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Length mismatch: Expected axis has 0 elements, new values have 1 elements

I have cloned Jing Wu's algo and want to add sma filter in coarseuniverse according to the example in API reference.
However, I got this error. Length mismatch: Expected axis has 0 elements, new values have 1 elements

Please help. Thanks.

 

from QuantConnect.Data.UniverseSelection import *
import math
import numpy as np
import pandas as pd
import scipy as sp
# import statsmodels.api as sm

class SelectionData(object):
def __init__(self, symbol, period):
self.symbol = symbol
self.sma = SimpleMovingAverage(period)
self.is_above_sma = False
self.volume = 0
self.Aprice = 0

def update(self, time, price, volume):
self.volume = volume
self.Aprice = price
if self.sma.Update(time, price):
self.is_above_sma = price > sma

class FundamentalFactorAlgorithm(QCAlgorithm):

def Initialize(self):

self.SetStartDate(2017, 1, 1) #Set Start Date
self.SetEndDate(2018, 1, 1) #Set Start Date
self.SetCash(10000) #Set Strategy Cash


self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.spy = self.AddEquity("SPY", Resolution.Minute).Symbol
self.holding_months = 1
self.num_screener = 100
self.num_stocks = 10
self.formation_days = 200
self.lowmom = False
self.month_count = self.holding_months
self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.At(0, 0), Action(self.monthly_rebalance))
self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.At(10, 0), Action(self.rebalance))
# rebalance the universe selection once a month
self.rebalence_flag = 0
# make sure to run the universe selection at the start of the algorithm even it's not the manth start
self.first_month_trade_flag = 1
self.trade_flag = 0
self.symbols = None
self.stateData = {}

def CoarseSelectionFunction(self, coarse):
if self.rebalence_flag or self.first_month_trade_flag:
# drop stocks which have no fundamental data or have too low prices
selected = [x for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 1) ]
for x in selected:
if x.Symbol not in self.stateData:
self.stateData[x.Symbol] = SelectionData(x.Symbol,150)

avg = self.stateData[x.Symbol]
avg.update(x.EndTime, x.AdjustedPrice, x.Volume)

values = list(filter(lambda x: x.is_above_sma & (x.volume > 500000), self.stateData.values()))
# rank the stocks by dollar volume
values.sort(key=lambda x: x.volume, reverse=True)

return [ x.Symbol for x in values[:200] ]
else:
return self.symbols


def FineSelectionFunction(self, fine):
if self.rebalence_flag or self.first_month_trade_flag:
hist = self.History([i.Symbol for i in fine], 1, Resolution.Daily)
try:
filtered_fine = [x for x in fine if (x.ValuationRatios.EVToEBITDA > 0)
and (x.EarningReports.BasicAverageShares.ThreeMonths > 0)
and float(x.EarningReports.BasicAverageShares.ThreeMonths) * hist.loc[str(x.Symbol)]['close'][0] > 2e9]
except:
filtered_fine = [x for x in fine if (x.ValuationRatios.EVToEBITDA > 0)
and (x.EarningReports.BasicAverageShares.ThreeMonths > 0)]

top = sorted(filtered_fine, key = lambda x: x.ValuationRatios.EVToEBITDA, reverse=True)[:self.num_screener]
self.symbols = [x.Symbol for x in top]

self.rebalence_flag = 0
self.first_month_trade_flag = 0
self.trade_flag = 1
return self.symbols
else:
return self.symbols

def OnData(self, data):
pass

def monthly_rebalance(self):
self.rebalence_flag = 1

def rebalance(self):
spy_hist = self.History([self.spy], 120, Resolution.Daily).loc[str(self.spy)]['close']
if self.Securities[self.spy].Price < spy_hist.mean():
for symbol in self.Portfolio.Keys:
if symbol.Value != "TLT":
self.Liquidate(symbol.Value)
self.AddEquity("TLT")
self.SetHoldings("TLT", 1)
return

if self.symbols is None: return
chosen_df = self.calc_return(self.symbols)
chosen_df = chosen_df.iloc[:self.num_stocks]

self.existing_pos = 0
add_symbols = []
for symbol in self.Portfolio.Keys:
if symbol.Value == 'SPY': continue
if (symbol.Value not in chosen_df.index):
self.SetHoldings(symbol, 0)
elif (symbol.Value in chosen_df.index):
self.existing_pos += 1

weight = 0.99/len(chosen_df)
for symbol in chosen_df.index:
self.AddEquity(symbol)
self.SetHoldings(symbol, weight)

def calc_return(self, stocks):
hist = self.History(stocks, self.formation_days, Resolution.Daily)
current = self.History(stocks, 1, Resolution.Minute)

self.price = {}
ret = {}

for symbol in stocks:
if str(symbol) in hist.index.levels[0] and str(symbol) in current.index.levels[0]:
self.price[symbol.Value] = list(hist.loc[str(symbol)]['close'])
self.price[symbol.Value].append(current.loc[str(symbol)]['close'][0])

for symbol in self.price.keys():
ret[symbol] = (self.price[symbol][-1] - self.price[symbol][0]) / self.price[symbol][0]
df_ret = pd.DataFrame.from_dict(ret, orient='index')
df_ret.columns = ['return']
sort_return = df_ret.sort_values(by = ['return'], ascending = self.lowmom)

return sort_return

 

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The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


Problem solved. Thanks.

0

Hi KY Chan , how did you fix this? 

0

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0

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


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