class Eiten:
def __init__(self):
print("\n--* Eiten has been initialized...")
self.HISTORY_TO_USE = history_to_use
self.DATA_GRANULARITY_MINUTES = data_granularity_minutes
self.FUTURE_BARS_FOR_TESTING = future_bars
self.APPLY_NOISE_FILTERING = apply_noise_filtering
self.IS_LONG_ONLY_PORTFOLIO = is_only_long
self.MARKET_INDEX = market_index
self.IS_TEST = is_test
self.EIGEN_PORTFOLIO_NUMBER = eigen_portfolio_number
self.STOCKS_FILE_PATH = stocks_file_path
# Create data engine
self.dataEngine = DataEngine(self.HISTORY_TO_USE,
self.DATA_GRANULARITY_MINUTES,
self.FUTURE_BARS_FOR_TESTING,
self.MARKET_INDEX, self.IS_TEST,
self.STOCKS_FILE_PATH)
# Monto carlo simulator
self.simulator = MontoCarloSimulator()
# Strategy manager
self.strategyManager = StrategyManager()
# Back tester
self.backTester = BackTester()
# Data dictionary
self.data_dictionary = {}
print('\n')
def calculate_percentage_change(self, old, new):
"""
Calculate percentage change
"""
return ((new - old) * 100) / old
def create_returns(self, historical_price_info):
"""
Create log return matrix, percentage return matrix, and mean return vector
"""
returns_matrix = []
returns_matrix_percentages = []
predicted_return_vectors = []
for i in range(0, len(historical_price_info)):
close_prices = list(historical_price_info[i]["Close"])
log_returns = [
math.log(close_prices[i] / close_prices[i - 1])
for i in range(1, len(close_prices))
]
percentage_returns = [
self.calculate_percentage_change(close_prices[i - 1],
close_prices[i])
for i in range(1, len(close_prices))
]
total_data = len(close_prices)
# Expected returns in future. We can either use historical returns as future returns on try to simulate future returns and take the mean. For simulation, you can modify the functions in simulator to use here.
future_expected_returns = np.mean([
(self.calculate_percentage_change(
close_prices[i - 1], close_prices[i])) / (total_data - i)
for i in range(1, len(close_prices))
]) # More focus on recent returns
# Add to matrices
returns_matrix.append(log_returns)
returns_matrix_percentages.append(percentage_returns)
# Add returns to vector
predicted_return_vectors.append(
future_expected_returns
) # Assuming that future returns are similar to past returns
# Convert to numpy arrays for one liner calculations
predicted_return_vectors = np.array(predicted_return_vectors)
returns_matrix = np.array(returns_matrix)
returns_matrix_percentages = np.array(returns_matrix_percentages)
return predicted_return_vectors, returns_matrix, returns_matrix_percentages
def load_data(self):
"""
Loads data needed for analysis
"""
# Gather data for all stocks in a dictionary format
# Dictionary keys will be -> historical_prices, future_prices
self.data_dictionary = self.dataEngine.collect_data_for_all_tickers()
# Add data to lists
symbol_names = list(sorted(self.data_dictionary.keys()))
historical_price_info, future_prices = [], []
for symbol in symbol_names:
historical_price_info.append(
self.data_dictionary[symbol]["historical_prices"])
future_prices.append(self.data_dictionary[symbol]["future_prices"])
# Get return matrices and vectors
predicted_return_vectors, returns_matrix, returns_matrix_percentages = self.create_returns(
historical_price_info)
return historical_price_info, future_prices, symbol_names, predicted_return_vectors, returns_matrix, returns_matrix_percentages
def run_strategies(self):
"""
Run strategies, back and future test them, and simulate the returns.
"""
historical_price_info, future_prices, symbol_names, predicted_return_vectors, returns_matrix, returns_matrix_percentages = self.load_data(
)
historical_price_market, future_prices_market = self.dataEngine.get_market_index_price(
)
# Calculate covariance matrix
covariance_matrix = np.cov(returns_matrix)
# Use random matrix theory to filter out the noisy eigen values
if self.APPLY_NOISE_FILTERING:
print(
"\n** Applying random matrix theory to filter out noise in the covariance matrix...\n"
)
covariance_matrix = self.strategyManager.random_matrix_theory_based_cov(
returns_matrix)
# Get weights for the portfolio
eigen_portfolio_weights_dictionary = self.strategyManager.calculate_eigen_portfolio(
symbol_names, covariance_matrix, self.EIGEN_PORTFOLIO_NUMBER)
mvp_portfolio_weights_dictionary = self.strategyManager.calculate_minimum_variance_portfolio(
symbol_names, covariance_matrix)
msr_portfolio_weights_dictionary = self.strategyManager.calculate_maximum_sharpe_portfolio(
symbol_names, covariance_matrix, predicted_return_vectors)
ga_portfolio_weights_dictionary = self.strategyManager.calculate_genetic_algo_portfolio(
symbol_names, returns_matrix_percentages)
# Print weights
print("\n*% Printing portfolio weights...")
self.print_and_plot_portfolio_weights(
eigen_portfolio_weights_dictionary, 'Eigen Portfolio', plot_num=1)
self.print_and_plot_portfolio_weights(
mvp_portfolio_weights_dictionary,
'Minimum Variance Portfolio (MVP)',
plot_num=2)
self.print_and_plot_portfolio_weights(msr_portfolio_weights_dictionary,
'Maximum Sharpe Portfolio (MSR)',
plot_num=3)
self.print_and_plot_portfolio_weights(ga_portfolio_weights_dictionary,
'Genetic Algo (GA)',
plot_num=4)
self.draw_plot()
# Back test
print("\n*& Backtesting the portfolios...")
self.backTester.back_test(symbol_names,
eigen_portfolio_weights_dictionary,
self.data_dictionary,
historical_price_market,
self.IS_LONG_ONLY_PORTFOLIO,
market_chart=True,
strategy_name='Eigen Portfolio')
self.backTester.back_test(
symbol_names,
mvp_portfolio_weights_dictionary,
self.data_dictionary,
historical_price_market,
self.IS_LONG_ONLY_PORTFOLIO,
market_chart=False,
strategy_name='Minimum Variance Portfolio (MVP)')
self.backTester.back_test(
symbol_names,
msr_portfolio_weights_dictionary,
self.data_dictionary,
historical_price_market,
self.IS_LONG_ONLY_PORTFOLIO,
market_chart=False,
strategy_name='Maximum Sharpe Portfolio (MSR)')
self.backTester.back_test(symbol_names,
ga_portfolio_weights_dictionary,
self.data_dictionary,
historical_price_market,
self.IS_LONG_ONLY_PORTFOLIO,
market_chart=False,
strategy_name='Genetic Algo (GA)')
self.draw_plot()
if self.IS_TEST:
print("\n#^ Future testing the portfolios...")
# Future test
self.backTester.future_test(symbol_names,
eigen_portfolio_weights_dictionary,
self.data_dictionary,
future_prices_market,
self.IS_LONG_ONLY_PORTFOLIO,
market_chart=True,
strategy_name='Eigen Portfolio')
self.backTester.future_test(
symbol_names,
mvp_portfolio_weights_dictionary,
self.data_dictionary,
future_prices_market,
self.IS_LONG_ONLY_PORTFOLIO,
market_chart=False,
strategy_name='Minimum Variance Portfolio (MVP)')
self.backTester.future_test(
symbol_names,
msr_portfolio_weights_dictionary,
self.data_dictionary,
future_prices_market,
self.IS_LONG_ONLY_PORTFOLIO,
market_chart=False,
strategy_name='Maximum Sharpe Portfolio (MSR)')
self.backTester.future_test(symbol_names,
ga_portfolio_weights_dictionary,
self.data_dictionary,
future_prices_market,
self.IS_LONG_ONLY_PORTFOLIO,
market_chart=False,
strategy_name='Genetic Algo (GA)')
self.draw_plot()
# Simulation
print("\n+$ Simulating future prices using monte carlo...")
self.simulator.simulate_portfolio(symbol_names,
eigen_portfolio_weights_dictionary,
self.data_dictionary,
future_prices_market,
self.IS_TEST,
market_chart=True,
strategy_name='Eigen Portfolio')
self.simulator.simulate_portfolio(
symbol_names,
eigen_portfolio_weights_dictionary,
self.data_dictionary,
future_prices_market,
self.IS_TEST,
market_chart=False,
strategy_name='Minimum Variance Portfolio (MVP)')
self.simulator.simulate_portfolio(
symbol_names,
eigen_portfolio_weights_dictionary,
self.data_dictionary,
future_prices_market,
self.IS_TEST,
market_chart=False,
strategy_name='Maximum Sharpe Portfolio (MSR)')
self.simulator.simulate_portfolio(symbol_names,
ga_portfolio_weights_dictionary,
self.data_dictionary,
future_prices_market,
self.IS_TEST,
market_chart=False,
strategy_name='Genetic Algo (GA)')
self.draw_plot()
def draw_plot(self):
"""
Draw plots
"""
plt.tight_layout()
plt.grid()
plt.legend(fontsize=14)
plt.show()
def print_and_plot_portfolio_weights(self, weights_dictionary, strategy,
plot_num):
print("\n-------- Weights for %s --------" % strategy)
symbols = list(sorted(weights_dictionary.keys()))
symbol_weights = []
for symbol in symbols:
print("Symbol: %s, Weight: %.4f" %
(symbol, weights_dictionary[symbol]))
symbol_weights.append(weights_dictionary[symbol])
# Plot
width = 0.1
x = np.arange(len(symbol_weights))
plt.bar(x + (width * (plot_num - 1)) + 0.05,
symbol_weights,
label=strategy,
width=width)
plt.xticks(x, symbols, fontsize=14)
plt.yticks(fontsize=14)
plt.xlabel("Symbols", fontsize=14)
plt.ylabel("Weight in Portfolio", fontsize=14)
plt.title("Portfolio Weights for Different Strategies", fontsize=14)
class Eiten:
def __init__(self, args):
plt.style.use('seaborn-white')
plt.rc('grid', linestyle="dotted", color='#a0a0a0')
plt.rcParams['axes.edgecolor'] = "#04383F"
plt.rcParams['figure.figsize'] = (12, 6)
print("\n--* Eiten has been initialized...")
self.args = args
# Create data engine
self.dataEngine = DataEngine(args)
# Monto carlo simulator
self.simulator = MontoCarloSimulator()
# Strategy manager
self.strategyManager = StrategyManager()
# Back tester
self.backTester = BackTester()
# Data dictionary
self.data_dictionary = {}
print('\n')
def calculate_percentage_change(self, old, new):
"""
Calculate percentage change
"""
return ((new - old) * 100) / old
def create_returns(self, historical_price_info):
"""
Create log return matrix, percentage return matrix, and mean return
vector
"""
returns_matrix = []
returns_matrix_percentages = []
predicted_return_vectors = []
for i in range(0, len(historical_price_info)):
close_prices = list(historical_price_info[i]["Close"])
log_returns = [
math.log(close_prices[i] / close_prices[i - 1])
for i in range(1, len(close_prices))
]
percentage_returns = [
self.calculate_percentage_change(close_prices[i - 1],
close_prices[i])
for i in range(1, len(close_prices))
]
total_data = len(close_prices)
# Expected returns in future. We can either use historical returns as future returns on try to simulate future returns and take the mean. For simulation, you can modify the functions in simulator to use here.
future_expected_returns = np.mean([
(self.calculate_percentage_change(
close_prices[i - 1], close_prices[i])) / (total_data - i)
for i in range(1, len(close_prices))
]) # More focus on recent returns
# Add to matrices
returns_matrix.append(log_returns)
returns_matrix_percentages.append(percentage_returns)
# Add returns to vector
# Assuming that future returns are similar to past returns
predicted_return_vectors.append(future_expected_returns)
# Convert to numpy arrays for one liner calculations
predicted_return_vectors = np.array(predicted_return_vectors)
returns_matrix = np.array(returns_matrix)
returns_matrix_percentages = np.array(returns_matrix_percentages)
return predicted_return_vectors, returns_matrix, returns_matrix_percentages
def load_data(self):
"""
Loads data needed for analysis
"""
# Gather data for all stocks in a dictionary format
# Dictionary keys will be -> historical_prices, future_prices
self.data_dictionary = self.dataEngine.collect_data_for_all_tickers()
# Add data to lists
symbol_names = list(sorted(self.data_dictionary.keys()))
historical_price_info, future_prices = [], []
for symbol in symbol_names:
historical_price_info.append(
self.data_dictionary[symbol]["historical_prices"])
future_prices.append(self.data_dictionary[symbol]["future_prices"])
# Get return matrices and vectors
predicted_return_vectors, returns_matrix, returns_matrix_percentages = self.create_returns(
historical_price_info)
return historical_price_info, future_prices, symbol_names, predicted_return_vectors, returns_matrix, returns_matrix_percentages
def run_strategies(self):
"""
Run strategies, back and future test them, and simulate the returns.
"""
historical_price_info, future_prices, symbol_names, predicted_return_vectors, returns_matrix, returns_matrix_percentages = self.load_data(
)
historical_price_market, future_prices_market = self.dataEngine.get_market_index_price(
)
# Calculate covariance matrix
covariance_matrix = np.cov(returns_matrix)
# Use random matrix theory to filter out the noisy eigen values
if self.args.apply_noise_filtering:
print(
"\n** Applying random matrix theory to filter out noise in the covariance matrix...\n"
)
covariance_matrix = self.strategyManager.random_matrix_theory_based_cov(
returns_matrix)
# Get weights for the portfolio
eigen_portfolio_weights_dictionary = self.strategyManager.calculate_eigen_portfolio(
symbol_names, covariance_matrix, self.args.eigen_portfolio_number)
mvp_portfolio_weights_dictionary = self.strategyManager.calculate_minimum_variance_portfolio(
symbol_names, covariance_matrix)
msr_portfolio_weights_dictionary = self.strategyManager.calculate_maximum_sharpe_portfolio(
symbol_names, covariance_matrix, predicted_return_vectors)
ga_portfolio_weights_dictionary = self.strategyManager.calculate_genetic_algo_portfolio(
symbol_names, returns_matrix_percentages)
# Print weights
print("\n*% Printing portfolio weights...")
self.print_and_plot_portfolio_weights(
eigen_portfolio_weights_dictionary, 'Eigen Portfolio', plot_num=1)
self.print_and_plot_portfolio_weights(
mvp_portfolio_weights_dictionary,
'Minimum Variance Portfolio (MVP)',
plot_num=2)
self.print_and_plot_portfolio_weights(msr_portfolio_weights_dictionary,
'Maximum Sharpe Portfolio (MSR)',
plot_num=3)
self.print_and_plot_portfolio_weights(ga_portfolio_weights_dictionary,
'Genetic Algo (GA)',
plot_num=4)
self.draw_plot("output/weights.png")
# Back test
print("\n*& Backtesting the portfolios...")
self.backTester.back_test(symbol_names,
eigen_portfolio_weights_dictionary,
self.data_dictionary,
historical_price_market,
self.args.only_long,
market_chart=True,
strategy_name='Eigen Portfolio')
self.backTester.back_test(
symbol_names,
mvp_portfolio_weights_dictionary,
self.data_dictionary,
historical_price_market,
self.args.only_long,
market_chart=False,
strategy_name='Minimum Variance Portfolio (MVP)')
self.backTester.back_test(
symbol_names,
msr_portfolio_weights_dictionary,
self.data_dictionary,
historical_price_market,
self.args.only_long,
market_chart=False,
strategy_name='Maximum Sharpe Portfolio (MSR)')
self.backTester.back_test(symbol_names,
ga_portfolio_weights_dictionary,
self.data_dictionary,
historical_price_market,
self.args.only_long,
market_chart=False,
strategy_name='Genetic Algo (GA)')
self.draw_plot("output/backtest.png")
if self.args.is_test:
print("\n#^ Future testing the portfolios...")
# Future test
self.backTester.future_test(symbol_names,
eigen_portfolio_weights_dictionary,
self.data_dictionary,
future_prices_market,
self.args.only_long,
market_chart=True,
strategy_name='Eigen Portfolio')
self.backTester.future_test(
symbol_names,
mvp_portfolio_weights_dictionary,
self.data_dictionary,
future_prices_market,
self.args.only_long,
market_chart=False,
strategy_name='Minimum Variance Portfolio (MVP)')
self.backTester.future_test(
symbol_names,
msr_portfolio_weights_dictionary,
self.data_dictionary,
future_prices_market,
self.args.only_long,
market_chart=False,
strategy_name='Maximum Sharpe Portfolio (MSR)')
self.backTester.future_test(symbol_names,
ga_portfolio_weights_dictionary,
self.data_dictionary,
future_prices_market,
self.args.only_long,
market_chart=False,
strategy_name='Genetic Algo (GA)')
self.draw_plot("output/future_tests.png")
# Simulation
print("\n+$ Simulating future prices using monte carlo...")
self.simulator.simulate_portfolio(symbol_names,
eigen_portfolio_weights_dictionary,
self.data_dictionary,
future_prices_market,
self.args.is_test,
market_chart=True,
strategy_name='Eigen Portfolio')
self.simulator.simulate_portfolio(
symbol_names,
eigen_portfolio_weights_dictionary,
self.data_dictionary,
future_prices_market,
self.args.is_test,
market_chart=False,
strategy_name='Minimum Variance Portfolio (MVP)')
self.simulator.simulate_portfolio(
symbol_names,
eigen_portfolio_weights_dictionary,
self.data_dictionary,
future_prices_market,
self.args.is_test,
market_chart=False,
strategy_name='Maximum Sharpe Portfolio (MSR)')
self.simulator.simulate_portfolio(symbol_names,
ga_portfolio_weights_dictionary,
self.data_dictionary,
future_prices_market,
self.args.is_test,
market_chart=False,
strategy_name='Genetic Algo (GA)')
self.draw_plot("output/monte_carlo.png")
def draw_plot(self, filename="output/graph.png"):
"""
Draw plots
"""
# Styling for plots
plt.grid()
plt.legend(fontsize=14)
plt.tight_layout()
plt.show()
"""if self.args.save_plot:
plt.savefig(filename)
else:
plt.tight_layout()
plt.show()""" # Plots were not being generated properly. Need to fix this.
def print_and_plot_portfolio_weights(self, weights_dictionary: dict,
strategy, plot_num: int) -> None:
print("\n-------- Weights for %s --------" % strategy)
symbols = list(sorted(weights_dictionary.keys()))
symbol_weights = []
for symbol in symbols:
print("Symbol: %s, Weight: %.4f" %
(symbol, weights_dictionary[symbol]))
symbol_weights.append(weights_dictionary[symbol])
# Plot
width = 0.1
x = np.arange(len(symbol_weights))
plt.bar(x + (width * (plot_num - 1)) + 0.05,
symbol_weights,
label=strategy,
width=width)
plt.xticks(x, symbols, fontsize=14)
plt.yticks(fontsize=14)
plt.xlabel("Symbols", fontsize=14)
plt.ylabel("Weight in Portfolio", fontsize=14)
plt.title("Portfolio Weights for Different Strategies", fontsize=14)