mirror of
https://github.com/OpenBB-finance/OpenBB.git
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072c413ac0
* Add separate view for finbrain for crypto curreny sentiment analysis * Move json with symbols to separate directory, little refactoring, update readme * Add screenshot of finbrain for PolkaDot * Add bitquery model and view * Move bitquery to onchain module * Bitquery added to onchain controller * Apply long number formatting for big numbers in bitquery commands * Update README with bitquery API key * Add error handling for cases when there is no token found, or dextrades response is empty. Use os library to take care of paths to files * Add helper method to extract dexTrades object from bitquery response. Add timout exception to request function. Add mapping for DECENTRALIZED_EXCHANGES to support lowercase choices: * Refactor bitquery model and views, prettify column names, remove redundant columns, change command names. Add test for api key * Update website docs for bitquery * Add difflib implementation for exchanges in bitquery ttcp command * Remove menu() from code * Remove tokens with less than 3 trades (doesn't make sense to show them, as most of them are scams). Add difflib for -coin parameter in baas command
399 lines
10 KiB
Python
399 lines
10 KiB
Python
"""The BitQuery view"""
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__docformat__ = "numpy"
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import os
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from tabulate import tabulate
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from gamestonk_terminal.cryptocurrency.dataframe_helpers import (
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very_long_number_formatter,
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prettify_column_names,
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)
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from gamestonk_terminal.cryptocurrency.onchain import bitquery_model
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from gamestonk_terminal.helper_funcs import export_data
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from gamestonk_terminal import feature_flags as gtff
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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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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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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: very_long_number_formatter(x)
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)
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df.columns = prettify_column_names(df.columns)
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if gtff.USE_TABULATE_DF:
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print(
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tabulate(
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df.head(top),
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headers=df.columns,
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floatfmt=".2f",
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showindex=False,
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tablefmt="fancy_grid",
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),
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"\n",
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)
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else:
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print(df.to_string, "\n")
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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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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: very_long_number_formatter(x)
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)
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df.columns = prettify_column_names(df.columns)
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if gtff.USE_TABULATE_DF:
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print(
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tabulate(
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df.head(top),
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headers=df.columns,
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floatfmt=".2f",
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showindex=False,
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tablefmt="fancy_grid",
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),
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"\n",
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)
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else:
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print(df.to_string, "\n")
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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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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,
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trade_amount_currency=trade_amount_currency,
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).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: very_long_number_formatter(x)
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)
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df.columns = prettify_column_names(df.columns)
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if gtff.USE_TABULATE_DF:
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print(
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tabulate(
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df.head(top),
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headers=df.columns,
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floatfmt=".2f",
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showindex=False,
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tablefmt="fancy_grid",
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),
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"\n",
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)
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else:
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print(df.to_string, "\n")
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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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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).sort_values(
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by=sortby, ascending=descend
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)
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df[["uniqueSenders", "transactions", "maximumGasPrice"]] = df[
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["uniqueSenders", "transactions", "maximumGasPrice"]
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].applymap(lambda x: 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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if gtff.USE_TABULATE_DF:
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print(
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tabulate(
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df,
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headers=df.columns,
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floatfmt=".2f",
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showindex=False,
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tablefmt="fancy_grid",
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),
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"\n",
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)
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else:
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print(df.to_string, "\n")
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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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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(
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exchange=exchange, limit=days
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).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: very_long_number_formatter(x)
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)
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df.columns = prettify_column_names(df.columns)
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if gtff.USE_TABULATE_DF:
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print(
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tabulate(
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df.head(top),
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headers=df.columns,
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floatfmt=".2f",
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showindex=False,
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tablefmt="fancy_grid",
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),
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"\n",
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)
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else:
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print(df.to_string, "\n")
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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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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(
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token=token, vs=vs, limit=days
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).sort_values(by=sortby, ascending=descend)
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df.columns = prettify_column_names(df.columns)
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if gtff.USE_TABULATE_DF:
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print(
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tabulate(
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df,
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headers=df.columns,
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floatfmt=".2f",
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showindex=False,
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tablefmt="fancy_grid",
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),
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"\n",
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)
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else:
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print(df.to_string, "\n")
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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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