def do_backtest():
# Load local data file that we just stored.
dv = DataView()
dv.load_dataview(folder_path=dataview_store_folder)
backtest_props = {
"start_date": dv.start_date, # start and end date of back-test
"end_date": dv.end_date,
"period": "month", # re-balance period length
"benchmark": dv.benchmark, # benchmark and universe
"universe": dv.universe,
"init_balance": 1e8, # Amount of money at the start of back-test
"position_ratio": 1.0, # Amount of money at the start of back-test
}
backtest_props.update(data_config)
backtest_props.update(trade_config)
# Create model context using AlphaTradeApi, AlphaStrategy, PortfolioManager and AlphaBacktestInstance.
# We can store anything, e.g., public variables in context.
trade_api = AlphaTradeApi()
strategy = AlphaStrategy(pc_method='market_value_weight')
pm = PortfolioManager()
bt = AlphaBacktestInstance()
context = model.Context(dataview=dv,
instance=bt,
strategy=strategy,
trade_api=trade_api,
pm=pm)
bt.init_from_config(backtest_props)
bt.run_alpha()
# After finishing back-test, we save trade results into a folder
bt.save_results(folder_path=backtest_result_folder)
def do_livetrade():
dv = DataView()
dv.load_dataview(folder_path=dataview_store_folder)
props = {"period": "day", "strategy_no": 1044, "init_balance": 1e6}
props.update(data_config)
props.update(trade_config)
strategy = AlphaStrategy(pc_method='market_value_weight')
pm = PortfolioManager()
bt = AlphaLiveTradeInstance()
trade_api = RealTimeTradeApi(props)
ds = RemoteDataService()
context = model.Context(dataview=dv,
instance=bt,
strategy=strategy,
trade_api=trade_api,
pm=pm,
data_api=ds)
bt.init_from_config(props)
bt.run_alpha()
goal_positions = strategy.goal_positions
print("Length of goal positions:", len(goal_positions))
task_id, msg = trade_api.goal_portfolio(goal_positions)
print(task_id, msg)
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)
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,
}
props.update(data_config)
props.update(trade_config)
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 >= 5) & (pe_ttm <= 80)
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 test_alpha_strategy_dataview():
dv = DataView()
dv.load_dataview(folder_path=dataview_dir_path)
props = {
"benchmark": BENCHMARK,
"universe": ','.join(dv.symbol),
"start_date": dv.start_date,
"end_date": dv.end_date,
"period": "day",
"days_delay": 0,
"init_balance": 1e8,
"position_ratio": 1.0,
"strategy_no": 44
}
props.update(data_config)
props.update(trade_config)
stock_selector = model.StockSelector()
stock_selector.add_filter(name='rank_ret_top10', func=my_selector)
strategy = AlphaStrategy(stock_selector=stock_selector,
pc_method='equal_weight')
pm = PortfolioManager()
if is_backtest:
bt = AlphaBacktestInstance()
trade_api = AlphaTradeApi()
ds = None
else:
bt = AlphaLiveTradeInstance()
trade_api = RealTimeTradeApi(props)
ds = RemoteDataService()
context = model.Context(dataview=dv,
instance=bt,
strategy=strategy,
trade_api=trade_api,
pm=pm,
data_api=ds)
stock_selector.register_context(context)
bt.init_from_config(props)
bt.run_alpha()
if is_backtest:
bt.save_results(folder_path=backtest_result_dir_path)
else:
goal_positions = strategy.goal_positions
print(goal_positions)
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,
'commission_rate': 0.0
}
props.update(data_config)
props.update(trade_config)
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 = jutil.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 do_backtest():
# Load local data file that we just stored.
dv = DataView()
dv.load_dataview(folder_path=dataview_store_folder)
backtest_props = {# start and end date of back-test
"start_date": dv.start_date,
"end_date": dv.end_date,
# re-balance period length
"period": "month",
# benchmark and universe
"benchmark": dv.benchmark,
"universe": dv.universe,
# Amount of money at the start of back-test
"init_balance": 1e8,
# Amount of money at the start of back-test
"position_ratio": 1.0,
}
backtest_props.update(data_config)
backtest_props.update(trade_config)
# We use trade_api to send orders
trade_api = AlphaTradeApi()
# This is our strategy
strategy = AlphaStrategy(pc_method='market_value_weight')
# PortfolioManager helps us to manage tasks, orders and calculate positions
pm = PortfolioManager()
# BacktestInstance is in charge of running the back-test
bt = AlphaBacktestInstance()
# Public variables are stored in context. We can also store anything in it
context = model.Context(dataview=dv,
instance=bt,
strategy=strategy,
trade_api=trade_api,
pm=pm)
bt.init_from_config(backtest_props)
bt.run_alpha()
# After finishing back-test, we save trade results into a folder
bt.save_results(folder_path=backtest_result_folder)
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)
def alpha_strategy_backtest():
dv = DataView()
dv.load_dataview(folder_path=dataview_folder)
props = {
"benchmark": benchmark,
"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,
}
props.update(data_config)
props.update(trade_config)
trade_api = AlphaTradeApi()
trade_api.init_from_config(props)
stock_selector = model.StockSelector()
stock_selector.add_filter(name='selector_roe_roa_not_new', func=selector_roe_roa_not_new)
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_folder)
def do_livetrade():
dv = DataView()
dv.load_dataview(folder_path=dataview_store_folder)
# print("total_mv", dv.get_ts('total_mv'))
# print("float_mv", dv.get_ts('float_mv'))
props = {"period": "day", "strategy_no": 1683, "init_balance": 1e6}
props.update(data_config)
props.update(trade_config)
strategy = AlphaStrategy(pc_method='market_value_weight')
pm = PortfolioManager()
bt = AlphaLiveTradeInstance()
trade_api = RealTimeTradeApi(props)
ds = RemoteDataService()
context = model.Context(dataview=dv,
instance=bt,
strategy=strategy,
trade_api=trade_api,
pm=pm,
data_api=ds)
trade_api.set_ordstatus_callback(on_orderstatus)
trade_api.set_trade_callback(on_trade)
trade_api.set_task_callback(on_taskstatus)
bt.init_from_config(props)
bt.run_alpha()
goal_positions = strategy.goal_positions
# print("strategy.weights", strategy.weights)
# print("Length of goal positions:", len(goal_positions))
# print(goal_positions)
task_id, msg = trade_api.goal_portfolio(goal_positions)
print(task_id, msg)