The world of cryptocurrency is driven by data — and understanding historical price movements is essential for making informed trading decisions. For investors and analysts tracking Metapocket (METAPK), access to reliable, structured historical data offers a competitive edge in identifying trends, testing strategies, and forecasting future performance.
This comprehensive guide dives into Metapocket’s price history, explores how to analyze K-line charts, and provides actionable insights on leveraging historical market data for technical analysis, risk management, and algorithmic trading.
Understanding Metapocket's Historical Price Movement
Tracking the historical price of Metapocket (METAPK) enables traders to evaluate past performance and uncover patterns that may influence future behavior. The dataset spans from July 4, 2024, to July 4, 2025, capturing critical market dynamics during this period.
As of July 4, 2025, Metapocket reached its all-time high value — surpassing an unprecedented peak in USD terms. This milestone marks a significant moment for early adopters and active traders alike. Conversely, the lowest point in Metapocket’s price trajectory — often referred to as its historical low — also occurred around the same timeframe, highlighting the asset’s volatility.
Despite growing interest, the current circulating supply of Metapocket remains approximately 0 tokens, with a maximum total supply capped at 11,145,391 METAPK. This limited availability could play a crucial role in shaping scarcity-driven demand in the future.
All pricing data presented here is sourced from trusted providers, ensuring accuracy and consistency across time intervals.
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Accessing High-Quality Historical Data
Reliable historical data is foundational for any serious crypto analysis. Whether you're conducting backtesting, building predictive models, or studying market cycles, having access to granular datasets makes all the difference.
What Data Is Available?
The complete Metapocket historical dataset includes:
- 1-minute, daily, weekly, and monthly OHLC (Open, High, Low, Close) prices
- Trading volume per interval
- Timestamped entries in UTC (GMT+0)
These datasets are rigorously validated for consistency and updated daily to reflect the latest market activity. Designed specifically for quantitative research and trading simulations, they are available for free download in CSV or Excel format.
Such precision allows traders to simulate strategies under real-world conditions and refine their entry/exit points before deploying capital.
How to Analyze Metapocket K-Line Charts
K-line (or candlestick) charts are among the most powerful tools in technical analysis. They provide a visual representation of price action over time, revealing market sentiment and potential turning points.
Structure of a K-Line
A typical candlestick consists of:
- Body: Represents the range between opening and closing prices.
- Wicks (shadows): Show the highest and lowest prices within the period.
Color coding:
- Green candle: Price increased during the period (close > open).
- Red candle: Price decreased (close < open).
By examining sequences of candles, traders can identify patterns such as doji reversals, bullish engulfing, or bearish hammers — all indicators of potential trend changes.
For example, sustained green candles with short wicks may signal strong buying pressure, while long red wicks suggest rejection at higher levels.
Practical Uses of Historical Data in Crypto Trading
Historical data isn't just for record-keeping — it's a strategic resource. Here’s how traders leverage Metapocket’s past performance:
1. Technical Analysis & Trend Identification
Using platforms like Python with libraries such as Pandas, NumPy, and Matplotlib, traders can import CSV data and generate custom visualizations. Plotting moving averages, RSI divergences, or Bollinger Bands helps detect momentum shifts and optimal trade setups.
Storing data in high-performance databases like GridDB enhances query speed and supports large-scale backtesting.
2. Price Prediction Modeling
Machine learning models thrive on historical data. By training algorithms on past price movements, traders can build predictive systems that forecast short-term fluctuations based on recurring patterns.
Features such as volatility clusters, volume spikes, and seasonality can be extracted from the dataset to improve model accuracy.
3. Risk Management
Understanding historical drawdowns and volatility helps assess the risk profile of holding Metapocket. Metrics like standard deviation, Value at Risk (VaR), and maximum peak-to-trough decline inform position sizing and stop-loss placement.
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4. Portfolio Optimization
Long-term investors use historical returns to evaluate asset performance within diversified portfolios. By comparing Metapocket’s behavior against other cryptocurrencies or benchmarks, one can adjust allocations to maximize returns while minimizing exposure.
5. Building Automated Trading Bots
Algorithmic traders rely heavily on OHLC data to train bots. Feeding years of minute-level data into reinforcement learning systems allows bots to adapt to various market regimes — from bull runs to consolidation phases.
Backtesting ensures strategies perform well across different scenarios before going live.
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Frequently Asked Questions
What is cryptocurrency historical data?
Cryptocurrency historical data refers to past market information for digital assets like Metapocket or Bitcoin. It includes metrics such as price (open, high, low, close), trading volume, and market capitalization. This data is essential for analyzing trends, conducting backtests, and building predictive models.
How can I get accurate historical prices for digital currencies?
While platforms like Google Finance offer limited visibility, they often lack downloadable formats. Web scraping poses legal risks and data integrity issues. The best method is to obtain structured data directly from reputable exchanges or data providers like Bitget or OKX, which offer reliable CSV exports for in-depth analysis.
Why should I use Bitget or similar platforms for crypto data?
Trusted platforms provide standardized, time-zone-consistent (GMT+0), and frequently updated datasets. Their commitment to data accuracy makes them ideal for academic research, institutional analysis, and algorithmic trading development.
In what format is the historical data provided?
Data is available in Excel (.xlsx) or CSV format, easily imported into Python, R, Excel, or database systems. Users needing format conversion (e.g., CSV to JSON) can use trusted tools like ConvertCSV without compromising data integrity.
How often is the historical data updated?
The datasets are refreshed daily, reflecting the previous day’s closing prices and volumes shortly after market settlement. This ensures users always work with the most current information.
Why do I see a "request frequency too high" error when downloading?
To prevent abuse, each cryptocurrency’s data can only be downloaded once per day per user. If you encounter this message, please wait until the next day to retry your download.
Final Thoughts
Access to high-quality Metapocket (METAPK) historical price data opens doors to deeper market understanding and smarter decision-making. From visualizing K-line patterns to training AI-driven trading bots, the applications are vast and impactful.
Whether you're a beginner learning technical analysis or a developer building the next generation of crypto algorithms, leveraging accurate, time-stamped datasets gives you a solid foundation for success.
👉 Start exploring live and historical crypto data with powerful analytical tools today.