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backtester.py
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backtester.py
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# Basic libraries
import os
import sys
import math
import scipy
import random
import collections
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
# Styling for plots
plt.style.use('seaborn-white')
plt.rc('grid', linestyle="dotted", color='#a0a0a0')
plt.rcParams['axes.edgecolor'] = "#04383F"
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)