'''
Test code for Floor/Ceiling code.
'''
import pandas as pd

# from swings import add_swings_fp
# from floor_ceiling import add_regime_fc

class FloorCeilingTest(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2021, 8, 3)
        self.SetEndDate(2021, 8, 17)
        self.SetCash(100000) 
        
        resolution = Resolution.Daily
        
        self.inst = self.AddForex('EURUSD', market=Market.Oanda, resolution=resolution, fillDataForward =False)
        # self.inst = self.AddForex('NZDUSD', market=Market.Oanda, resolution=resolution, fillDataForward =False)
        # self.inst = self.AddForex('USDJPY', market=Market.Oanda, resolution=resolution, fillDataForward =False)
        
        # Get history to populate the price DataFrame.
        self.inst_data_df = self.History(self.inst.Symbol, 1000)
        self.inst_data_df = self.inst_data_df.loc[self.inst.Symbol, ['high', 'low', 'close']]
        
        # self.Debug(str(self.inst_data_df.iloc[-2])
        #             + '\n' + str(self.inst_data_df.iloc[-1]))
        
        # Set up a chart for the regime data.
        test_chart = Chart('Test Plot')
        test_chart.AddSeries(Series('Close', SeriesType.Line, 0))
        test_chart.AddSeries(Series('H-High', SeriesType.Line, 0))
        test_chart.AddSeries(Series('H-Low', SeriesType.Line, 0))
        test_chart.AddSeries(Series('H-Close', SeriesType.Line, 0))
        self.AddChart(test_chart)
        

    def OnData(self, data):
        ''' 
        OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
        Parameters:
            data: Slice object keyed by symbol containing the price data
        '''
        # Update the instrument data DataFrame.
        new_row = { data[self.inst.Symbol].EndTime :
                        [data[self.inst.Symbol].High,
                        data[self.inst.Symbol].Low,
                        data[self.inst.Symbol].Close]}
        new_row = pd.DataFrame.from_dict(new_row, orient="index", columns=["high", "low", "close"])
                     
        self.inst_data_df = self.update_price_history(self.inst_data_df, new_row)
        
        # self.Debug(str(self.inst_data_df.iloc[-2])
        #             + '\n' + str(self.inst_data_df.iloc[-1]))
        
        self.Debug(str(self.Time) + ": THLC = " + str(self.inst_data_df.index[-1])
                                        + ", " + str(self.inst_data_df.iloc[-1]["high"])
                                        + ", " + str(self.inst_data_df.iloc[-1]["low"])
                                        + ", " + str(self.inst_data_df.iloc[-1]["close"]))
        
        self.Plot('Test Plot', 'Close', data[self.inst.Symbol].Close)
        self.Plot("Test Plot", "H-High", self.inst_data_df.iloc[-1]["high"])
        self.Plot("Test Plot", "H-Low", self.inst_data_df.iloc[-1]["low"])
        self.Plot("Test Plot", "H-Close", self.inst_data_df.iloc[-1]["close"])
        
    
    def update_price_history(self, history_df, new_row):
        '''
        Rolls the price history by adding the new row and dropping the oldest.
        
        Parameters
        ----------
        history_df : DataFrame
            The price history DataFrame.
        new_row : DataFrame
            The new row as a one-row DataFrame.
            
        Return
        ------
        history_df : DataFrame
            The supplied history rolled forward one data period.
        '''
        
        # self.Debug(str(self.Time) + ": new_row = " + str(new_row.name) + ": " + str(new_row))
        
        history_df = history_df.append(new_row)
        history_df.drop(history_df.index[0], inplace=True)
        
        # self.Debug(str(self.Time) + ": THLC = " + str(self.inst_data_df.index[-1])
        #                                 + ", " + str(self.inst_data_df.iloc[-1]["high"])
        #                                 + ", " + str(self.inst_data_df.iloc[-1]["low"])
        #                                 + ", " + str(self.inst_data_df.iloc[-1]["close"]))
        
        return history_df