Predictive Methods and Devices for K-Line Analysis

Introduction to the Invention

To address existing technical challenges, this invention provides a method and device for predicting K-line (candlestick chart) patterns in financial markets. The technical solution is outlined as follows:

First Aspect: Predictive Method for K-Line Patterns

The method comprises:

Data Acquisition: The server collects K-line data for a target financial product during:
- The current matching period
- Multiple historical matching periods (all periods having identical duration)
Similarity Calculation: The server computes similarity scores between:
- Current period K-line data
- Each historical period's K-line data
Target Period Selection: Based on similarity scores, the server selects the most relevant historical matching period as the target reference period.
Prediction Execution: Using the K-line data from:
- The prediction period following the target historical period (m time units after its conclusion)
  The server forecasts K-line data for:
- The prediction period following the current period (m time units after its conclusion)

Where m represents a positive integer time increment.

Key Technical Features

Similarity Parameters may include:
- Candlestick body similarity
- Upper/lower shadow similarity
- Bollinger Band® similarity
Advanced Selection Process:
- Primary filtering by similarity threshold
- Secondary filtering by extremum point distribution patterns
- Final selection of highest-similarity candidate
Prediction Output includes:
- Generated forecast information for trader reference
- Optional accuracy evaluation parameters assessing prediction reliability

Second Aspect: Predictive K-Line Device

The device implementation comprises:

Data acquisition module
Similarity computation module
Period selection module
Data prediction module
Optional information generation and accuracy evaluation modules

Technical Advantages

This automated solution:

Reduces reliance on subjective human analysis
Enhances prediction accuracy through quantitative pattern matching
Provides actionable trading insights based on historical pattern recurrence
Offers measurable prediction quality assessment

Detailed Implementation

Core Method Workflow

Data Collection Phase
- Gathers complete K-line datasets including:
  - Open/close/high/low prices
  - Bollinger Band® indicators (upper, middle, lower)
- Supports various financial products:
  - Precious metals (gold, silver)
  - Energy commodities
  - Equities and derivatives
Pattern Matching Engine
- Employs advanced similarity algorithms:
  - Pearson correlation coefficients
  - Euclidean distance metrics
  - Cosine similarity calculations
- Multi-parameter analysis:
```
similarity_score = 0.99*body_similarity + 
                   0.005*upper_shadow_similarity + 
                   0.005*lower_shadow_similarity
```
Prediction Generation
- Projects historical prediction period patterns onto current timeline
- Example: If historical 3-day prediction followed 10-day pattern in 2015, applies same projection to current 10-day pattern
Output Optimization
- Generates trading signals:
  - Price movement direction indicators
  - Entry/exit strategy suggestions
  - Expected price fluctuation ranges

Accuracy Enhancement Features

Parameter Optimization
- Automated testing of multiple parameter sets:
  - Period lengths
  - Similarity thresholds
  - Pattern constraints
- Selects highest-performing parameter combination
Performance Validation
- Multiple accuracy metrics:
  - Direction prediction accuracy
  - Entry timing precision
  - Profit potential estimation
Continuous Improvement
- Learning mechanism stores successful predictions
- Refines future similarity calculations

Practical Applications

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Typical Use Cases

Trend Prediction
- Identifies probable price movements
- Flags potential reversal points
Volatility Forecasting
- Estimates likely price ranges
- Calculates Bollinger Band® expansion/contraction
Risk Management
- Provides statistically validated trading signals
- Quantifies prediction confidence levels

FAQ Section

Q: How does this method compare to traditional technical analysis?
A: It automates pattern recognition while maintaining interpretability, combining quantitative rigor with actionable trading insights.

Q: What timeframes work best with this approach?
A: The system adapts to any timeframe (minutes to months), with optimization determining ideal period lengths for each asset.

Q: How frequently should the prediction parameters be updated?
A: Regular recalibration is recommended, particularly during market regime changes, with automated optimization available.

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Q: Can this predict cryptocurrency price movements?
A: Yes, the methodology applies equally to crypto markets, with parameter optimization recommended for each digital asset.

Q: What hardware requirements exist for running these predictions?
A: Standard server configurations suffice for individual assets, with scalable solutions available for portfolio-wide analysis.

Q: How quickly can the system identify new patterns?
A: The automated learning process continuously incorporates new data, typically recognizing emerging patterns within 2-3 cycles.