def display_dex_trades(
trade_amount_currency: str = "USD",
kind: str = "dex",
top: int = 20,
days: int = 90,
sortby: str = "tradeAmount",
descend: bool = False,
export: str = "",
) -> None:
"""Trades on Decentralized Exchanges aggregated by DEX or Month [Source: https://graphql.bitquery.io/]
Parameters
----------
kind: str
Aggregate trades by dex or time
trade_amount_currency: str
Currency of displayed trade amount. Default: USD
top: int
Number of records to display
sortby: str
Key by which to sort data
descend: bool
Flag to sort data descending
days: int
Last n days to query data. Maximum 365 (bigger numbers can cause timeouts
on server side)
export : str
Export dataframe data to csv,json,xlsx file
"""
if kind == "time":
df = bitquery_model.get_dex_trades_monthly(trade_amount_currency, days)
df = df.sort_values(by="date", ascending=descend)
else:
df = bitquery_model.get_dex_trades_by_exchange(trade_amount_currency,
days)
df = df.sort_values(by=sortby, ascending=descend)
df_data = df.copy()
df[["tradeAmount",
"trades"]] = df[["tradeAmount", "trades"
]].applymap(lambda x: very_long_number_formatter(x))
df.columns = prettify_column_names(df.columns)
print_rich_table(
df.head(top),
headers=list(df.columns),
show_index=False,
title="Trades on Decentralized Exchanges",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"lt",
df_data,
)
def display_spread_for_crypto_pair(
token="ETH",
vs="USDC",
days: int = 10,
sortby: str = "date",
descend: bool = False,
export: str = "",
) -> None:
"""Display an average bid and ask prices, average spread for given crypto pair for chosen time period.
[Source: https://graphql.bitquery.io/]
Parameters
----------
days: int
Last n days to query data
token: str
ERC20 token symbol
vs: str
Quoted currency.
sortby: str
Key by which to sort data
descend: bool
Flag to sort data descending
export : str
Export dataframe data to csv,json,xlsx file
Returns
-------
pd.DataFrame
Average bid and ask prices, spread for given crypto pair for chosen time period
"""
df = bitquery_model.get_spread_for_crypto_pair(token=token,
vs=vs,
limit=days).sort_values(
by=sortby,
ascending=descend)
df.columns = prettify_column_names(df.columns)
if gtff.USE_TABULATE_DF:
print(
tabulate(
df,
headers=df.columns,
floatfmt=".2f",
showindex=False,
tablefmt="fancy_grid",
),
"\n",
)
else:
print(df.to_string, "\n")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"baas",
df,
)
def display_most_traded_pairs(
exchange="Uniswap",
days: int = 10,
top: int = 10,
sortby: str = "tradeAmount",
descend: bool = False,
export: str = "",
) -> None:
"""Display most traded crypto pairs on given decentralized exchange in chosen time period.
[Source: https://graphql.bitquery.io/]
Parameters
----------
exchange:
Decentralized exchange name
days:
Number of days taken into calculation account.
sortby: str
Key by which to sort data
descend: bool
Flag to sort data descending
export : str
Export dataframe data to csv,json,xlsx file
Returns
-------
pd.DataFrame
Most traded crypto pairs on given decentralized exchange in chosen time period.
"""
df = bitquery_model.get_most_traded_pairs(
exchange=exchange, limit=days).sort_values(by=sortby,
ascending=descend)
df_data = df.copy()
df[["tradeAmount",
"trades"]] = df[["tradeAmount", "trades"
]].applymap(lambda x: very_long_number_formatter(x))
df.columns = prettify_column_names(df.columns)
if gtff.USE_TABULATE_DF:
print(
tabulate(
df.head(top),
headers=df.columns,
floatfmt=".2f",
showindex=False,
tablefmt="fancy_grid",
),
"\n",
)
else:
print(df.to_string, "\n")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"ttcp",
df_data,
)
def display_ethereum_unique_senders(
interval: str = "days",
limit: int = 10,
sortby: str = "date",
descend: bool = False,
export: str = "",
) -> None:
"""Display number of unique ethereum addresses which made a transaction in given time interval
[Source: https://graphql.bitquery.io/]
Parameters
----------
interval: str
Time interval in which ethereum address made transaction. month, week or day
limit: int
Number of records to display. It's calculated base on provided interval.
If interval is month then calculation is made in the way: limit * 30 = time period,
in case if interval is set to week, then time period is calculated as limit * 7.
For better user experience maximum time period in days is equal to 90.
sortby: str
Key by which to sort data
descend: bool
Flag to sort data descending
export : str
Export dataframe data to csv,json,xlsx file
Returns
-------
pd.DataFrame
Number of unique ethereum addresses which made a transaction in given time interval
"""
df = bitquery_model.get_ethereum_unique_senders(interval, limit)
if not df.empty:
df = df.sort_values(by=sortby, ascending=descend)
df[["uniqueSenders", "transactions", "maximumGasPrice"]] = df[[
"uniqueSenders", "transactions", "maximumGasPrice"
]].applymap(lambda x: lambda_very_long_number_formatter(x))
df_data = df.copy()
df.columns = prettify_column_names(df.columns)
print_rich_table(
df,
headers=list(df.columns),
show_index=False,
title="Unique Ethereum Addresses",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"ueat",
df_data,
)
def display_dex_volume_for_token(
token: str = "WBTC",
trade_amount_currency: str = "USD",
top: int = 10,
sortby: str = "tradeAmount",
descend: bool = False,
export: str = "",
) -> None:
"""Display token volume on different Decentralized Exchanges. [Source: https://graphql.bitquery.io/]
Parameters
----------
token: str
ERC20 token symbol or address
trade_amount_currency: str
Currency of displayed trade amount. Default: USD
top: int
Number of records to display
sortby: str
Key by which to sort data
descend: bool
Flag to sort data descending
export : str
Export dataframe data to csv,json,xlsx file
Returns
-------
pd.DataFrame
Token volume on different decentralized exchanges
"""
df = bitquery_model.get_token_volume_on_dexes(
token=token, trade_amount_currency=trade_amount_currency)
if not df.empty:
df = df.sort_values(by=sortby, ascending=descend)
df_data = df.copy()
df[["tradeAmount", "trades"]] = df[[
"tradeAmount", "trades"
]].applymap(lambda x: lambda_very_long_number_formatter(x))
df.columns = prettify_column_names(df.columns)
print_rich_table(
df.head(top),
headers=list(df.columns),
show_index=False,
title="Token Volume on Exchanges",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"tv",
df_data,
)
def display_spread_for_crypto_pair(
token="ETH",
vs="USDC",
days: int = 10,
sortby: str = "date",
descend: bool = False,
export: str = "",
) -> None:
"""Display an average bid and ask prices, average spread for given crypto pair for chosen time period.
[Source: https://graphql.bitquery.io/]
Parameters
----------
days: int
Last n days to query data
token: str
ERC20 token symbol
vs: str
Quoted currency.
sortby: str
Key by which to sort data
descend: bool
Flag to sort data descending
export : str
Export dataframe data to csv,json,xlsx file
Returns
-------
pd.DataFrame
Average bid and ask prices, spread for given crypto pair for chosen time period
"""
df = bitquery_model.get_spread_for_crypto_pair(token=token,
vs=vs,
limit=days)
if not df.empty:
df = df.sort_values(by=sortby, ascending=descend)
df.columns = prettify_column_names(df.columns)
print_rich_table(
df,
headers=list(df.columns),
show_index=False,
title="Average Spread for Given Crypto",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"baas",
df,
)
def display_most_traded_pairs(
exchange="Uniswap",
days: int = 10,
top: int = 10,
sortby: str = "tradeAmount",
descend: bool = False,
export: str = "",
) -> None:
"""Display most traded crypto pairs on given decentralized exchange in chosen time period.
[Source: https://graphql.bitquery.io/]
Parameters
----------
exchange:
Decentralized exchange name
days:
Number of days taken into calculation account.
sortby: str
Key by which to sort data
descend: bool
Flag to sort data descending
export : str
Export dataframe data to csv,json,xlsx file
Returns
-------
pd.DataFrame
Most traded crypto pairs on given decentralized exchange in chosen time period.
"""
df = bitquery_model.get_most_traded_pairs(exchange=exchange, limit=days)
if not df.empty:
df = df.sort_values(by=sortby, ascending=descend)
df_data = df.copy()
df[["tradeAmount", "trades"]] = df[[
"tradeAmount", "trades"
]].applymap(lambda x: lambda_very_long_number_formatter(x))
df.columns = prettify_column_names(df.columns)
print_rich_table(
df.head(top),
headers=list(df.columns),
show_index=False,
title="Most Traded Crypto Pairs",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"ttcp",
df_data,
)
def get_staking_account_info(address: str = "") -> Tuple[pd.DataFrame, str]:
"""Get staking info for provided terra account [Source: https://fcd.terra.dev/swagger]
Parameters
----------
address: str
terra blockchain address e.g. terra1jvwelvs7rdk6j3mqdztq5tya99w8lxk6l9hcqg
Returns
-------
Tuple[pd.DataFrame, str]:
luna delegations and summary report for given address
"""
response = _make_request(f"staking/{address}")
results: Dict[str, Any] = {"myDelegations": []}
for field in ["availableLuna", "delegationTotal"]:
results[field] = denominate_number(response.get(field, 0))
my_delegations = response.get("myDelegations")
if my_delegations:
for delegation in my_delegations:
validator = _adjust_delegation_info(delegation)
results["myDelegations"].append(validator)
df = pd.DataFrame(results["myDelegations"])
try:
df["validatorName"] = df["validatorName"].apply(
lambda x: lambda_replace_unicode(x))
df.columns = prettify_column_names(list(df.columns))
except KeyError:
df = pd.DataFrame()
results["totalRewards"] = denominate_number(
response.get("rewards", {}).get("total", 0))
report = f"""Overview:
Address: {address}
Available Luna: {results['availableLuna']}
Delegated Luna: {results['delegationTotal']}
Total Rewards: {results['totalRewards']}\n"""
report += "\nDelegations: " if not df.empty else "\nNo delegations found\n"
return df, report
def display_validators(top: int = 10,
sortby: str = "votingPower",
descend: bool = False,
export: str = "") -> None:
"""Display information about terra validators [Source: https://fcd.terra.dev/swagger]
Parameters
----------
top: int
Number of records to display
sortby: str
Key by which to sort data
descend: bool
Flag to sort data descending
export : str
Export dataframe data to csv,json,xlsx file
"""
df = terramoney_fcd_model.get_validators()
df_data = df.copy()
df = df.sort_values(by=sortby, ascending=descend)
df["tokensAmount"] = df["tokensAmount"].apply(
lambda x: lambda_very_long_number_formatter(x))
df.columns = [
x
if x not in ["Voting power", "Commission rate", "Uptime"] else f"{x} %"
for x in prettify_column_names(df.columns)
]
print_rich_table(
df.head(top),
headers=list(df.columns),
floatfmt=".2f",
show_index=False,
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"validators",
df_data,
)
def display_gov_proposals(
top: int = 10,
status: str = "all",
sortby: str = "id",
descend: bool = False,
export: str = "",
) -> None:
"""Display terra blockchain governance proposals list [Source: https://fcd.terra.dev/swagger]
Parameters
----------
top: int
Number of records to display
status: str
status of proposal, one from list: ['Voting','Deposit','Passed','Rejected']
sortby: str
Key by which to sort data
descend: bool
Flag to sort data descending
export : str
Export dataframe data to csv,json,xlsx file
"""
df = terramoney_fcd_model.get_proposals(status)
df_data = df.copy()
df = df.sort_values(by=sortby, ascending=descend).head(top)
df.columns = prettify_column_names(df.columns)
print_rich_table(
df,
headers=list(df.columns),
floatfmt=".2f",
show_index=False,
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"govp",
df_data,
)
def display_daily_volume_for_given_pair(
token: str = "WBTC",
vs: str = "USDT",
top: int = 20,
sortby: str = "date",
descend: bool = False,
export: str = "",
) -> None:
"""Display daily volume for given pair
[Source: https://graphql.bitquery.io/]
Parameters
----------
token: str
ERC20 token symbol or address
vs: str
Quote currency.
top: int
Number of records to display
sortby: str
Key by which to sort data
descend: bool
Flag to sort data descending
export : str
Export dataframe data to csv,json,xlsx file
Returns
-------
pd.DataFrame
Token volume on different decentralized exchanges
"""
df = bitquery_model.get_daily_dex_volume_for_given_pair(
token=token,
vs=vs,
limit=top,
)
if df.empty:
return
df = df.sort_values(by=sortby, ascending=descend)
df_data = df.copy()
df[["tradeAmount",
"trades"]] = df[["tradeAmount", "trades"
]].applymap(lambda x: very_long_number_formatter(x))
df.columns = prettify_column_names(df.columns)
if gtff.USE_TABULATE_DF:
print(
tabulate(
df.head(top),
headers=df.columns,
floatfmt=".2f",
showindex=False,
tablefmt="fancy_grid",
),
"\n",
)
else:
print(df.to_string, "\n")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"dvcp",
df_data,
)
def display_dex_volume_for_token(
token: str = "WBTC",
trade_amount_currency: str = "USD",
top: int = 10,
sortby: str = "tradeAmount",
descend: bool = False,
export: str = "",
) -> None:
"""Display token volume on different Decentralized Exchanges. [Source: https://graphql.bitquery.io/]
Parameters
----------
token: str
ERC20 token symbol or address
trade_amount_currency: str
Currency of displayed trade amount. Default: USD
top: int
Number of records to display
sortby: str
Key by which to sort data
descend: bool
Flag to sort data descending
export : str
Export dataframe data to csv,json,xlsx file
Returns
-------
pd.DataFrame
Token volume on different decentralized exchanges
"""
df = bitquery_model.get_token_volume_on_dexes(
token=token,
trade_amount_currency=trade_amount_currency,
).sort_values(by=sortby, ascending=descend)
df_data = df.copy()
df[["tradeAmount",
"trades"]] = df[["tradeAmount", "trades"
]].applymap(lambda x: very_long_number_formatter(x))
df.columns = prettify_column_names(df.columns)
if gtff.USE_TABULATE_DF:
print(
tabulate(
df.head(top),
headers=df.columns,
floatfmt=".2f",
showindex=False,
tablefmt="fancy_grid",
),
"\n",
)
else:
print(df.to_string, "\n")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"tv",
df_data,
)
