Adaptive Spike Detection in Financial Data Visualization
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Solution Overview
Problem
Existing methods for removing spikes from financial data are inadequate, particularly for signals with high dynamic ranges and during market closure periods, as they often misidentify significant price changes or irregular jumps, and are not effective for price time series data.
Innovation Solution
A method that analyzes financial data by identifying spikes within specific time intervals by comparing computed quantities from sub-intervals, using averages and adjustments for opening and closing values, to determine the presence of spikes and prevent them from affecting the graphical representation, allowing for user-controlled sensitivity and spike detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If threshold-based spike detection methods are used, then spikes can be detected in narrow range signals, but signals with high dynamic range are discriminated incorrectly and spikes are not detected
Solution Approach 1:
The patent applies dynamics by making the spike detection threshold adaptive rather than fixed. The threshold dynamically adjusts based on the local volatility of the financial instrument, calculated from the average true range (ATR) indicator. This allows the system to effectively detect spikes across signals with varying dynamic ranges, resolving the contradiction between detection precision and adaptability to different signal characteristics.
Solution Approach 2:
The patent changes the parameter used for threshold determination from a fixed value to a dynamically calculated value based on ATR. By modifying the threshold parameter to adapt to local market conditions and volatility, the system maintains high detection accuracy while becoming versatile across different dynamic ranges of financial signals.
2Object-generated harmful factors
If reference signal-based spike removal is used, then spikes can be removed from signals, but the method is not useful for price time series data
Solution Approach 1:
The patent applies self-service by having the financial instrument's own historical data serve as the reference for spike detection. Instead of using an external reference signal that doesn't capture price series characteristics, the system uses the instrument's own ATR-based volatility measurements to establish detection thresholds, making the method specifically suited for price time series data.
Solution Approach 2:
The patent introduces the ATR (Average True Range) indicator as an intermediary that bridges the gap between raw price data and spike detection. The ATR serves as a mediator that quantifies local volatility, enabling the system to distinguish between normal price variations and actual spikes in a manner specific to financial time series.
3Difficulty of detecting and measuring
If fixed threshold limits are used for spike detection, then spikes can be identified, but significant price changes during market closure are mistakenly identified as spikes
Solution Approach 1:
The patent applies dynamics by making the detection threshold adaptive to local volatility conditions rather than fixed. During periods of high volatility (such as around market closure), the ATR-based threshold automatically adjusts upward, preventing normal significant price changes from being misidentified as spikes. This dynamic adaptation maintains reliability across different market conditions while keeping detection straightforward.
4Ease of operation
If spike removal is applied to maintain chart usability, then charts remain useful during high volatility, but the method complexity increases
Solution Approach 1:
The patent changes the threshold parameter from a fixed value to one based on a standard financial indicator (ATR). This parameter transformation maintains relative simplicity by leveraging an already-widely-understood and calculated metric in technical analysis, rather than introducing a completely new complex algorithm. The approach preserves chart usability while minimizing the increase in method complexity.
Data Source
AI summary
A method for analyzing financial data generated in an exchange in communication with a client/server network. The method includes the steps of receiving a first set of data related to a financial instrument on the exchange, the first set of data being within a time interval and including: a high value for the financial instrument during the time interval; a low value for the financial instrument during the time interval; an opening value for the financial instrument during the time interval; and a closing value for the financial instrument during the time interval. The method also detects a spike in the time interval using a confidence value based upon the first set of data and displays a graph of a set of transactions of the financial instrument, the graph being scaled to prevent the spike from substantially affecting visual information relating to the set of transactions.