def initialize(context):
# Turn off the slippage model
set_slippage(slippage.FixedSlippage(spread=0.0))
# Set the commission model
set_commission(commission.PerShare(cost=0.01, min_trade_cost=1.0))
context.day = -1 # using zero-based counter for days
context.set_benchmark(symbol('DIA'))
context.assets = []
print('Setup investable assets...')
for ticker in asset_tickers:
#print(ticker)
context.assets.append(symbol(ticker))
context.n_asset = len(context.assets)
context.n_portfolio = 40 # num mean-variance efficient portfolios to compute
context.today = None
context.tau = None
context.min_data_window = 756 # min of 3 yrs data for calculations
context.first_rebal_date = None
context.first_rebal_idx = None
context.weights = None
# Schedule dynamic allocation calcs to occur 1 day before month end - note that
# actual trading will occur on the close on the last trading day of the month
schedule_function(rebalance,
date_rule=date_rules.month_end(days_offset=1),
time_rule=time_rules.market_close())
# Record some stuff every day
schedule_function(record_vars,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_close())
def initialize(context):
"""
A function to define things to do at the start of the strategy
"""
# set the account currency, only valid for backtests
set_account_currency("USD")
# lot-size (mini-lot for most brokers)
context.lot_size = 1000
# universe selection
context.securities = [
symbol('FXCM:AUD/USD'),
symbol('FXCM:EUR/CHF'),
symbol('FXCM:EUR/JPY'),
symbol('FXCM:EUR/USD'),
symbol('FXCM:GBP/USD'),
symbol('FXCM:NZD/USD'),
symbol('FXCM:USD/CAD'),
symbol('FXCM:USD/CHF'),
symbol('FXCM:USD/JPY'),
]
# define strategy parameters
context.params = {
'indicator_lookback': 375,
'indicator_freq': '1m',
'buy_signal_threshold': 0.5,
'sell_signal_threshold': -0.5,
'SMA_period_short': 15,
'SMA_period_long': 60,
'RSI_period': 60,
'trade_freq': 30,
'leverage': 1,
'pip_cost': 0.00003
}
# variable to control trading frequency
context.bar_count = 0
context.trading_hours = False
# 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(fx=commission.PipsCost(cost=context.params['pip_cost']))
set_slippage(fx=slippage.FixedSlippage(0.00))
# set a timeout for trading
schedule_function(stop_trading, date_rules.every_day(),
time_rules.market_close(hours=0, minutes=31))
# call square off to zero out positions 30 minutes before close.
schedule_function(daily_square_off, date_rules.every_day(),
time_rules.market_close(hours=0, minutes=30))
def initialize(context):
# This code runs once, when the sim starts up
log.debug('scheduling rebalance and recording')
set_slippage(slippage.FixedSlippage(spread=0.0))
set_commission(commission.PerShare(cost=0, min_trade_cost=0))
schedule_function(func=rebalance,
date_rule=date_rules.month_end(),
time_rule=time_rules.market_close(minutes=15))
schedule_function(func=record_daily,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_close())
def initialize(context):
"""
Called once at the start of the algorithm.
"""
feature_num = 11
context.orders_submitted = False
large_num = 9999999
least_num = 0
context.n_components = feature_num
context.security = symbol(SYMBOL) # Trade SPY
set_benchmark(symbol(SYMBOL)) # Set benchmarks
context.model = DecisionTreeClassifier(criterion='entropy',
max_depth=feature_num,
random_state=0)
context.lookback = 350 # Look back 62 days
context.history_range = 350 # Only consider the past 400 days' history
context.threshold = 4.05
context.longprices = large_num
context.shortprices = least_num
set_long_only()
# Generate a new model every week
schedule_function(create_model, date_rules.week_end(),
time_rules.market_close(minutes=10))
"""
# Generate a new model every week
schedule_function(create_model1, date_rules.week_end(), time_rules.market_close(minutes=10))
"""
# Trade at the start of every day
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open(minutes=1))
def initialize(self, context):
if self.verbose: print("Starting the robo advisor")
# Populate Portfolios
self.all_portfolios = initialize_portfolio(self.verbose)
# Set Commission model
self.initialize_commission(country=self.country, platform=self.trading_platform)
# Set Slippage model
set_slippage(slippage.FixedSlippage(spread=0.0)) # assume spread of 0
# Schedule Rebalancing check
rebalance_check_freq = date_rules.month_end()
if (self.rebalance_freq == 'daily'): rebalance_check_freq = date_rules.every_day()
elif (self.rebalance_freq == 'weekly'): rebalance_check_freq = date_rules.week_end()
if self.verbose: print('Rebalance checks will be done %s' % self.rebalance_freq)
schedule_function(
func=self.before_trading_starts,
date_rule=rebalance_check_freq,
time_rule=time_rules.market_open(hours=1))
# record daily weights at the end of each day
schedule_function(
func=record_current_weights,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_close()
)
def initialize(context):
"""
API function to define things to do at the start of the strategy.
"""
# set strategy parameters
context.lookback_data = 60
context.lookback_long = 20
context.leverage = 2.0
context.profit_target = 1.0
# reset everything at start
daily_reset(context)
# create our universe
create_universe(context)
# schedule calculation at the end of opening range (30 minutes)
schedule_function(calculate_trading_metrics, date_rules.every_day(),
time_rules.market_open(hours=0, minutes=30))
# schedule entry rules
schedule_function(no_more_entry, date_rules.every_day(),
time_rules.market_open(hours=1, minutes=30))
# schedule exit rules
schedule_function(unwind, date_rules.every_day(),
time_rules.market_close(hours=0, minutes=30))
# set trading costs
set_commission(commission.PerShare(cost=0.005, min_trade_cost=0.0))
set_slippage(slippage.FixedSlippage(0.00))
def initialize(context):
""" Called once at the start of the algorithm. """
set_slippage(slippage.VolumeShareSlippage(volume_limit=0.025, price_impact=0.1))
set_commission(commission.PerShare(cost=0.01, min_trade_cost=1.00))
set_max_leverage(1.0)
# Rebalance every day, 1 hour after market open.
schedule_function(
context.my_rebalance,
date_rules.every_day(),
time_rules.market_open(hours=1)
)
# Close all positions every day, 30 minutes before market close.
schedule_function(
context.close_positions,
date_rules.every_day(),
time_rules.market_close(minutes=30)
)
# Create risk manager
context.risk_manager = RiskManager(context, daily_risk)
# Create our dynamic stock selector.
attach_pipeline(context.make_screener(), 'stock_screener')
def initialize(context):
"""
Called once at the start of the algorithm.
"""
feature_num = 11
context.orders_submitted = False
large_num = 9999999
least_num = 0
context.n_components = feature_num
context.security = symbol(SYMBOL) # Trade SPY
set_benchmark(symbol(SYMBOL)) # Set benchmarks
context.model = SVC(kernel='rbf', tol=1e-3, random_state=0, gamma=0.2, C=10.0, verbose=True) # 8.05 for SVM model
context.lookback = 350 # Look back 62 days
context.history_range = 350 # Only consider the past 400 days' history
context.threshold = 4.05
context.longprices = large_num
context.shortprices = least_num
set_long_only()
# Generate a new model every week
schedule_function(create_model, date_rules.week_end(), time_rules.market_close(minutes=10))
"""
# Generate a new model every week
schedule_function(create_model1, date_rules.week_end(), time_rules.market_close(minutes=10))
"""
# Trade at the start of every day
schedule_function(rebalance, date_rules.every_day(), time_rules.market_open(minutes=1))
def initialize(context):
"""
A function to define things to do at the start of the strategy
"""
# set the account currency, only valid for backtests
set_account_currency("USD")
# universe selection
context.short_dollar_basket = {
symbol('FXCM:AUD/USD'):1,
symbol('FXCM:EUR/USD'):1,
symbol('FXCM:GBP/USD'):1,
symbol('FXCM:NZD/USD'):1,
symbol('FXCM:USD/CAD'):-1,
symbol('FXCM:USD/CHF'):-1,
symbol('FXCM:USD/JPY'):-1,
}
# Call rebalance function on the first trading day of each month after 2.5 hours from market open
schedule_function(rebalance,
date_rules.month_start(days_offset=0),
time_rules.market_close(hours=2, minutes=30))
# set trading cost and slippage to zero
set_commission(fx=commission.PipsCost(cost=0.00))
set_slippage(fx=slippage.FixedSlippage(0.00))
def initialize(context):
"""
Called once at the start of the algorithm.
"""
set_slippage(slippage.FixedSlippage(spread=0.00))
set_commission(commission.PerShare(cost=0, min_trade_cost=0))
schedule_function(rebalance, TRADE_FREQ,
date_rules.every_day(),
time_rules.market_open(hours=1, minutes=30),
)
schedule_function(record_vars, date_rules.every_day(),
time_rules.market_close())
ml_pipeline = make_ml_pipeline(universe,
n_forward_days=N_FORWARD_DAYS,
window_length=TRAINING_PERIOD)
# Create our dynamic stock selector.
attach_pipeline(ml_pipeline, 'ml_model')
context.past_predictions = {}
context.ic = 0
context.rmse = 0
context.mae = 0
context.returns_spread_bps = 0
def initialize(context):
"""
function to define things to do at the start of the strategy
"""
# set the account currency, only valid for backtests
set_account_currency("USD")
# trading pound parity!
# this should work after the European sovereign crisis settled down
# and before the Brexit noise started (2012-2015)
context.x = symbol('FXCM:GBP/USD')
context.y = symbol('FXCM:EUR/USD')
context.leverage = 5
context.signal = 0
# Trade entry and exit when the z_score is +/- entry_z_score and exit_z_score respectively
context.entry_z_score = 2.0
context.exit_z_score = 0.5
# Lookback window
context.lookback = 720
# used for zscore calculation
context.z_window = 360
# Call strategy function after the London open every day
schedule_function(pair_trading_strategy,
date_rules.every_day(),
time_rules.market_open(hours=9,minutes=30))
# set trading cost and slippage to zero
set_commission(fx=commission.PipsCost(cost=0.00))
set_slippage(fx=slippage.FixedSlippage(0.00))
# square off towards to NYC close
context.trading_hours = False
# set a timeout for trading
schedule_function(stop_trading,
date_rules.every_day(),
time_rules.market_close(hours=0, minutes=31))
# call square off to zero out positions 30 minutes before close.
schedule_function(daily_square_off,
date_rules.every_day(),
time_rules.market_close(hours=0, minutes=30))
def initialize(context):
"""
A function to define things to do at the start of the strategy
"""
# universe selection
context.long_portfolio = [symbol('ASIANPAINT'), symbol('TCS')]
# Call rebalance function on the first trading day of each month after 2.5 hours from market open
schedule_function(rebalance, date_rules.month_start(days_offset=0),
time_rules.market_close(hours=2, minutes=30))
def initialize(context):
'''
Called once at the start of the strategy execution.
This is the place to define things to do at the start of the strategy.
'''
# set the account base currency and strategy parameters
set_account_currency('USD')
context.params = {
'verbose': False,
'leverage': 1,
'rebalance_freq': '15m',
'no_overnight_position': True,
'pip_cost': 0.00008,
'rollover_spread': 0.00,
'BBands_period': 1440,
'SMA_period_short': 150,
'SMA_period_long': 600,
'indicator_lookback': 1440, # max of all lookbacks!!!
'indicator_freq': '1m',
'buy_signal_threshold': 0.5,
'sell_signal_threshold': -0.5
}
# define the strategy instruments universe
context.universe = [
symbol('FXCM:AUD/USD'),
symbol('FXCM:EUR/USD'),
symbol('FXCM:NZD/USD'),
symbol('FXCM:USD/CAD'),
symbol('FXCM:USD/CHF'),
]
context.ccy_universe = [
'AUD', 'CAD', 'CHF', 'EUR', 'GBP', 'JPY', 'NZD', 'USD'
]
# function to schedule roll-overs, at 5 PM EST or 9 PM UTC (3 hours before midnight)
schedule_function(compute_rollovers, date_rules.every_day(),
time_rules.market_close(hours=3, minutes=0))
# set up cost structures, we assume a $1 per $10K all-in cost
set_commission(fx=commission.PipsCost(cost=context.params['pip_cost']))
set_slippage(fx=slippage.FixedSlippage(spread=0.00))
# variables to track signals and target portfolio
context.signals = dict((security, 0) for security in context.universe)
context.weights = dict((security, 0) for security in context.universe)
# Call rebalance function, see below under standard helper functions to modify
rebalance_scheduler(context)
# make the back-test lighter
context.perf_tracker.keep_transactions = False
context.perf_tracker.keep_orders = False
def initialize(context):
"""
Called once at the start of the algorithm.
"""
feature_num = 11
context.orders_submitted = False
large_num = 9999999
least_num = 0
context.n_components = feature_num
context.security = symbol(SYMBOL) # Trade SPY
set_benchmark(symbol(SYMBOL)) # Set benchmarks
context.model2 = SVC(kernel='rbf',
tol=1e-3,
random_state=0,
gamma=0.2,
C=10.0,
verbose=True) # 8.05 for SVM model
context.model3 = KNeighborsClassifier(
n_neighbors=feature_num, p=3, metric='minkowski') # 7.05 for model
context.model = DecisionTreeClassifier(criterion='entropy',
max_depth=feature_num,
random_state=0)
context.model4 = RandomForestClassifier(criterion='entropy',
n_estimators=feature_num,
random_state=1,
n_jobs=2) # 5.2 for randomforest
context.model1 = LogisticRegression(random_state=0,
solver='lbfgs',
multi_class='multinomial')
context.modellist = {
'SVM': context.model2,
'KNeighbors': context.model3,
'DecisionTree': context.model,
'RandomForest': context.model4,
'LogisticRegression': context.model1
}
context.lookback = 350 # Look back 62 days
context.history_range = 350 # Only consider the past 400 days' history
context.threshold = 4.05
context.longprices = large_num
context.shortprices = least_num
set_long_only()
# Generate a new model every week
schedule_function(create_model, date_rules.week_end(),
time_rules.market_close(minutes=10))
"""
# Generate a new model every week
schedule_function(create_model1, date_rules.week_end(), time_rules.market_close(minutes=10))
"""
# Trade at the start of every day
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open(minutes=1))
def initialize(context):
'''
A function to define things to do at the start of the strategy
'''
# The context variables can be accessed by other methods
context.params = {'lookback': 12, 'percentile': 0.05, 'min_volume': 1E7}
# Call rebalance function on the first trading day of each month
schedule_function(strategy, date_rules.month_start(),
time_rules.market_close(minutes=1))
# Set up the pipe-lines for strategies
attach_pipeline(make_strategy_pipeline(context), name='strategy_pipeline')
def initialize(context):
__build_factor_basic_strategy(context)
attach_pipeline(make_pipeline(context), 'my_pipeline')
schedule_function(rebalance,
date_rules.week_end(days_offset=0),
half_days=True)
# record my portfolio variables at the end of day
schedule_function(func=recording_statements,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_close(),
half_days=True)
print("initialize over")
pass
def initialize(context):
# Define Symbol
context.security = symbol('MSFT')
# Define standard devation threshold
context.std_dev_threshold = 0.6
schedule_function(enter_position,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open())
schedule_function(square_off,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_close(minutes=1))
def initialize(context):
context.security = sid(8554) # Trade SPY
context.model = RandomForestRegressor()
context.lookback = 3 # Look back 3 days
context.history_range = 400 # Only consider the past 400 days' history
# Generate a new model every week
schedule_function(create_model, date_rules.week_end(),
time_rules.market_close(minutes=10))
# Trade at the start of every day
schedule_function(trade, date_rules.every_day(),
time_rules.market_open(minutes=1))
def rebalance_scheduler(context):
'''
function to schedule a rebalancing trade.
The rebalancing is done based on the weights determined in the strategy core
for each of the instruments defined in the trading universe.
'''
context.use_handle_data = False
rebalance_freq = context.params.get('rebalance_freq',None)
if rebalance_freq is None:
return
if context.params['verbose']:
print('setting up {} scheduler'.format(rebalance_freq))
if rebalance_freq == 'monthly':
schedule_function(rebalance,
date_rules.month_start(days_offset=0),
time_rules.market_open(hours=5, minutes=30))
elif rebalance_freq == 'weekly':
schedule_function(rebalance,
date_rules.week_start(days_offset=0),
time_rules.market_open(hours=5, minutes=30))
elif rebalance_freq == 'daily':
schedule_function(rebalance,
date_rules.every_day(),
time_rules.market_open(hours=5, minutes=30))
elif rebalance_freq.endswith('m'):
try:
context.trade_freq = int(rebalance_freq.split('m')[0])
print('trade freq {}'.format(context.trade_freq))
context.bar_count = 0
context.use_handle_data = True
except:
raise ValueError('Invalid minute frequency')
elif rebalance_freq.endswith('h'):
try:
context.trade_freq = int(rebalance_freq.split('h')[0])*60
print('trade freq {}'.format(context.trade_freq))
context.bar_count = 0
context.use_handle_data = True
except:
raise ValueError('Invalid hourly frequency')
else:
raise ValueError('Un-recognized rebalancing frequency')
if context.params['no_overnight_position']:
schedule_function(square_off,
date_rules.every_day(),
time_rules.market_close(hours=3, minutes=30))
def initialize(context):
# slippage model
set_slippage(slippage.FixedSlippage(spread=0))
# stock universe - list of tickers
securities = ['AAPL', 'ADBE', 'AIG', 'AMGN', 'BA']
# change string list of tickers into asset list using symbol function
context.sec_symbols = [symbol(s) for s in securities]
print(len(context.sec_symbols))
# schedule functions
schedule_function(trade_market_open, date_rules.every_day(),
time_rules.market_open())
schedule_function(cancel_open, date_rules.every_day(),
time_rules.market_close())
def initialize(context):
# This function runs once when sim starts.
# Put your `schedule_function()`, `set_slippage`, and `set_commission` calls here.
# FV is the five-year root symbol
# TU is the ten-year root symbol
context.asset_A = symbol('AAPL')
context.asset_B = symbol('MSFT')
# Keep state if we have crossed the ENTRY_THRESHOLD
context.position_initiated = False
# Run rebal function every day 15min before
# close.
schedule_function(func=rebal,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_close(minutes=15))
def initialize(context):
'''
Called once at the start of the strategy execution.
This is the place to define things to do at the start of the strategy.
'''
# set the account base currency and strategy parameters
set_account_currency('USD')
context.params = {
'verbose': False,
'leverage': 2,
'rebalance_freq': 'monthly',
'no_overnight_position': False,
'pip_cost': 0.0001,
'rollover_spread': 0.00,
'positions': 2,
'lookback': 26 * 6
}
# define the strategy instruments universe
context.universe = [
symbol('FXCM:AUD/USD'),
symbol('FXCM:EUR/CHF'),
symbol('FXCM:EUR/JPY'),
symbol('FXCM:EUR/USD'),
symbol('FXCM:GBP/JPY'),
symbol('FXCM:GBP/USD'),
symbol('FXCM:NZD/USD'),
symbol('FXCM:USD/CAD'),
symbol('FXCM:USD/CHF'),
symbol('FXCM:USD/JPY'),
]
context.ccy_universe = [
'AUD', 'CAD', 'CHF', 'EUR', 'GBP', 'JPY', 'NZD', 'USD'
]
# function to schedule roll-overs, at 5 PM EST or 9 PM UTC (3 hours before midnight)
schedule_function(compute_rollovers, date_rules.every_day(),
time_rules.market_close(hours=3, minutes=0))
# set up cost structures, we assume a $1 per $10K all-in cost
set_commission(fx=commission.PipsCost(cost=context.params['pip_cost']))
set_slippage(fx=slippage.FixedSlippage(spread=0.00))
# Call rebalance function, see below under standard helper functions to modify
rebalance_scheduler(context)
def initialize(context):
# Schedule our rebalance function to run at the start of
# each week, when the market opens.
# schedule_function(
# my_rebalance,
# date_rules.week_start(),
# time_rules.market_open()
# )
# Record variables at the end of each day.
schedule_function(
my_record_vars,
date_rules.every_day(),
time_rules.market_close()
)
# Create our pipeline and attach it to our algorithm.
my_pipe = make_pipeline(20, 2)
attach_pipeline(my_pipe, 'my_pipeline')
def initialize(context):
'''
A function to define things to do at the start of the strategy
'''
# universe selection
context.universe = [symbol('NIFTY-I'), symbol('BANKNIFTY-I')]
# define strategy parameters
context.params = {
'indicator_lookback': 375,
'indicator_freq': '1m',
'buy_signal_threshold': 0.5,
'sell_signal_threshold': -0.5,
'SMA_period_short': 15,
'SMA_period_long': 60,
'RSI_period': 300,
'BBands_period': 300,
'ADX_period': 120,
'trade_freq': 15,
'leverage': 1
}
# variable to control trading frequency
context.bar_count = 0
# variables to track target portfolio
context.weights = dict((security, 0.0) for security in context.universe)
# 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))
# create the list of experts as well as the agent controlling them
expert1 = Advisor('bbands_ea', expert_advisor_1, context.universe)
expert2 = Advisor('maxover_ea', expert_advisor_2, context.universe)
expert3 = Advisor('rsi_ea', expert_advisor_3, context.universe)
expert4 = Advisor('sup_res_ea', expert_advisor_4, context.universe)
context.agent = Agent([expert1, expert2, expert3, expert4], 0.35, method=1)
# schedule agent weights updates
schedule_function(update_agent_weights, date_rules.week_start(),
time_rules.market_close())
def initialize(context):
"""
Called once at the start of the algorithm.
"""
context.n_longs = N_LONGS
context.n_shorts = N_SHORTS
context.min_positions = MIN_POSITIONS
context.universe = assets
set_slippage(slippage.FixedSlippage(spread=0.00))
set_commission(commission.PerShare(cost=0, min_trade_cost=0))
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open(hours=1, minutes=30))
schedule_function(record_vars, date_rules.every_day(),
time_rules.market_close())
pipeline = compute_signals()
attach_pipeline(pipeline, 'signals')
def initialize(context):
attach_pipeline(make_pipeline(), 'data_pipeline')
context.technical_indicator_states = {}
context.window_length = 7
context.benchmark = deque(maxlen=context.window_length)
context.benchmark_asset = symbol('SPY')
context.benchmark_assets = symbols('QQQ', 'SPY')
#context.classifier = RandomForestClassifier() # Use a random forest classifier
context.classifier = DecisionTreeClassifier(max_depth=5, max_leaf_nodes=10)
# Clasifier training data
context.features = [
'RSI', 'EMA', 'MACD', 'SMA_5', 'SMA_10', 'bb_lower', 'bb_middle',
'bb_upper'
]
context.response = ['Class']
context.X = pd.DataFrame(
columns=context.features) # Independent, or input variables
context.Y = pd.DataFrame(
columns=context.response) # Dependent, or output variable
context.prediction = {} # Stores most recent prediction
context.tick = 0
context.total_buy = 0
context.positions = None
context.position_adjustment_days = 5 # Number of days to wait before adjusting positions
context.min_data_points = 500
context.max_data_points = 1500
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open(minutes=1))
schedule_function(record_vars, date_rules.every_day(),
time_rules.market_close())
set_benchmark(symbol('SPY'))
# Turn off the slippage model
set_slippage(slippage.FixedSlippage(spread=0.0))
# Set the commission model (Interactive Brokers Commission)
set_commission(commission.PerShare(cost=0.01, min_trade_cost=1.0))
def initialize(self, context):
"""
initialize() is called once at the start of the program. Any one-time
startup logic goes here.
"""
# An assortment of securities from different sectors:
# MSFT, UNH, CTAS, JNS, COG sid(5061),
context.security_list = [symbol("MSFT"), symbol('UNH'), symbol('CTAS'), symbol('JNS'), symbol('COG')]
#context.security_list = [sid(5061), sid(7792), sid(1941), sid(24556), sid(1746)]
# Rebalance every Monday (or the first trading day if it's a holiday)
# at market open.
schedule_function(self.rebalance,
date_rules.week_start(days_offset=0),
time_rules.market_open())
# Record variables at the end of each day.
schedule_function(self.record_vars,
date_rules.every_day(),
time_rules.market_close())
def initialize(context):
# Ethereum contract
context.contract = ContractHandler()
# Set Quandl API key and import fundamentals data from quandl
quandl.ApiConfig.api_key = environ['QUANDL_API_KEY']
context.fundamentals_earnings = quandl.get_table('SF0', qopts={'columns': ['ticker']}).\
to_numpy()
# Get blacklist of companies which returns a list of SIDs
blacklist = context.contract.getBlacklist()
set_do_not_order_list(blacklist)
# Dictionary of stocks and their respective weights
context.stock_weights = {}
# Count of days before rebalancing
context.days = 0
# Number of sectors to go long in
context.sect_numb = 2
# Sector mappings
context.sector_mappings = get_sectors()
# Rebalance weekly on the first day of the week at market open
schedule_function(rebalance,
date_rule=date_rules.week_start(),
time_rule=time_rules.market_open())
schedule_function(record_positions,
date_rule=date_rules.week_start(),
time_rule=time_rules.market_close())
# Register pipeline
fundamental_df = make_pipeline(context)
attach_pipeline(fundamental_df, 'fundamentals')
def initialize(context):
# -----------------------------------------------------------------------------------------------
c = context
c.STOCKS = symbols('QQQ')
c.BONDS = symbols('TLT', 'IEF')
c.LEV = 1.00
c.wt = {}
c.A = symbol('SLV')
c.B = symbol('GLD')
c.C = symbol('XLI')
c.D = symbol('XLU')
c.MKT = symbol('QQQ')
c.VOLA = 126
c.LB = 1.00
c.BULL = 1
c.COUNT = 0
c.OUT_DAY = 0
c.RET_INITIAL = 80
# -----------------------------------------------------------------------------------------------
schedule_function(daily_check, date_rules.every_day(),
time_rules.market_open(minutes=140))
schedule_function(record_vars, date_rules.every_day(),
time_rules.market_close())
def initialize (context) :
# Algorithm parameters are defined here
if ENVIRONMENT == 'RESEARCH':
context.benchmark = symbols('SPY')
context.risk_free = symbols('SHY')
context.cash_proxy = symbols('SHY')
elif ENVIRONMENT == 'ZIPLINE' or ENVIRONMENT == 'IDE':
context.benchmark = symbol('SPY')
context.risk_free = symbol('SHY')
context.cash_proxy = symbol('SHY')
context.algo_transforms = [(['price'], ROCP, ['mom_63'], 62),
(['price'], ROCP, ['mom_21'], 20),
(['price'], average_historic_volatility, ['hvol_20'], 63, 20),
(['price'], SMA, ['short_mavg'], 21),
(['price'], SMA, ['long_mavg'], 63)
]
if ENVIRONMENT == 'ZIPLINE' or ENVIRONMENT == 'IDE':
context.strategies = {
'strategy1': {
'strat_wt' : 1.0,
'strat_type' : 'SIGNAL',
'alloc_type' : 'EQWT',
'portfolios' : [
symbols('VHT', 'VGT', 'VCR', 'IBB', 'EDV', 'VB', 'VAW',
'TLT')
],
'weights' : [1.0],
'n_top' : [1],
'zscore_data' : ['+mom_63', '+mom_21', '-hvol_20'],
'zscore_weights' : [0.7, 0.0, 0.3],
# 'filter' : [lambda x,y: (x > y) & (x > 0), 'mom_21', ('mom_21', context.risk_free)]
# 'filter' : []
'filter' : [lambda x,y: x > y, 'mom_63', ('mom_63', context.risk_free)],
'buy_trigger' : [lambda x,y: (x > y) and (context.buy_flag==False),
'short_mavg', 'long_mavg'],
'sell_trigger' : [lambda x,y : (x < y) and (context.buy_flag==True),
'short_mavg', 'long_mavg']
}
}
elif ENVIRONMENT == 'RESEARCH':
context.strategies = {
'strategy1': {
'strat_wt' : 1.0,
'strat_type' : 'SIGNAL',
'alloc_type' : 'EQWT',
'portfolios' : [
[symbols('VHT'), symbols('VGT'), symbols('VCR'),
symbols('IBB'), symbols('EDV'), symbols('VB'),
symbols('VAW'), symbols('TLT')]
],
'weights' : [1.0],
'n_top' : [1],
'zscore_data' : ['+mom_63', '+mom_21', '-hvol_20'],
'zscore_weights' : [0.7, 0.0, 0.3],
# 'filter' : [lambda x,y: (x > y) & (x > 0), 'mom_21',
# ('mom_21', context.risk_free)]
# 'filter' : []
'filter' : [lambda x,y: x > y, 'mom_63', ('mom_63', context.risk_free)],
'buy_trigger' : [lambda x,y: (x > y) and (context.buy_flag==False),
'short_mavg', 'long_mavg'],
'sell_trigger' : [lambda x,y : (x < y) and (context.buy_flag==True),
'short_mavg', 'long_mavg']
}
}
# have to set this manually 'til I figure how to generate it programmatically from above parameters
context.max_lookback = 120 # need additional data for recursive indicators like EMA, historic_vol etc.
# set the appropriate time_rule offsets (hours, minutes) for order processing
context.order_time_rule = time_rules.market_close(hours=0, minutes=1)
context.threshold = 0.0 # only BUY/SELL if asset QTY changes by threshold%
set_commission(commission.PerTrade(cost=0.0))
set_slippage(slippage.FixedSlippage(spread=0))
#######################################################################################
# THE FOLLOWING MUST ALWAYS BE INCLUDED AND MUST PRECEDE ANY SCHEDULED FUNCTIONS!
# include this check to make sure that the algo parameters are formatted as required
check_algo_parameters (context)
# create a set of all the symbols used by the algorithm
create_symbol_list(context)
initialize_strat_variables(context)
if ENVIRONMENT != 'IDE':
add_strat_history(context)
#######################################################################################
schedule_function(generate_strat_data,
# date_rule=date_rules.month_start(days_offset=0),
date_rule=date_rules.month_start(days_offset=0),
time_rule=time_rules.market_open(hours=0, minutes=1),
half_days=True)
# schedule_function_by_interval(context, test_signal,
# date_rule=date_rules.every_day(),
# time_rule=time_rules.market_open(hours=0, minutes=5),
# half_days=True,
# freq=1)
# the 'freq' parameter is added to the norma schedule_function routine
# to enable intervals of 'freq' periods - 1,2,3,..... months
schedule_function_by_interval(context, rebalance,
date_rule=date_rules.month_start(days_offset=0),
time_rule=time_rules.market_open(hours=0, minutes=5),
half_days=True,
freq=1)
schedule_function_by_interval(context, handle_orders,
date_rule=date_rules.month_start(days_offset=0),
time_rule=time_rules.market_close(hours=0, minutes=15),
half_days=True,
freq=1)
def initialize(context):
# these must ALWAYS be present!
context.transforms = []
context.algo_rules = []
context.max_lookback = 63
context.outstanding = {} # orders which span multiple days
context.raw_data = {}
#############################################################
# set the following parameters as required
context.show_positions = True
# select records to show in algo.show_records()
context.show_records = True
# replace cash_proxy with risk_free if cantec.allow_cash_proxY_replacement is True
# and cash_proxy price is <= average cash_proxy price over last context.cash_proxy_lookback days
context.allow_cash_proxy_replacement = False
context.cash_proxy_lookback = 43 # must be <= context.max_lookback
context.update_metrics = False
# to calculate Sharpe ratio
context.calculate_SR = False
context.SR_lookback = 63 # must be <= context.max_lookback
context.SD_factor = 0
# position only changed if percentage change > threshold
context.threshold = 0.01
# the following can be changed
context.market_proxy = symbols('VFINX')[0]
context.risk_free = symbols('VFISX')[0]
# context.market_proxy = symbols('SPY')[0]
# context.risk_free = symbols('SHY')[0]
set_commission(commission.PerTrade(cost=10.0))
context.leverage = 1.0
#################################################################
# configure strategies
context.rebalance_interval = 3 # set interval to n = no of periods (default: months)
# if you want to change default period, change schedule reallocate below
# Strategy 1
rs0003 = StrategyParameters(
context,
name='rs0003',
portfolios=[symbols('NHMAX', 'FAGIX', 'VFIIX')],
# portfolios=[symbols('HYD','HYD','MBB')],
portfolio_allocation_modes=['EW'],
portfolio_allocation_kwargs=[{}],
security_weights=[None],
portfolio_allocation_formulas=[None],
scoring_methods=['RS'],
scoring_factors=[{
'+momentum': 1.0
}],
n_tops=[1],
protection_modes=['BY_RULE'],
protection_rules=['smma_rule'],
protection_formulas=[None],
cash_proxies=[symbol('VFISX')],
# cash_proxies=[symbol('SHY')],
strategy_allocation_mode='FIXED',
portfolio_weights=[1.0],
strategy_allocation_formula=None,
strategy_allocation_rule=None)
Configurator(context, define_transforms, define_rules, strategies=[rs0003])
############################
# configure algorithm
algo = Algo(context,
strategies=[rs0003.strategy],
allocation_model=AllocationModel(context,
mode='EW',
weights=None,
formula=None),
regime=None)
###########################################################################################################
# generate algo data every day at close
schedule_function(algo.update_data, date_rules.every_day(),
time_rules.market_close())
# daily functions to handle GTC orders
schedule_function(algo.check_for_unfilled_orders, date_rules.every_day(),
time_rules.market_close())
schedule_function(algo.fill_outstanding_orders, date_rules.every_day(),
time_rules.market_open())
if context.update_metrics:
# calculate metrics every day
schedule_function(algo.update_metrics, date_rules.every_day(),
time_rules.market_close())
if context.show_positions:
schedule_function(algo.show_positions,
date_rules.month_start(days_offset=0),
time_rules.market_open())
if context.show_records:
# show records every day
# edit the show_records function to include records required
schedule_function(algo.show_records, date_rules.every_day(),
time_rules.market_close())
schedule_function(algo.rebalance, date_rules.month_end(days_offset=2),
time_rules.market_open())
