Comparing the Dominance of Mining Pools Using ATLAS

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Last updated 26 days ago

For much of its history, the Bitcoin mining industry has been notoriously opaque. Luckily, it has become a common practice for many BTC mining pools to leave a subtle "miner signature" in each block's coinbase transaction (not to be confused with the popular crypto exchange by the same name). This transaction contains the miner reward— currently 6.25 BTC per block—but also leaves some extra space for arbitrary data, allowing the pool to publicly assert their claim. Using ATLAS v2, we're able to extract this miner signature, enabling us to derive a variety of mining pool metrics.

Resources

This notebook demonstrates basic functionality offered by the Coin Metrics Python API Client and ATLAS blockchain search engine.

Coin Metrics offers a vast assortment of data for hundreds of cryptoassets. The Python API Client allows for easy access to this data using Python without needing to create your own wrappers using requests and other such libraries.

To understand the data that Coin Metrics offers, feel free to peruse the resources below.

The Coin Metrics API v4 website contains the full set of endpoints and data offered by Coin Metrics.
The Coin Metrics Product Documentation gives detailed, conceptual explanations of the data that Coin Metrics offers.
The API Spec contains a full list of functions.

File Download

Download the entire notebook as either a jupyter notebook to run yourself or as a pdf from the two links below

Notebook Setup

from os import environ
import sys
import pandas as pd
import numpy as np
import logging
from datetime import date, datetime, timedelta
from coinmetrics.api_client import CoinMetricsClient
import json
import logging
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
from matplotlib.ticker import FormatStrFormatter, FuncFormatter
from matplotlib import font_manager
import cmasher as cmr
import re
%matplotlib inline
import plotly
from plotly.subplots import make_subplots

logging.basicConfig(
    format='%(asctime)s %(levelname)-8s %(message)s',
    level=logging.INFO,
    datefmt='%Y-%m-%d %H:%M:%S'
)

# We recommend privately storing your API key in your local environment.
try:
    api_key = environ["CM_API_KEY"]
    logging.info("Using API key found in environment")
except KeyError:
    api_key = ""
    logging.info("API key not found. Using community client")

client = CoinMetricsClient(api_key)

2024-10-04 13:46:00 INFO     Using API key found in environment

colors = np.unique(cmr.take_cmap_colors('Set3', 15, return_fmt='hex'))
colors

array(['#80B1D3', '#8DD3C7', '#B3DE69', '#BC80BD', '#BEBADA', '#CCEBC5',
       '#D9D9D9', '#FB8072', '#FCCDE5', '#FDB462', '#FFED6F', '#FFFFB3'],
      dtype='<U7')

ATLAS V2

Retrieve Block Info

block_info = client.get_list_of_blocks_v2(
    asset='btc',
    start_time=datetime.now()-timedelta(days=365),
    page_size=10000
).parallel(time_increment=timedelta(days=1)).to_dataframe()
block_info['extra_data'] = block_info['extra_data'].astype(str)

Exporting to dataframe type: 100%|██████████| 366/366 [00:06<00:00, 58.56it/s]

block_info.head()

	block_hash	height	consensus_time	miner_time	n_transactions	n_balance_updates	parent_block_hash	nonce	extra_data	version	difficulty	physical_size	consensus_size	consensus_size_limit
0	00000000000000000003cd8c9919b1073bab32cee67bfb...	810761	2023-10-05 13:54:00+00:00	2023-10-05 15:21:03+00:00	3658	22668	00000000000000000002667c723b1a1f559ed920504ab6...	3789b287	03095f0c1b4d696e656420627920416e74506f6f6c3830...	545259520	57321508229258.039062	1543713	3993396	4000000
1	000000000000000000000d070d5b4fb5c69ee6bae4bab6...	810762	2023-10-05 14:04:54+00:00	2023-10-05 15:26:11+00:00	2689	18732	00000000000000000003cd8c9919b1073bab32cee67bfb...	388afcd5	030a5f0c45022f62696e616e63652f323139fabe6d6d6d...	536993792	57321508229258.039062	1494856	3993547	4000000
2	00000000000000000004090783eb83951d6d9c91dd88f3...	810763	2023-10-05 14:28:43+00:00	2023-10-05 15:33:08+00:00	2528	17306	000000000000000000000d070d5b4fb5c69ee6bae4bab6...	9b7ba148	030b5f0c192f5669614254432f4d696e6564206279207a...	536928256	57321508229258.039062	1422613	3993322	4000000
3	00000000000000000004cd165ee9f93e68460c37173027...	810764	2023-10-05 14:36:46+00:00	2023-10-05 15:40:56+00:00	3227	20335	00000000000000000004090783eb83951d6d9c91dd88f3...	003804fb	030c5f0c194d696e656420627920416e74506f6f6c20f8...	805298176	57321508229258.039062	1572755	3993278	4000000
4	00000000000000000000fb654d8199a1fc240af3bbc173...	810765	2023-10-05 14:57:48+00:00	2023-10-05 15:50:03+00:00	3078	19352	00000000000000000004cd165ee9f93e68460c37173027...	903b489a	030d5f0c2cfabe6d6d0d426074ccdd9e3b0fe208ab9af1...	538968064	57321508229258.039062	1515726	3997698	4000000

# Assign 'empty' to rows where n_transactions == 1
block_info.loc[block_info['n_transactions'] == 1, 'category'] = 'Empty'

# Assign 'not empty' to remaining rows
block_info.loc[block_info['n_transactions'] != 1, 'category'] = 'Not Empty'

block_info['extra_data'][0]

'03095f0c1b4d696e656420627920416e74506f6f6c383036f800c8022ace0b75fabe6d6d7a90b864b377b7d7de125f87ab7890c46a47d58edcd9d602a6758485a2d7a7c2020000000000000026bb0000d455000000000000'

def convert_to_human_readable_ascii(s):
    # Check if the input string is a valid hexadecimal string
    if all(c in '0123456789abcdefABCDEF' for c in s):
        # Convert the input string to bytes
        b = bytes.fromhex(s)
        # Decode the bytes using the ISO-8859-1 encoding
        result = b.decode('ISO-8859-1')
        return result
    else:
        # Return an error message if the input string is not a valid hexadecimal string
        return 'Error: input is not a valid hexadecimal string'

block_info['miner_tag'] = block_info['extra_data'].apply(convert_to_human_readable_ascii)

block_signed = pd.DataFrame(block_info[['miner_tag','height','consensus_time','category']])
block_signed

	miner_tag	height	consensus_time	category
0	\t_ Mined by AntPool806ø�È*Î uú¾mmz¸d³w·×Þ...	810761	2023-10-05 13:54:00+00:00	Not Empty
1	\n_ E/binance/219ú¾mmm¡¾¼a04Vy>IC°Jgò ¡Ð0...	810762	2023-10-05 14:04:54+00:00	Not Empty
2	_ /ViaBTC/Mined by z180203/,ú¾mmñÊ1ä¾Ú!³K...	810763	2023-10-05 14:28:43+00:00	Not Empty
3	_ Mined by AntPool ø�¢1ê%-ú¾mm»âHjéZ¾®¨!M...	810764	2023-10-05 14:36:46+00:00	Not Empty
4	\r_ ,ú¾mm\rB`tÌÝ;â«ñ\tÆ¢èür¹³a\rmá\rÚ...	810765	2023-10-05 14:57:48+00:00	Not Empty
...	...	...	...	...
53385	/\r< OCEAN.XYZ >3OD�ã.³Lð\n��	864146	2024-10-04 15:50:04+00:00	Not Empty
53386	/\r,ú¾mm÷$@oÈ$~¸ÑÆn¹Ô3 ñîõä ¼��...	864147	2024-10-04 15:51:50+00:00	Not Empty
53387	/\rc�g/Foundry USA Pool #dropgold/+J�½�...	864148	2024-10-04 15:56:39+00:00	Not Empty
53388	/\rMined by AntPool èAx®øú¾mm6úq2iF...	864149	2024-10-04 15:56:44+00:00	Not Empty
53389	/\rMined by AntPool �µóú¾mm¹ý½kØ]"...	864150	2024-10-04 16:22:20+00:00	Not Empty

53390 rows × 4 columns

# List of strings included in coinbase signature
miners = ['AntPool', 'ViaBTC', 'binance', 'Binance', 'Foundry', 'Luxor', 
          'SlushPool', 'slush', 'BTC.com','BTC.COM','btc.com','btccom',
          'bitdeer','btcpool', 'F2Pool', 'poolin','xxxxxx.com','CKPool', 
          'BTC.TOP', 'BTCC', 'MARA Pool', 'Mara Pool', 'SpiderPool']

def detect_keywords(df, column, keywords):
    # Create a new column called 'miner'
    df['miner'] = None
    # Iterate over the miner names
    for miner in miners:
        # Use the update method to detect the miner name in the specified column
        # df['miner'].update(df[column].str.extract(rf'({miner})', expand=False))
        df.update({'miner':df[column].str.extract(rf'({miner})', expand=False)})
    # Replace any rows where the 'miner' column is None with 'Unknown'
    df['miner'] = df['miner'].replace({None: 'Other'})
    return df

miners_tagged = detect_keywords(block_signed, 'miner_tag', miners)
miners_tagged = miners_tagged.set_index('consensus_time')
miners_tagged

	miner_tag	height	category	miner
consensus_time
2023-10-05 13:54:00+00:00	\t_ Mined by AntPool806ø�È*Î uú¾mmz¸d³w·×Þ...	810761	Not Empty	AntPool
2023-10-05 14:04:54+00:00	\n_ E/binance/219ú¾mmm¡¾¼a04Vy>IC°Jgò ¡Ð0...	810762	Not Empty	binance
2023-10-05 14:28:43+00:00	_ /ViaBTC/Mined by z180203/,ú¾mmñÊ1ä¾Ú!³K...	810763	Not Empty	ViaBTC
2023-10-05 14:36:46+00:00	_ Mined by AntPool ø�¢1ê%-ú¾mm»âHjéZ¾®¨!M...	810764	Not Empty	AntPool
2023-10-05 14:57:48+00:00	\r_ ,ú¾mm\rB`tÌÝ;â«ñ\tÆ¢èür¹³a\rmá\rÚ...	810765	Not Empty	F2Pool
...	...	...	...	...
2024-10-04 15:50:04+00:00	/\r< OCEAN.XYZ >3OD�ã.³Lð\n��	864146	Not Empty	Other
2024-10-04 15:51:50+00:00	/\r,ú¾mm÷$@oÈ$~¸ÑÆn¹Ô3 ñîõä ¼��...	864147	Not Empty	F2Pool
2024-10-04 15:56:39+00:00	/\rc�g/Foundry USA Pool #dropgold/+J�½�...	864148	Not Empty	Foundry
2024-10-04 15:56:44+00:00	/\rMined by AntPool èAx®øú¾mm6úq2iF...	864149	Not Empty	AntPool
2024-10-04 16:22:20+00:00	/\rMined by AntPool �µóú¾mm¹ý½kØ]"...	864150	Not Empty	AntPool

miner_tag

height

category

miner

consensus_time

2023-10-05 13:54:00+00:00

\t_ Mined by AntPool806ø�È*Î uú¾mmz¸d³w·×Þ...

810761

Not Empty

AntPool

2023-10-05 14:04:54+00:00

\n_ E/binance/219ú¾mmm¡¾¼a04Vy>IC°Jgò ¡Ð0...

810762

Not Empty

binance

2023-10-05 14:28:43+00:00

_ /ViaBTC/Mined by z180203/,ú¾mmñÊ1ä¾Ú!³K...

810763

Not Empty

ViaBTC

2023-10-05 14:36:46+00:00

_ Mined by AntPool ø�¢1ê%-ú¾mm»âHjéZ¾®¨!M...

810764

Not Empty

AntPool

2023-10-05 14:57:48+00:00

\r_ ,ú¾mm\rB`tÌÝ;â«ñ\tÆ¢èür¹³a\rmá\rÚ...

810765

Not Empty

F2Pool

...

2024-10-04 15:50:04+00:00

/\r< OCEAN.XYZ >3OD�ã.³Lð\n��

864146

Not Empty

Other

2024-10-04 15:51:50+00:00

/\r,ú¾mm÷$@oÈ$~¸ÑÆn¹Ô3 ñîõä ¼��...

864147

Not Empty

F2Pool

2024-10-04 15:56:39+00:00

/\rc�g/Foundry USA Pool #dropgold/+J�½�...

864148

Not Empty

Foundry

2024-10-04 15:56:44+00:00

/\rMined by AntPool èAx®øú¾mm6úq2iF...

864149

Not Empty

AntPool

2024-10-04 16:22:20+00:00

/\rMined by AntPool �µóú¾mm¹ý½kØ]"...

864150

Not Empty

AntPool

53390 rows × 4 columns

def transform_index_to_date(index):
    datetime_index = pd.to_datetime(index)
    date_index = [datetime.date(datetime_obj) for datetime_obj in datetime_index]
    return date_index

date_index = transform_index_to_date(miners_tagged.index)
miners_tagged.index = pd.to_datetime(date_index)

miners_tagged

	miner_tag	height	category	miner
2023-10-05	\t_ Mined by AntPool806ø�È*Î uú¾mmz¸d³w·×Þ...	810761	Not Empty	AntPool
2023-10-05	\n_ E/binance/219ú¾mmm¡¾¼a04Vy>IC°Jgò ¡Ð0...	810762	Not Empty	binance
2023-10-05	_ /ViaBTC/Mined by z180203/,ú¾mmñÊ1ä¾Ú!³K...	810763	Not Empty	ViaBTC
2023-10-05	_ Mined by AntPool ø�¢1ê%-ú¾mm»âHjéZ¾®¨!M...	810764	Not Empty	AntPool
2023-10-05	\r_ ,ú¾mm\rB`tÌÝ;â«ñ\tÆ¢èür¹³a\rmá\rÚ...	810765	Not Empty	F2Pool
...	...	...	...	...
2024-10-04	/\r< OCEAN.XYZ >3OD�ã.³Lð\n��	864146	Not Empty	Other
2024-10-04	/\r,ú¾mm÷$@oÈ$~¸ÑÆn¹Ô3 ñîõä ¼��...	864147	Not Empty	F2Pool
2024-10-04	/\rc�g/Foundry USA Pool #dropgold/+J�½�...	864148	Not Empty	Foundry
2024-10-04	/\rMined by AntPool èAx®øú¾mm6úq2iF...	864149	Not Empty	AntPool
2024-10-04	/\rMined by AntPool �µóú¾mm¹ý½kØ]"...	864150	Not Empty	AntPool

53390 rows × 4 columns

other_miners = miners_tagged.loc[miners_tagged['miner'] == 'Other']
other_miners

	miner_tag	height	category	miner
2023-10-05	_ F°,ñ¿��/NiceHash/	810784	Not Empty	Other
2023-10-06	¬_ /ultimus/783p�?�DòuÄú¾mm(0³4yï@×ì7HWlù»...	810924	Not Empty	Other
2023-10-06	Ë_ \t\t\t \t\t \t_[��	810955	Not Empty	Other
2023-10-06	Ö_ /ultimus/787p�\ÏDú¾mmàÀ.,±r}?^º¿rE¨æ...	810966	Not Empty	Other
2023-10-07	` w !e/SBICrypto.com Pool/ø³bS��	811027	Not Empty	Other
...	...	...	...	...
2024-10-03	/\rMined by SecPool�)Õ ú¾mmÑÀ)Ñgò2e...	864005	Not Empty	Other
2024-10-03	/\rMined by SecPool�9ÕuRíú¾mm(5L,±kÏä½...	864006	Not Empty	Other
2024-10-04	C/\r \t\t\t \t\t \tÉ��\n��	864067	Not Empty	Other
2024-10-04	l/\rdÏÿf/SBICrypto.com Pool/\r$²v��	864108	Not Empty	Other
2024-10-04	/\r< OCEAN.XYZ >3OD�ã.³Lð\n��	864146	Not Empty	Other

3060 rows × 4 columns

miners_tagged['miner'] = miners_tagged['miner'].replace('xxxxxx.com', 'Poolin')
miners_tagged['miner'] = miners_tagged['miner'].replace('poolin', 'Poolin')
miners_tagged['miner'] = miners_tagged['miner'].replace('btccom', 'BTC.com')
miners_tagged['miner'] = miners_tagged['miner'].replace('btc.com', 'BTC.com')
miners_tagged['miner'] = miners_tagged['miner'].replace('btcpool', 'BTC.com')
miners_tagged['miner'] = miners_tagged['miner'].replace('bitdeer', 'BTC.com')
miners_tagged['miner'] = miners_tagged['miner'].replace('slush', 'Braiins Pool')
miners_tagged['miner'] = miners_tagged['miner'].replace('binance', 'Binance Pool')
miners_tagged['miner'] = miners_tagged['miner'].replace('Binance', 'Binance Pool')
miners_tagged['miner'] = miners_tagged['miner'].replace('Mara Pool', 'MARA Pool')

# Group the data by the date and calculate the relative proportions of each category
data = miners_tagged.groupby(miners_tagged.index.date)['miner'].value_counts(normalize=True)
data = data.unstack()

averages = data.sum()
averages = pd.DataFrame(averages.sort_values(ascending=False))
averages = averages.index.tolist()
data = data[averages]

data

miner	Foundry	AntPool	ViaBTC	F2Pool	Other	Binance Pool	MARA Pool	Luxor	SpiderPool	Braiins Pool	BTC.com	Poolin
2023-10-05	0.178571	0.303571	0.160714	0.160714	0.017857	0.125000	0.017857	0.035714	NaN	NaN	NaN	NaN
2023-10-06	0.237500	0.256250	0.112500	0.143750	0.018750	0.081250	0.062500	0.043750	NaN	0.006250	0.012500	0.025000
2023-10-07	0.263158	0.280702	0.087719	0.122807	0.046784	0.046784	0.040936	0.035088	NaN	0.011696	0.046784	0.017544
2023-10-08	0.310811	0.250000	0.121622	0.108108	0.033784	0.067568	0.027027	0.027027	NaN	0.033784	0.013514	0.006757
2023-10-09	0.269231	0.250000	0.115385	0.115385	0.038462	0.108974	0.038462	0.019231	NaN	0.012821	0.019231	0.012821
...	...	...	...	...	...	...	...	...	...	...	...	...
2024-09-30	0.357664	0.255474	0.065693	0.131387	0.058394	0.021898	0.021898	0.007299	0.058394	0.007299	NaN	0.014599
2024-10-01	0.324841	0.305732	0.133758	0.095541	0.057325	0.012739	0.019108	0.019108	0.006369	0.025478	NaN	NaN
2024-10-02	0.270968	0.219355	0.174194	0.083871	0.064516	0.032258	0.064516	0.025806	0.032258	0.012903	0.012903	0.006452
2024-10-03	0.234177	0.316456	0.101266	0.107595	0.082278	0.037975	0.037975	0.012658	0.031646	0.025316	0.012658	NaN
2024-10-04	0.300000	0.281818	0.181818	0.054545	0.027273	0.027273	0.009091	0.036364	0.018182	0.045455	0.018182	NaN

366 rows × 12 columns

# Plot the data as an area chart
ax = data.plot.area(figsize=(8, 6),fontsize=13.5,color=colors)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b'))
ax.set_title('\nBitcoin Mining Pool \nDominance (2023 - 2024)\n',fontsize=22,fontdict={'font':'arial'})
ax.axhline(0.5, linestyle='--', color='black')
plt.xlim([miners_tagged.index[0],miners_tagged.index[-1]])
plt.ylim(0,1)
plt.yticks([0.2, 0.4, 0.6, 0.8, 1.0], ['20%','40%','60%','80%','100%'],fontdict={'font':'arial','size':15})
plt.legend(bbox_to_anchor=(1,1),frameon=False)
plt.annotate('Source: Coin Metrics ATLAS',weight='book',font='arial',xy=(1.001, 0.001), xycoords='axes fraction',color='black',xytext=(-8, 6), textcoords='offset pixels',horizontalalignment='right',verticalalignment='bottom')
plt.savefig('./Pool-Dominance.png',facecolor='white',dpi=100)
plt.show()

total_blocks = pd.DataFrame(miners_tagged.index.value_counts())
total_blocks.rename(columns={total_blocks.columns[0]: 'Total Blocks'}, inplace=True)
total_blocks

	Total Blocks
2024-02-02	188
2024-01-25	181
2024-03-07	180
2024-07-22	179
2024-02-12	179
...	...
2024-01-15	114
2024-06-23	112
2024-10-04	110
2024-09-11	108
2023-10-05	56

366 rows × 1 columns

empty_blocks = miners_tagged.groupby([miners_tagged.index.date,miners_tagged.miner])['category'].value_counts()
empty_blocks = empty_blocks.unstack()

empty_blocks = pd.DataFrame(empty_blocks.reset_index()).fillna(0).set_index('level_0')
empty_blocks

category	miner	Empty	Not Empty
level_0
2023-10-05	AntPool	0.0	17.0
2023-10-05	Binance Pool	0.0	7.0
2023-10-05	F2Pool	1.0	8.0
2023-10-05	Foundry	0.0	10.0
2023-10-05	Luxor	0.0	2.0
...	...	...	...
2024-10-04	Luxor	0.0	4.0
2024-10-04	MARA Pool	0.0	1.0
2024-10-04	Other	0.0	3.0
2024-10-04	SpiderPool	0.0	2.0
2024-10-04	ViaBTC	0.0	20.0

4004 rows × 3 columns

df_pivot = empty_blocks.pivot_table(index=empty_blocks.index, columns="miner", values="Empty")
df_pivot = df_pivot.join(total_blocks[['Total Blocks']])
df_pivot = df_pivot.fillna(0)
df_pivot

	AntPool	BTC.com	Binance Pool	Braiins Pool	F2Pool	Foundry	Luxor	MARA Pool	Other	Poolin	SpiderPool	ViaBTC	Total Blocks
level_0
2023-10-05	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	56
2023-10-06	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	160
2023-10-07	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	171
2023-10-08	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	148
2023-10-09	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	156
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2024-09-30	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	137
2024-10-01	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	157
2024-10-02	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	155
2024-10-03	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	158
2024-10-04	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	110

366 rows × 13 columns

df_empty = df_pivot.iloc[:,0:].div(df_pivot['Total Blocks'], axis=0)
df_empty = df_empty.drop(df_empty.columns[-1:], axis=1)

df_empty = df_empty[averages]
df_empty

	Foundry	AntPool	ViaBTC	F2Pool	Other	Binance Pool	MARA Pool	Luxor	SpiderPool	Braiins Pool	BTC.com	Poolin
level_0
2023-10-05	0.0	0.000000	0.0	0.017857	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0
2023-10-06	0.0	0.000000	0.0	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0
2023-10-07	0.0	0.000000	0.0	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0
2023-10-08	0.0	0.000000	0.0	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0
2023-10-09	0.0	0.006410	0.0	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...
2024-09-30	0.0	0.000000	0.0	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0
2024-10-01	0.0	0.006369	0.0	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0
2024-10-02	0.0	0.006452	0.0	0.000000	0.0	0.0	0.0	0.006452	0.006452	0.0	0.0	0.0
2024-10-03	0.0	0.000000	0.0	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0
2024-10-04	0.0	0.000000	0.0	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0

366 rows × 12 columns

fig, ax = plt.subplots(figsize=(8, 6))
cumval = 0
i = 0
for col in df_empty.columns:
    plt.bar(df_empty.index, df_empty[col], bottom=cumval, label=col, color=colors[i])
    cumval = cumval+df_empty[col]
    i += 1
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b'))
plt.title('\nEmpty Blocks\nTotal Percentage (2023 - 2024)\n', fontdict = {'size':18, 'font': 'arial'})
plt.xticks(rotation=0)
plt.xlabel('')
plt.legend(bbox_to_anchor=(1,1),frameon=False)
plt.yticks([0.005, 0.010, 0.015, 0.02, 0.025], ['0.5%','1.0%','1.5%','2.0%','2.5%'],fontdict={'size':14})
plt.grid(True, axis='y',linestyle='--',alpha=0.5)
plt.annotate('Source: Coin Metrics ATLAS',weight='book',font='arial',xy=(1.00, 0.953), xycoords='axes fraction',color='black',xytext=(-8, 6), textcoords='offset pixels',horizontalalignment='right',verticalalignment='bottom')
plt.savefig('./Empty-Blocks-Total-2023-2024.png',facecolor='white',dpi=100)
plt.xlabel('');

empty_blocks_total = pd.DataFrame(miners_tagged.groupby([miners_tagged.miner])['category'].value_counts(normalize=True))
empty_blocks_total = empty_blocks_total.unstack().fillna(0)
empty_blocks_total.columns = empty_blocks_total.columns.droplevel(0)
empty_blocks_total = empty_blocks_total.reindex(averages)
empty_blocks_total

category	Empty	Not Empty
miner
Foundry	0.000000	1.000000
AntPool	0.005885	0.994115
ViaBTC	0.002395	0.997605
F2Pool	0.003208	0.996792
Other	0.003595	0.996405
Binance Pool	0.002993	0.997007
MARA Pool	0.000000	1.000000
Luxor	0.005000	0.995000
SpiderPool	0.035996	0.964004
Braiins Pool	0.002331	0.997669
BTC.com	0.004071	0.995929
Poolin	0.004640	0.995360

pool_total_blocks = pd.DataFrame(miners_tagged['miner'].value_counts())
pool_total_blocks = pool_total_blocks.drop('Other')
pool_total_blocks.rename(columns={pool_total_blocks.columns[0]: 'Total Blocks Mined'}, inplace=True)
pool_total_blocks

	Total Blocks Mined
miner
Foundry	15492
AntPool	13595
ViaBTC	6680
F2Pool	5923
Binance Pool	2339
MARA Pool	1986
Luxor	1400
SpiderPool	889
Braiins Pool	858
BTC.com	737
Poolin	431

row_num = empty_blocks_total.index.get_loc('Other')
colors_no_other = np.delete(colors, row_num)
empty_blocks_total = empty_blocks_total.drop('Other')

pool_total_blocks.reset_index()

	miner	Total Blocks Mined
0	Foundry	15492
1	AntPool	13595
2	ViaBTC	6680
3	F2Pool	5923
4	Binance Pool	2339
5	MARA Pool	1986
6	Luxor	1400
7	SpiderPool	889
8	Braiins Pool	858
9	BTC.com	737
10	Poolin	431

ax = empty_blocks_total['Empty'].plot.bar(figsize=(8, 6),stacked=True,fontsize=12,color=colors_no_other,width=0.8,zorder=2)
def format_percent(x, pos):
    return '{:.2%}'.format(x)
formatter = FuncFormatter(format_percent)
ax.yaxis.set_major_formatter(formatter)
plt.xticks(rotation=45,size=12)
plt.yticks(rotation=0,size=12)
plt.title('\nEmpty Blocks vs. Total Blocks\nby Mining Pool (Oct 2023 - Oct 2024)\n', fontdict = {'size':18, 'font': 'arial'})
plt.xlabel('');
plt.ylabel('Empty Blocks (%)',fontsize=14,labelpad=10);
plt.tick_params(axis='both', which='both', length=0, pad=8)
plt.ylim(0,0.04)
ax.grid(True, axis='y',linestyle='--',zorder=1,alpha=0.5)
ax2 = ax.twinx()
pool_total_blocks.plot.line(ax=ax2,color='black',linestyle='--',legend=True)
pool_total_blocks.reset_index().plot.scatter(x='miner',y='Total Blocks Mined',ax=ax2,color='black', s=50)
ax2.set_yticks([0,2000,4000,6000,8000,10000,12000,14000,16000])
plt.ylim(0,16000)
ax2.yaxis.set_major_formatter(mtick.StrMethodFormatter('{x:,.0f}'))
plt.legend(bbox_to_anchor=(1,1.0),frameon=False,fontsize=11.3)
plt.tick_params(axis='both', which='both', length=0, pad=8)
plt.yticks(rotation=0,size=12)
plt.ylabel('Total Blocks Mined\n',fontsize=14,labelpad=10);
plt.annotate(
    'Source: Coin Metrics ATLAS',
    weight='book',
    font='arial',
    fontsize=12,
    xy=(1.0, 1.0),
    xycoords='axes fraction',
    color='black',
    xytext=(-8, 6),
    textcoords='offset pixels',
    horizontalalignment='right',
    verticalalignment='bottom'
)
plt.savefig('./Empty-Blocks-by-Pool.png',facecolor='white',dpi=100)