def test_with_sma(self):
sigma = 0.3
x0 = 100
dt = 1. / 252
dW = np.random.normal(0, math.sqrt(dt), TestCompareWithFunctionalBacktest.num_days)
asset = []
asset.append(x0)
for i in xrange(1, TestCompareWithFunctionalBacktest.num_days):
xprev = asset[-1]
x = xprev + xprev * 0.02 * dt + sigma * xprev * dW[i]
asset.append(x)
instrument = 0
lot_size = 10000
symbols=['SPY', 'VXX', 'XLV', 'XIV']
dict_df = {}
self.df = self.get_df(asset=asset)
for symbol in symbols:
dict_df[symbol] = self.df
config = BacktestingConfig(id = None, stg_id='sma', portfolio_id='test2',
instrument_ids=[instrument],
subscription_types=[BarSubscriptionType(bar_type=BarType.Time, bar_size=BarSize.D1)],
from_date=TestCompareWithFunctionalBacktest.dates[0],
to_date=TestCompareWithFunctionalBacktest.dates[-1],
broker_id=Broker.Simulator,
feed_id=Feed.PandasMemory,
stg_configs={'qty': lot_size},
ref_data_mgr_type= None, persistence_config= None,
provider_configs = PandasMemoryDataFeedConfig(dict_df=dict_df))
self.init_context(symbols=symbols, asset=asset, app_config=config)
close = self.app_context.inst_data_mgr.get_series("Bar.%s.Time.86400" % instrument)
close.start(self.app_context)
strategy = SMAStrategy("sma", config.stg_configs)
strategy.start(self.app_context)
rets = strategy.get_portfolio().get_return()
bt_result = strategy.get_portfolio().get_result()
sma10 = talib.SMA(self.df.Close.values, 10)
sma25 = talib.SMA(self.df.Close.values, 25)
signal = pd.Series(1 * (sma10 > sma25), index=self.df.index)
cash = []
stock_value = []
cash.append(TestCompareWithFunctionalBacktest.init_cash)
stock_value.append(0)
for i in xrange(1, signal.shape[0]):
cash.append(cash[-1] - lot_size * (signal[i] - signal[i - 1]) * self.df['Close'][i])
stock_value.append(lot_size * signal[i] * self.df['Close'][i])
target_port = pd.DataFrame({"cash": cash,
"stock_value": stock_value})
target_port["total_equity"] = target_port["cash"] + target_port["stock_value"]
target_port["return"] = target_port["total_equity"].pct_change()
try:
np.testing.assert_almost_equal(target_port["return"].values[1:], rets.values, 5)
except AssertionError as e:
self.fail(e.message)
finally:
strategy.stop()
def test_with_sma(self):
sigma = 0.3
x0 = 100
dt = 1. / 252
dW = np.random.normal(0, math.sqrt(dt), TestCompareWithFunctionalBacktest.num_days)
asset = []
asset.append(x0)
for i in range(1, TestCompareWithFunctionalBacktest.num_days):
xprev = asset[-1]
x = xprev + xprev * 0.02 * dt + sigma * xprev * dW[i]
asset.append(x)
instrument = 0
lot_size = 10000
symbols = ['SPY', 'VXX', 'XLV', 'XIV']
dict_df = {}
self.df = self.get_df(asset=asset)
for symbol in symbols:
dict_df[symbol] = self.df
config = Config({
"Application": {
"type": "BackTesting",
"clockId": "Simulation",
"dataStoreId": "InMemory",
"persistenceMode": "Disable",
"createDBAtStart": True,
"deleteDBAtStop": False,
"feedId": "PandasMemory",
"brokerId": "Simulator",
"portfolioId": "test2",
"stg": "down2%",
"stgCls": "algotrader.strategy.down_2pct_strategy.Down2PctStrategy",
"instrumentIds": ["0"],
"subscriptionTypes": ["Bar.Yahoo.Time.D1"],
"fromDate": TestCompareWithFunctionalBacktest.dates[0],
"toDate": TestCompareWithFunctionalBacktest.dates[-1],
"plot": False
},
"DataStore": {"InMemory":
{
"file": "../data/algotrader_db.p",
"instCSV": "../data/refdata/instrument.csv",
"ccyCSV": "../data/refdata/ccy.csv",
"exchCSV": "../data/refdata/exch.csv"
}
},
"Strategy": {
"down2%": {
"qty": lot_size
}
}
})
self.init_context(symbols=symbols, asset=asset, config=config)
feed = self.app_context.get_feed()
feed.set_data_frame(dict_df)
close = self.app_context.inst_data_mgr.get_series("Bar.%s.Time.86400" % instrument)
close.start(self.app_context)
strategy = SMAStrategy("sma")
strategy.start(self.app_context)
rets = strategy.get_portfolio().get_return()
bt_result = strategy.get_portfolio().get_result()
sma10 = talib.SMA(self.df.Close.values, 10)
sma25 = talib.SMA(self.df.Close.values, 25)
signal = pd.Series(1 * (sma10 > sma25), index=self.df.index)
cash = []
stock_value = []
cash.append(TestCompareWithFunctionalBacktest.init_cash)
stock_value.append(0)
for i in range(1, signal.shape[0]):
cash.append(cash[-1] - lot_size * (signal[i] - signal[i - 1]) * self.df['Close'][i])
stock_value.append(lot_size * signal[i] * self.df['Close'][i])
target_port = pd.DataFrame({"cash": cash,
"stock_value": stock_value})
target_port["total_equity"] = target_port["cash"] + target_port["stock_value"]
target_port["return"] = target_port["total_equity"].pct_change()
try:
np.testing.assert_almost_equal(target_port["return"].values[1:], rets.values, 5)
except AssertionError as e:
self.fail(e.message)
finally:
strategy.stop()
def test_with_sma(self):
sigma = 0.3
x0 = 100
dt = 1. / 252
dW = np.random.normal(0, math.sqrt(dt),
TestCompareWithFunctionalBacktest.num_days)
asset = []
asset.append(x0)
for i in xrange(1, TestCompareWithFunctionalBacktest.num_days):
xprev = asset[-1]
x = xprev + xprev * 0.02 * dt + sigma * xprev * dW[i]
asset.append(x)
instrument = 0
lot_size = 10000
symbols = ['SPY', 'VXX', 'XLV', 'XIV']
dict_df = {}
self.df = self.get_df(asset=asset)
for symbol in symbols:
dict_df[symbol] = self.df
config = BacktestingConfig(
id=None,
stg_id='sma',
portfolio_id='test2',
instrument_ids=[instrument],
subscription_types=[
BarSubscriptionType(bar_type=BarType.Time, bar_size=BarSize.D1)
],
from_date=TestCompareWithFunctionalBacktest.dates[0],
to_date=TestCompareWithFunctionalBacktest.dates[-1],
broker_id=Broker.Simulator,
feed_id=Feed.PandasMemory,
stg_configs={'qty': lot_size},
ref_data_mgr_type=None,
persistence_config=None,
provider_configs=PandasMemoryDataFeedConfig(dict_df=dict_df))
self.init_context(symbols=symbols, asset=asset, app_config=config)
close = self.app_context.inst_data_mgr.get_series("Bar.%s.Time.86400" %
instrument)
close.start(self.app_context)
strategy = SMAStrategy("sma", config.stg_configs)
strategy.start(self.app_context)
rets = strategy.get_portfolio().get_return()
bt_result = strategy.get_portfolio().get_result()
sma10 = talib.SMA(self.df.Close.values, 10)
sma25 = talib.SMA(self.df.Close.values, 25)
signal = pd.Series(1 * (sma10 > sma25), index=self.df.index)
cash = []
stock_value = []
cash.append(TestCompareWithFunctionalBacktest.init_cash)
stock_value.append(0)
for i in xrange(1, signal.shape[0]):
cash.append(cash[-1] - lot_size *
(signal[i] - signal[i - 1]) * self.df['Close'][i])
stock_value.append(lot_size * signal[i] * self.df['Close'][i])
target_port = pd.DataFrame({"cash": cash, "stock_value": stock_value})
target_port[
"total_equity"] = target_port["cash"] + target_port["stock_value"]
target_port["return"] = target_port["total_equity"].pct_change()
try:
np.testing.assert_almost_equal(target_port["return"].values[1:],
rets.values, 5)
except AssertionError as e:
self.fail(e.message)
finally:
strategy.stop()
