def test_alpha_strategy_dataview():
save_dataview()
dv = DataView()
dv.load_dataview(folder_path=dataview_dir_path)
props = {
"start_date": dv.start_date,
"end_date": dv.end_date,
"period": "week",
"days_delay": 0,
"init_balance": 1e8,
"position_ratio": 0.7,
'commission_rate': 0.0
}
trade_api = AlphaTradeApi()
bt = AlphaBacktestInstance()
risk_model = model.FactorRiskModel()
signal_model = model.FactorRevenueModel()
cost_model = model.SimpleCostModel()
stock_selector = model.StockSelector()
signal_model.add_signal(name='my_factor', func=my_factor)
cost_model.consider_cost(name='my_commission',
func=my_commission,
options={'myrate': 1e-2})
stock_selector.add_filter(name='total_profit_growth', func=my_selector)
stock_selector.add_filter(name='no_new_stocks',
func=my_selector_no_new_stocks)
strategy = AlphaStrategy(revenue_model=signal_model,
stock_selector=stock_selector,
cost_model=cost_model,
risk_model=risk_model,
pc_method='factor_value_weight')
pm = PortfolioManager()
# strategy = AlphaStrategy(revenue_model=signal_model, pc_method='factor_value_weight')
# strategy = AlphaStrategy(stock_selector=stock_selector, pc_method='market_value_weight')
# strategy = AlphaStrategy()
context = model.AlphaContext(dataview=dv,
trade_api=trade_api,
instance=bt,
strategy=strategy,
pm=pm)
for mdl in [risk_model, signal_model, cost_model, stock_selector]:
mdl.register_context(context)
bt.init_from_config(props)
bt.run_alpha()
bt.save_results(folder_path=backtest_result_dir_path)
def run_strategy():
start_date = 20150501
end_date = 20171030
index = '399975.SZ'
ds = RemoteDataService()
ds.init_from_config()
symbol_list = ds.get_index_comp(index, start_date, start_date)
# add the benchmark index to the last position of symbol_list
symbol_list.append(index)
props = {
"symbol": ','.join(symbol_list),
"start_date": start_date,
"end_date": end_date,
"bar_type": "1d",
"init_balance": 1e7,
"std multiplier": 1.5,
"m": 10,
"n": 60,
"commission_rate": 2E-4
}
tapi = BacktestTradeApi()
ins = EventBacktestInstance()
strat = SectorRolling()
pm = PortfolioManager()
context = model.Context(data_api=ds,
trade_api=tapi,
instance=ins,
strategy=strat,
pm=pm)
ins.init_from_config(props)
ins.run()
ins.save_results(folder_path=result_dir_path)
ta = ana.EventAnalyzer()
ta.initialize(data_server_=ds, file_folder=result_dir_path)
df_bench, _ = ds.daily(index, start_date=start_date, end_date=end_date)
ta.data_benchmark = df_bench.set_index('trade_date').loc[:, ['close']]
ta.do_analyze(result_dir=result_dir_path,
selected_sec=props['symbol'].split(',')[:2])
def test_alpha_strategy_dataview():
dv = DataView()
dv.load_dataview(folder_path=dataview_dir_path)
props = {
"benchmark": "000300.SH",
"universe": ','.join(dv.symbol),
"start_date": dv.start_date,
"end_date": dv.end_date,
"period": "month",
"days_delay": 0,
"init_balance": 1e8,
"position_ratio": 1.0,
}
trade_api = AlphaTradeApi()
trade_api.init_from_config(props)
def selector_growth(context, user_options=None):
growth_rate = context.snapshot['net_profit_growth']
return (growth_rate >= 0.2) & (growth_rate <= 4)
def selector_pe(context, user_options=None):
pe_ttm = context.snapshot['pe_ttm']
return (pe_ttm >= 10) & (pe_ttm <= 20)
stock_selector = model.StockSelector()
stock_selector.add_filter(name='net_profit_growth', func=selector_growth)
stock_selector.add_filter(name='pe', func=selector_pe)
strategy = AlphaStrategy(stock_selector=stock_selector,
pc_method='equal_weight')
pm = PortfolioManager()
bt = AlphaBacktestInstance()
context = model.Context(dataview=dv,
instance=bt,
strategy=strategy,
trade_api=trade_api,
pm=pm)
stock_selector.register_context(context)
bt.init_from_config(props)
bt.run_alpha()
bt.save_results(folder_path=backtest_result_dir_path)
def run_strategy():
tapi = BacktestTradeApi()
ins = EventBacktestInstance()
ds = RemoteDataService()
strat = DualThrust()
pm = PortfolioManager()
context = model.Context(data_api=ds,
trade_api=tapi,
instance=ins,
strategy=strat,
pm=pm)
ins.init_from_config(props)
ins.run()
ins.save_results(folder_path=result_dir_path)
def test_alpha_strategy_dataview():
dv = DataView()
dv.load_dataview(folder_path=dataview_dir_path)
props = {
"start_date": dv.start_date,
"end_date": dv.end_date,
"period": "week",
"days_delay": 0,
"init_balance": 1e8,
"position_ratio": 0.7,
'commission_rate': 0.0
}
trade_api = AlphaTradeApi()
bt = AlphaBacktestInstance()
stock_selector = model.StockSelector()
stock_selector.add_filter(name='myselector', func=my_selector)
strategy = AlphaStrategy(stock_selector=stock_selector,
pc_method='equal_weight')
pm = PortfolioManager()
context = model.AlphaContext(dataview=dv, trade_api=trade_api,
instance=bt, strategy=strategy, pm=pm)
store = pd.HDFStore(ic_weight_hd5_path)
factorList = fileio.read_json(custom_data_path)
context.ic_weight = store['ic_weight']
context.factorList = factorList
store.close()
for mdl in [stock_selector]:
mdl.register_context(context)
bt.init_from_config(props)
bt.run_alpha()
bt.save_results(folder_path=backtest_result_dir_path)
def test_alpha_strategy_dataview():
dv = DataView()
dv.load_dataview(folder_path=dataview_dir_path)
props = {
"benchmark": "000300.SH",
"universe": ','.join(dv.symbol),
"start_date": 20170131,
"end_date": dv.end_date,
"period": "month",
"days_delay": 0,
"init_balance": 1e9,
"position_ratio": 1.0,
}
trade_api = AlphaTradeApi()
def singal_gq30(context, user_options=None):
import numpy as np
res = np.power(context.snapshot['gq30'], 8)
return res
signal_model = model.FactorRevenueModel()
signal_model.add_signal('signal_gq30', singal_gq30)
strategy = AlphaStrategy(revenue_model=signal_model, pc_method='factor_value_weight')
pm = PortfolioManager()
bt = AlphaBacktestInstance()
context = model.Context(dataview=dv, instance=bt, strategy=strategy, trade_api=trade_api, pm=pm)
signal_model.register_context(context)
bt.init_from_config(props)
bt.run_alpha()
bt.save_results(folder_path=backtest_result_dir_path)
def test_alpha_strategy_dataview():
dv = DataView()
dv.load_dataview(folder_path=dataview_dir_path)
props = {
"start_date": dv.start_date,
"end_date": dv.end_date,
"period": "week",
"days_delay": 0,
"init_balance": 1e8,
"position_ratio": 1.0,
}
gateway = AlphaTradeApi()
gateway.init_from_config(props)
context = model.Context(dataview=dv, gateway=gateway)
stock_selector = model.StockSelector()
stock_selector.add_filter(name='myselector', func=my_selector)
signal_model = model.FactorRevenueModel()
signal_model.add_signal(name='signalsize', func=signal_size)
strategy = AlphaStrategy(stock_selector=stock_selector, pc_method='factor_value_weight',
revenue_model=signal_model)
pm = PortfolioManager()
bt = AlphaBacktestInstance()
context = model.Context(dataview=dv, instance=bt, strategy=strategy, trade_api=trade_api, pm=pm)
for mdl in [signal_model, stock_selector]:
mdl.register_context(context)
bt.init_from_config(props)
bt.run_alpha()
bt.save_results(folder_path=backtest_result_dir_path)
def test_alpha_strategy_dataview():
dv = DataView()
dv.load_dataview(folder_path=dataview_dir_path)
props = {
"benchmark": "000300.SH",
"universe": ','.join(dv.symbol),
"start_date": dv.start_date,
"end_date": dv.end_date,
"period": "month",
"days_delay": 0,
"init_balance": 1e8,
"position_ratio": 1.0,
}
trade_api = AlphaTradeApi()
context = model.Context(dataview=dv, gateway=trade_api)
stock_selector = model.StockSelector(context)
stock_selector.add_filter(name='myrank', func=my_selector)
strategy = AlphaStrategy(stock_selector=stock_selector,
pc_method='equal_weight')
pm = PortfolioManager()
bt = AlphaBacktestInstance()
context = model.Context(dataview=dv,
instance=bt,
strategy=strategy,
trade_api=trade_api,
pm=pm)
stock_selector.register_context(context)
bt.init_from_config(props)
bt.run_alpha()
bt.save_results(folder_path=backtest_result_dir_path)
def run_strategy():
if is_backtest:
props = {
"symbol": "rb1710.SHF",
"start_date": 20170510,
"end_date": 20170930,
"bar_type": "1M", # '1d'
"init_balance": 2e4
}
tapi = BacktestTradeApi()
ins = EventBacktestInstance()
else:
props = {'symbol': 'rb1801.SHF'}
tapi = RealTimeTradeApi()
ins = EventRealTimeInstance()
tapi.use_strategy(8)
ds = RemoteDataService()
strat = DoubleMaStrategy()
pm = PortfolioManager()
context = model.Context(data_api=ds,
trade_api=tapi,
instance=ins,
strategy=strat,
pm=pm)
ins.init_from_config(props)
if not is_backtest:
ds.subscribe(props['symbol'])
ins.run()
if not is_backtest:
time.sleep(9999)
ins.save_results(folder_path=result_dir_path)