Blockchain.info Market Data Acquisition Framework

This notebook defines a standardized protocol for interfacing with the Blockchain.info public REST API to retrieve on-chain metric data for a single chart via a single function call.

1. Dependency Installation

[ ]

!pip install requests pandas

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Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests) (2026.2.25)
Requirement already satisfied: numpy>=1.26.0 in /usr/local/lib/python3.12/dist-packages (from pandas) (2.0.2)
Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas) (2.9.0.post0)
Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)
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Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)

2. Library Imports

[ ]

import warnings
warnings.filterwarnings("ignore")

import requests
import pandas as pd
from datetime import datetime

Code Logic

warnings.filterwarnings("ignore"): Suppresses non-critical runtime warnings to maintain clean output.
requests: Handles HTTP communication with the Blockchain.info public REST endpoint.
pandas: Provides the DataFrame structure for tabular time-series data.
datetime: Supplies datetime objects for date delta computation.

3. Available Chart Types

Blockchain.info exposes the following chart categories via the https://api.blockchain.info/charts/{chart_name} endpoint. Each chart_name value maps directly into the REST path.

3.1 Market Data

Chart Name	Description
`market-price`	Average USD market price across major exchanges
`market-cap`	Total USD value of all circulating Bitcoin
`trade-volume`	USD value of Bitcoin traded on major exchanges per day
`exchange-volume`	Total BTC volume traded on major exchanges per day

3.2 Blockchain Activity

Chart Name	Description
`n-transactions`	Total number of confirmed transactions per day
`n-unique-addresses`	Number of unique addresses used per day
`n-transactions-per-block`	Average number of transactions per block
`output-volume`	Total BTC output volume per day
`estimated-transaction-volume`	Estimated BTC transaction volume per day
`estimated-transaction-volume-usd`	Estimated USD transaction volume per day

3.3 Mining & Network

Chart Name	Description
`hash-rate`	Estimated network hash rate in GH/s
`difficulty`	Current mining difficulty target
`miners-revenue`	Total miner revenue (block reward + fees) in USD
`transaction-fees`	Total BTC paid in transaction fees per day
`transaction-fees-usd`	Total USD value of transaction fees per day
`cost-per-transaction`	Miner revenue per transaction in USD
`n-orphaned-blocks`	Number of orphaned blocks per day

3.4 Supply & Distribution

Chart Name	Description
`total-bitcoins`	Total number of BTC in circulation
`utxo-count`	Number of unspent transaction outputs
`bitcoin-days-destroyed`	Measure of BTC economic activity (age × volume)
`average-block-size`	Average block size in MB
`blocks-size`	Total size of all blocks per day in MB

[ ]

df_fees = fetch_chart("transaction-fees-usd", START_DATE, END_DATE)

print("--- Average Transaction Fee (USD) ---")
display(df_fees.head())
df_fees.info()

--- Average Transaction Fee (USD) ---

	timestamp	datetime	transaction_fees_usd
0	1704067200	2024-01-01	9.397140e+06
1	1704153600	2024-01-02	6.452516e+06
2	1704240000	2024-01-03	6.429705e+06
3	1704326400	2024-01-04	5.546797e+06
4	1704412800	2024-01-05	5.121602e+06

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8 entries, 0 to 7
Data columns (total 3 columns):
 #   Column                Non-Null Count  Dtype         
---  ------                --------------  -----         
 0   timestamp             8 non-null      int64         
 1   datetime              8 non-null      datetime64[ns]
 2   transaction_fees_usd  8 non-null      float64       
dtypes: datetime64[ns](1), float64(1), int64(1)
memory usage: 324.0 bytes

4. Configuration

[ ]

START_DATE = "2024-01-01"    # Format: YYYY-MM-DD
END_DATE   = "2024-01-08"    # Format: YYYY-MM-DD

BASE_URL   = "https://api.blockchain.info/charts/{chart_name}"

Code Logic

START_DATE / END_DATE: ISO 8601 date strings defining the retrieval window applied to all fetch calls below.
BASE_URL: Public Blockchain.info chart endpoint. No API key is required. The {chart_name} placeholder is resolved at call time.

5. Data Extraction Function

The fetch_chart function performs a single HTTP GET request to the Blockchain.info charts endpoint, maps the response to a standardized schema, and applies precision-safe type casting.

[ ]

def fetch_chart(chart_name, start_date, end_date):
    start_dt  = datetime.strptime(start_date, "%Y-%m-%d")
    end_dt    = datetime.strptime(end_date,   "%Y-%m-%d")
    days_diff = (end_dt - start_dt).days

    url    = BASE_URL.format(chart_name=chart_name)
    params = {
        "format":   "json",
        "start":    start_date,
        "timespan": f"{days_diff}days",
        "sampled":  "false",
    }

    response = requests.get(url, params=params, timeout=30)
    response.raise_for_status()

    data = response.json().get("values", [])

    if not data:
        return pd.DataFrame()

    column_name = chart_name.replace("-", "_")

    df = pd.DataFrame({
        "timestamp": [int(d["x"])   for d in data],
        "datetime":  pd.to_datetime([d["x"] for d in data], unit="s"),
        column_name: [float(d["y"]) for d in data],
    })

    df = df.astype({"timestamp": "int64", column_name: "float64"})
    df = df.drop_duplicates("timestamp").sort_values("timestamp", ignore_index=True)

    return df

Code Logic

datetime.strptime(...): Parses ISO 8601 date strings into datetime objects for delta computation.
(end_dt - start_dt).days: Derives the integer day span required by the timespan parameter.
BASE_URL.format(chart_name=chart_name): Resolves the {chart_name} placeholder using the provided chart identifier.
"format": "json": Instructs the API to return a structured JSON payload.
"start": start_date: Anchors the retrieval window to the specified start date.
"timespan": f"{days_diff}days": Defines the window width in days. The API returns all data points within this span in a single response — no pagination required.
"sampled": "false": Disables API-side data compression, ensuring all available data points are returned.
response.raise_for_status(): Raises an HTTPError on any non-2xx response code, surfacing failures immediately.
response.json().get("values", []): Extracts the time-series array from the response envelope; defaults to an empty list if the key is absent.
chart_name.replace("-", "_"): Converts the hyphenated chart name to a valid DataFrame column identifier (e.g., market-price → market_price).
[int(d["x"]) for d in data]: Extracts the Unix second timestamp from each data point dict.
[float(d["y"]) for d in data]: Extracts the metric value from each data point dict.
pd.to_datetime([...], unit="s"): Derives a human-readable datetime column from Unix second timestamps.
.astype({...}): Casts columns to typed numerics required for arithmetic computation.
.drop_duplicates("timestamp").sort_values(...): Removes duplicate timestamps and enforces chronological ordering.

6. Execution — One Call Per Chart Type

Each cell below demonstrates a single fetch call for one representative chart from each category defined in Section 3.

6.1 Market Data — Price

[ ]

df_price = fetch_chart("market-price", START_DATE, END_DATE)

print("--- Market Price ---")
display(df_price.head())
df_price.info()

--- Market Price ---

	timestamp	datetime	market_price
0	1704067200	2024-01-01	42249.69
1	1704153600	2024-01-02	44176.26
2	1704240000	2024-01-03	44958.98
3	1704326400	2024-01-04	42854.95
4	1704412800	2024-01-05	44190.10

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8 entries, 0 to 7
Data columns (total 3 columns):
 #   Column        Non-Null Count  Dtype         
---  ------        --------------  -----         
 0   timestamp     8 non-null      int64         
 1   datetime      8 non-null      datetime64[ns]
 2   market_price  8 non-null      float64       
dtypes: datetime64[ns](1), float64(1), int64(1)
memory usage: 324.0 bytes

6.2 Market Data — Trade Volume

[ ]

df_volume = fetch_chart("trade-volume", START_DATE, END_DATE)

print("--- Trade Volume (USD) ---")
display(df_volume.head())
df_volume.info()

--- Trade Volume (USD) ---

	timestamp	datetime	trade_volume
0	1704067200	2024-01-01	1.204915e+08
1	1704153600	2024-01-02	1.914118e+08
2	1704240000	2024-01-03	4.996471e+08
3	1704326400	2024-01-04	5.142611e+08
4	1704412800	2024-01-05	3.149481e+08

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8 entries, 0 to 7
Data columns (total 3 columns):
 #   Column        Non-Null Count  Dtype         
---  ------        --------------  -----         
 0   timestamp     8 non-null      int64         
 1   datetime      8 non-null      datetime64[ns]
 2   trade_volume  8 non-null      float64       
dtypes: datetime64[ns](1), float64(1), int64(1)
memory usage: 324.0 bytes

6.3 Blockchain Activity — Confirmed Transactions

[ ]

df_txn = fetch_chart("n-transactions", START_DATE, END_DATE)

print("--- Confirmed Transactions Per Day ---")
display(df_txn.head())
df_txn.info()

--- Confirmed Transactions Per Day ---

	timestamp	datetime	n_transactions
0	1704067200	2024-01-01	657752.0
1	1704153600	2024-01-02	367319.0
2	1704240000	2024-01-03	502749.0
3	1704326400	2024-01-04	482557.0
4	1704412800	2024-01-05	420884.0

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8 entries, 0 to 7
Data columns (total 3 columns):
 #   Column          Non-Null Count  Dtype         
---  ------          --------------  -----         
 0   timestamp       8 non-null      int64         
 1   datetime        8 non-null      datetime64[ns]
 2   n_transactions  8 non-null      float64       
dtypes: datetime64[ns](1), float64(1), int64(1)
memory usage: 324.0 bytes

6.4 Blockchain Activity — Unique Addresses

[ ]

df_addr = fetch_chart("n-unique-addresses", START_DATE, END_DATE)

print("--- Unique Active Addresses Per Day ---")
display(df_addr.head())
df_addr.info()

--- Unique Active Addresses Per Day ---

	timestamp	datetime	n_unique_addresses
0	1704067200	2024-01-01	624628.0
1	1704153600	2024-01-02	550846.0
2	1704240000	2024-01-03	677059.0
3	1704326400	2024-01-04	675009.0
4	1704412800	2024-01-05	645934.0

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8 entries, 0 to 7
Data columns (total 3 columns):
 #   Column              Non-Null Count  Dtype         
---  ------              --------------  -----         
 0   timestamp           8 non-null      int64         
 1   datetime            8 non-null      datetime64[ns]
 2   n_unique_addresses  8 non-null      float64       
dtypes: datetime64[ns](1), float64(1), int64(1)
memory usage: 324.0 bytes

6.5 Mining & Network — Hash Rate

[ ]

df_hash = fetch_chart("hash-rate", START_DATE, END_DATE)

print("--- Network Hash Rate (GH/s) ---")
display(df_hash.head())
df_hash.info()

--- Network Hash Rate (GH/s) ---

	timestamp	datetime	hash_rate
0	1704067200	2024-01-01	5.548140e+08
1	1704153600	2024-01-02	4.689073e+08
2	1704240000	2024-01-03	5.655523e+08
3	1704326400	2024-01-04	6.085056e+08
4	1704412800	2024-01-05	4.939634e+08

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8 entries, 0 to 7
Data columns (total 3 columns):
 #   Column     Non-Null Count  Dtype         
---  ------     --------------  -----         
 0   timestamp  8 non-null      int64         
 1   datetime   8 non-null      datetime64[ns]
 2   hash_rate  8 non-null      float64       
dtypes: datetime64[ns](1), float64(1), int64(1)
memory usage: 324.0 bytes

6.6 Mining & Network — Miner Revenue

[ ]

df_revenue = fetch_chart("miners-revenue", START_DATE, END_DATE)

print("--- Miner Revenue (USD) ---")
display(df_revenue.head())
df_revenue.info()

--- Miner Revenue (USD) ---

	timestamp	datetime	miners_revenue
0	1704067200	2024-01-01	5.087515e+07
1	1704153600	2024-01-02	4.351777e+07
2	1704240000	2024-01-03	4.994274e+07
3	1704326400	2024-01-04	5.174221e+07
4	1704412800	2024-01-05	4.291799e+07

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8 entries, 0 to 7
Data columns (total 3 columns):
 #   Column          Non-Null Count  Dtype         
---  ------          --------------  -----         
 0   timestamp       8 non-null      int64         
 1   datetime        8 non-null      datetime64[ns]
 2   miners_revenue  8 non-null      float64       
dtypes: datetime64[ns](1), float64(1), int64(1)
memory usage: 324.0 bytes

6.7 Mempool — Unconfirmed Transactions

[ ]

df_mempool = fetch_chart("mempool-size", START_DATE, END_DATE)

print("--- Mempool Size (Unconfirmed Transactions) ---")
display(df_mempool.head())
df_mempool.info()

--- Mempool Size (Unconfirmed Transactions) ---

	timestamp	datetime	mempool_size
0	1704067200	2024-01-01 00:00:00	82038643.0
1	1704068100	2024-01-01 00:15:00	83151660.0
2	1704069000	2024-01-01 00:30:00	82448230.0
3	1704069900	2024-01-01 00:45:00	82636299.0
4	1704070800	2024-01-01 01:00:00	82265967.5

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 673 entries, 0 to 672
Data columns (total 3 columns):
 #   Column        Non-Null Count  Dtype         
---  ------        --------------  -----         
 0   timestamp     673 non-null    int64         
 1   datetime      673 non-null    datetime64[ns]
 2   mempool_size  673 non-null    float64       
dtypes: datetime64[ns](1), float64(1), int64(1)
memory usage: 15.9 KB

6.8 Supply — Total BTC in Circulation

[ ]

df_supply = fetch_chart("total-bitcoins", START_DATE, END_DATE)

print("--- Total BTC in Circulation ---")
display(df_supply.head())
df_supply.info()

--- Total BTC in Circulation ---

	timestamp	datetime	total_bitcoins
0	1704068978	2024-01-01 00:29:38	19586168.75
1	1704069091	2024-01-01 00:31:31	19586175.00
2	1704070161	2024-01-01 00:49:21	19586181.25
3	1704070760	2024-01-01 00:59:20	19586187.50
4	1704071678	2024-01-01 01:14:38	19586193.75

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1010 entries, 0 to 1009
Data columns (total 3 columns):
 #   Column          Non-Null Count  Dtype         
---  ------          --------------  -----         
 0   timestamp       1010 non-null   int64         
 1   datetime        1010 non-null   datetime64[ns]
 2   total_bitcoins  1010 non-null   float64       
dtypes: datetime64[ns](1), float64(1), int64(1)
memory usage: 23.8 KB

6.9 Supply — UTXO Count

[ ]

df_utxo = fetch_chart("utxo-count", START_DATE, END_DATE)

print("--- Unspent Transaction Output Count ---")
display(df_utxo.head())
df_utxo.info()

--- Unspent Transaction Output Count ---

	timestamp	datetime	utxo_count
0	1704068978	2024-01-01 00:29:38	154349387.0
1	1704069091	2024-01-01 00:31:31	154348604.0
2	1704070161	2024-01-01 00:49:21	154349266.0
3	1704070760	2024-01-01 00:59:20	154353682.0
4	1704071678	2024-01-01 01:14:38	154355956.0

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1010 entries, 0 to 1009
Data columns (total 3 columns):
 #   Column      Non-Null Count  Dtype         
---  ------      --------------  -----         
 0   timestamp   1010 non-null   int64         
 1   datetime    1010 non-null   datetime64[ns]
 2   utxo_count  1010 non-null   float64       
dtypes: datetime64[ns](1), float64(1), int64(1)
memory usage: 23.8 KB

Conclusion

This notebook provides a robust framework for retrieving various Bitcoin blockchain metrics from the Blockchain.info API. The fetch_chart function standardizes the data acquisition process, allowing for easy and consistent access to market data, blockchain activity, mining and network statistics, and supply and distribution metrics. This allows for rapid exploration and analysis of on-chain data for various research or analytical purposes.

Blockchain Data Fetcher

Blockchain.info Market Data Acquisition Framework

1. Dependency Installation

2. Library Imports

Code Logic

3. Available Chart Types

3.1 Market Data

3.2 Blockchain Activity

3.3 Mining & Network

3.4 Supply & Distribution

4. Configuration

Code Logic

5. Data Extraction Function

Code Logic

6. Execution — One Call Per Chart Type

6.1 Market Data — Price

6.2 Market Data — Trade Volume

6.3 Blockchain Activity — Confirmed Transactions

6.4 Blockchain Activity — Unique Addresses

6.5 Mining & Network — Hash Rate

6.6 Mining & Network — Miner Revenue

6.7 Mempool — Unconfirmed Transactions

6.8 Supply — Total BTC in Circulation

6.9 Supply — UTXO Count

Conclusion