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):
# 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):
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):
context.i = 0
context.sym = symbol('GS')
set_commission(commission.PerDollar(cost=0.00165))
def initialize(context):
context.i = 0
context.sym = symbol('GS')
set_commission(commission.PerDollar(cost=0.00165)) # 매수/매도 시 수수료 0.165% 발생 -> 총 0.33%의 비용 발생
def initialize(context):
# PerDollar는 원래는 달러를 기준으로 수수료를 설정하는 것이지만 우리는 원 단위로 생각하면 되기 때문에
set_commission(commission.PerDollar(cost=0.00165))
context.i = 0
context.sym = symbol('GS')
context.hold = False
def initialize(context): #initial asset and trading pay
context.sym = symbol('SKHynix')
set_commission(commission.PerDollar(cost=0.00165))
def initialize(context):
set_commission(commission.PerDollar(cost=0.00165))
def initialize(context):
set_commission(commission.PerDollar(cost=0.00165))
add_history(5, '1d', 'price')
add_history(20, '1d', 'price')
context.i = 0
context.investment = False