def test_context():
r = random.random()
path = '../../output/tests/storage{:.6f}.pic'.format(r)
context = model.Context()
context.load_store(path)
assert len(context.storage) == 0
context.storage['me'] = 1.0
context.save_store(path)
context = model.Context()
context.load_store(path)
assert context.storage['me'] == 1.0
def test_livetrade():
props = {'symbol': 'rb1801.SHF', 'strategy_no': 46}
tapi = RealTimeTradeApi(trade_config)
ins = EventLiveTradeInstance()
props.update(data_config)
props.update(trade_config)
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)
ds.subscribe(props['symbol'])
ins.run()
time.sleep(3)
ins.stop()
ins.save_results(result_dir_path)
do_analyze()
def run_strategy():
"""
回测模式
"""
props = {"symbol": '510300.SH',
"start_date": 20160201,
"end_date": 20171231,
"fast_ma_length": 10,
"slow_ma_length": 20,
"bar_type": "1d",
"benchmark" : '000300.SH',
"init_balance": 50000}
tapi = BacktestTradeApi()
ins = EventBacktestInstance()
props.update(data_config)
props.update(trade_config)
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)
ins.run()
ins.save_results(folder_path=result_dir_path)
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 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 test_backtest():
props = {
"symbol": "rb1710.SHF",
"start_date": 20170710,
"end_date": 20170730,
"bar_type": "1M", # '1d'
"init_balance": 2e4
}
tapi = BacktestTradeApi()
ins = EventBacktestInstance()
props.update(data_config)
props.update(trade_config)
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)
ins.run()
ins.save_results(result_dir_path)
do_analyze()
def test_double_ma():
prop_file_path = fileio.join_relative_path("etc/backtest.json")
print prop_file_path
prop_file = open(prop_file_path, 'r')
props = json.load(prop_file)
enum_props = {'bar_type': common.QUOTE_TYPE}
for k, v in enum_props.iteritems():
props[k] = v.to_enum(props[k])
# strategy = CtaStrategy()
strategy = DoubleMaStrategy()
gateway = BarSimulatorGateway()
data_service = RemoteDataService()
context = model.Context()
context.register_data_api(data_service)
context.register_gateway(gateway)
context.register_trade_api(gateway)
backtest = EventBacktestInstance()
backtest.init_from_config(props, strategy, context=context)
# trade.run()
backtest.run()
report = backtest.generate_report(output_format="")
def run_strategy():
'''
universe可以自己定义吗，就是从我筛选的一组股票里面挑选
把universe改成symbol，然后值设置成一系列代码的字符串，用逗号隔开
:return:
'''
if is_backtest:
"""
回测模式
"""
props = {
"symbol": '600519.SH',
# "symbol": '002050.SZ',
# "benchmark": '002050.SZ',
"benchmark": '000300.SH',
"start_date": 20170101,
"end_date": 20171219,
"fast_ma_length": 3,
"slow_ma_length": 8,
"live_ma_length": 34,
"bar_type": "1d", # '1d'
"init_balance": 50000}
tapi = BacktestTradeApi()
ins = EventBacktestInstance()
else:
"""
实盘/仿真模式
"""
props = {'symbol': '600519.SH',
"fast_ma_length": 3,
"slow_ma_length": 5,
"live_ma_length": 34,
'strategy.no': 1062}
tapi = RealTimeTradeApi(trade_config)
ins = EventLiveTradeInstance()
props.update(data_config)
props.update(trade_config)
ds = RemoteDataService()
strat = TripleMaStrategy()
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)
def test_alpha_strategy_dataview():
dv_subfolder_name = 'test_dataview'
save_dataview(sub_folder=dv_subfolder_name)
dv = DataView()
fullpath = fileio.join_relative_path('../output/prepared',
dv_subfolder_name)
dv.load_dataview(folder=fullpath)
props = {
"benchmark": "000300.SH",
# "symbol": ','.join(dv.symbol),
"universe": ','.join(dv.symbol),
"start_date": dv.start_date,
"end_date": dv.end_date,
"period": "month",
"days_delay": 0,
"init_balance": 1e9,
"position_ratio": 0.7,
}
gateway = DailyStockSimGateway()
gateway.init_from_config(props)
context = model.Context()
context.register_gateway(gateway)
context.register_trade_api(gateway)
context.register_dataview(dv)
risk_model = model.FactorRiskModel()
signal_model = model.FactorRevenueModel_dv()
cost_model = model.SimpleCostModel()
risk_model.register_context(context)
signal_model.register_context(context)
cost_model.register_context(context)
signal_model.register_func('my_factor', my_factor)
signal_model.activate_func({'my_factor': {}})
cost_model.register_func('my_commission', my_commission)
cost_model.activate_func({'my_commission': {'myrate': 1e-2}})
strategy = DemoAlphaStrategy(risk_model, signal_model, cost_model)
# strategy.active_pc_method = 'equal_weight'
# strategy.active_pc_method = 'mc'
strategy.active_pc_method = 'factor_value_weight'
bt = AlphaBacktestInstance_dv()
bt.init_from_config(props, strategy, context=context)
bt.run_alpha()
bt.save_results(fileio.join_relative_path('../output/'))
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 run_strategy():
if is_backtest:
"""
回测模式
"""
props = {
"symbol": '600519.SH',
"start_date": 20170101,
"end_date": 20171104,
"fast_ma_length": 5,
"slow_ma_length": 15,
"bar_type": "1d", # '1d'
"init_balance": 50000
}
tapi = BacktestTradeApi()
ins = EventBacktestInstance()
else:
"""
实盘/仿真模式
"""
props = {
'symbol': '600519.SH',
"fast_ma_length": 5,
"slow_ma_length": 15,
'strategy.no': 1062
}
tapi = RealTimeTradeApi(trade_config)
ins = EventLiveTradeInstance()
props.update(data_config)
props.update(trade_config)
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)
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():
start_date = 20150501
end_date = 20171030
index = '399975.SZ'
ds = RemoteDataService()
ds.init_from_config(data_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
}
props.update(data_config)
props.update(trade_config)
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 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 = {
"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)
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)
#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 run_strategy():
dv = EventDataView()
dv.load_dataview(dataview_dir_path)
tapi = BacktestTradeApi()
ins = EventBacktestInstance()
strat = DoubleMaStrategy()
pm = PortfolioManager()
context = model.Context(dataview=dv, # data_api=ds,
trade_api=tapi, instance=ins,
strategy=strat, pm=pm)
ins.init_from_config(backtest_props)
ins.run()
ins.save_results(folder_path=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 = {
"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 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)
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', 'strategy.no': 46}
tapi = RealTimeTradeApi(trade_config)
ins = EventLiveTradeInstance()
props.update(data_config)
props.update(trade_config)
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)
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)
#
trade_api = RealTimeTradeApi({
"remote.trade.address": "tcp://1.85.40.235:58086",
"remote.trade.username": '******',
#"remote.trade.address": "tcp://127.0.0.1:58086",
#"remote.trade.username": "******",
"remote.trade.password": "******"
})
trade_api.init_from_config({
"remote.trade.address": "tcp://1.85.40.235:58086",
"remote.trade.username": "******",
#"remote.trade.address": "tcp://127.0.0.1:58086",
#"remote.trade.username": "******",
"remote.trade.password": "******"
})
context = model.Context(data_api=None, trade_api=trade_api, instance=None, strategy=None, pm=None)
#
for order in orders:
if order['order_qty'] == 0:
continue
if order['order_place_method'] == "TWAP":
offer_start_time = order['offer_start_time'].strftime("%H:%M:%S")
offer_stop_time = order['offer_stop_time'].strftime("%H:%M:%S")
elif order['order_place_method'] == "DMA":
now = datetime.now()
_date = now.date()
_time = now.time()
_time = max ( _time, order['offer_start_time'] )
offer_start_time = _time.strftime("%H:%M:%S")
def run_strategy():
if is_backtest:
"""
回测模式
"""
ds = RemoteDataService()
ds.init_from_config(data_config)
symbol_list = ds.query_index_member(index, start_date, end_date)
# symbol_list = ['600887.SH']
# symbol_list = sample(symbol_list, 20)
print(symbol_list)
# add the benchmark index to the last position of symbol_list
symbol_list.append(index)
props = {"symbol": ','.join(symbol_list),
"holding_Count": 15,
"start_date": start_date,
"end_date": end_date,
"bar_type": "1d", # '1d'
"init_balance": 300000,
"commission_rate": 2E-4}
tapi = BacktestTradeApi()
ins = EventBacktestInstance()
else:
"""
实盘/仿真模式
"""
props = {'symbol': '600519.SH',
"fast_ma_length": 5,
"slow_ma_length": 15,
'strategy.no': 1062}
tapi = RealTimeTradeApi(trade_config)
ins = EventLiveTradeInstance()
props.update(data_config)
props.update(trade_config)
ds = RemoteDataService()
strat = RNNStrategy()
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)
ta = ana.EventAnalyzer()
ds = RemoteDataService()
ds.init_from_config(data_config)
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']]
temp = pd.read_csv(result_dir_path + '/trades.csv')
symbols = set(temp['symbol'].unique())
print(symbols)
ta.do_analyze(result_dir=result_dir_path, selected_sec=list(symbols))
