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(self, window_length=100):
""" Slippage was messing up orders, setting to fixed corrected revisit this
"""
self.set_slippage(slippage.FixedSlippage())
self.buyplot = {sym: [pd.DataFrame()] for sym in sym_list}
self.plotmarks = {sym: [pd.DataFrame()] for sym in sym_list}
self.trade_log = {sym: 0 for sym in sym_list}
self.trade_dates = {sym: {'DATE': []} for sym in sym_list}
self.log = {
sym: {
'PAIR': [],
'ZSCORE': [],
'ACTION': [],
'SPREAD': []
}
for sym in sym_list
}
self.day_count = 0
self.dates = []
self.actions = {sym: {'ACTION': []} for sym in sym_list}
self.ratios = {sym: {'SPREAD': []} for sym in sym_list}
self.zscores = {sym: {'ZSCORE': []} for sym in sym_list}
self.spreads = {sym: [] for sym in sym_list}
self.gain_plot = {sym: {'GAIN': []} for sym in sym_list}
self.window_length = window_length
self.ols_transform = ols_transform(refresh_period=self.window_length,
window_length=self.window_length)
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
context.advisor = Advisor('bbands_ea',expert_advisor, context.universe)
# schedule agent weights updates
pass
def initialize(context):
context.start_date = trading_start
context.agent = agent
context.num_trials = 0
context.action = ACTION
context.values = deque(maxlen=21)
set_commission(commission.PerShare(cost=0.005, min_trade_cost=1.00))
set_slippage(slippage.FixedSlippage(0.00))
context.universe = universe_transform('2018-01-01')
schedule_function(rebalance_portfolio,
date_rules.week_start(days_offset=trading_day),
time_rules.market_open(hours=1))
#schedule_function(cancel_open_orders, date_rules.every_day(), time_rules.market_open())
schedule_function(prime_pipeline,
date_rules.week_start(days_offset=trading_day - 1),
time_rules.market_close())
context.Factor_weights = ENV.current_node.weights
context.weights = None
context.run_pipeline = True #We want to run stock selector immediately
context.weeks_since_rebalance = -1
attach_pipeline(make_pipeline(context), 'my_pipeline')
def initialize(context):
# Set benchmark to short-term Treasury note ETF (SHY) since strategy is dollar neutral
set_benchmark(symbol('AAPL'))
# Schedule our rebalance function to run at the end of each day.
# Modified the timing to 5 mins earlier than the market close to reduce the fail to book warnings
# schedule_function(my_rebalance, date_rules.every_day(), time_rules.market_close(minutes=5))
# Try to change it to open and see what happened -- HY
schedule_function(my_rebalance, date_rules.every_day(),
time_rules.market_open(minutes=5))
# Record variables at the end of each day.
# schedule_function(my_record_vars, date_rules.every_day(), time_rules.market_close())
# Get intraday prices today before the close if you are not skipping the most recent data
# schedule_function(get_prices,date_rules.every_day(), time_rules.market_close(minutes=10))
# Try to get the price data when the market is opening -- HY
# schedule_function(get_prices, date_rules.every_day(), time_rules.market_open(minutes=1))
# Set commissions and slippage to 0 to determine pure alpha
set_commission(commission.PerShare(cost=0, min_trade_cost=0))
set_slippage(slippage.FixedSlippage(spread=0))
# Number of quantiles for sorting returns for mean reversion
context.nq = 5
# Number of quantiles for sorting volatility over five-day mean reversion period
context.nq_vol = 3
# Create our pipeline and attach it to our algorithm.
my_pipe = make_pipeline()
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.securities = [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,
'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 initialize(self):
self.update = raw_input('Update Trends? [y/n]: ')
if self.update =='y':
for sym in sym_list:
self.get_trends(sym,self.update)
elif self.update =='n':
for sym in sym_list:
trend_df = pd.read_csv('trend_data/processed/'+sym_list[0]+'_trend_df.csv',index_col=0,parse_dates=True)
self.trend_dfs[sym] = trend_df
self.set_slippage(slippage.FixedSlippage())
self.dates = []
self.trends = {sym:[] for sym in sym_list}
self.zscores = {sym:[] for sym in sym_list}
self.zscores_s = {sym:[] for sym in sym_list}
self.chaikin_plot = {sym:[] for sym in sym_list}
self.prices = {sym:np.array([]) for sym in sym_list}
self.volume = {sym:np.array([]) for sym in sym_list}
self.highs = {sym:np.array([]) for sym in sym_list}
self.lows = {sym:np.array([]) for sym in sym_list}
self.day_count = 0
self.last_order = 0
self.rsi_plot = {sym:np.array([]) for sym in sym_list}
self.mfv = {sym:np.array([]) for sym in sym_list}
self.stops = {sym:[0,0] for sym in sym_list} #[take,stop]
self.buy_plot = {sym:[] for sym in sym_list}
self.sell_plot = {sym:[] for sym in sym_list}
self.atr_plot = {sym:{'profit':[],'loss':[]} for sym in sym_list}
self.gains = {sym:np.array([]) for sym in sym_list}
self.mfi_plot = {sym:np.array([]) for sym in sym_list}
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(self):
self.set_slippage(slippage.FixedSlippage())
self.dates = []
self.prices = {sym:np.array([]) for sym in feed}
self.vratio_plot = {sym:np.array([]) for sym in feed}
self.buy_plot = {sym:[] for sym in feed}
self.sell_plot = {sym:[] for sym in feed}
self.day_count = 0
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(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(algo, eps=1, window_length=5):
algo.stocks = STOCKS
algo.tickers = [symbol(ticker) for ticker in algo.stocks]
algo.m = len(algo.stocks)
algo.price = {}
algo.b_t = np.ones(algo.m) / algo.m
algo.eps = eps
algo.window_length = window_length
algo.set_commission(commission.PerShare(cost=0))
algo.set_slippage(slippage.FixedSlippage(spread=0))
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, asset):
context.set_commission(commission.PerShare(cost=0, min_trade_cost=0))
context.set_slippage(slippage.FixedSlippage(spread=0.0))
context.start_date = context.get_datetime()
context.asset = context.sid(asset)
context.counter = count()
# buy 1 first 4 days, close out all last day
context.order_amounts = np.repeat(0, len(self.equity_daily_bar_days))
context.order_amounts[0] = 2 # buy 2 lot on first day
context.order_amounts[69] = -1 # sell 1 lot on 69th day
context.order_amounts[-30] = -1 # close out final lot
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()
def initialize(self):
self.trend_df = self.get_trends()
self.set_slippage(slippage.FixedSlippage())
self.dates = []
self.trends = {sym: [] for sym in sym_list}
self.zscores = {sym: [] for sym in sym_list}
self.prices = {sym: np.array([]) for sym in sym_list}
self.highs = {sym: np.array([]) for sym in sym_list}
self.lows = {sym: np.array([]) for sym in sym_list}
self.day_count = 0
self.last_order = 0
self.stops = {sym: [0, 0] for sym in sym_list} #[take,stop]
self.buy_plot = {sym: [] for sym in sym_list}
self.sell_plot = {sym: [] for sym in sym_list}
self.atr_plot = {sym: {'profit': [], 'loss': []} for sym in sym_list}
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):
'''
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):
"""
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.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):
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(context):
# Quantopian backtester specific variables
set_slippage(slippage.FixedSlippage(spread=0))
set_commission(commission.PerShare(cost=0.01, min_trade_cost=1.0))
context.pairs = [
# STX, WDC
KalmanPairTrade(symbol('STX'),
symbol('WDC'),
initial_bars=300,
freq='1m',
delta=1e-3,
maxlen=300),
# CBI, JEC
KalmanPairTrade(symbol('CBI'),
symbol('JEC'),
initial_bars=300,
freq='1m',
delta=1e-3,
maxlen=300),
# MAS, VMC
KalmanPairTrade(symbol('MAS'),
symbol('VMC'),
initial_bars=300,
freq='1m',
delta=1e-3,
maxlen=300),
# JPM, C
KalmanPairTrade(symbol('JPM'),
symbol('C'),
initial_bars=300,
freq='1m',
delta=1e-3,
maxlen=300),
# AON, MMC
KalmanPairTrade(symbol('AON'),
symbol('MMC'),
initial_bars=300,
freq='1m',
delta=1e-3,
maxlen=300),
# COP, CVX
KalmanPairTrade(symbol('COP'),
symbol('CVX'),
initial_bars=300,
freq='1m',
delta=1e-3,
maxlen=300),
]
context.security_list = [
symbol('STX'),
symbol('WDC'),
symbol('CBI'),
symbol('JEC'),
symbol('MAS'),
symbol('VMC'),
symbol('JPM'),
symbol('C'),
symbol('AON'),
symbol('MMC'),
symbol('COP'),
symbol('CVX'),
]
weight = 1.8 / len(context.pairs)
for pair in context.pairs:
pair.leverage = weight
for minute in range(10, 390, 90):
for pair in context.pairs:
schedule_function(pair.trading_logic,
time_rule=time_rules.market_open(minutes=minute))
