Reinforcement Learning Order Execution for Stock Trading
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Solution Overview
Problem
Conventional algorithmic trading systems lack agility in responding to market changes, leading to inefficiencies when executing large stock orders, particularly in terms of market adaptability and cost optimization.
Innovation Solution
A reinforcement learning-based deep learning model is employed to derive and execute a strategy for stock transactions by using multiple neural networks to determine an action policy and estimate action values, allowing for real-time order execution and minimizing losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional algorithmic trading systems execute orders according to predetermined rules, then order execution can be automated, but the system lacks agility to respond to market changes
Solution Approach 1:
The patent implements a reinforcement learning-based deep learning model that dynamically adjusts order execution strategies based on real-time market data. The model continuously learns from market conditions and adapts its execution policy, transforming the static predetermined rules into a dynamic system that can respond to changing market environments while maintaining automated execution.
2Stability of the object's composition
If a time-weighted average price algorithm executes orders in equally divided parts, then execution is systematic, but market adaptability and efficiency are reduced
Solution Approach 1:
The patent changes the execution parameters from fixed equal divisions to dynamic allocations based on market conditions. The deep learning model adjusts order size, timing, and pricing parameters in real-time based on learned patterns from market data, enabling systematic execution that adapts to market opportunities and constraints.
Solution Approach 2:
The reinforcement learning model incorporates feedback mechanisms where execution results and market responses are continuously fed back into the model. This feedback loop allows the system to learn from past executions and improve future decisions, enhancing trading efficiency while maintaining systematic execution through the structured learning framework.
3Quantity of substance
If large orders are executed in the stock market, then trading volume is achieved, but stock price may sharply fall or rise making it difficult to trade at the right price
Solution Approach 1:
The patent segments large orders into multiple smaller execution batches, but unlike traditional equal divisions, the segmentation is optimized by the deep learning model based on market conditions. The model determines optimal split sizes, timing, and pricing for each segment to minimize market impact while achieving the total trading volume at favorable prices.
Data Source
AI summary
An order execution server for stock trading includes a data collection unit configured to collect trading data on at least one item, a model generation unit configured to generate a reinforcement learning-based deep learning model including two or more actors which are neural networks that determine an action policy of a reinforcement learning agent and a critic which is a neural network that estimates an action value of the reinforcement learning agent and train the reinforcement learning-based deep learning model to derive an order execution strategy for the at least one item based on the trading data and an order execution unit configured to perform order execution for the at least one item during a current period of time by using order information including the order execution strategy.