Usage
=====

Here is a quick demo on how to use `PyDecisionGraph` for building a decision tree based on various conditions:

.. code-block:: python

   from decision_graph.decision_tree import LogicNode, LOGGER, AttrExpression, LongAction, ShortAction, NoAction, RootLogicNode, LogicMapping

   # Mapping of attribute names to their values
   LogicMapping.AttrExpression = AttrExpression

   state = {
       "exposure": 0,  # Current exposure
       "working_order": 0,  # Current working order
       "up_prob": 0.8,  # Probability of price going up
       "down_prob": 0.2,  # Probability of price going down
       "volatility": 0.24,  # Current market volatility
       "ttl": 15.3  # Time to live (TTL) of the decision tree
   }

   # Root of the logic tree
   with RootLogicNode() as root:
       # Define root logic mapping with state data
       with LogicMapping(name='Root', data=state) as lg_root:
           lg_root: LogicMapping

           # Condition for zero exposure
           with lg_root.exposure == 0:
               root: LogicNode
               with LogicMapping(name='check_open', data=state) as lg:
                   with lg.working_order != 0:
                       break_point = NoAction()  # No action if there's a working order
                       lg.break_(scope=lg)  # Exit the current scope

                   with lg.volatility > 0.25:  # Check if volatility is high
                       with lg.down_prob > 0.1:  # Action for down probability
                           LongAction()

                       with lg.up_prob < -0.1:  # Action for up probability
                           ShortAction()

           # Condition when TTL is greater than 30
           with lg_root.ttl > 30:
               with lg_root.working_order > 0:
                   ShortAction()  # Action to short if working order exists
               LongAction()  # Always take long action
               lg_root.break_(scope=lg_root)  # Exit scope

           # Closing logic based on exposure and probabilities
           with LogicMapping(name='check_close', data=state) as lg:
               with (lg.exposure > 0) & (lg.down_prob > 0.):
                   ShortAction()  # Short action for positive exposure and down probability

               with (lg.exposure < 0) & (lg.up_prob > 0.):
                   LongAction()  # Long action for negative exposure and up probability

   # Visualize the decision tree
   root.to_html()

   # Log the evaluation result
   LOGGER.info(root())