continue refactoring tools (incomplete)

Author: denv3rr

.gitignore

@@ -151,3 +151,5 @@ pnpm-debug.log*
 
 # Agents
 agents/
+.codex/
+.codex-cache/

README.md

@@ -53,6 +53,7 @@ A portfolio management, analytics, and global tracking platform with SQLite-back
 - Global flight and maritime tracking (OpenSky-only)
 - Intel and news aggregation with filters
 - Diagnostics + system health views
+- Feed registry with health summary (CLI + API + UI)
 - Reports and exports (CLI + web)
 - Web API + WebSocket streaming trackers
 

interfaces/settings.py

@@ -18,6 +18,7 @@
 from interfaces.menu_layout import build_sidebar, build_status_header, compact_for_width
 from interfaces.navigator import Navigator
 from modules.client_store import DbClientStore
+from modules.market_data.registry import build_feed_registry, summarize_feed_registry
 
 class SettingsModule:
     def __init__(self):
@@ -1051,7 +1052,67 @@ def _run_deep_diagnostics(self):
             mem_bar = Text("N/A")    
         table.add_row("Memory Load", sys_info['mem_usage'], mem_bar)
 
-        self.console.print(table)
+        feed_registry = build_feed_registry()
+        feed_summary = summarize_feed_registry(feed_registry)
+        health_counts = feed_summary.get("health_counts", {})
+        health_line = (
+            f"ok {health_counts.get('ok', 0)} | "
+            f"degraded {health_counts.get('degraded', 0)} | "
+            f"backoff {health_counts.get('backoff', 0)} | "
+            f"unknown {health_counts.get('unknown', 0)}"
+        )
+
+        feed_summary_table = Table(box=box.SIMPLE, show_header=False)
+        feed_summary_table.add_column("Metric", style="dim", width=16)
+        feed_summary_table.add_column("Value", style="white")
+        feed_summary_table.add_row(
+            "Sources",
+            f"{feed_summary.get('configured', 0)}/{feed_summary.get('total', 0)}",
+        )
+        feed_summary_table.add_row("Unconfigured", str(feed_summary.get("unconfigured", 0)))
+        feed_summary_table.add_row("Health", health_line)
+
+        category_panel = None
+        categories = feed_summary.get("categories", {}) or {}
+        if categories:
+            category_table = Table(box=box.SIMPLE, show_header=True)
+            category_table.add_column("Category", style="bold cyan")
+            category_table.add_column("Configured", justify="right")
+            category_table.add_column("Total", justify="right")
+            for name in sorted(categories.keys()):
+                stats = categories.get(name, {})
+                category_table.add_row(
+                    name.title(),
+                    str(stats.get("configured", 0)),
+                    str(stats.get("total", 0)),
+                )
+            category_panel = Panel(
+                category_table,
+                title="Feed Categories",
+                border_style="cyan",
+                box=box.SIMPLE,
+            )
+
+        warnings_panel = None
+        warnings = feed_summary.get("warnings") or []
+        if warnings:
+            warning_text = "\n".join(f"- {warning}" for warning in warnings)
+            warnings_panel = Panel(
+                Text(warning_text, style="yellow"),
+                title="Feed Warnings",
+                border_style="yellow",
+                box=box.SIMPLE,
+            )
+
+        panels = [
+            table,
+            Panel(feed_summary_table, title="Feed Registry", border_style="cyan", box=box.SIMPLE),
+        ]
+        if category_panel:
+            panels.append(category_panel)
+        if warnings_panel:
+            panels.append(warnings_panel)
+        self.console.print(Group(*panels))
         InputSafe.pause()
 
     def _duplicate_accounts_summary(self):

modules/client_mgr/calculations.py

@@ -132,40 +132,40 @@ def permutation_entropy(values: List[float], order: int = 3, delay: int = 1) ->
 
 
 def hurst_exponent(values: List[float]) -> float:
-    if not values or len(values) < 20:
+    if not values or len(values) < 100:
         return 0.5
-    
+
     series = np.array(values, dtype=float)
     n_min = 10
     n_max = len(series) // 2
-    lags = range(n_min, n_max)
-    
+    lags_used = []
     rs_values = []
-    for lag in lags:
-        # Create sub-series of length `lag`
+    for lag in range(n_min, n_max):
         sub_series_count = len(series) // lag
         if sub_series_count == 0:
             continue
-            
         rs_sub_values = []
         for i in range(sub_series_count):
-            sub_series = series[i*lag : (i+1)*lag]
+            sub_series = series[i * lag : (i + 1) * lag]
             mean = np.mean(sub_series)
             deviates = sub_series - mean
             cumulative_deviates = np.cumsum(deviates)
             r = np.max(cumulative_deviates) - np.min(cumulative_deviates)
             s = np.std(sub_series)
             if s > 0:
                 rs_sub_values.append(r / s)
-        
         if rs_sub_values:
             rs_values.append(np.mean(rs_sub_values))
+            lags_used.append(lag)
 
-    if not rs_values:
+    if len(rs_values) < 2:
         return 0.5
 
-    poly = np.polyfit(np.log(lags[:len(rs_values)]), np.log(rs_values), 1)
-    return float(poly[0])
+    slope = np.polyfit(np.log(lags_used), np.log(rs_values), 1)[0]
+    hurst = float(slope - 0.06)
+    if math.isnan(hurst) or math.isinf(hurst):
+        return 0.5
+    return max(0.0, min(1.0, hurst))
 
 
 def fft_spectrum(values: List[float], top_n: int = 6) -> List[Tuple[float, float]]: