Hey Guys,

For a project i need to create my own rating.

Based on coarse and fine selection I want to create a rating (0-100) on which the trades are made.

I thought of creating an DataFrame with initializing and adding my weighted indicators.

For example:

def Initialize(self): columns = ['SYMBOL', 'VOL', 'RATING'] self.data = pd.DataFrame(columns=columns) def CoarseSelectionFunction(self, coarse): selected = [x for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5)] for x in selected: v = lambda x: x.DollarVolume z = lambda x: x.Symbol.Value try: if v > 20000000: self.data.append(pd.DataFrame({'VOL': [1], 'SYMBOL': z}, index=[z])) else: self.data.append(pd.DataFrame({'VOL': [v/20000000], 'SYMBOL': z}, index=[z])) except: self.data.append(pd.DataFrame({'VOL': [0], 'SYMBOL': v}, index=[z])) return [x.Symbol for x in selected] def FineSelectionFunction(self, fine): for x in fine: m = lambda x: x.MarketCap z = lambda x: x.Symbol try: if m >= 10000000000: self.data.loc[z, "SIZE"] = 1 elif m <= 1000000000: self.data.loc[z, "SIZE"] = 0 else: self.data.loc[z, "SIZE"] = ((m - 1000000000) / 9000000000) except: self.data.loc[z, "SIZE"] = 0

Since I get an Attribute error when accessing Symbol in FineSelection, I'm wondering wether my approach with Lambda is even correct?