Update PE Anomaly in Strategy Library

Author: AlinXlin

04 Strategy Library/92 Price Earnings Anomaly/02 Method.html

@@ -9,16 +9,15 @@
 </p>
 
 <div class="section-example-container">
-<pre class="python">
+    <pre class="python">
     def CoarseSelectionFunction(self, coarse):
-        if self.yearly_rebalance:
-            # drop stocks which have no fundamental data and have low price
-            CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData and x.Price > 5]
-            sortedByDollarVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=False) 
-            top = sortedByDollarVolume[:self._NumCoarseStocks]
-            return [i.Symbol for i in top]
-        else: 
-            return [] 
+        if self.Time.year == self.year: return []
+        
+        # drop stocks which have no fundamental data or have low price
+        CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData and x.Price > 5]
+        sortedByDollarVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=False) 
+        top = sortedByDollarVolume[:self._NumCoarseStocks]
+        return [i.Symbol for i in top] 
 </pre>
 </div>
 
@@ -27,19 +26,20 @@
 </p>
 
 <div class="section-example-container">
-<pre class="python">
+    <pre class="python">
 
     def FineSelectionFunction(self, fine):
-        if self.yearly_rebalance:
+        if self.Time.year == self.year:
+            self.filtered_fine = []
+        else:
             fine = [x for x in fine if (x.ValuationRatios.PERatio > 0)]
             for i in fine:
                 i.PERatio = float(i.ValuationRatios.PERatio)
-            sortedPERatio = sorted(fine, key=lambda x: x.PERatio,reverse=True)
+            sortedPERatio = sorted(fine, key=lambda x: x.PERatio)
+            
             self.filtered_fine = [i.Symbol for i in sortedPERatio[:self._NumStocksInPortfolio]]
-            self.yearly_rebalance = False
-            return self.filtered_fine
-        else:
-            return []
+            
+        return self.filtered_fine
 </pre>
 </div>
 

04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html

@@ -1,6 +1,6 @@
 div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_28c47727b202b88fd00d9b0b52dadedf.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_c1c62e5e8782b05cc465c5a503463616.html"></iframe>
     </div>
 </div>

04 Strategy Library/92 Price Earnings Anomaly/04 Summary.html

@@ -1,5 +1,5 @@
 <p>
-The portfolio significantly outperforms the benchmark, S&P 500, during the two years backtest period, from the beginning of the year 2016 to the end of the year 2017.
+The portfolio significantly outperforms the benchmark, S&P 500, during the three and half years backtest period, from the beginning of the year 2016 to July of the year 2019.
 </p>
 <p>
 The universe of stocks is filtered using dollar volume and price criteria. Most of the stocks chosen by the algorithm during the backtest period are small market capitalization stocks.