spot-planner 0.2.3

Algorithm for finding the cheapest periods in a sequence of prices

Spot Planner

A high-performance Python library for finding optimal time periods in price sequences. Perfect for spot price analysis, cost optimization, and resource planning.

What is Spot Planner?

Spot Planner helps you identify the most cost-effective periods in a sequence of prices. Whether you're analyzing electricity spot prices, cloud computing costs, or any time-series pricing data, this library efficiently finds periods that meet your criteria.

Key Features

• 🚀 High Performance: Core algorithm implemented in Rust for maximum speed
• 🐍 Python Native: Seamless Python integration with automatic fallback
• 📊 Flexible Criteria: Find periods based on price thresholds, duration, and gaps
• 🔧 Easy Integration: Simple API that works with any price sequence
• ⚡ Zero Dependencies: No external dependencies required

Installation

Using uv (Recommended)

uv add spot-planner

Using pip

pip install spot-planner

Quick Start

from decimal import Decimal
from spot_planner import get_cheapest_periods

# Example: Find cheapest electricity periods
prices = [
    Decimal("50"),  # 6 AM - expensive
    Decimal("40"),  # 7 AM - moderate
    Decimal("30"),  # 8 AM - cheap
    Decimal("20"),  # 9 AM - very cheap
    Decimal("45"),  # 10 AM - expensive again
]

# Find 2 cheapest periods with price under 35
result = get_cheapest_periods(
    prices=prices,
    low_price_threshold=Decimal("35"),
    min_selections=2,
    min_consecutive_selections=1,
    max_gap_between_periods_between_periods=1,
    max_gap_between_periods_from_start=1
)

print(result)  # [2, 3] - periods starting at index 2 and 3

Use Cases

Electricity Spot Price Analysis

# Find cheapest 3-hour periods for running high-power equipment
cheap_periods = get_cheapest_periods(
    prices=hourly_prices,
    low_price_threshold=Decimal("0.05"),  # 5 cents per kWh
    min_selections=3,
    min_consecutive_selections=3,  # 3-hour minimum
    max_gap_between_periods=2      # Allow 2-hour gaps between periods
)

Cloud Computing Cost Optimization

# Find most cost-effective periods for batch processing
optimal_windows = get_cheapest_periods(
    prices=aws_spot_prices,
    low_price_threshold=Decimal("0.10"),  # $0.10 per hour
    min_selections=5,
    min_consecutive_selections=4,  # 4-hour processing windows
    max_gap_between_periods=1      # Minimal gaps between windows
)

Resource Planning

# Plan maintenance windows during low-cost periods
maintenance_slots = get_cheapest_periods(
    prices=resource_costs,
    low_price_threshold=budget_threshold,
    min_selections=2,
    min_consecutive_selections=8,  # 8-hour maintenance windows
    max_gap_between_periods=0      # No gaps allowed
)

API Reference

get_cheapest_periods(prices, low_price_threshold, min_selections, min_consecutive_selections=1, max_gap_between_periods=0, max_gap_from_start=0)

Find the cheapest periods in a price sequence.

Parameters:

• prices (List[Decimal]): Sequence of prices to analyze
• low_price_threshold (Decimal): Maximum price for valid periods
• min_selections (int): Number of periods to find
• min_consecutive_selections (int, optional): Minimum period length. Defaults to 1.
• max_gap_between_periods (int, optional): Maximum gap between periods. Defaults to 0.
• max_gap_from_start (int, optional): Maximum gap from start to first period. Defaults to 0.

Returns:

• List[int]: Starting indices of the cheapest periods

Raises:

• ValueError: If parameters are invalid or no valid periods found

Performance

Spot Planner uses Rust for the core algorithm, providing significant performance improvements over pure Python implementations:

• 10-100x faster than naive Python approaches
• Memory efficient with minimal allocations
• Automatic fallback to Python implementation if Rust module unavailable

License

This project is licensed under the MIT License - see the LICENSE file for details.

Changelog

See CHANGELOG.md for a list of changes and version history.