def get_social_media(self) -> pd.DataFrame:
"""Get list of URLs to social media like twitter, facebook, reddit... [Source: CoinGecko]
Returns
-------
pandas.DataFrame
Urls to social media
Columns: Metric, Value
"""
social_dct = {}
links = self._get_links()
for (channel) in CHANNELS.keys(): # pylint: disable=consider-iterating-dictionary)
if channel in links:
value = links.get(channel, "")
if channel == "twitter_screen_name":
value = "https://twitter.com/" + value
elif channel == "bitcointalk_thread_identifier" and value is not None:
value = f"https://bitcointalk.org/index.php?topic={value}"
social_dct[channel] = value
social_dct["discord"] = find_discord(links.get("chat_url"))
dct = rename_columns_in_dct(social_dct, CHANNELS)
df = pd.Series(dct).to_frame().reset_index()
df.columns = ["Metric", "Value"]
df["Metric"] = df["Metric"].apply(
lambda x: replace_underscores_in_column_names(x)
if isinstance(x, str) else x)
return df[df["Value"].notna()]
def get_all_time_low(self, currency: str = "usd") -> pd.DataFrame:
"""Get all time low data for given coin. [Source: CoinGecko]
Returns
-------
pandas.DataFrame
All time low price data
Metric,Value
"""
market_data = self.coin.get("market_data", {})
if market_data == {}:
return pd.DataFrame()
ath_columns = [
"current_price",
"atl",
"atl_date",
"atl_change_percentage",
]
results = {}
for column in ath_columns:
results[column] = market_data[column].get(currency)
df = pd.Series(results).to_frame().reset_index()
df.columns = ["Metric", "Value"]
df["Metric"] = df["Metric"].apply(
lambda x: replace_underscores_in_column_names(x)
if isinstance(x, str) else x)
df["Metric"] = df["Metric"].apply(
lambda x: x.replace("Atl", "All Time Low"))
df["Metric"] = df["Metric"] + f" {currency.upper()}"
return df[df["Value"].notna()]
def get_base_info(self) -> pd.DataFrame:
"""Get all the base information about given coin. [Source: CoinGecko]
Returns
-------
pandas.DataFrame
Base information about coin
"""
regx = r'<a href="(.+?)">|</a>'
results = {}
for attr in BASE_INFO:
info_obj = self.coin.get(attr, {})
if attr == "description":
info_obj = info_obj.get("en")
info_obj = re.sub(regx, "", info_obj)
info_obj = re.sub(r"\r\n\r\n", " ", info_obj)
results[attr] = info_obj
results.update(self._get_base_market_data_info())
df = pd.Series(results).to_frame().reset_index()
df.columns = ["Metric", "Value"]
df["Metric"] = df["Metric"].apply(
lambda x: replace_underscores_in_column_names(x)
if isinstance(x, str) else x)
return df[df["Value"].notna()]
def get_nft_of_the_day() -> pd.DataFrame:
"""Scrapes data about nft of the day. [Source: CoinGecko]
Returns
-------
pandas.DataFrame
metric, value
"""
url = "https://www.coingecko.com/en/nft"
try:
scraped_data = scrape_gecko_data(url)
except RetryError as e:
print(e)
return pd.DataFrame()
row = scraped_data.find("div", class_="tw-px-4 tw-py-5 sm:tw-p-6")
try:
*author, description, _ = clean_row(row)
if len(author) > 3:
author, description = author[:3], author[3]
except (ValueError, IndexError):
return pd.DataFrame()
df = (pd.Series({
"author": " ".join(author),
"desc": description,
"url": GECKO_BASE_URL + row.find("a")["href"],
"img": row.find("img")["src"],
}).to_frame().reset_index())
df.columns = ["Metric", "Value"]
df["Metric"] = df["Metric"].apply(
lambda x: replace_underscores_in_column_names(x)
if isinstance(x, str) else x)
df = wrap_text_in_df(df, w=100)
return df
def get_global_info() -> pd.DataFrame:
"""Get global statistics about crypto from CoinGecko API like:
- market cap change
- number of markets
- icos
- number of active crypto
[Source: CoinGecko]
Returns
-------
pandas.DataFrame
Metric, Value
"""
client = CoinGeckoAPI()
results = client.get_global()
total_mcap = results.pop("market_cap_percentage")
eth, btc = total_mcap.get("btc"), total_mcap.get("eth")
for key in ["total_market_cap", "total_volume", "updated_at"]:
del results[key]
results["eth_market_cap_in_pct"] = eth
results["btc_market_cap_in_pct"] = btc
results["altcoin_market_cap_in_pct"] = 100 - (float(eth) + float(btc))
df = pd.Series(results).reset_index()
df.columns = ["Metric", "Value"]
df["Metric"] = df["Metric"].apply(
lambda x: replace_underscores_in_column_names(x)
if isinstance(x, str) else x)
return df
def get_holdings_overview(endpoint: str = "bitcoin") -> List[Any]:
"""Returns public companies that holds ethereum or bitcoin [Source: CoinGecko]
Parameters
----------
endpoint : str
"bitcoin" or "ethereum"
Returns
-------
List:
- str: Overall statistics
- pandas.DataFrame: Companies holding crypto
"""
cg = CoinGeckoAPI()
data = cg.get_companies_public_treasury_by_coin_id(coin_id=endpoint)
stats_str = f"""{len(data["companies"])} companies hold a total of {long_number_format_with_type_check(data["total_holdings"])} {endpoint} ({data["market_cap_dominance"]}% of market cap dominance) with the current value of {long_number_format_with_type_check(int(data["total_value_usd"]))} USD dollars""" # noqa
df = pd.json_normalize(data, record_path=["companies"])
df.columns = list(
map(
lambda x: replace_underscores_in_column_names(x)
if isinstance(x, str) else x,
df.columns,
))
return [stats_str, df]
def get_holdings_overview(endpoint: str = "bitcoin") -> pd.DataFrame:
"""Scrapes overview of public companies that holds ethereum or bitcoin
from "https://www.coingecko.com/en/public-companies-{bitcoin/ethereum}" [Source: CoinGecko]
Parameters
----------
endpoint : str
"bitcoin" or "ethereum"
Returns
-------
pandas.DataFrame
Metric, Value
"""
url = f"https://www.coingecko.com/en/public-companies-{endpoint}"
rows = scrape_gecko_data(url).find_all(
"span", class_="overview-box d-inline-block p-3 mr-2")
kpis = {}
for row in rows:
row_cleaned = clean_row(row)
if row_cleaned:
value, *kpi = row_cleaned
name = " ".join(kpi)
kpis[name] = value
df = pd.Series(kpis).to_frame().reset_index()
df.columns = ["Metric", "Value"]
df["Metric"] = df["Metric"].apply(
lambda x: replace_underscores_in_column_names(x)
if isinstance(x, str) else x)
return df
def all_time_high(self, currency="usd"):
"""Get all time high data for given coin
Returns
-------
pandas.DataFrame
Metric,Value
"""
market_data = self.coin.get("market_data")
ath_columns = [
"current_price",
"ath",
"ath_date",
"ath_change_percentage",
]
results = {}
for column in ath_columns:
results[column] = market_data[column].get(currency)
df = pd.Series(results).to_frame().reset_index()
df.columns = ["Metric", "Value"]
df["Metric"] = df["Metric"].apply(
lambda x: replace_underscores_in_column_names(x)
if isinstance(x, str) else x)
df["Metric"] = df["Metric"].apply(
lambda x: x.replace("Ath", "All Time High"))
df["Metric"] = df["Metric"] + f" {currency.upper()}"
return df[df["Value"].notna()]
def get_market_data(self) -> pd.DataFrame:
"""Get all the base market information about given coin. [Source: CoinGecko]
Returns
-------
pandas.DataFrame
Base market information about coin
Metric,Value
"""
market_data = self.coin.get("market_data", {})
market_columns_denominated = [
"market_cap",
"fully_diluted_valuation",
"total_volume",
"high_24h",
"low_24h",
]
denominated_data = create_dictionary_with_prefixes(
market_columns_denominated, market_data, DENOMINATION)
market_single_columns = [
"market_cap_rank",
"total_supply",
"max_supply",
"circulating_supply",
"price_change_percentage_24h",
"price_change_percentage_7d",
"price_change_percentage_30d",
"price_change_percentage_60d",
"price_change_percentage_1y",
"market_cap_change_24h",
]
single_stats = {}
for col in market_single_columns:
single_stats[col] = market_data.get(col)
single_stats.update(denominated_data)
try:
single_stats["circulating_supply_to_total_supply_ratio"] = (
single_stats["circulating_supply"] /
single_stats["total_supply"])
except (ZeroDivisionError, TypeError) as e:
console.print(e)
df = pd.Series(single_stats).to_frame().reset_index()
df.columns = ["Metric", "Value"]
df["Metric"] = df["Metric"].apply(
lambda x: replace_underscores_in_column_names(x)
if isinstance(x, str) else x)
return df[df["Value"].notna()]
def blockchain_explorers(self):
"""Get list of URLs to blockchain explorers for given coin:
Returns
-------
pandas.DataFrame
Metric, Value
"""
blockchain = self._get_links().get("blockchain_site")
if blockchain:
dct = filter_list(blockchain)
df = pd.Series(dct).to_frame().reset_index()
df.columns = ["Metric", "Value"]
df["Metric"] = df["Metric"].apply(
lambda x: replace_underscores_in_column_names(x)
if isinstance(x, str) else x)
return df[df["Value"].notna()]
return None
def websites(self):
"""Get list of URLs to websites like homepage of coin, forum,
Returns
-------
pandas.DataFrame
Metric, Value
"""
websites_dct = {}
links = self._get_links()
sites = ["homepage", "official_forum_url", "announcement_url"]
for site in sites:
websites_dct[site] = filter_list(links.get(site))
df = pd.Series(websites_dct).to_frame().reset_index()
df.columns = ["Metric", "Value"]
df["Value"] = df["Value"].apply(lambda x: ",".join(x))
df["Metric"] = df["Metric"].apply(
lambda x: replace_underscores_in_column_names(x)
if isinstance(x, str) else x)
return df[df["Value"].notna()]
def get_scores(self) -> pd.DataFrame:
"""Get different kind of scores for given coin. [Source: CoinGecko]
Returns
-------
pandas.DataFrame
Social, community, sentiment scores for coin
Metric,Value
"""
score_columns = [
"coingecko_rank",
"coingecko_score",
"developer_score",
"community_score",
"liquidity_score",
"sentiment_votes_up_percentage",
"sentiment_votes_down_percentage",
"public_interest_score",
"community_data",
"public_interest_stats",
]
single_stats = {col: self.coin.get(col) for col in score_columns[:-2]}
nested_stats = {}
for col in score_columns[-2:]:
_dct = self.coin.get(col, {})
for k, _ in _dct.items():
nested_stats[k] = _dct.get(k, {})
single_stats.update(nested_stats)
df = pd.Series(single_stats).reset_index()
df.replace({0: ""}, inplace=True)
df = df.fillna("")
df.columns = ["Metric", "Value"]
# pylint: disable=unsupported-assignment-operation
df["Metric"] = df["Metric"].apply(
lambda x: replace_underscores_in_column_names(x)
if isinstance(x, str) else x)
return df[df["Value"].notna()]
def get_global_defi_info():
"""Get global statistics about Decentralized Finances from CoinGecko API like:
Returns
-------
pandas.DataFrame
Metric, Value
"""
results = client.get_global_decentralized_finance_defi()
for key, value in results.items():
try:
results[key] = round(float(value), 4)
except (ValueError, TypeError):
pass
df = pd.Series(results).reset_index()
df.columns = ["Metric", "Value"]
df["Metric"] = df["Metric"].apply(
lambda x: replace_underscores_in_column_names(x)
if isinstance(x, str) else x)
return df
def developers_data(self):
"""Get coin development data from GitHub or BitBucket like:
number of pull requests, contributor etc
Returns
-------
pandas.DataFrame
Metric, Value
"""
dev = self.coin.get("developer_data")
useless_keys = (
"code_additions_deletions_4_weeks",
"last_4_weeks_commit_activity_series",
)
remove_keys(useless_keys, dev)
df = pd.Series(dev).to_frame().reset_index()
df.columns = ["Metric", "Value"]
df["Metric"] = df["Metric"].apply(
lambda x: replace_underscores_in_column_names(x)
if isinstance(x, str) else x)
return df[df["Value"].notna()]
def get_top_crypto_categories(
sort_filter: str = SORT_VALUES[0]) -> pd.DataFrame:
"""Returns top crypto categories [Source: CoinGecko]
Returns
-------
pandas.DataFrame
Rank, Name, Change_1h, Change_7d, Market_Cap, Volume_24h,Coins, Url
"""
if sort_filter in SORT_VALUES:
client = CoinGeckoAPI()
data = client.get_coins_categories()
df = pd.DataFrame(data)
del df["id"]
del df["content"]
del df["updated_at"]
df["top_3_coins"] = df["top_3_coins"].apply(coin_formatter)
df.columns = [
replace_underscores_in_column_names(col)
if isinstance(col, str) else col for col in df.columns
]
return df
return pd.DataFrame()
def display_defi_protocols(top: int,
sortby: str,
descend: bool,
description: bool,
export: str = "") -> None:
"""Display information about listed DeFi protocols, their current TVL and changes to it in the last hour/day/week.
[Source: https://docs.llama.fi/api]
Parameters
----------
top: int
Number of records to display
sortby: str
Key by which to sort data
descend: bool
Flag to sort data descending
description: bool
Flag to display description of protocol
export : str
Export dataframe data to csv,json,xlsx file
"""
df = llama_model.get_defi_protocols()
df_data = df.copy()
df = df.sort_values(by=sortby, ascending=descend)
df = df.drop(columns="chain")
df["tvl"] = df["tvl"].apply(lambda x: long_number_format(x))
if not description:
df.drop(["description", "url"], axis=1, inplace=True)
else:
df = df[[
"name",
"symbol",
"category",
"description",
"url",
]]
df.columns = [
replace_underscores_in_column_names(val) for val in df.columns
]
df.rename(
columns={
"Change 1H": "Change 1H (%)",
"Change 1D": "Change 1D (%)",
"Change 7D": "Change 7D (%)",
"Tvl": "TVL ($)",
},
inplace=True,
)
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__)),
"ldapps",
df_data,
)
