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