Tactic Tracking Engine for Noisy Tick Pattern Identification
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Solution Overview
Problem
Current search engines lack the capability to effectively identify trading tactics in stock markets due to background noise and the varied appearances of these tactics in market data, leading to difficulties in making informed trading decisions.
Innovation Solution
The Tactic Tracking, Evaluation and Identification Engine (TTEIE) system processes tick data to hypothesize and evaluate trading tactics, filtering out false positives and presenting identified tactics in a user-friendly interface, allowing traders to monitor multiple securities and understand buyer and seller activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search engines are used to search for trading tactics, then the search can be performed, but the background noise and varied appearances of tactics make it difficult to accurately identify trading tactics
Solution Approach 1:
The system segments the market data into distinct tactical patterns by analyzing specific characteristics such as order size, price changes, and time intervals. This segmentation allows the system to separate genuine trading tactics from background noise by examining individual pattern components rather than treating market data as a homogeneous stream.
Solution Approach 2:
The system changes the parameters used for analysis by transforming raw market data into normalized tactical patterns. It adjusts parameters such as minimum order sizes, time windows, and price thresholds to identify tactics that would be obscured in raw market data, thereby filtering out background noise while preserving tactical signals.
2Adaptability or versatility
If the system monitors multiple securities simultaneously, then comprehensive market coverage is achieved, but the complexity of processing and analyzing data increases
Solution Approach 1:
The system implements a universal tactic detection framework that can be applied across multiple securities using the same core algorithms and pattern recognition mechanisms. This multi-functional approach allows the system to monitor diverse securities without requiring security-specific processing logic, thereby managing complexity while achieving comprehensive coverage.
Solution Approach 2:
The system segments the monitoring process into independent modular components that can process each security separately before aggregating results. This segmentation allows the system to handle multiple securities simultaneously while maintaining manageable complexity through parallel processing and modular architecture.
3Reliability
If the system filters out false positives to improve accuracy, then tactic identification reliability increases, but the processing time and computational resources required increase
Solution Approach 1:
The system performs preliminary filtering actions by pre-defining tactical patterns and their characteristic parameters before actual detection occurs. This preliminary preparation allows the system to quickly compare incoming market data against pre-established criteria, reducing the time required for false positive filtering during real-time operation while maintaining high reliability.
Solution Approach 2:
The system uses periodic evaluation intervals where it systematically checks for tactical patterns at defined time windows. This periodic action allows the system to filter false positives in batches rather than continuously, reducing instantaneous processing time while maintaining reliability through systematic verification at each period.
Data Source
AI summary
The Tactic Tracking, Evaluation and Identification Engine Apparatuses, Methods and Systems (“TTEIE”) transforms subscription request, tick notification request inputs via TTEIE components into subscription response, identified tactic store request, user interface update notification outputs. A subscription request datastructure from a client is obtained. A set of tactic definition datastructures is retrieved. A tick notification comprising tick data for a tick associated with a target is obtained. A contact datastructure corresponding to each retrieved tactic definition datastructure is added to a tracking list of contact datastructures for the target. The tick data for the tick is appended for each contact datastructure in the tracking list. A contact datastructure's time series of ticks is evaluated with regard to the respective contact datastructure's corresponding time series of rules to classify the respective contact datastructure, for each contact datastructure in the tracking list. The client is notified regarding identified tactic contact datastructures.