fix more pandas future warnings

syscore/pandas/frequency.py

@@ -112,7 +112,7 @@ def merge_data_with_different_freq(
     dtype: int64
     """
 
-    list_as_concat_pd = pd.concat(list_of_data, axis=0)  # TODO 1463
+    list_as_concat_pd = pd.concat(list_of_data, axis=0)
     sorted_pd = list_as_concat_pd.sort_index()
     unique_pd = uniquets(sorted_pd)
 

sysdata/production/capital.py

@@ -164,18 +164,18 @@ def update_capital_value_for_strategy(
         if date is arg_not_supplied:
             date = datetime.datetime.now()
 
+        new_capital_item = pd.Series([new_capital_value], [date])
+
         try:
             capital_df = self.get_capital_pd_df_for_strategy(strategy_name)
         except missingData:
-            capital_series = pd.Series(dtype=float)
+            capital_series = new_capital_item
         else:
-            capital_series = df_to_series(capital_df)
-
-        new_capital_item = pd.Series([new_capital_value], [date])
-        updated_capital_series = pd.concat([capital_series, new_capital_item], axis=0)
-        updated_capital_df = updated_capital_series.to_frame()
+            capital_series = pd.concat(
+                [df_to_series(capital_df), new_capital_item], axis=0
+            )
 
-        self.update_capital_pd_df_for_strategy(strategy_name, updated_capital_df)
+        self.update_capital_pd_df_for_strategy(strategy_name, capital_series.to_frame())
 
     def delete_recent_capital_for_strategy(
         self, strategy_name: str, last_date: datetime.datetime

sysdata/production/margin.py

@@ -12,9 +12,6 @@ def final_value(self) -> float:
             raise missingData
         return self.values[-1]
 
-    def add_value(self, value: float, dateref=datetime.datetime.now()):
-        return seriesOfMargin(self._append(pd.Series([value], index=[dateref])))
-
 
 class marginData(object):
     def get_series_of_total_margin(self) -> seriesOfMargin:
@@ -48,10 +45,14 @@ def get_current_strategy_margin(self, strategy_name: str) -> float:
         return series_of_margin.final_value()
 
     def add_strategy_margin_entry(self, margin_entry: float, strategy_name: str):
+        new_val = pd.Series([margin_entry], index=[datetime.datetime.now()])
         existing_series = self.get_series_of_strategy_margin(strategy_name)
-        new_series = existing_series.add_value(margin_entry)
+        if existing_series.empty:
+            new_series = new_val
+        else:
+            new_series = pd.concat([existing_series, new_val])
         self._write_series_of_strategy_margin(
-            strategy_name, series_of_margin=new_series
+            strategy_name, series_of_margin=seriesOfMargin(new_series)
         )
 
     def get_series_of_strategy_margin(self, strategy_name: str) -> seriesOfMargin:

sysobjects/adjusted_prices.py

@@ -183,7 +183,7 @@ def _calc_new_multiple_prices(
 
     last_contract_in_price_data = prices_in_multiple_prices[price_contract_column][
         :last_date_in_current_adj
-    ][-1]
+    ].iloc[-1]
 
     new_multiple_price_data = prices_in_multiple_prices.prices_after_date(
         last_date_in_current_adj

sysproduction/update_historical_prices.py

@@ -482,13 +482,14 @@ def write_merged_prices_for_contract(
     diag_prices = diagPrices(data)
     price_updater = updatePrices(data)
 
-    list_of_data = [
-        diag_prices.get_prices_at_frequency_for_contract_object(
+    list_of_data = []
+    for frequency in list_of_frequencies:
+        prices = diag_prices.get_prices_at_frequency_for_contract_object(
             contract_object,
             frequency=frequency,
         )
-        for frequency in list_of_frequencies
-    ]
+        if len(prices) > 0:
+            list_of_data.append(prices)
 
     merged_prices = merge_data_with_different_freq(list_of_data)
 

sysquant/estimators/forecast_scalar.py

@@ -45,6 +45,6 @@ def forecast_scalar(
     scaling_factor = target_abs_forecast / avg_abs_value
 
     if backfill:
-        scaling_factor = scaling_factor.fillna(method="bfill")
+        scaling_factor = scaling_factor.bfill()
 
     return scaling_factor