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377 lines
10 KiB
Python
377 lines
10 KiB
Python
"""The BitQuery view"""
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__docformat__ = "numpy"
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import logging
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import os
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from gamestonk_terminal.cryptocurrency.dataframe_helpers import (
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prettify_column_names,
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lambda_very_long_number_formatter,
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)
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from gamestonk_terminal.cryptocurrency.onchain import bitquery_model
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from gamestonk_terminal.decorators import log_start_end
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from gamestonk_terminal.helper_funcs import export_data, print_rich_table
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from gamestonk_terminal.rich_config import console
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logger = logging.getLogger(__name__)
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@log_start_end(log=logger)
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def display_dex_trades(
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trade_amount_currency: str = "USD",
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kind: str = "dex",
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top: int = 20,
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days: int = 90,
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sortby: str = "tradeAmount",
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descend: bool = False,
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export: str = "",
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) -> None:
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"""Trades on Decentralized Exchanges aggregated by DEX or Month [Source: https://graphql.bitquery.io/]
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Parameters
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----------
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kind: str
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Aggregate trades by dex or time
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trade_amount_currency: str
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Currency of displayed trade amount. Default: USD
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top: int
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Number of records to display
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sortby: str
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Key by which to sort data
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descend: bool
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Flag to sort data descending
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days: int
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Last n days to query data. Maximum 365 (bigger numbers can cause timeouts
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on server side)
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export : str
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Export dataframe data to csv,json,xlsx file
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"""
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if kind == "time":
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df = bitquery_model.get_dex_trades_monthly(trade_amount_currency, days)
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if not df.empty:
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df = df.sort_values(by="date", ascending=descend)
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else:
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df = bitquery_model.get_dex_trades_by_exchange(trade_amount_currency, days)
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if not df.empty:
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df = df.sort_values(by=sortby, ascending=descend)
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if not df.empty:
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df_data = df.copy()
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df[["tradeAmount", "trades"]] = df[["tradeAmount", "trades"]].applymap(
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lambda x: lambda_very_long_number_formatter(x)
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)
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df.columns = prettify_column_names(df.columns)
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print_rich_table(
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df.head(top),
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headers=list(df.columns),
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show_index=False,
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title="Trades on Decentralized Exchanges",
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)
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console.print("")
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export_data(
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export,
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os.path.dirname(os.path.abspath(__file__)),
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"lt",
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df_data,
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)
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@log_start_end(log=logger)
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def display_daily_volume_for_given_pair(
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token: str = "WBTC",
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vs: str = "USDT",
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top: int = 20,
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sortby: str = "date",
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descend: bool = False,
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export: str = "",
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) -> None:
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"""Display daily volume for given pair
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[Source: https://graphql.bitquery.io/]
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Parameters
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----------
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token: str
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ERC20 token symbol or address
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vs: str
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Quote currency.
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top: int
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Number of records to display
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sortby: str
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Key by which to sort data
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descend: bool
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Flag to sort data descending
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export : str
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Export dataframe data to csv,json,xlsx file
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Returns
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-------
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pd.DataFrame
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Token volume on different decentralized exchanges
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"""
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df = bitquery_model.get_daily_dex_volume_for_given_pair(
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token=token,
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vs=vs,
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limit=top,
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)
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if df.empty:
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return
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df = df.sort_values(by=sortby, ascending=descend)
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df_data = df.copy()
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df[["tradeAmount", "trades"]] = df[["tradeAmount", "trades"]].applymap(
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lambda x: lambda_very_long_number_formatter(x)
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)
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df.columns = prettify_column_names(df.columns)
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print_rich_table(
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df.head(top),
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headers=list(df.columns),
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show_index=False,
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title="Daily Volume for Pair",
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)
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console.print("")
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export_data(
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export,
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os.path.dirname(os.path.abspath(__file__)),
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"dvcp",
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df_data,
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)
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@log_start_end(log=logger)
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def display_dex_volume_for_token(
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token: str = "WBTC",
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trade_amount_currency: str = "USD",
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top: int = 10,
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sortby: str = "tradeAmount",
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descend: bool = False,
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export: str = "",
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) -> None:
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"""Display token volume on different Decentralized Exchanges. [Source: https://graphql.bitquery.io/]
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Parameters
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----------
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token: str
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ERC20 token symbol or address
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trade_amount_currency: str
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Currency of displayed trade amount. Default: USD
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top: int
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Number of records to display
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sortby: str
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Key by which to sort data
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descend: bool
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Flag to sort data descending
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export : str
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Export dataframe data to csv,json,xlsx file
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Returns
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-------
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pd.DataFrame
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Token volume on different decentralized exchanges
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"""
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df = bitquery_model.get_token_volume_on_dexes(
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token=token, trade_amount_currency=trade_amount_currency
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)
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if not df.empty:
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df = df.sort_values(by=sortby, ascending=descend)
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df_data = df.copy()
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df[["tradeAmount", "trades"]] = df[["tradeAmount", "trades"]].applymap(
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lambda x: lambda_very_long_number_formatter(x)
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)
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df.columns = prettify_column_names(df.columns)
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print_rich_table(
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df.head(top),
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headers=list(df.columns),
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show_index=False,
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title="Token Volume on Exchanges",
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)
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console.print("")
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export_data(
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export,
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os.path.dirname(os.path.abspath(__file__)),
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"tv",
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df_data,
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)
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@log_start_end(log=logger)
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def display_ethereum_unique_senders(
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interval: str = "days",
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limit: int = 10,
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sortby: str = "date",
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descend: bool = False,
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export: str = "",
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) -> None:
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"""Display number of unique ethereum addresses which made a transaction in given time interval
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[Source: https://graphql.bitquery.io/]
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Parameters
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----------
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interval: str
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Time interval in which ethereum address made transaction. month, week or day
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limit: int
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Number of records to display. It's calculated base on provided interval.
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If interval is month then calculation is made in the way: limit * 30 = time period,
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in case if interval is set to week, then time period is calculated as limit * 7.
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For better user experience maximum time period in days is equal to 90.
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sortby: str
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Key by which to sort data
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descend: bool
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Flag to sort data descending
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export : str
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Export dataframe data to csv,json,xlsx file
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Returns
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-------
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pd.DataFrame
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Number of unique ethereum addresses which made a transaction in given time interval
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"""
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df = bitquery_model.get_ethereum_unique_senders(interval, limit)
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if not df.empty:
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df = df.sort_values(by=sortby, ascending=descend)
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df[["uniqueSenders", "transactions", "maximumGasPrice"]] = df[
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["uniqueSenders", "transactions", "maximumGasPrice"]
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].applymap(lambda x: lambda_very_long_number_formatter(x))
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df_data = df.copy()
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df.columns = prettify_column_names(df.columns)
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print_rich_table(
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df,
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headers=list(df.columns),
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show_index=False,
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title="Unique Ethereum Addresses",
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)
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console.print("")
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export_data(
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export,
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os.path.dirname(os.path.abspath(__file__)),
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"ueat",
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df_data,
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)
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@log_start_end(log=logger)
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def display_most_traded_pairs(
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exchange="Uniswap",
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days: int = 10,
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top: int = 10,
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sortby: str = "tradeAmount",
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descend: bool = False,
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export: str = "",
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) -> None:
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"""Display most traded crypto pairs on given decentralized exchange in chosen time period.
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[Source: https://graphql.bitquery.io/]
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Parameters
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----------
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exchange:
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Decentralized exchange name
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days:
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Number of days taken into calculation account.
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sortby: str
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Key by which to sort data
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descend: bool
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Flag to sort data descending
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export : str
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Export dataframe data to csv,json,xlsx file
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Returns
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-------
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pd.DataFrame
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Most traded crypto pairs on given decentralized exchange in chosen time period.
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"""
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df = bitquery_model.get_most_traded_pairs(exchange=exchange, limit=days)
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if not df.empty:
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df = df.sort_values(by=sortby, ascending=descend)
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df_data = df.copy()
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df[["tradeAmount", "trades"]] = df[["tradeAmount", "trades"]].applymap(
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lambda x: lambda_very_long_number_formatter(x)
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)
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df.columns = prettify_column_names(df.columns)
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print_rich_table(
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df.head(top),
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headers=list(df.columns),
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show_index=False,
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title="Most Traded Crypto Pairs",
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)
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console.print("")
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export_data(
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export,
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os.path.dirname(os.path.abspath(__file__)),
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"ttcp",
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df_data,
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)
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@log_start_end(log=logger)
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def display_spread_for_crypto_pair(
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token="ETH",
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vs="USDC",
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days: int = 10,
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sortby: str = "date",
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descend: bool = False,
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export: str = "",
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) -> None:
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"""Display an average bid and ask prices, average spread for given crypto pair for chosen time period.
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[Source: https://graphql.bitquery.io/]
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Parameters
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----------
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days: int
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Last n days to query data
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token: str
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ERC20 token symbol
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vs: str
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Quoted currency.
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sortby: str
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Key by which to sort data
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descend: bool
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Flag to sort data descending
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export : str
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Export dataframe data to csv,json,xlsx file
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Returns
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-------
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pd.DataFrame
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Average bid and ask prices, spread for given crypto pair for chosen time period
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"""
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df = bitquery_model.get_spread_for_crypto_pair(token=token, vs=vs, limit=days)
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if not df.empty:
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df = df.sort_values(by=sortby, ascending=descend)
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df.columns = prettify_column_names(df.columns)
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print_rich_table(
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df,
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headers=list(df.columns),
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show_index=False,
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title="Average Spread for Given Crypto",
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)
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console.print("")
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export_data(
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export,
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os.path.dirname(os.path.abspath(__file__)),
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"baas",
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df,
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)
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