Bitcoin Data Analysis
Objective:
To analyze Bitcoin price behavior using time series analysis, volatility measurement, market activity indicators, and technical trend evaluation from 2022–2025.
Tools Used:
Python
—Pandas
—NumPy
—Plotly
yFinance API
Power BI (optional dashboard integration)
Dataset:
Date
Open
High
Low
Close
Volume
Number of Trades
Quote Asset Volume
Taker Buy Volume
Goal
To analyze Bitcoin’s historical price movements, volatility, and market capitalization trends using time-series financial data.
Process
Imported and cleaned financial data using Python
Removed timestamp inconsistencies and duplicates
Calculated moving averages and volatility indicators
Added Market Cap column using supply-price logic
Built visualizations in Power BI and Python
Highlight
Identified volatility cycles and price momentum shifts through moving average crossovers.
Figure 1. This dashboard shows Bitcoin’s price movement over time using Open, High, Low, and Close values. The charts highlight strong price volatility, with major surges and pullbacks, especially during peak rally periods. Overall, the trend reflects significant long-term growth despite short-term fluctuations, illustrating Bitcoin’s high-risk, high-reward nature.
Figure 2. This candlestick chart illustrates Bitcoin’s daily price movement during Q1 2025, highlighting both upward momentum and short-term pullbacks. The steady climb through February suggests strong buying pressure, while the volatility in March reflects normal market corrections. Overall, the pattern shows a generally bullish quarter with typical fluctuations expected in a high-growth asset.
Figure 3. This dashboard compares Bitcoin’s price using normal scale and log scale views. The normal chart highlights the actual dollar increase, making recent price surges appear much larger, while the log chart shows percentage growth more evenly across time. Together, they reveal that although Bitcoin’s price has grown significantly, its growth pattern over the years follows a more balanced and gradual trend when viewed in percentage terms.
Summary
Bitcoin displayed pronounced cyclical regimes characterized by volatility clustering, momentum-driven expansions, and sharp mean-reversion phases. Price behavior reflected sentiment-driven acceleration patterns, with structural inflection points often preceded by volatility compression and liquidity shifts. Distinct bull and bear cycles revealed asymmetric risk profiles and amplified drawdown dynamics.
Conclusion & Analysis
This project examined Bitcoin as a high-volatility financial time-series, applying trend analysis, rolling volatility metrics, and regime observation to identify reversal signals and structural market transitions. The analysis highlighted how volatility clustering and momentum persistence shape cyclical price formation in speculative assets.
Beyond price tracking, the work strengthened my ability to interpret nonlinear financial behavior, detect regime shifts, and contextualize risk within broader market psychology. It reinforced a disciplined modeling approach — combining statistical observation, time-series logic, and structured interpretation — to transform volatile market data into coherent, decision-oriented financial insight.