dates = [start_date + timedelta(days=i) for i in range(num_days)]
sigma = 0.3
x0 = 100
dt = 1. / 252
from algotrader.models.sde_sim import euler
drift0 = lambda x, t: 0.01 * x
diffusion0 = lambda x, t: 0.05 * x
drift1 = lambda x, t: -0.01 * x
diffusion1 = lambda x, t: 0.05 * x
drift2 = lambda x, t: -0.03 * x
diffusion2 = lambda x, t: 0.08 * x
simpath0 = euler(drift0, diffusion0, 1.0, 2.0, num_days, 1)
simpath1 = euler(drift1, diffusion1, 10.0, 2.0, num_days, 1)
simpath2 = euler(drift2, diffusion2, 100.0, 2.0, num_days, 1)
df0 = pd.DataFrame({"dates": dates,
"Open": simpath0[0, :],
"High": simpath0[0, :],
"Low": simpath0[0, :],
"Close": simpath0[0, :],
"Volume": 10000 * np.ones(num_days)})
df0 = df0.set_index(keys="dates")
df1 = pd.DataFrame({"dates": dates,
"Open": simpath1[0, :],
"High": simpath1[0, :],
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)
dates = [start_date + timedelta(days=i) for i in range(num_days)]
sigma = 0.3
x0 = 100
dt = 1. / 252
from algotrader.models.sde_sim import euler
drift0 = lambda x, t: 0.01 * x
diffusion0 = lambda x, t: 0.05 * x
drift1 = lambda x, t: -0.01 * x
diffusion1 = lambda x, t: 0.05 * x
drift2 = lambda x, t: -0.03 * x
diffusion2 = lambda x, t: 0.08 * x
simpath0 = euler(drift0, diffusion0, 1.0, 2.0, num_days, 1)
simpath1 = euler(drift1, diffusion1, 10.0, 2.0, num_days, 1)
simpath2 = euler(drift2, diffusion2, 100.0, 2.0, num_days, 1)
df0 = pd.DataFrame({
"dates": dates,
"Open": simpath0[0, :],
"High": simpath0[0, :],
"Low": simpath0[0, :],
"Close": simpath0[0, :],
"Volume": 10000 * np.ones(num_days)
})
df0 = df0.set_index(keys="dates")
df1 = pd.DataFrame({
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)