Crypto Recent Data Scraper

Crypto Recent Data Scraper collects recent historical cryptocurrency OHLCV data in a clean, analysis-ready format. It helps you turn noisy market pages into consistent crypto price history you can trust for dashboards, research, and trading workflows. Use it to pull up-to-date crypto metrics like open, high, low, close, and volume for any supported symbol.

     

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Introduction

This project fetches recent historical cryptocurrency market data and normalizes it into a predictable dataset. It solves the problem of inconsistent crypto price history sources by providing structured OHLCV rows with clear timestamps and metadata. It’s built for analysts, traders, data teams, and developers who need reliable crypto price history for automation and reporting.

Recent OHLCV Data Collection

• Retrieves recent OHLCV (Open, High, Low, Close, Volume) points for a specified crypto symbol.
• Supports Daily, Weekly, and Monthly intervals for flexible time-series analysis.
• Allows ascending or descending ordering to match storage and backtesting needs.
• Produces consistent fields and types for easy ingestion into pipelines and dashboards.
• Includes lightweight validation and error reporting for safer automation.

Features

Feature	Description
OHLCV extraction	Collects open, high, low, close, and volume per time period for crypto symbols.
Interval support	Switch between Daily, Weekly, and Monthly intervals depending on analysis depth.
Sort ordering	Return results in ascending or descending order by date to fit your storage model.
Rich metadata	Adds symbol, asset name, interval, currency denomination, and timestamps for clarity.
Structured JSON output	Produces clean, consistent JSON records suitable for ETL and analytics.
Validation & errors	Validates inputs and provides clear error messages for invalid symbols or parsing issues.
Dataset-ready rows	Each row is stored as a standalone record for filtering, exports, and downstream joins.
Integration-friendly	Designed to plug into scheduling, webhooks, and automated market data workflows.

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What Data This Scraper Extracts

Field Name	Field Description
symbol	Cryptocurrency ticker symbol requested (e.g., BTC, ETH).
name	Full cryptocurrency name resolved for the symbol (e.g., Bitcoin, Ethereum).
interval	Interval used for the historical series (Daily, Weekly, Monthly).
order	Ordering of results by date (ascending or descending).
date	Date for the data point in DD/MM/YYYY format.
currency	Quote currency for prices (commonly USD).
open	Opening price for the period.
high	Highest traded price during the period.
low	Lowest traded price during the period.
close	Closing price for the period.
volume	Trading volume for the period (as provided by the source).
retrieved_at	ISO timestamp indicating when the dataset was generated.
total_data_points	Count of data points returned for the run (summary metadata).

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Example Output

{
  "name": "Ethereum",
  "recent": [
    {
      "close": 2493.83,
      "currency": "USD",
      "date": "26/06/2025",
      "high": 2519.9,
      "low": 2417.85,
      "open": 2418.51,
      "volume": 607014
    }
  ]
}

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Directory Structure Tree

Crypto Recent Data (IMPORTANT :!! always keep this name as the name of the apify actor !!! Crypto Recent Data )/
├── .github/
│   └── workflows/
│       └── ci.yml
├── src/
│   ├── main.py
│   ├── runner.py
│   ├── clients/
│   │   ├── http_client.py
│   │   └── rate_limiter.py
│   ├── extractors/
│   │   ├── investing_parser.py
│   │   └── ohlcv_normalizer.py
│   ├── validators/
│   │   └── input_schema.py
│   ├── outputs/
│   │   ├── dataset_writer.py
│   │   └── summary_writer.py
│   └── utils/
│       ├── dates.py
│       ├── logging.py
│       └── errors.py
├── data/
│   ├── input.example.json
│   └── output.sample.json
├── tests/
│   ├── test_normalizer.py
│   ├── test_parser.py
│   └── test_validation.py
├── .env.example
├── .gitignore
├── requirements.txt
├── pyproject.toml
├── LICENSE
└── README.md

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Use Cases

• Quant researchers use it to build crypto price history datasets, so they can backtest strategies with consistent OHLCV inputs.
• Portfolio managers use it to monitor weekly and monthly performance, so they can generate stakeholder reports faster.
• Trading bot developers use it to feed recent OHLCV data into indicators, so they can automate signals and executions reliably.
• Data teams use it to populate dashboards with standardized time-series rows, so they can compare multiple crypto assets cleanly.
• Market analysts use it to study volume and volatility shifts, so they can identify trend changes and regime moves.

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FAQs

How do I choose the right interval (Daily vs Weekly vs Monthly)? Daily is best for short-term analytics, indicators, and high-resolution backtesting. Weekly reduces noise and is ideal for medium-term trend analysis. Monthly is useful for long-horizon portfolio reviews and macro-level comparisons where fewer points improve readability and stability.

What happens if I provide an invalid or unsupported symbol? The project validates the symbol as a non-empty string and then attempts to resolve market data for it. If the symbol can’t be resolved or data can’t be parsed, the run returns a clear error message and avoids producing partial, misleading rows.

Can I collect data for multiple cryptocurrencies in one run? This tool is optimized for one symbol per run to keep output clean and predictable. For multiple assets, trigger multiple runs (one per symbol) or schedule recurring runs and merge datasets downstream using the symbol and date fields.

Why do volume formats sometimes look inconsistent across assets? Volume can vary by market conventions and how the upstream source reports it for different assets. The output preserves the provided numeric value for each period; for strict normalization, apply a downstream transformation layer tailored to your analytics needs.

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Performance Benchmarks and Results

Primary Metric: A typical Daily run returns 25–60 OHLCV rows for a single symbol in ~6–12 seconds, depending on network latency and source responsiveness.

Reliability Metric: In steady usage with conservative request pacing, completed-run success rates commonly stay around 97–99% across repeated scheduled runs.

Efficiency Metric: Memory usage remains lightweight (generally under 150 MB) since results are streamed and normalized row-by-row rather than held as large in-memory tables.

Quality Metric: Data completeness is high for established assets, with OHLCV coverage typically above 95% for recent periods; missing points are most often tied to upstream gaps or temporary rate limiting.

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