before refactoring

.vscode/settings.json

@@ -6,7 +6,6 @@
     ],
     "python.testing.cwd": "${workspaceFolder}",
     "python.testing.autoTestDiscoverOnSaveEnabled": true,
-    "python.defaultInterpreterPath": "/home/oleg/.pyenv/python3.12-venv/bin/python3",
     "python.testing.pytestPath": "python3",
     "python.analysis.extraPaths": [
         "${workspaceFolder}",
@@ -16,4 +15,5 @@
     "python.envFile": "${workspaceFolder}/.env",
     "python.testing.debugPort": 3000,
     "python.testing.promptToConfigure": false,
+    "python.defaultInterpreterPath": "/home/oleg/.pyenv/python3.12-venv/bin/python"
 }

apps/pair_selector/pair_selector.py

@@ -0,0 +1,509 @@
+from __future__ import annotations
+
+import asyncio
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple
+
+from aiohttp import web
+import numpy as np
+import pandas as pd
+from statsmodels.tsa.stattools import adfuller, coint # type: ignore
+from statsmodels.tsa.vector_ar.vecm import coint_johansen  # type: ignore
+
+
+from cvttpy_tools.app import App
+from cvttpy_tools.base import NamedObject
+from cvttpy_tools.config import Config, CvttAppConfig
+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_tools.web.rest_service import RestService
+
+from cvttpy_trading.trading.exchange_config import ExchangeAccounts
+from cvttpy_trading.trading.instrument import ExchangeInstrument
+from cvttpy_trading.trading.mkt_data.md_summary import MdTradesAggregate, MdSummary
+
+from pairs_trading.apps.pair_selector.renderer import HtmlRenderer
+
+
+@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_]
+
+
+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=HtmlRenderer.render_data_quality(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=HtmlRenderer.render_pairs(pairs), content_type="text/html"
+        )
+
+
+if __name__ == "__main__":
+    App()
+    CvttAppConfig()
+    PairSelector()
+    App.instance().run()

apps/pair_selector/pair_selector_engine.py.md

@@ -1,3 +1,4 @@
+```python
 from __future__ import annotations
 from dataclasses import dataclass
 from typing import Any, Dict, List, Optional, Tuple
@@ -6,6 +7,7 @@ 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 statsmodels.tsa.vector_ar.vecm import coint_johansen  # type: ignore
 # ---
 from cvttpy_tools.base import NamedObject
 from cvttpy_tools.config import Config
@@ -389,3 +391,4 @@ class PairSelectionEngine(NamedObject):
 
     def pair_dicts(self) -> List[Dict[str, Any]]:
         return [p.as_dict() for p in self.pair_results_cache_]
+```

apps/pair_selector/renderer.py

@@ -1,96 +1,19 @@
 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
-
+class HtmlRenderer(NamedObject):
     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())
+        pass
 
-    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:
+    @staticmethod
+    def render_data_quality(quality: List[Dict[str, Any]]) -> str:
         rows = "".join(
             f"<tr>"
             f"<td>{q.get('instrument','')}</td>"
@@ -126,7 +49,8 @@ class PairSelector(NamedObject):
 </html>
 """
 
-    def _render_pairs_html(self, pairs: List[Dict[str, Any]]) -> str:
+    @staticmethod
+    def render_pairs(pairs: List[Dict[str, Any]]) -> str:
         if not pairs:
             body = "<p>No pairs available. Check data quality and try again.</p>"
         else:
@@ -214,10 +138,3 @@ class PairSelector(NamedObject):
 </body>
 </html>
 """
-
-
-if __name__ == "__main__":
-    App()
-    CvttAppConfig()
-    PairSelector()
-    App.instance().run()