financegy 4.1.0

Unofficial Python library for accessing GSE (Guyana Stock Exchange) financial data

🏦 FinanceGY

FinanceGY is an unofficial Python library for accessing financial data from the Guyana Stock Exchange (GSE). It provides a simple and consistent interface for retrieving information on traded securities, recent trade data, and session details, all programmatically.

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Installation

pip install financegy

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Quick Start

import financegy

# --------------------------
# Core Data Retrieval
# --------------------------

# Get a list of all traded securities
securities = financegy.get_securities()

# Get active market securities (symbol + company name) from the most recent session page
active_securities = financegy.get_active_securities()

# Get the most recent trading session number
recent_session = financegy.get_recent_session()

# Get the full name of a security by its ticker symbol
security_name = financegy.get_security_by_symbol("DDL")

# Get the most recent trade data for a security
recent_trade = financegy.get_recent_trade("DDL")

# Get the most recent closing/last trade price (same most-recent session)
previous_close = financegy.get_previous_close("DDL")

# Get absolute price change vs previous session close
price_change = financegy.get_price_change("DDL")

# Get percent price change vs previous session close
price_change_percent = financegy.get_price_change_percent("DDL")

# Get all trade data for the most recent year (for the security)
recent_year_trades = financegy.get_security_recent_year("DDL")

# Get the earliest financial year available for the security
earliest_year = financegy.get_security_earliest_year("DDL")

# Get the latest financial year available for the security
latest_year = financegy.get_security_latest_year("DDL")

# Get the full trade history for a security (all years combined)
full_history = financegy.get_security_full_history("DDL")

# Get trade data for a specific trading session (all securities)
session_trades = financegy.get_session_trades("1136")

# Get trade data for a specific security in a session
security_session_trade = financegy.get_security_session_trade("DDL", "1136")

# Search for securities by name or symbol
search_results = financegy.search_securities("DDL")

# Get all trades for a specific year
year_trades = financegy.get_trades_for_year("DDL", "2019")

# Get historical trades within a date range - supports: yyyy / mm/yyyy / dd/mm/yyyy
historical_trades = financegy.get_historical_trades(
    symbol="DDL",
    start_date="01/06/2020",
    end_date="01/2022"
)

# --------------------------
# Analytics / Calculations
# --------------------------

# Get the latest session info (dict returned from most recent trade)
latest_session = financegy.get_latest_session_for_symbol("DDL")

# Average last traded price over a session range (inclusive)
avg_price_range = financegy.get_sessions_average_price("DDL", "1100", "1136")

# Average last traded price over the last N sessions (ending at latest session)
avg_price_latest = financegy.get_average_price("DDL", 30)

# Volatility over the last N sessions (weekly log-return volatility + annualized)
volatility = financegy.get_sessions_volatility("DDL", 30)

# Year-to-date high and low traded prices
ytd_high_low = financegy.get_ytd_high_low("DDL")

# --------------------------
# Portfolio / Position Calculations
# --------------------------

# Calculate the current market value of a position
position_value = financegy.calculate_position_value("DDL", shares=50)

# Calculate unrealized gain or loss for a position
position_return = financegy.calculate_position_return(
    symbol="DDL",
    shares=50,
    purchase_price=250
)

# Calculate percentage return for a position
position_return_percent = financegy.calculate_position_return_percent(
    symbol="DDL",
    shares=50,
    purchase_price=250
)

# Portfolio-level summary
portfolio = [
    {"symbol": "DTC", "shares": 100, "purchase_price": 300},
    {"symbol": "DDL", "shares": 50, "purchase_price": 250},
]

portfolio_summary = financegy.calculate_portfolio_summary(portfolio)

# --------------------------
# Utilities
# --------------------------

# Convert results to a DataFrame
df = financegy.to_dataframe(securities)

# Export to CSV / Excel
financegy.save_to_csv(securities, filename="securities.csv", silent=True)
financegy.save_to_excel(securities, filename="securities.xlsx", silent=True)

# Clear FinanceGY cache directory
financegy.clear_cache(silent=True)

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API Reference

Core Data Retrieval

Function	Description
get_securities()	Returns all currently traded securities on the GSE.
get_active_securities()	Returns active securities (symbol and company name) from the most recent session.
get_recent_session()	Returns the most recent trading session number.
get_security_by_symbol(symbol)	Returns the full security name for a ticker symbol.
get_recent_trade(symbol)	Returns the most recent trade information for the given security.
get_security_recent_year(symbol)	Returns all trade data for the most recent year available for the selected security.
get_security_earliest_year(symbol)	Returns the earliest financial year available for the selected security.
get_security_latest_year(symbol)	Returns the latest financial year available for the selected security.
get_security_full_history(symbol)	Returns the full trade history for the selected security across all available years.
get_session_trades(session)	Returns trade data for all securities during a specific trading session.
get_security_session_trade(symbol, session)	Returns trade data for a specific security during a specific session.
search_securities(query)	Searches securities whose names or ticker symbols match the given query.
get_trades_for_year(symbol, year)	Returns all trade records for a specific security during a given year.
get_historical_trades(symbol, start_date, end_date)	Returns historical trades within the specified date range.

Analytics / Calculation Functions

Function	Description
get_previous_close(symbol)	Returns the most recent closing/last trade price.
get_price_change(symbol)	Returns absolute price difference vs previous session close.
get_price_change_percent(symbol)	Returns percent price change vs previous session close.
get_latest_session_for_symbol(symbol)	Returns the latest trade dict for the symbol.
get_sessions_average_price(symbol, session_start, session_end)	Returns the average last traded price over a session range.
get_average_price(symbol, session_number)	Returns the average last traded price over the last N sessions.
get_sessions_volatility(symbol, session_number)	Returns volatility over the last N sessions.
get_ytd_high_low(symbol)	Returns year-to-date highest and lowest traded prices.

Portfolio / Position Functions

Function	Description
calculate_position_value(symbol, shares)	Calculates the current market value of a position using the latest trade price.
calculate_position_return(symbol, shares, purchase_price)	Calculates the unrealized gain or loss for a position.
calculate_position_return_percent(symbol, shares, purchase_price)	Calculates the percentage return for a position.
calculate_portfolio_summary(positions)	Computes a full portfolio summary including totals and per-position breakdown.

Utilities

Function	Description
to_dataframe(data)	Converts FinanceGY list/dict results into a pandas DataFrame.
save_to_csv(data, filename="output.csv", path=None, silent=False)	Saves data to a CSV file.
save_to_excel(data, filename="output.xlsx", path=None, silent=False)	Saves data to an Excel file.
clear_cache(silent=False)	Completely clears the FinanceGY cache directory.

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Caching System

FinanceGY includes a lightweight local caching system designed to speed up repeated requests and reduce unnecessary calls.

Whenever you call a data retrieval function (such as get_securities() or get_recent_trade()), FinanceGY automatically checks whether a cached response already exists for that specific query:

• If a valid cache file (less than 3 days old) is found, the result is returned instantly from the cache.
• If the cache is missing, disabled, or older than one week, FinanceGY fetches fresh data from the GSE and updates the cache automatically.

All cache files are stored in a local cache/ directory as small JSON files containing the retrieved data and a timestamp.

You can manually clear all cached data at any time:

import financegy

financegy.clear_cache()

This will delete all cached files and force the next data request to fetch fresh data directly from the source.

If you prefer to bypass the cache for a specific call, simply pass use_cache=False to any function. For example:

# Force a fresh fetch from the GSE, ignoring cached data
recent_trade = financegy.get_recent_trade("DDL", use_cache=False)

By default, caching is enabled for all supported functions unless explicitly turned off.

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License

This project is licensed under the MIT License

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Example Use Case

import financegy

ddl_recent = financegy.get_security_recent("DDL")
print(ddl_recent)