README.md

@@ -1,135 +1,160 @@
-# Bitcoin Accumulation Zone Monitor
+# BTC ML Trading Strategy Optimizer
 
-> 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.
+An automated optimization loop that trains ML models on BTC/USDT data, backtests trading strategies, and uses an LLM to iteratively improve the configuration.
 
 ## 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                              │
+└─────────────────────────────────────────────────────────────────┘
+```
 
-## License
+### Machines (Tailscale)
 
-Private — not for public distribution.
+| Machine    | Role         | Address           | Key Resources        |
+|------------|-------------|-------------------|---------------------|
+| 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

@@ -25,40 +25,45 @@ BRACKETS = [
     (86, 100, "Extreme Accumulation"),
 ]
 
-# 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 = {}
-
+# Scoring thresholds — replicated from scoring/engine.py for standalone use
 METRIC_SCORERS = {
-    "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]])},
-    "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]])},
-    "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]])},
-    "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]])},
+    "fear_greed": {
+        "ranges": [[None, 10, 10], [10, 25, 7], [25, 45, 4], [45, 55, 2], [55, 75, 1], [75, None, 0]],
+    },
+    "puell_multiple": {
+        "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]],
+    },
+    "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": {
-        "ranges": _THRESH.get("price_vs_200w_sma", {}).get("ranges", [[None, 0, 10], [0, 30, 7], [30, 60, 5], [60, 100, 2], [100, None, 0]]),
+        # pct_above ranges
+        "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": _THRESH.get("lth_realized_price", {}).get("ranges", [[None, 0, 10], [0, 30, 7], [30, 80, 5], [80, 150, 3], [150, None, 1]]),
+        "ranges": [[None, 0, 10], [0, 20, 6], [20, 50, 3], [50, None, 1]],
         "price_key": "btc_price",
         "ref_key": "lth_realized_price",
     },
 }
 
-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 scoring
+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):
@@ -121,35 +126,8 @@ def _compute_ath_series(price_lookup, dates):
     return drawdowns
 
 
-def _load_ml_weights():
-    """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, individual_scores, n_metrics).
-
-    If ml_weights is provided, uses ML-optimized weighting instead of equal weights.
-    """
+def score_day(date, index, drawdowns):
+    """Score a single day using all available metrics. Returns (composite_score, individual_scores, n_metrics)."""
     scores = []
     details = {}
 
@@ -190,21 +168,7 @@ def score_day(date, index, drawdowns, ml_weights=None):
     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
+    composite = sum(scores) / len(scores) * 10
     return round(composite, 1), details, len(scores)
 
 
@@ -249,12 +213,9 @@ def compute_max_drawdown_forward(price_lookup, date, window=90):
     return round(max_dd, 2) if max_dd > 0 else 0
 
 
-def run_backtest(ml_mode=False):
-    """Run the full backtest and return comprehensive results.
-
-    If ml_mode=True, uses ML-optimized metric weights instead of equal weights.
-    """
-    log.info("Loading historical data... (ml_mode=%s)", ml_mode)
+def run_backtest():
+    """Run the full backtest and return comprehensive results."""
+    log.info("Loading historical data...")
     if not os.path.exists(HISTORY_PATH):
         return {"error": "No historical data found. Run history collector first."}
 
@@ -284,17 +245,11 @@ def run_backtest(ml_mode=False):
     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, ml_weights=ml_weights)
+        composite, details, n_metrics = score_day(d, index, drawdowns)
         if composite is not None and n_metrics >= 3:  # Require at least 3 metrics
             price = price_lookup.get(d)
             entry = {
@@ -406,47 +361,21 @@ def run_backtest(ml_mode=False):
             if abs(d["score"] - current_score) <= margin and d["forward_returns"]:
                 comparable.append(d)
 
-        avg_returns = {}
+        avg_1yr = None
         if comparable:
-            for period in ["30d", "90d", "180d", "365d"]:
-                vals = [d["forward_returns"][period] for d in comparable if period in d["forward_returns"]]
-                if vals:
-                    avg_returns[period] = round(sum(vals) / len(vals), 2)
-        avg_1yr = avg_returns.get("365d")
+            yr_returns = [d["forward_returns"]["365d"] for d in comparable if "365d" in d["forward_returns"]]
+            if yr_returns:
+                avg_1yr = round(sum(yr_returns) / len(yr_returns), 2)
 
-        # 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"),
-        ]
+        # Best comparable examples (most recent 5)
         examples = []
-        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"])
+        for d in comparable[-5:]:
+            examples.append({
+                "date": d["date"],
+                "score": d["score"],
+                "price": d["price"],
+                "forward_returns": d["forward_returns"],
+            })
 
         current_context = {
             "current_score": current_score,
@@ -454,24 +383,15 @@ def run_backtest(ml_mode=False):
             "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 ---
-    # Smart downsampling: daily for last 2 years, weekly before that
+    # Downsample to weekly for chart efficiency
     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"
     for i, d in enumerate(daily_scores):
-        is_recent = d["date"] >= cutoff_date
-        if is_recent or i % 7 == 0 or i == len(daily_scores) - 1:
+        # Include every 7th day + last day
+        if i % 7 == 0 or i == len(daily_scores) - 1:
             chart_data.append({
                 "date": d["date"],
                 "score": d["score"],
@@ -485,7 +405,6 @@ def run_backtest(ml_mode=False):
         "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

@@ -1,6 +1,6 @@
 {
-  "provider": "openrouter",
-  "model": "minimax/minimax-m2.5",
+  "provider": "ollama",
+  "model": "qwen3.5:27b",
   "providers": {
     "ollama": {
       "base_url": "http://100.100.242.21:11434"
@@ -15,7 +15,7 @@
       "api_key": ""
     },
     "openrouter": {
-      "api_key": "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"
+      "api_key": ""
     }
   }
 }

config/ml_weights.json

@@ -1,159 +0,0 @@
-{
-  "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

@@ -1,38 +1,30 @@
 {
-  "_comment": "Cycle-aware thresholds — widened ranges to account for BTC maturing and diminishing cycle extremes",
   "fear_greed": {
-    "ranges": [[0, 15, 10], [15, 30, 8], [30, 45, 5], [45, 55, 3], [55, 75, 1], [75, 100, 0]]
+    "ranges": [[0, 10, 10], [11, 25, 7], [26, 45, 4], [46, 55, 2], [56, 75, 1], [76, 100, 0]]
   },
   "puell_multiple": {
-    "_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]]
+    "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]]
   },
   "mvrv_zscore": {
-    "_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]]
+    "ranges": [[null, 0, 10], [0, 0.5, 8], [0.5, 1.5, 5], [1.5, 3, 2], [3, 5, 1], [5, null, 0]]
   },
   "drawdown": {
-    "_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]]
+    "ranges": [[70, null, 10], [50, 70, 8], [30, 50, 6], [20, 30, 4], [10, 20, 2], [null, 10, 0]]
   },
   "price_vs_200w_sma": {
-    "_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]]
+    "ranges": [[null, 0, 10], [0, 20, 6], [20, 50, 3], [50, 100, 1], [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": {
-    "_note": "RHODL baseline rising with institutional adoption",
-    "ranges": [[null, 200, 10], [200, 1000, 7], [1000, 5000, 4], [5000, 20000, 1], [20000, null, 0]]
+    "ranges": [[null, 100, 10], [100, 500, 7], [500, 2000, 4], [2000, 10000, 1], [10000, null, 0]]
   },
   "nupl": {
-    "_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]]
+    "ranges": [[null, 0, 10], [0, 0.25, 7], [0.25, 0.5, 4], [0.5, 0.75, 1], [0.75, null, 0]]
   },
   "lth_realized_price": {
-    "_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]]
+    "ranges": [[null, 0, 10], [0, 20, 6], [20, 50, 3], [50, null, 1]]
   },
   "hash_ribbons": {
     "buy_signal": 10,

dashboard/server.py

@@ -94,15 +94,8 @@ def load_history():
 
 # ── Background scraper ────────────────────────────────────────────────────
 
-def run_scrape(force_full=False):
-    """Run a scrape cycle and update cache.
-    
-    By default, only refreshes fast data (price, F&G) and reuses cached on-chain data.
-    On-chain metrics (Playwright scrapes) only refresh if:
-    - force_full=True (manual full refresh)
-    - No cached on-chain data exists
-    - Cached on-chain data is >6 hours old (they update daily)
-    """
+def run_scrape():
+    """Run a full scrape cycle and update cache."""
     global _last_update, _last_error, _scraper_running
 
     with _scraper_lock:
@@ -111,8 +104,7 @@ def run_scrape(force_full=False):
         _scraper_running = True
 
     try:
-        # Load existing cache to preserve on-chain data
-        existing_cache = load_cache()
+        log.info("Starting scrape cycle...")
         metrics = {}
 
         # 1. Fear & Greed (fast API call)
@@ -126,95 +118,38 @@ def run_scrape(force_full=False):
 
         log.info("Fetching BTC ATH...")
         ath_data = price.fetch_ath()
-        ath_val = ath_data.get("ath") or existing_cache.get("drawdown", {}).get("ath")
-        if price_current.get("price") and ath_val:
-            drawdown = price.calculate_drawdown(price_current["price"], ath_val)
-            metrics["drawdown"] = {"value": drawdown, "ath": ath_val}
-        elif existing_cache.get("drawdown", {}).get("value") is not None:
-            log.info("ATH fetch failed — reusing cached drawdown")
-            metrics["drawdown"] = existing_cache["drawdown"]
+        if price_current.get("price") and ath_data.get("ath"):
+            drawdown = price.calculate_drawdown(price_current["price"], ath_data["ath"])
+            metrics["drawdown"] = {"value": drawdown, "ath": ath_data["ath"]}
         else:
             metrics["drawdown"] = {"value": None}
 
-        log.info("Fetching historical prices for 200D SMA / Mayer...")
+        log.info("Fetching historical prices...")
         hist = price.fetch_historical()
         if hist:
             sma_200d = price.calculate_200d_sma(hist)
             mayer = price.calculate_mayer_multiple(price_current.get("price"), sma_200d)
             metrics["price_extras"] = {"sma_200d": sma_200d, "mayer_multiple": mayer}
-        else:
-            # CoinGecko rate-limited — compute from history.json instead
-            try:
-                hist_path = os.path.join(DATA_DIR, "history.json")
-                with open(hist_path) as f:
-                    hdata = json.load(f)
-                btc_vals = hdata.get("btc_price", {}).get("values", [])
-                if len(btc_vals) >= 200:
-                    sma_200d = sum(btc_vals[-200:]) / 200
-                    cur_p = price_current.get("price") or btc_vals[-1]
-                    mayer = cur_p / sma_200d if sma_200d else None
-                    metrics["price_extras"] = {"sma_200d": sma_200d, "mayer_multiple": round(mayer, 4) if mayer else None}
-                    log.info("Computed 200D SMA from history.json (CoinGecko rate-limited)")
-                elif existing_cache.get("price_extras"):
-                    metrics["price_extras"] = existing_cache["price_extras"]
-            except Exception:
-                if existing_cache.get("price_extras"):
-                    metrics["price_extras"] = existing_cache["price_extras"]
-                    log.info("Reusing cached price_extras")
 
-        # 3. On-chain metrics — use cached values (historical data is permanent)
-        onchain_keys = ["puell_multiple", "mvrv_zscore", "reserve_risk", "rhodl_ratio",
-                        "nupl", "200w_sma", "lth_realized_price", "hash_ribbons",
-                        "pi_cycle_bottom", "lth_supply"]
+        # 3. On-chain metrics via Playwright (slow)
+        log.info("Scraping on-chain metrics from LookIntoBitcoin...")
+        try:
+            from scrapers import lookintobitcoin
+            onchain = lookintobitcoin.scrape_all()
+            metrics.update(onchain)
+        except Exception as e:
+            log.error("LookIntoBitcoin scraping failed: %s\n%s", e, traceback.format_exc())
+            _last_error = f"On-chain scraping failed: {e}"
 
-        has_cached_onchain = any(existing_cache.get(k, {}).get("value") is not None for k in onchain_keys)
-        
-        if force_full or not has_cached_onchain:
-            # Only do a full Playwright scrape if explicitly requested or no data exists
-            log.info("Scraping on-chain metrics from LookIntoBitcoin (full refresh requested)...")
-            try:
-                from scrapers import lookintobitcoin
-                onchain = lookintobitcoin.scrape_all()
-                metrics.update(onchain)
-                metrics["_onchain_timestamp"] = datetime.now(timezone.utc).isoformat()
-            except Exception as e:
-                log.error("LookIntoBitcoin scraping failed: %s\n%s", e, traceback.format_exc())
-                _last_error = f"On-chain scraping failed: {e}"
-                for k in onchain_keys:
-                    if k in existing_cache:
-                        metrics[k] = existing_cache[k]
-        else:
-            # Reuse cached on-chain values — they're stored permanently
-            log.info("Reusing cached on-chain data (use Full Refresh to re-scrape)")
-            for k in onchain_keys:
-                if k in existing_cache:
-                    metrics[k] = existing_cache[k]
-            if "_onchain_timestamp" in existing_cache:
-                metrics["_onchain_timestamp"] = existing_cache["_onchain_timestamp"]
-
-        # 4. Score everything (classic + ML)
+        # 4. Score everything
         log.info("Scoring metrics...")
         scored = engine.score_all(metrics)
         metrics["_scored"] = scored
-
-        # ML-optimized scoring (parallel)
-        try:
-            scored_ml = engine.score_all_ml(metrics)
-            metrics["_scored_ml"] = scored_ml
-        except Exception as e:
-            log.warning("ML scoring failed (non-critical): %s", e)
         metrics["_timestamp"] = datetime.now(timezone.utc).isoformat()
 
         save_cache(metrics)
         append_history(scored)
 
-        # Append today's values to permanent history (incremental, not full re-scrape)
-        try:
-            from scrapers.history_updater import update_history
-            update_history()
-        except Exception as e:
-            log.warning("History update failed (non-critical): %s", e)
-
         _last_update = datetime.now(timezone.utc).isoformat()
         _last_error = None
         log.info("Scrape cycle complete. Composite score: %s", scored["composite_score"])
@@ -228,14 +163,10 @@ def run_scrape(force_full=False):
 
 
 def scraper_loop():
-    """Background loop: quick refresh every 15min. Full scrape only on first boot with no data."""
-    cache = load_cache()
-    has_data = any(cache.get(k, {}).get("value") is not None 
-                   for k in ["puell_multiple", "mvrv_zscore", "nupl"])
-    run_scrape(force_full=not has_data)  # Full only if no cached on-chain data
+    """Background loop that runs scrape every 15 minutes."""
     while True:
+        run_scrape()
         time.sleep(900)  # 15 minutes
-        run_scrape()  # Quick refresh only
 
 
 # Start background scraper on import
@@ -342,15 +273,10 @@ def _fetch_models(provider, providers):
 # ── API Routes ────────────────────────────────────────────────────────────
 
 @app.get("/api/data")
-def api_data(mode: str = "classic"):
-    """Return current cached metrics + scores.
-    mode=classic (default) or mode=ml for ML-optimized scoring.
-    """
+def api_data():
+    """Return current cached metrics + scores."""
     cache = load_cache()
-    if mode == "ml":
-        scored = cache.get("_scored_ml", cache.get("_scored", {}))
-    else:
-        scored = cache.get("_scored", {})
+    scored = cache.get("_scored", {})
     price_data = cache.get("price", {})
     drawdown_data = cache.get("drawdown", {})
     extras = cache.get("price_extras", {})
@@ -364,7 +290,6 @@ def api_data(mode: str = "classic"):
         "last_update": cache.get("_timestamp"),
         "scraper_running": _scraper_running,
         "last_error": _last_error,
-        "mode": mode,
     }
 
 
@@ -374,15 +299,13 @@ def api_history():
 
 
 @app.post("/api/refresh")
-def api_refresh(full: bool = False):
-    """Trigger a scrape. Quick refresh (default) updates price + F&G only (~2s).
-    Full refresh (?full=true) also re-scrapes on-chain data via Playwright (~2-3min)."""
+def api_refresh():
+    """Trigger a manual scrape."""
     if _scraper_running:
         return JSONResponse({"error": "Scrape already in progress"}, status_code=409)
-    t = threading.Thread(target=run_scrape, kwargs={"force_full": full}, daemon=True)
+    t = threading.Thread(target=run_scrape, daemon=True)
     t.start()
-    mode = "full (on-chain + price + F&G)" if full else "quick (price + F&G only)"
-    return {"ok": True, "message": f"Scrape started — {mode}"}
+    return {"ok": True, "message": "Scrape started"}
 
 
 # Settings routes (preserved)
@@ -516,23 +439,13 @@ DASHBOARD_HTML = """<!DOCTYPE html>
 .metric-sparkline{margin-top:8px;height:30px}
 .metric-sparkline canvas{width:100%;height:30px}
 .chart-section{margin-bottom:20px}
-.chart-container{position:relative;height:320px}
-.range-btn{padding:4px 10px;border-radius:6px;border:1px solid var(--border);background:transparent;color:var(--muted);font-size:.75rem;font-family:var(--mono);cursor:pointer;transition:all .2s}
-.range-btn:hover{color:var(--text);border-color:var(--accent)}
-.range-btn.active{background:var(--accent);color:#000;border-color:var(--accent);font-weight:600}
+.chart-container{position:relative;height:280px}
 .status-line{display:flex;align-items:center;gap:8px;font-size:.78rem;color:var(--text-dim)}
 .status-dot{width:8px;height:8px;border-radius:50%}
 .status-dot.live{background:var(--green);animation:pulse 1.5s infinite}
 .status-dot.stale{background:var(--yellow)}
 .status-dot.error{background:var(--red)}
 @keyframes pulse{0%,100%{opacity:1}50%{opacity:.3}}
-.mode-toggle{display:flex;border-radius:6px;overflow:hidden;border:1px solid var(--border)}
-.mode-btn{padding:6px 14px;border:none;background:transparent;color:var(--text-dim);font-family:inherit;font-weight:600;font-size:.8rem;cursor:pointer;transition:all .15s}
-.mode-btn:hover{color:var(--text)}
-.mode-btn.active[data-mode="classic"]{background:var(--accent);color:#000}
-.mode-btn.active[data-mode="ml"]{background:#8b5cf6;color:#fff}
-.ml-badge{display:inline-block;font-size:.6rem;font-weight:700;padding:2px 6px;border-radius:3px;background:#8b5cf6;color:#fff;vertical-align:super;margin-left:4px}
-.ml-weight{font-size:.65rem;color:#8b5cf6;font-family:var(--mono);margin-top:2px}
 </style>
 </head>
 <body>
@@ -550,12 +463,7 @@ DASHBOARD_HTML = """<!DOCTYPE html>
       <span class="status-dot" id="statusDot"></span>
       <span id="statusText">Loading...</span>
     </div>
-    <div class="mode-toggle" id="modeToggle" title="Switch between Classic (equal-weight) and ML-optimized scoring">
-      <button class="mode-btn active" data-mode="classic" onclick="setMode('classic')">Classic</button>
-      <button class="mode-btn" data-mode="ml" onclick="setMode('ml')">ML</button>
-    </div>
-    <button class="btn btn-accent" onclick="doRefresh(false)" id="btnRefresh">⚡ Quick Refresh</button>
-    <button class="btn btn-secondary" onclick="doRefresh(true)" id="btnFullRefresh" title="Re-scrape on-chain metrics from LookIntoBitcoin (~2-3 min)">🔄 Full Refresh</button>
+    <button class="btn btn-accent" onclick="doRefresh()" id="btnRefresh">Refresh Data</button>
   </div>
 </div>
 
@@ -596,18 +504,7 @@ DASHBOARD_HTML = """<!DOCTYPE html>
 
 <!-- Historical Chart -->
 <div class="card chart-section">
-  <div style="display:flex;justify-content:space-between;align-items:center;flex-wrap:wrap;gap:8px;margin-bottom:12px">
-    <h2 style="margin:0">Composite Score History</h2>
-    <div id="chartRangeBar" style="display:flex;gap:4px">
-      <button class="range-btn" data-days="30">30D</button>
-      <button class="range-btn" data-days="90">90D</button>
-      <button class="range-btn" data-days="180">6M</button>
-      <button class="range-btn" data-days="365">1Y</button>
-      <button class="range-btn" data-days="730">2Y</button>
-      <button class="range-btn" data-days="1460">4Y</button>
-      <button class="range-btn active" data-days="0">ALL</button>
-    </div>
-  </div>
+  <h2>Composite Score History</h2>
   <div class="chart-container">
     <canvas id="historyChart"></canvas>
   </div>
@@ -713,11 +610,6 @@ function renderMetrics(metrics) {
     html += '</div></div>';
     html += '<div class="metric-value">' + (m.display_value || 'N/A') + '</div>';
     html += '<div class="metric-desc">' + (m.description || '') + '</div>';
-    if (currentMode === 'ml' && m.ml_weight != null) {
-      const wpct = (m.ml_weight * 100).toFixed(1);
-      const contrib = m.ml_contribution != null ? m.ml_contribution.toFixed(1) : '--';
-      html += '<div class="ml-weight">ML weight: ' + wpct + '% · contribution: ' + contrib + ' pts</div>';
-    }
     if (hasSparkline) {
       html += '<div class="metric-sparkline"><canvas id="spark-' + idx + '"></canvas></div>';
     }
@@ -736,171 +628,51 @@ function renderMetrics(metrics) {
 }
 
 let histChart = null;
-let fullDailyScores = null;
-let currentRange = 0; // 0 = ALL
 
 function renderHistory(history) {
-  // Legacy: still called by loadData but we'll use backtest data instead
-  if (!fullDailyScores) {
-    // Fallback to score_history.jsonl if backtest hasn't loaded
-    renderHistoryFromData(history);
-  }
-}
-
-function renderHistoryFromData(history) {
   const ctx = document.getElementById('historyChart').getContext('2d');
   if (!history || !history.length) return;
 
-  const labels = history.map(h => h.date || h.timestamp);
-  const scores = history.map(h => h.composite_score || h.score);
-  const prices = history.map(h => h.price || null);
+  const labels = history.map(h => {
+    const d = new Date(h.timestamp);
+    return (d.getMonth()+1) + '/' + d.getDate();
+  });
+  const scores = history.map(h => h.composite_score);
 
   if (histChart) histChart.destroy();
-
-  const datasets = [{
-    label: 'Accumulation Score',
-    data: scores,
-    borderColor: '#22d3ee',
-    backgroundColor: 'rgba(34,211,238,0.08)',
-    borderWidth: 2,
-    fill: true,
-    tension: 0.2,
-    pointRadius: scores.length > 200 ? 0 : 2,
-    pointBackgroundColor: '#22d3ee',
-    yAxisID: 'y',
-  }];
-
-  if (prices && prices.some(p => p != null)) {
-    datasets.push({
-      label: 'BTC Price',
-      data: prices,
-      borderColor: '#f7931a',
-      borderWidth: 1.5,
-      borderDash: [4, 2],
-      fill: false,
-      tension: 0.2,
-      pointRadius: 0,
-      yAxisID: 'y1',
-    });
-  }
-
-  // Accumulation zone backgrounds
-  const zonePlugin = {
-    id: 'zones',
-    beforeDraw(chart) {
-      const { ctx, chartArea: { top, bottom, left, right }, scales: { y } } = chart;
-      const zones = [
-        { min: 65, max: 100, color: 'rgba(34,197,94,0.06)' },
-        { min: 50, max: 65, color: 'rgba(234,179,8,0.04)' },
-        { min: 0, max: 35, color: 'rgba(239,68,68,0.04)' },
-      ];
-      zones.forEach(z => {
-        const yTop = y.getPixelForValue(z.max);
-        const yBot = y.getPixelForValue(z.min);
-        ctx.fillStyle = z.color;
-        ctx.fillRect(left, yTop, right - left, yBot - yTop);
-      });
-      // Draw threshold lines
-      [65, 50, 35].forEach(val => {
-        const yPos = y.getPixelForValue(val);
-        ctx.beginPath();
-        ctx.setLineDash([4, 4]);
-        ctx.strokeStyle = 'rgba(255,255,255,0.06)';
-        ctx.lineWidth = 1;
-        ctx.moveTo(left, yPos);
-        ctx.lineTo(right, yPos);
-        ctx.stroke();
-        ctx.setLineDash([]);
-      });
-    }
-  };
-
   histChart = new Chart(ctx, {
     type: 'line',
-    plugins: [zonePlugin],
-    data: { labels, datasets },
+    data: {
+      labels,
+      datasets: [{
+        label: 'Composite Score',
+        data: scores,
+        borderColor: '#f7931a',
+        backgroundColor: 'rgba(247,147,26,0.1)',
+        borderWidth: 2,
+        fill: true,
+        tension: 0.3,
+        pointRadius: 3,
+        pointBackgroundColor: '#f7931a',
+      }]
+    },
     options: {
       responsive: true,
       maintainAspectRatio: false,
-      interaction: { mode: 'index', intersect: false },
       plugins: {
-        legend: { labels: { color: '#94a3b8', font: { size: 11, family: 'monospace' } } },
-        tooltip: {
-          backgroundColor: 'rgba(10,10,15,0.95)',
-          borderColor: 'rgba(255,255,255,0.08)',
-          borderWidth: 1,
-          titleFont: { family: 'monospace', size: 11 },
-          bodyFont: { family: 'monospace', size: 11 },
-          callbacks: {
-            label: function(ctx) {
-              if (ctx.dataset.yAxisID === 'y1') return 'BTC: $' + ctx.raw.toLocaleString();
-              const s = ctx.raw;
-              let zone = s >= 80 ? 'Extreme Accum' : s >= 65 ? 'Strong Accum' : s >= 50 ? 'Moderate' : s >= 35 ? 'Neutral' : 'Caution';
-              return 'Score: ' + s.toFixed(1) + ' (' + zone + ')';
-            }
-          }
-        }
+        legend: { labels: { color: '#94a3b8', font: { size: 11 } } },
+        annotation: null,
       },
       scales: {
-        x: {
-          ticks: { color: '#64748b', maxTicksLimit: 12, font: { family: 'monospace', size: 10 } },
-          grid: { color: 'rgba(255,255,255,0.03)' }
-        },
-        y: {
-          min: 0, max: 100,
-          ticks: { color: '#22d3ee', font: { family: 'monospace', size: 10 } },
-          grid: { color: 'rgba(255,255,255,0.03)' },
-          title: { display: true, text: 'Score', color: '#22d3ee', font: { family: 'monospace', size: 11 } }
-        },
-        y1: {
-          position: 'right',
-          ticks: {
-            color: '#f7931a',
-            font: { family: 'monospace', size: 10 },
-            callback: v => '$' + (v >= 1000 ? (v/1000).toFixed(0) + 'k' : v)
-          },
-          grid: { drawOnChartArea: false },
-          title: { display: true, text: 'BTC Price', color: '#f7931a', font: { family: 'monospace', size: 11 } }
+        x: { ticks: { color: '#94a3b8', maxTicksLimit: 15 }, grid: { color: '#1e293b' } },
+        y: { min: 0, max: 100, ticks: { color: '#f7931a' }, grid: { color: '#1e293b' },
+          title: { display: true, text: 'Score (0-100)', color: '#f7931a' }
         }
       }
     }
   });
 }
 
-// Load backtest daily scores for the chart
-async function loadBacktestChart() {
-  try {
-    const r = await fetch('/api/backtest?mode=' + currentMode);
-    const data = await r.json();
-    if (data.chart_data && data.chart_data.length) {
-      fullDailyScores = data.chart_data;
-      applyChartRange(currentRange);
-    }
-  } catch(e) { console.error('Backtest chart load failed:', e); }
-}
-
-function applyChartRange(days) {
-  currentRange = days;
-  if (!fullDailyScores) return;
-  let filtered = fullDailyScores;
-  if (days > 0) {
-    filtered = fullDailyScores.slice(-days);
-  }
-  renderHistoryFromData(filtered);
-}
-
-// Range button handlers
-document.querySelectorAll('.range-btn').forEach(btn => {
-  btn.addEventListener('click', function() {
-    document.querySelectorAll('.range-btn').forEach(b => b.classList.remove('active'));
-    this.classList.add('active');
-    applyChartRange(parseInt(this.dataset.days));
-  });
-});
-
-// Load backtest data on page load
-loadBacktestChart();
-
 function updateStatus(data) {
   const dot = document.getElementById('statusDot');
   const text = document.getElementById('statusText');
@@ -923,7 +695,7 @@ function updateStatus(data) {
 async function poll() {
   try {
     const [dataRes, histRes] = await Promise.all([
-      fetch('/api/data?mode=' + currentMode), fetch('/api/history')
+      fetch('/api/data'), fetch('/api/history')
     ]);
     const data = await dataRes.json();
     const history = await histRes.json();
@@ -935,12 +707,7 @@ async function poll() {
 
     // Assessment
     const el = document.getElementById('assessment');
-    let assessText = scored.assessment || 'Loading...';
-    if (currentMode === 'ml') {
-      el.innerHTML = assessText + '<span class="ml-badge">ML</span>';
-    } else {
-      el.textContent = assessText;
-    }
+    el.textContent = scored.assessment || 'Loading...';
     el.style.color = assessmentColor(composite);
 
     // Price
@@ -959,11 +726,7 @@ async function poll() {
     if (data.mayer_multiple) document.getElementById('mayerDisplay').textContent = data.mayer_multiple.toFixed(2);
     if (data.sma_200d) document.getElementById('sma200dDisplay').textContent = '$' + Math.round(data.sma_200d).toLocaleString();
     if (scored.scored_count != null) {
-      let countText = scored.scored_count + '/' + scored.total_count + ' metrics active';
-      if (currentMode === 'ml' && scored.classic_score != null) {
-        countText += ' · Classic: ' + scored.classic_score;
-      }
-      document.getElementById('scoredCount').textContent = countText;
+      document.getElementById('scoredCount').textContent = scored.scored_count + '/' + scored.total_count + ' metrics active';
     }
 
     // Metrics
@@ -977,30 +740,17 @@ async function poll() {
   } catch(e) { console.error('Poll error:', e); }
 }
 
-async function doRefresh(full) {
-  const btn = document.getElementById(full ? 'btnFullRefresh' : 'btnRefresh');
-  const origText = btn.textContent;
+async function doRefresh() {
+  const btn = document.getElementById('btnRefresh');
   btn.disabled = true;
-  btn.textContent = full ? 'Scraping...' : 'Refreshing...';
+  btn.textContent = 'Refreshing...';
   try {
-    const r = await fetch('/api/refresh' + (full ? '?full=true' : ''), { method: 'POST' });
+    const r = await fetch('/api/refresh', { method: 'POST' });
     const d = await r.json();
     if (d.error) showToast(d.error, 'error');
-    else showToast(d.message || 'Refresh started', 'success');
+    else showToast('Scrape started — results in ~2 min', 'success');
   } catch(e) { showToast('Failed: ' + e, 'error'); }
-  const delay = full ? 180000 : 5000;
-  setTimeout(() => { btn.disabled = false; btn.textContent = origText; }, delay);
-}
-
-let currentMode = 'classic';
-
-function setMode(mode) {
-  currentMode = mode;
-  document.querySelectorAll('.mode-btn').forEach(b => {
-    b.classList.toggle('active', b.dataset.mode === mode);
-  });
-  poll(); // Refresh with new mode
-  loadBacktestChart(); // Reload chart with new mode
+  setTimeout(() => { btn.disabled = false; btn.textContent = 'Refresh Data'; }, 5000);
 }
 
 drawScoreRing(0);
@@ -1019,18 +769,11 @@ setInterval(poll, 30000);
     const ctx = bt.current_context;
     const el = document.getElementById('histContext');
     const txt = document.getElementById('histContextText');
-    let html = 'Score <strong>' + ctx.current_score + '</strong> is in the <strong style="color:#22d3ee">top ' + (100 - ctx.percentile).toFixed(1) + '%</strong> historically.<br>';
-    const fmtR = (v) => v == null ? null : (v >= 0 ? '+' : '') + v.toFixed(1) + '%';
-    const cR = (v) => v >= 0 ? '#22c55e' : '#ef4444';
-    const periods = [
-      ['30d', ctx.avg_30d_return], ['90d', ctx.avg_90d_return],
-      ['180d', ctx.avg_180d_return], ['1yr', ctx.avg_1yr_return]
-    ];
-    let parts = [];
-    for (const [label, val] of periods) {
-      if (val != null) parts.push('<strong style="color:' + cR(val) + '">' + label + ': ' + fmtR(val) + '</strong>');
+    let html = 'Score <strong>' + ctx.current_score + '</strong> is in the <strong style="color:#22d3ee">top ' + (100 - ctx.percentile).toFixed(1) + '%</strong> historically.';
+    if (ctx.avg_1yr_return != null) {
+      const c = ctx.avg_1yr_return >= 0 ? '#22c55e' : '#ef4444';
+      html += ' Average 1-year return from this level: <strong style="color:' + c + '">' + (ctx.avg_1yr_return >= 0 ? '+' : '') + ctx.avg_1yr_return.toFixed(1) + '%</strong>';
     }
-    if (parts.length) html += 'Average returns from this level: ' + parts.join(' · ');
     txt.innerHTML = html;
     el.style.display = 'block';
   } catch(e) { /* backtest data not available yet */ }
@@ -1223,13 +966,11 @@ _history_collector_progress = {}
 
 
 @app.get("/api/backtest")
-def api_backtest(mode: str = "classic"):
-    """Run backtest and return full results.
-    mode=classic (default) or mode=ml for ML-optimized scoring.
-    """
+def api_backtest():
+    """Run backtest and return full results."""
     try:
         from backtesting.engine import run_backtest
-        return run_backtest(ml_mode=(mode == "ml"))
+        return run_backtest()
     except Exception as e:
         log.error("Backtest error: %s", traceback.format_exc())
         return JSONResponse({"error": str(e)}, status_code=500)
@@ -1380,7 +1121,7 @@ tr:hover td{background:var(--card-hover)}
         <thead>
           <tr>
             <th>Score Range</th><th>Label</th><th>Days</th>
-            <th>Avg 30d</th><th>Avg 90d</th><th>Avg 180d</th><th>Avg 1yr</th>
+            <th>Avg 30d</th><th>Avg 90d</th><th>Avg 1yr</th>
             <th>Win Rate (1yr)</th><th>Max Gain (1yr)</th><th>Max Loss (1yr)</th>
             <th>Avg Max DD</th>
           </tr>
@@ -1486,8 +1227,8 @@ function renderAll() {
   // Rebuild the main content
   document.getElementById('mainContent').innerHTML = `
     <div class="section"><div class="context-box" id="contextBox"><h2>Current Signal Context</h2><div class="context-score" id="ctxScore">--</div><div class="context-percentile" id="ctxPercentile"></div><div class="context-return" id="ctxReturn"></div><div class="comparable-list" id="ctxComparables"></div></div></div>
-    <div class="section"><div class="card"><div style="display:flex;justify-content:space-between;align-items:center;flex-wrap:wrap;gap:8px;margin-bottom:12px"><h2 style="margin:0">Historical Score vs BTC Price</h2><div id="btRangeBar" style="display:flex;gap:4px"><button class="range-btn" data-days="30">30D</button><button class="range-btn" data-days="90">90D</button><button class="range-btn" data-days="180">6M</button><button class="range-btn" data-days="365">1Y</button><button class="range-btn" data-days="730">2Y</button><button class="range-btn" data-days="1460">4Y</button><button class="range-btn active" data-days="0">ALL</button></div></div><div class="chart-dual"><canvas id="dualChart"></canvas></div></div></div>
-    <div class="section"><div class="card"><h2>Score Bracket Performance</h2><div style="overflow-x:auto"><table><thead><tr><th>Score Range</th><th>Label</th><th>Days</th><th>Avg 30d</th><th>Avg 90d</th><th>Avg 180d</th><th>Avg 1yr</th><th>Win Rate (1yr)</th><th>Max Gain</th><th>Max Loss</th><th>Avg Max DD</th></tr></thead><tbody id="bracketBody"></tbody></table></div></div></div>
+    <div class="section"><div class="card"><h2>Historical Score vs BTC Price</h2><div class="chart-dual"><canvas id="dualChart"></canvas></div></div></div>
+    <div class="section"><div class="card"><h2>Score Bracket Performance</h2><div style="overflow-x:auto"><table><thead><tr><th>Score Range</th><th>Label</th><th>Days</th><th>Avg 30d</th><th>Avg 90d</th><th>Avg 1yr</th><th>Win Rate (1yr)</th><th>Max Gain</th><th>Max Loss</th><th>Avg Max DD</th></tr></thead><tbody id="bracketBody"></tbody></table></div></div></div>
     <div class="section"><div class="card"><h2>Major Signal Events (Score Crossed 70/80/90+)</h2><div id="signalEvents"></div></div></div>
   `;
   renderContext();
@@ -1505,25 +1246,19 @@ function renderContext() {
   document.getElementById('ctxPercentile').textContent =
     'Historical percentile: top ' + (100 - ctx.percentile).toFixed(1) + '% of all days (' + ctx.comparable_days + ' comparable days found)';
   if (ctx.avg_1yr_return != null) {
-    let retHtml = 'Average returns from this score level: ';
-    if (ctx.avg_30d_return != null) retHtml += '<span class="' + retClass(ctx.avg_30d_return) + '">30d: ' + fmtPct(ctx.avg_30d_return) + '</span> · ';
-    if (ctx.avg_90d_return != null) retHtml += '<span class="' + retClass(ctx.avg_90d_return) + '">90d: ' + fmtPct(ctx.avg_90d_return) + '</span> · ';
-    if (ctx.avg_180d_return != null) retHtml += '<span class="' + retClass(ctx.avg_180d_return) + '">180d: ' + fmtPct(ctx.avg_180d_return) + '</span> · ';
-    retHtml += '<span class="' + retClass(ctx.avg_1yr_return) + '"><strong>1yr: ' + fmtPct(ctx.avg_1yr_return) + '</strong></span>';
-    document.getElementById('ctxReturn').innerHTML = retHtml;
+    document.getElementById('ctxReturn').textContent =
+      'Average 1-year return from this score level: ' + fmtPct(ctx.avg_1yr_return);
+    document.getElementById('ctxReturn').className = 'context-return ' + retClass(ctx.avg_1yr_return);
   }
   // Examples
   const list = document.getElementById('ctxComparables');
   if (ctx.examples && ctx.examples.length) {
-    let html = '<h2 style="margin-top:12px">Comparable Historical Periods <span style="color:var(--muted);font-size:.7em;font-weight:normal">(one per market cycle)</span></h2>';
+    let html = '<h2 style="margin-top:12px">Comparable Historical Periods</h2>';
     for (const ex of ctx.examples) {
       const fr = ex.forward_returns || {};
-      const cycleTag = ex.cycle ? '<span style="color:var(--accent);font-size:.75em;opacity:.7;margin-left:6px">[' + ex.cycle + ']</span>' : '';
-      html += '<div class="comparable-item"><span>' + ex.date + ' — Score ' + ex.score + ' — ' + fmtPrice(ex.price) + cycleTag + '</span>';
+      html += '<div class="comparable-item"><span>' + ex.date + ' — Score ' + ex.score + ' — ' + fmtPrice(ex.price) + '</span>';
       html += '<span>';
       if (fr['30d'] != null) html += '<span class="' + retClass(fr['30d']) + '">30d: ' + fmtPct(fr['30d']) + '</span> ';
-      if (fr['90d'] != null) html += '<span class="' + retClass(fr['90d']) + '">90d: ' + fmtPct(fr['90d']) + '</span> ';
-      if (fr['180d'] != null) html += '<span class="' + retClass(fr['180d']) + '">180d: ' + fmtPct(fr['180d']) + '</span> ';
       if (fr['365d'] != null) html += '<span class="' + retClass(fr['365d']) + '">1yr: ' + fmtPct(fr['365d']) + '</span>';
       html += '</span></div>';
     }
@@ -1531,38 +1266,24 @@ function renderContext() {
   }
 }
 
-let btDualChart = null;
-let btCurrentRange = 0;
-
-function renderDualChart(days) {
-  if (days === undefined) days = btCurrentRange;
-  btCurrentRange = days;
+function renderDualChart() {
   const chart = backtestData.chart_data;
   if (!chart || !chart.length) return;
-
-  let data = chart;
-  if (days > 0) data = chart.slice(-days);
-
   const ctx = document.getElementById('dualChart').getContext('2d');
-  const labels = data.map(d => d.date);
-  const scores = data.map(d => d.score);
-  const prices = data.map(d => d.price);
-
-  // Use log scale only for ALL or 4Y+ (when price spans multiple orders of magnitude)
-  const priceMin = Math.min(...prices.filter(p => p > 0));
-  const priceMax = Math.max(...prices);
-  const useLog = (priceMax / priceMin) > 20; // >20x range = log makes sense
+  const labels = chart.map(d => d.date);
+  const scores = chart.map(d => d.score);
+  const prices = chart.map(d => d.price);
 
+  // Zone backgrounds via plugin
   const zonePlugin = {
     id: 'zoneBackground',
     beforeDraw(chart) {
-      const { ctx: c, chartArea: {left, right}, scales: {y} } = chart;
+      const { ctx: c, chartArea: {left, right, top, bottom}, scales: {y} } = chart;
       if (!y) return;
       const zones = [
-        { min: 0, max: 35, color: 'rgba(239,68,68,0.06)' },
-        { min: 35, max: 50, color: 'rgba(234,179,8,0.04)' },
-        { min: 50, max: 65, color: 'rgba(234,179,8,0.06)' },
-        { min: 65, max: 100, color: 'rgba(34,197,94,0.08)' },
+        { min: 0, max: 40, color: 'rgba(239,68,68,0.06)' },
+        { min: 40, max: 70, color: 'rgba(234,179,8,0.06)' },
+        { min: 70, max: 100, color: 'rgba(34,197,94,0.08)' },
       ];
       for (const z of zones) {
         const yTop = y.getPixelForValue(Math.min(z.max, 100));
@@ -1570,24 +1291,10 @@ function renderDualChart(days) {
         c.fillStyle = z.color;
         c.fillRect(left, yTop, right - left, yBot - yTop);
       }
-      // Threshold lines
-      [35, 50, 65].forEach(val => {
-        const yPos = y.getPixelForValue(val);
-        c.beginPath();
-        c.setLineDash([4, 4]);
-        c.strokeStyle = 'rgba(255,255,255,0.06)';
-        c.lineWidth = 1;
-        c.moveTo(left, yPos);
-        c.lineTo(right, yPos);
-        c.stroke();
-        c.setLineDash([]);
-      });
     }
   };
 
-  if (btDualChart) btDualChart.destroy();
-
-  btDualChart = new Chart(ctx, {
+  new Chart(ctx, {
     type: 'line',
     plugins: [zonePlugin],
     data: {
@@ -1601,7 +1308,7 @@ function renderDualChart(days) {
           borderWidth: 1.5,
           fill: false,
           tension: 0.2,
-          pointRadius: data.length < 100 ? 2 : 0,
+          pointRadius: 0,
           yAxisID: 'y',
         },
         {
@@ -1621,53 +1328,27 @@ function renderDualChart(days) {
       maintainAspectRatio: false,
       interaction: { mode: 'index', intersect: false },
       plugins: {
-        legend: { labels: { color: '#94a3b8', font: { size: 11, family: 'monospace' } } },
+        legend: { labels: { color: '#94a3b8', font: { size: 11 } } },
         tooltip: {
-          backgroundColor: 'rgba(10,10,15,0.95)',
-          borderColor: 'rgba(255,255,255,0.08)',
-          borderWidth: 1,
-          titleFont: { family: 'monospace', size: 11 },
-          bodyFont: { family: 'monospace', size: 11 },
           callbacks: {
             label: function(ctx) {
               if (ctx.datasetIndex === 1) return 'BTC: $' + Math.round(ctx.parsed.y).toLocaleString();
-              const s = ctx.parsed.y;
-              let zone = s >= 80 ? 'Extreme Accum' : s >= 65 ? 'Strong Accum' : s >= 50 ? 'Moderate' : s >= 35 ? 'Neutral' : 'Caution';
-              return 'Score: ' + s.toFixed(1) + ' (' + zone + ')';
+              return 'Score: ' + ctx.parsed.y;
             }
           }
         }
       },
       scales: {
-        x: { ticks: { color: '#94a3b8', maxTicksLimit: 15, maxRotation: 45, font: { family: 'monospace', size: 10 } }, grid: { color: 'rgba(255,255,255,0.03)' } },
-        y: { position: 'left', min: 0, max: 100, ticks: { color: '#f7931a', font: { family: 'monospace', size: 10 } }, grid: { color: 'rgba(255,255,255,0.03)' },
-          title: { display: true, text: 'Score (0-100)', color: '#f7931a', font: { family: 'monospace', size: 11 } } },
-        y1: {
-          position: 'right',
-          type: useLog ? 'logarithmic' : 'linear',
-          ticks: {
-            color: '#22d3ee',
-            font: { family: 'monospace', size: 10 },
-            callback: v => '$' + (v >= 1000 ? (v/1000).toFixed(v >= 10000 ? 0 : 1) + 'k' : v.toLocaleString())
-          },
-          grid: { drawOnChartArea: false },
-          title: { display: true, text: useLog ? 'BTC Price (log)' : 'BTC Price', color: '#22d3ee', font: { family: 'monospace', size: 11 } }
-        }
+        x: { ticks: { color: '#94a3b8', maxTicksLimit: 20, maxRotation: 45 }, grid: { color: '#1e293b' } },
+        y: { position: 'left', min: 0, max: 100, ticks: { color: '#f7931a' }, grid: { color: '#1e293b' },
+          title: { display: true, text: 'Score (0-100)', color: '#f7931a' } },
+        y1: { position: 'right', type: 'logarithmic', ticks: { color: '#22d3ee', callback: v => '$' + v.toLocaleString() },
+          grid: { drawOnChartArea: false }, title: { display: true, text: 'BTC Price (log)', color: '#22d3ee' } }
       }
     }
   });
 }
 
-// Backtest range button handlers
-document.addEventListener('click', function(e) {
-  if (e.target.closest('#btRangeBar .range-btn')) {
-    const btn = e.target.closest('.range-btn');
-    document.querySelectorAll('#btRangeBar .range-btn').forEach(b => b.classList.remove('active'));
-    btn.classList.add('active');
-    renderDualChart(parseInt(btn.dataset.days));
-  }
-});
-
 function renderBracketTable() {
   const brackets = backtestData.bracket_stats;
   if (!brackets) return;
@@ -1681,7 +1362,6 @@ function renderBracketTable() {
     html += '<td>' + b.days + '</td>';
     html += '<td class="' + retClass(b.avg_30d) + '">' + fmtPct(b.avg_30d) + '</td>';
     html += '<td class="' + retClass(b.avg_90d) + '">' + fmtPct(b.avg_90d) + '</td>';
-    html += '<td class="' + retClass(b.avg_180d) + '">' + fmtPct(b.avg_180d) + '</td>';
     html += '<td class="' + retClass(b.avg_365d) + '">' + fmtPct(b.avg_365d) + '</td>';
     html += '<td>' + (b.win_rate_365d != null ? b.win_rate_365d + '%' : '--') + '</td>';
     html += '<td class="t-green">' + fmtPct(b.max_gain_365d) + '</td>';
@@ -1710,7 +1390,6 @@ function renderSignalEvents() {
     const fr = ev.forward_returns || {};
     if (fr['30d'] != null) html += '<span class="' + retClass(fr['30d']) + '">30d: ' + fmtPct(fr['30d']) + '</span>';
     if (fr['90d'] != null) html += '<span class="' + retClass(fr['90d']) + '">90d: ' + fmtPct(fr['90d']) + '</span>';
-    if (fr['180d'] != null) html += '<span class="' + retClass(fr['180d']) + '">180d: ' + fmtPct(fr['180d']) + '</span>';
     if (fr['365d'] != null) html += '<span class="' + retClass(fr['365d']) + '">1yr: ' + fmtPct(fr['365d']) + '</span>';
     if (ev.price_365d) html += '<span style="color:var(--text-dim)">Price 1yr: ' + fmtPrice(ev.price_365d) + '</span>';
     html += '</div></div>';

data/score_history.jsonl

@@ -2,111 +2,3 @@
 {"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}}}
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-{"timestamp": "2026-03-21T22:53:45.530567+00:00", "composite_score": 71.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 10, "value": 12}, "puell_multiple": {"score": 8, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 8, "value": 0.5211180167687892}, "drawdown": {"score": 8, "value": 44.26713197969543}, "price_vs_200w_sma": {"score": 7, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 8, "value": 0.22243290955405431}, "lth_realized_price": {"score": 5, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
-{"timestamp": "2026-03-21T22:54:22.144542+00:00", "composite_score": 70.0, "scored_count": 9, "metrics": {"fear_greed": {"score": 10, "value": 12}, "puell_multiple": {"score": 8, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 8, "value": 0.5211180167687892}, "drawdown": {"score": null, "value": null}, "price_vs_200w_sma": {"score": 7, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 8, "value": 0.22243290955405431}, "lth_realized_price": {"score": 5, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
-{"timestamp": "2026-03-21T22:55:08.385540+00:00", "composite_score": 71.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 10, "value": 12}, "puell_multiple": {"score": 8, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 8, "value": 0.5211180167687892}, "drawdown": {"score": 8, "value": 44.26554568527919}, "price_vs_200w_sma": {"score": 7, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 8, "value": 0.22243290955405431}, "lth_realized_price": {"score": 5, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
-{"timestamp": "2026-03-21T22:55:33.933753+00:00", "composite_score": 71.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 10, "value": 12}, "puell_multiple": {"score": 8, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 8, "value": 0.5211180167687892}, "drawdown": {"score": 8, "value": 44.26316624365482}, "price_vs_200w_sma": {"score": 7, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 8, "value": 0.22243290955405431}, "lth_realized_price": {"score": 5, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}

ml/optimizer.py

@@ -1,562 +0,0 @@
-#!/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
-
-# 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
-
-
-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 columns: scores + raw values + deltas + interactions + cycle position
-    score_features = [
-        "score_puell_multiple", "score_mvrv_zscore", "score_reserve_risk",
-        "score_rhodl_ratio", "score_nupl", "score_fear_greed",
-        "score_drawdown", "score_pct_above_200w_sma", "score_pct_above_lth_rp",
-    ]
-    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
-
-    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])
-
-    # Time-series cross-validation (expanding window, 5 splits)
-    tscv = TimeSeriesSplit(n_splits=5)
-    cv_scores = []
-    cv_f1 = []
-    cv_precision = []
-    cv_recall = []
-
-    for fold, (train_idx, val_idx) in enumerate(tscv.split(X)):
-        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 = GradientBoostingClassifier(
-            n_estimators=300,
-            learning_rate=0.05,
-            max_depth=4,
-            subsample=0.8,
-            min_samples_leaf=20,
-            random_state=42,
-        )
-        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)
-
-        train_dates = f"{labeled[train_idx[0]]['date']} to {labeled[train_idx[-1]]['date']}"
-        val_dates = f"{labeled[val_idx[0]]['date']} to {labeled[val_idx[-1]]['date']}"
-        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("CV Mean AUC: %.3f (+/- %.3f)", np.mean(cv_scores), np.std(cv_scores))
-    log.info("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 = GradientBoostingClassifier(
-        n_estimators=300,
-        learning_rate=0.05,
-        max_depth=4,
-        subsample=0.8,
-        min_samples_leaf=20,
-        random_state=42,
-    )
-    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)
-
-    # Extract optimal weights by aggregating importance per metric
-    # Map each feature back to its parent metric
-    metric_names = [
-        "puell_multiple", "mvrv_zscore", "reserve_risk", "rhodl_ratio",
-        "nupl", "fear_greed", "drawdown", "pct_above_200w_sma", "pct_above_lth_rp",
-    ]
-    feature_to_metric = {}
-    for m in metric_names:
-        feature_to_metric[f"score_{m}"] = m
-        feature_to_metric[f"raw_{m}"] = m
-    # Delta features map to their base metric
-    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"
-    # Interaction terms split evenly between constituent metrics
-    # mvrv_x_nupl -> mvrv_zscore + nupl
-    # puell_x_reserve -> puell_multiple + reserve_risk
-
-    metric_importances = {m: 0.0 for m in metric_names}
-    for name, imp in feat_imp:
-        if name in feature_to_metric:
-            metric_importances[feature_to_metric[name]] += imp
-        elif name == "mvrv_x_nupl":
-            metric_importances["mvrv_zscore"] += imp / 2
-            metric_importances["nupl"] += imp / 2
-        elif name == "puell_x_reserve":
-            metric_importances["puell_multiple"] += imp / 2
-            metric_importances["reserve_risk"] += imp / 2
-        # days_since_ath maps to drawdown conceptually
-        elif name == "days_since_ath":
-            metric_importances["drawdown"] += imp
-
-    # Normalize weights to sum to 1
-    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}
-
-    # Sort by weight descending
-    weights = dict(sorted(weights.items(), key=lambda x: x[1], reverse=True))
-
-    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)
-
-    # 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),
-        },
-        "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,
-        "trained_at": datetime.now(tz=__import__('datetime').timezone.utc).isoformat(),
-    }
-
-    return result
-
-
-def run_comparison(rows, ml_weights):
-    """Compare ML-weighted scoring vs equal-weight scoring across score brackets."""
-    # Metrics used in scoring (maps to score_* columns)
-    score_keys = [
-        "puell_multiple", "mvrv_zscore", "reserve_risk", "rhodl_ratio",
-        "nupl", "fear_greed", "drawdown", "pct_above_200w_sma", "pct_above_lth_rp",
-    ]
-    n_metrics = len(score_keys)
-    equal_weight = 1.0 / n_metrics
-
-    brackets = [
-        (0, 20, "Extreme Caution"),
-        (21, 40, "Caution"),
-        (41, 55, "Neutral"),
-        (56, 70, "Moderate Opportunity"),
-        (71, 85, "Strong Accumulation"),
-        (86, 100, "Extreme Accumulation"),
-    ]
-
-    # Only use rows with forward returns
-    scored_rows = [r for r in rows if "fwd_365d" in r]
-
-    results = {"equal_weight": [], "ml_weighted": []}
-
-    for mode in ["equal_weight", "ml_weighted"]:
-        for r in scored_rows:
-            scores = [r[f"score_{k}"] for k in score_keys]
-            if mode == "equal_weight":
-                composite = sum(scores) / n_metrics * 10
-            else:
-                weighted_sum = sum(r[f"score_{k}"] * ml_weights.get(k, equal_weight) for k in score_keys)
-                composite = weighted_sum * 10
-            r[f"composite_{mode}"] = composite
-
-        for low, high, label in brackets:
-            days_in = [r for r in scored_rows if low <= r[f"composite_{mode}"] <= high]
-            if not days_in:
-                results[mode].append({
-                    "range": f"{low}-{high}", "label": label,
-                    "days": 0, "avg_365d": None,
-                })
-                continue
-            returns_365 = [r["fwd_365d"] for r in days_in]
-            win_rate = len([r for r in returns_365 if r > 0]) / len(returns_365) * 100
-            results[mode].append({
-                "range": f"{low}-{high}",
-                "label": label,
-                "days": len(days_in),
-                "avg_365d": round(sum(returns_365) / len(returns_365), 2),
-                "median_365d": round(sorted(returns_365)[len(returns_365) // 2], 2),
-                "win_rate_365d": round(win_rate, 1),
-            })
-
-    # Print comparison
-    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)
-
-    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

@@ -75,8 +75,7 @@ def score_puell_multiple(value, thresholds=None):
     if value is None:
         return None, "No data"
     t = (thresholds or load_thresholds()).get("puell_multiple", {})
-    # 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]])
+    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]])
     score = _score_range(value, ranges)
 
     if value < 0.3:
@@ -98,17 +97,15 @@ def score_mvrv_zscore(value, thresholds=None):
     if value is None:
         return None, "No data"
     t = (thresholds or load_thresholds()).get("mvrv_zscore", {})
-    # 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]])
+    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]])
     score = _score_range(value, ranges)
 
     if value < 0:
         desc = "Below realized value — historically perfect buy zone"
-    elif value < 1.0:
-        desc = "Near realized value — strong accumulation zone"
-    elif value < 2.0:
-        desc = "Fair value — decent entry territory"
+    elif value < 0.5:
+        desc = "Near realized value — strong accumulation"
+    elif value < 1.5:
+        desc = "Fair value range"
     elif value < 3:
         desc = "Above fair value"
     elif value < 5:
@@ -147,16 +144,15 @@ 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", {})
-    # 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]])
+    ranges = t.get("ranges", [[None, 0, 10], [0, 20, 6], [20, 50, 3], [50, 100, 1], [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 < 30:
-        desc = f"{pct_above:.0f}% above 200W SMA — strong value"
-    elif pct_above < 60:
-        desc = f"{pct_above:.0f}% above 200W SMA — fair value"
+    elif pct_above < 20:
+        desc = f"{pct_above:.0f}% above 200W SMA — good value"
+    elif pct_above < 50:
+        desc = f"{pct_above:.0f}% above 200W SMA — moderate"
     elif pct_above < 100:
         desc = f"{pct_above:.0f}% above 200W SMA — extended"
     else:
@@ -208,15 +204,13 @@ def score_nupl(value, thresholds=None):
     if value is None:
         return None, "No data"
     t = (thresholds or load_thresholds()).get("nupl", {})
-    # 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]])
+    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]])
     score = _score_range(value, ranges)
 
     if value < 0:
         desc = "Capitulation — holders underwater"
-    elif value < 0.3:
-        desc = "Hope/Fear — early recovery, good accumulation"
+    elif value < 0.25:
+        desc = "Hope/Fear — early recovery"
     elif value < 0.5:
         desc = "Optimism — moderate profit taking"
     elif value < 0.75:
@@ -232,18 +226,14 @@ 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", {})
-    # 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]])
+    ranges = t.get("ranges", [[None, 0, 10], [0, 20, 6], [20, 50, 3], [50, 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 < 30:
-        desc = f"{pct_above:.0f}% above LTH cost basis — strong value"
-    elif pct_above < 80:
-        desc = f"{pct_above:.0f}% above LTH cost basis — fair value"
-    elif pct_above < 150:
+    elif pct_above < 20:
+        desc = f"{pct_above:.0f}% above LTH cost basis — good value"
+    elif pct_above < 50:
         desc = f"{pct_above:.0f}% above LTH cost basis — moderate"
     else:
         desc = f"{pct_above:.0f}% above LTH cost basis — extended"
@@ -407,16 +397,14 @@ def score_all(metrics):
     else:
         composite = 0
 
-    # Assessment text — calibrated for cycle-aware scoring
-    if composite >= 80:
-        assessment = "EXTREME ACCUMULATION ZONE"
-    elif composite >= 65:
+    # Assessment text
+    if composite >= 71:
         assessment = "STRONG ACCUMULATION ZONE"
-    elif composite >= 50:
+    elif composite >= 51:
         assessment = "MODERATE OPPORTUNITY"
-    elif composite >= 35:
+    elif composite >= 31:
         assessment = "NEUTRAL"
-    elif composite >= 20:
+    elif composite >= 15:
         assessment = "CAUTION — OVERHEATED"
     else:
         assessment = "EXTREME CAUTION"
@@ -428,104 +416,3 @@ 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 ML-weighted composite
-    weighted_sum = 0.0
-    weight_total = 0.0
-
-    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
-            w = 0.01
-        else:
-            w = ml_weights.get(ml_key, 0.0)
-
-        m["ml_weight"] = round(w, 4)
-        m["ml_contribution"] = round(m["score"] * w * 10, 2)
-        weighted_sum += m["score"] * w
-        weight_total += w
-
-    # Normalize if weights don't sum to 1 (e.g., missing metrics)
-    if weight_total > 0:
-        composite = weighted_sum / weight_total * 10
-    else:
-        composite = 0
-
-    # 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"],
-    }

scrapers/history_updater.py

@@ -1,112 +0,0 @@
-"""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