Replace fillna with method with ffill/bfill

demo_scripts/common/timeseries_analysis_demo.py

@@ -46,7 +46,7 @@ def _single_series_usage(data_provider):
 def _dataframe_usage(data_provider):
     df = data_provider.get_price(
         tickers=many_tickers, fields=PriceField.Close, start_date=start_date, end_date=end_date)
-    df.fillna(method='ffill', inplace=True)
+    df.ffill(inplace=True)
     print(TimeseriesAnalysis.table_for_df(df))
     return df
 

qf_lib/analysis/common/abstract_document.py

@@ -207,7 +207,7 @@ def volatility(window):
     def _get_rolling_chart(self, timeseries_list, rolling_function, function_name):
         freq = timeseries_list[0].get_frequency()
         timeseries_list = [tms.dropna().to_prices(1) for tms in timeseries_list]
-        df = pd.concat(timeseries_list, axis=1).fillna(method='ffill')
+        df = pd.concat(timeseries_list, axis=1).ffill()
 
         rolling_window_len = int(freq.value / 2)  # 6M rolling
         step = round(freq.value / 6)  # 2M shift

qf_lib/analysis/rolling_analysis/rolling_analysis.py

@@ -32,7 +32,7 @@ def calculate_analysis(cls, strategy_tms: QFSeries, benchmark_tms: QFSeries):
         benchmark_name = benchmark_tms.name
         df[strategy_name] = strategy_tms.to_prices()
         df[benchmark_name] = benchmark_tms.to_prices()
-        df.fillna(method='ffill', inplace=True)
+        df.ffill(inplace=True)
 
         for window_info in windows:
             window = window_info[0]

qf_lib/analysis/tearsheets/portfolio_analysis_sheet.py

@@ -355,7 +355,7 @@ def _performance_series_for_ticker(self, df: QFDataFrame):
 
             pnl_series = QFSeries(
                 data=closed_positions_pnl["Realised PnL"] + open_positions_pnl['Total PnL of open position'],
-                index=time_index).fillna(method='ffill').fillna(0.0)
+                index=time_index).ffill().fillna(0.0)
             return_data[title] = pnl_series
 
         return return_data

qf_lib/analysis/trade_analysis/trades_generator.py

@@ -104,7 +104,7 @@ def create_trades_from_transactions(self, transactions: Sequence, portfolio_valu
         transactions_df.loc[new_positions_beginning, "position start"] = transactions_df.loc[
             new_positions_beginning].index
         transactions_df.loc[:, "position start"] = transactions_df.groupby(by="ticker", group_keys=False)[
-            "position start"].apply(lambda tms: tms.fillna(method="ffill"))
+            "position start"].apply(lambda tms: tms.ffill())
 
         trades_series = transactions_df.groupby(by="position start", group_keys=False)["transaction"].apply(
             lambda t: self._parse_position(t, portfolio_values))

qf_lib/backtesting/alpha_model/futures_model.py

@@ -148,7 +148,7 @@ def _compute_aggregated_volume(self, ticker: FutureTicker, start_date: datetime,
     def compute_atr(self, prices_df: PricesDataFrame):
         try:
             prices_df = prices_df[[PriceField.Close, PriceField.Open, PriceField.High, PriceField.Low]]
-            prices_df = prices_df.dropna(how='all').fillna(method='ffill').dropna()
+            prices_df = prices_df.dropna(how='all').ffill().dropna()
             #   Compute the ATR
             atr_values = average_true_range(prices_df.iloc[-self.num_of_bars_atr:], normalized=True)
         except Exception:

qf_lib/common/utils/returns/convert_dataframe_frequency.py

@@ -33,7 +33,7 @@ def convert_dataframe_frequency(dataframe: QFDataFrame, frequency: Frequency) ->
     if frequency == Frequency.DAILY:
         return dataframe.to_simple_returns()
 
-    filled_df = dataframe.to_prices().fillna(method="ffill")
+    filled_df = dataframe.to_prices().ffill()
     new_columns = {}
     for column in filled_df:
         new_columns[column] = get_aggregate_returns(filled_df[column], frequency)

qf_lib/indicators/market_stress_indicator_us.py

@@ -62,7 +62,7 @@ def get_indicator(self, years_rolling: float, start_date: datetime, end_date: da
         """
         underlying_start_date = start_date - timedelta(days=floor(years_rolling * 365 * 1.1))
         data = self.data_provider.get_price(self.tickers, PriceField.Close, underlying_start_date, end_date)
-        data = data.fillna(method='ffill')
+        data = data.ffill()
         # data = data.dropna() # this line can be enabled but it will shift starting point by the years_rolling
 
         window_size = floor(252 * years_rolling)

qf_lib/tests/manual_tests/futures_strategy.py

@@ -64,7 +64,7 @@ def calculate_exposure(self, ticker: FutureTicker, current_exposure: Exposure, c
         # Get the data frame containing High, Low, Close prices
         data_frame = self.futures_chain.get_price([PriceField.High, PriceField.Low, PriceField.Close], start_time,
                                                   current_time)
-        data_frame = data_frame.dropna(how='all').fillna(method='pad')
+        data_frame = data_frame.dropna(how='all').ffill()
 
         close_tms = data_frame[PriceField.Close]
         close_tms = close_tms.iloc[-self.slow_time_period:]