Systems and methods for bitcoin price prediction

Abstract

The present invention discloses a system and method for predicting Bitcoin prices using machine learning and comprehensive historical data analysis. The system comprises: a data collection module (110) gathering historical Bitcoin prices, blockchain event data, and whale activity information; a data preprocessing module (120) performing feature engineering and standardization; a model training module (130) selecting and optimizing a random forest model (132) through hyperparameter tuning; and a prediction module (140) generating Bitcoin price predictions. The method includes: collecting historical data; preprocessing data through feature engineering and standardization; training a random forest model by evaluating multiple models, selecting based on performance metrics, and tuning hyperparameters; and generating price predictions using the trained model. The system considers Bitcoin-specific events like halvings, hard forks, and protocol upgrades, alongside whale activity, to capture complex market dynamics. Periodic model retraining ensures adaptability to evolving cryptocurrency landscapes, providing a powerful tool for investors and researchers navigating volatile markets.

Description

SYSTEMS AND METHODS FOR BITCOIN PRICE PREDICTIONFIELD OF INVENTION

[0001] The present invention relates to the field of cryptocurrency analysis and prediction, more specifically to systems and methods for forecasting Bitcoin prices using advanced machine learning techniques that incorporate both historical data and blockchain- specific events.BACKGROUND OF THE INVENTION

[0002] Cryptocurrencies, particularly Bitcoin, have emerged as a significant asset class in the global financial ecosystem. The highly volatile nature of Bitcoin prices has attracted considerable attention from investors, traders, and researchers alike. Accurate price prediction models for Bitcoin are crucial for informed decisionmaking in this rapidly evolving market.

[0003] Currently, models for predicting Bitcoin price changes are often constructed by combining methods such as neural networks and multiple linear regression based on economic theory. For instance, the method disclosed in "Bitcoin Price Prediction Using AI-Based Programming '1utilizes artificial intelligence techniques to analyze historical price patterns. Similarly, the "Method and device for predicting price change trend of digital currency" employs multiple linear regression and neural network models based on economic theory.

[0004] However, these conventional approaches face significant limitations when applied to the unique ecosystem of Bitcoin. Traditional time-series analysis often fails to consider the profound impacts that Bitcoin- specific planned events can have on the price. The cryptocurrency market, and Bitcoin in particular, is influenced by a complex interplay of technological, economic, and social factors that are not typically considered in traditional financial models.

[0005] One critical limitation of prior art methods is their failure to adequately account for Bitcoin-specific events that can have profound impacts on price movements. These events include:

[0006] Halvings: Bitcoin halving occurs approximately every four years, reducing the block reward for miners and decreasing the supply of new Bitcoins entering circulation. Historically, this has led to price increases in the months following each halving event, as the decreased supply often results in increased demand if it remains constant. However, halvings can also contribute to short-term price volatility.

[0007] Hard Forks: A hard fork is a radical protocol change that creates a new cryptocurrency, with holders of the original coin also receiving the forked coin. Major hard forks like Bitcoin Cash and Bitcoin Gold have caused price fluctuations as some of the value of the original Bitcoin is transferred to the new forked coin. The price impact depends on the level of community support and market conditions around each fork.

[0008] Protocol Upgrades: Bitcoin upgrades and protocol changes have the potential to influence price, depending on how they are received by the community. Significant upgrades can lead to price appreciation if they are viewed as beneficial improvements to Bitcoin's functionality and adoption.

[0009] Whale Activities: Bitcoin whales are individuals or entities that hold a large amount of Bitcoin. When whales buy or sell large quantities, it can cause significant price movements due to Bitcoin's relatively illiquid market. Whale activity is closely monitored by analysts and traders, as it can provide clues to future price direction.

[0010] Existing prediction models often treat these events as exogenous shocks or fail to incorporate them altogether, leading to reduced accuracy in price forecasts, especially during periods of significant blockchain events.

[0011] Furthermore, the cryptocurrency market operates 24 / 7, unlike traditional financial markets, and is subject to rapid information dissemination through social media and online communities. This constant flow of information and market activity poses challenges for models that rely on daily closing prices or limited sets of technical indicators.

[0012] Another limitation of current approaches is their reliance on single modeling techniques, such as only using time series analysis or neural networks. This can lead to overfitting or underfitting of the model, particularly given the complex and often unpredictable nature of Bitcoin price movements.

[0013] Further complicating matters, all of these factors are interconnected and influence each other. For example, whales may time their buying or selling activity around anticipated halving or fork events. The complex correlations between the various factors make it very difficult to predict Bitcoin's price movements using existing economic or financial theory alone. Because the Bitcoin price is influenced by these complex factors beyond typical market forces, the prediction accuracy of models constructed based solely on existing economic or finance theory is low.

[0014] There is a pressing need for improved systems for prediction and forecasting of Bitcoin prices that take into account the combined impact of various historical events like halvings, forks, upgrades, and whale activity. Further, there is requirement for system that could bridges the gap between traditional financial modeling and the unique characteristics of the Bitcoin ecosystem.OBJECT OF INVENTION

[0015] The principal object of the embodiments herein is to provide_improved system and method for predicting Bitcoin prices that overcomes the limitations of existing prediction models by incorporating both historical price data and Bitcoinspecific blockchain events.

[0016] Another object of the embodiments herein to develop a comprehensive prediction model that systematically accounts for the impact of Bitcoin halvings, hard forks, protocol upgrades, and whale activities on price movements, thereby enhancing prediction accuracy.

[0017] A further object of the invention is to create a flexible and adaptable prediction system that leverages advanced machine learning techniques, particularly random forest modeling in combination with time series forecasting, to capture complex relationships between multiple variables affecting Bitcoin prices.

[0018] Yet another object of the invention is to offer a modular and scalable system that can accommodate various types of analog input devices, such as buttons, switches, or other user interface elements, across different applications and industries.

[0019] Yet another object of the invention is to advance the field of cryptocurrency analysis by introducing a novel approach that bridges the gap between traditional financial modeling and the unique characteristics of the Bitcoin ecosystem.

[0020] These and other objects of the present invention will become more apparent from the detailed description and accompanying drawings that follow.SUMMARY

[0021] The following presents a simplified summary of the disclosure in order to provide a basic understanding to the reader. This summary is not an extensive overview of the disclosure and it does not identify key / critical elements of the invention or delineate the scope of the invention. Its sole purpose is to present some concepts disclosed herein in a simplified form as a prelude to the more detailed description that is presented later.

[0022] A more complete appreciation of the present invention and the scope thereof can be obtained from the accompanying drawings which are briefly summarized below and the following detailed description of the presently preferred embodiments.

[0023] The present invention relates to a system and method for predicting Bitcoin prices using advanced machine learning techniques and comprehensive historical data analysis. The invention addresses the limitations of traditional time-series analyses by incorporating Bitcoin- specific events and market dynamics into its predictive model.

[0024] The embodiment of the invention contemplates to a computer-implemented system for Bitcoin price prediction. As illustrated in Figure 1, the system comprises a data collection module (110), a data preprocessing module (120), a model training module (130), and a prediction module (140). These modules work in concert to collect relevant historical data, process it into meaningful features, train an optimized machine learning model, and generate accurate price predictions.

[0025] The data collection module (110) gathers an extensive dataset including historical Bitcoin prices (112), blockchain event data (114) such as halvings, hard forks, and protocol upgrades, and whale activity information. This comprehensive approach ensures that the system considers a wide range of factors known to influence Bitcoin prices. The data preprocessing module (120) transforms the raw collected data into a format optimized for machine learning. It performs feature engineering (122) to compute new, informative features that capture the essence of the collected event data. These engineered features include time -based metrics related to halving events, binary indicators for hard forks and protocol upgrades, and quantitative measures of whale activity. The module also performs data standardization (124) to ensure consistent scaling across all inputs.

[0026] The model training module (130) forms the analytical core of the invention. It evaluates multiple machine learning models and selects a random forest model (132) as the primary predictive engine due to its superior ability to capture complex, non-linear relationships. The module then optimizes this model through an extensive hyperparameter tuning process, exploring various combinations of parameters such as the number of decision trees (n_estimators), maximum tree depth (max_depth), and minimum samples for node splitting (min_samples_split).

[0027] The prediction module (140) serves as the operational interface of the system. It accepts feature values (142) for future dates as input, preprocesses this data using the same procedures applied during training, and generates Bitcoin price predictions (144) using the trained and optimized random forest model. A key strength of the system lies in its adaptability. The model can be periodically retrained with new data, ensuring that it remains accurate and relevant as new events unfold in the dynamic cryptocurrency landscape.

[0028] Another embodiment of the invention contemplates to a computer- implemented method for predicting Bitcoin prices, encompassing the steps of data collection, preprocessing, model training, and prediction generation as described above.

[0029] The present invention represents a significant advancement in cryptocurrency price prediction technology. By harnessing the power of advanced machine learning techniques and a diverse array of historical and event-based data, this invention provides a powerful tool for investors, traders, and researchers seeking to navigate the complex and volatile world of cryptocurrency markets.

[0030] The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description.BRIEF DESCRIPTION OF FIGURES

[0031] System and method are illustrated in the accompanying drawings, throughout which like reference letters indicate corresponding parts in the various figures. The embodiments herein will be better understood from the following descriptions with reference to the drawings, in which:

[0032] Figure 1 shows a block diagram of system, according to the embodiments as disclosed herein; and

[0033] Figure 2 shows a flow chart of method, according to the embodiments as disclosed herein.DETAILED DESCRIPTION OF INVENTION

[0034] The embodiments herein and the various features and advantageous details thereof are explained in larger detail with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as not to unnecessarily obscure the embodiments herein. Also, the various embodiments described herein are not necessarily mutually exclusive, as some embodiments can be combined with one or more of the other embodiments to form new embodiments. The term “or” as used herein, refers to a non-exclusive or, unless otherwise indicated. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein can be practiced and to further enable those skilled in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

[0035] The present invention provides an advanced system and method for predicting Bitcoin prices based on historical price data and significant blockchain events. Figure 1 illustrates an overview of the system architecture and data flow.

[0036] At its core, the invention comprises four primary modules: a data collection module (110), a data preprocessing module (120), a model training module (130), and a prediction module (140). These modules are implemented as sophisticated software components running on high-performance servers or cloud computing platforms, working in concert to collect and process relevant data, train a state-of- the-art machine learning model, and generate accurate Bitcoin price predictions.

[0037] The data collection module (110) serves as the foundation of the system, gathering an extensive and diverse dataset that encompasses both historical Bitcoin prices (112) and key event data (114) known to impact cryptocurrency valuations. This module retrieves daily Bitcoin price data starting from the genesis block, providing a comprehensive historical context. Additionally, it collects data on four crucial categories of events:

[0038] 1. Bitcoin Halving Events: These periodic occurrences, happening approximately every four years, reduce the block reward for miners by half. Historically, halvings have led to price increases due to the resulting supply contraction. The module meticulously records the dates of past halvings and analyzes their short-term and long-term impacts on Bitcoin's price.

[0039] 2. Hard Forks: The Bitcoin network has experienced several significant hard forks, such as Bitcoin Cash and Bitcoin Gold, which can redistribute value across the original and new chains. The module gathers data on major hard forks, including their dates, the specific rule changes implemented, and the subsequent effects on Bitcoin's price.

[0040] 3. Protocol Upgrades: Substantial improvements to the Bitcoin protocol, like the implementation of SegWit (Segregated Witness), can influence price movements based on their reception by the community. The module tracks the dates and details of these upgrades, along with their corresponding price impacts.

[0041] 4. Bitcoin Whale Activity: The trading behavior of large Bitcoin holders, commonly referred to as "whales," can substantially sway market prices due to the relatively limited liquidity in cryptocurrency markets. The module aggregates data on changes in whale holdings and significant transactions, providing crucial insights into potential price movements.

[0042] By amassing this comprehensive dataset, the system establishes a robust foundation for its predictive modeling capabilities, ensuring that the most influential factors in Bitcoin's price history are considered. The data preprocessing module (120) transforms the raw collected data into a format optimized for machine learning algorithms. This module consists of two key submodules: feature engineering (122) and data standardization (124). The feature engineering submodule computes new, informative features that capture the essence of the collected event data.

[0043] These engineered features include:- Time-based metrics related to halving events, such as the duration since the last halving and the time remaining until the next expected halving.- Binary indicators signaling the occurrence of hard forks or major protocol upgrades on specific dates.- Quantitative measures of whale activity, such as the total Bitcoin volume transacted by major holders within defined time periods.

[0044] The data standardization submodule then normalizes these features to a consistent scale, addressing the challenge posed by Bitcoin's significant price variations over time. This normalization is crucial for ensuring that the subsequent machine learning model can effectively learn from all features without being undulyinfluenced by their differing scales. The model training module (130) forms the analytical core of the invention. This module evaluates an array of advanced machine learning models, including linear regression with time series components, decision trees, random forests, Long Short-Term Memory networks (LSTMs), and Facebook's Prophet algorithm. After rigorous testing, a random forest model (132) is selected as the primary predictive engine due to its superior ability to capture complex, non-linear relationships and efficiently handle a mix of categorical and continuous features.

[0045] The chosen random forest model undergoes an extensive hyperparameter tuning process to optimize its predictive accuracy for Bitcoin prices. This tuning involves a systematic exploration of various parameter combinations, selecting the configuration that yields the best performance on a validation dataset. The key hyperparameters subject to this optimization process include:

[0046] n_estimators: This parameter determines the number of decision trees in the random forest ensemble. The optimization process explores a range from 20 to 1000 trees, balancing the model's expressive power against computational efficiency.

[0047] max_depth: This sets the maximum allowed depth for each decision tree in the forest. The tuning process examines depths between 3 and 7 levels, striking a balance between the model's ability to capture intricate patterns and its resistance to overfitting.

[0048] min_samples_split: This parameter specifies the minimum number of samples required to split an internal node, tested across a range from 10% to 100% of the total sample size. This tuning helps prevent overfitting by avoiding splits that are too specific to the training data.

[0049] min_samples_leaf: This defines the minimum number of samples that must be present in a leaf node, with values tested from 1 to 100 samples. Thisparameter is crucial in ensuring the reliability of the model's predictions while maintaining its ability to capture nuanced patterns in the data.

[0050] The hyperparameter tuning process employs advanced search strategies such as grid search or random search, evaluating each parameter combination using cross-validation on the training dataset. The combination that yields the best average performance across validation folds is selected for the final model.

[0051] To ensure the model's generalizability, it undergoes a final evaluation on a held-out test set that was not used during the training or tuning phases. This step provides an unbiased estimate of the model's performance on new, unseen data, validating its ability to make reliable Bitcoin price predictions.

[0052] The prediction module (140) serves as the operational interface of the system. It accepts feature values (142) for future dates as input and generates Bitcoin price predictions (144) using the trained and optimized random forest model. To make a prediction, this module first preprocesses the input features using the same feature engineering and standardization procedures applied during the training phase. These pre-processed features are then fed into the trained random forest model, which estimates the corresponding Bitcoin price.

[0053] Referring to Fig. 2, the method for predicting Bitcoin prices begins with an extensive data collection process (Step 110). Historical Bitcoin price data (112) is gathered from the genesis block to the current date, including daily opening, closing, high, and low prices. Simultaneously, the system collects blockchain event data (114), which encompasses dates and details of Bitcoin halving events, information on hard forks (including dates and specific rule changes), and dates and details of significant protocol upgrades. Additionally, data on whale activity is aggregated, including changes in holdings of large Bitcoin accounts and significant transactions. This comprehensive data collection approach utilizes various techniques, andconnections to blockchain explorers to ensure the gathering of accurate and relevant information.

[0054] Once the raw data is collected, it undergoes a crucial preprocessing phase (Step 120). This step transforms the data into a format suitable for machine learning instructions through two main sub-steps: feature engineering (122) and data standardization (124). During feature engineering, the system computes time -based metrics related to halving events, such as the time elapsed since the last halving and the time remaining until the next expected halving. It creates binary indicators for the occurrence of hard forks or major protocol upgrades and calculates quantitative measures of whale activity, such as changes in the number of whale accounts and the volume of Bitcoin moved by whale accounts in specific time periods. The system may also derive additional features that capture market sentiment and technical indicators. Following feature engineering, data standardization normalizes all features to a consistent scale, typically between 0 and 1 or -1 and 1, handles missing data through imputation techniques, and encodes categorical variables if necessary.

[0055] The core of the method lies in the model training phase (Step 130). This step begins by splitting the preprocessed data into training, validation, and test sets. The system then evaluates multiple machine learning models, including but not limited to linear regression with time series components, decision trees, random forests, gradient boosting machines, and Long Short-Term Memory (LSTM) networks. Based on its superior performance in capturing non-linear relationships and handling a mix of feature types, the random forest model (132) is selected. The chosen model undergoes optimization through hyperparameter tuning, which involves defining a grid of hyperparameter combinations to explore, performing k- fold cross-validation for each combination, and selecting the best-performing hyperparameter set based on a chosen metric such as mean absolute error. Finally, the system trains the optimized model on the entire training set using the selected hyperparameters .

[0056] With the trained model in place, the method moves to the prediction generation phase (Step 140). This step begins by accepting input feature values (142) for future dates, which may include upcoming known events (e.g., scheduled halvings), current market conditions, and recent whale activity. The system preprocesses these input features using the same procedures applied during training. It then applies the trained random forest model to the preprocessed input features, generating Bitcoin price predictions (144) for the specified future dates.

[0057] To ensure ongoing accuracy and relevance, the method includes steps for model evaluation and periodic retraining. The system regularly assesses the model's performance on new, unseen data and compares predicted prices with actual prices as they become available. At set intervals (e.g., monthly or quarterly) or when performance metrics decline below a certain threshold, the model undergoes retraining. This retraining process incorporates new data and potentially new features, ensuring that the model remains adaptive to changing market conditions.

[0058] A key strength of this system lies in its adaptability. The model can be periodically retrained with new data, ensuring that it remains accurate and relevant as new events unfold in the dynamic cryptocurrency landscape. This retraining process allows the system to continually refine its predictive capabilities, incorporating the latest market trends and blockchain developments.

[0059] In short, the present invention represents a significant advancement in cryptocurrency price prediction technology. Its key innovations include:

[0060] -A comprehensive data collection system that aggregates historical Bitcoin prices and data on the most impactful events, providing a rich context for price prediction.

[0061] - An advanced data preprocessing pipeline that engineers informative features and standardizes data, optimizing it for machine learning applications.

[0062] - A sophisticated model training process that leverages the power of random forests, coupled with extensive hyperparameter tuning, to create a robust and accurate predictive model.

[0063] - An adaptive prediction system capable of generating future price forecasts and continuously improving its accuracy through periodic retraining.

[0064] - A modular and scalable architecture that facilitates easy updates and maintenance, ensuring the system remains at the forefront of Bitcoin price prediction technology.

[0065] By harnessing the power of advanced machine learning techniques and a diverse array of historical and event-based data, this invention provides a powerful tool for investors, traders, and researchers seeking to navigate the complex and volatile world of cryptocurrency markets. The system's ability to consider a wide range of factors, from network fundamentals to market dynamics, sets it apart as a comprehensive solution for Bitcoin price prediction, offering valuable insights to guide investment and trading decisions in this rapidly evolving financial landscape.

[0066] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and / or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognise that the embodiments herein can be practiced with appropriate modification within the scope of the embodiments as described herein.

Claims

CLAIMS:

1. A computer- implemented system for predicting Bitcoin prices, comprising: a) a data collection module (110) configured to gather: i) historical Bitcoin price data (112); ii) blockchain event data (114) including dates and details of Bitcoin halvings, hard forks, and protocol upgrades; iii) whale activity data; b) a data preprocessing module (120) configured to: i) engineer features (122) based on the gathered data, including:- time-based metrics related to halving events;- binary indicators for hard forks and protocol upgrades;- quantitative measures of whale activity; ii) standardize (124) the engineered features; c) a model training module (130) configured to: i) evaluate multiple machine learning models using the preprocessed data; ii) select and optimize a random forest model (132) by tuning hyperparameters including:- number of decision trees (n_estimators);- maximum depth of each tree (max_depth); minimum samples required to split an internal node (min_samples_split) ;- minimum samples required in a leaf node (min_samples_leaf); d) a prediction module (140) configured to: i) accept feature values (142) for future dates; ii) preprocess the input features; iii) generate Bitcoin price predictions (144) using the trained random forest model; e) at least one processor for executing the modules; and f) a non-transitory computer-readable medium storing instructions for the modules.

2. The system for predicting Bitcoin prices as claimed in claim 1, wherein the data collection module (110) is configured to collect data from the Bitcoin genesis block to a current date.

3. The system for predicting Bitcoin prices as claimed in claim 1, wherein the data preprocessing module (120) is configured to compute: a) time durations since the last halving event; b) time remaining until the next expected halving event; c) binary indicators for occurrences of hard forks or upgrades; d) changes in the number of Bitcoin whale accounts.

4. The system for predicting Bitcoin prices as claimed in claim 1, wherein the model training module (130) is configured to perform cross-validation on a training dataset to evaluate hyperparameter combinations.

5. The system for predicting Bitcoin prices as claimed in claim 1, further comprising a model evaluation module configured to assess the trained model's performance on a held-out test set.

6. The system for predicting Bitcoin prices as claimed in claim 1, wherein the prediction module (140) is configured to periodically retrain the random forest model (132) with new data to maintain prediction accuracy.

7. A computer-implemented method for predicting Bitcoin prices, comprising: a) collecting, by a data collection module (110), historical Bitcoin price data (112), blockchain event data (114), and whale activity data; b) preprocessing, by a data preprocessing module (120), the collected data by: i) engineering features (122) based on the gathered data; ii) standardizing (124) the engineered features;c) training, by a model training module (130), a random forest model (132) by: i) evaluating multiple machine learning models using the preprocessed data; ii) selecting the random forest model based on performance metrics; iii) tuning hyperparameters of the random forest model; d) generating, by a prediction module (140), Bitcoin price predictions (144) by: i) accepting feature values (142) for future dates; ii) preprocessing the input features; iii) applying the trained random forest model to the preprocessed input features.

8. The method for predicting Bitcoin prices as claimed in claim 7, wherein engineering features comprises computing: a) time durations since the last halving event; b) time remaining until the next expected halving event; c) binary indicators for occurrences of hard forks or upgrades; d) changes in the number of Bitcoin whale accounts.

9. The method for predicting Bitcoin prices as claimed in claim 7, wherein tuning hyperparameters comprises performing cross-validation on a training dataset to evaluate hyperparameter combinations.

10. The method for predicting Bitcoin prices as claimed in claim 7, further comprising assessing the trained model's performance on a held-out test set.

11. The method for predicting Bitcoin prices as claimed in claim 7, further comprising periodically retraining the random forest model (132) with new data to maintain prediction accuracy.

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