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_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(): 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()
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 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": 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 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)
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 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))