to discard

.gitignore

@@ -1,11 +1,10 @@
 # SpecStory explanation file
 __pycache__/
 __OLD__/
-.specstory/
 .history/
 .cursorindexingignore
 data
-.vscode/
+####.vscode/
 cvttpy
 # SpecStory explanation file
 .specstory/.what-is-this.md

PAIRS_TRADING_BACKTEST_USAGE.md

@@ -11,6 +11,7 @@ The enhanced `pt_backtest.py` script now supports multi-day and multi-instrument
 - Support for wildcard patterns in configuration files
 - CLI override for data file specification
 
+
 ### 2. Dynamic Instrument Selection
 - Auto-detection of instruments from database
 - CLI override for instrument specification

README.md

@@ -38,15 +38,12 @@ CONFIG = EQT_CONFIG      # For equity data
 ```
 
 Each configuration dictionary specifies:
-- `security_type`: "CRYPTO" or "EQUITY".
 - `data_directory`: Path to the data files.
 - `datafiles`: A list of database files to process. You can comment/uncomment specific files to include/exclude them from the backtest.
 - `db_table_name`: The name of the table within the SQLite database.
 - `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").
-- `min_required_points`: Minimum data points needed for statistical calculations.
-- `zero_threshold`: A small value to handle potential division by zero.
+- `stat_model_price`: The column in the data to be used as the price (e.g., "close").
 - `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

@@ -1,33 +0,0 @@
-{
-    "security_type": "CRYPTO",
-    "data_directory": "./data/crypto",
-    "datafiles": [
-        "2025*.mktdata.ohlcv.db"
-    ],
-    "db_table_name": "md_1min_bars",
-    "exchange_id": "BNBSPOT",
-    "instrument_id_pfx": "PAIR-",
-    # "instruments": [
-    #     "BTC-USDT",
-    #     "BCH-USDT",
-    #     "ETH-USDT",
-    #     "LTC-USDT",
-    #     "XRP-USDT",
-    #     "ADA-USDT",
-    #     "SOL-USDT",
-    #     "DOT-USDT"
-    # ],
-    "trading_hours": {
-        "begin_session": "00:00:00",
-        "end_session": "23:59:00",
-        "timezone": "UTC"
-    },
-    "price_column": "close",
-    "min_required_points": 30,
-    "zero_threshold": 1e-10,
-    "dis-equilibrium_open_trshld": 2.0,
-    "dis-equilibrium_close_trshld": 0.5,
-    "training_minutes": 120,
-    "funding_per_pair": 2000.0,
-    "strategy_class": "strategies.StaticFitStrategy"
-}

configuration/equity.cfg

@@ -2,7 +2,7 @@
     "security_type": "EQUITY",
     "data_directory": "./data/equity",
     "datafiles": [
-        "202506*.mktdata.ohlcv.db",
+        "20250618.mktdata.ohlcv.db",
     ],
     "db_table_name": "md_1min_bars",
     "exchange_id": "ALPACA",
@@ -19,8 +19,9 @@
     "dis-equilibrium_close_trshld": 1.0,
     "training_minutes": 120,
     "funding_per_pair": 2000.0,
-    "strategy_class": "strategies.StaticFitStrategy"
-    # "strategy_class": "strategies.SlidingFitStrategy"
-    "exclude_instruments": ["CAN"]
+    # "fit_method_class": "pt_trading.sliding_fit.SlidingFit",
+    "fit_method_class": "pt_trading.static_fit.StaticFit",
+    "exclude_instruments": ["CAN"],
+    "close_outstanding_positions": false
     
 }

configuration/equity_lg.cfg

@@ -0,0 +1,26 @@
+{
+    "security_type": "EQUITY",
+    "data_directory": "./data/equity",
+    "datafiles": [
+        "20250602.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",
+    "min_required_points": 30,
+    "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.fit_methods.StaticFit",
+    "exclude_instruments": ["CAN"]
+}
+# "fit_method_class": "pt_trading.fit_methods.SlidingFit",
+# "fit_method_class": "pt_trading.fit_methods.StaticFit",

configuration/vecm.cfg

@@ -0,0 +1,43 @@
+{
+    "market_data_loading": {
+        "CRYPTO": {
+    "data_directory": "./data/crypto",
+    "db_table_name": "md_1min_bars",
+    "instrument_id_pfx": "PAIR-",
+        },
+        "EQUITY": {
+            "data_directory": "./data/equity",
+            "db_table_name": "md_1min_bars",
+            "instrument_id_pfx": "STOCK-",
+        }
+    },
+
+    # ====== Funding ======
+    "funding_per_pair": 2000.0,
+  
+    # ====== Trading Parameters ======
+    "stat_model_price": "close", # "vwap"
+    "execution_price": {
+        "column": "vwap",
+        "shift": 1,
+    },
+    "dis-equilibrium_open_trshld": 2.0,
+    "dis-equilibrium_close_trshld": 1.0,
+    "training_minutes": 120,
+    "fit_method_class": "pt_trading.vecm_rolling_fit.VECMRollingFit",
+
+    # ====== Stop Conditions ======
+    "stop_close_conditions": {
+        "profit": 2.0,
+        "loss": -0.5
+    }
+
+    # ====== End of Session Closeout ======
+    "close_outstanding_positions": true,
+    # "close_outstanding_positions": false,
+    "trading_hours": {
+        "timezone": "America/New_York",
+        "begin_session": "9:30:00",
+        "end_session": "18:30:00",
+    }
+}

configuration/zscore.cfg

@@ -0,0 +1,42 @@
+{
+    "market_data_loading": {
+        "CRYPTO": {
+            "data_directory": "./data/crypto",
+            "db_table_name": "md_1min_bars",
+            "instrument_id_pfx": "PAIR-",
+        },
+        "EQUITY": {
+            "data_directory": "./data/equity",
+            "db_table_name": "md_1min_bars",
+            "instrument_id_pfx": "STOCK-",
+        }
+    },
+
+    # ====== Funding ======
+    "funding_per_pair": 2000.0,
+    # ====== Trading Parameters ======
+    "stat_model_price": "close",
+    "execution_price": {
+        "column": "vwap",
+        "shift": 1,
+    },
+    "dis-equilibrium_open_trshld": 2.0,
+    "dis-equilibrium_close_trshld": 0.5,
+    "training_minutes": 120,
+    "fit_method_class": "pt_trading.z-score_rolling_fit.ZScoreRollingFit",
+
+    # ====== Stop Conditions ======
+    "stop_close_conditions": {
+        "profit": 2.0,
+        "loss": -0.5
+    }
+
+    # ====== End of Session Closeout ======
+    "close_outstanding_positions": true,
+    # "close_outstanding_positions": false,
+    "trading_hours": {
+        "timezone": "America/New_York",
+        "begin_session": "9:30:00",
+        "end_session": "18:30:00",
+    }
+}

lg_notes.md

@@ -0,0 +1,115 @@
+07.11.2025
+pairs_trading/configuration <---- directory for config
+equity_lg.cfg <-------- copy of equity.cfg
+How to run a Program: TRIANGLEsquare ----> triangle EQUITY backtest
+Results are in  > results (timestamp table for all runs)
+table "...timestamp... .pt_backtest_results.equity.db"
+going to table  using sqlite
+> sqlite3 '/home/coder/results/20250721_175750.pt_backtest_results.equity.db'
+
+sqlite> .databases
+main: /home/coder/results/20250717_180122.pt_backtest_results.equity.db r/w
+sqlite> .tables
+config                 outstanding_positions  pt_bt_results   
+
+sqlite> PRAGMA table_info('pt_bt_results');
+0|date|DATE|0||0
+1|pair|TEXT|0||0
+2|symbol|TEXT|0||0
+3|open_time|DATETIME|0||0
+4|open_side|TEXT|0||0
+5|open_price|REAL|0||0
+6|open_quantity|INTEGER|0||0
+7|open_disequilibrium|REAL|0||0
+8|close_time|DATETIME|0||0
+9|close_side|TEXT|0||0
+10|close_price|REAL|0||0
+11|close_quantity|INTEGER|0||0
+12|close_disequilibrium|REAL|0||0
+13|symbol_return|REAL|0||0
+14|pair_return|REAL|0||0
+
+select count(*) as cnt from pt_bt_results;
+8
+
+select * from pt_bt_results;
+
+select 
+date, close_time, pair, symbol, symbol_return, pair_return
+from pt_bt_results ;
+
+select date, sum(symbol_return) as daily_return 
+from pt_bt_results where date = '2025-06-18' group by date;
+
+.quit
+
+sqlite3 '/home/coder/results/20250717_172435.pt_backtest_results.equity.db'
+
+sqlite> select date, sum(symbol_return) as daily_return 
+from pt_bt_results group by date;
+
+2025-06-02|1.29845390060828
+...
+2025-06-18|-43.5084977104115 <========== ????? ==========>
+2025-06-20|11.8605547517183
+
+
+select 
+date, close_time, pair, symbol, symbol_return, pair_return
+from pt_bt_results ;
+
+select date, close_time, pair, symbol, symbol_return, pair_return
+from pt_bt_results where date = '2025-06-18';
+
+
+./scripts/load_equity_pair_intraday.sh -A NVDA -B QQQ -d 20250701  -T ./intraday_md
+
+to inspect exactly what sources, formats, and processing steps you can open the script with:
+head -n 50 ./scripts/load_equity_pair_intraday.sh
+
+
+
+✓ Data file found: /home/coder/pairs_trading/data/crypto/20250605.mktdata.ohlcv.db
+
+sqlite3 '/home/coder/results/20250722_201930.pt_backtest_results.crypto.db'
+
+sqlite3 '/home/coder/results/xxxxxxxx_yyyyyy.pt_backtest_results.pseudo.db'
+
+11111111
+=== At your terminal, run these commands:
+sqlite3 '/home/coder/results/20250722_201930.pt_backtest_results.crypto.db'
+=== Then inside the SQLite prompt:
+.mode csv
+.headers on
+.output results_20250722.csv
+SELECT * FROM pt_bt_results;
+.output stdout
+.quit
+
+cd /home/coder/
+
+# === mode csv formats output as CSV
+# === headers on includes column names
+# === output my_table.csv directs output to that file
+# === Run your SELECT query, then revert output
+# === Open my_table.csv in Excel directly
+
+# ======== Using scp (Secure Copy)
+# === On your local machine, open a terminal and run:
+scp cvtt@953f6e8df266:/home/coder/results_20250722.csv ~/Downloads/
+
+
+# ===== convert cvs pandas dataframe ====== -->
+import pandas as pd
+# Replace with the actual path to your CSV file
+file_path = '/home/coder/results_20250722.csv'
+# Read the CSV file into a DataFrame
+df = pd.read_csv(file_path)
+# Show the first few rows
+print(df.head())
+
+
+
+
+
+

lib/cvtt/mkt_data.py

@@ -0,0 +1,188 @@
+#!/usr/bin/env python3
+
+import argparse
+from ast import Sub
+import asyncio
+from functools import partial
+import json
+import logging
+import uuid
+from dataclasses import dataclass
+from typing import Callable, Coroutine, Dict, List, Optional
+
+from numpy.strings import str_len
+import websockets
+from websockets.asyncio.client import ClientConnection
+
+MessageTypeT = str
+SubscriptionIdT = str
+MessageT = Dict
+UrlT = str
+CallbackT = Callable[[MessageTypeT, SubscriptionIdT, MessageT], Coroutine[None, str, None]]
+
+@dataclass
+class CvttPricesSubscription:
+    id_: str
+    exchange_config_name_: str
+    instrument_id_: str
+    interval_sec_: int
+    history_depth_sec_: int
+    is_subscribed_: bool
+    is_historical_: bool
+    callback_: CallbackT
+
+    def __init__(
+        self,
+        exchange_config_name: str,
+        instrument_id: str,
+        interval_sec: int,
+        history_depth_sec: int,
+        callback: CallbackT,
+    ):
+        self.exchange_config_name_ = exchange_config_name
+        self.instrument_id_ = instrument_id
+        self.interval_sec_ = interval_sec
+        self.history_depth_sec_ = history_depth_sec
+        self.callback_ = callback
+        self.id_ = str(uuid.uuid4())
+        self.is_subscribed_ = False
+        self.is_historical_ = history_depth_sec > 0
+
+
+class CvttPricerWebSockClient:
+    # Class members with type hints
+    ws_url_: UrlT
+    websocket_: Optional[ClientConnection]
+    subscriptions_: Dict[SubscriptionIdT, CvttPricesSubscription]
+    is_connected_: bool
+    logger_: logging.Logger
+
+    def __init__(self, url: str):
+        self.ws_url_ = url
+        self.websocket_ = None
+        self.is_connected_ = False
+        self.subscriptions_ = {}
+        self.logger_ = logging.getLogger(__name__)
+        logging.basicConfig(level=logging.INFO)
+
+    async def subscribe(
+        self, subscription: CvttPricesSubscription
+    ) -> str:  # returns subscription id
+
+        if not self.is_connected_:
+            try:
+                self.logger_.info(f"Connecting to {self.ws_url_}")
+                self.websocket_ = await websockets.connect(self.ws_url_)
+                self.is_connected_ = True
+            except Exception as e:
+                self.logger_.error(f"Unable to connect to {self.ws_url_}: {str(e)}")
+                raise e
+
+        subscr_msg = {
+            "type": "subscr",
+            "id": subscription.id_,
+            "subscr_type": "MD_AGGREGATE",
+            "exchange_config_name": subscription.exchange_config_name_,
+            "instrument_id": subscription.instrument_id_,
+            "interval_sec": subscription.interval_sec_,
+        }
+        if subscription.is_historical_:
+            subscr_msg["history_depth_sec"] = subscription.history_depth_sec_
+
+        assert self.websocket_ is not None
+        await self.websocket_.send(json.dumps(subscr_msg))
+
+        response = await self.websocket_.recv()
+        response_data = json.loads(response)
+        if not await self.handle_subscription_response(subscription, response_data):
+            await self.websocket_.close()
+            self.is_connected_ = False
+            raise Exception(f"Subscription failed: {str(response)}")
+
+        self.subscriptions_[subscription.id_] = subscription
+        return subscription.id_
+
+    async def handle_subscription_response(
+        self, subscription: CvttPricesSubscription, response: dict
+    ) -> bool:
+        if response.get("type") != "subscr" or response.get("id") != subscription.id_:
+            return False
+
+        if response.get("status") == "success":
+            self.logger_.info(f"Subscription successful: {json.dumps(response)}")
+            return True
+        elif response.get("status") == "error":
+            self.logger_.error(f"Subscription failed: {response.get('reason')}")
+            return False
+        return False
+
+    async def run(self) -> None:
+        assert self.websocket_
+        try:
+            while self.is_connected_:
+                try:
+                    message = await self.websocket_.recv()
+                    message_str = (
+                        message.decode("utf-8")
+                        if isinstance(message, bytes)
+                        else message
+                    )
+                    await self.process_message(json.loads(message_str))
+                except websockets.ConnectionClosed:
+                    self.logger_.warning("Connection closed")
+                    self.is_connected_ = False
+                    break
+        except Exception as e:
+            self.logger_.error(f"Error occurred: {str(e)}")
+            self.is_connected_ = False
+            await asyncio.sleep(5)  # Wait before reconnecting
+
+    async def process_message(self, message: Dict) -> None:
+        message_type = message.get("type")
+        if message_type in ["md_aggregate", "historical_md_aggregate"]:
+            subscription_id = message.get("subscr_id")
+            if subscription_id not in self.subscriptions_:
+                self.logger_.warning(f"Unknown subscription id: {subscription_id}")
+                return
+
+            subscription = self.subscriptions_[subscription_id]
+            await subscription.callback_(message_type, subscription_id, message)
+        else:
+            self.logger_.warning(f"Unknown message type: {message.get('type')}")
+
+
+async def main() -> None:
+    async def on_message(message_type: MessageTypeT, subscr_id: SubscriptionIdT, message: Dict, instrument_id: str) -> None:
+        print(f"{message_type=} {subscr_id=} {instrument_id}")
+        if message_type == "md_aggregate":
+            aggr = message.get("md_aggregate", [])
+            print(f"[{aggr['tstmp'][:19]}] *** RLTM ***  {message}")
+        elif message_type == "historical_md_aggregate":
+            for aggr in message.get("historical_data", []):
+                print(f"[{aggr['tstmp'][:19]}] *** HIST *** {aggr}")
+        else:
+            print(f"Unknown message type: {message_type}")
+        
+    pricer_client = CvttPricerWebSockClient(
+        "ws://localhost:12346/ws"
+    )
+    await pricer_client.subscribe(CvttPricesSubscription(
+        exchange_config_name="COINBASE_AT",
+        instrument_id="PAIR-BTC-USD",
+        interval_sec=60,
+        history_depth_sec=60*60*24,
+        callback=partial(on_message, instrument_id="PAIR-BTC-USD")
+    ))
+    await pricer_client.subscribe(CvttPricesSubscription(
+        exchange_config_name="COINBASE_AT",
+        instrument_id="PAIR-ETH-USD",
+        interval_sec=60,
+        history_depth_sec=60*60*24,
+        callback=partial(on_message, instrument_id="PAIR-ETH-USD")
+    ))
+
+    await pricer_client.run() 
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

lib/pt_trading/fit_method.py

@@ -0,0 +1,52 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from enum import Enum
+from typing import Dict, Optional, cast
+
+import pandas as pd 
+from pt_trading.results import BacktestResult
+from pt_trading.trading_pair import TradingPair
+
+NanoPerMin = 1e9
+
+
+class PairsTradingFitMethod(ABC):
+    TRADES_COLUMNS = [
+        "time",
+        "symbol",
+        "side",
+        "action",
+        "price",
+        "disequilibrium",
+        "scaled_disequilibrium",
+        "signed_scaled_disequilibrium",
+        "pair",
+    ]
+    @staticmethod
+    def create(config: Dict) -> PairsTradingFitMethod:
+        import importlib
+        fit_method_class_name = config.get("fit_method_class", None)
+        assert fit_method_class_name is not None
+        module_name, class_name = fit_method_class_name.rsplit(".", 1)
+        module = importlib.import_module(module_name)
+        fit_method = getattr(module, class_name)()
+        return cast(PairsTradingFitMethod, fit_method)
+
+    @abstractmethod
+    def run_pair(
+        self, pair: TradingPair, bt_result: BacktestResult
+    ) -> Optional[pd.DataFrame]: ...
+
+    @abstractmethod
+    def reset(self) -> None: ...
+
+    @abstractmethod
+    def create_trading_pair(
+        self,
+        config: Dict,
+        market_data: pd.DataFrame,
+        symbol_a: str,
+        symbol_b: str,
+    ) -> TradingPair: ...
+

lib/pt_trading/results.py

@@ -0,0 +1,743 @@
+import os
+import sqlite3
+from datetime import date, datetime
+from typing import Any, Dict, List, Optional, Tuple
+
+import pandas as pd
+from pt_trading.trading_pair import TradingPair
+
+
+# Recommended replacement adapters and converters for Python 3.12+
+# From: https://docs.python.org/3/library/sqlite3.html#sqlite3-adapter-converter-recipes
+def adapt_date_iso(val: date) -> str:
+    """Adapt datetime.date to ISO 8601 date."""
+    return val.isoformat()
+
+
+def adapt_datetime_iso(val: datetime) -> str:
+    """Adapt datetime.datetime to timezone-naive ISO 8601 date."""
+    return val.isoformat()
+
+
+def convert_date(val: bytes) -> date:
+    """Convert ISO 8601 date to datetime.date object."""
+    return datetime.fromisoformat(val.decode()).date()
+
+
+def convert_datetime(val: bytes) -> datetime:
+    """Convert ISO 8601 datetime to datetime.datetime object."""
+    return datetime.fromisoformat(val.decode())
+
+
+# Register the adapters and converters
+sqlite3.register_adapter(date, adapt_date_iso)
+sqlite3.register_adapter(datetime, adapt_datetime_iso)
+sqlite3.register_converter("date", convert_date)
+sqlite3.register_converter("datetime", convert_datetime)
+
+
+def create_result_database(db_path: str) -> None:
+    """
+    Create the SQLite database and required tables if they don't exist.
+    """
+    try:
+        # Create directory if it doesn't exist
+        db_dir = os.path.dirname(db_path)
+        if db_dir and not os.path.exists(db_dir):
+            os.makedirs(db_dir, exist_ok=True)
+            print(f"Created directory: {db_dir}")
+
+        conn = sqlite3.connect(db_path)
+        cursor = conn.cursor()
+
+        # Create the pt_bt_results table for completed trades
+        cursor.execute(
+            """
+            CREATE TABLE IF NOT EXISTS pt_bt_results (
+                date DATE,
+                pair TEXT,
+                symbol TEXT,
+                open_time DATETIME,
+                open_side TEXT,
+                open_price REAL,
+                open_quantity INTEGER,
+                open_disequilibrium REAL,
+                close_time DATETIME,
+                close_side TEXT,
+                close_price REAL,
+                close_quantity INTEGER,
+                close_disequilibrium REAL,
+                symbol_return REAL,
+                pair_return REAL,
+                close_condition TEXT
+            )
+        """
+        )
+        cursor.execute("DELETE FROM pt_bt_results;")
+
+        # Create the outstanding_positions table for open positions
+        cursor.execute(
+            """
+            CREATE TABLE IF NOT EXISTS outstanding_positions (
+                date DATE,
+                pair TEXT,
+                symbol TEXT,
+                position_quantity REAL,
+                last_price REAL,
+                unrealized_return REAL,
+                open_price REAL,
+                open_side TEXT
+            )
+        """
+        )
+        cursor.execute("DELETE FROM outstanding_positions;")
+
+        # Create the config table for storing configuration JSON for reference
+        cursor.execute(
+            """
+            CREATE TABLE IF NOT EXISTS config (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                run_timestamp DATETIME,
+                config_file_path TEXT,
+                config_json TEXT,
+                fit_method_class TEXT,
+                datafiles TEXT,
+                instruments TEXT
+            )
+        """
+        )
+        cursor.execute("DELETE FROM config;")
+
+        conn.commit()
+        conn.close()
+
+    except Exception as e:
+        print(f"Error creating result database: {str(e)}")
+        raise
+
+
+def store_config_in_database(
+    db_path: str,
+    config_file_path: str,
+    config: Dict,
+    fit_method_class: str,
+    datafiles: List[Tuple[str, str]],
+    instruments: List[Dict[str, str]],
+) -> None:
+    """
+    Store configuration information in the database for reference.
+    """
+    import json
+
+    if db_path.upper() == "NONE":
+        return
+
+    try:
+        conn = sqlite3.connect(db_path)
+        cursor = conn.cursor()
+
+        # Convert config to JSON string
+        config_json = json.dumps(config, indent=2, default=str)
+
+        # Convert lists to comma-separated strings for storage
+        datafiles_str = ", ".join([f"{datafile}" for _, datafile in datafiles])
+        instruments_str = ", ".join(
+            [
+                f"{inst['symbol']}:{inst['instrument_type']}:{inst['exchange_id']}"
+                for inst in instruments
+            ]
+        )
+
+        # Insert configuration record
+        cursor.execute(
+            """
+            INSERT INTO config (
+                run_timestamp, config_file_path, config_json, fit_method_class, datafiles, instruments
+            ) VALUES (?, ?, ?, ?, ?, ?)
+        """,
+            (
+                datetime.now(),
+                config_file_path,
+                config_json,
+                fit_method_class,
+                datafiles_str,
+                instruments_str,
+            ),
+        )
+
+        conn.commit()
+        conn.close()
+
+        print(f"Configuration stored in database")
+
+    except Exception as e:
+        print(f"Error storing configuration in database: {str(e)}")
+        import traceback
+
+        traceback.print_exc()
+
+
+def convert_timestamp(timestamp: Any) -> Optional[datetime]:
+    """Convert pandas Timestamp to Python datetime object for SQLite compatibility."""
+    if timestamp is None:
+        return None
+    if isinstance(timestamp, pd.Timestamp):
+        return timestamp.to_pydatetime()
+    elif isinstance(timestamp, datetime):
+        return timestamp
+    elif isinstance(timestamp, date):
+        return datetime.combine(timestamp, datetime.min.time())
+    elif isinstance(timestamp, str):
+        return datetime.strptime(timestamp, "%Y-%m-%d %H:%M:%S")
+    elif isinstance(timestamp, int):
+        return datetime.fromtimestamp(timestamp)
+    else:
+        raise ValueError(f"Unsupported timestamp type: {type(timestamp)}")
+
+
+
+class BacktestResult:
+    """
+    Class to handle backtest results, trades tracking, PnL calculations, and reporting.
+    """
+
+    def __init__(self, config: Dict[str, Any]):
+        self.config = config
+        self.trades: Dict[str, Dict[str, Any]] = {}
+        self.total_realized_pnl = 0.0
+        self.outstanding_positions: List[Dict[str, Any]] = []
+        self.pairs_trades_: Dict[str, List[Dict[str, Any]]] = {}
+        
+    def add_trade(
+        self,
+        pair_nm: str,
+        symbol: str,
+        side: str,
+        action: str,
+        price: Any,
+        disequilibrium: Optional[float] = None,
+        scaled_disequilibrium: Optional[float] = None,
+        timestamp: Optional[datetime] = None,
+        status: Optional[str] = None,
+    ) -> None:
+        """Add a trade to the results tracking."""
+        pair_nm = str(pair_nm)
+
+        if pair_nm not in self.trades:
+            self.trades[pair_nm] = {symbol: []}
+        if symbol not in self.trades[pair_nm]:
+            self.trades[pair_nm][symbol] = []
+        self.trades[pair_nm][symbol].append(
+            {
+                "symbol": symbol,
+                "side": side,
+                "action": action,
+                "price": price,
+                "disequilibrium": disequilibrium,
+                "scaled_disequilibrium": scaled_disequilibrium,
+                "timestamp": timestamp,
+                "status": status,
+            }
+        )
+
+    def add_outstanding_position(self, position: Dict[str, Any]) -> None:
+        """Add an outstanding position to tracking."""
+        self.outstanding_positions.append(position)
+
+    def add_realized_pnl(self, realized_pnl: float) -> None:
+        """Add realized PnL to the total."""
+        self.total_realized_pnl += realized_pnl
+
+    def get_total_realized_pnl(self) -> float:
+        """Get total realized PnL."""
+        return self.total_realized_pnl
+
+    def get_outstanding_positions(self) -> List[Dict[str, Any]]:
+        """Get all outstanding positions."""
+        return self.outstanding_positions
+
+    def get_trades(self) -> Dict[str, Dict[str, Any]]:
+        """Get all trades."""
+        return self.trades
+
+    def clear_trades(self) -> None:
+        """Clear all trades (used when processing new files)."""
+        self.trades.clear()
+
+    def collect_single_day_results(self, pairs_trades: List[pd.DataFrame]) -> None:
+        """Collect and process single day trading results."""
+        result = pd.concat(pairs_trades, ignore_index=True)
+        result["time"] = pd.to_datetime(result["time"])
+        result = result.set_index("time").sort_index()
+
+        print("\n --------------  Suggested Trades ")
+        print(result)
+
+        for row in result.itertuples():
+            side = row.side
+            action = row.action
+            symbol = row.symbol
+            price = row.price
+            disequilibrium = getattr(row, "disequilibrium", None)
+            scaled_disequilibrium = getattr(row, "scaled_disequilibrium", None)
+            if hasattr(row, "time"):
+                timestamp = getattr(row, "time")
+            else:
+                timestamp = convert_timestamp(row.Index)
+            status = row.status
+            self.add_trade(
+                pair_nm=str(row.pair),
+                symbol=str(symbol),
+                side=str(side),
+                action=str(action),
+                price=float(str(price)),
+                disequilibrium=disequilibrium,
+                scaled_disequilibrium=scaled_disequilibrium,
+                timestamp=timestamp,
+                status=str(status) if status is not None else "?",
+            )
+
+    def print_single_day_results(self) -> None:
+        """Print single day results summary."""
+        for pair, symbols in self.trades.items():
+            print(f"\n--- {pair} ---")
+            for symbol, trades in symbols.items():
+                for trade_data in trades:
+                    if len(trade_data) >= 2:
+                        side, price = trade_data[:2]
+                        print(f"{symbol} {side} at ${price}")
+
+    def print_results_summary(self, all_results: Dict[str, Dict[str, Any]]) -> None:
+        """Print summary of all processed files."""
+        print("\n====== Summary of All Processed Files ======")
+        for filename, data in all_results.items():
+            trade_count = sum(
+                len(trades)
+                for symbol_trades in data["trades"].values()
+                for trades in symbol_trades.values()
+            )
+            print(f"{filename}: {trade_count} trades")
+
+    def calculate_returns(self, all_results: Dict[str, Dict[str, Any]]) -> None:
+        """Calculate and print returns by day and pair."""
+        def _symbol_return(trade1_side: str, trade1_px: float, trade2_side: str, trade2_px: float) -> float:
+            if trade1_side == "BUY" and trade2_side == "SELL":
+                return (trade2_px - trade1_px) / trade1_px * 100
+            elif trade1_side == "SELL" and trade2_side == "BUY":
+                return (trade1_px - trade2_px) / trade1_px * 100
+            else:
+                return 0
+
+        print("\n====== Returns By Day and Pair ======")
+
+        trades = []
+        for filename, data in all_results.items():
+            pairs = list(data["trades"].keys())
+            for pair in pairs:
+                self.pairs_trades_[pair] = []
+                trades_dict = data["trades"][pair]
+                for symbol in trades_dict.keys():
+                    trades.extend(trades_dict[symbol])
+            trades = sorted(trades, key=lambda x: (x["timestamp"], x["symbol"]))
+
+            print(f"\n--- {filename} ---")
+
+            self.outstanding_positions = data["outstanding_positions"]
+            
+            day_return = 0.0
+            for idx in range(0, len(trades), 4):
+                symbol_a = trades[idx]["symbol"]
+                trade_a_1 = trades[idx]
+                trade_a_2 = trades[idx + 2]
+
+                symbol_b = trades[idx + 1]["symbol"]
+                trade_b_1 = trades[idx + 1]
+                trade_b_2 = trades[idx + 3]
+
+                symbol_return = 0
+                assert (
+                    trade_a_1["timestamp"] < trade_a_2["timestamp"]
+                ), f"Trade 1: {trade_a_1['timestamp']} is not less than Trade 2: {trade_a_2['timestamp']}"
+                assert (
+                    trade_a_1["action"] == "OPEN" and trade_a_2["action"] == "CLOSE"
+                ), f"Trade 1: {trade_a_1['action']} and Trade 2: {trade_a_2['action']} are the same"
+
+                # Calculate return based on action combination
+                trade_return = 0
+                symbol_a_return = _symbol_return(trade_a_1["side"], trade_a_1["price"], trade_a_2["side"], trade_a_2["price"])
+                symbol_b_return = _symbol_return(trade_b_1["side"], trade_b_1["price"], trade_b_2["side"], trade_b_2["price"])
+
+                pair_return = symbol_a_return + symbol_b_return
+
+                self.pairs_trades_[pair].append(
+                    {
+                        "symbol": symbol_a,
+                        "open_side": trade_a_1["side"],
+                        "open_action": trade_a_1["action"],
+                        "open_price": trade_a_1["price"],
+                        "close_side": trade_a_2["side"],
+                        "close_action": trade_a_2["action"],
+                        "close_price": trade_a_2["price"],
+                        "symbol_return": symbol_a_return,
+                        "open_disequilibrium": trade_a_1["disequilibrium"],
+                        "open_scaled_disequilibrium": trade_a_1["scaled_disequilibrium"],
+                        "close_disequilibrium": trade_a_2["disequilibrium"],
+                        "close_scaled_disequilibrium": trade_a_2["scaled_disequilibrium"],
+                        "open_time": trade_a_1["timestamp"],
+                        "close_time": trade_a_2["timestamp"],
+                        "shares": self.config["funding_per_pair"] / 2  / trade_a_1["price"],
+                        "is_completed": True,
+                        "close_condition": trade_a_2["status"],
+                        "pair_return": pair_return
+                    }
+                )
+                self.pairs_trades_[pair].append(
+                    {
+                        "symbol": symbol_b,
+                        "open_side": trade_b_1["side"],
+                        "open_action": trade_b_1["action"],
+                        "open_price": trade_b_1["price"],
+                        "close_side": trade_b_2["side"],
+                        "close_action": trade_b_2["action"],
+                        "close_price": trade_b_2["price"],
+                        "symbol_return": symbol_b_return,
+                        "open_disequilibrium": trade_b_1["disequilibrium"],
+                        "open_scaled_disequilibrium": trade_b_1["scaled_disequilibrium"],
+                        "close_disequilibrium": trade_b_2["disequilibrium"],
+                        "close_scaled_disequilibrium": trade_b_2["scaled_disequilibrium"],
+                        "open_time": trade_b_1["timestamp"],
+                        "close_time": trade_b_2["timestamp"],
+                        "shares": self.config["funding_per_pair"] / 2  / trade_b_1["price"],
+                        "is_completed": True,
+                        "close_condition": trade_b_2["status"],
+                        "pair_return": pair_return
+                    }
+                )
+
+
+            # Print pair returns with disequilibrium information
+            day_return = 0.0
+            if pair in self.pairs_trades_:
+
+                print(f"{pair}:")
+                pair_return = 0.0
+                for trd in self.pairs_trades_[pair]:
+                    disequil_info = ""
+                    if (
+                        trd["open_scaled_disequilibrium"] is not None
+                        and trd["open_scaled_disequilibrium"] is not None
+                    ):
+                        disequil_info = (
+                            f' | Open Dis-eq: {trd["open_scaled_disequilibrium"]:.2f},'
+                            f' Close Dis-eq: {trd["close_scaled_disequilibrium"]:.2f}'
+                        )
+
+                    print(
+                        f' {trd["open_time"].time()}-{trd["close_time"].time()}  {trd["symbol"]}: '
+                        f' {trd["open_side"]} @ ${trd["open_price"]:.2f},'
+                        f' {trd["close_side"]} @ ${trd["close_price"]:.2f},'
+                        f' Return: {trd["symbol_return"]:.2f}%{disequil_info}'
+                    )
+                    pair_return += trd["symbol_return"]
+
+                print(f"    Pair Total Return: {pair_return:.2f}%")
+                day_return += pair_return
+
+            # Print day total return and add to global realized PnL
+            if day_return != 0:
+                print(f"  Day Total Return: {day_return:.2f}%")
+                self.add_realized_pnl(day_return)
+
+    def print_outstanding_positions(self) -> None:
+        """Print all outstanding positions with share quantities and current values."""
+        if not self.get_outstanding_positions():
+            print("\n====== NO OUTSTANDING POSITIONS ======")
+            return
+
+        print(f"\n====== OUTSTANDING POSITIONS ======")
+        print(
+            f"{'Pair':<15}"
+            f" {'Symbol':<10}"
+            f" {'Side':<4}"
+            f" {'Shares':<10}"
+            f" {'Open $':<8}"
+            f" {'Current $':<10}"
+            f" {'Value $':<12}"
+            f" {'Disequilibrium':<15}"
+        )
+        print("-" * 100)
+
+        total_value = 0.0
+
+        for pos in self.get_outstanding_positions():
+            # Print position A
+            print(
+                f"{pos['pair']:<15}"
+                f" {pos['symbol_a']:<10}"
+                f" {pos['side_a']:<4}"
+                f" {pos['shares_a']:<10.2f}"
+                f" {pos['open_px_a']:<8.2f}"
+                f" {pos['current_px_a']:<10.2f}"
+                f" {pos['current_value_a']:<12.2f}"
+                f" {'':<15}"
+            )
+
+            # Print position B
+            print(
+                f"{'':<15}"
+                f" {pos['symbol_b']:<10}"
+                f" {pos['side_b']:<4}"
+                f" {pos['shares_b']:<10.2f}"
+                f" {pos['open_px_b']:<8.2f}"
+                f" {pos['current_px_b']:<10.2f}"
+                f" {pos['current_value_b']:<12.2f}"
+            )
+
+            # Print pair totals with disequilibrium info
+            print(
+                f"{'':<15}"
+                f" {'PAIR TOTAL':<10}"
+                f" {'':<4}"
+                f" {'':<10}"
+                f" {'':<8}"
+                f" {'':<10}"
+                f" {pos['total_current_value']:<12.2f}"
+            )
+
+            # Print disequilibrium details
+            print(
+                f"{'':<15}"
+                f" {'DISEQUIL':<10}"
+                f" {'':<4}"
+                f" {'':<10}"
+                f" {'':<8}"
+                f" {'':<10}"
+                f" Raw: {pos['current_disequilibrium']:<6.4f}"
+                f" Scaled: {pos['current_scaled_disequilibrium']:<6.4f}"
+            )
+
+            print("-" * 100)
+
+            total_value += pos["total_current_value"]
+
+        print(f"{'TOTAL OUTSTANDING VALUE':<80} ${total_value:<12.2f}")
+
+    def print_grand_totals(self) -> None:
+        """Print grand totals across all pairs."""
+        print(f"\n====== GRAND TOTALS ACROSS ALL PAIRS ======")
+        print(f"Total Realized PnL: {self.get_total_realized_pnl():.2f}%")
+
+    def handle_outstanding_position(
+        self,
+        pair: TradingPair,
+        pair_result_df: pd.DataFrame,
+        last_row_index: int,
+        open_side_a: str,
+        open_side_b: str,
+        open_px_a: float,
+        open_px_b: float,
+        open_tstamp: datetime,
+    ) -> Tuple[float, float, float]:
+        """
+        Handle calculation and tracking of outstanding positions when no close signal is found.
+
+        Args:
+            pair: TradingPair object
+            pair_result_df: DataFrame with pair results
+            last_row_index: Index of the last row in the data
+            open_side_a, open_side_b: Trading sides for symbols A and B
+            open_px_a, open_px_b: Opening prices for symbols A and B
+            open_tstamp: Opening timestamp
+        """
+        if pair_result_df is None or pair_result_df.empty:
+            return 0, 0, 0
+
+        last_row = pair_result_df.loc[last_row_index]
+        last_tstamp = last_row["tstamp"]
+        colname_a, colname_b = pair.exec_prices_colnames()
+        last_px_a = last_row[colname_a]
+        last_px_b = last_row[colname_b]
+
+        # Calculate share quantities based on funding per pair
+        # Split funding equally between the two positions
+        funding_per_position = self.config["funding_per_pair"] / 2
+        shares_a = funding_per_position / open_px_a
+        shares_b = funding_per_position / open_px_b
+
+        # Calculate current position values (shares * current price)
+        current_value_a = shares_a * last_px_a * (-1 if open_side_a == "SELL" else 1)
+        current_value_b = shares_b * last_px_b * (-1 if open_side_b == "SELL" else 1)
+        total_current_value = current_value_a + current_value_b
+
+        # Get disequilibrium information
+        current_disequilibrium = last_row["disequilibrium"]
+        current_scaled_disequilibrium = last_row["scaled_disequilibrium"]
+
+        # Store outstanding positions
+        self.add_outstanding_position(
+            {
+                "pair": str(pair),
+                "symbol_a": pair.symbol_a_,
+                "symbol_b": pair.symbol_b_,
+                "side_a": open_side_a,
+                "side_b": open_side_b,
+                "shares_a": shares_a,
+                "shares_b": shares_b,
+                "open_px_a": open_px_a,
+                "open_px_b": open_px_b,
+                "current_px_a": last_px_a,
+                "current_px_b": last_px_b,
+                "current_value_a": current_value_a,
+                "current_value_b": current_value_b,
+                "total_current_value": total_current_value,
+                "open_time": open_tstamp,
+                "last_time": last_tstamp,
+                "current_abs_term": current_scaled_disequilibrium,
+                "current_disequilibrium": current_disequilibrium,
+                "current_scaled_disequilibrium": current_scaled_disequilibrium,
+            }
+        )
+
+        # Print position details
+        print(f"{pair}: NO CLOSE SIGNAL FOUND - Position held until end of session")
+        print(f"  Open: {open_tstamp} | Last: {last_tstamp}")
+        print(
+            f"  {pair.symbol_a_}: {open_side_a} {shares_a:.2f} shares @ ${open_px_a:.2f} -> ${last_px_a:.2f} | Value: ${current_value_a:.2f}"
+        )
+        print(
+            f"  {pair.symbol_b_}: {open_side_b} {shares_b:.2f} shares @ ${open_px_b:.2f} -> ${last_px_b:.2f} | Value: ${current_value_b:.2f}"
+        )
+        print(f"  Total Value: ${total_current_value:.2f}")
+        print(
+            f"  Disequilibrium: {current_disequilibrium:.4f} | Scaled: {current_scaled_disequilibrium:.4f}"
+        )
+
+        return current_value_a, current_value_b, total_current_value
+    
+    def store_results_in_database(
+        self, db_path: str, day: str
+    ) -> None:
+        """
+        Store backtest results in the SQLite database.
+        """
+        if db_path.upper() == "NONE":
+            return
+
+        try:
+            # Extract date from datafile name (assuming format like 20250528.mktdata.ohlcv.db)
+            date_str = day
+
+            # Convert to proper date format
+            try:
+                date_obj = datetime.strptime(date_str, "%Y%m%d").date()
+            except ValueError:
+                # If date parsing fails, use current date
+                date_obj = datetime.now().date()
+
+            conn = sqlite3.connect(db_path)
+            cursor = conn.cursor()
+
+            # Process each trade from bt_result
+            trades = self.get_trades()
+
+            for pair_name, _ in trades.items():
+
+                # Second pass: insert completed trade records into database
+                for trade_pair in sorted(self.pairs_trades_[pair_name], key=lambda x: x["open_time"]):
+                    # Only store completed trades in pt_bt_results table
+                    cursor.execute(
+                        """
+                        INSERT INTO pt_bt_results (
+                            date, pair, symbol, open_time, open_side, open_price, 
+                            open_quantity, open_disequilibrium, close_time, close_side, 
+                            close_price, close_quantity, close_disequilibrium, 
+                            symbol_return, pair_return, close_condition
+                        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                    """,
+                        (
+                            date_obj,
+                            pair_name,
+                            trade_pair["symbol"],
+                            trade_pair["open_time"],
+                            trade_pair["open_side"],
+                            trade_pair["open_price"],
+                            trade_pair["shares"],
+                            trade_pair["open_scaled_disequilibrium"],
+                            trade_pair["close_time"],
+                            trade_pair["close_side"],
+                            trade_pair["close_price"],
+                            trade_pair["shares"],
+                            trade_pair["close_scaled_disequilibrium"],
+                            trade_pair["symbol_return"],
+                            trade_pair["pair_return"],
+                            trade_pair["close_condition"]
+                        ),
+                    )
+
+            # Store outstanding positions in separate table
+            outstanding_positions = self.get_outstanding_positions()
+            for pos in outstanding_positions:
+                # Calculate position quantity (negative for SELL positions)
+                position_qty_a = (
+                    pos["shares_a"] if pos["side_a"] == "BUY" else -pos["shares_a"]
+                )
+                position_qty_b = (
+                    pos["shares_b"] if pos["side_b"] == "BUY" else -pos["shares_b"]
+                )
+
+                # Calculate unrealized returns
+                # For symbol A: (current_price - open_price) / open_price * 100 * position_direction
+                unrealized_return_a = (
+                    (pos["current_px_a"] - pos["open_px_a"]) / pos["open_px_a"] * 100
+                ) * (1 if pos["side_a"] == "BUY" else -1)
+                unrealized_return_b = (
+                    (pos["current_px_b"] - pos["open_px_b"]) / pos["open_px_b"] * 100
+                ) * (1 if pos["side_b"] == "BUY" else -1)
+
+                # Store outstanding position for symbol A
+                cursor.execute(
+                    """
+                    INSERT INTO outstanding_positions (
+                        date, pair, symbol, position_quantity, last_price, unrealized_return, open_price, open_side
+                    ) VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+                """,
+                    (
+                        date_obj,
+                        pos["pair"],
+                        pos["symbol_a"],
+                        position_qty_a,
+                        pos["current_px_a"],
+                        unrealized_return_a,
+                        pos["open_px_a"],
+                        pos["side_a"],
+                    ),
+                )
+
+                # Store outstanding position for symbol B
+                cursor.execute(
+                    """
+                    INSERT INTO outstanding_positions (
+                        date, pair, symbol, position_quantity, last_price, unrealized_return, open_price, open_side
+                    ) VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+                """,
+                    (
+                        date_obj,
+                        pos["pair"],
+                        pos["symbol_b"],
+                        position_qty_b,
+                        pos["current_px_b"],
+                        unrealized_return_b,
+                        pos["open_px_b"],
+                        pos["side_b"],
+                    ),
+                )
+
+            conn.commit()
+            conn.close()
+
+        except Exception as e:
+            print(f"Error storing results in database: {str(e)}")
+            import traceback
+
+            traceback.print_exc()
+

lib/pt_trading/rolling_window_fit.py

@@ -0,0 +1,317 @@
+from abc import ABC, abstractmethod
+from enum import Enum
+from typing import Any, Dict, Optional, cast
+
+import pandas as pd  # type: ignore[import]
+from pt_trading.fit_method import PairsTradingFitMethod
+from pt_trading.results import BacktestResult
+from pt_trading.trading_pair import PairState, TradingPair
+from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults
+
+NanoPerMin = 1e9
+
+
+class RollingFit(PairsTradingFitMethod):
+    """
+    N O T E:
+    =========
+    - This class remains to be abstract
+    - The following methods are to be implemented in the subclass:
+        - create_trading_pair()
+    =========
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def run_pair(
+        self, pair: TradingPair, bt_result: BacktestResult
+    ) -> Optional[pd.DataFrame]:
+        print(f"***{pair}*** STARTING....")
+        config = pair.config_
+
+        curr_training_start_idx = pair.get_begin_index()
+        end_index = pair.get_end_index()
+
+        pair.user_data_["state"] = PairState.INITIAL
+        # Initialize trades DataFrame with proper dtypes to avoid concatenation warnings
+        pair.user_data_["trades"] = pd.DataFrame(columns=self.TRADES_COLUMNS).astype(
+            {
+                "time": "datetime64[ns]",
+                "symbol": "string",
+                "side": "string",
+                "action": "string",
+                "price": "float64",
+                "disequilibrium": "float64",
+                "scaled_disequilibrium": "float64",
+                "pair": "object",
+            }
+        )
+
+        training_minutes = config["training_minutes"]
+        curr_predicted_row_idx = 0
+        while True:
+            print(curr_training_start_idx, end="\r")
+            pair.get_datasets(
+                training_minutes=training_minutes,
+                training_start_index=curr_training_start_idx,
+                testing_size=1,
+            )
+
+            if len(pair.training_df_) < training_minutes:
+                print(
+                    f"{pair}: current offset={curr_training_start_idx}"
+                    f" * Training data length={len(pair.training_df_)} < {training_minutes}"
+                    " * Not enough training data. Completing the job."
+                )
+                break
+
+            try:
+                # ================================ PREDICTION ================================
+                self.pair_predict_result_ = pair.predict()
+            except Exception as e:
+                raise RuntimeError(
+                    f"{pair}: TrainingPrediction failed: {str(e)}"
+                ) from e
+
+                # break
+
+            curr_training_start_idx += 1
+            if curr_training_start_idx > end_index:
+                break
+            curr_predicted_row_idx += 1
+
+        self._create_trading_signals(pair, config, bt_result)
+        print(f"***{pair}*** FINISHED *** Num Trades:{len(pair.user_data_['trades'])}")
+
+        return pair.get_trades()
+
+    def _create_trading_signals(
+        self, pair: TradingPair, config: Dict, bt_result: BacktestResult
+    ) -> None:
+
+        predicted_df = self.pair_predict_result_
+        assert predicted_df is not None
+
+        open_threshold = config["dis-equilibrium_open_trshld"]
+        close_threshold = config["dis-equilibrium_close_trshld"]
+        for curr_predicted_row_idx in range(len(predicted_df)):
+            pred_row = predicted_df.iloc[curr_predicted_row_idx]
+            scaled_disequilibrium = pred_row["scaled_disequilibrium"]
+
+            if pair.user_data_["state"] in [
+                PairState.INITIAL,
+                PairState.CLOSE,
+                PairState.CLOSE_POSITION,
+                PairState.CLOSE_STOP_LOSS,
+                PairState.CLOSE_STOP_PROFIT,
+            ]:
+                if scaled_disequilibrium >= open_threshold:
+                    open_trades = self._get_open_trades(
+                        pair, row=pred_row, open_threshold=open_threshold
+                    )
+                    if open_trades is not None:
+                        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:
+                if scaled_disequilibrium <= 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"] = PairState.CLOSE.name
+                        print(f"CLOSE TRADES:\n{close_trades}")
+                        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:
+            print(f"{pair}:  *** Position is NOT CLOSED. ***")
+            # outstanding positions
+            if config["close_outstanding_positions"]:
+                close_position_row = pd.Series(pair.market_data_.iloc[-2])
+                close_position_row["disequilibrium"] = 0.0
+                close_position_row["scaled_disequilibrium"] = 0.0
+                close_position_row["signed_scaled_disequilibrium"] = 0.0
+                
+                close_position_trades = self._get_close_trades(
+                    pair=pair, row=close_position_row, close_threshold=close_threshold
+                )
+                if close_position_trades is not None:
+                    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.CLOSE_POSITION
+                    pair.on_close_trades(close_position_trades)
+            else:
+                if predicted_df is not None:
+                    bt_result.handle_outstanding_position(
+                        pair=pair,
+                        pair_result_df=predicted_df,
+                        last_row_index=0,
+                        open_side_a=pair.user_data_["open_side_a"],
+                        open_side_b=pair.user_data_["open_side_b"],
+                        open_px_a=pair.user_data_["open_px_a"],
+                        open_px_b=pair.user_data_["open_px_b"],
+                        open_tstamp=pair.user_data_["open_tstamp"],
+                    )
+
+    def _get_open_trades(
+        self, pair: TradingPair, row: pd.Series, open_threshold: float
+    ) -> Optional[pd.DataFrame]:
+        colname_a, colname_b = pair.exec_prices_colnames()
+
+        open_row = row
+
+        open_tstamp = open_row["tstamp"]
+        open_disequilibrium = open_row["disequilibrium"]
+        open_scaled_disequilibrium = open_row["scaled_disequilibrium"]
+        signed_scaled_disequilibrium = open_row["signed_scaled_disequilibrium"]
+        open_px_a = open_row[f"{colname_a}"]
+        open_px_b = open_row[f"{colname_b}"]
+
+        # creating the trades
+        # use outer single quotes so we can reference DataFrame keys with double quotes inside
+        print(f'OPEN_TRADES: {open_tstamp} open_scaled_disequilibrium={open_scaled_disequilibrium}')
+        if open_disequilibrium > 0:
+            open_side_a = "SELL"
+            open_side_b = "BUY"
+            close_side_a = "BUY"
+            close_side_b = "SELL"
+        else:
+            open_side_a = "BUY"
+            open_side_b = "SELL"
+            close_side_a = "SELL"
+            close_side_b = "BUY"
+
+        # save closing sides
+        pair.user_data_["open_side_a"] = open_side_a
+        pair.user_data_["open_side_b"] = open_side_b
+        pair.user_data_["open_px_a"] = open_px_a
+        pair.user_data_["open_px_b"] = open_px_b
+
+        pair.user_data_["open_tstamp"] = open_tstamp
+
+        pair.user_data_["close_side_a"] = close_side_a
+        pair.user_data_["close_side_b"] = close_side_b
+
+        # create opening trades
+        trd_signal_tuples = [
+            (
+                open_tstamp,
+                pair.symbol_a_,
+                open_side_a,
+                "OPEN",
+                open_px_a,
+                open_disequilibrium,
+                open_scaled_disequilibrium,
+                signed_scaled_disequilibrium,
+                pair,
+            ),
+            (
+                open_tstamp,
+                pair.symbol_b_,
+                open_side_b,
+                "OPEN",
+                open_px_b,
+                open_disequilibrium,
+                open_scaled_disequilibrium,
+                signed_scaled_disequilibrium,
+                pair,
+            ),
+        ]
+        # Create DataFrame 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",
+                "signed_scaled_disequilibrium": "float64",
+                "pair": "object",
+            }
+        )
+
+    def _get_close_trades(
+        self, pair: TradingPair, row: pd.Series, close_threshold: float
+    ) -> Optional[pd.DataFrame]:
+        colname_a, colname_b = pair.exec_prices_colnames()
+
+        close_row = row
+        close_tstamp = close_row["tstamp"]
+        close_disequilibrium = close_row["disequilibrium"]
+        close_scaled_disequilibrium = close_row["scaled_disequilibrium"]
+        signed_scaled_disequilibrium = close_row["signed_scaled_disequilibrium"]
+        close_px_a = close_row[f"{colname_a}"]
+        close_px_b = close_row[f"{colname_b}"]
+
+        close_side_a = pair.user_data_["close_side_a"]
+        close_side_b = pair.user_data_["close_side_b"]
+
+        trd_signal_tuples = [
+            (
+                close_tstamp,
+                pair.symbol_a_,
+                close_side_a,
+                "CLOSE",
+                close_px_a,
+                close_disequilibrium,
+                close_scaled_disequilibrium,
+                signed_scaled_disequilibrium,
+                pair,
+            ),
+            (
+                close_tstamp,
+                pair.symbol_b_,
+                close_side_b,
+                "CLOSE",
+                close_px_b,
+                close_disequilibrium,
+                close_scaled_disequilibrium,
+                signed_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",
+                "signed_scaled_disequilibrium": "float64",
+                "pair": "object",
+            }
+        )
+
+    def reset(self) -> None:
+        curr_training_start_idx = 0

lib/pt_trading/trading_pair.py

@@ -0,0 +1,380 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from enum import Enum
+from typing import Any, Dict, List, Optional
+
+import pandas as pd  # type:ignore
+
+
+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
+
+    tstamp_: pd.Timestamp
+    pair_: str
+    eg_pvalue_: float
+    johansen_lr1_: float
+    johansen_cvt_: float
+    eg_is_cointegrated_: bool
+    johansen_is_cointegrated_: bool
+
+    def __init__(self, pair: TradingPair):
+        training_df = pair.training_df_
+
+        assert training_df is not None
+        from statsmodels.tsa.vector_ar.vecm import coint_johansen
+
+        df = training_df[pair.colnames()].reset_index(drop=True)
+
+        # Run Johansen cointegration test
+        result = coint_johansen(df, det_order=0, k_ar_diff=1)
+        self.johansen_lr1_ = result.lr1[0]
+        self.johansen_cvt_ = result.cvt[0, 1]
+        self.johansen_is_cointegrated_ = self.johansen_lr1_ > self.johansen_cvt_
+
+        # Run Engle-Granger cointegration test
+        from statsmodels.tsa.stattools import coint  # type: ignore
+
+        col1, col2 = pair.colnames()
+        assert training_df is not None
+        series1 = training_df[col1].reset_index(drop=True)
+        series2 = training_df[col2].reset_index(drop=True)
+
+        self.eg_pvalue_ = float(coint(series1, series2)[1])
+        self.eg_is_cointegrated_ = bool(self.eg_pvalue_ < self.EG_PVALUE_THRESHOLD)
+
+        self.tstamp_ = training_df.index[-1]
+        self.pair_ = pair.name()
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "tstamp": self.tstamp_,
+            "pair": self.pair_,
+            "eg_pvalue": self.eg_pvalue_,
+            "johansen_lr1": self.johansen_lr1_,
+            "johansen_cvt": self.johansen_cvt_,
+            "eg_is_cointegrated": self.eg_is_cointegrated_,
+            "johansen_is_cointegrated": self.johansen_is_cointegrated_,
+        }
+
+    def __repr__(self) -> str:
+        return f"CointegrationData(tstamp={self.tstamp_}, pair={self.pair_}, eg_pvalue={self.eg_pvalue_}, johansen_lr1={self.johansen_lr1_}, johansen_cvt={self.johansen_cvt_}, eg_is_cointegrated={self.eg_is_cointegrated_}, johansen_is_cointegrated={self.johansen_is_cointegrated_})"
+
+
+class TradingPair(ABC):
+    market_data_: pd.DataFrame
+    symbol_a_: str
+    symbol_b_: str
+    stat_model_price_: str
+
+    training_mu_: float
+    training_std_: float
+
+    training_df_: pd.DataFrame
+    testing_df_: pd.DataFrame
+
+    user_data_: Dict[str, Any]
+
+    # predicted_df_: Optional[pd.DataFrame]
+
+    def __init__(
+        self,
+        config: Dict[str, Any],
+        market_data: pd.DataFrame,
+        symbol_a: str,
+        symbol_b: str,
+    ):
+        self.symbol_a_ = symbol_a
+        self.symbol_b_ = symbol_b
+        self.stat_model_price_ = config["stat_model_price"]
+        self.user_data_ = {}
+        self.predicted_df_ = None
+        self.config_ = config
+
+        self._set_market_data(market_data)
+
+    def _set_market_data(self, market_data: pd.DataFrame) -> None:
+        self.market_data_ = pd.DataFrame(
+            self._transform_dataframe(market_data)[["tstamp"] + self.colnames()]
+        )
+
+        self.market_data_ = self.market_data_.dropna().reset_index(drop=True)
+        self.market_data_["tstamp"] = pd.to_datetime(self.market_data_["tstamp"])
+        self.market_data_ = self.market_data_.sort_values("tstamp")
+        self._set_execution_price_data()
+        pass
+        
+    def _set_execution_price_data(self) -> None:
+        if "execution_price" not in self.config_:
+            self.market_data_[f"exec_price_{self.symbol_a_}"] = self.market_data_[f"{self.stat_model_price_}_{self.symbol_a_}"]
+            self.market_data_[f"exec_price_{self.symbol_b_}"] = self.market_data_[f"{self.stat_model_price_}_{self.symbol_b_}"]
+            return
+        execution_price_column = self.config_["execution_price"]["column"]
+        execution_price_shift = self.config_["execution_price"]["shift"]
+        self.market_data_[f"exec_price_{self.symbol_a_}"] = self.market_data_[f"{self.stat_model_price_}_{self.symbol_a_}"].shift(-execution_price_shift)
+        self.market_data_[f"exec_price_{self.symbol_b_}"] = self.market_data_[f"{self.stat_model_price_}_{self.symbol_b_}"].shift(-execution_price_shift)
+        self.market_data_ = self.market_data_.dropna().reset_index(drop=True)
+
+        
+        
+
+    def get_begin_index(self) -> int:
+        if "trading_hours" not in self.config_:
+            return 0
+        assert "timezone" in self.config_["trading_hours"]
+        assert "begin_session" in self.config_["trading_hours"]
+        start_time = (
+            pd.to_datetime(self.config_["trading_hours"]["begin_session"])
+            .tz_localize(self.config_["trading_hours"]["timezone"])
+            .time()
+        )
+        mask = self.market_data_["tstamp"].dt.time >= start_time
+        return int(self.market_data_.index[mask].min())
+
+    def get_end_index(self) -> int:
+        if "trading_hours" not in self.config_:
+            return 0
+        assert "timezone" in self.config_["trading_hours"]
+        assert "end_session" in self.config_["trading_hours"]
+        end_time = (
+            pd.to_datetime(self.config_["trading_hours"]["end_session"])
+            .tz_localize(self.config_["trading_hours"]["timezone"])
+            .time()
+        )
+        mask = self.market_data_["tstamp"].dt.time <= end_time
+        return int(self.market_data_.index[mask].max())
+
+    def _transform_dataframe(self, df: pd.DataFrame) -> pd.DataFrame:
+        # Select only the columns we need
+        df_selected: pd.DataFrame = pd.DataFrame(
+            df[["tstamp", "symbol", self.stat_model_price_]]
+        )
+
+        # Start with unique timestamps
+        result_df: pd.DataFrame = (
+            pd.DataFrame(df_selected["tstamp"]).drop_duplicates().reset_index(drop=True)
+        )
+
+        # For each unique symbol, add a corresponding close price column
+
+        symbols = df_selected["symbol"].unique()
+        for symbol in symbols:
+            # Filter rows for this symbol
+            df_symbol = df_selected[df_selected["symbol"] == symbol].reset_index(
+                drop=True
+            )
+
+            # Create column name like "close-COIN"
+            new_price_column = f"{self.stat_model_price_}_{symbol}"
+
+            # Create temporary dataframe with timestamp and price
+            temp_df = pd.DataFrame(
+                {
+                    "tstamp": df_symbol["tstamp"],
+                    new_price_column: df_symbol[self.stat_model_price_],
+                }
+            )
+
+            # Join with our result dataframe
+            result_df = pd.merge(result_df, temp_df, on="tstamp", how="left")
+            result_df = result_df.reset_index(
+                drop=True
+            )  # do not dropna() since irrelevant symbol would affect dataset
+
+        return result_df.dropna()
+
+    def get_datasets(
+        self,
+        training_minutes: int,
+        training_start_index: int = 0,
+        testing_size: Optional[int] = None,
+    ) -> None:
+
+        testing_start_index = training_start_index + training_minutes
+        self.training_df_ = self.market_data_.iloc[
+            training_start_index:testing_start_index, :training_minutes
+        ].copy()
+        assert self.training_df_ is not None
+        self.training_df_ = self.training_df_.dropna().reset_index(drop=True)
+
+        testing_start_index = training_start_index + training_minutes
+        if testing_size is None:
+            self.testing_df_ = self.market_data_.iloc[testing_start_index:, :].copy()
+        else:
+            self.testing_df_ = self.market_data_.iloc[
+                testing_start_index : testing_start_index + testing_size, :
+            ].copy()
+        assert self.testing_df_ is not None
+        self.testing_df_ = self.testing_df_.dropna().reset_index(drop=True)
+
+    def colnames(self) -> List[str]:
+        return [
+            f"{self.stat_model_price_}_{self.symbol_a_}",
+            f"{self.stat_model_price_}_{self.symbol_b_}",
+        ]
+
+    def exec_prices_colnames(self) -> List[str]:
+        return [
+            f"exec_price_{self.symbol_a_}",
+            f"exec_price_{self.symbol_b_}",
+        ]
+
+    def add_trades(self, trades: pd.DataFrame) -> None:
+        if self.user_data_["trades"] is None or len(self.user_data_["trades"]) == 0:
+            # If trades is empty or None, just assign the new trades directly
+            self.user_data_["trades"] = trades.copy()
+        else:
+            # Ensure both DataFrames have the same columns and dtypes before concatenation
+            existing_trades = self.user_data_["trades"]
+
+            # If existing trades is empty, just assign the new trades
+            if len(existing_trades) == 0:
+                self.user_data_["trades"] = trades.copy()
+            else:
+                # Ensure both DataFrames have the same columns
+                if set(existing_trades.columns) != set(trades.columns):
+                    # Add missing columns to trades with appropriate default values
+                    for col in existing_trades.columns:
+                        if col not in trades.columns:
+                            if col == "time":
+                                trades[col] = pd.Timestamp.now()
+                            elif col in ["action", "symbol"]:
+                                trades[col] = ""
+                            elif col in [
+                                "price",
+                                "disequilibrium",
+                                "scaled_disequilibrium",
+                            ]:
+                                trades[col] = 0.0
+                            elif col == "pair":
+                                trades[col] = None
+                            else:
+                                trades[col] = None
+
+                # Concatenate with explicit dtypes to avoid warnings
+                self.user_data_["trades"] = pd.concat(
+                    [existing_trades, trades], ignore_index=True, copy=False
+                )
+
+    def get_trades(self) -> pd.DataFrame:
+        return (
+            self.user_data_["trades"] if "trades" in self.user_data_ else pd.DataFrame()
+        )
+
+    def cointegration_check(self) -> Optional[pd.DataFrame]:
+        print(f"***{self}*** STARTING....")
+        config = self.config_
+
+        curr_training_start_idx = 0
+
+        COINTEGRATION_DATA_COLUMNS = {
+            "tstamp": "datetime64[ns]",
+            "pair": "string",
+            "eg_pvalue": "float64",
+            "johansen_lr1": "float64",
+            "johansen_cvt": "float64",
+            "eg_is_cointegrated": "bool",
+            "johansen_is_cointegrated": "bool",
+        }
+        # Initialize trades DataFrame with proper dtypes to avoid concatenation warnings
+        result: pd.DataFrame = pd.DataFrame(
+            columns=[col for col in COINTEGRATION_DATA_COLUMNS.keys()]
+        )  # .astype(COINTEGRATION_DATA_COLUMNS)
+
+        training_minutes = config["training_minutes"]
+        while True:
+            print(curr_training_start_idx, end="\r")
+            self.get_datasets(
+                training_minutes=training_minutes,
+                training_start_index=curr_training_start_idx,
+                testing_size=1,
+            )
+
+            if len(self.training_df_) < training_minutes:
+                print(
+                    f"{self}: current offset={curr_training_start_idx}"
+                    f" * Training data length={len(self.training_df_)} < {training_minutes}"
+                    " * Not enough training data. Completing the job."
+                )
+                break
+            new_row = pd.Series(CointegrationData(self).to_dict())
+            result.loc[len(result)] = new_row
+            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"]:
+                print(f"STOP PROFIT: {current_return}")
+                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"]:
+                print(f"STOP LOSS: {current_return}")
+                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 _single_instrument_return(symbol: str) -> float:
+                instrument_open_trades = open_trades[open_trades["symbol"] == symbol]
+                instrument_open_price = instrument_open_trades["price"].iloc[0]
+
+                sign = -1 if instrument_open_trades["side"].iloc[0] == "SELL" else 1
+                instrument_price = predicted_row[f"{self.stat_model_price_}_{symbol}"]
+                instrument_return = (
+                    sign
+                    * (instrument_price - instrument_open_price)
+                    / instrument_open_price
+                )
+                return float(instrument_return) * 100.0
+
+            instrument_a_return = _single_instrument_return(self.symbol_a_)
+            instrument_b_return = _single_instrument_return(self.symbol_b_)
+            return instrument_a_return + instrument_b_return
+        return 0.0
+
+    def __repr__(self) -> str:
+        return self.name()
+
+    def name(self) -> str:
+        return f"{self.symbol_a_} & {self.symbol_b_}"
+        # return f"{self.symbol_a_} & {self.symbol_b_}"
+
+    @abstractmethod
+    def predict(self) -> pd.DataFrame: ...
+
+    # @abstractmethod
+    # def predicted_df(self) -> Optional[pd.DataFrame]: ...

lib/pt_trading/vecm_rolling_fit.py

@@ -0,0 +1,193 @@
+# original script moved to vecm_rolling_fit_01.py
+
+# 09.09.25  Added GARCH model - predicting volatility
+
+# Rule of thumb:
+# alpha + beta ≈ 1 → strong volatility clustering, persistence.
+# If much lower → volatility mean reverts quickly.
+# If > 1 → model is unstable / non-stationary (bad).
+
+# the VECM disequilibrium (mean reversion signal) and 
+# the GARCH volatility forecast (risk measure).
+# combine them → e.g., only enter trades when:
+
+# high_volatility = 1 → persistence > 0.95 or volatility > 2 (rule of thumb: unstable / risky regime).
+# high_volatility = 0 → stable regime.
+
+
+# VECM disequilibrium z-score > threshold and
+# GARCH-forecasted volatility is not too high (avoid noise-driven signals).
+# This creates a volatility-adjusted pairs trading strategy, more robust than plain VECM
+
+# now pair_predict_result_ DataFrame includes:
+# disequilibrium, scaled_disequilibrium, z-scores, garch_alpha, garch_beta, garch_persistence (α+β rule-of-thumb)
+# garch_vol_forecast (1-step volatility forecast)
+
+# Would you like me to also add a warning flag column 
+# (e.g., "high_volatility" = 1 if persistence > 0.95 or vol_forecast > threshold) 
+# so you can easily detect unstable regimes?
+
+# VECM/GARCH 
+# vecm_rolling_fit.py:
+from typing import Any, Dict, Optional, cast
+
+import numpy as np
+import pandas as pd
+from typing import Any, Dict, Optional
+from pt_trading.results import BacktestResult
+from pt_trading.rolling_window_fit import RollingFit
+from pt_trading.trading_pair import TradingPair
+from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults
+from arch import arch_model
+
+NanoPerMin = 1e9
+
+class VECMTradingPair(TradingPair):
+    vecm_fit_: Optional[VECMResults]
+    pair_predict_result_: Optional[pd.DataFrame]
+
+    def __init__(
+        self,
+        config: Dict[str, Any],
+        market_data: pd.DataFrame,
+        symbol_a: str,
+        symbol_b: str,
+    ):
+        super().__init__(config, market_data, symbol_a, symbol_b)
+        self.vecm_fit_ = None
+        self.pair_predict_result_ = None
+        self.garch_fit_ = None
+        self.sigma_spread_forecast_ = None
+        self.garch_alpha_ = None
+        self.garch_beta_ = None
+        self.garch_persistence_ = None
+        self.high_volatility_flag_ = None
+
+    def _train_pair(self) -> None:
+        self._fit_VECM()
+        assert self.vecm_fit_ is not None
+
+        diseq_series = self.training_df_[self.colnames()] @ self.vecm_fit_.beta
+        self.training_mu_ = float(diseq_series[0].mean())
+        self.training_std_ = float(diseq_series[0].std())
+
+        self.training_df_["disequilibrium"] = diseq_series
+        self.training_df_["scaled_disequilibrium"] = (
+            diseq_series - self.training_mu_
+        ) / self.training_std_
+
+    def _fit_VECM(self) -> None:
+        assert self.training_df_ is not None
+        vecm_df = self.training_df_[self.colnames()].reset_index(drop=True)
+        vecm_model = VECM(vecm_df, coint_rank=1)
+        vecm_fit = vecm_model.fit()
+        self.vecm_fit_ = vecm_fit
+
+        # Error Correction Term (spread)
+        ect_series = (vecm_df @ vecm_fit.beta).iloc[:, 0]
+
+        # Difference the spread for stationarity
+        dz = ect_series.diff().dropna()
+
+        if len(dz) < 30:
+            print("Not enough data for GARCH fitting.")
+            return
+
+        # Rescale if variance too small
+        if dz.std() < 0.1:
+            dz = dz * 1000
+        # print("Scale check:", dz.std())
+
+        try:
+            garch = arch_model(dz, vol="GARCH", p=1, q=1, mean="Zero", dist="normal")
+            garch_fit = garch.fit(disp="off")
+            self.garch_fit_ = garch_fit
+
+            # Extract parameters
+            params = garch_fit.params
+            self.garch_alpha_ = params.get("alpha[1]", np.nan)
+            self.garch_beta_ = params.get("beta[1]", np.nan)
+            self.garch_persistence_ = self.garch_alpha_ + self.garch_beta_
+
+            # print (f"GARCH α: {self.garch_alpha_:.4f}, β: {self.garch_beta_:.4f}, "
+            #       f"α+β (persistence): {self.garch_persistence_:.4f}")
+
+            # One-step-ahead volatility forecast
+            forecast = garch_fit.forecast(horizon=1)
+            sigma_next = np.sqrt(forecast.variance.iloc[-1, 0])
+            self.sigma_spread_forecast_ = float(sigma_next)
+            # print("GARCH sigma forecast:", self.sigma_spread_forecast_)
+
+            # Rule of thumb: persistence close to 1 or large volatility forecast
+            self.high_volatility_flag_ = int(
+                (self.garch_persistence_ is not None and self.garch_persistence_ > 0.95)
+                or (self.sigma_spread_forecast_ is not None and self.sigma_spread_forecast_ > 2)
+            )
+
+        except Exception as e:
+            print(f"GARCH fit failed: {e}")
+            self.garch_fit_ = None
+            self.sigma_spread_forecast_ = None
+            self.high_volatility_flag_ = None
+
+    def predict(self) -> pd.DataFrame:
+        self._train_pair()
+        assert self.testing_df_ is not None
+        assert self.vecm_fit_ is not None
+
+        # VECM predictions
+        predicted_prices = self.vecm_fit_.predict(steps=len(self.testing_df_))
+        predicted_df = pd.merge(
+            self.testing_df_.reset_index(drop=True),
+            pd.DataFrame(predicted_prices, columns=pd.Index(self.colnames()), dtype=float),
+            left_index=True,
+            right_index=True,
+            suffixes=("", "_pred"),
+        ).dropna()
+
+        # Disequilibrium and z-scores
+        predicted_df["disequilibrium"] = (
+            predicted_df[self.colnames()] @ self.vecm_fit_.beta
+        )
+        predicted_df["signed_scaled_disequilibrium"] = (
+            predicted_df["disequilibrium"] - self.training_mu_
+        ) / self.training_std_
+        predicted_df["scaled_disequilibrium"] = abs(
+            predicted_df["signed_scaled_disequilibrium"]
+        )
+
+        # Add GARCH parameters + volatility forecast
+        predicted_df["garch_alpha"] = self.garch_alpha_
+        predicted_df["garch_beta"] = self.garch_beta_
+        predicted_df["garch_persistence"] = self.garch_persistence_
+        predicted_df["garch_vol_forecast"] = self.sigma_spread_forecast_
+        predicted_df["high_volatility"] = self.high_volatility_flag_
+
+        # Save results
+        if self.pair_predict_result_ is None:
+            self.pair_predict_result_ = predicted_df
+        else:
+            self.pair_predict_result_ = pd.concat(
+                [self.pair_predict_result_, predicted_df], ignore_index=True
+            )
+
+        return self.pair_predict_result_
+
+
+class VECMRollingFit(RollingFit):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def create_trading_pair(
+        self,
+        config: Dict,
+        market_data: pd.DataFrame,
+        symbol_a: str,
+        symbol_b: str,
+    ) -> TradingPair:
+        return VECMTradingPair(
+            config=config,
+            market_data=market_data,
+            symbol_a = symbol_a,
+            symbol_b = symbol_b,
+        )

lib/pt_trading/z-score_rolling_fit.py

@@ -0,0 +1,124 @@
+from typing import Any, Dict, Optional
+
+import pandas as pd
+import statsmodels.api as sm
+
+from pt_trading.rolling_window_fit import RollingFit
+from pt_trading.trading_pair import TradingPair
+
+NanoPerMin = 1e9
+
+
+class ZScoreTradingPair(TradingPair):
+    """TradingPair implementation that fits a hedge ratio with OLS and
+    computes a standardized spread (z-score).
+
+    The class stores training spread mean/std and hedge ratio so the model
+    can be applied to testing data consistently.
+    """
+
+    zscore_model_: Optional[sm.regression.linear_model.RegressionResultsWrapper]
+    pair_predict_result_: Optional[pd.DataFrame]
+    zscore_df_: Optional[pd.Series]
+    hedge_ratio_: Optional[float]
+    spread_mean_: Optional[float]
+    spread_std_: Optional[float]
+
+    def __init__(
+        self,
+        config: Dict[str, Any],
+        market_data: pd.DataFrame,
+        symbol_a: str,
+        symbol_b: str,
+    ):
+        super().__init__(config, market_data, symbol_a, symbol_b)
+        self.zscore_model_ = None
+        self.pair_predict_result_ = None
+        self.zscore_df_ = None
+        self.hedge_ratio_ = None
+        self.spread_mean_ = None
+        self.spread_std_ = None
+
+    def _fit_zscore(self) -> None:
+        """Fit OLS on the training window and compute training z-score."""
+        assert self.training_df_ is not None
+
+        # Extract price series for the two symbols from the training frame.
+        px_df = self.training_df_[self.colnames()]
+        symbol_a_px = px_df.iloc[:, 0]
+        symbol_b_px = px_df.iloc[:, 1]
+
+        # Align indexes and fit OLS: symbol_a ~ const + symbol_b
+        symbol_a_px, symbol_b_px = symbol_a_px.align(symbol_b_px, join="inner")
+        X = sm.add_constant(symbol_b_px)
+        self.zscore_model_ = sm.OLS(symbol_a_px, X).fit()
+
+        # Hedge ratio is the slope on symbol_b
+        params = self.zscore_model_.params
+        self.hedge_ratio_ = float(params.iloc[1]) if len(params) > 1 else 0.0
+
+        # Training spread and its standardized z-score
+        spread = symbol_a_px - self.hedge_ratio_ * symbol_b_px
+        self.spread_mean_ = float(spread.mean())
+        self.spread_std_ = float(spread.std(ddof=0)) if spread.std(ddof=0) != 0 else 1.0
+        self.zscore_df_ = (spread - self.spread_mean_) / self.spread_std_
+
+    def predict(self) -> pd.DataFrame:
+        """Apply fitted hedge ratio to the testing frame and return a
+        dataframe with canonical columns:
+          - disequilibrium: signed z-score
+          - scaled_disequilibrium: absolute z-score
+          - signed_scaled_disequilibrium: same as disequilibrium (keeps sign)
+        """
+        # Fit on training window
+        self._fit_zscore()
+        assert self.zscore_df_ is not None
+        assert self.hedge_ratio_ is not None
+        assert self.spread_mean_ is not None and self.spread_std_ is not None
+
+        # Keep training columns for inspection
+        self.training_df_["disequilibrium"] = self.zscore_df_
+        self.training_df_["scaled_disequilibrium"] = self.zscore_df_.abs()
+
+        # Apply model to testing frame
+        assert self.testing_df_ is not None
+        test_df = self.testing_df_.copy()
+        px_test = test_df[self.colnames()]
+        a_test = px_test.iloc[:, 0]
+        b_test = px_test.iloc[:, 1]
+        a_test, b_test = a_test.align(b_test, join="inner")
+
+        # Compute test spread and standardize using training mean/std
+        test_spread = a_test - self.hedge_ratio_ * b_test
+        test_zscore = (test_spread - self.spread_mean_) / self.spread_std_
+
+        # Attach canonical columns
+        # Align back to test_df index if needed
+        test_zscore = test_zscore.reindex(test_df.index)
+        test_df["disequilibrium"] = test_zscore
+        test_df["signed_scaled_disequilibrium"] = test_zscore
+        test_df["scaled_disequilibrium"] = test_zscore.abs()
+
+        # Reset index and accumulate results across windows
+        test_df = test_df.reset_index(drop=True)
+        if self.pair_predict_result_ is None:
+            self.pair_predict_result_ = test_df
+        else:
+            self.pair_predict_result_ = pd.concat(
+                [self.pair_predict_result_, test_df], ignore_index=True
+            )
+
+        self.pair_predict_result_ = self.pair_predict_result_.reset_index(drop=True)
+        return self.pair_predict_result_.dropna()
+
+
+class ZScoreRollingFit(RollingFit):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def create_trading_pair(
+        self, config: Dict, market_data: pd.DataFrame, symbol_a: str, symbol_b: str
+    ) -> TradingPair:
+        return ZScoreTradingPair(
+            config=config, market_data=market_data, symbol_a=symbol_a, symbol_b=symbol_b
+        )

lib/tools/config.py

@@ -0,0 +1,17 @@
+import hjson
+from typing import Dict
+from datetime import datetime
+
+
+def load_config(config_path: str) -> Dict:
+    with open(config_path, "r") as f:
+        config = hjson.load(f)
+    return dict(config)
+
+
+def expand_filename(filename: str) -> str:
+    # expand %T
+    res = filename.replace("%T", datetime.now().strftime("%Y%m%d_%H%M%S"))
+    # expand %D
+    return res.replace("%D", datetime.now().strftime("%Y%m%d"))
+

lib/tools/data_loader.py

@@ -0,0 +1,151 @@
+from __future__ import annotations
+
+import sqlite3
+from typing import Dict, List, cast
+import pandas as pd
+
+
+def load_sqlite_to_dataframe(db_path:str, query:str) -> pd.DataFrame:
+    df: pd.DataFrame = pd.DataFrame()
+    import os
+    if not os.path.exists(db_path):
+        print(f"WARNING: database file {db_path} does not exist")
+        return df
+
+    try:
+        conn = sqlite3.connect(db_path)
+
+        df = pd.read_sql_query(query, conn)
+        return df
+    except sqlite3.Error as excpt:
+        print(f"SQLite error: {excpt}")
+        raise
+    except Exception as excpt:
+        print(f"Error: {excpt}")
+        raise Exception() from excpt
+    finally:
+        if "conn" in locals():
+            conn.close()
+
+
+def convert_time_to_UTC(value: str, timezone: str, extra_minutes: int = 0) -> str:
+
+    from zoneinfo import ZoneInfo
+    from datetime import datetime, timedelta
+
+    # Parse it to naive datetime object
+    local_dt = datetime.strptime(value, "%Y-%m-%d %H:%M:%S")
+    local_dt = local_dt + timedelta(minutes=extra_minutes)
+
+    zinfo = ZoneInfo(timezone)
+    result: datetime = local_dt.replace(tzinfo=zinfo).astimezone(ZoneInfo("UTC"))
+
+    return result.strftime("%Y-%m-%d %H:%M:%S")
+
+
+def load_market_data(
+    datafile: str,
+    instruments: List[Dict[str, str]],
+    db_table_name: str,
+    trading_hours: Dict = {},
+    extra_minutes: int = 0,
+) -> pd.DataFrame:
+
+    insts = [
+        '"' + instrument["instrument_id_pfx"] + instrument["symbol"] + '"'
+        for instrument in instruments
+    ]
+    instrument_ids = list(set(insts))
+    exchange_ids = list(
+        set(['"' + instrument["exchange_id"] + '"' for instrument in instruments])
+    )
+
+    query = "select"
+    query += " tstamp"
+    query += ", tstamp_ns as time_ns"
+
+    query += f", substr(instrument_id, instr(instrument_id, '-') + 1) as symbol"
+    query += ", open"
+    query += ", high"
+    query += ", low"
+    query += ", close"
+    query += ", volume"
+    query += ", num_trades"
+    query += ", vwap"
+
+    query += f" from {db_table_name}"
+    query += f" where exchange_id in ({','.join(exchange_ids)})"
+    query += f" and instrument_id in ({','.join(instrument_ids)})"
+
+    df = load_sqlite_to_dataframe(db_path=datafile, query=query)
+
+    # Trading Hours
+    if len(df) > 0 and len(trading_hours) > 0:
+        date_str = df["tstamp"][0][0:10]
+
+        start_time = convert_time_to_UTC(
+            f"{date_str} {trading_hours['begin_session']}", trading_hours["timezone"]
+        )
+        end_time = convert_time_to_UTC(
+            f"{date_str} {trading_hours['end_session']}", trading_hours["timezone"], extra_minutes=extra_minutes # to get execution price
+        )
+
+        # Perform boolean selection
+        df = df[(df["tstamp"] >= start_time) & (df["tstamp"] <= end_time)]
+        df["tstamp"] = pd.to_datetime(df["tstamp"])
+
+    return cast(pd.DataFrame, df)
+
+
+# def get_available_instruments_from_db(datafile: str, config: Dict) -> List[str]:
+#     """
+#     Auto-detect available instruments from the database by querying distinct instrument_id values.
+#     Returns instruments without the configured prefix.
+#     """
+#     try:
+#         conn = sqlite3.connect(datafile)
+
+#         # Build exclusion list with full instrument_ids
+#         exclude_instruments = config.get("exclude_instruments", [])
+#         prefix = config.get("instrument_id_pfx", "")
+#         exclude_instrument_ids = [f"{prefix}{inst}" for inst in exclude_instruments]
+
+#         # Query to get distinct instrument_ids
+#         query = f"""
+#         SELECT DISTINCT instrument_id
+#         FROM {config['db_table_name']}
+#         WHERE exchange_id = ?
+#         """
+
+#         # Add exclusion clause if there are instruments to exclude
+#         if exclude_instrument_ids:
+#             placeholders = ",".join(["?" for _ in exclude_instrument_ids])
+#             query += f" AND instrument_id NOT IN ({placeholders})"
+#             cursor = conn.execute(
+#                 query, (config["exchange_id"],) + tuple(exclude_instrument_ids)
+#             )
+#         else:
+#             cursor = conn.execute(query, (config["exchange_id"],))
+#         instrument_ids = [row[0] for row in cursor.fetchall()]
+#         conn.close()
+
+#         # Remove the configured prefix to get instrument symbols
+#         instruments = []
+#         for instrument_id in instrument_ids:
+#             if instrument_id.startswith(prefix):
+#                 symbol = instrument_id[len(prefix) :]
+#                 instruments.append(symbol)
+#             else:
+#                 instruments.append(instrument_id)
+
+#         return sorted(instruments)
+
+#     except Exception as e:
+#         print(f"Error auto-detecting instruments from {datafile}: {str(e)}")
+#         return []
+
+
+# if __name__ == "__main__":
+#     df1 = load_sqlite_to_dataframe(sys.argv[1], table_name="md_1min_bars")
+
+#     print(df1)

lib/utils/db_inspector.py

@@ -25,7 +25,7 @@ def list_tables(db_path: str) -> List[str]:
     conn.close()
     return tables
 
-def view_table_schema(db_path: str, table_name: str):
+def view_table_schema(db_path: str, table_name: str) -> None:
     """View the schema of a specific table."""
     conn = sqlite3.connect(db_path)
     cursor = conn.cursor()
@@ -44,13 +44,13 @@ def view_table_schema(db_path: str, table_name: str):
     
     conn.close()
 
-def view_config_table(db_path: str, limit: int = 10):
+def view_config_table(db_path: str, limit: int = 10) -> None:
     """View entries from the config table."""
     conn = sqlite3.connect(db_path)
     cursor = conn.cursor()
     
     cursor.execute(f"""
-        SELECT id, run_timestamp, config_file_path, strategy_class, 
+        SELECT id, run_timestamp, config_file_path, fit_method_class, 
                datafiles, instruments, config_json
         FROM config 
         ORDER BY run_timestamp DESC
@@ -67,17 +67,17 @@ def view_config_table(db_path: str, limit: int = 10):
     print("=" * 80)
     
     for row in rows:
-        id, run_timestamp, config_file_path, strategy_class, datafiles, instruments, config_json = row
+        id, run_timestamp, config_file_path, fit_method_class, datafiles, instruments, config_json = row
         
         print(f"ID: {id} | {run_timestamp}")
-        print(f"Config: {config_file_path} | Strategy: {strategy_class}")
+        print(f"Config: {config_file_path} | Strategy: {fit_method_class}")
         print(f"Files: {datafiles}")
         print(f"Instruments: {instruments}")
         print("-" * 80)
     
     conn.close()
 
-def view_results_summary(db_path: str):
+def view_results_summary(db_path: str) -> None:
     """View summary of trading results."""
     conn = sqlite3.connect(db_path)
     cursor = conn.cursor()
@@ -119,7 +119,7 @@ def view_results_summary(db_path: str):
     
     conn.close()
 
-def main():
+def main() -> None:
     if len(sys.argv) < 2:
         print("Usage: python db_inspector.py <database_path> [command]")
         print("Commands:")

pyproject.toml

@@ -0,0 +1,66 @@
+[build-system]
+requires = ["setuptools>=45", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "pairs-trading"
+version = "0.1.0"
+description = "Pairs Trading Backtesting Framework"
+requires-python = ">=3.8"
+
+[tool.black]
+line-length = 88
+target-version = ['py38']
+include = '\.pyi?$'
+extend-exclude = '''
+/(
+  # directories
+  \.eggs
+  | \.git
+  | \.hg
+  | \.mypy_cache
+  | \.tox
+  | \.venv
+  | build
+  | dist
+)/
+'''
+
+[tool.flake8]
+max-line-length = 88
+extend-ignore = ["E203", "W503"]
+exclude = [
+    ".git",
+    "__pycache__",
+    "build",
+    "dist",
+    ".venv",
+    ".mypy_cache",
+    ".tox"
+]
+
+[tool.mypy]
+python_version = "3.8"
+warn_return_any = true
+warn_unused_configs = true
+disallow_untyped_defs = true
+disallow_incomplete_defs = true
+check_untyped_defs = true
+disallow_untyped_decorators = true
+no_implicit_optional = true
+warn_redundant_casts = true
+warn_unused_ignores = true
+warn_no_return = true
+warn_unreachable = true
+strict_equality = true
+
+[[tool.mypy.overrides]]
+module = [
+    "numpy.*",
+    "pandas.*",
+    "matplotlib.*",
+    "seaborn.*",
+    "scipy.*",
+    "sklearn.*"
+]
+ignore_missing_imports = true 

pyrightconfig.json

@@ -0,0 +1,24 @@
+{
+    "include": [
+        "lib"
+    ],
+    "exclude": [
+        "**/node_modules",
+        "**/__pycache__",
+        "**/.*",
+        "results",
+        "data"
+    ],
+    "ignore": [],
+    "defineConstant": {},
+    "typeCheckingMode": "basic",
+    "useLibraryCodeForTypes": true,
+    "autoImportCompletions": true,
+    "autoSearchPaths": true,
+    "extraPaths": [
+        "lib"
+    ],
+    "stubPath": "./typings",
+    "venvPath": ".",
+    "venv": "python3.12-venv"
+}

requirements.txt

@@ -4,6 +4,7 @@ async-timeout>=4.0.2
 attrs>=21.2.0
 beautifulsoup4>=4.10.0
 black>=23.3.0
+flake8>=6.0.0
 certifi>=2020.6.20
 chardet>=4.0.0
 charset-normalizer>=3.1.0
@@ -23,11 +24,14 @@ hjson>=3.0.2
 html5lib>=1.1
 httplib2>=0.20.2
 idna>=3.3
+ipython>=8.18.1
+ipywidgets>=8.1.1
 ifaddr>=0.1.7
 IMDbPY>=2021.4.18
 ipykernel>=6.29.5
 jeepney>=0.7.1
 jsonschema>=3.2.0
+jupyter>=1.0.0
 keyring>=23.5.0
 launchpadlib>=1.10.16
 lazr.restfulclient>=0.14.4
@@ -41,19 +45,23 @@ more-itertools>=8.10.0
 multidict>=6.0.4
 mypy>=0.942
 mypy-extensions>=0.4.3
+nbformat>=5.10.2
 netaddr>=0.8.0
 ######### netifaces>=0.11.0
+numpy>=1.26.4,<2.3.0
 oauthlib>=3.2.0
 packaging>=23.1
+pandas>=2.2.3
 pathspec>=0.11.1
 pexpect>=4.8.0
 Pillow>=9.0.1
 platformdirs>=3.2.0
+plotly>=5.19.0
 protobuf>=3.12.4
 psutil>=5.9.0
 ptyprocess>=0.7.0
 pycurl>=7.44.1
-pyelftools>=0.27
+# pyelftools>=0.27
 Pygments>=2.11.2
 pyparsing>=2.4.7
 pyrsistent>=0.18.1
@@ -61,11 +69,12 @@ python-debian>=0.1.43 #+ubuntu1.1
 python-dotenv>=0.19.2
 python-magic>=0.4.24
 python-xlib>=0.29
-pyxdg>=0.27
+# pyxdg>=0.27
 PyYAML>=6.0
 reportlab>=3.6.8
 requests>=2.25.1
 requests-file>=1.5.1
+scipy<1.13.0
 seaborn>=0.13.2
 SecretStorage>=3.3.1
 setproctitle>=1.2.2
@@ -73,113 +82,113 @@ six>=1.16.0
 soupsieve>=2.3.1
 ssh-import-id>=5.11
 statsmodels>=0.14.4
-texttable>=1.6.4
+# texttable>=1.6.4
 tldextract>=3.1.2
 tomli>=1.2.2
 ######## typed-ast>=1.4.3
-types-aiofiles>=0.1
-types-annoy>=1.17
-types-appdirs>=1.4
-types-atomicwrites>=1.4
-types-aws-xray-sdk>=2.8
-types-babel>=2.9
-types-backports-abc>=0.5
-types-backports.ssl-match-hostname>=3.7
-types-beautifulsoup4>=4.10
-types-bleach>=4.1
-types-boto>=2.49
-types-braintree>=4.11
-types-cachetools>=4.2
-types-caldav>=0.8
-types-certifi>=2020.4
-types-characteristic>=14.3
-types-chardet>=4.0
-types-click>=7.1
-types-click-spinner>=0.1
-types-colorama>=0.4
-types-commonmark>=0.9
-types-contextvars>=0.1
-types-croniter>=1.0
-types-cryptography>=3.3
-types-dataclasses>=0.1
-types-dateparser>=1.0
-types-DateTimeRange>=0.1
-types-decorator>=0.1
-types-Deprecated>=1.2
-types-docopt>=0.6
-types-docutils>=0.17
-types-editdistance>=0.5
-types-emoji>=1.2
-types-entrypoints>=0.3
-types-enum34>=1.1
-types-filelock>=3.2
-types-first>=2.0
-types-Flask>=1.1
-types-freezegun>=1.1
-types-frozendict>=0.1
-types-futures>=3.3
-types-html5lib>=1.1
-types-httplib2>=0.19
-types-humanfriendly>=9.2
-types-ipaddress>=1.0
-types-itsdangerous>=1.1
-types-JACK-Client>=0.1
-types-Jinja2>=2.11
-types-jmespath>=0.10
-types-jsonschema>=3.2
-types-Markdown>=3.3
-types-MarkupSafe>=1.1
-types-mock>=4.0
-types-mypy-extensions>=0.4
-types-mysqlclient>=2.0
-types-oauthlib>=3.1
-types-orjson>=3.6
-types-paramiko>=2.7
-types-Pillow>=8.3
-types-polib>=1.1
-types-prettytable>=2.1
-types-protobuf>=3.17
-types-psutil>=5.8
-types-psycopg2>=2.9
-types-pyaudio>=0.2
-types-pycurl>=0.1
-types-pyfarmhash>=0.2
-types-Pygments>=2.9
-types-PyMySQL>=1.0
-types-pyOpenSSL>=20.0
-types-pyRFC3339>=0.1
-types-pysftp>=0.2
-types-pytest-lazy-fixture>=0.6
-types-python-dateutil>=2.8
-types-python-gflags>=3.1
-types-python-nmap>=0.6
-types-python-slugify>=5.0
-types-pytz>=2021.1
-types-pyvmomi>=7.0
-types-PyYAML>=5.4
-types-redis>=3.5
-types-requests>=2.25
-types-retry>=0.9
-types-selenium>=3.141
-types-Send2Trash>=1.8
-types-setuptools>=57.4
-types-simplejson>=3.17
-types-singledispatch>=3.7
-types-six>=1.16
-types-slumber>=0.7
-types-stripe>=2.59
-types-tabulate>=0.8
-types-termcolor>=1.1
-types-toml>=0.10
-types-toposort>=1.6
-types-ttkthemes>=3.2
-types-typed-ast>=1.4
-types-tzlocal>=0.1
-types-ujson>=0.1
-types-vobject>=0.9
-types-waitress>=0.1
-types-Werkzeug>=1.0
-types-xxhash>=2.0
+# types-aiofiles>=0.1
+# types-annoy>=1.17
+# types-appdirs>=1.4
+# types-atomicwrites>=1.4
+# types-aws-xray-sdk>=2.8
+# types-babel>=2.9
+# types-backports-abc>=0.5
+# types-backports.ssl-match-hostname>=3.7
+# types-beautifulsoup4>=4.10
+# types-bleach>=4.1
+# types-boto>=2.49
+# types-braintree>=4.11
+# types-cachetools>=4.2
+# types-caldav>=0.8
+# types-certifi>=2020.4
+# types-characteristic>=14.3
+# types-chardet>=4.0
+# types-click>=7.1
+# types-click-spinner>=0.1
+# types-colorama>=0.4
+# types-commonmark>=0.9
+# types-contextvars>=0.1
+# types-croniter>=1.0
+# types-cryptography>=3.3
+# types-dataclasses>=0.1
+# types-dateparser>=1.0
+# types-DateTimeRange>=0.1
+# types-decorator>=0.1
+# types-Deprecated>=1.2
+# types-docopt>=0.6
+# types-docutils>=0.17
+# types-editdistance>=0.5
+# types-emoji>=1.2
+# types-entrypoints>=0.3
+# types-enum34>=1.1
+# types-filelock>=3.2
+# types-first>=2.0
+# types-Flask>=1.1
+# types-freezegun>=1.1
+# types-frozendict>=0.1
+# types-futures>=3.3
+# types-html5lib>=1.1
+# types-httplib2>=0.19
+# types-humanfriendly>=9.2
+# types-ipaddress>=1.0
+# types-itsdangerous>=1.1
+# types-JACK-Client>=0.1
+# types-Jinja2>=2.11
+# types-jmespath>=0.10
+# types-jsonschema>=3.2
+# types-Markdown>=3.3
+# types-MarkupSafe>=1.1
+# types-mock>=4.0
+# types-mypy-extensions>=0.4
+# types-mysqlclient>=2.0
+# types-oauthlib>=3.1
+# types-orjson>=3.6
+# types-paramiko>=2.7
+# types-Pillow>=8.3
+# types-polib>=1.1
+# types-prettytable>=2.1
+# types-protobuf>=3.17
+# types-psutil>=5.8
+# types-psycopg2>=2.9
+# types-pyaudio>=0.2
+# types-pycurl>=0.1
+# types-pyfarmhash>=0.2
+# types-Pygments>=2.9
+# types-PyMySQL>=1.0
+# types-pyOpenSSL>=20.0
+# types-pyRFC3339>=0.1
+# types-pysftp>=0.2
+# types-pytest-lazy-fixture>=0.6
+# types-python-dateutil>=2.8
+# types-python-gflags>=3.1
+# types-python-nmap>=0.6
+# types-python-slugify>=5.0
+# types-pytz>=2021.1
+# types-pyvmomi>=7.0
+# types-PyYAML>=5.4
+# types-redis>=3.5
+# types-requests>=2.25
+# types-retry>=0.9
+# types-selenium>=3.141
+# types-Send2Trash>=1.8
+# types-setuptools>=57.4
+# types-simplejson>=3.17
+# types-singledispatch>=3.7
+# types-six>=1.16
+# types-slumber>=0.7
+# types-stripe>=2.59
+# types-tabulate>=0.8
+# types-termcolor>=1.1
+# types-toml>=0.10
+# types-toposort>=1.6
+# types-ttkthemes>=3.2
+# types-typed-ast>=1.4
+# types-tzlocal>=0.1
+# types-ujson>=0.1
+# types-vobject>=0.9
+# types-waitress>=0.1
+#types-Werkzeug>=1.0
+#types-xxhash>=2.0
 typing-extensions>=3.10.0.2
 Unidecode>=1.3.3
 urllib3>=1.26.5

research/cointegration_test.py

@@ -0,0 +1,127 @@
+import argparse
+import glob
+import importlib
+import os
+from datetime import date, datetime
+from typing import Any, Dict, List, Optional
+
+import pandas as pd
+
+from tools.config import expand_filename, load_config
+from tools.data_loader import get_available_instruments_from_db
+
+from pt_trading.results import (
+    BacktestResult,
+    create_result_database,
+    store_config_in_database,
+    store_results_in_database,
+)
+
+from pt_trading.fit_method import PairsTradingFitMethod
+from pt_trading.trading_pair import TradingPair
+
+from research.research_tools import create_pairs, resolve_datafiles
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Run pairs trading backtest.")
+    parser.add_argument(
+        "--config", type=str, required=True, help="Path to the configuration file."
+    )
+    parser.add_argument(
+        "--datafile",
+        type=str,
+        required=False,
+        help="Market data file to process.",
+    )
+    parser.add_argument(
+        "--instruments",
+        type=str,
+        required=False,
+        help = "Comma-separated list of instrument symbols (e.g., COIN,GBTC). If not provided, auto-detects from database.",
+    )
+    args = parser.parse_args()
+
+    config: Dict = load_config(args.config)
+
+    # Resolve data files (CLI takes priority over config)
+    datafile = resolve_datafiles(config, args.datafile)[0]
+
+    if not datafile:
+        print("No data files found to process.")
+        return
+
+    print(f"Found {datafile} data files to process:")
+
+    # # Create result database if needed
+    # if args.result_db.upper() != "NONE":
+    #     args.result_db = expand_filename(args.result_db)
+    #     create_result_database(args.result_db)
+
+    # # Initialize a dictionary to store all trade results
+    # all_results: Dict[str, Dict[str, Any]] = {}
+
+    # # Store configuration in database for reference
+    # if args.result_db.upper() != "NONE":
+    #     # Get list of all instruments for storage
+    #     all_instruments = []
+    #     for datafile in datafiles:
+    #         if args.instruments:
+    #             file_instruments = [
+    #                 inst.strip() for inst in args.instruments.split(",")
+    #             ]
+    #         else:
+    #             file_instruments = get_available_instruments_from_db(datafile, config)
+    #         all_instruments.extend(file_instruments)
+
+    #     # Remove duplicates while preserving order
+    #     unique_instruments = list(dict.fromkeys(all_instruments))
+
+    #     store_config_in_database(
+    #         db_path=args.result_db,
+    #         config_file_path=args.config,
+    #         config=config,
+    #         fit_method_class=fit_method_class_name,
+    #         datafiles=datafiles,
+    #         instruments=unique_instruments,
+    #     )
+
+    # Process each data file
+    stat_model_price = config["stat_model_price"]
+
+    print(f"\n====== Processing {os.path.basename(datafile)} ======")
+
+    # Determine instruments to use
+    if args.instruments:
+        # Use CLI-specified instruments
+        instruments = [inst.strip() for inst in args.instruments.split(",")]
+        print(f"Using CLI-specified instruments: {instruments}")
+    else:
+        # Auto-detect instruments from database
+        instruments = get_available_instruments_from_db(datafile, config)
+        print(f"Auto-detected instruments: {instruments}")
+
+    if not instruments:
+        print(f"No instruments found in {datafile}...")
+        return
+    # Process data for this file
+    try:
+        cointegration_data: pd.DataFrame = pd.DataFrame()
+        for pair in create_pairs(datafile, stat_model_price, config, instruments):
+            cointegration_data = pd.concat([cointegration_data, pair.cointegration_check()])
+
+        pd.set_option('display.width', 400)
+        pd.set_option('display.max_colwidth', None)
+        pd.set_option('display.max_columns', None)
+        with pd.option_context('display.max_rows', None, 'display.max_columns', None):
+            print(f"cointegration_data:\n{cointegration_data}")
+
+    except Exception as err:
+        print(f"Error processing {datafile}: {str(err)}")
+        import traceback
+
+        traceback.print_exc()
+
+
+
+if __name__ == "__main__":
+    main()

research/notebooks/pt_pair_backtest.ipynb

@@ -0,0 +1,16 @@
+{
+ "cells": [],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

research/pt_backtest.py

@@ -0,0 +1,232 @@
+import argparse
+import glob
+import importlib
+import os
+from datetime import date, datetime
+from typing import Any, Dict, List, Optional, Tuple
+
+import pandas as pd
+
+from research.research_tools import create_pairs
+from tools.config import expand_filename, load_config
+from pt_trading.results import (
+    BacktestResult,
+    create_result_database,
+    store_config_in_database,
+)
+from pt_trading.fit_method import PairsTradingFitMethod
+from pt_trading.trading_pair import TradingPair
+
+DayT = str
+DataFileNameT = str
+
+def resolve_datafiles(
+    config: Dict, date_pattern: str, instruments: List[Dict[str, str]]
+) -> List[Tuple[DayT, DataFileNameT]]:
+    resolved_files: List[Tuple[DayT, DataFileNameT]] = []
+    for inst in instruments:
+        pattern = date_pattern
+        inst_type = inst["instrument_type"]
+        data_dir = config["market_data_loading"][inst_type]["data_directory"]
+        if "*" in pattern or "?" in pattern:
+            # Handle wildcards
+            if not os.path.isabs(pattern):
+                pattern = os.path.join(data_dir, f"{pattern}.mktdata.ohlcv.db")
+            matched_files = glob.glob(pattern)
+            for matched_file in matched_files:
+                import re
+                match = re.search(r"(\d{8})\.mktdata\.ohlcv\.db$", matched_file)
+                assert match is not None
+                day = match.group(1)
+                resolved_files.append((day, matched_file))
+        else:
+            # Handle explicit file path
+            if not os.path.isabs(pattern):
+                pattern = os.path.join(data_dir, f"{pattern}.mktdata.ohlcv.db")
+            resolved_files.append((date_pattern, pattern))
+    return sorted(list(set(resolved_files)))  # Remove duplicates and sort
+
+
+def get_instruments(args: argparse.Namespace, config: Dict) -> List[Dict[str, str]]:
+
+    instruments = [
+        {
+            "symbol": inst.split(":")[0],
+            "instrument_type": inst.split(":")[1],
+            "exchange_id": inst.split(":")[2],
+            "instrument_id_pfx": config["market_data_loading"][inst.split(":")[1]][
+                "instrument_id_pfx"
+            ],
+            "db_table_name": config["market_data_loading"][inst.split(":")[1]][
+                "db_table_name"
+            ],
+        }
+        for inst in args.instruments.split(",")
+    ]
+    return instruments
+
+
+def run_backtest(
+    config: Dict,
+    datafiles: List[str],
+    fit_method: PairsTradingFitMethod,
+    instruments: List[Dict[str, str]],
+) -> BacktestResult:
+    """
+    Run backtest for all pairs using the specified instruments.
+    """
+    bt_result: BacktestResult = BacktestResult(config=config)
+    # if len(datafiles) < 2:
+    #     print(f"WARNING: insufficient data files: {datafiles}")
+    #     return bt_result
+
+    if not all([os.path.exists(datafile) for datafile in datafiles]):
+        print(f"WARNING: data file {datafiles} does not exist")
+        return bt_result
+
+    pairs_trades = []
+
+    pairs = create_pairs(
+        datafiles=datafiles,
+        fit_method=fit_method,
+        config=config,
+        instruments=instruments,
+    )
+    for pair in pairs:
+        single_pair_trades = fit_method.run_pair(pair=pair, bt_result=bt_result)
+        if single_pair_trades is not None and len(single_pair_trades) > 0:
+            pairs_trades.append(single_pair_trades)
+    print(f"pairs_trades:\n{pairs_trades}")
+    # Check if result_list has any data before concatenating
+    if len(pairs_trades) == 0:
+        print("No trading signals found for any pairs")
+        return bt_result
+
+    bt_result.collect_single_day_results(pairs_trades)
+    return bt_result
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Run pairs trading backtest.")
+    parser.add_argument(
+        "--config", type=str, required=True, help="Path to the configuration file."
+    )
+    parser.add_argument(
+        "--date_pattern",
+        type=str,
+        required=True,
+        help="Date YYYYMMDD, allows * and ? wildcards",
+    )
+    parser.add_argument(
+        "--instruments",
+        type=str,
+        required=True,
+        help="Comma-separated list of instrument symbols (e.g., COIN:EQUITY,GBTC:CRYPTO)",
+    )
+    parser.add_argument(
+        "--result_db",
+        type=str,
+        required=True,
+        help="Path to SQLite database for storing results. Use 'NONE' to disable database output.",
+    )
+
+    args = parser.parse_args()
+
+    config: Dict = load_config(args.config)
+
+    # Dynamically instantiate fit method class
+    fit_method = PairsTradingFitMethod.create(config)
+
+    # Resolve data files (CLI takes priority over config)
+    instruments = get_instruments(args, config)
+    datafiles = resolve_datafiles(config, args.date_pattern, instruments)
+
+    days = list(set([day for day, _ in datafiles]))
+    print(f"Found {len(datafiles)} data files to process:")
+    for df in datafiles:
+        print(f"  - {df}")
+
+    # Create result database if needed
+    if args.result_db.upper() != "NONE":
+        args.result_db = expand_filename(args.result_db)
+        create_result_database(args.result_db)
+
+    # Initialize a dictionary to store all trade results
+    all_results: Dict[str, Dict[str, Any]] = {}
+    is_config_stored = False
+    # Process each data file
+
+    for day in sorted(days):
+        md_datafiles = [datafile for md_day, datafile in datafiles if md_day == day]
+        if not all([os.path.exists(datafile) for datafile in md_datafiles]):
+            print(f"WARNING: insufficient data files: {md_datafiles}")
+            continue
+        print(f"\n====== Processing {day} ======")
+
+        if not is_config_stored:    
+            store_config_in_database(
+                db_path=args.result_db,
+                config_file_path=args.config,
+                config=config,
+                fit_method_class=config["fit_method_class"],
+                datafiles=datafiles,
+                instruments=instruments,
+            )
+            is_config_stored = True
+
+        # Process data for this file
+        try:
+            fit_method.reset()
+
+            bt_results = run_backtest(
+                config=config,
+                datafiles=md_datafiles,
+                fit_method=fit_method,
+                instruments=instruments,
+            )
+            
+            if bt_results.trades is None or len(bt_results.trades) == 0:
+                print(f"No trades found for {day}")
+                continue
+
+            # Store results with day name as key
+            filename = os.path.basename(day)
+            all_results[filename] = {
+                "trades": bt_results.trades.copy(),
+                "outstanding_positions": bt_results.outstanding_positions.copy(),
+            }
+
+            # Store results in database
+            if args.result_db.upper() != "NONE":
+                bt_results.calculate_returns(
+                    {
+                        filename: {
+                            "trades": bt_results.trades.copy(),
+                            "outstanding_positions": bt_results.outstanding_positions.copy(),
+                        }
+                    }
+                )
+                bt_results.store_results_in_database(db_path=args.result_db, day=day)
+
+            print(f"Successfully processed {filename}")
+
+        except Exception as err:
+            print(f"Error processing {day}: {str(err)}")
+            import traceback
+
+            traceback.print_exc()
+
+    # Calculate and print results using a new BacktestResult instance for aggregation
+    if all_results:
+        aggregate_bt_results = BacktestResult(config=config)
+        aggregate_bt_results.calculate_returns(all_results)
+        aggregate_bt_results.print_grand_totals()
+        aggregate_bt_results.print_outstanding_positions()
+
+        if args.result_db.upper() != "NONE":
+            print(f"\nResults stored in database: {args.result_db}")
+    else:
+        print("No results to display.")
+
+
+if __name__ == "__main__":
+    main()

research/research_tools.py

@@ -0,0 +1,93 @@
+import glob
+import os
+from typing import Dict, List, Optional
+
+import pandas as pd
+from pt_trading.fit_method import PairsTradingFitMethod
+
+def resolve_datafiles(config: Dict, cli_datafiles: Optional[str] = None) -> List[str]:
+    """
+    Resolve the list of data files to process.
+    CLI datafiles take priority over config datafiles.
+    Supports wildcards in config but not in CLI.
+    """
+    if cli_datafiles:
+        # CLI override - comma-separated list, no wildcards
+        datafiles = [f.strip() for f in cli_datafiles.split(",")]
+        # Make paths absolute relative to data directory
+        data_dir = config.get("data_directory", "./data")
+        resolved_files = []
+        for df in datafiles:
+            if not os.path.isabs(df):
+                df = os.path.join(data_dir, df)
+            resolved_files.append(df)
+        return resolved_files
+
+    # Use config datafiles with wildcard support
+    config_datafiles = config.get("datafiles", [])
+    data_dir = config.get("data_directory", "./data")
+    resolved_files = []
+
+    for pattern in config_datafiles:
+        if "*" in pattern or "?" in pattern:
+            # Handle wildcards
+            if not os.path.isabs(pattern):
+                pattern = os.path.join(data_dir, pattern)
+            matched_files = glob.glob(pattern)
+            resolved_files.extend(matched_files)
+        else:
+            # Handle explicit file path
+            if not os.path.isabs(pattern):
+                pattern = os.path.join(data_dir, pattern)
+            resolved_files.append(pattern)
+
+    return sorted(list(set(resolved_files)))  # Remove duplicates and sort
+
+
+def create_pairs(
+    datafiles: List[str],
+    fit_method: PairsTradingFitMethod,
+    config: Dict,
+    instruments: List[Dict[str, str]],
+) -> List:
+    from pt_trading.trading_pair import TradingPair
+    from tools.data_loader import load_market_data
+
+    all_indexes = range(len(instruments))
+    unique_index_pairs = [(i, j) for i in all_indexes for j in all_indexes if i < j]
+    pairs = []
+
+    # Update config to use the specified instruments
+    config_copy = config.copy()
+    config_copy["instruments"] = instruments
+
+    market_data_df = pd.DataFrame()
+    extra_minutes = 0
+    if "execution_price" in config_copy:
+        extra_minutes = config_copy["execution_price"]["shift"]
+
+    for datafile in datafiles:
+        md_df = load_market_data(
+            datafile = datafile,
+            instruments = instruments,
+            db_table_name = config_copy["market_data_loading"][instruments[0]["instrument_type"]]["db_table_name"],
+            trading_hours=config_copy["trading_hours"],
+            extra_minutes=extra_minutes,
+        )
+        market_data_df = pd.concat([market_data_df, md_df])
+
+    if len(set(market_data_df["symbol"])) != 2: # both symbols must be present for a pair
+        print(f"WARNING: insufficient data in files: {datafiles}")
+        return []
+
+    for a_index, b_index in unique_index_pairs:
+        symbol_a=instruments[a_index]["symbol"]
+        symbol_b=instruments[b_index]["symbol"]
+        pair = fit_method.create_trading_pair(
+            config=config_copy,
+            market_data=market_data_df,
+            symbol_a=symbol_a,
+            symbol_b=symbol_b,
+        )
+        pairs.append(pair)
+    return pairs

scripts/load_crypto_1min.sh

@@ -12,11 +12,18 @@
 # -------------------------------------
 # --- Current month - all files
 # -------------------------------------
-cd $(realpath $(dirname $0))
+cd $(realpath $(dirname $0))/..
 mkdir -p ./data/crypto
 pushd ./data/crypto
 
-rsync -ahvv cvtt@hs01.cvtt.vpn:/works/cvtt/md_archive/crypto/sim/*.gz ./
+Files=$1
+if [ -z "$Files" ]; then
+    Files="*.gz"
+fi
+
+Cmd="rsync -ahvv cvtt@hs01.cvtt.vpn:/works/cvtt/md_archive/crypto/sim/${Files} ./"
+echo $Cmd
+eval $Cmd
 # -------------------------------------
 
 for srcfname in $(ls *.db.gz); do
@@ -24,8 +31,12 @@ for srcfname in $(ls *.db.gz); do
     tgtfile=${dt}.mktdata.ohlcv.db
     echo "${srcfname} -> ${tgtfile}"
 
-    gunzip -c $srcfname > temp.db
-    rm -f ${tgtfile} && sqlite3 temp.db ".dump md_1min_bars" | sqlite3  ${tgtfile} && rm ${srcfname}
+    Cmd="gunzip -c $srcfname > temp.db"
+    echo $Cmd
+    eval $Cmd
+    Cmd="rm -f ${tgtfile} && sqlite3 temp.db \".dump md_1min_bars\" | sqlite3  ${tgtfile} && rm ${srcfname}"
+    echo $Cmd
+    eval $Cmd
 done
 rm temp.db
 popd

scripts/load_equity_1min.sh

@@ -26,8 +26,12 @@ for srcfname in $(ls *.db.gz); do
     tgtfile=${dt}.mktdata.ohlcv.db
     echo "${srcfname} -> ${tgtfile}"
 
-    gunzip -c $srcfname > temp.db
-    rm -f ${tgtfile} && sqlite3 temp.db ".dump md_1min_bars" | sqlite3  ${tgtfile} && rm ${srcfname}
+    Cmd="gunzip -c $srcfname > temp.db && rm $srcfname"
+    echo ${Cmd}
+    eval ${Cmd}
+    Cmd="rm -f ${tgtfile} && sqlite3 temp.db '.dump md_1min_bars' | sqlite3  ${tgtfile}"
+    echo ${Cmd}
+    eval ${Cmd}
 done
 rm temp.db
 popd

src/notebooks/pt_sliding.ipynb

@@ -1,858 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Sliding Fit Strategy Visualization Notebook\n",
-    "\n",
-    "This notebook is specifically designed for the SlidingFitStrategy, which uses a sliding window approach.\n",
-    "It re-trains the model every minute and shows how cointegration, model parameters, and trading signals evolve over time.\n",
-    "You can visualize the dynamic nature of the sliding window and how the relationship between instruments changes."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 🎯 Key Features:\n",
-    "\n",
-    "1. **Interactive Configuration**: \n",
-    "   - Easy switching between CRYPTO and EQUITY configurations\n",
-    "   - Simple parameter adjustment for thresholds and training periods\n",
-    "\n",
-    "2. **Single Pair Focus**: \n",
-    "   - Instead of running multiple pairs, focuses on one pair at a time\n",
-    "   - Allows deep analysis of the relationship between two instruments\n",
-    "\n",
-    "3. **Step-by-Step Visualization**:\n",
-    "   - **Raw price data**: Individual prices, normalized comparison, and price ratios\n",
-    "   - **Training analysis**: Cointegration testing and VECM model fitting\n",
-    "   - **Dis-equilibrium visualization**: Both raw and scaled dis-equilibrium with threshold lines\n",
-    "   - **Strategy execution**: Trading signal generation and visualization\n",
-    "   - **Prediction analysis**: Actual vs predicted prices with trading signals overlaid\n",
-    "\n",
-    "4. **Rich Analytics**:\n",
-    "   - Cointegration status and VECM model details\n",
-    "   - Statistical summaries for all stages\n",
-    "   - Threshold crossing analysis\n",
-    "   - Trading signal breakdown\n",
-    "\n",
-    "5. **Interactive Experimentation**:\n",
-    "   - Easy parameter modification\n",
-    "   - Re-run capabilities for different configurations\n",
-    "   - Support for both StaticFitStrategy and SlidingFitStrategy\n",
-    "\n",
-    "### 🚀 How to Use:\n",
-    "\n",
-    "1. **Start Jupyter**:\n",
-    "   ```bash\n",
-    "   cd src/notebooks\n",
-    "   jupyter notebook pairs_trading_visualization.ipynb\n",
-    "   ```\n",
-    "\n",
-    "2. **Customize Your Analysis**:\n",
-    "   - Change `SYMBOL_A` and `SYMBOL_B` to your desired trading pair\n",
-    "   - Switch between `CRYPTO_CONFIG` and `EQT_CONFIG`\n",
-    "   - Choose your strategy (StaticFitStrategy or SlidingFitStrategy)\n",
-    "   - Adjust thresholds and parameters as needed\n",
-    "\n",
-    "3. **Run and Visualize**:\n",
-    "   - Execute cells step by step to see the analysis unfold\n",
-    "   - Rich matplotlib visualizations show relationships and signals\n",
-    "   - Comprehensive summary at the end\n",
-    "\n",
-    "The notebook provides exactly what you requested - a way to visualize the relationship between two instruments and their scaled dis-equilibrium, with all the stages of your pairs trading strategy clearly displayed and analyzed.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup and Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Trading Parameters Configuration\n",
-    "# Specify your configuration file, trading symbols and date here\n",
-    "\n",
-    "# Configuration file selection\n",
-    "CONFIG_FILE = \"equity\"  # Options: \"equity\", \"crypto\", or custom filename (without .cfg extension)\n",
-    "\n",
-    "# Trading pair symbols\n",
-    "SYMBOL_A = \"COIN\"   # Change this to your desired symbol A\n",
-    "SYMBOL_B = \"MSTR\"   # Change this to your desired symbol B\n",
-    "\n",
-    "# Date for data file selection (format: YYYYMMDD)\n",
-    "TRADING_DATE = \"20250605\"  # Change this to your desired date\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "import os\n",
-    "sys.path.append('..')\n",
-    "\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from typing import Dict, List, Optional\n",
-    "from IPython.display import clear_output\n",
-    "\n",
-    "# Import our modules\n",
-    "from strategies import SlidingFitStrategy, PairState\n",
-    "from tools.data_loader import load_market_data\n",
-    "from tools.trading_pair import TradingPair\n",
-    "from results import BacktestResult\n",
-    "\n",
-    "# Set plotting style\n",
-    "plt.style.use('seaborn-v0_8')\n",
-    "sns.set_palette(\"husl\")\n",
-    "plt.rcParams['figure.figsize'] = (15, 10)\n",
-    "\n",
-    "print(\"Setup complete!\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load Configuration from Configuration Files using HJSON\n",
-    "import hjson\n",
-    "import os\n",
-    "\n",
-    "def load_config_from_file(config_type=\"equity\"):\n",
-    "    \"\"\"Load configuration from configuration files using HJSON\"\"\"\n",
-    "    config_file = f\"../../configuration/{config_type}.cfg\"\n",
-    "    \n",
-    "    try:\n",
-    "        with open(config_file, 'r') as f:\n",
-    "            # HJSON handles comments, trailing commas, and other human-friendly features\n",
-    "            config = hjson.load(f)\n",
-    "        \n",
-    "        # Convert relative paths to absolute paths from notebook perspective\n",
-    "        if 'data_directory' in config:\n",
-    "            data_dir = config['data_directory']\n",
-    "            if data_dir.startswith('./'):\n",
-    "                # Convert relative path to absolute path from notebook's perspective\n",
-    "                config['data_directory'] = os.path.abspath(f\"../../{data_dir[2:]}\")\n",
-    "        \n",
-    "        return config\n",
-    "        \n",
-    "    except FileNotFoundError:\n",
-    "        print(f\"Configuration file not found: {config_file}\")\n",
-    "        return None\n",
-    "    except hjson.HjsonDecodeError as e:\n",
-    "        print(f\"HJSON parsing error in {config_file}: {e}\")\n",
-    "        return None\n",
-    "    except Exception as e:\n",
-    "        print(f\"Unexpected error loading config from {config_file}: {e}\")\n",
-    "        return None\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f\"Trading Parameters:\")\n",
-    "print(f\"  Configuration: {CONFIG_FILE}\")\n",
-    "print(f\"  Symbol A: {SYMBOL_A}\")\n",
-    "print(f\"  Symbol B: {SYMBOL_B}\")\n",
-    "print(f\"  Trading Date: {TRADING_DATE}\")\n",
-    "\n",
-    "# Load the specified configuration\n",
-    "print(f\"\\nLoading {CONFIG_FILE} configuration using HJSON...\")\n",
-    "test_config = load_config_from_file(CONFIG_FILE)\n",
-    "assert test_config is not None\n",
-    "BT_TEST_CONFIG = test_config\n",
-    "\n",
-    "if BT_TEST_CONFIG:\n",
-    "    print(f\"✓ Successfully loaded {BT_TEST_CONFIG['security_type']} configuration\")\n",
-    "    print(f\"  Data directory: {BT_TEST_CONFIG['data_directory']}\")\n",
-    "    print(f\"  Database table: {BT_TEST_CONFIG['db_table_name']}\")\n",
-    "    print(f\"  Exchange: {BT_TEST_CONFIG['exchange_id']}\")\n",
-    "    print(f\"  Training window: {BT_TEST_CONFIG['training_minutes']} minutes\")\n",
-    "    print(f\"  Open threshold: {BT_TEST_CONFIG['dis-equilibrium_open_trshld']}\")\n",
-    "    print(f\"  Close threshold: {BT_TEST_CONFIG['dis-equilibrium_close_trshld']}\")\n",
-    "    \n",
-    "    # Automatically construct data file name based on date and config type\n",
-    "    # if CONFIG['security_type'] == \"CRYPTO\":\n",
-    "    DATA_FILE = f\"{TRADING_DATE}.mktdata.ohlcv.db\"\n",
-    "    # elif CONFIG['security_type'] == \"EQUITY\":\n",
-    "    #     DATA_FILE = f\"{TRADING_DATE}.alpaca_sim_md.db\"\n",
-    "    # else:\n",
-    "    #     DATA_FILE = f\"{TRADING_DATE}.mktdata.db\"  # Default fallback\n",
-    "\n",
-    "    # Update CONFIG with the specific data file and instruments\n",
-    "    BT_TEST_CONFIG[\"datafiles\"] = [DATA_FILE]\n",
-    "    BT_TEST_CONFIG[\"instruments\"] = [SYMBOL_A, SYMBOL_B]\n",
-    "    \n",
-    "    print(f\"\\nData Configuration:\")\n",
-    "    print(f\"  Data File: {DATA_FILE}\")\n",
-    "    print(f\"  Security Type: {BT_TEST_CONFIG['security_type']}\")\n",
-    "    \n",
-    "    # Verify data file exists\n",
-    "    import os\n",
-    "    data_file_path = f\"{BT_TEST_CONFIG['data_directory']}/{DATA_FILE}\"\n",
-    "    if os.path.exists(data_file_path):\n",
-    "        print(f\"  ✓ Data file found: {data_file_path}\")\n",
-    "    else:\n",
-    "        print(f\"  ⚠ Data file not found: {data_file_path}\")\n",
-    "        print(f\"    Please check if the date and file exist in the data directory\")\n",
-    "        \n",
-    "        # List available files in the data directory\n",
-    "        try:\n",
-    "            data_dir = BT_TEST_CONFIG['data_directory']\n",
-    "            if os.path.exists(data_dir):\n",
-    "                available_files = [f for f in os.listdir(data_dir) if f.endswith('.db')]\n",
-    "                print(f\"    Available files in {data_dir}:\")\n",
-    "                for file in sorted(available_files)[:5]:  # Show first 5 files\n",
-    "                    print(f\"      - {file}\")\n",
-    "                if len(available_files) > 5:\n",
-    "                    print(f\"      ... and {len(available_files)-5} more files\")\n",
-    "        except Exception as e:\n",
-    "            print(f\"    Could not list files in data directory: {e}\")\n",
-    "else:\n",
-    "    print(\"⚠ Failed to load configuration. Please check the configuration file.\")\n",
-    "    print(\"Available configuration files:\")\n",
-    "    config_dir = \"../../configuration\"\n",
-    "    if os.path.exists(config_dir):\n",
-    "        config_files = [f for f in os.listdir(config_dir) if f.endswith('.cfg')]\n",
-    "        for file in config_files:\n",
-    "            print(f\"  - {file}\")\n",
-    "    else:\n",
-    "        print(f\"  Configuration directory not found: {config_dir}\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Select Trading Pair and Initialize Strategy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize Strategy\n",
-    "# Trading pair and data file are now defined in the previous cell\n",
-    "\n",
-    "# Initialize SlidingFitStrategy\n",
-    "STRATEGY = SlidingFitStrategy()\n",
-    "\n",
-    "print(f\"Strategy Initialization:\")\n",
-    "print(f\"  Selected pair: {SYMBOL_A} & {SYMBOL_B}\")\n",
-    "print(f\"  Data file: {DATA_FILE}\")\n",
-    "print(f\"  Strategy: {type(STRATEGY).__name__}\")\n",
-    "print(f\"\\nStrategy characteristics:\")\n",
-    "print(f\"  - Sliding window training every minute\")\n",
-    "print(f\"  - Dynamic cointegration testing\")\n",
-    "print(f\"  - State-based position management\")\n",
-    "print(f\"  - Continuous model re-training\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load and Prepare Market Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load market data\n",
-    "datafile_path = f\"{BT_TEST_CONFIG['data_directory']}/{DATA_FILE}\"\n",
-    "print(f\"Loading data from: {datafile_path}\")\n",
-    "\n",
-    "market_data_df = load_market_data(datafile_path, config=BT_TEST_CONFIG)\n",
-    "\n",
-    "print(f\"Loaded {len(market_data_df)} rows of market data\")\n",
-    "print(f\"Symbols in data: {market_data_df['symbol'].unique()}\")\n",
-    "print(f\"Time range: {market_data_df['tstamp'].min()} to {market_data_df['tstamp'].max()}\")\n",
-    "\n",
-    "# Create trading pair\n",
-    "pair = TradingPair(\n",
-    "    market_data=market_data_df,\n",
-    "    symbol_a=SYMBOL_A,\n",
-    "    symbol_b=SYMBOL_B,\n",
-    "    price_column=BT_TEST_CONFIG[\"price_column\"]\n",
-    ")\n",
-    "\n",
-    "print(f\"\\nCreated trading pair: {pair}\")\n",
-    "print(f\"Market data shape: {pair.market_data_.shape}\")\n",
-    "print(f\"Column names: {pair.colnames()}\")\n",
-    "\n",
-    "# Calculate maximum possible iterations for sliding window\n",
-    "training_minutes = BT_TEST_CONFIG[\"training_minutes\"]\n",
-    "max_iterations = len(pair.market_data_) - training_minutes\n",
-    "print(f\"\\nSliding window analysis:\")\n",
-    "print(f\"  Training window size: {training_minutes} minutes\")\n",
-    "print(f\"  Maximum iterations: {max_iterations}\")\n",
-    "print(f\"  Total analysis time: ~{max_iterations} minutes\")\n",
-    "\n",
-    "# Display sample data\n",
-    "print(f\"\\nSample data:\")\n",
-    "display(pair.market_data_.head())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run SlidingFitStrategy with Real-Time Visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Run the sliding strategy with detailed tracking\n",
-    "print(f\"Running SlidingFitStrategy on {pair}...\")\n",
-    "print(f\"This will process {max_iterations} minutes of data with sliding training windows.\\n\")\n",
-    "\n",
-    "# Initialize tracking variables\n",
-    "iteration_data = []\n",
-    "cointegration_history = []\n",
-    "beta_history = []\n",
-    "alpha_history = []\n",
-    "state_history = []\n",
-    "disequilibrium_history = []\n",
-    "scaled_disequilibrium_history = []\n",
-    "timestamp_history = []\n",
-    "training_mu_history = []\n",
-    "training_std_history = []\n",
-    "\n",
-    "# Initialize the strategy state\n",
-    "pair.user_data_['state'] = PairState.INITIAL\n",
-    "pair.user_data_[\"trades\"] = pd.DataFrame(columns=pd.Index(STRATEGY.TRADES_COLUMNS, dtype=str))\n",
-    "pair.user_data_[\"is_cointegrated\"] = False\n",
-    "\n",
-    "bt_result = BacktestResult(config=BT_TEST_CONFIG)\n",
-    "training_minutes = BT_TEST_CONFIG[\"training_minutes\"]\n",
-    "open_threshold = BT_TEST_CONFIG[\"dis-equilibrium_open_trshld\"]\n",
-    "close_threshold = BT_TEST_CONFIG[\"dis-equilibrium_close_trshld\"]\n",
-    "\n",
-    "# Limit iterations for demonstration (change this to max_iterations for full run)\n",
-    "max_demo_iterations = min(200, max_iterations)  # Process first 200 minutes\n",
-    "print(f\"Processing first {max_demo_iterations} iterations for demonstration...\\n\")\n",
-    "\n",
-    "for curr_training_start_idx in range(max_demo_iterations):\n",
-    "    if curr_training_start_idx % 20 == 0:\n",
-    "        print(f\"Processing iteration {curr_training_start_idx}/{max_demo_iterations}...\")\n",
-    "\n",
-    "    # Get datasets for this iteration\n",
-    "    pair.get_datasets(\n",
-    "        training_minutes=training_minutes,\n",
-    "        training_start_index=curr_training_start_idx,\n",
-    "        testing_size=1\n",
-    "    )\n",
-    "\n",
-    "    if len(pair.training_df_) < training_minutes:\n",
-    "        print(f\"Iteration {curr_training_start_idx}: Not enough training data. Stopping.\")\n",
-    "        break\n",
-    "\n",
-    "    # Record timestamp for this iteration\n",
-    "    current_timestamp = pair.testing_df_['tstamp'].iloc[0] if len(pair.testing_df_) > 0 else None\n",
-    "    timestamp_history.append(current_timestamp)\n",
-    "\n",
-    "    # Train and test cointegration\n",
-    "    try:\n",
-    "        is_cointegrated = pair.train_pair()\n",
-    "        cointegration_history.append(is_cointegrated)\n",
-    "\n",
-    "        if is_cointegrated:\n",
-    "            # Record model parameters\n",
-    "            beta_history.append(pair.vecm_fit_.beta.flatten())\n",
-    "            alpha_history.append(pair.vecm_fit_.alpha.flatten())\n",
-    "            training_mu_history.append(pair.training_mu_)\n",
-    "            training_std_history.append(pair.training_std_)\n",
-    "\n",
-    "            # Generate prediction for current minute\n",
-    "            pair.predict()\n",
-    "\n",
-    "            if len(pair.predicted_df_) > 0:\n",
-    "                current_disequilibrium = pair.predicted_df_['disequilibrium'].iloc[0]\n",
-    "                current_scaled_disequilibrium = pair.predicted_df_['scaled_disequilibrium'].iloc[0]\n",
-    "                disequilibrium_history.append(current_disequilibrium)\n",
-    "                scaled_disequilibrium_history.append(current_scaled_disequilibrium)\n",
-    "            else:\n",
-    "                disequilibrium_history.append(np.nan)\n",
-    "                scaled_disequilibrium_history.append(np.nan)\n",
-    "        else:\n",
-    "            # No cointegration\n",
-    "            beta_history.append(None)\n",
-    "            alpha_history.append(None)\n",
-    "            training_mu_history.append(np.nan)\n",
-    "            training_std_history.append(np.nan)\n",
-    "            disequilibrium_history.append(np.nan)\n",
-    "            scaled_disequilibrium_history.append(np.nan)\n",
-    "\n",
-    "    except Exception as e:\n",
-    "        print(f\"Iteration {curr_training_start_idx}: Training failed: {str(e)}\")\n",
-    "        cointegration_history.append(False)\n",
-    "        beta_history.append(None)\n",
-    "        alpha_history.append(None)\n",
-    "        training_mu_history.append(np.nan)\n",
-    "        training_std_history.append(np.nan)\n",
-    "        disequilibrium_history.append(np.nan)\n",
-    "        scaled_disequilibrium_history.append(np.nan)\n",
-    "\n",
-    "    # Record current state\n",
-    "    current_state = pair.user_data_.get('state', PairState.INITIAL)\n",
-    "    state_history.append(current_state)\n",
-    "\n",
-    "print(f\"\\nCompleted {len(cointegration_history)} iterations\")\n",
-    "print(f\"Cointegration rate: {sum(cointegration_history)/len(cointegration_history)*100:.1f}%\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualize Sliding Window Results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create comprehensive visualization of sliding window results\n",
-    "fig, axes = plt.subplots(6, 1, figsize=(18, 24))\n",
-    "\n",
-    "# Filter valid timestamps\n",
-    "valid_timestamps = [ts for ts in timestamp_history if ts is not None]\n",
-    "n_points = len(valid_timestamps)\n",
-    "\n",
-    "if n_points == 0:\n",
-    "    print(\"No valid data points to visualize\")\n",
-    "else:\n",
-    "    # 1. Cointegration Status Over Time\n",
-    "    cointegration_values = [1 if coint else 0 for coint in cointegration_history[:n_points]]\n",
-    "    axes[0].plot(valid_timestamps, cointegration_values, 'o-', alpha=0.7, markersize=3)\n",
-    "    axes[0].fill_between(valid_timestamps, cointegration_values, alpha=0.3)\n",
-    "    axes[0].set_title('Cointegration Status Over Time (1=Cointegrated, 0=Not Cointegrated)')\n",
-    "    axes[0].set_ylabel('Cointegrated')\n",
-    "    axes[0].set_ylim(-0.1, 1.1)\n",
-    "    axes[0].grid(True)\n",
-    "\n",
-    "    # 2. Beta Coefficients Evolution\n",
-    "    valid_betas = []\n",
-    "    beta_timestamps = []\n",
-    "    for i, beta in enumerate(beta_history[:n_points]):\n",
-    "        if beta is not None and i < len(valid_timestamps):\n",
-    "            valid_betas.append(beta)\n",
-    "            beta_timestamps.append(valid_timestamps[i])\n",
-    "\n",
-    "    if valid_betas:\n",
-    "        beta_array = np.array(valid_betas)\n",
-    "        axes[1].plot(beta_timestamps, beta_array[:, 1], 'o-', alpha=0.7, markersize=2,\n",
-    "                    label='Beta[1]', color='red')\n",
-    "        axes[1].set_title('VECM Beta[1] Coefficient Evolution (Beta[0] = 1.0 by normalization)')\n",
-    "        axes[1].set_ylabel('Beta[1] Value')\n",
-    "        axes[1].legend()\n",
-    "        axes[1].grid(True)\n",
-    "\n",
-    "    # 3. Training Mean and Std Evolution\n",
-    "    valid_mu = [mu for mu in training_mu_history[:n_points] if not np.isnan(mu)]\n",
-    "    valid_std = [std for std in training_std_history[:n_points] if not np.isnan(std)]\n",
-    "    mu_timestamps = [valid_timestamps[i] for i, mu in enumerate(training_mu_history[:n_points]) if not np.isnan(mu)]\n",
-    "\n",
-    "    if valid_mu:\n",
-    "        axes[2].plot(mu_timestamps, valid_mu, 'b-', alpha=0.7, label='Training Mean', linewidth=1)\n",
-    "        ax2_twin = axes[2].twinx()\n",
-    "        ax2_twin.plot(mu_timestamps, valid_std, 'r-', alpha=0.7, label='Training Std', linewidth=1)\n",
-    "        axes[2].set_title('Training Dis-equilibrium Statistics Evolution')\n",
-    "        axes[2].set_ylabel('Mean', color='b')\n",
-    "        ax2_twin.set_ylabel('Std', color='r')\n",
-    "        axes[2].grid(True)\n",
-    "        axes[2].legend(loc='upper left')\n",
-    "        ax2_twin.legend(loc='upper right')\n",
-    "\n",
-    "    # 4. Raw Dis-equilibrium Over Time\n",
-    "    valid_diseq = [diseq for diseq in disequilibrium_history[:n_points] if not np.isnan(diseq)]\n",
-    "    diseq_timestamps = [valid_timestamps[i] for i, diseq in enumerate(disequilibrium_history[:n_points]) if not np.isnan(diseq)]\n",
-    "\n",
-    "    if valid_diseq:\n",
-    "        axes[3].plot(diseq_timestamps, valid_diseq, 'g-', alpha=0.7, linewidth=1)\n",
-    "        # Add rolling mean\n",
-    "        if len(valid_diseq) > 10:\n",
-    "            rolling_mean = pd.Series(valid_diseq).rolling(window=10, min_periods=1).mean()\n",
-    "            axes[3].plot(diseq_timestamps, rolling_mean, 'r-', alpha=0.8, linewidth=2, label='10-period MA')\n",
-    "            axes[3].legend()\n",
-    "        axes[3].set_title('Raw Dis-equilibrium Over Time')\n",
-    "        axes[3].set_ylabel('Dis-equilibrium')\n",
-    "        axes[3].grid(True)\n",
-    "\n",
-    "    # 5. Scaled Dis-equilibrium with Thresholds\n",
-    "    valid_scaled_diseq = [diseq for diseq in scaled_disequilibrium_history[:n_points] if not np.isnan(diseq)]\n",
-    "    scaled_diseq_timestamps = [valid_timestamps[i] for i, diseq in enumerate(scaled_disequilibrium_history[:n_points]) if not np.isnan(diseq)]\n",
-    "\n",
-    "    if valid_scaled_diseq:\n",
-    "        axes[4].plot(scaled_diseq_timestamps, valid_scaled_diseq, 'purple', alpha=0.7, linewidth=1)\n",
-    "        axes[4].axhline(y=open_threshold, color='red', linestyle='--', alpha=0.8,\n",
-    "                       label=f'Open Threshold ({open_threshold})')\n",
-    "        axes[4].axhline(y=close_threshold, color='blue', linestyle='--', alpha=0.8,\n",
-    "                       label=f'Close Threshold ({close_threshold})')\n",
-    "        axes[4].axhline(y=0, color='black', linestyle='-', alpha=0.5, linewidth=0.5)\n",
-    "        axes[4].set_title('Scaled Dis-equilibrium with Trading Thresholds')\n",
-    "        axes[4].set_ylabel('Scaled Dis-equilibrium')\n",
-    "        axes[4].legend()\n",
-    "        axes[4].grid(True)\n",
-    "\n",
-    "    # 6. Price Data with Training Windows\n",
-    "    # Show original price data with indication of training windows\n",
-    "    colname_a, colname_b = pair.colnames()\n",
-    "    price_data = pair.market_data_[:n_points + training_minutes].copy()\n",
-    "\n",
-    "    axes[5].plot(price_data['tstamp'], price_data[colname_a], alpha=0.7, label=f'{SYMBOL_A}', linewidth=1)\n",
-    "    axes[5].plot(price_data['tstamp'], price_data[colname_b], alpha=0.7, label=f'{SYMBOL_B}', linewidth=1)\n",
-    "\n",
-    "    # Highlight training windows\n",
-    "    for i in range(0, min(n_points, 10), max(1, n_points//20)):  # Show every 20th window\n",
-    "        start_idx = i\n",
-    "        end_idx = i + training_minutes\n",
-    "        if end_idx < len(price_data):\n",
-    "            window_data = price_data.iloc[start_idx:end_idx]\n",
-    "            axes[5].axvspan(window_data['tstamp'].iloc[0], window_data['tstamp'].iloc[-1],\n",
-    "                           alpha=0.1, color='gray')\n",
-    "\n",
-    "    axes[5].set_title(f'Price Data with Training Windows (Gray bands show some training windows)')\n",
-    "    axes[5].set_ylabel('Price')\n",
-    "    axes[5].set_xlabel('Time')\n",
-    "    axes[5].legend()\n",
-    "    axes[5].grid(True)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "# Print summary statistics\n",
-    "print(f\"\\n\" + \"=\"*80)\n",
-    "print(f\"SLIDING WINDOW ANALYSIS SUMMARY\")\n",
-    "print(f\"=\"*80)\n",
-    "print(f\"Total iterations processed: {n_points}\")\n",
-    "print(f\"Cointegration episodes: {sum(cointegration_history[:n_points])}\")\n",
-    "print(f\"Cointegration rate: {sum(cointegration_history[:n_points])/n_points*100:.1f}%\")\n",
-    "if valid_betas:\n",
-    "    print(f\"Beta coefficient stability: Std = {np.std(beta_array, axis=0)}\")\n",
-    "if valid_scaled_diseq:\n",
-    "    threshold_breaches = sum(1 for x in valid_scaled_diseq if abs(x) > open_threshold)\n",
-    "    print(f\"Open threshold breaches: {threshold_breaches} ({threshold_breaches/len(valid_scaled_diseq)*100:.1f}%)\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Analyze Training Window Evolution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Detailed analysis of how training windows evolve\n",
-    "print(\"TRAINING WINDOW EVOLUTION ANALYSIS\")\n",
-    "print(\"=\" * 50)\n",
-    "\n",
-    "# Analyze cointegration stability\n",
-    "if len(cointegration_history) > 1:\n",
-    "    # Find cointegration change points\n",
-    "    change_points = []\n",
-    "    for i in range(1, len(cointegration_history)):\n",
-    "        if cointegration_history[i] != cointegration_history[i - 1]:\n",
-    "            change_points.append(\n",
-    "                (\n",
-    "                    i,\n",
-    "                    cointegration_history[i],\n",
-    "                    valid_timestamps[i] if i < len(valid_timestamps) else None,\n",
-    "                )\n",
-    "            )\n",
-    "\n",
-    "    print(f\"\\nCointegration Change Points:\")\n",
-    "    if change_points:\n",
-    "        for idx, status, timestamp in change_points[:10]:  # Show first 10\n",
-    "            status_str = \"GAINED\" if status else \"LOST\"\n",
-    "            print(f\"  Iteration {idx}: {status_str} cointegration at {timestamp}\")\n",
-    "        if len(change_points) > 10:\n",
-    "            print(f\"  ... and {len(change_points)-10} more changes\")\n",
-    "    else:\n",
-    "        print(f\"  No cointegration changes detected\")\n",
-    "\n",
-    "# Analyze beta stability when cointegrated\n",
-    "if valid_betas and len(valid_betas) > 10:\n",
-    "    beta_df = pd.DataFrame(\n",
-    "        valid_betas,\n",
-    "        columns=pd.Index([f\"Beta_{i}\" for i in range(len(valid_betas[0]))], dtype=str),\n",
-    "    )\n",
-    "    beta_df[\"timestamp\"] = beta_timestamps\n",
-    "\n",
-    "    print(f\"\\nBeta Coefficient Analysis:\")\n",
-    "    print(f\"  Number of valid beta estimates: {len(valid_betas)}\")\n",
-    "    print(f\"  Beta statistics:\")\n",
-    "    for col in beta_df.columns[:-1]:  # Exclude timestamp\n",
-    "        print(\n",
-    "            f\"    {col}: Mean={beta_df[col].mean():.4f}, Std={beta_df[col].std():.4f}\"\n",
-    "        )\n",
-    "\n",
-    "    # Check for beta regime changes\n",
-    "    beta_changes = []\n",
-    "    threshold = 0.1  # 10% change threshold\n",
-    "    for i in range(1, len(valid_betas)):\n",
-    "        if np.any(\n",
-    "            np.abs(np.array(valid_betas[i]) - np.array(valid_betas[i - 1])) > threshold\n",
-    "        ):\n",
-    "            beta_changes.append(i)\n",
-    "\n",
-    "    print(f\"  Significant beta changes (>{threshold*100}%): {len(beta_changes)}\")\n",
-    "    if beta_changes:\n",
-    "        print(\n",
-    "            f\"    Change frequency: {len(beta_changes)/len(valid_betas)*100:.1f}% of cointegrated periods\"\n",
-    "        )\n",
-    "\n",
-    "# Analyze dis-equilibrium characteristics\n",
-    "if valid_scaled_diseq:\n",
-    "    scaled_diseq_series = pd.Series(valid_scaled_diseq)\n",
-    "\n",
-    "    print(f\"\\nDis-equilibrium Analysis:\")\n",
-    "    print(f\"  Mean: {scaled_diseq_series.mean():.4f}\")\n",
-    "    print(f\"  Std: {scaled_diseq_series.std():.4f}\")\n",
-    "    print(f\"  Min: {scaled_diseq_series.min():.4f}\")\n",
-    "    print(f\"  Max: {scaled_diseq_series.max():.4f}\")\n",
-    "\n",
-    "    # Threshold analysis\n",
-    "    open_breaches = sum(1 for x in valid_scaled_diseq if abs(x) >= open_threshold)\n",
-    "    close_opportunities = sum(\n",
-    "        1 for x in valid_scaled_diseq if abs(x) <= close_threshold\n",
-    "    )\n",
-    "\n",
-    "    print(\n",
-    "        f\"  Open threshold breaches: {open_breaches} ({open_breaches/len(valid_scaled_diseq)*100:.1f}%)\"\n",
-    "    )\n",
-    "    print(\n",
-    "        f\"  Close opportunities: {close_opportunities} ({close_opportunities/len(valid_scaled_diseq)*100:.1f}%)\"\n",
-    "    )\n",
-    "\n",
-    "    # Mean reversion analysis\n",
-    "    zero_crossings = 0\n",
-    "    for i in range(1, len(valid_scaled_diseq)):\n",
-    "        if (valid_scaled_diseq[i - 1] * valid_scaled_diseq[i]) < 0:  # Sign change\n",
-    "            zero_crossings += 1\n",
-    "\n",
-    "    print(f\"  Zero crossings (mean reversion events): {zero_crossings}\")\n",
-    "    if zero_crossings > 0:\n",
-    "        print(\n",
-    "            f\"  Average time between mean reversions: {len(valid_scaled_diseq)/zero_crossings:.1f} minutes\"\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run Complete Strategy (Optional)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Optional: Run the complete strategy to generate actual trades\n",
-    "# Warning: This may take several minutes depending on data size\n",
-    "\n",
-    "RUN_COMPLETE_STRATEGY = False  # Set to True to run full strategy\n",
-    "\n",
-    "if RUN_COMPLETE_STRATEGY:\n",
-    "    print(\"Running complete SlidingFitStrategy...\")\n",
-    "    print(\"This may take several minutes...\")\n",
-    "\n",
-    "    # Reset strategy state\n",
-    "    STRATEGY.curr_training_start_idx_ = 0\n",
-    "\n",
-    "    # Create new pair and result objects\n",
-    "    pair_full = TradingPair(\n",
-    "        market_data=market_data_df,\n",
-    "        symbol_a=SYMBOL_A,\n",
-    "        symbol_b=SYMBOL_B,\n",
-    "        price_column=BT_TEST_CONFIG[\"price_column\"]\n",
-    "    )\n",
-    "\n",
-    "    bt_result_full = BacktestResult(config=BT_TEST_CONFIG)\n",
-    "\n",
-    "    # Run strategy\n",
-    "    pair_trades = STRATEGY.run_pair(config=BT_TEST_CONFIG, pair=pair_full, bt_result=bt_result_full)\n",
-    "\n",
-    "    if pair_trades is not None and len(pair_trades) > 0:\n",
-    "        print(f\"\\nGenerated {len(pair_trades)} trading signals:\")\n",
-    "        display(pair_trades)\n",
-    "\n",
-    "        # Analyze trades\n",
-    "        trade_times = pair_trades['time'].unique()\n",
-    "        print(f\"\\nTrade Analysis:\")\n",
-    "        print(f\"  Unique trade times: {len(trade_times)}\")\n",
-    "        print(f\"  Trade frequency: {len(trade_times)/max_iterations*100:.2f}% of total periods\")\n",
-    "\n",
-    "        # Group trades by time\n",
-    "        for trade_time in trade_times[:5]:  # Show first 5 trade times\n",
-    "            trades_at_time = pair_trades[pair_trades['time'] == trade_time]\n",
-    "            print(f\"\\n  Trade at {trade_time}:\")\n",
-    "            for _, trade in trades_at_time.iterrows():\n",
-    "                print(f\"    {trade['action']} {trade['symbol']} @ ${trade['price']:.2f} \"\n",
-    "                      f\"(dis-eq: {trade['scaled_disequilibrium']:.2f})\")\n",
-    "    else:\n",
-    "        print(\"\\nNo trading signals generated\")\n",
-    "        print(\"Possible reasons:\")\n",
-    "        print(\"  - Insufficient cointegration periods\")\n",
-    "        print(\"  - Dis-equilibrium never exceeded thresholds\")\n",
-    "        print(\"  - Strategy-specific conditions not met\")\n",
-    "else:\n",
-    "    print(\"Complete strategy execution is disabled.\")\n",
-    "    print(\"Set RUN_COMPLETE_STRATEGY = True to run the full strategy.\")\n",
-    "    print(\"Note: This may take several minutes depending on your data size.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Interactive Parameter Analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Interactive analysis for parameter optimization\n",
-    "print(\"PARAMETER SENSITIVITY ANALYSIS\")\n",
-    "print(\"=\"*40)\n",
-    "\n",
-    "print(f\"Current parameters:\")\n",
-    "print(f\"  Training window: {BT_TEST_CONFIG['training_minutes']} minutes\")\n",
-    "print(f\"  Open threshold: {BT_TEST_CONFIG['dis-equilibrium_open_trshld']}\")\n",
-    "print(f\"  Close threshold: {BT_TEST_CONFIG['dis-equilibrium_close_trshld']}\")\n",
-    "\n",
-    "# Recommendations based on observed data\n",
-    "if valid_scaled_diseq:\n",
-    "    diseq_stats = pd.Series(valid_scaled_diseq).describe()\n",
-    "    print(f\"\\nObserved scaled dis-equilibrium statistics:\")\n",
-    "    print(f\"  75th percentile: {diseq_stats['75%']:.2f}\")\n",
-    "    print(f\"  95th percentile: {np.percentile(valid_scaled_diseq, 95):.2f}\")\n",
-    "    print(f\"  99th percentile: {np.percentile(valid_scaled_diseq, 99):.2f}\")\n",
-    "\n",
-    "    # Suggest optimal thresholds\n",
-    "    suggested_open = np.percentile(np.abs(valid_scaled_diseq), 85)\n",
-    "    suggested_close = np.percentile(np.abs(valid_scaled_diseq), 30)\n",
-    "\n",
-    "    print(f\"\\nSuggested threshold optimization:\")\n",
-    "    print(f\"  Suggested open threshold: {suggested_open:.2f} (85th percentile)\")\n",
-    "    print(f\"  Suggested close threshold: {suggested_close:.2f} (30th percentile)\")\n",
-    "\n",
-    "    if suggested_open != open_threshold or suggested_close != close_threshold:\n",
-    "        print(f\"\\nTo test these parameters, modify the CONFIG dictionary:\")\n",
-    "        print(f\"  CONFIG['dis-equilibrium_open_trshld'] = {suggested_open:.2f}\")\n",
-    "        print(f\"  CONFIG['dis-equilibrium_close_trshld'] = {suggested_close:.2f}\")\n",
-    "\n",
-    "# Training window recommendations\n",
-    "if len(cointegration_history) > 0:\n",
-    "    cointegration_rate = sum(cointegration_history)/len(cointegration_history)\n",
-    "    print(f\"\\nTraining window analysis:\")\n",
-    "    print(f\"  Current cointegration rate: {cointegration_rate*100:.1f}%\")\n",
-    "\n",
-    "    if cointegration_rate < 0.3:\n",
-    "        print(f\"  Recommendation: Consider increasing training window (current: {training_minutes})\")\n",
-    "        print(f\"  Suggested: {int(training_minutes * 1.5)} minutes\")\n",
-    "    elif cointegration_rate > 0.8:\n",
-    "        print(f\"  Recommendation: Consider decreasing training window for more responsive model\")\n",
-    "        print(f\"  Suggested: {int(training_minutes * 0.75)} minutes\")\n",
-    "    else:\n",
-    "        print(f\"  Current training window appears appropriate\")\n",
-    "\n",
-    "print(f\"\\nTo re-run analysis with different parameters:\")\n",
-    "print(f\"1. Modify the CONFIG dictionary above\")\n",
-    "print(f\"2. Re-run from the 'Run SlidingFitStrategy' cell\")\n",
-    "print(f\"3. Compare results with current analysis\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Summary and Conclusions\n",
-    "\n",
-    "This notebook demonstrates the SlidingFitStrategy's dynamic approach to pairs trading.\n",
-    "Key insights from the sliding window analysis:\n",
-    "\n",
-    "1. **Cointegration Stability**: How often the pair maintains cointegration\n",
-    "2. **Model Parameter Evolution**: How VECM coefficients change over time\n",
-    "3. **Threshold Effectiveness**: How well current thresholds capture trading opportunities\n",
-    "4. **Mean Reversion Patterns**: Frequency and timing of dis-equilibrium corrections\n",
-    "\n",
-    "The sliding approach allows for:\n",
-    "- **Adaptive modeling**: Responds to changing market conditions\n",
-    "- **Dynamic thresholding**: Can be optimized based on observed patterns\n",
-    "- **Real-time monitoring**: Provides continuous assessment of pair relationships\n",
-    "- **Risk management**: Early detection of cointegration breakdown"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "python3.12-venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

src/notebooks/pt_static.ipynb

@@ -1,710 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Pairs Trading Visualization Notebook\n",
-    "\n",
-    "This notebook allows you to visualize pairs trading strategies on individual instrument pairs.\n",
-    "You can examine the relationship between two instruments, their dis-equilibrium, and trading signals."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### \ud83c\udfaf Key Features:\n",
-    "\n",
-    "1. **Interactive Configuration**: \n",
-    "   - Easy switching between CRYPTO and EQUITY configurations\n",
-    "   - Simple parameter adjustment for thresholds and training periods\n",
-    "\n",
-    "2. **Single Pair Focus**: \n",
-    "   - Instead of running multiple pairs, focuses on one pair at a time\n",
-    "   - Allows deep analysis of the relationship between two instruments\n",
-    "\n",
-    "3. **Step-by-Step Visualization**:\n",
-    "   - **Raw price data**: Individual prices, normalized comparison, and price ratios\n",
-    "   - **Training analysis**: Cointegration testing and VECM model fitting\n",
-    "   - **Dis-equilibrium visualization**: Both raw and scaled dis-equilibrium with threshold lines\n",
-    "   - **Strategy execution**: Trading signal generation and visualization\n",
-    "   - **Prediction analysis**: Actual vs predicted prices with trading signals overlaid\n",
-    "\n",
-    "4. **Rich Analytics**:\n",
-    "   - Cointegration status and VECM model details\n",
-    "   - Statistical summaries for all stages\n",
-    "   - Threshold crossing analysis\n",
-    "   - Trading signal breakdown\n",
-    "\n",
-    "5. **Interactive Experimentation**:\n",
-    "   - Easy parameter modification\n",
-    "   - Re-run capabilities for different configurations\n",
-    "   - Support for both StaticFitStrategy and SlidingFitStrategy\n",
-    "\n",
-    "### \ud83d\ude80 How to Use:\n",
-    "\n",
-    "1. **Start Jupyter**:\n",
-    "   ```bash\n",
-    "   cd src/notebooks\n",
-    "   jupyter notebook pairs_trading_visualization.ipynb\n",
-    "   ```\n",
-    "\n",
-    "2. **Customize Your Analysis**:\n",
-    "   - Change `SYMBOL_A` and `SYMBOL_B` to your desired trading pair\n",
-    "   - Switch between `CRYPTO_CONFIG` and `EQT_CONFIG`\n",
-    "   - Only **StaticFitStrategy** is supported. \n",
-    "   - Adjust thresholds and parameters as needed\n",
-    "\n",
-    "3. **Run and Visualize**:\n",
-    "   - Execute cells step by step to see the analysis unfold\n",
-    "   - Rich matplotlib visualizations show relationships and signals\n",
-    "   - Comprehensive summary at the end\n",
-    "\n",
-    "The notebook provides exactly what you requested - a way to visualize the relationship between two instruments and their scaled dis-equilibrium, with all the stages of your pairs trading strategy clearly displayed and analyzed.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup and Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "import os\n",
-    "sys.path.append('..')\n",
-    "\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from typing import Dict, List, Optional\n",
-    "\n",
-    "# Import our modules\n",
-    "from strategies import StaticFitStrategy, SlidingFitStrategy\n",
-    "from tools.data_loader import load_market_data\n",
-    "from tools.trading_pair import TradingPair\n",
-    "from results import BacktestResult\n",
-    "\n",
-    "# Set plotting style\n",
-    "plt.style.use('seaborn-v0_8')\n",
-    "sns.set_palette(\"husl\")\n",
-    "plt.rcParams['figure.figsize'] = (12, 8)\n",
-    "\n",
-    "print(\"Setup complete!\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configuration - Choose between CRYPTO_CONFIG or EQT_CONFIG\n",
-    "\n",
-    "CRYPTO_CONFIG = {\n",
-    "    \"security_type\": \"CRYPTO\",\n",
-    "    \"data_directory\": \"../../data/crypto\",\n",
-    "    \"datafiles\": [\n",
-    "        \"20250519.mktdata.ohlcv.db\",\n",
-    "    ],\n",
-    "    \"db_table_name\": \"bnbspot_ohlcv_1min\",\n",
-    "    \"exchange_id\": \"BNBSPOT\",\n",
-    "    \"instrument_id_pfx\": \"PAIR-\",\n",
-    "    \"instruments\": [\n",
-    "        \"BTC-USDT\",\n",
-    "        \"BCH-USDT\",\n",
-    "        \"ETH-USDT\",\n",
-    "        \"LTC-USDT\",\n",
-    "        \"XRP-USDT\",\n",
-    "        \"ADA-USDT\",\n",
-    "        \"SOL-USDT\",\n",
-    "        \"DOT-USDT\",\n",
-    "    ],\n",
-    "    \"trading_hours\": {\n",
-    "        \"begin_session\": \"00:00:00\",\n",
-    "        \"end_session\": \"23:59:00\",\n",
-    "        \"timezone\": \"UTC\",\n",
-    "    },\n",
-    "    \"price_column\": \"close\",\n",
-    "    \"min_required_points\": 30,\n",
-    "    \"zero_threshold\": 1e-10,\n",
-    "    \"dis-equilibrium_open_trshld\": 2.0,\n",
-    "    \"dis-equilibrium_close_trshld\": 0.5,\n",
-    "    \"training_minutes\": 120,\n",
-    "    \"funding_per_pair\": 2000.0,\n",
-    "}\n",
-    "\n",
-    "EQT_CONFIG = {\n",
-    "    \"security_type\": \"EQUITY\",\n",
-    "    \"data_directory\": \"../../data/equity\",\n",
-    "    \"datafiles\": {\n",
-    "        \"0508\":   \"20250508.alpaca_sim_md.db\",\n",
-    "        \"0509\":   \"20250509.alpaca_sim_md.db\",\n",
-    "        \"0510\":   \"20250510.alpaca_sim_md.db\",\n",
-    "        \"0511\":   \"20250511.alpaca_sim_md.db\",\n",
-    "        \"0512\":   \"20250512.alpaca_sim_md.db\",\n",
-    "        \"0513\":   \"20250513.alpaca_sim_md.db\",\n",
-    "        \"0514\":   \"20250514.alpaca_sim_md.db\",\n",
-    "        \"0515\":   \"20250515.alpaca_sim_md.db\",\n",
-    "        \"0516\":   \"20250516.alpaca_sim_md.db\",\n",
-    "        \"0517\":   \"20250517.alpaca_sim_md.db\",\n",
-    "        \"0518\":   \"20250518.alpaca_sim_md.db\",\n",
-    "        \"0519\":   \"20250519.alpaca_sim_md.db\",\n",
-    "        \"0520\":   \"20250520.alpaca_sim_md.db\",\n",
-    "        \"0521\":   \"20250521.alpaca_sim_md.db\",\n",
-    "        \"0522\":   \"20250522.alpaca_sim_md.db\",\n",
-    "    },\n",
-    "    \"db_table_name\": \"md_1min_bars\",\n",
-    "    \"exchange_id\": \"ALPACA\",\n",
-    "    \"instrument_id_pfx\": \"STOCK-\",\n",
-    "    \"instruments\": [\n",
-    "        \"COIN\",\n",
-    "        \"GBTC\",\n",
-    "        \"HOOD\",\n",
-    "        \"MSTR\",\n",
-    "        \"PYPL\",\n",
-    "    ],\n",
-    "    \"trading_hours\": {\n",
-    "        \"begin_session\": \"9:30:00\",\n",
-    "        \"end_session\": \"16:00:00\",\n",
-    "        \"timezone\": \"America/New_York\",\n",
-    "    },\n",
-    "    \"price_column\": \"close\",\n",
-    "    \"min_required_points\": 30,\n",
-    "    \"zero_threshold\": 1e-10,\n",
-    "    \"dis-equilibrium_open_trshld\": 2.0,\n",
-    "    \"dis-equilibrium_close_trshld\": 1.0, #0.5,\n",
-    "    \"training_minutes\": 120,\n",
-    "    \"funding_per_pair\": 2000.0,\n",
-    "}\n",
-    "\n",
-    "# Choose your configuration\n",
-    "CONFIG = EQT_CONFIG  # Change to CRYPTO_CONFIG if you want to use crypto data\n",
-    "\n",
-    "print(f\"Using {CONFIG['security_type']} configuration\")\n",
-    "print(f\"Available instruments: {CONFIG['instruments']}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Select Trading Pair and Data File"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Select your trading pair and strategy\n",
-    "SYMBOL_A = \"COIN\"  # Change these to your desired symbols\n",
-    "SYMBOL_B = \"GBTC\"\n",
-    "DATA_FILE = CONFIG[\"datafiles\"][\"0509\"]\n",
-    "\n",
-    "# Choose strategy\n",
-    "STRATEGY = StaticFitStrategy()\n",
-    "\n",
-    "print(f\"Selected pair: {SYMBOL_A} & {SYMBOL_B}\")\n",
-    "print(f\"Data file: {DATA_FILE}\")\n",
-    "print(f\"Strategy: {type(STRATEGY).__name__}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load Market Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load market data\n",
-    "datafile_path = f\"{CONFIG['data_directory']}/{DATA_FILE}\"\n",
-    "print(f\"Current working directory: {os.getcwd()}\")\n",
-    "print(f\"Loading data from: {datafile_path}\")\n",
-    "\n",
-    "market_data_df = load_market_data(datafile_path, config=CONFIG)\n",
-    "\n",
-    "print(f\"Loaded {len(market_data_df)} rows of market data\")\n",
-    "print(f\"Symbols in data: {market_data_df['symbol'].unique()}\")\n",
-    "print(f\"Time range: {market_data_df['tstamp'].min()} to {market_data_df['tstamp'].max()}\")\n",
-    "\n",
-    "# Display first few rows\n",
-    "market_data_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Trading Pair and Analyze"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create trading pair\n",
-    "pair = TradingPair(\n",
-    "    market_data=market_data_df,\n",
-    "    symbol_a=SYMBOL_A,\n",
-    "    symbol_b=SYMBOL_B,\n",
-    "    price_column=CONFIG[\"price_column\"]\n",
-    ")\n",
-    "\n",
-    "print(f\"Created trading pair: {pair}\")\n",
-    "print(f\"Market data shape: {pair.market_data_.shape}\")\n",
-    "print(f\"Column names: {pair.colnames()}\")\n",
-    "\n",
-    "# Display first few rows of pair data\n",
-    "pair.market_data_.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Split Data into Training and Testing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get training and testing datasets\n",
-    "training_minutes = CONFIG[\"training_minutes\"]\n",
-    "pair.get_datasets(training_minutes=training_minutes)\n",
-    "\n",
-    "print(f\"Training data: {len(pair.training_df_)} rows\")\n",
-    "print(f\"Testing data: {len(pair.testing_df_)} rows\")\n",
-    "print(f\"Training period: {pair.training_df_['tstamp'].iloc[0]} to {pair.training_df_['tstamp'].iloc[-1]}\")\n",
-    "print(f\"Testing period: {pair.testing_df_['tstamp'].iloc[0]} to {pair.testing_df_['tstamp'].iloc[-1]}\")\n",
-    "\n",
-    "# Check for any missing data\n",
-    "print(f\"Training data null values: {pair.training_df_.isnull().sum().sum()}\")\n",
-    "print(f\"Testing data null values: {pair.testing_df_.isnull().sum().sum()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualize Raw Price Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plot raw price data\n",
-    "fig, axes = plt.subplots(3, 1, figsize=(15, 12))\n",
-    "\n",
-    "# Combined price plot\n",
-    "colname_a, colname_b = pair.colnames()\n",
-    "all_data = pd.concat([pair.training_df_, pair.testing_df_]).reset_index(drop=True)\n",
-    "\n",
-    "# Plot individual prices\n",
-    "axes[0].plot(all_data['tstamp'], all_data[colname_a], label=f'{SYMBOL_A}', alpha=0.8)\n",
-    "axes[0].plot(all_data['tstamp'], all_data[colname_b], label=f'{SYMBOL_B}', alpha=0.8)\n",
-    "axes[0].axvline(x=pair.training_df_['tstamp'].iloc[-1], color='red', linestyle='--', alpha=0.7, label='Train/Test Split')\n",
-    "axes[0].set_title(f'Price Comparison: {SYMBOL_A} vs {SYMBOL_B}')\n",
-    "axes[0].set_ylabel('Price')\n",
-    "axes[0].legend()\n",
-    "axes[0].grid(True)\n",
-    "\n",
-    "# Normalized prices for comparison\n",
-    "norm_a = all_data[colname_a] / all_data[colname_a].iloc[0]\n",
-    "norm_b = all_data[colname_b] / all_data[colname_b].iloc[0]\n",
-    "\n",
-    "axes[1].plot(all_data['tstamp'], norm_a, label=f'{SYMBOL_A} (normalized)', alpha=0.8)\n",
-    "axes[1].plot(all_data['tstamp'], norm_b, label=f'{SYMBOL_B} (normalized)', alpha=0.8)\n",
-    "axes[1].axvline(x=pair.training_df_['tstamp'].iloc[-1], color='red', linestyle='--', alpha=0.7, label='Train/Test Split')\n",
-    "axes[1].set_title('Normalized Price Comparison')\n",
-    "axes[1].set_ylabel('Normalized Price')\n",
-    "axes[1].legend()\n",
-    "axes[1].grid(True)\n",
-    "\n",
-    "# Price ratio\n",
-    "price_ratio = all_data[colname_a] / all_data[colname_b]\n",
-    "axes[2].plot(all_data['tstamp'], price_ratio, label=f'{SYMBOL_A}/{SYMBOL_B} Ratio', color='green', alpha=0.8)\n",
-    "axes[2].axvline(x=pair.training_df_['tstamp'].iloc[-1], color='red', linestyle='--', alpha=0.7, label='Train/Test Split')\n",
-    "axes[2].set_title('Price Ratio')\n",
-    "axes[2].set_ylabel('Ratio')\n",
-    "axes[2].set_xlabel('Time')\n",
-    "axes[2].legend()\n",
-    "axes[2].grid(True)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train the Pair and Check Cointegration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Train the pair and check cointegration\n",
-    "try:\n",
-    "    is_cointegrated = pair.train_pair()\n",
-    "    print(f\"Pair {pair} cointegration status: {is_cointegrated}\")\n",
-    "\n",
-    "    if is_cointegrated:\n",
-    "        print(f\"VECM Beta coefficients: {pair.vecm_fit_.beta.flatten()}\")\n",
-    "        print(f\"Training dis-equilibrium mean: {pair.training_mu_:.6f}\")\n",
-    "        print(f\"Training dis-equilibrium std: {pair.training_std_:.6f}\")\n",
-    "\n",
-    "        # Display VECM summary\n",
-    "        print(\"\\nVECM Model Summary:\")\n",
-    "        print(pair.vecm_fit_.summary())\n",
-    "    else:\n",
-    "        print(\"Pair is not cointegrated. Cannot proceed with strategy.\")\n",
-    "\n",
-    "except Exception as e:\n",
-    "    print(f\"Training failed: {str(e)}\")\n",
-    "    is_cointegrated = False"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualize Training Period Dis-equilibrium"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if is_cointegrated:\n",
-    "    # fig, axes = plt.subplots(, 1, figsize=(15, 10))\n",
-    "\n",
-    "    # # Raw dis-equilibrium\n",
-    "    # axes[0].plot(pair.training_df_['tstamp'], pair.training_df_['dis-equilibrium'],\n",
-    "    #              color='blue', alpha=0.8, label='Raw Dis-equilibrium')\n",
-    "    # axes[0].axhline(y=pair.training_mu_, color='red', linestyle='--', alpha=0.7, label='Mean')\n",
-    "    # axes[0].axhline(y=pair.training_mu_ + pair.training_std_, color='orange', linestyle='--', alpha=0.5, label='+1 Std')\n",
-    "    # axes[0].axhline(y=pair.training_mu_ - pair.training_std_, color='orange', linestyle='--', alpha=0.5, label='-1 Std')\n",
-    "    # axes[0].set_title('Training Period: Raw Dis-equilibrium')\n",
-    "    # axes[0].set_ylabel('Dis-equilibrium')\n",
-    "    # axes[0].legend()\n",
-    "    # axes[0].grid(True)\n",
-    "\n",
-    "    # Scaled dis-equilibrium\n",
-    "    fig, axes = plt.subplots(1, 1, figsize=(15, 5))\n",
-    "    axes.plot(pair.training_df_['tstamp'], pair.training_df_['scaled_dis-equilibrium'],\n",
-    "                 color='green', alpha=0.8, label='Scaled Dis-equilibrium')\n",
-    "    axes.axhline(y=0, color='red', linestyle='--', alpha=0.7, label='Mean (0)')\n",
-    "    axes.axhline(y=1, color='orange', linestyle='--', alpha=0.5, label='+1 Std')\n",
-    "    axes.axhline(y=-1, color='orange', linestyle='--', alpha=0.5, label='-1 Std')\n",
-    "    axes.axhline(y=CONFIG['dis-equilibrium_open_trshld'], color='purple',\n",
-    "                    linestyle=':', alpha=0.7, label=f\"Open Threshold ({CONFIG['dis-equilibrium_open_trshld']})\")\n",
-    "    axes.axhline(y=CONFIG['dis-equilibrium_close_trshld'], color='brown',\n",
-    "                    linestyle=':', alpha=0.7, label=f\"Close Threshold ({CONFIG['dis-equilibrium_close_trshld']})\")\n",
-    "    axes.set_title('Training Period: Scaled Dis-equilibrium')\n",
-    "    axes.set_ylabel('Scaled Dis-equilibrium')\n",
-    "    axes.set_xlabel('Time')\n",
-    "    axes.legend()\n",
-    "    axes.grid(True)\n",
-    "\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()\n",
-    "\n",
-    "    # Print statistics\n",
-    "    print(f\"Training dis-equilibrium statistics:\")\n",
-    "    print(f\"  Mean: {pair.training_df_['dis-equilibrium'].mean():.6f}\")\n",
-    "    print(f\"  Std: {pair.training_df_['dis-equilibrium'].std():.6f}\")\n",
-    "    print(f\"  Min: {pair.training_df_['dis-equilibrium'].min():.6f}\")\n",
-    "    print(f\"  Max: {pair.training_df_['dis-equilibrium'].max():.6f}\")\n",
-    "\n",
-    "    print(f\"\\nScaled dis-equilibrium statistics:\")\n",
-    "    print(f\"  Mean: {pair.training_df_['scaled_dis-equilibrium'].mean():.6f}\")\n",
-    "    print(f\"  Std: {pair.training_df_['scaled_dis-equilibrium'].std():.6f}\")\n",
-    "    print(f\"  Min: {pair.training_df_['scaled_dis-equilibrium'].min():.6f}\")\n",
-    "    print(f\"  Max: {pair.training_df_['scaled_dis-equilibrium'].max():.6f}\")\n",
-    "else:\n",
-    "    print(\"The pair is not cointegrated\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate Predictions and Run Strategy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if is_cointegrated:\n",
-    "    try:\n",
-    "        # Generate predictions\n",
-    "        pair.predict()\n",
-    "        print(f\"Generated predictions for {len(pair.predicted_df_)} rows\")\n",
-    "\n",
-    "        # Display prediction data structure\n",
-    "        print(f\"Prediction columns: {list(pair.predicted_df_.columns)}\")\n",
-    "        print(f\"Prediction period: {pair.predicted_df_['tstamp'].iloc[0]} to {pair.predicted_df_['tstamp'].iloc[-1]}\")\n",
-    "\n",
-    "        # Run strategy\n",
-    "        bt_result = BacktestResult(config=CONFIG)\n",
-    "        pair_trades = STRATEGY.run_pair(config=CONFIG, pair=pair, bt_result=bt_result)\n",
-    "\n",
-    "        if pair_trades is not None and len(pair_trades) > 0:\n",
-    "            print(f\"\\nGenerated {len(pair_trades)} trading signals:\")\n",
-    "            print(pair_trades)\n",
-    "        else:\n",
-    "            print(\"\\nNo trading signals generated\")\n",
-    "\n",
-    "    except Exception as e:\n",
-    "        print(f\"Prediction/Strategy failed: {str(e)}\")\n",
-    "        pair_trades = None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualize Predictions and Dis-equilibrium"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if is_cointegrated and hasattr(pair, 'predicted_df_'):\n",
-    "    fig, axes = plt.subplots(4, 1, figsize=(16, 16))\n",
-    "\n",
-    "    # Actual vs Predicted Prices\n",
-    "    colname_a, colname_b = pair.colnames()\n",
-    "\n",
-    "    axes[0].plot(pair.predicted_df_['tstamp'], pair.predicted_df_[colname_a],\n",
-    "                 label=f'{SYMBOL_A} Actual', alpha=0.8)\n",
-    "    axes[0].plot(pair.predicted_df_['tstamp'], pair.predicted_df_[f'{colname_a}_pred'],\n",
-    "                 label=f'{SYMBOL_A} Predicted', alpha=0.8, linestyle='--')\n",
-    "    axes[0].set_title('Actual vs Predicted Prices - Symbol A')\n",
-    "    axes[0].set_ylabel('Price')\n",
-    "    axes[0].legend()\n",
-    "    axes[0].grid(True)\n",
-    "\n",
-    "    axes[1].plot(pair.predicted_df_['tstamp'], pair.predicted_df_[colname_b],\n",
-    "                 label=f'{SYMBOL_B} Actual', alpha=0.8)\n",
-    "    axes[1].plot(pair.predicted_df_['tstamp'], pair.predicted_df_[f'{colname_b}_pred'],\n",
-    "                 label=f'{SYMBOL_B} Predicted', alpha=0.8, linestyle='--')\n",
-    "    axes[1].set_title('Actual vs Predicted Prices - Symbol B')\n",
-    "    axes[1].set_ylabel('Price')\n",
-    "    axes[1].legend()\n",
-    "    axes[1].grid(True)\n",
-    "\n",
-    "    # Raw dis-equilibrium\n",
-    "    axes[2].plot(pair.predicted_df_['tstamp'], pair.predicted_df_['disequilibrium'],\n",
-    "                 color='blue', alpha=0.8, label='Dis-equilibrium')\n",
-    "    axes[2].axhline(y=pair.training_mu_, color='red', linestyle='--', alpha=0.7, label='Training Mean')\n",
-    "    axes[2].set_title('Testing Period: Raw Dis-equilibrium')\n",
-    "    axes[2].set_ylabel('Dis-equilibrium')\n",
-    "    axes[2].legend()\n",
-    "    axes[2].grid(True)\n",
-    "\n",
-    "    # Scaled dis-equilibrium with trading signals\n",
-    "    axes[3].plot(pair.predicted_df_['tstamp'], pair.predicted_df_['scaled_disequilibrium'],\n",
-    "                 color='green', alpha=0.8, label='Scaled Dis-equilibrium')\n",
-    "\n",
-    "    # Add threshold lines\n",
-    "    axes[3].axhline(y=CONFIG['dis-equilibrium_open_trshld'], color='purple',\n",
-    "                    linestyle=':', alpha=0.7, label=f\"Open Threshold ({CONFIG['dis-equilibrium_open_trshld']})\")\n",
-    "    axes[3].axhline(y=CONFIG['dis-equilibrium_close_trshld'], color='brown',\n",
-    "                    linestyle=':', alpha=0.7, label=f\"Close Threshold ({CONFIG['dis-equilibrium_close_trshld']})\")\n",
-    "\n",
-    "    # Add trading signals if they exist\n",
-    "    if pair_trades is not None and len(pair_trades) > 0:\n",
-    "        for _, trade in pair_trades.iterrows():\n",
-    "            color = 'red' if 'BUY' in trade['action'] else 'blue'\n",
-    "            marker = '^' if 'BUY' in trade['action'] else 'v'\n",
-    "            axes[3].scatter(trade['time'], trade['scaled_disequilibrium'],\n",
-    "                           color=color, marker=marker, s=100, alpha=0.8,\n",
-    "                           label=f\"{trade['action']} {trade['symbol']}\" if _ < 2 else \"\")\n",
-    "\n",
-    "    axes[3].set_title('Testing Period: Scaled Dis-equilibrium with Trading Signals')\n",
-    "    axes[3].set_ylabel('Scaled Dis-equilibrium')\n",
-    "    axes[3].set_xlabel('Time')\n",
-    "    axes[3].legend()\n",
-    "    axes[3].grid(True)\n",
-    "\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()\n",
-    "\n",
-    "    # Print prediction statistics\n",
-    "    print(f\"\\nTesting dis-equilibrium statistics:\")\n",
-    "    print(f\"  Mean: {pair.predicted_df_['disequilibrium'].mean():.6f}\")\n",
-    "    print(f\"  Std: {pair.predicted_df_['disequilibrium'].std():.6f}\")\n",
-    "    print(f\"  Min: {pair.predicted_df_['disequilibrium'].min():.6f}\")\n",
-    "    print(f\"  Max: {pair.predicted_df_['disequilibrium'].max():.6f}\")\n",
-    "\n",
-    "    print(f\"\\nTesting scaled dis-equilibrium statistics:\")\n",
-    "    print(f\"  Mean: {pair.predicted_df_['scaled_disequilibrium'].mean():.6f}\")\n",
-    "    print(f\"  Std: {pair.predicted_df_['scaled_disequilibrium'].std():.6f}\")\n",
-    "    print(f\"  Min: {pair.predicted_df_['scaled_disequilibrium'].min():.6f}\")\n",
-    "    print(f\"  Max: {pair.predicted_df_['scaled_disequilibrium'].max():.6f}\")\n",
-    "\n",
-    "    # Count threshold crossings\n",
-    "    open_crossings = (pair.predicted_df_['scaled_disequilibrium'] >= CONFIG['dis-equilibrium_open_trshld']).sum()\n",
-    "    close_crossings = (pair.predicted_df_['scaled_disequilibrium'] <= CONFIG['dis-equilibrium_close_trshld']).sum()\n",
-    "    print(f\"\\nThreshold crossings:\")\n",
-    "    print(f\"  Open threshold ({CONFIG['dis-equilibrium_open_trshld']}): {open_crossings} times\")\n",
-    "    print(f\"  Close threshold ({CONFIG['dis-equilibrium_close_trshld']}): {close_crossings} times\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Summary and Analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"=\" * 60)\n",
-    "print(\"PAIRS TRADING ANALYSIS SUMMARY\")\n",
-    "print(\"=\" * 60)\n",
-    "\n",
-    "print(f\"\\nPair: {SYMBOL_A} & {SYMBOL_B}\")\n",
-    "print(f\"Strategy: {type(STRATEGY).__name__}\")\n",
-    "print(f\"Data file: {DATA_FILE}\")\n",
-    "print(f\"Training period: {training_minutes} minutes\")\n",
-    "\n",
-    "print(f\"\\nCointegration Status: {'\u2713 COINTEGRATED' if is_cointegrated else '\u2717 NOT COINTEGRATED'}\")\n",
-    "\n",
-    "if is_cointegrated:\n",
-    "    print(f\"\\nVECM Model:\")\n",
-    "    print(f\"  Beta coefficients: {pair.vecm_fit_.beta.flatten()}\")\n",
-    "    print(f\"  Training mean: {pair.training_mu_:.6f}\")\n",
-    "    print(f\"  Training std: {pair.training_std_:.6f}\")\n",
-    "\n",
-    "    if pair_trades is not None and len(pair_trades) > 0:\n",
-    "        print(f\"\\nTrading Signals: {len(pair_trades)} generated\")\n",
-    "        unique_times = pair_trades['time'].unique()\n",
-    "        print(f\"  Unique trade times: {len(unique_times)}\")\n",
-    "\n",
-    "        # Group by time to see paired trades\n",
-    "        for trade_time in unique_times:\n",
-    "            trades_at_time = pair_trades[pair_trades['time'] == trade_time]\n",
-    "            print(f\"\\n  Trade at {trade_time}:\")\n",
-    "            for _, trade in trades_at_time.iterrows():\n",
-    "                print(f\"    {trade['action']} {trade['symbol']} @ ${trade['price']:.2f} (dis-eq: {trade['scaled_disequilibrium']:.2f})\")\n",
-    "    else:\n",
-    "        print(f\"\\nTrading Signals: None generated\")\n",
-    "        print(\"  Possible reasons:\")\n",
-    "        print(\"    - Dis-equilibrium never exceeded open threshold\")\n",
-    "        print(\"    - Insufficient testing data\")\n",
-    "        print(\"    - Strategy-specific conditions not met\")\n",
-    "\n",
-    "else:\n",
-    "    print(\"\\nCannot proceed with trading strategy - pair is not cointegrated\")\n",
-    "    print(\"Consider:\")\n",
-    "    print(\"  - Trying different symbol pairs\")\n",
-    "    print(\"  - Adjusting training period length\")\n",
-    "    print(\"  - Using different data timeframe\")\n",
-    "\n",
-    "print(\"\\n\" + \"=\" * 60)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Interactive Analysis (Optional)\n",
-    "\n",
-    "You can modify the parameters below and re-run the analysis:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Interactive parameter adjustment\n",
-    "print(\"Current parameters:\")\n",
-    "print(f\"  Open threshold: {CONFIG['dis-equilibrium_open_trshld']}\")\n",
-    "print(f\"  Close threshold: {CONFIG['dis-equilibrium_close_trshld']}\")\n",
-    "print(f\"  Training minutes: {CONFIG['training_minutes']}\")\n",
-    "\n",
-    "# Uncomment and modify these to experiment:\n",
-    "# CONFIG['dis-equilibrium_open_trshld'] = 1.5\n",
-    "# CONFIG['dis-equilibrium_close_trshld'] = 0.3\n",
-    "# CONFIG['training_minutes'] = 180\n",
-    "\n",
-    "print(\"\\nTo re-run with different parameters:\")\n",
-    "print(\"1. Modify the parameters above\")\n",
-    "print(\"2. Re-run from the 'Split Data into Training and Testing' cell\")\n",
-    "print(\"3. Or try different symbol pairs by changing SYMBOL_A and SYMBOL_B\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "python3.12-venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

src/results.py

@@ -1,656 +0,0 @@
-from typing import Any, Dict, List
-import pandas as pd
-import sqlite3
-import os
-from datetime import datetime, date
-
-
-# Recommended replacement adapters and converters for Python 3.12+
-# From: https://docs.python.org/3/library/sqlite3.html#sqlite3-adapter-converter-recipes
-def adapt_date_iso(val):
-    """Adapt datetime.date to ISO 8601 date."""
-    return val.isoformat()
-
-def adapt_datetime_iso(val):
-    """Adapt datetime.datetime to timezone-naive ISO 8601 date."""
-    return val.isoformat()
-
-def convert_date(val):
-    """Convert ISO 8601 date to datetime.date object."""
-    return datetime.fromisoformat(val.decode()).date()
-
-def convert_datetime(val):
-    """Convert ISO 8601 datetime to datetime.datetime object."""
-    return datetime.fromisoformat(val.decode())
-
-# Register the adapters and converters
-sqlite3.register_adapter(date, adapt_date_iso)
-sqlite3.register_adapter(datetime, adapt_datetime_iso)
-sqlite3.register_converter("date", convert_date)
-sqlite3.register_converter("datetime", convert_datetime)
-
-
-def create_result_database(db_path: str) -> None:
-    """
-    Create the SQLite database and required tables if they don't exist.
-    """
-    try:
-        conn = sqlite3.connect(db_path)
-        cursor = conn.cursor()
-        
-        # Create the pt_bt_results table for completed trades
-        cursor.execute('''
-            CREATE TABLE IF NOT EXISTS pt_bt_results (
-                date DATE,
-                pair TEXT,
-                symbol TEXT,
-                open_time DATETIME,
-                open_side TEXT,
-                open_price REAL,
-                open_quantity INTEGER,
-                open_disequilibrium REAL,
-                close_time DATETIME,
-                close_side TEXT,
-                close_price REAL,
-                close_quantity INTEGER,
-                close_disequilibrium REAL,
-                symbol_return REAL,
-                pair_return REAL
-            )
-        ''')
-        cursor.execute("DELETE FROM pt_bt_results;")
-
-        # Create the outstanding_positions table for open positions
-        cursor.execute('''
-            CREATE TABLE IF NOT EXISTS outstanding_positions (
-                date DATE,
-                pair TEXT,
-                symbol TEXT,
-                position_quantity REAL,
-                last_price REAL,
-                unrealized_return REAL,
-                open_price REAL,
-                open_side TEXT
-            )
-        ''')
-        cursor.execute("DELETE FROM outstanding_positions;")
-        
-        # Create the config table for storing configuration JSON for reference
-        cursor.execute('''
-            CREATE TABLE IF NOT EXISTS config (
-                id INTEGER PRIMARY KEY AUTOINCREMENT,
-                run_timestamp DATETIME,
-                config_file_path TEXT,
-                config_json TEXT,
-                strategy_class TEXT,
-                datafiles TEXT,
-                instruments TEXT
-            )
-        ''')
-        cursor.execute("DELETE FROM config;")
-        
-        conn.commit()
-        conn.close()
-        
-    except Exception as e:
-        print(f"Error creating result database: {str(e)}")
-        raise
-
-
-def store_config_in_database(db_path: str, config_file_path: str, config: Dict, strategy_class: str, datafiles: List[str], instruments: List[str]) -> None:
-    """
-    Store configuration information in the database for reference.
-    """
-    import json
-    
-    if db_path.upper() == "NONE":
-        return
-        
-    try:
-        conn = sqlite3.connect(db_path)
-        cursor = conn.cursor()
-        
-        # Convert config to JSON string
-        config_json = json.dumps(config, indent=2, default=str)
-        
-        # Convert lists to comma-separated strings for storage
-        datafiles_str = ', '.join(datafiles)
-        instruments_str = ', '.join(instruments)
-        
-        # Insert configuration record
-        cursor.execute('''
-            INSERT INTO config (
-                run_timestamp, config_file_path, config_json, strategy_class, datafiles, instruments
-            ) VALUES (?, ?, ?, ?, ?, ?)
-        ''', (
-            datetime.now(),
-            config_file_path,
-            config_json,
-            strategy_class,
-            datafiles_str,
-            instruments_str
-        ))
-        
-        conn.commit()
-        conn.close()
-        
-        print(f"Configuration stored in database")
-        
-    except Exception as e:
-        print(f"Error storing configuration in database: {str(e)}")
-        import traceback
-        traceback.print_exc()
-
-
-def store_results_in_database(db_path: str, datafile: str, bt_result: 'BacktestResult') -> None:
-    """
-    Store backtest results in the SQLite database.
-    """
-    if db_path.upper() == "NONE":
-        return
-    
-    def convert_timestamp(timestamp):
-        """Convert pandas Timestamp to Python datetime object for SQLite compatibility."""
-        if timestamp is None:
-            return None
-        if hasattr(timestamp, 'to_pydatetime'):
-            return timestamp.to_pydatetime()
-        return timestamp
-    
-    try:
-        # Extract date from datafile name (assuming format like 20250528.mktdata.ohlcv.db)
-        filename = os.path.basename(datafile)
-        date_str = filename.split('.')[0]  # Extract date part
-        
-        # Convert to proper date format
-        try:
-            date_obj = datetime.strptime(date_str, '%Y%m%d').date()
-        except ValueError:
-            # If date parsing fails, use current date
-            date_obj = datetime.now().date()
-        
-        conn = sqlite3.connect(db_path)
-        cursor = conn.cursor()
-        
-        # Process each trade from bt_result
-        trades = bt_result.get_trades()
-        
-        for pair_name, symbols in trades.items():
-            # Calculate pair return for this pair
-            pair_return = 0.0
-            pair_trades = []
-            
-            # First pass: collect all trades and calculate returns
-            for symbol, symbol_trades in symbols.items():
-                if len(symbol_trades) == 0:  # No trades for this symbol
-                    print(f"Warning: No trades found for symbol {symbol} in pair {pair_name}")
-                    continue
-                    
-                elif len(symbol_trades) >= 2:  # Completed trades (entry + exit)
-                    # Handle both old and new tuple formats
-                    if len(symbol_trades[0]) == 2:  # Old format: (action, price)
-                        entry_action, entry_price = symbol_trades[0]
-                        exit_action, exit_price = symbol_trades[1]
-                        open_disequilibrium = 0.0  # Fallback for old format
-                        open_scaled_disequilibrium = 0.0
-                        close_disequilibrium = 0.0
-                        close_scaled_disequilibrium = 0.0
-                        open_time = datetime.now()
-                        close_time = datetime.now()
-                    else:  # New format: (action, price, disequilibrium, scaled_disequilibrium, timestamp)
-                        entry_action, entry_price, open_disequilibrium, open_scaled_disequilibrium, open_time = symbol_trades[0]
-                        exit_action, exit_price, close_disequilibrium, close_scaled_disequilibrium, close_time = symbol_trades[1]
-                        
-                        # Handle None values
-                        open_disequilibrium = open_disequilibrium if open_disequilibrium is not None else 0.0
-                        open_scaled_disequilibrium = open_scaled_disequilibrium if open_scaled_disequilibrium is not None else 0.0
-                        close_disequilibrium = close_disequilibrium if close_disequilibrium is not None else 0.0
-                        close_scaled_disequilibrium = close_scaled_disequilibrium if close_scaled_disequilibrium is not None else 0.0
-                        
-                        # Convert pandas Timestamps to Python datetime objects
-                        open_time = convert_timestamp(open_time) or datetime.now()
-                        close_time = convert_timestamp(close_time) or datetime.now()
-                    
-                    # Calculate actual share quantities based on funding per pair
-                    # Split funding equally between the two positions
-                    funding_per_position = bt_result.config["funding_per_pair"] / 2
-                    shares = funding_per_position / entry_price
-                    
-                    # Calculate symbol return
-                    symbol_return = 0.0
-                    if entry_action == "BUY" and exit_action == "SELL":
-                        symbol_return = (exit_price - entry_price) / entry_price * 100
-                    elif entry_action == "SELL" and exit_action == "BUY":
-                        symbol_return = (entry_price - exit_price) / entry_price * 100
-                    
-                    pair_return += symbol_return
-                    
-                    pair_trades.append({
-                        'symbol': symbol,
-                        'entry_action': entry_action,
-                        'entry_price': entry_price,
-                        'exit_action': exit_action,
-                        'exit_price': exit_price,
-                        'symbol_return': symbol_return,
-                        'open_disequilibrium': open_disequilibrium,
-                        'open_scaled_disequilibrium': open_scaled_disequilibrium,
-                        'close_disequilibrium': close_disequilibrium,
-                        'close_scaled_disequilibrium': close_scaled_disequilibrium,
-                        'open_time': open_time,
-                        'close_time': close_time,
-                        'shares': shares,
-                        'is_completed': True
-                    })
-                    
-                # Skip one-sided trades - they will be handled by outstanding_positions table
-                elif len(symbol_trades) == 1:
-                    print(f"Skipping one-sided trade for {symbol} in pair {pair_name} - will be stored in outstanding_positions table")
-                    continue
-                    
-                else:
-                    # This should not happen, but handle unexpected cases
-                    print(f"Warning: Unexpected number of trades ({len(symbol_trades)}) for symbol {symbol} in pair {pair_name}")
-                    continue
-            
-            # Second pass: insert completed trade records into database
-            for trade in pair_trades:
-                # Only store completed trades in pt_bt_results table
-                cursor.execute('''
-                    INSERT INTO pt_bt_results (
-                        date, pair, symbol, open_time, open_side, open_price, 
-                        open_quantity, open_disequilibrium, close_time, close_side, 
-                        close_price, close_quantity, close_disequilibrium, 
-                        symbol_return, pair_return
-                    ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
-                ''', (
-                    date_obj,
-                    pair_name,
-                    trade['symbol'],
-                    trade['open_time'],
-                    trade['entry_action'],
-                    trade['entry_price'],
-                    trade['shares'],
-                    trade['open_scaled_disequilibrium'],
-                    trade['close_time'],
-                    trade['exit_action'],
-                    trade['exit_price'],
-                    trade['shares'],
-                    trade['close_scaled_disequilibrium'],
-                    trade['symbol_return'],
-                    pair_return
-                ))
-        
-        # Store outstanding positions in separate table
-        outstanding_positions = bt_result.get_outstanding_positions()
-        for pos in outstanding_positions:
-            # Calculate position quantity (negative for SELL positions)
-            position_qty_a = pos['shares_a'] if pos['side_a'] == 'BUY' else -pos['shares_a']
-            position_qty_b = pos['shares_b'] if pos['side_b'] == 'BUY' else -pos['shares_b']
-            
-            # Calculate unrealized returns
-            # For symbol A: (current_price - open_price) / open_price * 100 * position_direction
-            unrealized_return_a = ((pos['current_px_a'] - pos['open_px_a']) / pos['open_px_a'] * 100) * (1 if pos['side_a'] == 'BUY' else -1)
-            unrealized_return_b = ((pos['current_px_b'] - pos['open_px_b']) / pos['open_px_b'] * 100) * (1 if pos['side_b'] == 'BUY' else -1)
-            
-            # Store outstanding position for symbol A
-            cursor.execute('''
-                INSERT INTO outstanding_positions (
-                    date, pair, symbol, position_quantity, last_price, unrealized_return, open_price, open_side
-                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?)
-            ''', (
-                date_obj,
-                pos['pair'],
-                pos['symbol_a'],
-                position_qty_a,
-                pos['current_px_a'],
-                unrealized_return_a,
-                pos['open_px_a'],
-                pos['side_a']
-            ))
-            
-            # Store outstanding position for symbol B
-            cursor.execute('''
-                INSERT INTO outstanding_positions (
-                    date, pair, symbol, position_quantity, last_price, unrealized_return, open_price, open_side
-                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?)
-            ''', (
-                date_obj,
-                pos['pair'],
-                pos['symbol_b'],
-                position_qty_b,
-                pos['current_px_b'],
-                unrealized_return_b,
-                pos['open_px_b'],
-                pos['side_b']
-            ))
-        
-        conn.commit()
-        conn.close()
-        
-    except Exception as e:
-        print(f"Error storing results in database: {str(e)}")
-        import traceback
-        traceback.print_exc()
-
-
-class BacktestResult:
-    """
-    Class to handle backtest results, trades tracking, PnL calculations, and reporting.
-    """
-
-    def __init__(self, config: Dict[str, Any]):
-        self.config = config
-        self.trades: Dict[str, Dict[str, Any]] = {}
-        self.total_realized_pnl = 0.0
-        self.outstanding_positions: List[Dict[str, Any]] = []
-
-    def add_trade(self, pair_nm, symbol, action, price, disequilibrium=None, scaled_disequilibrium=None, timestamp=None):
-        """Add a trade to the results tracking."""
-        pair_nm = str(pair_nm)
-
-        if pair_nm not in self.trades:
-            self.trades[pair_nm] = {symbol: []}
-        if symbol not in self.trades[pair_nm]:
-            self.trades[pair_nm][symbol] = []
-        self.trades[pair_nm][symbol].append((action, price, disequilibrium, scaled_disequilibrium, timestamp))
-
-    def add_outstanding_position(self, position: Dict[str, Any]):
-        """Add an outstanding position to tracking."""
-        self.outstanding_positions.append(position)
-
-    def add_realized_pnl(self, realized_pnl: float):
-        """Add realized PnL to the total."""
-        self.total_realized_pnl += realized_pnl
-
-    def get_total_realized_pnl(self) -> float:
-        """Get total realized PnL."""
-        return self.total_realized_pnl
-
-    def get_outstanding_positions(self) -> List[Dict[str, Any]]:
-        """Get all outstanding positions."""
-        return self.outstanding_positions
-
-    def get_trades(self) -> Dict[str, Dict[str, Any]]:
-        """Get all trades."""
-        return self.trades
-
-    def clear_trades(self):
-        """Clear all trades (used when processing new files)."""
-        self.trades.clear()
-
-    def collect_single_day_results(self, result):
-        """Collect and process single day trading results."""
-        if result is None:
-            return
-
-        print("\n --------------  Suggested Trades ")
-        print(result)
-
-        for row in result.itertuples():
-            action = row.action
-            symbol = row.symbol
-            price = row.price
-            disequilibrium = getattr(row, 'disequilibrium', None)
-            scaled_disequilibrium = getattr(row, 'scaled_disequilibrium', None)
-            timestamp = getattr(row, 'time', None)
-            self.add_trade(
-                pair_nm=row.pair, action=action, symbol=symbol, price=price,
-                disequilibrium=disequilibrium, scaled_disequilibrium=scaled_disequilibrium,
-                timestamp=timestamp
-            )
-
-    def print_single_day_results(self):
-        """Print single day results summary."""
-        for pair, symbols in self.trades.items():
-            print(f"\n--- {pair} ---")
-            for symbol, trades in symbols.items():
-                for trade_data in trades:
-                    if len(trade_data) >= 2:
-                        side, price = trade_data[:2]
-                        print(f"{symbol} {side} at ${price}")
-
-    def print_results_summary(self, all_results):
-        """Print summary of all processed files."""
-        print("\n====== Summary of All Processed Files ======")
-        for filename, data in all_results.items():
-            trade_count = sum(
-                len(trades)
-                for symbol_trades in data["trades"].values()
-                for trades in symbol_trades.values()
-            )
-            print(f"{filename}: {trade_count} trades")
-
-    def calculate_returns(self, all_results: Dict):
-        """Calculate and print returns by day and pair."""
-        print("\n====== Returns By Day and Pair ======")
-
-        for filename, data in all_results.items():
-            day_return = 0
-            print(f"\n--- {filename} ---")
-
-            # Process each pair
-            for pair, symbols in data["trades"].items():
-                pair_return = 0
-                pair_trades = []
-
-                # Calculate individual symbol returns in the pair
-                for symbol, trades in symbols.items():
-                    if len(trades) >= 2:  # Need at least entry and exit
-                        # Get entry and exit trades - handle both old and new tuple formats
-                        if len(trades[0]) == 2:  # Old format: (action, price)
-                            entry_action, entry_price = trades[0]
-                            exit_action, exit_price = trades[1]
-                            open_disequilibrium = None
-                            open_scaled_disequilibrium = None
-                            close_disequilibrium = None
-                            close_scaled_disequilibrium = None
-                        else:  # New format: (action, price, disequilibrium, scaled_disequilibrium, timestamp)
-                            entry_action, entry_price = trades[0][:2]
-                            exit_action, exit_price = trades[1][:2]
-                            open_disequilibrium = trades[0][2] if len(trades[0]) > 2 else None
-                            open_scaled_disequilibrium = trades[0][3] if len(trades[0]) > 3 else None
-                            close_disequilibrium = trades[1][2] if len(trades[1]) > 2 else None
-                            close_scaled_disequilibrium = trades[1][3] if len(trades[1]) > 3 else None
-
-                        # Calculate return based on action
-                        symbol_return = 0
-                        if entry_action == "BUY" and exit_action == "SELL":
-                            # Long position
-                            symbol_return = (exit_price - entry_price) / entry_price * 100
-                        elif entry_action == "SELL" and exit_action == "BUY":
-                            # Short position
-                            symbol_return = (entry_price - exit_price) / entry_price * 100
-
-                        pair_trades.append(
-                            (
-                                symbol,
-                                entry_action,
-                                entry_price,
-                                exit_action,
-                                exit_price,
-                                symbol_return,
-                                open_scaled_disequilibrium,
-                                close_scaled_disequilibrium,
-                            )
-                        )
-                        pair_return += symbol_return
-
-                # Print pair returns with disequilibrium information
-                if pair_trades:
-                    print(f"  {pair}:")
-                    for (
-                        symbol,
-                        entry_action,
-                        entry_price,
-                        exit_action,
-                        exit_price,
-                        symbol_return,
-                        open_scaled_disequilibrium,
-                        close_scaled_disequilibrium,
-                    ) in pair_trades:
-                        disequil_info = ""
-                        if open_scaled_disequilibrium is not None and close_scaled_disequilibrium is not None:
-                            disequil_info = f" | Open Dis-eq: {open_scaled_disequilibrium:.2f}, Close Dis-eq: {close_scaled_disequilibrium:.2f}"
-                        
-                        print(
-                            f"    {symbol}: {entry_action} @ ${entry_price:.2f}, {exit_action} @ ${exit_price:.2f}, Return: {symbol_return:.2f}%{disequil_info}"
-                        )
-                    print(f"    Pair Total Return: {pair_return:.2f}%")
-                    day_return += pair_return
-
-            # Print day total return and add to global realized PnL
-            if day_return != 0:
-                print(f"  Day Total Return: {day_return:.2f}%")
-                self.add_realized_pnl(day_return)
-
-    def print_outstanding_positions(self):
-        """Print all outstanding positions with share quantities and current values."""
-        if not self.get_outstanding_positions():
-            print("\n====== NO OUTSTANDING POSITIONS ======")
-            return
-
-        print(f"\n====== OUTSTANDING POSITIONS ======")
-        print(
-            f"{'Pair':<15}"
-            f" {'Symbol':<10}"
-            f" {'Side':<4}"
-            f" {'Shares':<10}"
-            f" {'Open $':<8}"
-            f" {'Current $':<10}"
-            f" {'Value $':<12}"
-            f" {'Disequilibrium':<15}"
-        )
-        print("-" * 100)
-
-        total_value = 0.0
-
-        for pos in self.get_outstanding_positions():
-            # Print position A
-            print(
-                f"{pos['pair']:<15}"
-                f" {pos['symbol_a']:<10}"
-                f" {pos['side_a']:<4}"
-                f" {pos['shares_a']:<10.2f}"
-                f" {pos['open_px_a']:<8.2f}"
-                f" {pos['current_px_a']:<10.2f}"
-                f" {pos['current_value_a']:<12.2f}"
-                f" {'':<15}"
-            )
-
-            # Print position B
-            print(
-                f"{'':<15}"
-                f" {pos['symbol_b']:<10}"
-                f" {pos['side_b']:<4}"
-                f" {pos['shares_b']:<10.2f}"
-                f" {pos['open_px_b']:<8.2f}"
-                f" {pos['current_px_b']:<10.2f}"
-                f" {pos['current_value_b']:<12.2f}"
-            )
-
-            # Print pair totals with disequilibrium info
-            print(
-                f"{'':<15}"
-                f" {'PAIR TOTAL':<10}"
-                f" {'':<4}"
-                f" {'':<10}"
-                f" {'':<8}"
-                f" {'':<10}"
-                f" {pos['total_current_value']:<12.2f}"
-            )
-
-            # Print disequilibrium details
-            print(
-                f"{'':<15}"
-                f" {'DISEQUIL':<10}"
-                f" {'':<4}"
-                f" {'':<10}"
-                f" {'':<8}"
-                f" {'':<10}"
-                f" Raw: {pos['current_disequilibrium']:<6.4f}"
-                f" Scaled: {pos['current_scaled_disequilibrium']:<6.4f}"
-            )
-
-            print("-" * 100)
-
-            total_value += pos["total_current_value"]
-
-        print(f"{'TOTAL OUTSTANDING VALUE':<80} ${total_value:<12.2f}")
-
-    def print_grand_totals(self):
-        """Print grand totals across all pairs."""
-        print(f"\n====== GRAND TOTALS ACROSS ALL PAIRS ======")
-        print(f"Total Realized PnL: {self.get_total_realized_pnl():.2f}%")
-
-    def handle_outstanding_position(self, pair, pair_result_df, last_row_index,
-                                  open_side_a, open_side_b, open_px_a, open_px_b,
-                                  open_tstamp):
-        """
-        Handle calculation and tracking of outstanding positions when no close signal is found.
-
-        Args:
-            pair: TradingPair object
-            pair_result_df: DataFrame with pair results
-            last_row_index: Index of the last row in the data
-            open_side_a, open_side_b: Trading sides for symbols A and B
-            open_px_a, open_px_b: Opening prices for symbols A and B
-            open_tstamp: Opening timestamp
-        """
-        if pair_result_df is None or pair_result_df.empty:
-            return 0, 0, 0
-
-        last_row = pair_result_df.loc[last_row_index]
-        last_tstamp = last_row["tstamp"]
-        colname_a, colname_b = pair.colnames()
-        last_px_a = last_row[colname_a]
-        last_px_b = last_row[colname_b]
-
-        # Calculate share quantities based on funding per pair
-        # Split funding equally between the two positions
-        funding_per_position = self.config["funding_per_pair"] / 2
-        shares_a = funding_per_position / open_px_a
-        shares_b = funding_per_position / open_px_b
-
-        # Calculate current position values (shares * current price)
-        current_value_a = shares_a * last_px_a
-        current_value_b = shares_b * last_px_b
-        total_current_value = current_value_a + current_value_b
-
-        # Get disequilibrium information
-        current_disequilibrium = last_row["disequilibrium"]
-        current_scaled_disequilibrium = last_row["scaled_disequilibrium"]
-
-        # Store outstanding positions
-        self.add_outstanding_position(
-            {
-                "pair": str(pair),
-                "symbol_a": pair.symbol_a_,
-                "symbol_b": pair.symbol_b_,
-                "side_a": open_side_a,
-                "side_b": open_side_b,
-                "shares_a": shares_a,
-                "shares_b": shares_b,
-                "open_px_a": open_px_a,
-                "open_px_b": open_px_b,
-                "current_px_a": last_px_a,
-                "current_px_b": last_px_b,
-                "current_value_a": current_value_a,
-                "current_value_b": current_value_b,
-                "total_current_value": total_current_value,
-                "open_time": open_tstamp,
-                "last_time": last_tstamp,
-                "current_abs_term": current_scaled_disequilibrium,
-                "current_disequilibrium": current_disequilibrium,
-                "current_scaled_disequilibrium": current_scaled_disequilibrium,
-            }
-        )
-
-        # Print position details
-        print(f"{pair}: NO CLOSE SIGNAL FOUND - Position held until end of session")
-        print(f"  Open: {open_tstamp} | Last: {last_tstamp}")
-        print(f"  {pair.symbol_a_}: {open_side_a} {shares_a:.2f} shares @ ${open_px_a:.2f} -> ${last_px_a:.2f} | Value: ${current_value_a:.2f}")
-        print(f"  {pair.symbol_b_}: {open_side_b} {shares_b:.2f} shares @ ${open_px_b:.2f} -> ${last_px_b:.2f} | Value: ${current_value_b:.2f}")
-        print(f"  Total Value: ${total_current_value:.2f}")
-        print(f"  Disequilibrium: {current_disequilibrium:.4f} | Scaled: {current_scaled_disequilibrium:.4f}")
-
-        return current_value_a, current_value_b, total_current_value

src/strategies.py

@@ -1,420 +0,0 @@
-from abc import ABC, abstractmethod
-from enum import Enum
-
-from typing import Dict, Optional, cast
-
-import pandas as pd  # type: ignore[import]
-
-from tools.trading_pair import TradingPair
-from results import BacktestResult
-
-NanoPerMin = 1e9
-
-class PairsTradingStrategy(ABC):
-    TRADES_COLUMNS = [
-        "time",
-        "action",
-        "symbol",
-        "price",
-        "disequilibrium",
-        "scaled_disequilibrium",
-        "pair",
-    ]
-    @abstractmethod
-    def run_pair(self, config: Dict, pair: TradingPair, bt_result: BacktestResult) -> Optional[pd.DataFrame]:
-        ...
-        
-    @abstractmethod
-    def reset(self):
-        ...
-
-class StaticFitStrategy(PairsTradingStrategy):
-
-    def run_pair(self, config: Dict, pair: TradingPair, bt_result: BacktestResult) -> Optional[pd.DataFrame]: # abstractmethod
-        pair.get_datasets(training_minutes=config["training_minutes"])
-        try:
-            is_cointegrated = pair.train_pair()
-            if not is_cointegrated:
-                print(f"{pair} IS NOT COINTEGRATED")
-                return None
-        except Exception as e:
-            print(f"{pair}: Training failed: {str(e)}")
-            return None
-
-        try:
-            pair.predict()
-        except Exception as e:
-            print(f"{pair}: Prediction failed: {str(e)}")
-            return None
-
-        pair_trades = self.create_trading_signals(pair=pair, config=config, result=bt_result)
-
-        return pair_trades
-
-    def create_trading_signals(self, pair: TradingPair, config: Dict, result: BacktestResult) -> pd.DataFrame:
-        beta = pair.vecm_fit_.beta  # type: ignore
-        colname_a, colname_b = pair.colnames()
-
-        predicted_df = pair.predicted_df_
-
-        open_threshold = config["dis-equilibrium_open_trshld"]
-        close_threshold = config["dis-equilibrium_close_trshld"]
-
-        # Iterate through the testing dataset to find the first trading opportunity
-        open_row_index = None
-        for row_idx in range(len(predicted_df)):
-            curr_disequilibrium = predicted_df["scaled_disequilibrium"][row_idx]
-
-            # Check if current row has sufficient disequilibrium (not near-zero)
-            if curr_disequilibrium >= open_threshold:
-                open_row_index = row_idx
-                break
-
-        # If no row with sufficient disequilibrium found, skip this pair
-        if open_row_index is None:
-            print(f"{pair}: Insufficient disequilibrium in testing dataset. Skipping.")
-            return pd.DataFrame()
-
-        # Look for close signal starting from the open position
-        trading_signals_df = (
-            predicted_df["scaled_disequilibrium"][open_row_index:] < close_threshold
-        )
-
-        # Adjust indices to account for the offset from open_row_index
-        close_row_index = None
-        for idx, value in trading_signals_df.items():
-            if value:
-                close_row_index = idx
-                break
-
-        open_row = predicted_df.loc[open_row_index]
-        open_tstamp = open_row["tstamp"]
-        open_disequilibrium = open_row["disequilibrium"]
-        open_scaled_disequilibrium = open_row["scaled_disequilibrium"]
-        open_px_a = open_row[f"{colname_a}"]
-        open_px_b = open_row[f"{colname_b}"]
-
-        abs_beta = abs(beta[1])
-        pred_px_b = predicted_df.loc[open_row_index][f"{colname_b}_pred"]
-        pred_px_a = predicted_df.loc[open_row_index][f"{colname_a}_pred"]
-
-        if pred_px_b * abs_beta - pred_px_a > 0:
-            open_side_a = "BUY"
-            open_side_b = "SELL"
-            close_side_a = "SELL"
-            close_side_b = "BUY"
-        else:
-            open_side_b = "BUY"
-            open_side_a = "SELL"
-            close_side_b = "SELL"
-            close_side_a = "BUY"
-
-        # If no close signal found, print position and unrealized PnL
-        if close_row_index is None:
-
-            last_row_index = len(predicted_df) - 1
-
-            # Use the new method from BacktestResult to handle outstanding positions
-            result.handle_outstanding_position(
-                pair=pair,
-                pair_result_df=predicted_df,
-                last_row_index=last_row_index,
-                open_side_a=open_side_a,
-                open_side_b=open_side_b,
-                open_px_a=open_px_a,
-                open_px_b=open_px_b,
-                open_tstamp=open_tstamp,
-            )
-
-            # Return only open trades (no close trades)
-            trd_signal_tuples = [
-                (
-                    open_tstamp,
-                    open_side_a,
-                    pair.symbol_a_,
-                    open_px_a,
-                    open_disequilibrium,
-                    open_scaled_disequilibrium,
-                    pair,
-                ),
-                (
-                    open_tstamp,
-                    open_side_b,
-                    pair.symbol_b_,
-                    open_px_b,
-                    open_disequilibrium,
-                    open_scaled_disequilibrium,
-                    pair,
-                ),
-            ]
-        else:
-            # Close signal found - create complete trade
-            close_row = predicted_df.loc[close_row_index]
-            close_tstamp = close_row["tstamp"]
-            close_disequilibrium = close_row["disequilibrium"]
-            close_scaled_disequilibrium = close_row["scaled_disequilibrium"]
-            close_px_a = close_row[f"{colname_a}"]
-            close_px_b = close_row[f"{colname_b}"]
-
-            print(f"{pair}: Close signal found at index {close_row_index}")
-
-            trd_signal_tuples = [
-                (
-                    open_tstamp,
-                    open_side_a,
-                    pair.symbol_a_,
-                    open_px_a,
-                    open_disequilibrium,
-                    open_scaled_disequilibrium,
-                    pair,
-                ),
-                (
-                    open_tstamp,
-                    open_side_b,
-                    pair.symbol_b_,
-                    open_px_b,
-                    open_disequilibrium,
-                    open_scaled_disequilibrium,
-                    pair,
-                ),
-                (
-                    close_tstamp,
-                    close_side_a,
-                    pair.symbol_a_,
-                    close_px_a,
-                    close_disequilibrium,
-                    close_scaled_disequilibrium,
-                    pair,
-                ),
-                (
-                    close_tstamp,
-                    close_side_b,
-                    pair.symbol_b_,
-                    close_px_b,
-                    close_disequilibrium,
-                    close_scaled_disequilibrium,
-                    pair,
-                ),
-            ]
-
-        # Add tuples to data frame
-        return pd.DataFrame(
-            trd_signal_tuples,
-            columns=self.TRADES_COLUMNS,  # type: ignore
-        )
-    
-    def reset(self):
-        pass
-
-class PairState(Enum):
-    INITIAL = 1
-    OPEN = 2
-    CLOSED = 3
-
-class SlidingFitStrategy(PairsTradingStrategy):
-    def __init__(self):
-        super().__init__()
-        self.curr_training_start_idx_ = 0
-
-    def run_pair(self, config: Dict, pair: TradingPair, bt_result: BacktestResult) -> Optional[pd.DataFrame]:
-        print(f"***{pair}*** STARTING....")
-
-        pair.user_data_['state'] = PairState.INITIAL
-        pair.user_data_["trades"] = pd.DataFrame(columns=self.TRADES_COLUMNS)  # type: ignore
-        pair.user_data_["is_cointegrated"] = False
-
-        open_threshold = config["dis-equilibrium_open_trshld"]
-        close_threshold = config["dis-equilibrium_open_trshld"]
-
-        training_minutes = config["training_minutes"]
-        while True:
-            print(self.curr_training_start_idx_, end='\r')
-            pair.get_datasets(
-                training_minutes=training_minutes,
-                training_start_index=self.curr_training_start_idx_,
-                testing_size=1
-            )
-
-            if len(pair.training_df_) < training_minutes:  # type: ignore
-                print(f"{pair}:  {self.curr_training_start_idx_} Not enough training data. Completing the job.")
-                if pair.user_data_["state"] == PairState.OPEN:
-                    print(f"{pair}:  {self.curr_training_start_idx_} Position is not closed.")
-                    # outstanding positions
-                    # last_row_index = self.curr_training_start_idx_ + training_minutes
-
-                    bt_result.handle_outstanding_position(
-                        pair=pair,
-                        pair_result_df=pair.predicted_df_,
-                        last_row_index=0,
-                        open_side_a=pair.user_data_["open_side_a"],
-                        open_side_b=pair.user_data_["open_side_b"],
-                        open_px_a=pair.user_data_["open_px_a"],
-                        open_px_b=pair.user_data_["open_px_b"],
-                        open_tstamp=pair.user_data_["open_tstamp"],
-                    )
-                break
-
-            try:
-                is_cointegrated = pair.train_pair()
-            except Exception as e:
-                raise RuntimeError(f"{pair}: Training failed: {str(e)}") from e
-
-            if pair.user_data_["is_cointegrated"] != is_cointegrated:
-                    pair.user_data_["is_cointegrated"] = is_cointegrated
-                    if not is_cointegrated:
-                        if pair.user_data_["state"] == PairState.OPEN:
-                            print(f"{pair}  {self.curr_training_start_idx_} LOST COINTEGRATION. Consider closing positions...")
-                        else:
-                            print(f"{pair} {self.curr_training_start_idx_} IS NOT COINTEGRATED. Moving on")
-                    else:
-                        print('*' * 80)
-                        print(f"Pair {pair} ({self.curr_training_start_idx_}) IS COINTEGRATED")
-                        print('*' * 80)
-            if not is_cointegrated:
-                self.curr_training_start_idx_ += 1
-                continue
-
-            try:
-                pair.predict()
-            except Exception as e:
-                raise RuntimeError(f"{pair}: Prediction failed: {str(e)}") from e
-
-            if pair.user_data_["state"] == PairState.INITIAL:
-
-                open_trades = self._get_open_trades(pair, open_threshold=open_threshold)
-                if open_trades is not None:
-                    pair.user_data_["trades"] = open_trades
-                    pair.user_data_["state"] = PairState.OPEN
-            elif pair.user_data_["state"] == PairState.OPEN:
-                close_trades = self._get_close_trades(pair, close_threshold=close_threshold)
-                if close_trades is not None:
-                    pair.user_data_["trades"] = pd.concat([pair.user_data_["trades"], close_trades], ignore_index=True)
-                    pair.user_data_["state"] = PairState.CLOSED
-                    break
-
-            self.curr_training_start_idx_ += 1
-
-        print(f"***{pair}*** FINISHED ... {len(pair.user_data_['trades'])}")
-        return pair.user_data_["trades"]
-
-    def _get_open_trades(self, pair: TradingPair, open_threshold: float) -> Optional[pd.DataFrame]:
-        colname_a, colname_b = pair.colnames()
-
-        predicted_df = pair.predicted_df_
-        
-        # Check if we have any data to work with
-        if len(predicted_df) == 0:
-            return None
-
-        open_row = predicted_df.iloc[0]
-        open_tstamp = open_row["tstamp"]
-        open_disequilibrium = open_row["disequilibrium"]
-        open_scaled_disequilibrium = open_row["scaled_disequilibrium"]
-        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
-        if open_disequilibrium > 0:
-            open_side_a = "SELL"
-            open_side_b = "BUY"
-            close_side_a = "BUY"
-            close_side_b = "SELL"
-        else:
-            open_side_a = "BUY"
-            open_side_b = "SELL"
-            close_side_a = "SELL"
-            close_side_b = "BUY"
-
-        # save closing sides
-        pair.user_data_["open_side_a"] = open_side_a
-        pair.user_data_["open_side_b"] = open_side_b
-        pair.user_data_["open_px_a"] = open_px_a
-        pair.user_data_["open_px_b"] = open_px_b
-
-        pair.user_data_["open_tstamp"] = open_tstamp
-
-        pair.user_data_["close_side_a"] = close_side_a
-        pair.user_data_["close_side_b"] = close_side_b
-
-
-        # create opening trades
-        trd_signal_tuples = [
-            (
-                open_tstamp,
-                open_side_a,
-                pair.symbol_a_,
-                open_px_a,
-                open_disequilibrium,
-                open_scaled_disequilibrium,
-                pair,
-            ),
-            (
-                open_tstamp,
-                open_side_b,
-                pair.symbol_b_,
-                open_px_b,
-                open_disequilibrium,
-                open_scaled_disequilibrium,
-                pair,
-            ),
-        ]
-        return pd.DataFrame(
-            trd_signal_tuples,
-            columns=self.TRADES_COLUMNS,  # type: ignore
-        )
-
-    def _get_close_trades(self, pair: TradingPair, close_threshold: float) -> Optional[pd.DataFrame]:
-        colname_a, colname_b = pair.colnames()
-
-        # Check if we have any data to work with
-        if len(pair.predicted_df_) == 0:
-            return None
-
-        close_row = pair.predicted_df_.iloc[0]
-        close_tstamp = close_row["tstamp"]
-        close_disequilibrium = close_row["disequilibrium"]
-        close_scaled_disequilibrium = close_row["scaled_disequilibrium"]
-        close_px_a = close_row[f"{colname_a}"]
-        close_px_b = close_row[f"{colname_b}"]
-
-        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,
-                close_side_a,
-                pair.symbol_a_,
-                close_px_a,
-                close_disequilibrium,
-                close_scaled_disequilibrium,
-                pair,
-            ),
-            (
-                close_tstamp,
-                close_side_b,
-                pair.symbol_b_,
-                close_px_b,
-                close_disequilibrium,
-                close_scaled_disequilibrium,
-                pair,
-            ),
-        ]
-
-        # Add tuples to data frame
-        return pd.DataFrame(
-            trd_signal_tuples,
-            columns=self.TRADES_COLUMNS,  # type: ignore
-        )
-
-    def reset(self):
-        self.curr_training_start_idx_ = 0
-
-
-

src/tools/data_loader.py

@@ -1,138 +0,0 @@
-import sqlite3
-from typing import Dict, List, cast
-import pandas as pd
-
-
-
-def load_sqlite_to_dataframe(db_path, query):
-    try:
-        conn = sqlite3.connect(db_path)
-
-        df = pd.read_sql_query(query, conn)
-        return df
-    except sqlite3.Error as excpt:
-        print(f"SQLite error: {excpt}")
-        raise
-    except Exception as excpt:
-        print(f"Error: {excpt}")
-        raise Exception() from excpt
-    finally:
-        if "conn" in locals():
-            conn.close()
-
-
-def convert_time_to_UTC(value: str, timezone: str) -> str:
-
-    from zoneinfo import ZoneInfo
-    from datetime import datetime
-
-    # Parse it to naive datetime object
-    local_dt = datetime.strptime(value, "%Y-%m-%d %H:%M:%S")
-
-    zinfo = ZoneInfo(timezone)
-    result: datetime = local_dt.replace(tzinfo=zinfo).astimezone(ZoneInfo("UTC"))
-
-    return result.strftime("%Y-%m-%d %H:%M:%S")
-
-
-def load_market_data(datafile: str, config: Dict) -> pd.DataFrame:
-    from tools.data_loader import load_sqlite_to_dataframe
-
-    instrument_ids = [
-        '"' + config["instrument_id_pfx"] + instrument + '"'
-        for instrument in config["instruments"]
-    ]
-    security_type = config["security_type"]
-    exchange_id = config["exchange_id"]
-
-    query = "select"
-    if security_type == "CRYPTO":
-        query += " strftime('%Y-%m-%d %H:%M:%S', tstamp_ns/1000000000, 'unixepoch') as tstamp"
-        query += ", tstamp as time_ns"
-    else:
-        query += " tstamp"
-        query += ", tstamp_ns as time_ns"
-
-    query += f", substr(instrument_id, {len(config['instrument_id_pfx']) + 1}) as symbol"
-    query += ", open"
-    query += ", high"
-    query += ", low"
-    query += ", close"
-    query += ", volume"
-    query += ", num_trades"
-    query += ", vwap"
-
-    query += f" from {config['db_table_name']}"
-    query += f" where exchange_id ='{exchange_id}'"
-    query += f" and instrument_id in ({','.join(instrument_ids)})"
-
-    df = load_sqlite_to_dataframe(db_path=datafile, query=query)
-
-    # Trading Hours
-    date_str = df["tstamp"][0][0:10]
-    trading_hours = config["trading_hours"]
-
-    start_time = convert_time_to_UTC(
-        f"{date_str} {trading_hours['begin_session']}", trading_hours["timezone"]
-    )
-    end_time = convert_time_to_UTC(
-        f"{date_str} {trading_hours['end_session']}", trading_hours["timezone"]
-    )
-
-    # Perform boolean selection
-    df = df[(df["tstamp"] >= start_time) & (df["tstamp"] <= end_time)]
-    df["tstamp"] = pd.to_datetime(df["tstamp"])
-
-    return cast(pd.DataFrame, df)
-
-
-def get_available_instruments_from_db(datafile: str, config: Dict) -> List[str]:
-    """
-    Auto-detect available instruments from the database by querying distinct instrument_id values.
-    Returns instruments without the configured prefix.
-    """
-    try:
-        conn = sqlite3.connect(datafile)
-
-        # Build exclusion list with full instrument_ids
-        exclude_instruments = config.get("exclude_instruments", [])
-        prefix = config.get("instrument_id_pfx", "")
-        exclude_instrument_ids = [f"{prefix}{inst}" for inst in exclude_instruments]
-        
-        # Query to get distinct instrument_ids
-        query = f"""
-        SELECT DISTINCT instrument_id 
-        FROM {config['db_table_name']} 
-        WHERE exchange_id = ?
-        """
-        
-        # Add exclusion clause if there are instruments to exclude
-        if exclude_instrument_ids:
-            placeholders = ','.join(['?' for _ in exclude_instrument_ids])
-            query += f" AND instrument_id NOT IN ({placeholders})"
-            cursor = conn.execute(query, (config["exchange_id"],) + tuple(exclude_instrument_ids))
-        else:
-            cursor = conn.execute(query, (config["exchange_id"],))
-        instrument_ids = [row[0] for row in cursor.fetchall()]
-        conn.close()
-
-        # Remove the configured prefix to get instrument symbols
-        instruments = []
-        for instrument_id in instrument_ids:
-            if instrument_id.startswith(prefix):
-                symbol = instrument_id[len(prefix) :]
-                instruments.append(symbol)
-            else:
-                instruments.append(instrument_id)
-
-        return sorted(instruments)
-
-    except Exception as e:
-        print(f"Error auto-detecting instruments from {datafile}: {str(e)}")
-        return []
-
-
-# if __name__ == "__main__":
-#     df1 = load_sqlite_to_dataframe(sys.argv[1], table_name="md_1min_bars")
-
-#     print(df1)

src/tools/trading_pair.py

@@ -1,208 +0,0 @@
-from typing import Any, Dict, List, Optional
-import pandas as pd  # type:ignore
-from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults  # type:ignore
-
-
-class TradingPair:
-    market_data_: pd.DataFrame
-    symbol_a_: str
-    symbol_b_: str
-    price_column_: str
-
-    training_mu_: float
-    training_std_: float
-
-    training_df_: pd.DataFrame
-    testing_df_: pd.DataFrame
-
-    vecm_fit_: VECMResults
-
-    user_data_: Dict[str, Any]
-
-    def __init__(
-        self, market_data: pd.DataFrame, symbol_a: str, symbol_b: str, price_column: str
-    ):
-        self.symbol_a_ = symbol_a
-        self.symbol_b_ = symbol_b
-        self.price_column_ = price_column
-        self.market_data_ = pd.DataFrame(
-            self._transform_dataframe(market_data)[["tstamp"] + self.colnames()]
-        )
-
- 
-        self.user_data_ = {}
-
-    def _transform_dataframe(self, df: pd.DataFrame) -> pd.DataFrame:
-        # Select only the columns we need
-        df_selected: pd.DataFrame = pd.DataFrame(
-            df[["tstamp", "symbol", self.price_column_]]
-        )
-
-        # Start with unique timestamps
-        result_df: pd.DataFrame = (
-            pd.DataFrame(df_selected["tstamp"]).drop_duplicates().reset_index(drop=True)
-        )
-
-        # For each unique symbol, add a corresponding close price column
-
-        symbols = df_selected["symbol"].unique()
-        for symbol in symbols:
-            # Filter rows for this symbol
-            df_symbol = df_selected[df_selected["symbol"] == symbol].reset_index(
-                drop=True
-            )
-
-            # Create column name like "close-COIN"
-            new_price_column = f"{self.price_column_}_{symbol}"
-
-            # Create temporary dataframe with timestamp and price
-            temp_df = pd.DataFrame(
-                {
-                    "tstamp": df_symbol["tstamp"],
-                    new_price_column: df_symbol[self.price_column_],
-                }
-            )
-
-            # Join with our result dataframe
-            result_df = pd.merge(result_df, temp_df, on="tstamp", how="left")
-            result_df = result_df.reset_index(
-                drop=True
-            )  # do not dropna() since irrelevant symbol would affect dataset
-
-        return result_df
-
-    def get_datasets(
-        self,
-        training_minutes: int,
-        training_start_index: int = 0,
-        testing_size: Optional[int] = None,
-    ) -> None:
-
-        testing_start_index = training_start_index + training_minutes
-        self.training_df_ = self.market_data_.iloc[
-            training_start_index:testing_start_index, :
-        ].copy()
-        assert self.training_df_ is not None
-        self.training_df_ = self.training_df_.dropna().reset_index(drop=True)
-
-        testing_start_index = training_start_index + training_minutes
-        if testing_size is None:
-            self.testing_df_ = self.market_data_.iloc[testing_start_index:, :].copy()
-        else:
-            self.testing_df_ = self.market_data_.iloc[
-                testing_start_index : testing_start_index + testing_size, :
-            ].copy()
-        assert self.testing_df_ is not None
-        self.testing_df_ = self.testing_df_.dropna().reset_index(drop=True)
-
-    def colnames(self) -> List[str]:
-        return [
-            f"{self.price_column_}_{self.symbol_a_}",
-            f"{self.price_column_}_{self.symbol_b_}",
-        ]
-
-    def fit_VECM(self):
-        assert self.training_df_ is not None
-        vecm_df = self.training_df_[self.colnames()].reset_index(drop=True)
-        vecm_model = VECM(vecm_df, coint_rank=1)
-        vecm_fit = vecm_model.fit()
-
-        assert vecm_fit is not None
-
-        # URGENT check beta and alpha
-
-        # Check if the model converged properly
-        if not hasattr(vecm_fit, "beta") or vecm_fit.beta is None:
-            print(f"{self}: VECM model failed to converge properly")
-
-        self.vecm_fit_ = vecm_fit
-        # print(f"{self}: beta={self.vecm_fit_.beta} alpha={self.vecm_fit_.alpha}"  )
-        # print(f"{self}: {self.vecm_fit_.summary()}")
-        pass
-
-    def check_cointegration_johansen(self):
-        assert self.training_df_ is not None
-        from statsmodels.tsa.vector_ar.vecm import coint_johansen
-
-        df = self.training_df_[self.colnames()].reset_index(drop=True)
-        result = coint_johansen(df, det_order=0, k_ar_diff=1)
-        print(
-            f"{self}: lr1={result.lr1[0]} > cvt={result.cvt[0, 1]}? {result.lr1[0] > result.cvt[0, 1]}"
-        )
-        is_cointegrated = result.lr1[0] > result.cvt[0, 1]
-
-        return is_cointegrated
-
-    def check_cointegration_engle_granger(self):
-        from statsmodels.tsa.stattools import coint
-
-        col1, col2 = self.colnames()
-        assert self.training_df_ is not None
-        series1 = self.training_df_[col1].reset_index(drop=True)
-        series2 = self.training_df_[col2].reset_index(drop=True)
-
-        # Run Engle-Granger cointegration test
-        pvalue = coint(series1, series2)[1]
-        # Define cointegration if p-value < 0.05 (i.e., reject null of no cointegration)
-        is_cointegrated = pvalue < 0.05
-        print(f"{self}: is_cointegrated={is_cointegrated} pvalue={pvalue}")
-        return is_cointegrated
-
-    def train_pair(self) -> bool:
-        is_cointegrated_johansen = self.check_cointegration_johansen()
-        is_cointegrated_engle_granger = self.check_cointegration_engle_granger()
-        if not is_cointegrated_johansen and not is_cointegrated_engle_granger:
-            return False
-            pass
-
-        # print('*' * 80 + '\n' + f"**************** {self} IS COINTEGRATED ****************\n" + '*' * 80)
-        self.fit_VECM()
-        assert self.training_df_ is not None and self.vecm_fit_ is not None
-        diseq_series = self.training_df_[self.colnames()] @ self.vecm_fit_.beta
-        print(diseq_series.shape)
-        self.training_mu_ = float(diseq_series[0].mean())
-        self.training_std_ = float(diseq_series[0].std())
-
-        self.training_df_["dis-equilibrium"] = (
-            self.training_df_[self.colnames()] @ self.vecm_fit_.beta
-        )
-        # Normalize the dis-equilibrium
-        self.training_df_["scaled_dis-equilibrium"] = (
-            diseq_series - self.training_mu_
-        ) / self.training_std_
-
-        return True
-
-    def predict(self) -> pd.DataFrame:
-        assert self.testing_df_ is not None
-        assert self.vecm_fit_ is not None
-        predicted_prices = self.vecm_fit_.predict(steps=len(self.testing_df_))
-
-        # Convert prediction to a DataFrame for readability
-        # predicted_df =
-
-        self.predicted_df_ = pd.merge(
-            self.testing_df_.reset_index(drop=True),
-            pd.DataFrame(
-                predicted_prices, columns=pd.Index(self.colnames()), dtype=float
-            ),
-            left_index=True,
-            right_index=True,
-            suffixes=("", "_pred"),
-        ).dropna()
-
-        self.predicted_df_["disequilibrium"] = (
-            self.predicted_df_[self.colnames()] @ self.vecm_fit_.beta
-        )
-
-        self.predicted_df_["scaled_disequilibrium"] = (
-            abs(self.predicted_df_["disequilibrium"] - self.training_mu_)
-            / self.training_std_
-        )
-
-        # Reset index to ensure proper indexing
-        self.predicted_df_ = self.predicted_df_.reset_index()
-        return self.predicted_df_
-
-    def __repr__(self) -> str:
-        return f"{self.symbol_a_} & {self.symbol_b_}"

strategy/pair_strategy.py

@@ -1,106 +1,29 @@
 import argparse
-import hjson
-import importlib
+import asyncio
 import glob
+import importlib
 import os
-import sqlite3
-from datetime import datetime, date
-
+from datetime import date, datetime
 from typing import Any, Dict, List, Optional
 
+import hjson
 import pandas as pd
 
 from tools.data_loader import get_available_instruments_from_db, load_market_data
-from tools.trading_pair import TradingPair
-from results import BacktestResult, create_result_database, store_results_in_database, store_config_in_database
+from pt_trading.results import (
+    BacktestResult,
+    create_result_database,
+    store_config_in_database,
+    store_results_in_database,
+)
+from pt_trading.fit_methods import PairsTradingFitMethod
+from pt_trading.trading_pair import TradingPair
 
 
-def load_config(config_path: str) -> Dict:
-    with open(config_path, "r") as f:
-        config = hjson.load(f)
-    return config
-
-
-# def get_available_instruments_from_db(datafile: str, config: Dict) -> List[str]:
-#     """
-#     Auto-detect available instruments from the database by querying distinct instrument_id values.
-#     Returns instruments without the configured prefix.
-#     """
-#     try:
-#         conn = sqlite3.connect(datafile)
-
-#         # Query to get distinct instrument_ids
-#         query = f"""
-#         SELECT DISTINCT instrument_id 
-#         FROM {config['db_table_name']} 
-#         WHERE exchange_id = ?
-#         """
-
-#         cursor = conn.execute(query, (config["exchange_id"],))
-#         instrument_ids = [row[0] for row in cursor.fetchall()]
-#         conn.close()
-
-#         # Remove the configured prefix to get instrument symbols
-#         prefix = config.get("instrument_id_pfx", "")
-#         instruments = []
-#         for instrument_id in instrument_ids:
-#             if instrument_id.startswith(prefix):
-#                 symbol = instrument_id[len(prefix) :]
-#                 instruments.append(symbol)
-#             else:
-#                 instruments.append(instrument_id)
-
-#         return sorted(instruments)
-
-#     except Exception as e:
-#         print(f"Error auto-detecting instruments from {datafile}: {str(e)}")
-#         return []
-
-
-def resolve_datafiles(config: Dict, cli_datafiles: Optional[str] = None) -> List[str]:
-    """
-    Resolve the list of data files to process.
-    CLI datafiles take priority over config datafiles.
-    Supports wildcards in config but not in CLI.
-    """
-    if cli_datafiles:
-        # CLI override - comma-separated list, no wildcards
-        datafiles = [f.strip() for f in cli_datafiles.split(",")]
-        # Make paths absolute relative to data directory
-        data_dir = config.get("data_directory", "./data")
-        resolved_files = []
-        for df in datafiles:
-            if not os.path.isabs(df):
-                df = os.path.join(data_dir, df)
-            resolved_files.append(df)
-        return resolved_files
-
-    # Use config datafiles with wildcard support
-    config_datafiles = config.get("datafiles", [])
-    data_dir = config.get("data_directory", "./data")
-    resolved_files = []
-
-    for pattern in config_datafiles:
-        if "*" in pattern or "?" in pattern:
-            # Handle wildcards
-            if not os.path.isabs(pattern):
-                pattern = os.path.join(data_dir, pattern)
-            matched_files = glob.glob(pattern)
-            resolved_files.extend(matched_files)
-        else:
-            # Handle explicit file path
-            if not os.path.isabs(pattern):
-                pattern = os.path.join(data_dir, pattern)
-            resolved_files.append(pattern)
-
-    return sorted(list(set(resolved_files)))  # Remove duplicates and sort
-
-
-def run_backtest(
+def run_strategy(
     config: Dict,
     datafile: str,
-    price_column: str,
-    strategy,
+    fit_method: PairsTradingFitMethod,
     instruments: List[str],
 ) -> BacktestResult:
     """
@@ -118,21 +41,27 @@ def run_backtest(
         config_copy = config.copy()
         config_copy["instruments"] = instruments
 
-        market_data_df = load_market_data(datafile, config=config_copy)
+        market_data_df = load_market_data(
+            datafile=datafile,
+            exchange_id=config_copy["exchange_id"],
+            instruments=config_copy["instruments"],
+            instrument_id_pfx=config_copy["instrument_id_pfx"],
+            db_table_name=config_copy["db_table_name"],
+            trading_hours=config_copy["trading_hours"],
+        )
 
         for a_index, b_index in unique_index_pairs:
-            pair = TradingPair(
+            pair = fit_method.create_trading_pair(
                 market_data=market_data_df,
                 symbol_a=instruments[a_index],
                 symbol_b=instruments[b_index],
-                price_column=price_column,
             )
             pairs.append(pair)
         return pairs
 
     pairs_trades = []
     for pair in _create_pairs(config, instruments):
-        single_pair_trades = strategy.run_pair(
+        single_pair_trades = fit_method.run_pair(
             pair=pair, config=config, bt_result=bt_result
         )
         if single_pair_trades is not None and len(single_pair_trades) > 0:
@@ -179,11 +108,12 @@ def main() -> None:
 
     config: Dict = load_config(args.config)
 
-    # Dynamically instantiate strategy class
-    strategy_class_name = config.get("strategy_class", "strategies.StaticFitStrategy")
-    module_name, class_name = strategy_class_name.rsplit(".", 1)
+    # Dynamically instantiate fit method class
+    fit_method_class_name = config.get("fit_method_class", None)
+    assert fit_method_class_name is not None
+    module_name, class_name = fit_method_class_name.rsplit(".", 1)
     module = importlib.import_module(module_name)
-    strategy = getattr(module, class_name)()
+    fit_method = getattr(module, class_name)()
 
     # Resolve data files (CLI takes priority over config)
     datafiles = resolve_datafiles(config, args.datafiles)
@@ -202,32 +132,33 @@ def main() -> None:
 
     # Initialize a dictionary to store all trade results
     all_results: Dict[str, Dict[str, Any]] = {}
-    
+
     # Store configuration in database for reference
     if args.result_db.upper() != "NONE":
         # Get list of all instruments for storage
         all_instruments = []
         for datafile in datafiles:
             if args.instruments:
-                file_instruments = [inst.strip() for inst in args.instruments.split(",")]
+                file_instruments = [
+                    inst.strip() for inst in args.instruments.split(",")
+                ]
             else:
                 file_instruments = get_available_instruments_from_db(datafile, config)
             all_instruments.extend(file_instruments)
-        
+
         # Remove duplicates while preserving order
         unique_instruments = list(dict.fromkeys(all_instruments))
-        
+
         store_config_in_database(
             db_path=args.result_db,
             config_file_path=args.config,
             config=config,
-            strategy_class=strategy_class_name,
+            fit_method_class=fit_method_class_name,
             datafiles=datafiles,
-            instruments=unique_instruments
+            instruments=unique_instruments,
         )
 
     # Process each data file
-    price_column = config["price_column"]
 
     for datafile in datafiles:
         print(f"\n====== Processing {os.path.basename(datafile)} ======")
@@ -248,13 +179,12 @@ def main() -> None:
 
         # Process data for this file
         try:
-            strategy.reset()
-            
-            bt_results = run_backtest(
+            fit_method.reset()
+
+            bt_results = run_strategy(
                 config=config,
                 datafile=datafile,
-                price_column=price_column,
-                strategy=strategy,
+                fit_method=fit_method,
                 instruments=instruments,
             )
 
@@ -288,4 +218,4 @@ def main() -> None:
 
 
 if __name__ == "__main__":
-    main()
+    asyncio.run(main())