add indicator comparison functionality

Author: areed1192

README.md

@@ -9,7 +9,7 @@
 
 ## Overview
 
-Current Version: **0.1.0**
+Current Version: **0.1.1**
 
 A trading robot written in Python that can run automated strategies using a technical analysis.
 The robot is designed to mimic a few common scenarios:

pyrobot/indicators.py

@@ -46,6 +46,9 @@ def __init__(self, price_data_frame: StockFrame) -> None:
         self._current_indicators = {}
         self._indicator_signals = {}
         self._frame = self._stock_frame.frame
+
+        self._indicators_comp_key = []
+        self._indicators_key = []
 
         if self.is_multi_index:
             True
@@ -66,11 +69,10 @@ def get_indicator_signal(self, indicator: Optional[str]= None) -> Dict:
             return self._indicator_signals[indicator]
         else:      
             return self._indicator_signals
-
 
     def set_indicator_signal(self, indicator: str, buy: float, sell: float, condition_buy: Any, condition_sell: Any, 
-                                buy_max: float = None, sell_max: float = None, condition_buy_max: Any = None, condition_sell_max: Any = None) -> None:
-        """Return the raw Pandas Dataframe Object.
+                            buy_max: float = None, sell_max: float = None, condition_buy_max: Any = None, condition_sell_max: Any = None) -> None:
+        """Used to set an indicator where one indicator crosses above or below a certain numerical threshold.
 
         Arguments:
         ----
@@ -102,7 +104,8 @@ def set_indicator_signal(self, indicator: str, buy: float, sell: float, conditio
         # Add the key if it doesn't exist.
         if indicator not in self._indicator_signals:
             self._indicator_signals[indicator] = {}
-        
+            self._indicators_key.append(indicator)      
+
         # Add the signals.
         self._indicator_signals[indicator]['buy'] = buy     
         self._indicator_signals[indicator]['sell'] = sell
@@ -115,6 +118,52 @@ def set_indicator_signal(self, indicator: str, buy: float, sell: float, conditio
         self._indicator_signals[indicator]['buy_operator_max'] = condition_buy_max
         self._indicator_signals[indicator]['sell_operator_max'] = condition_sell_max
 
+    def set_indicator_signal_compare(self, indicator_1: str, indicator_2: str, condition_buy: Any, condition_sell: Any) -> None:
+        """Used to set an indicator where one indicator is compared to another indicator.
+
+        Overview:
+        ----
+        Some trading strategies depend on comparing one indicator to another indicator.
+        For example, the Simple Moving Average crossing above or below the Exponential
+        Moving Average. This will be used to help build those strategies that depend
+        on this type of structure.
+
+        Arguments:
+        ----
+        indicator_1 {str} -- The first indicator key, for example `ema` or `sma`.
+
+        indicator_2 {str} -- The second indicator key, this is the indicator we will compare to. For example,
+            is the `sma` greater than the `ema`.
+
+        condition_buy {str} -- The operator which is used to evaluate the `buy` condition. For example, `">"` would
+            represent greater than or from the `operator` module it would represent `operator.gt`.
+        
+        condition_sell {str} -- The operator which is used to evaluate the `sell` condition. For example, `">"` would
+            represent greater than or from the `operator` module it would represent `operator.gt`.
+        """
+
+        # Define the key.
+        key = "{ind_1}_comp_{ind_2}".format(
+            ind_1=indicator_1,
+            ind_2=indicator_2
+        )
+
+        # Add the key if it doesn't exist.
+        if key not in self._indicator_signals:
+            self._indicator_signals[key] = {}
+            self._indicators_comp_key.append(key)   
+
+        # Grab the dictionary.
+        indicator_dict = self._indicator_signals[key]
+        
+        # Add the signals.
+        indicator_dict['type'] = 'comparison'
+        indicator_dict['indicator_1'] = indicator_1
+        indicator_dict['indicator_2'] = indicator_2
+        indicator_dict['buy_operator'] = condition_buy
+        indicator_dict['sell_operator'] = condition_sell
+
+
     @property
     def price_data_frame(self) -> pd.DataFrame:
         """Return the raw Pandas Dataframe Object.
@@ -1010,6 +1059,9 @@ def refresh(self):
 
             # Update the function.
             indicator_function(**indicator_argument)
+    
+    def grab_comparison_indicator(self) -> dict:
+        pass
 
     def check_signals(self) -> Union[pd.DataFrame, None]:
         """Checks to see if any signals have been generated.
@@ -1020,7 +1072,11 @@ def check_signals(self) -> Union[pd.DataFrame, None]:
             is returned otherwise nothing is returned.
         """
 
-        signals_df = self._stock_frame._check_signals(indicators=self._indicator_signals)
+        signals_df = self._stock_frame._check_signals(
+            indicators=self._indicator_signals,
+            indciators_comp_key=self._indicators_comp_key,
+            indicators_key=self._indicators_key
+        )
 
         return signals_df
 

pyrobot/robot.py

@@ -3,7 +3,6 @@
 import pprint
 import pathlib
 import pandas as pd
-import pkg_resources
 
 from datetime import time
 from datetime import datetime
@@ -19,15 +18,11 @@
 from pyrobot.portfolio import Portfolio
 from pyrobot.stock_frame import StockFrame
 
-current_td_version = pkg_resources.get_distribution('td-ameritrade-python-api').version
-
 from td.client import TDClient
+from td.utils import TDUtilities
 
-if current_td_version == '0.3.0':
-    from td.utils import TDUtilities
-    milliseconds_since_epoch = TDUtilities().milliseconds_since_epoch
-else:
-    from td.utils import milliseconds_since_epoch
+# We are going to be doing some timestamp conversions.
+milliseconds_since_epoch = TDUtilities().milliseconds_since_epoch
 
 
 class PyRobot():
@@ -557,8 +552,8 @@ def get_latest_bar(self) -> List[dict]:
         bar_type = self._bar_type
 
         # Define the start and end date.
-        start_date = datetime.today()
-        end_date = start_date - timedelta(minutes=bar_size * 15)
+        end_date = datetime.today()
+        start_date = end_date - timedelta(days=1)
         start = str(milliseconds_since_epoch(dt_object=start_date))
         end = str(milliseconds_since_epoch(dt_object=end_date))
 
@@ -636,11 +631,11 @@ def wait_till_next_bar(self, last_bar_timestamp: pd.DatetimeIndex) -> None:
         print("-"*80)
         print("Curr Time: {time_curr}".format(
             time_curr=curr_bar_time.strftime("%Y-%m-%d %H:%M:%S")
-            )
+        )
         )
         print("Next Time: {time_next}".format(
             time_next=next_bar_time.strftime("%Y-%m-%d %H:%M:%S")
-            )
+        )
         )
         print("Sleep Time: {seconds}".format(seconds=time_to_wait_now))
         print("-"*80)
@@ -691,8 +686,8 @@ def execute_signals(self, signals: List[pd.Series], trades_to_execute: dict) ->
                 }
             >>> signals = indicator_client.check_signals()
             >>> trading_robot.execute_signals(
-                signals=signals,
-                trades_to_execute=trades_dict
+                    signals=signals,
+                    trades_to_execute=trades_dict
                 )
         """