def display_holdings_overview(coin: str, show_bar: bool, export: str, top: int) -> None:
"""Shows overview of public companies that holds ethereum or bitcoin. [Source: CoinGecko]
Parameters
----------
coin: str
Cryptocurrency: ethereum or bitcoin
export: str
Export dataframe data to csv,json,xlsx
"""
res = gecko.get_holdings_overview(coin)
stats_string = res[0]
df = res[1]
df = df.head(top)
if df.empty:
console.print("\nZero companies holding this crypto\n")
else:
if show_bar:
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
for _, row in df.iterrows():
ax.bar(x=row["Symbol"], height=row["Total Holdings"])
ax.set_ylabel("BTC Number")
ax.get_yaxis().set_major_formatter(
ticker.FuncFormatter(lambda x, _: long_number_format_with_type_check(x))
)
ax.set_xlabel("Company Symbol")
fig.tight_layout(pad=8)
ax.set_title("Total BTC Holdings per company")
ax.tick_params(axis="x", labelrotation=90)
console.print(f"\n{stats_string}\n")
df = df.applymap(lambda x: long_number_format_with_type_check(x))
print_rich_table(
df,
headers=list(df.columns),
show_index=False,
title="Public Companies Holding BTC or ETH",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"cghold",
df,
)
def display_price_supply(coin_id: str, currency: str, export: str) -> None:
"""Get ticker information for single coin [Source: CoinPaprika]
Parameters
----------
coin_id: str
Identifier of coin for CoinPaprika API
currency: str
Quoted currency
export: str
Export dataframe data to csv,json,xlsx
"""
df = coinpaprika_model.get_tickers_info_for_coin(coin_id, currency)
if df.empty:
console.print("No data found", "\n")
return
df = df.applymap(lambda x: long_number_format_with_type_check(x))
print_rich_table(df,
headers=list(df.columns),
show_index=False,
title="Coin Information")
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"ps",
df,
)
def display_global_market(export: str) -> None:
"""Return data frame with most important global crypto statistics like:
market_cap_usd, volume_24h_usd, bitcoin_dominance_percentage, cryptocurrencies_number,
market_cap_ath_value, market_cap_ath_date, volume_24h_ath_value, volume_24h_ath_date,
market_cap_change_24h, volume_24h_change_24h, last_updated [Source: CoinPaprika]
Parameters
----------
export : str
Export dataframe data to csv,json,xlsx file
"""
df = paprika.get_global_market()
df_data = df.copy()
df["Value"] = df["Value"].apply(
lambda x: long_number_format_with_type_check(x))
print_rich_table(df,
headers=list(df.columns),
show_index=False,
title="Global Crypto Statistics")
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"global",
df_data,
)
def display_categories(sortby: str, top: int, export: str, pie: bool) -> None:
"""Shows top cryptocurrency categories by market capitalization
The cryptocurrency category ranking is based on market capitalization. [Source: CoinGecko]
Parameters
----------
top: int
Number of records to display
sortby: str
Key by which to sort data
export : str
Export dataframe data to csv,json,xlsx file
"""
df = gecko.get_top_crypto_categories(sortby)
df_data = df
if not df.empty:
if pie:
df_data[f"% relative to top {top}"] = (
df_data["Market Cap"] / df_data["Market Cap"].sum()
) * 100
stables_to_display = df_data[df_data[f"% relative to top {top}"] >= 1]
other_stables = df_data[df_data[f"% relative to top {top}"] < 1]
values_list = list(stables_to_display[f"% relative to top {top}"].values)
values_list.append(other_stables[f"% relative to top {top}"].sum())
labels_list = list(stables_to_display["Name"].values)
labels_list.append("Others")
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
ax.pie(
values_list,
labels=labels_list,
wedgeprops={"linewidth": 0.5, "edgecolor": "white"},
autopct="%1.0f%%",
startangle=90,
)
ax.set_title(f"Market Cap distribution of top {top} crypto categories")
if gtff.USE_ION:
plt.ion()
plt.show()
df = df.applymap(lambda x: long_number_format_with_type_check(x))
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__)),
"cgcategories",
df_data,
)
else:
console.print("\nUnable to retrieve data from CoinGecko.\n")
def display_all_exchanges(currency: str, sortby: str, descend: bool, top: int,
export: str) -> None:
"""List exchanges from CoinPaprika API. [Source: CoinPaprika]
Parameters
----------
currency: str
Quoted currency
top: int
Number of records to display
sortby: str
Key by which to sort data
descend: bool
Flag to sort data descending
links: bool
Flag to display urls
export : str
Export dataframe data to csv,json,xlsx file
"""
df = paprika.get_list_of_exchanges(quotes=currency).sort_values(
by=sortby, ascending=descend)
df_data = df.copy()
if df.empty:
print("No data found", "\n")
return
cols = [col for col in df.columns if col != "rank"]
df[cols] = df[cols].applymap(
lambda x: long_number_format_with_type_check(x))
print(f"\nDisplaying data vs {currency}")
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__)),
"exchanges",
df_data,
)
def price_supply(coin_id: str, other_args: List[str]):
"""Get ticker information for single coin
Parameters
----------
coin_id: str
Identifier of coin for CoinPaprika API
other_args: List[str]
Arguments to pass to argparse
"""
parser = argparse.ArgumentParser(
prog="ps",
add_help=False,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="""Get price and supply related metrics for given coin.""",
)
parser.add_argument(
"--vs",
help="Quoted currency. Default USD",
dest="vs",
default="USD",
type=str,
choices=CURRENCIES,
)
try:
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
df = paprika.get_tickers_info_for_coin(coin_id, ns_parser.vs)
if df.empty:
print("No data found", "\n")
return
df = df.applymap(lambda x: long_number_format_with_type_check(x))
print(
tabulate(
df,
headers=df.columns,
floatfmt=".2f",
showindex=False,
tablefmt="fancy_grid",
),
"\n",
)
except Exception as e:
print(e, "\n")
def display_all_coins_market_info(currency: str, sortby: str, descend: bool,
top: int, export: str) -> None:
"""Displays basic market information for all coins from CoinPaprika API. [Source: CoinPaprika]
Parameters
----------
currency: str
Quoted currency
top: int
Number of records to display
sortby: str
Key by which to sort data
descend: bool
Flag to sort data descending
links: bool
Flag to display urls
export : str
Export dataframe data to csv,json,xlsx file
"""
df = paprika.get_coins_market_info(quotes=currency).sort_values(
by=sortby, ascending=descend)
df_data = df.copy()
if df.empty:
console.print("No data found", "\n")
return
cols = [col for col in df.columns if col != "rank"]
df[cols] = df[cols].applymap(
lambda x: long_number_format_with_type_check(x))
console.print(f"\nDisplaying data vs {currency}")
print_rich_table(
df.head(top),
headers=list(df.columns),
show_index=False,
title="Basic Market Information",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"markets",
df_data,
)
def display_price_supply(coin_id: str, currency: str, export: str) -> None:
"""Get ticker information for single coin [Source: CoinPaprika]
Parameters
----------
coin_id: str
Identifier of coin for CoinPaprika API
currency: str
Quoted currency
export: str
Export dataframe data to csv,json,xlsx
"""
df = coinpaprika_model.get_tickers_info_for_coin(coin_id, currency)
if df.empty:
print("No data found", "\n")
return
df = df.applymap(lambda x: long_number_format_with_type_check(x))
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__)),
"ps",
df,
)
def global_market(other_args: List[str]):
"""Return data frame with most important global crypto statistics like:
market_cap_usd, volume_24h_usd, bitcoin_dominance_percentage, cryptocurrencies_number,
market_cap_ath_value, market_cap_ath_date, volume_24h_ath_value, volume_24h_ath_date,
market_cap_change_24h, volume_24h_change_24h, last_updated,
Parameters
----------
other_args: List[str]
Arguments to pass to argparse
"""
parser = argparse.ArgumentParser(
add_help=False,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
prog="global",
description="""Show most important global crypto statistics like:
market_cap_usd, volume_24h_usd, bitcoin_dominance_percentage, cryptocurrencies_number,
market_cap_ath_value, market_cap_ath_date, volume_24h_ath_value, volume_24h_ath_date,
market_cap_change_24h, volume_24h_change_24h, last_updated.""",
)
try:
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
df = paprika.get_global_market()
df["Value"] = df["Value"].apply(lambda x: long_number_format_with_type_check(x))
print(
tabulate(
df,
headers=df.columns,
floatfmt=".1f",
showindex=False,
tablefmt="fancy_grid",
)
)
print("")
except Exception as e:
print(e, "\n")
def display_global_market(export: str) -> None:
"""Return data frame with most important global crypto statistics like:
market_cap_usd, volume_24h_usd, bitcoin_dominance_percentage, cryptocurrencies_number,
market_cap_ath_value, market_cap_ath_date, volume_24h_ath_value, volume_24h_ath_date,
market_cap_change_24h, volume_24h_change_24h, last_updated [Source: CoinPaprika]
Parameters
----------
export : str
Export dataframe data to csv,json,xlsx file
"""
df = paprika.get_global_market()
df_data = df.copy()
df["Value"] = df["Value"].apply(
lambda x: long_number_format_with_type_check(x))
if gtff.USE_TABULATE_DF:
print(
tabulate(
df,
headers=df.columns,
floatfmt=".1f",
showindex=False,
tablefmt="fancy_grid",
),
"\n",
)
else:
print(df.to_string, "\n")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"global",
df_data,
)
def display_stablecoins(
top: int, export: str, sortby: str, descend: bool, pie: bool
) -> None:
"""Shows stablecoins data [Source: CoinGecko]
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 = gecko.get_stable_coins(top)
if not df.empty:
total_market_cap = int(df["market_cap"].sum())
df[f"Percentage [%] of top {top}"] = (df["market_cap"] / total_market_cap) * 100
df_data = df
df = df.sort_values(by=sortby, ascending=descend).head(top)
df = df.set_axis(
[
"Symbol",
"Name",
"Price [$]",
"Market Cap [$]",
"Market Cap Rank",
"Change 24h [%]",
"Change 7d [%]",
"Volume [$]",
f"Percentage [%] of top {top}",
],
axis=1,
inplace=False,
)
df = df.applymap(lambda x: long_number_format_with_type_check(x))
if pie:
stables_to_display = df_data[df_data[f"Percentage [%] of top {top}"] >= 1]
other_stables = df_data[df_data[f"Percentage [%] of top {top}"] < 1]
values_list = list(
stables_to_display[f"Percentage [%] of top {top}"].values
)
values_list.append(other_stables[f"Percentage [%] of top {top}"].sum())
labels_list = list(stables_to_display["name"].values)
labels_list.append("Others")
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
ax.pie(
values_list,
labels=labels_list,
wedgeprops={"linewidth": 0.5, "edgecolor": "white"},
labeldistance=1.05,
autopct="%1.0f%%",
startangle=90,
)
ax.set_title(f"Market cap distribution of top {top} Stablecoins")
if gtff.USE_ION:
plt.ion()
plt.show()
console.print(
f"""
First {top} stablecoins have a total {long_number_format_with_type_check(total_market_cap)} dollars of market cap.
"""
)
print_rich_table(
df.head(top),
headers=list(df.columns),
show_index=False,
title="Stablecoin Data",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"cgstables",
df,
)
else:
console.print("\nUnable to retrieve data from CoinGecko.\n")
def all_exchanges(other_args: List[str]):
"""List exchanges from CoinPaprika API
Parameters
----------
other_args: List[str]
Arguments to pass to argparse
"""
parser = argparse.ArgumentParser(
add_help=False,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
prog="exchanges",
description="""Show all exchanges from CoinPaprika
You can display only top N number of coins with --top parameter.
You can sort data by rank, name, currencies, markets, fiats, confidence,
volume_24h,volume_7d ,volume_30d, sessions_per_month --sort parameter
and also with --descend flag to sort descending.
Displays:
rank, name, currencies, markets, fiats, confidence, volume_24h,
volume_7d ,volume_30d, sessions_per_month""",
)
parser.add_argument(
"--vs",
help="Quoted currency. Default USD",
dest="vs",
default="USD",
type=str,
choices=CURRENCIES,
)
parser.add_argument(
"-t",
"--top",
default=20,
dest="top",
help="Limit of records",
type=check_positive,
)
parser.add_argument(
"-s",
"--sort",
dest="sortby",
type=str,
help="Sort by given column. Default: rank",
default="rank",
choices=[
"rank",
"name",
"currencies",
"markets",
"fiats",
"confidence",
"volume_24h",
"volume_7d",
"volume_30d",
"sessions_per_month",
],
)
parser.add_argument(
"--descend",
action="store_false",
help="Flag to sort in descending order (lowest first)",
dest="descend",
default=True,
)
try:
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
df = paprika.get_list_of_exchanges(quotes=ns_parser.vs).sort_values(
by=ns_parser.sortby, ascending=ns_parser.descend
)
if df.empty:
print("No data found", "\n")
return
cols = [col for col in df.columns if col != "rank"]
df[cols] = df[cols].applymap(lambda x: long_number_format_with_type_check(x))
print("")
print(f"Displaying data vs {ns_parser.vs}")
print(
tabulate(
df.head(ns_parser.top),
headers=df.columns,
floatfmt=".2f",
showindex=False,
tablefmt="fancy_grid",
)
)
print("")
except Exception as e:
print(e, "\n")
