from blueshift_library.technicals.indicators import bollinger_band, doji from zipline.finance import commission, slippage from zipline.api import(symbol, get_datetime, order_target_percent, schedule_function, date_rules, time_rules, attach_pipeline, pipeline_output, set_commission, set_slippage, get_open_orders, cancel_order, ) def initialize(context): # schedule_function(run_strategy, # date_rules.every_day(), # time_rules.market_open(hours = 9, minutes = 16), # ) # context.securities = [symbol('HINDPETRO'),symbol('ZEEL'), symbol("AXISBANK"), symbol("INFRATEL")] context.securities = [symbol('AXISBANK'), symbol("BPCL")] context.params = {'indicator_lookback':375, 'indicator_freq':'1m', 'buy_signal_threshold':0.5, 'sell_signal_threshold':-0.5, 'ROC_period_short':30, 'ROC_period_long':120, 'BBands_period':300, 'trade_freq':5, 'leverage':2} # variable to control trading frequency context.bar_count = 0 # variables to track signals and target portfolio context.signals = dict((security,0) for security in context.securities) context.target_position = dict((security,0) for security in context.securities) # set trading cost and slippage to zero set_commission(commission.PerShare(cost=0.0, min_trade_cost=0.0)) set_slippage(slippage.FixedSlippage(0.00)) def handle_data(context, data): """ A function to define things to do at every bar """ context.bar_count = context.bar_count + 1 if context.bar_count < context.params['trade_freq']: return # time to trade, call the strategy function context.bar_count = 0 run_strategy(context, data) def run_strategy(context, data): """ A function to define core strategy steps """ generate_signals(context, data) generate_target_position(context, data) rebalance(context, data) def rebalance(context,data): """ A function to rebalance - all execution logic goes here """ for security in context.securities: order_target_percent(security, context.target_position[security]) def generate_target_position(context, data): ''' A function to define target portfolio ''' num_secs = len(context.securities) weight = round(1.0/num_secs,2)context.params['leverage'] for security in context.securities: if context.signals[security] > context.params['buy_signal_threshold']: context.target_position[security] = weight elif context.signals[security] < context.params['sell_signal_threshold']: context.target_position[security] = -weight else: context.target_position[security] = 0 def generate_signals(context, data): """ A function to define define the signal generation """ try: price_data = data.history(context.securities, ['open','high','low','close'], context.params['indicator_lookback'], context.params['indicator_freq']) except: return for security in context.securities: px = price_data.minor_xs(security) context.signals[security] = signal_function(px, context.params, context.signals[security]) def signal_function(px, params, last_signal): """ The main trading logic goes here, called by generate_signals above """ ind1 = doji(px) upper, mid, lower = bollinger_band(px.close.values,params['BBands_period']) last_px = px.close.values[-1] dist_to_upper = 100(upper - last_px)/(upper - lower) if ind1 > 0 and dist_to_upper < 30: return 1 elif ind1 > 0 and dist_to_upper > 70: return -1 else: return last_signal
i, i want to see trading logs of this strategy. plz assist me what modification needs to be done in this code to getting trading logs. thanks and regards Akshay
Unfortunately, this place does not make it easy to copy and paste a properly formatted peiece of code. And looking at code without proper formatting can be maddening!
Can you pleas send your query to blueshift-support@quantinsti.com with a properly formatted code file attached, and also adding explanation of what you mean by 'trade logs'.
sure sir