def display_token_historical_prices(
address: str,
top: int = 30,
sortby: str = "date",
descend: bool = False,
export: str = "",
) -> None:
"""Display token historical prices with volume and market cap, and average price. [Source: Ethplorer]
Parameters
----------
address: str
Token balance e.g. 0x1f9840a85d5aF5bf1D1762F925BDADdC4201F984
top: int
Limit of transactions. Maximum 100
sortby: str
Key to sort by.
descend: str
Sort in descending order.
export : str
Export dataframe data to csv,json,xlsx file
"""
df = ethplorer_model.get_token_historical_price(address)
df_data = df.copy()
if df.empty:
console.print(f"No results found for balance: {address}\n")
return
df["volumeConverted"] = df["volumeConverted"].apply(
lambda x: lambda_very_long_number_formatter(x))
df["cap"] = df["cap"].apply(lambda x: lambda_very_long_number_formatter(x))
df = df.sort_values(by=sortby, ascending=descend)
print_rich_table(
df.head(top),
headers=list(df.columns),
show_index=False,
title="Historical Token Prices",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"prices",
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_coins(category: str,
top: int = 250,
sortby: str = "Symbol",
export: str = "") -> None:
"""Display top coins [Source: CoinGecko]
Parameters
----------
category: str
Coingecko category. If no category is passed it will search for all coins. (E.g., smart-contract-platform)
top: int
Number of records to display
sortby: str
Key to sort data
export : str
Export dataframe data to csv,json,xlsx file
"""
df = pycoingecko_model.get_coins(top=top, category=category)
if not df.empty:
df = df[[
"symbol",
"name",
"total_volume",
"market_cap",
"market_cap_rank",
"price_change_percentage_7d_in_currency",
"price_change_percentage_24h_in_currency",
]]
df = df.set_axis(
COINS_COLUMNS,
axis=1,
inplace=False,
)
if sortby in COINS_COLUMNS:
df = df[(df["Volume [$]"].notna())
& (df["Market Cap [$]"].notna())].sort_values(
by=sortby, ascending=False)
for col in ["Volume [$]", "Market Cap [$]"]:
if col in df.columns:
df[col] = df[col].apply(
lambda x: lambda_very_long_number_formatter(x))
print_rich_table(
df.head(top),
headers=list(df.columns),
show_index=False,
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"cgtop",
df,
)
else:
console.print("\nUnable to retrieve data from CoinGecko.\n")
def display_token_history(
address: str,
top: int = 10,
sortby: str = "timestamp",
hash_: bool = False,
descend: bool = False,
export: str = "",
) -> None:
"""Display info about token history. [Source: Ethplorer]
Parameters
----------
address: str
Token balance e.g. 0x1f9840a85d5aF5bf1D1762F925BDADdC4201F984
top: int
Limit of transactions. Maximum 100
sortby: str
Key to sort by.
descend: str
Sort in descending order.
hash_: bool,
Flag to show transaction hash.
export : str
Export dataframe data to csv,json,xlsx file
"""
df = ethplorer_model.get_token_history(address)
df_data = df.copy()
if df.empty:
console.print(f"No results found for balance: {address}\n")
return
df["value"] = df["value"].apply(
lambda x: lambda_very_long_number_formatter(x))
df = df.sort_values(by=sortby, ascending=descend)
if hash_:
df.drop(["from", "to"], axis=1, inplace=True)
else:
df.drop("transactionHash", inplace=True, axis=1)
print_rich_table(
df.head(top),
headers=list(df.columns),
show_index=False,
title="Token History Information",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"th",
df_data,
)
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 display_uni_tokens(
skip: int = 0,
limit: int = 20,
sortby: str = "index",
descend: bool = True,
export: str = "",
) -> None:
"""Displays tokens trade-able on Uniswap DEX.
[Source: https://thegraph.com/en/]
Parameters
----------
skip: int
Number of records to skip
limit: 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 = graph_model.get_uni_tokens(skip=skip)
df_data = df.copy()
df = df.sort_values(by=sortby, ascending=descend)
df[["totalLiquidity", "tradeVolumeUSD"
]] = df[["totalLiquidity", "tradeVolumeUSD"
]].applymap(lambda x: lambda_very_long_number_formatter(x))
print_rich_table(
df.head(limit),
headers=list(df.columns),
show_index=False,
title="UniSwarp DEX Trade-able Tokens",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"tokens",
df_data,
)
def display_address_info(
address: str,
top: int = 15,
sortby: str = "index",
descend: bool = True,
export: str = "",
) -> None:
"""Display info about tokens for given ethereum blockchain balance e.g. ETH balance, balance of all tokens with
name and symbol. [Source: Ethplorer]
Parameters
----------
address: str
Ethereum balance.
top: int
Limit of transactions. Maximum 100
sortby: str
Key to sort by.
descend: str
Sort in descending order.
export : str
Export dataframe data to csv,json,xlsx file
"""
df = ethplorer_model.get_address_info(address)
df_data = df.copy()
df = df.sort_values(by=sortby, ascending=descend)
df["balance"] = df["balance"].apply(
lambda x: lambda_very_long_number_formatter(x)
if x >= 10000 else round(float(x), 4))
print_rich_table(
df.head(top),
headers=list(df.columns),
show_index=False,
title="Blockchain Token Information",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"balance",
df_data,
)
def display_gainers(period: str = "1h",
top: int = 20,
sortby: str = "Symbol",
export: str = "") -> None:
"""Shows Largest Gainers - coins which gain the most in given period. [Source: CoinGecko]
Parameters
----------
period: str
Time period by which data is displayed. One from [1h, 24h, 7d, 14d, 30d, 60d, 1y]
top: int
Number of records to display
sortby: str
Key to sort data
export : str
Export dataframe data to csv,json,xlsx file
"""
df = pycoingecko_model.get_gainers_or_losers(top=top,
period=period,
typ="gainers")
if not df.empty:
if sortby in COINS_COLUMNS:
df = df[(df["Volume [$]"].notna())
& (df["Market Cap [$]"].notna())].sort_values(
by=sortby, ascending=False)
for col in ["Volume [$]", "Market Cap [$]"]:
if col in df.columns:
df[col] = df[col].apply(
lambda x: lambda_very_long_number_formatter(x))
print_rich_table(
df.head(top),
headers=list(df.columns),
show_index=False,
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"gainers",
df,
)
else:
console.print("\nUnable to retrieve data from CoinGecko.\n")
def display_address_history(
address: str,
top: int = 10,
sortby: str = "timestamp",
descend: bool = False,
export: str = "",
) -> None:
"""Display information about balance historical transactions. [Source: Ethplorer]
Parameters
----------
address: str
Ethereum nlockchain balance e.g. 0x3cD751E6b0078Be393132286c442345e5DC49699
top: int
Limit of transactions. Maximum 100
sortby: str
Key to sort by.
descend: str
Sort in descending order.
export : str
Export dataframe data to csv,json,xlsx file
"""
df = ethplorer_model.get_address_history(address)
df_data = df.copy()
df = df.sort_values(by=sortby, ascending=descend)
df["value"] = df["value"].apply(
lambda x: lambda_very_long_number_formatter(x)
if x >= 10000 else round(float(x), 4))
print_rich_table(
df.head(top),
headers=list(df.columns),
show_index=False,
title="Historical Transactions Information",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"hist",
df_data,
)
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_top_token_holders(
address: str,
top: int = 10,
sortby: str = "balance",
descend: bool = False,
export: str = "",
) -> None:
"""Display info about top ERC20 token holders. [Source: Ethplorer]
Parameters
----------
address: str
Token balance e.g. 0x1f9840a85d5aF5bf1D1762F925BDADdC4201F984
top: int
Limit of transactions. Maximum 100
sortby: str
Key to sort by.
descend: str
Sort in descending order.
export : str
Export dataframe data to csv,json,xlsx file
"""
df = ethplorer_model.get_top_token_holders(address)
df_data = df.copy()
df = df.sort_values(by=sortby, ascending=descend)
df["balance"] = df["balance"].apply(
lambda x: lambda_very_long_number_formatter(x))
print_rich_table(
df.head(top),
headers=list(df.columns),
show_index=False,
title="ERC20 Token Holder Info",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"holders",
df_data,
)
def display_top_tokens(
top: int = 15,
sortby: str = "rank",
descend: bool = True,
export: str = "",
) -> None:
"""Display top ERC20 tokens [Source: Ethplorer]
Parameters
----------
top: int
Limit of transactions. Maximum 100
sortby: str
Key to sort by.
descend: str
Sort in descending order.
export : str
Export dataframe data to csv,json,xlsx file
"""
df = ethplorer_model.get_top_tokens()
df_data = df.copy()
df.fillna("", inplace=True)
df = df.sort_values(by=sortby, ascending=descend)
for col in ["txsCount", "transfersCount", "holdersCount"]:
if col in df.columns:
df[col] = df[col].apply(
lambda x: lambda_very_long_number_formatter(x))
print_rich_table(
df.head(top),
headers=list(df.columns),
show_index=False,
title="Top ERC20 Tokens",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"top",
df_data,
)
def display_token_info(
address: str,
social: bool = False,
export: str = "",
) -> None:
"""Display info about ERC20 token. [Source: Ethplorer]
Parameters
----------
address: str
Token balance e.g. 0x1f9840a85d5aF5bf1D1762F925BDADdC4201F984
social: bool
Flag to display social media links
export : str
Export dataframe data to csv,json,xlsx file
"""
df = ethplorer_model.get_token_info(address)
df_data = df.copy()
df["Value"] = df["Value"].apply(
lambda x: lambda_very_long_number_formatter(x))
socials = ["website", "telegram", "reddit", "twitter", "coingecko"]
if social:
df = df[df["Metric"].isin(["balance", "name", "symbol"] + socials)]
else:
df = df[~df["Metric"].isin(socials)]
print_rich_table(df,
headers=list(df.columns),
show_index=False,
title="ERC20 Token Information")
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"info",
df_data,
)
def display_uni_pools(top: int = 20,
sortby: str = "volumeUSD",
descend: bool = False,
export: str = "") -> None:
"""Displays uniswap pools by volume.
[Source: https://thegraph.com/en/]
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
export : str
Export dataframe data to csv,json,xlsx file
"""
df = graph_model.get_uni_pools_by_volume().sort_values(by=sortby,
ascending=descend)
df["volumeUSD"] = df["volumeUSD"].apply(
lambda x: lambda_very_long_number_formatter(x))
df_data = df.copy()
print_rich_table(df.head(top),
headers=list(df.columns),
show_index=False,
title="Uniswap Pools")
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"pools",
df_data,
)
def get_uniswap_stats() -> pd.DataFrame:
"""Get base statistics about Uniswap DEX. [Source: https://thegraph.com/en/]
uniswapFactory id: 0x5C69bEe701ef814a2B6a3EDD4B1652CB9cc5aA6f - ethereum address on which Uniswap Factory
smart contract was deployed. The factory contract is deployed once from the off-chain source code, and it contains
functions that make it possible to create exchange contracts for any ERC20 token that does not already have one.
It also functions as a registry of ERC20 tokens that have been added to the system, and the exchange with which they
are associated. More: https://docs.uniswap.org/protocol/V1/guides/connect-to-uniswap
We use 0x5C69bEe701ef814a2B6a3EDD4B1652CB9cc5aA6f address to fetch all smart contracts that were
created with usage of this factory.
Returns
-------
pd.DataFrame
Uniswap DEX statistics like liquidity, volume, number of pairs, number of transactions.
"""
query = """
{
uniswapFactory(id: "0x5C69bEe701ef814a2B6a3EDD4B1652CB9cc5aA6f"){
totalVolumeUSD
totalLiquidityUSD
pairCount
txCount
totalLiquidityUSD
totalLiquidityETH
}
}
"""
data = query_graph(UNI_URL, query)
if not data:
return pd.DataFrame()
df = pd.Series(data["uniswapFactory"]).reset_index()
df.columns = ["Metric", "Value"]
df["Value"] = df["Value"].apply(
lambda x: lambda_very_long_number_formatter(x))
return df
def display_last_uni_swaps(top: int = 20,
sortby: str = "timestamp",
descend: bool = False,
export: str = "") -> None:
"""Displays last swaps done on Uniswap
[Source: https://thegraph.com/en/]
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
export : str
Export dataframe data to csv,json,xlsx file
"""
df = graph_model.get_last_uni_swaps(limit=top).sort_values(
by=sortby, ascending=descend)
df["amountUSD"] = df["amountUSD"].apply(
lambda x: lambda_very_long_number_formatter(x))
df_data = df.copy()
print_rich_table(df,
headers=list(df.columns),
show_index=False,
title="Last Uniswap Swaps")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"swaps",
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: 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="Daily Volume for Pair",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"dvcp",
df_data,
)
def display_recently_added(
top: int = 20,
days: int = 7,
min_volume: int = 20,
min_liquidity: int = 0,
min_tx: int = 100,
sortby: str = "created",
descend: bool = False,
export: str = "",
) -> None:
"""Displays Lastly added pairs on Uniswap DEX.
[Source: https://thegraph.com/en/]
Parameters
----------
top: int
Number of records to display
days: int
Number of days the pair has been active,
min_volume: int
Minimum trading volume,
min_liquidity: int
Minimum liquidity
min_tx: int
Minimum number of transactions
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 = graph_model.get_uniswap_pool_recently_added(
last_days=days,
min_volume=min_volume,
min_liquidity=min_liquidity,
min_tx=min_tx,
)
df_data = df.copy()
df = df.sort_values(by=sortby, ascending=descend)
df[["volumeUSD", "totalSupply"
]] = df[["volumeUSD", "totalSupply"
]].applymap(lambda x: lambda_very_long_number_formatter(x))
print_rich_table(
df.head(top),
headers=list(df.columns),
show_index=False,
title="Latest Added Pairs on Uniswap DEX",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"pairs",
df_data,
)
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)
if not df.empty:
df = df.sort_values(by="date", ascending=descend)
else:
df = bitquery_model.get_dex_trades_by_exchange(trade_amount_currency,
days)
if not df.empty:
df = df.sort_values(by=sortby, ascending=descend)
if not df.empty:
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="Trades on Decentralized Exchanges",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"lt",
df_data,
)
