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api.py
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api.py
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import pandas as pd
import requests
class CoinglassAPI:
""" Unofficial Python client for Coinglass API """
def __init__(self, api_key: str):
"""
Args:
api_key: key from Coinglass, get one at https://www.coinglass.com/pricing
"""
self.__api_key = api_key
self._base_url = "https://open-api.coinglass.com/public/v2/indicator/"
self._session = requests.Session()
def _get(self, endpoint: str, params: dict = None) -> dict:
headers = {
"accept": "application/json",
"coinglassSecret": self.__api_key
}
url = self._base_url + endpoint
return self._session.request('GET', url, params=params, headers=headers, timeout=30).json()
@staticmethod
def _create_dataframe(data: list[dict], time_col: str) -> pd.DataFrame:
""" Create pandas DataFrame from list of dicts """
df = pd.DataFrame(data)
df["time"] = pd.to_datetime(df[time_col], unit="ms")
df.drop(columns=[time_col], inplace=True)
df.set_index("time", inplace=True, drop=True)
if "t" in df.columns:
df.drop(columns=["t"], inplace=True)
return df
@staticmethod
def _check_for_errors(response: dict) -> None:
""" Check for errors in response """
if not response["success"]:
raise Exception(f"Code {response['code']}: {response['msg']}")
def funding(
self,
ex: str,
pair: str,
interval: str,
limit: int = 500,
start_time: int = None,
end_time: int = None
) -> pd.DataFrame:
"""
Funding rate for a given pair
Args:
ex: exchange to get funding rate for (e.g. Binance, dYdX, etc.)
pair: pair to get funding rate for (e.g. BTCUSDT on Binance, BTC-USD on dYdX, etc.)
interval: interval to get funding rate for (e.g. m1, m5, m15, m30, h1, h4, etc.)
limit: number of data points to return (default: 500)
start_time: start time in milliseconds
end_time: end time in milliseconds
Returns:
pandas DataFrame with funding rate
"""
response = self._get(
endpoint="funding",
params={"ex": ex, "pair": pair, "interval": interval, "limit": limit,
"start_time": start_time, "end_time": end_time}
)
self._check_for_errors(response)
data = response["data"]
return self._create_dataframe(data, time_col="createTime")
def funding_ohlc(
self,
ex: str,
pair: str,
interval: str,
limit: int = 500,
start_time: int = None,
end_time: int = None
) -> pd.DataFrame:
"""
Funding rate in OHLC format for an exchange pair
Args:
ex: exchange to get funding rate for (e.g. Binance, dYdX, etc.)
pair: pair to get funding rate for (e.g. BTCUSDT on Binance, BTC-USD on dYdX, etc.)
interval: interval to get funding rate for (e.g. m1, m5, m15, m30, h1, h4, etc.)
limit: number of data points to return (default: 500)
start_time: start time in milliseconds
end_time: end time in milliseconds
Returns:
pandas DataFrame with funding rate in OHLC format for an exchange pair
"""
response = self._get(
endpoint="funding_ohlc",
params={"ex": ex, "pair": pair, "interval": interval, "limit": limit,
"start_time": start_time, "end_time": end_time}
)
self._check_for_errors(response)
data = response["data"]
return self._create_dataframe(data, time_col="t")
def funding_average(
self,
symbol: str,
interval: str,
limit: int = 500,
start_time: int = None,
end_time: int = None
) -> pd.DataFrame:
"""
Average funding rate for a symbol
Args:
symbol: symbol to get funding rate for
interval: interval to get funding rate for (e.g. m1, m5, m15, m30, h1, h4, etc.)
limit: number of data points to return (default: 500)
start_time: start time in milliseconds
end_time: end time in milliseconds
Returns:
pandas DataFrame with funding rate
"""
response = self._get(
endpoint="funding_avg",
params={"symbol": symbol, "interval": interval, "limit": limit,
"start_time": start_time, "end_time": end_time}
)
self._check_for_errors(response)
data = response["data"]
return self._create_dataframe(data, time_col="createTime")
def open_interest_ohlc(
self,
ex: str,
pair: str,
interval: str,
limit: int = 500,
start_time: int = None,
end_time: int = None
) -> pd.DataFrame:
"""
Open interest in OHLC format for an exchange pair
Args:
ex: exchange to get OI for (e.g. Binance, dYdX, etc.)
pair: pair to get OI for (e.g. BTCUSDT on Binance, BTC-USD on dYdX, etc.)
interval: interval to get OI for (e.g. m1, m5, m15, m30, h1, h4, etc.)
limit: number of data points to return (default: 500)
start_time: start time in milliseconds
end_time: end time in milliseconds
Returns:
pandas DataFrame with open interest in OHLC format for an exchange pair
"""
response = self._get(
endpoint="open_interest_ohlc",
params={"ex": ex, "pair": pair, "interval": interval, "limit": limit,
"start_time": start_time, "end_time": end_time}
)
self._check_for_errors(response)
data = response["data"]
return self._create_dataframe(data, time_col="t")
def open_interest_aggregated_ohlc(
self,
symbol: str,
interval: str,
limit: int = 500,
start_time: int = None,
end_time: int = None
) -> pd.DataFrame:
"""
Aggregated open interest in OHLC format for a symbol
Args:
symbol: symbol to get OI for
interval: interval to get OI for (e.g. m1, m5, m15, m30, h1, h4, etc.)
limit: number of data points to return (default: 500)
start_time: start time in milliseconds
end_time: end time in milliseconds
Returns:
pandas DataFrame with aggregated open interest in OHLC format
"""
response = self._get(
endpoint="open_interest_aggregated_ohlc",
params={"symbol": symbol, "interval": interval, "limit": limit,
"start_time": start_time, "end_time": end_time}
)
self._check_for_errors(response)
data = response["data"]
return self._create_dataframe(data, time_col="t")
def liquidation_symbol(
self,
symbol: str,
interval: str,
limit: int = 500,
start_time: int = None,
end_time: int = None
) -> pd.DataFrame:
"""
Liquidation data for a symbol
Args:
symbol: symbol to get liquidation data for
interval: interval to get liquidation data for (e.g. m1, m5, m15, m30, h1, h4, etc.)
limit: number of data points to return (default: 500)
start_time: start time in milliseconds
end_time: end time in milliseconds
Returns:
pandas DataFrame with liquidation data
"""
response = self._get(
endpoint="liquidation_symbol",
params={"symbol": symbol, "interval": interval, "limit": limit,
"start_time": start_time, "end_time": end_time}
)
self._check_for_errors(response)
data = response["data"]
return self._create_dataframe(data, time_col="createTime")
def liquidation_pair(
self,
ex: str,
pair: str,
interval: str,
limit: int = 500,
start_time: int = None,
end_time: int = None
) -> pd.DataFrame:
"""
Liquidation data for an exchange pair
Args:
ex: exchange to get liquidation data for (e.g. Binance, dYdX, etc.)
pair: pair to get liquidation data for (e.g. BTCUSDT on Binance, BTC-USD on dYdX, etc.)
interval: interval to get liquidation data for (e.g. m1, m5, m15, m30, h1, h4, etc.)
limit: number of data points to return (default: 500)
start_time: start time in milliseconds
end_time: end time in milliseconds
Returns:
pandas DataFrame with liquidation data for an exchange pair
"""
response = self._get(
endpoint="funding_ohlc",
params={"ex": ex, "pair": pair, "interval": interval, "limit": limit,
"start_time": start_time, "end_time": end_time}
)
self._check_for_errors(response)
data = response["data"]
return self._create_dataframe(data, time_col="t")
def long_short_accounts(
self,
ex: str,
pair: str,
interval: str,
limit: int = 500,
start_time: int = None,
end_time: int = None
) -> pd.DataFrame:
response = self._get(
endpoint="long_short_accounts",
params={"ex": ex, "pair": pair, "interval": interval, "limit": limit,
"start_time": start_time, "end_time": end_time}
)
self._check_for_errors(response)
data = response["data"]
return self._create_dataframe(data, time_col="createTime")
def long_short_symbol(
self,
symbol: str,
interval: str,
limit: int = 500,
start_time: int = None,
end_time: int = None
) -> pd.DataFrame:
"""
Long/short ratio for a symbol
Args:
symbol: symbol to get long/short ratio for
interval: interval to get long/short ratio for (e.g. m1, m5, m15, m30, h1, h4, etc.)
limit: number of data points to return (default: 500)
start_time: start time in milliseconds
end_time: end time in milliseconds
Returns:
pandas DataFrame with long/short ratio
"""
response = self._get(
endpoint="long_short_symbol",
params={"symbol": symbol, "interval": interval, "limit": limit,
"start_time": start_time, "end_time": end_time}
)
self._check_for_errors(response)
data = response["data"]
return self._create_dataframe(data, time_col="t")