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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.

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

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
markettimecontract_countvalue_usddatabase_timeexchange_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_assettimeopen_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()
markettimecoin_metrics_idamountpricetypedatabase_timeside

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_assetmetrics

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_assettimeliquidations_reported_future_buy_usd_1hliquidations_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()
assettimeopen_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
marketmin_timemax_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
markettimedatabase_timerateperiodinterval

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()
assettimefutures_aggregate_funding_rate_all_margin_1d_periodfutures_aggregate_funding_rate_all_margin_1y_periodfutures_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()

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_assetmetricfrequencymin_timemax_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_assettimefutures_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
time2023-01-01 00:00:00+00:002023-01-02 00:00:00+00:002023-01-03 00:00:00+00:002023-01-04 00:00:00+00:002023-01-05 00:00:00+00:002023-01-06 00:00:00+00:002023-01-07 00:00:00+00:002023-01-08 00:00:00+00:002023-01-09 00:00:00+00:002023-01-10 00:00:00+00:00...2023-12-22 00:00:00+00:002023-12-23 00:00:00+00:002023-12-24 00:00:00+00:002023-12-25 00:00:00+00:002023-12-26 00:00:00+00:002023-12-27 00:00:00+00:002023-12-28 00:00:00+00:002023-12-29 00:00:00+00:002023-12-30 00:00:00+00:002023-12-31 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()

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()
assettimefutures_cumulative_funding_rate_all_margin_rolling_1dfutures_cumulative_funding_rate_all_margin_rolling_30dfutures_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()
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