Getting Started With Futures Data

This notebook demonstrates basic functionality offered by the Coin Metrics Python API Client and Market Data Feed.

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.

Resources

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

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MDF_futures_overview.ipynb

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MDF_futures_overview.pdf

pdf

Notebook Setup

import os
from os import environ
import sys
import pandas as pd
import numpy as np
import seaborn as sns
import logging
from datetime import date, datetime, timedelta
from coinmetrics.api_client import CoinMetricsClient
import json
import logging
from pytz import timezone as timezone_conv
from datetime import timezone as timezone_info
import matplotlib.ticker as mticker
from matplotlib.ticker import ScalarFormatter
from matplotlib.ticker import FuncFormatter
from matplotlib.dates import DateFormatter
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
%matplotlib inline

sns.set_theme()
sns.set(rc={'figure.figsize':(8,6)})

logging.basicConfig(
    format='%(asctime)s %(levelname)-8s %(message)s',
    level=logging.INFO,
    datefmt='%Y-%m-%d %H:%M:%S'
)
now = datetime.utcnow()
last_day_date_time = now - timedelta(hours = 24)

# 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)

2024-09-16 16:41:57 INFO     Using API key found in environment

Futures Catalog

Futures contracts are standardized contracts that allow counterparties to enter into an agreement to buy or sell a standardized asset under contract specifications that are defined by the exchange. Each specific futures contract offered by a specific exchange will have identical contract specifications regardless of who is the counterparty.

The contract specifications include information such as the underlying base and quote asset, the margin asset, the contract size, the listing time, expiration time, and other terms.

Coin Metrics offers contract specifications for both futures and options. Here we define futures to include both non-perpetual futures that expire and perpetual futures (sometimes called perpetual swaps).

market_reference = client.reference_data_markets(
    type='future',
    page_size=10000
).to_dataframe()

print('Total number of supported futures markets: ' + str(len(market_reference)))

Total number of supported futures markets: 18682

# Perpetual futures markets are any futures market with null expiration
print('Total number of perpetual futures markets: ' + str(len(market_reference.loc[market_reference['expiration'].isna()])))

Total number of perpetual futures markets: 4222

# Filter by base or quote asset
print('Total number of supported BTC futures markets: ' + str(len(market_reference.loc[market_reference['base'] == 'btc'])))

Total number of supported BTC futures markets: 3109

# Select first BTC futures market as an example
market_reference.loc[market_reference['base'] == 'btc'].iloc[0]

market                            binance-BTCBUSD-future
exchange                                         binance
base                                                 btc
quote                                               busd
pair                                            btc-busd
symbol                                           BTCBUSD
type                                              future
size_asset                                           btc
margin_asset                                        busd
strike                                              <NA>
option_contract_type                                <NA>
is_european                                         <NA>
contract_size                                        1.0
tick_size                                            0.1
multiplier_size                                     <NA>
listing                   2021-01-11T08:00:00.000000000Z
expiration                                          <NA>
settlement_price                                    <NA>
pool_config_id                                      <NA>
contract_address                                    <NA>
fee                                                 <NA>
price_includes_fee                                  <NA>
variable_fee                                        <NA>
base_address                                        <NA>
quote_address                                       <NA>
status                                              <NA>
order_amount_increment                             0.001
order_amount_min                                   0.001
order_amount_max                                     500
order_price_increment                                0.1
order_price_min                                    557.6
order_price_max                                4529890.0
order_size_min                                       5.0
order_taker_fee                                     <NA>
order_maker_fee                                     <NA>
margin_trading_enabled                              <NA>
experimental                                        <NA>
Name: 151, dtype: object

Open Interest

Open interest represents the number of contracts that are currently outstanding and not settled for a specific derivatives market.

Open Interest is available at various levels:

• Assets level (i.e btc)

• Asset Pair level (i.e. btc-usd)

• Exchange level (i.e. binance)

• Exchange-Asset level (i.e. binance-btc)

• Market level (i.e. binance-BTCUSDT-future)

BTC Open Interest at the Market Level

binance_btcusdt_oi = client.get_market_open_interest(
    markets = 'binance-BTCUSDT-future',
    start_time = datetime.utcnow() - timedelta(days=1),
).to_dataframe()

binance_btcusdt_oi

	market	time	contract_count	value_usd	database_time	exchange_time
0	binance-BTCUSDT-future	2024-09-15 21:42:00+00:00	87506.236	5214374094.5096	2024-09-15 21:42:31.800754+00:00	2024-09-15 21:42:00+00:00
1	binance-BTCUSDT-future	2024-09-15 21:43:00+00:00	87493.551	5213766952.1553	2024-09-15 21:43:55.721238+00:00	2024-09-15 21:43:00+00:00
2	binance-BTCUSDT-future	2024-09-15 21:44:00+00:00	87505.378	5215066763.2038	2024-09-15 21:44:48.749434+00:00	2024-09-15 21:44:00+00:00
3	binance-BTCUSDT-future	2024-09-15 21:45:00+00:00	87499.076	5215364925.1648	2024-09-15 21:45:41.693858+00:00	2024-09-15 21:45:00+00:00
4	binance-BTCUSDT-future	2024-09-15 21:46:00+00:00	87503.139	5214338303.9517	2024-09-15 21:46:35.001759+00:00	2024-09-15 21:46:00+00:00
...	...	...	...	...	...	...
1435	binance-BTCUSDT-future	2024-09-16 21:37:00+00:00	84817.5	4903884915.75	2024-09-16 21:37:39.951283+00:00	2024-09-16 21:37:00+00:00
1436	binance-BTCUSDT-future	2024-09-16 21:38:00+00:00	84825.836	4902466778.702	2024-09-16 21:38:33.480165+00:00	2024-09-16 21:38:00+00:00
1437	binance-BTCUSDT-future	2024-09-16 21:39:00+00:00	84832.474	4902417772.9756	2024-09-16 21:39:26.976255+00:00	2024-09-16 21:39:00+00:00
1438	binance-BTCUSDT-future	2024-09-16 21:40:00+00:00	84842.805	4902471801.315	2024-09-16 21:40:46.680850+00:00	2024-09-16 21:40:00+00:00
1439	binance-BTCUSDT-future	2024-09-16 21:41:00+00:00	84851.162	4904380193.3676	2024-09-16 21:41:40.406353+00:00	2024-09-16 21:41:00+00:00

1440 rows × 6 columns

BTC Open Interest by Exchange (Exchange-Asset Endpoint)

oi_catalog = client.catalog_exchange_asset_metrics_v2(metrics='open_interest_reported_future_usd').to_dataframe()
oi_catalog = oi_catalog[oi_catalog['exchange_asset'].str.split('-').str[1] == 'btc']
exchange_assets = oi_catalog['exchange_asset'].to_list()

Use the get_exchange_asset_metrics client function to pull the all BTC exchange-asset pairs at daily frequency:

btc_oi = client.get_exchange_asset_metrics(
    exchange_assets = exchange_assets,
    metrics = 'open_interest_reported_future_usd',
    start_time = datetime.utcnow() - timedelta(days=365),
    frequency = '1d'
).to_dataframe()

# Convert 'open_interest_reported_future_usd' to numeric
btc_oi['open_interest_reported_future_usd'] = btc_oi['open_interest_reported_future_usd'].astype(np.float64)

# Convert 'time' to datetime
btc_oi['time'] = btc_oi['time'].dt.tz_localize(None).astype('datetime64[ns]')
btc_oi

	exchange_asset	time	open_interest_reported_future_usd
0	binance-btc	2023-09-18	3.074676e+09
1	binance-btc	2023-09-19	3.220249e+09
2	binance-btc	2023-09-20	3.359060e+09
3	binance-btc	2023-09-21	3.272911e+09
4	binance-btc	2023-09-22	3.280109e+09
...	...	...	...
3280	okex-btc	2024-09-12	2.346645e+09
3281	okex-btc	2024-09-13	2.431933e+09
3282	okex-btc	2024-09-14	2.693548e+09
3283	okex-btc	2024-09-15	2.608818e+09
3284	okex-btc	2024-09-16	2.569876e+09

3285 rows × 3 columns

# Drop rows with missing data
btc_oi.dropna(inplace=True)

exchanges = btc_oi['exchange_asset'].unique()
dates = btc_oi['time'].unique() 
stacked_data = [btc_oi[btc_oi['exchange_asset'] == exchange]['open_interest_reported_future_usd'].values for exchange in exchanges]

fig, ax = plt.subplots(figsize=(12, 7))
ax.stackplot(dates, stacked_data, labels=exchanges, edgecolor='none')

ax.set_title('\nBTC Open Interest\nby Exchange\n', fontsize=16)
ax.set_xlabel('', fontsize=14)
ax.set_ylabel('Reported Open Interest (USD)\n', fontsize=14)
ax.legend(loc='upper left', title='Exchange Asset', bbox_to_anchor=(1,1), frameon=False)
ax.grid(True, linestyle='--', alpha=0.5, color='gray')
ax.set_facecolor('white')

# Format y-axis in billions of dollars
def billions(x, pos):
    return f'${x * 1e-9:.1f}B'

ax.yaxis.set_major_formatter(FuncFormatter(billions))

ax.set_xlim([btc_oi['time'].min(), btc_oi['time'].max()])
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b\n%Y'))
fig.autofmt_xdate()
ax.tick_params(axis='x', which='major', pad=10) 
_ = plt.xticks(rotation=0)
plt.tight_layout()
plt.show()

Liquidations

Next, we'll take a look at liquidations data. As a reminder, exchanges which offer futures markets utilize a risk management system that will attempt to close a user’s position before the point at which the user begins to owe more than what is in the user's account. The trade or order that closes the user's position is referred to as a liquidation.

This time, we'll use the get_market_liquidations client function to pull all BTCUSDT liquidations over the last 24 hours on Binance:

market = 'binance-BTCUSDT-future'

liquidations_df = client.get_market_liquidations(
    markets = market,
    start_time = datetime.utcnow() - timedelta(days=1),
).to_dataframe()
liquidations_df['amount'] = liquidations_df['amount'].astype(np.float64)
liquidations_df['price'] = liquidations_df['price'].astype(np.float64)

liquidations_df.head()

	market	time	coin_metrics_id	amount	price	type	database_time	side
0	binance-BTCUSDT-future	2024-09-15 21:54:34.145000+00:00	1726437274145000000	0.011	59648.0	trade	2024-09-15 21:54:34.297968+00:00	buy
1	binance-BTCUSDT-future	2024-09-15 21:57:52.361000+00:00	1726437472361000000	0.006	59663.1	trade	2024-09-15 21:57:53.574117+00:00	buy
2	binance-BTCUSDT-future	2024-09-15 22:02:17.426000+00:00	1726437737426000000	0.242	59511.0	trade	2024-09-15 22:02:18.244282+00:00	sell
3	binance-BTCUSDT-future	2024-09-15 22:02:19.433000+00:00	1726437739433000000	0.041	59503.1	trade	2024-09-15 22:02:20.362299+00:00	sell
4	binance-BTCUSDT-future	2024-09-15 22:02:33.095000+00:00	1726437753095000000	0.009	59479.2	trade	2024-09-15 22:02:34.376645+00:00	sell

# Get volume-weighted average price of the futures contract from the market-candles endpoint
price = client.get_market_candles(
        markets = market,
        start_time = datetime.utcnow() - timedelta(days=1),
        end_time = datetime.utcnow(),
        frequency='1m'
).to_dataframe()
price['vwap'] = price['vwap'].astype(np.float64)

import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter

plt.figure(figsize=(13,7))
scaling_factor = 300  # Adjust this value to get the desired point size
color_map = {'buy': 'green', 'sell': 'red'}
liqs = plt.scatter(
    x=liquidations_df['time'],
    y=liquidations_df['price'],
    s=liquidations_df['amount'] * scaling_factor,  # Scale point sizes by the scaling factor
    c=liquidations_df['side'].map(color_map),
    alpha=0.6
)

plt.plot(price['time'], price['vwap'], color='black', linestyle='-', label='VWAP')

mean_price = liquidations_df['price'].mean()
std_price = liquidations_df['price'].std()
plt.ylim(mean_price - 3*std_price, mean_price + 3*std_price)
plt.xlabel("", fontsize=15)
plt.ylabel("Price\n", font='arial',fontsize=15)
plt.title('\n' + str(market) + '\nLiquidations\n', size=20)

# Format the xtick labels
date_format = DateFormatter('%D\n%H:%M')
plt.gca().xaxis.set_major_formatter(date_format)

legend_labels = ['BUY', 'SELL', 'VWAP']
legend_handles = [
    plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=color_map['buy'], markersize=12),
    plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=color_map['sell'], markersize=12),
    plt.Line2D([0], [0], color='black', lw=2)  # Legend entry for VWAP
]
legend = plt.legend(legend_handles, legend_labels, loc='lower right', fontsize=14, ncol=2, framealpha=0, bbox_to_anchor=(0.99, 1.02))

plt.gca().set_facecolor('white')
plt.grid(color='black', linestyle='dotted')

plt.tight_layout() 
plt.show()

png

Notice that this timeseries also includes the liquidation type. Some exchanges report “liquidations orders” in which they will report the creation of a liquidation order when a trader’s position initially enters liquidation. When a trader’s position enters liquidation, an exchange will typically enter a limit order at the price at which the trader will be bankruptcy price. The liquidation orders will show the amount of the position that is being liquidated and the liquidation price, but will not represent the matched trades that are executed as a result of the liquidation. Other exchanges will report “liquidation trades” which represent the actual matched trade as a result of a liquidation order but will not report liquidation orders. Some exchanges will report both liquidation orders and liquidation trades.

Aggregated Liquidation Metrics

In addition to examining individual liquidations, we can also leverage aggregated liquidations metrics. This allows us to quickly view the total amount of USD-denominated liquidations that have occurred over large timeframes, without needing to aggregate the amounts at the trade level.

metrics = ['liquidations_reported_future_buy_usd_1h', 'liquidations_reported_future_sell_usd_1h']

liq_catalog = client.catalog_exchange_asset_metrics_v2(metrics=metrics).to_dataframe()
liq_catalog = liq_catalog[liq_catalog['exchange_asset'].str.split('-').str[1] == 'btc']
liq_catalog

	exchange_asset	metrics
61	binance-btc	[{'metric': 'liquidations_reported_future_buy_...
335	bitfinex-btc	[{'metric': 'liquidations_reported_future_buy_...
401	bitmex-btc	[{'metric': 'liquidations_reported_future_buy_...
577	bybit-btc	[{'metric': 'liquidations_reported_future_buy_...
925	deribit-btc	[{'metric': 'liquidations_reported_future_buy_...
978	ftx-btc	[{'metric': 'liquidations_reported_future_buy_...
1213	huobi-btc	[{'metric': 'liquidations_reported_future_buy_...
1475	kraken-btc	[{'metric': 'liquidations_reported_future_buy_...
1688	okex-btc	[{'metric': 'liquidations_reported_future_buy_...

agg_liqs = client.get_exchange_asset_metrics(
    exchange_assets=liq_catalog['exchange_asset'].to_list(),
    metrics = metrics,
    start_time = datetime.utcnow() - timedelta(days=1.5),
    frequency='1h'
).to_dataframe()
agg_liqs.replace('None', np.nan, inplace=True)
agg_liqs[metrics[0]] = agg_liqs['liquidations_reported_future_buy_usd_1h'].astype(np.float64)
agg_liqs['liquidations_reported_future_sell_usd_1h'] = -1 * agg_liqs['liquidations_reported_future_sell_usd_1h'].astype(np.float64)

agg_liqs = agg_liqs.fillna(0)
agg_liqs

	exchange_asset	time	liquidations_reported_future_buy_usd_1h	liquidations_reported_future_sell_usd_1h
0	binance-btc	2024-09-15 10:00:00+00:00	41189.76644	-123119.61981
1	binance-btc	2024-09-15 11:00:00+00:00	2219.32844	-26757.74386
2	binance-btc	2024-09-15 12:00:00+00:00	20766.66452	-540.03552
3	binance-btc	2024-09-15 13:00:00+00:00	166694.89730	-28269.05000
4	binance-btc	2024-09-15 14:00:00+00:00	208587.82170	-319846.10928
...	...	...	...	...
167	okex-btc	2024-09-16 16:00:00+00:00	2309.60000	-231.20680
168	okex-btc	2024-09-16 17:00:00+00:00	753306.85950	-676171.41858
169	okex-btc	2024-09-16 18:00:00+00:00	0.00000	-245113.87800
170	okex-btc	2024-09-16 19:00:00+00:00	0.00000	-18840.23928
171	okex-btc	2024-09-16 20:00:00+00:00	52104.18704	-131510.72160

172 rows × 4 columns

btc_total_oi = client.get_asset_metrics(
    assets='btc',
    metrics='open_interest_reported_future_usd',
    frequency='1h', 
    start_time = datetime.utcnow() - timedelta(days=2)
).to_dataframe()
btc_total_oi.head()

	asset	time	open_interest_reported_future_usd
0	btc	2024-09-14 22:00:00+00:00	27085472082.8134
1	btc	2024-09-14 23:00:00+00:00	27086895034.416401
2	btc	2024-09-15 00:00:00+00:00	27089846618.242401
3	btc	2024-09-15 01:00:00+00:00	27103605277.447899
4	btc	2024-09-15 02:00:00+00:00	27206441557.918598

df = agg_liqs

melted_df = df.melt(id_vars=['time', 'exchange_asset'], 
                    value_vars=['liquidations_reported_future_buy_usd_1h', 'liquidations_reported_future_sell_usd_1h'],
                    var_name='transaction_type', value_name='amount')
melted_df['amount'] /= 1e6
melted_df['time'] = melted_df['time'].dt.tz_localize(None)

fig, ax = plt.subplots(figsize=(12,7))
plt.gca().set_facecolor('white')
plt.grid(color='gray', linestyle='dotted',alpha=0.3)
ax2 = ax.twinx()

unique_assets = melted_df['exchange_asset'].unique()
colormap = plt.cm.tab20
colors = {asset: colormap(i) for i, asset in enumerate(unique_assets)}

for asset in unique_assets:
    subset = melted_df[melted_df['exchange_asset'] == asset]
    ax.bar(subset['time'], subset['amount'], width=0.01, label=asset, color=colors[asset]) 

# Plot open interest on secondary y-axis
btc_total_oi['time'] = btc_total_oi['time'].dt.tz_localize(None)
ax2.plot(btc_total_oi['time'], btc_total_oi['open_interest_reported_future_usd'], color='black', label='Open Interest (USD)', linewidth=1, linestyle='--')

ax.set_xlabel('')
ax.set_ylabel('Amount Liquidated (USD)', fontsize=14)
ax2.set_ylabel('\nOpen Interest (USD)', fontsize=14)
ax.set_title('\nBTC Hourly Liquidations \nand Open Interest (USD)\n', fontsize=16)

locator = mdates.HourLocator(interval=6)  
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%D\n%H:%M'))  

# Format y-axis ticks for liquidations
def y_formatter(x, pos):
    if x < 0:
        return f"-${abs(x):.1f}M"
    else:
        return f"${x:.1f}M"

ax.yaxis.set_major_formatter(mticker.FuncFormatter(y_formatter))
ax.yaxis.grid(True, linestyle='--', which='major')  
ax2.yaxis.grid(False)  
ax.yaxis.tick_left()  
ax.tick_params(axis='both', length=0, labelsize=12)
ax2.tick_params(axis='both', length=0, labelsize=12) 

# Format y-axis ticks for open interest in billions
def y_formatter_billion(x, pos):
    return f"${x*1e-9:.2f}B"
ax2.yaxis.set_major_formatter(mticker.FuncFormatter(y_formatter_billion))

num_ticks = 10
yticks = np.linspace(melted_df['amount'].min(), melted_df['amount'].max(), num_ticks)
ax.set_yticks(yticks)

# Set y-ticks based on a similar range
yticks2 = np.linspace(btc_total_oi['open_interest_reported_future_usd'].min(), 
                      btc_total_oi['open_interest_reported_future_usd'].max(), num_ticks)
ax2.set_yticks(yticks2)

# Legend for both axes
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax2.legend(lines + lines2, labels + labels2, loc='upper right', fontsize=10, ncol=2, framealpha=0, bbox_to_anchor=(1.08, 1.19))

plt.tight_layout()

for spine in ax2.spines.values():
    spine.set_visible(False)
for spine in ax.spines.values():
    spine.set_visible(False)

plt.show()

png

Funding Rates

Funding rates are a mechanism that exchanges use to ensure that perpetual futures trade at a price that is close to the price of the underlying spot markets. The funding rate is used to calculate the funding fee which long position holders pay short position holders, or vice versa, as a way to incentivize market participants to take positions that keep perpetual futures prices close to the underlying.

Coin Metrics funding rate data from the timeseries/market-funding-rates endpoint includes the following fields:

• market: The id of the market. Market ids use the following naming convention: exchangeName-baseAsset-quoteAsset-spot for spot markets, exchangeName-futuresSymbol-future for futures markets, and exchangeName-optionsSymbol-option for options markets.

• time: The exchange-reported time in ISO 8601 date-time format. Always with nanoseconds precision.

• rate: The funding rate expressed as a percentage over the period. For example, if the funding rate is 0.10%, expressed as an 8 hour rate and calculated over the past 8 hours, the rate is 0.0010.

• period: The periodicity of the funding rate. If the rate is 0.0010then this rate would be applied every period defined by this field.

• interval: The interval of time over which the funding rate is calculated.

• database_time: The timestamp when the data was saved in the database in ISO 8601 date-time format with nanoseconds precision.

fr_catalog = client.catalog_market_funding_rates_v2(exchange='binance').to_dataframe()

fr_catalog

	market	min_time	max_time
0	binance-1000BONKUSDC-future	2024-05-01 00:00:00+00:00	2024-09-16 20:00:00+00:00
1	binance-1000BONKUSDT-future	2023-11-22 16:00:00+00:00	2024-09-16 20:00:00+00:00
2	binance-1000BTTCUSDT-future	2022-01-26 08:00:00.001000+00:00	2022-04-11 08:00:00+00:00
3	binance-1000FLOKIUSDT-future	2023-05-06 16:00:00+00:00	2024-09-16 16:00:00+00:00
4	binance-1000LUNCBUSD-future	2022-05-30 16:00:00.005000+00:00	2023-06-08 08:00:00+00:00
...	...	...	...
442	binance-ZILUSDT-future	2020-06-17 08:00:00.007000+00:00	2024-09-16 16:00:00+00:00
443	binance-ZILUSD_PERP-future	2022-04-06 08:00:00.013000+00:00	2022-12-26 08:00:00.014000+00:00
444	binance-ZKUSDT-future	2024-06-17 12:00:00+00:00	2024-09-16 20:00:00+00:00
445	binance-ZROUSDT-future	2024-06-20 16:00:00+00:00	2024-09-16 20:00:00+00:00
446	binance-ZRXUSDT-future	2020-06-24 08:00:00+00:00	2024-09-16 16:00:00+00:00

447 rows × 3 columns

fr_markets = [
    'bitmex-XBTUSD-future',
    'bybit-BTCUSD-future',
    'okex-BTC-USD-SWAP-future'
]

fr_raw = client.get_market_funding_rates(
    markets = fr_markets,
    start_time=datetime.utcnow() - timedelta(days=7),
).to_dataframe()
fr_raw

	market	time	database_time	rate	period	interval
0	bitmex-XBTUSD-future	2024-09-10 04:00:00+00:00	2024-09-10 04:00:55.910555+00:00	-0.000116	08:00:00	08:00:00
1	bitmex-XBTUSD-future	2024-09-10 12:00:00+00:00	2024-09-10 12:00:40.929397+00:00	0.000037	08:00:00	08:00:00
2	bitmex-XBTUSD-future	2024-09-10 20:00:00+00:00	2024-09-10 20:00:01.451626+00:00	0.000048	08:00:00	08:00:00
3	bitmex-XBTUSD-future	2024-09-11 04:00:00+00:00	2024-09-11 04:00:15.978545+00:00	0.0001	08:00:00	08:00:00
4	bitmex-XBTUSD-future	2024-09-11 12:00:00+00:00	2024-09-11 12:00:04.727025+00:00	0.0001	08:00:00	08:00:00
...	...	...	...	...	...	...
58	okex-BTC-USD-SWAP-future	2024-09-15 08:00:00+00:00	2024-09-15 08:00:05.108766+00:00	0.000062	08:00:00	08:00:00
59	okex-BTC-USD-SWAP-future	2024-09-15 16:00:00+00:00	2024-09-15 16:00:12.545520+00:00	0.000061	08:00:00	08:00:00
60	okex-BTC-USD-SWAP-future	2024-09-16 00:00:00+00:00	2024-09-16 00:00:04.734967+00:00	0.000046	08:00:00	08:00:00
61	okex-BTC-USD-SWAP-future	2024-09-16 08:00:00+00:00	2024-09-16 08:00:07.052638+00:00	0.000013	08:00:00	08:00:00
62	okex-BTC-USD-SWAP-future	2024-09-16 16:00:00+00:00	2024-09-16 16:00:06.345528+00:00	0.000011	08:00:00	08:00:00

63 rows × 6 columns

# Convert 'time' to datetime for plotting, if not already in this format
fr_raw['time'] = pd.to_datetime(fr_raw['time'])
fr_raw = fr_raw.sort_values(by='time')
for column in fr_raw.columns:
    if column not in ['time', 'market']:
        fr_raw[column] = pd.to_numeric(fr_raw[column], errors='coerce') * 100

# Create a color map
markets = fr_raw['market'].unique()
colors = plt.cm.jet(np.linspace(0, 1, len(markets)))  # Generating a color for each market
color_map = dict(zip(markets, colors))

# Plotting
plt.figure(figsize=(15, 6))
plt.gca().set_facecolor('white')
# Plot bars for each market
for market in markets:
    market_data = fr_raw[fr_raw['market'] == market]
    plt.bar(market_data['time'], market_data['rate'], color=color_map[market], label=market, width=0.07, alpha=0.9)

formatter = mticker.FuncFormatter(lambda y, _: '{:.4f}%'.format(y))
plt.gca().yaxis.set_major_formatter(formatter)
plt.grid(True, linestyle='--', which='major', color='gray', alpha=0.3)  
plt.xticks(rotation=45)
plt.xlabel('')
plt.ylabel('Funding Rate (%)')
plt.title('\nPerpetual Futures\n Funding Rates\n',fontsize=16)
plt.legend(loc='upper left', fontsize=10, bbox_to_anchor=(0.76,1.2), frameon=False)

plt.show()

png

Aggregated Funding Rates

Coin Metrics also calculates several aggregated funding rate metrics.

Aggregate Funding Rate is the average funding rate weighted by open interest, published once per hour and representing the average funding rate converted to 8 hour, 1 day, 30 day, and 1 year time periods.

• futures_aggregate_funding_rate_usd_margin_*: metrics represent the average funding rate weighted by open interest from perpetual futures markets where the margin asset is U.S. dollars or stablecoins converted to a specified time period.

• futures_aggregate_funding_rate_coin_margin_*: represent the average funding rate weighted by open interest from perpetual futures markets where the margin asset is equivalent to the underlying base asset converted to a specified period.

• futures_aggregate_funding_rate_all_margin_*: represent the average funding rate weighted by open interest from all perpetual futures markets, regardless of the margin asset, converted to a specified time period.

btc_fr = client.get_asset_metrics(
    assets='btc',
    start_time='2023-10-01',
    metrics = [
        'futures_aggregate_funding_rate_all_margin_1d_period',
        'futures_aggregate_funding_rate_all_margin_30d_period',
        'futures_aggregate_funding_rate_all_margin_1y_period'
        ]
).to_dataframe()

btc_fr.head()

	asset	time	futures_aggregate_funding_rate_all_margin_1d_period	futures_aggregate_funding_rate_all_margin_1y_period	futures_aggregate_funding_rate_all_margin_30d_period
0	btc	2023-10-01 00:00:00+00:00	0.000101	0.036712	0.003017
1	btc	2023-10-02 00:00:00+00:00	0.000087	0.031662	0.002602
2	btc	2023-10-03 00:00:00+00:00	0.000086	0.031306	0.002573
3	btc	2023-10-04 00:00:00+00:00	0.000109	0.039942	0.003283
4	btc	2023-10-05 00:00:00+00:00	0.000071	0.025923	0.002131

plt.figure(figsize=(14, 8))

plt.plot(btc_fr['time'], btc_fr['futures_aggregate_funding_rate_all_margin_1d_period'] * 100, label='1D Period', color='blue')
plt.plot(btc_fr['time'], btc_fr['futures_aggregate_funding_rate_all_margin_30d_period'] * 100, label='30D Period', color='green')
plt.plot(btc_fr['time'], btc_fr['futures_aggregate_funding_rate_all_margin_1y_period'] * 100, label='1Y Period', color='red')

plt.gca().set_facecolor('white')
plt.grid(color='gray', linestyle='dotted', alpha=0.3)

plt.title('Bitcoin Perpetual Futures\nAggregated Funding Rate\n', fontsize=16)
plt.xlabel('')
plt.ylabel('Aggregate\nFunding Rate\n', fontsize=14)
plt.grid(True, alpha=0.3, linestyle='--')

# Set the formatter for the Y-axis to display percentages
formatter = mticker.FuncFormatter(lambda y, _: '{:.0f}%'.format(y))
plt.gca().yaxis.set_major_formatter(formatter)

plt.gca().xaxis.set_major_locator(mdates.AutoDateLocator())
plt.gca().xaxis.set_major_formatter(mdates.ConciseDateFormatter(mdates.AutoDateLocator()))
plt.legend(loc='upper right', fontsize=10, ncol=1, framealpha=0, bbox_to_anchor=(0.99, 1.13))
plt.show()

png

Plotting a heatmap of BTC funding rates across exchanges

btc_exch_fr_catalog = client.catalog_exchange_asset_metrics_v2(metrics='futures_aggregate_funding_rate_all_margin_1y_period').to_dataframe()
btc_exch_fr_catalog = btc_exch_fr_catalog.loc[btc_exch_fr_catalog['metric']=='futures_aggregate_funding_rate_all_margin_1y_period']
btc_exch_fr_catalog = btc_exch_fr_catalog[btc_exch_fr_catalog['exchange_asset'].str.contains(r'-btc$', case=False)]
btc_exch_fr_catalog

2024-09-16 16:42:29 WARNING  /catalog/ endpoints will be deprecated in the future. Consider using /catalog-v2/ and /reference-data/ endpoints instead.

	exchange_asset	metric	frequency	min_time	max_time
4711	binance-btc	futures_aggregate_funding_rate_all_margin_1y_p...	1d	2020-07-27 18:00:00+00:00	2024-09-16 21:00:00+00:00
24034	bitfinex-btc	futures_aggregate_funding_rate_all_margin_1y_p...	1d	2020-12-02 09:00:00+00:00	2024-09-16 21:00:00+00:00
29530	bitmex-btc	futures_aggregate_funding_rate_all_margin_1y_p...	1d	2020-07-27 18:00:00+00:00	2024-09-16 21:00:00+00:00
41723	bybit-btc	futures_aggregate_funding_rate_all_margin_1y_p...	1d	2021-05-01 20:00:00+00:00	2024-09-16 21:00:00+00:00
66927	deribit-btc	futures_aggregate_funding_rate_all_margin_1y_p...	1d	2020-07-27 18:00:00+00:00	2024-09-16 21:00:00+00:00
69721	ftx-btc	futures_aggregate_funding_rate_all_margin_1y_p...	1d	2020-07-27 18:00:00+00:00	2022-11-12 04:00:00+00:00
88682	huobi-btc	futures_aggregate_funding_rate_all_margin_1y_p...	1d	2020-07-27 18:00:00+00:00	2024-09-16 21:00:00+00:00
105482	kraken-btc	futures_aggregate_funding_rate_all_margin_1y_p...	1d	2020-10-09 09:00:00+00:00	2024-09-16 21:00:00+00:00
126904	okex-btc	futures_aggregate_funding_rate_all_margin_1y_p...	1d	2020-10-30 09:00:00+00:00	2024-09-16 21:00:00+00:00

btc_fr_exchanges = client.get_exchange_asset_metrics(
    exchange_assets=btc_exch_fr_catalog['exchange_asset'].to_list(),
    start_time='2023-01-01',
    end_time='2023-12-31',
    metrics = 'futures_aggregate_funding_rate_all_margin_1y_period',
    frequency='1d'
).to_dataframe()
btc_fr_exchanges

	exchange_asset	time	futures_aggregate_funding_rate_all_margin_1y_period
0	binance-btc	2023-01-01 00:00:00+00:00	0.1095
1	binance-btc	2023-01-02 00:00:00+00:00	0.012148
2	binance-btc	2023-01-03 00:00:00+00:00	0.046845
3	binance-btc	2023-01-04 00:00:00+00:00	0.041311
4	binance-btc	2023-01-05 00:00:00+00:00	0.061746
...	...	...	...
2915	okex-btc	2023-12-27 00:00:00+00:00	0.376005
2916	okex-btc	2023-12-28 00:00:00+00:00	0.254648
2917	okex-btc	2023-12-29 00:00:00+00:00	0.536332
2918	okex-btc	2023-12-30 00:00:00+00:00	0.455873
2919	okex-btc	2023-12-31 00:00:00+00:00	0.548358

2920 rows × 3 columns

df = btc_fr_exchanges
pivot_df = df.pivot(index='exchange_asset', columns='time', values='futures_aggregate_funding_rate_all_margin_1y_period')
pivot_df = pivot_df.astype(float)
pivot_df

time	2023-01-01 00:00:00+00:00	2023-01-02 00:00:00+00:00	2023-01-03 00:00:00+00:00	2023-01-04 00:00:00+00:00	2023-01-05 00:00:00+00:00	2023-01-06 00:00:00+00:00	2023-01-07 00:00:00+00:00	2023-01-08 00:00:00+00:00	2023-01-09 00:00:00+00:00	2023-01-10 00:00:00+00:00	...	2023-12-22 00:00:00+00:00	2023-12-23 00:00:00+00:00	2023-12-24 00:00:00+00:00	2023-12-25 00:00:00+00:00	2023-12-26 00:00:00+00:00	2023-12-27 00:00:00+00:00	2023-12-28 00:00:00+00:00	2023-12-29 00:00:00+00:00	2023-12-30 00:00:00+00:00	2023-12-31 00:00:00+00:00
exchange_asset
binance-btc	0.109500	0.012148	0.046845	0.041311	0.061746	0.077517	0.096432	0.089722	0.057266	0.017031	...	0.281263	0.206533	0.154560	0.195473	0.425674	0.383043	0.379663	0.549031	0.457254	0.387193
bitfinex-btc	0.240054	0.382189	0.211039	0.229558	0.074891	0.131655	0.131765	0.160125	0.113086	0.129979	...	0.270820	0.156269	0.125486	0.308607	0.465474	0.147093	0.655639	0.688972	0.453521	0.265981
bitmex-btc	0.109500	-0.147477	-0.027653	-0.075540	0.109140	0.058906	-0.078846	0.109500	0.109500	0.109500	...	0.124057	0.120385	0.105715	0.111534	0.109102	0.118988	0.178754	0.469497	0.127023	0.127770
bybit-btc	0.332716	0.328811	0.106620	0.109500	0.108059	0.056104	0.076392	-0.001772	0.090156	-0.066673	...	0.109500	0.109500	0.109500	0.109500	0.109500	0.109500	0.109500	0.109500	0.109500	0.109500
deribit-btc	0.004014	-0.004356	0.000194	-0.000777	-0.000004	-0.003702	-0.000003	0.000411	0.000128	0.009021	...	0.162735	0.222283	0.009642	0.074016	0.344480	0.472755	0.558913	0.589233	0.398686	0.509796
huobi-btc	0.109500	0.099561	0.109500	0.109500	-0.031955	0.109500	0.109500	0.815508	-0.012098	0.062326	...	0.261585	1.119745	0.330376	0.585262	0.487209	1.132452	0.657258	0.739781	0.549361	0.700592
kraken-btc	0.030208	0.040457	-0.095853	-0.027047	0.061041	-0.130703	-0.061580	0.036233	-0.006607	0.217927	...	0.316554	0.166807	0.167266	0.130185	0.474480	0.381600	0.226822	0.361264	0.169106	0.289377
okex-btc	0.209290	0.191624	-0.019627	0.007415	0.206787	0.104460	-0.038630	0.124934	0.025828	0.020786	...	0.396916	0.417243	-0.002470	0.271116	0.250061	0.376005	0.254648	0.536332	0.455873	0.548358

8 rows × 365 columns

# Plotting the heatmap
plt.figure(figsize=(16, 8))
ax = sns.heatmap(pivot_df, cmap='viridis', annot=False)
plt.title('BTC Perp Futures\nAggregate Funding Rate (APR)\n', fontsize=14)

# Set x-ticks for monthly intervals
date_labels = [pd.to_datetime(label).strftime('%b %Y') for label in pivot_df.columns]
monthly_intervals = [i for i, label in enumerate(pivot_df.columns) if pd.to_datetime(label).day == 1]

ax.set_xticks(monthly_intervals)
ax.set_xticklabels([date_labels[i] for i in monthly_intervals], rotation=0)
ax.set_yticklabels(ax.get_yticklabels(), rotation=0)

# Formatting colorbar labels as percentages
colorbar = ax.collections[0].colorbar
colorbar.ax.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f'{x:.0%}'))

plt.xlabel('')
plt.ylabel('')
plt.show()

png

Cumulative Funding Rate

Cumulative Funding Rate is the cumulative average funding rate that would be accumulated by contract holders over a specified time period. Published once per hour, representing the cumulative realized funding rate over the previous 1 day, 7 day, and 30 day time periods.

• futures_cumulative_funding_rate_usd_margin_*: metrics represent the cumulative average funding rate weighted by open interest from futures markets where the margin asset is U.S. dollars or stablecoins over the previous specified time period.

• futures_cumulative_funding_rate_coin_margin_*: metrics represent the cumulative average funding rate weighted by open interest from futures markets where the margin asset is equivalent to the underlying base asset over the previous specified time period.

• futures_cumulative_funding_rate_all_margin_*: metrics represent the cumulative average funding rate weighted by open interest from all futures markets, regardless of the margin asset, over the previous specified time period.

btc_cumulative_fr = client.get_asset_metrics(
    assets='btc',
    start_time='2023-01-01',
    end_time='2023-12-31',
    metrics = [
        'futures_cumulative_funding_rate_all_margin_rolling_1d',
        'futures_cumulative_funding_rate_all_margin_rolling_7d',
        'futures_cumulative_funding_rate_all_margin_rolling_30d'
        ]
).to_dataframe()
btc_cumulative_fr.head()

	asset	time	futures_cumulative_funding_rate_all_margin_rolling_1d	futures_cumulative_funding_rate_all_margin_rolling_30d	futures_cumulative_funding_rate_all_margin_rolling_7d
0	btc	2023-01-01 00:00:00+00:00	0.00041	0.002146	0.0015
1	btc	2023-01-02 00:00:00+00:00	0.000358	0.002479	0.001736
2	btc	2023-01-03 00:00:00+00:00	0.000128	0.002587	0.001735
3	btc	2023-01-04 00:00:00+00:00	0.000176	0.002641	0.001754
4	btc	2023-01-05 00:00:00+00:00	0.000194	0.002957	0.001745

for column in btc_cumulative_fr.columns:
    if column != 'time':
        btc_cumulative_fr[column] = pd.to_numeric(btc_cumulative_fr[column], errors='coerce')

# Convert the 'time' column to datetime format if it's not already
btc_cumulative_fr['time'] = pd.to_datetime(btc_cumulative_fr['time'], errors='coerce')

# Ensure 'futures_cumulative_funding_rate_all_margin_7d' is numeric, replacing non-numeric values with numpy.nan
btc_cumulative_fr['futures_cumulative_funding_rate_all_margin_7d'] = pd.to_numeric(btc_cumulative_fr['futures_cumulative_funding_rate_all_margin_rolling_7d'], errors='coerce')

# Plotting with safe checking
valid_7d_indices = ~btc_cumulative_fr['futures_cumulative_funding_rate_all_margin_rolling_7d'].isna()
valid_30d_indices = ~btc_cumulative_fr['futures_cumulative_funding_rate_all_margin_rolling_30d'].isna()

plt.plot(btc_cumulative_fr['time'], btc_cumulative_fr['futures_cumulative_funding_rate_all_margin_rolling_1d'] * 100, label='1D Period', color='blue')
plt.plot(btc_cumulative_fr['time'][valid_7d_indices], btc_cumulative_fr['futures_cumulative_funding_rate_all_margin_rolling_7d'][valid_7d_indices] * 100, label='7D Period', color='green')
plt.plot(btc_cumulative_fr['time'][valid_30d_indices], btc_cumulative_fr['futures_cumulative_funding_rate_all_margin_rolling_30d'][valid_30d_indices] * 100, label='30D Period', color='red')

plt.gca().set_facecolor('white')
plt.grid(color='gray', linestyle='dotted', alpha=0.3)

plt.title('Bitcoin Perpetual Futures\nCumulative Funding Rate\n', fontsize=16)
plt.xlabel('')
plt.ylabel('Cumulative\nFunding Rate\n', fontsize=14)
plt.grid(True, alpha=0.3, linestyle='--')

formatter = mticker.FuncFormatter(lambda y, _: '{:.2f}%'.format(y))
plt.gca().yaxis.set_major_formatter(formatter)
plt.gca().xaxis.set_major_locator(mdates.AutoDateLocator())
plt.gca().xaxis.set_major_formatter(mdates.ConciseDateFormatter(mdates.AutoDateLocator()))
plt.legend(loc='upper right', fontsize=10, ncol=1, framealpha=0, bbox_to_anchor=(0.99, 1.13))
plt.show()

png