def initialize(context): # Let's set a look up date inside our backtest to ensure we grab the correct security #set_symbol_lookup_date('2015-01-01') # Use a very liquid set of stocks for quick order fills context.symbol = symbol('SPY') #context.stocks = symbols(['TWX','AIG','PSX','EMC','YHOO','MDY','TNA','CHK','FXI', # 'PEP','SBUX','VZ','VWO','TWC','HAL','MDLZ','CAT','TSLA', # 'MU','PM','WYNN','MET',NOV BRK_B SNDK ESRX YELP]) #set_universe(universe.DollarVolumeUniverse(99.5, 100)) #set_benchmark(symbol('SPY')) # set a more realistic commission for IB, remove both this and slippage when live trading in IB set_commission(commission.PerShare(cost=0.014, min_trade_cost=1.4)) # Default slippage values, but here to mess with for fun. set_slippage( slippage.VolumeShareSlippage(volume_limit=0.25, price_impact=0.1)) # Use dicts to store items for plotting or comparison context.next_pred_price = {} # Current cycles prediction #Change us! context.history_len = 500 # How many days in price history for training set context.out_of_sameple_bin_size = 2 context.score_filter = -1000.0 context.action_to_move_percent = 0.0 # Register 2 histories that track daily prices, # one with a 100 window and one with a 300 day window add_history(context.history_len, '1d', 'price') context.i = 0
def initialize(context): # Register 2 histories that track daily prices, # one with a 100 window and one with a 300 day window add_history(100, '1d', 'price') add_history(300, '1d', 'price') context.i = 0
def initialize_magc(context): set_commission(commission.PerDollar(cost = COMMISSION)) add_history(20, '1d', 'price') add_history(60, '1d', 'price') context.i = 0 context.investment = False context.buy_price = 0
def initialize_bband(context): set_commission(commission.PerDollar(cost = COMMISSION)) context.i = 0 context.investment = False context.buy_price = 0 context.position = 0.0 add_history(20, '1d', 'price')
def initialize(context): # Let's set a look up date inside our backtest to ensure we grab the correct security #set_symbol_lookup_date('2015-01-01') # Use a very liquid set of stocks for quick order fills context.symbol = symbol('SPY') #context.stocks = symbols(['TWX','AIG','PSX','EMC','YHOO','MDY','TNA','CHK','FXI', # 'PEP','SBUX','VZ','VWO','TWC','HAL','MDLZ','CAT','TSLA', # 'MU','PM','WYNN','MET',NOV BRK_B SNDK ESRX YELP]) #set_universe(universe.DollarVolumeUniverse(99.5, 100)) #set_benchmark(symbol('SPY')) # set a more realistic commission for IB, remove both this and slippage when live trading in IB set_commission(commission.PerShare(cost=0.014, min_trade_cost=1.4)) # Default slippage values, but here to mess with for fun. set_slippage(slippage.VolumeShareSlippage(volume_limit=0.25, price_impact=0.1)) # Use dicts to store items for plotting or comparison context.next_pred_price = {} # Current cycles prediction #Change us! context.history_len = 500 # How many days in price history for training set context.out_of_sameple_bin_size = 2 context.score_filter = -1000.0 context.action_to_move_percent = 0.0 # Register 2 histories that track daily prices, # one with a 100 window and one with a 300 day window add_history(context.history_len, '1d', 'price') context.i = 0
def initialize(context): # Register 2 histories that track daily prices, # one with a 100 window and one with a 300 day window add_history(25, '1d', 'price') set_commission(commission.PerDollar(0.0003)) context.i = 0 context.pct = 0.1
def initialize(context): #register 2 histories to track daily prices add_history(100, '1d', 'price') add_history(300, '1d', 'price') context.i = 0 context.invested = False
def initialize(context): # Register 2 histories that track daily prices, # one with a 100 window and one with a 300 day window add_history(100, "1d", "price") add_history(300, "1d", "price") context.sym = symbol("AAPL") context.i = 0
def initialize(context): context.count = 252 #context.stock = symbol('CSCO') context.stock = symbol('MMM') add_history(bar_count=252, frequency='1d', field='price') #spy_data = history(252, '1d', 'price') context.other_stocks = {'AAPL'} add_history(bar_count=252, frequency='1d', field='price')
def initialize(context): context.stocks = ['VNQ', 'XLE', 'VTI','VEA', 'VWO', 'VIG', 'SCHP', 'MUB', 'LQD', 'EMB'] context.leverage = 2 add_history(950, '1d', 'price') context.x0 = 1.0*np.ones(len(context.stocks))/len(context.stocks) #context.i = 0 context.day_count = -1
def initialize(context): logging.debug('enter initialize') context.set_slippage(FixedSlippage()) context.set_commission(commission.PerTrade(cost=5)) context.LOW_RSI = initialize.low_RSI context.HIGH_RSI = initialize.high_RSI context.rsi_window = initialize.rsi_window add_history(context.rsi_window, '1d', 'price') context.i = 0 context.invested = False
def initialize(context): # Register 2 histories that track daily prices, # one with a 100 window and one with a 300 day window # mid 此处可以多次add_history(),但是,程序在历史数据管理中,对相同参数,只是大小不同的数据不会开辟多个panel # mid 而是会按最长的空间开辟buffer add_history(20, '1d', 'price') add_history(40, '1d', 'price') context.sym = symbol('AAPL') context.i = 0
def initialize(context, eps = 10, window_length = 50): #init context.stocks = STOCKS context.sids = SIDS #context.sids = [context.symbol(symb) for symb in context.stocks] context.m = np.size(STOCKS) context.price = {} context.b_t = np.ones(context.m)/float(context.m) context.prev_weights = np.ones(context.m)/float(context.m) context.eps = eps context.init = True context.days = 0 context.window_length = window_length add_history(window_length, '1d', 'price')
def initialize(self, context): add_history(200, '1d', 'price') set_slippage(slippage.FixedSlippage(spread=0.0)) set_commission(commission.PerShare(cost=0.01, min_trade_cost=1.0)) context.tick = 0 dp_data = self.data df_data = pd.DataFrame(index=dp_data.axes[1]) df_data['close'] = dp_data[:, :, 'close'] df_data['open'] = dp_data[:, :, 'open'] df_data['high'] = dp_data[:, :, 'high'] df_data['low'] = dp_data[:, :, 'low'] df_data['volume'] = dp_data[:, :, 'volume'] self.atr = atr_per_close(df_data, atrLen = self.atr_len) context.longstop = 0
def initialize(context, eps=10, window_length=50): #init context.stocks = STOCKS context.sids = SIDS #context.sids = [context.symbol(symb) for symb in context.stocks] context.m = np.size(STOCKS) context.price = {} context.b_t = np.ones(context.m) / float(context.m) context.prev_weights = np.ones(context.m) / float(context.m) context.eps = eps context.init = True context.days = 0 context.window_length = window_length add_history(window_length, '1d', 'price')
def initialize(context): add_history(190, '1d', 'price') add_history(365, '1d', 'price') context.syms = symbols('AAPL', 'AXP', 'BA', 'CAT', 'CSCO', 'CVX', 'DD', 'DIS', 'GE', 'GS', 'HD', 'IBM', 'INTC', 'JNJ', 'JPM', 'KO', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PFE', 'PG', 'TRV', 'UNH', 'UTX', 'V', 'VZ', 'WMT', 'XOM') context.stocks_to_long = 10 context.stocks_to_short = 10 context.day = 0 context.n = 0 context.weekCounter = 0 algo.set_commission(commission.PerShare(cost=0.15))
def initialize(context): # Register 2 histories that track daily prices, # one with a 100 window and one with a 300 day window context.N = 5 + 1 context.k = 0.7 add_history(context.N, '1d', 'close') add_history(context.N, '1d', 'open') add_history(context.N, '1d', 'high') add_history(context.N, '1d', 'low') context.i = 0 context.invested = False
def initialize(context): ''' Called once at the very beginning of a backtest (and live trading). Use this method to set up any bookkeeping variables. The context object is passed to all the other methods in your algorithm. Parameters context: An initialized and empty Python dictionary that has been augmented so that properties can be accessed using dot notation as well as the traditional bracket notation. Returns None ''' # Register history container to keep a window of the last 100 prices. add_history(10, '1d', 'price') # Turn off the slippage model set_slippage(slippage.FixedSlippage(spread=0.0)) # Set the commission model (Interactive Brokers Commission) set_commission(commission.PerShare(cost=0.0, min_trade_cost=0.0)) context.tick = 0
def initialize(context): # Hack to manually add the assets into the universe so they register with # the history container. for s in context.asset_finder.sids: context._current_universe.add(s) add_history(5, '1d', 'price')
def initialize(self, context): add_history(60, '1d', 'price') set_slippage(slippage.FixedSlippage(spread=0.0)) set_commission(commission.PerShare(cost=0.01, min_trade_cost=1.0)) context.tick = 0
def add_strat_history(context): for p in ['open_price', 'high', 'low', 'close_price', 'volume', 'price']: add_history(context.max_lookback, '1d', p)
def initialize(context): add_history(5, '1d', 'price') add_history(20, '1d', 'price') context.i = 0 context.stockCd = '035720'
def initialize(context): add_history(120, '1d', 'price') set_slippage(slippage.FixedSlippage(spread=0.0)) set_commission(commission.PerShare(cost=0.01, min_trade_cost=1.0)) context.tick = 0
def initialize(context): set_commission(commission.PerDollar(cost=0.00165)) add_history(5, '1d', 'price') add_history(20, '1d', 'price') context.i = 0 context.investment = False