finfetcher 0.1.0

A robust wrapper around yfinance for market-aware financial data fetching.

FinFetcher

Quick Summary: A robust Python library for fetching clean, market-aware historical financial data. It acts as a smart wrapper around yfinance, automatically handling asset-specific market hours and timezones to ensure data integrity for quantitative modeling and backtesting.

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📊 The Problem it Solves

When fetching daily data during active market hours, standard tools often return an "unfinished" candle for the current day. Using this incomplete data point can introduce significant look-ahead bias or noise in statistical models.

FinFetcher solves this by:

1. Identifying the asset's exchange timezone (e.g., NYSE vs. Tokyo SE vs. Crypto).
2. Checking the exact market status relative to the server time.
3. Automatically filtering out the current day's row if the market hasn't closed yet.
4. Calculating the valid target_date (next trading day) for forecasting targets.

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🏆 Key Capabilities

Component	Feature	Details
Smart Ingestion	Market-aware cleaning.	Removes incomplete daily candles based on precise closing times (e.g., 16:20 ET for US Equities to account for delay).
Multi-Asset	Native handling for various types.	EQUITY (Stocks/ETFs), CRYPTOCURRENCY (24/7), FUTURES, FOREX, INDEX.
Forecasting Prep	Target Date Calculation.	Automatically computes the next valid business day (or calendar day for Crypto) for predictive labeling.
Resiliency	Robust Error Handling.	Wraps yfinance with retry logic and custom exceptions for better pipeline stability.

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📦 Installation

pip install finfetcher

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🚀 Usage

Basic Example

from finfetcher import DataFetcher

# Initialize for an asset (Equity)
fetcher = DataFetcher("AAPL")

# Fetch data (default: period="4y", interval="1d")
# This returns a pandas DataFrame with Date index
df = fetcher.get_data(period="1mo")

print(f"Data shape: {df.shape}")
print(f"Last available close: {df.index[-1]}")

# Access the calculated target date (next trading day)
print(f"Prediction Target Date: {fetcher.target_date}")

Handling Cryptocurrencies

Crypto markets never close, so the logic adjusts to use a 23:59 UTC cutoff.

crypto_fetcher = DataFetcher("BTC-USD")
df = crypto_fetcher.get_data(period="5d")

# Target date will be tomorrow (calendar day), not business day
print(f"Next Target: {crypto_fetcher.target_date}")

Custom Market Configuration

You can override default market hours or add new asset types by passing a custom_cutoffs dictionary.

from finfetcher import DataFetcher

# Example: Change US Equity close to 13:00 (e.g. half-day)
custom_config = {
    "EQUITY": {
        "timezones": {
            "America/New_York": {"hour": 13, "minute": 0}
        }
    }
}

fetcher = DataFetcher("AAPL", custom_cutoffs=custom_config)
df = fetcher.get_data()

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🛠️ Logic Details

The library contains a set of configuration of market closing times (src/finfetcher/config.py) to handle timezone conversions accurately.

• US Equities: Closes at 16:00 ET (buffered to 16:20 to handle API delays).
• European Markets: Handled via specific timezones (London, Paris, Frankfurt, etc.).
• Asian Markets: Tokyo, Hong Kong, Singapore, etc.
• Crypto: UTC based cutoff.

💻 Tech Stack

• Python 3.10+
• Pandas & NumPy
• yfinance
• pytz