def initialize(context):
schedule_function(func=trade,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(),
half_days=True)
schedule_function(func=cancel,
time_rule=time_rules.market_close(minutes=5),
date_rule=date_rules.every_day(),
half_days=True)
schedule_function(func=reorder,
time_rule=time_rules.market_open(minutes=5),
date_rule=date_rules.every_day(),
half_days=True)
context.asserts = symbols('SPY')
context.bonds = symbol('SHY')
context.rebalance_date = 0
context.fired = False
context.rebalance_inteval = 'D' #'Q', #'D', #'M' #'Q' #'Y'
context.top_n_by_momentum = pd.Series()
#Choose X stocks out of portfolio of Y stocks- how many stocks to hold - top X by momentum
context.stocks = 1
#Lookback for momentum calculation
context.momentum_days = 60
#set at less than 1 to ensure no leverage
context.leverage_buffer = 0.99
#Set to 0 to reject any stocks with negative momentum, set to -1 to accept stocks with negative momentum
context.trend = 0.0
context.reorder_dict = {}
def initialize(context):
ws.send(msg_placeholder % "Simulation Start")
context.lookback = 252 # Period to calculate slope and drawdown
context.max_leverage = 1.0 # Leverage
context.profit_take = 1.96 # 95% of bollinger band
context.minimum_return = 0.1 # Enter if and only if annualized slope exceeds this level
context.max_drawdown = 0.10 # Avoid if too much drawdown
context.market_impact = 0.2 # Max order is 10% of market trading volume
context.weights = {} # Slope at time of entry
context.drawdown = {} # Drawdown at time of entry
context.shares = {} # Daily target share
schedule_function(func=stop_loss,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(minutes=30))
ws.send(
msg_placeholder %
"Execution of stop loss scheduled at 30 minutes after market open")
schedule_function(func=regression,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(minutes=50))
ws.send(
msg_placeholder %
"Execution of regression computation scheduled at 50 minutes after market open"
)
schedule_function(func=trade,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(minutes=100))
ws.send(
msg_placeholder %
"Execution of transaction planner scheduled at 100 minutes after market open"
)
for thirty_minute_interval in range(30, 391, 30):
schedule_function(
execute_transactions, date_rules.every_day(),
time_rules.market_open(minutes=thirty_minute_interval
)) # execute every 30 minutes
ws.send(msg_placeholder %
"Execution of transactions scheduled at every 30 minutes")
attach_pipeline(create_high_dollar_volume_pipeline(),
'top_dollar_volume')
ws.send(msg_placeholder %
"High Dollar Volume pipeline filter attached")
def initialize(context):
c = context
# Some Risk-On universes
core_etf = symbols('QQQ', 'XLP', 'TLT', 'IEF')
tact_etf = symbols('XLV', 'XLY', 'TLT', 'GLD')
VAAG12 = symbols('SPY', 'IWM', 'QQQ', 'VGK', 'EWJ', 'VWO', 'VNQ', 'GSG',
'GLD', 'TLT', 'LQD', 'HYG')
VAAG4 = symbols('SPY', 'VEA', 'VWO', 'BND')
c.RISK = VAAG4 # Which Riskasset universe to use?
alletf = symbols('QQQ', 'XLP', 'TLT', 'IEF', 'SPY', 'IWM', 'QQQ', 'VGK',
'EWJ', 'VWO', 'VNQ', 'GSG', 'GLD', 'TLT', 'LQD', 'HYG',
'XLV', 'XLY', 'GLD', 'VEA') # 'TLO' repleced by 'TLT'
# Some Risk Off (Cash) universes
c.CASH = symbols('SHY', 'IEF', 'LQD')
schedule_function(define_weights, date_rules.month_end(Month_Offset),
time_rules.market_open(minutes=64))
log.info('define_weights for last trading day of the month')
schedule_function(trade_sell, date_rules.month_end(Month_Offset),
time_rules.market_open(minutes=65))
log.info('trade_sell for last trading day of the month')
if TplusX > 0:
schedule_function(
trade_buy, date_rules.month_start(max(0,
TplusX - 1 - Month_Offset)),
time_rules.market_open(minutes=66))
log.info('trade_buy for month day ' +
str(max(0, TplusX - Month_Offset)))
else:
schedule_function(trade_buy, date_rules.month_end(Month_Offset),
time_rules.market_open(minutes=66))
log.info('trade_buy last trading day of the month')
stocks = core_etf + tact_etf
context.sell = {}
context.buy = {}
for stock in stocks:
context.sell[stock] = 0
context.buy[stock] = 0
context.difference_perc = 0.02
context.stops = {}
context.stoploss = 0.30
context.stoppedout = []
def get_date_rules(freq):
if freq == "day":
start_rule = date_rules.every_day()
end_rule = date_rules.every_day()
elif freq == "week":
start_rule = date_rules.week_start()
end_rule = date_rules.week_end()
elif freq == "month":
start_rule = date_rules.month_start()
end_rule = date_rules.month_end()
else:
start_rule = date_rules.every_day()
end_rule = date_rules.every_day()
date_rule = {"start": start_rule, "end": end_rule}
time_rule = {
"start": time_rules.market_open(),
"end": time_rules.market_close()
}
return date_rule, time_rule
def initialize(context):
"""
Called once at the start of the algorithm.
"""
c = context
c.etf_universe = StaticAssets(
symbols('XLY', 'XLP', 'XLE', 'XLF', 'XLV', 'XLI', 'XLB', 'XLK', 'XLU'))
c.alphas = pd.DataFrame()
# Rebalance every day, 1 hour after market open.
schedule_function(
rebalance,
date_rules.every_day(),
time_rules.market_open(hours=1),
)
# Record tracking variables at the end of each day.
schedule_function(
record_vars,
date_rules.every_day(),
time_rules.market_close(),
)
# Create our dynamic stock selector.
attach_pipeline(make_pipeline(context), 'pipeline')
attach_pipeline(make_pipeinit(context), 'pipeinit')
c.first_trading_day = True
c.factor_name_list = make_factor().keys()
def initialize(context):
set_benchmark(symbol("SPY"))
set_cancel_policy(EODCancel())
# set_max_order_count(1)
# set_max_leverage(1.0)
context.longSpread = False
context.shortSpread = False
context.longStock = symbol('SH')
context.shortStock = symbol('SPY')
#
# schedule_function(fnTripleMovingAverageCrossover,
# date_rules.every_day(),
# time_rules.market_open(minutes = 20))
schedule_function(
handle_data,
date_rules.every_day(),
# time_rules.market_close(minutes = 45))
time_rules.market_open(minutes=45))
# schedule_function(fnLiquidatePositions,
# date_rules.every_day())
# time_rules.market_open(minutes = 1))
schedule_function(recordVars, date_rules.every_day())
def initialize(context):
print("init")
context.asset = symbol('IBM')
context.natr_period = 10
context.long_multi = 0.2
context.short_multi = 0.2
context.pre_period = 1
schedule_function(rebalance, date_rules.every_day(), time_rules.market_open())
def initialize(context):
print("init")
context.asset = symbol('JD')
context.bop_window = 20
context.over_buy = 0.5
context.over_sell = -0.5
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open())
def initialize(context):
print('初始化策略')
context.asset = symbol('IBM')
context.adx_buy_line = 40
context.adx_sell_line = 20
context.adx_window = 7
schedule_function(rebalance, date_rules.every_day(), time_rules.market_open())
def initialize(context):
print("init")
context.asset = symbol('IBM')
context.adosc_window = 14
context.fast_period = 3
context.slow_period = 10
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open())
def initialize(context):
print("init")
context.asset = symbol('IBM')
context.cmo_window = 30
context.over_sell = -10
context.over_buy = 10
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open())
def initialize(context):
print("init")
context.asset = symbol('IBM')
context.bar_count = 60
context.short_window = 5
context.long_window = 20
context.ama_window = 10
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open())
def initialize_testing_algo(self, context):
set_commission(PerShare())
set_slippage(VolumeShareSlippage())
if self.syms is None:
self.syms = self.__validate_symbols__(self.ticker_syms)
self.portfolio_memory = PortfolioMemory(len(self.syms),
self.config.n_history)
schedule_function(self.testing_rebalance,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(minutes=1))
def initialize(context):
print("init")
context.asset = symbol('IBM')
context.T = 10
# 自定义的初始化函数
init_local_context(context)
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open())
def initialize(context):
print("init")
context.asset = symbol('IBM')
context.bar_count = 60
context.period = 26
# 设定AR的超卖线，低于它则买入
context.over_sell = 80
# 设定AR的超买线，高于它则卖出
context.over_buy = 150
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open())
def initialize(self, context):
# Schedule our rebalance function to run every day, after a 1 hour, when the market opens.
schedule_function(self.rebalance, date_rules.every_day(),
time_rules.market_open(hours=1, minutes=0))
# max_minute = 6 * 60 + 30
#
# for minute in range(30, max_minute, 60):
# schedule_function(self.rebalance, date_rules.every_day(), time_rules.market_open(minutes=minute))
my_pipe = self.make_pipeline()
attach_pipeline(my_pipe, "my_pipeline")
def initialize(context):
print("init")
context.asset = symbol('JD')
context.bar_count = 20
context.atr_period = 5
context.pre_period = 1
# 多头ATR的倍数
context.long_multi = 1.5
# 空头ATR的倍数
context.short_multi = 2.0
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open())
def initialize(context):
print('初始化策略')
context.asset = symbol('IBM')
# 止损线
context.portfolio_stop_loss = 0.95
# 止盈线
context.portfolio_stop_win = 1.05
# 是否已经触发止损
context.stop_loss_triggered = False
# 是否已经触发止盈
context.stop_win_triggered = False
schedule_function(rebalance, date_rules.every_day(), time_rules.market_open())
def initialize(context):
print("init")
context.asset = symbol('AAPL')
context.ema_fast_window = 5
context.ema_slow_window = 20
context.stop_loss_line = 5
context.stop_win_line = 5
# 记录净值的最大值，用于计算持仓的止损回撤
context.max_up_to_now = None
# 记录净值的最大值，用于计算持仓的止盈回撤
context.min_up_to_now = None
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open())
def initialize(context):
ws.send(msg_placeholder % "Simulation Start")
context.security = symbol(ticker)
context.model = RandomForestRegressor()
context.trained = False
context.lookback = 3
context.history_range = 400
schedule_function(create_model, date_rules.week_end(), time_rules.market_close(minutes=10))
schedule_function(trade, date_rules.every_day(), time_rules.market_open(minutes=minutes))
ws.send(msg_placeholder % "Execution of Training and Trading functions scheduled")
def initialize(context):
"""
Called once at the start of the algorithm.
"""
context.assets = [
(symbol('IEF'), 0.40), # bond etf
(symbol('QQQ'), 0.60)
] # Nasdaq etf
context.first_rebalance = 0
set_commission(commission.PerShare(cost=0.005, min_trade_cost=1))
# Rebalance every month, at market open.
schedule_function(my_rebalance, date_rules.month_end(days_offset=0),
time_rules.market_open(minutes=1))
def initialize(context):
"""
Called once at the start of the algorithm.
"""
# Rebalance every day, 1 hour after market open.
schedule_function(rebalance, date_rules.month_end(),
time_rules.market_open(hours=1))
# Record tracking variables at the end of each day.
schedule_function(
record_vars,
date_rules.every_day(),
time_rules.market_close(),
)
# Create our dynamic stock selector.
print('ATTACH PIPELINE')
attach_pipeline(make_pipeline(), 'pipeline')
print('PIPELINE ATTACHED')
def initialize(context):
print("init")
context.asset = symbol('IBM')
# 历史窗口大小
context.window_size = 10
# 用户自定义的变量，可以被handle_data使用，触发空头的range.当K1<K2时，多头相对容易被触发,当K1>K2时，空头相对容易被触发
context.K1 = 0.5
context.K2 = 0.3
# 止损线
context.portfolio_stop_loss = 0.95
# 止盈线
context.portfolio_stop_win = 1.05
# 是否已经触发止损
context.stop_loss_triggered = False
# 是否已经触发止盈
context.stop_win_triggered = False
schedule_function(rebalance, date_rules.every_day(), time_rules.market_open())
def initialize(context):
print("init")
context.asset = symbol('IBM')
# 快线回看周期为9
context.fastk_period = 9
# 慢线回看周期为3
context.slowk_period = 3
context.slowd_period = 3
# 使用简单平均
context.slowk_matype = 0
# 历史窗口
context.kdj_window = 100
# 超买信号线
context.over_buy_signal = 80
# 超卖信号线
context.over_sell_signal = 20
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open())
def initialize(context):
print("init")
context.asset = symbol('IBM')
# 止损线
context.portfolio_stop_loss = 0.9
# 止盈线
context.portfolio_stop_win = 1.1
# 是否已经触发止损
context.stop_loss_triggered = False
# 是否已经触发止盈
context.stop_win_triggered = False
# 买入线
context.lower_rsi = 30
# 卖出线
context.upper_rsi = 80
# 历史长度
context.rsi_window = 21
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open())
def initialize(context):
ws.send(msg_placeholder % "Simulation Start")
context.security = symbol(ticker)
context.long_threshold = 0
context.short_threshold = -0.06
context.no_shorts = no_shorts
if context.no_shorts:
set_long_only()
context.n_clusters = 9
context.ret_windows = [30]
context.window_lengths = [30, 90, 150, 210, 240]
context.lookback = 8 * 250
context.refresh_frequency = 30
context.use_classifier = use_clf
if context.use_classifier:
context.ret_buckets = {
"gen": [-0.04, 0, 0.04]
}
context.long_prob_lb = 0.5
context.short_prob_ub = 0.3
context.model = {}
context.return_projections = {}
context.price_projections = {}
context.bucket_probs = {}
context.days_traded = 0
context.last_traded_date = 0
schedule_function(rebalance, date_rule=date_rules.every_day(), time_rule=time_rules.market_open(hours=1))
ws.send(msg_placeholder % "Execution of clustering scheduled at 1 hour after market open")
def initialize(context):
print("init")
context.asset = symbol('IBM')
# 底仓价格
context.base_price = None
# 计算移动均值所需的历史bar数目，用户自定义的变量，可以被handle_data使用
context.sma_window_size = 24
# 确定当前price可否作为base_price的依据就是当前price是否小于20日均线*price_to_sma_threshold
context.price_to_sma_threshold = 1
# 止损线，用户自定义的变量，可以被handle_data使用
context.portfolio_stop_loss = 0.90
# 用户自定义变量，记录下是否已经触发止损
context.stop_loss_triggered = False
# 止盈线，用户自定义的变量，可以被handle_data使用
context.portfolio_stop_win = 1.10
# 用户自定义变量，记录下是否已经触发止盈
context.stop_win_triggered = False
# 设置网格的4个档位的买入价格（相对于基础价的百分比）
context.buy4, context.buy3, context.buy2, context.buy1 = 0.88, 0.91, 0.94, 0.97
# 设置网格的4个档位的卖出价格（相对于基础价的百分比）
context.sell4, context.sell3, context.sell2, context.sell1 = 1.8, 1.6, 1.4, 1.2
schedule_function(rebalance, date_rules.every_day(), time_rules.market_open())
def initialize(context):
context.asset = symbol('IBM')
SVM_train(context)
schedule_function(rebalance, date_rules.every_day(), time_rules.market_open())
def initialize(context):
print("init")
context.asset = symbol('IBM')
context.tema_window = 60
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open())
def initialize(context):
context.asset = symbol('IBM')
set_commission(commission.PerShare(cost=0.0001, min_trade_cost=0))
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open())
