.

apps/pair_selector.py

@@ -81,16 +81,18 @@ class PairSelector(NamedObject):
         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")
+        return web.Response(text=HtmlRenderer.render_data_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")
+        return web.Response(text=HtmlRenderer.render_pair_selection_html(pairs), content_type="text/html")
 
-    def _render_quality_html(self, quality: List[Dict[str, Any]]) -> str:
+class HtmlRenderer:
+    @staticmethod
+    def render_data_quality_html(quality: List[Dict[str, Any]]) -> str:
         rows = "".join(
             f"<tr>"
             f"<td>{q.get('instrument','')}</td>"
@@ -126,7 +128,8 @@ class PairSelector(NamedObject):
 </html>
 """
 
-    def _render_pairs_html(self, pairs: List[Dict[str, Any]]) -> str:
+    @staticmethod
+    def render_pair_selection_html(pairs: List[Dict[str, Any]]) -> str:
         if not pairs:
             body = "<p>No pairs available. Check data quality and try again.</p>"
         else:

lib/pair_selector_engine.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 from dataclasses import dataclass
+from readline import insert_text
 from typing import Any, Dict, List, Optional, Tuple
 
 import numpy as np
@@ -28,6 +29,7 @@ class InstrumentQuality(NamedObject):
 
 @dataclass
 class PairStats(NamedObject):
+    tstamp_: pd.Timestamp
     instrument_a_: ExchangeInstrument
     instrument_b_: ExchangeInstrument
     pvalue_eg_: Optional[float]
@@ -110,7 +112,7 @@ class QualityChecker(NamedObject):
 
         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_)
+        recency_cutoff = now_ts - pd.Timedelta(seconds=10 * self.interval_sec_)
         if latest_ts <= recency_cutoff:
             return InstrumentQuality(
                 instrument_=inst,
@@ -163,6 +165,21 @@ class QualityChecker(NamedObject):
             return float(prev_nt)
         return (prev_nt + next_nt) / 2.0
 
+class PairOfInstruments(NamedObject):
+    intrument_a_: ExchangeInstrument
+    instrument_b_: ExchangeInstrument
+    stats_history_: List[PairStats]
+
+
+    def __init__(self, instrument_a: ExchangeInstrument, instrument_b: ExchangeInstrument) -> None:
+        self.instrument_a_ = instrument_a
+        self.instrument_b_ = instrument_b
+        self.stats_history_ = []
+    
+    def add_stats(self, stats: PairStats) -> None:
+        self.stats_history_.append(stats)
+
+
 
 class PairAnalyzer(NamedObject):
     price_field_: str
@@ -172,27 +189,24 @@ class PairAnalyzer(NamedObject):
         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 analyze(self, pairs: List[PairOfInstruments], series: Dict[ExchangeInstrument, pd.DataFrame]) -> None:
+        for pair in pairs:
+            inst_a = pair.instrument_a_
+            inst_b = pair.instrument_b_
+            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:
+                pair.add_stats(stats)
+                
+        self._rank(pairs)
 
     def _compute_stats(
         self,
@@ -250,6 +264,7 @@ class PairAnalyzer(NamedObject):
             trace_stat = None
 
         return PairStats(
+            tstamp_=pd.Timestamp.utcnow(),
             instrument_a_=inst_a,
             instrument_b_=inst_b,
             pvalue_eg_=p_eg,
@@ -258,13 +273,15 @@ class PairAnalyzer(NamedObject):
             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:
+    def _rank(self, pairs: List[PairOfInstruments]) -> None:
+        last_stats: List[PairStats] = [pair.stats_history_[-1] for pair in pairs]
+    
+        self._assign_ranks(last_stats, key=lambda r: r.pvalue_eg_, attr="rank_eg_")
+        self._assign_ranks(last_stats, key=lambda r: r.pvalue_adf_, attr="rank_adf_")
+        self._assign_ranks(last_stats, key=lambda r: r.pvalue_j_, attr="rank_j_")
+        for res in last_stats:
             res.composite_rank_ = res.rank_eg_ + res.rank_adf_ + res.rank_j_
-        results.sort(key=lambda r: r.composite_rank_)
+        last_stats.sort(key=lambda r: r.composite_rank_)
 
     @staticmethod
     def _assign_ranks(
@@ -293,6 +310,9 @@ class PairSelectionEngine(NamedObject):
     data_quality_cache_: List[InstrumentQuality]
     pair_results_cache_: List[PairStats]
     
+    pairs_: List[PairOfInstruments]
+
+
     def __init__(
         self,
         config: Config,
@@ -324,6 +344,14 @@ class PairSelectionEngine(NamedObject):
         self.data_quality_cache_ = []
         self.pair_results_cache_ = []
         
+        self.create_pairs(instruments)
+
+    def create_pairs(self, instruments: List[ExchangeInstrument]) -> None:
+        self.pairs_ = []    
+        for i in range(len(instruments)):
+            for j in range(i + 1, len(instruments)):
+                self.pairs_.append(PairOfInstruments(instruments[i], instruments[j]))
+                
     async def run_once(self) -> None:
         quality_results: List[InstrumentQuality] = []
         price_series: Dict[ExchangeInstrument, pd.DataFrame] = {}
@@ -338,8 +366,9 @@ class PairSelectionEngine(NamedObject):
             df = self._to_dataframe(aggr, inst)
             if len(df) > 0:
                 price_series[inst] = df
+        self.analyzer_.analyze(pairs=self.pairs_, series=price_series)
         self.data_quality_cache_ = quality_results
-        self.pair_results_cache_ = self.analyzer_.analyze(price_series)
+        self.pair_results_cache_ = [pair.stats_history_[-1] for pair in self.pairs_]
 
     def _to_dataframe(self, aggr: List[MdTradesAggregate], inst: ExchangeInstrument) -> pd.DataFrame:
         rows: List[Dict[str, Any]] = []
@@ -388,4 +417,4 @@ class PairSelectionEngine(NamedObject):
         return res
 
     def pair_dicts(self) -> List[Dict[str, Any]]:
-        return [p.as_dict() for p in self.pair_results_cache_]
+        return [p.as_dict() for p in [pair.stats_history_[-1] for pair in self.pairs_]]