Python
pip install velodata
Examples
Quick Start
from velodata import lib as velo
# new velo client
client = velo.client('api_key')
# get futures and pick one
future = client.get_futures()[0]
# get futures columns and pick two
columns = client.get_futures_columns()[:2]
# last 10 minutes in 1 minute resolution
params = {
'type': 'futures',
'columns': columns,
'exchanges': [future['exchange']],
'products': [future['product']],
'begin': client.timestamp() - 1000 * 60 * 11,
'end': client.timestamp(),
'resolution': '1m'
}
# returns dataframe
print(client.get_rows(params))Aggregated Spot CVD
from velodata import lib as velo
day_in_ms = 1000 * 60 * 60 * 24
# new velo client
client = velo.client('api_key')
# from one day ago in 10 minute resolution
params = {
'type': 'spot',
'columns': ['buy_dollar_volume', 'sell_dollar_volume'],
'exchanges': ['coinbase', 'binance', 'bybit-spot'],
'products': ['ETHUSDT', 'ETH-USD'],
'begin': client.timestamp() - day_in_ms,
'end': client.timestamp(),
'resolution': '10m'
}
# returns dataframe
df = client.get_rows(params)
# aggregate all exchanges
df = df.groupby(df['time']).sum(numeric_only=True)
# compute volume delta
df['delta'] = df['buy_dollar_volume'] - df['sell_dollar_volume']
# cumulative
print(df['delta'].cumsum())OI-Weighted Funding Rate
from velodata import lib as velo
hour_in_ms = 1000 * 60 * 60
# new velo client
client = velo.client('api_key')
# from one hour ago in 1 minute resolution
params = {
'type': 'futures',
'columns': ['funding_rate', 'coin_open_interest_close'],
'exchanges': ['binance-futures', 'bybit', 'okex-swap'],
'coins': ['SOL'],
'begin': client.timestamp() - hour_in_ms,
'end': client.timestamp(),
'resolution': '1m'
}
# returns dataframe
df = client.get_rows(params)
# oi-weighted funding = SUM(funding*OI) / SUM(OI)
df['funding_rate'] = df['funding_rate'] * df['coin_open_interest_close']
df = df.groupby(df['time']).sum(numeric_only=True)
df['funding_rate'] = df['funding_rate'] / df['coin_open_interest_close']
print(df['funding_rate'])Spot-Vol Correlation
from velodata import lib as velo
day_in_ms = 1000 * 60 * 60 * 24
# new velo client
client = velo.client('api_key')
# from 5 days ago in 1 hour resolution
params = {
'type': 'options',
'columns': ['dvol_close', 'index_price'],
'exchanges': ['deribit'],
'products': ['BTC'],
'begin': client.timestamp() - day_in_ms * 5,
'end': client.timestamp(),
'resolution': '1h'
}
# returns dataframe
df = client.get_rows(params)
# simple rolling 24 hour correlation
print(
df['dvol_close'].pct_change().rolling(24).corr(df['index_price'].pct_change())
)Futures Basis
Special case: `3m_basis_ann` is available for BTC and ETH. To request this data, do not specify products, exchanges, or any additional columns
from velodata import lib as velo
day_in_ms = 1000 * 60 * 60 * 24
# new velo client
client = velo.client('api_key')
# from 5 days ago in 1 hour resolution
params = {
'type': 'futures',
'columns': ['3m_basis_ann'],
'coins': ['BTC', 'ETH'],
'begin': client.timestamp() - day_in_ms * 5,
'end': client.timestamp(),
'resolution': '1h'
}
# returns dataframe
print(client.get_rows(params))Large Request
Both get_rows and stream_rows handle batching of large requests automatically, but if you wish to receive batch responses individually rather than in one final DataFrame you should use stream_rows.
from velodata import lib as velo
day_in_ms = 1000 * 60 * 60 * 24
# new velo client
client = velo.client('api_key')
# from 5 days ago in 1 minute resolution
# 2 columns * 4 products * 1 exchange * 7200 rows = 57600 values
params = {
'type': 'futures',
'columns': ['open_price', 'close_price'],
'exchanges': ['binance-futures'],
'products': ['LTCUSDT', 'ETCUSDT', 'BCHUSDT', 'SOLUSDT'],
'begin': client.timestamp() - day_in_ms * 5,
'end': client.timestamp(),
'resolution': '1m'
}
# creates 3 batches (57600 values / 22500 limit)
batches = client.batch_rows(params)
# prints each batch as it finishes
for df in client.stream_rows(batches):
print(df) Helper Methods
Key status:
get_status()Supported futures:
get_futures()Supported options:
get_options()Supported spot pairs:
get_spot()Supported futures columns:
get_futures_columns()Supported options columns:
get_options_columns()Supported spot columns:
get_spot_columns()Millisecond timestamp:
timestamp()
Data Methods
Get rows
get_rows(params)
type: 'futures', 'options', or 'spot'
exchanges, products, coins, columns: lists
begin, end: millisecond timestamps
resolution: minutes (integer) or resolution (string)
Returns DataFrame
If both `coins` and `products` are specified, only `products` will be used
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