stacking-sats-pipeline 0.4.0

Hypertrial's Stacking Sats Library - Optimized Bitcoin DCA

Stacking Sats Pipeline

A data engineering pipeline for extracting, loading, and merging cryptocurrency and financial data from multiple sources.

Requirements

• Python 3.11 or 3.12
• pip

Installation

pip install stacking-sats-pipeline

Quick Start

Data Extraction

Extract all data sources to local files for offline analysis:

CLI Usage

# Extract all data to CSV format
stacking-sats --extract-data csv

# Extract all data to Parquet format (smaller files, better compression)
stacking-sats --extract-data parquet

# Extract to specific directory
stacking-sats --extract-data csv --output-dir data/
stacking-sats --extract-data parquet -o exports/

Python API

from stacking_sats_pipeline import extract_all_data

# Extract all data to CSV in current directory
extract_all_data("csv")

# Extract all data to Parquet in specific directory
extract_all_data("parquet", "data/exports/")

Data Loading

from stacking_sats_pipeline import load_data

# Load Bitcoin price data
df = load_data()

# Load specific data source
from stacking_sats_pipeline.data import CoinMetricsLoader
loader = CoinMetricsLoader()
btc_data = loader.load_from_web()

What gets extracted:

• 📈 Bitcoin Price Data (CoinMetrics) → btc_coinmetrics.csv/parquet
• 😨 Fear & Greed Index (Alternative.me) → fear_greed.csv/parquet
• 💵 U.S. Dollar Index (FRED) → dxy_fred.csv/parquet*

*Requires FRED_API_KEY environment variable. Get a free key at FRED API

File Format Benefits:

• CSV: Human-readable, universally compatible
• Parquet: ~50% smaller files, faster loading, preserves data types

Multi-Source Data Loading

from stacking_sats_pipeline.data import MultiSourceDataLoader

# Load and merge data from all available sources
loader = MultiSourceDataLoader()
available_sources = loader.get_available_sources()
merged_df = loader.load_and_merge(available_sources)

# Available sources: coinmetrics, feargreed, fred (if API key available)
print(f"Available data sources: {available_sources}")
print(f"Merged data shape: {merged_df.shape}")

Data Sources

CoinMetrics (Bitcoin Price Data)

from stacking_sats_pipeline.data import CoinMetricsLoader

loader = CoinMetricsLoader(data_dir="data/")
df = loader.load_from_web()  # Fetch latest data
df = loader.load_from_file()  # Load cached data (fetches if missing)

# Extract to files
csv_path = loader.extract_to_csv()
parquet_path = loader.extract_to_parquet()

Fear & Greed Index

from stacking_sats_pipeline.data import FearGreedLoader

loader = FearGreedLoader(data_dir="data/")
df = loader.load_from_web()

FRED (Federal Reserve Economic Data)

import os
os.environ['FRED_API_KEY'] = 'your_api_key_here'

from stacking_sats_pipeline.data import FREDLoader

loader = FREDLoader(data_dir="data/")
df = loader.load_from_web()  # DXY (Dollar Index) data

Development

For development and testing:

Requirements: Python 3.11 or 3.12

# Clone the repository
git clone https://github.com/hypertrial/stacking_sats_pipeline.git
cd stacking_sats_pipeline

# Set up development environment (installs dependencies + pre-commit hooks)
make setup-dev

# OR manually:
pip install -e ".[dev]"
pre-commit install

# Run tests
make test
# OR: pytest

# Code quality (MANDATORY - CI will fail if not clean)
make lint          # Fix linting issues
make format        # Format code
make check         # Check without fixing (CI-style)

# Run specific test categories
pytest -m "not integration"  # Skip integration tests
pytest -m integration        # Run only integration tests

Code Quality Standards

⚠️ MANDATORY: All code must pass ruff linting and formatting checks.

• Linting/Formatting: We use ruff for both linting and code formatting
• Pre-commit hooks: Automatically run on every commit to catch issues early
• CI enforcement: Pull requests will fail if code doesn't meet standards

Quick commands:

make help          # Show all available commands
make lint          # Fix ALL issues (autopep8 + ruff + format)
make autopep8      # Fix line length issues specifically
make format        # Format code with ruff only
make format-all    # Comprehensive formatting (autopep8 + ruff)
make check         # Check code quality (what CI runs)

For detailed testing documentation, see TESTS.md.

Contributing Data Sources

The data loading system is designed to be modular and extensible. To add new data sources (exchanges, APIs, etc.), see the Data Loader Contribution Guide which provides step-by-step instructions for implementing new data loaders.

Command Line Options

# Extract data
stacking-sats --extract-data csv --output-dir data/
stacking-sats --extract-data parquet -o exports/

# Show help
stacking-sats --help

Project Structure

├── stacking_sats_pipeline/
│   ├── main.py                    # Pipeline orchestrator and CLI
│   ├── config.py                  # Configuration constants
│   ├── data/                      # Modular data loading system
│   │   ├── coinmetrics_loader.py  # CoinMetrics data source
│   │   ├── fear_greed_loader.py   # Fear & Greed Index data source
│   │   ├── fred_loader.py         # FRED economic data source
│   │   ├── data_loader.py         # Multi-source data loader
│   │   └── CONTRIBUTE.md          # Guide for adding data sources
│   └── __init__.py                # Package exports
├── tutorials/examples.py          # Interactive examples
└── tests/                         # Comprehensive test suite

API Reference

Core Functions

from stacking_sats_pipeline import (
    extract_all_data,           # Extract all data sources to files
    load_data,                  # Load Bitcoin price data
    validate_price_data,        # Validate price data quality
    extract_btc_data_to_csv,    # Extract Bitcoin data to CSV
    extract_btc_data_to_parquet # Extract Bitcoin data to Parquet
)

Configuration Constants

from stacking_sats_pipeline import (
    BACKTEST_START,    # Default start date for data range
    BACKTEST_END,      # Default end date for data range
    CYCLE_YEARS,       # Default cycle period
    MIN_WEIGHT,        # Minimum weight threshold
    PURCHASE_FREQ      # Default purchase frequency
)

Data Validation

All data sources include built-in validation:

from stacking_sats_pipeline import validate_price_data

# Validate Bitcoin price data
df = load_data()
is_valid = validate_price_data(df)

# Custom validation with specific requirements
requirements = {
    'required_columns': ['PriceUSD', 'Volume'],
    'min_price': 100,
    'max_price': 1000000
}
is_valid = validate_price_data(df, **requirements)

File Format Support

The pipeline supports both CSV and Parquet formats:

• CSV: Universal compatibility, human-readable
• Parquet: Better compression (~50% smaller), faster loading, preserves data types

# CSV format
extract_all_data("csv", "output_dir/")

# Parquet format
extract_all_data("parquet", "output_dir/")

Timestamp Handling

All data sources normalize timestamps to midnight UTC for consistent merging:

loader = MultiSourceDataLoader()
merged_df = loader.load_and_merge(['coinmetrics', 'fred'])

# All timestamps are normalized to 00:00:00 UTC
print(merged_df.index.tz)  # UTC
print(merged_df.index.time[0])  # 00:00:00

Error Handling

The pipeline includes comprehensive error handling:

try:
    df = extract_all_data("csv")
except Exception as e:
    print(f"Data extraction failed: {e}")
    # Partial extraction may have succeeded

Individual data sources fail gracefully - if one source is unavailable, others will still be extracted.