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| # Data Insights from SEC-10K filings | ||
| # FinTech-Lab-Summer-2024 | ||
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| ### Insights from SEC-10K filings using LLM | ||
| ## Project Overview | ||
| This project focuses on extracting, analyzing, and visualizing key financial insights from 10-K filings of public firms using a large language model (LLM). The insights are presented through a web interface built with Streamlit, which provides a dynamic way to explore financial trends and data-driven insights. | ||
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| ## Features | ||
| - **Data Extraction**: | ||
| 1. Python library SEC-Edgar-Download is used to extract SEC filings. | ||
| 2. The filings of any firms can be downloaded using the script located in | ||
| `src/data/raw_data/download_sec_filings.py` | ||
| 3. `Downloader.log` containes information about the missing as well as the downloaded files. | ||
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| - **Data Pre-processing** | ||
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| Pre-processing steps takes in 3 parts : | ||
| 1. First the raw html,css contained is removed from downloaded filings and stored in `src\data\pre_processed_data\cleaned-sec-edgar-filings` | ||
| 2. Then lemmetization and Stemming is performed on the cleaned data and only 5 words before and after the numerical feautres were selected and stored in | ||
| `src\data\pre_processed_data\processed-numeric-contexts` | ||
| 3. Finally using all feautres that are considered finacially insightful(*mentioned below*) are extracted using regex expression and finally stored in | ||
| `src\data\pre_processed_data\feature` | ||
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| ## Installation | ||
| ```bash | ||
| git clone https://github.com/siddharth7113/Fintech-Lab-Summer-2024 | ||
| cd siddharth7113/Fintech-Lab-Summer-2024 | ||
| pip install -r requirements.txt | ||
| - **Text Analysis**: | ||
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| 1. Each of these features files are sent to LLM (**Mixtral-7b-Instruct**) via OpenRouterAPI and saved in `src/output/output-responses` | ||
| 2. Using a python script these annual files for each firm is combined in text format which is stored in `src/output/pre-analysis_combined`. | ||
| 3. This combined text is again sent to LLM (**Mixtral-7b-Instruct**) via OpenRouterAPI to obtain two types of files : text_insights, csv_insights. | ||
| (These files are used to ) | ||
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| ``` | ||
| ## Usage | ||
| ``` | ||
| python main.py | ||
| ``` | ||
| ## Contributing | ||
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| Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate. | ||
| - **Data Visualization**: Interactive charts and graphs to display financial metrics and trends. | ||
| - **Web Interface**: A Streamlit application that allows users to select different firms and view corresponding financial insights and visualizations. | ||
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| --- | ||
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| ### Important terms and their need : | ||
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| ### Financial Statements and Performance Metrics | ||
| - **Revenue**: Represents the total income generated by a business. | ||
| - **Expenses**: Refers to the costs incurred by a business in its operations. | ||
| - **Net Income**: Calculated as revenue minus expenses, indicating the profitability of a company. | ||
| - **Assets**: Include all resources owned by a company that have economic value. | ||
| - **Liabilities**: Represent the company's debts or obligations. | ||
| - **Equity**: Reflects the ownership interest in a company's assets after deducting liabilities. | ||
| - **Cash Flow**: Shows the movement of cash in and out of a business. | ||
| - **Operating Margin**: Indicates the profitability of a company's core business activities. | ||
| - **Gross Margin**: Represents the percentage of revenue that exceeds the cost of goods sold. | ||
| - **EBITDA**: Stands for Earnings Before Interest, Taxes, Depreciation, and Amortization. | ||
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| ### Financial Analysis and Reporting | ||
| - **Financial Ratios**: Include metrics like debt-to-equity ratio and return on equity used to assess a company's financial health. | ||
| - **Earnings Per Share**: Calculated as net income divided by the number of outstanding shares. | ||
| - **Tax Rate**: Refers to the percentage of income that a company pays in taxes. | ||
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| ### Investment and Risk Management | ||
| - **Debt**: Represents borrowed funds that a company must repay. | ||
| - **Investment Gains/Losses**: Reflect the profits or losses from investment activities. | ||
| - **Hedging Activities**: Strategies used to reduce risks associated with price fluctuations. | ||
| - **Derivative Instruments**: Financial contracts whose value is derived from an underlying asset. | ||
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| ### Other Financial Terms | ||
| - **Common Stock**: Represents ownership in a company and typically carries voting rights. | ||
| - **Subsequent Events**: Events occurring after the end of a reporting period that may impact financial statements. | ||
| - **Fair Value Measurements**: Refers to the estimated value of an asset or liability based on market conditions. | ||
| - **Geographic Concentration Risk**: Risk associated with a company's heavy reliance on a particular geographic region. | ||
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| --- | ||
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| ## Directory Structure | ||
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| FinTech-Lab-Summer-2024/ | ||
| │ | ||
| ├── .github/workflows # CI/CD pipelines for automated testing and deployment. | ||
| │ └── python-ci.yml | ||
| │ | ||
| ├── docs # Documentation related to the project. | ||
| │ | ||
| ├── src # Source code for the project. | ||
| │ ├── analysis # Scripts for data analysis. | ||
| │ │ ├── csv # CSV files with analyzed financial data. | ||
| │ │ ├── text-summaries # Textual summaries extracted from 10-K filings. | ||
| │ │ └── txt_to_csv.py # Script to convert text data to CSV. | ||
| │ │ | ||
| │ ├── app # Streamlit application. | ||
| │ │ └── streamlit_app.py # Main application script. | ||
| │ │ | ||
| │ ├── scripts # Utility scripts for data processing. | ||
| │ │ ├── data_extraction.py # Script for downloading SEC filings. | ||
| │ │ ├── feature_extraction.py # Script for feature extraction from text. | ||
| │ │ └── lemmitization.py # Script for text normalization. | ||
| │ │ | ||
| │ └── data # Data used or generated by the scripts. | ||
| │ ├── pre-processed_data # Preprocessed datasets. | ||
| │ └── pre-processing_scripts # Scripts that preprocess data. | ||
| │ | ||
| ├── tests # Automated tests for the project. | ||
| │ └── test_analysis.py # Test cases for data analysis scripts. | ||
| │ | ||
| ├── requirements.txt # Project dependencies. | ||
| └── README.md # Project overview and setup instructions. | ||
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| --- | ||
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| ## **Streamlit Link** | ||
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| [url-link](www.google.com) | ||
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| ## Tech-Stack | ||
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| ## Contributing | ||
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| Contributions are welcome! Please fork the repository and open a pull request with your features or fixes. | ||
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| ## License | ||
| This project is licensed under the terms of the MIT license. | ||
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