simulor 0.2.0b1

Event-driven backtesting framework with institutional-grade analytics

Simulor

Simulor is a sophisticated backtesting framework designed for quantitative traders and researchers who demand institutional-grade realism and performance. Built on an event-driven architecture with pluggable components, it seamlessly transitions from historical backtesting to paper trading and live execution.

⚠️ Active Development: Simulor is currently in beta. APIs may change before v1.0. We welcome feedback and contributions!

Key Features

• Event-Driven Architecture: True event-driven simulation with point-in-time data delivery, multiple resolutions (tick/minute/hour/daily), and compositional data structures
• Pluggable Strategy Components: Modular alpha models, portfolio construction, risk management, execution models, and universe selection. Swap components without rewriting strategy logic
• Institutional-Grade Execution: Realistic fill models, transaction costs (commissions, spreads, slippage, market impact), T+0 settlement, and corporate actions handling
• Comprehensive Analytics: Performance metrics (Sharpe, Sortino, Calmar), risk analysis (VaR, CVaR, beta), trade statistics, and interactive Plotly visualizations
• Environment Parity: Same strategy code runs in backtest, paper trading, and live execution modes. Seamless transition from research to production

Installation

pip install simulor

Quick Start

from decimal import Decimal
from pathlib import Path
from simulor import (
    Strategy, Engine, Fund,
    MovingAverageCrossover, EqualWeight, PositionLimit, Immediate, Static,
    CsvFeed, SimulatedBroker, Resolution, Instrument, Tearsheet
)

# Define strategy with pluggable components
# Workflow: Universe Selection -> Alpha Model -> Portfolio Construction -> Risk Management -> Execution
strategy = Strategy(
    name='MA_Crossover',
    universe=Static([
        Instrument.stock('SPY'),
        Instrument.stock('QQQ'),
        Instrument.stock('IWM')
    ]),
    alpha=MovingAverageCrossover(fast_period=10, slow_period=20),
    construction=EqualWeight(),
    risk=PositionLimit(max_position=Decimal('0.1')),
    execution=Immediate()
)

# Run backtest
engine = Engine(
    data=CsvFeed(path=Path('data/bars.csv'), resolution=Resolution.DAILY),
    fund=Fund(
        strategies=[strategy],  # Add more strategies as needed
        capital=Decimal('100000')
    ),
    broker=SimulatedBroker()
)
results = engine.run(
    start='2020-01-01',
    end='2025-12-31',
    mode='backtest'
)

# Analyze results
print(f"Total Return: {results.total_return:.2%}")
print(f"Sharpe Ratio: {results.sharpe_ratio:.2f}")
print(f"Max Drawdown: {results.max_drawdown:.2%}")

# Generate tearsheet
tearsheet = Tearsheet(results)
tearsheet.save('tearsheet.html')

Roadmap

v0.1.0

• Event-driven engine with CSV data provider
• Pluggable strategy framework
• Basic execution (InstantFill, SpreadFill)
• Core analytics (returns, Sharpe, drawdown)
• Simple transaction costs

v0.2.0+ (Planned)

• Parquet data provider and advanced data layer features
• History API with type-safe lookback
• Advanced fill models (L2 matching, probabilistic)
• T+2 settlement and realistic cash management
• Overfitting detection (WFA, PBO, CSCV)
• Advanced analytics (execution quality, attribution)
• ML integration and model registry
• Broker integrations for live trading

Acknowledgments

Simulor implements the industry-standard modular pipeline architecture (Universe Selection → Alpha Generation → Portfolio Construction → Risk Management → Execution). This separation of concerns allows for maximum flexibility, testability, and maintainability, similar to the architecture found in institutional quantitative systems and platforms like Lean.

Contributing

Contributions are welcome!

License

This project is licensed under the MIT License.