def get_trades(product_id: str,
limit: int = 1000,
side: Optional[Any] = None) -> pd.DataFrame:
"""Get last N trades for chosen trading pair. [Source: Coinbase]
Parameters
----------
product_id: str
Trading pair of coins on Coinbase e.g ETH-USDT or UNI-ETH
limit: int
Last <limit> of trades. Maximum is 1000.
side: str
You can chose either sell or buy side. If side is not set then all trades will be displayed.
Returns
-------
pd.DataFrame
Last N trades for chosen trading pairs.
"""
params = {"limit": limit}
if side is not None and side in ["buy", "sell"]:
params["side"] = side
product_id = check_validity_of_product(product_id)
product = make_coinbase_request(f"/products/{product_id}/trades",
params=params)
return pd.DataFrame(product)[["time", "price", "size", "side"]]
def get_candles(product_id: str, interval: str = "24h") -> pd.DataFrame:
"""Get candles for chosen trading pair and time interval. [Source: Coinbase]
Parameters
----------
product_id: str
Trading pair of coins on Coinbase e.g ETH-USDT or UNI-ETH
interval: str
Time interval. One from 1min, 5min ,15min, 1hour, 6hour, 24hour
Returns
-------
pd.DataFrame
Candles for chosen trading pair.
"""
interval_map = {
"1min": 60,
"5min": 300,
"15min": 900,
"1hour": 3600,
"6hour": 21600,
"24hour": 86400,
"1day": 86400,
}
if interval not in interval_map:
console.print(
f"Wrong interval. Please use on from {list(interval_map.keys())}\n"
)
return pd.DataFrame()
granularity: int = interval_map[interval]
product_id = check_validity_of_product(product_id)
candles = make_coinbase_request(f"/products/{product_id}/candles",
params={"granularity": granularity})
df = pd.DataFrame(candles)
df.columns = [
"date",
"Low",
"High",
"Open",
"Close",
"Volume",
]
return df[[
"date",
"Open",
"High",
"Low",
"Close",
"Volume",
]]
def get_order_book(
product_id: str) -> Tuple[np.ndarray, np.ndarray, str, dict]:
"""Get orders book for chosen trading pair. [Source: Coinbase]
Parameters
----------
product_id: str
Trading pair of coins on Coinbase e.g ETH-USDT or UNI-ETH
Returns
-------
Tuple[np.array, np.array, str, dict]
array with bid prices, order sizes and cumulative order sizes
array with ask prices, order sizes and cumulative order sizes
trading pair
dict with raw data
"""
# TODO: Order with price much higher then current price. E.g current price 200 USD, sell order with 10000 USD
# makes chart look very ugly (bad scaling). Think about removing outliers or add log scale ?
product_id = check_validity_of_product(product_id)
market_book = make_coinbase_request(f"/products/{product_id}/book?level=2")
size = min(len(market_book["bids"]),
len(market_book["asks"])) # arrays needs to have equal size.
market_book["bids"] = market_book["bids"][:size]
market_book["asks"] = market_book["asks"][:size]
market_book.pop("sequence")
bids = np.asarray(market_book["bids"], dtype=float)[:size]
asks = np.asarray(market_book["asks"], dtype=float)[:size]
bids = np.insert(bids, 3, (bids[:, 1] * bids[:, 2]).cumsum(), axis=1)
asks = np.insert(asks,
3,
np.flipud(asks[:, 1] * asks[:, 2]).cumsum(),
axis=1)
bids = np.delete(bids, 2, axis=1)
asks = np.delete(asks, 2, axis=1)
return bids, asks, product_id, market_book
def get_trading_pair_info(product_id: str) -> pd.DataFrame:
"""Get information about chosen trading pair. [Source: Coinbase]
Parameters
----------
product_id: str
Trading pair of coins on Coinbase e.g ETH-USDT or UNI-ETH
Returns
-------
pd.DataFrame
Basic information about given trading pair
"""
product_id = check_validity_of_product(product_id)
pair = make_coinbase_request(f"/products/{product_id}")
df = pd.Series(pair).to_frame().reset_index()
df.columns = ["Metric", "Value"]
console.print(df)
return df
def get_product_stats(product_id: str) -> pd.DataFrame:
"""Get 24 hr stats for the product. Volume is in base currency units.
Open, high and low are in quote currency units. [Source: Coinbase]
Parameters
----------
product_id: str
Trading pair of coins on Coinbase e.g ETH-USDT or UNI-ETH
Returns
-------
pd.DataFrame
24h stats for chosen trading pair
"""
product_id = check_validity_of_product(product_id)
product = make_coinbase_request(f"/products/{product_id}/stats")
df = pd.Series(product).reset_index()
df.columns = ["Metric", "Value"]
return df