Hi,
I am unable to print anything on the blueshift platform. The code freezes after all the backtest is done at 100% but doesnt show up anything after that. Any one else experienced that ?
I am trying to execute a simple strategy based on moviing average but the way it looks no order is getting executed. Sharing the code below
What i want to do is, buy if the price is higher than 3,5 and 13 bar average; sell if the price is lower than 3,5 and 13 bar average and square off at the end of the day.
from blueshift_library.technicals.indicators import bollinger_band, ema
from blueshift.finance import commission, slippage
from blueshift.api import( symbol,
order_target_percent,
set_commission,
set_slippage,
get_datetime,
schedule_function,
date_rules,
time_rules
)
def initialize(context):
"""
A function to define things to do at the start of the strategy
"""
# universe selection
context.securities = [symbol('RELIANCE'),
symbol('DIVISLAB'),
symbol('SUNPHARMA'),
symbol('MARUTI'),
symbol('AMARAJABAT'),
symbol('BPCL'),
symbol('BAJFINANCE'),
symbol('HDFCBANK'),
symbol('ASIANPAINT'),
symbol('TCS')]
# define strategy parameters
context.params = {'indicator_lookback':15,
'indicator_freq':'1m',
'buy_signal_threshold':0.5,
'sell_signal_threshold':-0.5,
'SMA_period_short':5,
'SMA_period_mid':8,
'SMA_period_long':13,
'BBands_period':300,
'trade_freq':1,
'leverage':.7}
# 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=20.0, min_trade_cost=0.0))
set_slippage(slippage.FixedSlippage(2.00))
# if the day closes quare off
schedule_function(square_off, date_rules.every_day(), time_rules.market_close(hours=0,minutes = 5))
def handle_data(context, data):
"""
A function to define things to do at every bar
"""
# Compute 5,8.13 bar moving average
# if the current price higher than 5,8,13 then buy, if it goes lower than 5, sell
print(get_datetime())
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 generate_signals(context, data):
"""
A function to define define the signal generation
"""
try:
price_data = data.history(context.securities, 'close',
context.params['indicator_lookback'],
context.params['indicator_freq'])
except:
return
for security in context.securities:
px = price_data.loc[:,security].values
context.signals[security] = signal_function(px, context.params)
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
def signal_function(px, params):
"""
The main trading logic goes here, called by generate_signals above
"""
ind5 = ema(px, params['SMA_period_short'])
ind8 = ema(px, params['SMA_period_mid'])
ind13 = ema(px, params['SMA_period_long'])
last_px = px[-1]
if (last_px>=ind5) & (last_px>=ind8) & (last_px>=ind13):
return 1
elif (last_px<ind5) & (last_px<=ind8) & (last_px<=ind13):
return -1
else:
return 0
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 square_off(context,data):
for sec in context.securities:
order_target_percent(sec,0)
