def init_context(self, symbols, asset, app_config):
self.app_context = ApplicationContext(app_config=app_config)
inst_df = build_inst_dataframe_from_list(symbols)
ccy_df = pd.DataFrame({
"ccy_id": ["USD", "HKD"],
"name": ["US Dollar", "HK Dollar"]
})
exchange_df = pd.DataFrame({
"exch_id": ["NYSE"],
"name": ["New York Stock Exchange"]
})
ref_data_mgr = MockRefDataManager(inst_df=inst_df,
ccy_df=ccy_df,
exch_df=exchange_df)
self.app_context.ref_data_mgr = ref_data_mgr
self.app_context.start()
self.portfolio = self.app_context.portf_mgr.new_portfolio(
portf_id='test2', cash=TestCompareWithFunctionalBacktest.init_cash)
self.portfolio.start(self.app_context)
symbols = ['SPY', 'VXX', 'XLV', 'XIV']
inst_df = build_inst_dataframe_from_list(symbols)
# ccy_id,name
ccy_df = pd.DataFrame({
"ccy_id": ["USD", "HKD"],
"name": ["US Dollar", "HK Dollar"]
})
exchange_df = pd.DataFrame({
"exch_id": ["NYSE"],
"name": ["New York Stock Exchange"]
})
mgr = MockRefDataManager(inst_df, ccy_df, exchange_df)
feed = PandaH5DataFeed('/Users/jchan/workspace/data/Equity/US/etf.h5',
ref_data_mgr=mgr)
today = date.today()
startDate = date(2011, 1, 1)
sub_key0 = HistDataSubscriptionKey(inst_id=0,
provider_id=PandaH5DataFeed.ID,
data_type=Bar,
bar_size=BarSize.D1,
from_date=startDate,
to_date=today)
sub_key1 = HistDataSubscriptionKey(inst_id=1,
provider_id=PandaH5DataFeed.ID,
def main():
symbols = ['SPY', 'VXX', 'XLV', 'XIV']
inst_df = build_inst_dataframe_from_list(symbols)
ccy_df = pd.DataFrame({
"ccy_id": ["USD", "HKD"],
"name": ["US Dollar", "HK Dollar"]
})
exchange_df = pd.DataFrame({
"exch_id": ["NYSE"],
"name": ["New York Stock Exchange"]
})
mgr = MockRefDataManager(inst_df=inst_df,
ccy_df=ccy_df,
exch_df=exchange_df)
portfolio = Portfolio(portf_id='test', cash=100000)
start_date = datetime(2000, 1, 1)
num_days = 3000
dates = [start_date + timedelta(days=i) for i in range(num_days)]
sigma = 0.3
x0 = 100
dt = 1. / 252
dW = np.random.normal(0, math.sqrt(dt), num_days)
asset = []
asset.append(x0)
for i in xrange(1, num_days):
xprev = asset[-1]
x = xprev + xprev * 0.02 * dt + sigma * xprev * dW[i]
asset.append(x)
df = pd.DataFrame({
"dates": dates,
"Open": asset,
"High": asset,
"Low": asset,
"Close": asset,
"Volume": 10000 * np.ones(num_days)
})
df = df.set_index(keys="dates")
dict_df = {'SPY': df, 'VXX': df, 'XLV': df, 'XIV': df}
feed = PandasMemoryDataFeed(dict_df, ref_data_mgr=mgr)
broker = Simulator()
instrument = 0
config = BacktestingConfig(stg_id="sma",
portfolio_id='test',
instrument_ids=[instrument],
subscription_types=[
BarSubscriptionType(bar_type=BarType.Time,
bar_size=BarSize.D1)
],
from_date=dates[0],
to_date=dates[-1],
broker_id=Simulator.ID,
feed_id=PandasMemoryDataFeed.ID)
close = inst_data_mgr.get_series("Bar.%s.Time.86400" % instrument)
mgr.get_insts([instrument])
mgr.get_inst(instrument)
# strategy = Down2PctStrategy("down2%", portfolio,
# instrument=0, qty=1000, trading_config=config, ref_data_mgr=mgr)
strategy = SMAStrategy("sma", stg_configs={'qty': 1})
runner = BacktestRunner(strategy)
runner.start()
print portfolio.get_result()
# pyfolio
rets = strategy.get_portfolio().get_return()
# import pyfolio as pf
# pf.create_returns_tear_sheet(rets)
# pf.create_full_tear_sheet(rets)
# build in plot
plotter = StrategyPlotter(strategy)
plotter.plot(instrument=0)
# import matplotlib.pyplot as plt
# plt.show()
import talib
sma10 = talib.SMA(df.Close.values, 10)
sma25 = talib.SMA(df.Close.values, 25)
# signal = pd.Series(1*(sma10 > sma25),index=df.index.tz_localize("UTC"))
signal = pd.Series(1 * (sma10 > sma25), index=df.index)
target_rets = df["Close"].pct_change() * signal.shift(1)
target_rets.index = target_rets.index.tz_localize("UTC")
print target_rets.values[1:] - rets.values
def main():
symbols = ['XXX', 'YYY']
inst_df = build_inst_dataframe_from_list(symbols)
ccy_df = pd.DataFrame({
"ccy_id": ["USD", "HKD"],
"name": ["US Dollar", "HK Dollar"]
})
exchange_df = pd.DataFrame({
"exch_id": ["NYSE"],
"name": ["New York Stock Exchange"]
})
mgr = MockRefDataManager(inst_df=inst_df,
ccy_df=ccy_df,
exch_df=exchange_df)
portfolio = Portfolio(cash=100000)
start_date = datetime(2000, 1, 1)
num_days = 300
dates = [start_date + timedelta(days=i) for i in range(num_days)]
drift = lambda x, t: 0.02 * x
diffusion = lambda x, t: 0.3 * x
ou_k = 2.0
ou_theta = 0.25
ou_eta = 0.08
ou_drift = lambda x, t: ou_k * (ou_theta - x)
ou_diffusion = lambda x, t: ou_eta * x
sim_asset_paths = euler(drift, diffusion, 100.0, 1.0, num_days, 10)
sim_spread_paths = euler(ou_drift, ou_diffusion, 0.1, 1.0, num_days, 10)
asset_x = sim_asset_paths[1, :]
spread = sim_spread_paths[1, :]
asset_y = np.exp(np.log(asset_x) + spread)
asset_x_df = pd.DataFrame({
"dates": dates,
"Open": asset_x,
"High": asset_x,
"Low": asset_x,
"Close": asset_x,
"Volume": 10000 * np.ones(num_days)
})
asset_y_df = pd.DataFrame({
"dates": dates,
"Open": asset_y,
"High": asset_y,
"Low": asset_y,
"Close": asset_y,
"Volume": 10000 * np.ones(num_days)
})
asset_x_df = asset_x_df.set_index(keys="dates")
asset_y_df = asset_y_df.set_index(keys="dates")
dict_df = {'XXX': asset_x_df, 'YYY': asset_y_df}
feed = PandasMemoryDataFeed(dict_df, ref_data_mgr=mgr)
broker = Simulator()
config = BacktestingConfig(stg_id="pairou",
portfolio_id='test',
instrument_ids=[0, 1],
subscription_types=[
BarSubscriptionType(bar_type=BarType.Time,
bar_size=BarSize.D1)
],
from_date=dates[0],
to_date=dates[-1],
broker_id=Simulator.ID,
feed_id=PandasMemoryDataFeed.ID)
ou_params = {"k": ou_k, "theta": ou_theta, "eta": ou_eta}
strategy = PairTradingWithOUSpread("pairou",
ou_params=ou_params,
gamma=1.0,
trading_config=config,
ref_data_mgr=mgr)
runner = BacktestRunner(strategy)
runner.start()
print portfolio.get_result()
# pyfolio
rets = strategy.get_portfolio().get_return()
# import pyfolio as pf
# pf.create_returns_tear_sheet(rets)
# pf.create_full_tear_sheet(rets)
# build in plot
plotter = StrategyPlotter(strategy)
plotter.plot(instrument=0)
def main():
symbols = ['SPY', 'VXX', 'XLV', 'XIV']
inst_df = build_inst_dataframe_from_list(symbols)
ccy_df = pd.DataFrame({"ccy_id": ["USD", "HKD"],
"name": ["US Dollar", "HK Dollar"]})
exchange_df = pd.DataFrame({"exch_id": ["NYSE"],
"name": ["New York Stock Exchange"]})
mgr = MockRefDataManager(inst_df=inst_df, ccy_df=ccy_df, exch_df=exchange_df)
portfolio = Portfolio(portf_id='test', cash=100000)
start_date = datetime(2000, 1, 1)
num_days = 3000
dates = [start_date + timedelta(days=i) for i in range(num_days)]
sigma = 0.3
x0 = 100
dt = 1. / 252
dW = np.random.normal(0, math.sqrt(dt), num_days)
asset = []
asset.append(x0)
for i in xrange(1, num_days):
xprev = asset[-1]
x = xprev + xprev * 0.02 * dt + sigma * xprev * dW[i]
asset.append(x)
df = pd.DataFrame({"dates": dates,
"Open": asset,
"High": asset,
"Low": asset,
"Close": asset,
"Volume": 10000 * np.ones(num_days)})
df = df.set_index(keys="dates")
dict_df = {'SPY': df,
'VXX': df,
'XLV': df,
'XIV': df}
feed = PandasMemoryDataFeed(dict_df, ref_data_mgr=mgr)
broker = Simulator()
instrument = 0
config = BacktestingConfig(stg_id="sma", portfolio_id='test',
instrument_ids=[instrument],
subscription_types=[BarSubscriptionType(bar_type=BarType.Time, bar_size=BarSize.D1)],
from_date=dates[0], to_date=dates[-1],
broker_id=Simulator.ID,
feed_id=PandasMemoryDataFeed.ID)
close = inst_data_mgr.get_series("Bar.%s.Time.86400" % instrument)
mgr.get_insts([instrument])
mgr.get_inst(instrument)
# strategy = Down2PctStrategy("down2%", portfolio,
# instrument=0, qty=1000, trading_config=config, ref_data_mgr=mgr)
strategy = SMAStrategy("sma", stg_configs={'qty':1})
runner = BacktestRunner(strategy)
runner.start()
print portfolio.get_result()
# pyfolio
rets = strategy.get_portfolio().get_return()
# import pyfolio as pf
# pf.create_returns_tear_sheet(rets)
# pf.create_full_tear_sheet(rets)
# build in plot
plotter = StrategyPlotter(strategy)
plotter.plot(instrument=0)
# import matplotlib.pyplot as plt
# plt.show()
import talib
sma10 = talib.SMA(df.Close.values, 10)
sma25 = talib.SMA(df.Close.values, 25)
# signal = pd.Series(1*(sma10 > sma25),index=df.index.tz_localize("UTC"))
signal = pd.Series(1 * (sma10 > sma25), index=df.index)
target_rets = df["Close"].pct_change() * signal.shift(1)
target_rets.index = target_rets.index.tz_localize("UTC")
print target_rets.values[1:] - rets.values