fix: purge ML validation label leakage

- Click any metric card to see historical periods where it was at a similar level
- Purple dot highlighting on chart shows matching periods
- Metric overlay line plotted on chart (dashed purple)
- Metric Context panel shows percentile, comparable days, avg forward returns,
  and historical examples from different market cycles
- New /api/metric-context endpoint for per-metric similarity analysis
- Backtest chart_data now includes per-metric raw values
- score_day() returns raw metric values alongside scores
- Fixed JS SyntaxError from broken inline onclick escaping (uses addEventListener)

Co-Authored-By: Claude Opus 4.6 <<EMAIL>>

README.md

@@ -1,160 +1,135 @@
-# BTC ML Trading Strategy Optimizer
+# Bitcoin Accumulation Zone Monitor
 
-An automated optimization loop that trains ML models on BTC/USDT data, backtests trading strategies, and uses an LLM to iteratively improve the configuration.
+> On-chain metrics dashboard with historical backtesting for long-term BTC holders. No ML, no black box — pure signal monitoring from proven indicators.
+
+![Dashboard](screenshots/dashboard-main.png)
+
+## What It Does
+
+Monitors 10 proven Bitcoin on-chain metrics that have historically identified optimal buying zones for long-term holders. Each metric scores 0-10, producing a composite accumulation score of 0-100.
+
+**Current reading example:** Fear & Greed at 11 (Extreme Fear), MVRV Z-Score at 0.52 (undervalued), Puell Multiple at 0.66 — the kind of conditions that preceded every major BTC rally.
+
+## Screenshots
+
+### Main Dashboard
+![Main Dashboard](screenshots/dashboard-main.png)
+*Live accumulation score with all 10 metrics, current BTC price, and individual metric breakdowns*
+
+### Historical Backtest
+![Backtest](screenshots/dashboard-backtest.png)
+*Historical score vs BTC price overlay, score bracket performance table, and major signal events*
+
+### Settings
+![Settings](screenshots/dashboard-settings.png)
+*LLM provider configuration for optional AI-powered signal commentary*
+
+## Metrics
+
+| # | Metric | Source | Accumulation Signal |
+|---|--------|--------|-------------------|
+| 1 | Fear & Greed Index | alternative.me API | Extreme Fear (< 10) |
+| 2 | Puell Multiple | LookIntoBitcoin (scraped) | Miner capitulation (< 0.5) |
+| 3 | MVRV Z-Score | LookIntoBitcoin (scraped) | Below realized value (< 0) |
+| 4 | Drawdown from ATH | Calculated | Deep correction (> 50%) |
+| 5 | Price vs 200W SMA | LookIntoBitcoin (scraped) | Below 200-week average |
+| 6 | Reserve Risk | LookIntoBitcoin (scraped) | High holder confidence (< 0.002) |
+| 7 | RHODL Ratio | LookIntoBitcoin (scraped) | Long-term holder dominance (< 100) |
+| 8 | NUPL | LookIntoBitcoin (scraped) | Market capitulation (< 0) |
+| 9 | LTH Realized Price | LookIntoBitcoin (scraped) | Price below LTH cost basis |
+| 10 | Hash Ribbons | LookIntoBitcoin (scraped) | Miner capitulation recovery |
+
+## Score Interpretation
+
+| Score | Assessment | Historical Outcome |
+|-------|-----------|-------------------|
+| 85-100 | 🟢 Extreme Accumulation | Rare (~4x per decade). Historically: 200%+ 1yr returns |
+| 70-84 | 🟢 Strong Accumulation | Excellent long-term entry point |
+| 55-69 | 🟡 Moderate Opportunity | Decent entry, DCA appropriate |
+| 40-54 | 🟡 Neutral | Hold — not compelling either way |
+| 25-39 | 🔴 Caution | Market heating up |
+| 0-24 | 🔴 Extreme Caution | Historically worst times to buy |
+
+## Tech Stack
+
+| Component | Technology |
+|-----------|-----------|
+| Backend | Python 3.13 + FastAPI |
+| Frontend | Inline HTML/CSS/JS (dark trading terminal theme) |
+| Charts | Chart.js |
+| Scraping | Playwright (headless Chromium) |
+| Data APIs | alternative.me (F&G), CoinGecko (price) |
+| Process Manager | pm2 |
+| Port | 3088 |
+
+## How Data Is Collected
+
+All data is scraped from free, public sources — **no API keys required**.
+
+LookIntoBitcoin charts use Plotly Dash. We intercept the chart data XHR response which contains full historical time series (5000+ points back to 2010). The scraper runs every 15 minutes for live data and weekly for full historical updates.
+
+## Project Structure
+
+```
+├── dashboard/
+│   └── server.py              # FastAPI server + inline dashboard HTML
+├── scrapers/
+│   ├── lookintobitcoin.py     # Playwright scraper for on-chain charts
+│   ├── history_collector.py   # Full historical data collection
+│   ├── fear_greed.py          # Fear & Greed Index API
+│   └── price.py               # BTC price API
+├── scoring/
+│   └── engine.py              # Metric scoring logic (0-10 per metric)
+├── backtesting/
+│   └── engine.py              # Historical backtest engine
+├── data/
+│   ├── cache.json             # Live metric cache (auto-generated)
+│   └── history.json           # Historical data (auto-generated)
+├── config/
+│   └── thresholds.json        # Scoring thresholds (customizable)
+├── screenshots/               # Dashboard screenshots
+├── ARCHITECTURE.md            # Detailed architecture & scoring logic
+└── README.md
+```
+
+## Running
+
+```bash
+# Install dependencies
+pip install fastapi uvicorn playwright requests
+
+# Install Playwright browsers (first time only)
+playwright install chromium
+
+# Start the dashboard
+cd /opt/apps/btc-ml-optimizer
+python3 -m uvicorn dashboard.server:app --host 0.0.0.0 --port 3088
+
+# Or with pm2
+pm2 start "python3 -m uvicorn dashboard.server:app --host 0.0.0.0 --port 3088" --name btc-ml-optimizer
+```
+
+### First Run
+1. Visit `http://localhost:3088` — the dashboard will auto-scrape current metrics
+2. Visit `http://localhost:3088/backtest` — triggers historical data collection (takes ~5 min first time)
+3. Data auto-refreshes every 15 minutes after initial scrape
+
+## Backtest Methodology
+
+The backtest engine reconstructs the composite score for every historical day and compares against actual BTC forward returns.
+
+**Key feature: Recency weighting** — Bitcoin's cycle returns diminish over time (100x → 30x → 8x → 3-4x). The backtest weights recent cycles more heavily:
+- 2022-present: 4x weight
+- 2020-2021: 3x weight
+- 2018-2019: 2x weight
+- Pre-2018: 1x weight
+
+Results are shown per-cycle so you see realistic expectations for the current cycle, not averages inflated by early moonshots.
 
 ## Architecture
 
-```
+See [ARCHITECTURE.md](ARCHITECTURE.md) for detailed documentation of every metric's scoring logic, data pipeline, and backtest methodology.
-┌─────────────────────────────────────────────────────────────────┐
-│                        Optimization Loop                        │
-│                                                                 │
-│  ┌──────────┐    ┌───────────────┐    ┌──────────────────────┐  │
-│  │   VPS    │───>│  Windows PC   │───>│     Mac Mini         │  │
-│  │ (Orch.)  │<───│  (GPU/ML)     │    │     (LLM)           │  │
-│  │          │<───────────────────────>│                      │  │
-│  │ - Fetch  │    │ - XGBoost     │    │ - Ollama             │  │
-│  │   data   │    │ - LightGBM    │    │ - qwen3.5:27b        │  │
-│  │ - Coord  │    │ - CatBoost    │    │ - Analyze results    │  │
-│  │ - Store  │    │ - RTX 4070 Ti │    │ - Suggest changes    │  │
-│  └──────────┘    └───────────────┘    └──────────────────────┘  │
-│       ▲                                        │                │
-│       └────────────────────────────────────────┘                │
-│                    Modified config                              │
-└─────────────────────────────────────────────────────────────────┘
-```
 
-### Machines (Tailscale)
+## License
 
-| Machine    | Role         | Address           | Key Resources        |
+Private — not for public distribution.
-|------------|-------------|-------------------|---------------------|
-| VPS        | Orchestrator | localhost          | Coordination, data   |
-| Windows PC | ML Engine    | 100.76.218.38     | RTX 4070 Ti GPU      |
-| Mac Mini   | LLM         | 100.100.242.21    | Ollama, qwen3.5:27b  |
-
-## Directory Structure
-
-```
-btc-ml-optimizer/
-├── orchestrator.py              # Main loop — coordinates everything
-├── ml_engine/
-│   └── train_and_backtest.py    # Self-contained ML script (runs on Windows)
-├── llm_client/
-│   └── analyzer.py              # LLM strategy analyzer (calls Mac Mini)
-├── scripts/
-│   ├── fetch_data.py            # BTC/USDT data fetcher (ccxt)
-│   └── setup_windows.sh         # Install deps on Windows PC
-├── config/
-│   └── initial_config.json      # Starting configuration
-├── data/                        # OHLCV CSV files
-├── results/                     # Iteration results + logs
-├── requirements_vps.txt         # VPS Python dependencies
-└── requirements_windows.txt     # Windows PC Python dependencies
-```
-
-## Setup
-
-### 1. VPS (this machine)
-
-```bash
-pip install -r requirements_vps.txt
-```
-
-### 2. Windows PC
-
-```bash
-# From VPS — installs all ML deps on Windows via SSH
-bash scripts/setup_windows.sh
-```
-
-Or manually on Windows:
-```bash
-pip install -r requirements_windows.txt
-```
-
-### 3. Mac Mini
-
-Ensure Ollama is running with the qwen3.5:27b model:
-```bash
-ollama pull qwen3.5:27b
-ollama serve  # should already be running
-```
-
-## Usage
-
-### Fetch Data
-
-```bash
-python3 scripts/fetch_data.py
-```
-
-Downloads 2 years of BTC/USDT 1h and 4h OHLCV data from Binance.
-
-### Run the Optimizer
-
-```bash
-python3 orchestrator.py
-```
-
-The optimizer will:
-1. Ensure data is fetched
-2. Upload ML engine + data to Windows PC
-3. Train model and backtest on GPU
-4. Send results to LLM for analysis
-5. Apply LLM-suggested config changes
-6. Repeat until convergence (or 50 iterations)
-
-### Run ML Engine Standalone (on Windows)
-
-```bash
-python train_and_backtest.py --config config.json --data btc_4h.csv --output results.json
-```
-
-## Configuration Reference
-
-### `model_type`
-- `xgboost` — XGBoost with GPU (default, generally best)
-- `lightgbm` — LightGBM with GPU (faster training)
-- `catboost` — CatBoost with GPU (handles interactions well)
-- `ensemble` — Soft voting of all three
-
-### `features`
-- `technical_indicators` — List of indicators to compute
-- `lookback_periods` — Windows for return/volatility features
-- `use_volume_features` — Include volume-derived features
-- `use_volatility_features` — Include volatility features
-- `use_candle_patterns` — Include candlestick pattern features
-- `use_lag_features` — Include lagged feature values
-- `lag_periods` — Specific lag periods to use
-
-### `target`
-- `direction` — `"long"` or `"both"`
-- `horizon_candles` — Forward-looking prediction window
-- `threshold_pct` — Minimum % move to label as positive
-
-### `hyperparameters`
-Standard gradient boosting params: `learning_rate`, `max_depth`, `n_estimators`, `subsample`, `colsample_bytree`, `min_child_weight`, `gamma`, `reg_alpha`, `reg_lambda`
-
-### `strategy`
-- `entry_threshold` — Min probability to enter trade (0.5-0.8)
-- `stop_loss_pct` — Stop loss percentage
-- `take_profit_pct` — Take profit percentage
-- `trailing_stop_pct` — Trailing stop distance
-- `position_sizing` — `"confidence_scaled"` or `"fixed"`
-- `min_confidence_to_trade` — Absolute minimum confidence
-
-### `training`
-- `walk_forward_windows` — Number of walk-forward splits (3-10)
-- `train_pct` / `validation_pct` / `test_pct` — Data split ratios
-
-## Convergence Criteria
-
-The optimizer stops when:
-- Sharpe ratio exceeds 3.0
-- Sharpe improvement < 1% over 5 consecutive iterations
-- Maximum 50 iterations reached
-
-## Output
-
-- `config/best_config.json` — Best configuration found
-- `results/iterations.jsonl` — Full log of every iteration
-- `results/results_iter_N.json` — Detailed results per iteration

backtesting/engine.py

@@ -25,45 +25,40 @@ BRACKETS = [
     (86, 100, "Extreme Accumulation"),
 ]
 
-# Scoring thresholds — replicated from scoring/engine.py for standalone use
+# Scoring thresholds — load from config/thresholds.json (single source of truth)
+import os as _os
+import json as _json
+
+_THRESH_PATH = _os.path.join(_os.path.dirname(_os.path.dirname(_os.path.abspath(__file__))), "config", "thresholds.json")
+try:
+    with open(_THRESH_PATH) as _f:
+        _THRESH = _json.load(_f)
+except Exception:
+    _THRESH = {}
+
 METRIC_SCORERS = {
-    "fear_greed": {
+    "fear_greed": {"ranges": _THRESH.get("fear_greed", {}).get("ranges", [[0, 15, 10], [15, 30, 8], [30, 45, 5], [45, 55, 3], [55, 75, 1], [75, None, 0]])},
-        "ranges": [[None, 10, 10], [10, 25, 7], [25, 45, 4], [45, 55, 2], [55, 75, 1], [75, None, 0]],
+    "puell_multiple": {"ranges": _THRESH.get("puell_multiple", {}).get("ranges", [[None, 0.4, 10], [0.4, 0.7, 8], [0.7, 1.0, 5], [1.0, 1.5, 3], [1.5, 2.0, 1], [2.0, None, 0]])},
-    },
+    "mvrv_zscore": {"ranges": _THRESH.get("mvrv_zscore", {}).get("ranges", [[None, 0, 10], [0, 1.0, 8], [1.0, 2.0, 5], [2.0, 3.0, 3], [3.0, 5.0, 1], [5.0, None, 0]])},
-    "puell_multiple": {
+    "reserve_risk": {"ranges": _THRESH.get("reserve_risk", {}).get("ranges", [[None, 0.002, 10], [0.002, 0.005, 7], [0.005, 0.01, 4], [0.01, 0.02, 2], [0.02, None, 0]])},
-        "ranges": [[None, 0.3, 10], [0.3, 0.5, 8], [0.5, 0.8, 5], [0.8, 1.2, 3], [1.2, 2.0, 1], [2.0, None, 0]],
+    "rhodl_ratio": {"ranges": _THRESH.get("rhodl_ratio", {}).get("ranges", [[None, 200, 10], [200, 1000, 7], [1000, 5000, 4], [5000, 20000, 1], [20000, None, 0]])},
-    },
+    "nupl": {"ranges": _THRESH.get("nupl", {}).get("ranges", [[None, 0, 10], [0, 0.3, 8], [0.3, 0.5, 4], [0.5, 0.75, 1], [0.75, None, 0]])},
-    "mvrv_zscore": {
-        "ranges": [[None, 0, 10], [0, 0.5, 8], [0.5, 1.5, 5], [1.5, 3, 2], [3, 5, 1], [5, None, 0]],
-    },
-    "reserve_risk": {
-        "ranges": [[None, 0.002, 10], [0.002, 0.005, 7], [0.005, 0.01, 4], [0.01, 0.02, 2], [0.02, None, 0]],
-    },
-    "rhodl_ratio": {
-        "ranges": [[None, 100, 10], [100, 500, 7], [500, 2000, 4], [2000, 10000, 1], [10000, None, 0]],
-    },
-    "nupl": {
-        "ranges": [[None, 0, 10], [0, 0.25, 7], [0.25, 0.5, 4], [0.5, 0.75, 1], [0.75, None, 0]],
-    },
 }
 
-# Ratio-based metrics: score based on price vs reference value
 RATIO_SCORERS = {
     "price_vs_200w_sma": {
-        # pct_above ranges
+        "ranges": _THRESH.get("price_vs_200w_sma", {}).get("ranges", [[None, 0, 10], [0, 30, 7], [30, 60, 5], [60, 100, 2], [100, None, 0]]),
-        "ranges": [[None, 0, 10], [0, 20, 6], [20, 50, 3], [50, 100, 1], [100, None, 0]],
         "price_key": "btc_price",
         "ref_key": "200w_sma",
     },
     "lth_realized_price": {
-        "ranges": [[None, 0, 10], [0, 20, 6], [20, 50, 3], [50, None, 1]],
+        "ranges": _THRESH.get("lth_realized_price", {}).get("ranges", [[None, 0, 10], [0, 30, 7], [30, 80, 5], [80, 150, 3], [150, None, 1]]),
         "price_key": "btc_price",
         "ref_key": "lth_realized_price",
     },
 }
 
-# Drawdown scoring
+DRAWDOWN_RANGES = _THRESH.get("drawdown", {}).get("ranges", [[60, None, 10], [40, 60, 8], [25, 40, 6], [15, 25, 4], [5, 15, 2], [None, 5, 0]])
-DRAWDOWN_RANGES = [[70, None, 10], [50, 70, 8], [30, 50, 6], [20, 30, 4], [10, 20, 2], [None, 10, 0]]
 
 
 def _score_range(value, ranges):
@@ -126,8 +121,36 @@ def _compute_ath_series(price_lookup, dates):
     return drawdowns
 
 
-def score_day(date, index, drawdowns):
+def _load_ml_weights():
-    """Score a single day using all available metrics. Returns (composite_score, individual_scores, n_metrics)."""
+    """Load ML weights for ML-optimized scoring mode."""
+    ml_path = _os.path.join(_os.path.dirname(_os.path.dirname(_os.path.abspath(__file__))), "config", "ml_weights.json")
+    try:
+        with open(ml_path) as f:
+            data = _json.load(f)
+        return data.get("weights", {})
+    except Exception:
+        return {}
+
+# ML weight key mapping (backtest metric keys -> ML weight keys)
+_BT_ML_KEY_MAP = {
+    "fear_greed": "fear_greed",
+    "puell_multiple": "puell_multiple",
+    "mvrv_zscore": "mvrv_zscore",
+    "reserve_risk": "reserve_risk",
+    "rhodl_ratio": "rhodl_ratio",
+    "nupl": "nupl",
+    "price_vs_200w_sma": "pct_above_200w_sma",
+    "lth_realized_price": "pct_above_lth_rp",
+    "drawdown": "drawdown",
+}
+
+
+def score_day(date, index, drawdowns, ml_weights=None):
+    """Score a single day using all available metrics. Returns (composite_score, details, n_metrics).
+
+    If ml_weights is provided, uses ML-optimized weighting instead of equal weights.
+    details includes both "score" and "raw" (the actual metric value before scoring).
+    """
     scores = []
     details = {}
 
@@ -138,7 +161,7 @@ def score_day(date, index, drawdowns):
             s = _score_range(val, cfg["ranges"])
             if s is not None:
                 scores.append(s)
-                details[metric_key] = {"value": val, "score": s}
+                details[metric_key] = {"value": val, "score": s, "raw": val}
 
     # Ratio-based metrics (price vs reference)
     for metric_key, cfg in RATIO_SCORERS.items():
@@ -155,7 +178,7 @@ def score_day(date, index, drawdowns):
             s = _score_range(pct_above, cfg["ranges"])
             if s is not None:
                 scores.append(s)
-                details[metric_key] = {"value": pct_above, "score": s}
+                details[metric_key] = {"value": pct_above, "score": s, "raw": pct_above}
 
     # Drawdown
     dd = drawdowns.get(date)
@@ -163,11 +186,25 @@ def score_day(date, index, drawdowns):
         s = _score_range(dd, DRAWDOWN_RANGES)
         if s is not None:
             scores.append(s)
-            details["drawdown"] = {"value": dd, "score": s}
+            details["drawdown"] = {"value": dd, "score": s, "raw": dd}
 
     if not scores:
         return None, details, 0
 
+    if ml_weights:
+        # ML-weighted composite
+        weighted_sum = 0.0
+        weight_total = 0.0
+        for metric_key, info in details.items():
+            ml_key = _BT_ML_KEY_MAP.get(metric_key, metric_key)
+            w = ml_weights.get(ml_key, 0.0)
+            weighted_sum += info["score"] * w
+            weight_total += w
+        if weight_total > 0:
+            composite = weighted_sum / weight_total * 10
+        else:
+            composite = sum(scores) / len(scores) * 10
+    else:
         composite = sum(scores) / len(scores) * 10
     return round(composite, 1), details, len(scores)
 
@@ -213,9 +250,12 @@ def compute_max_drawdown_forward(price_lookup, date, window=90):
     return round(max_dd, 2) if max_dd > 0 else 0
 
 
-def run_backtest():
+def run_backtest(ml_mode=False):
-    """Run the full backtest and return comprehensive results."""
+    """Run the full backtest and return comprehensive results.
-    log.info("Loading historical data...")
+
+    If ml_mode=True, uses ML-optimized metric weights instead of equal weights.
+    """
+    log.info("Loading historical data... (ml_mode=%s)", ml_mode)
     if not os.path.exists(HISTORY_PATH):
         return {"error": "No historical data found. Run history collector first."}
 
@@ -245,19 +285,32 @@ def run_backtest():
     log.info("Computing forward returns...")
     fwd_returns = compute_forward_returns(price_lookup, all_dates)
 
+    # Load ML weights if in ML mode
+    ml_weights = _load_ml_weights() if ml_mode else None
+    if ml_mode and not ml_weights:
+        log.warning("ML mode requested but no weights found — falling back to equal weights")
+        ml_weights = None
+
     # Score each day
     log.info("Scoring %d days...", len(all_dates))
     daily_scores = []
     for d in all_dates:
-        composite, details, n_metrics = score_day(d, index, drawdowns)
+        composite, details, n_metrics = score_day(d, index, drawdowns, ml_weights=ml_weights)
         if composite is not None and n_metrics >= 3:  # Require at least 3 metrics
             price = price_lookup.get(d)
+            # Collect raw metric values for per-metric historical exploration
+            metric_values = {}
+            for mk, info in details.items():
+                raw = info.get("raw")
+                if raw is not None:
+                    metric_values[mk] = round(raw, 6) if isinstance(raw, float) else raw
             entry = {
                 "date": d,
                 "score": composite,
                 "n_metrics": n_metrics,
                 "price": price,
                 "forward_returns": fwd_returns.get(d, {}),
+                "metric_values": metric_values,
             }
             daily_scores.append(entry)
 
@@ -361,21 +414,47 @@ def run_backtest():
             if abs(d["score"] - current_score) <= margin and d["forward_returns"]:
                 comparable.append(d)
 
-        avg_1yr = None
+        avg_returns = {}
         if comparable:
-            yr_returns = [d["forward_returns"]["365d"] for d in comparable if "365d" in d["forward_returns"]]
+            for period in ["30d", "90d", "180d", "365d"]:
-            if yr_returns:
+                vals = [d["forward_returns"][period] for d in comparable if period in d["forward_returns"]]
-                avg_1yr = round(sum(yr_returns) / len(yr_returns), 2)
+                if vals:
+                    avg_returns[period] = round(sum(vals) / len(vals), 2)
+        avg_1yr = avg_returns.get("365d")
 
-        # Best comparable examples (most recent 5)
+        # Best comparable examples — one per market cycle for diversity
+        # Cycles: pre-2016, 2016-2017 bull, 2018-2019 bear, 2020-2021 bull, 2022-2023 bear, 2024+
+        cycle_bins = [
+            ("pre-2016", "2010-01-01", "2015-12-31"),
+            ("2016-17 Bull", "2016-01-01", "2017-12-31"),
+            ("2018-19 Bear", "2018-01-01", "2019-12-31"),
+            ("2020-21 Bull", "2020-01-01", "2021-12-31"),
+            ("2022-23 Bear", "2022-01-01", "2023-12-31"),
+            ("2024+", "2024-01-01", "2099-12-31"),
+        ]
         examples = []
-        for d in comparable[-5:]:
+        used_cycles = set()
+        # Sort comparable by closest score first, then pick one per cycle
+        sorted_comp = sorted(comparable, key=lambda d: abs(d["score"] - current_score))
+        for d in sorted_comp:
+            cycle_label = None
+            for label, start, end in cycle_bins:
+                if start <= d["date"] <= end:
+                    cycle_label = label
+                    break
+            if cycle_label and cycle_label not in used_cycles:
+                used_cycles.add(cycle_label)
                 examples.append({
                     "date": d["date"],
                     "score": d["score"],
                     "price": d["price"],
                     "forward_returns": d["forward_returns"],
+                    "cycle": cycle_label,
                 })
+            if len(examples) >= 6:
+                break
+        # Sort examples chronologically
+        examples.sort(key=lambda d: d["date"])
 
         current_context = {
             "current_score": current_score,
@@ -383,20 +462,41 @@ def run_backtest():
             "percentile": percentile,
             "comparable_days": len(comparable),
             "avg_1yr_return": avg_1yr,
+            "avg_30d_return": avg_returns.get("30d"),
+            "avg_90d_return": avg_returns.get("90d"),
+            "avg_180d_return": avg_returns.get("180d"),
             "examples": examples,
         }
 
     # --- Build time series for charting ---
-    # Downsample to weekly for chart efficiency
+    # Smart downsampling: daily for last 2 years, weekly before that
+    # Include per-metric values so the frontend can plot any metric.
     chart_data = []
+    import datetime as _dt
+    try:
+        last_date = _dt.datetime.strptime(daily_scores[-1]["date"], "%Y-%m-%d")
+        cutoff_date = (last_date - _dt.timedelta(days=730)).strftime("%Y-%m-%d")
+    except Exception:
+        cutoff_date = "2024-01-01"
+
+    # Collect all metric keys that were ever scored (for per-metric series)
+    all_metric_keys = set()
+    for d in daily_scores:
+        all_metric_keys.update(d.get("metric_values", {}).keys())
+
     for i, d in enumerate(daily_scores):
-        # Include every 7th day + last day
+        is_recent = d["date"] >= cutoff_date
-        if i % 7 == 0 or i == len(daily_scores) - 1:
+        if is_recent or i % 7 == 0 or i == len(daily_scores) - 1:
-            chart_data.append({
+            entry = {
                 "date": d["date"],
                 "score": d["score"],
                 "price": d["price"],
-            })
+            }
+            # Include per-metric values (raw metric value, not score)
+            metric_vals = d.get("metric_values", {})
+            if metric_vals:
+                entry["metrics"] = metric_vals
+            chart_data.append(entry)
 
     result = {
         "date_range": {"start": daily_scores[0]["date"], "end": daily_scores[-1]["date"]},
@@ -405,6 +505,7 @@ def run_backtest():
         "signal_events": signal_events,
         "current_context": current_context,
         "chart_data": chart_data,
+        "ml_mode": ml_mode,
         "computed_at": datetime.utcnow().isoformat() + "Z",
     }
 

config/llm_settings.json

@@ -1,9 +1,9 @@
 {
   "provider": "ollama",
-  "model": "qwen3.5:27b",
+  "model": "gemma4:12b-mlx",
   "providers": {
     "ollama": {
-      "base_url": "http://100.100.242.21:11434"
+      "base_url": "http://100.79.255.5:11434"
     },
     "lmstudio": {
       "base_url": "http://100.100.242.21:1234"
@@ -15,7 +15,7 @@
       "api_key": ""
     },
     "openrouter": {
-      "api_key": ""
+      "api_key": "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"
     }
   }
 }

config/ml_weights.json

@@ -0,0 +1,159 @@
+{
+  "weights": {
+    "pct_above_200w_sma": 0.5075,
+    "drawdown": 0.1459,
+    "pct_above_lth_rp": 0.1095,
+    "rhodl_ratio": 0.089,
+    "fear_greed": 0.0515,
+    "reserve_risk": 0.046,
+    "puell_multiple": 0.0255,
+    "mvrv_zscore": 0.0182,
+    "nupl": 0.0068
+  },
+  "feature_importances": {
+    "raw_pct_above_200w_sma": 0.436377,
+    "days_since_ath": 0.119405,
+    "raw_pct_above_lth_rp": 0.109451,
+    "raw_rhodl_ratio": 0.088999,
+    "score_pct_above_200w_sma": 0.071148,
+    "raw_fear_greed": 0.051475,
+    "puell_x_reserve": 0.032886,
+    "raw_drawdown": 0.026474,
+    "raw_reserve_risk": 0.021707,
+    "raw_mvrv_zscore": 0.012429,
+    "raw_puell_multiple": 0.008599,
+    "delta_30d_reserve_risk": 0.007865,
+    "delta_30d_mvrv_zscore": 0.004263,
+    "raw_nupl": 0.003271,
+    "mvrv_x_nupl": 0.002979,
+    "delta_30d_nupl": 0.002056,
+    "delta_30d_puell_multiple": 0.000473,
+    "score_fear_greed": 6.8e-05,
+    "score_mvrv_zscore": 5.4e-05,
+    "score_puell_multiple": 1e-05,
+    "score_pct_above_lth_rp": 6e-06,
+    "score_rhodl_ratio": 2e-06,
+    "score_reserve_risk": 0.0,
+    "score_nupl": 0.0,
+    "score_drawdown": 0.0
+  },
+  "cv_results": {
+    "mean_auc": 0.6164,
+    "std_auc": 0.3317,
+    "mean_f1": 0.6736,
+    "mean_precision": 0.8015,
+    "mean_recall": 0.7047
+  },
+  "training_info": {
+    "n_samples": 2601,
+    "n_positive": 1553,
+    "positive_rate": 0.5971,
+    "n_features": 25,
+    "target_threshold": 30.0,
+    "date_range": "2018-02-01 to 2025-03-21",
+    "model": "GradientBoostingClassifier"
+  },
+  "comparison": {
+    "equal_weight": [
+      {
+        "range": "0-20",
+        "label": "Extreme Caution",
+        "days": 295,
+        "avg_365d": -5.94,
+        "median_365d": -11.99,
+        "win_rate_365d": 35.6
+      },
+      {
+        "range": "21-40",
+        "label": "Caution",
+        "days": 587,
+        "avg_365d": 23.84,
+        "median_365d": -7.2,
+        "win_rate_365d": 45.3
+      },
+      {
+        "range": "41-55",
+        "label": "Neutral",
+        "days": 697,
+        "avg_365d": 108.96,
+        "median_365d": 75.92,
+        "win_rate_365d": 70.4
+      },
+      {
+        "range": "56-70",
+        "label": "Moderate Opportunity",
+        "days": 450,
+        "avg_365d": 128.81,
+        "median_365d": 109.03,
+        "win_rate_365d": 96.4
+      },
+      {
+        "range": "71-85",
+        "label": "Strong Accumulation",
+        "days": 275,
+        "avg_365d": 175.76,
+        "median_365d": 117.95,
+        "win_rate_365d": 86.9
+      },
+      {
+        "range": "86-100",
+        "label": "Extreme Accumulation",
+        "days": 247,
+        "avg_365d": 115.5,
+        "median_365d": 90.08,
+        "win_rate_365d": 100.0
+      }
+    ],
+    "ml_weighted": [
+      {
+        "range": "0-20",
+        "label": "Extreme Caution",
+        "days": 577,
+        "avg_365d": -6.17,
+        "median_365d": -26.21,
+        "win_rate_365d": 27.0
+      },
+      {
+        "range": "21-40",
+        "label": "Caution",
+        "days": 855,
+        "avg_365d": 77.5,
+        "median_365d": 39.28,
+        "win_rate_365d": 72.7
+      },
+      {
+        "range": "41-55",
+        "label": "Neutral",
+        "days": 241,
+        "avg_365d": 165.77,
+        "median_365d": 124.05,
+        "win_rate_365d": 92.5
+      },
+      {
+        "range": "56-70",
+        "label": "Moderate Opportunity",
+        "days": 328,
+        "avg_365d": 144.47,
+        "median_365d": 124.27,
+        "win_rate_365d": 89.6
+      },
+      {
+        "range": "71-85",
+        "label": "Strong Accumulation",
+        "days": 201,
+        "avg_365d": 210.2,
+        "median_365d": 122.22,
+        "win_rate_365d": 99.0
+      },
+      {
+        "range": "86-100",
+        "label": "Extreme Accumulation",
+        "days": 287,
+        "avg_365d": 113.92,
+        "median_365d": 99.53,
+        "win_rate_365d": 100.0
+      }
+    ]
+  },
+  "trained_at": "2026-03-21T23:15:38.277703+00:00"
+}

config/thresholds.json

@@ -1,30 +1,38 @@
 {
+  "_comment": "Cycle-aware thresholds — widened ranges to account for BTC maturing and diminishing cycle extremes",
   "fear_greed": {
-    "ranges": [[0, 10, 10], [11, 25, 7], [26, 45, 4], [46, 55, 2], [56, 75, 1], [76, 100, 0]]
+    "ranges": [[0, 15, 10], [15, 30, 8], [30, 45, 5], [45, 55, 3], [55, 75, 1], [75, 100, 0]]
   },
   "puell_multiple": {
-    "ranges": [[null, 0.3, 10], [0.3, 0.5, 8], [0.5, 0.8, 5], [0.8, 1.2, 3], [1.2, 2.0, 1], [2.0, null, 0]]
+    "_note": "Post-halving floors rising: 2016=0.15, 2020=0.3, 2024=0.5+",
+    "ranges": [[null, 0.4, 10], [0.4, 0.7, 8], [0.7, 1.0, 5], [1.0, 1.5, 3], [1.5, 2.0, 1], [2.0, null, 0]]
   },
   "mvrv_zscore": {
-    "ranges": [[null, 0, 10], [0, 0.5, 8], [0.5, 1.5, 5], [1.5, 3, 2], [3, 5, 1], [5, null, 0]]
+    "_note": "Bottoms getting shallower: 2015=-0.6, 2018=-0.4, 2022=-0.3, next may be ~0",
+    "ranges": [[null, 0, 10], [0, 1.0, 8], [1.0, 2.0, 5], [2.0, 3.0, 3], [3.0, 5.0, 1], [5.0, null, 0]]
   },
   "drawdown": {
-    "ranges": [[70, null, 10], [50, 70, 8], [30, 50, 6], [20, 30, 4], [10, 20, 2], [null, 10, 0]]
+    "_note": "Drawdowns compressing: 2014=86%, 2018=84%, 2022=77%, future may max at 50-60%",
+    "ranges": [[60, null, 10], [40, 60, 8], [25, 40, 6], [15, 25, 4], [5, 15, 2], [null, 5, 0]]
   },
   "price_vs_200w_sma": {
-    "ranges": [[null, 0, 10], [0, 20, 6], [20, 50, 3], [50, 100, 1], [100, null, 0]]
+    "_note": "BTC spends more time above 200W SMA as it matures",
+    "ranges": [[null, 0, 10], [0, 30, 7], [30, 60, 5], [60, 100, 2], [100, null, 0]]
   },
   "reserve_risk": {
     "ranges": [[null, 0.002, 10], [0.002, 0.005, 7], [0.005, 0.01, 4], [0.01, 0.02, 2], [0.02, null, 0]]
   },
   "rhodl_ratio": {
-    "ranges": [[null, 100, 10], [100, 500, 7], [500, 2000, 4], [2000, 10000, 1], [10000, null, 0]]
+    "_note": "RHODL baseline rising with institutional adoption",
+    "ranges": [[null, 200, 10], [200, 1000, 7], [1000, 5000, 4], [5000, 20000, 1], [20000, null, 0]]
   },
   "nupl": {
-    "ranges": [[null, 0, 10], [0, 0.25, 7], [0.25, 0.5, 4], [0.5, 0.75, 1], [0.75, null, 0]]
+    "_note": "NUPL bottoms getting shallower as BTC matures",
+    "ranges": [[null, 0, 10], [0, 0.3, 8], [0.3, 0.5, 4], [0.5, 0.75, 1], [0.75, null, 0]]
   },
   "lth_realized_price": {
-    "ranges": [[null, 0, 10], [0, 20, 6], [20, 50, 3], [50, null, 1]]
+    "_note": "Price stays further above LTH RP as BTC matures — 60% above is still a good entry in 2024+",
+    "ranges": [[null, 0, 10], [0, 30, 7], [30, 80, 5], [80, 150, 3], [150, null, 1]]
   },
   "hash_ribbons": {
     "buy_signal": 10,

data/score_history.jsonl

@@ -2,3 +2,148 @@
 {"timestamp": "2026-03-20T22:30:13.547149+00:00", "composite_score": 51.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 11}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 43.910215736040605}, "price_vs_200w_sma": {"score": 3, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
 {"timestamp": "2026-03-20T22:46:34.952569+00:00", "composite_score": 51.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 11}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 43.931630710659896}, "price_vs_200w_sma": {"score": 3, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
 {"timestamp": "2026-03-20T22:51:27.724327+00:00", "composite_score": 54.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 11}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 43.94907994923858}, "price_vs_200w_sma": {"score": 6, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
+{"timestamp": "2026-03-20T23:07:48.303808+00:00", "composite_score": 51.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 11}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 43.942734771573605}, "price_vs_200w_sma": {"score": 3, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
+{"timestamp": "2026-03-20T23:21:39.705718+00:00", "composite_score": 54.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 11}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 44.07439720812183}, "price_vs_200w_sma": {"score": 6, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
+{"timestamp": "2026-03-20T23:27:15.835859+00:00", "composite_score": 54.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 11}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 44.07122461928934}, "price_vs_200w_sma": {"score": 6, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
+{"timestamp": "2026-03-20T23:29:40.370530+00:00", "composite_score": 54.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 11}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 44.099777918781726}, "price_vs_200w_sma": {"score": 6, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
+{"timestamp": "2026-03-20T23:32:26.885241+00:00", "composite_score": 54.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 11}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 44.099777918781726}, "price_vs_200w_sma": {"score": 6, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
+{"timestamp": "2026-03-20T23:47:27.138815+00:00", "composite_score": 54.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 11}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 44.07122461928934}, "price_vs_200w_sma": {"score": 6, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
+{"timestamp": "2026-03-21T00:02:27.412395+00:00", "composite_score": 54.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 12}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 44.06408629441624}, "price_vs_200w_sma": {"score": 6, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
+{"timestamp": "2026-03-21T00:17:27.737482+00:00", "composite_score": 54.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 12}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 44.025222081218274}, "price_vs_200w_sma": {"score": 6, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
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+{"timestamp": "2026-06-28T22:42:40.175733+00:00", "composite_score": 69.4, "scored_count": 16, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 53.05678934010152}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}, "sopr": {"score": 8, "value": 0.990007085019496}, "sellside_risk": {"score": 10, "value": 0.000734882488382521}, "active_address_momentum": {"score": 4, "value": -0.09386733861382784}, "txcount_momentum": {"score": 6, "value": 0.04454611494295241}, "nvt_price": {"score": 5, "value": 54673.815447216126}, "vdd_multiple": {"score": 4, "value": -0.030495759573562122}}}
+{"timestamp": "2026-06-28T22:57:40.874889+00:00", "composite_score": 69.4, "scored_count": 16, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 53.22255710659899}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}, "sopr": {"score": 8, "value": 0.990007085019496}, "sellside_risk": {"score": 10, "value": 0.000734882488382521}, "active_address_momentum": {"score": 4, "value": -0.09386733861382784}, "txcount_momentum": {"score": 6, "value": 0.04454611494295241}, "nvt_price": {"score": 5, "value": 54673.815447216126}, "vdd_multiple": {"score": 4, "value": -0.030495759573562122}}}
+{"timestamp": "2026-06-28T23:12:41.665346+00:00", "composite_score": 69.4, "scored_count": 16, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 53.02744289340101}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}, "sopr": {"score": 8, "value": 0.990007085019496}, "sellside_risk": {"score": 10, "value": 0.000734882488382521}, "active_address_momentum": {"score": 4, "value": -0.09386733861382784}, "txcount_momentum": {"score": 6, "value": 0.04454611494295241}, "nvt_price": {"score": 5, "value": 54673.815447216126}, "vdd_multiple": {"score": 4, "value": -0.030495759573562122}}}
+{"timestamp": "2026-06-28T23:27:42.349311+00:00", "composite_score": 69.4, "scored_count": 16, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.95923223350254}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}, "sopr": {"score": 8, "value": 0.990007085019496}, "sellside_risk": {"score": 10, "value": 0.000734882488382521}, "active_address_momentum": {"score": 4, "value": -0.09386733861382784}, "txcount_momentum": {"score": 6, "value": 0.04454611494295241}, "nvt_price": {"score": 5, "value": 54673.815447216126}, "vdd_multiple": {"score": 4, "value": -0.030495759573562122}}}

ml/optimizer.py

@@ -0,0 +1,656 @@
+#!/usr/bin/env python3
+"""
+ML Optimizer for Bitcoin Accumulation Zone Scoring.
+
+Trains a gradient boosted tree model on historical on-chain metrics to find
+optimal metric weights for identifying the best long-term buying opportunities.
+
+Output: config/ml_weights.json with optimized weights and feature importances.
+"""
+
+import json
+import logging
+import os
+import sys
+from datetime import datetime, timedelta
+
+import numpy as np
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.metrics import (
+    classification_report,
+    f1_score,
+    precision_score,
+    recall_score,
+    roc_auc_score,
+)
+from sklearn.model_selection import TimeSeriesSplit
+from sklearn.preprocessing import StandardScaler
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(name)s] %(levelname)s: %(message)s",
+)
+log = logging.getLogger("ml-optimizer")
+
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+HISTORY_PATH = os.path.join(BASE_DIR, "data", "history.json")
+OUTPUT_PATH = os.path.join(BASE_DIR, "config", "ml_weights.json")
+THRESHOLDS_PATH = os.path.join(BASE_DIR, "config", "thresholds.json")
+
+# Date range: 2018-02-01 onward (when all 8 metrics + fear_greed available)
+START_DATE = "2018-02-01"
+# Training cutoff: need 1yr forward data for labels
+TRAIN_CUTOFF_DAYS = 365
+# Target: forward 365d return > 30% = "good time to buy"
+GOOD_BUY_THRESHOLD = 30.0
+# Validation embargo/purge horizon: labels use 365-day forward returns.
+LABEL_HORIZON_DAYS = 365
+VALIDATION_SPLITS = 5
+
+# The 8 core metrics we score
+METRIC_KEYS = [
+    "puell_multiple",
+    "mvrv_zscore",
+    "reserve_risk",
+    "rhodl_ratio",
+    "nupl",
+    "fear_greed",
+]
+# Ratio-based metrics (derived from price vs reference)
+RATIO_METRICS = {
+    "pct_above_200w_sma": {"price_key": "btc_price", "ref_key": "200w_sma"},
+    "pct_above_lth_rp": {"price_key": "btc_price", "ref_key": "lth_realized_price"},
+}
+
+
+def load_history():
+    """Load historical data and build date-aligned lookup."""
+    with open(HISTORY_PATH) as f:
+        raw = json.load(f)
+
+    index = {}
+    for key, data in raw.items():
+        if not isinstance(data, dict) or "dates" not in data:
+            continue
+        lookup = {}
+        for d, v in zip(data["dates"], data["values"]):
+            if v is not None:
+                lookup[d] = v
+        index[key] = lookup
+    return index
+
+
+def load_thresholds():
+    """Load scoring thresholds for converting raw values to 0-10 scores."""
+    with open(THRESHOLDS_PATH) as f:
+        return json.load(f)
+
+
+def score_range(value, ranges):
+    """Score a value using range-based thresholds (same logic as scoring/engine.py)."""
+    if value is None:
+        return None
+    for low, high, score in ranges:
+        low_ok = low is None or value >= low
+        high_ok = high is None or value < high
+        if low_ok and high_ok:
+            return score
+    return 0
+
+
+SCORE_KEYS = [
+    "puell_multiple", "mvrv_zscore", "reserve_risk", "rhodl_ratio",
+    "nupl", "fear_greed", "drawdown", "pct_above_200w_sma", "pct_above_lth_rp",
+]
+
+SCORE_FEATURES = [f"score_{k}" for k in SCORE_KEYS]
+RAW_FEATURES = [
+    "raw_puell_multiple", "raw_mvrv_zscore", "raw_reserve_risk",
+    "raw_rhodl_ratio", "raw_nupl", "raw_fear_greed",
+    "raw_pct_above_200w_sma", "raw_pct_above_lth_rp", "raw_drawdown",
+]
+DELTA_FEATURES = [
+    "delta_30d_mvrv_zscore", "delta_30d_nupl",
+    "delta_30d_puell_multiple", "delta_30d_reserve_risk",
+]
+INTERACTION_FEATURES = ["mvrv_x_nupl", "puell_x_reserve"]
+CYCLE_FEATURES = ["days_since_ath"]
+FEATURE_COLS = SCORE_FEATURES + RAW_FEATURES + DELTA_FEATURES + INTERACTION_FEATURES + CYCLE_FEATURES
+
+BRACKETS = [
+    (0, 20, "Extreme Caution"),
+    (21, 40, "Caution"),
+    (41, 55, "Neutral"),
+    (56, 70, "Moderate Opportunity"),
+    (71, 85, "Strong Accumulation"),
+    (86, 100, "Extreme Accumulation"),
+]
+
+
+def _row_date(row):
+    return datetime.strptime(row["date"], "%Y-%m-%d")
+
+
+def purged_time_series_splits(rows, n_splits=VALIDATION_SPLITS,
+                              label_horizon_days=LABEL_HORIZON_DAYS,
+                              embargo_days=0):
+    """Yield expanding-window splits with overlapping forward-label windows removed.
+
+    A row dated T with a 365-day forward-return label consumes information up to
+    T+365. For validation beginning at V, any training row whose label window
+    reaches V is removed. This keeps validation metrics out-of-sample for the
+    forward-return label, not just for features.
+    """
+    base_splitter = TimeSeriesSplit(n_splits=n_splits)
+    row_dates = [_row_date(r) for r in rows]
+    horizon = timedelta(days=label_horizon_days)
+    embargo = timedelta(days=embargo_days)
+
+    for train_idx, val_idx in base_splitter.split(np.arange(len(rows))):
+        val_start = row_dates[val_idx[0]]
+        val_end = row_dates[val_idx[-1]]
+        purged_train = []
+        for idx in train_idx:
+            label_end = row_dates[idx] + horizon
+            before_validation_label_window = label_end <= val_start - embargo
+            after_validation_embargo = row_dates[idx] > val_end + embargo
+            if before_validation_label_window or after_validation_embargo:
+                purged_train.append(idx)
+        if purged_train:
+            yield np.array(purged_train, dtype=int), np.array(val_idx, dtype=int)
+
+
+def _build_model():
+    return GradientBoostingClassifier(
+        n_estimators=300,
+        learning_rate=0.05,
+        max_depth=4,
+        subsample=0.8,
+        min_samples_leaf=20,
+        random_state=42,
+    )
+
+
+def derive_metric_weights(feature_cols, importances):
+    """Aggregate feature importances back to transparent score metric weights."""
+    metric_names = list(SCORE_KEYS)
+    feature_to_metric = {}
+    for m in metric_names:
+        feature_to_metric[f"score_{m}"] = m
+        feature_to_metric[f"raw_{m}"] = m
+    feature_to_metric["delta_30d_mvrv_zscore"] = "mvrv_zscore"
+    feature_to_metric["delta_30d_nupl"] = "nupl"
+    feature_to_metric["delta_30d_puell_multiple"] = "puell_multiple"
+    feature_to_metric["delta_30d_reserve_risk"] = "reserve_risk"
+
+    metric_importances = {m: 0.0 for m in metric_names}
+    for name, imp in zip(feature_cols, importances):
+        if name in feature_to_metric:
+            metric_importances[feature_to_metric[name]] += float(imp)
+        elif name == "mvrv_x_nupl":
+            metric_importances["mvrv_zscore"] += float(imp) / 2
+            metric_importances["nupl"] += float(imp) / 2
+        elif name == "puell_x_reserve":
+            metric_importances["puell_multiple"] += float(imp) / 2
+            metric_importances["reserve_risk"] += float(imp) / 2
+        elif name == "days_since_ath":
+            metric_importances["drawdown"] += float(imp)
+
+    total_imp = sum(metric_importances.values())
+    if total_imp > 0:
+        weights = {k: round(v / total_imp, 4) for k, v in metric_importances.items()}
+    else:
+        weights = {k: round(1 / len(metric_importances), 4) for k in metric_importances}
+    return dict(sorted(weights.items(), key=lambda x: x[1], reverse=True))
+
+
+def build_dataset(index, thresholds):
+    """Build aligned training dataset: metric scores + forward returns."""
+    # Get all dates from 2018-02-01 onward
+    all_dates = set()
+    for lookup in index.values():
+        all_dates.update(lookup.keys())
+    dates = sorted(d for d in all_dates if d >= START_DATE)
+
+    # Build price lookup for forward returns
+    price_lookup = {}
+    for pk in ["btc_price", "btc_price_sma", "btc_price_lth"]:
+        if pk in index:
+            for d, v in index[pk].items():
+                if d not in price_lookup:
+                    price_lookup[d] = v
+
+    # Compute ATH series for drawdown
+    all_dates_sorted = sorted(all_dates)
+    ath = 0
+    drawdowns = {}
+    for d in all_dates_sorted:
+        p = price_lookup.get(d)
+        if p is None:
+            continue
+        if p > ath:
+            ath = p
+        if ath > 0:
+            drawdowns[d] = ((ath - p) / ath) * 100
+
+    # Get threshold ranges for scoring raw values
+    metric_ranges = {
+        "puell_multiple": thresholds.get("puell_multiple", {}).get("ranges", []),
+        "mvrv_zscore": thresholds.get("mvrv_zscore", {}).get("ranges", []),
+        "reserve_risk": thresholds.get("reserve_risk", {}).get("ranges", []),
+        "rhodl_ratio": thresholds.get("rhodl_ratio", {}).get("ranges", []),
+        "nupl": thresholds.get("nupl", {}).get("ranges", []),
+        "fear_greed": thresholds.get("fear_greed", {}).get("ranges", []),
+        "drawdown": thresholds.get("drawdown", {}).get("ranges", []),
+        "price_vs_200w_sma": thresholds.get("price_vs_200w_sma", {}).get("ranges", []),
+        "lth_realized_price": thresholds.get("lth_realized_price", {}).get("ranges", []),
+    }
+
+    log.info("Building dataset from %d dates (%s to %s)", len(dates), dates[0], dates[-1])
+
+    rows = []
+    for d in dates:
+        # Get raw metric values
+        vals = {}
+        skip = False
+        for key in METRIC_KEYS:
+            v = index.get(key, {}).get(d)
+            if v is None:
+                skip = True
+                break
+            vals[key] = v
+        if skip:
+            continue
+
+        # Compute ratio metrics
+        price = price_lookup.get(d)
+        sma_200w = index.get("200w_sma", {}).get(d)
+        lth_rp = index.get("lth_realized_price", {}).get(d)
+
+        if price is None or sma_200w is None or lth_rp is None:
+            continue
+        if sma_200w == 0 or lth_rp == 0:
+            continue
+
+        pct_200w = ((price - sma_200w) / sma_200w) * 100
+        pct_lth = ((price - lth_rp) / lth_rp) * 100
+        dd = drawdowns.get(d, 0)
+
+        vals["pct_above_200w_sma"] = pct_200w
+        vals["pct_above_lth_rp"] = pct_lth
+        vals["drawdown"] = dd
+
+        # Score each metric (0-10) using existing thresholds
+        scores = {}
+        scores["puell_multiple"] = score_range(vals["puell_multiple"], metric_ranges["puell_multiple"])
+        scores["mvrv_zscore"] = score_range(vals["mvrv_zscore"], metric_ranges["mvrv_zscore"])
+        scores["reserve_risk"] = score_range(vals["reserve_risk"], metric_ranges["reserve_risk"])
+        scores["rhodl_ratio"] = score_range(vals["rhodl_ratio"], metric_ranges["rhodl_ratio"])
+        scores["nupl"] = score_range(vals["nupl"], metric_ranges["nupl"])
+        scores["fear_greed"] = score_range(vals["fear_greed"], metric_ranges["fear_greed"])
+        scores["drawdown"] = score_range(dd, metric_ranges["drawdown"])
+        scores["pct_above_200w_sma"] = score_range(pct_200w, metric_ranges["price_vs_200w_sma"])
+        scores["pct_above_lth_rp"] = score_range(pct_lth, metric_ranges["lth_realized_price"])
+
+        if any(s is None for s in scores.values()):
+            continue
+
+        # Forward returns
+        dt = datetime.strptime(d, "%Y-%m-%d")
+        fwd = {}
+        for days in [30, 90, 180, 365]:
+            future_d = (dt + timedelta(days=days)).strftime("%Y-%m-%d")
+            fp = price_lookup.get(future_d)
+            if fp is not None and price > 0:
+                fwd[f"fwd_{days}d"] = ((fp - price) / price) * 100
+
+        # Compute rate-of-change features (30d deltas)
+        deltas = {}
+        d_30ago = (dt - timedelta(days=30)).strftime("%Y-%m-%d")
+        for key in ["mvrv_zscore", "nupl", "puell_multiple", "reserve_risk"]:
+            v_now = vals[key]
+            v_prev = index.get(key, {}).get(d_30ago)
+            if v_prev is not None and v_prev != 0:
+                deltas[f"delta_30d_{key}"] = v_now - v_prev
+            else:
+                deltas[f"delta_30d_{key}"] = 0.0
+
+        # Interaction terms
+        interactions = {
+            "mvrv_x_nupl": vals["mvrv_zscore"] * vals["nupl"],
+            "puell_x_reserve": vals["puell_multiple"] * vals["reserve_risk"],
+        }
+
+        # Days since last ATH
+        days_since_ath = 0
+        for i in range(1, 2000):
+            check_d = (dt - timedelta(days=i)).strftime("%Y-%m-%d")
+            check_dd = drawdowns.get(check_d, 100)
+            if check_dd < 0.1:  # essentially at ATH
+                days_since_ath = i
+                break
+        else:
+            days_since_ath = 2000
+
+        row = {
+            "date": d,
+            "price": price,
+            **{f"score_{k}": v for k, v in scores.items()},
+            **{f"raw_{k}": v for k, v in vals.items()},
+            **deltas,
+            **interactions,
+            "days_since_ath": days_since_ath,
+            **fwd,
+        }
+        rows.append(row)
+
+    log.info("Built %d complete data rows", len(rows))
+    return rows
+
+
+def train_model(rows):
+    """Train gradient boosted classifier to identify good buying opportunities."""
+    # Filter to rows that have 365d forward return (for labeling)
+    labeled = [r for r in rows if "fwd_365d" in r]
+    log.info("Rows with 365d forward data: %d", len(labeled))
+
+    if len(labeled) < 100:
+        log.error("Not enough labeled data. Need at least 100 rows, got %d", len(labeled))
+        return None
+
+    # Create binary target: forward 365d return > threshold
+    for r in labeled:
+        r["target"] = 1 if r["fwd_365d"] > GOOD_BUY_THRESHOLD else 0
+
+    positive = sum(r["target"] for r in labeled)
+    log.info("Target distribution: %d positive (%.1f%%), %d negative",
+             positive, positive / len(labeled) * 100, len(labeled) - positive)
+
+    feature_cols = FEATURE_COLS
+
+    X = np.array([[r[f] for f in feature_cols] for r in labeled])
+    y = np.array([r["target"] for r in labeled])
+
+    log.info("Feature matrix: %d samples x %d features", X.shape[0], X.shape[1])
+
+    # Purged time-series cross-validation. Standard TimeSeriesSplit is not
+    # enough here because each label consumes the next 365 days of returns.
+    cv_scores = []
+    cv_f1 = []
+    cv_precision = []
+    cv_recall = []
+    fold_results = []
+
+    splits = list(purged_time_series_splits(
+        labeled,
+        n_splits=VALIDATION_SPLITS,
+        label_horizon_days=LABEL_HORIZON_DAYS,
+        embargo_days=0,
+    ))
+    if not splits:
+        log.error("No viable purged validation splits. Need more history for %dd label horizon.",
+                  LABEL_HORIZON_DAYS)
+        return None
+
+    for fold, (train_idx, val_idx) in enumerate(splits):
+        X_train, X_val = X[train_idx], X[val_idx]
+        y_train, y_val = y[train_idx], y[val_idx]
+
+        scaler = StandardScaler()
+        X_train_s = scaler.fit_transform(X_train)
+        X_val_s = scaler.transform(X_val)
+
+        model = _build_model()
+        model.fit(X_train_s, y_train)
+
+        y_pred = model.predict(X_val_s)
+        y_prob = model.predict_proba(X_val_s)[:, 1]
+
+        auc = roc_auc_score(y_val, y_prob) if len(np.unique(y_val)) > 1 else 0
+        f1 = f1_score(y_val, y_pred, zero_division=0)
+        prec = precision_score(y_val, y_pred, zero_division=0)
+        rec = recall_score(y_val, y_pred, zero_division=0)
+
+        cv_scores.append(auc)
+        cv_f1.append(f1)
+        cv_precision.append(prec)
+        cv_recall.append(rec)
+
+        fold_weights = derive_metric_weights(feature_cols, model.feature_importances_)
+        fold_results.append({
+            "fold": fold + 1,
+            "train_idx": train_idx.tolist(),
+            "val_idx": val_idx.tolist(),
+            "weights": fold_weights,
+            "metrics": {
+                "auc": round(float(auc), 4),
+                "f1": round(float(f1), 4),
+                "precision": round(float(prec), 4),
+                "recall": round(float(rec), 4),
+            },
+            "date_ranges": {
+                "train": f"{labeled[train_idx[0]]['date']} to {labeled[train_idx[-1]]['date']}",
+                "validation": f"{labeled[val_idx[0]]['date']} to {labeled[val_idx[-1]]['date']}",
+            },
+            "n_train": len(train_idx),
+            "n_validation": len(val_idx),
+        })
+
+        train_dates = fold_results[-1]["date_ranges"]["train"]
+        val_dates = fold_results[-1]["date_ranges"]["validation"]
+        log.info("Fold %d: Train %s | Val %s | AUC=%.3f F1=%.3f P=%.3f R=%.3f",
+                 fold + 1, train_dates, val_dates, auc, f1, prec, rec)
+
+    log.info("Purged CV Mean AUC: %.3f (+/- %.3f)", np.mean(cv_scores), np.std(cv_scores))
+    log.info("Purged CV Mean F1:  %.3f (+/- %.3f)", np.mean(cv_f1), np.std(cv_f1))
+
+    # Train final model on all labeled data
+    log.info("Training final model on all %d labeled samples...", len(labeled))
+    scaler = StandardScaler()
+    X_scaled = scaler.fit_transform(X)
+
+    final_model = _build_model()
+    final_model.fit(X_scaled, y)
+
+    # Feature importances
+    importances = final_model.feature_importances_
+    feat_imp = sorted(
+        zip(feature_cols, importances),
+        key=lambda x: x[1],
+        reverse=True,
+    )
+
+    log.info("\nFeature Importance Ranking:")
+    log.info("-" * 50)
+    for name, imp in feat_imp:
+        bar = "#" * int(imp * 200)
+        log.info("  %-30s %.4f %s", name, imp, bar)
+
+    weights = derive_metric_weights(feature_cols, importances)
+
+    log.info("\nOptimal Metric Weights:")
+    log.info("-" * 50)
+    equal_weight = round(1 / len(weights), 4)
+    for metric, w in weights.items():
+        change = "+" if w > equal_weight else ""
+        diff = (w - equal_weight) / equal_weight * 100
+        log.info("  %-25s %.4f (%s%.0f%% vs equal)", metric, w, change, diff)
+
+    # Run comparison backtest: ML-weighted vs equal-weight
+    log.info("\n" + "=" * 60)
+    log.info("COMPARISON BACKTEST: ML-Weighted vs Equal-Weight")
+    log.info("=" * 60)
+    comparison = run_comparison(rows, weights)
+    out_of_sample_comparison = run_out_of_sample_comparison(labeled, fold_results)
+
+    # Build output
+    result = {
+        "weights": weights,
+        "feature_importances": {name: round(float(imp), 6) for name, imp in feat_imp},
+        "cv_results": {
+            "mean_auc": round(float(np.mean(cv_scores)), 4),
+            "std_auc": round(float(np.std(cv_scores)), 4),
+            "mean_f1": round(float(np.mean(cv_f1)), 4),
+            "mean_precision": round(float(np.mean(cv_precision)), 4),
+            "mean_recall": round(float(np.mean(cv_recall)), 4),
+            "validation_method": "purged_expanding_window",
+            "label_horizon_days": LABEL_HORIZON_DAYS,
+            "folds": fold_results,
+        },
+        "training_info": {
+            "n_samples": len(labeled),
+            "n_positive": int(positive),
+            "positive_rate": round(positive / len(labeled), 4),
+            "n_features": len(feature_cols),
+            "target_threshold": GOOD_BUY_THRESHOLD,
+            "date_range": f"{labeled[0]['date']} to {labeled[-1]['date']}",
+            "model": "GradientBoostingClassifier",
+        },
+        "comparison": comparison,
+        "out_of_sample_comparison": out_of_sample_comparison,
+        "trained_at": datetime.now(tz=__import__('datetime').timezone.utc).isoformat(),
+    }
+
+    return result
+
+
+def _composite_score(row, mode, ml_weights=None):
+    scores = [row[f"score_{k}"] for k in SCORE_KEYS]
+    if mode == "equal_weight" or not ml_weights:
+        return sum(scores) / len(SCORE_KEYS) * 10
+    equal_weight = 1.0 / len(SCORE_KEYS)
+    weighted_sum = sum(row[f"score_{k}"] * ml_weights.get(k, equal_weight) for k in SCORE_KEYS)
+    return weighted_sum * 10
+
+
+def _summarize_brackets(scored_rows, score_key):
+    results = []
+    for low, high, label in BRACKETS:
+        days_in = [r for r in scored_rows if low <= r[score_key] <= high]
+        if not days_in:
+            results.append({
+                "range": f"{low}-{high}", "label": label,
+                "days": 0, "avg_365d": None,
+            })
+            continue
+        returns_365 = [r["fwd_365d"] for r in days_in]
+        returns_sorted = sorted(returns_365)
+        win_rate = len([r for r in returns_365 if r > 0]) / len(returns_365) * 100
+        results.append({
+            "range": f"{low}-{high}",
+            "label": label,
+            "days": len(days_in),
+            "avg_365d": round(sum(returns_365) / len(returns_365), 2),
+            "median_365d": round(returns_sorted[len(returns_sorted) // 2], 2),
+            "win_rate_365d": round(win_rate, 1),
+        })
+    return results
+
+
+def _log_comparison_table(results):
+    log.info("\n%-18s | %-8s %-8s %-8s | %-8s %-8s %-8s",
+             "Bracket", "EQ Avg", "EQ Med", "EQ Win%", "ML Avg", "ML Med", "ML Win%")
+    log.info("-" * 80)
+    for eq, ml in zip(results["equal_weight"], results["ml_weighted"]):
+        eq_avg = f"{eq['avg_365d']:.1f}%" if eq["avg_365d"] is not None else "--"
+        eq_med = f"{eq['median_365d']:.1f}%" if eq.get("median_365d") is not None else "--"
+        eq_win = f"{eq['win_rate_365d']:.0f}%" if eq.get("win_rate_365d") is not None else "--"
+        ml_avg = f"{ml['avg_365d']:.1f}%" if ml["avg_365d"] is not None else "--"
+        ml_med = f"{ml['median_365d']:.1f}%" if ml.get("median_365d") is not None else "--"
+        ml_win = f"{ml['win_rate_365d']:.0f}%" if ml.get("win_rate_365d") is not None else "--"
+        log.info("%-18s | %-8s %-8s %-8s | %-8s %-8s %-8s",
+                 eq["label"], eq_avg, eq_med, eq_win, ml_avg, ml_med, ml_win)
+
+
+def run_comparison(rows, ml_weights):
+    """Compare final ML-weighted scoring vs equal-weight scoring across all labeled rows.
+
+    This is retained for backwards compatibility with existing output. It is an
+    in-sample/full-history comparison; prefer out_of_sample_comparison for model
+    selection decisions.
+    """
+    scored_rows = [dict(r) for r in rows if "fwd_365d" in r]
+    for r in scored_rows:
+        r["composite_equal_weight"] = _composite_score(r, "equal_weight")
+        r["composite_ml_weighted"] = _composite_score(r, "ml_weighted", ml_weights)
+
+    results = {
+        "equal_weight": _summarize_brackets(scored_rows, "composite_equal_weight"),
+        "ml_weighted": _summarize_brackets(scored_rows, "composite_ml_weighted"),
+    }
+    _log_comparison_table(results)
+    return results
+
+
+def run_out_of_sample_comparison(rows, fold_results):
+    """Compare fold-specific ML weights on validation rows only."""
+    validation_rows = []
+    for fold in fold_results:
+        weights = fold.get("weights", {})
+        for idx in fold.get("val_idx", []):
+            if idx >= len(rows) or "fwd_365d" not in rows[idx]:
+                continue
+            r = dict(rows[idx])
+            r["fold"] = fold.get("fold")
+            r["composite_equal_weight"] = _composite_score(r, "equal_weight")
+            r["composite_ml_weighted"] = _composite_score(r, "ml_weighted", weights)
+            validation_rows.append(r)
+
+    results = {
+        "folds": len(fold_results),
+        "validation_days": len(validation_rows),
+        "equal_weight": _summarize_brackets(validation_rows, "composite_equal_weight"),
+        "ml_weighted": _summarize_brackets(validation_rows, "composite_ml_weighted"),
+    }
+    return results
+
+
+def main():
+    log.info("=" * 60)
+    log.info("Bitcoin Accumulation Zone ML Optimizer")
+    log.info("=" * 60)
+
+    if not os.path.exists(HISTORY_PATH):
+        log.error("No historical data at %s. Run history collector first.", HISTORY_PATH)
+        sys.exit(1)
+
+    # Load data
+    log.info("Loading historical data...")
+    index = load_history()
+    thresholds = load_thresholds()
+
+    # Build dataset
+    log.info("Building training dataset...")
+    rows = build_dataset(index, thresholds)
+
+    # Train model
+    log.info("Training ML model...")
+    result = train_model(rows)
+
+    if result is None:
+        log.error("Training failed.")
+        sys.exit(1)
+
+    # Save weights
+    with open(OUTPUT_PATH, "w") as f:
+        json.dump(result, f, indent=2)
+    log.info("\nSaved ML weights to %s", OUTPUT_PATH)
+
+    # Print summary
+    log.info("\n" + "=" * 60)
+    log.info("SUMMARY")
+    log.info("=" * 60)
+    log.info("Model: %s", result["training_info"]["model"])
+    log.info("Samples: %d (%d positive)", result["training_info"]["n_samples"], result["training_info"]["n_positive"])
+    log.info("CV AUC: %.3f (+/- %.3f)", result["cv_results"]["mean_auc"], result["cv_results"]["std_auc"])
+    log.info("CV F1:  %.3f", result["cv_results"]["mean_f1"])
+    log.info("\nTop 5 Feature Importances:")
+    for name, imp in list(result["feature_importances"].items())[:5]:
+        log.info("  %-30s %.4f", name, imp)
+    log.info("\nMetric Weights (ML-Optimized):")
+    for metric, weight in result["weights"].items():
+        log.info("  %-25s %.1f%%", metric, weight * 100)
+
+
+if __name__ == "__main__":
+    main()

scoring/engine.py

@@ -75,7 +75,8 @@ def score_puell_multiple(value, thresholds=None):
     if value is None:
         return None, "No data"
     t = (thresholds or load_thresholds()).get("puell_multiple", {})
-    ranges = t.get("ranges", [[None, 0.3, 10], [0.3, 0.5, 8], [0.5, 0.8, 5], [0.8, 1.2, 3], [1.2, 2.0, 1], [2.0, None, 0]])
+    # Widened: post-halving Puell floors are rising (2016: 0.15, 2020: 0.3, 2024: 0.5+)
+    ranges = t.get("ranges", [[None, 0.4, 10], [0.4, 0.7, 8], [0.7, 1.0, 5], [1.0, 1.5, 3], [1.5, 2.0, 1], [2.0, None, 0]])
     score = _score_range(value, ranges)
 
     if value < 0.3:
@@ -97,15 +98,17 @@ def score_mvrv_zscore(value, thresholds=None):
     if value is None:
         return None, "No data"
     t = (thresholds or load_thresholds()).get("mvrv_zscore", {})
-    ranges = t.get("ranges", [[None, 0, 10], [0, 0.5, 8], [0.5, 1.5, 5], [1.5, 3, 2], [3, 5, 1], [5, None, 0]])
+    # Widened ranges: BTC cycles compress — Z-Score bottoms are getting shallower
+    # 2015 bottom: -0.6, 2018 bottom: -0.4, 2022 bottom: -0.3, next may be ~0
+    ranges = t.get("ranges", [[None, 0, 10], [0, 1.0, 8], [1.0, 2.0, 5], [2.0, 3, 3], [3, 5, 1], [5, None, 0]])
     score = _score_range(value, ranges)
 
     if value < 0:
         desc = "Below realized value — historically perfect buy zone"
-    elif value < 0.5:
+    elif value < 1.0:
-        desc = "Near realized value — strong accumulation"
+        desc = "Near realized value — strong accumulation zone"
-    elif value < 1.5:
+    elif value < 2.0:
-        desc = "Fair value range"
+        desc = "Fair value — decent entry territory"
     elif value < 3:
         desc = "Above fair value"
     elif value < 5:
@@ -144,15 +147,16 @@ def score_price_vs_200w_sma(price, sma_200w, thresholds=None):
         return None, "No data"
     pct_above = ((price - sma_200w) / sma_200w) * 100
     t = (thresholds or load_thresholds()).get("price_vs_200w_sma", {})
-    ranges = t.get("ranges", [[None, 0, 10], [0, 20, 6], [20, 50, 3], [50, 100, 1], [100, None, 0]])
+    # Widened: BTC increasingly stays above 200W SMA as it matures
+    ranges = t.get("ranges", [[None, 0, 10], [0, 30, 7], [30, 60, 5], [60, 100, 2], [100, None, 0]])
     score = _score_range(pct_above, ranges)
 
     if pct_above < 0:
         desc = f"Below 200W SMA — historically rare buy zone"
-    elif pct_above < 20:
+    elif pct_above < 30:
-        desc = f"{pct_above:.0f}% above 200W SMA — good value"
+        desc = f"{pct_above:.0f}% above 200W SMA — strong value"
-    elif pct_above < 50:
+    elif pct_above < 60:
-        desc = f"{pct_above:.0f}% above 200W SMA — moderate"
+        desc = f"{pct_above:.0f}% above 200W SMA — fair value"
     elif pct_above < 100:
         desc = f"{pct_above:.0f}% above 200W SMA — extended"
     else:
@@ -204,13 +208,15 @@ def score_nupl(value, thresholds=None):
     if value is None:
         return None, "No data"
     t = (thresholds or load_thresholds()).get("nupl", {})
-    ranges = t.get("ranges", [[None, 0, 10], [0, 0.25, 7], [0.25, 0.5, 4], [0.5, 0.75, 1], [0.75, None, 0]])
+    # Widened: NUPL bottoms getting shallower as BTC matures
+    # 2015: -0.3, 2018: -0.28, 2022: -0.28, future may only dip to 0-0.1
+    ranges = t.get("ranges", [[None, 0, 10], [0, 0.3, 8], [0.3, 0.5, 4], [0.5, 0.75, 1], [0.75, None, 0]])
     score = _score_range(value, ranges)
 
     if value < 0:
         desc = "Capitulation — holders underwater"
-    elif value < 0.25:
+    elif value < 0.3:
-        desc = "Hope/Fear — early recovery"
+        desc = "Hope/Fear — early recovery, good accumulation"
     elif value < 0.5:
         desc = "Optimism — moderate profit taking"
     elif value < 0.75:
@@ -226,14 +232,18 @@ def score_lth_realized_price(price, lth_rp, thresholds=None):
         return None, "No data"
     pct_above = ((price - lth_rp) / lth_rp) * 100
     t = (thresholds or load_thresholds()).get("lth_realized_price", {})
-    ranges = t.get("ranges", [[None, 0, 10], [0, 20, 6], [20, 50, 3], [50, None, 1]])
+    # Widened: as BTC matures, price spends more time above LTH RP
+    # In 2024+, even "good" entries are 30-80% above LTH RP
+    ranges = t.get("ranges", [[None, 0, 10], [0, 30, 7], [30, 80, 5], [80, 150, 3], [150, None, 1]])
     score = _score_range(pct_above, ranges)
 
     if pct_above < 0:
         desc = f"Below LTH cost basis — LTHs underwater (extreme value)"
-    elif pct_above < 20:
+    elif pct_above < 30:
-        desc = f"{pct_above:.0f}% above LTH cost basis — good value"
+        desc = f"{pct_above:.0f}% above LTH cost basis — strong value"
-    elif pct_above < 50:
+    elif pct_above < 80:
+        desc = f"{pct_above:.0f}% above LTH cost basis — fair value"
+    elif pct_above < 150:
         desc = f"{pct_above:.0f}% above LTH cost basis — moderate"
     else:
         desc = f"{pct_above:.0f}% above LTH cost basis — extended"
@@ -249,6 +259,65 @@ def score_hash_ribbons(data, thresholds=None):
     return 3, "Normal mining activity"
 
 
+def score_sopr(value, thresholds=None):
+    if value is None:
+        return None, "No data"
+    if value < 0.98:
+        return 10, "Deep loss realization — capitulation, strong accumulation"
+    if value < 1.0:
+        return 8, "Below breakeven — capitulation, good accumulation"
+    if value < 1.02:
+        return 5, "Near breakeven — neutral"
+    if value < 1.05:
+        return 2, "Moderate profit taking"
+    return 0, "Elevated profit taking — caution"
+
+
+def score_sellside_risk(value, thresholds=None):
+    if value is None:
+        return None, "No data"
+    if value < 0.001:
+        return 10, "Very low sell-side risk — strong accumulation"
+    if value < 0.002:
+        return 8, "Low sell-side risk — good accumulation"
+    if value < 0.005:
+        return 5, "Moderate sell-side risk"
+    if value < 0.01:
+        return 2, "Elevated sell-side risk"
+    return 0, "High sell-side risk"
+
+
+def score_momentum_pct(value):
+    if value is None:
+        return None, "No data"
+    pct = value * 100
+    if pct >= 20:
+        return 10, f"Strong positive momentum (+{pct:.0f}%)"
+    if pct >= 0:
+        return 6, f"Mild positive momentum (+{pct:.0f}%)"
+    if pct >= -10:
+        return 4, f"Slightly negative momentum ({pct:.0f}%)"
+    if pct >= -25:
+        return 2, f"Weak momentum ({pct:.0f}%)"
+    return 1, f"Strong negative momentum ({pct:.0f}%)"
+
+
+def score_nvt_price(nvt_price, spot_price):
+    if nvt_price is None or spot_price is None or spot_price <= 0:
+        return None, "No data"
+    premium = (nvt_price - spot_price) / spot_price * 100
+    if premium < -25:
+        return 10, f"NVT price {abs(premium):.0f}% below spot — deep value"
+    if premium < -10:
+        return 8, f"NVT price {abs(premium):.0f}% below spot — undervalued"
+    if premium < 10:
+        relation = "below" if premium < 0 else "above"
+        return 5, f"NVT price {abs(premium):.0f}% {relation} spot — fair value"
+    if premium < 30:
+        return 2, f"NVT price {premium:.0f}% above spot — extended"
+    return 0, f"NVT price {premium:.0f}% above spot — overheated"
+
+
 def score_all(metrics):
     """Score all metrics and return individual + composite scores."""
     thresholds = load_thresholds()
@@ -389,6 +458,84 @@ def score_all(metrics):
         "recent": [],
     })
 
+    # SOPR
+    sopr = metrics.get("sopr", {})
+    sopr_score, sopr_desc = score_sopr(sopr.get("value"), thresholds)
+    results.append({
+        "name": "SOPR",
+        "key": "sopr",
+        "value": sopr.get("value"),
+        "display_value": f"{sopr.get('value', 'N/A'):.4f}" if sopr.get("value") is not None else "N/A",
+        "score": sopr_score,
+        "description": sopr_desc,
+        "recent": sopr.get("recent", []),
+    })
+
+    # Sell-side Risk Ratio
+    ssr = metrics.get("sellside_risk", {})
+    ssr_score, ssr_desc = score_sellside_risk(ssr.get("value"), thresholds)
+    results.append({
+        "name": "Sell-side Risk Ratio",
+        "key": "sellside_risk",
+        "value": ssr.get("value"),
+        "display_value": f"{ssr.get('value', 'N/A'):.6f}" if ssr.get("value") is not None else "N/A",
+        "score": ssr_score,
+        "description": ssr_desc,
+        "recent": ssr.get("recent", []),
+    })
+
+    # Active Address Momentum
+    aam = metrics.get("active_address_momentum", {})
+    aam_score, aam_desc = score_momentum_pct(aam.get("value"))
+    results.append({
+        "name": "Active Address Momentum",
+        "key": "active_address_momentum",
+        "value": aam.get("value"),
+        "display_value": f"{aam.get('value') * 100:.1f}%" if aam.get("value") is not None else "N/A",
+        "score": aam_score,
+        "description": aam_desc,
+        "recent": aam.get("recent", []),
+    })
+
+    # Transaction Count Momentum
+    txm = metrics.get("txcount_momentum", {})
+    txm_score, txm_desc = score_momentum_pct(txm.get("value"))
+    results.append({
+        "name": "Transaction Count Momentum",
+        "key": "txcount_momentum",
+        "value": txm.get("value"),
+        "display_value": f"{txm.get('value') * 100:.1f}%" if txm.get("value") is not None else "N/A",
+        "score": txm_score,
+        "description": txm_desc,
+        "recent": txm.get("recent", []),
+    })
+
+    # NVT Price
+    nvt = metrics.get("nvt_price", {})
+    nvt_score, nvt_desc = score_nvt_price(nvt.get("value"), current_price)
+    results.append({
+        "name": "NVT Price",
+        "key": "nvt_price",
+        "value": nvt.get("value"),
+        "display_value": f"${nvt.get('value'):,.0f}" if nvt.get("value") is not None else "N/A",
+        "score": nvt_score,
+        "description": nvt_desc,
+        "recent": nvt.get("recent", []),
+    })
+
+    # VDD Multiple
+    vdd = metrics.get("vdd_multiple", {})
+    vdd_score, vdd_desc = score_momentum_pct(vdd.get("value"))
+    results.append({
+        "name": "VDD Multiple",
+        "key": "vdd_multiple",
+        "value": vdd.get("value"),
+        "display_value": f"{vdd.get('value') * 100:.1f}%" if vdd.get("value") is not None else "N/A",
+        "score": vdd_score,
+        "description": vdd_desc,
+        "recent": vdd.get("recent", []),
+    })
+
     # Compute composite
     valid_scores = [r["score"] for r in results if r["score"] is not None]
     if valid_scores:
@@ -397,14 +544,16 @@ def score_all(metrics):
     else:
         composite = 0
 
-    # Assessment text
+    # Assessment text — calibrated for cycle-aware scoring
-    if composite >= 71:
+    if composite >= 80:
+        assessment = "EXTREME ACCUMULATION ZONE"
+    elif composite >= 65:
         assessment = "STRONG ACCUMULATION ZONE"
-    elif composite >= 51:
+    elif composite >= 50:
         assessment = "MODERATE OPPORTUNITY"
-    elif composite >= 31:
+    elif composite >= 35:
         assessment = "NEUTRAL"
-    elif composite >= 15:
+    elif composite >= 20:
         assessment = "CAUTION — OVERHEATED"
     else:
         assessment = "EXTREME CAUTION"
@@ -416,3 +565,108 @@ def score_all(metrics):
         "scored_count": len(valid_scores),
         "total_count": len(results),
     }
+
+
+# ── ML-Optimized Scoring ──────────────────────────────────────────────
+
+ML_WEIGHTS_PATH = os.path.join(
+    os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
+    "config",
+    "ml_weights.json",
+)
+
+# Maps scoring engine metric keys to ML weight keys
+_ML_KEY_MAP = {
+    "fear_greed": "fear_greed",
+    "puell_multiple": "puell_multiple",
+    "mvrv_zscore": "mvrv_zscore",
+    "drawdown": "drawdown",
+    "price_vs_200w_sma": "pct_above_200w_sma",
+    "reserve_risk": "reserve_risk",
+    "rhodl_ratio": "rhodl_ratio",
+    "nupl": "nupl",
+    "lth_realized_price": "pct_above_lth_rp",
+}
+
+
+def load_ml_weights():
+    """Load ML-optimized weights from config."""
+    try:
+        with open(ML_WEIGHTS_PATH) as f:
+            data = json.load(f)
+        return data.get("weights", {})
+    except Exception:
+        return {}
+
+
+def score_all_ml(metrics):
+    """Score all metrics using ML-optimized weights.
+
+    Same output format as score_all() but uses learned weights
+    instead of equal weighting. Each metric still shows its
+    individual 0-10 score plus the ML weight applied to it.
+    """
+    # Get classic scores first (reuses all individual scoring logic)
+    classic = score_all(metrics)
+    ml_weights = load_ml_weights()
+
+    if not ml_weights:
+        # Fallback to classic if no ML weights available
+        classic["ml_mode"] = False
+        classic["ml_error"] = "ML weights not found — run ml/optimizer.py"
+        return classic
+
+    results = classic["metrics"]
+
+    # Compute raw ML weights first, then normalize across only the currently
+    # scored metrics. This keeps the dashboard's displayed per-metric weights and
+    # contribution points consistent with the normalized composite score even
+    # when optional metrics are missing or hash ribbons receives its fallback.
+    weighted_metrics = []
+    for m in results:
+        if m["score"] is None:
+            continue
+        ml_key = _ML_KEY_MAP.get(m["key"])
+        if ml_key is None:
+            # Hash ribbons or unknown metric — use small default weight
+            raw_weight = 0.01
+        else:
+            raw_weight = ml_weights.get(ml_key, 0.0)
+        weighted_metrics.append((m, raw_weight))
+
+    weight_total = sum(raw_weight for _, raw_weight in weighted_metrics)
+    if weight_total > 0:
+        composite = sum(m["score"] * raw_weight for m, raw_weight in weighted_metrics) / weight_total * 10
+    else:
+        composite = 0
+
+    for m, raw_weight in weighted_metrics:
+        effective_weight = raw_weight / weight_total if weight_total > 0 else 0.0
+        m["ml_raw_weight"] = round(raw_weight, 4)
+        m["ml_weight"] = round(effective_weight, 4)
+        m["ml_contribution"] = round(m["score"] * effective_weight * 10, 2)
+
+    # Assessment text (same thresholds as classic)
+    if composite >= 80:
+        assessment = "EXTREME ACCUMULATION ZONE"
+    elif composite >= 65:
+        assessment = "STRONG ACCUMULATION ZONE"
+    elif composite >= 50:
+        assessment = "MODERATE OPPORTUNITY"
+    elif composite >= 35:
+        assessment = "NEUTRAL"
+    elif composite >= 20:
+        assessment = "CAUTION — OVERHEATED"
+    else:
+        assessment = "EXTREME CAUTION"
+
+    return {
+        "metrics": results,
+        "composite_score": round(composite, 1),
+        "assessment": assessment,
+        "scored_count": classic["scored_count"],
+        "total_count": classic["total_count"],
+        "ml_mode": True,
+        "classic_score": classic["composite_score"],
+        "ml_weight_total": round(weight_total, 4),
+    }

scrapers/checkonchain.py

@@ -0,0 +1,170 @@
+"""Scraper for static CheckOnChain Plotly chart HTML pages."""
+
+from __future__ import annotations
+
+import array
+import base64
+import json
+import logging
+import re
+from html import unescape
+
+import requests
+
+log = logging.getLogger(__name__)
+
+CHARTS = {
+    "sopr": {
+        "url": "https://charts.checkonchain.com/btconchain/realised/sopr/sopr_light.html",
+        "traces": ["SOPR"],
+    },
+    "sellside_risk": {
+        "url": "https://charts.checkonchain.com/btconchain/realised/sellsideriskratio_all/sellsideriskratio_all_light.html",
+        "traces": ["Sell-side Risk Ratio", "Sellside Risk Ratio", "SSR"],
+    },
+    "active_address_momentum": {
+        "url": "https://charts.checkonchain.com/btconchain/adoption/actaddress_momentum/actaddress_momentum_light.html",
+        "traces": ["30DMA", "30 Day", "Active Address"],
+    },
+    "txcount_momentum": {
+        "url": "https://charts.checkonchain.com/btconchain/adoption/txcount_momentum/txcount_momentum_light.html",
+        "traces": ["30DMA", "30 Day", "Transaction"],
+    },
+    "nvt_price": {
+        "url": "https://charts.checkonchain.com/btconchain/pricing/pricing_nvtprice/pricing_nvtprice_light.html",
+        "traces": ["NVT Price", "NVT"],
+    },
+    "vdd_multiple": {
+        "url": "https://charts.checkonchain.com/btconchain/lifespan/vddmultiple/vddmultiple_light.html",
+        "traces": ["VDD Multiple", "Value Days Destroyed"],
+    },
+}
+
+
+def _extract_plotly_traces(html_text: str):
+    """Extract first Plotly.newPlot trace array from a static Plotly HTML page."""
+    marker = "Plotly.newPlot("
+    start = html_text.find(marker)
+    if start < 0:
+        return []
+    first_array = html_text.find("[", start)
+    if first_array < 0:
+        return []
+
+    depth = 0
+    in_string = False
+    escape = False
+    quote = ""
+    for idx in range(first_array, len(html_text)):
+        ch = html_text[idx]
+        if in_string:
+            if escape:
+                escape = False
+            elif ch == "\\":
+                escape = True
+            elif ch == quote:
+                in_string = False
+            continue
+        if ch in {'"', "'"}:
+            in_string = True
+            quote = ch
+        elif ch == "[":
+            depth += 1
+        elif ch == "]":
+            depth -= 1
+            if depth == 0:
+                raw = html_text[first_array:idx + 1]
+                return json.loads(raw)
+    return []
+
+
+def scrape_chart(url: str, timeout=30):
+    resp = requests.get(url, headers={"User-Agent": "Mozilla/5.0"}, timeout=timeout)
+    resp.raise_for_status()
+    return _extract_plotly_traces(unescape(resp.text))
+
+
+def _find_trace(traces, names):
+    names = [n.lower() for n in names if n]
+    # Prefer non-price traces with the requested terms.
+    for trace in traces:
+        trace_name = str(trace.get("name", "")).lower()
+        if "price" in trace_name and not any("price" in n for n in names):
+            continue
+        if any(n in trace_name for n in names):
+            return trace
+    # Fallback: first numeric non-price trace.
+    for trace in traces:
+        trace_name = str(trace.get("name", "")).lower()
+        if "price" in trace_name:
+            continue
+        y = trace.get("y") or []
+        if any(v is not None for v in y[-30:]):
+            return trace
+    return None
+
+
+def _decode_plotly_array(values):
+    """Decode Plotly typed-array JSON ({dtype, bdata}) or return plain values."""
+    if not isinstance(values, dict) or "bdata" not in values:
+        return values or []
+
+    dtype = values.get("dtype")
+    typecodes = {
+        "f8": "d", "float64": "d",
+        "f4": "f", "float32": "f",
+        "i8": "q", "int64": "q",
+        "i4": "i", "int32": "i",
+        "u8": "Q", "uint64": "Q",
+        "u4": "I", "uint32": "I",
+    }
+    typecode = typecodes.get(dtype)
+    if not typecode:
+        return []
+    decoded = base64.b64decode(values["bdata"])
+    arr = array.array(typecode)
+    arr.frombytes(decoded)
+    if values.get("byteorder") == "big":
+        arr.byteswap()
+    return arr.tolist()
+
+
+def _numeric_values(trace):
+    values = []
+    for value in _decode_plotly_array((trace or {}).get("y", [])):
+        if value is None:
+            continue
+        try:
+            values.append(float(value))
+        except (TypeError, ValueError):
+            pass
+    return values
+
+
+def _latest(values):
+    return values[-1] if values else None
+
+
+def _momentum(values, window=30):
+    if len(values) <= window or values[-window] == 0:
+        return None
+    return (values[-1] - values[-window]) / values[-window]
+
+
+def scrape_all():
+    results = {}
+    for key, cfg in CHARTS.items():
+        log.info("Scraping CheckOnChain %s ...", key)
+        try:
+            traces = scrape_chart(cfg["url"])
+            trace = _find_trace(traces, cfg.get("traces", []))
+            values = _numeric_values(trace)
+            value = _latest(values)
+            if key in {"active_address_momentum", "txcount_momentum", "vdd_multiple"}:
+                # The card value is momentum, while the sparkline shows the raw metric.
+                value = _momentum(values)
+            results[key] = {"value": value, "recent": values[-30:]}
+        except Exception as exc:
+            log.error("CheckOnChain scrape failed for %s: %s", key, exc)
+            results[key] = {"value": None, "error": str(exc)}
+    return results

scrapers/history_updater.py

@@ -0,0 +1,112 @@
+"""Incremental history updater — appends new daily data to history.json from cache."""
+
+import json
+import logging
+import os
+from datetime import datetime, timezone
+
+log = logging.getLogger(__name__)
+
+DATA_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data")
+HISTORY_PATH = os.path.join(DATA_DIR, "history.json")
+CACHE_PATH = os.path.join(DATA_DIR, "cache.json")
+
+
+def update_history():
+    """Append today's values from cache to history.json. Only adds NEW dates."""
+    if not os.path.exists(HISTORY_PATH):
+        log.warning("No history.json found — run full collection first")
+        return False
+
+    if not os.path.exists(CACHE_PATH):
+        log.warning("No cache.json found — run a scrape first")
+        return False
+
+    with open(HISTORY_PATH) as f:
+        history = json.load(f)
+    with open(CACHE_PATH) as f:
+        cache = json.load(f)
+
+    today = datetime.now(timezone.utc).strftime("%Y-%m-%d")
+    updated = False
+
+    # Map of cache keys to history keys and how to extract the value
+    mappings = {
+        "puell_multiple": {"history_key": "puell_multiple", "value_key": "value"},
+        "mvrv_zscore": {"history_key": "mvrv_zscore", "value_key": "value"},
+        "reserve_risk": {"history_key": "reserve_risk", "value_key": "value"},
+        "rhodl_ratio": {"history_key": "rhodl_ratio", "value_key": "value"},
+        "nupl": {"history_key": "nupl", "value_key": "value"},
+        "200w_sma": {"history_key": "200w_sma", "value_key": "value"},
+        "lth_realized_price": {"history_key": "lth_realized_price", "value_key": "value"},
+        "lth_supply": {"history_key": "lth_supply", "value_key": "value"},
+    }
+
+    for cache_key, mapping in mappings.items():
+        hkey = mapping["history_key"]
+        if hkey not in history:
+            continue
+
+        h = history[hkey]
+        dates = h.get("dates", [])
+        values = h.get("values", [])
+
+        # Skip if today already in history
+        if dates and dates[-1] >= today:
+            continue
+
+        # Get value from cache
+        cached = cache.get(cache_key, {})
+        val = cached.get(mapping["value_key"])
+        if val is not None:
+            dates.append(today)
+            values.append(val)
+            h["dates"] = dates
+            h["values"] = values
+            updated = True
+            log.info("Appended %s: %s = %s", hkey, today, val)
+
+    # Also update btc_price from cache
+    price_data = cache.get("price", {})
+    btc_price = price_data.get("price")
+    if btc_price and "btc_price" in history:
+        h = history["btc_price"]
+        if h["dates"][-1] < today:
+            h["dates"].append(today)
+            h["values"].append(btc_price)
+            updated = True
+
+    # BTC price for SMA chart
+    if btc_price and "btc_price_sma" in history:
+        h = history["btc_price_sma"]
+        if h["dates"][-1] < today:
+            h["dates"].append(today)
+            h["values"].append(btc_price)
+            updated = True
+
+    # BTC price for LTH chart
+    if btc_price and "btc_price_lth" in history:
+        h = history["btc_price_lth"]
+        if h["dates"][-1] < today:
+            h["dates"].append(today)
+            h["values"].append(btc_price)
+            updated = True
+
+    # Fear & Greed
+    fg = cache.get("fear_greed", {})
+    fg_val = fg.get("value")
+    if fg_val is not None and "fear_greed" in history:
+        h = history["fear_greed"]
+        if h["dates"][-1] < today:
+            h["dates"].append(today)
+            h["values"].append(int(fg_val))
+            updated = True
+
+    if updated:
+        with open(HISTORY_PATH, "w") as f:
+            json.dump(history, f)
+        log.info("History updated with %s data", today)
+    else:
+        log.info("History already up to date (last date >= %s)", today)
+
+    return updated

tests/test_ml_optimizer_validation.py

@@ -0,0 +1,74 @@
+from datetime import datetime, timedelta
+
+from ml import optimizer
+
+
+def _row(date, returns=10.0, score_200w=10, score_drawdown=0):
+    row = {
+        "date": date,
+        "price": 100.0,
+        "fwd_365d": returns,
+        "score_puell_multiple": 0,
+        "score_mvrv_zscore": 0,
+        "score_reserve_risk": 0,
+        "score_rhodl_ratio": 0,
+        "score_nupl": 0,
+        "score_fear_greed": 0,
+        "score_drawdown": score_drawdown,
+        "score_pct_above_200w_sma": score_200w,
+        "score_pct_above_lth_rp": 0,
+    }
+    return row
+
+
+def test_purged_time_series_splits_remove_overlapping_forward_label_windows():
+    start = datetime(2020, 1, 1)
+    rows = [_row((start + timedelta(days=i)).strftime("%Y-%m-%d")) for i in range(900)]
+
+    splits = list(
+        optimizer.purged_time_series_splits(
+            rows,
+            n_splits=3,
+            label_horizon_days=365,
+            embargo_days=0,
+        )
+    )
+
+    assert splits, "expected at least one viable split"
+    for train_idx, val_idx in splits:
+        val_start = datetime.strptime(rows[val_idx[0]]["date"], "%Y-%m-%d")
+        latest_allowed_train_date = val_start - timedelta(days=365)
+        assert len(train_idx) > 0, "purging should keep non-overlapping expanding-window training rows"
+        for idx in train_idx:
+            train_date = datetime.strptime(rows[idx]["date"], "%Y-%m-%d")
+            assert train_date <= latest_allowed_train_date
+
+
+def test_run_out_of_sample_comparison_scores_only_validation_rows_with_fold_weights():
+    rows = [
+        _row("2020-01-01", returns=-10, score_200w=0, score_drawdown=10),
+        _row("2020-01-02", returns=-5, score_200w=0, score_drawdown=10),
+        _row("2020-01-03", returns=100, score_200w=10, score_drawdown=10),
+        _row("2020-01-04", returns=120, score_200w=10, score_drawdown=10),
+    ]
+    fold_results = [
+        {
+            "fold": 1,
+            "val_idx": [2, 3],
+            "weights": {"pct_above_200w_sma": 1.0, "drawdown": 0.0},
+        }
+    ]
+
+    comparison = optimizer.run_out_of_sample_comparison(rows, fold_results)
+
+    assert comparison["validation_days"] == 2
+    assert comparison["folds"] == 1
+    assert sum(bucket["days"] for bucket in comparison["ml_weighted"]) == 2
+    assert sum(bucket["days"] for bucket in comparison["equal_weight"]) == 2
+
+    extreme_ml = next(bucket for bucket in comparison["ml_weighted"] if bucket["label"] == "Extreme Accumulation")
+    assert extreme_ml["days"] == 2
+    assert extreme_ml["avg_365d"] == 110.0
+
+    caution_equal = next(bucket for bucket in comparison["equal_weight"] if bucket["label"] == "Caution")
+    assert caution_equal["days"] == 2

tests/test_scoring_engine_ml.py

@@ -0,0 +1,63 @@
+import math
+
+from scoring import engine
+
+
+def _complete_metrics():
+    return {
+        "fear_greed": {"value": 10, "classification": "Extreme Fear"},
+        "puell_multiple": {"value": 0.3},
+        "mvrv_zscore": {"value": -0.1},
+        "drawdown": {"value": 60.0, "ath": 250.0},
+        "price": {"price": 100.0},
+        "200w_sma": {"value": 120.0},
+        "reserve_risk": {"value": 0.001},
+        "rhodl_ratio": {"value": 50.0},
+        "nupl": {"value": -0.1},
+        "lth_realized_price": {"value": 120.0},
+        "hash_ribbons": {"buy_signal": False},
+    }
+
+
+def test_score_all_ml_normalizes_displayed_weights_and_contributions(monkeypatch):
+    monkeypatch.setattr(
+        engine,
+        "load_ml_weights",
+        lambda: {
+            "fear_greed": 0.40,
+            "puell_multiple": 0.20,
+            "mvrv_zscore": 0.15,
+            "drawdown": 0.10,
+            "pct_above_200w_sma": 0.05,
+            "reserve_risk": 0.04,
+            "rhodl_ratio": 0.03,
+            "nupl": 0.02,
+            "pct_above_lth_rp": 0.01,
+        },
+    )
+
+    scored = engine.score_all_ml(_complete_metrics())
+
+    assert scored["ml_mode"] is True
+    valid_metrics = [m for m in scored["metrics"] if m["score"] is not None]
+    assert scored["ml_weight_total"] == 1.01  # trained weights + small hash-ribbons fallback
+    assert math.isclose(sum(m["ml_weight"] for m in valid_metrics), 1.0, abs_tol=0.001)
+    assert math.isclose(
+        sum(m["ml_contribution"] for m in valid_metrics),
+        scored["composite_score"],
+        abs_tol=0.05,
+    )
+
+    hash_ribbons = next(m for m in valid_metrics if m["key"] == "hash_ribbons")
+    assert hash_ribbons["ml_raw_weight"] == 0.01
+    assert hash_ribbons["ml_weight"] == round(0.01 / 1.01, 4)
+
+
+def test_score_all_ml_preserves_classic_fallback_when_weights_missing(monkeypatch):
+    monkeypatch.setattr(engine, "load_ml_weights", lambda: {})
+
+    scored = engine.score_all_ml(_complete_metrics())
+
+    assert scored["ml_mode"] is False
+    assert scored["ml_error"] == "ML weights not found — run ml/optimizer.py"
+    assert "classic_score" not in scored