Merge pull request #7 from silvavn/master

Improved organization and removed redundancy

.gitignore

@@ -0,0 +1,3 @@
+
+*.pyc
+*.png

argchecker.py

@@ -0,0 +1,25 @@
+class ArgChecker:
+    """
+    Argument checker
+    """
+
+    def __init__(self, args):
+        print("Checking arguments...")
+        self.check_arguments(args)
+
+    def check_arguments(self, args):
+        granularity_constraints_list = [1, 5, 10, 15, 30, 60, 3600]
+        granularity_constraints_list_string = ''.join(
+            str(value) + "," for value in granularity_constraints_list).strip(",")
+
+        assert not(args.data_granularity_minutes not in granularity_constraints_list), "You can only choose the following values for 'data_granularity_minutes' argument -> %s\nExiting now..." % granularity_constraints_list_string
+
+        assert not(args.is_test == 1 and args.future_bars <
+                   2), "You want to test but the future bars are less than 2. That does not give us enough data to test the model properly. Please use a value larger than 2.\nExiting now..."
+
+        assert not(args.history_to_use != "all" and int(args.history_to_use_int) <
+                   args.future_bars), "It is a good idea to use more history and less future bars. Please change these two values and try again.\nExiting now..."
+
+        args.market_index = str(args.market_index).upper()
+        if args.history_to_use != "all":
+            args.history_to_use = int(args.history_to_use)

backtester.py

@@ -18,100 +18,101 @@
 
 
 class BackTester:
-	"""
-	Backtester module that does both backward and forward testing for our portfolios.
-	"""
-	def __init__(self):
-		print("\n--# Backtester has been initialized")
-
-	def calculate_percentage_change(self, old, new):
-		"""
-		Percentage change
-		"""
-		return ((new - old) * 100) / old
-
-	def portfolio_weight_manager(self, weight, is_long_only):
-		"""
-		Manage portfolio weights. If portfolio is long only, set the negative weights to zero.
-		"""
-		if is_long_only == 1:
-			weight = max(weight, 0)
-		else:
-			weight = weight
-		return weight
-
-	def back_test(self, symbol_names, portfolio_weights_dictionary, portfolio_data_dictionary, historical_price_market, is_long_only, market_chart, strategy_name):
-		"""
-		Main backtest function. Takes in the portfolio weights and compares the portfolio returns with a market index of your choice.
-		"""
-
-		# Get market returns during the backtesting time
-		historical_price_market = list(historical_price_market["Close"])
-		market_returns = [self.calculate_percentage_change(historical_price_market[i - 1], historical_price_market[i]) for i in range(1, len(historical_price_market))]
-		market_returns_cumulative = np.cumsum(market_returns)
-
-		# Get invidiual returns for each stock in our portfolio
-		normal_returns_matrix = []
-		for symbol in symbol_names:
-			symbol_historical_prices = list(portfolio_data_dictionary[symbol]["historical_prices"]["Close"])
-			symbol_historical_returns = [self.calculate_percentage_change(symbol_historical_prices[i - 1], symbol_historical_prices[i]) for i in range(1, len(symbol_historical_prices))]
-			normal_returns_matrix.append(symbol_historical_returns)
-
-		# Get portfolio returns
-		normal_returns_matrix = np.array(normal_returns_matrix).transpose()
-		portfolio_weights_vector = np.array([self.portfolio_weight_manager(portfolio_weights_dictionary[symbol], is_long_only) for symbol in portfolio_weights_dictionary]).transpose()
-		portfolio_returns = np.dot(normal_returns_matrix, portfolio_weights_vector)
-		portfolio_returns_cumulative = np.cumsum(portfolio_returns)
-
-		# Plot returns
-		x = np.arange(len(portfolio_returns_cumulative))
-		plt.plot(x, portfolio_returns_cumulative, linewidth = 2.0, label = strategy_name)
-		plt.axhline(y = 0, linestyle = 'dotted', alpha = 0.3, color = 'black')
-		if market_chart:
-			x = np.arange(len(market_returns_cumulative))
-			plt.plot(x, market_returns_cumulative, linewidth = 2.0, color = '#282828', label = 'Market Index', linestyle = '--')
-
-		# Plotting styles
-		plt.title("Backtest Results", fontsize = 14)
-		plt.xlabel("Bars (Time Sorted)", fontsize = 14)
-		plt.ylabel("Cumulative Percentage Return", fontsize = 14)
-		plt.xticks(fontsize = 14)
-		plt.yticks(fontsize = 14)
-
-	def future_test(self, symbol_names, portfolio_weights_dictionary, portfolio_data_dictionary, future_price_market, is_long_only, market_chart, strategy_name):
-		"""
-		Main future test function. If future data is available i.e is_test is set to 1 and future_bars set to > 0, this takes in the portfolio weights and compares the portfolio returns with a market index of your choice in the future.
-		"""
-
-		# Get future prices
-		future_price_market = [item[4] for item in list(future_price_market)]
-		market_returns = [self.calculate_percentage_change(future_price_market[i - 1], future_price_market[i]) for i in range(1, len(future_price_market))]
-		market_returns_cumulative = np.cumsum(market_returns)
-
-		# Get invidiual returns for each stock in our portfolio
-		normal_returns_matrix = []
-		for symbol in symbol_names:
-			symbol_historical_prices = [item[4] for item in list(portfolio_data_dictionary[symbol]["future_prices"])]
-			symbol_historical_returns = [self.calculate_percentage_change(symbol_historical_prices[i - 1], symbol_historical_prices[i]) for i in range(1, len(symbol_historical_prices))]
-			normal_returns_matrix.append(symbol_historical_returns)
-
-		# Get portfolio returns
-		normal_returns_matrix = np.array(normal_returns_matrix).transpose()
-		portfolio_weights_vector = np.array([self.portfolio_weight_manager(portfolio_weights_dictionary[symbol], is_long_only) for symbol in portfolio_weights_dictionary]).transpose()
-		portfolio_returns = np.dot(normal_returns_matrix, portfolio_weights_vector)
-		portfolio_returns_cumulative = np.cumsum(portfolio_returns)
-
-		# Plot
-		x = np.arange(len(portfolio_returns_cumulative))
-		plt.axhline(y = 0, linestyle = 'dotted', alpha = 0.3, color = 'black')
-		plt.plot(x, portfolio_returns_cumulative, linewidth = 2.0, label = strategy_name)
-		if market_chart:
-			x = np.arange(len(market_returns_cumulative))
-			plt.plot(x, market_returns_cumulative, linewidth = 2.0, color = '#282828', label = 'Market Index', linestyle = '--')
-
-		# Plotting styles
-		plt.title("Future Test Results", fontsize = 14)
-		plt.xlabel("Bars (Time Sorted)", fontsize = 14)
-		plt.ylabel("Cumulative Percentage Return", fontsize = 14)
-		plt.xticks(fontsize = 14)
-		plt.yticks(fontsize = 14)
+    """
+    Backtester module that does both backward and forward testing for our portfolios.
+    """
+    def __init__(self):
+        print("\n--# Backtester has been initialized")
+
+    def calculate_percentage_change(self, old, new):
+        """
+        Percentage change
+        """
+        return ((new - old) * 100) / old
+
+    def portfolio_weight_manager(self, weight, is_long_only):
+        """
+        Manage portfolio weights. If portfolio is long only, set the negative weights to zero.
+        """
+        if is_long_only == 1:
+            weight = max(weight, 0)
+        else:
+            weight = weight
+        return weight
+
+    def back_test(self, symbol_names, portfolio_weights_dictionary, portfolio_data_dictionary, historical_price_market, is_long_only, market_chart, strategy_name):
+        """
+        Main backtest function. Takes in the portfolio weights and compares the portfolio returns with a market index of your choice.
+        """
+
+        # Get market returns during the backtesting time
+        historical_price_market = list(historical_price_market["Close"])
+        market_returns = [self.calculate_percentage_change(historical_price_market[i - 1], historical_price_market[i]) for i in range(1, len(historical_price_market))]
+        market_returns_cumulative = np.cumsum(market_returns)
+
+        # Get invidiual returns for each stock in our portfolio
+        normal_returns_matrix = []
+        for symbol in symbol_names:
+            symbol_historical_prices = list(portfolio_data_dictionary[symbol]["historical_prices"]["Close"])
+            symbol_historical_returns = [self.calculate_percentage_change(symbol_historical_prices[i - 1], symbol_historical_prices[i]) for i in range(1, len(symbol_historical_prices))]
+            normal_returns_matrix.append(symbol_historical_returns)
+
+        # Get portfolio returns
+        normal_returns_matrix = np.array(normal_returns_matrix).transpose()
+        portfolio_weights_vector = np.array([self.portfolio_weight_manager(portfolio_weights_dictionary[symbol], is_long_only) for symbol in portfolio_weights_dictionary]).transpose()
+        portfolio_returns = np.dot(normal_returns_matrix, portfolio_weights_vector)
+        portfolio_returns_cumulative = np.cumsum(portfolio_returns)
+
+        # Plot returns
+        x = np.arange(len(portfolio_returns_cumulative))
+        plt.plot(x, portfolio_returns_cumulative, linewidth = 2.0, label = strategy_name)
+        plt.axhline(y = 0, linestyle = 'dotted', alpha = 0.3, color = 'black')
+        if market_chart:
+            x = np.arange(len(market_returns_cumulative))
+            plt.plot(x, market_returns_cumulative, linewidth = 2.0, color = '#282828', label = 'Market Index', linestyle = '--')
+
+        # Plotting styles
+        plt.title("Backtest Results", fontsize = 14)
+        plt.xlabel("Bars (Time Sorted)", fontsize = 14)
+        plt.ylabel("Cumulative Percentage Return", fontsize = 14)
+        plt.xticks(fontsize = 14)
+        plt.yticks(fontsize = 14)
+
+    def future_test(self, symbol_names, portfolio_weights_dictionary, portfolio_data_dictionary, future_price_market, is_long_only, market_chart, strategy_name):
+        """
+        Main future test function. If future data is available i.e is_test is set to 1 and future_bars set to > 0, this takes in the portfolio weights and compares the portfolio returns with a market index of your choice in the future.
+        """
+
+        # Get future prices
+        print(future_price_market)
+        future_price_market = [item[4] for item in list(future_price_market)]
+        market_returns = [self.calculate_percentage_change(future_price_market[i - 1], future_price_market[i]) for i in range(1, len(future_price_market))]
+        market_returns_cumulative = np.cumsum(market_returns)
+
+        # Get invidiual returns for each stock in our portfolio
+        normal_returns_matrix = []
+        for symbol in symbol_names:
+            symbol_historical_prices = [item[4] for item in list(portfolio_data_dictionary[symbol]["future_prices"])]
+            symbol_historical_returns = [self.calculate_percentage_change(symbol_historical_prices[i - 1], symbol_historical_prices[i]) for i in range(1, len(symbol_historical_prices))]
+            normal_returns_matrix.append(symbol_historical_returns)
+
+        # Get portfolio returns
+        normal_returns_matrix = np.array(normal_returns_matrix).transpose()
+        portfolio_weights_vector = np.array([self.portfolio_weight_manager(portfolio_weights_dictionary[symbol], is_long_only) for symbol in portfolio_weights_dictionary]).transpose()
+        portfolio_returns = np.dot(normal_returns_matrix, portfolio_weights_vector)
+        portfolio_returns_cumulative = np.cumsum(portfolio_returns)
+
+        # Plot
+        x = np.arange(len(portfolio_returns_cumulative))
+        plt.axhline(y = 0, linestyle = 'dotted', alpha = 0.3, color = 'black')
+        plt.plot(x, portfolio_returns_cumulative, linewidth = 2.0, label = strategy_name)
+        if market_chart:
+            x = np.arange(len(market_returns_cumulative))
+            plt.plot(x, market_returns_cumulative, linewidth = 2.0, color = '#282828', label = 'Market Index', linestyle = '--')
+
+        # Plotting styles
+        plt.title("Future Test Results", fontsize = 14)
+        plt.xlabel("Bars (Time Sorted)", fontsize = 14)
+        plt.ylabel("Cumulative Percentage Return", fontsize = 14)
+        plt.xticks(fontsize = 14)
+        plt.yticks(fontsize = 14)

commands.json

@@ -0,0 +1,79 @@
+[{
+        "comm": "--history_to_use",
+        "type": "str",
+        "default": "all",
+        "help": "How many bars of 1 hour do you want to use for the anomaly detection model. Either an integer or all"
+    },
+    {
+        "comm": "--is_load_from_dictionary",
+        "type": "int",
+        "default": "0",
+        "help": "Whether to load data from dictionary or get it from yahoo finance."
+    },
+    {
+        "comm": "--data_dictionary_path",
+        "type": "str",
+        "default": "dictionaries/data_dictionary.npy",
+        "help": "Data dictionary path."
+    },
+    {
+        "comm": "--is_save_dictionary",
+        "type": "int",
+        "default": "1",
+        "help": "Whether to save data in a dictionary."
+    },
+    {
+        "comm": "--data_granularity_minutes",
+        "type": "int",
+        "default": "15",
+        "help": "Minute level data granularity that you want to use. Default is 60 minute bars."
+    },
+    {
+        "comm": "--is_test",
+        "type": "int",
+        "default": "1",
+        "help": "Whether to test the tool or just predict for future. When testing you should set the future_bars to larger than 1."
+    },
+    {
+        "comm": "--future_bars",
+        "type": "int",
+        "default": "30",
+        "help": "How many bars to keep for testing purposes."
+    },
+    {
+        "comm": "--apply_noise_filtering",
+        "type": "int",
+        "default": "1",
+        "help": "Whether to apply the random matrix theory to filter out the eigen values."
+    },
+    {
+        "comm": "--only_long",
+        "type": "int",
+        "default": "1",
+        "help": "Whether to only long the stocks or do both long and short."
+    },
+    {
+        "comm": "--market_index",
+        "type": "str",
+        "default": "SPY",
+        "help": "Which index to use for comparisons."
+    },
+    {
+        "comm": "--eigen_portfolio_number",
+        "type": "int",
+        "default": "2",
+        "help": "Which eigen portfolio to choose. By default the 2nd one is choosen as it gives the most risk and reward."
+    },
+    {
+        "comm": "--stocks_file_path",
+        "type": "str",
+        "default": "stocks/stocks.txt",
+        "help": "Stocks file that contains the list of stocks you want to build your portfolio with."
+    },
+    {
+        "comm": "--save_plot",
+        "type": "bool",
+        "default": "False",
+        "help": "Save plot instead of rendering it immediately."
+    }
+]