def __init__(self, symbol, bars, strategy, opt, initial_capital=100000.0):
self.symbol = symbol
self.cash = initial_capital
self.holding = 0
self.start = bars.beg
self.stop = bars.end
self.bars = bars
self.strategy = strategy
self.length = self.bars.date.get_loc(bars.end) - \
self.bars.date.get_loc(bars.beg) + 1
self.current_mark = 0
self.optimizer = opt
self.initial_capital = float(initial_capital)
self.positions = pd.DataFrame(columns=self.symbol)
self.position = {}
self.direction = {}
self.portfolio = pd.DataFrame(columns=self.symbol)
self.order_engine = OrderEngine()
self.book = Book(self.symbol, self.bars, initial_capital=100000.0)
self.book.book.ix[self.symbol, self.bars.ind, 'Pos'] = 0
self.book.book.ix[self.symbol, self.bars.ind, 'PrePos'] = 0
self.stop_mark = False
def __init__(self, symbol, bars, strategy, opt, initial_capital=100000.0):
self.symbol = symbol
self.cash = initial_capital
self.holding = 0
self.start = bars.beg
self.stop = bars.end
self.bars = bars
self.strategy = strategy
self.length = self.bars.date.get_loc(bars.end) - self.bars.date.get_loc(bars.beg) + 1
self.current_mark = 0
self.optimizer = opt
self.initial_capital = float(initial_capital)
self.positions = pd.DataFrame(columns=self.symbol)
self.position = {}
self.direction = {}
self.portfolio = pd.DataFrame(columns=self.symbol)
self.order_engine = OrderEngine()
self.book = Book(self.symbol, self.bars, initial_capital=100000.0)
self.book.book.ix[self.symbol, self.bars.ind, "Pos"] = 0
self.book.book.ix[self.symbol, self.bars.ind, "PrePos"] = 0
self.stop_mark = False
class MarketOnClosePortfolio(Portfolio):
"""Inherits Portfolio to create a system that purchases 100 units of
a particular symbol upon a long/short signal, assuming the market
open price of a bar.
Parameters:
----------
symbol - A stock symbol which forms the basis of the portfolio.
bars - A DataFrame of bars for a symbol set.
signals - A pandas DataFrame of signals (1, 0, -1) for each symbol.
initial_capital - The amount in cash at the start of the portfolio.
Notes:
----------
In addition, there are zero transaction costs and cash can be immediately
borrowed for shorting (no margin posting or interest requirements).
"""
def __init__(self, symbol, bars, strategy, opt, initial_capital=100000.0):
self.symbol = symbol
self.cash = initial_capital
self.holding = 0
self.start = bars.beg
self.stop = bars.end
self.bars = bars
self.strategy = strategy
self.length = self.bars.date.get_loc(bars.end) - \
self.bars.date.get_loc(bars.beg) + 1
self.current_mark = 0
self.optimizer = opt
self.initial_capital = float(initial_capital)
self.positions = pd.DataFrame(columns=self.symbol)
self.position = {}
self.direction = {}
self.portfolio = pd.DataFrame(columns=self.symbol)
self.order_engine = OrderEngine()
self.book = Book(self.symbol, self.bars, initial_capital=100000.0)
self.book.book.ix[self.symbol, self.bars.ind, 'Pos'] = 0
self.book.book.ix[self.symbol, self.bars.ind, 'PrePos'] = 0
self.stop_mark = False
def generate_positions(self):
"""generate a list of expected positions of each symbol
Parameters
----------
symbol : list of strings
list of interested symbols
bars :
pass in data so that I can know its structures
initial_capital:
Well, we need some initial capital for investment
default value is 1 million
Notes
----------
Creates a 'positions' DataFrame that simply longs or shorts
100 of the particular symbol based on the forecast signals of
{1, 0, -1} from the signals DataFrame.
"""
self.optimizer.gen_positions(self.strategy.signals, self.book)
def loop(self):
# update bars for one day, acquiring new data
self.bars.update_bars()
# prepare book status for use
self.book.prepare()
# then we construct portfolio, we assume tradings occur at close
# we generate signals
self.strategy.generate_signals()
self.current_mark += 1
if self.strategy.na:
# strategy says no trading required
pass
else:
# generate positions
self.optimizer.gen_positions(self.strategy.signals, self.book)
# check if constraints have been violated
if self.optimizer.constraints.drawdown_mark:
print "\nStop: Drawdown reaches limit "+ \
str(self.optimizer.constraints.drawdown)
self.stop_mark = True
if self.optimizer.constraints.leverage_mark:
print "\nWarning: leverage reached limit "+ \
str(self.optimizer.constraints.leverage)
# we execute orders
self.order_engine.execute(self.book)
# we compile book at the end of every day
self.book.compile()
def backtest_portfolio(self, beg=0, end=0, worker=None, job=None):
"""Constructs a portfolio from the positions DataFrame by
"""
if beg != 0:
self.start = beg
if end != 0:
self.stop = end
# first we update data book
self.book.prepare()
# start backtest
print "-------------"
print "Start Backtest:"
j = 0
while self.bars.proceed == "OK": # if the light is green
if self.stop_mark: # if the stop flag is raised, quit
break
self.loop()
# we provide summary of our daily trading
if int(float(self.current_mark) / self.length * 100.0) > j:
worker.send_job_status(job, \
int(float(self.current_mark)/self.length*100.0), 100)
j = int(float(self.current_mark) / self.length * 100.0)
print "\n\n------------------------------------"
print "Msg: backtest finished successfully"
return self.book
def backtest_portfolio_external(
self,
beg=0,
end=0,
):
"""Constructs a portfolio from the positions DataFrame by
"""
if beg != 0:
self.start = beg
if end != 0:
self.stop = end
# first we update data feed
self.book.prepare()
incre = 20.0 / self.length
print "-------------"
print "Start Backtest:"
while self.bars.proceed == "OK":
if self.stop_mark:
break
self.loop()
# we provide summary of our daily trading
sys.stdout.write("\r Progress: [" + "=" \
* int(self.current_mark * incre) +
" " * int(20 - self.current_mark * incre) + "] "
+ str(self.current_mark)+"/"+str(self.length))
# this is for debug purpose
#self.book.print_daily_summary()
# ------------------------
print "\n\n------------------------------------"
print "Msg: backtest finished successfully"
return self.book
class MarketOnClosePortfolio(Portfolio):
"""Inherits Portfolio to create a system that purchases 100 units of
a particular symbol upon a long/short signal, assuming the market
open price of a bar.
Parameters:
----------
symbol - A stock symbol which forms the basis of the portfolio.
bars - A DataFrame of bars for a symbol set.
signals - A pandas DataFrame of signals (1, 0, -1) for each symbol.
initial_capital - The amount in cash at the start of the portfolio.
Notes:
----------
In addition, there are zero transaction costs and cash can be immediately
borrowed for shorting (no margin posting or interest requirements).
"""
def __init__(self, symbol, bars, strategy, opt, initial_capital=100000.0):
self.symbol = symbol
self.cash = initial_capital
self.holding = 0
self.start = bars.beg
self.stop = bars.end
self.bars = bars
self.strategy = strategy
self.length = self.bars.date.get_loc(bars.end) - self.bars.date.get_loc(bars.beg) + 1
self.current_mark = 0
self.optimizer = opt
self.initial_capital = float(initial_capital)
self.positions = pd.DataFrame(columns=self.symbol)
self.position = {}
self.direction = {}
self.portfolio = pd.DataFrame(columns=self.symbol)
self.order_engine = OrderEngine()
self.book = Book(self.symbol, self.bars, initial_capital=100000.0)
self.book.book.ix[self.symbol, self.bars.ind, "Pos"] = 0
self.book.book.ix[self.symbol, self.bars.ind, "PrePos"] = 0
self.stop_mark = False
def generate_positions(self):
"""generate a list of expected positions of each symbol
Parameters
----------
symbol : list of strings
list of interested symbols
bars :
pass in data so that I can know its structures
initial_capital:
Well, we need some initial capital for investment
default value is 1 million
Notes
----------
Creates a 'positions' DataFrame that simply longs or shorts
100 of the particular symbol based on the forecast signals of
{1, 0, -1} from the signals DataFrame.
"""
self.optimizer.gen_positions(self.strategy.signals, self.book)
def loop(self):
# update bars for one day, acquiring new data
self.bars.update_bars()
# prepare book status for use
self.book.prepare()
# then we construct portfolio, we assume tradings occur at close
# we generate signals
self.strategy.generate_signals()
self.current_mark += 1
if self.strategy.na:
# strategy says no trading required
pass
else:
# generate positions
self.optimizer.gen_positions(self.strategy.signals, self.book)
# check if constraints have been violated
if self.optimizer.constraints.drawdown_mark:
print "\nStop: Drawdown reaches limit " + str(self.optimizer.constraints.drawdown)
self.stop_mark = True
if self.optimizer.constraints.leverage_mark:
print "\nWarning: leverage reached limit " + str(self.optimizer.constraints.leverage)
# we execute orders
self.order_engine.execute(self.book)
# we compile book at the end of every day
self.book.compile()
def backtest_portfolio(self, beg=0, end=0, worker=None, job=None):
"""Constructs a portfolio from the positions DataFrame by
"""
if beg != 0:
self.start = beg
if end != 0:
self.stop = end
# first we update data book
self.book.prepare()
# start backtest
print "-------------"
print "Start Backtest:"
j = 0
while self.bars.proceed == "OK": # if the light is green
if self.stop_mark: # if the stop flag is raised, quit
break
self.loop()
# we provide summary of our daily trading
if int(float(self.current_mark) / self.length * 100.0) > j:
worker.send_job_status(job, int(float(self.current_mark) / self.length * 100.0), 100)
j = int(float(self.current_mark) / self.length * 100.0)
print "\n\n------------------------------------"
print "Msg: backtest finished successfully"
return self.book
def backtest_portfolio_external(self, beg=0, end=0):
"""Constructs a portfolio from the positions DataFrame by
"""
if beg != 0:
self.start = beg
if end != 0:
self.stop = end
# first we update data feed
self.book.prepare()
incre = 20.0 / self.length
print "-------------"
print "Start Backtest:"
while self.bars.proceed == "OK":
if self.stop_mark:
break
self.loop()
# we provide summary of our daily trading
sys.stdout.write(
"\r Progress: ["
+ "=" * int(self.current_mark * incre)
+ " " * int(20 - self.current_mark * incre)
+ "] "
+ str(self.current_mark)
+ "/"
+ str(self.length)
)
# this is for debug purpose
# self.book.print_daily_summary()
# ------------------------
print "\n\n------------------------------------"
print "Msg: backtest finished successfully"
return self.book
