为什么会发生此 AttributeError？答案

【问题标题】：Why does this AttributeError happen?为什么会发生此 AttributeError？
【发布时间】：2017-11-23 09:28:29
【问题描述】：

下午好，

我是一名学生，我正在尝试在 Quantopian 平台上实施 WaveTrend Oscillator 策略：https://www.tradingview.com/script/2KE8wTuF-Indicator-WaveTrend-Oscillator-WT/ 我想做的是在指标高时卖出 AAPL，低时买入。

它一直给我这个错误：

AttributeError: 'zipline.assets._assets.Equity' object has no attribute 'history'

谁能帮帮我？

import talib
import pandas

# ---------------------------------------------------  
n1, n2, period, stock = 10, 21, 12, sid(24)
# --------------------------------------------------- 
def initialize(context): 
    schedule_function(open_positions, date_rules.week_start(), time_rules.market_open())

def handle_data(context, data):
    if get_open_orders(): return
    close = stock.history(stock, 'close', period + 1, '1d')
    low = stock.history(stock, 'low', period + 1, '1d') 
    high = stock.history(stock, 'high', period + 1, '1d') 
    ap = (high+low+close)/3
    esa = talib.EMA(ap, timeperiod=n1)
    d = talib.EMA(abs(ap - esa), timeperiod=n1)
    ci = (ap - esa) / (0.015 * d)    
    wt1 = talib.EMA(ci, timeperiod=n2)
    wt1 = wt1.dropna()
    wt2 = talib.SMA(wt1, timeperiod=4)
    wt2 = wt2.dropna()

def open_positions(context, data):
    if data.can_trade(stock <  wt1):
        order_target_percent(stock, 2)
    elif data.can_trade(stock > wt2):
        order_target_percent(stock, -1)

【问题讨论】：

从代码和错误中我认为stock 没有您尝试使用的history 方法
@Roars 是对的——sid(...) 方法正在返回一些没有history() 方法的对象。 quantopian.com/help#ide-history 有帮助吗？

标签： python error-handling attributeerror quantitative-finance quantopian

【解决方案1】：

好的，我想我让它正常工作了：

    import talib

# ---------------------------------------------------
n1, n2, period, stock = 10, 21, 60, sid(24)
# ---------------------------------------------------
def initialize(context):
    schedule_function(trade, date_rules.week_start(), time_rules.market_open())

def trade(context, data):
    ob = 80 #"Over Bought Level"  
    os = -80 #"Over Sold Level"
    if get_open_orders(): return
    close = data.history(stock, 'close', period + 1, '1d').dropna()
    low = data.history(stock, 'low', period + 1, '1d').dropna()
    high = data.history(stock, 'high', period + 1, '1d').dropna()
    ap = (high + low + close) / 3
    esa = talib.EMA(ap, timeperiod=n1)
    d = talib.EMA(abs(ap - esa), timeperiod=n1)
    ci = (ap - esa) / (0.015 * d)
    wt1 = talib.EMA(ci, timeperiod=n2)
    record(wt1 = wt1[-1], ob = ob,os = os)
    if data.can_trade(stock):
        if  wt1[-1] > os:
            order_target_percent(stock, 2)
        elif wt1[-1] < ob:
            order_target_percent(stock, 0)

【讨论】：