def task_backtest():
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)
function_list = {}
exec generate_signals in function_list
# Create a portfolio
portfolio = MarketOnClosePortfolio(symbol, bars, rfs, opt, initial_capital=1000000.0)
exec generate_signals in function_list
portfolio.strategy.sig_generator = function_list["generate_signals"]
# Backtest our portfolio and store result in book
book = portfolio.backtest_portfolio_external()
ret = book.nav_to_json()
print ret
return json.dumps(ret)
def task_backtest():
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)
function_list = {}
exec generate_signals in function_list
# Create a portfolio
portfolio = MarketOnClosePortfolio(symbol, bars, rfs, opt, \
initial_capital=1000000.0)
exec generate_signals in function_list
portfolio.strategy.sig_generator = function_list["generate_signals"]
# Backtest our portfolio and store result in book
book = portfolio.backtest_portfolio_external()
ret = book.nav_to_json()
print ret
return json.dumps(ret)
