progress: stop signals

README.md

@@ -45,7 +45,6 @@ Each configuration dictionary specifies:
 - `instruments`: A list of symbols to consider for forming trading pairs.
 - `trading_hours`: Defines the session start and end times, crucial for equity markets.
 - `price_column`: The column in the data to be used as the price (e.g., "close").
-- `zero_threshold`: A small value to handle potential division by zero.
 - `dis-equilibrium_open_trshld`: The threshold (in standard deviations) of the dis-equilibrium for opening a trade.
 - `dis-equilibrium_close_trshld`: The threshold (in standard deviations) of the dis-equilibrium for closing an open trade.
 - `training_minutes`: The length of the rolling window (in minutes) used to train the model (e.g., calculate cointegration, mean, and standard deviation of the dis-equilibrium).

configuration/crypto.cfg

@@ -7,24 +7,27 @@
     "db_table_name": "md_1min_bars",
     "exchange_id": "BNBSPOT",
     "instrument_id_pfx": "PAIR-",
-    "trading_hours": {
-        "begin_session": "00:00:00",
-        "end_session": "23:59:00",
-        "timezone": "UTC"
-    },
-    "price_column": "close",
-    "zero_threshold": 1e-10,
-    "dis-equilibrium_open_trshld": 2.0,
-    "dis-equilibrium_close_trshld": 0.5,
-    "training_minutes": 120,
     "funding_per_pair": 2000.0,
+
+    # ====== Trading Parameters ======
+    "price_column": "close",
+    "dis-equilibrium_open_trshld": 2.0,
+    "dis-equilibrium_close_trshld": 1.0,
+    "training_minutes": 120,
     "fit_method_class": "pt_trading.sliding_fit.SlidingFit",
-    # "fit_method_class": "pt_trading.static_fit.StaticFit",
+
-    "close_outstanding_positions": true,
+    # ====== Stop Conditions ======
-    "trading_hours": {
+    "stop_close_conditions": {
-        "begin_session": "06:00:00",
+        "profit": 2.0,
-        "end_session": "16:00:00",
+        "loss": -0.5
-        "timezone": "America/New_York"
     }
 
+    # ====== End of Session Closeout ======
+    "close_outstanding_positions": true,
+    # "close_outstanding_positions": false,
+    "trading_hours": {
+        "begin_session": "9:30:00",
+        "end_session": "21:30:00",
+        "timezone": "America/New_York"
+    }
 }

configuration/equity.cfg

@@ -7,24 +7,20 @@
     "db_table_name": "md_1min_bars",
     "exchange_id": "ALPACA",
     "instrument_id_pfx": "STOCK-",
-    "trading_hours": {
-        "begin_session": "9:30:00",
-        "end_session": "16:00:00",
-        "timezone": "America/New_York"
-    },
-    "price_column": "close",
-    "funding_per_pair": 2000.0,
-    "zero_threshold": 1e-10,
-    # 
-    "dis-equilibrium_open_trshld": 2.0,
-    "dis-equilibrium_close_trshld": 1.0,
-    "training_minutes": 150,
-    "fit_method_class": "pt_trading.sliding_fit.SlidingFit",
     "exclude_instruments": ["CAN"],
 
+    "funding_per_pair": 2000.0,
+
+    # ====== Trading Parameters ======
+    "price_column": "close",
+    "dis-equilibrium_open_trshld": 2.0,
+    "dis-equilibrium_close_trshld": 1.0,
+    "training_minutes": 120,
+    "fit_method_class": "pt_trading.sliding_fit.SlidingFit",
+
     # ====== Stop Conditions ======
-    "stop_conditions": {
+    "stop_close_conditions": {
-        "profit": 1.0,
+        "profit": 2.0,
         "loss": -0.5
     }
 

configuration/equity_single.cfg

@@ -1,26 +0,0 @@
-{
-    "security_type": "EQUITY",
-    "data_directory": "./data/equity",
-    # "datafiles": [
-    #     "20250604.mktdata.ohlcv.db",
-    # ],
-    "db_table_name": "md_1min_bars",
-    "exchange_id": "ALPACA",
-    "instrument_id_pfx": "STOCK-",
-    "trading_hours": {
-        "begin_session": "9:30:00",
-        "end_session": "16:00:00",
-        "timezone": "America/New_York"
-    },
-    "price_column": "close",
-    "zero_threshold": 1e-10,
-    "dis-equilibrium_open_trshld": 2.0,
-    "dis-equilibrium_close_trshld": 1.0,
-    "training_minutes": 120,
-    "funding_per_pair": 2000.0,
-    "fit_method_class": "pt_trading.sliding_fit.SlidingFit",
-    # "fit_method_class": "pt_trading.static_fit.StaticFit",
-    "exclude_instruments": ["CAN"],
-    "close_outstanding_positions": false
-    
-}

lib/pt_trading/fit_method.py

@@ -29,8 +29,3 @@ class PairsTradingFitMethod(ABC):
     def reset(self) -> None: ...
 
 
-class PairState(Enum):
-    INITIAL = 1
-    OPEN = 2
-    CLOSED = 3
-    CLOSED_POSITIONS = 4

lib/pt_trading/sliding_fit.py

@@ -3,9 +3,9 @@ from enum import Enum
 from typing import Dict, Optional, cast
 
 import pandas as pd  # type: ignore[import]
-from pt_trading.fit_method import PairState, PairsTradingFitMethod
+from pt_trading.fit_method import PairsTradingFitMethod
 from pt_trading.results import BacktestResult
-from pt_trading.trading_pair import CointegrationData, TradingPair
+from pt_trading.trading_pair import CointegrationData, TradingPair, PairState
 
 NanoPerMin = 1e9
 
@@ -72,7 +72,7 @@ class SlidingFit(PairsTradingFitMethod):
             curr_predicted_row_idx += 1
 
         self._create_trading_signals(pair, config, bt_result)
-        print(f"***{pair}*** FINISHED ... {len(pair.user_data_['trades'])}")
+        print(f"***{pair}*** FINISHED *** Num Trades:{len(pair.user_data_['trades'])}")
         return pair.get_trades()
 
     def _create_trading_signals(
@@ -86,24 +86,41 @@ class SlidingFit(PairsTradingFitMethod):
         close_threshold = config["dis-equilibrium_close_trshld"]
         for curr_predicted_row_idx in range(len(pair.predicted_df_)):
             pred_row = pair.predicted_df_.iloc[curr_predicted_row_idx]
-            if pair.user_data_["state"] in [PairState.INITIAL, PairState.CLOSED, PairState.CLOSED_POSITIONS]:
+            scaled_disequilibrium = pred_row["scaled_disequilibrium"]
-                open_trades = self._get_open_trades(
+
-                    pair, row=pred_row, open_threshold=open_threshold
+            if pair.user_data_["state"] in [PairState.INITIAL, PairState.CLOSE, PairState.CLOSE_POSITION]:
-                )
+                if scaled_disequilibrium >= open_threshold:                    
-                if open_trades is not None:
+                    open_trades = self._get_open_trades(
-                    open_trades["status"] = "OPEN"
+                        pair, row=pred_row, open_threshold=open_threshold
-                    print(f"OPEN TRADES:\n{open_trades}")
+                    )
-                    pair.add_trades(open_trades)
+                    if open_trades is not None:
-                    pair.user_data_["state"] = PairState.OPEN
+                        open_trades["status"] = PairState.OPEN.name
+                        print(f"OPEN TRADES:\n{open_trades}")
+                        pair.add_trades(open_trades)
+                        pair.user_data_["state"] = PairState.OPEN
+                        pair.on_open_trades(open_trades)
+
             elif pair.user_data_["state"] == PairState.OPEN:
-                close_trades = self._get_close_trades(
+                if scaled_disequilibrium <= close_threshold:
-                    pair, row=pred_row, close_threshold=close_threshold
+                    close_trades = self._get_close_trades(
-                )
+                        pair, row=pred_row, close_threshold=close_threshold
-                if close_trades is not None:
+                    )
-                    close_trades["status"] = "CLOSE"
+                    if close_trades is not None:
-                    print(f"CLOSE TRADES:\n{close_trades}")
+                        close_trades["status"] = PairState.CLOSE.name
-                    pair.add_trades(close_trades)
+                        print(f"CLOSE TRADES:\n{close_trades}")
-                    pair.user_data_["state"] = PairState.CLOSED
+                        pair.add_trades(close_trades)
+                        pair.user_data_["state"] = PairState.CLOSE
+                        pair.on_close_trades(close_trades)
+                elif pair.to_stop_close_conditions(predicted_row=pred_row):
+                    close_trades = self._get_close_trades(
+                        pair, row=pred_row, close_threshold=close_threshold
+                    )
+                    if close_trades is not None:
+                        close_trades["status"] = pair.user_data_["stop_close_state"].name
+                        print(f"STOP CLOSE TRADES:\n{close_trades}")
+                        pair.add_trades(close_trades)
+                        pair.user_data_["state"] = pair.user_data_["stop_close_state"] 
+                        pair.on_close_trades(close_trades)
 
         # Outstanding positions
         if pair.user_data_["state"] == PairState.OPEN:
@@ -112,16 +129,17 @@ class SlidingFit(PairsTradingFitMethod):
             )
             # outstanding positions
             if config["close_outstanding_positions"]:
-                close_position_trades = self._get_close_position_trades(
+                close_position_trades = self._get_close_trades(
                     pair=pair,
                     row=pred_row,
                     close_threshold=close_threshold,
                 )
                 if close_position_trades is not None:
-                    close_position_trades["status"] = "CLOSE_POSITION"
+                    close_position_trades["status"] = PairState.CLOSE_POSITION.name
                     print(f"CLOSE_POSITION TRADES:\n{close_position_trades}")
                     pair.add_trades(close_position_trades)
-                    pair.user_data_["state"] = PairState.CLOSED_POSITIONS
+                    pair.user_data_["state"] = PairState.CLOSE_POSITION
+                    pair.on_close_trades(close_position_trades)
             else:
                 if pair.predicted_df_ is not None:
                     bt_result.handle_outstanding_position(
@@ -154,9 +172,6 @@ class SlidingFit(PairsTradingFitMethod):
         open_px_a = open_row[f"{colname_a}"]
         open_px_b = open_row[f"{colname_b}"]
 
-        if open_scaled_disequilibrium < open_threshold:
-            return None
-
         # creating the trades
         print(f"OPEN_TRADES: {row["tstamp"]} {open_scaled_disequilibrium=}")
         if open_disequilibrium > 0:
@@ -237,8 +252,6 @@ class SlidingFit(PairsTradingFitMethod):
         close_side_a = pair.user_data_["close_side_a"]
         close_side_b = pair.user_data_["close_side_b"]
 
-        if close_scaled_disequilibrium > close_threshold:
-            return None
         trd_signal_tuples = [
             (
                 close_tstamp,
@@ -276,61 +289,114 @@ class SlidingFit(PairsTradingFitMethod):
             "pair": "object"
         })
 
-    def _get_close_position_trades(
+    # def _get_stop_close_trades(
-        self, pair: TradingPair, row: pd.Series, close_threshold: float
+    #     self, pair: TradingPair, row: pd.Series, close_threshold: float
-    ) -> Optional[pd.DataFrame]:
+    # ) -> Optional[pd.DataFrame]:
-        colname_a, colname_b = pair.colnames()
+    #     colname_a, colname_b = pair.colnames()
+    #     assert pair.predicted_df_ is not None
+    #     if len(pair.predicted_df_) == 0:
+    #         return None
         
-        assert pair.predicted_df_ is not None
+    #     stop_close_row = row
-        if len(pair.predicted_df_) == 0:
+    #     stop_close_tstamp = stop_close_row["tstamp"]    
-            return None
+    #     stop_close_disequilibrium = stop_close_row["disequilibrium"]
+    #     stop_close_scaled_disequilibrium = stop_close_row["scaled_disequilibrium"]
+    #     stop_close_px_a = stop_close_row[f"{colname_a}"]
+    #     stop_close_px_b = stop_close_row[f"{colname_b}"]
         
-        close_position_row = row
+    #     stop_close_side_a = pair.user_data_["close_side_a"]
-        close_position_tstamp = close_position_row["tstamp"]
+    #     stop_close_side_b = pair.user_data_["close_side_b"]
-        close_position_disequilibrium = close_position_row["disequilibrium"]
-        close_position_scaled_disequilibrium = close_position_row["scaled_disequilibrium"]
-        close_position_px_a = close_position_row[f"{colname_a}"]
-        close_position_px_b = close_position_row[f"{colname_b}"]
         
-        close_position_side_a = pair.user_data_["close_side_a"]
+    #     trd_signal_tuples = [
-        close_position_side_b = pair.user_data_["close_side_b"]
+    #         (
+    #             stop_close_tstamp,
+    #             stop_close_side_a,  
+    #             pair.symbol_a_,
+    #             stop_close_px_a,
+    #             stop_close_disequilibrium,
+    #             stop_close_scaled_disequilibrium,
+    #             pair,
+    #         ),  
+    #         (
+    #             stop_close_tstamp,
+    #             stop_close_side_b,
+    #             pair.symbol_b_,
+    #             stop_close_px_b,
+    #             stop_close_disequilibrium,  
+    #             stop_close_scaled_disequilibrium,
+    #             pair,
+    #         ),
+    #     ]
+    #     df = pd.DataFrame(
+    #         trd_signal_tuples,
+    #         columns=self.TRADES_COLUMNS,
+    #     )
+    #     # Ensure consistent dtypes
+    #     return df.astype({
+    #         "time": "datetime64[ns]",
+    #         "action": "string", 
+    #         "symbol": "string", 
+    #         "price": "float64",
+    #         "disequilibrium": "float64",
+    #         "scaled_disequilibrium": "float64",
+    #         "pair": "object"
+    #     })  
 
-        trd_signal_tuples = [
+    # def _get_close_position_trades(
-            (
+    #     self, pair: TradingPair, row: pd.Series, close_threshold: float
-                close_position_tstamp,
+    # ) -> Optional[pd.DataFrame]:
-                close_position_side_a,
+    #     colname_a, colname_b = pair.colnames()
-                pair.symbol_a_,
-                close_position_px_a,
-                close_position_disequilibrium,
-                close_position_scaled_disequilibrium,
-                pair,
-            ),
-            (
-                close_position_tstamp,
-                close_position_side_b,
-                pair.symbol_b_,
-                close_position_px_b,
-                close_position_disequilibrium,
-                close_position_scaled_disequilibrium,
-                pair,
-            ),
-        ]
 
-        # Add tuples to data frame with explicit dtypes to avoid concatenation warnings
+    #     assert pair.predicted_df_ is not None
-        df = pd.DataFrame(
+    #     if len(pair.predicted_df_) == 0:
-            trd_signal_tuples,
+    #         return None
-            columns=self.TRADES_COLUMNS,
+
-        )
+    #     close_position_row = row
-        # Ensure consistent dtypes
+    #     close_position_tstamp = close_position_row["tstamp"]
-        return df.astype({
+    #     close_position_disequilibrium = close_position_row["disequilibrium"]
-            "time": "datetime64[ns]",
+    #     close_position_scaled_disequilibrium = close_position_row["scaled_disequilibrium"]
-            "action": "string",
+    #     close_position_px_a = close_position_row[f"{colname_a}"]
-            "symbol": "string", 
+    #     close_position_px_b = close_position_row[f"{colname_b}"]
-            "price": "float64",
+
-            "disequilibrium": "float64",
+    #     close_position_side_a = pair.user_data_["close_side_a"]
-            "scaled_disequilibrium": "float64",
+    #     close_position_side_b = pair.user_data_["close_side_b"]
-            "pair": "object"
+
-        })
+    #     trd_signal_tuples = [
+    #         (
+    #             close_position_tstamp,
+    #             close_position_side_a,
+    #             pair.symbol_a_,
+    #             close_position_px_a,
+    #             close_position_disequilibrium,
+    #             close_position_scaled_disequilibrium,
+    #             pair,
+    #         ),
+    #         (
+    #             close_position_tstamp,
+    #             close_position_side_b,
+    #             pair.symbol_b_,
+    #             close_position_px_b,
+    #             close_position_disequilibrium,
+    #             close_position_scaled_disequilibrium,
+    #             pair,
+    #         ),
+    #     ]
+
+    #     # Add tuples to data frame with explicit dtypes to avoid concatenation warnings
+    #     df = pd.DataFrame(
+    #         trd_signal_tuples,
+    #         columns=self.TRADES_COLUMNS,
+    #     )
+    #     # Ensure consistent dtypes
+    #     return df.astype({
+    #         "time": "datetime64[ns]",
+    #         "action": "string",
+    #         "symbol": "string", 
+    #         "price": "float64",
+    #         "disequilibrium": "float64",
+    #         "scaled_disequilibrium": "float64",
+    #         "pair": "object"
+    #     })
 
     def reset(self) -> None:
         curr_training_start_idx = 0

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()