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(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():
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(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)
# dff = pandas.DataFrame(df) # dff.plot(x='Date', y='Open') # print(df['Open']) # plt.plot(df['Date'], df['Close'], linestyle='-', linewidth=1) # plt.plot(df['Date'], df['Open'], linestyle='-', linewidth=1) # plt.legend(['Close', 'Open'], loc='upper left') # mac = MovingAverageCrossStrategy(df, short_window=100, long_window=400) signals = mac.generate_signals() print(signals) portfolio = MarketOnClosePortfolio(df, signals, initial_capital=10000) print(portfolio) \ # mavg = Series(df).rolling(window=40, center=False).mean() # mavg = pd.rolling_mean(df['Close'], 40) # mavg.plot(label='mavg') # plt.legend() # mavg = pd.rolling_mean(df['Close'], 40) # print(mavg[-10:]) # This line is necessary for the plot to appear in a Jupyter notebook # % matplotlib inline # Control the default size of figures in this Jupyter notebook # % pylab inline # pylab.rcParams['figure.figsize'] = (15, 9) # Change the size of plots
