def __init__(self, strategies, data_dir, start_automatically=True):
if not strategies:
raise ValueError("Empty strategies list in your algo file.")
# Single market object will be used for all backtesting instances
self.market = BacktestMarketData(
data_dir, #should come from script loader
strategies[0].SYMBOL_LIST,
date_from = getattr(strategies[0], 'DATE_FROM', datetime.now() - timedelta(weeks=52)),
date_to = getattr(strategies[0], 'DATE_TO', datetime.now()),
normalize_prices = getattr(strategies[0], 'NORMALIZE_PRICES', settings.NORMALIZE_PRICES),
normalize_volume = getattr(strategies[0], 'NORMALIZE_VOLUME', settings.NORMALIZE_VOLUME),
round_decimals = getattr(strategies[0], 'ROUND_DECIMALS', settings.ROUND_DECIMALS),
)
self.portfolios = []
for strategy in strategies:
port = BacktestPortfolio(self.market, strategy)
self.portfolios.append(port)
logging.info("Backtesting {} {} with {} symbols from {} to {}".format(
len(self.portfolios),
'strategies' if len(self.portfolios) > 1 else 'strategy',
len(self.market.symbol_list),
self.market.date_from.strftime('%Y-%m-%d'),
self.market.date_to.strftime('%Y-%m-%d'),
))
logging.info("Data load took {}".format(str(self.market.load_time)))
if start_automatically:
self.start()
self.calc_performance()
def __init__(self, strategies, data_dir, start_automatically=True):
if not strategies:
raise ValueError("Empty strategies list in your algo file.")
# Single market object will be used for all backtesting instances
self.market = BacktestMarketData(
data_dir, #should come from script loader
strategies[0].SYMBOL_LIST,
date_from=getattr(strategies[0], 'DATE_FROM',
datetime.now() - timedelta(weeks=52)),
date_to=getattr(strategies[0], 'DATE_TO', datetime.now()),
normalize_prices=getattr(strategies[0], 'NORMALIZE_PRICES',
settings.NORMALIZE_PRICES),
normalize_volume=getattr(strategies[0], 'NORMALIZE_VOLUME',
settings.NORMALIZE_VOLUME),
round_decimals=getattr(strategies[0], 'ROUND_DECIMALS',
settings.ROUND_DECIMALS),
)
self.portfolios = []
for strategy in strategies:
port = BacktestPortfolio(self.market, strategy)
self.portfolios.append(port)
logging.info("Backtesting {} {} with {} symbols from {} to {}".format(
len(self.portfolios),
'strategies' if len(self.portfolios) > 1 else 'strategy',
len(self.market.symbol_list),
self.market.date_from.strftime('%Y-%m-%d'),
self.market.date_to.strftime('%Y-%m-%d'),
))
logging.info("Data load took {}".format(str(self.market.load_time)))
if start_automatically:
self.start()
self.calc_performance()
class BacktestEngine(object):
def __init__(self, strategies, data_dir, start_automatically=True):
if not strategies:
raise ValueError("Empty strategies list in your algo file.")
# Single market object will be used for all backtesting instances
self.market = BacktestMarketData(
data_dir, #should come from script loader
strategies[0].SYMBOL_LIST,
date_from = getattr(strategies[0], 'DATE_FROM', datetime.now() - timedelta(weeks=52)),
date_to = getattr(strategies[0], 'DATE_TO', datetime.now()),
normalize_prices = getattr(strategies[0], 'NORMALIZE_PRICES', settings.NORMALIZE_PRICES),
normalize_volume = getattr(strategies[0], 'NORMALIZE_VOLUME', settings.NORMALIZE_VOLUME),
round_decimals = getattr(strategies[0], 'ROUND_DECIMALS', settings.ROUND_DECIMALS),
)
self.portfolios = []
for strategy in strategies:
port = BacktestPortfolio(self.market, strategy)
self.portfolios.append(port)
logging.info("Backtesting {} {} with {} symbols from {} to {}".format(
len(self.portfolios),
'strategies' if len(self.portfolios) > 1 else 'strategy',
len(self.market.symbol_list),
self.market.date_from.strftime('%Y-%m-%d'),
self.market.date_to.strftime('%Y-%m-%d'),
))
logging.info("Data load took {}".format(str(self.market.load_time)))
if start_automatically:
self.start()
self.calc_performance()
def start(self):
"""
Starts backtesting for all portfolios created.
New market events are handled on this level to allow for multiple portfolios
to run simultaneously with a single market object
"""
start_time = datetime.now()
while self.market.continue_execution:
for _ in self.market._update_bars():
[portfolio.run_cycle() for portfolio in self.portfolios]
[portfolio.update_last_positions_and_holdings() for portfolio in self.portfolios]
logging.info("Backtest took " + str((datetime.now()-start_time)))
def calc_performance(self, order_by='sharpe'):
start_time = datetime.now()
[portfolio.calc_performance() for portfolio in self.portfolios]
# Order engines by sharpe (used for plotting)
self.portfolios = sorted(self.portfolios, key=lambda x: x.performance[order_by], reverse=True)
# Calc results dataframe that contains performance for all portfolios
self.results = pd.DataFrame(
[[
str(portfolio.strategy),
portfolio.performance['total_return'],
portfolio.performance['ann_return'],
portfolio.performance['sharpe'],
portfolio.performance['trades'],
portfolio.performance['pct_trades_profit'],
portfolio.performance['dangerous'],
portfolio.performance['dd_max'],
] for portfolio in self.portfolios ],
columns=[
'strategy',
'total returns',
'annualised return',
'sharpe',
'# trades',
'profit %',
'dangerous',
'max dd',
],
)
logging.debug("Performance calculated in {}".format(str(datetime.now()-start_time)))
def plot_open_positions(self):
import matplotlib.pyplot as plt
for portfolio in self.portfolios:
ax = portfolio.performance['all_positions']['open_trades'].plot()
ax.set_title(portfolio.strategy)
plt.grid()
plt.show()
def plot_results(self):
import matplotlib.pyplot as plt
for portfolio in self.portfolios:
ax = portfolio.performance['equity_curve'].plot()
ax.set_title(portfolio.strategy)
plt.grid()
plt.show()
def plot_symbol_subscriptions(self):
import matplotlib.pyplot as plt
for portfolio in self.portfolios:
portfolio.performance['subscribed_symbols'].plot()
plt.show()
def print_all_trades(self):
for port in self.portfolios:
print(port.performance['all_trades'])
def strategy_finishing_methods(self):
[portfolio.strategy.backtest_done(portfolio.performance) for portfolio in self.portfolios]
class BacktestEngine(object):
def __init__(self, strategies, data_dir, start_automatically=True):
if not strategies:
raise ValueError("Empty strategies list in your algo file.")
# Single market object will be used for all backtesting instances
self.market = BacktestMarketData(
data_dir, #should come from script loader
strategies[0].SYMBOL_LIST,
date_from=getattr(strategies[0], 'DATE_FROM',
datetime.now() - timedelta(weeks=52)),
date_to=getattr(strategies[0], 'DATE_TO', datetime.now()),
normalize_prices=getattr(strategies[0], 'NORMALIZE_PRICES',
settings.NORMALIZE_PRICES),
normalize_volume=getattr(strategies[0], 'NORMALIZE_VOLUME',
settings.NORMALIZE_VOLUME),
round_decimals=getattr(strategies[0], 'ROUND_DECIMALS',
settings.ROUND_DECIMALS),
)
self.portfolios = []
for strategy in strategies:
port = BacktestPortfolio(self.market, strategy)
self.portfolios.append(port)
logging.info("Backtesting {} {} with {} symbols from {} to {}".format(
len(self.portfolios),
'strategies' if len(self.portfolios) > 1 else 'strategy',
len(self.market.symbol_list),
self.market.date_from.strftime('%Y-%m-%d'),
self.market.date_to.strftime('%Y-%m-%d'),
))
logging.info("Data load took {}".format(str(self.market.load_time)))
if start_automatically:
self.start()
self.calc_performance()
def start(self):
"""
Starts backtesting for all portfolios created.
New market events are handled on this level to allow for multiple portfolios
to run simultaneously with a single market object
"""
start_time = datetime.now()
while self.market.continue_execution:
for _ in self.market._update_bars():
[portfolio.run_cycle() for portfolio in self.portfolios]
[
portfolio.update_last_positions_and_holdings()
for portfolio in self.portfolios
]
logging.info("Backtest took " + str((datetime.now() - start_time)))
def calc_performance(self, order_by='sharpe'):
start_time = datetime.now()
[portfolio.calc_performance() for portfolio in self.portfolios]
# Order engines by sharpe (used for plotting)
self.portfolios = sorted(self.portfolios,
key=lambda x: x.performance[order_by],
reverse=True)
# Calc results dataframe that contains performance for all portfolios
self.results = pd.DataFrame(
[[
str(portfolio.strategy),
portfolio.performance['total_return'],
portfolio.performance['ann_return'],
portfolio.performance['sharpe'],
portfolio.performance['trades'],
portfolio.performance['pct_trades_profit'],
portfolio.performance['dangerous'],
portfolio.performance['dd_max'],
] for portfolio in self.portfolios],
columns=[
'strategy',
'total returns',
'annualised return',
'sharpe',
'# trades',
'profit %',
'dangerous',
'max dd',
],
)
logging.debug("Performance calculated in {}".format(
str(datetime.now() - start_time)))
def plot_open_positions(self):
import matplotlib.pyplot as plt
for portfolio in self.portfolios:
ax = portfolio.performance['all_positions']['open_trades'].plot()
ax.set_title(portfolio.strategy)
plt.grid()
plt.show()
def plot_results(self):
import matplotlib.pyplot as plt
for portfolio in self.portfolios:
ax = portfolio.performance['equity_curve'].plot()
ax.set_title(portfolio.strategy)
plt.grid()
plt.show()
def plot_symbol_subscriptions(self):
import matplotlib.pyplot as plt
for portfolio in self.portfolios:
portfolio.performance['subscribed_symbols'].plot()
plt.show()
def print_all_trades(self):
for port in self.portfolios:
print(port.performance['all_trades'])
def strategy_finishing_methods(self):
[
portfolio.strategy.backtest_done(portfolio.performance)
for portfolio in self.portfolios
]