BIRP: Bitcoin Information Retrieval Prediction Model Based on Multimodal Pattern Matching (2308.08558v1)

Abstract: Financial time series have historically been assumed to be a martingale process under the Random Walk hypothesis. Instead of making investment decisions using the raw prices alone, various multimodal pattern matching algorithms have been developed to help detect subtly hidden repeatable patterns within the financial market. Many of the chart-based pattern matching tools only retrieve similar past chart (PC) patterns given the current chart (CC) pattern, and leaves the entire interpretive and predictive analysis, thus ultimately the final investment decision, to the investors. In this paper, we propose an approach of ranking similar PC movements given the CC information and show that exploiting this as additional features improves the directional prediction capacity of our model. We apply our ranking and directional prediction modeling methodologies on Bitcoin due to its highly volatile prices that make it challenging to predict its future movements.

• The paper presents a novel Bitcoin prediction model that employs multimodal pattern matching to enhance directional forecasts.
• It refines technical indicators by leveraging four ranking strategies, including TS2Vec and crypto news embeddings, to improve prediction accuracy.
• Experimental results demonstrate that integrating IR-based feature engineering significantly outperforms traditional models in volatile market conditions.

An Overview of the BIRP: Bitcoin Information Retrieval Prediction Model

The paper "BIRP: Bitcoin Information Retrieval Prediction Model Based on Multimodal Pattern Matching" presents a novel framework integrating multimodal pattern matching into Bitcoin price directional prediction models. This approach is particularly significant due to Bitcoin's high volatility, which poses unique challenges in forecasting price movements compared to traditional financial markets.

Core Contributions

The primary contributions of the paper are twofold. First, it introduces four distinct ranking methodologies designed to enhance pattern matching capabilities for Bitcoin price data. Second, it incorporates a directional forecasting model for trading BTCUSDT perpetual futures, demonstrating that utilizing historical pattern data can significantly enhance prediction performance.

Methodological Framework

The paper refines several well-known technical indicators to rank historical patterns similar to the current Bitcoin price movements:

• Balance of Power (BOP): An oscillator reflecting market sentiment.
• Even Better Sinewave (EBSW): A volatility-focused indicator.
• Chaikin Money Flow (CMF): Assesses accumulation and distribution in markets.
• Differences (DIFF): Utilizes differencing across various periods to capture market shifts.
• Intra-Ratios (INTRA): Derives ratios from intra-period data, capturing immediate market dynamics.

The directional forecasting model leverages an XGBoost classifier, employing these chart-based features alongside additional information derived from various pattern matching and ranking methods.

Ranking Methodologies

The authors propose four ranking strategies:

1. Random Sampling: Serves as a baseline by randomly selecting past patterns.
2. Euclidean Distance: Calculates similarity strictly based on raw feature distances.
3. TS2Vec Embedding: Leverages the TS2Vec time series representation technique, optimizing data embeddings specific to financial time series contexts.
4. Multimodal Embedding: Combines chart patterns with financial news embeddings, utilizing Crypto DeBERTa LLMs to create enriched, multimodal representations.

Each of these methods draws on either traditional or advanced AI techniques, providing a comprehensive evaluation through their application on real-world Bitcoin data.

Experimental Findings

Experiments conducted in the paper reveal:

• All ranking strategies outperform the baseline model that does not incorporate Information Retrieval (IR)-based Feature Engineering (FE).
• Multimodal embeddings demonstrate the highest improvement, indicating the value of integrating news data with traditional chart patterns.
• The model with IR-based FE using the multimodal ranking strategy achieved a mean increase in prediction accuracy and F1 score over traditional methods without pattern matching.

Implications and Future Directions

The integration of multimodal pattern matching in financial machine learning highlights the potential of combining disparate data sources for improved predictive capabilities. The findings suggest practical applications in developing advanced trading systems that can more accurately anticipate market movements under volatile conditions.

Future developments may explore extending the methodology to other cryptocurrencies and financial instruments. Moreover, further enhancements in the multimodal approach could be achieved by applying more complex natural language processing techniques or larger datasets. Additionally, exploration into causal inference in pattern relationships might provide deeper insights into the underlying mechanisms of price movements.

In summary, the paper provides a significant contribution to the field of financial machine learning by pushing the boundaries of predictive modeling in the cryptocurrency domain through innovative feature engineering and pattern recognition techniques. This work lays a foundation for future research aiming to increase the precision of financial forecasts amidst evolving market conditions.