from quantopian.pipeline import Pipeline
from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.factors import SimpleMovingAverage
from quantopian.pipeline.filters.morningstar import Q1500US
from quantopian.pipeline.factors import AnnualizedVolatility
from quantopian.pipeline.factors.morningstar import MarketCap
from quantopian.pipeline import factors, filters, classifiers
Market_Cap=(MarketCap > 1000000000)
def lowvolport():
return filters.make_us_equity_universe(
target_size=50,
rankby=factors.AnnualizedVolatility(window_length=90),
mask=Market_Cap,
)
def initialize(context):
# Schedule our rebalance function to run at the start of each week.
schedule_function(my_rebalance, date_rules.week_start(), time_rules.market_open(hours=1))
# Record variables at the end of each day.
schedule_function(my_record_vars, date_rules.every_day(), time_rules.market_close())
# Create our pipeline and attach it to our algorithm.
my_pipe = make_pipeline()
attach_pipeline(my_pipe, 'my_pipeline')
def make_pipeline():
"""
Create our pipeline.
"""
# Base universe set to the Q1500US.
base_universe = Q1500US()
Market_Cap = (MarketCap > 1000000000)
# Filter to select securities to long.
volatility_bottom = AnnualizedVolatility(inputs=[USEquityPricing.close], window_length=90, mask=base_universe)
volatility_bottom_50=volatility_bottom.bottom(50)
# Filter for all securities that we want to trade.
securities_to_trade = (Market_Cap & volatility_bottom_50)
return Pipeline(
columns={
'Market_Cap': Market_Cap
},
screen=(securities_to_trade),
)
def my_compute_weights(context):
"""
Compute ordering weights.
"""
# Compute even target weights for our long positions and short positions.
long_weight = 0.5 / len(context.longs)
short_weight = -0.5 / len(context.shorts)
return long_weight, short_weight
def before_trading_start(context, data):
# Gets our pipeline output every day.
context.output = pipeline_output('my_pipeline')
# Go long in securities for which the 'longs' value is True.
context.longs = context.output[context.output['longs']].index.tolist()
# Go short in securities for which the 'shorts' value is True.
context.shorts = context.output[context.output['shorts']].index.tolist()
context.long_weight, context.short_weight = my_compute_weights(context)
def my_rebalance(context, data):
"""
Rebalance weekly.
"""
for security in context.portfolio.positions:
if security not in context.longs and security not in context.shorts and data.can_trade(security):
order_target_percent(security, 0)
for security in context.longs:
if data.can_trade(security):
order_target_percent(security, context.long_weight)
for security in context.shorts:
if data.can_trade(security):
order_target_percent(security, context.short_weight)
def my_record_vars(context, data):
"""
Record variables at the end of each day.
"""
longs = shorts = 0
for position in context.portfolio.positions.itervalues():
if position.amount > 0:
longs += 1
elif position.amount < 0:
shorts += 1
# Record our variables.
record(leverage=context.account.leverage, long_count=longs, short_count=shorts)
こんにちは、私は Python の初心者で、Matlab の経験があります。コードは、私が最近 Quantopian で行ったものです。エラーメッセージは
AttributeError: 'bool' object has no attribute 'ndim'
There was a runtime error on line 27.
27行目は
my_pipe = make_pipeline()
上記は私の最初の質問です。2 番目の質問は、既存のアルゴリズムに基づいて、式を使用して 3 か月ごとに VAR モデルを実行するにはどうすればよいかということです。
Yt = a0 + a1Yt-1 + ..... + apYt-p + b1Xt-1 + ..... + bpXt-p + ut
Yt は 90 日間のリターンで、Xt-1、...、Xt-p はボラティリティのラグですか?
少し早いですがお礼を！詳細を指定する必要がある場合はお知らせください。