Electricity Trading Simulation Framework

A Python framework for simulating and analyzing electricity intraday trading strategies, developed for academic research on market dynamics and trading algorithm performance. It's designed to be simple and extensible - no need for detailed order book data or complex market models to get started.

Overview

This framework enables researchers and practitioners to:

• Simulate electricity intraday markets with customizable clearing mechanisms
• Test trading strategies under these clearing mechanisms
• Analyze performance metrics including fill rates, execution prices, and time-to-fill
• Compare different market designs and their impact on trading outcomes
• Validate theoretical models with empirical data integration

Key Features

• Strategy Framework: Extensible base class for implementing custom trading algorithms
• Clearing Mechanisms: Pluggable market clearing implementations (continuous, batch, reference-based)
• Data Integration: Support for real market data (day-ahead prices, intraday trades)
• Metrics: Performance analysis and visualization

Installation

# Clone the repository
git clone https://github.com/yourusername/electricity-trading-sim.git
cd electricity-trading-sim

# Install dependencies
pip install -e .

Quick Start

Basic Simulation

import datetime
from src.sim import TradingSimulation
from src.strategy import Strategy
from src.clearing import ClearingMechanism

# Define a simple strategy
class MyStrategy(Strategy):
    def update_orders(self, current_time):
        # Your trading logic here
        return new_orders, updated_orders, canceled_orders

# Define a clearing mechanism
class MyClearing(ClearingMechanism):
    def clear(self, current_time, active_orders):
        # Your market clearing logic here
        return filled_orders

# Run simulation
sim = TradingSimulation(
    start_time=datetime.datetime(2023, 1, 1, 10, 0),
    end_time=datetime.datetime(2023, 1, 1, 16, 0),
    time_step=datetime.timedelta(minutes=15),
    strategies=[MyStrategy()],
    clearing_mechanism=MyClearing()
)

results = sim.run()

Performance Analysis

from src.metrics import SimulationMetrics
from src.plot import SimulationVisualizer

# Calculate metrics
metrics = SimulationMetrics(results['order_history'])
performance = metrics.run_all()

# Visualize results
visualizer = SimulationVisualizer(results)
visualizer.plot_strategy_metrics(performance)

Case Studies

The framework includes two case studies demonstrating how the framework can be utilized:

Case Study 1: Using Publicly Available Market Data

• Objective: Compare different clearing mechanisms' impact on trading outcomes
• Data Sources: Day-ahead prices from SMARD and intraday prices from NordPool.
• Strategies: Urgency-based trading with time-dependent price adjustments
• Clearing: Day-ahead reference, intraday VWAP, and time-horizon based clearing.
• Results: Analysis of key metrics like execution prices, time to fill and fill rates.

Case Study 2: Using Synthetic Trade-level Data

• Objective: Evaluate different trading strategy performance under trade level data
• Strategies: Urgency-based vs. forecast-based approaches
• Data: Synthetically generated trade-level data Clearing: Hourly Vwap-based clearing and volume-based clearing
• Results: Analysis of key metrics like execution prices, time to fill and fill rates.

Contributing

This framework is designed for research. Contributions are welcome for:

• New trading strategies
• Additional clearing mechanisms
• Performance metrics
• Data source integrations
• Documentation improvements