MANIFEST.in	- MANIFEST.in within the d1_library directory ensures the inclusion of all SQL scripts located in the sql_tools/sql_scripts subdirectory into the distribution package - This is crucial for the project's functionality as it guarantees the necessary SQL scripts are packaged and available for use in the application's database operations.
pyproject.toml	- The 'pyproject.toml' in the 'd1_library' directory outlines the build system and project metadata for the D-one Trading Platform Library - It specifies the required Python version, build dependencies, and the backend used for building - Additionally, it lists the project's dependencies, ensuring the correct packages are installed for successful operation.
setup.py	- The 'setup.py' in the 'd1_library' directory configures the D-one Trading Platform Library - It specifies the library's metadata, required Python version, dependencies, and included package data - This setup file is crucial for the library's distribution and installation, ensuring the correct environment for the trading platform's operation.

d1_library

requirements.txt

- The requirements.txt in the d1_library directory specifies the necessary libraries for the project - It includes trading APIs, data processing tools, database utilities, and other utilities - These libraries enable the project to interact with trading platforms, process data, manage databases, and perform other utility functions.

strategies

portfolio.py	- OptimizationPortfolio in portfolio.py manages the optimization of a financial portfolio - It calculates the efficient frontier, allocates assets discretely based on the latest prices, and provides the new portfolio - It also determines which stocks to buy or sell - This is integral to the project's financial analysis and investment strategy formulation.
momentum.py	- Momentum.py, located in the d1_library/strategies directory, is a crucial component of the project - It calculates the momentum score of financial time series data, filters data by date, and identifies top performing symbols based on momentum - This module aids in the selection of optimal portfolio components within the broader codebase architecture.

stock_parser

fmp_fetcher.py	- FMPFetcher, located in the d1_library/stock_parser directory, serves as a data fetcher and parser for stock market data from the Financial Modeling Prep (FMP) API - It retrieves historical and daily stock data, handles rate limits, and prepares the data for database insertion.
stock_list_fetcher.py	- StockListFetcher, located in the d1_library/stock_parser directory, fetches and parses information about companies traded on 'NYSE', 'NASDAQ', 'AMEX' markets - It retrieves data from the NASDAQ API, processes it into a pandas DataFrame, and filters it based on selected columns, providing a streamlined dataset for further analysis.
rapid_fetcher.py	- Rapid_fetcher.py, located in the stock_parser directory, serves as a data fetcher from the Alpha Vantage API - It retrieves daily adjusted stock data for specified symbols, including opening, closing, high, and low prices, volume, dividends, and split coefficients - The data is returned as a Pandas DataFrame, supporting the project's data analysis and visualization tasks.
tdameritrade_fetcher.py	- TdameritradeFetcher, located in the d1_library, interacts with the TD Ameritrade API to fetch stock market data - It checks if the market is open, retrieves daily data for specified symbols, and organizes the data into a pandas DataFrame - The module dynamically loads credentials from a specified secrets file.
yahoo_fetcher.py	- YahooFinanceFetcher, located in the stock_parser directory, serves as a market status checker for a specific stock symbol - It fetches the trading history of the symbol from Yahoo Finance and determines whether the market is open based on the latest trading date - This functionality is integral to the overall project's ability to interact with real-time stock market data.

sql_tools

db_fetcher.py	- DatabaseFetcher in db_fetcher.py establishes a connection to a PostgreSQL database using provided credentials - It executes SQL scripts to fetch daily data, specific daily data, exchange IDs, vendor IDs, symbol IDs, and actual symbols - It also provides functionality to close the database connection.
db_insert.py	- DatabaseInsert in db_insert.py facilitates data loading into a database - It dynamically loads secrets for database credentials, constructs a database engine URL, and executes SQL queries to insert data - It also handles the insertion of symbols and daily data, with error handling and connection disposal included.

sql_scripts

daily_spec_data.sql	- The daily_spec_data.sql script within the SQL tools of the D1 library retrieves specific daily data for a given symbol within a specified date range - It forms a crucial part of the project's data extraction and manipulation process, enabling targeted data analysis and decision-making.
exchange_id.sql	- The 'exchange_id.sql' script, located in the 'sql_tools/sql_scripts' directory of the 'd1_library', retrieves the unique identifier of a specific exchange from the 'exchange' table in the database - This is achieved by matching the provided abbreviation, thereby facilitating the identification and management of different exchanges within the system.
symbols.sql	- In the context of the entire codebase architecture, the script located at d1_library/d1_library/sql_tools/sql_scripts/symbols.sql serves to extract unique symbols from a database - It specifically retrieves symbols that have been updated since a specified date, ensuring the most recent data is always utilized.
insert_symbols.sql	- The insert_symbols.sql script in the d1_library/sql_tools/sql_scripts directory updates the 'symbols' table in the database - It populates the table with data from a temporary table, handling any conflicts by updating existing records - The script also removes the temporary table after the operation.
vendor_id.sql	- Vendor_id.sql within the sql_scripts directory of the d1_library retrieves the unique identifier of a specific data vendor from the data_vendor table - This operation is crucial for linking vendor-specific data across the entire codebase, ensuring accurate data tracking and management.
symbol_id.sql	- Symbol_id.sql, located in the sql_scripts subdirectory of the d1_library, retrieves the id and ticker from the symbols table - This operation is integral to the project's overall functionality, enabling the identification and tracking of different symbols within the system.
daily_data.sql	- Daily_data.sql, located in the sql_scripts directory under the sql_tools of the d1_library, retrieves the closing price of a specific symbol between two dates - This script plays a crucial role in the codebase architecture by providing time-bound financial data for further analysis or processing.
insert_daily_data.sql	- The SQL script in the d1_library/sql_tools/sql_scripts directory serves to update the daily_data table with new stock market data - It pulls this data from a temporary table, handles any conflicts with existing entries based on a unique key, and then removes the temporary table.

telegram_bot

notificationsender.py

- Notificationsender.py, located in the telegram_bot directory of the d1_library, is responsible for interacting with the Telegram API - It retrieves updates from a specific bot using a unique token, enabling the system to receive and process incoming messages from the Telegram platform.

terminal

ibkr.py	- IBAPI, located in the terminal directory, serves as an interface to the Interactive Brokers (IB) trading platform - It establishes a connection, handles requests, and manages trading operations such as retrieving account details, positions, portfolio, and news, as well as submitting orders - It leverages the ib_insync library to interact with the IB's API, ensuring seamless trading operations.
alpaca.py	- AlpacaTrader, located in the terminal directory of the d1_library, interfaces with the Alpaca Trade API - It initializes the API, retrieves position data, and submits orders - The class also validates order side input and optionally includes average entry price in position data - This component plays a crucial role in managing and executing trades within the larger codebase.