test optomization

test_app.py

@@ -27,10 +27,10 @@ def test_utils(self):
 		#Test if bollinger_bands returns TypeError if given a non-numpy list type
 		self.assertRaises(TypeError, utils.algorithm_output, 'bollinger_bands', [1, 2, 3], backtest=True)
 
-	#Test backtest_module.py
+	#Test backtest_module.py, using backtest plot data is because theres no much diffirence (optomization)
 	def test_backtest(self):
 		prices, timestamps, _ = price.get_prices()
-		backtest_data = backtest_module.backtest('bollinger_bands', prices, timestamps)
+		backtest_plot_data = backtest_module.backtest('bollinger_bands', prices, timestamps, plot=True)
 		#Test back test data keys are correct and backtest data exsits
 		backtest_dict = {
 		  'transactions': [],
@@ -43,10 +43,14 @@ def test_backtest(self):
 		  'profit': 1,
 		  'profit_percentage %': 1
 		}
-		self.assertIsNotNone(backtest_data)
-		self.assertCountEqual(backtest_dict.keys(), backtest_data.keys())
+
+		self.assertIsNotNone(backtest_plot_data)
+		self.assertCountEqual(backtest_dict.keys(), backtest_plot_data.keys())
 		#Test transactions keys are correct and and transactions exsits
 
+		backtest_plot = backtest_module.plot(backtest_plot_data)
+		self.assertIsNotNone(backtest_plot)
+
 		transactions_dict = {
 		  'price': price,
 		  'signal': 'signal',
@@ -56,52 +60,29 @@ def test_backtest(self):
 		  'timestamp': 1
 		}
 
-		transactions = backtest_data['transactions'][0]  #take the first transaction of transactions list
+		transactions = backtest_plot_data['transactions'][0]  #take the first transaction of transactions list
 		self.assertIsNotNone(transactions_dict)
 		self.assertCountEqual(transactions_dict.keys(), transactions.keys())
 		#Test if algorithm is correct (using bollinger bands change whenever needed) and algorithm variable exsits
-		self.assertIsNotNone(backtest_data['algorithm'])
-		self.assertEqual('bollinger_bands', backtest_data['algorithm'])
+		self.assertIsNotNone(backtest_plot_data['algorithm'])
+		self.assertEqual('bollinger_bands', backtest_plot_data['algorithm'])
 		#Test if start_balance is greater than 0 and exsits
-		self.assertIsNotNone(backtest_data['start_balance'])
-		self.assertGreater(backtest_data['start_balance'], 0)
+		self.assertIsNotNone(backtest_plot_data['start_balance'])
+		self.assertGreater(backtest_plot_data['start_balance'], 0)
 		#Test if strength_to_usd is greater than 0 and exsits
-		self.assertIsNotNone(backtest_data['strength_to_usd'])
-		self.assertGreater(backtest_data['strength_to_usd'], 0)
+		self.assertIsNotNone(backtest_plot_data['strength_to_usd'])
+		self.assertGreater(backtest_plot_data['strength_to_usd'], 0)
 		#Test if all of these keys are inegers and exsits (raises error if nan)
-		self.assertTrue(type(backtest_data['balance']), int)
-		self.assertTrue(type(backtest_data['final_total']), int)
-		self.assertTrue(type(backtest_data['shares']), int)
-		self.assertTrue(type(backtest_data['profit']), int)
-		self.assertTrue(type(backtest_data['profit_percentage %']), int)
-
-	#Test backtest_plot function
-	def test_backtest_plot(self):
-		prices, timestamps, _ = price.get_prices()
-		backtest_plot_data = backtest_module.backtest('bollinger_bands', prices, timestamps, plot=True)
-		#Test back test data keys are correct and backtest data exsits
-		backtest_dict = {
-		  'transactions': [],
-		  'algorithm': 'bollinger_bands',
-		  'balance': 1,
-		  'start_balance': 1,
-		  'final_total': 1,
-		  'strength_to_usd': 1,
-		  'shares': 1,
-		  'profit': 1,
-		  'profit_percentage %': 1
-		}
-		self.assertIsNotNone(backtest_plot_data)
-		self.assertCountEqual(backtest_dict.keys(), backtest_plot_data.keys())
-		#Test if backtest plot exsits and not raising any errors
-		backtest_plot = backtest_module.plot(backtest_plot_data)
-		self.assertIsNotNone(backtest_plot)
+		self.assertTrue(type(backtest_plot_data['balance']), int)
+		self.assertTrue(type(backtest_plot_data['final_total']), int)
+		self.assertTrue(type(backtest_plot_data['shares']), int)
+		self.assertTrue(type(backtest_plot_data['profit']), int)
+		self.assertTrue(type(backtest_plot_data['profit_percentage %']), int)
 
-	#Test app views
 	def test_views(self):
 		application = app.get_app()
 		app_client = application.test_client()
-		intervals = price.supported_intervals
+		intervals = price.cached_intervals
 		algorithms = utils.get_algorithms()
 		#Test backtest && backtest plot && plot views
 		for algorithm in algorithms: