1 and its agent-based architecture."
]
}
]
}
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to Fundamental Analysis with IPython Notebooks\n",
"\n",
"This tutorial will guide you through the basics of fundamental analysis using IPython notebooks. We'll cover how to access and analyze financial data, calculate key metrics, and interpret the results."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Setting Up the Environment\n",
"\n",
"First, we need to import the necessary libraries. We'll use `pandas` for data manipulation and `yfinance` to retrieve financial data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import yfinance as yf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Retrieving Financial Data\n",
"\n",
"Let's retrieve financial data for a specific company, for example, Apple (AAPL). We'll get the income statement, balance sheet, and cash flow statement."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ticker = yf.Ticker(\"AAPL\")\n",
"income_statement = ticker.income_stmt\n",
"balance_sheet = ticker.balance_sheet\n",
"cash_flow = ticker.cashflow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Analyzing the Income Statement\n",
"\n",
"The income statement shows a company's financial performance over a period. Let's look at the revenue and net income."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"revenue = income_statement.loc['Total Revenue']\n",
"net_income = income_statement.loc['Net Income']\n",
"\n",
"print(\"Revenue:\\n\", revenue)\n",
"print(\"\\nNet Income:\\n\", net_income)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Analyzing the Balance Sheet\n",
"\n",
"The balance sheet shows a company's assets, liabilities, and equity at a specific point in time. Let's look at total assets and total liabilities."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"total_assets = balance_sheet.loc['Total Assets']\n",
"total_liabilities = balance_sheet.loc['Total Liabilities']\n",
"\n",
"print(\"Total Assets:\\n\", total_assets)\n",
"print(\"\\nTotal Liabilities:\\n\", total_liabilities)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Analyzing the Cash Flow Statement\n",
"\n",
"The cash flow statement shows how a company generates and uses cash. Let's look at the operating cash flow."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"operating_cash_flow = cash_flow.loc['Operating Cash Flow']\n",
"\n",
"print(\"Operating Cash Flow:\\n\", operating_cash_flow)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Calculating Key Ratios\n",
"\n",
"Now, let's calculate some key financial ratios, such as the debt-to-equity ratio and the profit margin."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"debt_to_equity = total_liabilities / (total_assets - total_liabilities)\n",
"profit_margin = net_income / revenue\n",
"\n",
"print(\"Debt-to-Equity Ratio:\\n\", debt_to_equity)\n",
"print(\"\\nProfit Margin:\\n\", profit_margin)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Interpreting the Results\n",
"\n",
"Based on the calculated ratios and financial data, you can assess the company's financial health and performance. For example, a high debt-to-equity ratio may indicate higher financial risk, while a high profit margin suggests strong profitability.\n",
"\n",
"**Note:** This is a basic introduction to fundamental analysis. Further analysis would involve comparing these metrics with industry averages and historical trends."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Further Exploration\n",
"\n",
"You can extend this tutorial by:\n",
"* Analyzing other companies and industries.\n",
"* Calculating additional financial ratios.\n",
"* Visualizing the data using `matplotlib` or `seaborn`.\n",
"* Integrating with other data sources for a more comprehensive analysis."
]
}
]
}
#data_exploration.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Exploration in Adam v19.1\n",
"\n",
"This notebook demonstrates how to load and explore data within the Adam v19.1 system."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Importing Libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import json"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Simulated Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Simulate loading data from various sources\n",
"data = {\n",
" 'stock_prices': pd.DataFrame(np.random.rand(100, 4), columns=['Open', 'High', 'Low', 'Close']),\n",
" 'economic_indicators': pd.DataFrame(np.random.rand(50, 3), columns=['GDP', 'Inflation', 'Unemployment']),\n",
" 'company_financials': pd.DataFrame(np.random.rand(20, 5), columns=['Revenue', 'NetIncome', 'Assets', 'Liabilities', 'Equity'])\n",
"}\n",
"\n",
"# Simulate loading data from the Knowledge Base\n",
"knowledge_base_data = {\n",
" 'financial_concepts': {\n",
" 'valuation_methods': ['DCF', 'Comparable Company Analysis', 'Precedent Transactions'],\n",
" 'risk_management': ['Market Risk', 'Credit Risk', 'Liquidity Risk']\n",
" }\n",
"}\n",
"\n",
"print(\"Stock Prices:\\n\", data['stock_prices'].head())\n",
"print(\"\\nEconomic Indicators:\\n\", data['economic_indicators'].head())\n",
"print(\"\\nCompany Financials:\\n\", data['company_financials'].head())\n",
"print(\"\\nKnowledge Base Financial Concepts:\\n\", knowledge_base_data['financial_concepts'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Data Exploration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Basic data exploration\n",
"print(\"Stock Prices Descriptive Statistics:\\n\", data['stock_prices'].describe())\n",
"print(\"\\nEconomic Indicators Missing Values:\\n\", data['economic_indicators'].isnull().sum())"
]
}
]
}
#model_evaluation.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Evaluation in Adam v19.1\n",
"\n",
"This notebook demonstrates how to evaluate machine learning models within the Adam v19.1 system."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Importing Libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import json"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Simulated Model Results Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Simulate loading model results\n",
"model_results = {\n",
" 'stock_price_prediction': {\n",
" 'actual': np.random.rand(100),\n",
" 'predicted': np.random.rand(100)\n",
" },\n",
" 'economic_forecast': {\n",
" 'actual': np.random.rand(50),\n",
" 'predicted': np.random.rand(50)\n",
" }\n",
"}\n",
"\n",
"#Simulate loading data from the simulation results archive\n",
"simulation_results = {\n",
" \"Monte_carlo\": {\n",
" \"run_1\": {\n",
" \"result\": 1\n",
" }\n",
" }\n",
"}\n",
"print(\"Stock Price Prediction Results:\\n\", pd.DataFrame(model_results['stock_price_prediction']).head())\n",
"print(\"\\nEconomic Forecast Results:\\n\", pd.DataFrame(model_results['economic_forecast']).head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Model Evaluation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Calculate evaluation metrics\n",
"from sklearn.metrics import mean_squared_error\n",
"\n",
"stock_mse = mean_squared_error(model_results['stock_price_prediction']['actual'], model_results['stock_price_prediction']['predicted'])\n",
"economic_mse = mean_squared_error(model_results['economic_forecast']['actual'], model_results['economic_forecast']['predicted'])\n",
"\n",
"print(\"Stock Price Prediction MSE:\", stock_mse)\n",
"print(\"Economic Forecast MSE:\", economic_mse)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Model Visualization"
]
},
{
"cell_type": "code"
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Visualize model results\n",
"plt.figure(figsize=(12, 6))\n",
"\n",
"plt.subplot(1, 2, 1)\n",
"plt.scatter(model_results['stock_price_prediction']['actual'], model_results['stock_price_prediction']['predicted'])\n",
"plt.title(\"Stock Price Prediction\")\n",
"plt.xlabel(\"Actual\")\n",
"plt.ylabel(\"Predicted\")\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.scatter(model_results['economic_forecast']['actual'], model_results['economic_forecast']['predicted'])\n",
"plt.title(\"Economic Forecast\")\n",
"plt.xlabel(\"Actual\")\n",
"plt.ylabel(\"Predicted\")\n",
"\n",
"plt.show()"
]
}
]
}
#report_generation.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Report Generation in Adam v19.1\n",
"\n",
"This notebook demonstrates how to generate reports using analysis results within the Adam v19.1 system."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Importing Libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import json"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Simulated Analysis Results Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Simulate loading analysis results\n",
"analysis_results = {\n",
" 'market_sentiment': 'Bullish',\n",
" 'stock_recommendations': ['AAPL', 'GOOG', 'MSFT'],\n",
" 'economic_outlook': 'Positive'\n",
"}\n",
"\n",
"#Simulate loading report templates\n",
"report_templates = {\n",
" 'market_report': '# Market Report\\n\\nSentiment: {sentiment}\\n\\nRecommendations: {recommendations}\\n\\nOutlook: {outlook}',\n",
" 'company_report': '# Company Report\\n\\nCompany: {company}\\n\\nAnalysis: {analysis}'\n",
"}\n",
"print(\"Analysis Results:\\n\", analysis_results)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Report Generation (Markdown)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Generate a market report in Markdown format\n",
"market_report_markdown = report_templates['market_report'].format(\n",
" sentiment=analysis_results['market_sentiment'],\n",
" recommendations=', '.join(analysis_results['stock_recommendations']),\n",
" outlook=analysis_results['economic_outlook']\n",
")\n",
"\n",
"print(\"Market Report (Markdown):\\n\\n\", market_report_markdown)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Report Generation (HTML)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Generate a market report in HTML format\n",
"market_report_html = report_templates['market_report'].format(\n",
" sentiment=analysis_results['market_sentiment'],\n",
" recommendations='- ' + '
- '.join(analysis_results['stock_recommendations']) + '
',\n",
" outlook=analysis_results['economic_outlook']\n",
")\n",
"market_report_html = market_report_html.replace('\\n','
')\n",
"print(\"Market Report (HTML):\\n\\n\", market_report_html)"
]
}
]
}