rename fast_simulation parameter to fast_mode

jesse/modes/backtest_mode.py

@@ -224,8 +224,8 @@ def load_candles(start_date_str: str, finish_date_str: str) -> Dict[str, Dict[st
     return candles
 
 
-def simulator(*args, fast_simulation: bool = False, **kwargs) -> dict:
-    if fast_simulation:
+def simulator(*args, fast_mode: bool = False, **kwargs) -> dict:
+    if fast_mode:
         return _skip_simulator(*args, **kwargs)
 
     return _step_simulator(*args, **kwargs)

jesse/research/backtest.py

@@ -17,7 +17,7 @@ def backtest(
         generate_json: bool = False,
         generate_logs: bool = False,
         hyperparameters: dict = None,
-        fast_simulation: bool = True
+        fast_mode: bool = True
 ) -> dict:
     """
     An isolated backtest() function which is perfect for using in research, and AI training
@@ -66,7 +66,7 @@ def backtest(
         generate_equity_curve=generate_equity_curve,
         generate_hyperparameters=generate_hyperparameters,
         generate_logs=generate_logs,
-        fast_simulation=fast_simulation,
+        fast_mode=fast_mode,
     )
 
 
@@ -86,7 +86,7 @@ def _isolated_backtest(
         generate_equity_curve: bool = False,
         generate_hyperparameters: bool = False,
         generate_logs: bool = False,
-        fast_simulation: bool = True,
+        fast_mode: bool = True,
 ) -> dict:
     from jesse.services.validators import validate_routes
     from jesse.modes.backtest_mode import simulator
@@ -163,7 +163,7 @@ def _isolated_backtest(
         generate_equity_curve=generate_equity_curve,
         generate_hyperparameters=generate_hyperparameters,
         generate_logs=generate_logs,
-        fast_simulation=fast_simulation,
+        fast_mode=fast_mode,
     )
 
     result = {