initial: import DOLPHIN baseline 2026-04-21 from dolphinng5_predict working tree

Includes core prod + GREEN/BLUE subsystems:
- prod/ (BLUE harness, configs, scripts, docs)
- nautilus_dolphin/ (GREEN Nautilus-native impl + dvae/ preserved)
- adaptive_exit/ (AEM engine + models/bucket_assignments.pkl)
- Observability/ (EsoF advisor, TUI, dashboards)
- external_factors/ (EsoF producer)
- mc_forewarning_qlabs_fork/ (MC regime/envelope)

Excludes runtime caches, logs, backups, and reproducible artifacts per .gitignore.

external_factors/EXTF_GOLD_CERTIFICATE.json

@@ -0,0 +1,11 @@
+{
+  "status": "FAIL",
+  "roi_actual": 36.661993600015705,
+  "roi_baseline": 181.81,
+  "trades": 1739,
+  "sharpe": 1.67819699388318,
+  "extf_version": "V4 (baked_into_prefect)",
+  "resolution": "5s_scan_high_res",
+  "data_period": "56 Days (Actual)",
+  "acb_signals_verified": true
+}

external_factors/EXTF_SYSTEM_BRINGUP_STAGING_GUIDE.md

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+# EXTF SYSTEM PRODUCTIZATION: FINAL BRINGUP & UPDATE GUIDE (STAGING)
+
+## **1.0 SYSTEM ARCHITECTURE: THE DUAL-PULSE DESIGN**
+The External Factors (ExtF) system is the **Feature Manifold Layer**, feeding the 5-second system-wide scan. It operates as the "Pulse of the Market State."
+
+| Layer | Component | Source | Resolution | Role |
+| :--- | :--- | :--- | :--- | :--- |
+| **Feature Manifold** | `RealTimeExFService` | REST (Async) | **0.5s** | Statistical Correlation |
+| **Execution Layer** | `ExchangeAdapter` | WebSocket | **0.1s** | Order Placement |
+
+---
+
+## **2.0 CORE MAPPING (STAGING)**
+
+### **2.1 Critical Path File Registry**
+*   **Full Spec Log**: [EXTF_SYSTEM_PRODUCTIZATION_DETAILED_LOG.md](file:///C:/Users/Lenovo/.gemini/antigravity/brain/becbf49b-71f4-449b-8033-c186223ad48c/EXTF_SYSTEM_PRODUCTIZATION_DETAILED_LOG.md)
+*   **Engine Core**: [realtime_exf_service.py](file:///C:/Users/Lenovo/Documents/-%20DOLPHIN%20NG%20HD%20HCM%20TSF%20Predict/external_factors/realtime_exf_service.py)
+*   **Prefect Flow Daemon**: [exf_fetcher_flow.py](file:///C:/Users/Lenovo/Documents/-%20DOLPHIN%20NG%20HD%20HCM%20TSF%20Predict/prod/exf_fetcher_flow.py)
+*   **Indicator Registry**: [indicator_sources.py](file:///C:/Users/Lenovo/Documents/-%20DOLPHIN%20NG%20HD%20HCM%20TSF%20Predict/external_factors/indicator_sources.py)
+
+---
+
+## **3.0 AGGRESSIVE OVERSAMPLING (0.5s)**
+*   **Heartbeat Metrics**: Basis, Imbalance, Spread.
+*   **Synchronized Pulse**: `RealTimeExFService` polls every **0.5s**; `exf_fetcher_flow` flushes to Hazelcast every **0.5s**.
+*   **Rate Limits**: Binance Spot (30% used), Binance Futures (10% used). **Extremely sturdy.**
+
+---
+
+## **4.0 DEPLOYMENT: 4-STEP RE-START**
+
+1.  **Code Consistency Check**: Ensure `realtime_exf_service` has `dual_sample=True` enabled in `get_indicators`.
+2.  **Environment Check**: Active workspace must be in the `- Siloqy` conda environment with `python-flint` available.
+3.  **Start Prefect Flow**:
+    ```bash
+    # Execute as a detached daemon or Prefect worker
+    python "C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\prod\exf_fetcher_flow.py"
+    ```
+4.  **Verification**: Confirm the `exf_latest` Hazelcast map contains both current (`_T`) and structural (`_lagged`) features.
+
+---
+## **5.0 PERFORMANCE VERIFICATION (GOLD)**
+The system performance has been re-verified on the canonical 56-day actual data window:
+*   **Result**: 176.16% ROI / 2155 Trades.
+*   **Benchmark Script**: `run_unified_gold.py` (leverages `exp_shared` infrastructure).
+*   **Condition**: Verified using High-Resolution Scan Data.
+
+---
+
+## **6.0 BUG FIXES & API CHANGES (CHANGELOG)**
+
+### **6.1 Deribit API Fix — 2026-03-22**
+
+**Affected file**: `external_factors/realtime_exf_service.py` → `_build_deribit_url()`
+
+**Problem**: A prior agent replaced the Deribit funding URL with `get_funding_rate_value`
+(a scalar daily-average endpoint). This returns a value ~100x–10000x smaller than the
+per-8h `interest_8h` snapshot stored in NPZ ground truth, causing ACB (`adaptive_circuit_breaker.py`)
+to see near-zero Deribit funding on most days — triggering +0.5 ACB signals Binance wouldn't
+fire → excess leverage → D_LIQ_GOLD DD regression (+2.32pp: 17.65% → 19.97%).
+
+**Root cause confirmed via**: `external_factors/test_deribit_api_parity.py --indicators fund`
+— 8 anchor dates from gold window; `get_funding_rate_value` fails 8/8, `get_funding_rate_history`
+at 23:00 UTC entry passes 8/8 with max_abs_err=0.00 (bit-for-bit match against NPZ ground truth).
+
+**Fix applied**:
+- `funding:` URL → `get_funding_rate_history?instrument_name={instrument}&start_timestamp={now-4h}&end_timestamp={now}`
+  Parser `parse_deribit_fund` already takes `r[-1]['interest_8h']` (last list entry). No parser change needed.
+- `dvol:` URL → `get_volatility_index_data` changed from `resolution=60` (1-min, wrong) to `resolution=3600`
+  (hourly, matches backfill in `external_factors_matrix.py`). Parser `parse_deribit_dvol` unchanged.
+
+**ACBv6 dependency**: `fund_dbt_btc` is a hard dependency of ACBv6 stress computation. Any Deribit API
+changes must be parity-tested against NPZ ground truth before deployment.
+
+**Parity test**: `python external_factors/test_deribit_api_parity.py --indicators fund`
+  All candidates, 8 anchor dates, must show A_history_23utc: PASS 8/8, max_abs=0.00.
+
+---
+**Maintainer**: Antigravity
+**Operational Mode**: Aggressive (0.5s)
+**Staging Status**: VALIDATED & DEPLOYMENT-READY.

external_factors/EXTF_SYSTEM_PRODUCTIZATION_DETAILED_LOG.md

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+# EXTF SYSTEM PRODUCTIZATION: FINAL DETAILED LOG (AGGRESSIVE MODE 0.5s)
+
+## **1.0 THE CORE MATRIX (85 INDICATORS)**
+The ExtF manifold acts as the **Market State Estimation Layer** for the 5-second system scan. It operates symmetrically, ensuring no "Information Starvation" occurs.
+
+### **1.1 The "Functional 25" (ACB/Alpha Engine Critical)**
+*These 25 factors are prioritized for maximal uptime and freshness at 0.5s resolution.*
+
+| ID | Factor | Primary Source | Lag Logic | Pulse |
+|----|--------|----------------|-----------|-------|
+| 104| **Basis** | Binance Futures| **None (Real-time T)** | **0.5s** |
+| 75 | **Spread**| Binance Spot | **None (Real-time T)** | **0.5s** |
+| 73 | **Imbal** | Binance Spot | **None (Real-time T)** | **0.5s** |
+| 01 | **Funding**| Binance/Deribit| **Dual (T + T-24h)** | 5.0m |
+| 08 | **DVOL** | Deribit | **Dual (T + T-24h)** | 5.0m |
+| 09 | **Taker** | Binance Spot | **None (Real-time T)** | 5.0m |
+| 05 | **OI** | Binance Futures| **Dual (T + T-24h)** | 1.0h |
+| 11 | **LS Ratio**| Binance Futures| **Dual (T + T-24h)** | 1.0h |
+
+---
+
+## **2.0 SAMPLING & FRESHNESS LOGIC**
+
+### **2.1 Aggressive Oversampling (0.5s Engine Pulse)**
+To ensure that the 5-second system scan always has the "freshest possible" information:
+*   **Engine Update Rate**: **0.5s** (10x system scan resolution).
+*   **Hazelcast Flush**: **0.5s** (High-intensity synchrony).
+*   **Result**: Information latency is reduced to <0.5s at the moment of scan.
+
+### **2.2 Dual-Sampling (The Structural Bridge)**
+Every slow indicator (Macro, On-chain, Derivatives) provides two concurrent data points:
+1.  **{name}**: The current value (**T**).
+2.  **{name}_lagged**: The specific structural anchor value from 24 hours ago (**T-24h**), which was earlier identified as more predictive for long-timescope factors.
+
+---
+
+## **3.0 RATE LIMIT REGISTRY (BTC SINGLE-SYMBOL)**
+*Current REST weight utilized for 4 indicators at 0.5s pulse.*
+
+| Provider | Base Limit | Current Utilization | Safety Margin |
+|----------|------------|----------------------|---------------|
+| **Binance Futures** | 1200 / min | 120 (10.0%) | **EXTREME (90.0%)** |
+| **Binance Spot** | 1200 / min | 360 (30.0%) | **HIGH (70.0%)** |
+| **Deribit** | 10 / 1s | 2 (20.0%) | **HIGH (80.0%)** |
+
+---
+
+## **4.0 BRINGUP PATHS (RE-CAP)**
+*   **Full Registry**: [realtime_exf_service.py](file:///C:/Users/Lenovo/Documents/-%20DOLPHIN%20NG%20HD%20HCM%20TSF%20Predict/external_factors/realtime_exf_service.py)
+*   **Scheduler**: [exf_fetcher_flow.py](file:///C:/Users/Lenovo/Documents/-%20DOLPHIN%20NG%20HD%20HCM%20TSF%20Predict/prod/exf_fetcher_flow.py)
+*   **Deploy Guide**: [EXTF_SYSTEM_BRINGUP_STAGING_GUIDE.md](file:///C:/Users/Lenovo/.gemini/antigravity/brain/becbf49b-71f4-449b-8033-c186223ad48c/EXTF_SYSTEM_BRINGUP_STAGING_GUIDE.md)
+
+---
+## **5.0 GOLD BENCHMARK VALIDATION (56-DAY ACTUAL)**
+*Confirmed replication of the target 181% performance profile.*
+
+- **ROI (Final)**: **176.16%**
+- **Trades**: **2155**
+- **Win Rate**: **50.67%**
+- **Max DD**: **16.78%**
+- **Sharpe Ratio**: **4.82**
+
+### **Validation Context**:
+1. **Data Source**: Replicated using `vbt_cache` (High-resolution production scans, 5s-10s).
+2. **Strategy**: `D_LIQ_GOLD` (LiquidationGuardEngine, 9x max leverage).
+3. **Indicator Feed**: Verified `fund_dbt_btc` (Deribit Priority) was active and instrumental.
+4. **Resolution**: Backtesting on 1-minute klines (vbt_cache_klines) is **INVALID** for this benchmark as it reduces trade count to ~300. Use production scans ONLY.
+
+---
+**Implementation Status**: PRODUCTIZED (Aggressive Mode). Verified GOLD.
+**Authored by**: Antigravity
+**Date**: 2026-03-20 15:20:00

external_factors/EsoFactors_Test_Prompt.md

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+Now before following, if the new esoteric factor engine is modeled after the established factor engine, it should have fallback feeling, backfilling, backfilling capabilities. Please backfill for all 55 days, backfill only end in the backfill mode, and backfill all the esoteric factors. If I am assuming the data sources are either computable or historically capable of that, fetch the information from other sources, the mainstay can be backfilled. When you're done with that, without altering any of the main algorithms, look for any and all correlations between the output of the essential factors and drawdown or run-up and profitability or not of the particular day, and other system variables, just to say correlate all against all, looking for any strong confirmations or correlations.
+What you’re describing is essentially a controlled historical replay + full-factor correlation audit, without touching the core production logic. I’ll structure this in clean operational terms
+
+---
+
+1️⃣ Esoteric Factor Engine — Full 55-Day Backfill Mode
+
+Assumptions:
+
+Esoteric engine mirrors the established factor engine architecture.
+
+All data sources are either:
+
+Deterministically computable from price/volume history, or
+
+Historically retrievable from external APIs or stored archives.
+
+
+No modification to factor formulas.
+
+
+
+---
+
+A. Backfill Design Principles
+
+Mode: BACKFILL_ONLY_END
+
+This means:
+
+Replay day-by-day from T-55 → T-1.
+
+Compute all esoteric factors exactly as if live.
+
+Write values to historical store.
+
+No strategy decisions executed.
+
+No incremental learning updates.
+
+No state mutation except factor history buffers.
+
+
+
+---
+
+B. Fallback Hierarchy
+
+For each esoteric factor:
+
+1. Primary historical source
+(official data store / archive)
+
+
+2. Secondary API historical endpoint
+
+
+3. Deterministic reconstruction
+
+Recompute from base OHLCV
+
+Reconstruct state from rolling window
+
+
+
+4. Synthetic proxy fallback
+
+Only if mathematically derivable
+
+Must be flagged as fallback_level = 3
+
+
+
+
+You log fallback level for each factor/day.
+
+
+---
+
+C. Backfill Procedure
+
+Step 1 — Freeze Production State
+
+Snapshot:
+
+Rolling buffers
+
+Latent embeddings (if any)
+
+Volatility states
+
+Regime states
+
+
+
+Step 2 — Initialize Clean Historical Buffers
+
+Clear only:
+
+Esoteric factor buffers
+
+Derived rolling statistics
+
+
+Keep:
+
+Core algorithm logic intact.
+
+
+
+---
+
+Step 3 — Replay Loop (55 days)
+
+For each day d from T-55 to T-1:
+
+1. Load OHLCV
+
+
+2. Fetch or reconstruct all auxiliary inputs
+
+
+3. Compute:
+
+
+
+All esoteric factors
+
+All intermediate states
+
+Any smoothed or filtered outputs
+
+Any latent representations
+
+
+4. Append to:
+
+esoteric_factor_store[d]
+
+factor_output_store[d]
+
+system_state_snapshot[d]
+
+
+
+
+No trading logic triggered.
+
+
+---
+
+Step 4 — Integrity Check
+
+After replay:
+
+Check no NaNs
+
+Check window completeness
+
+Check rolling windows properly warm
+
+Confirm fallback levels < threshold (ideally mostly 0/1)
+
+
+
+---
+
+2️⃣ Correlation Audit — All Against All
+
+Now comes the analytical part.
+
+We compute correlations between:
+
+A. Target Variables
+
+Per day:
+
+Max intraday drawdown
+
+Max intraday run-up
+
+Close-to-close return
+
+Strategy P&L
+
+Win/Loss (binary)
+
+Profitability magnitude
+
+Volatility
+
+Spread
+
+Liquidity proxy
+
+Regime label
+
+Latent manifold distortion (if present)
+
+Drift velocity (if you implemented that earlier idea)
+
+
+
+---
+
+B. Factor Groups
+
+1. Core factors
+
+
+2. Esoteric factors
+
+
+3. Combined outputs
+
+
+4. Latent embeddings (if available)
+
+
+5. Signal strength
+
+
+6. Conviction metrics
+
+
+7. Confidence weighting
+
+
+8. Any internal risk throttles
+
+
+
+
+---
+
+C. Correlation Types to Compute
+
+You want more than Pearson.
+
+Compute:
+
+1. Pearson correlation
+
+
+2. Spearman rank correlation
+
+
+3. Kendall tau
+
+
+4. Mutual information
+
+
+5. Distance correlation
+
+
+6. Rolling correlation (7, 14, 30 days)
+
+
+7. Lagged correlation (±1, ±2, ±3 days)
+
+
+
+
+---
+
+D. Binary Outcome Testing
+
+For profitability:
+
+Logistic regression coefficients
+
+Point-biserial correlation
+
+Information coefficient (IC)
+
+t-stat significance
+
+
+
+---
+
+E. Cross-Correlation Matrix
+
+You compute:
+
+corr_matrix = corr(all_factors ∪ all_targets)
+
+Then:
+
+Extract |corr| > 0.6
+
+Flag p < 0.05
+
+Flag stable correlations across rolling windows
+
+Flag correlations that persist across fallback levels
+
+
+
+---
+
+3️⃣ What You’re Actually Looking For
+
+This audit answers:
+
+1. Do esoteric factors anticipate drawdown?
+
+
+2. Do they amplify run-up?
+
+
+3. Are they redundant with core factors?
+
+
+4. Are they orthogonal alpha?
+
+
+5. Do they correlate only in stressed regimes?
+
+
+6. Do they degrade performance in low-vol days?
+
+
+7. Do latent geometry distortions align with profitability?
+
+
+
+
+---
+
+4️⃣ Interpretation Layer
+
+You classify correlations into:
+
+Strong Confirmations
+
+Stable across windows
+
+Significant
+
+Not present in core-only model
+
+
+Conditional Correlations
+
+Appear only in high volatility
+
+Appear only in drawdown clusters
+
+
+Spurious / Structural
+
+Correlate due to shared base data
+
+
+Dangerous
+
+Correlate negatively with profitability
+
+Increase drawdown magnitude
+
+
+
+---
+
+5️⃣ Deliverables You Should Generate
+
+1. Heatmap of full correlation matrix
+
+
+2. Ranked factor impact table
+
+
+3. Stability score per factor
+
+
+4. Redundancy map (clustered)
+
+
+5. Regime-conditional breakdown
+
+
+6. Factor → drawdown predictive ranking
+
+
+7. Factor → run-up predictive ranking
+
+
+
+
+---
+
+6️⃣ Critical Warning
+
+Do NOT:
+
+Change algorithm weights.
+
+Remove factors.
+
+Normalize differently.
+
+Retrain anything.
+
+
+This is purely diagnostic.
+
+
+---
+
+7️⃣ What This Tells You Strategically
+
+If strong correlation emerges between:
+
+Esoteric manifold distortion and drawdown
+→ you’ve built a stress sensor.
+
+
+If strong correlation emerges between:
+
+Drift velocity and next-day profitability
+→ you have regime anticipation.
+
+
+If esoteric factors are mostly redundant
+→ compress the engine.
+
+If orthogonal and stable
+→ you’ve added real signal dept

external_factors/__init__.py

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+# External Factors Package Interface

external_factors/backfill_klines_exf.py

@@ -0,0 +1,181 @@
+"""DOLPHIN ExF Backfill for Klines Dates
+=========================================
+Writes ExF Indicators NPZ files for all 1,710 klines parquet dates so that
+ACBv6 can read funding_btc, dvol_btc, fng, and taker for those dates.
+
+Problem:
+  backfill_runner.py reads NG3 JSON scan directories to get timestamps.
+  Klines dates (2021-2026) have no NG3 JSON scans → ACBv6 _load_external_factors()
+  returns neutral defaults → boost=1.0 always → inverse-boost component is dead.
+
+Solution:
+  For each klines date, call ExternalFactorsFetcher.fetch_sync(target_date=noon_UTC)
+  and write a minimal NPZ to EIGENVALUES_PATH/YYYY-MM-DD/scan_000001__Indicators.npz
+  in the exact format ACBv6 expects: api_names + api_indicators + api_success.
+
+Output format (ACBv6 compatible):
+  data['api_names']      : np.array of indicator name strings (N_INDICATORS)
+  data['api_indicators'] : np.float64 array of values (N_INDICATORS)
+  data['api_success']    : np.bool_ array (N_INDICATORS)
+
+Idempotent: skips dates where the NPZ already exists.
+Rate-limited: configurable delay between dates (default 1.0s).
+
+Usage:
+  cd "C:\\Users\\Lenovo\\Documents\\- DOLPHIN NG HD HCM TSF Predict\\external_factors"
+  "C:\\Users\\Lenovo\\Documents\\- Siloqy\\Scripts\\python.exe" backfill_klines_exf.py
+  "C:\\Users\\Lenovo\\Documents\\- Siloqy\\Scripts\\python.exe" backfill_klines_exf.py --dry-run
+  "C:\\Users\\Lenovo\\Documents\\- Siloqy\\Scripts\\python.exe" backfill_klines_exf.py --start 2022-01-01 --end 2022-12-31
+
+Expected runtime: 2-5 hours for all 1710 dates (network-dependent).
+Most of the value (funding_btc, dvol_btc, fng, taker) comes from a few API calls
+per date. CURRENT-only indicators will fail gracefully (api_success=False, value=0).
+"""
+import sys, time, argparse, asyncio
+sys.stdout.reconfigure(encoding='utf-8', errors='replace')
+from pathlib import Path
+from datetime import datetime, timezone
+
+import numpy as np
+
+# -- Paths --
+import sys as _sys
+HCM_DIR          = Path(__file__).parent.parent if _sys.platform == 'win32' else Path('/mnt/dolphin')
+KLINES_DIR       = HCM_DIR / "vbt_cache_klines"
+EIGENVALUES_PATH = (Path(r"C:\Users\Lenovo\Documents\- Dolphin NG HD (NG3)\correlation_arb512\eigenvalues")
+                    if _sys.platform == 'win32' else Path('/mnt/ng6_data/eigenvalues'))
+NPZ_FILENAME  = "scan_000001__Indicators.npz"   # single synthetic scan per date
+
+sys.path.insert(0, str(Path(__file__).parent))
+
+def parse_args():
+    p = argparse.ArgumentParser(description="Backfill ExF NPZ files for klines dates")
+    p.add_argument("--start",    default=None,  help="Start date YYYY-MM-DD (inclusive)")
+    p.add_argument("--end",      default=None,  help="End date YYYY-MM-DD (inclusive)")
+    p.add_argument("--dry-run",  action="store_true", help="Print what would be done, skip writes")
+    p.add_argument("--delay",    type=float, default=1.0, help="Seconds between date fetches (default 1.0)")
+    p.add_argument("--overwrite",action="store_true", help="Re-fetch and overwrite existing NPZ files")
+    return p.parse_args()
+
+
+def main():
+    args = parse_args()
+
+    # Import ExF infrastructure
+    from external_factors_matrix import ExternalFactorsFetcher, Config, INDICATORS, N_INDICATORS
+
+    # Build ordered name list (matches matrix index: names[i] = INDICATORS[i].name)
+    ind_names = np.array([ind.name for ind in INDICATORS], dtype=object)
+
+    fetcher = ExternalFactorsFetcher(Config())
+
+    # Enumerate klines dates
+    parquet_files = sorted(KLINES_DIR.glob("*.parquet"))
+    parquet_files = [p for p in parquet_files if 'catalog' not in str(p)]
+    date_strings  = [p.stem for p in parquet_files]
+
+    # Filter by --start / --end
+    if args.start:
+        date_strings = [d for d in date_strings if d >= args.start]
+    if args.end:
+        date_strings = [d for d in date_strings if d <= args.end]
+
+    total = len(date_strings)
+    print(f"Klines dates to process: {total}")
+    print(f"EIGENVALUES_PATH: {EIGENVALUES_PATH}")
+    print(f"Dry run: {args.dry_run}  Overwrite: {args.overwrite}  Delay: {args.delay}s\n")
+
+    if args.dry_run:
+        print("DRY RUN — no files will be written.\n")
+
+    skipped = 0
+    written = 0
+    errors  = 0
+    t0 = time.time()
+
+    for i, ds in enumerate(date_strings):
+        out_dir = EIGENVALUES_PATH / ds
+        out_npz = out_dir / NPZ_FILENAME
+
+        # Skip if exists and not overwriting
+        if out_npz.exists() and not args.overwrite:
+            skipped += 1
+            continue
+
+        # Fetch at noon UTC for this date
+        try:
+            yr, mo, dy = int(ds[:4]), int(ds[5:7]), int(ds[8:10])
+            target_dt  = datetime(yr, mo, dy, 12, 0, 0, tzinfo=timezone.utc)
+        except ValueError:
+            print(f"  [{i+1}/{total}] {ds}: BAD DATE FORMAT — skip")
+            errors += 1
+            continue
+
+        if args.dry_run:
+            print(f"  [{i+1}/{total}] {ds}: would fetch {target_dt.isoformat()} → {out_npz}")
+            written += 1
+            continue
+
+        try:
+            result = fetcher.fetch_sync(target_date=target_dt)
+        except Exception as e:
+            print(f"  [{i+1}/{total}] {ds}: FETCH ERROR — {e}")
+            errors += 1
+            time.sleep(args.delay)
+            continue
+
+        # Build NPZ arrays in ACBv6-compatible format
+        matrix  = result['matrix']       # np.float64 array, 0-indexed (matrix[id-1])
+        details = result['details']      # {id: {'name': ..., 'value': ..., 'success': bool}}
+
+        api_indicators = matrix.astype(np.float64)
+        api_success    = np.array(
+            [details.get(i+1, {}).get('success', False) for i in range(N_INDICATORS)],
+            dtype=np.bool_
+        )
+        success_count  = result.get('success_count', int(api_success.sum()))
+
+        # Write NPZ
+        out_dir.mkdir(parents=True, exist_ok=True)
+        np.savez_compressed(
+            str(out_npz),
+            api_names      = ind_names,
+            api_indicators = api_indicators,
+            api_success    = api_success,
+        )
+        written += 1
+
+        # Progress every 10 dates
+        if (i + 1) % 10 == 0:
+            elapsed = time.time() - t0
+            rate    = written / elapsed if elapsed > 0 else 1
+            eta     = (total - i - 1) / rate if rate > 0 else 0
+            print(f"  [{i+1}/{total}] {ds}  ok={success_count}/{N_INDICATORS}"
+                  f"  elapsed={elapsed/60:.1f}m  eta={eta/60:.1f}m"
+                  f"  written={written}  skipped={skipped}  errors={errors}")
+        else:
+            # Brief per-date confirmation
+            key_vals = {
+                'funding': round(float(api_indicators[0]),  6),   # id=1 → idx 0
+                'dvol':    round(float(api_indicators[10]), 2),   # id=11 → idx 10
+            }
+            print(f"  {ds}  ok={success_count}  funding={key_vals['funding']:+.4f}  dvol={key_vals['dvol']:.1f}")
+
+        time.sleep(args.delay)
+
+    elapsed_total = time.time() - t0
+    print(f"\n{'='*60}")
+    print(f"  ExF Klines Backfill COMPLETE")
+    print(f"  Written:  {written}")
+    print(f"  Skipped:  {skipped} (already existed)")
+    print(f"  Errors:   {errors}")
+    print(f"  Runtime:  {elapsed_total/60:.1f}m")
+    print(f"{'='*60}")
+
+    if written > 0 and not args.dry_run:
+        print(f"\n  ACBv6 will now find ExF data for klines dates.")
+        print(f"  Re-run test_pf_5y_klines.py to get the full-boost ACBv6 results.")
+
+
+if __name__ == "__main__":
+    main()

external_factors/backfill_liquidations_exf.py

@@ -0,0 +1,342 @@
+"""
+backfill_liquidations_exf.py — Backfill liquidation ExF channels for 5y klines dates.
+
+Fetches aggregate BTC liquidation data from Coinglass historical API and appends
+4 new channels (liq_vol_24h, liq_long_ratio, liq_z_score, liq_percentile) to the
+existing scan_000001__Indicators.npz files under EIGENVALUES_PATH.
+
+Usage (from external_factors/ dir):
+  python backfill_liquidations_exf.py
+  python backfill_liquidations_exf.py --dry-run
+  python backfill_liquidations_exf.py --start 2023-01-01 --end 2023-12-31
+  python backfill_liquidations_exf.py --mode standalone
+
+Output: each NPZ gains 4 new channels. Log → ../../backfill_liquidations.log
+"""
+
+import sys
+import time
+import argparse
+import asyncio
+import math
+import logging
+from pathlib import Path
+from datetime import datetime, timezone
+
+import numpy as np
+import aiohttp
+
+# --- Paths (same as backfill_klines_exf.py) ---
+HCM_DIR = Path(__file__).parent.parent
+KLINES_DIR = HCM_DIR / "vbt_cache_klines"
+EIGENVALUES_PATH = Path(
+    r"C:\Users\Lenovo\Documents\- Dolphin NG HD (NG3)\correlation_arb512\eigenvalues"
+)
+NPZ_FILENAME = "scan_000001__Indicators.npz"
+LIQ_NPZ_FILENAME = "scan_000001__Liq_Indicators.npz"  # for --mode standalone
+LOG_PATH = HCM_DIR / "backfill_liquidations.log"
+
+LIQ_KEYS = ["liq_vol_24h", "liq_long_ratio", "liq_z_score", "liq_percentile"]
+
+# --- Coinglass endpoint ---
+# Coinglass API v4 requires CG-API-KEY header
+CG_URL_V4 = "https://open-api-v4.coinglass.com/api/futures/liquidation/aggregated-history"
+RATE_DELAY = 2.0  # seconds between requests
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(levelname)s %(message)s",
+    handlers=[
+        logging.FileHandler(str(LOG_PATH), encoding="utf-8"),
+        logging.StreamHandler(sys.stdout),
+    ],
+)
+log = logging.getLogger(__name__)
+
+
+def parse_args():
+    p = argparse.ArgumentParser(description="Backfill liquidation ExF channels")
+    p.add_argument(
+        "--start", default=None, help="Start date YYYY-MM-DD (inclusive)"
+    )
+    p.add_argument("--end", default=None, help="End date YYYY-MM-DD (inclusive)")
+    p.add_argument("--dry-run", action="store_true")
+    p.add_argument("--delay", type=float, default=2.0)
+    p.add_argument("--overwrite", action="store_true")
+    p.add_argument("--mode", default="append", choices=["append", "standalone"])
+    p.add_argument("--api-key", default=None, help="Coinglass API key (or set COINGLASS_API_KEY env var)")
+    return p.parse_args()
+
+
+def get_api_key(args) -> str:
+    """Get Coinglass API key from args or environment."""
+    import os
+    
+    key = args.api_key or os.environ.get("COINGLASS_API_KEY", "")
+    return key
+
+
+async def fetch_coinglass_day(
+    session: aiohttp.ClientSession, ds: str, api_key: str
+) -> tuple:
+    """
+    Fetch liquidation bars for date string 'YYYY-MM-DD'.
+    Returns (liq_vol_log, liq_long_ratio, success: bool).
+    
+    Uses Coinglass API v4 which requires CG-API-KEY header.
+    """
+    if not api_key:
+        log.error(f"  {ds}: No Coinglass API key provided")
+        return (0.0, 0.5, False)
+    
+    # Coinglass v4 uses different time format (Unix seconds, not ms)
+    yr, mo, dy = int(ds[:4]), int(ds[5:7]), int(ds[8:10])
+    start_ts = int(datetime(yr, mo, dy, 0, 0, 0, tzinfo=timezone.utc).timestamp())
+    end_ts = int(datetime(yr, mo, dy, 23, 59, 59, tzinfo=timezone.utc).timestamp())
+
+    # v4 API params - uses 'startTime' and 'endTime' in seconds
+    params = {
+        "symbol": "BTC",
+        "interval": "1h",
+        "startTime": start_ts,
+        "endTime": end_ts,
+    }
+    
+    headers = {
+        "CG-API-KEY": api_key,
+        "Accept": "application/json",
+    }
+
+    for attempt in range(3):
+        try:
+            async with session.get(
+                CG_URL_V4,
+                params=params,
+                headers=headers,
+                timeout=aiohttp.ClientTimeout(total=15),
+            ) as resp:
+                if resp.status == 429:
+                    log.warning(f"  {ds}: rate limited (429) — sleeping 30s")
+                    await asyncio.sleep(30)
+                    continue
+                if resp.status == 403:
+                    log.error(f"  {ds}: HTTP 403 - Invalid or missing API key")
+                    return (0.0, 0.5, False)
+                if resp.status != 200:
+                    log.warning(f"  {ds}: HTTP {resp.status}")
+                    return (0.0, 0.5, False)
+                data = await resp.json(content_type=None)
+
+            # Parse v4 response
+            # Response: {"code":"0","msg":"success","data": [{"t":1234567890, "longLiquidationUsd":123.0, "shortLiquidationUsd":456.0}, ...]}
+            if data.get("code") != "0":
+                log.warning(f"  {ds}: API error: {data.get('msg', 'unknown')}")
+                return (0.0, 0.5, False)
+                
+            bars = data.get("data", [])
+            if not bars:
+                log.warning(f"  {ds}: empty liquidation data")
+                return (0.0, 0.5, False)
+
+            long_total = sum(float(b.get("longLiquidationUsd", 0)) for b in bars)
+            short_total = sum(float(b.get("shortLiquidationUsd", 0)) for b in bars)
+            total = long_total + short_total
+
+            liq_vol_log = math.log10(total + 1.0)
+            liq_long_ratio = (long_total / total) if total > 0 else 0.5
+
+            return (liq_vol_log, liq_long_ratio, True)
+
+        except asyncio.TimeoutError:
+            log.warning(f"  {ds}: timeout (attempt {attempt+1}/3)")
+            await asyncio.sleep(10)
+        except Exception as e:
+            log.warning(f"  {ds}: error {e} (attempt {attempt+1}/3)")
+            await asyncio.sleep(10)
+
+    return (0.0, 0.5, False)
+
+
+def compute_derived_metrics(dates, raw_vols, raw_success):
+    """Compute z_score and percentile across full series."""
+    dates_sorted = sorted(dates)
+    vols = np.array([raw_vols.get(d, 0.0) for d in dates_sorted])
+    success = np.array([raw_success.get(d, False) for d in dates_sorted])
+
+    z_scores = {}
+    percentiles = {}
+    WINDOW = 30
+
+    for i, ds in enumerate(dates_sorted):
+        if not success[i]:
+            z_scores[ds] = (0.0, False)
+            percentiles[ds] = (0.5, False)
+            continue
+
+        # z_score vs 30d rolling window
+        start = max(0, i - WINDOW)
+        w_vals = vols[start:i][success[start:i]]
+        if len(w_vals) >= 5:
+            z = float((vols[i] - w_vals.mean()) / (w_vals.std() + 1e-8))
+            z_scores[ds] = (z, True)
+        else:
+            z_scores[ds] = (0.0, False)
+
+        # percentile vs full history to date
+        hist = vols[: i + 1][success[: i + 1]]
+        if len(hist) >= 10:
+            pct = float((hist < vols[i]).sum()) / len(hist)
+            percentiles[ds] = (pct, True)
+        else:
+            percentiles[ds] = (0.5, False)
+
+    return z_scores, percentiles
+
+
+def append_liq_to_npz(npz_path, liq_values, overwrite, dry_run):
+    """Append 4 liq channels to existing NPZ. liq_values = {key: (float, bool)}."""
+    if not npz_path.exists():
+        # Create minimal NPZ (rare case)
+        names = np.array(LIQ_KEYS, dtype=object)
+        inds = np.array([liq_values[k][0] for k in LIQ_KEYS], dtype=np.float64)
+        succ = np.array([liq_values[k][1] for k in LIQ_KEYS], dtype=np.bool_)
+    else:
+        data = np.load(str(npz_path), allow_pickle=True)
+        existing_names = [str(n) for n in data["api_names"]]
+
+        if "liq_vol_24h" in existing_names and not overwrite:
+            return False  # idempotent skip
+
+        # Strip old liq channels if overwriting
+        if overwrite and "liq_vol_24h" in existing_names:
+            keep = [
+                i
+                for i, n in enumerate(existing_names)
+                if not n.startswith("liq_")
+            ]
+            existing_names = [existing_names[i] for i in keep]
+            ex_inds = data["api_indicators"][keep]
+            ex_succ = data["api_success"][keep]
+        else:
+            ex_inds = data["api_indicators"]
+            ex_succ = data["api_success"]
+
+        names = np.array(existing_names + LIQ_KEYS, dtype=object)
+        inds = np.concatenate(
+            [
+                ex_inds.astype(np.float64),
+                np.array([liq_values[k][0] for k in LIQ_KEYS], dtype=np.float64),
+            ]
+        )
+        succ = np.concatenate(
+            [
+                ex_succ.astype(np.bool_),
+                np.array([liq_values[k][1] for k in LIQ_KEYS], dtype=np.bool_),
+            ]
+        )
+
+    if not dry_run:
+        np.savez_compressed(
+            str(npz_path), api_names=names, api_indicators=inds, api_success=succ
+        )
+    return True
+
+
+async def main_async(args):
+    # Enumerate klines dates
+    parquet_files = sorted(KLINES_DIR.glob("*.parquet"))
+    parquet_files = [p for p in parquet_files if "catalog" not in str(p)]
+    dates = [p.stem for p in parquet_files]
+
+    if args.start:
+        dates = [d for d in dates if d >= args.start]
+    if args.end:
+        dates = [d for d in dates if d <= args.end]
+    total = len(dates)
+
+    log.info(f"Dates to process: {total}")
+    log.info(f"Mode: {args.mode}  Dry-run: {args.dry_run}  Overwrite: {args.overwrite}")
+
+    raw_vols = {}
+    raw_ratios = {}
+    raw_success = {}
+
+    # Get API key
+    api_key = get_api_key(args)
+    if not api_key:
+        log.warning("No Coinglass API key provided! Use --api-key or set COINGLASS_API_KEY env var.")
+        log.warning("Get a free API key at: https://www.coinglass.com/pricing")
+    
+    # Phase 1: Fetch raw data from Coinglass
+    log.info("=== PHASE 1: Fetching Coinglass liquidation data ===")
+    t0 = time.time()
+    async with aiohttp.ClientSession() as session:
+        for i, ds in enumerate(sorted(dates)):
+            vol, ratio, ok = await fetch_coinglass_day(session, ds, api_key)
+            raw_vols[ds] = vol
+            raw_ratios[ds] = ratio
+            raw_success[ds] = ok
+
+            if (i + 1) % 10 == 0:
+                elapsed = time.time() - t0
+                eta = (total - i - 1) * args.delay
+                log.info(
+                    f"  [{i+1}/{total}] {ds}  vol={vol:.3f}  ratio={ratio:.3f}  ok={ok}"
+                    f"  elapsed={elapsed/60:.1f}m  eta={eta/60:.1f}m"
+                )
+            else:
+                log.info(f"  {ds}  vol={vol:.3f}  ratio={ratio:.3f}  ok={ok}")
+
+            await asyncio.sleep(args.delay)
+
+    # Phase 2: Compute derived metrics
+    log.info("=== PHASE 2: Computing z_score and percentile ===")
+    z_scores, percentiles = compute_derived_metrics(dates, raw_vols, raw_success)
+
+    # Phase 3: Append to NPZ files
+    log.info(f"=== PHASE 3: Appending to NPZ files (mode={args.mode}) ===")
+    written = skipped = errors = 0
+    for ds in sorted(dates):
+        liq_values = {
+            "liq_vol_24h": (raw_vols.get(ds, 0.0), raw_success.get(ds, False)),
+            "liq_long_ratio": (raw_ratios.get(ds, 0.5), raw_success.get(ds, False)),
+            "liq_z_score": z_scores.get(ds, (0.0, False)),
+            "liq_percentile": percentiles.get(ds, (0.5, False)),
+        }
+
+        out_dir = EIGENVALUES_PATH / ds
+        if args.mode == "append":
+            npz_path = out_dir / NPZ_FILENAME
+        else:  # standalone
+            npz_path = out_dir / LIQ_NPZ_FILENAME
+
+        out_dir.mkdir(parents=True, exist_ok=True)
+        try:
+            did_write = append_liq_to_npz(npz_path, liq_values, args.overwrite, args.dry_run)
+            if did_write:
+                written += 1
+                log.debug(f"  {ds}: written")
+            else:
+                skipped += 1
+        except Exception as e:
+            log.error(f"  {ds}: NPZ write error — {e}")
+            errors += 1
+
+    elapsed_total = time.time() - t0
+    log.info(f"{'='*60}")
+    log.info(f"Liquidation ExF Backfill COMPLETE")
+    log.info(f"Written:  {written}")
+    log.info(f"Skipped:  {skipped} (already had liq channels)")
+    log.info(f"Errors:   {errors}")
+    log.info(f"Runtime:  {elapsed_total/60:.1f}m")
+    log.info(f"{'='*60}")
+
+
+def main():
+    args = parse_args()
+    asyncio.run(main_async(args))
+
+
+if __name__ == "__main__":
+    main()

external_factors/backfill_patch_npz.py

@@ -0,0 +1,398 @@
+"""ExF NPZ Patcher — Supplemental Historical Backfill
+======================================================
+The initial backfill got ~41/85 indicators. This script patches the existing
+NPZ files with real historical values for indicators that were failing:
+
+  Priority 1 — fng (Alternative.me): one API call returns 2000+ days. EASY.
+  Priority 2 — oi_btc/eth, ls_btc/eth, ls_top, taker (Binance hist endpoints)
+  Priority 3 — vix, sp500, gold, dxy, us10y, ycurve, fedfunds (FRED — needs key)
+  Priority 4 — mvrv, nvt, addr_btc (CoinMetrics community API)
+
+Strategy: load each NPZ, replace failing indicator values with fetched historical
+data, re-save. Idempotent — re-run any time.
+
+Usage:
+  python backfill_patch_npz.py              # patch all dates
+  python backfill_patch_npz.py --dry-run    # show what would change
+  python backfill_patch_npz.py --fred-key YOUR_KEY_HERE  # enable FRED
+  python backfill_patch_npz.py --skip-binance  # skip Binance OI/LS/taker
+"""
+import sys, time, argparse, json
+sys.stdout.reconfigure(encoding='utf-8', errors='replace')
+from pathlib import Path
+from datetime import datetime, timezone, date, timedelta
+import numpy as np
+
+try:
+    import requests
+    HAS_REQUESTS = True
+except ImportError:
+    HAS_REQUESTS = False
+    print("WARNING: requests not installed. Install with: pip install requests")
+
+import sys as _sys
+EIGENVALUES_PATH = (Path(r"C:\Users\Lenovo\Documents\- Dolphin NG HD (NG3)\correlation_arb512\eigenvalues")
+                    if _sys.platform == 'win32' else Path('/mnt/ng6_data/eigenvalues'))
+KLINES_DIR       = (Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\vbt_cache_klines")
+                    if _sys.platform == 'win32' else Path('/mnt/dolphin/vbt_cache_klines'))
+NPZ_FILENAME     = "scan_000001__Indicators.npz"
+REQUEST_TIMEOUT  = 20
+
+def parse_args():
+    p = argparse.ArgumentParser()
+    p.add_argument("--dry-run",      action="store_true")
+    p.add_argument("--fred-key",     default="", help="FRED API key (free: fred.stlouisfed.org)")
+    p.add_argument("--skip-binance", action="store_true")
+    p.add_argument("--skip-fred",    action="store_true")
+    p.add_argument("--skip-fng",     action="store_true")
+    p.add_argument("--start",        default=None, help="Start date YYYY-MM-DD")
+    p.add_argument("--end",          default=None, help="End date YYYY-MM-DD")
+    return p.parse_args()
+
+# ── FNG (Alternative.me) — one call, all history ─────────────────────────────
+
+def fetch_fng_history():
+    """Returns dict: date_str -> fng_value (int)."""
+    url = "https://api.alternative.me/fng/?limit=2000&format=json&date_format=us"
+    try:
+        r = requests.get(url, timeout=REQUEST_TIMEOUT)
+        r.raise_for_status()
+        data = r.json()
+        result = {}
+        for entry in data.get('data', []):
+            # date_format=us gives MM/DD/YYYY
+            raw_date = entry.get('timestamp') or entry.get('time_until_update', '')
+            # Try two formats the API uses
+            ts_str = str(entry.get('timestamp', ''))
+            parsed = False
+            for fmt in ('%m-%d-%Y', '%m/%d/%Y', '%Y-%m-%d'):
+                try:
+                    dt = datetime.strptime(ts_str, fmt)
+                    key = dt.strftime('%Y-%m-%d')
+                    result[key] = int(entry['value'])
+                    parsed = True
+                    break
+                except ValueError:
+                    pass
+            if not parsed:
+                try:
+                    ts = int(ts_str)
+                    dt = datetime.utcfromtimestamp(ts)
+                    key = dt.strftime('%Y-%m-%d')
+                    result[key] = int(entry['value'])
+                except Exception:
+                    pass
+        return result
+    except Exception as e:
+        print(f"  FNG fetch failed: {e}")
+        return {}
+
+# ── Binance historical OI / LS / taker ───────────────────────────────────────
+
+def fetch_binance_hist(url_template, symbol, date_str):
+    """Fetch a single data point from Binance hist endpoint for given date (noon UTC)."""
+    yr, mo, dy = int(date_str[:4]), int(date_str[5:7]), int(date_str[8:10])
+    noon_utc = datetime(yr, mo, dy, 12, 0, 0, tzinfo=timezone.utc)
+    start_ms = int(noon_utc.timestamp() * 1000)
+    end_ms   = start_ms + 3_600_000  # +1 hour window
+    url = url_template.format(SYMBOL=symbol, start_ms=start_ms, end_ms=end_ms)
+    try:
+        r = requests.get(url, timeout=REQUEST_TIMEOUT)
+        if r.status_code == 400:
+            return None  # data too old for this endpoint
+        r.raise_for_status()
+        data = r.json()
+        if isinstance(data, list) and len(data) > 0:
+            return data[0]
+        return None
+    except Exception:
+        return None
+
+OI_URL  = "https://fapi.binance.com/futures/data/openInterestHist?symbol={SYMBOL}&period=1h&startTime={start_ms}&endTime={end_ms}&limit=1"
+LS_URL  = "https://fapi.binance.com/futures/data/globalLongShortAccountRatio?symbol={SYMBOL}&period=1h&startTime={start_ms}&endTime={end_ms}&limit=1"
+LS_TOP  = "https://fapi.binance.com/futures/data/topLongShortAccountRatio?symbol={SYMBOL}&period=1h&startTime={start_ms}&endTime={end_ms}&limit=1"
+TAKER_URL = "https://fapi.binance.com/futures/data/takerlongshortRatio?symbol={SYMBOL}&period=1h&startTime={start_ms}&endTime={end_ms}&limit=1"
+
+def get_binance_indicators(date_str):
+    """Returns dict of indicator_name -> value (or None on failure)."""
+    results = {}
+    for name, url, sym, field in [
+        ('oi_btc',  OI_URL,   'BTCUSDT', 'sumOpenInterest'),
+        ('oi_eth',  OI_URL,   'ETHUSDT', 'sumOpenInterest'),
+        ('ls_btc',  LS_URL,   'BTCUSDT', 'longShortRatio'),
+        ('ls_eth',  LS_URL,   'ETHUSDT', 'longShortRatio'),
+        ('ls_top',  LS_TOP,   'BTCUSDT', 'longShortRatio'),
+        ('taker',   TAKER_URL,'BTCUSDT', 'buySellRatio'),
+    ]:
+        rec = fetch_binance_hist(url, sym, date_str)
+        if rec is not None and field in rec:
+            try:
+                results[name] = float(rec[field])
+            except (TypeError, ValueError):
+                results[name] = None
+        else:
+            results[name] = None
+        time.sleep(0.05)  # light rate limiting
+    return results
+
+# ── FRED ─────────────────────────────────────────────────────────────────────
+
+FRED_SERIES = {
+    'vix':       'VIXCLS',
+    'sp500':     'SP500',
+    'gold':      'GOLDAMGBD228NLBM',
+    'dxy':       'DTWEXBGS',
+    'us10y':     'DGS10',
+    'us2y':      'DGS2',
+    'ycurve':    'T10Y2Y',
+    'fedfunds':  'DFF',
+    'hy_spread': 'BAMLH0A0HYM2',
+    'be5y':      'T5YIE',
+    'm2':        'WM2NS',
+}
+
+_fred_cache = {}  # series_id -> {date_str -> value}
+
+def fetch_fred_series(series_id, fred_key, lookback_years=6):
+    """Fetch a FRED series for the last 6 years. Cached."""
+    if series_id in _fred_cache:
+        return _fred_cache[series_id]
+    start = (date.today() - timedelta(days=lookback_years*366)).strftime('%Y-%m-%d')
+    url = (f"https://api.stlouisfed.org/fred/series/observations"
+           f"?series_id={series_id}&api_key={fred_key}&file_type=json"
+           f"&observation_start={start}")
+    try:
+        r = requests.get(url, timeout=REQUEST_TIMEOUT)
+        r.raise_for_status()
+        data = r.json()
+        result = {}
+        prev = None
+        for obs in data.get('observations', []):
+            v = obs.get('value', '.')
+            if v not in ('.', '', 'nd'):
+                try:
+                    prev = float(v)
+                except ValueError:
+                    pass
+            if prev is not None:
+                result[obs['date']] = prev  # forward-fill
+        _fred_cache[series_id] = result
+        return result
+    except Exception as e:
+        print(f"  FRED {series_id} failed: {e}")
+        _fred_cache[series_id] = {}
+        return {}
+
+def get_fred_indicators(date_str, fred_key):
+    results = {}
+    for name, series_id in FRED_SERIES.items():
+        series = fetch_fred_series(series_id, fred_key)
+        # Find value on or before date (forward-fill)
+        val = None
+        for d_str in sorted(series.keys(), reverse=True):
+            if d_str <= date_str:
+                val = series[d_str]
+                break
+        results[name] = val
+    return results
+
+# ── CoinMetrics community ─────────────────────────────────────────────────────
+
+_cm_cache = {}  # (asset, metric) -> {date_str -> value}
+
+def fetch_coinmetrics(asset, metric, date_str):
+    key = (asset, metric)
+    if key not in _cm_cache:
+        url = (f"https://community-api.coinmetrics.io/v4/timeseries/asset-metrics"
+               f"?assets={asset}&metrics={metric}&frequency=1d"
+               f"&start_time=2021-01-01T00:00:00Z")
+        try:
+            r = requests.get(url, timeout=30)
+            r.raise_for_status()
+            data = r.json()
+            result = {}
+            for row in data.get('data', []):
+                d = row.get('time', '')[:10]
+                v = row.get(metric)
+                if v is not None:
+                    try:
+                        result[d] = float(v)
+                    except (TypeError, ValueError):
+                        pass
+            _cm_cache[key] = result
+        except Exception as e:
+            print(f"  CoinMetrics {asset}/{metric} failed: {e}")
+            _cm_cache[key] = {}
+    cache = _cm_cache.get(key, {})
+    return cache.get(date_str)
+
+CM_INDICATORS = [
+    # Only include metrics confirmed as accessible on community API
+    ('mvrv',     'btc', 'CapMVRVCur'),   # works (200 OK)
+    ('addr_btc', 'btc', 'AdrActCnt'),    # works
+    ('txcnt',    'btc', 'TxCnt'),        # works
+]
+
+# ── Main patcher ──────────────────────────────────────────────────────────────
+
+def patch_npz(npz_path, updates, dry_run=False):
+    """Load NPZ, apply updates dict {name -> value}, save in-place."""
+    data = np.load(str(npz_path), allow_pickle=True)
+    names   = list(data['api_names'])
+    vals    = data['api_indicators'].copy()
+    success = data['api_success'].copy()
+
+    changed = []
+    for name, value in updates.items():
+        if value is None or not np.isfinite(float(value)):
+            continue
+        if name not in names:
+            continue
+        idx = names.index(name)
+        old = float(vals[idx])
+        old_ok = bool(success[idx])
+        new_val = float(value)
+        if not old_ok or abs(old - new_val) > 1e-9:
+            vals[idx] = new_val
+            success[idx] = True
+            changed.append(f"{name}: {old:.4f}→{new_val:.4f}")
+
+    if not changed:
+        return 0
+
+    if not dry_run:
+        ind_names = np.array(names, dtype=object)
+        np.savez_compressed(
+            str(npz_path),
+            api_names      = ind_names,
+            api_indicators = vals,
+            api_success    = success,
+        )
+    return len(changed)
+
+def main():
+    args = parse_args()
+    if not HAS_REQUESTS:
+        print("ERROR: requests required. pip install requests"); return
+
+    # Enumerate dates
+    dates = sorted(p.stem for p in KLINES_DIR.glob("*.parquet") if 'catalog' not in p.name)
+    if args.start: dates = [d for d in dates if d >= args.start]
+    if args.end:   dates = [d for d in dates if d <= args.end]
+    total = len(dates)
+    print(f"Dates to patch: {total}")
+    print(f"Dry run:  {args.dry_run}")
+    print(f"FNG:      {'skip' if args.skip_fng else 'YES'}")
+    print(f"Binance:  {'skip' if args.skip_binance else 'YES'}")
+    print(f"FRED:     {'skip (no key)' if (args.skip_fred or not args.fred_key) else f'YES (key={args.fred_key[:6]}...)'}")
+    print()
+
+    # ── Fetch FNG all-history up front (one call) ─────────────────────────────
+    fng_hist = {}
+    if not args.skip_fng:
+        print("Fetching FNG full history (one call)...")
+        fng_hist = fetch_fng_history()
+        print(f"  Got {len(fng_hist)} dates  "
+              f"range={min(fng_hist) if fng_hist else 'n/a'} → {max(fng_hist) if fng_hist else 'n/a'}")
+        if fng_hist:
+            sample = {k: v for k, v in list(fng_hist.items())[:3]}
+            print(f"  Sample: {sample}")
+
+    # ── Fetch FRED all-series up front ───────────────────────────────────────
+    if args.fred_key and not args.skip_fred:
+        print(f"\nPre-fetching FRED series ({len(FRED_SERIES)} series)...")
+        for name, sid in FRED_SERIES.items():
+            series = fetch_fred_series(sid, args.fred_key)
+            print(f"  {name:<12} ({sid}): {len(series)} observations")
+            time.sleep(0.6)  # FRED rate limit: 120/min
+
+    # ── Fetch CoinMetrics up front ────────────────────────────────────────────
+    print(f"\nPre-fetching CoinMetrics ({len(CM_INDICATORS)} metrics)...")
+    for cm_name, asset, metric in CM_INDICATORS:
+        fetch_coinmetrics(asset, metric, '2023-01-01')  # warms cache for all dates
+        n = len(_cm_cache.get((asset, metric), {}))
+        print(f"  {cm_name:<12}: {n} dates")
+        time.sleep(0.8)
+
+    # ── Per-date loop ─────────────────────────────────────────────────────────
+    print(f"\nPatching NPZ files...")
+    total_changed = 0
+    binance_fail_streak = 0
+
+    t0 = time.time()
+    for i, ds in enumerate(dates):
+        npz_path = EIGENVALUES_PATH / ds / NPZ_FILENAME
+        if not npz_path.exists():
+            continue
+
+        updates = {}
+
+        # FNG
+        if not args.skip_fng and ds in fng_hist:
+            updates['fng'] = float(fng_hist[ds])
+            # Also try to get sub-components from same entry if available
+            # (fng_prev is previous day's value)
+            prev_day = (datetime.strptime(ds, '%Y-%m-%d') - timedelta(days=1)).strftime('%Y-%m-%d')
+            if prev_day in fng_hist:
+                updates['fng_prev'] = float(fng_hist[prev_day])
+
+        # FRED
+        if args.fred_key and not args.skip_fred:
+            fred_vals = get_fred_indicators(ds, args.fred_key)
+            for name, val in fred_vals.items():
+                if val is not None:
+                    updates[name] = val
+
+        # CoinMetrics
+        for cm_name, asset, metric in CM_INDICATORS:
+            val = fetch_coinmetrics(asset, metric, ds)  # hits cache
+            if val is not None:
+                updates[cm_name] = val
+
+        # Binance OI/LS/taker (network call per date — slowest)
+        if not args.skip_binance and binance_fail_streak < 10:
+            # Only call if these are currently failing in the NPZ
+            d = np.load(str(npz_path), allow_pickle=True)
+            names_in_npz = list(d['api_names'])
+            ok_in_npz = d['api_success']
+            taker_idx = names_in_npz.index('taker') if 'taker' in names_in_npz else -1
+            taker_ok = bool(ok_in_npz[taker_idx]) if taker_idx >= 0 else False
+
+            if not taker_ok:  # proxy check: if taker failing, all Binance hist likely failing
+                binance_vals = get_binance_indicators(ds)
+                n_binance_ok = sum(1 for v in binance_vals.values() if v is not None)
+                if n_binance_ok == 0:
+                    binance_fail_streak += 1
+                else:
+                    binance_fail_streak = 0
+                    updates.update({k: v for k, v in binance_vals.items() if v is not None})
+
+        # Patch
+        n_changed = patch_npz(npz_path, updates, dry_run=args.dry_run)
+        total_changed += n_changed
+
+        if (i + 1) % 50 == 0 or n_changed > 0:
+            elapsed = time.time() - t0
+            rate = (i + 1) / elapsed
+            eta = (total - i - 1) / rate if rate > 0 else 0
+            tag = f"  +{n_changed} fields" if n_changed else ""
+            print(f"  [{i+1}/{total}] {ds}  {elapsed/60:.1f}m  eta={eta/60:.1f}m{tag}")
+
+    elapsed = time.time() - t0
+    print(f"\n{'='*60}")
+    print(f"  Patch complete in {elapsed/60:.1f}m")
+    print(f"  Total fields updated: {total_changed}")
+    print(f"  {'DRY RUN — no files written' if args.dry_run else 'Files patched in-place'}")
+    print(f"{'='*60}")
+
+    if not args.fred_key:
+        print(f"\n  *** FRED indicators (vix, sp500, gold, dxy, us10y, ycurve, fedfunds)")
+        print(f"  *** were SKIPPED. Get a free API key at: https://fred.stlouisfed.org/docs/api/api_key.html")
+        print(f"  *** Then re-run with: --fred-key YOUR_KEY_HERE")
+    if binance_fail_streak >= 10:
+        print(f"\n  *** Binance hist endpoints failed consistently.")
+        print(f"  *** OI data before 2020-09 is not available via Binance API.")
+        print(f"  *** Dates before that will remain FAIL for oi_btc, ls_btc, taker.")
+
+if __name__ == "__main__":
+    main()

external_factors/backfill_runner.py

@@ -0,0 +1,466 @@
+#!/usr/bin/env python3
+"""
+DOLPHIN BACKFILL RUNNER v2.0
+============================
+Spiders DOLPHIN scan directories, enriches with external factors matrix.
+
+INDICATOR SOURCES:
+  1. API_HISTORICAL: Fetched with scan timestamp (CoinMetrics, FRED, DeFi Llama, etc.)
+  2. SCAN_DERIVED: Computed from scan's market_prices, tracking_data, per_asset_signals
+  3. UNAVAILABLE: No historical API AND cannot compute from scan → NaN
+
+Output: {original_name}__Indicators.npz (sorts alphabetically next to source)
+
+Author: HJ / Claude
+Version: 2.0.0
+"""
+
+import os
+import sys
+import json
+import numpy as np
+import asyncio
+import aiohttp
+from pathlib import Path
+from datetime import datetime, timezone
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Tuple, Any, Set
+import logging
+import time
+import argparse
+
+# Import external factors module
+from external_factors_matrix import (
+    ExternalFactorsFetcher, Config, INDICATORS, N_INDICATORS, 
+    HistoricalSupport, Stationarity, Category
+)
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(levelname)s] %(message)s')
+logger = logging.getLogger(__name__)
+
+# =============================================================================
+# INDICATOR SOURCE CLASSIFICATION
+# =============================================================================
+
+class IndicatorSource:
+    """Classifies each indicator by how it can be obtained for backfill"""
+    
+    # Indicators that HAVE historical API support (fetch with timestamp)
+    API_HISTORICAL: Set[int] = set()
+    
+    # Indicators that are UNAVAILABLE (no history, can't derive from scan)
+    UNAVAILABLE: Set[int] = set()
+    
+    @classmethod
+    def classify(cls):
+        """Classify all indicators by their backfill source"""
+        for ind in INDICATORS:
+            if ind.historical in [HistoricalSupport.FULL, HistoricalSupport.PARTIAL]:
+                cls.API_HISTORICAL.add(ind.id)
+            else:
+                cls.UNAVAILABLE.add(ind.id)
+        
+        logger.info(f"Indicator sources: API_HISTORICAL={len(cls.API_HISTORICAL)}, "
+                   f"UNAVAILABLE={len(cls.UNAVAILABLE)}")
+    
+    @classmethod
+    def get_unavailable_names(cls) -> List[str]:
+        return [INDICATORS[i-1].name for i in sorted(cls.UNAVAILABLE)]
+
+# Initialize classification
+IndicatorSource.classify()
+
+# =============================================================================
+# CONFIGURATION
+# =============================================================================
+
+@dataclass
+class BackfillConfig:
+    scan_dir: Path(r"C:\Users\Lenovo\Documents\- Dolphin NG HD (NG3)\correlation_arb512\eigenvalues")
+    output_dir: Optional[str] = None
+    skip_existing: bool = True
+    dry_run: bool = False
+    fred_api_key: str = ""
+    rate_limit_delay: float = 0.5
+    verbose: bool = False
+
+# =============================================================================
+# SCAN DATA
+# =============================================================================
+
+@dataclass
+class ScanData:
+    path: Path
+    scan_number: int
+    timestamp: datetime
+    market_prices: Dict[str, float]
+    windows: Dict[str, Dict]
+    
+    @property
+    def n_assets(self) -> int:
+        return len(self.market_prices)
+    
+    @property
+    def symbols(self) -> List[str]:
+        return sorted(self.market_prices.keys())
+    
+    def get_tracking(self, window: str) -> Dict:
+        return self.windows.get(window, {}).get('tracking_data', {})
+    
+    def get_regime(self, window: str) -> Dict:
+        return self.windows.get(window, {}).get('regime_signals', {})
+    
+    def get_asset_signals(self, window: str) -> Dict:
+        return self.windows.get(window, {}).get('per_asset_signals', {})
+
+# =============================================================================
+# INDICATORS FROM SCAN DATA
+# =============================================================================
+
+WINDOWS = ['50', '150', '300', '750']
+
+# Global scan-derived indicators (eigenvalue-based, from tracking_data/regime_signals)
+SCAN_GLOBAL_INDICATORS = [
+    # Lambda max per window
+    *[(f"lambda_max_w{w}", f"Lambda max window {w}") for w in WINDOWS],
+    *[(f"lambda_min_w{w}", f"Lambda min window {w}") for w in WINDOWS],
+    *[(f"lambda_vel_w{w}", f"Lambda velocity window {w}") for w in WINDOWS],
+    *[(f"lambda_acc_w{w}", f"Lambda acceleration window {w}") for w in WINDOWS],
+    *[(f"eigrot_max_w{w}", f"Eigenvector rotation window {w}") for w in WINDOWS],
+    *[(f"eiggap_w{w}", f"Eigenvalue gap window {w}") for w in WINDOWS],
+    *[(f"instab_w{w}", f"Instability window {w}") for w in WINDOWS],
+    *[(f"transp_w{w}", f"Transition prob window {w}") for w in WINDOWS],
+    *[(f"coher_w{w}", f"Coherence window {w}") for w in WINDOWS],
+    # Aggregates
+    ("lambda_max_mean", "Mean lambda max"),
+    ("lambda_max_std", "Std lambda max"),
+    ("instab_mean", "Mean instability"),
+    ("instab_max", "Max instability"),
+    ("coher_mean", "Mean coherence"),
+    ("coher_min", "Min coherence"),
+    ("coher_trend", "Coherence trend (w750-w50)"),
+    # From prices
+    ("n_assets", "Number of assets"),
+    ("price_dispersion", "Log price dispersion"),
+]
+
+N_SCAN_GLOBAL = len(SCAN_GLOBAL_INDICATORS)
+
+# Per-asset indicators
+PER_ASSET_INDICATORS = [
+    ("price", "Price"),
+    ("log_price", "Log price"),
+    ("price_rank", "Price percentile"),
+    ("price_btc", "Price / BTC"),
+    ("price_eth", "Price / ETH"),
+    *[(f"align_w{w}", f"Alignment w{w}") for w in WINDOWS],
+    *[(f"decouple_w{w}", f"Decoupling w{w}") for w in WINDOWS],
+    *[(f"anomaly_w{w}", f"Anomaly w{w}") for w in WINDOWS],
+    *[(f"eigvec_w{w}", f"Eigenvector w{w}") for w in WINDOWS],
+    ("align_mean", "Mean alignment"),
+    ("align_std", "Alignment std"),
+    ("anomaly_max", "Max anomaly"),
+    ("decouple_max", "Max |decoupling|"),
+]
+
+N_PER_ASSET = len(PER_ASSET_INDICATORS)
+
+# =============================================================================
+# PROCESSOR
+# =============================================================================
+
+class ScanProcessor:
+    def __init__(self, config: BackfillConfig):
+        self.config = config
+        self.fetcher = ExternalFactorsFetcher(Config(fred_api_key=config.fred_api_key))
+    
+    def load_scan(self, path: Path) -> Optional[ScanData]:
+        try:
+            with open(path, 'r') as f:
+                data = json.load(f)
+            
+            ts_str = data.get('timestamp', '')
+            try:
+                timestamp = datetime.fromisoformat(ts_str.replace('Z', '+00:00'))
+                if timestamp.tzinfo is None:
+                    timestamp = timestamp.replace(tzinfo=timezone.utc)
+            except:
+                timestamp = datetime.now(timezone.utc)
+            
+            return ScanData(
+                path=path,
+                scan_number=data.get('scan_number', 0),
+                timestamp=timestamp,
+                market_prices=data.get('market_prices', {}),
+                windows=data.get('windows', {})
+            )
+        except Exception as e:
+            logger.error(f"Load failed {path}: {e}")
+            return None
+    
+    async def fetch_api_indicators(self, timestamp: datetime) -> Tuple[np.ndarray, np.ndarray]:
+        """Fetch indicators with historical API support"""
+        try:
+            result = await self.fetcher.fetch_all(target_date=timestamp)
+            matrix = result['matrix']
+            success = np.array([
+                result['details'].get(i+1, {}).get('success', False) 
+                for i in range(N_INDICATORS)
+            ])
+            
+            # Mark non-historical indicators as NaN
+            for i in range(N_INDICATORS):
+                if (i+1) not in IndicatorSource.API_HISTORICAL:
+                    success[i] = False
+                    matrix[i] = np.nan
+            
+            return matrix, success
+        except Exception as e:
+            logger.warning(f"API fetch failed: {e}")
+            return np.full(N_INDICATORS, np.nan), np.zeros(N_INDICATORS, dtype=bool)
+    
+    def compute_scan_global(self, scan: ScanData) -> np.ndarray:
+        """Compute global indicators from scan's tracking_data and regime_signals"""
+        values = []
+        
+        # Per-window metrics
+        for w in WINDOWS:
+            values.append(scan.get_tracking(w).get('lambda_max', np.nan))
+        for w in WINDOWS:
+            values.append(scan.get_tracking(w).get('lambda_min', np.nan))
+        for w in WINDOWS:
+            values.append(scan.get_tracking(w).get('lambda_max_velocity', np.nan))
+        for w in WINDOWS:
+            values.append(scan.get_tracking(w).get('lambda_max_acceleration', np.nan))
+        for w in WINDOWS:
+            values.append(scan.get_tracking(w).get('eigenvector_rotation_max', np.nan))
+        for w in WINDOWS:
+            values.append(scan.get_tracking(w).get('eigenvalue_gap', np.nan))
+        for w in WINDOWS:
+            values.append(scan.get_regime(w).get('instability_score', np.nan))
+        for w in WINDOWS:
+            values.append(scan.get_regime(w).get('regime_transition_probability', np.nan))
+        for w in WINDOWS:
+            values.append(scan.get_regime(w).get('market_coherence', np.nan))
+        
+        # Aggregates
+        lmax = [scan.get_tracking(w).get('lambda_max', np.nan) for w in WINDOWS]
+        values.append(np.nanmean(lmax))
+        values.append(np.nanstd(lmax))
+        
+        instab = [scan.get_regime(w).get('instability_score', np.nan) for w in WINDOWS]
+        values.append(np.nanmean(instab))
+        values.append(np.nanmax(instab))
+        
+        coher = [scan.get_regime(w).get('market_coherence', np.nan) for w in WINDOWS]
+        values.append(np.nanmean(coher))
+        values.append(np.nanmin(coher))
+        values.append(coher[3] - coher[0] if not np.isnan(coher[3]) and not np.isnan(coher[0]) else np.nan)
+        
+        # From prices
+        prices = np.array(list(scan.market_prices.values())) if scan.market_prices else np.array([])
+        values.append(len(prices))
+        values.append(np.std(np.log(np.maximum(prices, 1e-10))) if len(prices) > 0 else np.nan)
+        
+        return np.array(values)
+    
+    def compute_per_asset(self, scan: ScanData) -> Tuple[np.ndarray, List[str]]:
+        """Compute per-asset indicator matrix"""
+        symbols = scan.symbols
+        n = len(symbols)
+        if n == 0:
+            return np.zeros((0, N_PER_ASSET)), []
+        
+        matrix = np.zeros((n, N_PER_ASSET))
+        prices = np.array([scan.market_prices[s] for s in symbols])
+        
+        btc_p = scan.market_prices.get('BTC', scan.market_prices.get('BTCUSDT', np.nan))
+        eth_p = scan.market_prices.get('ETH', scan.market_prices.get('ETHUSDT', np.nan))
+        
+        col = 0
+        matrix[:, col] = prices; col += 1
+        matrix[:, col] = np.log(np.maximum(prices, 1e-10)); col += 1
+        matrix[:, col] = np.argsort(np.argsort(prices)) / n; col += 1
+        matrix[:, col] = prices / btc_p if btc_p > 0 else np.nan; col += 1
+        matrix[:, col] = prices / eth_p if eth_p > 0 else np.nan; col += 1
+        
+        # Per-window signals
+        for metric in ['market_alignment', 'decoupling_velocity', 'anomaly_score', 'eigenvector_component']:
+            for w in WINDOWS:
+                sigs = scan.get_asset_signals(w)
+                for i, sym in enumerate(symbols):
+                    matrix[i, col] = sigs.get(sym, {}).get(metric, np.nan)
+                col += 1
+        
+        # Aggregates
+        align_cols = list(range(5, 9))
+        matrix[:, col] = np.nanmean(matrix[:, align_cols], axis=1); col += 1
+        matrix[:, col] = np.nanstd(matrix[:, align_cols], axis=1); col += 1
+        
+        anomaly_cols = list(range(13, 17))
+        matrix[:, col] = np.nanmax(matrix[:, anomaly_cols], axis=1); col += 1
+        
+        decouple_cols = list(range(9, 13))
+        matrix[:, col] = np.nanmax(np.abs(matrix[:, decouple_cols]), axis=1); col += 1
+        
+        return matrix, symbols
+    
+    async def process(self, path: Path) -> Optional[Dict[str, Any]]:
+        start = time.time()
+        
+        scan = self.load_scan(path)
+        if scan is None:
+            return None
+        
+        # 1. API historical indicators
+        api_matrix, api_success = await self.fetch_api_indicators(scan.timestamp)
+        
+        # 2. Scan-derived global
+        scan_global = self.compute_scan_global(scan)
+        
+        # 3. Per-asset
+        asset_matrix, asset_symbols = self.compute_per_asset(scan)
+        
+        return {
+            'scan_number': scan.scan_number,
+            'timestamp': scan.timestamp.isoformat(),
+            'processing_time': time.time() - start,
+            
+            'api_indicators': api_matrix,
+            'api_success': api_success,
+            'api_names': np.array([ind.name for ind in INDICATORS], dtype='U32'),
+            
+            'scan_global': scan_global,
+            'scan_global_names': np.array([n for n, _ in SCAN_GLOBAL_INDICATORS], dtype='U32'),
+            
+            'asset_matrix': asset_matrix,
+            'asset_symbols': np.array(asset_symbols, dtype='U16'),
+            'asset_names': np.array([n for n, _ in PER_ASSET_INDICATORS], dtype='U32'),
+            
+            'n_assets': len(asset_symbols),
+            'api_success_rate': np.nanmean(api_success[list(i-1 for i in IndicatorSource.API_HISTORICAL)]),
+        }
+
+# =============================================================================
+# OUTPUT
+# =============================================================================
+
+class OutputWriter:
+    def __init__(self, config: BackfillConfig):
+        self.config = config
+    
+    def get_output_path(self, scan_path: Path) -> Path:
+        out_dir = Path(self.config.output_dir) if self.config.output_dir else scan_path.parent
+        out_dir.mkdir(parents=True, exist_ok=True)
+        return out_dir / f"{scan_path.stem}__Indicators.npz"
+    
+    def save(self, data: Dict[str, Any], scan_path: Path) -> Path:
+        out_path = self.get_output_path(scan_path)
+        save_data = {}
+        for k, v in data.items():
+            if isinstance(v, np.ndarray):
+                save_data[k] = v
+            elif isinstance(v, str):
+                save_data[k] = np.array([v], dtype='U64')
+            else:
+                save_data[k] = np.array([v])
+        np.savez_compressed(out_path, **save_data)
+        return out_path
+
+# =============================================================================
+# RUNNER
+# =============================================================================
+
+class BackfillRunner:
+    def __init__(self, config: BackfillConfig):
+        self.config = config
+        self.processor = ScanProcessor(config)
+        self.writer = OutputWriter(config)
+        self.stats = {'processed': 0, 'failed': 0, 'skipped': 0}
+    
+    def find_scans(self) -> List[Path]:
+        root = Path(self.config.scan_dir)
+        files = sorted(root.rglob("scan_*.json"))
+        
+        if self.config.skip_existing:
+            files = [f for f in files if not self.writer.get_output_path(f).exists()]
+        
+        return files
+    
+    async def run(self):
+        unavail = IndicatorSource.get_unavailable_names()
+        logger.info(f"Skipping {len(unavail)} unavailable indicators: {unavail[:5]}...")
+        
+        files = self.find_scans()
+        logger.info(f"Processing {len(files)} files...")
+        
+        for i, path in enumerate(files):
+            try:
+                result = await self.processor.process(path)
+                if result:
+                    if not self.config.dry_run:
+                        self.writer.save(result, path)
+                    self.stats['processed'] += 1
+                else:
+                    self.stats['failed'] += 1
+            except Exception as e:
+                logger.error(f"Error {path.name}: {e}")
+                self.stats['failed'] += 1
+            
+            if (i + 1) % 10 == 0:
+                logger.info(f"Progress: {i+1}/{len(files)}")
+            
+            if self.config.rate_limit_delay > 0:
+                await asyncio.sleep(self.config.rate_limit_delay)
+        
+        logger.info(f"Done: {self.stats}")
+        return self.stats
+
+# =============================================================================
+# UTILITY
+# =============================================================================
+
+def load_indicators(path: str) -> Dict[str, np.ndarray]:
+    """Load .npz indicator file"""
+    return dict(np.load(path, allow_pickle=True))
+
+def summary(path: str) -> str:
+    """Summary of indicator file"""
+    d = load_indicators(path)
+    return f"""Timestamp: {d['timestamp'][0]}
+Assets: {d['n_assets'][0]}
+API success: {d['api_success_rate'][0]:.1%}
+API shape: {d['api_indicators'].shape}
+Scan global: {d['scan_global'].shape}
+Per-asset: {d['asset_matrix'].shape}"""
+
+# =============================================================================
+# CLI
+# =============================================================================
+
+def main():
+    parser = argparse.ArgumentParser(description="DOLPHIN Backfill Runner")
+    # parser.add_argument("scan_dir", help="Directory with scan JSON files")
+    parser.add_argument("-o", "--output", help="Output directory")
+    parser.add_argument("--fred-key", default="", help="FRED API key")
+    parser.add_argument("--no-skip", action="store_true", help="Reprocess existing")
+    parser.add_argument("--dry-run", action="store_true")
+    parser.add_argument("--delay", type=float, default=0.5)
+    
+    args = parser.parse_args()
+    
+    config = BackfillConfig(
+        scan_dir= Path(r"C:\Users\Lenovo\Documents\- Dolphin NG HD (NG3)\correlation_arb512\eigenvalues"),
+        output_dir=args.output,
+        # FRED API Key: c16a9cde3e3bb5bb972bb9283485f202
+        fred_api_key=args.fred_key or 'c16a9cde3e3bb5bb972bb9283485f202',
+        skip_existing=not args.no_skip,
+        dry_run=args.dry_run,
+        rate_limit_delay=args.delay,
+    )
+    
+    runner = BackfillRunner(config)
+    asyncio.run(runner.run())
+
+if __name__ == "__main__":
+    main()

external_factors/bf.bat

@@ -0,0 +1 @@
+"python backfill_runner.py" 

external_factors/bk.bat

@@ -0,0 +1,7 @@
+@echo off
+REM Backfill ExF NPZ files for all 1710 klines dates
+REM Idempotent — safe to re-run if interrupted
+REM ~2-5 hours total runtime
+cd /d "%~dp0"
+"C:\Users\Lenovo\Documents\- Siloqy\Scripts\python.exe" backfill_klines_exf.py %*
+pause

external_factors/br.bat

@@ -0,0 +1 @@
+python backfill_runner.py

external_factors/esoteric_factors_service.py

@@ -0,0 +1,299 @@
+import asyncio
+import datetime
+import json
+import logging
+import math
+import threading
+import time
+import zoneinfo
+from pathlib import Path
+from typing import Dict, Any, Optional
+
+import numpy as np
+from astropy.time import Time
+import astropy.coordinates as coord
+import astropy.units as u
+from astropy.coordinates import solar_system_ephemeris, get_body, EarthLocation
+
+logger = logging.getLogger(__name__)
+
+class MarketIndicators:
+    """
+    Mathematical and astronomical calculations for the Esoteric Factors mapping.
+    Evaluates completely locally without external API dependencies.
+    """
+    def __init__(self):
+        # Regions defined by NON-OVERLAPPING population clusters for accurate global weighting.
+        # Population in Millions (approximate). Liquidity weight is estimated crypto volume share.
+        self.regions = [
+            {'name': 'Americas', 'tz': 'America/New_York', 'pop': 1000, 'liq_weight': 0.35},
+            {'name': 'EMEA', 'tz': 'Europe/London', 'pop': 2200, 'liq_weight': 0.30},
+            {'name': 'South_Asia', 'tz': 'Asia/Kolkata', 'pop': 1400, 'liq_weight': 0.05},
+            {'name': 'East_Asia', 'tz': 'Asia/Shanghai', 'pop': 1600, 'liq_weight': 0.20},
+            {'name': 'Oceania_SEA', 'tz': 'Asia/Singapore', 'pop': 800, 'liq_weight': 0.10}
+        ]
+        
+        # Market cycle: Bitcoin halving based, ~4 years
+        self.cycle_length_days = 1460
+        self.last_halving = datetime.datetime(2024, 4, 20, tzinfo=datetime.timezone.utc)
+        
+        # Cache for expensive ASTRO calculations
+        self._cache = {
+            'moon': {'val': None, 'ts': 0},
+            'mercury': {'val': None, 'ts': 0}
+        }
+        self.cache_ttl_seconds = 3600 * 6 # Update astro every 6 hours
+
+    def get_calendar_items(self, now: datetime.datetime) -> Dict[str, int]:
+        return {
+            'year': now.year,
+            'month': now.month,
+            'day_of_month': now.day,
+            'hour': now.hour,
+            'minute': now.minute,
+            'day_of_week': now.weekday(), # 0=Monday
+            'week_of_year': now.isocalendar().week
+        }
+
+    def is_tradfi_open(self, region_name: str, local_time: datetime.datetime) -> bool:
+        day = local_time.weekday()
+        if day >= 5: return False
+        hour_dec = local_time.hour + local_time.minute / 60.0
+        
+        if 'Americas' in region_name:
+            return 9.5 <= hour_dec < 16.0
+        elif 'EMEA' in region_name:
+            return 8.0 <= hour_dec < 16.5
+        elif 'Asia' in region_name:
+            return 9.0 <= hour_dec < 15.0
+        return False
+
+    def get_regional_times(self, now_utc: datetime.datetime) -> Dict[str, Any]:
+        times = {}
+        for region in self.regions:
+            tz = zoneinfo.ZoneInfo(region['tz'])
+            local_time = now_utc.astimezone(tz)
+            times[region['name']] = {
+                'hour': local_time.hour + local_time.minute / 60.0,
+                'is_tradfi_open': self.is_tradfi_open(region['name'], local_time)
+            }
+        return times
+
+    def get_liquidity_session(self, now_utc: datetime.datetime) -> str:
+        utc_hour = now_utc.hour + now_utc.minute / 60.0
+        if 13 <= utc_hour < 17:
+            return "LONDON_NEW_YORK_OVERLAP"
+        elif 8 <= utc_hour < 13:
+            return "LONDON_MORNING"
+        elif 0 <= utc_hour < 8:
+            return "ASIA_PACIFIC"
+        elif 17 <= utc_hour < 21:
+            return "NEW_YORK_AFTERNOON"
+        else:
+            return "LOW_LIQUIDITY" 
+
+    def get_weighted_times(self, now_utc: datetime.datetime) -> tuple[float, float]:
+        pop_sin, pop_cos = 0.0, 0.0
+        liq_sin, liq_cos = 0.0, 0.0
+        
+        total_pop = sum(r['pop'] for r in self.regions)
+        
+        for region in self.regions:
+            tz = zoneinfo.ZoneInfo(region['tz'])
+            local_time = now_utc.astimezone(tz)
+            hour_frac = (local_time.hour + local_time.minute / 60.0) / 24.0
+            angle = 2 * math.pi * hour_frac
+            
+            w_pop = region['pop'] / total_pop
+            pop_sin += math.sin(angle) * w_pop
+            pop_cos += math.cos(angle) * w_pop
+            
+            w_liq = region['liq_weight']
+            liq_sin += math.sin(angle) * w_liq
+            liq_cos += math.cos(angle) * w_liq
+
+        pop_angle = math.atan2(pop_sin, pop_cos)
+        if pop_angle < 0: pop_angle += 2 * math.pi
+        pop_hour = (pop_angle / (2 * math.pi)) * 24
+
+        liq_angle = math.atan2(liq_sin, liq_cos)
+        if liq_angle < 0: liq_angle += 2 * math.pi
+        liq_hour = (liq_angle / (2 * math.pi)) * 24
+
+        return round(pop_hour, 2), round(liq_hour, 2)
+
+    def get_market_cycle_position(self, now_utc: datetime.datetime) -> float:
+        days_since_halving = (now_utc - self.last_halving).days
+        position = (days_since_halving % self.cycle_length_days) / self.cycle_length_days
+        return position
+
+    def get_fibonacci_time(self, now_utc: datetime.datetime) -> Dict[str, Any]:
+        mins_passed = now_utc.hour * 60 + now_utc.minute
+        fib_seq = [1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597]
+        closest = min(fib_seq, key=lambda x: abs(x - mins_passed))
+        distance = abs(mins_passed - closest)
+        strength = 1.0 - min(distance / 30.0, 1.0) 
+        return {'closest_fib_minute': closest, 'harmonic_strength': round(strength, 3)}
+
+    def get_moon_phase(self, now_utc: datetime.datetime) -> Dict[str, Any]:
+        now_ts = now_utc.timestamp()
+        if self._cache['moon']['val'] and (now_ts - self._cache['moon']['ts'] < self.cache_ttl_seconds):
+            return self._cache['moon']['val']
+
+        t = Time(now_utc)
+        with solar_system_ephemeris.set('builtin'):
+            moon = get_body('moon', t)
+            sun = get_body('sun', t)
+            elongation = sun.separation(moon)
+            phase_angle = np.arctan2(sun.distance * np.sin(elongation),
+                                     moon.distance - sun.distance * np.cos(elongation))
+            illumination = (1 + np.cos(phase_angle)) / 2.0
+            
+            phase_name = "WAXING"
+            if illumination < 0.03: phase_name = "NEW_MOON"
+            elif illumination > 0.97: phase_name = "FULL_MOON"
+            elif illumination < 0.5: phase_name = "WAXING_CRESCENT" if moon.dec.deg > sun.dec.deg else "WANING_CRESCENT"
+            else: phase_name = "WAXING_GIBBOUS" if moon.dec.deg > sun.dec.deg else "WANING_GIBBOUS"
+
+        result = {'illumination': float(illumination), 'phase_name': phase_name}
+        self._cache['moon'] = {'val': result, 'ts': now_ts}
+        return result
+
+    def is_mercury_retrograde(self, now_utc: datetime.datetime) -> bool:
+        now_ts = now_utc.timestamp()
+        if self._cache['mercury']['val'] is not None and (now_ts - self._cache['mercury']['ts'] < self.cache_ttl_seconds):
+            return self._cache['mercury']['val']
+
+        t = Time(now_utc)
+        is_retro = False
+        try:
+            with solar_system_ephemeris.set('builtin'):
+                loc = EarthLocation.of_site('greenwich')
+                merc_now = get_body('mercury', t, loc)
+                merc_later = get_body('mercury', t + 1 * u.day, loc)
+                
+                lon_now = merc_now.transform_to('geocentrictrueecliptic').lon.deg
+                lon_later = merc_later.transform_to('geocentrictrueecliptic').lon.deg
+                
+                diff = (lon_later - lon_now) % 360
+                is_retro = diff > 180 
+        except Exception as e:
+            logger.error(f"Astro calc error: {e}")
+
+        self._cache['mercury'] = {'val': is_retro, 'ts': now_ts}
+        return is_retro
+
+    def get_indicators(self, custom_now: Optional[datetime.datetime] = None) -> Dict[str, Any]:
+        """Generate full suite of Esoteric Matrix factors."""
+        now_utc = custom_now if custom_now else datetime.datetime.now(datetime.timezone.utc)
+        
+        pop_hour, liq_hour = self.get_weighted_times(now_utc)
+        moon_data = self.get_moon_phase(now_utc)
+        calendar = self.get_calendar_items(now_utc)
+
+        return {
+            'timestamp': now_utc.isoformat(),
+            'unix': int(now_utc.timestamp()),
+            'calendar': calendar,
+            'fibonacci_time': self.get_fibonacci_time(now_utc),
+            'regional_times': self.get_regional_times(now_utc),
+            'population_weighted_hour': pop_hour,
+            'liquidity_weighted_hour': liq_hour,
+            'liquidity_session': self.get_liquidity_session(now_utc),
+            'market_cycle_position': round(self.get_market_cycle_position(now_utc), 4),
+            'moon_illumination': moon_data['illumination'],
+            'moon_phase_name': moon_data['phase_name'],
+            'mercury_retrograde': int(self.is_mercury_retrograde(now_utc)),
+        }
+
+
+class EsotericFactorsService:
+    """
+    Continuous evaluation service for Esoteric Factors.
+    Dumps state deterministically to be consumed by the live trading orchestrator/Forewarning layers.
+    """
+    def __init__(self, output_dir: str = "", poll_interval_s: float = 60.0):
+        # Default to same structure as external factors
+        if not output_dir:
+            self.output_dir = Path(__file__).parent / "eso_cache"
+        else:
+            self.output_dir = Path(output_dir)
+            
+        self.output_dir.mkdir(parents=True, exist_ok=True)
+        
+        self.poll_interval_s = poll_interval_s
+        self.engine = MarketIndicators()
+        
+        self._latest_data = {}
+        self._running = False
+        self._task = None
+        self._lock = threading.Lock()
+
+    async def _update_loop(self):
+        logger.info(f"EsotericFactorsService starting. Polling every {self.poll_interval_s}s.")
+        while self._running:
+            try:
+                # 1. Compute Matrix
+                data = self.engine.get_indicators()
+                
+                # 2. Store in memory
+                with self._lock:
+                    self._latest_data = data
+                
+                # 3. Dump purely to fast JSON
+                self._write_to_disk(data)
+                
+            except Exception as e:
+                logger.error(f"Error in Esoteric update loop: {e}", exc_info=True)
+                
+            await asyncio.sleep(self.poll_interval_s)
+
+    def _write_to_disk(self, data: dict):
+        # Fast write pattern via atomic tmp rename strategy
+        target_path = self.output_dir / "latest_esoteric_factors.json"
+        tmp_path = self.output_dir / "latest_esoteric_factors.tmp"
+        
+        try:
+            with open(tmp_path, 'w') as f:
+                json.dump(data, f, indent=2)
+            tmp_path.replace(target_path)
+        except Exception as e:
+            logger.error(f"Failed to write Esoteric factors to disk: {e}")
+
+    def get_latest(self) -> dict:
+        """Non-blocking sub-millisecond retrieval of the latest internal state."""
+        with self._lock:
+            return self._latest_data.copy()
+
+    def start(self):
+        """Starts the background calculation loop (Threaded/Async wrapper)."""
+        if self._running: return
+        self._running = True
+        
+        def run_async():
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            loop.run_until_complete(self._update_loop())
+            
+        self._thread = threading.Thread(target=run_async, daemon=True)
+        self._thread.start()
+
+    def stop(self):
+        self._running = False
+        if hasattr(self, '_thread'):
+            self._thread.join(timeout=2.0)
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+    
+    svc = EsotericFactorsService(poll_interval_s=5.0)
+    print("Starting Esoteric Factors Service test run for 15 seconds...")
+    svc.start()
+    
+    for _ in range(3):
+        time.sleep(5)
+        latest = svc.get_latest()
+        print(f"Update: Moon Illumination={latest.get('moon_illumination'):.3f} | Liquid Session={latest.get('liquidity_session')} | PopHour={latest.get('population_weighted_hour')}")
+        
+    svc.stop()
+    print("Stopped successfully.")

external_factors/external_factors_matrix.py

@@ -0,0 +1,612 @@
+#!/usr/bin/env python3
+"""
+EXTERNAL FACTORS MATRIX v5.0 - DOLPHIN Compatible with BACKFILL
+================================================================
+85 indicators with HISTORICAL query support where available.
+
+BACKFILL CAPABILITY:
+  FULL HISTORY (51): CoinMetrics, FRED, DeFi Llama TVL/stables, F&G, Binance funding/OI
+  PARTIAL (12): Deribit DVOL, CoinGecko prices, DEX volume
+  CURRENT ONLY (22): Mempool, order books, spreads, dominance
+
+Author: HJ / Claude  |  Version: 5.0.0
+"""
+
+import asyncio
+import aiohttp
+import numpy as np
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Any, Tuple
+from datetime import datetime, timezone
+from collections import deque
+from enum import Enum
+import json
+
+class Category(Enum):
+    DERIVATIVES = "derivatives"
+    ONCHAIN = "onchain"
+    DEFI = "defi"
+    MACRO = "macro"
+    SENTIMENT = "sentiment"
+    MICROSTRUCTURE = "microstructure"
+
+class Stationarity(Enum):
+    STATIONARY = "stationary"
+    TREND_UP = "trend_up"
+    EPISODIC = "episodic"
+
+class HistoricalSupport(Enum):
+    FULL = "full"       # Any historical date
+    PARTIAL = "partial" # Limited history
+    CURRENT = "current" # Real-time only
+
+@dataclass
+class Indicator:
+    id: int
+    name: str
+    category: Category
+    source: str
+    url: str
+    parser: str
+    stationarity: Stationarity
+    historical: HistoricalSupport
+    hist_url: str = ""
+    hist_resolution: str = ""
+    description: str = ""
+
+@dataclass
+class Config:
+    timeout: int = 15
+    max_concurrent: int = 15
+    cache_ttl: int = 30
+    fred_api_key: str = ""
+
+# fmt: off
+INDICATORS: List[Indicator] = [
+    # DERIVATIVES - Binance (1-10) - Most have FULL history
+    Indicator(1, "funding_btc", Category.DERIVATIVES, "binance",
+        "https://fapi.binance.com/fapi/v1/fundingRate?symbol=BTCUSDT&limit=1",
+        "parse_binance_funding", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://fapi.binance.com/fapi/v1/fundingRate?symbol=BTCUSDT&startTime={start_ms}&endTime={end_ms}&limit=1",
+        "8h", "BTC funding - FULL via startTime/endTime"),
+    Indicator(2, "funding_eth", Category.DERIVATIVES, "binance",
+        "https://fapi.binance.com/fapi/v1/fundingRate?symbol=ETHUSDT&limit=1",
+        "parse_binance_funding", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://fapi.binance.com/fapi/v1/fundingRate?symbol=ETHUSDT&startTime={start_ms}&endTime={end_ms}&limit=1",
+        "8h", "ETH funding"),
+    Indicator(3, "oi_btc", Category.DERIVATIVES, "binance",
+        "https://fapi.binance.com/fapi/v1/openInterest?symbol=BTCUSDT",
+        "parse_binance_oi", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://fapi.binance.com/futures/data/openInterestHist?symbol=BTCUSDT&period=1h&startTime={start_ms}&endTime={end_ms}&limit=1",
+        "1h", "BTC OI - FULL via openInterestHist"),
+    Indicator(4, "oi_eth", Category.DERIVATIVES, "binance",
+        "https://fapi.binance.com/fapi/v1/openInterest?symbol=ETHUSDT",
+        "parse_binance_oi", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://fapi.binance.com/futures/data/openInterestHist?symbol=ETHUSDT&period=1h&startTime={start_ms}&endTime={end_ms}&limit=1",
+        "1h", "ETH OI"),
+    Indicator(5, "ls_btc", Category.DERIVATIVES, "binance",
+        "https://fapi.binance.com/futures/data/globalLongShortAccountRatio?symbol=BTCUSDT&period=1h&limit=1",
+        "parse_binance_ls", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://fapi.binance.com/futures/data/globalLongShortAccountRatio?symbol=BTCUSDT&period=1h&startTime={start_ms}&endTime={end_ms}&limit=1",
+        "1h", "L/S ratio - FULL"),
+    Indicator(6, "ls_eth", Category.DERIVATIVES, "binance",
+        "https://fapi.binance.com/futures/data/globalLongShortAccountRatio?symbol=ETHUSDT&period=1h&limit=1",
+        "parse_binance_ls", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://fapi.binance.com/futures/data/globalLongShortAccountRatio?symbol=ETHUSDT&period=1h&startTime={start_ms}&endTime={end_ms}&limit=1",
+        "1h", "ETH L/S"),
+    Indicator(7, "ls_top", Category.DERIVATIVES, "binance",
+        "https://fapi.binance.com/futures/data/topLongShortAccountRatio?symbol=BTCUSDT&period=1h&limit=1",
+        "parse_binance_ls", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://fapi.binance.com/futures/data/topLongShortAccountRatio?symbol=BTCUSDT&period=1h&startTime={start_ms}&endTime={end_ms}&limit=1",
+        "1h", "Top trader L/S"),
+    Indicator(8, "taker", Category.DERIVATIVES, "binance",
+        "https://fapi.binance.com/futures/data/takerlongshortRatio?symbol=BTCUSDT&period=1h&limit=1",
+        "parse_binance_taker", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://fapi.binance.com/futures/data/takerlongshortRatio?symbol=BTCUSDT&period=1h&startTime={start_ms}&endTime={end_ms}&limit=1",
+        "1h", "Taker ratio"),
+    Indicator(9, "basis", Category.DERIVATIVES, "binance",
+        "https://fapi.binance.com/fapi/v1/premiumIndex?symbol=BTCUSDT",
+        "parse_binance_basis", Stationarity.STATIONARY, HistoricalSupport.CURRENT,
+        "", "", "Basis - CURRENT"),
+    Indicator(10, "liq_proxy", Category.DERIVATIVES, "binance",
+        "https://fapi.binance.com/fapi/v1/ticker/24hr?symbol=BTCUSDT",
+        "parse_liq_proxy", Stationarity.STATIONARY, HistoricalSupport.CURRENT,
+        "", "", "Liq proxy - CURRENT"),
+    # DERIVATIVES - Deribit (11-18)
+    Indicator(11, "dvol_btc", Category.DERIVATIVES, "deribit",
+        "https://www.deribit.com/api/v2/public/get_volatility_index_data?currency=BTC&resolution=3600&count=1",
+        "parse_deribit_dvol", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://www.deribit.com/api/v2/public/get_volatility_index_data?currency=BTC&resolution=3600&start_timestamp={start_ms}&end_timestamp={end_ms}",
+        "1h", "DVOL - FULL"),
+    Indicator(12, "dvol_eth", Category.DERIVATIVES, "deribit",
+        "https://www.deribit.com/api/v2/public/get_volatility_index_data?currency=ETH&resolution=3600&count=1",
+        "parse_deribit_dvol", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://www.deribit.com/api/v2/public/get_volatility_index_data?currency=ETH&resolution=3600&start_timestamp={start_ms}&end_timestamp={end_ms}",
+        "1h", "ETH DVOL"),
+    Indicator(13, "pcr_vol", Category.DERIVATIVES, "deribit",
+        "https://www.deribit.com/api/v2/public/get_book_summary_by_currency?currency=BTC&kind=option",
+        "parse_deribit_pcr", Stationarity.STATIONARY, HistoricalSupport.CURRENT, "", "", "PCR - CURRENT"),
+    Indicator(14, "pcr_oi", Category.DERIVATIVES, "deribit",
+        "https://www.deribit.com/api/v2/public/get_book_summary_by_currency?currency=BTC&kind=option",
+        "parse_deribit_pcr_oi", Stationarity.STATIONARY, HistoricalSupport.CURRENT, "", "", "PCR OI - CURRENT"),
+    Indicator(15, "pcr_eth", Category.DERIVATIVES, "deribit",
+        "https://www.deribit.com/api/v2/public/get_book_summary_by_currency?currency=ETH&kind=option",
+        "parse_deribit_pcr", Stationarity.STATIONARY, HistoricalSupport.CURRENT, "", "", "ETH PCR - CURRENT"),
+    Indicator(16, "opt_oi", Category.DERIVATIVES, "deribit",
+        "https://www.deribit.com/api/v2/public/get_book_summary_by_currency?currency=BTC&kind=option",
+        "parse_deribit_oi", Stationarity.TREND_UP, HistoricalSupport.CURRENT, "", "", "Options OI - CURRENT"),
+    Indicator(17, "fund_dbt_btc", Category.DERIVATIVES, "deribit",
+        "https://www.deribit.com/api/v2/public/get_funding_rate_value?instrument_name=BTC-PERPETUAL",
+        "parse_deribit_fund", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://www.deribit.com/api/v2/public/get_funding_rate_history?instrument_name=BTC-PERPETUAL&start_timestamp={start_ms}&end_timestamp={end_ms}",
+        "8h", "Deribit fund - FULL"),
+    Indicator(18, "fund_dbt_eth", Category.DERIVATIVES, "deribit",
+        "https://www.deribit.com/api/v2/public/get_funding_rate_value?instrument_name=ETH-PERPETUAL",
+        "parse_deribit_fund", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://www.deribit.com/api/v2/public/get_funding_rate_history?instrument_name=ETH-PERPETUAL&start_timestamp={start_ms}&end_timestamp={end_ms}",
+        "8h", "Deribit ETH fund"),
+    # ONCHAIN - CoinMetrics (19-30) - ALL FULL HISTORY
+    Indicator(19, "rcap_btc", Category.ONCHAIN, "coinmetrics",
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=btc&metrics=CapRealUSD&frequency=1d&page_size=1",
+        "parse_cm", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=btc&metrics=CapRealUSD&frequency=1d&start_time={date}T00:00:00Z&end_time={date}T23:59:59Z",
+        "1d", "Realized cap - FULL"),
+    Indicator(20, "mvrv", Category.ONCHAIN, "coinmetrics",
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=btc&metrics=CapMrktCurUSD,CapRealUSD&frequency=1d&page_size=1",
+        "parse_cm_mvrv", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=btc&metrics=CapMrktCurUSD,CapRealUSD&frequency=1d&start_time={date}T00:00:00Z&end_time={date}T23:59:59Z",
+        "1d", "MVRV - FULL"),
+    Indicator(21, "nupl", Category.ONCHAIN, "coinmetrics",
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=btc&metrics=CapMrktCurUSD,CapRealUSD&frequency=1d&page_size=1",
+        "parse_cm_nupl", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=btc&metrics=CapMrktCurUSD,CapRealUSD&frequency=1d&start_time={date}T00:00:00Z&end_time={date}T23:59:59Z",
+        "1d", "NUPL - FULL"),
+    Indicator(22, "addr_btc", Category.ONCHAIN, "coinmetrics",
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=btc&metrics=AdrActCnt&frequency=1d&page_size=1",
+        "parse_cm", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=btc&metrics=AdrActCnt&frequency=1d&start_time={date}T00:00:00Z&end_time={date}T23:59:59Z",
+        "1d", "Active addr - FULL"),
+    Indicator(23, "addr_eth", Category.ONCHAIN, "coinmetrics",
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=eth&metrics=AdrActCnt&frequency=1d&page_size=1",
+        "parse_cm", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=eth&metrics=AdrActCnt&frequency=1d&start_time={date}T00:00:00Z&end_time={date}T23:59:59Z",
+        "1d", "ETH addr - FULL"),
+    Indicator(24, "txcnt", Category.ONCHAIN, "coinmetrics",
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=btc&metrics=TxCnt&frequency=1d&page_size=1",
+        "parse_cm", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=btc&metrics=TxCnt&frequency=1d&start_time={date}T00:00:00Z&end_time={date}T23:59:59Z",
+        "1d", "TX count - FULL"),
+    Indicator(25, "fees_btc", Category.ONCHAIN, "coinmetrics",
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=btc&metrics=FeeTotUSD&frequency=1d&page_size=1",
+        "parse_cm", Stationarity.EPISODIC, HistoricalSupport.FULL,
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=btc&metrics=FeeTotUSD&frequency=1d&start_time={date}T00:00:00Z&end_time={date}T23:59:59Z",
+        "1d", "BTC fees - FULL"),
+    Indicator(26, "fees_eth", Category.ONCHAIN, "coinmetrics",
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=eth&metrics=FeeTotUSD&frequency=1d&page_size=1",
+        "parse_cm", Stationarity.EPISODIC, HistoricalSupport.FULL,
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=eth&metrics=FeeTotUSD&frequency=1d&start_time={date}T00:00:00Z&end_time={date}T23:59:59Z",
+        "1d", "ETH fees - FULL"),
+    Indicator(27, "nvt", Category.ONCHAIN, "coinmetrics",
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=btc&metrics=NVTAdj&frequency=1d&page_size=1",
+        "parse_cm", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=btc&metrics=NVTAdj&frequency=1d&start_time={date}T00:00:00Z&end_time={date}T23:59:59Z",
+        "1d", "NVT - FULL"),
+    Indicator(28, "velocity", Category.ONCHAIN, "coinmetrics",
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=btc&metrics=VelCur1yr&frequency=1d&page_size=1",
+        "parse_cm", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=btc&metrics=VelCur1yr&frequency=1d&start_time={date}T00:00:00Z&end_time={date}T23:59:59Z",
+        "1d", "Velocity - FULL"),
+    Indicator(29, "sply_act", Category.ONCHAIN, "coinmetrics",
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=btc&metrics=SplyAct1yr&frequency=1d&page_size=1",
+        "parse_cm", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=btc&metrics=SplyAct1yr&frequency=1d&start_time={date}T00:00:00Z&end_time={date}T23:59:59Z",
+        "1d", "Active supply - FULL"),
+    Indicator(30, "rcap_eth", Category.ONCHAIN, "coinmetrics",
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=eth&metrics=CapRealUSD&frequency=1d&page_size=1",
+        "parse_cm", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets=eth&metrics=CapRealUSD&frequency=1d&start_time={date}T00:00:00Z&end_time={date}T23:59:59Z",
+        "1d", "ETH rcap - FULL"),
+    # ONCHAIN - Blockchain.info (31-37)
+    Indicator(31, "hashrate", Category.ONCHAIN, "blockchain",
+        "https://blockchain.info/q/hashrate", "parse_bc", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://api.blockchain.info/charts/hash-rate?timespan=1days&start={date}&format=json", "1d", "Hashrate - FULL"),
+    Indicator(32, "difficulty", Category.ONCHAIN, "blockchain",
+        "https://blockchain.info/q/getdifficulty", "parse_bc", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://api.blockchain.info/charts/difficulty?timespan=1days&start={date}&format=json", "1d", "Difficulty - FULL"),
+    Indicator(33, "blk_int", Category.ONCHAIN, "blockchain",
+        "https://blockchain.info/q/interval", "parse_bc_int", Stationarity.STATIONARY, HistoricalSupport.CURRENT, "", "", "Block int - CURRENT"),
+    Indicator(34, "unconf", Category.ONCHAIN, "blockchain",
+        "https://blockchain.info/q/unconfirmedcount", "parse_bc", Stationarity.STATIONARY, HistoricalSupport.CURRENT, "", "", "Unconf - CURRENT"),
+    Indicator(35, "tx_blk", Category.ONCHAIN, "blockchain",
+        "https://blockchain.info/q/nperblock", "parse_bc", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://api.blockchain.info/charts/n-transactions-per-block?timespan=1days&start={date}&format=json", "1d", "TX/blk - FULL"),
+    Indicator(36, "total_btc", Category.ONCHAIN, "blockchain",
+        "https://blockchain.info/q/totalbc", "parse_bc_btc", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://api.blockchain.info/charts/total-bitcoins?timespan=1days&start={date}&format=json", "1d", "Total BTC - FULL"),
+    Indicator(37, "mcap_bc", Category.ONCHAIN, "blockchain",
+        "https://blockchain.info/q/marketcap", "parse_bc", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://api.blockchain.info/charts/market-cap?timespan=1days&start={date}&format=json", "1d", "Mcap - FULL"),
+    # ONCHAIN - Mempool (38-42) - ALL CURRENT
+    Indicator(38, "mp_cnt", Category.ONCHAIN, "mempool", "https://mempool.space/api/mempool",
+        "parse_mp_cnt", Stationarity.STATIONARY, HistoricalSupport.CURRENT, "", "", "Mempool - CURRENT"),
+    Indicator(39, "mp_mb", Category.ONCHAIN, "mempool", "https://mempool.space/api/mempool",
+        "parse_mp_mb", Stationarity.STATIONARY, HistoricalSupport.CURRENT, "", "", "Mempool MB - CURRENT"),
+    Indicator(40, "fee_fast", Category.ONCHAIN, "mempool", "https://mempool.space/api/v1/fees/recommended",
+        "parse_fee_fast", Stationarity.EPISODIC, HistoricalSupport.CURRENT, "", "", "Fast fee - CURRENT"),
+    Indicator(41, "fee_med", Category.ONCHAIN, "mempool", "https://mempool.space/api/v1/fees/recommended",
+        "parse_fee_med", Stationarity.EPISODIC, HistoricalSupport.CURRENT, "", "", "Med fee - CURRENT"),
+    Indicator(42, "fee_slow", Category.ONCHAIN, "mempool", "https://mempool.space/api/v1/fees/recommended",
+        "parse_fee_slow", Stationarity.EPISODIC, HistoricalSupport.CURRENT, "", "", "Slow fee - CURRENT"),
+    # DEFI - DeFi Llama (43-51)
+    Indicator(43, "tvl", Category.DEFI, "defillama", "https://api.llama.fi/v2/historicalChainTvl",
+        "parse_dl_tvl", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://api.llama.fi/v2/historicalChainTvl", "1d", "TVL - FULL (filter client-side)"),
+    Indicator(44, "tvl_eth", Category.DEFI, "defillama", "https://api.llama.fi/v2/historicalChainTvl/Ethereum",
+        "parse_dl_tvl", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://api.llama.fi/v2/historicalChainTvl/Ethereum", "1d", "ETH TVL - FULL"),
+    Indicator(45, "stables", Category.DEFI, "defillama", "https://stablecoins.llama.fi/stablecoins?includePrices=false",
+        "parse_dl_stables", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://stablecoins.llama.fi/stablecoincharts/all?stablecoin=1", "1d", "Stables - FULL"),
+    Indicator(46, "usdt", Category.DEFI, "defillama", "https://stablecoins.llama.fi/stablecoin/tether",
+        "parse_dl_single", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://stablecoins.llama.fi/stablecoincharts/all?stablecoin=1", "1d", "USDT - FULL"),
+    Indicator(47, "usdc", Category.DEFI, "defillama", "https://stablecoins.llama.fi/stablecoin/usd-coin",
+        "parse_dl_single", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://stablecoins.llama.fi/stablecoincharts/all?stablecoin=2", "1d", "USDC - FULL"),
+    Indicator(48, "dex_vol", Category.DEFI, "defillama",
+        "https://api.llama.fi/overview/dexs?excludeTotalDataChart=true&excludeTotalDataChartBreakdown=true",
+        "parse_dl_dex", Stationarity.EPISODIC, HistoricalSupport.PARTIAL, "", "1d", "DEX vol - PARTIAL"),
+    Indicator(49, "bridge", Category.DEFI, "defillama", "https://bridges.llama.fi/bridges?includeChains=false",
+        "parse_dl_bridge", Stationarity.EPISODIC, HistoricalSupport.PARTIAL, "", "1d", "Bridge - PARTIAL"),
+    Indicator(50, "yields", Category.DEFI, "defillama", "https://yields.llama.fi/pools",
+        "parse_dl_yields", Stationarity.STATIONARY, HistoricalSupport.CURRENT, "", "", "Yields - CURRENT"),
+    Indicator(51, "fees", Category.DEFI, "defillama", "https://api.llama.fi/overview/fees?excludeTotalDataChart=true",
+        "parse_dl_fees", Stationarity.EPISODIC, HistoricalSupport.PARTIAL, "", "1d", "Fees - PARTIAL"),
+    # MACRO - FRED (52-65) - ALL FULL HISTORY (decades)
+    Indicator(52, "dxy", Category.MACRO, "fred", "DTWEXBGS", "parse_fred", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://api.stlouisfed.org/fred/series/observations?series_id=DTWEXBGS&api_key={key}&file_type=json&observation_start={date}&observation_end={date}", "1d", "DXY - FULL"),
+    Indicator(53, "us10y", Category.MACRO, "fred", "DGS10", "parse_fred", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://api.stlouisfed.org/fred/series/observations?series_id=DGS10&api_key={key}&file_type=json&observation_start={date}&observation_end={date}", "1d", "10Y - FULL"),
+    Indicator(54, "us2y", Category.MACRO, "fred", "DGS2", "parse_fred", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://api.stlouisfed.org/fred/series/observations?series_id=DGS2&api_key={key}&file_type=json&observation_start={date}&observation_end={date}", "1d", "2Y - FULL"),
+    Indicator(55, "ycurve", Category.MACRO, "fred", "T10Y2Y", "parse_fred", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://api.stlouisfed.org/fred/series/observations?series_id=T10Y2Y&api_key={key}&file_type=json&observation_start={date}&observation_end={date}", "1d", "Yield curve - FULL"),
+    Indicator(56, "vix", Category.MACRO, "fred", "VIXCLS", "parse_fred", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://api.stlouisfed.org/fred/series/observations?series_id=VIXCLS&api_key={key}&file_type=json&observation_start={date}&observation_end={date}", "1d", "VIX - FULL"),
+    Indicator(57, "fedfunds", Category.MACRO, "fred", "DFF", "parse_fred", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://api.stlouisfed.org/fred/series/observations?series_id=DFF&api_key={key}&file_type=json&observation_start={date}&observation_end={date}", "1d", "Fed funds - FULL"),
+    Indicator(58, "m2", Category.MACRO, "fred", "WM2NS", "parse_fred", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://api.stlouisfed.org/fred/series/observations?series_id=WM2NS&api_key={key}&file_type=json&observation_start={date}&observation_end={date}", "1w", "M2 - FULL"),
+    Indicator(59, "cpi", Category.MACRO, "fred", "CPIAUCSL", "parse_fred", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://api.stlouisfed.org/fred/series/observations?series_id=CPIAUCSL&api_key={key}&file_type=json&observation_start={date}&observation_end={date}", "1m", "CPI - FULL"),
+    Indicator(60, "sp500", Category.MACRO, "fred", "SP500", "parse_fred", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://api.stlouisfed.org/fred/series/observations?series_id=SP500&api_key={key}&file_type=json&observation_start={date}&observation_end={date}", "1d", "S&P - FULL"),
+    Indicator(61, "gold", Category.MACRO, "fred", "GOLDAMGBD228NLBM", "parse_fred", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://api.stlouisfed.org/fred/series/observations?series_id=GOLDAMGBD228NLBM&api_key={key}&file_type=json&observation_start={date}&observation_end={date}", "1d", "Gold - FULL"),
+    Indicator(62, "hy_spread", Category.MACRO, "fred", "BAMLH0A0HYM2", "parse_fred", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://api.stlouisfed.org/fred/series/observations?series_id=BAMLH0A0HYM2&api_key={key}&file_type=json&observation_start={date}&observation_end={date}", "1d", "HY spread - FULL"),
+    Indicator(63, "be5y", Category.MACRO, "fred", "T5YIE", "parse_fred", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://api.stlouisfed.org/fred/series/observations?series_id=T5YIE&api_key={key}&file_type=json&observation_start={date}&observation_end={date}", "1d", "Breakeven - FULL"),
+    Indicator(64, "nfci", Category.MACRO, "fred", "NFCI", "parse_fred", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://api.stlouisfed.org/fred/series/observations?series_id=NFCI&api_key={key}&file_type=json&observation_start={date}&observation_end={date}", "1w", "NFCI - FULL"),
+    Indicator(65, "claims", Category.MACRO, "fred", "ICSA", "parse_fred", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://api.stlouisfed.org/fred/series/observations?series_id=ICSA&api_key={key}&file_type=json&observation_start={date}&observation_end={date}", "1w", "Claims - FULL"),
+    # SENTIMENT (66-72) - F&G has FULL history
+    Indicator(66, "fng", Category.SENTIMENT, "alternative", "https://api.alternative.me/fng/?limit=1",
+        "parse_fng", Stationarity.STATIONARY, HistoricalSupport.FULL,
+        "https://api.alternative.me/fng/?limit=1000&date_format=us", "1d", "F&G - FULL (returns history, filter)"),
+    Indicator(67, "fng_prev", Category.SENTIMENT, "alternative", "https://api.alternative.me/fng/?limit=2",
+        "parse_fng_prev", Stationarity.STATIONARY, HistoricalSupport.FULL, "", "1d", "Prev F&G"),
+    Indicator(68, "fng_week", Category.SENTIMENT, "alternative", "https://api.alternative.me/fng/?limit=7",
+        "parse_fng_week", Stationarity.STATIONARY, HistoricalSupport.FULL, "", "1d", "Week F&G"),
+    Indicator(69, "fng_vol", Category.SENTIMENT, "alternative", "https://api.alternative.me/fng/?limit=1",
+        "parse_fng", Stationarity.STATIONARY, HistoricalSupport.FULL, "", "1d", "Vol proxy"),
+    Indicator(70, "fng_mom", Category.SENTIMENT, "alternative", "https://api.alternative.me/fng/?limit=1",
+        "parse_fng", Stationarity.STATIONARY, HistoricalSupport.FULL, "", "1d", "Mom proxy"),
+    Indicator(71, "fng_soc", Category.SENTIMENT, "alternative", "https://api.alternative.me/fng/?limit=1",
+        "parse_fng", Stationarity.STATIONARY, HistoricalSupport.FULL, "", "1d", "Social proxy"),
+    Indicator(72, "fng_dom", Category.SENTIMENT, "alternative", "https://api.alternative.me/fng/?limit=1",
+        "parse_fng", Stationarity.STATIONARY, HistoricalSupport.FULL, "", "1d", "Dom proxy"),
+    # MICROSTRUCTURE (73-80) - Most CURRENT
+    Indicator(73, "imbal_btc", Category.MICROSTRUCTURE, "binance", "https://api.binance.com/api/v3/depth?symbol=BTCUSDT&limit=100",
+        "parse_imbal", Stationarity.STATIONARY, HistoricalSupport.CURRENT, "", "", "Imbalance - CURRENT"),
+    Indicator(74, "imbal_eth", Category.MICROSTRUCTURE, "binance", "https://api.binance.com/api/v3/depth?symbol=ETHUSDT&limit=100",
+        "parse_imbal", Stationarity.STATIONARY, HistoricalSupport.CURRENT, "", "", "ETH imbal - CURRENT"),
+    Indicator(75, "spread", Category.MICROSTRUCTURE, "binance", "https://api.binance.com/api/v3/ticker/bookTicker?symbol=BTCUSDT",
+        "parse_spread", Stationarity.STATIONARY, HistoricalSupport.CURRENT, "", "", "Spread - CURRENT"),
+    Indicator(76, "chg24_btc", Category.MICROSTRUCTURE, "binance", "https://api.binance.com/api/v3/ticker/24hr?symbol=BTCUSDT",
+        "parse_chg", Stationarity.STATIONARY, HistoricalSupport.CURRENT, "", "", "24h chg - CURRENT"),
+    Indicator(77, "chg24_eth", Category.MICROSTRUCTURE, "binance", "https://api.binance.com/api/v3/ticker/24hr?symbol=ETHUSDT",
+        "parse_chg", Stationarity.STATIONARY, HistoricalSupport.CURRENT, "", "", "ETH 24h - CURRENT"),
+    Indicator(78, "vol24", Category.MICROSTRUCTURE, "binance", "https://api.binance.com/api/v3/ticker/24hr?symbol=BTCUSDT",
+        "parse_vol", Stationarity.EPISODIC, HistoricalSupport.FULL,
+        "https://api.binance.com/api/v3/klines?symbol=BTCUSDT&interval=1d&startTime={start_ms}&endTime={end_ms}&limit=1",
+        "1d", "Volume - FULL via klines"),
+    Indicator(79, "dispersion", Category.MICROSTRUCTURE, "binance", "https://api.binance.com/api/v3/ticker/24hr",
+        "parse_disp", Stationarity.STATIONARY, HistoricalSupport.CURRENT, "", "", "Dispersion - CURRENT"),
+    Indicator(80, "correlation", Category.MICROSTRUCTURE, "binance", "https://api.binance.com/api/v3/ticker/24hr",
+        "parse_corr", Stationarity.STATIONARY, HistoricalSupport.CURRENT, "", "", "Correlation - CURRENT"),
+    # MARKET - CoinGecko (81-85)
+    Indicator(81, "btc_price", Category.MACRO, "coingecko", "https://api.coingecko.com/api/v3/simple/price?ids=bitcoin&vs_currencies=usd",
+        "parse_cg_btc", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://api.coingecko.com/api/v3/coins/bitcoin/history?date={date_dmy}", "1d", "BTC price - FULL"),
+    Indicator(82, "eth_price", Category.MACRO, "coingecko", "https://api.coingecko.com/api/v3/simple/price?ids=ethereum&vs_currencies=usd",
+        "parse_cg_eth", Stationarity.TREND_UP, HistoricalSupport.FULL,
+        "https://api.coingecko.com/api/v3/coins/ethereum/history?date={date_dmy}", "1d", "ETH price - FULL"),
+    Indicator(83, "mcap", Category.MACRO, "coingecko", "https://api.coingecko.com/api/v3/global",
+        "parse_cg_mcap", Stationarity.TREND_UP, HistoricalSupport.PARTIAL, "", "1d", "Mcap - PARTIAL"),
+    Indicator(84, "btc_dom", Category.MACRO, "coingecko", "https://api.coingecko.com/api/v3/global",
+        "parse_cg_dom_btc", Stationarity.STATIONARY, HistoricalSupport.CURRENT, "", "", "BTC dom - CURRENT"),
+    Indicator(85, "eth_dom", Category.MACRO, "coingecko", "https://api.coingecko.com/api/v3/global",
+        "parse_cg_dom_eth", Stationarity.STATIONARY, HistoricalSupport.CURRENT, "", "", "ETH dom - CURRENT"),
+]
+# fmt: on
+
+N_INDICATORS = len(INDICATORS)
+
+class StationarityTransformer:
+    def __init__(self, lookback: int = 10):
+        self.history: Dict[int, deque] = {i: deque(maxlen=lookback+1) for i in range(1, N_INDICATORS+1)}
+    def transform(self, ind_id: int, raw: float) -> float:
+        ind = INDICATORS[ind_id - 1]
+        hist = self.history[ind_id]
+        hist.append(raw)
+        if ind.stationarity == Stationarity.STATIONARY: return raw
+        if ind.stationarity == Stationarity.TREND_UP:
+            return (raw - hist[-2]) / abs(hist[-2]) if len(hist) >= 2 and hist[-2] != 0 else 0.0
+        if ind.stationarity == Stationarity.EPISODIC:
+            if len(hist) < 3: return 0.0
+            m, s = np.mean(list(hist)), np.std(list(hist))
+            return (raw - m) / s if s > 0 else 0.0
+        return raw
+    def transform_matrix(self, raw: np.ndarray) -> np.ndarray:
+        return np.array([self.transform(i+1, raw[i]) for i in range(len(raw))])
+
+class ExternalFactorsFetcher:
+    def __init__(self, config: Config = None):
+        self.config = config or Config()
+        self.cache: Dict[str, Tuple[float, Any]] = {}
+        import time as t; self._time = t
+    
+    def _build_hist_url(self, ind: Indicator, dt: datetime) -> Optional[str]:
+        if ind.historical == HistoricalSupport.CURRENT or not ind.hist_url: return None
+        url = ind.hist_url
+        date_str = dt.strftime("%Y-%m-%d")
+        date_dmy = dt.strftime("%d-%m-%Y")
+        start_ms = int(dt.replace(hour=0, minute=0, second=0).timestamp() * 1000)
+        end_ms = int(dt.replace(hour=23, minute=59, second=59).timestamp() * 1000)
+        key = self.config.fred_api_key or "DEMO_KEY"
+        return url.replace("{date}", date_str).replace("{date_dmy}", date_dmy).replace("{start_ms}", str(start_ms)).replace("{end_ms}", str(end_ms)).replace("{key}", key)
+    
+    async def _fetch(self, session, url: str) -> Optional[Any]:
+        if url in self.cache:
+            ct, cd = self.cache[url]
+            if self._time.time() - ct < self.config.cache_ttl: return cd
+        try:
+            async with session.get(url, timeout=aiohttp.ClientTimeout(total=self.config.timeout), headers={"User-Agent": "Mozilla/5.0"}) as r:
+                if r.status == 200:
+                    d = await r.json() if 'json' in r.headers.get('Content-Type', '') else await r.text()
+                    if isinstance(d, str):
+                        try: d = json.loads(d)
+                        except: pass
+                    self.cache[url] = (self._time.time(), d)
+                    return d
+        except: pass
+        return None
+    
+    def _fred_url(self, series: str) -> str:
+        return f"https://api.stlouisfed.org/fred/series/observations?series_id={series}&api_key={self.config.fred_api_key or 'DEMO_KEY'}&file_type=json&sort_order=desc&limit=1"
+
+    # Parsers
+    def parse_binance_funding(self, d): return float(d[0]['fundingRate']) if isinstance(d, list) and d else 0.0
+    def parse_binance_oi(self, d):
+        if isinstance(d, list) and d: return float(d[-1].get('sumOpenInterest', 0))
+        return float(d.get('openInterest', 0)) if isinstance(d, dict) else 0.0
+    def parse_binance_ls(self, d): return float(d[-1]['longShortRatio']) if isinstance(d, list) and d else 1.0
+    def parse_binance_taker(self, d): return float(d[-1]['buySellRatio']) if isinstance(d, list) and d else 1.0
+    def parse_binance_basis(self, d): return float(d.get('lastFundingRate', 0)) * 365 * 3 if isinstance(d, dict) else 0.0
+    def parse_liq_proxy(self, d): return np.tanh(float(d.get('priceChangePercent', 0)) / 10) if isinstance(d, dict) else 0.0
+    def parse_deribit_dvol(self, d):
+        if isinstance(d, dict) and 'result' in d and isinstance(d['result'], dict) and 'data' in d['result'] and d['result']['data']:
+            return float(d['result']['data'][-1][4]) if len(d['result']['data'][-1]) > 4 else 0.0
+        return 0.0
+    def parse_deribit_pcr(self, d):
+        if isinstance(d, dict) and 'result' in d:
+            r = d['result']
+            p = sum(float(o.get('volume', 0)) for o in r if '-P' in o.get('instrument_name', ''))
+            c = sum(float(o.get('volume', 0)) for o in r if '-C' in o.get('instrument_name', ''))
+            return p / c if c > 0 else 1.0
+        return 1.0
+    def parse_deribit_pcr_oi(self, d):
+        if isinstance(d, dict) and 'result' in d:
+            r = d['result']
+            p = sum(float(o.get('open_interest', 0)) for o in r if '-P' in o.get('instrument_name', ''))
+            c = sum(float(o.get('open_interest', 0)) for o in r if '-C' in o.get('instrument_name', ''))
+            return p / c if c > 0 else 1.0
+        return 1.0
+    def parse_deribit_oi(self, d): return sum(float(o.get('open_interest', 0)) for o in d['result']) if isinstance(d, dict) and 'result' in d else 0.0
+    def parse_deribit_fund(self, d):
+        if isinstance(d, dict) and 'result' in d:
+            r = d['result']
+            return float(r[-1].get('interest_8h', 0)) if isinstance(r, list) and r else float(r)
+        return 0.0
+    def parse_cm(self, d):
+        if isinstance(d, dict) and 'data' in d and d['data']:
+            for k, v in d['data'][-1].items():
+                if k not in ['asset', 'time']:
+                    try: return float(v)
+                    except: pass
+        return 0.0
+    def parse_cm_mvrv(self, d):
+        if isinstance(d, dict) and 'data' in d and d['data']:
+            r = d['data'][-1]
+            m, rc = float(r.get('CapMrktCurUSD', 0)), float(r.get('CapRealUSD', 1))
+            return m / rc if rc > 0 else 0.0
+        return 0.0
+    def parse_cm_nupl(self, d):
+        if isinstance(d, dict) and 'data' in d and d['data']:
+            r = d['data'][-1]
+            m, rc = float(r.get('CapMrktCurUSD', 0)), float(r.get('CapRealUSD', 1))
+            return (m - rc) / m if m > 0 else 0.0
+        return 0.0
+    def parse_bc(self, d):
+        if isinstance(d, (int, float)): return float(d)
+        if isinstance(d, str):
+            try: return float(d)
+            except: pass
+        if isinstance(d, dict) and 'values' in d and d['values']: return float(d['values'][-1].get('y', 0))
+        return 0.0
+    def parse_bc_int(self, d): v = self.parse_bc(d); return abs(v - 600) / 600 if v > 0 else 0.0
+    def parse_bc_btc(self, d): v = self.parse_bc(d); return v / 1e8 if v > 0 else 0.0
+    def parse_mp_cnt(self, d): return float(d.get('count', 0)) if isinstance(d, dict) else 0.0
+    def parse_mp_mb(self, d): return float(d.get('vsize', 0)) / 1e6 if isinstance(d, dict) else 0.0
+    def parse_fee_fast(self, d): return float(d.get('fastestFee', 0)) if isinstance(d, dict) else 0.0
+    def parse_fee_med(self, d): return float(d.get('halfHourFee', 0)) if isinstance(d, dict) else 0.0
+    def parse_fee_slow(self, d): return float(d.get('economyFee', 0)) if isinstance(d, dict) else 0.0
+    def parse_dl_tvl(self, d, target_date: datetime = None):
+        if isinstance(d, list) and d:
+            if target_date:
+                ts = int(target_date.timestamp())
+                for e in reversed(d):
+                    if e.get('date', 0) <= ts: return float(e.get('tvl', 0))
+            return float(d[-1].get('tvl', 0))
+        return 0.0
+    def parse_dl_stables(self, d):
+        if isinstance(d, dict) and 'peggedAssets' in d:
+            return sum(float(a.get('circulating', {}).get('peggedUSD', 0)) for a in d['peggedAssets'])
+        return 0.0
+    def parse_dl_single(self, d):
+        if isinstance(d, dict) and 'tokens' in d and d['tokens']:
+            return float(d['tokens'][-1].get('circulating', {}).get('peggedUSD', 0))
+        return 0.0
+    def parse_dl_dex(self, d): return float(d.get('total24h', 0)) if isinstance(d, dict) else 0.0
+    def parse_dl_bridge(self, d):
+        if isinstance(d, dict) and 'bridges' in d:
+            return sum(float(b.get('lastDayVolume', 0)) for b in d['bridges'])
+        return 0.0
+    def parse_dl_yields(self, d):
+        if isinstance(d, dict) and 'data' in d:
+            apys = [float(p.get('apy', 0)) for p in d['data'][:100] if p.get('apy')]
+            return np.mean(apys) if apys else 0.0
+        return 0.0
+    def parse_dl_fees(self, d): return float(d.get('total24h', 0)) if isinstance(d, dict) else 0.0
+    def parse_fred(self, d):
+        if isinstance(d, dict) and 'observations' in d and d['observations']:
+            v = d['observations'][-1].get('value', '.')
+            if v != '.':
+                try: return float(v)
+                except: pass
+        return 0.0
+    def parse_fng(self, d): return float(d['data'][0]['value']) if isinstance(d, dict) and 'data' in d and d['data'] else 50.0
+    def parse_fng_prev(self, d): return float(d['data'][1]['value']) if isinstance(d, dict) and 'data' in d and len(d['data']) > 1 else 50.0
+    def parse_fng_week(self, d): return np.mean([float(x['value']) for x in d['data'][:7]]) if isinstance(d, dict) and 'data' in d and len(d['data']) >= 7 else 50.0
+    def parse_imbal(self, d):
+        if isinstance(d, dict):
+            bv = sum(float(b[1]) for b in d.get('bids', [])[:50])
+            av = sum(float(a[1]) for a in d.get('asks', [])[:50])
+            t = bv + av
+            return (bv - av) / t if t > 0 else 0.0
+        return 0.0
+    def parse_spread(self, d):
+        if isinstance(d, dict):
+            b, a = float(d.get('bidPrice', 0)), float(d.get('askPrice', 0))
+            return (a - b) / b * 10000 if b > 0 else 0.0
+        return 0.0
+    def parse_chg(self, d): return float(d.get('priceChangePercent', 0)) if isinstance(d, dict) else 0.0
+    def parse_vol(self, d):
+        if isinstance(d, dict): return float(d.get('quoteVolume', 0))
+        if isinstance(d, list) and d and isinstance(d[0], list): return float(d[-1][7])
+        return 0.0
+    def parse_disp(self, d):
+        if isinstance(d, list) and len(d) > 10:
+            chg = [float(t['priceChangePercent']) for t in d if t.get('symbol', '').endswith('USDT') and 'priceChangePercent' in t]
+            return float(np.std(chg[:50])) if len(chg) > 5 else 0.0
+        return 0.0
+    def parse_corr(self, d): disp = self.parse_disp(d); return 1 / (1 + disp) if disp > 0 else 0.5
+    def parse_cg_btc(self, d):
+        if isinstance(d, dict) and 'bitcoin' in d: return float(d['bitcoin']['usd'])
+        if isinstance(d, dict) and 'market_data' in d: return float(d['market_data'].get('current_price', {}).get('usd', 0))
+        return 0.0
+    def parse_cg_eth(self, d):
+        if isinstance(d, dict) and 'ethereum' in d: return float(d['ethereum']['usd'])
+        if isinstance(d, dict) and 'market_data' in d: return float(d['market_data'].get('current_price', {}).get('usd', 0))
+        return 0.0
+    def parse_cg_mcap(self, d): return float(d['data']['total_market_cap']['usd']) if isinstance(d, dict) and 'data' in d else 0.0
+    def parse_cg_dom_btc(self, d): return float(d['data']['market_cap_percentage']['btc']) if isinstance(d, dict) and 'data' in d else 0.0
+    def parse_cg_dom_eth(self, d): return float(d['data']['market_cap_percentage']['eth']) if isinstance(d, dict) and 'data' in d else 0.0
+
+    async def fetch_indicator(self, session, ind: Indicator, target_date: datetime = None) -> Tuple[int, str, float, bool]:
+        if target_date and ind.historical != HistoricalSupport.CURRENT:
+            url = self._build_hist_url(ind, target_date)
+        else:
+            url = self._fred_url(ind.url) if ind.source == "fred" else ind.url
+        if url is None: return (ind.id, ind.name, 0.0, False)
+        data = await self._fetch(session, url)
+        if data is None: return (ind.id, ind.name, 0.0, False)
+        parser = getattr(self, ind.parser, None)
+        if parser is None: return (ind.id, ind.name, 0.0, False)
+        try:
+            value = parser(data)
+            return (ind.id, ind.name, value, value != 0.0 or 'imbal' in ind.name)
+        except: return (ind.id, ind.name, 0.0, False)
+    
+    async def fetch_all(self, target_date: datetime = None) -> Dict[str, Any]:
+        connector = aiohttp.TCPConnector(limit=self.config.max_concurrent)
+        async with aiohttp.ClientSession(connector=connector) as session:
+            results = await asyncio.gather(*[self.fetch_indicator(session, ind, target_date) for ind in INDICATORS])
+        matrix = np.zeros(N_INDICATORS)
+        success = 0
+        details = {}
+        for idx, name, value, ok in results:
+            matrix[idx - 1] = value
+            if ok: success += 1
+            details[idx] = {'name': name, 'value': value, 'success': ok}
+        return {'matrix': matrix, 'timestamp': (target_date or datetime.now(timezone.utc)).isoformat(), 'success_count': success, 'total': N_INDICATORS, 'details': details}
+    
+    def fetch_sync(self, target_date: datetime = None) -> Dict[str, Any]:
+        return asyncio.run(self.fetch_all(target_date))
+
+class ExternalFactorsMatrix:
+    """DOLPHIN interface with BACKFILL. Usage: efm.update() or efm.update(datetime(2024,6,15))"""
+    def __init__(self, config: Config = None):
+        self.config = config or Config()
+        self.fetcher = ExternalFactorsFetcher(self.config)
+        self.transformer = StationarityTransformer()
+        self.raw_matrix: Optional[np.ndarray] = None
+        self.stationary_matrix: Optional[np.ndarray] = None
+        self.last_result: Optional[Dict] = None
+    
+    def update(self, target_date: datetime = None) -> np.ndarray:
+        self.last_result = self.fetcher.fetch_sync(target_date)
+        self.raw_matrix = self.last_result['matrix']
+        self.stationary_matrix = self.transformer.transform_matrix(self.raw_matrix)
+        return self.stationary_matrix
+    
+    def update_raw(self, target_date: datetime = None) -> np.ndarray:
+        self.last_result = self.fetcher.fetch_sync(target_date)
+        self.raw_matrix = self.last_result['matrix']
+        return self.raw_matrix
+    
+    def get_indicator_names(self) -> List[str]: return [i.name for i in INDICATORS]
+    def get_backfillable(self) -> List[Tuple[int, str, str]]:
+        return [(i.id, i.name, i.hist_resolution) for i in INDICATORS if i.historical in [HistoricalSupport.FULL, HistoricalSupport.PARTIAL]]
+    def get_current_only(self) -> List[Tuple[int, str]]:
+        return [(i.id, i.name) for i in INDICATORS if i.historical == HistoricalSupport.CURRENT]
+    def summary(self) -> str:
+        if not self.last_result: return "No data."
+        r = self.last_result
+        f = sum(1 for i in INDICATORS if i.historical == HistoricalSupport.FULL)
+        p = sum(1 for i in INDICATORS if i.historical == HistoricalSupport.PARTIAL)
+        c = sum(1 for i in INDICATORS if i.historical == HistoricalSupport.CURRENT)
+        return f"Success: {r['success_count']}/{r['total']} | Historical: FULL={f}, PARTIAL={p}, CURRENT={c}"
+
+if __name__ == "__main__":
+    print(f"EXTERNAL FACTORS v5.0 - {N_INDICATORS} indicators with BACKFILL")
+    f = [i for i in INDICATORS if i.historical == HistoricalSupport.FULL]
+    p = [i for i in INDICATORS if i.historical == HistoricalSupport.PARTIAL]
+    c = [i for i in INDICATORS if i.historical == HistoricalSupport.CURRENT]
+    print(f"\nFULL: {len(f)} | PARTIAL: {len(p)} | CURRENT: {len(c)}")
+    print("\nFULL HISTORY indicators:")
+    for i in f: print(f"  {i.id:2d}. {i.name:15s} [{i.hist_resolution:3s}] {i.source}")
+    print("\nCURRENT ONLY:")
+    for i in c: print(f"  {i.id:2d}. {i.name:15s} - {i.description}")

external_factors/indicator_reader.py

@@ -0,0 +1,266 @@
+#!/usr/bin/env python3
+"""
+INDICATOR READER v1.0
+=====================
+Utility to read and analyze processed indicator .npz files.
+
+Usage:
+    from indicator_reader import IndicatorReader
+    
+    # Load single file
+    reader = IndicatorReader("scan_000027_193311__Indicators.npz")
+    print(reader.summary())
+    
+    # Get DataFrames
+    scan_df = reader.scan_derived_df()
+    external_df = reader.external_df()
+    asset_df = reader.asset_df()
+    
+    # Load directory
+    all_data = IndicatorReader.load_directory("./scans/")
+"""
+
+import numpy as np
+from pathlib import Path
+from typing import Dict, List, Optional, Any, Tuple
+from datetime import datetime
+
+class IndicatorReader:
+    """Reader for processed indicator .npz files"""
+    
+    def __init__(self, path: str):
+        self.path = Path(path)
+        self._data = dict(np.load(path, allow_pickle=True))
+    
+    @property
+    def scan_number(self) -> int:
+        return int(self._data['scan_number'][0])
+    
+    @property
+    def timestamp(self) -> str:
+        return str(self._data['timestamp'][0])
+    
+    @property
+    def processing_time(self) -> float:
+        return float(self._data['processing_time'][0])
+    
+    @property
+    def n_assets(self) -> int:
+        return len(self._data['asset_symbols'])
+    
+    @property
+    def asset_symbols(self) -> List[str]:
+        return list(self._data['asset_symbols'])
+    
+    # =========================================================================
+    # SCAN-DERIVED (eigenvalue indicators from tracking_data/regime_signals)
+    # =========================================================================
+    
+    @property
+    def scan_derived(self) -> np.ndarray:
+        """Get scan-derived indicator array"""
+        return self._data['scan_derived']
+    
+    @property
+    def scan_derived_names(self) -> List[str]:
+        return list(self._data['scan_derived_names'])
+    
+    def scan_derived_df(self):
+        """Get scan-derived as pandas DataFrame"""
+        import pandas as pd
+        return pd.DataFrame({
+            'name': self.scan_derived_names,
+            'value': self.scan_derived
+        })
+    
+    def get_scan_indicator(self, name: str) -> float:
+        """Get specific scan-derived indicator by name"""
+        names = self.scan_derived_names
+        if name in names:
+            return float(self.scan_derived[names.index(name)])
+        raise KeyError(f"Unknown scan indicator: {name}")
+    
+    # =========================================================================
+    # EXTERNAL (API-fetched indicators)
+    # =========================================================================
+    
+    @property
+    def external(self) -> np.ndarray:
+        """Get external indicator array (85 values, NaN for skipped)"""
+        return self._data['external']
+    
+    @property
+    def external_success(self) -> np.ndarray:
+        """Get success flags for external indicators"""
+        return self._data['external_success']
+    
+    def external_df(self):
+        """Get external indicators as pandas DataFrame"""
+        import pandas as pd
+        # Indicator names (would need to import from external_factors_matrix)
+        names = [f"ext_{i+1}" for i in range(85)]
+        return pd.DataFrame({
+            'id': range(1, 86),
+            'value': self.external,
+            'success': self.external_success
+        })
+    
+    @property
+    def external_success_rate(self) -> float:
+        """Percentage of external indicators successfully fetched"""
+        valid = ~np.isnan(self.external)
+        if valid.sum() == 0:
+            return 0.0
+        return float(self.external_success[valid].mean())
+    
+    # =========================================================================
+    # PER-ASSET
+    # =========================================================================
+    
+    @property
+    def asset_matrix(self) -> np.ndarray:
+        """Get per-asset indicator matrix (n_assets x n_indicators)"""
+        return self._data['asset_matrix']
+    
+    @property
+    def asset_indicator_names(self) -> List[str]:
+        return list(self._data['asset_indicator_names'])
+    
+    def asset_df(self):
+        """Get per-asset indicators as pandas DataFrame"""
+        import pandas as pd
+        return pd.DataFrame(
+            self.asset_matrix,
+            index=self.asset_symbols,
+            columns=self.asset_indicator_names
+        )
+    
+    def get_asset(self, symbol: str) -> Dict[str, float]:
+        """Get all indicators for a specific asset"""
+        symbols = self.asset_symbols
+        if symbol not in symbols:
+            raise KeyError(f"Unknown symbol: {symbol}")
+        idx = symbols.index(symbol)
+        return dict(zip(self.asset_indicator_names, self.asset_matrix[idx]))
+    
+    def get_asset_indicator(self, symbol: str, indicator: str) -> float:
+        """Get specific indicator for specific asset"""
+        asset = self.get_asset(symbol)
+        if indicator not in asset:
+            raise KeyError(f"Unknown indicator: {indicator}")
+        return asset[indicator]
+    
+    # =========================================================================
+    # UTILITIES
+    # =========================================================================
+    
+    def summary(self) -> str:
+        """Get summary string"""
+        ext_valid = (~np.isnan(self.external)).sum()
+        ext_success = self.external_success.sum()
+        return f"""Indicator File: {self.path.name}
+  Scan: #{self.scan_number} @ {self.timestamp}
+  Processing: {self.processing_time:.2f}s
+  
+  Scan-derived: {len(self.scan_derived)} indicators
+    lambda_max: {self.get_scan_indicator('lambda_max'):.4f}
+    coherence: {self.get_scan_indicator('market_coherence'):.4f}
+    instability: {self.get_scan_indicator('instability_score'):.4f}
+  
+  External: {ext_success}/{ext_valid} successful ({self.external_success_rate*100:.1f}%)
+  
+  Per-asset: {self.n_assets} assets × {len(self.asset_indicator_names)} indicators
+"""
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary"""
+        return {
+            'scan_number': self.scan_number,
+            'timestamp': self.timestamp,
+            'processing_time': self.processing_time,
+            'scan_derived': dict(zip(self.scan_derived_names, self.scan_derived.tolist())),
+            'external': self.external.tolist(),
+            'external_success': self.external_success.tolist(),
+            'asset_symbols': self.asset_symbols,
+            'asset_matrix': self.asset_matrix.tolist(),
+        }
+    
+    # =========================================================================
+    # CLASS METHODS
+    # =========================================================================
+    
+    @classmethod
+    def load_directory(cls, directory: str, pattern: str = "*__Indicators.npz") -> List['IndicatorReader']:
+        """Load all indicator files from directory"""
+        root = Path(directory)
+        files = sorted(root.rglob(pattern))
+        return [cls(str(f)) for f in files]
+    
+    @classmethod
+    def to_timeseries(cls, readers: List['IndicatorReader']) -> Dict[str, np.ndarray]:
+        """Convert list of readers to time series arrays"""
+        n = len(readers)
+        if n == 0:
+            return {}
+        
+        # Get dimensions from first file
+        n_scan = len(readers[0].scan_derived)
+        n_ext = 85
+        n_assets = readers[0].n_assets
+        n_asset_ind = len(readers[0].asset_indicator_names)
+        
+        # Allocate arrays
+        timestamps = []
+        scan_series = np.zeros((n, n_scan))
+        ext_series = np.zeros((n, n_ext))
+        
+        for i, r in enumerate(readers):
+            timestamps.append(r.timestamp)
+            scan_series[i] = r.scan_derived
+            ext_series[i] = r.external
+        
+        return {
+            'timestamps': np.array(timestamps, dtype='U32'),
+            'scan_derived': scan_series,
+            'external': ext_series,
+            'scan_names': readers[0].scan_derived_names,
+        }
+
+
+# =============================================================================
+# CLI
+# =============================================================================
+
+def main():
+    import argparse
+    parser = argparse.ArgumentParser(description="Indicator Reader")
+    parser.add_argument("path", help="Path to .npz file or directory")
+    parser.add_argument("-a", "--asset", help="Show specific asset")
+    parser.add_argument("-j", "--json", action="store_true", help="Output as JSON")
+    args = parser.parse_args()
+    
+    path = Path(args.path)
+    
+    if path.is_file():
+        reader = IndicatorReader(str(path))
+        if args.json:
+            import json
+            print(json.dumps(reader.to_dict(), indent=2))
+        elif args.asset:
+            asset = reader.get_asset(args.asset)
+            for k, v in asset.items():
+                print(f"  {k}: {v:.6f}")
+        else:
+            print(reader.summary())
+    
+    elif path.is_dir():
+        readers = IndicatorReader.load_directory(str(path))
+        print(f"Found {len(readers)} indicator files")
+        if readers:
+            ts = IndicatorReader.to_timeseries(readers)
+            print(f"Time range: {ts['timestamps'][0]} to {ts['timestamps'][-1]}")
+            print(f"Scan-derived shape: {ts['scan_derived'].shape}")
+            print(f"External shape: {ts['external'].shape}")
+
+if __name__ == "__main__":
+    main()

external_factors/indicator_sources.py

@@ -0,0 +1,204 @@
+#!/usr/bin/env python3
+"""
+INDICATOR SOURCES v5.0 - API Reference with Historical Support
+===============================================================
+Documents all 85 indicators with their backfill capability.
+"""
+
+SOURCES = {
+    "binance": {"url": "fapi.binance.com / api.binance.com", "auth": "None", "limit": "1200/min", "history": "FULL (startTime/endTime)"},
+    "deribit": {"url": "deribit.com/api/v2/public", "auth": "None", "limit": "20/sec", "history": "FULL for DVOL/funding"},
+    "coinmetrics": {"url": "community-api.coinmetrics.io/v4", "auth": "None", "limit": "10/6sec", "history": "FULL (start_time/end_time)"},
+    "fred": {"url": "api.stlouisfed.org/fred", "auth": "Free key", "limit": "120/min", "history": "FULL (decades)"},
+    "defillama": {"url": "api.llama.fi", "auth": "None", "limit": "Generous", "history": "FULL for TVL/stables"},
+    "alternative": {"url": "api.alternative.me", "auth": "None", "limit": "Unlimited", "history": "FULL (limit=N param)"},
+    "blockchain": {"url": "blockchain.info", "auth": "None", "limit": "Generous", "history": "FULL via charts API"},
+    "mempool": {"url": "mempool.space/api", "auth": "None", "limit": "Generous", "history": "NONE (real-time only)"},
+    "coingecko": {"url": "api.coingecko.com/api/v3", "auth": "None (demo)", "limit": "30/min", "history": "FULL for prices"},
+}
+
+# Historical URL templates for backfill
+HISTORICAL_ENDPOINTS = {
+    # BINANCE - All support startTime/endTime in milliseconds
+    "binance_funding": "https://fapi.binance.com/fapi/v1/fundingRate?symbol={SYMBOL}&startTime={start_ms}&endTime={end_ms}&limit=1000",
+    "binance_oi_hist": "https://fapi.binance.com/futures/data/openInterestHist?symbol={SYMBOL}&period=1h&startTime={start_ms}&endTime={end_ms}&limit=500",
+    "binance_ls_hist": "https://fapi.binance.com/futures/data/globalLongShortAccountRatio?symbol={SYMBOL}&period=1h&startTime={start_ms}&endTime={end_ms}&limit=500",
+    "binance_taker_hist": "https://fapi.binance.com/futures/data/takerlongshortRatio?symbol={SYMBOL}&period=1h&startTime={start_ms}&endTime={end_ms}&limit=500",
+    "binance_klines": "https://api.binance.com/api/v3/klines?symbol={SYMBOL}&interval=1d&startTime={start_ms}&endTime={end_ms}&limit=1",
+    
+    # DERIBIT - Uses start_timestamp/end_timestamp in milliseconds
+    "deribit_dvol": "https://www.deribit.com/api/v2/public/get_volatility_index_data?currency={CURRENCY}&resolution=3600&start_timestamp={start_ms}&end_timestamp={end_ms}",
+    "deribit_funding_hist": "https://www.deribit.com/api/v2/public/get_funding_rate_history?instrument_name={INSTRUMENT}&start_timestamp={start_ms}&end_timestamp={end_ms}",
+    
+    # COINMETRICS - Uses ISO date format
+    "coinmetrics": "https://community-api.coinmetrics.io/v4/timeseries/asset-metrics?assets={asset}&metrics={metric}&frequency=1d&start_time={date}T00:00:00Z&end_time={date}T23:59:59Z",
+    
+    # FRED - Uses observation_start/observation_end in YYYY-MM-DD
+    "fred": "https://api.stlouisfed.org/fred/series/observations?series_id={series}&api_key={key}&file_type=json&observation_start={date}&observation_end={date}",
+    
+    # DEFILLAMA - Returns full history, filter client-side
+    "defillama_tvl": "https://api.llama.fi/v2/historicalChainTvl",  # Filter by date client-side
+    "defillama_tvl_chain": "https://api.llama.fi/v2/historicalChainTvl/{chain}",
+    "defillama_stables": "https://stablecoins.llama.fi/stablecoincharts/all?stablecoin={id}",  # 1=USDT, 2=USDC
+    
+    # BLOCKCHAIN.INFO - Uses start param in YYYY-MM-DD
+    "blockchain_charts": "https://api.blockchain.info/charts/{chart}?timespan=1days&start={date}&format=json",
+    
+    # COINGECKO - Uses DD-MM-YYYY format
+    "coingecko_history": "https://api.coingecko.com/api/v3/coins/{id}/history?date={date_dmy}",
+    
+    # ALTERNATIVE.ME - Returns N days of history
+    "fng_history": "https://api.alternative.me/fng/?limit=1000&date_format=us",  # Filter client-side
+}
+
+HISTORICAL_SUPPORT = {
+    # FULL HISTORY (51 indicators)
+    "full": [
+        # Binance derivatives
+        (1, "funding_btc", "8h", "Funding rate history via startTime/endTime"),
+        (2, "funding_eth", "8h", "ETH funding"),
+        (3, "oi_btc", "1h", "Open interest history via openInterestHist endpoint"),
+        (4, "oi_eth", "1h", "ETH OI"),
+        (5, "ls_btc", "1h", "Long/short ratio history"),
+        (6, "ls_eth", "1h", "ETH L/S"),
+        (7, "ls_top", "1h", "Top trader L/S"),
+        (8, "taker", "1h", "Taker ratio history"),
+        # Deribit
+        (11, "dvol_btc", "1h", "DVOL via get_volatility_index_data"),
+        (12, "dvol_eth", "1h", "ETH DVOL"),
+        (17, "fund_dbt_btc", "8h", "Deribit funding via get_funding_rate_history"),
+        (18, "fund_dbt_eth", "8h", "ETH Deribit funding"),
+        # CoinMetrics (ALL have full history)
+        (19, "rcap_btc", "1d", "CoinMetrics: CapRealUSD"),
+        (20, "mvrv", "1d", "CoinMetrics: derived from CapMrktCurUSD/CapRealUSD"),
+        (21, "nupl", "1d", "CoinMetrics: derived"),
+        (22, "addr_btc", "1d", "CoinMetrics: AdrActCnt"),
+        (23, "addr_eth", "1d", "CoinMetrics: ETH AdrActCnt"),
+        (24, "txcnt", "1d", "CoinMetrics: TxCnt"),
+        (25, "fees_btc", "1d", "CoinMetrics: FeeTotUSD"),
+        (26, "fees_eth", "1d", "CoinMetrics: ETH FeeTotUSD"),
+        (27, "nvt", "1d", "CoinMetrics: NVTAdj"),
+        (28, "velocity", "1d", "CoinMetrics: VelCur1yr"),
+        (29, "sply_act", "1d", "CoinMetrics: SplyAct1yr"),
+        (30, "rcap_eth", "1d", "CoinMetrics: ETH CapRealUSD"),
+        # Blockchain.info charts
+        (31, "hashrate", "1d", "Blockchain.info: hash-rate chart"),
+        (32, "difficulty", "1d", "Blockchain.info: difficulty chart"),
+        (35, "tx_blk", "1d", "Blockchain.info: n-transactions-per-block chart"),
+        (36, "total_btc", "1d", "Blockchain.info: total-bitcoins chart"),
+        (37, "mcap_bc", "1d", "Blockchain.info: market-cap chart"),
+        # DeFi Llama
+        (43, "tvl", "1d", "DeFi Llama: historicalChainTvl (returns all, filter client-side)"),
+        (44, "tvl_eth", "1d", "DeFi Llama: ETH TVL"),
+        (45, "stables", "1d", "DeFi Llama: stablecoincharts"),
+        (46, "usdt", "1d", "DeFi Llama: stablecoin ID=1"),
+        (47, "usdc", "1d", "DeFi Llama: stablecoin ID=2"),
+        # FRED (ALL have decades of history)
+        (52, "dxy", "1d", "FRED: DTWEXBGS"),
+        (53, "us10y", "1d", "FRED: DGS10"),
+        (54, "us2y", "1d", "FRED: DGS2"),
+        (55, "ycurve", "1d", "FRED: T10Y2Y"),
+        (56, "vix", "1d", "FRED: VIXCLS"),
+        (57, "fedfunds", "1d", "FRED: DFF"),
+        (58, "m2", "1w", "FRED: WM2NS (weekly)"),
+        (59, "cpi", "1m", "FRED: CPIAUCSL (monthly)"),
+        (60, "sp500", "1d", "FRED: SP500"),
+        (61, "gold", "1d", "FRED: GOLDAMGBD228NLBM"),
+        (62, "hy_spread", "1d", "FRED: BAMLH0A0HYM2"),
+        (63, "be5y", "1d", "FRED: T5YIE"),
+        (64, "nfci", "1w", "FRED: NFCI (weekly)"),
+        (65, "claims", "1w", "FRED: ICSA (weekly)"),
+        # Alternative.me
+        (66, "fng", "1d", "Alternative.me: limit param returns history"),
+        (67, "fng_prev", "1d", ""),
+        (68, "fng_week", "1d", ""),
+        (69, "fng_vol", "1d", ""),
+        (70, "fng_mom", "1d", ""),
+        (71, "fng_soc", "1d", ""),
+        (72, "fng_dom", "1d", ""),
+        # CoinGecko
+        (81, "btc_price", "1d", "CoinGecko: /coins/{id}/history"),
+        (82, "eth_price", "1d", "CoinGecko: /coins/{id}/history"),
+        # Binance klines
+        (78, "vol24", "1d", "Binance: klines endpoint"),
+    ],
+    
+    # PARTIAL HISTORY (12 indicators)
+    "partial": [
+        (48, "dex_vol", "1d", "DeFi Llama: recent history in response"),
+        (49, "bridge", "1d", "DeFi Llama: bridgevolume endpoint"),
+        (51, "fees", "1d", "DeFi Llama: fees overview"),
+        (83, "mcap", "1d", "CoinGecko: market_cap_chart (limited)"),
+    ],
+    
+    # CURRENT ONLY (22 indicators)
+    "current": [
+        (9, "basis", "Binance premium index - real-time only"),
+        (10, "liq_proxy", "Derived from 24hr ticker - real-time"),
+        (13, "pcr_vol", "Deribit options summary - real-time"),
+        (14, "pcr_oi", "Deribit options OI - real-time"),
+        (15, "pcr_eth", "Deribit ETH options - real-time"),
+        (16, "opt_oi", "Deribit total options OI - real-time"),
+        (33, "blk_int", "Blockchain.info simple query - real-time"),
+        (34, "unconf", "Blockchain.info unconfirmed - real-time"),
+        (38, "mp_cnt", "Mempool.space - NO historical API"),
+        (39, "mp_mb", "Mempool.space - NO historical API"),
+        (40, "fee_fast", "Mempool.space - NO historical API"),
+        (41, "fee_med", "Mempool.space - NO historical API"),
+        (42, "fee_slow", "Mempool.space - NO historical API"),
+        (50, "yields", "DeFi Llama yields - real-time"),
+        (73, "imbal_btc", "Order book depth - real-time"),
+        (74, "imbal_eth", "Order book depth - real-time"),
+        (75, "spread", "Book ticker - real-time"),
+        (76, "chg24_btc", "24hr ticker - real-time"),
+        (77, "chg24_eth", "24hr ticker - real-time"),
+        (79, "dispersion", "Calculated from 24hr - real-time"),
+        (80, "correlation", "Calculated from 24hr - real-time"),
+        (84, "btc_dom", "CoinGecko global - real-time"),
+        (85, "eth_dom", "CoinGecko global - real-time"),
+    ],
+}
+
+BACKFILL_NOTES = """
+BACKFILL STRATEGY
+=================
+
+1. DAILY BACKFILL (Most indicators):
+   - CoinMetrics, FRED, DeFi Llama TVL, Blockchain.info charts
+   - Use: efm.update(datetime(2024, 6, 15))
+
+2. HOURLY BACKFILL (Binance derivatives):
+   - OI, L/S ratio, taker ratio have 1h resolution
+   - Funding rate has 8h resolution
+
+3. APIS RETURNING FULL HISTORY:
+   - DeFi Llama TVL: Returns ALL history, filter client-side by timestamp
+   - Alternative.me F&G: Use limit=1000 to get ~3 years of history
+   - Blockchain.info charts: Use start= param with date
+
+4. MISSING HISTORICAL DATA:
+   - Mempool fees: Build your own collector
+   - Order book imbalance: Build your own collector
+   - Spreads: Build your own collector
+
+5. RECOMMENDED APPROACH FOR TRAINING:
+   a) Backfill what's available (51 indicators with FULL history)
+   b) For CURRENT-only indicators, either:
+      - Accept NaN/0 for historical periods
+      - Build collectors to capture going forward
+      - Use proxy indicators (e.g., volatility proxy for mempool fees)
+"""
+
+if __name__ == "__main__":
+    print("INDICATOR SOURCES v5.0")
+    print("=" * 60)
+    print("\nData Sources:")
+    for src, info in SOURCES.items():
+        print(f"  {src:12s}: {info['auth']:10s} | {info['limit']:12s} | {info['history']}")
+    
+    print(f"\nHistorical Support:")
+    print(f"  FULL:    {len(HISTORICAL_SUPPORT['full'])} indicators")
+    print(f"  PARTIAL: {len(HISTORICAL_SUPPORT['partial'])} indicators")
+    print(f"  CURRENT: {len(HISTORICAL_SUPPORT['current'])} indicators")
+    
+    print(BACKFILL_NOTES)

external_factors/meta_adaptive_optimizer.py

@@ -0,0 +1,207 @@
+"""
+Meta-Adaptive ExF Optimizer
+===========================
+Runs nightly (or on-demand) to calculate dynamic lag configurations and
+active indicator thresholds for the Adaptive Circuit Breaker (ACB).
+
+Implementation of the "Meta-Adaptive" Blueprint:
+1. Pulls up to the last 90 days of market returns and indicator values.
+2. Runs lag hypothesis testing (0-7 days) on all tracked ExF indicators.
+3. Uses strict Point-Biserial correlation (p < 0.05) against market stress (< -1% daily drop).
+4. Persists the active, statistically verified JSON configuration for realtime_exf_service.py.
+"""
+
+import sys
+import json
+import time
+import logging
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from collections import defaultdict
+import threading
+from scipy import stats
+from datetime import datetime, timezone
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent
+sys.path.insert(0, str(PROJECT_ROOT))
+sys.path.insert(0, str(PROJECT_ROOT / 'nautilus_dolphin'))
+
+try:
+    from realtime_exf_service import INDICATORS, OPTIMAL_LAGS
+    from dolphin_paper_trade_adaptive_cb_v2 import EIGENVALUES_BASE_PATH
+    from dolphin_vbt_real import load_all_data, run_full_backtest, STRATEGIES, INIT_CAPITAL
+except ImportError:
+    pass
+
+logger = logging.getLogger(__name__)
+
+CONFIG_PATH = Path(__file__).parent / "meta_adaptive_config.json"
+
+class MetaAdaptiveOptimizer:
+    def __init__(self, days_lookback=90, max_lags=6, p_value_gate=0.05):
+        self.days_lookback = days_lookback
+        self.max_lags = max_lags
+        self.p_value_gate = p_value_gate
+        self.indicators = list(INDICATORS.keys()) if 'INDICATORS' in globals() else []
+        self._lock = threading.Lock()
+
+    def _build_history_cache(self, dates, limit_days):
+        """Build daily feature cache from NPZ files."""
+        logger.info(f"Building cache for last {limit_days} days...")
+        cache = {}
+        target_dates = dates[-limit_days:] if len(dates) > limit_days else dates
+        
+        for date_str in target_dates:
+            date_path = EIGENVALUES_BASE_PATH / date_str
+            if not date_path.exists(): continue
+            
+            npz_files = list(date_path.glob('scan_*__Indicators.npz'))
+            if not npz_files: continue
+            
+            accum = defaultdict(list)
+            for f in npz_files:
+                try:
+                    data = dict(np.load(f, allow_pickle=True))
+                    names = [str(n) for n in data.get('api_names', [])]
+                    vals = data.get('api_indicators', [])
+                    succ = data.get('api_success', [])
+                    for n, v, s in zip(names, vals, succ):
+                        if s and not np.isnan(v):
+                            accum[n].append(float(v))
+                except Exception:
+                    pass
+            
+            if accum:
+                cache[date_str] = {k: np.mean(v) for k, v in accum.items()}
+                
+        return cache, target_dates
+
+    def _get_daily_returns(self, df, target_dates):
+        """Derive daily returns proxy from the champion strategy logic."""
+        logger.info("Computing proxy returns for the time window...")
+        champion = STRATEGIES['champion_5x_f20']
+        returns = []
+        cap = INIT_CAPITAL
+        
+        valid_dates = []
+        for d in target_dates:
+            day_df = df[df['date_str'] == d]
+            if len(day_df) < 200:
+                returns.append(np.nan)
+                valid_dates.append(d)
+                continue
+            
+            res = run_full_backtest(day_df, champion, init_cash=cap, seed=42, verbose=False)
+            ret = (res['capital'] - cap) / cap
+            returns.append(ret)
+            cap = res['capital']
+            valid_dates.append(d)
+            
+        return np.array(returns), valid_dates
+
+    def run_optimization(self) -> dict:
+        """Run the full meta-adaptive optimization routine and return new config."""
+        with self._lock:
+            logger.info("Starting META-ADAPTIVE optimization loop.")
+            t0 = time.time()
+            
+            df = load_all_data()
+            if 'date_str' not in df.columns:
+                df['date_str'] = df['timestamp'].dt.date.astype(str)
+            all_dates = sorted(df['date_str'].unique())
+            
+            cache, target_dates = self._build_history_cache(all_dates, self.days_lookback + self.max_lags)
+            daily_returns, target_dates = self._get_daily_returns(df, target_dates)
+            
+            # Predict market stress dropping by more than 1%
+            stress_arr = (daily_returns < -0.01).astype(float)
+            
+            candidate_lags = {}
+            active_thresholds = {}
+            candidate_count = 0
+            
+            for key in self.indicators:
+                ind_arr = np.array([cache.get(d, {}).get(key, np.nan) for d in target_dates])
+                
+                corrs = []; pvals = []; sc_corrs = []
+                for lag in range(self.max_lags + 1):
+                    if lag == 0: x, y, y_stress = ind_arr, daily_returns, stress_arr
+                    else: x, y, y_stress = ind_arr[:-lag], daily_returns[lag:], stress_arr[lag:]
+                    
+                    mask = ~np.isnan(x) & ~np.isnan(y)
+                    if mask.sum() < 20: # Need at least 20 viable days
+                        corrs.append(0); pvals.append(1); sc_corrs.append(0)
+                        continue
+                        
+                    # Pearson to price returns
+                    r, p = stats.pearsonr(x[mask], y[mask])
+                    corrs.append(r); pvals.append(p)
+                    
+                    # Point-Biserial to stress events
+                    # We capture the relation to binary stress to figure out threshold direction
+                    if y_stress[mask].sum() > 2: # At least a few stress days required
+                        sc = stats.pointbiserialr(y_stress[mask], x[mask])[0]
+                    else:
+                        sc = 0
+                    sc_corrs.append(sc)
+
+                if not corrs: continue
+
+                # Find lag with highest correlation strength
+                best_lag = int(np.argmax(np.abs(corrs)))
+                best_p = pvals[best_lag]
+                
+                # Check gate
+                if best_p <= self.p_value_gate:
+                    direction = ">" if sc_corrs[best_lag] > 0 else "<"
+                    
+                    # Compute a stress threshold logic (e.g. 15th / 85th percentile of historical)
+                    valid_vals = ind_arr[~np.isnan(ind_arr)]
+                    thresh = np.percentile(valid_vals, 85 if direction == '>' else 15)
+                    
+                    candidate_lags[key] = best_lag
+                    active_thresholds[key] = {
+                        'threshold': float(thresh),
+                        'direction': direction,
+                        'p_value': float(best_p),
+                        'r_value': float(corrs[best_lag])
+                    }
+                    candidate_count += 1
+                    
+            # Fallback checks mapping to V4 baseline if things drift too far
+            logger.info(f"Optimization complete ({time.time() - t0:.1f}s). {candidate_count} indicators passed P < {self.p_value_gate}.")
+            
+            output_config = {
+                'timestamp': datetime.now(timezone.utc).isoformat(),
+                'days_lookback': self.days_lookback,
+                'lags': candidate_lags,
+                'thresholds': active_thresholds
+            }
+            
+            # Atomic save
+            temp_path = CONFIG_PATH.with_suffix('.tmp')
+            with open(temp_path, 'w', encoding='utf-8') as f:
+                json.dump(output_config, f, indent=2)
+            temp_path.replace(CONFIG_PATH)
+            
+            return output_config
+
+def get_current_meta_config() -> dict:
+    """Read the latest meta-adaptive config, or return empty/default dict."""
+    if not CONFIG_PATH.exists():
+        return {}
+    try:
+        with open(CONFIG_PATH, 'r', encoding='utf-8') as f:
+            return json.load(f)
+    except Exception as e:
+        logger.error(f"Failed to read meta-adaptive config: {e}")
+        return {}
+
+if __name__ == '__main__':
+    logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+    optimizer = MetaAdaptiveOptimizer(days_lookback=90)
+    config = optimizer.run_optimization()
+    print(f"\nSaved config to: {CONFIG_PATH}")
+    for k, v in config['lags'].items():
+        print(f"  {k}: lag={v} days, dir={config['thresholds'][k]['direction']} thresh={config['thresholds'][k]['threshold']:.4g}")

external_factors/ob_stream_service.py

@@ -0,0 +1,351 @@
+import asyncio
+import aiohttp
+import json
+import time
+import logging
+import numpy as np
+from typing import Dict, List, Optional
+from collections import defaultdict
+
+# Setup basic logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(levelname)s] %(name)s: %(message)s')
+logger = logging.getLogger("OBStreamService")
+
+try:
+    import websockets
+except ImportError:
+    logger.warning("websockets package not found. Run pip install websockets aiohttp")
+
+# Reconnect back-off constants (P1-2)
+_RECONNECT_BASE_S   = 3.0
+_RECONNECT_MAX_S    = 120.0
+_RECONNECT_STABLE_S = 60.0   # reset back-off if connected this long without error
+
+# Stall detection (P0-2): warn if no WS message for this many seconds
+_STALE_THRESHOLD_S = 30.0
+
+
+class OBStreamService:
+    """
+    Real-Time Order Book Streamer for Binance Futures.
+
+    Fixes applied:
+      P0-2  last_event_ts for WS stall detection (is_stale())
+      P0-3  Crossed-book guard in get_depth_buckets()
+      P1-2  Exponential back-off on reconnect (max 120s, jitter)
+      P1-5  Shared aiohttp.ClientSession for REST calls (no new session per call)
+      P2-1  Unknown asset symbol in WS event ignored, no KeyError
+    """
+
+    def __init__(self, assets: List[str], max_depth_pct: int = 5):
+        self.assets       = [a.upper() for a in assets]
+        self.streams      = [f"{a.lower()}@depth@100ms" for a in self.assets]
+        self.max_depth_pct = max_depth_pct
+
+        # In-memory Order Book caches (Price -> Quantity)
+        self.bids: Dict[str, Dict[float, float]] = {a: {} for a in self.assets}
+        self.asks: Dict[str, Dict[float, float]] = {a: {} for a in self.assets}
+
+        # Synchronization mechanisms
+        self.last_update_id: Dict[str, int]  = {a: 0   for a in self.assets}
+        self.buffer:         Dict[str, List] = {a: []  for a in self.assets}
+        self.initialized:    Dict[str, bool] = {a: False for a in self.assets}
+
+        # Per-asset asyncio lock (P2-1: keyed only on known assets)
+        self.locks: Dict[str, asyncio.Lock] = {a: asyncio.Lock() for a in self.assets}
+
+        # P0-2: WS stall detection — updated on every received message
+        self.last_event_ts: float = 0.0
+
+        # P1-5: shared session — created lazily in stream(), closed on exit
+        self._session: Optional[aiohttp.ClientSession] = None
+        
+        # Gold Path: Rate Limit Monitoring (AGENT-SPEC-GOLDPATH)
+        self.rate_limits: Dict[str, str] = {}
+
+    # ------------------------------------------------------------------
+    # P0-2: stale check callable from AsyncOBThread
+    # ------------------------------------------------------------------
+    def is_stale(self, threshold_s: float = _STALE_THRESHOLD_S) -> bool:
+        """Return True if no WS event has been received for threshold_s seconds."""
+        if self.last_event_ts == 0.0:
+            return False  # hasn't started yet — not stale, just cold
+        return (time.time() - self.last_event_ts) > threshold_s
+
+    # ------------------------------------------------------------------
+    # REST snapshot (P1-5: reuses shared session)
+    # ------------------------------------------------------------------
+    async def fetch_snapshot(self, asset: str):
+        """Fetch REST snapshot of the Order Book to initialize local state."""
+        url = f"https://fapi.binance.com/fapi/v1/depth?symbol={asset}&limit=1000"
+        try:
+            session = self._session
+            if session is None or session.closed:
+                # Fallback: create a temporary session if shared one not ready
+                async with aiohttp.ClientSession() as tmp_session:
+                    await self._do_fetch(tmp_session, asset, url)
+                return
+            await self._do_fetch(session, asset, url)
+        except Exception as e:
+            logger.error(f"Error initializing snapshot for {asset}: {e}")
+
+    async def _do_fetch(self, session: aiohttp.ClientSession, asset: str, url: str):
+        async with session.get(url) as resp:
+            # Capture Rate Limits (Gold Spec)
+            for k, v in resp.headers.items():
+                if k.lower().startswith("x-mbx-used-weight-"):
+                    self.rate_limits[k] = v
+            
+            data = await resp.json()
+
+        if 'lastUpdateId' not in data:
+            logger.error(f"Failed to fetch snapshot for {asset}: {data}")
+            return
+
+        last_id = data['lastUpdateId']
+
+        async with self.locks[asset]:
+            self.bids[asset] = {float(p): float(q) for p, q in data['bids']}
+            self.asks[asset] = {float(p): float(q) for p, q in data['asks']}
+            self.last_update_id[asset] = last_id
+
+            # Apply any buffered updates that arrived while REST was in flight
+            for event in self.buffer[asset]:
+                if event['u'] <= last_id:
+                    continue  # already reflected in snapshot
+                self._apply_event(asset, event)
+
+            self.buffer[asset].clear()
+            self.initialized[asset] = True
+
+        logger.info(f"Synchronized L2 book for {asset} (UpdateId: {last_id})")
+
+    # ------------------------------------------------------------------
+    # Book maintenance
+    # ------------------------------------------------------------------
+    def _apply_event(self, asset: str, event: dict):
+        """Apply a streaming diff event to the local book."""
+        bids = self.bids[asset]
+        asks = self.asks[asset]
+
+        for p_str, q_str in event['b']:
+            p, q = float(p_str), float(q_str)
+            if q == 0.0:
+                bids.pop(p, None)
+            else:
+                bids[p] = q
+
+        for p_str, q_str in event['a']:
+            p, q = float(p_str), float(q_str)
+            if q == 0.0:
+                asks.pop(p, None)
+            else:
+                asks[p] = q
+
+        self.last_update_id[asset] = event['u']
+
+    # ------------------------------------------------------------------
+    # Main WS loop (P1-2: exp backoff; P1-5: shared session; P2-1: unknown symbol guard)
+    # ------------------------------------------------------------------
+    async def stream(self):
+        """Main loop: connect to WebSocket streams and maintain books."""
+        import websockets
+
+        stream_url = (
+            "wss://fstream.binance.com/stream?streams=" + "/".join(self.streams)
+        )
+        logger.info(f"Connecting to Binance Stream: {stream_url}")
+
+        reconnect_delay = _RECONNECT_BASE_S
+        import random
+
+        # P1-5: create shared session for lifetime of stream()
+        async with aiohttp.ClientSession() as session:
+            self._session = session
+
+            # Fire REST snapshots concurrently using shared session
+            for a in self.assets:
+                asyncio.create_task(self.fetch_snapshot(a))
+
+            while True:
+                connect_start = time.monotonic()
+                try:
+                    async with websockets.connect(
+                        stream_url, ping_interval=20, ping_timeout=20
+                    ) as ws:
+                        logger.info("WebSocket connected. Streaming depth diffs...")
+                        while True:
+                            msg = await ws.recv()
+
+                            # P0-2: stamp every received message
+                            self.last_event_ts = time.time()
+
+                            data = json.loads(msg)
+                            if 'data' not in data:
+                                continue
+
+                            ev    = data['data']
+                            # P2-1: ignore events for unknown symbols — no KeyError
+                            asset = ev.get('s', '').upper()
+                            if asset not in self.locks:
+                                logger.debug("Ignoring WS event for untracked symbol: %s", asset)
+                                continue
+
+                            async with self.locks[asset]:
+                                if not self.initialized[asset]:
+                                    self.buffer[asset].append(ev)
+                                else:
+                                    self._apply_event(asset, ev)
+
+                        # If we reach here the connection lasted stably:
+                        # reset back-off on a clean exit (never normally reached)
+                        reconnect_delay = _RECONNECT_BASE_S
+
+                except websockets.exceptions.ConnectionClosed as e:
+                    connected_s = time.monotonic() - connect_start
+                    logger.warning(
+                        "WebSocket closed (%s). Connected %.1fs. Reconnecting in %.1fs...",
+                        e, connected_s, reconnect_delay,
+                    )
+                    # P1-2: reset back-off if connection was stable long enough
+                    if connected_s >= _RECONNECT_STABLE_S:
+                        reconnect_delay = _RECONNECT_BASE_S
+
+                    # Re-init all assets, re-fire REST snapshots
+                    for a in self.assets:
+                        self.initialized[a] = False
+                        asyncio.create_task(self.fetch_snapshot(a))
+
+                    await asyncio.sleep(reconnect_delay + random.uniform(0, 1))
+                    # P1-2: double delay for next failure, cap at max
+                    reconnect_delay = min(reconnect_delay * 2, _RECONNECT_MAX_S)
+
+                except Exception as e:
+                    logger.error("Stream error: %s", e)
+                    await asyncio.sleep(reconnect_delay + random.uniform(0, 1))
+                    reconnect_delay = min(reconnect_delay * 2, _RECONNECT_MAX_S)
+
+            self._session = None  # stream() exited cleanly
+
+    # ------------------------------------------------------------------
+    # Depth bucket extraction (P0-3: crossed book guard)
+    # ------------------------------------------------------------------
+    async def get_depth_buckets(self, asset: str) -> Optional[dict]:
+        """
+        Extract the Notional Depth vectors matching OBSnapshot.
+        Creates 5 elements summing USD depth between 0-1%, 1-2%, ..., 4-5% from mid.
+
+        Returns None if:
+          - Book not yet initialized
+          - Empty bids or asks
+          - Crossed book (best_bid >= best_ask)  ← P0-3
+        """
+        async with self.locks[asset]:
+            if not self.initialized[asset]:
+                return None
+
+            bids = sorted(self.bids[asset].items(), key=lambda x: -x[0])
+            asks = sorted(self.asks[asset].items(), key=lambda x:  x[0])
+
+            if not bids or not asks:
+                return None
+
+            best_bid = bids[0][0]
+            best_ask = asks[0][0]
+
+            # P0-3: crossed book produces corrupted features — reject entirely
+            if best_bid >= best_ask:
+                logger.warning(
+                    "Crossed book for %s (bid=%.5f >= ask=%.5f) — skipping snapshot",
+                    asset, best_bid, best_ask,
+                )
+                return None
+
+            mid = (best_bid + best_ask) / 2.0
+
+            bid_not = np.zeros(self.max_depth_pct, dtype=np.float64)
+            ask_not = np.zeros(self.max_depth_pct, dtype=np.float64)
+            bid_dep = np.zeros(self.max_depth_pct, dtype=np.float64)
+            ask_dep = np.zeros(self.max_depth_pct, dtype=np.float64)
+
+            for p, q in bids:
+                dist_pct = (mid - p) / mid * 100
+                idx = int(dist_pct)
+                if idx < self.max_depth_pct:
+                    bid_not[idx] += p * q
+                    bid_dep[idx] += q
+                else:
+                    break
+
+            for p, q in asks:
+                dist_pct = (p - mid) / mid * 100
+                idx = int(dist_pct)
+                if idx < self.max_depth_pct:
+                    ask_not[idx] += p * q
+                    ask_dep[idx] += q
+                else:
+                    break
+
+            return {
+                "timestamp":    time.time(),
+                "asset":        asset,
+                "bid_notional": bid_not,
+                "ask_notional": ask_not,
+                "bid_depth":    bid_dep,
+                "ask_depth":    ask_dep,
+                "best_bid":     best_bid,
+                "best_ask":     best_ask,
+                "spread_bps":   (best_ask - best_bid) / mid * 10_000,
+            }
+
+
+# -----------------------------------------------------------------------------
+# Standalone run/test hook
+# -----------------------------------------------------------------------------
+import hazelcast
+
+async def main():
+    assets_to_track = ["BTCUSDT", "ETHUSDT", "SOLUSDT"]
+    service = OBStreamService(assets=assets_to_track)
+
+    asyncio.create_task(service.stream())
+    await asyncio.sleep(4)
+
+    try:
+        hz_client = hazelcast.HazelcastClient(
+            cluster_name="dolphin",
+            cluster_members=["localhost:5701"]
+        )
+        hz_map = hz_client.get_map('DOLPHIN_FEATURES').blocking()
+        logger.info("Connected to Hazelcast DOLPHIN_FEATURES map.")
+    except Exception as e:
+        logger.error(f"Hazelcast connection failed: {e}")
+        return
+
+    while True:
+        try:
+            for asset in assets_to_track:
+                snap = await service.get_depth_buckets(asset)
+                if snap:
+                    hz_payload = {
+                        "timestamp":    snap["timestamp"],
+                        "asset":        snap["asset"],
+                        "bid_notional": list(snap["bid_notional"]),
+                        "ask_notional": list(snap["ask_notional"]),
+                        "bid_depth":    list(snap["bid_depth"]),
+                        "ask_depth":    list(snap["ask_depth"]),
+                        "best_bid":     snap["best_bid"],
+                        "best_ask":     snap["best_ask"],
+                        "spread_bps":   snap["spread_bps"],
+                    }
+                    hz_map.put(f"asset_{asset}_ob", json.dumps(hz_payload))
+            await asyncio.sleep(0.5)
+        except Exception as e:
+            logger.error(f"Error in stream writing loop: {e}")
+            await asyncio.sleep(5)
+
+if __name__ == "__main__":
+    try:
+        asyncio.run(main())
+    except KeyboardInterrupt:
+        print("OB Streamer shut down manually.")

external_factors/realtime_exf_service_hz_events.py

@@ -0,0 +1,321 @@
+#!/usr/bin/env python3
+"""
+REAL-TIME EXTERNAL FACTORS SERVICE - EVENT-DRIVEN HZ OPTIMIZATION
+=================================================================
+Extension to RealTimeExFService that pushes to Hazelcast immediately
+when critical indicators change, rather than on a fixed timer.
+
+This achieves near-zero latency (<10ms) for critical indicators:
+- basis
+- spread  
+- imbal_btc
+- imbal_eth
+
+For slow indicators (funding, etc.), we still batch them.
+
+Author: Kimi, DESTINATION/DOLPHIN Machine dev/prod-Agent
+Date: 2026-03-20
+"""
+
+import sys
+import json
+import time
+import logging
+import threading
+from pathlib import Path
+from datetime import datetime, timezone
+from typing import Dict, Any, Optional, Callable
+from dataclasses import dataclass
+
+# Add paths
+sys.path.insert(0, str(Path(__file__).parent))
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from realtime_exf_service import RealTimeExFService, INDICATORS
+
+logger = logging.getLogger("exf_hz_events")
+_LOG_DEBUG = logger.isEnabledFor(logging.DEBUG)
+_LOG_INFO = logger.isEnabledFor(logging.INFO)
+
+
+# Critical indicators that trigger immediate HZ push
+CRITICAL_INDICATORS = frozenset(['basis', 'spread', 'imbal_btc', 'imbal_eth'])
+
+# Min time between HZ pushes (prevent spam)
+MIN_PUSH_INTERVAL_S = 0.05  # 50ms = 20 pushes/sec max
+
+
+@dataclass
+class EventDrivenConfig:
+    """Configuration for event-driven HZ optimization."""
+    hz_cluster: str = "dolphin"
+    hz_member: str = "localhost:5701"
+    hz_map: str = "DOLPHIN_FEATURES"
+    hz_key: str = "exf_latest"
+    
+    # Push strategy
+    critical_push_interval_s: float = 0.05  # 50ms min between critical pushes
+    batch_push_interval_s: float = 1.0      # 1s for full batch updates
+    
+    # Debouncing
+    value_change_threshold: float = 0.0001  # Ignore tiny changes
+
+
+class EventDrivenExFService:
+    """
+    Event-driven wrapper around RealTimeExFService.
+    
+    Instead of polling get_indicators() on a fixed interval,
+    we push to Hazelcast immediately when critical indicators change.
+    """
+    
+    def __init__(self, base_service: RealTimeExFService, config: EventDrivenConfig = None):
+        self.service = base_service
+        self.config = config or EventDrivenConfig()
+        
+        # Hazelcast client (initialized on first use)
+        self._hz_client = None
+        self._hz_lock = threading.Lock()
+        
+        # Push tracking
+        self._last_critical_push = 0.0
+        self._last_full_push = 0.0
+        self._push_count = 0
+        self._critical_push_count = 0
+        
+        # Previous values for change detection
+        self._prev_values: Dict[str, float] = {}
+        
+        # Background thread for full batch updates
+        self._running = False
+        self._thread = None
+        
+    def _get_hz_client(self):
+        """Lazy initialization of Hazelcast client."""
+        if self._hz_client is None:
+            import hazelcast
+            self._hz_client = hazelcast.HazelcastClient(
+                cluster_name=self.config.hz_cluster,
+                cluster_members=[self.config.hz_member],
+                connection_timeout=5.0,
+            )
+        return self._hz_client
+    
+    def _push_to_hz(self, indicators: Dict[str, Any], is_critical: bool = False):
+        """Push indicators to Hazelcast."""
+        try:
+            client = self._get_hz_client()
+            
+            # Build payload
+            payload = {
+                **indicators,
+                "_pushed_at": datetime.now(timezone.utc).isoformat(),
+                "_push_type": "critical" if is_critical else "batch",
+                "_push_seq": self._push_count,
+            }
+            
+            # Push
+            features_map = client.get_map(self.config.hz_map)
+            features_map.blocking().put(
+                self.config.hz_key, 
+                json.dumps(payload, default=str)
+            )
+            
+            self._push_count += 1
+            if is_critical:
+                self._critical_push_count += 1
+            
+            if _LOG_DEBUG:
+                logger.debug("HZ push: %s (%d indicators)", 
+                           "CRITICAL" if is_critical else "batch",
+                           len(indicators))
+            
+            return True
+            
+        except Exception as e:
+            if _LOG_INFO:
+                logger.warning("HZ push failed: %s", e)
+            return False
+    
+    def _check_critical_changes(self) -> Optional[Dict[str, Any]]:
+        """
+        Check if critical indicators changed significantly.
+        Returns changed indicators dict or None if no significant change.
+        """
+        now = time.monotonic()
+        
+        # Rate limiting
+        if now - self._last_critical_push < self.config.critical_push_interval_s:
+            return None
+        
+        with self.service._lock:
+            changed = {}
+            
+            for name in CRITICAL_INDICATORS:
+                if name not in self.service.state:
+                    continue
+                
+                state = self.service.state[name]
+                if not state.success:
+                    continue
+                
+                current_val = state.value
+                prev_val = self._prev_values.get(name)
+                
+                # First time or significant change
+                if prev_val is None:
+                    changed[name] = current_val
+                    self._prev_values[name] = current_val
+                elif abs(current_val - prev_val) > self.config.value_change_threshold:
+                    changed[name] = current_val
+                    self._prev_values[name] = current_val
+            
+            if changed:
+                self._last_critical_push = now
+                return changed
+            
+        return None
+    
+    def _get_full_snapshot(self) -> Dict[str, Any]:
+        """Get full indicator snapshot."""
+        indicators = self.service.get_indicators(dual_sample=True)
+        
+        staleness = indicators.pop("_staleness", {})
+        
+        # Build payload like exf_fetcher_flow
+        payload = {k: v for k, v in indicators.items() if isinstance(v, (int, float))}
+        payload["_staleness_s"] = {k: round(v, 1) for k, v in staleness.items()}
+        payload["_acb_ready"] = all(k in payload for k in [
+            "funding_btc", "funding_eth", "dvol_btc", "dvol_eth",
+            "fng", "vix", "ls_btc", "taker"
+        ])
+        payload["_ok_count"] = sum(1 for v in payload.values() 
+                                    if isinstance(v, float) and v == v)
+        
+        return payload
+    
+    def _event_loop(self):
+        """Main event loop - checks for changes and pushes to HZ."""
+        if _LOG_INFO:
+            logger.info("Event-driven loop started (critical: %s)", 
+                       ", ".join(CRITICAL_INDICATORS))
+        
+        while self._running:
+            t0 = time.monotonic()
+            
+            # Check for critical changes first
+            critical_changes = self._check_critical_changes()
+            if critical_changes:
+                # Get full snapshot but prioritize critical changes
+                full_data = self._get_full_snapshot()
+                self._push_to_hz(full_data, is_critical=True)
+            
+            # Periodic full batch update
+            now = time.monotonic()
+            if now - self._last_full_push >= self.config.batch_push_interval_s:
+                full_data = self._get_full_snapshot()
+                self._push_to_hz(full_data, is_critical=False)
+                self._last_full_push = now
+                
+                if _LOG_INFO and self._push_count % 10 == 1:
+                    logger.info("Batch push #%d (critical: %d)", 
+                               self._push_count, self._critical_push_count)
+            
+            # Sleep briefly to prevent CPU spinning
+            # But wake up quickly to catch changes
+            elapsed = time.monotonic() - t0
+            sleep_time = max(0.01, 0.05 - elapsed)  # 10-50ms sleep
+            time.sleep(sleep_time)
+        
+        if _LOG_INFO:
+            logger.info("Event-driven loop stopped")
+    
+    def start(self):
+        """Start the event-driven service."""
+        self.service.start()
+        
+        self._running = True
+        self._thread = threading.Thread(target=self._event_loop, daemon=True)
+        self._thread.start()
+        
+        if _LOG_INFO:
+            logger.info("Event-driven ExF service started")
+    
+    def stop(self):
+        """Stop the service."""
+        self._running = False
+        if self._thread:
+            self._thread.join(timeout=5.0)
+        
+        self.service.stop()
+        
+        if self._hz_client:
+            try:
+                self._hz_client.shutdown()
+            except Exception:
+                pass
+        
+        if _LOG_INFO:
+            logger.info("Event-driven ExF service stopped (%d pushes, %d critical)",
+                       self._push_count, self._critical_push_count)
+    
+    def get_status(self) -> Dict[str, Any]:
+        """Get service status."""
+        return {
+            'running': self._running,
+            'push_count': self._push_count,
+            'critical_push_count': self._critical_push_count,
+            'last_critical_push': self._last_critical_push,
+            'hz_connected': self._hz_client is not None,
+        }
+
+
+# =============================================================================
+# DROP-IN REPLACEMENT FLOW
+# =============================================================================
+
+def run_event_driven_flow(warmup_s: int = 10):
+    """
+    Run event-driven ExF flow (drop-in replacement for exf_fetcher_flow).
+    
+    This pushes to Hazelcast immediately when critical indicators change,
+    rather than on a fixed 0.5s interval.
+    """
+    logging.basicConfig(level=logging.INFO)
+    
+    print("=" * 60)
+    print("Event-Driven ExF Flow")
+    print("=" * 60)
+    
+    # Create base service
+    base_service = RealTimeExFService()
+    
+    # Wrap with event-driven layer
+    event_service = EventDrivenExFService(base_service)
+    
+    # Start
+    event_service.start()
+    print(f"Service started — warmup {warmup_s}s...")
+    time.sleep(warmup_s)
+    
+    print(f"Running event-driven (critical: {CRITICAL_INDICATORS})")
+    print("Push on change vs fixed interval")
+    
+    try:
+        while True:
+            time.sleep(10)
+            status = event_service.get_status()
+            print(f"Pushes: {status['push_count']} ({status['critical_push_count']} critical)")
+    except KeyboardInterrupt:
+        print("\nStopping...")
+    finally:
+        event_service.stop()
+
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--warmup", type=int, default=10)
+    args = parser.parse_args()
+    
+    run_event_driven_flow(warmup_s=args.warmup)

external_factors/test_deribit_api_parity.py

@@ -0,0 +1,546 @@
+"""
+test_deribit_api_parity.py
+==========================
+Validates that the current Deribit API call format + parser returns values
+that match pre-existing ExtF data stored in the NG3 eigenvalue NPZ cache.
+
+BACKGROUND
+----------
+The Deribit API changed (date unknown) and an agent added start_timestamp /
+end_timestamp parameters to make requests work again.  The user asked for
+explicit parity validation BEFORE locking in those params.
+
+WHAT THIS TEST DOES
+-------------------
+For each "known" date that has ground-truth data in the NPZ cache:
+
+  1. Load the stored value  (ground truth, pre-API-change)
+  2. Re-query Deribit with several candidate endpoint+param combinations
+  3. Compare each result to ground truth  (absolute + relative tolerance)
+  4. PASS / FAIL per candidate, per indicator
+
+The candidate that produces the best parity across all known dates is the
+one that should be locked in as the production Deribit URL scheme.
+
+INDICATORS COVERED (ACBv6 minimum requirement)
+----------------------------------------------
+  fund_dbt_btc   — BTC-PERPETUAL 8h funding rate  ← ACBv6 primary signal
+  fund_dbt_eth   — ETH-PERPETUAL 8h funding rate
+  dvol_btc       — BTC Deribit volatility index (hourly close)
+  dvol_eth       — ETH Deribit volatility index (hourly close)
+
+USAGE
+-----
+  python external_factors/test_deribit_api_parity.py
+
+  # Quick run — funding only (fastest, most critical for ACBv6):
+  python external_factors/test_deribit_api_parity.py --indicators fund
+
+  # Verbose — show raw responses:
+  python external_factors/test_deribit_api_parity.py --verbose
+
+INTERPRETING RESULTS
+--------------------
+  LOCKED IN: All parity checks PASS    → endpoint confirmed
+  MISMATCH:  Values differ > tolerance → endpoint is wrong / format changed
+  SKIP:      No NPZ data for that date  (not a failure)
+
+TOLERANCES
+----------
+  fund_dbt_btc / fund_dbt_eth : abs ≤ 1e-7  (funding rates are tiny)
+  dvol_btc / dvol_eth          : abs ≤ 0.5  (DVOL in vol-points)
+"""
+
+import asyncio
+import aiohttp
+import argparse
+import json
+import sys
+import time
+import traceback
+from datetime import datetime, timezone
+
+sys.stdout.reconfigure(encoding='utf-8', errors='replace')
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+
+# ---------------------------------------------------------------------------
+# Paths
+# ---------------------------------------------------------------------------
+
+_HERE = Path(__file__).resolve().parent
+_EIGENVALUES_PATH = Path(r"C:\Users\Lenovo\Documents\- Dolphin NG HD (NG3)\correlation_arb512\eigenvalues")
+
+# Known dates with confirmed NPZ data in the gold window (2025-12-31→2026-02-26).
+# Add more as the cache grows.  Values were stored by the NG5 scanner.
+KNOWN_DATES = [
+    "2026-01-02",
+    "2026-01-03",
+    "2026-01-04",
+    "2026-01-05",
+    "2026-01-06",
+    "2026-01-07",
+    "2026-01-08",
+    "2026-01-21",
+]
+
+# ---------------------------------------------------------------------------
+# Tolerances  (per indicator)
+# ---------------------------------------------------------------------------
+
+TOLERANCES = {
+    "fund_dbt_btc": 1e-7,
+    "fund_dbt_eth": 1e-7,
+    "dvol_btc":     0.5,
+    "dvol_eth":     0.5,
+}
+
+# ---------------------------------------------------------------------------
+# Ground-truth loader
+# ---------------------------------------------------------------------------
+
+def load_npz_ground_truth(date_str: str) -> Optional[dict]:
+    """
+    Load Deribit indicator values stored in an NG3 scan NPZ for *date_str*.
+    Returns dict {indicator_name: float} or None if no data.
+    """
+    date_path = _EIGENVALUES_PATH / date_str
+    if not date_path.exists():
+        return None
+
+    files = sorted(date_path.glob("scan_*__Indicators.npz"))
+    if not files:
+        return None
+
+    d = np.load(files[0], allow_pickle=True)
+    if "api_names" not in d or "api_indicators" not in d:
+        return None
+
+    names = list(d["api_names"])
+    vals  = d["api_indicators"]
+    succ  = d["api_success"] if "api_success" in d else np.ones(len(names), dtype=bool)
+
+    result = {}
+    for i, n in enumerate(names):
+        if succ[i]:
+            target_names = {"fund_dbt_btc", "fund_dbt_eth", "dvol_btc", "dvol_eth"}
+            if n in target_names:
+                result[n] = float(vals[i])
+    return result if result else None
+
+# ---------------------------------------------------------------------------
+# Endpoint candidates
+# ---------------------------------------------------------------------------
+
+def _day_epoch_ms(date_str: str, hour: int = 0) -> int:
+    """Return Unix milliseconds for a given date + hour (UTC)."""
+    dt = datetime(int(date_str[:4]), int(date_str[5:7]), int(date_str[8:10]),
+                  hour, 0, 0, tzinfo=timezone.utc)
+    return int(dt.timestamp() * 1000)
+
+
+def ts23utc(date_str: str) -> int:
+    """Return Unix ms for 23:00 UTC on date_str — canonical NG5 scanner capture time."""
+    return _day_epoch_ms(date_str, hour=23)
+
+
+def build_candidate_urls(date_str: str) -> dict:
+    """
+    Build all candidate URL variants for a historical date.
+    Returns dict: { candidate_label: {indicator: url, ...} }
+    """
+    day_start  = _day_epoch_ms(date_str, hour=0)
+    next_start = day_start + 86400_000   # +24h
+    ts23       = ts23utc(date_str)       # 23:00 UTC — canonical NG5 capture time
+
+    # Funding: NG5 scanner confirmed to run at 23:00 UTC.
+    # We use get_funding_rate_history (full day) then extract the 23:00 UTC entry.
+    # Candidate variants test different windows and parsers.
+    fund_urls = {
+        # CANDIDATE A (EXPECTED CORRECT): get_funding_rate_history full day → 23:00 UTC entry
+        "A_history_23utc": {
+            "fund_dbt_btc": (
+                f"https://www.deribit.com/api/v2/public/get_funding_rate_history"
+                f"?instrument_name=BTC-PERPETUAL"
+                f"&start_timestamp={day_start}&end_timestamp={next_start}",
+                "parse_history_at_23utc",
+                ts23,
+            ),
+            "fund_dbt_eth": (
+                f"https://www.deribit.com/api/v2/public/get_funding_rate_history"
+                f"?instrument_name=ETH-PERPETUAL"
+                f"&start_timestamp={day_start}&end_timestamp={next_start}",
+                "parse_history_at_23utc",
+                ts23,
+            ),
+        },
+        # CANDIDATE B (AGENT FIX — expected wrong): get_funding_rate_value over full day
+        "B_value_fullday_agentfix": {
+            "fund_dbt_btc": (
+                f"https://www.deribit.com/api/v2/public/get_funding_rate_value"
+                f"?instrument_name=BTC-PERPETUAL"
+                f"&start_timestamp={day_start}&end_timestamp={next_start}",
+                "parse_scalar_result",
+                0,
+            ),
+            "fund_dbt_eth": (
+                f"https://www.deribit.com/api/v2/public/get_funding_rate_value"
+                f"?instrument_name=ETH-PERPETUAL"
+                f"&start_timestamp={day_start}&end_timestamp={next_start}",
+                "parse_scalar_result",
+                0,
+            ),
+        },
+        # CANDIDATE C: get_funding_rate_history narrow window (±2h around 23:00) → last entry
+        "C_history_narrow23": {
+            "fund_dbt_btc": (
+                f"https://www.deribit.com/api/v2/public/get_funding_rate_history"
+                f"?instrument_name=BTC-PERPETUAL"
+                f"&start_timestamp={ts23 - 7200_000}&end_timestamp={ts23 + 3600_000}",
+                "parse_history_at_23utc",
+                ts23,
+            ),
+            "fund_dbt_eth": (
+                f"https://www.deribit.com/api/v2/public/get_funding_rate_history"
+                f"?instrument_name=ETH-PERPETUAL"
+                f"&start_timestamp={ts23 - 7200_000}&end_timestamp={ts23 + 3600_000}",
+                "parse_history_at_23utc",
+                ts23,
+            ),
+        },
+    }
+
+    # DVOL: hourly resolution; scanner at 23:00 UTC → take candle closest to 23:00
+    dvol_urls = {
+        # CANDIDATE D: get_volatility_index_data, 1h resolution, full day
+        "D_dvol_1h_fullday": {
+            "dvol_btc": (
+                f"https://www.deribit.com/api/v2/public/get_volatility_index_data"
+                f"?currency=BTC&resolution=3600"
+                f"&start_timestamp={day_start}&end_timestamp={next_start}",
+                "parse_dvol_at_23utc",
+                ts23,
+            ),
+            "dvol_eth": (
+                f"https://www.deribit.com/api/v2/public/get_volatility_index_data"
+                f"?currency=ETH&resolution=3600"
+                f"&start_timestamp={day_start}&end_timestamp={next_start}",
+                "parse_dvol_at_23utc",
+                ts23,
+            ),
+        },
+        # CANDIDATE E: agent's variant — 60-min resolution + count=10
+        "E_dvol_60min_count10": {
+            "dvol_btc": (
+                f"https://www.deribit.com/api/v2/public/get_volatility_index_data"
+                f"?currency=BTC&resolution=60&count=10"
+                f"&start_timestamp={day_start}&end_timestamp={next_start}",
+                "parse_dvol_last",
+                0,
+            ),
+            "dvol_eth": (
+                f"https://www.deribit.com/api/v2/public/get_volatility_index_data"
+                f"?currency=ETH&resolution=60&count=10"
+                f"&start_timestamp={day_start}&end_timestamp={next_start}",
+                "parse_dvol_last",
+                0,
+            ),
+        },
+    }
+
+    # Merge
+    all_candidates = {}
+    all_candidates.update(fund_urls)
+    all_candidates.update(dvol_urls)
+    return all_candidates
+
+
+# ---------------------------------------------------------------------------
+# Parsers
+# ---------------------------------------------------------------------------
+
+def parse_history_at_23utc(d: dict, target_ts_ms: int = 0) -> Optional[float]:
+    """
+    Parse get_funding_rate_history response.
+    Returns interest_8h from the entry CLOSEST to 23:00 UTC on the target date.
+    The NG5 scanner runs at 23:00 UTC daily — this is the canonical capture time.
+    Falls back to last entry if 23:00 UTC entry not found (e.g. live/realtime call).
+    """
+    if not isinstance(d, dict) or "result" not in d:
+        return None
+    r = d["result"]
+    if not isinstance(r, list) or not r:
+        return None
+    try:
+        r_sorted = sorted(r, key=lambda x: x.get("timestamp", 0))
+        if target_ts_ms > 0:
+            # Find entry closest to 23:00 UTC for the target date
+            best = min(r_sorted, key=lambda x: abs(x.get("timestamp", 0) - target_ts_ms))
+        else:
+            # Live call: take last entry (most recent)
+            best = r_sorted[-1]
+        return float(best.get("interest_8h", 0))
+    except (TypeError, KeyError, ValueError):
+        return None
+
+
+def parse_scalar_result(d: dict) -> Optional[float]:
+    """Parse get_funding_rate_value response — result is a scalar."""
+    if not isinstance(d, dict) or "result" not in d:
+        return None
+    r = d["result"]
+    if isinstance(r, list) and r:
+        # Fallback: if API returned list anyway, take last interest_8h
+        try:
+            return float(sorted(r, key=lambda x: x.get("timestamp", 0))[-1].get("interest_8h", 0))
+        except (TypeError, KeyError, ValueError):
+            return None
+    try:
+        return float(r)
+    except (TypeError, ValueError):
+        return None
+
+
+def parse_dvol_last(d: dict, target_ts_ms: int = 0) -> Optional[float]:
+    """Parse get_volatility_index_data — returns close from entry closest to target_ts_ms (or last)."""
+    if not isinstance(d, dict) or "result" not in d:
+        return None
+    r = d["result"]
+    if not isinstance(r, dict) or "data" not in r:
+        return None
+    data = r["data"]
+    if not data:
+        return None
+    # data row format: [timestamp_ms, open, high, low, close]
+    try:
+        rows = sorted(data, key=lambda x: x[0])
+        if target_ts_ms > 0:
+            best = min(rows, key=lambda x: abs(x[0] - target_ts_ms))
+        else:
+            best = rows[-1]
+        return float(best[4]) if len(best) > 4 else float(best[-1])
+    except (TypeError, IndexError, ValueError):
+        return None
+
+
+def parse_dvol_at_23utc(d: dict, target_ts_ms: int = 0) -> Optional[float]:
+    """Alias for parse_dvol_last — explicit 23:00 UTC variant."""
+    return parse_dvol_last(d, target_ts_ms)
+
+
+PARSERS = {
+    "parse_history_at_23utc": parse_history_at_23utc,
+    "parse_history_last":     lambda d, ts=0: parse_history_at_23utc(d, 0),
+    "parse_scalar_result":    lambda d, ts=0: parse_scalar_result(d),
+    "parse_dvol_last":        parse_dvol_last,
+    "parse_dvol_at_23utc":    parse_dvol_at_23utc,
+}
+
+# ---------------------------------------------------------------------------
+# HTTP fetcher
+# ---------------------------------------------------------------------------
+
+async def fetch_json(session: aiohttp.ClientSession, url: str, verbose: bool = False) -> Optional[dict]:
+    try:
+        async with session.get(url, timeout=aiohttp.ClientTimeout(total=15)) as resp:
+            if resp.status != 200:
+                if verbose:
+                    print(f"    HTTP {resp.status} for {url[:80]}...")
+                return None
+            text = await resp.text()
+            d = json.loads(text)
+            if verbose:
+                preview = str(d)[:200]
+                print(f"    RAW: {preview}")
+            return d
+    except Exception as e:
+        if verbose:
+            print(f"    FETCH ERROR: {e} — {url[:80]}")
+        return None
+
+# ---------------------------------------------------------------------------
+# Main parity checker
+# ---------------------------------------------------------------------------
+
+async def run_parity_check(dates: list, indicators_filter: Optional[set],
+                            verbose: bool) -> dict:
+    """
+    Run parity check for all dates × candidates.
+    Returns nested dict: results[candidate][indicator] = {pass: int, fail: int, details: [...]}
+    """
+    results = {}   # candidate → indicator → {pass, fail, abs_diffs, details}
+
+    async with aiohttp.ClientSession(
+        headers={"User-Agent": "DOLPHIN-ExtF-Parity-Test/1.0"}
+    ) as session:
+
+        for date_str in dates:
+            print(f"\n{'='*60}")
+            print(f"DATE: {date_str}")
+            print(f"{'='*60}")
+
+            # Ground truth
+            gt = load_npz_ground_truth(date_str)
+            if gt is None:
+                print("  [SKIP] No NPZ data available for this date.")
+                continue
+
+            print(f"  Ground truth (NPZ): {gt}")
+
+            # Build candidates
+            candidates = build_candidate_urls(date_str)
+
+            for cand_label, indicator_urls in candidates.items():
+                for ind_name, url_spec in indicator_urls.items():
+                    # Unpack 3-tuple (url, parser_name, target_ts_ms)
+                    url, parser_name, target_ts = url_spec
+
+                    # Filter
+                    if indicators_filter and ind_name not in indicators_filter:
+                        continue
+                    if ind_name not in gt:
+                        continue  # no ground truth for this indicator on this date
+
+                    gt_val = gt[ind_name]
+                    tol    = TOLERANCES.get(ind_name, 1e-6)
+
+                    if verbose:
+                        print(f"\n  [{cand_label}] {ind_name}")
+                        print(f"    URL: {url[:100]}...")
+
+                    # Fetch + parse
+                    raw = await fetch_json(session, url, verbose=verbose)
+                    if raw is None:
+                        got_val = None
+                        status = "FETCH_FAIL"
+                    else:
+                        parser = PARSERS[parser_name]
+                        got_val = parser(raw, target_ts)
+                        if got_val is None:
+                            status = "PARSE_FAIL"
+                        else:
+                            abs_diff = abs(got_val - gt_val)
+                            rel_diff = abs_diff / max(abs(gt_val), 1e-12)
+                            if abs_diff <= tol:
+                                status = "PASS"
+                            else:
+                                status = f"FAIL (abs={abs_diff:.2e}, rel={rel_diff:.1%})"
+
+                    # Store
+                    if cand_label not in results:
+                        results[cand_label] = {}
+                    if ind_name not in results[cand_label]:
+                        results[cand_label][ind_name] = {"pass": 0, "fail": 0, "skip": 0, "abs_diffs": []}
+                    rec = results[cand_label][ind_name]
+
+                    if status == "PASS":
+                        rec["pass"] += 1
+                        rec["abs_diffs"].append(abs(got_val - gt_val))
+                    elif status == "FETCH_FAIL" or status == "PARSE_FAIL":
+                        rec["skip"] += 1
+                    else:
+                        rec["fail"] += 1
+
+                    icon = "OK" if status == "PASS" else ("~~" if "FAIL" not in status else "XX")
+                    got_str = f"{got_val:.6e}" if got_val is not None else "None"
+                    print(f"  {icon} [{cand_label}] {ind_name:16s}  gt={gt_val:.6e}  got={got_str}  {status}")
+
+                    # Rate-limit courtesy
+                    await asyncio.sleep(0.15)
+
+    return results
+
+
+def print_summary(results: dict):
+    """Print pass/fail summary table and recommend endpoint."""
+    print(f"\n{'='*70}")
+    print("PARITY SUMMARY")
+    print(f"{'='*70}")
+    print(f"{'Candidate':<30} {'Indicator':<16} {'PASS':>5} {'FAIL':>5} {'SKIP':>5} {'Verdict'}")
+    print("-" * 70)
+
+    winner = {}   # indicator → best candidate
+
+    for cand_label, ind_results in results.items():
+        for ind_name, rec in sorted(ind_results.items()):
+            p, f, s = rec["pass"], rec["fail"], rec["skip"]
+            if p + f == 0:
+                verdict = "NO DATA"
+            elif f == 0:
+                max_abs = max(rec["abs_diffs"]) if rec["abs_diffs"] else 0
+                verdict = f"LOCKED IN OK  (max_abs={max_abs:.2e})"
+                if ind_name not in winner:
+                    winner[ind_name] = (cand_label, max_abs)
+                elif max_abs < winner[ind_name][1]:
+                    winner[ind_name] = (cand_label, max_abs)
+            else:
+                verdict = f"MISMATCH XX  ({f} failures)"
+            print(f"{cand_label:<30} {ind_name:<16} {p:>5} {f:>5} {s:>5}  {verdict}")
+
+    print(f"\n{'='*70}")
+    print("RECOMMENDED ENDPOINT PER INDICATOR")
+    print(f"{'='*70}")
+    if winner:
+        for ind_name, (cand, max_abs) in sorted(winner.items()):
+            print(f"  {ind_name:<16} → {cand}  (max abs diff = {max_abs:.2e})")
+    else:
+        print("  WARNING: No candidate passed parity for any indicator.")
+        print("  Possible causes:")
+        print("    • Deribit API response format changed (check raw output with --verbose)")
+        print("    • parser needs updating for new response structure")
+        print("    • timestamps or window size wrong — try different KNOWN_DATES")
+
+    print()
+
+
+# ---------------------------------------------------------------------------
+# Entry point
+# ---------------------------------------------------------------------------
+
+def main():
+    parser = argparse.ArgumentParser(description="Deribit ExtF API parity test")
+    parser.add_argument("--indicators", choices=["fund", "dvol", "all"], default="all",
+                        help="Which indicator groups to test (default: all)")
+    parser.add_argument("--dates", nargs="*", default=None,
+                        help="Override KNOWN_DATES list (e.g. 2026-01-02 2026-01-05)")
+    parser.add_argument("--verbose", action="store_true",
+                        help="Print raw API responses for debugging")
+    args = parser.parse_args()
+
+    dates = args.dates if args.dates else KNOWN_DATES
+
+    ind_filter = None
+    if args.indicators == "fund":
+        ind_filter = {"fund_dbt_btc", "fund_dbt_eth"}
+    elif args.indicators == "dvol":
+        ind_filter = {"dvol_btc", "dvol_eth"}
+
+    print("DOLPHIN — Deribit ExtF API Parity Test")
+    print(f"Testing {len(dates)} known dates × {args.indicators} indicators")
+    print(f"Ground truth: {_EIGENVALUES_PATH}")
+    print()
+
+    results = asyncio.run(run_parity_check(dates, ind_filter, args.verbose))
+    print_summary(results)
+
+    # Exit non-zero if any critical indicator (fund_dbt_btc) has failures
+    critical = results.get("A_history_fullday", {}).get("fund_dbt_btc", {})
+    if critical.get("fail", 0) > 0 or critical.get("pass", 0) == 0:
+        # Try to find ANY passing candidate for fund_dbt_btc
+        any_pass = any(
+            results.get(c, {}).get("fund_dbt_btc", {}).get("pass", 0) > 0 and
+            results.get(c, {}).get("fund_dbt_btc", {}).get("fail", 0) == 0
+            for c in results
+        )
+        if not any_pass:
+            print("CRITICAL: No valid endpoint found for fund_dbt_btc (ACBv6 dependency)")
+            sys.exit(1)
+        else:
+            print("ℹ️  fund_dbt_btc: preferred candidate (A_history_fullday) failed but another passed.")
+            print("   Update _build_deribit_url() in realtime_exf_service.py to use the passing candidate.")
+
+
+if __name__ == "__main__":
+    main()