OpenEngine – Building Your First Event-Driven Backtesting Engine: A Step-by-Step Research Note

Algorithmic trading requires a realistic and robust simulation environment. This note describes how we are planning to build an opensource – event‑driven backtesting engine optimized for 1‑minute data using DuckDB for fast historical data storage and querying. In addition, it details the integration of a broker API for order execution—using the OpenAlgo PlaceOrder API—as an example of how to enable live trading with minimal code changes. This modular, unified architecture helps ensure that your strategy’s behavior remains consistent from simulation to production.

1. Introduction

Before deploying trading strategies live, a high‑quality backtesting engine is essential for understanding performance under real market conditions. In this note, we explain why an event‑driven approach is advantageous compared to a purely vector‑based method, especially when using granular 1‑minute data. We also demonstrate how to integrate with a broker’s order placement API (OpenAlgo PlaceOrder) to seamlessly transition from simulation to live execution.

2. Background and Motivation

2.1 Vector-Based vs. Event-Driven Backtesting

• Vector-Based Backtesting:
  • Overview: Processes large data batches using vectorized operations (e.g., with pandas or NumPy).
  • Pros: High performance and simplicity for statistical operations.
  • Cons: Not ideal for simulating the sequential and dynamic aspects of live trading (e.g., latency, partial fills, order rejections).

• Event-Driven Backtesting:
  • Overview: Simulates trading by processing discrete events (market data updates, signals, orders, fills) in order.
  • Pros: Closely mirrors live trading, supports stateful strategies, and unifies simulation and live execution within the same framework.
  • Cons: More architecturally complex and requires careful management of events, though this is usually manageable at a 1‑minute resolution.

Given the requirement for a unified codebase for both backtesting and live trading, the event‑driven approach is strongly recommended.

2.2 Why Use DuckDB?

DuckDB is an embeddable, high‑performance OLAP database that:

• Provides Speed: Efficiently processes large volumes of columnar data such as 1‑minute historical market data.
• Is Simple to Integrate: With its Python API, it allows you to store, query, and manage data with minimal setup.
• Scales Well: Suitable for local testing and scalable enough for larger production deployments.

3. Key Components of the Backtesting Engine

A modular, event‑driven backtesting engine should consist of the following components:

1. Data Handling and Storage:
   • Historical Data Loader: Import 1‑minute data into DuckDB.
   • DuckDB Integration: Use SQL queries to efficiently retrieve and process data during simulation.
   • Real-Time Data Feed: (For live trading) Ingest live data via APIs or WebSockets.
2. Event System:
   • Event Types: Define events such as Market Data Events, Signal Events, Order Events, Fill Events, and Portfolio Update Events.
   • Event Queue and Dispatcher: Ensure events are processed sequentially and routed to the appropriate engine modules.
3. Strategy Engine:
   • Signal Generation: Process incoming market events to generate buy/sell signals.
   • State Management: Maintain necessary context for sequential or stateful strategies.
4. Order Management and Execution:
   • Order Manager: Translate signals into orders, simulate order fills in backtesting, and handle order status updates.
   • Broker Interface: Abstract the connection to broker APIs, ensuring that the same interface is used for both backtesting and live trading.
   • API Integration: In live trading, use endpoints like the OpenAlgo PlaceOrder API to send orders to the broker.
5. Portfolio and Risk Management:
   • Position Sizing and Allocation: Determine trade sizes based on risk controls.
   • Risk Controls: Enforce stop-loss, take-profit, and other risk management rules.
   • Portfolio Valuation: Continuously update positions and track performance metrics (drawdowns, Sharpe ratio, etc.).
6. Reporting and Analytics:
   • Metrics Calculation: Generate performance reports and visualizations.
   • Logging: Maintain detailed logs for debugging, auditing, and performance analysis.
7. Configuration and Environment Management:
   • User Settings: Enable customization of strategy parameters, capital allocation, and risk limits.
   • Unified Environment: Ensure that settings work identically in both backtesting and live modes.

4. Integrating Broker API: OpenAlgo PlaceOrder Function

To execute orders in live trading (and even simulate them during backtesting), you need a robust API interface. Here’s an overview of the OpenAlgo PlaceOrder API function:

4.1 API Details

• Endpoint URL:
  • Local Host: POST http://127.0.0.1:5000/api/v1/placeorder
  • Ngrok Domain: POST https://<your-ngrok-domain>.ngrok-free.app/api/v1/placeorder
  • Custom Domain: POST https://<your-custom-domain>/api/v1/placeorder
• Request JSON Format:

{
    "apikey": "<your_app_apikey>",
    "strategy": "Test Strategy", 
    "symbol": "SAIL", 
    "action": "BUY", 
    "exchange": "NSE", 
    "pricetype": "MARKET", 
    "product": "MIS", 
    "quantity": "1",
    "price": "0",
    "trigger_price": "0",
    "disclosed_quantity": "0"
}

Parameter Descriptions:

Sample API Response:

{
    "orderid": "240307000614705",
    "status": "success"
}

4.2 Integration in the Order Manager

When designing your Order Manager module, abstract the execution of orders so that in a live trading scenario, it calls the API. In backtesting, you might simulate the API call or use a similar interface for consistency. For example:

import requests

class BrokerInterface:
    def __init__(self, base_url, apikey):
        self.base_url = base_url
        self.apikey = apikey

    def place_order(self, strategy, symbol, action, exchange, product="MIS",
                    pricetype="MARKET", quantity="1", price="0", trigger_price="0", disclosed_quantity="0"):
        endpoint = f"{self.base_url}/api/v1/placeorder"
        payload = {
            "apikey": self.apikey,
            "strategy": strategy,
            "exchange": exchange,
            "symbol": symbol,
            "action": action,
            "product": product,
            "pricetype": pricetype,
            "quantity": quantity,
            "price": price,
            "trigger_price": trigger_price,
            "disclosed_quantity": disclosed_quantity
        }
        response = requests.post(endpoint, json=payload)
        if response.status_code == 200:
            return response.json()
        else:
            raise Exception("Order placement failed", response.text)

In your live trading engine, you would call this method when an order signal is generated. In backtesting, you can simulate this call to log the order details and update simulated portfolio values.

5. Step-by-Step Process for Building the Engine

Step 1: Data Acquisition and Storage

• Collect Historical Data:
  Obtain your 1‑minute historical market data from Yahoo Finance or other providers.
• Integrate DuckDB:
  Create a schema to store the data and load it using DuckDB’s Python API:

import duckdb
con = duckdb.connect('market_data.duckdb')
con.execute("""
  CREATE TABLE IF NOT EXISTS market_data (
      timestamp TIMESTAMP,
      open DOUBLE,
      high DOUBLE,
      low DOUBLE,
      close DOUBLE,
      volume BIGINT
  )
""")
con.execute("COPY market_data FROM '1min_data.csv' (FORMAT CSV, HEADER)")

Step 2: Design the Event‑Driven Architecture

• Define Event Types:
  Create classes (or named tuples) for events such as MarketDataEvent, SignalEvent, OrderEvent, and FillEvent.
• Implement the Event Queue and Dispatcher:
  Use Python’s built‑in libraries (e.g., queue.Queue) to manage event sequencing.

Step 3: Develop the Strategy Module

• Base Strategy Interface:
  Define an abstract strategy class to generate signals.
• Concrete Strategy Implementation:
  For instance, a moving average crossover strategy that processes each 1‑minute market event and outputs a signal.
• State Management:
  Ensure your strategy maintains any necessary state (e.g., moving averages, current positions).

Step 4: Implement Order Management and Execution Simulation

• Order Manager Module:
  Build a component to handle signal-to-order translation, using the BrokerInterface (as shown above) to place orders.
• Broker Interface Integration:
  Integrate the OpenAlgo PlaceOrder API so that order execution is identical for live and simulated environments.

Step 5: Build Portfolio and Risk Management Components

• Portfolio Manager:
  Track positions, cash balances, and overall portfolio value.
• Risk Engine:
  Incorporate stop-loss, take-profit, and maximum exposure controls.

Step 6: Integrate the Simulation Loop

• Data Replay:
  Develop a loop that reads 1‑minute data from DuckDB, converts each row into a market data event, and processes it through your event‑driven architecture.
• Live Simulation:
  Ensure that the loop is capable of running in real time, processing live events and calling the order placement API when required.

Step 7: Reporting, Analytics, and Logging

• Performance Metrics:
  Calculate cumulative returns, drawdowns, and other key indicators.
• Visualization:
  Use libraries such as Matplotlib or Plotly to visualize equity curves and trade performance.
• Logging:
  Implement detailed logging to trace events, order placements, and strategy decisions.

Step 8: Transitioning to Live Trading

• Unified Engine Design:
  Ensure that your event‑processing loop and core modules remain identical whether processing historical data or live feeds.
• API Integration:
  Replace simulated order fills with real API calls (e.g., using the BrokerInterface to call the OpenAlgo PlaceOrder endpoint).
• Testing:
  Rigorously test in a paper trading or simulated environment before full live deployment.

6. Discussion and Future Directions

By following this detailed, step‑by‑step process and leveraging an event‑driven design, you can build a backtesting engine that mirrors live trading conditions. Integration with DuckDB provides efficient data handling for granular 1‑minute data, and incorporating the OpenAlgo PlaceOrder API ensures a smooth transition to live execution.

Future Enhancements Might Include:

• Support for sub‑minute or tick data.
• Advanced order types and more realistic execution simulations.
• Asynchronous event processing to further reduce latency.
• Integration of machine learning models for dynamic strategy adjustments.

7. Conclusion

Developing a unified backtesting and live trading engine is challenging yet rewarding. By building an event‑driven system with robust data handling via DuckDB and seamless order execution through the OpenAlgo PlaceOrder API, you can create a framework that minimizes discrepancies between simulated and live performance. This research note has outlined the architectural considerations and practical steps needed to get started, providing a solid foundation for building and refining your trading engine.