update strategy for quantile models

src/strategy/strategy.py

@@ -1,5 +1,6 @@
 import numpy as np
 import pandas as pd
+import logging
 from typing import Dict, Any
 
 EXIT_POSITION = 0
@@ -36,19 +37,29 @@ class BuyAndHoldStrategy(StrategyBase):
 
 class ModelPredictionsStrategyBase(StrategyBase):
     """Base class for strategies based on model predictions."""
+
     def __init__(self,
                  predictions,
-                 name: str = None):
+                 name: str = None,
+                 future: int = 1,
+                 exchange_fee: int = 0.001,
+                 target: str = 'close_price'):
         self.predictions = predictions
         assert 'time_index' in self.predictions.columns
         assert 'group_id' in self.predictions.columns
         assert 'prediction' in self.predictions.columns
 
         self.name = name
-
+        self.future = future
+        self.target = target
+        self.exchange_fee = exchange_fee
 
     def info(self):
-        return {'strategy_name': self.name or 'Unknown model'}
+        return {
+            'strategy_name': self.name or 'Unknown model',
+            'future': self.future,
+            'target': self.target
+        }
 
     def run(self, data):
         # Adds predictions to data, if prediction is unknown for a given
@@ -56,71 +67,90 @@ class ModelPredictionsStrategyBase(StrategyBase):
         merged_data = pd.merge(
             data, self.predictions, on=['time_index', 'group_id'],
             how='left')
-        
+
         return self.get_positions(merged_data)
 
     def get_positions(self, data):
         raise NotImplementedError()
 
 
-class ReturnsPredictionStrategy(ModelPredictionsStrategyBase):
+class ModelQuantilePredictionsStrategy(ModelPredictionsStrategyBase):
-    """Strategy that selects position based on returns predictions."""
-
-    def __init__(
-            self,
-            predictions,
-            threshold=0.001,
-            name=None):
-        super().__init__(predictions, name=name)
-        self.threshold = threshold
-
-    def get_positions(self, data):
-        arr = data['prediction']
-        positions = []
-        for i in range(len(arr)):
-            if arr[i] > self.threshold:
-                positions.append(LONG_POSITION)
-            elif arr[i] < -self.threshold:
-                positions.append(EXIT_POSITION)
-            elif not len(positions):
-                positions.append(EXIT_POSITION)
-            else:
-                positions.append(positions[-1])
-
-        return np.array(positions, dtype=np.int32)
-
-
-class PriceQuantilePredictionStrategy(ModelPredictionsStrategyBase):
     def __init__(
         self,
         predictions,
-        name=None):
+        quantiles,
-        super().__init__(predictions, name=name)
+        quantile_enter_long=None,
+        quantile_exit_long=None,
+        quantile_enter_short=None,
+        quantile_exit_short=None,
+        name: str = None,
+        future: int = 1,
+        target: str = 'close_price',
+        exchange_fee: int = 0.001
+    ):
+        super().__init__(
+            predictions,
+            name=name,
+            future=future,
+            target=target,
+            exchange_fee=exchange_fee)
 
-    def info(self):
+        self.quantiles = quantiles
-        return {'strategy_name': self.name}
+        self.quantile_enter_long = quantile_enter_long
+        self.quantile_exit_long = quantile_exit_long
+        self.quantile_enter_short = quantile_enter_short
+        self.quantile_exit_short = quantile_exit_short
 
     def get_positions(self, data):
-
         arr_preds = data['prediction'].to_numpy()
-        arr_close_price = data['close_price'].to_numpy()
+        arr_target = data[self.target].to_numpy()
 
-        positions = []
+        positions = [EXIT_POSITION]
         for i in range(len(arr_preds)):
-            if not np.isnan(arr_preds[i]).any():
+
-                price = arr_close_price[i]
+            # If strategy does not have prediction
-                pred_low = arr_preds[i][0]
+            # keep the current position.
-                pred_high = arr_preds[i][-1]
+            if np.isnan(arr_preds[i]).any():
-                if (pred_low - price) / price > 0.001:
+                # logging.warning(f"Missing value for time index {i}.")
-                    positions.append(LONG_POSITION)
+                positions.append(positions[-1])
-                    continue
+                continue
-                elif (pred_high - price) / price < -0.001:
+
-                    positions.append(EXIT_POSITION)
+            target = arr_target[i]
-                    continue
+            prediction = arr_preds[i][self.future - 1]
-            
+
-            if not len(positions):
+            # Enter long position
+            if (self.quantile_enter_long and
+                    (prediction[self.get_quantile_idx(
+                        round(1 - self.quantile_enter_long, 2)
+                    )] - target)
+                    / target > self.exchange_fee):
+                positions.append(LONG_POSITION)
+
+            # Enter short position
+            elif (self.quantile_enter_short and
+                  (prediction[self.get_quantile_idx(
+                    self.quantile_enter_short)] - target)
+                    / target < -self.exchange_fee):
+                positions.append(SHORT_POSITION)
+
+            # Exit long position
+            elif (self.quantile_exit_long and
+                  (prediction[self.get_quantile_idx(
+                    self.quantile_exit_long)] - target)
+                    / target < -self.exchange_fee):
                 positions.append(EXIT_POSITION)
+
+            # Exit short postion
+            elif (self.quantile_exit_short and
+                  (prediction[self.get_quantile_idx(
+                    round(1 - self.quantile_exit_short, 2)
+                  )] - target) / target > self.exchange_fee):
+                positions.append(EXIT_POSITION)
+
             else:
                 positions.append(positions[-1])
 
-        return np.array(positions, dtype=np.int32)
+        return np.array(positions[1:], dtype=np.int32)
+
+    def get_quantile_idx(self, quantile):
+        return self.quantiles.index(quantile)