Market Data Simulation for Realistic Order Book Backtesting
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Solution Overview
Problem
Current methods for backtesting algorithmic trading systems, such as using shared test markets or historical data, fail to account for realistic market feedback, leading to unrealistic strategic validation as they do not simulate the complex dynamics of real market interactions.
Innovation Solution
A data-driven simulation system that includes an exchange simulator and an event processing engine, which receives market data and order requests, makes probabilistic inferences to produce simulated market data and updated order requests, effectively mimicking real market conditions by incorporating feedback mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If historical data is used for backtesting, then computational resources are saved and testing is simpler, but realistic market feedback effects are lost leading to unrealistic strategic validation
Solution Approach 1:
The patent creates a simulated market environment that copies the essential dynamics of real markets, including order book mechanics and feedback mechanisms, allowing backtesting to occur in a realistic yet controlled setting rather than using simple historical data replay
Solution Approach 2:
The patent introduces an inference algorithm as an intermediary between historical data and the backtesting process, which probabilistically infers hidden market actions (orders, cancellations, trades) to generate realistic market feedback without requiring actual real-time market participation
2Reliability
If shared test markets are used for backtesting, then real market conditions are simulated, but real risk exposure and resource consumption occur
Solution Approach 1:
The patent creates a virtual copy of the real market environment that replicates market dynamics, order book behavior, and feedback mechanisms, allowing strategies to be tested under realistic conditions without exposing capital to actual market risk
Solution Approach 2:
The patent uses simulated market data and virtual order books that can be rapidly generated and discarded, replacing the need for expensive and risky real market testing while maintaining the essential characteristics needed for valid strategy evaluation
3Device complexity
If simple historical data replay is used, then computational complexity is reduced, but feedback mechanisms are ignored leading to inaccurate performance evaluation
Solution Approach 1:
The patent implements feedback mechanisms by using inference algorithms that probabilistically determine market actions based on observed price changes, creating a closed-loop system where strategy actions generate realistic market responses that feed back into subsequent pricing and order book states
Solution Approach 2:
The patent transforms static historical data replay into a dynamic simulation where the market state evolves in response to strategy actions, with the order book and price levels changing realistically based on inferred market participant behavior and exchange rules
Data Source
AI summary
A system and a method are disclosed for simulating data driven market order exchange mechanics. An event processing engine receives a feed of market data and forwards it to an exchange simulator. The feed of market data may be recorded market data, live relayed market data, or simulated market data. A series of order requests is also received. The order requests are market order or limit orders, and can include new orders, amend orders, or cancel orders. The feed of market data is analyzed and an inference algorithm is applied by making probabilistic inferences to determine what actions may have occurred to produce the received feed of market data. A second series of order requests are produced. The received order requests and the second series of order requests are combined with normal exchange rules to produce a stream of simulated market data and a series of updated order requests.