├── .gitignore ├── LICENSE ├── README.md ├── financial_datasets ├── __init__.py ├── dataset.py ├── filings.py ├── generator.py ├── llm │ ├── __init__.py │ └── openai.py ├── parser.py ├── prompts.py └── tools.py ├── poetry.lock ├── pyproject.toml └── tests ├── __init__.py ├── test_generator.py └── test_parser.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/#use-with-ide 110 | .pdm.toml 111 | 112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 113 | __pypackages__/ 114 | 115 | # Celery stuff 116 | celerybeat-schedule 117 | celerybeat.pid 118 | 119 | # SageMath parsed files 120 | *.sage.py 121 | 122 | # Environments 123 | .env 124 | .venv 125 | env/ 126 | venv/ 127 | ENV/ 128 | env.bak/ 129 | venv.bak/ 130 | 131 | # Spyder project settings 132 | .spyderproject 133 | .spyproject 134 | 135 | # Rope project settings 136 | .ropeproject 137 | 138 | # mkdocs documentation 139 | /site 140 | 141 | # mypy 142 | .mypy_cache/ 143 | .dmypy.json 144 | dmypy.json 145 | 146 | # Pyre type checker 147 | .pyre/ 148 | 149 | # pytype static type analyzer 150 | .pytype/ 151 | 152 | # Cython debug symbols 153 | cython_debug/ 154 | 155 | # PyCharm 156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 158 | # and can be added to the global gitignore or merged into this file. For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | .idea/ 161 | 162 | */env -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Virat Singh 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Financial Datasets 🧪 2 | 3 | Financial Datasets is an open-source Python library 4 | that lets you create question & answer financial datasets 5 | using Large Language Models (LLMs). With this library, 6 | you can easily generate realistic financial datasets from a 10-K, 7 | 10-Q, PDF, and other financial texts. 8 | 9 | [![Twitter Follow](https://img.shields.io/twitter/follow/virattt?style=social)](https://twitter.com/virattt) 10 | 11 | ## Usage 12 | 13 | **Example generated dataset:** 14 | 15 | ```json 16 | [ 17 | { 18 | "question": "What was Airbnb's revenue in 2023?", 19 | "answer": "$9.9 billion", 20 | "context": "In 2023, revenue increased by 18% to $9.9 billion compared to 2022, primarily due to a 14% increase in Nights and Experiences Booked of 54.5 million combined with higher average daily rates driving a 16% increase in Gross Booking Value of $10.0 billion." 21 | }, 22 | { 23 | "question": "By what percentage did Airbnb's net income increase in 2023 compared to the prior year?", 24 | "answer": "153%", 25 | "context": "Net income in 2023 increased by 153% to $4.8 billion, compared to the prior year, driven by our revenue growth, increased interest income, discipline in managing our cost structure, and the release of a portion of our valuation allowance on deferred tax assets of $2.9 billion." 26 | } 27 | ] 28 | ``` 29 | 30 | **Example #1 - generate from any text** 31 | 32 | Most flexible option. Generates dataset using a list of string `texts`. Colab code 33 | example [here](https://colab.research.google.com/gist/virattt/f9b5a0ae82cc0caab57df5dedc2927c9/intro-financial-datasets.ipynb). 34 | 35 | ```python 36 | from financial_datasets.generator import DatasetGenerator 37 | 38 | # Your list of texts 39 | texts = ... 40 | 41 | # Create dataset generator 42 | generator = DatasetGenerator(model="gpt-4-turbo", api_key="your-openai-key") 43 | 44 | # Generate dataset from texts 45 | dataset = generator.generate_from_texts( 46 | texts=texts, 47 | max_questions=100, 48 | ) 49 | ``` 50 | 51 | **Example #2 - generate from PDF** 52 | 53 | Generate a dataset using a PDF `url` only. Colab code 54 | example [here](https://colab.research.google.com/gist/virattt/b04442ee7c6c0d0bb3c9371af2283a20/intro-financial-datasets.ipynb). 55 | 56 | ```python 57 | from financial_datasets.generator import DatasetGenerator 58 | 59 | # Create dataset generator 60 | generator = DatasetGenerator(model="gpt-4-turbo", api_key="your-openai-key") 61 | 62 | # Generate dataset from PDF url 63 | dataset = generator.generate_from_pdf( 64 | url="https://www.berkshirehathaway.com/letters/2023ltr.pdf", 65 | max_questions=100, 66 | ) 67 | ``` 68 | 69 | **Example #3 - generate from 10-K** 70 | 71 | Generate a dataset using a `ticker` and `year`. Colab code 72 | example [here](https://colab.research.google.com/gist/virattt/743872e143034987d20e6a6c7bb9d0a1/intro-financial-datasets.ipynb). 73 | 74 | ```python 75 | from financial_datasets.generator import DatasetGenerator 76 | 77 | # Create dataset generator 78 | generator = DatasetGenerator(model="gpt-4-turbo", api_key="your-openai-key") 79 | 80 | # Generate dataset from 10-K 81 | dataset = generator.generate_from_10K( 82 | ticker="AAPL", 83 | year=2023, 84 | max_questions=100, 85 | item_names=["Item 1", "Item 7"], # optional - specify Item names to use 86 | ) 87 | ``` 88 | 89 | ## Installation 90 | 91 | ### Using pip 92 | 93 | You can install the Financial Datasets library using pip: 94 | 95 | ``` 96 | pip install financial-datasets 97 | ``` 98 | 99 | ### Using Poetry 100 | 101 | If you prefer to use Poetry for dependency management, you can add Financial Datasets to your project: 102 | 103 | ``` 104 | poetry add financial-datasets 105 | ``` 106 | 107 | ### From the Repository 108 | 109 | If you want to install the library directly from the repository, follow these steps: 110 | 111 | 1. Clone the repository: 112 | ``` 113 | git clone https://github.com/virattt/financial-datasets.git 114 | ``` 115 | 116 | 2. Navigate to the project directory: 117 | ``` 118 | cd financial-datasets 119 | ``` 120 | 121 | 3. Install the dependencies using Poetry: 122 | ``` 123 | poetry install 124 | ``` 125 | 126 | 4. You can now use the library in your Python projects. 127 | 128 | ## Contributing 129 | 130 | Contributions are welcome! If you find any issues or have suggestions for improvements, 131 | please open an issue or submit a pull request. 132 | 133 | ## License 134 | 135 | This project is licensed under the [MIT License](link-to-license-file). 136 | 137 | ## Contributors 138 | 139 | 140 | 141 | 142 | -------------------------------------------------------------------------------- /financial_datasets/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/virattt/financial-datasets/9856644262caab56cf174109924bba1d81330335/financial_datasets/__init__.py -------------------------------------------------------------------------------- /financial_datasets/dataset.py: -------------------------------------------------------------------------------- 1 | from typing import List 2 | 3 | from pydantic.main import BaseModel 4 | 5 | 6 | class DatasetItem(BaseModel): 7 | question: str 8 | answer: str 9 | context: str 10 | 11 | 12 | class Dataset(BaseModel): 13 | items: List[DatasetItem] 14 | -------------------------------------------------------------------------------- /financial_datasets/filings.py: -------------------------------------------------------------------------------- 1 | from datetime import datetime 2 | 3 | from enum import Enum 4 | 5 | 6 | class FilingItem(Enum): 7 | ITEM_1 = "Item 1" 8 | ITEM_1A = "Item 1A" 9 | ITEM_1B = "Item 1B" 10 | ITEM_2 = "Item 2" 11 | ITEM_3 = "Item 3" 12 | ITEM_4 = "Item 4" 13 | ITEM_5 = "Item 5" 14 | ITEM_6 = "Item 6" 15 | ITEM_7 = "Item 7" 16 | ITEM_7A = "Item 7A" 17 | ITEM_8 = "Item 8" 18 | ITEM_9 = "Item 9" 19 | ITEM_9A = "Item 9A" 20 | ITEM_9B = "Item 9B" 21 | ITEM_10 = "Item 10" 22 | ITEM_11 = "Item 11" 23 | ITEM_12 = "Item 12" 24 | ITEM_13 = "Item 13" 25 | ITEM_14 = "Item 14" 26 | ITEM_15 = "Item 15" 27 | ITEM_16 = "Item 16" 28 | 29 | 30 | def filter_filings(filings, form: str, year: int): 31 | """ 32 | Filter filings by form and year. 33 | 34 | :param filings: List of EntityFilings. 35 | :param form: The form to filter by. 36 | :param year: The year to filter by. 37 | :return: The filtered EntityFiling. 38 | """ 39 | for filing in filings: 40 | report_date_str = filing.report_date 41 | report_date = datetime.strptime(report_date_str, "%Y-%m-%d") 42 | if filing.form == form and report_date.year == year: 43 | return filing 44 | 45 | raise ValueError(f"No {form} filing found for the year {year}.") 46 | -------------------------------------------------------------------------------- /financial_datasets/generator.py: -------------------------------------------------------------------------------- 1 | import json 2 | import time 3 | from io import BytesIO 4 | from typing import List 5 | 6 | import requests 7 | from PyPDF2 import PdfReader 8 | from langchain_text_splitters import TokenTextSplitter 9 | from tqdm import tqdm 10 | 11 | from financial_datasets.dataset import DatasetItem, Dataset 12 | from financial_datasets.llm.openai import chat_completion_request 13 | from financial_datasets.parser import FilingParser 14 | from financial_datasets.prompts import default_prompt 15 | from financial_datasets.tools import generate_dataset 16 | 17 | default_sec_identity = "gary gary@financialdatasets.org" 18 | 19 | 20 | class DatasetGenerator: 21 | def __init__(self, model: str, api_key: str): 22 | # Ensure model begins with gpt- 23 | if not model.startswith('gpt-'): 24 | raise NotImplementedError(f'Model {model} is not supported yet.') 25 | 26 | if not api_key: 27 | raise ValueError("API key is required.") 28 | 29 | self._model = model 30 | 31 | def generate_from_texts( 32 | self, 33 | texts: List[str], 34 | max_questions=10, 35 | **kwargs, 36 | ) -> Dataset: 37 | """ 38 | Generate questions from a list of texts. 39 | 40 | :param texts: List of texts to generate questions from. 41 | :param max_questions: Maximum number of questions to generate. 42 | 43 | :return: Dataset containing the generated questions. 44 | """ 45 | 46 | # Get optional system prompt from kwargs 47 | system_prompt = kwargs.get("system_prompt", default_prompt) 48 | 49 | # Determine how many questions to generate per text 50 | num_texts = len(texts) 51 | questions_per_text = max_questions // num_texts 52 | 53 | # Keep track of remaining questions to generate 54 | num_remaining_questions = max_questions % num_texts 55 | 56 | # Keep track of usage 57 | input_tokens = 0 58 | output_tokens = 0 59 | 60 | progress_bar = tqdm(total=max_questions, desc="Generating questions", colour='green') 61 | 62 | # Generate dataset items 63 | items: List[DatasetItem] = [] 64 | for index, text in enumerate(texts): 65 | try: 66 | # Determine the number of questions to generate for the current text 67 | current_max_questions = questions_per_text 68 | if index < num_remaining_questions: 69 | current_max_questions += 1 70 | 71 | # Generate questions 72 | response = chat_completion_request( 73 | model=self._model, 74 | tools=generate_dataset(), 75 | tool_choice={"type": "function", "function": {"name": "generate_dataset"}}, 76 | messages=[ 77 | {"role": "system", "content": system_prompt}, 78 | {"role": "user", "content": f"Generate {current_max_questions} questions for the following block of text: {text}"} 79 | ], 80 | ) 81 | 82 | # Update usage 83 | usage = response.usage 84 | input_tokens += usage.prompt_tokens 85 | output_tokens += usage.completion_tokens 86 | 87 | # Parse tool call 88 | tool_call = response.choices[0].message.tool_calls[0] 89 | if tool_call: 90 | function_params = json.loads(tool_call.function.arguments) 91 | dataset_items = function_params.get("dataset_items") 92 | 93 | if not dataset_items: 94 | # Skip to the next text if no questions were generated 95 | continue 96 | 97 | # Convert the dataset_items (list of dicts) to list of DatasetItem 98 | dataset_items = [DatasetItem(**item) for item in dataset_items] 99 | 100 | # Add the generated items to our total list of questions 101 | items.extend(dataset_items) 102 | 103 | # Update the progress bar by the number of questions generated 104 | progress_bar.update(len(dataset_items)) 105 | 106 | # Stop generating questions if we have reached the maximum number of questions 107 | if len(items) >= max_questions: 108 | break 109 | 110 | except Exception as e: 111 | print(f"Failed to generate questions for batch {index + 1}: {e}") 112 | continue 113 | 114 | # Sleep for 1 second to avoid overloading the LLM 115 | time.sleep(1) 116 | 117 | # Ensure the progress bar is closed 118 | progress_bar.close() 119 | 120 | return Dataset( 121 | items=items[:max_questions], 122 | ) 123 | 124 | def generate_from_pdf( 125 | self, 126 | url: str, 127 | max_questions=10, 128 | **kwargs, 129 | ) -> Dataset: 130 | """ 131 | Generate questions from a PDF file. 132 | 133 | :param url: The URL of the PDF file. 134 | :param max_questions: Maximum number of questions to generate. 135 | :param kwargs: Additional arguments like chunk_size, chunk_overlap, etc. 136 | :return: Dataset containing the generated questions. 137 | """ 138 | # Download the PDF file 139 | response = requests.get(url) 140 | pdf_file = BytesIO(response.content) 141 | 142 | # Extract text from the PDF file 143 | reader = PdfReader(pdf_file) 144 | text = "" 145 | for page in reader.pages: 146 | text += page.extract_text() 147 | 148 | # Remove any newline characters 149 | text = text.replace("\n", " ") 150 | 151 | # Chunk the text to prevent exceeding the context window of models at the question generation step. 152 | chunk_size = kwargs.get("chunk_size", 1024) 153 | chunk_overlap = kwargs.get("chunk_overlap", 100) 154 | 155 | # Split by tokens 156 | token_splitter = TokenTextSplitter( 157 | chunk_size=chunk_size, 158 | chunk_overlap=chunk_overlap, 159 | ) 160 | 161 | # Chunk the text 162 | texts = token_splitter.split_text(text) 163 | 164 | return self.generate_from_texts(texts=texts, max_questions=max_questions, kwargs=kwargs) 165 | 166 | def generate_from_10K( 167 | self, 168 | ticker: str, 169 | year: int, 170 | max_questions=10, 171 | sec_identity=default_sec_identity, 172 | **kwargs, 173 | ) -> Dataset: 174 | """ 175 | Generate questions from a specific SEC filing for a given ticker. 176 | 177 | :param ticker: The stock ticker symbol. 178 | :param year: The year of the filing. 179 | :param max_questions: Maximum number of questions to generate. 180 | :param sec_identity: The identity to use when making requests to the SEC API. 181 | 182 | :return: Dataset containing the generated questions. 183 | """ 184 | 185 | # Get optional item names from kwargs 186 | item_names = kwargs.get("item_names", []) 187 | 188 | # Get the items from the 10-K 189 | filing_parser = FilingParser() 190 | items = filing_parser.get_10K_items(ticker, year, item_names, sec_identity) 191 | 192 | # Chunk Items to prevent exceeding the context window of models at the question generation step. 193 | chunk_size = kwargs.get("chunk_size", 1024) 194 | chunk_overlap = kwargs.get("chunk_overlap", 100) 195 | token_splitter = TokenTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) 196 | texts = [] # List to hold the chunked items 197 | for item in items: 198 | chunks = token_splitter.split_text(item) 199 | texts.extend(chunks) 200 | 201 | # Generate questions from the extracted text 202 | return self.generate_from_texts(texts=texts, max_questions=max_questions, kwargs=kwargs) 203 | 204 | def generate_from_10Q( 205 | self, 206 | ticker: str, 207 | year: int, 208 | quarter: int, 209 | max_questions=10, 210 | sec_identity=default_sec_identity, 211 | **kwargs, 212 | ) -> Dataset: 213 | """ 214 | Generate questions from a specific SEC filing for a given ticker. 215 | 216 | :param ticker: The stock ticker symbol. 217 | :param year: The year of the filing. 218 | :param quarter: The quarter of the filing. 219 | :param max_questions: Maximum number of questions to generate. 220 | :param sec_identity: The identity to use when making requests to the SEC API. 221 | 222 | :return: Dataset containing the generated questions. 223 | """ 224 | 225 | # Get optional item names from kwargs 226 | item_names = kwargs.get("item_names", []) 227 | 228 | # Get the items from the 10-Q 229 | filing_parser = FilingParser() 230 | items = filing_parser.get_10Q_items(ticker, year, quarter, item_names, sec_identity) 231 | 232 | # Chunk Items to prevent exceeding the context window of models at the question generation step. 233 | chunk_size = kwargs.get("chunk_size", 1024) 234 | chunk_overlap = kwargs.get("chunk_overlap", 100) 235 | token_splitter = TokenTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) 236 | texts = [] # List to hold the chunked items 237 | for item in items: 238 | chunks = token_splitter.split_text(item) 239 | texts.extend(chunks) 240 | 241 | # Generate questions from the extracted text 242 | return self.generate_from_texts(texts=texts, max_questions=max_questions, kwargs=kwargs) 243 | -------------------------------------------------------------------------------- /financial_datasets/llm/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/virattt/financial-datasets/9856644262caab56cf174109924bba1d81330335/financial_datasets/llm/__init__.py -------------------------------------------------------------------------------- /financial_datasets/llm/openai.py: -------------------------------------------------------------------------------- 1 | from openai import OpenAI 2 | from tenacity import retry, wait_random_exponential, stop_after_attempt 3 | 4 | client = OpenAI() 5 | 6 | 7 | @retry(wait=wait_random_exponential(multiplier=1, max=40), stop=stop_after_attempt(3)) 8 | def chat_completion_request(model: str, messages, tools=None, tool_choice=None): 9 | try: 10 | response = client.chat.completions.create( 11 | model=model, 12 | messages=messages, 13 | tools=tools, 14 | tool_choice=tool_choice, 15 | ) 16 | return response 17 | except Exception as e: 18 | print("Unable to generate ChatCompletion response") 19 | print(f"Exception: {e}") 20 | return e 21 | -------------------------------------------------------------------------------- /financial_datasets/parser.py: -------------------------------------------------------------------------------- 1 | import re 2 | from typing import List 3 | 4 | from edgar import Company, set_identity, get_filings 5 | 6 | from financial_datasets.filings import filter_filings, FilingItem 7 | 8 | default_sec_identity = "gary gary@financialdatasets.org" 9 | 10 | valid_item_names = set([item.value for item in FilingItem]) 11 | 12 | 13 | class FilingParser: 14 | 15 | def get_10K_items( 16 | self, 17 | ticker: str, 18 | year: int, 19 | item_names: List[str] = [], 20 | sec_identity=default_sec_identity, 21 | ) -> List[str]: 22 | """ 23 | Get the items from a 10-K filing for a given company and year. 24 | 25 | :param ticker: The stock ticker symbol. 26 | :param year: The year of the filing. 27 | :param item_names: List of Items to retrieve from the 10-K. 28 | :param sec_identity: The identity to use when making requests to the SEC API. 29 | 30 | :return: A list of items from the 10-K filing. 31 | """ 32 | 33 | # Ensure item_names are valid 34 | if any(item_name not in valid_item_names for item_name in item_names): 35 | raise ValueError(f"Item names may only be one of {sorted(valid_item_names)}.") 36 | 37 | # Tell the SEC who is making the request 38 | set_identity(sec_identity) 39 | 40 | if not ticker: 41 | raise ValueError("Ticker symbol is required.") 42 | 43 | if not year: 44 | raise ValueError("Year is required.") 45 | 46 | # Create a Company object 47 | company = Company(ticker) 48 | 49 | # Retrieve the SEC filing 50 | filings = company.get_filings(form="10-K") 51 | filing = filter_filings(filings, "10-K", year).obj() 52 | 53 | # Get the Item names in the filing 54 | filing_item_names = set([item_name for item_name in filing.items]) 55 | 56 | # Figure out which Items to return 57 | items_to_return = [] if item_names else filing.items 58 | for item_name in item_names: 59 | if item_name in filing_item_names: 60 | items_to_return.append(item_name) 61 | 62 | # Get the items 63 | items = [filing[item] for item in items_to_return] 64 | 65 | # Remove any newline characters and trim outer whitespace 66 | items = [item.replace("\n", " ").strip() for item in items] 67 | 68 | # Remove any sequence of 3 or more '-' or '.' characters 69 | pattern1 = r'-{3,}|\.{3,}' 70 | items = [re.sub(pattern1, '', item) for item in items] 71 | 72 | # Remove any sequence of 2 or more '+' characters 73 | pattern2 = r'\+{2,}' 74 | items = [re.sub(pattern2, '', item) for item in items] 75 | 76 | return items 77 | 78 | def get_10Q_items( 79 | self, 80 | ticker: str, 81 | year: int, 82 | quarter: int, 83 | item_names: List[str] = [], 84 | sec_identity=default_sec_identity, 85 | ) -> List[str]: 86 | """ 87 | Get the items from a 10-Q filing for a given company, year, and quarter. 88 | 89 | :param ticker: The stock ticker symbol. 90 | :param year: The year of the filing. 91 | :param quarter: The quarter of the filing. 92 | :param item_names: List of Items to retrieve from the 10-Q. 93 | :param sec_identity: The identity to use when making requests to the SEC API. 94 | 95 | :return: A list of items from the 10-Q filing. 96 | """ 97 | 98 | # Ensure item_names are valid 99 | if any(item_name not in valid_item_names for item_name in item_names): 100 | raise ValueError(f"Item names may only be one of {sorted(valid_item_names)}.") 101 | 102 | # Tell the SEC who is making the request 103 | set_identity(sec_identity) 104 | 105 | if not ticker: 106 | raise ValueError("Ticker symbol is required.") 107 | 108 | if not year: 109 | raise ValueError("Year is required.") 110 | 111 | # Get all 10-Q filings for the given year and quarter 112 | filings = get_filings(year, quarter, form="10-Q") 113 | 114 | # Create a Company object 115 | company = Company(ticker) 116 | 117 | # Filter the filings to find the one for the company 118 | filing = next((f for f in filings if f.cik == company.cik), None) 119 | if not filing: 120 | raise ValueError(f"No 10-Q filing found for {ticker} in {year} Q{quarter}.") 121 | 122 | # Get the filing entities for the company 123 | company_filings = company.get_filings(accession_number=filing.accession_number) 124 | if not company_filings: 125 | raise ValueError(f"No 10-Q filing found for {ticker} in {year} Q{quarter}.") 126 | 127 | # Get the exact 10-Q filing for the company 128 | company_filing = company_filings[0].obj() 129 | 130 | # Get the Item names in the filing 131 | filing_item_names = set([item_name for item_name in company_filing.items]) 132 | 133 | # Figure out which Items to return 134 | items_to_return = [] if item_names else company_filing.items 135 | for item_name in item_names: 136 | if item_name in filing_item_names: 137 | items_to_return.append(item_name) 138 | 139 | # Get the items 140 | items = [company_filing[item] for item in items_to_return] 141 | 142 | # Remove any newline characters and trim outer whitespace 143 | items = [item.replace("\n", " ").strip() for item in items] 144 | 145 | # Remove any sequence of 3 or more '-' or '.' characters 146 | pattern1 = r'-{3,}|\.{3,}' 147 | items = [re.sub(pattern1, '', item) for item in items] 148 | 149 | # Remove any sequence of 2 or more '+' characters 150 | pattern2 = r'\+{2,}' 151 | items = [re.sub(pattern2, '', item) for item in items] 152 | 153 | return items 154 | -------------------------------------------------------------------------------- /financial_datasets/prompts.py: -------------------------------------------------------------------------------- 1 | default_prompt = """ 2 | You are an expert at understanding financial documents and generating datasets. 3 | The types of texts include 10-Ks, 10-Qs, earnings call transcripts, PDFs, and other financial documents. 4 | Your task involves creating question and answer pairs that stand alone without reference to any specific documents. 5 | These questions and answers will be used independently in future applications such as LLM evaluation and fine-tuning, 6 | where no background document will be available. 7 | 8 | You must follow these rules: 9 | 10 | 1. Direct Derivation: Answers must be directly derived from the provided content without implying the existence of the text. 11 | 2. Self-contained Questions: Ensure that questions are fully answerable from the information given and do not imply that there is a larger document. 12 | 3. Clarity and Precision: Questions should be clear, precise, and not ambiguous. 13 | 4. Prohibited References: Explicitly avoid phrases like "according to the document", "in the text", "as mentioned in the document", or any implication of external texts. Do not construct questions that require knowledge of the document's structure or location of information within it. 14 | 5. Context Inclusion: Include the specific information from the content that supports the answer. The context should enable the answer to stand independently of any external text. 15 | 6. Sufficiency of Information: If the content lacks enough information to form a complete question-answer pair, do not force one. 16 | 7. Original Responses: Answers should be paraphrased in your own words; direct copying from the content is not allowed. 17 | 18 | Good generated examples: 19 | ``` 20 | [ 21 | { 22 | "question": "What was Airbnb's revenue in 2023?", 23 | "answer": "$9.9 billion", 24 | "context": "In 2023, revenue increased by 18% to $9.9 billion compared to 2022, primarily due to a 14% increase in Nights and Experiences Booked of 54.5 million combined with higher average daily rates driving a 16% increase in Gross Booking Value of $10.0 billion." 25 | }, 26 | { 27 | "question": "By what percentage did Airbnb's net income increase in 2023 compared to the prior year?", 28 | "answer": "153%", 29 | "context": "Net income in 2023 increased by 153% to $4.8 billion, compared to the prior year, driven by our revenue growth, increased interest income, discipline in managing our cost structure, and the release of a portion of our valuation allowance on deferred tax assets of $2.9 billion." 30 | } 31 | ] 32 | ``` 33 | 34 | Bad generated examples: 35 | ``` 36 | [ 37 | { 38 | "answer": "Part IV hereof refers to a specific section within the document that precedes the inclusion of the consolidated financial statements.", 39 | "context": "The term Part IV hereof in the document refers to the section that directly precedes the consolidated financial statements.", 40 | "question": "What does Part IV hereof refer to in the context of a document layout?", 41 | }, 42 | { 43 | "answer": "No, the consolidated financial statements are not presented directly within the body of the Annual Report on Form 10-K; they are incorporated by reference.", 44 | "context": "The consolidated financial statements are incorporated by reference in the Annual Report on Form 10-K, meaning they are not presented in full directly within the documents body.", 45 | "question": "Are the consolidated financial statements presented directly within the body of the Annual Report on form 10-K?" 46 | } 47 | ] 48 | ``` 49 | 50 | NEVER mention the words "document", "text", "layout", "filing", "text", "table" in your questions or answers. 51 | ALWAYS ensure all questions and answers are accurate, self-contained, and relevant without relying on or implying the existence of any original document or text 52 | while strictly avoiding any fabrication or speculation. 53 | """ -------------------------------------------------------------------------------- /financial_datasets/tools.py: -------------------------------------------------------------------------------- 1 | from typing import List 2 | 3 | 4 | def generate_dataset() -> List[dict]: 5 | return [ 6 | { 7 | "type": "function", 8 | "function": { 9 | "name": 'generate_dataset', 10 | "description": 'This function generates a list of dataset items. 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(>=0.2.1)"] 1975 | 1976 | [metadata] 1977 | lock-version = "2.0" 1978 | python-versions = "^3.10" 1979 | content-hash = "f455a26d0e7cd1353db0d841ca33008038fd67b7826c0c5c5d3720267216fc71" 1980 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.poetry] 2 | name = "financial-datasets" 3 | version = "0.1.18" 4 | description = "Financial datasets for LLMs" 5 | authors = ["Virat Singh "] 6 | license = "MIT" 7 | readme = "README.md" 8 | packages = [{ include = "financial_datasets"}] 9 | 10 | [tool.poetry.dependencies] 11 | python = "^3.10" 12 | openai = "^1.28.1" 13 | tiktoken = "^0.6.0" 14 | pypdf2 = "^3.0.1" 15 | langchain-text-splitters = "^0.0.1" 16 | edgartools = "2.21.1" 17 | pydantic = "2.7.1" 18 | python-dotenv = "^1.0.1" 19 | 20 | [tool.poetry.group.dev.dependencies] 21 | pytest = "^8.1.1" 22 | twine = "^5.0.0" 23 | setuptools = "^69.5.1" 24 | 25 | [build-system] 26 | requires = ["poetry-core"] 27 | build-backend = "poetry.core.masonry.api" 28 | -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/virattt/financial-datasets/9856644262caab56cf174109924bba1d81330335/tests/__init__.py -------------------------------------------------------------------------------- /tests/test_generator.py: -------------------------------------------------------------------------------- 1 | import os 2 | from unittest.mock import MagicMock 3 | 4 | import pytest 5 | from dotenv import load_dotenv 6 | 7 | from financial_datasets.dataset import Dataset, DatasetItem 8 | from financial_datasets.generator import DatasetGenerator 9 | 10 | # Load the environment variables from .env file 11 | load_dotenv() 12 | 13 | 14 | def test_create_generator() -> None: 15 | def create_openai_generator() -> DatasetGenerator: 16 | return DatasetGenerator( 17 | model="gpt-3.5-turbo-0125", 18 | api_key="fake_key" 19 | ) 20 | 21 | def create_anthropic_generator() -> DatasetGenerator: 22 | return DatasetGenerator( 23 | model="claude-3-haiku-20240307", 24 | api_key="fake_key" 25 | ) 26 | 27 | # Creating OpenAI generator should not raise an exception 28 | openai_generator = create_openai_generator() 29 | assert openai_generator is not None 30 | 31 | # Creating Anthropic generator should raise an exception 32 | with pytest.raises(NotImplementedError): 33 | create_anthropic_generator() 34 | 35 | 36 | def test_generate_from_texts() -> None: 37 | def create_openai_generator() -> DatasetGenerator: 38 | return DatasetGenerator( 39 | model="gpt-3.5-turbo-0125", 40 | api_key=os.environ.get('OPENAI_API_KEY') 41 | ) 42 | 43 | # Create OpenAI generator 44 | generator = create_openai_generator() 45 | 46 | # Define some texts from a 10-K 47 | texts = [ 48 | "Our business depends on Hosts maintaining their listings on our platform and engaging in practices that encourage guests to book those listings, including increasing the number of nights and experiences that are available to book, providing timely responses to inquiries from guests, offering a variety of desirable and differentiated listings at competitive prices that meet the expectations of guests, and offering exceptional hospitality, services, and experiences to guests", 49 | "In 2023, revenue increased by 18% to $9.9 billion compared to 2022, primarily due to a 14% increase in Nights and Experiences Booked of 54.5 million combined with higher average daily rates driving a 16% increase in Gross Booking Value of $10.0 billion. The growth in revenue demonstrated continued strong travel demand. On a constant-currency basis, revenue increased 17% in 2023 compared to 2022." 50 | ] 51 | 52 | # Generate questions 53 | dataset = generator.generate_from_texts(texts=texts, max_questions=10) 54 | items = dataset.items 55 | assert len(items) <= 10 56 | 57 | 58 | def test_generate_from_texts_with_mock(): 59 | # Create a mock response 60 | mock_response = Dataset( 61 | items=[ 62 | DatasetItem(question="Question 1", answer="Answer 1", context="Context 1"), 63 | DatasetItem(question="Question 2", answer="Answer 2", context="Context 2"), 64 | ] 65 | ) 66 | 67 | # Create a mock OpenAI client 68 | mock_client = MagicMock() 69 | mock_client.chat.completions.create.return_value = mock_response 70 | 71 | # Create a DatasetGenerator instance with the mock client 72 | dataset_generator = DatasetGenerator(model="gpt-3.5-turbo", api_key="mock_api_key") 73 | dataset_generator._client = mock_client 74 | 75 | # Define the input texts and max_questions 76 | texts = ["Text 1", "Text 2", "Text 3"] 77 | max_questions = 6 78 | 79 | # Call the generate_from_texts method 80 | dataset = dataset_generator.generate_from_texts(texts, max_questions) 81 | 82 | # Assert the length of items generated 83 | assert len(dataset.items) == max_questions 84 | 85 | # Assert the number of times the OpenAI client's create method was called 86 | assert mock_client.chat.completions.create.call_count <= len(texts) 87 | 88 | 89 | def test_generate_from_10K(): 90 | def create_openai_generator() -> DatasetGenerator: 91 | return DatasetGenerator( 92 | model="gpt-3.5-turbo-0125", 93 | api_key=os.environ.get('OPENAI_API_KEY'), 94 | ) 95 | 96 | # Create OpenAI generator 97 | generator = create_openai_generator() 98 | 99 | # Generate questions from the SEC filing 100 | dataset = generator.generate_from_10K( 101 | ticker="TSLA", 102 | year=2023, 103 | max_questions=100, 104 | chunk_size=2048, 105 | item_names=[ 106 | "Item 1", 107 | "Item 3", 108 | "Item 6", 109 | "Item 7A", 110 | "Item 8", 111 | ], 112 | ) 113 | assert len(dataset.items) <= 5 114 | 115 | 116 | def test_generate_from_10Q(): 117 | def create_openai_generator() -> DatasetGenerator: 118 | return DatasetGenerator( 119 | model="gpt-3.5-turbo-0125", 120 | api_key=os.environ.get('OPENAI_API_KEY') 121 | ) 122 | 123 | # Create OpenAI generator 124 | generator = create_openai_generator() 125 | 126 | # Generate questions from Costco's 2024 Q1 10-Q filing 127 | dataset = generator.generate_from_10Q( 128 | ticker="COST", 129 | year=2024, 130 | quarter=1, 131 | max_questions=5, 132 | ) 133 | 134 | assert len(dataset.items) <= 5 135 | 136 | 137 | def test_generate_from_pdf(): 138 | def create_openai_generator() -> DatasetGenerator: 139 | return DatasetGenerator( 140 | model="gpt-3.5-turbo-0125", 141 | api_key=os.environ.get('OPENAI_API_KEY') 142 | ) 143 | 144 | # Create OpenAI generator 145 | generator = create_openai_generator() 146 | 147 | # Generate questions from Berkshire's 2023 letter 148 | dataset = generator.generate_from_pdf( 149 | url="https://www.berkshirehathaway.com/letters/2023ltr.pdf", 150 | max_questions=5, 151 | ) 152 | assert len(dataset.items) <= 5 153 | -------------------------------------------------------------------------------- /tests/test_parser.py: -------------------------------------------------------------------------------- 1 | from financial_datasets.parser import FilingParser 2 | 3 | 4 | def test_get_10K_items(): 5 | # Given 6 | parser = FilingParser() 7 | 8 | # When 9 | items = parser.get_10K_items( 10 | ticker="SNOW", 11 | year=2023, 12 | ) 13 | 14 | # Then 15 | assert len(items) > 0 16 | assert items[0].startswith("ITEM 1.") 17 | 18 | 19 | def test_get_10K_items_with_items_param(): 20 | # Given 21 | parser = FilingParser() 22 | 23 | # When 24 | items = parser.get_10K_items( 25 | ticker="SNOW", 26 | year=2023, 27 | item_names=["Item 1A", "Item 2"], 28 | ) 29 | 30 | # Then 31 | assert len(items) == 2 32 | assert items[0].startswith("ITEM 1A") 33 | assert items[1].startswith("ITEM 2") 34 | 35 | 36 | def test_get_10Q_items(): 37 | # Given 38 | parser = FilingParser() 39 | 40 | # When 41 | items = parser.get_10Q_items( 42 | ticker="TSLA", 43 | year=2023, 44 | quarter=4, 45 | ) 46 | 47 | # Then 48 | assert len(items) > 0 49 | assert items[0].startswith("ITEM 1") 50 | 51 | 52 | def test_get_10Q_items_with_items_param(): 53 | # Given 54 | parser = FilingParser() 55 | 56 | # When 57 | items = parser.get_10Q_items( 58 | ticker="SNOW", 59 | year=2023, 60 | quarter=2, 61 | item_names=["Item 1A", "Item 2"], 62 | ) 63 | 64 | # Then 65 | assert len(items) == 2 66 | assert items[0].startswith("ITEM 1A") 67 | assert items[1].startswith("ITEM 2") 68 | --------------------------------------------------------------------------------