我是Quantopian的新手，我正在尝试从Morningstar获取ipo的日期。我在Quantopian上找到了一些代码，并将其嵌入到pipline中。 （其余代码被复制用于测试目的。
我收到的错误消息是TypeError：类型为'NoneType'的对象在xxx行上没有len（）。我查看了其他库存溢出帮助，他们在其中分析了相同的错误，并且鉴于那里应该有数据，所以我不确定出什么问题了。
错误在线：out [：] = np.apply_along_axis（get_delta_days，0，ipo_dates）
代码是： “” Morningstar的样本测试。
"""
# Import the libraries we will use here
import pandas as pd
import numpy as np
from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.factors import AverageDollarVolume, Returns
from quantopian.pipeline.data import Fundamentals
#from quantopian.pipeline.data import morningstar as mstar
from quantopian.pipeline import CustomFactor
def initialize(context):
"""
The initialize function is the place to create your pipeline (security
selector),
and set trading conditions such as commission and slippage. It is called
once
at the start of the simulation and also where context variables can be
set.
"""
# Define context variables that can be accessed in other methods of
# the algorithm.
context.long_leverage = 0.5
context.short_leverage = -0.5
context.returns_lookback = 5
# Rebalance on the first trading day of each week at 11AM.
schedule_function(rebalance,
date_rules.every_day(),#week_start(days_offset=0),
time_rules.market_open(hours = 1, minutes = 30))
# Record tracking variables at the end of each day.
schedule_function(record_vars,
date_rules.every_day(),
time_rules.market_close(minutes=1))
# Create and attach our pipeline (dynamic security selector), defined
below.
attach_pipeline(make_pipeline(context), 'mean_reversion_example')
class StockAge(CustomFactor):
inputs = [Fundamentals.ipo_date]
window_length = 1
def compute(self, today, assets, out, ipo_dates):
def get_delta_days(ipo_dates):
# Convert last known (ie -1) ipo_date to Timestamps
# Subtract ipo date from current date
# Return delta days
ipo = pd.Timestamp(ipo_dates[-1], tz='UTC', offset='C')
delta = today - ipo
print ('Hello, world!')
return None if pd.isnull(delta) else float(delta.days)
# Apply the above function across each column and output the values
out[:] = np.apply_along_axis(get_delta_days, 0, ipo_dates)
def make_pipeline(context):
# Create a pipeline object.
pipe = Pipeline()
stk_age = StockAge()
stk_top = stk_age.notnan()
pipe.add(stk_top,'ipo')
return pipe
def before_trading_start(context, data):
"""
Called every day before market open. This is where we get the securities
that made it through the pipeline.
"""
context.myipo = context.output[context.output['ipo']]
# Keep a list reference and a set reference to all of our pipeline securities
# (set has much faster lookup)
context.security_list = context.myipo.index.tolist()
context.security_set = set(context.security_list)
def assign_weights(context):
"""
Assign weights to ou long and short target positions.
"""
# Set the allocations to even weights for each long position, and even weights
# for each short position.
def rebalance(context,data):
"""
This rebalancing function is called according to our schedule_function
settings.
"""
assign_weights(context)
for security in context.security_list:
order_target_percent(security, context.short_weight)
for security in context.portfolio.positions:
if security not in context.security_set and data.can_trade(security):
order_target_percent(security, 0)
order_target_percent
# Log the long and short orders each week.
log.info("This week's ipos: "+", ".join([ipo_.symbol for ipo_ in context.ipo.index]))
def record_vars(context, data):
"""
This function is called at the end of each day and plots certain variables.
"""
# Check how many long and short positions we have.
longs = shorts = 0
for position in context.portfolio.positions.itervalues():
if position.amount > 0:
longs += 1
if position.amount < 0:
shorts += 1
# Record and plot the leverage of our portfolio over time as well as the
# number of long and short positions. Even in minute mode, only the end-of-day
# leverage is plotted.
record(leverage = context.account.leverage, long_count=longs, short_count=shorts)