class TestPriceFunctions():
def setup_method(self, test_method):
self.pf = PriceFunctions()
self.coins = ['BTC', 'ETH', 'DASH', 'DOGE', 'LTC', 'STR', 'XMR', 'XRP']
def test_to_same_starting(self):
self.coins = ['BTC', 'ETH', 'DASH', 'DOGE', 'LTC', 'STR', 'XMR', 'XRP']
dfs = {}
for coin in self.coins:
dfs[coin] = self.pf.get_pandas(coin=coin,
targetdays=24,
absolute=True)
dfs = self.pf.to_same_starting(dfs)
assert (sum(dfs['BTC'].index != dfs['ETH'].index) == 0)
assert (sum(dfs['ETH'].index != dfs['DASH'].index) == 0)
assert (sum(dfs['DASH'].index != dfs['DOGE'].index) == 0)
assert (sum(dfs['DOGE'].index != dfs['LTC'].index) == 0)
assert (sum(dfs['LTC'].index != dfs['STR'].index) == 0)
assert (sum(dfs['STR'].index != dfs['XMR'].index) == 0)
assert (sum(dfs['XMR'].index != dfs['XRP'].index) == 0)
def test_get_pandas(self):
for coin in self.coins:
df = PriceFunctions().get_pandas(coin=coin, targetdays=24)
assert (sum(df.index[1:] - df.index[:-1] != 3600) == 0)
assert ({
'Open', 'Open', 'High', 'Low', 'Volume', 'Percentage Change',
'Classification'
}.issubset(df.columns))
def test_mergeData(self):
pf = PriceFunctions()
self.btc = pf.get_pandas(coin='BTC', targetdays=24,
absolute=True) #as the data is hourly
self.btc = self.btc.reset_index()
xmr = pd.read_csv('CryptoScraper/cache/XMR.csv')
dash = pd.read_csv('CryptoScraper/cache/DASH.csv')
doge = pd.read_csv('CryptoScraper/cache/DOGE.csv')
eth = pd.read_csv('CryptoScraper/cache/ETH.csv')
ltc = pd.read_csv('CryptoScraper/cache/LTC.csv')
str = pd.read_csv('CryptoScraper/cache/STR.csv')
xrp = pd.read_csv('CryptoScraper/cache/XRP.csv')
self.btcXmr = self.btc[self.btc['Date'] >= xmr.iloc[0]['Date']]
self.btcXmr.reset_index(inplace=True, drop=True)
assert (sum(self.btcXmr['Date'] != xmr['Date']) == 0)
self.btcDash = self.btc[self.btc['Date'] >= dash.iloc[0]['Date']]
self.btcDash.reset_index(inplace=True, drop=True)
assert (sum(self.btcDash['Date'] != dash['Date']) == 0)
self.btcDoge = self.btc[self.btc['Date'] >= doge.iloc[0]['Date']]
self.btcDoge.reset_index(inplace=True, drop=True)
assert (sum(self.btcDoge['Date'] != doge['Date']) == 0)
self.btcEth = self.btc[self.btc['Date'] >= eth.iloc[0]['Date']]
self.btcEth.reset_index(inplace=True, drop=True)
assert (sum(self.btcEth['Date'] != eth['Date']) == 0)
self.btcLtc = self.btc[self.btc['Date'] >= ltc.iloc[0]['Date']]
self.btcLtc.reset_index(inplace=True, drop=True)
assert (sum(self.btcLtc['Date'] != ltc['Date']) == 0)
self.btcStr = self.btc[self.btc['Date'] >= str.iloc[0]['Date']]
self.btcStr.reset_index(inplace=True, drop=True)
assert (sum(self.btcStr['Date'] != str['Date']) == 0)
self.btcXrp = self.btc[self.btc['Date'] >= xrp.iloc[0]['Date']]
self.btcXrp.reset_index(inplace=True, drop=True)
assert (sum(self.btcXrp['Date'] != xrp['Date']) == 0)
def test_get_pandas(self):
for coin in self.coins:
df = PriceFunctions().get_pandas(coin=coin, targetdays=24)
assert (sum(df.index[1:] - df.index[:-1] != 3600) == 0)
assert ({
'Open', 'Open', 'High', 'Low', 'Volume', 'Percentage Change',
'Classification'
}.issubset(df.columns))
def setup_method(self, test_method):
self.pf = PriceFunctions()
self.coins = ['BTC', 'ETH', 'DASH', 'DOGE', 'LTC', 'STR', 'XMR', 'XRP']
def __init__(self, symbol):
pf = PriceFunctions()
self.btc = pf.get_pandas(coin='BTC', targetdays=24, absolute=True) #as the data is hourly
self.btc = self.btc.reset_index()
self.url = "https://poloniex.com/public?command=returnChartData¤cyPair=BTC_{}&start={}&end=9999999999&period={}" #gives half an hour data
self.symbol = symbol