def is1hSMA50200Bull(self):
try:
if self.isSimulation() and isinstance(self.sma50200_1h_cache,
pd.DataFrame):
df_data = self.sma50200_1h_cache
if self.exchange == 'coinbasepro':
api = CBPublicAPI()
df_data = api.getHistoricalData(self.market, 3600)
self.sma50200_1h_cache = df_data
elif self.exchange == 'binance':
api = BPublicAPI()
df_data = api.getHistoricalData(self.market, '1h')
self.sma50200_1h_cache = df_data
else:
return False
ta = TechnicalAnalysis(df_data)
if 'sma50' not in df_data:
ta.addSMA(50)
if 'sma200' not in df_data:
ta.addSMA(200)
df_last = ta.getDataFrame().copy().iloc[-1, :]
df_last['bull'] = df_last['sma50'] > df_last['sma200']
return bool(df_last['bull'])
except Exception:
return False
def is6hSMA50200Bull(self):
try:
if self.exchange == 'coinbasepro':
api = CBPublicAPI()
df_data = api.getHistoricalData(self.market, 21600)
elif self.exchange == 'binance':
api = BPublicAPI()
df_data = api.getHistoricalData(self.market, '6h')
else:
return False
ta = TechnicalAnalysis(df_data)
ta.addSMA(50)
ta.addSMA(200)
df_last = ta.getDataFrame().copy().iloc[-1, :]
df_last['bull'] = df_last['sma50'] > df_last['sma200']
return bool(df_last['bull'])
except Exception:
return False
def is1hSMA50200Bull(self, iso8601end: str = ''):
try:
if self.isSimulation() and isinstance(self.sma50200_1h_cache,
pd.DataFrame):
df_data = self.sma50200_1h_cache[(
self.sma50200_1h_cache['date'] <= iso8601end)]
elif self.exchange == 'coinbasepro':
api = CBPublicAPI()
df_data = api.getHistoricalData(self.market, 3600)
self.sma50200_1h_cache = df_data
elif self.exchange == 'binance':
api = BPublicAPI()
df_data = api.getHistoricalData(self.market, '1h')
self.sma50200_1h_cache = df_data
else:
return False
ta = TechnicalAnalysis(df_data)
if 'sma50' not in df_data:
ta.addSMA(50)
if 'sma200' not in df_data:
ta.addSMA(200)
df_last = ta.getDataFrame().copy().iloc[-1, :]
Logger.debug("---- SMA50200 1H Check----")
if self.isSimulation():
Logger.debug("simdate: " + str(df_last['date']))
Logger.debug("sma50 1h: " + str(df_last['sma50']))
Logger.debug("sma200 1h: " + str(df_last['sma200']))
Logger.debug("bull 1h: " +
str(df_last['sma50'] > df_last['sma200']))
df_last['bull'] = df_last['sma50'] > df_last['sma200']
return bool(df_last['bull'])
except Exception:
return False
def isCryptoRecession(self):
try:
if self.exchange == 'coinbasepro':
api = CBPublicAPI()
df_data = api.getHistoricalData(self.market, 86400)
elif self.exchange == 'binance':
api = BPublicAPI()
df_data = api.getHistoricalData(self.market, '1d')
else:
return False # if there is an API issue, default to False to avoid hard sells
if len(df_data) <= 200:
return False # if there is unsufficient data, default to False to avoid hard sells
ta = TechnicalAnalysis(df_data)
ta.addSMA(50)
ta.addSMA(200)
df_last = ta.getDataFrame().copy().iloc[-1, :]
df_last['crypto_recession'] = df_last['sma50'] < df_last['sma200']
return bool(df_last['crypto_recession'])
except Exception:
return False
def test_should_calculate_addSMA_20():
"""
Add the Simple Moving Average (SMA) to the DataFrame :
"""
# GIVEN a series of values
closes_list = [
0.0003, 0.0004, 0.0010, 0.0020, 0.0009, 0.0008, 0.0009, 0.0010, 0.0012,
0.0015, 0.0025, 0.0015, 0.0014, 0.0016, 0.0030, 0.0032, 0.0035, 0.0024,
0.0023, 0.0022, 0.0021, 0.0020
]
df = pd.DataFrame({
'date': [
'2021-10-10 14:30:00', '2021-10-10 14:31:00',
'2021-10-10 14:32:00', '2021-10-10 14:33:00',
'2021-10-10 14:34:00', '2021-10-10 14:35:00',
'2021-10-10 14:36:00', '2021-10-10 14:37:00',
'2021-10-10 14:38:00', '2021-10-10 14:39:00',
'2021-10-10 14:40:00', '2021-10-10 14:41:00',
'2021-10-10 14:42:00', '2021-10-10 14:43:00',
'2021-10-10 14:44:00', '2021-10-10 14:45:00',
'2021-10-10 14:46:00', '2021-10-10 14:47:00',
'2021-10-10 14:48:00', '2021-10-10 14:49:00',
'2021-10-10 14:50:00', '2021-10-10 14:51:00'
],
'close':
closes_list
})
df['date'] = pd.to_datetime(df['date'], format="%Y-%d-%m %H:%M:%S")
df.set_index(['date'])
ta = TechnicalAnalysis(df)
# WHEN calculate the cumulative moving average 20
ta.addSMA(20)
# THEN
actual = ta.getDataFrame()
expected = pd.DataFrame({
'date': [
'2021-10-10 14:30:00', '2021-10-10 14:31:00',
'2021-10-10 14:32:00', '2021-10-10 14:33:00',
'2021-10-10 14:34:00', '2021-10-10 14:35:00',
'2021-10-10 14:36:00', '2021-10-10 14:37:00',
'2021-10-10 14:38:00', '2021-10-10 14:39:00',
'2021-10-10 14:40:00', '2021-10-10 14:41:00',
'2021-10-10 14:42:00', '2021-10-10 14:43:00',
'2021-10-10 14:44:00', '2021-10-10 14:45:00',
'2021-10-10 14:46:00', '2021-10-10 14:47:00',
'2021-10-10 14:48:00', '2021-10-10 14:49:00',
'2021-10-10 14:50:00', '2021-10-10 14:51:00'
],
'close':
closes_list,
'sma20': [
calculate_mean_on_range(0, 1, closes_list),
calculate_mean_on_range(0, 2, closes_list),
calculate_mean_on_range(0, 3, closes_list),
calculate_mean_on_range(0, 4, closes_list),
calculate_mean_on_range(0, 5, closes_list),
calculate_mean_on_range(0, 6, closes_list),
calculate_mean_on_range(0, 7, closes_list),
calculate_mean_on_range(0, 8, closes_list),
calculate_mean_on_range(0, 9, closes_list),
calculate_mean_on_range(0, 10, closes_list),
calculate_mean_on_range(0, 11, closes_list),
calculate_mean_on_range(0, 12, closes_list),
calculate_mean_on_range(0, 13, closes_list),
calculate_mean_on_range(0, 14, closes_list),
calculate_mean_on_range(0, 15, closes_list),
calculate_mean_on_range(0, 16, closes_list),
calculate_mean_on_range(0, 17, closes_list),
calculate_mean_on_range(0, 18, closes_list),
calculate_mean_on_range(0, 19, closes_list),
calculate_mean_on_range(0, 20, closes_list),
calculate_mean_on_range(1, 21, closes_list),
calculate_mean_on_range(2, 22, closes_list)
]
})
expected['date'] = pd.to_datetime(df['date'], format="%Y-%d-%m %H:%M:%S")
expected.set_index(['date'])
assert_frame_equal(actual, expected)