progress 0.0.7

.
fix
2026-02-01 23:36:46 +00:00 · 2026-01-31 20:11:07 +00:00 · 2026-01-28 01:00:17 +00:00 · 2026-01-26 21:46:50 +00:00 · 2026-01-24 20:35:59 +00:00 · 2026-01-23 20:15:24 +00:00
48 changed files with 7508 additions and 6659 deletions
@@ -0,0 +1 @@
+source /home/oleg/.pyenv/python3.12-venv/bin/activate
@@ -9,3 +9,4 @@ cvttpy
 # SpecStory explanation file
 .specstory/.what-is-this.md
 results/
+tmp/
@@ -18,199 +18,61 @@
            },
        },
        {
-            "name": "-------- Live Pair Trading --------",
-        },
-        {
-            "name": "PAIRS TRADER",
-            "type": "debugpy",
-            "request": "launch",
-            "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
-            "program": "${workspaceFolder}/bin/pairs_trader.py",
-            "console": "integratedTerminal",
-            "env": {
-                "PYTHONPATH": "${workspaceFolder}/.."
-            },
-            "args": [
-                "--config=${workspaceFolder}/configuration/pairs_trader.cfg",
-                "--pair=PAIR-ADA-USDT:BNBSPOT,PAIR-SOL-USDT:BNBSPOT",
-            ],
-        },
-        {
-            "name": "-------- OLS --------",
-        },
-        {
-            "name": "CRYPTO OLS (rolling)",
-            "type": "debugpy",
-            "request": "launch",
-            "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
-            "program": "${workspaceFolder}/research/backtest.py",
-            "args": [
-                "--config=${workspaceFolder}/configuration/ols.cfg",
-                "--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
-                "--date_pattern=20250605",
-                "--result_db=${workspaceFolder}/research/results/crypto/%T.ols.ADA-SOL.20250605.crypto_results.db",            
-            ],
-            "env": {
-                "PYTHONPATH": "${workspaceFolder}/lib"
-            },
-            "console": "integratedTerminal"
-        },
-        {
-            "name": "CRYPTO OLS (optimized)",
-            "type": "debugpy",
-            "request": "launch",
-            "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
-            "program": "${workspaceFolder}/research/backtest.py",
-            "args": [
-                "--config=${workspaceFolder}/configuration/ols-opt.cfg",
-                "--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
-                "--date_pattern=20250605",
-                "--result_db=${workspaceFolder}/research/results/crypto/%T.ols-opt.ADA-SOL.20250605.crypto_results.db",            
-            ],
-            "env": {
-                "PYTHONPATH": "${workspaceFolder}/lib"
-            },
-            "console": "integratedTerminal"
-        },
-        // {
-        //     "name": "CRYPTO OLS (expanding)",
-        //     "type": "debugpy",
-        //     "request": "launch",
-        //     "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
-        //     "program": "${workspaceFolder}/research/backtest.py",
-        //     "args": [
-        //         "--config=${workspaceFolder}/configuration/ols-exp.cfg",
-        //         "--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
-        //         "--date_pattern=20250605",
-        //         "--result_db=${workspaceFolder}/research/results/crypto/%T.ols-exp.ADA-SOL.20250605.crypto_results.db",            
-        //     ],
-        //     "env": {
-        //         "PYTHONPATH": "${workspaceFolder}/lib"
-        //     },
-        //     "console": "integratedTerminal"
-        // },
-        {
-            "name": "EQUITY OLS (rolling)",
-            "type": "debugpy",
-            "request": "launch",
-            "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
-            "program": "${workspaceFolder}/research/backtest.py",
-            "args": [
-                "--config=${workspaceFolder}/configuration/ols.cfg",
-                "--instruments=COIN:EQUITY:ALPACA,MSTR:EQUITY:ALPACA",
-                "--date_pattern=20250605",
-                "--result_db=${workspaceFolder}/research/results/equity/%T.ols.COIN-MSTR.20250605.equity_results.db",            
-            ],
-            "env": {
-                "PYTHONPATH": "${workspaceFolder}/lib"
-            },
-            "console": "integratedTerminal"
-        },
-        {
-            "name": "EQUITY-CRYPTO OLS (rolling)",
-            "type": "debugpy",
-            "request": "launch",
-            "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
-            "program": "${workspaceFolder}/research/backtest.py",
-            "args": [
-                "--config=${workspaceFolder}/configuration/ols.cfg",
-                "--instruments=COIN:EQUITY:ALPACA,BTC-USDT:CRYPTO:BNBSPOT",
-                "--date_pattern=20250605",
-                "--result_db=${workspaceFolder}/research/results/intermarket/%T.ols.COIN-BTC.20250605.equity_results.db",            
-            ],
-            "env": {
-                "PYTHONPATH": "${workspaceFolder}/lib"
-            },
-            "console": "integratedTerminal"
-        },
-       {
            "name": "-------- VECM --------",
        },
        {
-            "name": "CRYPTO VECM (rolling)",
+            "name": "CRYPTO VECM BACKTEST (optimized)",
            "type": "debugpy",
            "request": "launch",
            "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
            "program": "${workspaceFolder}/research/backtest.py",
            "args": [
-                "--config=${workspaceFolder}/configuration/vecm.cfg",
-                "--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
-                "--date_pattern=20250605",
-                "--result_db=${workspaceFolder}/research/results/crypto/%T.vecm.ADA-SOL.20250605.crypto_results.db",            
-            ],
-            "env": {
-                "PYTHONPATH": "${workspaceFolder}/lib"
-            },
-            "console": "integratedTerminal"
-        },
-        {
-            "name": "CRYPTO VECM (optimized)",
-            "type": "debugpy",
-            "request": "launch",
-            "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
-            "program": "${workspaceFolder}/research/backtest.py",
-            "args": [
-                "--config=${workspaceFolder}/configuration/vecm-opt.cfg",
-                "--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
-                "--date_pattern=20250605",
+                "--config=http://cloud16.cvtt.vpn:6789/apps/pairs_trading/backtest",
+                "--instruments=CRYPTO:BNBSPOT:PAIR-ADA-USDT,CRYPTO:BNBSPOT:PAIR-SOL-USDT",
+                "--date_pattern=20250911",
                "--result_db=${workspaceFolder}/research/results/crypto/%T.vecm-opt.ADA-SOL.20250605.crypto_results.db",            
            ],
            "env": {
-                "PYTHONPATH": "${workspaceFolder}/lib"
+                "PYTHONPATH": "${workspaceFolder}/..",
+                "CONFIG_SERVICE": "cloud16.cvtt.vpn:6789",
+                "MODEL_CONFIG": "vecm-opt"
            },
            "console": "integratedTerminal"
        },
        // {
-        //     "name": "CRYPTO VECM (expanding)",
+        //     "name": "EQUITY VECM (rolling)",
        //     "type": "debugpy",
        //     "request": "launch",
        //     "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
        //     "program": "${workspaceFolder}/research/backtest.py",
        //     "args": [
-        //         "--config=${workspaceFolder}/configuration/vecm-exp.cfg",
-        //         "--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
+        //         "--config=${workspaceFolder}/configuration/vecm.cfg",
+        //         "--instruments=COIN:EQUITY:ALPACA,MSTR:EQUITY:ALPACA",
        //         "--date_pattern=20250605",
-        //         "--result_db=${workspaceFolder}/research/results/crypto/%T.vecm-exp.ADA-SOL.20250605.crypto_results.db",            
+        //         "--result_db=${workspaceFolder}/research/results/equity/%T.vecm.COIN-MSTR.20250605.equity_results.db",            
+        //     ],
+        //     "env": {
+        //         "PYTHONPATH": "${workspaceFolder}/lib"
+        //     },
+        //     "console": "integratedTerminal"
+        // },
+        // {
+        //     "name": "EQUITY-CRYPTO VECM (rolling)",
+        //     "type": "debugpy",
+        //     "request": "launch",
+        //     "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
+        //     "program": "${workspaceFolder}/research/backtest.py",
+        //     "args": [
+        //         "--config=${workspaceFolder}/configuration/vecm.cfg",
+        //         "--instruments=COIN:EQUITY:ALPACA,BTC-USDT:CRYPTO:BNBSPOT",
+        //         "--date_pattern=20250605",
+        //         "--result_db=${workspaceFolder}/research/results/intermarket/%T.vecm.COIN-BTC.20250601.equity_results.db",            
        //     ],
        //     "env": {
        //         "PYTHONPATH": "${workspaceFolder}/lib"
        //     },
        //     "console": "integratedTerminal"
        // },
-        {
-            "name": "EQUITY VECM (rolling)",
-            "type": "debugpy",
-            "request": "launch",
-            "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
-            "program": "${workspaceFolder}/research/backtest.py",
-            "args": [
-                "--config=${workspaceFolder}/configuration/vecm.cfg",
-                "--instruments=COIN:EQUITY:ALPACA,MSTR:EQUITY:ALPACA",
-                "--date_pattern=20250605",
-                "--result_db=${workspaceFolder}/research/results/equity/%T.vecm.COIN-MSTR.20250605.equity_results.db",            
-            ],
-            "env": {
-                "PYTHONPATH": "${workspaceFolder}/lib"
-            },
-            "console": "integratedTerminal"
-        },
-        {
-            "name": "EQUITY-CRYPTO VECM (rolling)",
-            "type": "debugpy",
-            "request": "launch",
-            "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
-            "program": "${workspaceFolder}/research/backtest.py",
-            "args": [
-                "--config=${workspaceFolder}/configuration/vecm.cfg",
-                "--instruments=COIN:EQUITY:ALPACA,BTC-USDT:CRYPTO:BNBSPOT",
-                "--date_pattern=20250605",
-                "--result_db=${workspaceFolder}/research/results/intermarket/%T.vecm.COIN-BTC.20250601.equity_results.db",            
-            ],
-            "env": {
-                "PYTHONPATH": "${workspaceFolder}/lib"
-            },
-            "console": "integratedTerminal"
-        },
        {
            "name": "-------- B a t c h e s --------",
        },
@@ -1,10 +1,10 @@
 {
-	"folders": [
-		{
-			"path": ".."
-		}
-	],
+    "folders": [
+        {
+            "path": ".."
+        }
+    ],
    "settings": {
-        "workbench.colorTheme": "Noctis Minimus"
+        "workbench.colorTheme": "Dracula Theme"
    }
 }
@@ -1,112 +0,0 @@
-{
-    "PythonVersion": "3.12",
-    "[python]": {
-        "editor.defaultFormatter": "ms-python.black-formatter"
-    },
-    // ===========================================================
-    "workbench.activityBar.orientation": "vertical",
-    // ===========================================================
-
-    // "markdown.styles": [
-    //     "/home/oleg/develop/cvtt2/.vscode/light-theme.css"
-    // ],
-    "markdown.preview.background": "#ffffff",
-    "markdown.preview.textEditorTheme": "light",
-    "markdown-pdf.styles": [
-        "/home/oleg/develop/cvtt2/.vscode/light-theme.css"
-    ],
-    "editor.detectIndentation": false,
-	// Configure editor settings to be overridden for [yaml] language.
-	"[yaml]":  {
-		"editor.insertSpaces": true,
-		"editor.tabSize": 4,
-	},
-    "pylint.args": [
-        "--disable=missing-docstring"
-        , "--disable=invalid-name"
-        , "--disable=too-few-public-methods"
-        , "--disable=broad-exception-raised"
-        , "--disable=broad-exception-caught"
-        , "--disable=pointless-string-statement"
-        , "--disable=unused-argument"
-        , "--disable=line-too-long"
-        , "--disable=import-outside-toplevel"
-        , "--disable=fixme"
-        , "--disable=protected-access"
-        , "--disable=logging-fstring-interpolation"
-    ],
-    
-    // ===== TESTING CONFIGURATION =====
-    "python.testing.unittestEnabled": false,
-    "python.testing.pytestEnabled": true,
-    "python.testing.pytestArgs": [
-        "-v",
-        "--tb=short",
-        "--disable-warnings"
-    ],
-    "python.testing.envVars": {
-        "PYTHONPATH": "${workspaceFolder}/lib:${workspaceFolder}/.."
-    },
-    "python.testing.cwd": "${workspaceFolder}",
-    "python.testing.autoTestDiscoverOnSaveEnabled": true,
-    "python.testing.pytestPath": "/home/oleg/.pyenv/python3.12-venv/bin/pytest",
-    "python.testing.promptToConfigure": false,
-    "python.testing.pytest.enabled": true,
-    
-   
-    // Python interpreter settings
-    "python.defaultInterpreterPath": "/home/oleg/.pyenv/python3.12-venv/bin/python3.12",
-    
-    // Environment variables for Python execution
-    "python.envFile": "${workspaceFolder}/.vscode/.env",
-    "python.terminal.activateEnvironment": false,
-    "python.terminal.activateEnvInCurrentTerminal": false,
-    
-    // Global environment variables for VS Code Python extension
-    "terminal.integrated.env.linux": {
-        "PYTHONPATH": "/home/oleg/develop/:${env:PYTHONPATH}"
-    },
-    
-    "pylint.enabled": true,
-    "github.copilot.enable": false,
-    "markdown.extension.print.theme": "dark",
-    "python.analysis.extraPaths": [
-        "${workspaceFolder}/..", 
-        "${workspaceFolder}/lib"
-    ],
-    
-    // Try enabling regular Python language server alongside CursorPyright
-    "python.languageServer": "None",
-    "python.analysis.diagnosticMode": "workspace",
-    "workbench.colorTheme": "Atom One Dark",
-    "cursorpyright.analysis.enable": false,
-    "cursorpyright.analysis.extraPaths": [
-        "${workspaceFolder}/..", 
-        "${workspaceFolder}/lib"
-    ],
-    
-    // Enable quick fixes for unused imports
-    "python.analysis.autoImportCompletions": true,
-    "python.analysis.fixAll": ["source.unusedImports"],
-    "python.analysis.typeCheckingMode": "basic",
-    
-    // Enable code actions for CursorPyright
-    "cursorpyright.analysis.autoImportCompletions": true,
-    "cursorpyright.analysis.typeCheckingMode": "off",
-    "cursorpyright.reportUnusedImport": "warning",
-    "cursorpyright.reportUnusedVariable": "warning",
-    "cursorpyright.analysis.diagnosticMode": "workspace",
-    
-    // Force enable code actions
-    "editor.lightBulb.enabled": true,
-    "editor.codeActionsOnSave": {
-        "source.organizeImports": "explicit",
-        "source.fixAll": "explicit",
-        "source.unusedImports": "explicit"
-    },
-    
-    // Enable Python-specific code actions
-    "python.analysis.completeFunctionParens": true,
-    "python.analysis.addImport.exactMatchOnly": false,
-    "workbench.tree.indent": 24,
-}
@@ -6,7 +6,7 @@
    ],
    "python.testing.cwd": "${workspaceFolder}",
    "python.testing.autoTestDiscoverOnSaveEnabled": true,
-    "python.defaultInterpreterPath": "/usr/bin/python3",
+    "python.defaultInterpreterPath": "/home/oleg/.pyenv/python3.12-venv/bin/python3",
    "python.testing.pytestPath": "python3",
    "python.analysis.extraPaths": [
        "${workspaceFolder}",
@@ -15,12 +15,5 @@
    ],
    "python.envFile": "${workspaceFolder}/.env",
    "python.testing.debugPort": 3000,
-    "python.linting.enabled": true,
-    "python.linting.pylintEnabled": false,
-    "python.linting.mypyEnabled": true,
-    "files.associations": {
-        "*.py": "python"
-    },
    "python.testing.promptToConfigure": false,
-    "workbench.colorTheme": "Dracula Theme Soft"
 }
@@ -0,0 +1 @@
+0.0.7
@@ -0,0 +1,223 @@
+from __future__ import annotations
+
+import asyncio
+from typing import Any, Dict, List
+
+from aiohttp import web
+
+from cvttpy_tools.app import App
+from cvttpy_tools.base import NamedObject
+from cvttpy_tools.config import CvttAppConfig
+from cvttpy_tools.logger import Log
+from cvttpy_tools.web.rest_service import RestService
+from cvttpy_trading.trading.exchange_config import ExchangeAccounts
+from cvttpy_trading.trading.instrument import ExchangeInstrument
+
+from pairs_trading.lib.pair_selector_engine import PairSelectionEngine
+
+
+class PairSelector(NamedObject):
+    instruments_: List[ExchangeInstrument]
+    engine_: PairSelectionEngine
+    rest_service_: RestService
+
+    def __init__(self) -> None:
+        App.instance().add_cmdline_arg("--oneshot", action="store_true", default=False)
+        App.instance().add_call(App.Stage.Config, self._on_config())
+        App.instance().add_call(App.Stage.Run, self.run())
+
+    async def _on_config(self) -> None:
+        cfg = CvttAppConfig.instance()
+        self.instruments_ = self._load_instruments(cfg)
+        price_field = cfg.get_value("model/stat_model_price", "close")
+
+        self.engine_ = PairSelectionEngine(
+            config=cfg,
+            instruments=self.instruments_,
+            price_field=price_field,
+        )
+
+        self.rest_service_ = RestService(config_key="/api/REST")
+        self.rest_service_.add_handler("GET", "/data_quality", self._on_data_quality)
+        self.rest_service_.add_handler("GET", "/pair_selection", self._on_pair_selection)
+
+    def _load_instruments(self, cfg: CvttAppConfig) -> List[ExchangeInstrument]:
+        instruments_cfg = cfg.get_value("instruments", [])
+        instruments: List[ExchangeInstrument] = []
+        assert len(instruments_cfg) >= 2, "at least two instruments required"
+        for item in instruments_cfg:
+            if isinstance(item, str):
+                parts = item.split(":", 1)
+                if len(parts) != 2:
+                    raise ValueError(f"invalid instrument format: {item}")
+                exch_acct, instrument_id = parts
+            elif isinstance(item, dict):
+                exch_acct = item.get("exch_acct", "")
+                instrument_id = item.get("instrument_id", "")
+                if not exch_acct or not instrument_id:
+                    raise ValueError(f"invalid instrument config: {item}")
+            else:
+                raise ValueError(f"unsupported instrument entry: {item}")
+
+            exch_inst = ExchangeAccounts.instance().get_exchange_instrument(
+                exch_acct=exch_acct, instrument_id=instrument_id
+            )
+            assert exch_inst is not None, f"no ExchangeInstrument for {exch_acct}:{instrument_id}"
+            exch_inst.user_data_["exch_acct"] = exch_acct
+            instruments.append(exch_inst)
+        return instruments
+
+    async def run(self) -> None:
+        oneshot = App.instance().get_argument("oneshot", False)
+        while True:
+            await self.engine_.run_once()
+            if oneshot:
+                break
+            sleep_for = self.engine_.sleep_seconds_until_next_cycle()
+            await asyncio.sleep(sleep_for)
+
+    async def _on_data_quality(self, request: web.Request) -> web.Response:
+        fmt = request.query.get("format", "html").lower()
+        quality = self.engine_.quality_dicts()
+        if fmt == "json":
+            return web.json_response(quality)
+        return web.Response(text=self._render_quality_html(quality), content_type="text/html")
+
+    async def _on_pair_selection(self, request: web.Request) -> web.Response:
+        fmt = request.query.get("format", "html").lower()
+        pairs = self.engine_.pair_dicts()
+        if fmt == "json":
+            return web.json_response(pairs)
+        return web.Response(text=self._render_pairs_html(pairs), content_type="text/html")
+
+    def _render_quality_html(self, quality: List[Dict[str, Any]]) -> str:
+        rows = "".join(
+            f"<tr>"
+            f"<td>{q.get('instrument','')}</td>"
+            f"<td>{q.get('record_count','')}</td>"
+            f"<td>{q.get('latest_tstamp','')}</td>"
+            f"<td>{q.get('status','')}</td>"
+            f"<td>{q.get('reason','')}</td>"
+            f"</tr>"
+            for q in sorted(quality, key=lambda x: str(x.get("instrument", "")))
+        )
+        return f"""
+<!DOCTYPE html>
+<html>
+<head>
+    <meta charset='utf-8'/>
+    <title>Data Quality</title>
+    <style>
+        body {{ font-family: Arial, sans-serif; margin: 20px; }}
+        table {{ border-collapse: collapse; width: 100%; }}
+        th, td {{ border: 1px solid #ccc; padding: 8px; text-align: left; }}
+        th {{ background: #f2f2f2; }}
+    </style>
+</head>
+<body>
+    <h2>Data Quality</h2>
+    <table>
+        <thead>
+            <tr><th>Instrument</th><th>Records</th><th>Latest</th><th>Status</th><th>Reason</th></tr>
+        </thead>
+        <tbody>{rows}</tbody>
+    </table>
+</body>
+</html>
+"""
+
+    def _render_pairs_html(self, pairs: List[Dict[str, Any]]) -> str:
+        if not pairs:
+            body = "<p>No pairs available. Check data quality and try again.</p>"
+        else:
+            body_rows = []
+            for p in pairs:
+                body_rows.append(
+                    "<tr>"
+                    f"<td>{p.get('instrument_a','')}</td>"
+                    f"<td>{p.get('instrument_b','')}</td>"
+                    f"<td data-value='{p.get('rank_eg',0)}'>{p.get('rank_eg','')}</td>"
+                    f"<td data-value='{p.get('rank_adf',0)}'>{p.get('rank_adf','')}</td>"
+                    f"<td data-value='{p.get('rank_j',0)}'>{p.get('rank_j','')}</td>"
+                    f"<td data-value='{p.get('pvalue_eg','')}'>{p.get('pvalue_eg','')}</td>"
+                    f"<td data-value='{p.get('pvalue_adf','')}'>{p.get('pvalue_adf','')}</td>"
+                    f"<td data-value='{p.get('pvalue_j','')}'>{p.get('pvalue_j','')}</td>"
+                    "</tr>"
+                )
+            body = "\n".join(body_rows)
+
+        return f"""
+<!DOCTYPE html>
+<html>
+<head>
+    <meta charset='utf-8'/>
+    <title>Pair Selection</title>
+    <style>
+        body {{ font-family: Arial, sans-serif; margin: 20px; }}
+        table {{ border-collapse: collapse; width: 100%; }}
+        th, td {{ border: 1px solid #ccc; padding: 8px; text-align: left; }}
+        th.sortable {{ cursor: pointer; background: #f2f2f2; }}
+    </style>
+</head>
+<body>
+    <h2>Pair Selection</h2>
+    <table id="pairs-table">
+        <thead>
+            <tr>
+                <th>Instrument A</th>
+                <th>Instrument B</th>
+                <th class="sortable" data-type="num">Rank-EG</th>
+                <th class="sortable" data-type="num">Rank-ADF</th>
+                <th class="sortable" data-type="num">Rank-J</th>
+                <th>EG p-value</th>
+                <th>ADF p-value</th>
+                <th>Johansen pseudo p</th>
+            </tr>
+        </thead>
+        <tbody>
+            {body}
+        </tbody>
+    </table>
+    <script>
+    (function() {{
+        const table = document.getElementById('pairs-table');
+        if (!table) return;
+        const getValue = (cell) => {{
+            const val = cell.getAttribute('data-value');
+            const num = parseFloat(val);
+            return isNaN(num) ? val : num;
+        }};
+        const toggleSort = (index, isNumeric) => {{
+            const tbody = table.querySelector('tbody');
+            const rows = Array.from(tbody.querySelectorAll('tr'));
+            const th = table.querySelectorAll('th')[index];
+            const dir = th.getAttribute('data-dir') === 'asc' ? 'desc' : 'asc';
+            th.setAttribute('data-dir', dir);
+            rows.sort((a, b) => {{
+                const va = getValue(a.children[index]);
+                const vb = getValue(b.children[index]);
+                if (isNumeric && !isNaN(va) && !isNaN(vb)) {{
+                    return dir === 'asc' ? va - vb : vb - va;
+                }}
+                return dir === 'asc'
+                    ? String(va).localeCompare(String(vb))
+                    : String(vb).localeCompare(String(va));
+            }});
+            tbody.innerHTML = '';
+            rows.forEach(r => tbody.appendChild(r));
+        }};
+        table.querySelectorAll('th.sortable').forEach((th, idx) => {{
+            th.addEventListener('click', () => toggleSort(idx, th.dataset.type === 'num'));
+        }});
+    }})();
+    </script>
+</body>
+</html>
+"""
+
+
+if __name__ == "__main__":
+    App()
+    CvttAppConfig()
+    PairSelector()
+    App.instance().run()
@@ -0,0 +1,169 @@
+from __future__ import annotations
+
+import asyncio
+from typing import Callable, Coroutine, Dict, List
+import aiohttp.web as web
+
+from cvttpy_tools.app import App
+from cvttpy_tools.config import Config
+from cvttpy_tools.base import NamedObject
+from cvttpy_tools.config import CvttAppConfig
+from cvttpy_tools.logger import Log
+from cvttpy_tools.settings.cvtt_types import BookIdT
+from cvttpy_tools.web.rest_service import RestService
+
+# ---
+from cvttpy_trading.trading.instrument import ExchangeInstrument
+from cvttpy_trading.trading.mkt_data.md_summary import MdTradesAggregate
+from cvttpy_trading.trading.exchange_config import ExchangeAccounts
+# ---
+from pairs_trading.lib.live.mkt_data_client import CvttRestMktDataClient
+
+'''
+config http://cloud16.cvtt.vpn/apps/pairs_trading
+'''
+
+HistMdCbT = Callable[[List[MdTradesAggregate]], Coroutine]
+UpdateMdCbT = Callable[[MdTradesAggregate], Coroutine]
+
+class PairTrader(NamedObject):
+    config_: CvttAppConfig
+    instruments_: List[ExchangeInstrument]
+    book_id_: BookIdT
+
+    live_strategy_: "PtLiveStrategy" #type: ignore
+    ti_sender_: "TradingInstructionsSender" #type: ignore
+    pricer_client_: CvttRestMktDataClient
+    rest_service_: RestService
+    
+    latest_history_: Dict[ExchangeInstrument, List[MdTradesAggregate]]
+    
+    def __init__(self) -> None:
+        self.instruments_ = []
+        self.latest_history_ = {}
+        
+        App.instance().add_cmdline_arg(
+            "--instrument_A",
+            type=str,
+            required=True,
+            help=(
+                " Instrument A in pair (e.g., COINBASE_AT:PAIR-BTC-USD)"
+            ),
+        )
+        App.instance().add_cmdline_arg(
+            "--instrument_B",
+            type=str,
+            required=True,
+            help=(
+                " Instrument B in pair (e.g., COINBASE_AT:PAIR-ETH-USD)"
+            ),
+        )
+
+        App.instance().add_cmdline_arg(
+            "--book_id",
+            type=str,
+            required=True,
+            help="Book ID"
+        )
+        App.instance().add_call(App.Stage.Config, self._on_config())
+        App.instance().add_call(App.Stage.Run, self.run())
+        
+    async def _on_config(self) -> None:
+        self.config_ = CvttAppConfig.instance()
+        self.book_id_ = App.instance().get_argument(name="book_id")
+        
+        # ------- PARSE INSTRUMENTS -------
+        instr_list: List[str] = []
+        instr_str = App.instance().get_argument("instrument_A", "")
+        assert instr_str != "", "Missing insrument A" 
+        instr_list.append(instr_str)
+        instr_str = App.instance().get_argument("instrument_B", "")
+        assert instr_str != "", "Missing insrument B" 
+        instr_list.append(instr_str)
+ 
+        for instr in instr_list:
+            instr_parts = instr.split(":")
+            if len(instr_parts) != 2:
+                raise ValueError(f"Invalid pair format: {instr}")   
+            exch_acct = instr_parts[0]
+            instrument_id = instr_parts[1]
+            exch_inst = ExchangeAccounts.instance().get_exchange_instrument(exch_acct=exch_acct, instrument_id=instrument_id)
+            assert exch_inst is not None, f"No ExchangeInstrument for {instr}"
+            exch_inst.user_data_["exch_acct"] = exch_acct
+            self.instruments_.append(exch_inst)
+
+        Log.info(f"{self.fname()} Instruments: {self.instruments_[0].details_short()} <==> {self.instruments_[1].details_short()}")
+        
+        # ------- CREATE STRATEGY -------
+        from pairs_trading.lib.pt_strategy.live.live_strategy import PtLiveStrategy
+        strategy_config = CvttAppConfig.instance() #self.config_.get_subconfig("strategy_config", Config({}))
+        self.live_strategy_ = PtLiveStrategy(
+            config=strategy_config,
+            pairs_trader=self,
+        )
+        Log.info(f"{self.fname()} Strategy created: {self.live_strategy_}")  
+        model_name = self.config_.get_value("model/name", "?model/name?")
+        self.config_.set_value("strategy_id", f"{self.live_strategy_.__class__.__name__}:{model_name}")
+        
+        # # ------- CREATE PRICER CLIENT -------
+        self.pricer_client_ = CvttRestMktDataClient(config=self.config_)
+        Log.info(f"{self.fname()} MD client created: {self.pricer_client_}")  
+        
+        # ------- CREATE TRADER CLIENT -------
+        from pairs_trading.lib.live.ti_sender import TradingInstructionsSender
+        self.ti_sender_ = TradingInstructionsSender(config=self.config_, pairs_trader=self)
+        Log.info(f"{self.fname()} TI sender created: {self.ti_sender_}")
+
+        # # ------- CREATE REST SERVER -------
+        self.rest_service_ = RestService(
+            config_key=f"/api/REST"
+        )
+        
+        # --- Strategy Handlers
+        self.rest_service_.add_handler(
+            method="POST",
+            url="/api/strategy",
+            handler=self._on_api_request,
+        )
+        
+    async def subscribe_md(self) -> None:
+        from functools import partial
+        for exch_inst in self.instruments_:
+            exch_acct = exch_inst.user_data_.get("exch_acct", "?exch_acct?")
+            instrument_id = exch_inst.instrument_id()
+            
+            await self.pricer_client_.add_subscription(
+                exch_acct=exch_acct,
+                instrument_id=instrument_id,
+                interval_sec=self.live_strategy_.interval_sec(),
+                history_depth_sec=self.live_strategy_.history_depth_sec(),
+                callback=partial(self._on_md_summary, exch_inst=exch_inst)
+            )
+
+    async def _on_md_summary(self, history: List[MdTradesAggregate], exch_inst: ExchangeInstrument) -> None:
+        Log.info(f"{self.fname()}: got {exch_inst.details_short()} data")
+        self.latest_history_[exch_inst] = history
+        if len(self.latest_history_) == 2:
+            from itertools import chain
+            all_aggrs = sorted(list(chain.from_iterable(self.latest_history_.values())), key=lambda X: X.aggr_time_ns_)
+
+            await self.live_strategy_.on_mkt_data_hist_snapshot(hist_aggr=all_aggrs)
+            self.latest_history_ = {}
+
+    async def _on_api_request(self, request: web.Request) -> web.Response:
+        # TODO choose pair
+        # TODO confirm chosen pair (after selection is implemented)
+        return web.Response() # TODO  API request handler implementation
+    
+    
+    async def run(self) -> None:
+        Log.info(f"{self.fname()} ...")
+        while True:
+            await asyncio.sleep(0.1)
+        pass
+
+if __name__ == "__main__":
+    App()
+    CvttAppConfig()
+    PairTrader()
+    App.instance().run()
@@ -1,103 +0,0 @@
-from __future__ import annotations
-
-from functools import partial
-from typing import Dict, List
-
-from cvttpy_tools.settings.cvtt_types import JsonDictT
-from cvttpy_tools.app import App
-from cvttpy_tools.base import NamedObject
-from cvttpy_tools.config import CvttAppConfig
-from cvttpy_tools.logger import Log
-from pairs_trading.lib.pt_strategy.live.live_strategy import PtLiveStrategy
-from pairs_trading.lib.pt_strategy.live.pricer_md_client import PtMktDataClient
-from pairs_trading.lib.pt_strategy.live.ti_sender import TradingInstructionsSender
-
-# import sys
-# print("PYTHONPATH directories:")
-# for path in sys.path:
-#     print(path)
-
-
-class PairTradingRunner(NamedObject):
-    config_: CvttAppConfig
-    instruments_: List[JsonDictT]
-
-    live_strategy_: PtLiveStrategy
-    pricer_client_: PtMktDataClient
-    
-    def __init__(self) -> None:
-        self.instruments_ = []
-        
-        App.instance().add_cmdline_arg(
-            "--pair",
-            type=str,
-            required=True,
-            help=(
-                "Comma-separated pair of instrument symbols"
-                " with exchange config name"
-                " (e.g., PAIR-BTC-USD:BNBSPOT,PAIR-ETH-USD:BNBSPOT)"
-            ),
-        )
-
-        App.instance().add_call(App.Stage.Config, self._on_config())
-        App.instance().add_call(App.Stage.Run, self.run())
-        
-    async def _on_config(self) -> None:
-        self.config_ = CvttAppConfig.instance()
-        
-        # ------- PARSE INSTRUMENTS -------
-        instr_str = App.instance().get_argument("pair", "")
-        if not instr_str:
-            raise ValueError("Pair is required")
-        instr_list = instr_str.split(",")
-        for instr in instr_list:
-            instr_parts = instr.split(":")
-            if len(instr_parts) != 2:
-                raise ValueError(f"Invalid pair format: {instr}")   
-            instrument_id = instr_parts[0]
-            exchange_config_name = instr_parts[1]
-            self.instruments_.append({
-                "exchange_config_name": exchange_config_name,
-                "instrument_id": instrument_id
-            })  
-            
-        assert len(self.instruments_) == 2, "Only two instruments are supported"        
-        Log.info(f"{self.fname()} Instruments: {self.instruments_}")
-        
-        # # ------- CREATE TI (trading instructions) CLIENT -------
-        # ti_config = self.config_.get_subconfig("ti_config", {})
-        # self.ti_sender_ = TradingInstructionsSender(config=ti_config)
-        # Log.info(f"{self.fname()} TI client created: {self.ti_sender_}")  
-        
-        # ------- CREATE CVTT CLIENT -------
-        ti_config = self.config_.get_subconfig("ti_config", {})
-        self.ti_sender_ = TradingInstructionsSender(config=ti_config)
-        Log.info(f"{self.fname()} TI client created: {self.ti_sender_}")  
-
-
-        # ------- CREATE STRATEGY -------
-        strategy_config = self.config_.get_value("strategy_config", {})
-        self.live_strategy_ = PtLiveStrategy(
-            config=strategy_config,
-            instruments=self.instruments_,
-            ti_sender=self.ti_sender_
-        )
-        Log.info(f"{self.fname()} Strategy created: {self.live_strategy_}")  
-        
-        # # ------- CREATE PRICER CLIENT -------
-        # pricer_config = self.config_.get_subconfig("pricer_config", {})
-        # self.pricer_client_ = PtMktDataClient(
-        #     live_strategy=self.live_strategy_,
-        #     pricer_config=pricer_config
-        # )
-        # Log.info(f"{self.fname()} CVTT Pricer client created: {self.pricer_client_}")  
-
-    async def run(self) -> None:
-        Log.info(f"{self.fname()} ...")
-        pass
-
-if __name__ == "__main__":
-    App()
-    CvttAppConfig()
-    PairTradingRunner()
-    App.instance().run()
@@ -0,0 +1,186 @@
+#!/usr/bin/env bash
+
+# ---------------- Settings
+
+repo=git@cloud21.cvtt.vpn:/works/git/cvtt2/research/pairs_trading.git
+
+dist_root=/home/cvttdist/software/cvtt2
+dist_user=cvttdist
+dist_host="cloud21.cvtt.vpn"
+dist_ssh_port="22"
+
+dist_locations="cloud21.cvtt.vpn:22 hs01.cvtt.vpn:22"
+version_file="VERSION"
+
+prj=pairs_trading
+brnch=master
+interactive=N
+
+# ---------------- Settings
+
+# ---------------- cmdline
+
+usage() {
+  echo "Usage: $0 [-b <branch (master)> -i (interactive)"
+  exit 1
+}
+
+while getopts "b:i" opt; do
+  case ${opt} in
+    b )
+      brnch=$OPTARG
+      ;;
+    i )
+      interactive=Y
+      ;;
+    \? )
+      echo "Invalid option: -$OPTARG" >&2
+      usage
+      ;;
+    : )
+      echo "Option -$OPTARG requires an argument." >&2
+      usage
+      ;;
+  esac
+done
+# ---------------- cmdline
+
+confirm() {
+    if [ "${interactive}" == "Y" ]; then
+        echo "--------------------------------"
+        echo -n "Press <Enter> to continue" && read
+    fi
+}
+
+
+if [ "${interactive}" == "Y" ]; then
+    echo -n "Enter project [${prj}]: "
+    read project
+    if [ "${project}" == "" ]
+    then
+        project=${prj}
+    fi
+else
+    project=${prj}
+fi
+
+# repo=${git_repo_arr[${project}]}
+if [ -z ${repo} ]; then
+    echo "ERROR: Project repository for ${project} not found"
+    exit -1
+fi
+echo "Project repo: ${repo}"
+
+if [ "${interactive}" == "Y" ]; then
+    echo -n "Enter branch to build release from [${brnch}]: "
+    read branch
+    if [ "${branch}" == "" ]
+    then
+        branch=${brnch}
+    fi
+else
+    branch=${brnch}
+fi
+
+tmp_dir=$(mktemp -d)
+function cleanup {
+    cd ${HOME}
+    rm -rf ${tmp_dir}
+}
+trap cleanup EXIT
+
+
+prj_dir="${tmp_dir}/${prj}"
+
+cmd_arr=()
+Cmd="git clone ${repo} ${prj_dir}"
+cmd_arr+=("${Cmd}")
+
+Cmd="cd ${prj_dir}"
+cmd_arr+=("${Cmd}")
+
+if [ "${interactive}" == "Y" ]; then
+    echo "------------------------------------"
+    echo "The following commands will execute:"
+    echo "------------------------------------"
+    for cmd in "${cmd_arr[@]}"
+    do
+        echo ${cmd}
+    done
+fi
+
+confirm
+
+for cmd in "${cmd_arr[@]}"
+do
+    echo ${cmd} && eval ${cmd}
+done
+
+Cmd="git checkout ${branch}"
+echo ${Cmd} && eval ${Cmd}
+if [ "${?}" != "0" ]; then
+    echo "ERROR: Branch ${branch} is not found"
+    cd ${HOME} && rm -rf ${tmp_dir}
+    exit -1
+fi
+
+
+release_version=$(cat ${version_file} | awk -F',' '{print $1}')
+whats_new=$(cat ${version_file} | awk -F',' '{print $2}')
+
+
+echo "--------------------------------"
+echo "Version file: ${version_file}"
+echo "Release version: ${release_version}"
+
+confirm
+
+version_tag="v${release_version}"
+if [ "$(git tag -l "${version_tag}")" != "" ]; then
+    version_tag="${version_tag}.$(date +%Y%m%d_%H%M)"
+fi
+version_comment="'${version_tag} ${project} ${branch} $(date +%Y-%m-%d)\n${whats_new}'"
+
+cmd_arr=()
+Cmd="git tag -a ${version_tag} -m ${version_comment}"
+cmd_arr+=("${Cmd}")
+
+Cmd="git push origin --tags"
+cmd_arr+=("${Cmd}")
+
+Cmd="rm -rf .git"
+cmd_arr+=("${Cmd}")
+
+SourceLoc=../${project}
+
+dist_path="${dist_root}/${project}/${release_version}"
+
+for dist_loc in ${dist_locations}; do
+    dhp=(${dist_loc//:/ })
+    dist_host=${dhp[0]}
+    dist_port=${dhp[1]}
+    Cmd="rsync -avzh"
+    Cmd="${Cmd} --rsync-path=\"mkdir -p ${dist_path}"
+    Cmd="${Cmd} && rsync\" -e \"ssh -p ${dist_ssh_port}\""
+    Cmd="${Cmd} $SourceLoc ${dist_user}@${dist_host}:${dist_path}/"
+    cmd_arr+=("${Cmd}")
+done
+
+if [ "${interactive}" == "Y" ]; then
+    echo "------------------------------------"
+    echo "The following commands will execute:"
+    echo "------------------------------------"
+    for cmd in "${cmd_arr[@]}"
+    do
+        echo ${cmd}
+    done
+fi
+
+confirm
+
+for cmd in "${cmd_arr[@]}"
+do
+    pwd && echo ${cmd} && eval ${cmd}
+done
+
+echo "$0 Done ${project} ${release_version}"
@@ -23,8 +23,8 @@
    "dis-equilibrium_open_trshld": 2.0,
    "dis-equilibrium_close_trshld": 0.5,
    "training_size": 120,
-    "model_class": "pt_strategy.models.OLSModel",
-    "model_data_policy_class": "pt_strategy.model_data_policy.ExpandingWindowDataPolicy",
+    "model_class": "pairs_trading.lib.pt_strategy.models.OLSModel",
+    "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.ExpandingWindowDataPolicy",

    # ====== Stop Conditions ======
    "stop_close_conditions": {
@@ -22,11 +22,11 @@
    },
    "dis-equilibrium_open_trshld": 1.75,
    "dis-equilibrium_close_trshld": 0.9,
-    "model_class": "pt_strategy.models.OLSModel",
+    "model_class": "pairs_trading.lib.pt_strategy.models.OLSModel",

-    # "model_data_policy_class": "pt_strategy.model_data_policy.EGOptimizedWndDataPolicy",
-    # "model_data_policy_class": "pt_strategy.model_data_policy.ADFOptimizedWndDataPolicy",
-    "model_data_policy_class": "pt_strategy.model_data_policy.JohansenOptdWndDataPolicy",
+    # "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.EGOptimizedWndDataPolicy",
+    # "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.ADFOptimizedWndDataPolicy",
+    "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.JohansenOptdWndDataPolicy",
    "min_training_size": 60,
    "max_training_size": 150,

@@ -22,11 +22,11 @@
    },
    "dis-equilibrium_open_trshld": 1.75,
    "dis-equilibrium_close_trshld": 0.9,
-    "model_class": "pt_strategy.models.OLSModel",
+    "model_class": "pairs_trading.lib.pt_strategy.models.OLSModel",

    "training_size": 120,
-    "model_data_policy_class": "pt_strategy.model_data_policy.RollingWindowDataPolicy",
-    # "model_data_policy_class": "pt_strategy.model_data_policy.OptimizedWindowDataPolicy",
+    "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.RollingWindowDataPolicy",
+    # "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.OptimizedWindowDataPolicy",
    # "min_training_size": 60,
    # "max_training_size": 150,

@@ -23,11 +23,11 @@
    },
    "dis-equilibrium_open_trshld": 1.75,
    "dis-equilibrium_close_trshld": 1.0,
-    "model_class": "pt_strategy.models.VECMModel",
+    "model_class": "pairs_trading.lib.pt_strategy.models.VECMModel",

    "training_size": 120,
-    "model_data_policy_class": "pt_strategy.model_data_policy.RollingWindowDataPolicy",
-    # "model_data_policy_class": "pt_strategy.model_data_policy.OptimizedWindowDataPolicy",
+    "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.RollingWindowDataPolicy",
+    # "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.OptimizedWindowDataPolicy",
    # "min_training_size": 60,
    # "max_training_size": 150,

@@ -0,0 +1,46 @@
+{
+    "refdata": {
+        "assets": @inc=http://@env{CONFIG_SERVICE}/refdata/assets
+        , "instruments": @inc=http://@env{CONFIG_SERVICE}/refdata/instruments
+        , "exchange_instruments": @inc=http://@env{CONFIG_SERVICE}/refdata/exchange_instruments
+        , "dynamic_instrument_exchanges": ["ALPACA"]
+        , "exchanges": @inc=http://@env{CONFIG_SERVICE}/refdata/exchanges
+    }, 
+    "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,
+
+    # ====== Model =======
+    "model": @inc=http://@env{CONFIG_SERVICE}/apps/common/models/@env{MODEL_CONFIG} 
+
+    # ====== Trading ======= 
+    "execution_price": {
+        "column": "vwap",
+        "shift": 1,
+    },
+    # ====== 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": "7:30:00",
+        "end_session": "18:30:00",
+    }
+}
@@ -1,5 +1,5 @@
 {
-    "strategy_config": @inc=file:///home/oleg/develop/pairs_trading/configuration/ols.cfg
+    "strategy_config": @inc=file:///home/oleg/develop/pairs_trading/configuration/vecm-opt.cfg
    "pricer_config": {
        "pricer_url": "ws://localhost:12346/ws",
        "history_depth_sec": 86400 #"60*60*24",  # use simpleeval
@@ -1,19 +1,26 @@
 {
-    "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-",
-        }
-    },
+    # "refdata": {
+    #     "assets": @inc=http://@env{CONFIG_SERVICE}/refdata/assets
+    #     , "instruments": @inc=http://@env{CONFIG_SERVICE}/refdata/instruments
+    #     , "exchange_instruments": @inc=http://@env{CONFIG_SERVICE}/refdata/exchange_instruments
+    #     , "dynamic_instrument_exchanges": ["ALPACA"]
+    #     , "exchanges": @inc=http://@env{CONFIG_SERVICE}/refdata/exchanges
+    # }, 
+    # "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,
+    # # ====== Funding ======
+    # "funding_per_pair": 2000.0,
  
    # ====== Trading Parameters ======
    "stat_model_price": "close", # "vwap"
@@ -24,26 +31,26 @@
    "dis-equilibrium_open_trshld": 1.75,
    "dis-equilibrium_close_trshld": 1.0,

-    "model_class": "pt_strategy.models.VECMModel",
+    "model_class": "pairs_trading.lib.pt_strategy.models.VECMModel",

    # "training_size": 120,
-    # "model_data_policy_class": "pt_strategy.model_data_policy.RollingWindowDataPolicy",
-    "model_data_policy_class": "pt_strategy.model_data_policy.ADFOptimizedWndDataPolicy",
+    # "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.RollingWindowDataPolicy",
+    "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.ADFOptimizedWndDataPolicy",
    "min_training_size": 60,
    "max_training_size": 150,

-    # ====== Stop Conditions ======
-    "stop_close_conditions": {
-        "profit": 2.0,
-        "loss": -0.5
-    }
+    # # ====== 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": "7:30:00",
-        "end_session": "18:30:00",
-    }
+    # # ====== End of Session Closeout ======
+    # "close_outstanding_positions": true,
+    # # "close_outstanding_positions": false,
+    # "trading_hours": {
+    #     "timezone": "America/New_York",
+    #     "begin_session": "7:30:00",
+    #     "end_session": "18:30:00",
+    # }
 }
@@ -1,213 +0,0 @@
-from __future__ import annotations
-
-from typing import Dict, Any, List, Optional
-import time
-
-import requests
-
-from cvttpy_tools.base import NamedObject
-from cvttpy_tools.logger import Log
-from cvttpy_tools.config import Config
-from cvttpy_tools.timer import Timer
-
-from cvttpy_trading.trading.mkt_data.historical_md import HistMdBar
-
-
-class RESTSender(NamedObject):
-    session_: requests.Session
-    base_url_: str
-
-    def __init__(self, base_url: str) -> None:
-        self.base_url_ = base_url
-        self.session_ = requests.Session()
-
-    def is_ready(self) -> bool:
-        """Checks if the server is up and responding"""
-        url = f"{self.base_url_}/ping"
-        try:
-            response = self.session_.get(url)
-            response.raise_for_status()
-            return True
-        except requests.exceptions.RequestException:
-            return False
-
-    def send_post(self, endpoint: str, post_body: Dict) -> requests.Response:
-
-        while not self.is_ready():
-            print("Waiting for FrontGateway to start...")
-            time.sleep(5)
-
-        url = f"{self.base_url_}/{endpoint}"
-        try:
-            return self.session_.request(
-                method="POST",
-                url=url,
-                json=post_body,
-                headers={"Content-Type": "application/json"},
-            )
-        except requests.exceptions.RequestException as excpt:
-            raise ConnectionError(
-                f"Failed to send status={excpt.response.status_code} {excpt.response.text}"  # type: ignore
-            ) from excpt
-
-    def send_get(self, endpoint: str) -> requests.Response:
-        while not self.is_ready():
-            print("Waiting for FrontGateway to start...")
-            time.sleep(5)
-
-        url = f"{self.base_url_}/{endpoint}"
-        try:
-            return self.session_.request(method="GET", url=url)
-        except requests.exceptions.RequestException as excpt:
-            raise ConnectionError(
-                f"Failed to send status={excpt.response.status_code} {excpt.response.text}"  # type: ignore
-            ) from excpt
-
-
-class MdSummary(HistMdBar):
-    def __init__(
-        self,
-        ts_ns: int,
-        open: float,
-        high: float,
-        low: float,
-        close: float,
-        volume: float,
-        vwap: float,
-        num_trades: int,
-    ):
-        super().__init__(ts=ts_ns)
-        self.open_ = open
-        self.high_ = high
-        self.low_ = low
-        self.close_ = close
-        self.volume_ = volume
-        self.vwap_ = vwap
-        self.num_trades_ = num_trades
-
-    @classmethod
-    def from_REST_response(cls, response: requests.Response) -> List[MdSummary]:
-        res: List[MdSummary] = []
-        jresp = response.json()
-        hist_data = jresp.get("historical_data", [])
-        for hd in hist_data:
-            res.append(
-                MdSummary(
-                    ts_ns=hd["time_ns"],
-                    open=hd["open"],
-                    high=hd["high"],
-                    low=hd["low"],
-                    close=hd["close"],
-                    volume=hd["volume"],
-                    vwap=hd["vwap"],
-                    num_trades=hd["num_trades"],
-                )
-            )
-        return res
-
-
-class MdSummaryCollector(NamedObject):
-    sender_: RESTSender
-    exch_acct_: str
-    instrument_id_: str
-    interval_sec_: int
-    history_depth_sec_: int
-
-    history_: List[MdSummary]
-    timer_: Optional[Timer]
-
-    def __init__(
-        self,
-        sender: RESTSender,
-        exch_acct: str,
-        instrument_id: str,
-        interval_sec: int,
-        history_depth_sec: int,
-    ) -> None:
-        self.sender_ = sender
-        self.exch_acct_ = exch_acct
-        self.instrument_id_ = instrument_id
-        self.interval_sec_ = interval_sec
-        self.history_depth_sec_ = history_depth_sec
-
-        self.history_depth_sec_ = []
-        self.timer_ = None
-
-    def rqst_data(self) -> Dict[str, Any]:
-        return {
-            "exch_acct": self.exch_acct_,
-            "instrument_id": self.instrument_id_,
-            "interval_sec": self.interval_sec_,
-            "history_depth_sec": self.history_depth_sec_,
-        }
-
-    def get_history(self) -> List[MdSummary]:
-        response: requests.Response = self.sender_.send_post(
-            endpoint="md_summary", post_body=self.rqst_data()
-        )
-        return MdSummary.from_REST_response(response=response)
-
-    def get_last(self) -> Optional[MdSummary]:
-        rqst_data = self.rqst_data()
-        rqst_data["history_depth_sec"] = self.interval_sec_
-        response: requests.Response = self.sender_.send_post(
-            endpoint="md_summary", post_body=rqst_data
-        )
-        res = MdSummary.from_REST_response(response=response)
-        return None if len(res) == 0 else res[-1]
-
-    async def start(self) -> None:
-        if self.timer_:
-            Log.error(f"{self.fname()}: Timer is already started")
-            return
-        self.history_ = self.get_history()
-        self.timer_ = Timer(
-            start_in_sec=self.interval_sec_,
-            is_periodic=True,
-            period_interval=self.interval_sec_,
-            func=self._load_new,
-        )
-
-    async def _load_new(self) -> None:
-        last: Optional[MdSummary] = self.get_last()
-        if not last:
-            # URGENT logging
-            return
-        if last.ts_ns_ <= self.history_[-1].ts_ns_:
-            # URGENT logging
-            return 
-        self.history_.append(last)
-        # URGENT implement notification
-        
-    def stop(self) -> None:
-        if self.timer_:
-            self.timer_.cancel()
-            self.timer_ = None
-
-class CvttRESTClient(NamedObject):
-    config_: Config
-    sender_: RESTSender
-
-    def __init__(self, config: Config) -> None:
-        self.config_ = config
-        base_url = self.config_.get_value("cvtt_base_url", default="")
-        assert base_url
-        self.sender_ = RESTSender(base_url=base_url)
-
-
-if __name__ == "__main__":
-    config = Config(json_src={"cvtt_base_url": "http://cvtt-tester-01.cvtt.vpn:23456"})
-
-    cvtt_client = CvttRESTClient(config)
-
-    mdsc = MdSummaryCollector(
-        sender=cvtt_client.sender_,
-        exch_acct="COINBASE_AT",
-        instrument_id="PAIR-BTC-USD",
-        interval_sec=60,
-        history_depth_sec=24 * 3600,
-    )
-
-    hist = mdsc.get_history()
-    last = mdsc.get_last()
-    pass
@@ -1,220 +0,0 @@
-#!/usr/bin/env python3
-
-import asyncio
-import json
-import uuid
-from dataclasses import dataclass
-from functools import partial
-from typing import Callable, Coroutine, Dict, Optional
-
-import websockets
-from cvttpy_tools.logger import Log
-from cvttpy_tools.settings.cvtt_types import JsonDictT
-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 CvttWebSockClient:
-    ws_url_: UrlT
-    websocket_: Optional[ClientConnection]
-    is_connected_: bool
-    
-    def __init__(self, url: str):
-        self.ws_url_ = url
-        self.websocket_ = None
-        self.is_connected_ = False
-
-    async def connect(self) -> None:
-        self.websocket_ = await websockets.connect(self.ws_url_)
-        self.is_connected_ = True
-
-    async def close(self) -> None:
-        if self.websocket_ is not None:
-            await self.websocket_.close()
-            self.is_connected_ = False
-
-    async def receive_message(self) -> JsonDictT:
-        assert self.websocket_ is not None
-        assert self.is_connected_
-        message = await self.websocket_.recv()
-        message_str = (
-            message.decode("utf-8")
-            if isinstance(message, bytes)
-            else message
-        )
-        res = json.loads(message_str)
-        assert res is not None
-        assert isinstance(res, dict)
-        return res
-    
-    @classmethod
-    async def check_connection(cls, url: str) -> bool:
-        try:
-            async with websockets.connect(url) as websocket:
-                result = True
-        except Exception as e:
-            Log.error(f"Unable to connect to {url}: {str(e)}")
-            result = False
-        return result
-    
-class CvttPricerWebSockClient(CvttWebSockClient):
-    # Class members with type hints
-    subscriptions_: Dict[SubscriptionIdT, CvttPricesSubscription]
-
-    def __init__(self, url: str):
-        super().__init__(url)
-        self.subscriptions_ = {}
-
-    async def subscribe(
-        self, subscription: CvttPricesSubscription
-    ) -> str:  # returns subscription id
-
-        if not self.is_connected_:
-            try:
-                Log.info(f"Connecting to {self.ws_url_}")
-                await self.connect()
-            except Exception as e:
-                Log.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":
-            Log.info(f"Subscription successful: {json.dumps(response)}")
-            return True
-        elif response.get("status") == "error":
-            Log.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:
-                    msg_dict: JsonDictT = await self.receive_message()
-                except websockets.ConnectionClosed:
-                    Log.warning("Connection closed")
-                    self.is_connected_ = False
-                    break
-                except Exception as e:
-                    Log.error(f"Error occurred: {str(e)}")
-                    self.is_connected_ = False
-                    await asyncio.sleep(5)  # Wait before reconnecting
-
-                await self.process_message(msg_dict)
-
-        except Exception as e:
-            Log.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_:
-                Log.warning(f"Unknown subscription id: {subscription_id}")
-                return
-
-            subscription = self.subscriptions_[subscription_id]
-            await subscription.callback_(message_type, subscription_id, message)
-        else:
-            Log.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['tstamp'][:19]}] *** RLTM ***  {message}")
-        elif message_type == "historical_md_aggregate":
-            for aggr in message.get("historical_data", []):
-                print(f"[{aggr['tstamp'][: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())
@@ -0,0 +1,277 @@
+from __future__ import annotations
+
+import asyncio
+from typing import Dict, Any, List, Optional, Set
+
+import requests
+
+from cvttpy_tools.base import NamedObject
+from cvttpy_tools.logger import Log
+from cvttpy_tools.config import Config
+from cvttpy_tools.timer import Timer
+from cvttpy_tools.timeutils import NanosT, current_seconds
+from cvttpy_tools.settings.cvtt_types import InstrumentIdT, IntervalSecT
+from cvttpy_tools.web.rest_client import RESTSender
+# ---
+from cvttpy_trading.trading.instrument import ExchangeInstrument
+from cvttpy_trading.trading.accounting.exch_account import ExchangeAccountNameT
+from cvttpy_trading.trading.mkt_data.md_summary import MdTradesAggregate, MdSummary, MdSummaryCallbackT
+from cvttpy_trading.trading.exchange_config import ExchangeAccounts
+# ---
+
+
+# class MdSummary(HistMdBar):
+#     def __init__(
+#         self,
+#         ts_ns: int,
+#         open: float,
+#         high: float,
+#         low: float,
+#         close: float,
+#         volume: float,
+#         vwap: float,
+#         num_trades: int,
+#     ):
+#         super().__init__(ts=ts_ns)
+#         self.open_ = open
+#         self.high_ = high
+#         self.low_ = low
+#         self.close_ = close
+#         self.volume_ = volume
+#         self.vwap_ = vwap
+#         self.num_trades_ = num_trades
+
+#     @classmethod
+#     def from_REST_response(cls, response: requests.Response) -> List[MdSummary]:
+#         res: List[MdSummary] = []
+#         jresp = response.json()
+#         hist_data = jresp.get("historical_data", [])
+#         for hd in hist_data:
+#             res.append(
+#                 MdSummary(
+#                     ts_ns=hd["time_ns"],
+#                     open=hd["open"],
+#                     high=hd["high"],
+#                     low=hd["low"],
+#                     close=hd["close"],
+#                     volume=hd["volume"],
+#                     vwap=hd["vwap"],
+#                     num_trades=hd["num_trades"],
+#                 )
+#             )
+#         return res
+
+#     def create_md_trades_aggregate(
+#         self,
+#         exch_acct: ExchangeAccountNameT,
+#         exch_inst: ExchangeInstrument,
+#         interval_sec: IntervalSecT,
+#     ) -> MdTradesAggregate:
+#         res = MdTradesAggregate(
+#             exch_acct=exch_acct,
+#             exch_inst=exch_inst,
+#             interval_ns=interval_sec * NanoPerSec,
+#         )
+#         res.set(mdbar=self)
+#         return res
+
+
+# MdSummaryCallbackT = Callable[[List[MdTradesAggregate]], Coroutine]
+
+
+class MdSummaryCollector(NamedObject):
+    sender_: RESTSender
+    exch_acct_: ExchangeAccountNameT
+    exch_inst_: ExchangeInstrument
+    interval_sec_: IntervalSecT
+    history_depth_sec_: IntervalSecT
+
+    history_: List[MdTradesAggregate]
+
+    callbacks_: List[MdSummaryCallbackT]
+    timer_: Optional[Timer]
+
+    def __init__(
+        self,
+        sender: RESTSender,
+        exch_acct: ExchangeAccountNameT,
+        instrument_id: InstrumentIdT,
+        interval_sec: IntervalSecT,
+        history_depth_sec: IntervalSecT,
+    ) -> None:
+        self.sender_ = sender
+        self.exch_acct_ = exch_acct
+
+        exch_inst = ExchangeAccounts.instance().get_exchange_instrument(
+            exch_acct=exch_acct, instrument_id=instrument_id
+        )
+        assert exch_inst is not None, f"Unable to find Exchange instrument for {exch_acct}/{instrument_id}"
+        self.exch_inst_ = exch_inst
+        self.interval_sec_ = interval_sec
+        self.history_depth_sec_ = history_depth_sec
+
+        self.history_ = []
+        self.callbacks_ = []
+        self.timer_ = None
+
+    def add_callback(self, cb: MdSummaryCallbackT) -> None:
+        self.callbacks_.append(cb)
+
+    def __hash__(self):
+        return hash(
+            (
+                self.exch_acct_,
+                self.exch_inst_.instrument_id(),
+                self.interval_sec_,
+                self.history_depth_sec_,
+            )
+        )
+
+    def rqst_data(self) -> Dict[str, Any]:
+        return {
+            "exch_acct": self.exch_acct_,
+            "instrument_id": self.exch_inst_.instrument_id(),
+            "interval_sec": self.interval_sec_,
+            "history_depth_sec": self.history_depth_sec_,
+        }
+
+    def get_history(self) -> List[MdSummary]:
+        response: requests.Response = self.sender_.send_post(
+            endpoint="md_summary", post_body=self.rqst_data()
+        )
+        if response.status_code not in (200, 201):
+            Log.error(
+                f"{self.fname()}: Received error: {response.status_code} - {response.text}"
+            )
+            return []
+        return MdSummary.from_REST_response(response=response)
+
+    def get_last(self) -> Optional[MdSummary]:
+        Log.info(f"{self.fname()}: for {self.exch_inst_.details_short()}")
+        rqst_data = self.rqst_data()
+        rqst_data["history_depth_sec"] = self.interval_sec_ * 2
+        response: requests.Response = self.sender_.send_post(
+            endpoint="md_summary", post_body=rqst_data
+        )
+        if response.status_code not in (200, 201):
+            Log.error(
+                f"{self.fname()}: Received error: {response.status_code} - {response.text}"
+            )
+            return None
+        res = MdSummary.from_REST_response(response=response)
+        Log.info(f"DEBUG *** {self.exch_inst_.base_asset_id_}: {res[-1].tstamp_}")
+        return None if len(res) == 0 else res[-1]
+
+    def is_empty(self) -> bool:
+        return len(self.history_) == 0
+
+    async def start(self) -> None:
+        if self.timer_:
+            Log.error(f"{self.fname()}: Timer is already started")
+            return
+        mdsum_hist = self.get_history()
+        self.history_ = [
+            mdsum.create_md_trades_aggregate(
+                exch_acct=self.exch_acct_,
+                exch_inst=self.exch_inst_,
+                interval_sec=self.interval_sec_,
+            )
+            for mdsum in mdsum_hist
+        ]
+        await self.run_callbacks()
+        self.set_timer()
+
+    def set_timer(self):
+        if self.timer_:
+            self.timer_.cancel()
+        start_in = self.next_load_time() - current_seconds()
+        self.timer_ = Timer(
+            start_in_sec=start_in,
+            func=self._load_new,
+        )
+        Log.info(f"{self.fname()} Timer for {self.exch_inst_.details_short()} is set to run in {start_in} sec")
+
+    def next_load_time(self) -> NanosT:
+        ALLOW_LAG_SEC = 1
+        curr_sec = int(current_seconds())
+        return (curr_sec - curr_sec % self.interval_sec_) + self.interval_sec_ + ALLOW_LAG_SEC
+
+    async def _load_new(self) -> None:
+
+        last: Optional[MdSummary] = self.get_last()
+        if not last:
+            Log.warning(f"{self.fname()}: did not get last update")
+        elif not self.is_empty() and last.ts_ns_ <= self.history_[-1].aggr_time_ns_:
+            Log.info(
+                f"{self.fname()}: Received {last}. Already Have: {self.history_[-1]}"
+            )
+        else:
+            self.history_.append(last.create_md_trades_aggregate(exch_acct=self.exch_acct_, exch_inst=self.exch_inst_, interval_sec=self.interval_sec_))
+            await self.run_callbacks()
+        self.set_timer()
+
+    async def run_callbacks(self) -> None:
+        [await cb(self.history_) for cb in self.callbacks_]
+
+    def stop(self) -> None:
+        if self.timer_:
+            self.timer_.cancel()
+            self.timer_ = None
+
+
+class CvttRestMktDataClient(NamedObject):
+    config_: Config
+    sender_: RESTSender
+    collectors_: Set[MdSummaryCollector]
+
+    def __init__(self, config: Config) -> None:
+        self.config_ = config
+        base_url = self.config_.get_value("cvtt_base_url", default="")
+        assert base_url
+        self.sender_ = RESTSender(base_url=base_url)
+        self.collectors_ = set()
+
+    async def add_subscription(
+        self,
+        exch_acct: ExchangeAccountNameT,
+        instrument_id: InstrumentIdT,
+        interval_sec: IntervalSecT,
+        history_depth_sec: IntervalSecT,
+        callback: MdSummaryCallbackT,
+    ) -> None:
+        mdsc = MdSummaryCollector(
+            sender=self.sender_,
+            exch_acct=exch_acct,
+            instrument_id=instrument_id,
+            interval_sec=interval_sec,
+            history_depth_sec=history_depth_sec,
+        )
+        mdsc.add_callback(callback)
+        self.collectors_.add(mdsc)
+        await mdsc.start()
+
+
+if __name__ == "__main__":
+    config = Config(json_src={"cvtt_base_url": "http://cvtt-tester-01.cvtt.vpn:23456"})
+    # config = Config(json_src={"cvtt_base_url": "http://dev-server-02.cvtt.vpn:23456"})
+
+    async def _calback(history: List[MdTradesAggregate]) -> None:
+        Log.info(
+            f"MdSummary Hist Length is {len(history)}. Last summary: {history[-1] if  len(history) > 0 else '[]'}"
+        )
+
+    async def __run() -> None:
+        Log.info("Starting...")
+        cvtt_client = CvttRestMktDataClient(config)
+        await cvtt_client.add_subscription(
+            exch_acct="COINBASE_AT",
+            instrument_id="PAIR-BTC-USD",
+            interval_sec=60,
+            history_depth_sec=24 * 3600,
+            callback=_calback,
+        )
+        while True:
+            await asyncio.sleep(5)
+
+    asyncio.run(__run())
+    pass
@@ -0,0 +1,60 @@
+```python
+from __future__ import annotations
+
+from typing import Dict
+import time
+
+import requests
+
+from cvttpy_tools.base import NamedObject
+
+class RESTSender(NamedObject):
+    session_: requests.Session
+    base_url_: str
+
+    def __init__(self, base_url: str) -> None:
+        self.base_url_ = base_url
+        self.session_ = requests.Session()
+
+    def is_ready(self) -> bool:
+        """Checks if the server is up and responding"""
+        url = f"{self.base_url_}/ping"
+        try:
+            response = self.session_.get(url)
+            response.raise_for_status()
+            return True
+        except requests.exceptions.RequestException:
+            return False
+
+    def send_post(self, endpoint: str, post_body: Dict) -> requests.Response:
+
+        while not self.is_ready():
+            print("Waiting for FrontGateway to start...")
+            time.sleep(5)
+
+        url = f"{self.base_url_}/{endpoint}"
+        try:
+            return self.session_.request(
+                method="POST",
+                url=url,
+                json=post_body,
+                headers={"Content-Type": "application/json"},
+            )
+        except requests.exceptions.RequestException as excpt:
+            raise ConnectionError(
+                f"Failed to send status={excpt.response.status_code} {excpt.response.text}"  # type: ignore
+            ) from excpt
+
+    def send_get(self, endpoint: str) -> requests.Response:
+        while not self.is_ready():
+            print("Waiting for FrontGateway to start...")
+            time.sleep(5)
+
+        url = f"{self.base_url_}/{endpoint}"
+        try:
+            return self.session_.request(method="GET", url=url)
+        except requests.exceptions.RequestException as excpt:
+            raise ConnectionError(
+                f"Failed to send status={excpt.response.status_code} {excpt.response.text}"  # type: ignore
+            ) from excpt
+```
@@ -0,0 +1,50 @@
+from enum import Enum
+
+import requests
+
+# import aiohttp
+from cvttpy_tools.base import NamedObject
+from cvttpy_tools.config import Config
+from cvttpy_tools.logger import Log
+from cvttpy_tools.web.rest_client import RESTSender
+# ---
+from cvttpy_trading.trading.trading_instructions import TradingInstructions
+# ---
+from pairs_trading.apps.pair_trader import PairTrader
+
+
+class TradingInstructionsSender(NamedObject):
+    config_: Config
+    sender_: RESTSender
+    pairs_trader_: PairTrader
+
+    class TradingInstType(str, Enum):
+        TARGET_POSITION = "TARGET_POSITION"
+        DIRECT_ORDER = "DIRECT_ORDER"
+        MARKET_MAKING = "MARKET_MAKING"
+        NONE = "NONE"
+
+    def __init__(self, config: Config, pairs_trader: PairTrader) -> None:
+        self.config_ = config
+        base_url = self.config_.get_value("cvtt_base_url", default="")
+        assert base_url
+        self.sender_ = RESTSender(base_url=base_url)
+        self.pairs_trader_ = pairs_trader
+        
+        self.book_id_ = self.pairs_trader_.book_id_
+        assert self.book_id_, "book_id is required"
+        
+        self.strategy_id_ = config.get_value("strategy_id", "")
+        assert self.strategy_id_, "strategy_id is required"
+        
+
+    async def send_trading_instructions(self, ti: TradingInstructions) -> None:
+        Log.info(f"{self.fname()}: sending {ti=}")
+        response: requests.Response = self.sender_.send_post(
+            endpoint="trading_instructions", post_body=ti.to_dict()
+        )
+        if response.status_code not in (200, 201):
+            Log.error(
+                f"{self.fname()}: Received error: {response.status_code} - {response.text}"
+            )
+
@@ -0,0 +1,391 @@
+from __future__ import annotations
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+from statsmodels.tsa.stattools import adfuller, coint
+from statsmodels.tsa.vector_ar.vecm import coint_johansen
+# ---
+from cvttpy_tools.base import NamedObject
+from cvttpy_tools.config import Config
+from cvttpy_tools.logger import Log
+from cvttpy_tools.timeutils import NanoPerSec, SecPerHour, current_nanoseconds
+from cvttpy_tools.web.rest_client import RESTSender
+# ---
+from cvttpy_trading.trading.instrument import ExchangeInstrument
+from cvttpy_trading.trading.mkt_data.md_summary import MdTradesAggregate, MdSummary
+
+
+@dataclass
+class InstrumentQuality(NamedObject):
+    instrument_: ExchangeInstrument
+    record_count_: int
+    latest_tstamp_: Optional[pd.Timestamp]
+    status_: str
+    reason_: str
+
+
+@dataclass
+class PairStats(NamedObject):
+    instrument_a_: ExchangeInstrument
+    instrument_b_: ExchangeInstrument
+    pvalue_eg_: Optional[float]
+    pvalue_adf_: Optional[float]
+    pvalue_j_: Optional[float]
+    trace_stat_j_: Optional[float]
+    rank_eg_: int = 0
+    rank_adf_: int = 0
+    rank_j_: int = 0
+    composite_rank_: int = 0
+
+    def as_dict(self) -> Dict[str, Any]:
+        return {
+            "instrument_a": self.instrument_a_.instrument_id(),
+            "instrument_b": self.instrument_b_.instrument_id(),
+            "pvalue_eg": self.pvalue_eg_,
+            "pvalue_adf": self.pvalue_adf_,
+            "pvalue_j": self.pvalue_j_,
+            "trace_stat_j": self.trace_stat_j_,
+            "rank_eg": self.rank_eg_,
+            "rank_adf": self.rank_adf_,
+            "rank_j": self.rank_j_,
+            "composite_rank": self.composite_rank_,
+        }
+
+
+class DataFetcher(NamedObject):
+    sender_: RESTSender
+    interval_sec_: int
+    history_depth_sec_: int
+
+    def __init__(
+        self,
+        base_url: str,
+        interval_sec: int,
+        history_depth_sec: int,
+    ) -> None:
+        self.sender_ = RESTSender(base_url=base_url)
+        self.interval_sec_ = interval_sec
+        self.history_depth_sec_ = history_depth_sec
+
+    def fetch(self, exch_acct: str, inst: ExchangeInstrument) -> List[MdTradesAggregate]:
+        rqst_data = {
+            "exch_acct": exch_acct,
+            "instrument_id": inst.instrument_id(),
+            "interval_sec": self.interval_sec_,
+            "history_depth_sec": self.history_depth_sec_,
+        }
+        response = self.sender_.send_post(endpoint="md_summary", post_body=rqst_data)
+        if response.status_code not in (200, 201):
+            Log.error(
+                f"{self.fname()}: error {response.status_code} for {inst.details_short()}: {response.text}")
+            return []
+        mdsums: List[MdSummary] = MdSummary.from_REST_response(response=response)
+        return [
+            mdsum.create_md_trades_aggregate(
+                exch_acct=exch_acct, exch_inst=inst, interval_sec=self.interval_sec_
+            )
+            for mdsum in mdsums
+        ]
+
+
+class QualityChecker(NamedObject):
+    interval_sec_: int
+
+    def __init__(self, interval_sec: int) -> None:
+        self.interval_sec_ = interval_sec
+
+    def evaluate(self, inst: ExchangeInstrument, aggr: List[MdTradesAggregate]) -> InstrumentQuality:
+        if len(aggr) == 0:
+            return InstrumentQuality(
+                instrument_=inst,
+                record_count_=0,
+                latest_tstamp_=None,
+                status_="FAIL",
+                reason_="no records",
+            )
+
+        aggr_sorted = sorted(aggr, key=lambda a: a.aggr_time_ns_)
+
+        latest_ts = pd.to_datetime(aggr_sorted[-1].aggr_time_ns_, unit="ns", utc=True)
+        now_ts = pd.Timestamp.utcnow()
+        recency_cutoff = now_ts - pd.Timedelta(seconds=2 * self.interval_sec_)
+        if latest_ts <= recency_cutoff:
+            return InstrumentQuality(
+                instrument_=inst,
+                record_count_=len(aggr_sorted),
+                latest_tstamp_=latest_ts,
+                status_="FAIL",
+                reason_=f"stale: latest {latest_ts} <= cutoff {recency_cutoff}",
+            )
+
+        gaps_ok, reason = self._check_gaps(aggr_sorted)
+        status = "PASS" if gaps_ok else "FAIL"
+        return InstrumentQuality(
+            instrument_=inst,
+            record_count_=len(aggr_sorted),
+            latest_tstamp_=latest_ts,
+            status_=status,
+            reason_=reason,
+        )
+
+    def _check_gaps(self, aggr: List[MdTradesAggregate]) -> Tuple[bool, str]:
+        NUM_TRADES_THRESHOLD = 50
+        if len(aggr) < 2:
+            return True, "ok"
+
+        interval_ns = self.interval_sec_ * NanoPerSec
+        for idx in range(1, len(aggr)):
+            prev = aggr[idx - 1]
+            curr = aggr[idx]
+            delta = curr.aggr_time_ns_ - prev.aggr_time_ns_
+            missing_intervals = int(delta // interval_ns) - 1
+            if missing_intervals <= 0:
+                continue
+            
+            prev_nt = prev.num_trades_
+            next_nt = curr.num_trades_
+            estimate = self._approximate_num_trades(prev_nt, next_nt)
+            if estimate > NUM_TRADES_THRESHOLD:
+                return False, (
+                    f"gap of {missing_intervals} interval(s), est num_trades={estimate} > {NUM_TRADES_THRESHOLD}"
+                )
+        return True, "ok"
+
+    @staticmethod
+    def _approximate_num_trades(prev_nt: int, next_nt: int) -> float:
+        if prev_nt is None and next_nt is None:
+            return 0.0
+        if prev_nt is None:
+            return float(next_nt)
+        if next_nt is None:
+            return float(prev_nt)
+        return (prev_nt + next_nt) / 2.0
+
+
+class PairAnalyzer(NamedObject):
+    price_field_: str
+    interval_sec_: int
+
+    def __init__(self, price_field: str, interval_sec: int) -> None:
+        self.price_field_ = price_field
+        self.interval_sec_ = interval_sec
+
+    def analyze(self, series: Dict[ExchangeInstrument, pd.DataFrame]) -> List[PairStats]:
+        instruments = list(series.keys())
+        results: List[PairStats] = []
+        for i in range(len(instruments)):
+            for j in range(i + 1, len(instruments)):
+                inst_a = instruments[i]
+                inst_b = instruments[j]
+                df_a = series[inst_a][["tstamp", "price"]].rename(
+                    columns={"price": "price_a"}
+                )
+                df_b = series[inst_b][["tstamp", "price"]].rename(
+                    columns={"price": "price_b"}
+                )
+                merged = pd.merge(df_a, df_b, on="tstamp", how="inner").sort_values(
+                    "tstamp"
+                )
+                stats = self._compute_stats(inst_a, inst_b, merged)
+                if stats:
+                    results.append(stats)
+        self._rank(results)
+        return results
+
+    def _compute_stats(
+        self,
+        inst_a: ExchangeInstrument,
+        inst_b: ExchangeInstrument,
+        merged: pd.DataFrame,
+    ) -> Optional[PairStats]:
+        if len(merged) < 2:
+            return None
+        px_a = merged["price_a"].astype(float)
+        px_b = merged["price_b"].astype(float)
+
+        std_a = float(px_a.std())
+        std_b = float(px_b.std())
+        if std_a == 0 or std_b == 0:
+            return None
+
+        z_a = (px_a - float(px_a.mean())) / std_a
+        z_b = (px_b - float(px_b.mean())) / std_b
+
+        p_eg: Optional[float]
+        p_adf: Optional[float]
+        p_j: Optional[float]
+        trace_stat: Optional[float]
+
+        try:
+            p_eg = float(coint(z_a, z_b)[1])
+        except Exception as exc:
+            Log.warning(f"{self.fname()}: EG failed for {inst_a.details_short()}/{inst_b.details_short()}: {exc}")
+            p_eg = None
+
+        try:
+            spread = z_a - z_b
+            p_adf = float(adfuller(spread, maxlag=1, regression="c")[1])
+        except Exception as exc:
+            Log.warning(f"{self.fname()}: ADF failed for {inst_a.details_short()}/{inst_b.details_short()}: {exc}")
+            p_adf = None
+
+        try:
+            data = np.column_stack([z_a, z_b])
+            res = coint_johansen(data, det_order=0, k_ar_diff=1)
+            trace_stat = float(res.lr1[0])
+            cv10, cv5, cv1 = res.cvt[0]
+            if trace_stat > cv1:
+                p_j = 0.01
+            elif trace_stat > cv5:
+                p_j = 0.05
+            elif trace_stat > cv10:
+                p_j = 0.10
+            else:
+                p_j = 1.0
+        except Exception as exc:
+            Log.warning(f"{self.fname()}: Johansen failed for {inst_a.details_short()}/{inst_b.details_short()}: {exc}")
+            p_j = None
+            trace_stat = None
+
+        return PairStats(
+            instrument_a_=inst_a,
+            instrument_b_=inst_b,
+            pvalue_eg_=p_eg,
+            pvalue_adf_=p_adf,
+            pvalue_j_=p_j,
+            trace_stat_j_=trace_stat,
+        )
+
+    def _rank(self, results: List[PairStats]) -> None:
+        self._assign_ranks(results, key=lambda r: r.pvalue_eg_, attr="rank_eg_")
+        self._assign_ranks(results, key=lambda r: r.pvalue_adf_, attr="rank_adf_")
+        self._assign_ranks(results, key=lambda r: r.pvalue_j_, attr="rank_j_")
+        for res in results:
+            res.composite_rank_ = res.rank_eg_ + res.rank_adf_ + res.rank_j_
+        results.sort(key=lambda r: r.composite_rank_)
+
+    @staticmethod
+    def _assign_ranks(
+        results: List[PairStats], key, attr: str
+    ) -> None:
+        values = [key(r) for r in results]
+        sorted_vals = sorted([v for v in values if v is not None])
+        for res in results:
+            val = key(res)
+            if val is None:
+                setattr(res, attr, len(sorted_vals) + 1)
+                continue
+            rank = 1 + sum(1 for v in sorted_vals if v < val)
+            setattr(res, attr, rank)
+
+
+class PairSelectionEngine(NamedObject):
+    config_: object
+    instruments_: List[ExchangeInstrument]
+    price_field_: str
+    fetcher_: DataFetcher
+    quality_: QualityChecker
+    analyzer_: PairAnalyzer
+    interval_sec_: int
+    history_depth_sec_: int
+    data_quality_cache_: List[InstrumentQuality]
+    pair_results_cache_: List[PairStats]
+
+    def __init__(
+        self,
+        config: Config,
+        instruments: List[ExchangeInstrument],
+        price_field: str,
+    ) -> None:
+        self.config_ = config
+        self.instruments_ = instruments
+        self.price_field_ = price_field
+
+        interval_sec = int(config.get_value("interval_sec", 0))
+        history_depth_sec = int(config.get_value("history_depth_hours", 0)) * SecPerHour
+        base_url = config.get_value("cvtt_base_url", None)
+        assert interval_sec > 0, "interval_sec must be > 0"
+        assert history_depth_sec > 0, "history_depth_sec must be > 0"
+        assert base_url, "cvtt_base_url must be set"
+
+        self.fetcher_ = DataFetcher(
+            base_url=base_url,
+            interval_sec=interval_sec,
+            history_depth_sec=history_depth_sec,
+        )
+        self.quality_ = QualityChecker(interval_sec=interval_sec)
+        self.analyzer_ = PairAnalyzer(price_field=price_field, interval_sec=interval_sec)
+
+        self.interval_sec_ = interval_sec
+        self.history_depth_sec_ = history_depth_sec
+
+        self.data_quality_cache_ = []
+        self.pair_results_cache_ = []
+
+    async def run_once(self) -> None:
+        quality_results: List[InstrumentQuality] = []
+        price_series: Dict[ExchangeInstrument, pd.DataFrame] = {}
+
+        for inst in self.instruments_:
+            exch_acct = inst.user_data_.get("exch_acct") or inst.exchange_id_
+            aggr = self.fetcher_.fetch(exch_acct=exch_acct, inst=inst)
+            q = self.quality_.evaluate(inst, aggr)
+            quality_results.append(q)
+            if q.status_ != "PASS":
+                continue
+            df = self._to_dataframe(aggr, inst)
+            if len(df) > 0:
+                price_series[inst] = df
+        self.data_quality_cache_ = quality_results
+        self.pair_results_cache_ = self.analyzer_.analyze(price_series)
+
+    def _to_dataframe(self, aggr: List[MdTradesAggregate], inst: ExchangeInstrument) -> pd.DataFrame:
+        rows: List[Dict[str, Any]] = []
+        for item in aggr:
+            rows.append(
+                {
+                    "tstamp": pd.to_datetime(item.aggr_time_ns_, unit="ns", utc=True),
+                    "price": self._extract_price(item, inst),
+                    "num_trades": item.num_trades_,
+                }
+            )
+        df = pd.DataFrame(rows)
+        return df.sort_values("tstamp").reset_index(drop=True)
+
+    def _extract_price(self, aggr: MdTradesAggregate, inst: ExchangeInstrument) -> float:
+        price_field = self.price_field_
+        # MdTradesAggregate inherits hist bar with fields open_, high_, low_, close_, vwap_
+        field_map = {
+            "open": aggr.open_,
+            "high": aggr.high_,
+            "low": aggr.low_,
+            "close": aggr.close_,
+            "vwap": aggr.vwap_,
+        }
+        raw = field_map.get(price_field, aggr.close_)
+        return inst.get_price(raw)
+
+    def sleep_seconds_until_next_cycle(self) -> float:
+        now_ns = current_nanoseconds()
+        interval_ns = self.interval_sec_ * NanoPerSec
+        next_boundary = (now_ns // interval_ns + 1) * interval_ns
+        return max(0.0, (next_boundary - now_ns) / NanoPerSec)
+
+    def quality_dicts(self) -> List[Dict[str, Any]]:
+        res: List[Dict[str, Any]] = []
+        for q in self.data_quality_cache_:
+            res.append(
+                {
+                    "instrument": q.instrument_.instrument_id(),
+                    "record_count": q.record_count_,
+                    "latest_tstamp": q.latest_tstamp_.isoformat() if q.latest_tstamp_ else None,
+                    "status": q.status_,
+                    "reason": q.reason_,
+                }
+            )
+        return res
+
+    def pair_dicts(self) -> List[Dict[str, Any]]:
+        return [p.as_dict() for p in self.pair_results_cache_]
@@ -1,351 +1,351 @@
 from __future__ import annotations

-from dataclasses import dataclass
 from typing import Any, Dict, List, Optional
-from enum import Enum

 import pandas as pd
+
 # ---
 from cvttpy_tools.base import NamedObject
+from cvttpy_tools.app import App
+from cvttpy_tools.config import Config
+from cvttpy_tools.settings.cvtt_types import IntervalSecT
+from cvttpy_tools.timeutils import NanosT, SecPerHour, current_nanoseconds, NanoPerSec, format_nanos_utc
 from cvttpy_tools.logger import Log
-from cvttpy_tools.settings.cvtt_types import JsonDictT
+
 # ---
 from cvttpy_trading.trading.instrument import ExchangeInstrument
+from cvttpy_trading.trading.mkt_data.md_summary import MdTradesAggregate
+from cvttpy_trading.trading.trading_instructions import TradingInstructions
+from cvttpy_trading.trading.trading_instructions import TargetPositionSignal
+
 # ---
-from pt_strategy.live.ti_sender import TradingInstructionsSender
-from pt_strategy.model_data_policy import ModelDataPolicy
-from pt_strategy.pt_market_data import RealTimeMarketData
-from pt_strategy.pt_model import Prediction
-from pt_strategy.trading_pair import PairState, TradingPair
-
-"""
-  --config=pair.cfg
-  --pair=PAIR-BTC-USDT:COINBASE_AT,PAIR-ETH-USDT:COINBASE_AT
-"""
-
-
-class TradingInstructionType(Enum):
-    TARGET_POSITION = "TARGET_POSITION"
-
-@dataclass
-class TradingInstruction(NamedObject):
-    type_: TradingInstructionType
-    exch_instr_: ExchangeInstrument
-    specifics_: Dict[str, Any]
+from pairs_trading.lib.pt_strategy.model_data_policy import ModelDataPolicy
+from pairs_trading.lib.pt_strategy.pt_model import Prediction
+from pairs_trading.lib.pt_strategy.trading_pair import LiveTradingPair
+from pairs_trading.apps.pair_trader import PairTrader
+from pairs_trading.lib.pt_strategy.pt_market_data import LiveMarketData


 class PtLiveStrategy(NamedObject):
-    config_: Dict[str, Any]
-    trading_pair_: TradingPair
+    config_: Config
+    instruments_: List[ExchangeInstrument]
+
+    interval_sec_: IntervalSecT
+    history_depth_sec_: IntervalSecT
+    open_threshold_: float
+    close_threshold_: float
+
+    trading_pair_: LiveTradingPair
    model_data_policy_: ModelDataPolicy
-    pt_mkt_data_: RealTimeMarketData
-    ti_sender_: TradingInstructionsSender
+    pairs_trader_: PairTrader

    # for presentation: history of prediction values and trading signals
-    predictions_: pd.DataFrame
-    trading_signals_: pd.DataFrame
+    predictions_df_: pd.DataFrame
+    trading_signals_df_: pd.DataFrame
+    allowed_md_lag_sec_: int
+

    def __init__(
        self,
-        config: Dict[str, Any],
-        instruments: List[Dict[str, str]],
-        ti_sender: TradingInstructionsSender,
+        config: Config,
+        pairs_trader: PairTrader,
    ):
-
        self.config_ = config
-        self.trading_pair_ = TradingPair(config=config, instruments=instruments)
-        self.predictions_ = pd.DataFrame()
-        self.trading_signals_ = pd.DataFrame()
-        self.ti_sender_ = ti_sender

-        import copy
-
-        # modified config must be passed to PtMarketData
-        config_copy = copy.deepcopy(config)
-        config_copy["instruments"] = instruments
-        self.pt_mkt_data_ = RealTimeMarketData(config=config_copy)
-        self.model_data_policy_ = ModelDataPolicy.create(
-            config, is_real_time=True, pair=self.trading_pair_
+        self.pairs_trader_ = pairs_trader
+        self.trading_pair_ = LiveTradingPair(
+            config=config,
+            instruments=self.pairs_trader_.instruments_,
        )
-        self.open_threshold_ = self.config_.get("dis-equilibrium_open_trshld", 0.0)
-        assert self.open_threshold_ > 0, "open_threshold must be greater than 0"
-        self.close_threshold_ = self.config_.get("dis-equilibrium_close_trshld", 0.0)
-        assert self.close_threshold_ > 0, "close_threshold must be greater than 0"
+        self.model_data_policy_ = ModelDataPolicy.create(
+            self.config_,
+            is_real_time=True,
+            pair=self.trading_pair_,
+        )
+        assert (
+            self.model_data_policy_ is not None
+        ), f"{self.fname()}: Unable to create ModelDataPolicy"
+
+        self.predictions_df_ = pd.DataFrame()
+        self.trading_signals_df_ = pd.DataFrame()
+
+        self.instruments_ = self.pairs_trader_.instruments_
+
+        App.instance().add_call(
+            stage=App.Stage.Config, func=self._on_config(), can_run_now=True
+        )
+
+    async def _on_config(self) -> None:
+        self.interval_sec_ = self.config_.get_value("interval_sec", 0)
+        assert self.interval_sec_ > 0, "interval_sec cannot be 0"
+        self.history_depth_sec_ = (
+            self.config_.get_value("history_depth_hours", 0) * SecPerHour
+        )
+        assert self.history_depth_sec_ > 0, "history_depth_hours cannot be 0"
+
+        self.allowed_md_lag_sec_ = self.config_.get_value("allowed_md_lag_sec", 3)
+
+        self.open_threshold_ = self.config_.get_value(
+            "model/disequilibrium/open_trshld", 0.0
+        )
+        self.close_threshold_ = self.config_.get_value(
+            "model/disequilibrium/close_trshld", 0.0
+        )
+
+        assert (
+            self.open_threshold_ > 0
+        ), "disequilibrium/open_trshld must be greater than 0"
+        assert (
+            self.close_threshold_ > 0
+        ), "disequilibrium/close_trshld must be greater than 0"
+
+        await self.pairs_trader_.subscribe_md()
+

    def __repr__(self) -> str:
        return f"{self.classname()}: trading_pair={self.trading_pair_}, mdp={self.model_data_policy_.__class__.__name__}, "

-    async def on_mkt_data_hist_snapshot(self, aggr: JsonDictT) -> None:
-        Log.info(f"on_mkt_data_hist_snapshot: {aggr}")
-        await self.pt_mkt_data_.on_mkt_data_hist_snapshot(snapshot=aggr)
-        pass
+    async def on_mkt_data_hist_snapshot(
+        self, hist_aggr: List[MdTradesAggregate]
+    ) -> None:
+        if not self._is_md_actual(hist_aggr=hist_aggr):
+            return

-    async def on_mkt_data_update(self, aggr: JsonDictT) -> None:
-        market_data_df = await self.pt_mkt_data_.on_mkt_data_update(update=aggr)
-        if market_data_df is not None:
-            self.trading_pair_.market_data_ = market_data_df
-            self.model_data_policy_.advance()
-            prediction = self.trading_pair_.run(
-                market_data_df, self.model_data_policy_.advance()
+        market_data_df: pd.DataFrame = self._create_md_df(hist_aggr=hist_aggr)
+        if len(market_data_df) == 0:
+            Log.warning(f"{self.fname()} Unable to create market data df")
+            return
+
+        self.trading_pair_.market_data_ = market_data_df
+
+        Log.info(f"{self.fname()}: Running prediction for pair: {self.trading_pair_}")
+        prediction = self.trading_pair_.run(
+            market_data_df, self.model_data_policy_.advance()
+        )
+        self.predictions_df_ = pd.concat(
+            [self.predictions_df_, prediction.to_df()], ignore_index=True
+        )
+
+        trading_instructions: List[TradingInstructions] = (
+            self._create_trading_instructions(
+                prediction=prediction, last_row=market_data_df.iloc[-1]
            )
-            self.predictions_ = pd.concat(
-                [self.predictions_, prediction.to_df()], ignore_index=True
+        )
+        if trading_instructions is not None:
+            await self._send_trading_instructions(trading_instructions)
+
+    def _is_md_actual(self, hist_aggr: List[MdTradesAggregate]) -> bool:
+        if len(hist_aggr) == 0:
+            Log.warning(f"{self.fname()} list of aggregates IS EMPTY")
+            return False
+
+        curr_ns = current_nanoseconds()
+        
+        # MAYBE check market data length
+        
+        # at 18:05:01 we should see data for 18:04:00
+        lag_sec = (curr_ns - hist_aggr[-1].aggr_time_ns_) / NanoPerSec - self.interval_sec()
+        if lag_sec > self.allowed_md_lag_sec_:
+            Log.warning(
+                f"{self.fname()} {hist_aggr[-1].exch_inst_.details_short()}"
+                f" Lagging {int(lag_sec)} > {self.allowed_md_lag_sec_} seconds:"
+                f"\n{len(hist_aggr)} records"
+                f"\n{hist_aggr[-1].exch_inst_.base_asset_id_}: {hist_aggr[-1].tstamp()}"
+                f"\n{hist_aggr[-2].exch_inst_.base_asset_id_}: {hist_aggr[-2].tstamp()}"
+            )
+            return False
+        else:
+            Log.info(
+                f"{self.fname()} {hist_aggr[-1].exch_inst_.details_short()}"
+                f" Lag {int(lag_sec)} <= {self.allowed_md_lag_sec_} seconds"
+                f"\n{len(hist_aggr)} records"
+                f"\n{hist_aggr[-1].exch_inst_.base_asset_id_}: {hist_aggr[-1].tstamp()}"
+                f"\n{hist_aggr[-2].exch_inst_.base_asset_id_}: {hist_aggr[-2].tstamp()}"
+            )
+        return True
+
+    def _create_md_df(self, hist_aggr: List[MdTradesAggregate]) -> pd.DataFrame:
+        """
+                          tstamp              time_ns    symbol      open      high       low     close     volume  num_trades        vwap
+        0    2025-09-10 11:30:00  1757503800000000000  ADA-USDT    0.8750    0.8750    0.8743    0.8743  50710.500           0    0.874489
+        1    2025-09-10 11:30:00  1757503800000000000  SOL-USDT  219.9700  219.9800  219.6600  219.7000   2648.582           0  219.787847
+        2    2025-09-10 11:31:00  1757503860000000000  SOL-USDT  219.7000  219.7300  219.6200  219.6200   1134.886           0  219.663460
+        3    2025-09-10 11:31:00  1757503860000000000  ADA-USDT    0.8743    0.8745    0.8741    0.8741  10696.400           0    0.874234
+        4    2025-09-10 11:32:00  1757503920000000000  ADA-USDT    0.8742    0.8742    0.8739    0.8740  18546.900           0    0.874037
+        """
+
+        rows: List[Dict[str, Any]] = []
+
+        for aggr in hist_aggr:
+            exch_inst = aggr.exch_inst_
+
+            rows.append(
+                {
+                    # convert nanoseconds → tz-aware pandas timestamp
+                    "tstamp": pd.to_datetime(aggr.aggr_time_ns_, unit="ns", utc=True),
+                    "time_ns": aggr.aggr_time_ns_,
+                    "symbol": exch_inst.instrument_id().split("-", 1)[1],
+                    "exchange_id": exch_inst.exchange_id_,
+                    "instrument_id": exch_inst.instrument_id(),
+                    "open": exch_inst.get_price(aggr.open_),
+                    "high": exch_inst.get_price(aggr.high_),
+                    "low": exch_inst.get_price(aggr.low_),
+                    "close": exch_inst.get_price(aggr.close_),
+                    "volume": exch_inst.get_quantity(aggr.volume_),
+                    "num_trades": aggr.num_trades_,
+                    "vwap": exch_inst.get_price(aggr.vwap_),
+                }
            )

-            trading_instructions: List[TradingInstruction] = (
-                self._create_trading_instructions(
-                    prediction=prediction, last_row=market_data_df.iloc[-1]
-                )
-            )
-            if len(trading_instructions) > 0:
-                await self._send_trading_instructions(trading_instructions)
-            # trades = self._create_trades(prediction=prediction, last_row=market_data_df.iloc[-1])
-        # URGENT implement this
-        pass
+        source_md_df = pd.DataFrame(
+            rows,
+            columns=[
+                "tstamp",
+                "time_ns",
+                "symbol",
+                "exchange_id",
+                "instrument_id",
+                "open",
+                "high",
+                "low",
+                "close",
+                "volume",
+                "num_trades",
+                "vwap",
+            ],
+        )
+
+        # automatic sorting
+        source_md_df.sort_values(
+            by=["time_ns", "symbol"],
+            ascending=True,
+            inplace=True,
+            kind="mergesort",  # stable sort
+        )
+
+        source_md_df.reset_index(drop=True, inplace=True)
+
+        pt_mkt_data = LiveMarketData(config=self.config_, instruments=self.instruments_)
+        pt_mkt_data.origin_mkt_data_df_ = source_md_df
+        pt_mkt_data.set_market_data()
+
+        return pt_mkt_data.market_data_df_
+
+    def interval_sec(self) -> IntervalSecT:
+        return self.interval_sec_
+
+    def history_depth_sec(self) -> IntervalSecT:
+        return self.history_depth_sec_

    async def _send_trading_instructions(
-        self, trading_instructions: pd.DataFrame
+        self, trading_instructions: List[TradingInstructions]
    ) -> None:
-        pass
+        for ti in trading_instructions:
+            Log.info(f"{self.fname()} Sending trading instructions {ti}")
+            await self.pairs_trader_.ti_sender_.send_trading_instructions(ti)

    def _create_trading_instructions(
        self, prediction: Prediction, last_row: pd.Series
-    ) -> List[TradingInstruction]:
+    ) -> List[TradingInstructions]:
+        trd_instructions: List[TradingInstructions] = []
        pair = self.trading_pair_
-        trd_instructions: List[TradingInstruction] = []

        scaled_disequilibrium = prediction.scaled_disequilibrium_
        abs_scaled_disequilibrium = abs(scaled_disequilibrium)

-        if pair.is_closed():
-            if abs_scaled_disequilibrium >= self.open_threshold_:
-                trd_instructions = self._create_open_trade_instructions(
-                    pair, row=last_row, prediction=prediction
-                )
-        elif pair.is_open():
-            if abs_scaled_disequilibrium <= self.close_threshold_:
-                trd_instructions = self._create_close_trade_instructions(
-                    pair, row=last_row, prediction=prediction
-                )
-            elif pair.to_stop_close_conditions(predicted_row=last_row):
-                trd_instructions = self._create_close_trade_instructions(
-                    pair, row=last_row
-                )
+        if abs_scaled_disequilibrium >= self.open_threshold_:
+            trd_instructions = self._create_open_trade_instructions(
+                pair, row=last_row, prediction=prediction
+            )
+
+        elif abs_scaled_disequilibrium <= self.close_threshold_ or pair.to_stop_close_conditions(predicted_row=last_row):
+            trd_instructions = self._create_close_trade_instructions(
+                pair, row=last_row  # , prediction=prediction
+            )
+

        return trd_instructions
    
+    def _strength(self, scaled_disequilibrium: float) -> float:
+        # TODO PtLiveStrategy._strength()
+        return 1.0
+
    def _create_open_trade_instructions(
-        self, pair: TradingPair, row: pd.Series, prediction: Prediction
-    ) -> List[TradingInstruction]:
-        scaled_disequilibrium = prediction.scaled_disequilibrium_
-
-        if scaled_disequilibrium > 0:
-            side_a = "SELL"
-            trd_inst_a = TradingInstruction(
-                type=TradingInstructionType.TARGET_POSITION,
-                exch_instr=pair.get_instrument_a(),
-                specifics={"side": "SELL", "strength": -1},
-            )
-            side_b = "BUY"
-        else:
-            side_a = "BUY"
-            side_b = "SELL"
-
-        # save closing sides
-        pair.user_data_["open_side_a"] = side_a  # used in oustanding positions
-        pair.user_data_["open_side_b"] = side_b
-        pair.user_data_["open_px_a"] = px_a
-        pair.user_data_["open_px_b"] = px_b
-        pair.user_data_["open_tstamp"] = tstamp
-
-        pair.user_data_["close_side_a"] = side_b  # used for closing trades
-        pair.user_data_["close_side_b"] = side_a
-
-        # create opening trades
-        df.loc[len(df)] = {
-            "time": tstamp,
-            "symbol": pair.symbol_a_,
-            "side": side_a,
-            "action": "OPEN",
-            "price": px_a,
-            "disequilibrium": diseqlbrm,
-            "signed_scaled_disequilibrium": scaled_disequilibrium,
-            "scaled_disequilibrium": abs(scaled_disequilibrium),
-            # "pair": pair,
-        }
-        df.loc[len(df)] = {
-            "time": tstamp,
-            "symbol": pair.symbol_b_,
-            "side": side_b,
-            "action": "OPEN",
-            "price": px_b,
-            "disequilibrium": diseqlbrm,
-            "scaled_disequilibrium": abs(scaled_disequilibrium),
-            "signed_scaled_disequilibrium": scaled_disequilibrium,
-            # "pair": pair,
-        }
-        return df
-
-    def _handle_outstanding_positions(self) -> Optional[pd.DataFrame]:
-        trades = None
-        pair = self.trading_pair_
-
-        # Outstanding positions
-        if pair.user_data_["state"] == PairState.OPEN:
-            print(f"{pair}:  *** Position is NOT CLOSED. ***")
-            # outstanding positions
-            if self.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
-
-                trades = self._create_close_trades(
-                    pair=pair, row=close_position_row, prediction=None
-                )
-                if trades is not None:
-                    trades["status"] = PairState.CLOSE_POSITION.name
-                    print(f"CLOSE_POSITION TRADES:\n{trades}")
-                    pair.user_data_["state"] = PairState.CLOSE_POSITION
-                    pair.on_close_trades(trades)
-            else:
-                pair.add_outstanding_position(
-                    symbol=pair.symbol_a_,
-                    open_side=pair.user_data_["open_side_a"],
-                    open_px=pair.user_data_["open_px_a"],
-                    open_tstamp=pair.user_data_["open_tstamp"],
-                    last_mkt_data_row=pair.market_data_.iloc[-1],
-                )
-                pair.add_outstanding_position(
-                    symbol=pair.symbol_b_,
-                    open_side=pair.user_data_["open_side_b"],
-                    open_px=pair.user_data_["open_px_b"],
-                    open_tstamp=pair.user_data_["open_tstamp"],
-                    last_mkt_data_row=pair.market_data_.iloc[-1],
-                )
-        return trades
-
-    def _trades_df(self) -> pd.DataFrame:
-        types = {
-            "time": "datetime64[ns]",
-            "action": "string",
-            "symbol": "string",
-            "side": "string",
-            "price": "float64",
-            "disequilibrium": "float64",
-            "scaled_disequilibrium": "float64",
-            "signed_scaled_disequilibrium": "float64",
-            # "pair": "object",
-        }
-        columns = list(types.keys())
-        return pd.DataFrame(columns=columns).astype(types)
-
-    def _create_open_trades(
-        self, pair: TradingPair, row: pd.Series, prediction: Prediction
-    ) -> Optional[pd.DataFrame]:
-        colname_a, colname_b = pair.exec_prices_colnames()
-
-        tstamp = row["tstamp"]
+        self, pair: LiveTradingPair, row: pd.Series, prediction: Prediction
+    ) -> List[TradingInstructions]:
        diseqlbrm = prediction.disequilibrium_
        scaled_disequilibrium = prediction.scaled_disequilibrium_
-        px_a = row[f"{colname_a}"]
-        px_b = row[f"{colname_b}"]
-
-        # creating the trades
-        df = self._trades_df()
-
-        print(f"OPEN_TRADES: {row["tstamp"]} {scaled_disequilibrium=}")
        if diseqlbrm > 0:
-            side_a = "SELL"
-            side_b = "BUY"
+            side_a = -1
+            side_b = 1
        else:
-            side_a = "BUY"
-            side_b = "SELL"
+            side_a = 1
+            side_b = -1

-        # save closing sides
-        pair.user_data_["open_side_a"] = side_a  # used in oustanding positions
-        pair.user_data_["open_side_b"] = side_b
-        pair.user_data_["open_px_a"] = px_a
-        pair.user_data_["open_px_b"] = px_b
-        pair.user_data_["open_tstamp"] = tstamp
+        ti_a: Optional[TradingInstructions] = TradingInstructions(
+            book=self.pairs_trader_.book_id_,
+            strategy_id=self.__class__.__name__,
+            ti_type=TradingInstructions.Type.TARGET_POSITION,
+            issued_ts_ns=current_nanoseconds(),
+            data=TargetPositionSignal(
+                strength=side_a * self._strength(scaled_disequilibrium),
+                exchange_id=pair.get_instrument_a().exchange_id_,
+                base_asset=pair.get_instrument_a().base_asset_id_,
+                quote_asset=pair.get_instrument_a().quote_asset_id_,
+                user_data={}
+            ),
+        )
+        if not ti_a:
+            return []
+        ti_b: Optional[TradingInstructions] = TradingInstructions(
+            book=self.pairs_trader_.book_id_,
+            strategy_id=self.__class__.__name__,
+            ti_type=TradingInstructions.Type.TARGET_POSITION,
+            issued_ts_ns=current_nanoseconds(),
+            data=TargetPositionSignal(
+                strength=side_b * self._strength(scaled_disequilibrium),
+                exchange_id=pair.get_instrument_b().exchange_id_,
+                base_asset=pair.get_instrument_b().base_asset_id_,
+                quote_asset=pair.get_instrument_b().quote_asset_id_,
+                user_data={}
+            ),
+        )
+        if not ti_b:
+            return []
+        return [ti_a, ti_b]

-        pair.user_data_["close_side_a"] = side_b  # used for closing trades
-        pair.user_data_["close_side_b"] = side_a

-        # create opening trades
-        df.loc[len(df)] = {
-            "time": tstamp,
-            "symbol": pair.symbol_a_,
-            "side": side_a,
-            "action": "OPEN",
-            "price": px_a,
-            "disequilibrium": diseqlbrm,
-            "signed_scaled_disequilibrium": scaled_disequilibrium,
-            "scaled_disequilibrium": abs(scaled_disequilibrium),
-            # "pair": pair,
-        }
-        df.loc[len(df)] = {
-            "time": tstamp,
-            "symbol": pair.symbol_b_,
-            "side": side_b,
-            "action": "OPEN",
-            "price": px_b,
-            "disequilibrium": diseqlbrm,
-            "scaled_disequilibrium": abs(scaled_disequilibrium),
-            "signed_scaled_disequilibrium": scaled_disequilibrium,
-            # "pair": pair,
-        }
-        return df
-
-    def _create_close_trades(
-        self, pair: TradingPair, row: pd.Series, prediction: Optional[Prediction] = None
-    ) -> Optional[pd.DataFrame]:
-        colname_a, colname_b = pair.exec_prices_colnames()
-
-        tstamp = row["tstamp"]
-        if prediction is not None:
-            diseqlbrm = prediction.disequilibrium_
-            signed_scaled_disequilibrium = prediction.scaled_disequilibrium_
-            scaled_disequilibrium = abs(prediction.scaled_disequilibrium_)
-        else:
-            diseqlbrm = 0.0
-            signed_scaled_disequilibrium = 0.0
-            scaled_disequilibrium = 0.0
-        px_a = row[f"{colname_a}"]
-        px_b = row[f"{colname_b}"]
-
-        # creating the trades
-        df = self._trades_df()
-
-        # create opening trades
-        df.loc[len(df)] = {
-            "time": tstamp,
-            "symbol": pair.symbol_a_,
-            "side": pair.user_data_["close_side_a"],
-            "action": "CLOSE",
-            "price": px_a,
-            "disequilibrium": diseqlbrm,
-            "scaled_disequilibrium": scaled_disequilibrium,
-            "signed_scaled_disequilibrium": signed_scaled_disequilibrium,
-            # "pair": pair,
-        }
-        df.loc[len(df)] = {
-            "time": tstamp,
-            "symbol": pair.symbol_b_,
-            "side": pair.user_data_["close_side_b"],
-            "action": "CLOSE",
-            "price": px_b,
-            "disequilibrium": diseqlbrm,
-            "scaled_disequilibrium": scaled_disequilibrium,
-            "signed_scaled_disequilibrium": signed_scaled_disequilibrium,
-            # "pair": pair,
-        }
-        del pair.user_data_["close_side_a"]
-        del pair.user_data_["close_side_b"]
-
-        del pair.user_data_["open_tstamp"]
-        del pair.user_data_["open_px_a"]
-        del pair.user_data_["open_px_b"]
-        del pair.user_data_["open_side_a"]
-        del pair.user_data_["open_side_b"]
-        return df
+    def _create_close_trade_instructions(
+        self, pair: LiveTradingPair, row: pd.Series
+    ) -> List[TradingInstructions]:
+        ti_a: Optional[TradingInstructions] = TradingInstructions(
+            book=self.pairs_trader_.book_id_,
+            strategy_id=self.__class__.__name__,
+            ti_type=TradingInstructions.Type.TARGET_POSITION,
+            issued_ts_ns=current_nanoseconds(),
+            data=TargetPositionSignal(
+                strength=0,
+                exchange_id=pair.get_instrument_a().exchange_id_,
+                base_asset=pair.get_instrument_a().base_asset_id_,
+                quote_asset=pair.get_instrument_a().quote_asset_id_,
+                user_data={}
+            ),
+        )
+        if not ti_a:
+            return []
+        ti_b: Optional[TradingInstructions] = TradingInstructions(
+            book=self.pairs_trader_.book_id_,
+            strategy_id=self.__class__.__name__,
+            ti_type=TradingInstructions.Type.TARGET_POSITION,
+            issued_ts_ns=current_nanoseconds(),
+            data=TargetPositionSignal(
+                strength=0,
+                exchange_id=pair.get_instrument_b().exchange_id_,
+                base_asset=pair.get_instrument_b().base_asset_id_,
+                quote_asset=pair.get_instrument_b().quote_asset_id_,
+                user_data={}
+            ),
+        )
+        if not ti_b:
+            return []
+        return [ti_a, ti_b]
@@ -1,85 +0,0 @@
-from __future__ import annotations
-
-from functools import partial
-from typing import Dict, List
-
-from cvtt_client.mkt_data import (CvttPricerWebSockClient,
-                                  CvttPricesSubscription, MessageTypeT,
-                                  SubscriptionIdT)
-from cvttpy_tools.app import App
-from cvttpy_tools.base import NamedObject
-from cvttpy_tools.config import Config
-from cvttpy_tools.logger import Log
-from cvttpy_tools.settings.cvtt_types import JsonDictT
-from pt_strategy.live.live_strategy import PtLiveStrategy
-from pt_strategy.trading_pair import TradingPair
-
-"""
-  --config=pair.cfg
-  --pair=PAIR-BTC-USDT:COINBASE_AT,PAIR-ETH-USDT:COINBASE_AT
-"""
-
-
-class PtMktDataClient(NamedObject):
-    config_: Config
-    live_strategy_: PtLiveStrategy
-    pricer_client_: CvttPricerWebSockClient
-    subscriptions_: List[CvttPricesSubscription]
-
-    def __init__(self, live_strategy: PtLiveStrategy, pricer_config: Config):
-        self.config_ = pricer_config
-        self.live_strategy_ = live_strategy
-
-        App.instance().add_call(App.Stage.Start, self._on_start())
-        App.instance().add_call(App.Stage.Run, self.run())
-
-    async def _on_start(self) -> None:
-        pricer_url = self.config_.get_value("pricer_url")
-        assert pricer_url is not None, "pricer_url is not found in config"
-        self.pricer_client_ = CvttPricerWebSockClient(url=pricer_url)
-
-
-    async def _subscribe(self) -> None:
-        history_depth_sec = self.config_.get_value("history_depth_sec", 86400)
-        interval_sec = self.config_.get_value("interval_sec", 60)
-
-        pair: TradingPair = self.live_strategy_.trading_pair_
-        subscriptions = [CvttPricesSubscription(
-            exchange_config_name=instrument["exchange_config_name"],
-            instrument_id=instrument["instrument_id"],
-            interval_sec=interval_sec,
-            history_depth_sec=history_depth_sec,
-            callback=partial(
-                self.on_message, instrument_id=instrument["instrument_id"]
-            ),
-        ) for instrument in pair.instruments_]
-
-        for subscription in subscriptions:
-            Log.info(f"{self.fname()} Subscribing to {subscription}")
-            await self.pricer_client_.subscribe(subscription)
-
-    async def on_message(
-        self,
-        message_type: MessageTypeT,
-        subscr_id: SubscriptionIdT,
-        message: Dict,
-        instrument_id: str,
-    ) -> None:
-        Log.info(f"{self.fname()}: {message_type=} {subscr_id=} {instrument_id}")
-        aggr: JsonDictT
-        if message_type == "md_aggregate":
-            aggr = message.get("md_aggregate", {})
-            await self.live_strategy_.on_mkt_data_update(aggr)
-        elif message_type == "historical_md_aggregate":
-            aggr = message.get("historical_data", {})
-            await self.live_strategy_.on_mkt_data_hist_snapshot(aggr)
-        else:
-            Log.info(f"Unknown message type: {message_type}")
-
-    async def run(self) -> None:
-        if not await CvttPricerWebSockClient.check_connection(self.pricer_client_.ws_url_):
-            Log.error(f"Unable to connect to {self.pricer_client_.ws_url_}")
-            raise Exception(f"Unable to connect to {self.pricer_client_.ws_url_}")
-        await self._subscribe()
-
-        await self.pricer_client_.run()
@@ -1,86 +0,0 @@
-import time
-from enum import Enum
-from typing import Tuple
-
-# import aiohttp
-from cvttpy_tools.app import App
-from cvttpy_tools.base import NamedObject
-from cvttpy_tools.config import Config
-from cvttpy_tools.logger import Log
-from cvttpy_tools.timer import Timer
-from cvttpy_tools.timeutils import NanoPerSec
-from cvttpy_tools.web.rest_client import REST_RequestProcessor
-
-
-class TradingInstructionsSender(NamedObject):
-
-    class TradingInstType(str, Enum):
-        TARGET_POSITION = "TARGET_POSITION"
-        DIRECT_ORDER = "DIRECT_ORDER"
-        MARKET_MAKING = "MARKET_MAKING"
-        NONE = "NONE"
-
-    config_: Config
-    ti_method_: str
-    ti_url_: str
-    health_check_method_: str
-    health_check_url_: str
-
-    def __init__(self, config: Config):
-        self.config_ = config
-        base_url = config.get_value("url", "ws://localhost:12346/ws")
-        
-        self.book_id_ = config.get_value("book_id", "")
-        assert self.book_id_, "book_id is required"
-        
-        self.strategy_id_ = config.get_value("strategy_id", "")
-        assert self.strategy_id_, "strategy_id is required"
-        
-        endpoint_uri = config.get_value("ti_endpoint/url", "/trading_instructions")
-        endpoint_method = config.get_value("ti_endpoint/method", "POST")
-
-        health_check_uri = config.get_value("health_check_endpoint/url", "/ping")
-        health_check_method = config.get_value("health_check_endpoint/method", "GET")
-
-
-        
-        self.ti_method_ = endpoint_method
-        self.ti_url_ = f"{base_url}{endpoint_uri}"
-        
-        self.health_check_method_ = health_check_method
-        self.health_check_url_ = f"{base_url}{health_check_uri}"
-        
-        App.instance().add_call(App.Stage.Start, self._set_health_check_timer(), can_run_now=True)
- 
-    async def _set_health_check_timer(self) -> None:
-        # TODO: configurable interval
-        self.health_check_timer_ = Timer(is_periodic=True, period_interval=15, start_in_sec=0, func=self._health_check)
-        Log.info(f"{self.fname()} Health check timer set to 15 seconds")
-            
-    async def _health_check(self) -> None:
-        rqst = REST_RequestProcessor(method=self.health_check_method_, url=self.health_check_url_)
-        async with rqst as (status, msg, headers):
-            if status != 200:
-                Log.error(f"{self.fname()} CVTT Service is not responding")
-
-    async def send_tgt_positions(self, strength: float, base_asset: str, quote_asset: str) -> Tuple[int, str]:
-        instr = {
-            "type": self.TradingInstType.TARGET_POSITION.value,
-            "book_id": self.book_id_,
-            "strategy_id": self.strategy_id_,
-            "issued_ts_ns": int(time.time() * NanoPerSec),
-            "data": {
-                "strength": strength,
-                "base_asset": base_asset,
-                "quote_asset": quote_asset,
-                "user_data": {},
-            },
-        }
-        
-        rqst = REST_RequestProcessor(method=self.ti_method_, url=self.ti_url_, params=instr)
-        async with rqst as (status, msg, headers):
-            if status != 200:
-                raise ConnectionError(f"Failed to send trading instructions: {msg}")
-            return (status, msg)
-
-
@@ -8,31 +8,25 @@ from typing import Any, Dict, Optional, cast
 import numpy as np
 import pandas as pd

+from cvttpy_tools.config import Config

@dataclass
 class DataWindowParams:
-    training_size: int
-    training_start_index: int
+    training_size_: int
+    training_start_index_: int


 class ModelDataPolicy(ABC):
-    config_: Dict[str, Any]
+    config_: Config
    current_data_params_: DataWindowParams
    count_: int
    is_real_time_: bool

-    def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
+    def __init__(self, config: Config, *args: Any, **kwargs: Any):
        self.config_ = config
-        training_size = config.get("training_size", 120)
-        training_start_index = 0
-        if kwargs.get("is_real_time", False):
-            training_size = 120
-            training_start_index = 0
-        else:
-            training_size = config.get("training_size", 120)
        self.current_data_params_ = DataWindowParams(
-            training_size=config.get("training_size", 120),
-            training_start_index=0,
+            training_size_=config.get_value("model/training_size", 120),
+            training_start_index_=0,
        )
        self.count_ = 0
        self.is_real_time_ = kwargs.get("is_real_time", False)
@@ -40,14 +34,15 @@ class ModelDataPolicy(ABC):
    @abstractmethod
    def advance(self, mkt_data_df: Optional[pd.DataFrame] = None) -> DataWindowParams:
        self.count_ += 1
-        print(self.count_, end="\r")
+        if not self.is_real_time_:
+            print(self.count_, end="\r")
        return self.current_data_params_

    @staticmethod
-    def create(config: Dict[str, Any], *args: Any, **kwargs: Any) -> ModelDataPolicy:
+    def create(config: Config, *args: Any, **kwargs: Any) -> ModelDataPolicy:
        import importlib

-        model_data_policy_class_name = config.get("model_data_policy_class", None)
+        model_data_policy_class_name = config.get_value("model/model_data_policy_class", None)
        assert model_data_policy_class_name is not None
        module_name, class_name = model_data_policy_class_name.rsplit(".", 1)
        module = importlib.import_module(module_name)
@@ -58,16 +53,18 @@ class ModelDataPolicy(ABC):


 class RollingWindowDataPolicy(ModelDataPolicy):
-    def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
+    def __init__(self, config: Config, *args: Any, **kwargs: Any):
        super().__init__(config, *args, **kwargs)
        self.count_ = 1

    def advance(self, mkt_data_df: Optional[pd.DataFrame] = None) -> DataWindowParams:
        super().advance(mkt_data_df)
        if self.is_real_time_:
-            self.current_data_params_.training_start_index = -self.current_data_params_.training_size
+            self.current_data_params_.training_start_index_ = 0
+            if mkt_data_df and len(mkt_data_df) > self.curren_data_params_.training_size_:
+                self.current_data_params_.training_start_index_ = -self.curren_data_params_.training_size_ 
        else:
-            self.current_data_params_.training_start_index += 1
+            self.current_data_params_.training_start_index_ += 1
        return self.current_data_params_


@@ -80,18 +77,17 @@ class OptimizedWndDataPolicy(ModelDataPolicy, ABC):
    prices_a_: np.ndarray
    prices_b_: np.ndarray

-    def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
+    def __init__(self, config: Config, *args: Any, **kwargs: Any):
        super().__init__(config, *args, **kwargs)
        assert (
            kwargs.get("pair") is not None
        ), "pair must be provided"
-        assert (
-            "min_training_size" in config and "max_training_size" in config
-        ), "min_training_size and max_training_size must be provided"
-        self.min_training_size_ = cast(int, config.get("min_training_size"))
-        self.max_training_size_ = cast(int, config.get("max_training_size"))
+        assert (config.key_exists("model/max_training_size") and config.key_exists("model/min_training_size")
+            ), "min_training_size and max_training_size must be provided"
+        self.min_training_size_ = cast(int, config.get_value("model/min_training_size"))
+        self.max_training_size_ = cast(int, config.get_value("model/max_training_size"))

-        from pt_strategy.trading_pair import TradingPair
+        from pairs_trading.lib.pt_strategy.trading_pair import TradingPair
        self.pair_ = cast(TradingPair, kwargs.get("pair"))        
        
        if "mkt_data" in kwargs:
@@ -110,12 +106,12 @@ class OptimizedWndDataPolicy(ModelDataPolicy, ABC):
        if self.is_real_time_:
            self.end_index_ = len(self.mkt_data_df_) - 1
        else:
-            self.end_index_ = self.current_data_params_.training_start_index + self.max_training_size_
+            self.end_index_ = self.current_data_params_.training_start_index_ + self.max_training_size_
            if self.end_index_ > len(self.mkt_data_df_) - 1:
                self.end_index_ = len(self.mkt_data_df_) - 1
-                self.current_data_params_.training_start_index = self.end_index_ - self.max_training_size_
-                if self.current_data_params_.training_start_index < 0:
-                    self.current_data_params_.training_start_index = 0
+                self.current_data_params_.training_start_index_ = self.end_index_ - self.max_training_size_
+                if self.current_data_params_.training_start_index_ < 0:
+                    self.current_data_params_.training_start_index_ = 0

        col_a, col_b = self.pair_.colnames()
        self.prices_a_ = np.array(self.mkt_data_df_[col_a])
@@ -133,7 +129,7 @@ class EGOptimizedWndDataPolicy(OptimizedWndDataPolicy):
    # Engle-Granger cointegration test
    *** VERY SLOW ***
    '''
-    def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
+    def __init__(self, config: Config, *args: Any, **kwargs: Any):
        super().__init__(config, *args, **kwargs)

    def optimize_window_size(self) -> DataWindowParams:
@@ -152,8 +148,8 @@ class EGOptimizedWndDataPolicy(OptimizedWndDataPolicy):
            eg_pvalue = float(coint(series_a, series_b)[1])
            if eg_pvalue < last_pvalue:
                last_pvalue = eg_pvalue
-                result.training_size = trn_size
-                result.training_start_index = start_index
+                result.training_size_ = trn_size
+                result.training_start_index_ = start_index

        # print(
        #     f"*** DEBUG *** end_index={self.end_index_}, best_trn_size={self.current_data_params_.training_size}, {last_pvalue=}"
@@ -162,7 +158,7 @@ class EGOptimizedWndDataPolicy(OptimizedWndDataPolicy):

 class ADFOptimizedWndDataPolicy(OptimizedWndDataPolicy):
    # Augmented Dickey-Fuller test
-    def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
+    def __init__(self, config: Config, *args: Any, **kwargs: Any):
        super().__init__(config, *args, **kwargs)

    def optimize_window_size(self) -> DataWindowParams:
@@ -196,8 +192,8 @@ class ADFOptimizedWndDataPolicy(OptimizedWndDataPolicy):

            if adf_pvalue < last_pvalue:
                last_pvalue = adf_pvalue
-                result.training_size = trn_size
-                result.training_start_index = start_index
+                result.training_size_ = trn_size
+                result.training_start_index_ = start_index

        # print(
        #     f"*** DEBUG *** end_index={self.end_index_},"
@@ -208,7 +204,7 @@ class ADFOptimizedWndDataPolicy(OptimizedWndDataPolicy):
    
 class JohansenOptdWndDataPolicy(OptimizedWndDataPolicy):
    # Johansen test
-    def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
+    def __init__(self, config: Config, *args: Any, **kwargs: Any):
        super().__init__(config, *args, **kwargs)

    def optimize_window_size(self) -> DataWindowParams:
@@ -246,8 +242,8 @@ class JohansenOptdWndDataPolicy(OptimizedWndDataPolicy):
                continue

        if best_trn_size > 0:
-            result.training_size = best_trn_size
-            result.training_start_index = best_start_index
+            result.training_size_ = best_trn_size
+            result.training_start_index_ = best_start_index
        else:
            print("*** WARNING: No valid cointegration window found.")

@@ -6,8 +6,8 @@ import statsmodels.api as sm



-from pt_strategy.pt_model import PairsTradingModel, Prediction
-from pt_strategy.trading_pair import TradingPair
+from pairs_trading.lib.pt_strategy.pt_model import PairsTradingModel, Prediction
+from pairs_trading.lib.pt_strategy.trading_pair import TradingPair


 class OLSModel(PairsTradingModel):
@@ -1,35 +1,103 @@
 from __future__ import annotations

+from abc import ABC, abstractmethod
 from typing import Any, Dict, List, Optional

 import pandas as pd
+
+# ---
+from cvttpy_tools.base import NamedObject
+from cvttpy_tools.config import Config
 from cvttpy_tools.settings.cvtt_types import JsonDictT
-from tools.data_loader import load_market_data
+
+# ---
+from cvttpy_trading.trading.mkt_data.md_summary import MdTradesAggregate
+from cvttpy_trading.trading.instrument import ExchangeInstrument
+# ---
+from pairs_trading.lib.tools.data_loader import load_market_data


-class PtMarketData():
-    config_: Dict[str, Any]
+class PtMarketData(NamedObject, ABC):
+    config_: Config
    origin_mkt_data_df_: pd.DataFrame
    market_data_df_: pd.DataFrame
+    stat_model_price_: str
+    instruments_: List[ExchangeInstrument]
+    symbol_a_: str
+    symbol_b_: str

-    def __init__(self, config: Dict[str, Any]):
+    def __init__(self, config: Config, instruments: List[ExchangeInstrument]):
        self.config_ = config
        self.origin_mkt_data_df_ = pd.DataFrame()
        self.market_data_df_ = pd.DataFrame()
+        self.stat_model_price_ = self.config_.get_value("model/stat_model_price")

+        self.instruments_ = instruments
+        assert len(self.instruments_) > 0, "No instruments found in config"
+        self.symbol_a_ = self.instruments_[0].instrument_id().split("-", 1)[1]
+        self.symbol_b_ = self.instruments_[1].instrument_id().split("-", 1)[1]
+
+    @abstractmethod
+    def md_columns(self) -> List[str]: ...
+
+    @abstractmethod
+    def rename_columns(self, symbol_df: pd.DataFrame) -> pd.DataFrame: ...
+    
+    @abstractmethod
+    def tranform_df_target_colnames(self) -> List[str]: ...
+        
+    def set_market_data(self) -> None:
+        self.market_data_df_ = pd.DataFrame(
+            self._transform_dataframe(self.origin_mkt_data_df_)[
+                ["tstamp"] + self.tranform_df_target_colnames()
+            ]
+        )
+
+        self.market_data_df_ = self.market_data_df_.dropna().reset_index(drop=True)
+        self.market_data_df_["tstamp"] = pd.to_datetime(self.market_data_df_["tstamp"])
+        self.market_data_df_ = self.market_data_df_.sort_values("tstamp")
+
+    def colnames(self) -> List[str]:
+        return [
+            f"{self.stat_model_price_}_{self.symbol_a_}",
+            f"{self.stat_model_price_}_{self.symbol_b_}",
+        ]
+
+    def _transform_dataframe(self, df: pd.DataFrame) -> pd.DataFrame:
+        df_selected: pd.DataFrame = pd.DataFrame(df[self.md_columns()])
+        result_df = (
+            pd.DataFrame(df_selected["tstamp"]).drop_duplicates().reset_index(drop=True)
+        )
+
+        # For each unique symbol, add a corresponding stat_model_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"
+            temp_df: pd.DataFrame = self.rename_columns(df_symbol)
+            # 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()
    
 class ResearchMarketData(PtMarketData):
    current_index_: int
-    
    is_execution_price_: bool

-    def __init__(self, config: Dict[str, Any]):
-        super().__init__(config)
+    def __init__(self, config: Config, instruments: List[ExchangeInstrument]):
+        super().__init__(config, instruments)
        self.current_index_ = 0
-        self.is_execution_price_ = "execution_price" in self.config_
+        self.is_execution_price_ = self.config_.key_exists("execution_price")
        if self.is_execution_price_:
-            self.execution_price_column_ = self.config_["execution_price"]["column"]
-            self.execution_price_shift_ = self.config_["execution_price"]["shift"]
+            self.execution_price_column_ = self.config_.get_value("execution_price")["column"]
+            self.execution_price_shift_ = self.config_.get_value("execution_price")["shift"]
        else:
            self.execution_price_column_ = None
            self.execution_price_shift_ = 0
@@ -43,187 +111,113 @@ class ResearchMarketData(PtMarketData):
        return result

    def load(self) -> None:
-        datafiles: List[str] = self.config_.get("datafiles", [])
-        instruments: List[Dict[str, str]] = self.config_.get("instruments", [])
-        assert len(instruments) > 0, "No instruments found in config"
+        datafiles: List[str] = self.config_.get_value("datafiles", [])
        assert len(datafiles) > 0, "No datafiles found in config"
-        self.symbol_a_ = instruments[0]["symbol"]
-        self.symbol_b_ = instruments[1]["symbol"]
-        self.stat_model_price_ = self.config_["stat_model_price"]

-        extra_minutes: int
-        extra_minutes = self.execution_price_shift_
+        extra_minutes: int = self.execution_price_shift_

        for datafile in datafiles:
            md_df = load_market_data(
                datafile=datafile,
-                instruments=instruments,
-                db_table_name=self.config_["market_data_loading"][instruments[0]["instrument_type"]]["db_table_name"],
-                trading_hours=self.config_["trading_hours"],
+                instruments=self.instruments_,
+                db_table_name=self.config_.get_value("market_data_loading")[
+                    self.instruments_[0].user_data_.get("instrument_type", "?instrument_type?")
+                ]["db_table_name"],
+                trading_hours=self.config_.get_value("trading_hours"),
                extra_minutes=extra_minutes,
            )
            self.origin_mkt_data_df_ = pd.concat([self.origin_mkt_data_df_, md_df])

        self.origin_mkt_data_df_ = self.origin_mkt_data_df_.sort_values(by="tstamp")
-        self.origin_mkt_data_df_ = self.origin_mkt_data_df_.dropna().reset_index(drop=True)
-        self._set_market_data()
-        
-    def _set_market_data(self, ) -> None:
-        if self.is_execution_price_:
-            self.market_data_df_ = pd.DataFrame(
-                self._transform_dataframe(self.origin_mkt_data_df_)[["tstamp"] + self.colnames() + self.orig_exec_prices_colnames()]
-            )
-        else:
-            self.market_data_df_ = pd.DataFrame(
-                self._transform_dataframe(self.origin_mkt_data_df_)[["tstamp"] + self.colnames()]
-            )
-
-        self.market_data_df_ = self.market_data_df_.dropna().reset_index(drop=True)
-        self.market_data_df_["tstamp"] = pd.to_datetime(self.market_data_df_["tstamp"])
-        self.market_data_df_ = self.market_data_df_.sort_values("tstamp")
+        self.origin_mkt_data_df_ = self.origin_mkt_data_df_.dropna().reset_index(
+            drop=True
+        )
+        self.set_market_data()
        self._set_execution_price_data()

-    def _transform_dataframe(self, df: pd.DataFrame) -> pd.DataFrame:
-        df_selected: pd.DataFrame
-        if self.is_execution_price_:
-            execution_price_column = self.config_["execution_price"]["column"]
-
-            df_selected = pd.DataFrame(
-                df[["tstamp", "symbol", self.stat_model_price_, execution_price_column]]
-            )
-        else:
-            df_selected = pd.DataFrame(
-                df[["tstamp", "symbol", self.stat_model_price_]]
-            )
-
-        result_df = pd.DataFrame(df_selected["tstamp"]).drop_duplicates().reset_index(drop=True)
-
-        # For each unique symbol, add a corresponding stat_model_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}"
-            if self.is_execution_price_:
-                new_execution_price_column = f"{self.execution_price_column_}_{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_],
-                        new_execution_price_column: df_symbol[execution_price_column],
-                    }
-                )
-            else:
-                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 _set_execution_price_data(self) -> None:
-        if "execution_price" not in self.config_:
-            self.market_data_df_[f"exec_price_{self.symbol_a_}"] = self.market_data_df_[f"{self.stat_model_price_}_{self.symbol_a_}"]
-            self.market_data_df_[f"exec_price_{self.symbol_b_}"] = self.market_data_df_[f"{self.stat_model_price_}_{self.symbol_b_}"]
+        if not self.is_execution_price_:
            return
-        execution_price_column = self.config_["execution_price"]["column"]
-        execution_price_shift = self.config_["execution_price"]["shift"]
-        self.market_data_df_[f"exec_price_{self.symbol_a_}"] = self.market_data_df_[f"{execution_price_column}_{self.symbol_a_}"].shift(-execution_price_shift)
-        self.market_data_df_[f"exec_price_{self.symbol_b_}"] = self.market_data_df_[f"{execution_price_column}_{self.symbol_b_}"].shift(-execution_price_shift)
+        if not self.config_.key_exists("execution_price"):
+            self.market_data_df_[f"exec_price_{self.symbol_a_}"] = self.market_data_df_[
+                f"{self.stat_model_price_}_{self.symbol_a_}"
+            ]
+            self.market_data_df_[f"exec_price_{self.symbol_b_}"] = self.market_data_df_[
+                f"{self.stat_model_price_}_{self.symbol_b_}"
+            ]
+            return
+        execution_price_column = self.config_.get_value("execution_price")["column"]
+        execution_price_shift = self.config_.get_value("execution_price")["shift"]
+        self.market_data_df_[f"exec_price_{self.symbol_a_}"] = self.market_data_df_[
+            f"{execution_price_column}_{self.symbol_a_}"
+        ].shift(-execution_price_shift)
+        self.market_data_df_[f"exec_price_{self.symbol_b_}"] = self.market_data_df_[
+            f"{execution_price_column}_{self.symbol_b_}"
+        ].shift(-execution_price_shift)
        self.market_data_df_ = self.market_data_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 md_columns(self) -> List[str]: 
+        # @abstractmethod
+        if self.is_execution_price_:
+            return ["tstamp", "symbol", self.stat_model_price_, self.execution_price_column_]
+        else:
+            return ["tstamp", "symbol", self.stat_model_price_]
+
+    def rename_columns(self, selected_symbol_df: pd.DataFrame) -> pd.DataFrame:  
+        # @abstractmethod
+        symbol = selected_symbol_df.iloc[0]["symbol"]
+        new_price_column = f"{self.stat_model_price_}_{symbol}"
+        if self.is_execution_price_:
+            new_execution_price_column = f"{self.execution_price_column_}_{symbol}"
+
+            # Create temporary dataframe with timestamp and price
+            temp_df = pd.DataFrame(
+                {
+                    "tstamp": selected_symbol_df["tstamp"],
+                    new_price_column: selected_symbol_df[self.stat_model_price_],
+                    new_execution_price_column: selected_symbol_df[self.execution_price_column_],
+                }
+            )
+        else:
+            temp_df = pd.DataFrame(
+                {
+                    "tstamp": selected_symbol_df["tstamp"],
+                    new_price_column: selected_symbol_df[self.stat_model_price_],
+                }
+            )
+        return temp_df
+
+    def tranform_df_target_colnames(self):
+        # @abstractmethod
+        return self.colnames() + self.orig_exec_prices_colnames()
    
    def orig_exec_prices_colnames(self) -> List[str]:
        return [
            f"{self.execution_price_column_}_{self.symbol_a_}",
            f"{self.execution_price_column_}_{self.symbol_b_}",
-        ]
+        ] if self.is_execution_price_ else []

-    def exec_prices_colnames(self) -> List[str]:
-        return [
-            f"exec_price_{self.symbol_a_}",
-            f"exec_price_{self.symbol_b_}",
-        ]
+class LiveMarketData(PtMarketData):

-class RealTimeMarketData(PtMarketData):
+    def __init__(self, config: Config, instruments: List[ExchangeInstrument]):
+        super().__init__(config, instruments)

-    def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
-        super().__init__(config, *args, **kwargs)
+    def md_columns(self) -> List[str]: 
+        # @abstractmethod
+        return ["tstamp", "symbol", self.stat_model_price_]

-    async def on_mkt_data_hist_snapshot(self, snapshot: JsonDictT) -> None:
-        # URGENT
-        # create origin_mkt_data_df_ from snapshot
-        # verify that the data for both instruments are present
-        
-        # transform it to market_data_df_ tstamp, close_symbolA, close_symbolB
-        '''
-        # from cvttpy/exchanges/binance/spot/mkt_data.py
-        values = {
-            "time_ns": time_ns,
-            "tstamp": format_nanos_utc(time_ns),
-            "exchange_id": exch_inst.exchange_id_,
-            "instrument_id": exch_inst.instrument_id(),
-            "interval_ns": interval_sec * 1_000_000_000,
-            "open": float(kline[1]),
-            "high": float(kline[2]),
-            "low": float(kline[3]),
-            "close": float(kline[4]),
-            "volume": float(kline[5]),
-            "num_trades": kline[8],
-            "vwap": float(kline[7]) / float(kline[5]) if float(kline[5]) > 0 else 0.0  # Calculate VWAP
-        }
-        '''
-
-
-        pass
-    
-    async def on_mkt_data_update(self, update: JsonDictT) -> Optional[pd.DataFrame]:
-        # URGENT
-        # make sure update has both instruments
-        # create DataFrame tmp1 from update
-        # transform tmp1 into temp. datframe  tmp2
-        # add tmp1 to origin_mkt_data_df_
-        # add tmp2 to market_data_df_
-        # return market_data_df_
-        '''
-        class MdTradesAggregate(NamedObject):
-        def to_dict(self) -> Dict[str, Any]:
-            return {
-                "time_ns": self.time_ns_,
-                "tstamp": format_nanos_utc(self.time_ns_),
-                "exchange_id": self.exch_inst_.exchange_id_,
-                "instrument_id": self.exch_inst_.instrument_id(),
-                "interval_ns": self.interval_ns_,
-                "open": self.exch_inst_.get_price(self.open_),
-                "high": self.exch_inst_.get_price(self.high_),
-                "low": self.exch_inst_.get_price(self.low_),
-                "close": self.exch_inst_.get_price(self.close_),
-                "volume": self.exch_inst_.get_quantity(self.volume_),
-                "vwap": self.exch_inst_.get_price(self.vwap_),
-                "num_trades": self.exch_inst_.get_quantity(self.num_trades_),
+    def rename_columns(self, selected_symbol_df: pd.DataFrame) -> pd.DataFrame:  
+        # @abstractmethod
+        symbol = selected_symbol_df.iloc[0]["symbol"]
+        new_price_column = f"{self.stat_model_price_}_{symbol}"
+        temp_df = pd.DataFrame(
+            {
+                "tstamp": selected_symbol_df["tstamp"],
+                new_price_column: selected_symbol_df[self.stat_model_price_],
            }
-        '''
+        )
+        return temp_df

-        return pd.DataFrame()
+    def tranform_df_target_colnames(self):
+        # @abstractmethod
+        return self.colnames()
@@ -3,8 +3,11 @@ from __future__ import annotations
 from abc import ABC, abstractmethod
 from typing import Any, Dict, cast

-from pt_strategy.prediction import Prediction
-
+# ---
+from cvttpy_tools.config import Config
+# ---
+from pairs_trading.lib.pt_strategy.prediction import Prediction
+from pairs_trading.lib.pt_strategy.trading_pair import TradingPair
    
 class PairsTradingModel(ABC):

@@ -13,10 +16,10 @@ class PairsTradingModel(ABC):
        ...
    
    @staticmethod
-    def create(config: Dict[str, Any]) -> PairsTradingModel:
+    def create(config: Config) -> PairsTradingModel:
        import importlib
        
-        model_class_name = config.get("model_class", None)
+        model_class_name = config.get_value("model/model_class", None)
        assert model_class_name is not None
        module_name, class_name = model_class_name.rsplit(".", 1)
        module = importlib.import_module(module_name)
@@ -1,54 +1,56 @@
 from __future__ import annotations

-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Tuple

 import pandas as pd
-from pt_strategy.model_data_policy import ModelDataPolicy
-from pt_strategy.pt_market_data import ResearchMarketData
-from pt_strategy.pt_model import Prediction
-from pt_strategy.trading_pair import PairState, TradingPair
-
+# ---
+from cvttpy_tools.config import Config
+# ---
+from cvttpy_trading.trading.instrument import ExchangeInstrument
+# ---
+from pairs_trading.lib.pt_strategy.model_data_policy import ModelDataPolicy
+from pairs_trading.lib.pt_strategy.pt_market_data import ResearchMarketData
+from pairs_trading.lib.pt_strategy.pt_model import Prediction
+from pairs_trading.lib.pt_strategy.trading_pair import PairState, TradingPair, ResearchTradingPair

 class PtResearchStrategy:
-    config_: Dict[str, Any]
-    trading_pair_: TradingPair
+    config_: Config
+    trading_pair_: ResearchTradingPair
    model_data_policy_: ModelDataPolicy
    pt_mkt_data_: ResearchMarketData 

    trades_: List[pd.DataFrame]
-    predictions_: pd.DataFrame
+    predictions_df_: pd.DataFrame

    def __init__(
        self,
-        config: Dict[str, Any],
-        datafiles: List[str],
-        instruments: List[Dict[str, str]],
+        config: Config,
+        instruments: List[ExchangeInstrument]
    ):
-        from pt_strategy.model_data_policy import ModelDataPolicy
-        from pt_strategy.trading_pair import TradingPair
+        from pairs_trading.lib.pt_strategy.model_data_policy import ModelDataPolicy
+        from pairs_trading.lib.pt_strategy.trading_pair import TradingPair

        self.config_ = config
        self.trades_ = []
-        self.trading_pair_ = TradingPair(config=config, instruments=instruments)
-        self.predictions_ = pd.DataFrame()
+        self.trading_pair_ = ResearchTradingPair(config=config, instruments=instruments)
+        self.predictions_df_ = pd.DataFrame()

        import copy

        # modified config must be passed to PtMarketData
        config_copy = copy.deepcopy(config)
-        config_copy["instruments"] = instruments
-        config_copy["datafiles"] = datafiles
-        self.pt_mkt_data_ = ResearchMarketData(config=config_copy)
+        config_copy.set_value("instruments", instruments)
+        self.pt_mkt_data_ = ResearchMarketData(config=config_copy, instruments=instruments)
        self.pt_mkt_data_.load()
        self.model_data_policy_ = ModelDataPolicy.create(
-            config, mkt_data=self.pt_mkt_data_.market_data_df_, pair=self.trading_pair_
+            config_copy, mkt_data=self.pt_mkt_data_.market_data_df_, pair=self.trading_pair_
        )

    def outstanding_positions(self) -> List[Dict[str, Any]]:
        return list(self.trading_pair_.user_data_.get("outstanding_positions", []))

    def run(self) -> None:
-        training_minutes = self.config_.get("training_minutes", 120)
+        training_minutes = self.config_.get_value("training_minutes", 120)
        market_data_series: pd.Series
        market_data_df = pd.DataFrame()

@@ -72,8 +74,8 @@ class PtResearchStrategy:
            prediction = self.trading_pair_.run(
                market_data_df, self.model_data_policy_.advance(mkt_data_df=market_data_df)
            )
-            self.predictions_ = pd.concat(
-                [self.predictions_, prediction.to_df()], ignore_index=True
+            self.predictions_df_ = pd.concat(
+                [self.predictions_df_, prediction.to_df()], ignore_index=True
            )
            assert prediction is not None

@@ -93,8 +95,8 @@ class PtResearchStrategy:
        pair = self.trading_pair_
        trades = None

-        open_threshold = self.config_["dis-equilibrium_open_trshld"]
-        close_threshold = self.config_["dis-equilibrium_close_trshld"]
+        open_threshold = self.config_.get_value("model/disequilibrium/open_trshld")
+        close_threshold = self.config_.get_value("model/disequilibrium/close_trshld")
        scaled_disequilibrium = prediction.scaled_disequilibrium_
        abs_scaled_disequilibrium = abs(scaled_disequilibrium)

@@ -143,7 +145,7 @@ class PtResearchStrategy:
        if pair.user_data_["state"] == PairState.OPEN:
            print(f"{pair}:  *** Position is NOT CLOSED. ***")
            # outstanding positions
-            if self.config_["close_outstanding_positions"]:
+            if self.config_.get_value("close_outstanding_positions", False):
                close_position_row = pd.Series(pair.market_data_.iloc[-2])
                # close_position_row["disequilibrium"] = 0.0
                # close_position_row["scaled_disequilibrium"] = 0.0
@@ -159,14 +161,14 @@ class PtResearchStrategy:
                    pair.on_close_trades(trades)
            else:
                pair.add_outstanding_position(
-                    symbol=pair.symbol_a_,
+                    symbol=pair.symbol_a(),
                    open_side=pair.user_data_["open_side_a"],
                    open_px=pair.user_data_["open_px_a"],
                    open_tstamp=pair.user_data_["open_tstamp"],
                    last_mkt_data_row=pair.market_data_.iloc[-1],
                )
                pair.add_outstanding_position(
-                    symbol=pair.symbol_b_,
+                    symbol=pair.symbol_b(),
                    open_side=pair.user_data_["open_side_b"],
                    open_px=pair.user_data_["open_px_b"],
                    open_tstamp=pair.user_data_["open_tstamp"],
@@ -190,7 +192,7 @@ class PtResearchStrategy:
        return pd.DataFrame(columns=columns).astype(types)

    def _create_open_trades(
-        self, pair: TradingPair, row: pd.Series, prediction: Prediction
+        self, pair: ResearchTradingPair, row: pd.Series, prediction: Prediction
    ) -> Optional[pd.DataFrame]:
        colname_a, colname_b = pair.exec_prices_colnames()

@@ -224,7 +226,7 @@ class PtResearchStrategy:
        # create opening trades
        df.loc[len(df)] = {
            "time": tstamp,
-            "symbol": pair.symbol_a_,
+            "symbol": pair.symbol_a(),
            "side": side_a,
            "action": "OPEN",
            "price": px_a,
@@ -235,7 +237,7 @@ class PtResearchStrategy:
        }
        df.loc[len(df)] = {
            "time": tstamp,
-            "symbol": pair.symbol_b_,
+            "symbol": pair.symbol_b(),
            "side": side_b,
            "action": "OPEN",
            "price": px_b,
@@ -247,7 +249,7 @@ class PtResearchStrategy:
        return df

    def _create_close_trades(
-        self, pair: TradingPair, row: pd.Series, prediction: Optional[Prediction] = None
+        self, pair: ResearchTradingPair, row: pd.Series, prediction: Optional[Prediction] = None
    ) -> Optional[pd.DataFrame]:
        colname_a, colname_b = pair.exec_prices_colnames()

@@ -269,7 +271,7 @@ class PtResearchStrategy:
        # create opening trades
        df.loc[len(df)] = {
            "time": tstamp,
-            "symbol": pair.symbol_a_,
+            "symbol": pair.symbol_a(),
            "side": pair.user_data_["close_side_a"],
            "action": "CLOSE",
            "price": px_a,
@@ -280,7 +282,7 @@ class PtResearchStrategy:
        }
        df.loc[len(df)] = {
            "time": tstamp,
-            "symbol": pair.symbol_b_,
+            "symbol": pair.symbol_b(),
            "side": pair.user_data_["close_side_b"],
            "action": "CLOSE",
            "price": px_b,
@@ -4,8 +4,12 @@ from datetime import date, datetime
 from typing import Any, Dict, List, Optional, Tuple

 import pandas as pd
-from pt_strategy.trading_pair import TradingPair
-
+# ---
+from cvttpy_tools.config import Config
+# ---
+from cvttpy_trading.trading.instrument import ExchangeInstrument
+# ---
+from pairs_trading.lib.pt_strategy.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
@@ -18,12 +22,10 @@ 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())
@@ -118,9 +120,9 @@ def create_result_database(db_path: str) -> None:
 def store_config_in_database(
    db_path: str,
    config_file_path: str,
-    config: Dict,
+    config: Config,
    datafiles: List[Tuple[str, str]],
-    instruments: List[Dict[str, str]],
+    instruments: List[ExchangeInstrument],
 ) -> None:
    """
    Store configuration information in the database for reference.
@@ -135,13 +137,13 @@ def store_config_in_database(
        cursor = conn.cursor()

        # Convert config to JSON string
-        config_json = json.dumps(config, indent=2, default=str)
+        config_json = json.dumps(config.data(), 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']}"
+                inst.details_short()
                for inst in instruments
            ]
        )
@@ -204,9 +206,9 @@ class PairResearchResult:
    trades_: Dict[DayT, pd.DataFrame]
    outstanding_positions_: Dict[DayT, List[OutstandingPositionT]]
    symbol_roundtrip_trades_: Dict[str, List[Dict[str, Any]]]
+    config_: Config

-
-    def __init__(self, config: Dict[str, Any]) -> None:
+    def __init__(self, config: Config) -> None:
        self.config_ = config
        self.trades_ = {}
        self.outstanding_positions_ = {}
@@ -218,13 +220,6 @@ class PairResearchResult:
        self.trades_[day] = trades
        self.outstanding_positions_[day] = outstanding_positions

-    # def all_trades(self) -> List[TradeT]:
-    #     """Get all trades across all days as a flat list."""
-    #     all_trades_list: List[TradeT] = []
-    #     for day_trades in self.trades_.values():
-    #         all_trades_list.extend(day_trades.to_dict(orient="records"))
-    #     return all_trades_list
-    
    def outstanding_positions(self) -> List[OutstandingPositionT]:
        """Get all outstanding positions across all days as a flat list."""
        res: List[Dict[str, Any]] = []
@@ -292,7 +287,7 @@ class PairResearchResult:
                pair_return = symbol_a_return + symbol_b_return
                
                # Create round-trip records for both symbols
-                funding_per_position = self.config_.get("funding_per_pair", 10000) / 2
+                funding_per_position = self.config_.get_value("funding_per_pair", 10000) / 2
                
                # Symbol A round-trip
                day_roundtrips.append({
@@ -1,13 +1,21 @@
 from __future__ import annotations

+from abc import ABC, abstractmethod
 from datetime import datetime
 from enum import Enum
 from typing import Any, Dict, List

 import pandas as pd

-from pt_strategy.model_data_policy import DataWindowParams
-from pt_strategy.prediction import Prediction
+# ---
+from cvttpy_tools.base import NamedObject
+from cvttpy_tools.config import Config
+# ---
+from cvttpy_trading.trading.instrument import ExchangeInstrument
+# ---
+from pairs_trading.lib.pt_strategy.model_data_policy import DataWindowParams
+from pairs_trading.lib.pt_strategy.prediction import Prediction
+


 class PairState(Enum):
@@ -19,59 +27,76 @@ class PairState(Enum):
    CLOSE_STOP_PROFIT = 6


-def get_symbol(instrument: Dict[str, str]) -> str:
-    if "symbol" in instrument:
-        return instrument["symbol"]
-    elif "instrument_id" in instrument:
-        instrument_id = instrument["instrument_id"]
-        instrument_pfx = instrument_id[:instrument_id.find("-") + 1]
-        symbol = instrument_id[len(instrument_pfx):]
-        instrument["symbol"] = symbol
-        instrument["instrument_id_pfx"] = instrument_pfx
-        return symbol
-    else:
-        raise ValueError(f"Invalid instrument: {instrument}, missing symbol or instrument_id")
-
-class TradingPair:
-    config_: Dict[str, Any]
+class TradingPair(NamedObject, ABC):
+    config_: Config
+    model_: Any # "PairsTradingModel"
    market_data_: pd.DataFrame
-    instruments_: List[Dict[str, str]]
-    symbol_a_: str
-    symbol_b_: str
-    
-    stat_model_price_: str
-    model_: PairsTradingModel  # type: ignore[assignment]

    user_data_: Dict[str, Any]
+    stat_model_price_: str
+
+    instruments_: List[ExchangeInstrument]

    def __init__(
        self,
-        config: Dict[str, Any],
-        instruments: List[Dict[str, str]],
+        config: Config,
+        instruments: List[ExchangeInstrument],
    ):
-        
-        from pt_strategy.pt_model import PairsTradingModel
-        
-        assert len(instruments) == 2, "Trading pair must have exactly 2 instruments"
+        from pairs_trading.lib.pt_strategy.pt_model import PairsTradingModel

        self.config_ = config
-        self.instruments_ = instruments
-        self.symbol_a_ = get_symbol(instruments[0])
-        self.symbol_b_ = get_symbol(instruments[1])
        self.model_ = PairsTradingModel.create(config)
-        self.stat_model_price_ = config["stat_model_price"]
-        self.user_data_ = {
-            "state": PairState.INITIAL,
-        }
+        self.user_data_ = {}
+        self.instruments_ = instruments
+        self.instruments_[0].user_data_["symbol"] = instruments[0].instrument_id().split("-", 1)[1]
+        self.instruments_[1].user_data_["symbol"] = instruments[1].instrument_id().split("-", 1)[1]        
+        self.stat_model_price_ = config.get_value("model/stat_model_price")
+        
+    def run(self, market_data: pd.DataFrame, data_params: DataWindowParams) -> Prediction:  # type: ignore[assignment]
+        self.market_data_ = market_data[
+            data_params.training_start_index_ : data_params.training_start_index_ + data_params.training_size_
+        ]
+        return self.model_.predict(pair=self)
+
+    def colnames(self) -> List[str]:
+        return [
+            f"{self.stat_model_price_}_{self.symbol_a()}",
+            f"{self.stat_model_price_}_{self.symbol_b()}",
+        ]
+    def symbol_a(self) -> str:
+        return self.get_instrument_a().user_data_["symbol"]
+    
+    def symbol_b(self) -> str:
+        return self.get_instrument_b().user_data_["symbol"]
+
+    def get_instrument_a(self) -> ExchangeInstrument:
+        return self.instruments_[0]
+
+    def get_instrument_b(self) -> ExchangeInstrument:
+        return self.instruments_[1]

    def __repr__(self) -> str:
        return (
            f"{self.__class__.__name__}:"
-            f" symbol_a={self.symbol_a_},"
-            f" symbol_b={self.symbol_b_},"
+            f" symbol_a={self.symbol_a()},"
+            f" symbol_b={self.symbol_b()},"
            f" model={self.model_.__class__.__name__}"
        )
    
+class ResearchTradingPair(TradingPair):
+
+    def __init__(
+        self,
+        config: Config,
+        instruments: List[ExchangeInstrument],
+    ):
+        assert len(instruments) == 2, "Trading pair must have exactly 2 instruments"
+        super().__init__(config=config, instruments=instruments)
+
+        self.user_data_ = {
+            "state": PairState.INITIAL,
+        }
+        
    def is_closed(self) -> bool:
        return self.user_data_["state"] in [
            PairState.CLOSE,
@@ -79,39 +104,34 @@ class TradingPair:
            PairState.CLOSE_STOP_LOSS,
            PairState.CLOSE_STOP_PROFIT,
        ]
-    def is_open(self) -> bool:
-        return self.user_data_["state"] == PairState.OPEN

-    def colnames(self) -> List[str]:
-        return [
-            f"{self.stat_model_price_}_{self.symbol_a_}",
-            f"{self.stat_model_price_}_{self.symbol_b_}",
-        ]
+    def is_open(self) -> bool:
+        return not self.is_closed()

    def exec_prices_colnames(self) -> List[str]:
        return [
-            f"exec_price_{self.symbol_a_}",
-            f"exec_price_{self.symbol_b_}",
+            f"exec_price_{self.symbol_a()}",
+            f"exec_price_{self.symbol_b()}",
        ]

    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
+            not config.key_exists("stop_close_conditions")
+            or config.get_value("stop_close_conditions") is None
        ):
            return False
-        if "profit" in config["stop_close_conditions"]:
+        if "profit" in config.get_value("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"]:
+            if current_return >= config.get_value("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"]:
+        if "loss" in config.get_value("stop_close_conditions"):
+            if current_return <= config.get_value("stop_close_conditions")["loss"]:
                print(f"STOP LOSS: {current_return}")
                self.user_data_["stop_close_state"] = PairState.CLOSE_STOP_LOSS
                return True
@@ -136,8 +156,8 @@ class TradingPair:
                )
                return float(instrument_return) * 100.0

-            instrument_a_return = _single_instrument_return(self.symbol_a_)
-            instrument_b_return = _single_instrument_return(self.symbol_b_)
+            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

@@ -158,20 +178,22 @@ class TradingPair:
        open_tstamp: datetime,
        last_mkt_data_row: pd.Series,
    ) -> None:
-        assert symbol in [self.symbol_a_, self.symbol_b_], "Symbol must be one of the pair's symbols"
+        assert symbol in [
+            self.symbol_a(),
+            self.symbol_b(),
+        ], "Symbol must be one of the pair's symbols"
        assert open_side in ["BUY", "SELL"], "Open side must be either BUY or SELL"
        assert open_px > 0, "Open price must be greater than 0"
        assert open_tstamp is not None, "Open timestamp must be provided"
        assert last_mkt_data_row is not None, "Last market data row must be provided"

        exec_prices_col_a, exec_prices_col_b = self.exec_prices_colnames()
-        if symbol == self.symbol_a_:
+        if symbol == self.symbol_a():
            last_px = last_mkt_data_row[exec_prices_col_a]
        else:
            last_px = last_mkt_data_row[exec_prices_col_b]

-
-        funding_per_position = self.config_["funding_per_pair"] / 2
+        funding_per_position = self.config_.get_value("funding_per_pair") / 2
        shares = funding_per_position / open_px
        if open_side == "SELL":
            shares = -shares
@@ -179,21 +201,26 @@ class TradingPair:
        if "outstanding_positions" not in self.user_data_:
            self.user_data_["outstanding_positions"] = []

-        self.user_data_["outstanding_positions"].append({
-            "symbol": symbol,
-            "open_side": open_side,
-            "open_px": open_px,
-            "shares": shares,
-            "open_tstamp": open_tstamp,
-            "last_px": last_px,
-            "last_tstamp": last_mkt_data_row["tstamp"],
-            "last_value": last_px * shares,
-        })
-
-
-    def run(self, market_data: pd.DataFrame, data_params: DataWindowParams) -> Prediction: # type: ignore[assignment]
-        self.market_data_ = market_data[data_params.training_start_index:data_params.training_start_index + data_params.training_size]
-        return self.model_.predict(pair=self)
-
+        self.user_data_["outstanding_positions"].append(
+            {
+                "symbol": symbol,
+                "open_side": open_side,
+                "open_px": open_px,
+                "shares": shares,
+                "open_tstamp": open_tstamp,
+                "last_px": last_px,
+                "last_tstamp": last_mkt_data_row["tstamp"],
+                "last_value": last_px * shares,
+            }
+        )
+
+class LiveTradingPair(TradingPair):
+
+    def __init__(self, config: Config, instruments: List[ExchangeInstrument]):
+        super().__init__(config, instruments)
+
+    def to_stop_close_conditions(self, predicted_row: pd.Series) -> bool:
+        # TODO LiveTradingPair.to_stop_close_conditions()
+        return False
        

@@ -1,12 +1,12 @@
 import hjson
 from typing import Dict
 from datetime import datetime
+# ---
+from cvttpy_tools.config import Config


-def load_config(config_path: str) -> Dict:
-    with open(config_path, "r") as f:
-        config = hjson.load(f)
-    return dict(config)
+def load_config(config_path: str) -> Config:
+    return Config(json_src=f"file://{config_path}")


 def expand_filename(filename: str) -> str:
@@ -1,9 +1,10 @@
 from __future__ import annotations

 import sqlite3
-from typing import Dict, List, cast
+from typing import Any, Dict, List, Tuple, cast
 import pandas as pd

+from cvttpy_trading.trading.instrument import ExchangeInstrument

 def load_sqlite_to_dataframe(db_path:str, query:str) -> pd.DataFrame:
    df: pd.DataFrame = pd.DataFrame()
@@ -45,19 +46,17 @@ def convert_time_to_UTC(value: str, timezone: str, extra_minutes: int = 0) -> st

 def load_market_data(
    datafile: str,
-    instruments: List[Dict[str, str]],
+    instruments: List[ExchangeInstrument],
    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))
+
+    inst_ids = ['"' + exch_inst.instrument_id() + '"' for exch_inst in instruments]
+    instrument_ids = list(set(inst_ids))
    exchange_ids = list(
-        set(['"' + instrument["exchange_id"] + '"' for instrument in instruments])
+        set(['"' + instrument.exchange_id() + '"' for instrument in instruments])
    )

    query = "select"
@@ -1,18 +1,22 @@
 import os
 import glob
 from typing import Dict, List, Tuple
+# ---
+from cvttpy_tools.config import Config
+# ---
+from cvttpy_trading.trading.instrument import ExchangeInstrument

 DayT = str
 DataFileNameT = str

 def resolve_datafiles(
-    config: Dict, date_pattern: str, instruments: List[Dict[str, str]]
+    config: Config, date_pattern: str, instruments: List[ExchangeInstrument]
 ) -> List[Tuple[DayT, DataFileNameT]]:
    resolved_files: List[Tuple[DayT, DataFileNameT]] = []
-    for inst in instruments:
+    for exch_inst in instruments:
        pattern = date_pattern
-        inst_type = inst["instrument_type"]
-        data_dir = config["market_data_loading"][inst_type]["data_directory"]
+        inst_type = exch_inst.user_data_.get("instrument_type", "?instrument_type?")
+        data_dir = config.get_value(f"market_data_loading/{inst_type}/data_directory")
        if "*" in pattern or "?" in pattern:
            # Handle wildcards
            if not os.path.isabs(pattern):
@@ -1,21 +0,0 @@
-import argparse
-from typing import Dict, List
-
-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
-
@@ -1,4 +1,4 @@
-from pt_strategy.research_strategy import PtResearchStrategy
+from pairs_trading.lib.pt_strategy.research_strategy import PtResearchStrategy


 def visualize_prices(strategy: PtResearchStrategy, trading_date: str) -> None: 
@@ -8,8 +8,8 @@ def visualize_prices(strategy: PtResearchStrategy, trading_date: str) -> None:
    import seaborn as sns

    pair = strategy.trading_pair_
-    SYMBOL_A = pair.symbol_a_
-    SYMBOL_B = pair.symbol_b_
+    SYMBOL_A = pair.symbol_a()
+    SYMBOL_B = pair.symbol_b()
    TRD_DATE = f"{trading_date[0:4]}-{trading_date[4:6]}-{trading_date[6:8]}"

    plt.style.use('seaborn-v0_8')
@@ -1,13 +1,8 @@
 from __future__ import annotations

-import os
-from typing import Any, Dict

-from pt_strategy.results import (PairResearchResult, create_result_database,
-                                 store_config_in_database)
-from pt_strategy.research_strategy import PtResearchStrategy
-from tools.filetools import resolve_datafiles
-from tools.instruments import get_instruments
+from pairs_trading.lib.pt_strategy.results import (PairResearchResult)
+from pairs_trading.lib.pt_strategy.research_strategy import PtResearchStrategy


 def visualize_trades(strategy: PtResearchStrategy, results: PairResearchResult, trading_date: str) -> None: 
@@ -25,8 +20,8 @@ def visualize_trades(strategy: PtResearchStrategy, results: PairResearchResult,
    origin_mkt_data_df = strategy.pt_mkt_data_.origin_mkt_data_df_
    mkt_data_df = strategy.pt_mkt_data_.market_data_df_
    TRD_DATE = f"{trading_date[0:4]}-{trading_date[4:6]}-{trading_date[6:8]}"
-    SYMBOL_A = pair.symbol_a_
-    SYMBOL_B = pair.symbol_b_
+    SYMBOL_A = pair.symbol_a()
+    SYMBOL_B = pair.symbol_b()

    
    print(f"\nCreated trading pair: {pair}")
@@ -51,7 +46,7 @@ def visualize_trades(strategy: PtResearchStrategy, results: PairResearchResult,
    timeline_df = pd.DataFrame({'tstamp': all_timestamps})

    # Merge with predicted data to get dis-equilibrium values
-    timeline_df = timeline_df.merge(strategy.predictions_[['tstamp', 'disequilibrium', 'scaled_disequilibrium', 'signed_scaled_disequilibrium']], 
+    timeline_df = timeline_df.merge(strategy.predictions_df_[['tstamp', 'disequilibrium', 'scaled_disequilibrium', 'signed_scaled_disequilibrium']], 
                                    on='tstamp', how='left')

    # Get Symbol_A and Symbol_B market data
@@ -110,8 +105,8 @@ def visualize_trades(strategy: PtResearchStrategy, results: PairResearchResult,
        type="line",
        x0=timeline_df['tstamp'].min(),
        x1=timeline_df['tstamp'].max(),
-        y0=strategy.config_['dis-equilibrium_open_trshld'],
-        y1=strategy.config_['dis-equilibrium_open_trshld'],
+        y0=strategy.config_.get_value('model/disequilibrium/open_trshld'),
+        y1=strategy.config_.get_value('model/disequilibrium/open_trshld'),
        line=dict(color="purple", width=2, dash="dot"),
        opacity=0.7,
        row=1, col=1
@@ -121,8 +116,8 @@ def visualize_trades(strategy: PtResearchStrategy, results: PairResearchResult,
        type="line",
        x0=timeline_df['tstamp'].min(),
        x1=timeline_df['tstamp'].max(),
-        y0=-strategy.config_['dis-equilibrium_open_trshld'],
-        y1=-strategy.config_['dis-equilibrium_open_trshld'],
+        y0=-strategy.config_.get_value('model/disequilibrium/open_trshld'),
+        y1=-strategy.config_.get_value('model/disequilibrium/open_trshld'),
        line=dict(color="purple", width=2, dash="dot"),
        opacity=0.7,
        row=1, col=1
@@ -132,8 +127,8 @@ def visualize_trades(strategy: PtResearchStrategy, results: PairResearchResult,
        type="line",
        x0=timeline_df['tstamp'].min(),
        x1=timeline_df['tstamp'].max(),
-        y0=strategy.config_['dis-equilibrium_close_trshld'],
-        y1=strategy.config_['dis-equilibrium_close_trshld'],
+        y0=strategy.config_.get_value('model/disequilibrium/close_trshld'),
+        y1=strategy.config_.get_value('model/disequilibrium/close_trshld'),
        line=dict(color="brown", width=2, dash="dot"),
        opacity=0.7,
        row=1, col=1
@@ -143,8 +138,8 @@ def visualize_trades(strategy: PtResearchStrategy, results: PairResearchResult,
        type="line",
        x0=timeline_df['tstamp'].min(),
        x1=timeline_df['tstamp'].max(),
-        y0=-strategy.config_['dis-equilibrium_close_trshld'],
-        y1=-strategy.config_['dis-equilibrium_close_trshld'],
+        y0=-strategy.config_.get_value('model/disequilibrium/close_trshld'),
+        y1=-strategy.config_.get_value('model/disequilibrium/close_trshld'),
        line=dict(color="brown", width=2, dash="dot"),
        opacity=0.7,
        row=1, col=1
@@ -1,66 +0,0 @@
-[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 
@@ -1,25 +0,0 @@
-{
-    "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"
-}
@@ -170,6 +170,7 @@ types-PyYAML>=5.4
 types-redis>=3.5
 types-requests>=2.25
 types-retry>=0.9
+types-seaborn>0.13.2
 types-selenium>=3.141
 types-Send2Trash>=1.8
 types-setuptools>=57.4
@@ -1,106 +1,139 @@
 from __future__ import annotations

 import os
-from typing import Any, Dict
+from typing import Any, Dict, List, Tuple

-from pt_strategy.results import (
+# ---
+from cvttpy_tools.app import App
+from cvttpy_tools.base import NamedObject
+from cvttpy_tools.config import CvttAppConfig
+
+# ---
+from cvttpy_trading.trading.instrument import ExchangeInstrument
+from cvttpy_trading.settings.instruments import Instruments
+
+# ---
+from pairs_trading.lib.pt_strategy.results import (
    PairResearchResult,
    create_result_database,
    store_config_in_database,
 )
-from pt_strategy.research_strategy import PtResearchStrategy
-from tools.filetools import resolve_datafiles
-from tools.instruments import get_instruments
+from pairs_trading.lib.pt_strategy.research_strategy import PtResearchStrategy
+from pairs_trading.lib.tools.filetools import resolve_datafiles
+
+InstrumentTypeT = str


-def main() -> None:
-    import argparse
+class Runner(NamedObject):
+    def __init__(self):
+        App()
+        CvttAppConfig()

-    from tools.config import expand_filename, load_config
+        # App.instance().add_cmdline_arg(
+        #     "--config", type=str, required=True, help="Path to the configuration file."
+        # )
+        App.instance().add_cmdline_arg(
+            "--date_pattern",
+            type=str,
+            required=True,
+            help="Date YYYYMMDD, allows * and ? wildcards",
+        )
+        App.instance().add_cmdline_arg(
+            "--instruments",
+            type=str,
+            required=True,
+            help="Comma-separated list of instrument symbols (e.g., COIN:EQUITY,GBTC:CRYPTO)",
+        )
+        App.instance().add_cmdline_arg(
+            "--result_db",
+            type=str,
+            required=True,
+            help="Path to SQLite database for storing results. Use 'NONE' to disable database output.",
+        )

-    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.",
-    )
+        App.instance().add_call(stage=App.Stage.Config, func=self._on_config())
+        App.instance().add_call(stage=App.Stage.Run, func=self.run())

-    args = parser.parse_args()
+    async def _on_config(self) -> None:
+        # Resolve data files (CLI takes priority over config)
+        instruments: List[ExchangeInstrument] = self._get_instruments()
+        datafiles = resolve_datafiles(
+            config=CvttAppConfig.instance(),
+            date_pattern=App.instance().get_argument("date_pattern"),
+            instruments=instruments,
+        )

-    config: Dict = load_config(args.config)
+        days = list(set([day for day, _ in datafiles]))
+        print(f"Found {len(datafiles)} data files to process:")
+        for df in datafiles:
+            print(f"  - {df}")

-    # Resolve data files (CLI takes priority over config)
-    instruments = get_instruments(args, config)
-    datafiles = resolve_datafiles(config, args.date_pattern, instruments)
+        # Create result database if needed
+        if App.instance().get_argument("result_db").upper() != "NONE":
+            create_result_database(App.instance().get_argument("result_db"))

-    days = list(set([day for day, _ in datafiles]))
-    print(f"Found {len(datafiles)} data files to process:")
-    for df in datafiles:
-        print(f"  - {df}")
+        # Initialize a dictionary to store all trade results
+        all_results: Dict[str, Dict[str, Any]] = {}
+        is_config_stored = False
+        # Process each data file

-    # 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)
+        results = PairResearchResult(config=CvttAppConfig.instance())
+        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}")
+                exit(1)
+            print(f"\n====== Processing {day} ======")

-    # Initialize a dictionary to store all trade results
-    all_results: Dict[str, Dict[str, Any]] = {}
-    is_config_stored = False
-    # Process each data file
+            if not is_config_stored:
+                store_config_in_database(
+                    db_path=App.instance().get_argument("result_db"),
+                    config_file_path=App.instance().get_argument("config"),
+                    config=CvttAppConfig.instance(),
+                    datafiles=datafiles,
+                    instruments=instruments,
+                )
+                is_config_stored = True

-    results = PairResearchResult(config=config)
-    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,
-                datafiles=datafiles,
+            CvttAppConfig.instance().set_value("datafiles", md_datafiles)
+            pt_strategy = PtResearchStrategy(
+                config=CvttAppConfig.instance(),
                instruments=instruments,
            )
-            is_config_stored = True
+            pt_strategy.run()
+            results.add_day_results(
+                day=day,
+                trades=pt_strategy.day_trades(),
+                outstanding_positions=pt_strategy.outstanding_positions(),
+            )

-        pt_strategy = PtResearchStrategy(
-            config=config, datafiles=md_datafiles, instruments=instruments
-        )
-        pt_strategy.run()
-        results.add_day_results(
-            day=day,
-            trades=pt_strategy.day_trades(),
-            outstanding_positions=pt_strategy.outstanding_positions(),
-        )
+        results.analyze_pair_performance()

+    def _get_instruments(self) -> List[ExchangeInstrument]:
+        res: List[ExchangeInstrument] = []

-    results.analyze_pair_performance()
+        for inst in App.instance().get_argument("instruments").split(","):
+            instrument_type = inst.split(":")[0]
+            exchange_id = inst.split(":")[1]
+            instrument_id = inst.split(":")[2]
+            exch_inst: ExchangeInstrument = Instruments.instance().get_exch_inst(
+                exch_id=exchange_id, inst_id=instrument_id, src=f"{self.fname()}"
+            )
+            exch_inst.user_data_["instrument_type"] = instrument_type
+            res.append(exch_inst)

+        return res

-    if args.result_db.upper() != "NONE":
-        print(f"\nResults stored in database: {args.result_db}")
-    else:
-        print("No results to display.")
+    async def run(self) -> None:
+
+        if App.instance().get_argument("result_db").upper() != "NONE":
+            print(
+                f'\nResults stored in database: {App.instance().get_argument("result_db")}'
+            )
+        else:
+            print("No results to display.")


 if __name__ == "__main__":
-    main()
+    Runner()
+    App.instance().run()
@@ -1,94 +0,0 @@
-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
@@ -1,111 +0,0 @@
-from __future__ import annotations
-
-import os
-from typing import Any, Dict
-
-from pt_strategy.results import (PairResearchResult, create_result_database,
-                                 store_config_in_database)
-from pt_strategy.research_strategy import PtResearchStrategy
-from tools.filetools import resolve_datafiles
-from tools.instruments import get_instruments
-from tools.viz.viz_trades import visualize_trades
-
-
-def main() -> None:
-    import argparse
-
-    from tools.config import expand_filename, load_config
-
-    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=False,
-        default="NONE",
-        help="Path to SQLite database for storing results. Use 'NONE' to disable database output.",
-    )
-
-    args = parser.parse_args()
-
-    config: Dict = load_config(args.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
-
-    results = PairResearchResult(config=config)
-    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,
-                datafiles=datafiles,
-                instruments=instruments,
-            )
-            is_config_stored = True
-
-        pt_strategy = PtResearchStrategy(
-            config=config, datafiles=md_datafiles, instruments=instruments
-        )
-        pt_strategy.run()
-        results.add_day_results(
-            day=day,
-            trades=pt_strategy.day_trades(),
-            outstanding_positions=pt_strategy.outstanding_positions(),
-        )
-
-
-    results.analyze_pair_performance()
-    
-    
-    visualize_trades(pt_strategy, results, day)
-
-
-    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()
Author	SHA1	Message	Date
Oleg Sheynin	e4a3795793	progress 0.0.7	2026-02-01 23:36:46 +00:00
Oleg Sheynin	f311315ef8	.	2026-01-31 20:11:07 +00:00
Oleg Sheynin	76f9a80ad6	fix	2026-01-28 01:00:17 +00:00
Oleg Sheynin	bf25eb7fb5	progress 0.0.5	2026-01-26 21:46:50 +00:00
Oleg Sheynin	f2a5d6a7ad	progress 0.0.4	2026-01-24 20:35:59 +00:00
Oleg Sheynin	b9d479ae8c	progress 0.0.4	2026-01-23 20:15:24 +00:00
Oleg Sheynin	e6ae62ebb6	progress 0.0.3	2026-01-22 23:52:17 +00:00
Oleg Sheynin	170e48d646	minor	2026-01-19 18:04:05 +00:00
Oleg Sheynin	d5f00f557b	progress	2026-01-15 02:05:35 +00:00
Oleg Sheynin	c0fabcb429	progress	2026-01-12 21:26:15 +00:00
Oleg Sheynin	bd6cf1d4d0	progress. Initial untested version	2026-01-11 18:17:05 +00:00
Oleg Sheynin	b196863a34	progress	2026-01-11 13:33:58 +00:00
oleg	6dd0f97d74	dev progress	2026-01-01 22:18:02 +00:00
oleg	002f797751	dev progress	2026-01-01 22:12:04 +00:00
oleg	4bf1d46208	dev progress	2026-01-01 18:36:18 +00:00
oleg	842eb3ec62	dev progress	2026-01-01 01:36:31 +00:00
oleg	69a0b19e9f	dev progress	2025-12-31 08:03:26 +00:00
oleg	121c85def0	dev progress	2025-12-30 10:52:33 +00:00
Oleg Sheynin	2e32b26fad	dev progress	2025-12-28 19:30:00 +00:00
Oleg Sheynin	ba2a6cd2eb	progress	2025-12-23 03:14:41 +00:00
Oleg Sheynin	8b115cee75	renewed development	2025-12-22 23:58:41 +00:00
				`@@ -0,0 +1 @@`
				`source /home/oleg/.pyenv/python3.12-venv/bin/activate`