class Coin:
"""Coin class, it holds loaded coin"""
def __init__(self, symbol: str, load_from_api: bool = False):
self.client = CoinGeckoAPI()
if load_from_api:
self._coin_list = self.client.get_coins_list()
else:
self._coin_list = read_file_data("coingecko_coins.json")
self.coin_symbol, self.symbol = self._validate_coin(symbol)
if self.coin_symbol:
self.coin: Dict[Any, Any] = self._get_coin_info()
def __str__(self):
return f"{self.coin_symbol}"
def _validate_coin(
self, search_coin: str) -> Tuple[Optional[Any], Optional[Any]]:
"""Validate if given coin symbol or id exists in list of available coins on CoinGecko.
If yes it returns coin id. [Source: CoinGecko]
Parameters
----------
symbol: str
Either coin symbol or coin id
Returns
-------
Tuple[str, str]
- str with coin
- str with symbol
"""
coin = None
symbol = None
for dct in self._coin_list:
if search_coin.lower() in [
dct["id"],
dct["symbol"],
]:
coin = dct.get("id")
symbol = dct.get("symbol")
return coin, symbol
raise ValueError(
f"Could not find coin with the given id: {search_coin}\n")
def coin_list(self) -> list:
"""List all available coins [Source: CoinGecko]
Returns
-------
list
list of all available coin ids
"""
return [token.get("id") for token in self._coin_list]
def _get_coin_info(self) -> dict:
"""Helper method which fetch the coin information by id from CoinGecko API like:
(name, price, market, ... including exchange tickers) [Source: CoinGecko]
Returns
-------
dict
Coin information
"""
params = dict(localization="false", tickers="false", sparkline=True)
return self.client.get_coin_by_id(self.coin_symbol, **params)
def _get_links(self) -> Dict:
"""Helper method that extracts links from coin [Source: CoinGecko]
Returns
-------
dict
Links related to coin
"""
return self.coin.get("links", {})
@property
def get_repositories(self) -> Optional[Any]:
"""Get list of all repositories for given coin [Source: CoinGecko]
Returns
-------
list
Repositories related to coin
"""
return self._get_links().get("repos_url")
@property
def get_developers_data(self) -> pd.DataFrame:
"""Get coin development data from GitHub or BitBucket like:
number of pull requests, contributor etc [Source: CoinGecko]
Returns
-------
pandas.DataFrame
Developers Data
Columns: 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()]
@property
def get_blockchain_explorers(self) -> Union[pd.DataFrame, Any]:
"""Get list of URLs to blockchain explorers for given coin. [Source: CoinGecko]
Returns
-------
pandas.DataFrame
Blockchain Explorers
Columns: 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
@property
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()]
@property
def get_websites(self) -> pd.DataFrame:
"""Get list of URLs to websites like homepage of coin, forum. [Source: CoinGecko]
Returns
-------
pandas.DataFrame
Urls to website, homepage, forum
Columns: 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()]
@property
def get_categories(self) -> Union[Dict[Any, Any], List[Any]]:
"""Coins categories. [Source: CoinGecko]
Returns
-------
list/dict
Coin categories
"""
return self.coin.get("categories", {})
def _get_base_market_data_info(self) -> dict:
"""Helper method that fetches all the base market/price information about given coin. [Source: CoinGecko]
Returns
-------
dict
All market related information for given coin
"""
market_dct = {}
market_data = self.coin.get("market_data", {})
for stat in [
"total_supply",
"max_supply",
"circulating_supply",
"price_change_percentage_24h",
"price_change_percentage_7d",
"price_change_percentage_30d",
]:
market_dct[stat] = market_data.get(stat)
prices = create_dictionary_with_prefixes(["current_price"],
market_data, DENOMINATION)
market_dct.update(prices)
return market_dct
@property
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()]
@property
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 get_all_time_high(self, currency: str = "usd") -> pd.DataFrame:
"""Get all time high data for given coin. [Source: CoinGecko]
Returns
-------
pandas.DataFrame
All time high price data
Metric,Value
"""
market_data = self.coin.get("market_data", {})
if market_data == {}:
return pd.DataFrame()
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_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()]
@property
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_coin_market_chart(self,
vs_currency: str = "usd",
days: int = 30,
**kwargs: Any) -> pd.DataFrame:
"""Get prices for given coin. [Source: CoinGecko]
Parameters
----------
vs_currency: str
currency vs which display data
days: int
number of days to display the data
kwargs
Returns
-------
pandas.DataFrame
Prices for given coin
Columns: time, price, currency
"""
prices = self.client.get_coin_market_chart_by_id(
self.coin_symbol, vs_currency, days, **kwargs)
prices = prices["prices"]
df = pd.DataFrame(data=prices, columns=["time", "price"])
df["time"] = pd.to_datetime(df.time, unit="ms")
df = df.set_index("time")
df["currency"] = vs_currency
return df
def get_ohlc(self,
vs_currency: str = "usd",
days: int = 90) -> pd.DataFrame:
"""Get Open, High, Low, Close prices for given coin. [Source: CoinGecko]
Parameters
----------
vs_currency: str
currency vs which display data
days: int
number of days to display the data
on from (1/7/14/30/90/180/365, max)
Returns
-------
pandas.DataFrame
OHLC data for coin
Columns: time, price, currency
"""
prices = self.client.get_coin_ohlc_by_id(self.coin_symbol, vs_currency,
days)
df = pd.DataFrame(data=prices,
columns=["time", "open", "high", "low", "close"])
df["time"] = pd.to_datetime(df.time, unit="ms")
df = df.set_index("time")
df["currency"] = vs_currency
return df
class Coin:
"""Coin class, it holds loaded coin"""
def __init__(self, symbol):
self.client = CoinGeckoAPI()
self._coin_list = self.client.get_coins_list()
self.coin_symbol = self._validate_coin(symbol)
if self.coin_symbol:
self.coin = self._get_coin_info()
def __str__(self):
return f"{self.coin_symbol}"
def _validate_coin(self, symbol):
"""Validate if given coin symbol or id exists in list of available coins on CoinGecko.
If yes it returns coin id.
Parameters
----------
symbol: str
Either coin symbol or coin id
Returns
-------
id of the coin on CoinGecko service.
"""
coin = None
for dct in self._coin_list:
if symbol.lower() in list(dct.values()):
coin = dct.get("id")
if not coin:
raise ValueError(
f"Could not find coin with the given id: {symbol}\n")
return coin
def coin_list(self):
"""
Returns
-------
list of all available coin ids
"""
return [token.get("id") for token in self._coin_list]
def _get_coin_info(self):
"""Helper method which fetch the coin information by id from CoinGecko API like:
(name, price, market, ... including exchange tickers)
Returns
-------
dict
"""
params = dict(localization="false", tickers="false", sparkline=True)
return self.client.get_coin_by_id(self.coin_symbol, **params)
def _get_links(self):
"""Helper method that extracts links from coin
Returns
-------
dict
"""
return self.coin.get("links")
@property
def repositories(self):
"""Get list of all repositories for given coin
Returns
-------
list with repositories
"""
return self._get_links().get("repos_url")
@property
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"]
return df
@property
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"]
return df
return None
@property
def social_media(self):
"""Get list of URLs to social media like twitter, facebook, reddit...
Returns
-------
pandas.DataFrame
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"]
return df
@property
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))
return df
@property
def categories(self):
"""Coins categories
Returns
-------
list/dict
"""
return self.coin.get("categories")
def _get_base_market_data_info(self):
"""Helper method that fetches all the base market/price information about given coin
Returns
-------
dict
"""
market_dct = {}
market_data = self.coin.get("market_data")
for stat in [
"total_supply",
"max_supply",
"circulating_supply",
"price_change_percentage_24h",
"price_change_percentage_7d",
"price_change_percentage_30d",
]:
market_dct[stat] = market_data.get(stat)
prices = create_dictionary_with_prefixes(["current_price"],
market_data, DENOMINATION)
market_dct.update(prices)
return market_dct
@property
def base_info(self):
"""Get all the base information about given coin
Returns
-------
pandas.DataFrame
"""
results = {}
for attr in BASE_INFO:
info_obj = self.coin.get(attr)
if attr == "description":
info_obj = info_obj.get("en")
results[attr] = info_obj
results.update(self._get_base_market_data_info())
return pd.Series(results).to_frame().reset_index()
@property
def market_data(self):
"""Get all the base market information about given coin
Returns
-------
pandas.DataFrame
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:
print(e)
df = pd.Series(single_stats).to_frame().reset_index()
df.columns = ["Metric", "Value"]
return df
@property
def all_time_high(self):
"""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 = create_dictionary_with_prefixes(ath_columns, market_data,
DENOMINATION)
df = pd.Series(results).to_frame().reset_index()
df.columns = ["Metric", "Value"]
return df
@property
def all_time_low(self):
"""Get all time low data for given coin
Returns
-------
pandas.DataFrame
Metric,Value
"""
market_data = self.coin.get("market_data")
ath_columns = [
"current_price",
"atl",
"atl_date",
"atl_change_percentage",
]
results = create_dictionary_with_prefixes(ath_columns, market_data,
DENOMINATION)
df = pd.Series(results).to_frame().reset_index()
df.columns = ["Metric", "Value"]
return df
@property
def scores(self):
"""Get different kind of scores for given coin
Returns
-------
pandas.DataFrame
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"]
return df
def get_coin_market_chart(self, vs_currency="usd", days=30, **kwargs):
"""Get prices for given coin
Parameters
----------
vs_currency: str
currency vs which display data
days: int
number of days to display the data
kwargs
Returns
-------
pandas.DataFrame
time, price, currency
"""
prices = self.client.get_coin_market_chart_by_id(
self.coin_symbol, vs_currency, days, **kwargs)
prices = prices["prices"]
df = pd.DataFrame(data=prices, columns=["time", "price"])
df["time"] = pd.to_datetime(df.time, unit="ms")
df = df.set_index("time")
df["currency"] = vs_currency
return df
def get_ohlc(self, vs_currency="usd", days=90):
"""Get Open, High, Low, Close prices for given coin
Parameters
----------
vs_currency: str
currency vs which display data
days: int
number of days to display the data
on from (1/7/14/30/90/180/365, max)
Returns
-------
pandas.DataFrame
time, price, currency
"""
prices = self.client.get_coin_ohlc_by_id(self.coin_symbol, vs_currency,
days)
df = pd.DataFrame(data=prices,
columns=["time", "open", "high", "low", "close"])
df["time"] = pd.to_datetime(df.time, unit="ms")
df = df.set_index("time")
df["currency"] = vs_currency
return df
