def initialize(context):
# Ethereum contract
#context.contract = ContractHandler()
# Get blacklist of sectors which returns a list of codes
#context.blacklist = context.contract.getBlacklist()
# for testing purpose without Ethereum exclude Defense, Beer & Alcohol, Tabacco and Coal
context.blacklist = [26.0, 4.0, 5.0, 29.0]
# Only run get_fundamentals when necessary based on the rebalance function
context.initial = True
# 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(key='code')
context.ticker_sector_dict = get_sector_code()
# TODO: Update this accordingly (weekly?)
# Rebalance monthly on the first day of the month at market open
schedule_function(rebalance,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open())
def initialize(context):
# 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 = {
101.0: "Basic Materials",
102.0: "Consumer Cyclical",
103.0: "Financial Services",
104.0: "Real Estate",
205.0: "Consumer Defensive",
206.0: "Healthcare",
207.0: "Utilites",
308.0: "Communication Services",
309.0: "Energy",
310.0: "Industrials",
311.0: "Technology",
}
# Rebalance monthly on the first day of the month at market open
schedule_function(rebalance, date_rule=date_rules.month_start(), time_rule=time_rules.market_open())
def initialize(context):
set_commission(commission.PerDollar(cost=0.0055))
# context.strategy_name = "FOF_{mdd}"
context.mdd = context.namespace["mdd"]
context.fof_code = context.namespace["fof_code"]
context.save2mysql = context.namespace.get("save_method", 0)
context.i = 0
schedule_function(scheduled_rebalance,
date_rules.month_start(days_offset=0),
time_rules.market_open())
context.init_value = pd.Series() # 记录自最近一次调仓以来，组合内各基金所达到的最大净值
context.adjust_date = 0 # 记录距离最近一次季调的时间
context.stop_date = 0 # 记录距离最近一次止损的时间
# 基金池
stock = [
'000478.OF', '050002.OF', '110006.OF', '161120.OF', '000217.OF',
"501018.OF", '000071.OF', '070031.OF', '000342.OF', 'B00.IPE',
"HSCI.HI", '037.CS', "em_bond", "reit", "SPTAUUSDOZ.IDC", '096001.OF',
'SPX.GI', '000905.SH'
]
context.stocks = []
for sym in stock:
context.stocks.append(symbol(sym))
# 指数与基金的对应关系 fixed
index = [
"HSCI.HI", '037.CS', "EM_BOND", "REIT", "SPTAUUSDOZ.IDC", "000905.SH",
"B00.IPE", "NDX.GI", 'SPX.GI'
]
index_etf = ["000071.OF","161120.OF","000342.OF",\
"070031.OF","000217.OF","000478.OF",\
"501018.OF","160213.OF",'096001.OF']
context.df_index = pd.DataFrame(
data=index_etf,
index=index,
)
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):
"""
Initialization and trading logic
"""
# Set comission and slippage
if enable_commission:
comm_model = PerDollar(cost=commission_pct)
else:
comm_model = PerDollar(cost=0.0)
set_commission(comm_model)
if enable_slippage:
slippage_model = VolumeShareSlippage(
volume_limit=slippage_volume_limit, price_impact=slippage_impact)
else:
slippage_model = FixedSlippage(spread=0.0)
# Used only for progress output
context.last_month = initial_portfolio
# Store index membership
context.index_members = pd.read_csv('./data/sp500_members.csv',
index_col=0,
parse_dates=[0])
# Schedule rebalance monthly
schedule_function(func=rebalance,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open())
def initialize(context):
print("{}:inside initialize".format(get_datetime()))
schedule_function(rebalance, date_rule=date_rules.month_start(),
time_rule=time_rules.market_open())
context.frequency = 120
context.loop_count = 0
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):
'''
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 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):
context.i = int(input("Start index?: "))
# 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=.005, min_trade_cost=1.0))
context.set_slippage(slippage.VolumeShareSlippage())
context.schedule_function(func=run,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(),
half_days=True,
calendar=None)
def initialize(context):
pipe = Pipeline()
attach_pipeline(pipe, 'my_pipeline')
pb_ratios = PriceBookRatio()
pipe.add(pb_ratios, 'pb_ratio')
# If Zipline has trouble pulling the default benchmark, try setting the
# benchmark to something already in your bundle
# set_benchmark(symbol("change this to a symbol in your data"))
# Rebalance monthly
schedule_function(rebalance, date_rules.month_start())
context.set_commission(commission.PerShare(cost=.0075, min_trade_cost=1.0))
def initialize(context):
# ETFs and target weights for a balanced and hedged portfolio
context.securities = {
'SPY': 0.25,
'TLT': 0.3,
'IEF': 0.3,
'GLD': 0.075,
'DBC': 0.075
}
# Schedule rebalance for once a month
schedule_function(rebalance, date_rules.month_start(),
time_rules.market_open())
# Set up a benchmark to measure against
context.set_benchmark(symbol('SPY'))
def initialize(context):
# add_history(LOOKBACK_PERIOD, '1d', 'price')
#set_max_leverage(1.25)
context.i = 0
set_commission(commission.PerDollar(cost=0.006))
context.fof_code = context.namespace["fof_code"]
context.save2mysql = context.namespace.get("save_method", 0)
schedule_function(scheduled_rebalance,
date_rules.month_start(days_offset=0),
time_rules.market_open())
context.weights = pd.Series()
stock = [house,oil,Emerging_market_bonds,America_stock,Hongkong_stock,\
gold,"HSCI.HI", "SPX.GI",'B00.IPE',"em_bond","reit",
"SPTAUUSDOZ.IDC"]
context.stocks = []
for i in stock:
context.stocks.append(symbol(i))
def initialize(context):
# weights for rebalance
context.weights = {}
# set up the factor models
vol_factor = FactorModel('vol_factor', VolatilityFactor, 0.05, False)
skew_factor = FactorModel('skew_factor', SkewFactor, 0.05, True)
pca_factor = FactorModel('pca_factor', PCAFactor, 0.05, False)
mom_factor = FactorModel('mom_factor', MomentumFactor, 0.05, True)
context.agent = Agent([vol_factor, skew_factor, pca_factor, mom_factor],
0.35,
method=1)
# set up pipeline - for efficient computation
attach_pipeline(factor_pipe(context), name='factor_pipe')
# our rebalabce function
schedule_function(run_strategy, date_rules.month_start(days_offset=0),
time_rules.market_open(hours=2, minutes=30))
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())
Pipeline algorithm that buys recent winners and sells recent losers.
Specifically:
- rank stocks by their performance over the past MOMENTUM_WINDOW days
- ignore very recent performance by excluding the last RANKING_PERIOD_GAP
days from the ranking window (as commonly recommended for UMD)
- buy the TOP_N_DECILES deciles of highest performing stocks and short the TOP_N_DECILES
deciles of lowest performing stocks
- rebalance the portfolio according to REBALANCE_INTERVAL
"""
MOMENTUM_WINDOW = 252
RANKING_PERIOD_GAP = 22
TOP_N_DECILES = 5
REBALANCE_INTERVAL = date_rules.month_start()
class Momentum(CustomFactor):
"""
Calculates the percent change in close price over MOMENTUM_WINDOW,
excluding the most recent RANKING_PERIOD_GAP periods.
"""
inputs = [USEquityPricing.close]
window_length = MOMENTUM_WINDOW
def compute(self, today, assets, out, close):
out[:] = (close[-RANKING_PERIOD_GAP] - close[0]) / close[0]
def make_pipeline():
def initialize(context):
context.record_rank = {}
context.save2mysql = 0
# 模型训练参数
context.horizon = 6
context.percent = 5
context.model_name = 'adaBoost'
context.rolling = 1 # 是否为滚动，0为不滚动
context.ishistory = 0 #是否用历史分类。0为不使用
context.train_period = 12 * 1
context.i = 0
set_slippage(slippage.FixedSlippage(spread=0.00))
set_commission(commission.PerDollar(cost=0.00325))
month = 21
week = 5
rf = 0.025
c = 252
if context.namespace["fund_type"] == 'stock':
benchmark = '000300.SH'
fund_type = 1
elif context.namespace["fund_type"] == 'hybrid':
benchmark = '000300.SH'
fund_type = 2
elif context.namespace["fund_type"] == 'bond':
benchmark = '037.CS'
fund_type = 3
# 选择基金池和benchmark
ben_sid = symbol(benchmark).sid
df_stock = df.query("update_time == update_time.max() and type_code=={"\
"type}".format(type = fund_type))
# 基金900 开头的不需要，专门去掉
df_stock = df_stock[df_stock["fund_code"] < '900000'].fund_code + ".OF"
sfilt = SpecificAssets(symbols(
benchmark, *tuple(df_stock))) # 使用到的数据，包括基金和对应benchmark指数
sample_filt = SpecificAssets(symbols(
*tuple(df_stock))) # 只包含基金，因为评级不应该包含benchmark，故在事后screen去掉benchmark
# 只包含基金，因为评级不应该包含benchmark，故在事后screen去掉benchmark
## 16个因子指标
down_sharpe = Down_sharpe(window_length=9 * month, rf=rf, c=c, mask=sfilt)
drawdown = Drawdown(window_length=9 * month, mask=sfilt)
dvar = Down_variation(window_length=9 * month, rf=rf, c=c, mask=sfilt)
mean_r = Average_return(window_length=9 * month, mask=sfilt)
lasting = Lasting(window_length=9 * month,
mask=sfilt,
time=6 * month,
percent=20)
emr = Emr(window_length=9 * month, mask=sfilt, time=6 * month)
rlt_var = Negative_variation(window_length=9 * month, c=c, mask=sfilt)
mcv_in = Mcv_in(window_length=9 * month,
rf=rf,
c=c,
mask=sfilt,
time=week,
index_sid=ben_sid)
rank_stable = Rank_stable(window_length=9 * month,
mask=sfilt,
time=6 * month)
select_time, select_stock = CL(window_length=9 * month,
rf=rf,
mask=sfilt,
time=week,
index_sid=ben_sid)
hit_rate = Hit_rate(window_length=10 * month,
mask=sfilt,
time=6 * month,
time_window=9 * month,
index_sid=ben_sid)
value_at_risk = Value_at_risk(window_length=9 * month,
mask=sfilt,
q_value=5,
time=6 * month)
beta_in = Beta_in(window_length=9 * month,
mask=sfilt,
rf=rf,
time=week,
index_sid=ben_sid)
bias_in = Bias_in(window_length=9 * month, mask=sfilt, time=126)
ir = Information_ratio(window_length=9 * month,
mask=sfilt,
time=1,
index_sid=ben_sid)
# 预测因变量Y
_ry = Lag_Return(window_length=context.horizon * month, mask=sfilt)
_sp = Lag_Sharpe(window_length=context.horizon * month, mask=sfilt)
pipe = Pipeline(columns={
"dhsp": down_sharpe,
"drwd": drawdown,
"dvar": dvar,
"mean_r": mean_r,
"lasting": lasting,
"emr": emr,
"rlt_var": rlt_var,
"mcv_in": mcv_in,
"rank_stable": rank_stable,
"select_time": select_time,
"select_stock": select_stock,
"hit_rate": hit_rate,
"value_at_risk": value_at_risk,
"beta_in": beta_in,
"bias_in": bias_in,
"ir": ir,
"_ry": _sp
},
screen=sample_filt)
attach_pipeline(pipe, 'my_pipeline')
set_max_leverage(1.1)
schedule_function(rebalance,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open())
# 初始化记录变量
context.rank_score = pd.Series()
context.f = pd.Panel()
context.f_dict = {}
context.reb_flag = False
context.B = {}
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)