Bitcoin cityscape with market volatility heatmap

Decoding Bitcoin Volatility: Can AI Predict the Next Market Swing?

Lena Kashyap in Tech & Innovation December 2025 • 4 min read.

"New research explores how artificial intelligence and order flow image representation can forecast short-term cryptocurrency price fluctuations, offering insights for traders and investors."

Bitcoin, known for its dramatic price swings, presents a challenge for traders and investors alike. Predicting volatility—the degree of price fluctuation—is crucial for managing risk and making informed decisions. While much research has focused on long-term trends, the ability to forecast short-term volatility remains a key objective, particularly in the fast-paced world of cryptocurrency trading.

A recent study tackles this challenge head-on, proposing a novel method for predicting short-term Bitcoin volatility. The approach leverages the power of artificial intelligence (AI) and a unique way of representing market data: order flow images. By transforming complex trading information into visual patterns, the researchers aim to unlock predictive insights that traditional methods might miss.

This research delves into the intricacies of order flow, employing sophisticated neural networks to decipher market dynamics. The results suggest a promising path toward more accurate short-term volatility forecasting, potentially offering a significant advantage for those navigating the cryptocurrency markets.

Turning Order Flow into Images: A New Way to See Market Dynamics

Bitcoin cityscape with market volatility heatmap

The core of this innovative approach lies in transforming raw order flow data into images. Order flow encompasses a wealth of information, including the size and direction of trades, as well as the dynamics of the limit order book (LOB). The LOB essentially lists all buy and sell orders at different price levels, providing a snapshot of market supply and demand.

Researchers map this multi-faceted data into image color channels. Imagine each image as a representation of market activity over a specific time interval. The color of each pixel reflects the intensity of buying or selling pressure, the size of trades, and the depth of the limit order book. This visual representation allows AI models to identify patterns and relationships that might be obscured in raw data.

Trade Size and Direction: The size and direction of each trade (buy or sell) are encoded into the image, providing insight into market sentiment.
Limit Order Book Dynamics: The state of the limit order book, including the volume of buy and sell orders at various price levels, is captured in the image, revealing potential support and resistance levels.
Time Interval Snapshots: These images represent market activity over fixed time intervals, creating a time series of visual data that AI models can learn from.

By converting order flow into images, researchers create a unique dataset suitable for training convolutional neural networks (CNNs). CNNs are a type of AI model particularly well-suited for image recognition, allowing them to identify subtle patterns and features within the order flow images that are indicative of future volatility.

The Future of Volatility Prediction: Beyond Images

While this study demonstrates the potential of order flow image representation for predicting Bitcoin volatility, the researchers suggest avenues for further exploration. One promising direction involves incorporating temporal information more explicitly. Since market dynamics evolve over time, using recurrent neural networks (RNNs), such as LSTMs, in conjunction with CNNs could capture the sequential dependencies in order flow patterns.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2304.02472,

Title: Learning To Predict Short-Term Volatility With Order Flow Image Representation

Subject: q-fin.rm cs.lg q-fin.tr

Authors: Artem Lensky, Mingyu Hao

Published: 04-04-2023

Everything You Need To Know

What is the primary objective of the research presented, and why is it important for those involved in cryptocurrency?

The primary objective is to predict short-term Bitcoin volatility using artificial intelligence. This is crucial for cryptocurrency traders and investors because Bitcoin is known for its dramatic price swings. Predicting volatility allows for better risk management and more informed trading decisions. Accurate volatility forecasts can provide a significant advantage in the fast-paced cryptocurrency market.

How does the study leverage artificial intelligence to forecast Bitcoin's volatility, and what novel method is employed?

The study uses artificial intelligence, specifically convolutional neural networks (CNNs), to analyze order flow images. The novel method involves transforming raw order flow data into visual images. Order flow encompasses trade size, direction, and limit order book (LOB) dynamics. This data is mapped into image color channels. CNNs then analyze these images to identify patterns indicative of future volatility.

Explain the concept of order flow images and how they are created from market data. What information is encoded within these images?

Order flow images are visual representations of market activity, created by transforming raw order flow data into images. This process involves encoding various aspects of trading data into the image's color channels. The images include: trade size and direction, indicating market sentiment; limit order book (LOB) dynamics, showing buy and sell order volumes at different price levels; and time interval snapshots, creating a time series of visual data for AI models. Each pixel's color reflects the intensity of buying or selling pressure, the size of trades, and the depth of the limit order book.

What are the potential advantages of using order flow images compared to traditional methods in predicting Bitcoin volatility, and what AI models are used?

Order flow images offer a new way to visualize and analyze market data, potentially revealing patterns that traditional methods might miss. By using convolutional neural networks (CNNs) specifically designed for image recognition, the AI models can identify subtle patterns and features within the order flow images that are indicative of future volatility. This approach may provide a more accurate and timely forecast compared to older techniques.

What are the suggested future research directions to improve Bitcoin volatility prediction, as highlighted by the researchers?

The researchers suggest incorporating temporal information more explicitly to improve the accuracy of volatility prediction. One promising direction involves using recurrent neural networks (RNNs), such as LSTMs, alongside CNNs. RNNs can capture sequential dependencies in order flow patterns, allowing for a more comprehensive understanding of market dynamics as they evolve over time. This combination could lead to even more accurate short-term volatility forecasting.