progress: stop signals

lib/pt_trading/trading_pair.py

@@ -1,9 +1,18 @@
 from __future__ import annotations
 
+from enum import Enum
 from typing import Any, Dict, List, Optional
 
 import pandas as pd  # type:ignore
-from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults  # type:ignore
+from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults
+
+class PairState(Enum):
+    INITIAL = 1
+    OPEN = 2
+    CLOSE = 3
+    CLOSE_POSITION = 4
+    CLOSE_STOP_LOSS = 5
+    CLOSE_STOP_PROFIT = 6
 
 class CointegrationData:
     EG_PVALUE_THRESHOLD = 0.05
@@ -288,13 +297,6 @@ class TradingPair:
             / self.training_std_
         )
         
-        # print("*** PREDICTED DF")
-        # print(predicted_df)
-        # print("*" * 80) 
-        # print("*** SELF.PREDICTED_DF")
-        # print(self.predicted_df_)
-        # print("*" * 80) 
-
         predicted_df = predicted_df.reset_index(drop=True)
         if self.predicted_df_ is None:
             self.predicted_df_ = predicted_df
@@ -343,6 +345,49 @@ class TradingPair:
             curr_training_start_idx += 1
         return result
 
+    def to_stop_close_conditions(self, predicted_row: pd.Series) -> bool:
+        config = self.config_
+        if ("stop_close_conditions" not in config or config["stop_close_conditions"] is None) :
+            return False
+        if "profit" in config["stop_close_conditions"]:
+            current_return = self._current_return(predicted_row)
+            #
+            # print(f"time={predicted_row['tstamp']} current_return={current_return}")
+            # 
+            if current_return >= config["stop_close_conditions"]["profit"]:
+                self.user_data_["stop_close_state"] = PairState.CLOSE_STOP_PROFIT
+                return True
+        if "loss" in config["stop_close_conditions"]:
+            if current_return <= config["stop_close_conditions"]["loss"]:
+                self.user_data_["stop_close_state"] = PairState.CLOSE_STOP_LOSS
+                return True
+        return False
+    
+    def on_open_trades(self, trades: pd.DataFrame) -> None:
+        if "close_trades" in self.user_data_: del self.user_data_["close_trades"]
+        self.user_data_["open_trades"] = trades
+
+    def on_close_trades(self, trades: pd.DataFrame) -> None:
+        del self.user_data_["open_trades"]
+        self.user_data_["close_trades"] = trades
+
+    def _current_return(self, predicted_row: pd.Series) -> float:
+        if "open_trades" in self.user_data_:
+            open_trades = self.user_data_["open_trades"]
+            if len(open_trades) == 0:
+                return 0.0
+            def _stock_return(stock: str) -> float:
+                stock_open_trades = open_trades[open_trades["symbol"] == stock]
+                stock_sign = -1 if stock_open_trades["action"].iloc[0]  == "SELL" else 1
+                stock_price = predicted_row[f"{self.price_column_}_{stock}"]
+                stock_return = stock_sign * (stock_price - stock_open_trades["price"].iloc[0]) / stock_open_trades["price"].iloc[0]
+                return float(stock_return)
+            
+            stock_a_return = _stock_return(self.symbol_a_)
+            stock_b_return = _stock_return(self.symbol_b_)
+            return (stock_a_return + stock_b_return) * 100.0
+        return 0.0
+        
     def __repr__(self) -> str:
         return self.name()