def task_backtest(gearman_worker, gearman_job):
symbol = ['000001', '603993']
bars = bindata.BackTestData(bindata.raw)
# Apply our current strategy on the chosen stock pool
rfs = CurrentStrategy(symbol, bars)
# specify constraints, here is the default one
cons = Constraint()
# specify a naive optimizer
opt = NaiveOptimizer(cons)
data = json.loads(gearman_job.data)
function_list = {}
signal_generator = compile(data["code"], '', 'exec')
exec signal_generator in function_list
# Create a portfolio
portfolio = MarketOnClosePortfolio(symbol, bars, rfs, \
opt, initial_capital=1000000.0)
portfolio.strategy.sig_generator = function_list["generate_signals"]
# Backtest our portfolio and store result in book
book = portfolio.backtest_portfolio(worker=gearman_worker, job=gearman_job)
ret = book.nav_to_json()
return json.dumps(ret)
def task_backtest(gearman_worker, gearman_job):
symbol = ['000001', '603993']
bars = bindata.BackTestData(bindata.raw)
# Apply our current strategy on the chosen stock pool
rfs = CurrentStrategy(symbol, bars)
# specify constraints, here is the default one
cons = Constraint()
# specify a naive optimizer
opt = NaiveOptimizer(cons)
data = json.loads(gearman_job.data)
function_list = {}
signal_generator = compile(data["code"], '', 'exec')
exec signal_generator in function_list
# Create a portfolio
portfolio = MarketOnClosePortfolio(symbol, bars, rfs, \
opt, initial_capital=1000000.0)
portfolio.strategy.sig_generator = function_list["generate_signals"]
# Backtest our portfolio and store result in book
book = portfolio.backtest_portfolio(worker=gearman_worker, job=gearman_job)
ret = book.nav_to_json()
return json.dumps(ret)
