def test_model():
model.StockSelector()
model.SimpleCostModel()
model.AlphaContext()
model.FactorRiskModel()
model.FactorSignalModel()
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.FactorSignalModel()
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(signal_model=signal_model,
stock_selector=stock_selector,
cost_model=cost_model,
risk_model=risk_model,
pc_method='factor_value_weight')
pm = PortfolioManager()
# strategy = AlphaStrategy(signal_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 test_alpha_strategy_dataview():
dv = DataView()
dv.load_dataview(folder_path=dataview_dir_path)
#回测参数选择
props = {
"benchmark": "000905.SH",
"universe": ','.join(dv.symbol),
"start_date": 20170605,
"end_date": 20180807,
"period": "day",
"days_delay": 0,
"init_balance": 1e9,
"position_ratio": 1.0,
"commission_rate": 0.0015, #手续费
"n_periods": 2
}
props.update(data_config)
props.update(trade_config)
trade_api = AlphaTradeApi()
signal_model = model.FactorSignalModel()
#添加信号
signal_model.add_signal('alpha3', alpha) #在使用新因子时，alpha3应改为新因子的名称
stock_selector = model.StockSelector()
stock_selector.add_filter(name='myselector', func=my_selector)
strategy = AlphaStrategy(stock_selector=stock_selector,
signal_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)
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": 20170131,
"end_date": dv.end_date,
"period": "month",
"days_delay": 0,
"init_balance": 1e9,
"position_ratio": 1.0,
}
props.update(data_config)
props.update(trade_config)
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.FactorSignalModel()
signal_model.add_signal('signal_gq30', singal_gq30)
strategy = AlphaStrategy(signal_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,
}
props.update(data_config)
props.update(trade_config)
trade_api = AlphaTradeApi()
stock_selector = model.StockSelector()
stock_selector.add_filter(name='myselector', func=my_selector)
signal_model = model.FactorSignalModel()
signal_model.add_signal(name='signalsize', func=signal_size)
strategy = AlphaStrategy(stock_selector=stock_selector,
pc_method='factor_value_weight',
signal_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 = {
"symbol": dv.symbol,
"universe": ','.join(dv.symbol),
"start_date": dv.start_date,
"end_date": dv.end_date,
"period": "week",
"days_delay": 0,
"init_balance": 1e7,
"position_ratio": 1.0,
"commission_rate": 2E-4 # 手续费万2
}
props.update(data_config)
props.update(trade_config)
trade_api = AlphaTradeApi()
signal_model = model.FactorSignalModel()
signal_model.add_signal('stockWeight', stockWeight)
strategy = AlphaStrategy(signal_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)