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The ranking of portfolios sets out the preference of the factor model, i.e. First, according to the factor order, we place our universe stocks into 5 distinct quintile portfolios, named P1, P2, P3, P4 and P5.
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The second step is using different selected factor variables to calculate an equally weighted composite factor score for each stock. Step 2: Calculate equally weighted composite factor scores The order is descending if the factor correlation is negative, it is ascending if the factor correlation is positive. For each pre-selected factor, we rank the stocks by those factor values. We choose 4 factors: FCFYield, PriceChange1M, BookValuePerShare and RevenueGrowth.įirst, we remove the stocks without fundamental data or have zero factor value. Return test_result Factor Significance Testing Result Loss_port_excess_return = np.array(excess_return)
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Win_port_excess_return = np.array(excess_return) Loss_prob = loss_unt()/float(len(win_excess)) Win_excess = df_port_return.iloc - df_port_return.iloc Loss_excess = df_port_return.iloc - df_port_return.iloc If np.array(total_return) > np.array(total_return): Total_return = (df_port_return + 1).T.cumprod().iloc - 1Īnnual_return = (total_return+1)**(1./6)-1Įxcess_return = annual_return - np.array(annual_return)Ĭorrelation = annual_rr(pd.Series(,index = annual_return.index)) The excess return of win portfolio should be greater than 0.25, while the excess return of loss portfolio should be lower than 0.05.ĭef calculate_criteria(self,df_port_return):.If the factor is significant, both loss and win probability should greater than 0.4. The win probability is the probability that the win portfolio return outperform the benchmark return. The loss probability is the probability that the loss portfolio return underperform the benchmark. If the return of the rank first portfolio larger than the portfolio at the bottom of the return rankings, we define it the win portfolio and the loss portfolio and vice versa.The absolute value of the correlation coefficient should larger than 0.8. The first metrics is the correlation between the portfolio' returns and their rank.Then We generate three metrics to judge the significance of each factor.
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Step 3: Generate the metrics to test the factor significanceĪfter getting the monthly returns of portfolios and the benchmark, we compute the average annual return and excess return over benchmark of each portfolio across the whole backtesting period. Port_avg_return.append(benchmark_monthly_return) Close) for slice in history_benchmark]īenchmark_monthly_return = (benchmark_daily_price/benchmark_daily_price) - 1 History_benchmark = self.History(20,Resolution.Daily)īenchmark_daily_price =. # add return of "SPY" as the benchmark to the end of the return listīenchmark_syl = self.AddEquity("SPY").Symbol Port_avg_return = np.mean(reshape_return,axis=1).tolist() # calculate the average return of different portfolios Reshape_return = np.reshape(monthly_return, (self.numOfPortfolio, len(monthly_return)/self.numOfPortfolio)) Because coarse selection function takes all the equities including ETFs which have no fundamental data into account, we need the property x.HasFundamentalData to exclude them from our candidate stocks pool.
#Stock symbol generator algorithm code
Here we use Scheduled events API to trigger code to run at the first trading day each month and use three flag variables to control the rebalancing of CoarseSelection, FineSelection and Ondata functions.Ĭoarse Universe selection is the built-in universe data provided by QuantConnect which allows you to filter the universe of over 16000 symbols to perform rough filtering before your algorithm. This is controlled by the variable UniverseSettings.Resolution. You can refer to the documentation for more details. Universes are refreshed every day by default, but can be refreshed as often as required. There is a convenient way using our universe selection API. First, we sort the stocks by daily dollar volume and take the top stocks with the highest dollar volumes as our candidates. Step 1: Ranking the stocks by factor valuesġ. # schedule an event to fire at the first trading day of SPY Self.df_return = pd.DataFrame(index = range(self.numOfPortfolio+1)) # store the monthly returns of different portfolios in a dataframe Self.flag3 = 0 # variable to record the number of rebalancing times Self.flag2 = 0 # variable to control the monthly rebalance of OnData function Self.flag1 = 1 # variable to control the monthly rebalance of coarse and fine selection function Self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) Self.SetStartDate(2005,01,01) #Set Start Date