def run_example(run_optimization=False):
universe = ['000300.zicn']
start_date = dt.datetime(2005, 1, 1)
end_date = dt.datetime(2017, 4, 13)
window_min_max = config('GX_WINDOW_MIN_MAX', default=215, cast=int)
window_ma = config('GX_WINDOW_MA', default=7, cast=int)
vol_diff_slice = config('GX_VOL_DIFF_SLICE', default=True, cast=bool)
if run_optimization:
params_set, params_name = parameters_generator()
run_param_grid_search(strat=GXRPS,
universe=universe,
start_date=start_date,
end_date=end_date,
params_set=params_set,
params_name=params_name)
else:
strategyRunner(userStrategy=GXRPS,
strategyParameters=(window_min_max, window_ma,
vol_diff_slice),
symbolList=universe,
startDate=start_date,
endDate=end_date,
benchmark='000300.zicn',
logLevel='info',
saveFile=True,
portfolioType=PortfolioType.CashManageable,
plot=True,
freq='D')
def run_example():
indexes = ['000016.zicn', '000300.zicn', '000905.zicn']
# futures to trade
ihs = ['ih15%02d' % i for i in range(4, 13)]
ifs = ['if15%02d' % i for i in range(4, 13)]
ics = ['ic15%02d' % i for i in range(4, 13)]
futures = ihs + ifs + ics
universe = indexes + futures
initialCapital = 75000.0
startDate = dt.datetime(2015, 4, 22)
endDate = dt.datetime(2015, 11, 13)
strategyRunner(userStrategy=MovingAverageCrossStrategy,
initialCapital=initialCapital,
symbolList=universe,
startDate=startDate,
endDate=endDate,
dataSource=DataSource.DataYes,
saveFile=True,
plot=False,
benchmark='000300.zicn',
portfolioType=PortfolioType.FullNotional)
def run_example():
indexes = ['000016.zicn', '000300.zicn', '000905.zicn']
# futures to trade
ihs = ['ih15%02d' % i for i in range(4, 13)]
ifs = ['if15%02d' % i for i in range(4, 13)]
ics = ['ic15%02d' % i for i in range(4, 13)]
futures = ihs + ifs + ics
universe = indexes + futures
initialCapital = 75000.0
startDate = dt.datetime(2015, 4, 22)
endDate = dt.datetime(2015, 11, 19)
strategyRunner(userStrategy=MovingAverageCrossStrategy,
initialCapital=initialCapital,
symbolList=universe,
startDate=startDate,
endDate=endDate,
dataSource=DataSource.DataYes,
saveFile=True,
plot=True,
benchmark='000300.zicn',
portfolioType=PortfolioType.FullNotional)
def run_example():
stocks = set_universe('000300.zicn')
futures = ['if15%02d' % i for i in range(4, 13)]
universes = stocks + futures
strategyRunner(userStrategy=MovingAverageCrossStrategy,
symbolList=universes,
startDate=dt.datetime(2015, 4, 22),
endDate=dt.datetime(2015, 11, 30),
benchmark='000300.zicn')
def run_example():
csvDir = "data"
universe = ['aapl', 'msft', 'ibm']
initialCapital = 1000000.0
strategyRunner(userStrategy=MovingAverageCrossStrategy,
initialCapital=initialCapital,
symbolList=universe,
dataSource=DataSource.CSV,
csvDir=csvDir,
saveFile=False,
plot=True)
def run_example():
universe = ['aapl', 'msft', 'ibm']
initialCapital = 100000.0
startDate = dt.datetime(1990, 1, 1)
endDate = dt.datetime(2015, 9, 15)
strategyRunner(userStrategy=MovingAverageCrossStrategy,
initialCapital=initialCapital,
symbolList=universe,
startDate=startDate,
endDate=endDate,
dataSource=DataSource.YAHOO,
saveFile=False)
def run_example():
universe = set_universe('000300.zicn')[:20]
startDate = dt.datetime(2006, 1, 1)
endDate = dt.datetime(2015, 10, 1)
strategyRunner(userStrategy=MovingAverageCrossStrategy,
symbolList=universe,
startDate=startDate,
endDate=endDate,
dataSource=DataSource.DataYes,
benchmark='000300.zicn',
logLevel='info',
saveFile=True,
plot=True)
def run_example():
universe = set_universe('000300.zicn')
initialCapital = 100000.0
startDate = dt.datetime(2006, 1, 1)
endDate = dt.datetime(2015, 10, 1)
strategyRunner(userStrategy=MovingAverageCrossStrategy,
initialCapital=initialCapital,
symbolList=universe,
startDate=startDate,
endDate=endDate,
dataSource=DataSource.DataYes,
freq=0,
benchmark='000300.zicn',
saveFile=False,
plot=True)
def run_param_grid_search(strat, universe, start_date, end_date, params_set, params_name, **kwargs):
universe = universe
start_date = start_date
end_date = end_date
benchmark = kwargs.get('benchmark', universe[0])
log_level = kwargs.get('logLevel', 'critical')
ptf_type = kwargs.get('portfolioType', PortfolioType.CashManageable)
freq = kwargs.get('freq', 'D')
record = pd.DataFrame()
for params in params_set:
result = strategyRunner(userStrategy=strat,
strategyParameters=params,
symbolList=universe,
startDate=start_date,
endDate=end_date,
benchmark=benchmark,
logLevel=log_level,
portfolioType=ptf_type,
freq=freq,
plot=False,
saveFile=False)['perf_metric']
row = pd.Series(index=params_name, data=np.array(params))
row_to_append = pd.concat([result['metrics'], row], axis=0)
record = pd.concat([record, row_to_append], axis=1)
record = record.T
record.to_csv('optimizing_result.csv')
def run_example():
universe = ['600000.xshg', '000300.zicn']
initialCapital = 1e5
startDate = dt.datetime(2014, 12, 1)
endDate = dt.datetime(2015, 11, 1)
strategyRunner(userStrategy=MonitoringIndexStrategy,
initialCapital=initialCapital,
symbolList=universe,
startDate=startDate,
endDate=endDate,
dataSource=DataSource.DXDataCenter,
freq=0,
benchmark='000300.zicn',
logLevel="warning",
saveFile=True,
plot=True)
def run_example():
universe = ['000905.zicn']
start_date = dt.datetime(2016, 1, 1)
end_date = dt.datetime(2017, 4, 24)
alpha = config('GF_LLT_ALPHA', default=2.0/61, cast=float)
strategyRunner(userStrategy=GFLLT,
strategyParameters=[alpha],
symbolList=universe,
startDate=start_date,
endDate=end_date,
benchmark='000300.zicn',
logLevel='info',
saveFile=True,
portfolioType=PortfolioType.CashManageable,
plot=True,
freq='D')
def run_example():
universe = ['000300.zicn']
start_date = dt.datetime(2005, 1, 1)
end_date = dt.datetime(2017, 1, 1)
ma_short = config('GF_MA_SHORT', default=20, cast=int)
ma_long = config('GF_MA_LONG', default=90, cast=int)
strategyRunner(userStrategy=GFMovingAverageCrossStrategy,
strategyParameters=(ma_short, ma_long),
symbolList=universe,
startDate=start_date,
endDate=end_date,
benchmark='000300.zicn',
dataSource=DataSource.WIND,
logLevel='info',
saveFile=True,
portfolioType=PortfolioType.CashManageable,
plot=True,
freq='D')
def run_example():
universe = ["600000.xshg", "000300.zicn"]
initialCapital = 1e5
startDate = dt.datetime(2014, 12, 1)
endDate = dt.datetime(2015, 11, 1)
strategyRunner(
userStrategy=MonitoringIndexStrategy,
initialCapital=initialCapital,
symbolList=universe,
startDate=startDate,
endDate=endDate,
dataSource=DataSource.DXDataCenter,
freq=0,
benchmark="000300.zicn",
logLevel="warning",
saveFile=True,
plot=True,
)
def test_gf_llt_strat(self):
for i in range(len(self._data)):
data = self._data[i]
perf = strategyRunner(userStrategy=GFLLT,
strategyParameters=(data['alpha'],),
symbolList=data['universe'],
dataSource=DataSource.CSV,
csvDir=data['csv_dir'],
saveFile=False,
portfolioType=PortfolioType.CashManageable,
plot=False
)['perf_metric']
assert_frame_equal(perf, self._expected[i])
def run_example():
universe = ['510300.xshg', '510500.xshg', '511010.xshg', '518880.xshg']
assets = {
'510300.xshg': {
'target_weight': 0.0,
'default_weight': 0.075
},
'510500.xshg': {
'target_weight': 0.0,
'default_weight': 0.075
},
'511010.xshg': {
'target_weight': 0.0,
'default_weight': 0.75
},
'518880.xshg': {
'target_weight': 0.0,
'default_weight': 0.1
}
}
start_date = dt.datetime(2013, 8, 1)
end_date = dt.datetime(2017, 2, 28)
window = config('RISKPARITY_WINDOW', cast=int)
tiaocang_freq = config('RISKPARITY_TIAOCANG_FREQ', cast=int)
print strategyRunner(userStrategy=RISKPARITY,
strategyParameters=(window, assets, tiaocang_freq),
symbolList=universe,
startDate=start_date,
endDate=end_date,
benchmark='000300.zicn',
dataSource=DataSource.WIND,
logLevel='info',
saveFile=True,
portfolioType=PortfolioType.CashManageable,
plot=True,
freq='D',
priceAdj='F')
def test_gx_rps_strat(self):
for i in range(len(self._data)):
data = self._data[i]
perf = strategyRunner(userStrategy=GXRPS,
strategyParameters=(data['window_min_max'],
data['window_ma'],
data['vol_diff_slice']),
symbolList=data['universe'],
dataSource=DataSource.CSV,
csvDir=data['csv_dir'],
saveFile=False,
portfolioType=PortfolioType.CashManageable,
plot=False)['perf_metric']
assert_frame_equal(perf, self._expected[i])
def run_example():
# universe = ['if15%02d.ccfx' % i for i in range(1, 13)]
universe = ['if15%02d.ccfx' % i for i in range(10, 13)
] + ['if16%02d.ccfx' % i for i in range(1, 4)]
start_date = dt.datetime(2015, 11, 10)
end_date = dt.datetime(2016, 2, 1)
window_up = config('DT_WINDOW_UP', cast=float)
multi_up = config('DT_MULTI_UP', cast=float)
window_down = config('DT_WINDOW_DOWN', cast=float)
multi_down = config('DT_MULTI_DOWN', cast=float)
strategyRunner(userStrategy=DualThrust,
strategyParameters=(window_up, multi_up, window_down,
multi_down),
symbolList=universe,
startDate=start_date,
endDate=end_date,
benchmark='000300.zicn',
dataSource=DataSource.WIND,
logLevel='info',
saveFile=True,
plot=True,
freq='min5')
def test_risk_parity_strat(self):
for i in range(len(self._data)):
data = self._data[i]
perf = strategyRunner(userStrategy=RISKPARITY,
strategyParameters=(data['window'],
data['assets'],
data['tiaocang_freq'],
data['weight_type']),
symbolList=data['universe'],
dataSource=DataSource.CSV,
csvDir=data['csv_dir'],
saveFile=False,
portfolioType=PortfolioType.CashManageable,
plot=False)['perf_metric']
assert_frame_equal(perf, self._expected[i])
