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):
"""
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 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):
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):
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):
context.assets = {
'a1': symbol('AAPL'),
'a2': symbol('GE'),
'a3': symbol('WMT')
}
schedule_function(cte_mix_handle, date_rules.every_day())
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("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('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):
context.assets = {
'a1': symbol('AAPL'),
'a2': symbol('GE'),
'a3': symbol('WMT')
}
context.has_ordered = False
schedule_function(buy_and_hold, date_rules.every_day())
def initialize(context):
context.assets = {
'a1': symbol('AAPL'),
'a2': symbol('GE'),
'a3': symbol('WMT')
}
context.time_window = 20
schedule_function(volatility_handle, date_rules.every_day())
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(context):
context.assets = {
'a1': symbol('AAPL'),
'a2': symbol('GE'),
'a3': symbol('WMT')
}
context.long_period = 30
context.short_period = 7
schedule_function(ma_crossover, date_rules.every_day())
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(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(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('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):
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):
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):
attach_pipeline(make_pipeline(), 'my_pipeline')
# Rebalance each day. In daily mode, this is equivalent to putting
# `rebalance` in our handle_data, but in minute mode, it's equivalent to
# running at the start of the day each day.
schedule_function(rebalance, date_rules.every_day())
# Explicitly set the commission/slippage to the "old" value until we can
# rebuild example data.
# github.com/quantopian/zipline/blob/master/tests/resources/
# rebuild_example_data#L105
context.set_commission(commission.PerShare(cost=.0075, min_trade_cost=1.0))
context.set_slippage(slippage.VolumeShareSlippage())
def initialize(context):
spy_tlt = StaticAssets([symbol('AAPL'), symbol('IBM')])
print(symbol('AAPL'), symbols('AAPL'))
# Create an arbitrary factor
price = USEquityPricing.close.latest
pipe = Pipeline(screen=spy_tlt, columns={
'price': price,
})
attach_pipeline(pipe, 'my_pipeline')
schedule_function(rebalance, date_rules.every_day())
pass
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):
"""
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')
# 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):
ws.send(msg_placeholder % "Simulation Start")
pipe = Pipeline()
attach_pipeline(pipe, "volume_pipeline")
# 100 day average dollar volume factor
dollar_volume = AverageDollarVolume(window_length=100)
pipe.add(dollar_volume, "100_day_dollar_volume")
# filter out only the top stocks by dollar volume
high_dollar_volume = dollar_volume.percentile_between(99, 100)
pipe.set_screen(high_dollar_volume)
# set the global variables
context.dev_multiplier = 2
context.max_notional = 1000000
context.min_notional = -1000000
context.days_traded = 0
ws.send(msg_placeholder % "Pipeline filter attached")
schedule_function(func=choose_and_order,
date_rule=date_rules.every_day())
