def initialize(context):
# List of Major World Indices Yahoo tickers - https://finance.yahoo.com/world-indices
with open('tickers.pickle', 'rb') as handle:
indices_tickers = pickle.load(handle) # load in tickers from pickle
os.remove('tickers.pickle') # delete tickers pickle file
context.indices = [symbol(ticker) for ticker in indices_tickers
] # create list of ticker symbols
context.days_of_correction = [
0 for _ in indices_tickers
] # create list of days since correction has begun
set_benchmark(symbol('^GSPC'))
set_commission(
commission.PerTrade(cost=15.0)
) # commission for IBKR, UK for Stocks, ETF's & Warrants - https://www.interactivebrokers.co.uk/en/index.php?f=39753&p=stocks1
'''-----------------------------PARAMETERS TO BE OPTIMISED--------------------------------'''
context.correction_margin = 0.1 # the percentage drawdown considered a correction
print('Drawdown percentage range from peak for correction: ' +
str(round(context.correction_margin * 100)) + '%')
context.upturn_coefficient = 0.24 # the ratio upturn from trough indicating end of correction - used in calculate_required_upturn function
print('Upturn Coefficient: ' + str(round(context.upturn_coefficient, 4)))
context.min_gain = 0.05 # the highest the price can be from peak and still be considered for ordering
print('Mainimum potential gain from peak to be considered: ' +
str(round(context.min_gain * 100, 0)) + '%')
context.state_threshold = -10.0 # 0.0002 threshold between bull and bear markets
print('Market state threshhold: ' +
str(round(context.state_threshold, 4)) + '%')
def initialize(self, context):
context.set_slippage(slippage.FixedSlippage(spread=0))
context.set_commission(commission.PerTrade(cost=0))
context.target_duration = 30 # How long to test strategies for
context.num_parents = 4 # Number of parents each new species has
context.generation_size = 5 # How many species to generate per generation
context.frames = deque(maxlen=70) # Store past frames for past prices
context.species = [] # All species are stored here
self.new_generation(
context) # Create a new generation (starts testing on 70th day)
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(self,stockA,stockB, window_length=14):
self.spreads = []
self.capital_base=10000
self.invested = 0
self.window_length = window_length
self.instant_fill=True #Pairslog results are built using EOD data. (I assumed same day of signal)
self.stockA=stockA
self.stockB=stockB
self.posSizeA=self.capital_base
self.posSizeB=self.capital_base #I assumed 50% margin for both long and short trades
self.set_commission(commission.PerTrade(cost=0)) #Pairslog results do not consider commissions.
self.set_slippage(slippage.FixedSlippage(spread=0.0)) #Pairslog results are built using EOD data and do not consider liquidity factor.
self.txnumber=0
self.trades = pd.DataFrame()