chore: persist Mac Mini Docker deployment

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

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

.dockerignore

@@ -0,0 +1,11 @@
+.git
+.gitea
+.github
+.venv
+.playwright
+__pycache__
+*.py[cod]
+*.log
+.env
+results
+screenshots

.gitea/workflows/ci.yml

@@ -0,0 +1,28 @@
+name: CI
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.11.6"
+          enable-cache: true
+
+      - name: Validate lockfile and install dependencies
+        run: uv sync --locked --group runtime --group ml --group dev
+
+      - name: Compile Python sources
+        run: uv run --frozen python -m compileall -q dashboard scrapers scoring backtesting ml ml_engine llm_client scripts orchestrator.py
+
+      - name: Run tests
+        run: uv run --frozen pytest

.gitignore

@@ -1,7 +1,13 @@
 __pycache__/
 *.pyc
+.venv/
+.playwright/
+.pytest_cache/
 data/cache.json
 data/history.json
+data/score_history.jsonl
+data/jobs.json
+data/*.lock
 config/llm_settings.json
 results/
 *.log

Dockerfile

@@ -0,0 +1,31 @@
+# syntax=docker/dockerfile:1.7
+FROM ghcr.io/astral-sh/uv:0.11.6 AS uv
+FROM python:3.13.5-slim-bookworm
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    PLAYWRIGHT_BROWSERS_PATH=/ms-playwright \
+    UV_COMPILE_BYTECODE=1 \
+    UV_LINK_MODE=copy
+
+COPY --from=uv /uv /uvx /bin/
+
+WORKDIR /app
+
+COPY pyproject.toml uv.lock ./
+RUN uv sync --frozen --no-install-project --no-dev --group runtime --group ml \
+    && uv run --frozen --no-dev --group runtime --group ml \
+       playwright install --with-deps chromium \
+    && chmod -R a+rX /ms-playwright
+
+COPY --chown=10001:10001 . .
+RUN mkdir -p /app/data /app/config \
+    && chown -R 10001:10001 /app/data /app/config
+
+USER 10001:10001
+EXPOSE 3088
+
+HEALTHCHECK --interval=30s --timeout=5s --start-period=30s --retries=3 \
+  CMD ["/app/.venv/bin/python", "-c", "import urllib.request; urllib.request.urlopen('http://127.0.0.1:3088/health/live', timeout=3)"]
+
+CMD ["/app/.venv/bin/python", "-m", "uvicorn", "dashboard.server:app", "--host", "0.0.0.0", "--port", "3088"]

README.md

@@ -1,134 +1,268 @@
 # Bitcoin Accumulation Zone Monitor
 
-> On-chain metrics dashboard with historical backtesting for long-term BTC holders. No ML, no black box — pure signal monitoring from proven indicators.
+> Bitcoin on-chain metrics dashboard with classic equal-weight scoring, ML-optimized scoring, historical backtesting, and click-to-select metric context for long-term BTC accumulation decisions.
 
 ![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.
+Monitors Bitcoin accumulation conditions using 16 scored market/on-chain indicators plus optional informational cycle metrics. Each scored metric receives a 0-10 score and rolls into a 0-100 accumulation score.
 
-**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.
+The dashboard now supports two scoring modes:
+
+- **Classic** — transparent equal-weight scoring across every active metric.
+- **ML** — feature-importance weights trained against historical 365-day forward returns, with displayed per-metric weights and point contributions.
+
+Historical backtests show score-vs-price behavior, score bracket performance, major signal events, and current-score context. Metric cards are clickable: selecting a metric overlays its historical series on the score chart and shows comparable historical periods with forward returns.
 
 ## Screenshots
 
-### Main Dashboard
+### Main Dashboard — ML mode + metric context
 ![Main Dashboard](screenshots/dashboard-main.png)
-*Live accumulation score with all 10 metrics, current BTC price, and individual metric breakdowns*
+*Live BTC price, Classic/ML scoring toggle, 16 active scored metrics, ML weights/contributions, metric sparklines, and click-to-select historical context.*
 
 ### Historical Backtest
 ![Backtest](screenshots/dashboard-backtest.png)
-*Historical score vs BTC price overlay, score bracket performance table, and major signal events*
+*Current signal percentile, comparable historical periods by cycle, score-vs-BTC chart, bracket performance, and major signal events.*
 
 ### Settings
 ![Settings](screenshots/dashboard-settings.png)
-*LLM provider configuration for optional AI-powered signal commentary*
+*LLM provider configuration for optional AI-powered signal commentary and local/cloud model selection.*
+
+## Feature Highlights
+
+- **16 scored metrics** from market sentiment, miner stress, valuation, holder behavior, network activity, and velocity signals.
+- **Classic vs ML scoring toggle** on the dashboard and backtest API.
+- **ML score explainability**: metric cards show learned weight and contribution in points.
+- **Leakage-resistant ML validation**: training uses purged time-series splits so 365-day forward-return labels do not overlap validation windows.
+- **Historical context panel**: compares the current composite score against historical periods and forward returns.
+- **Clickable metric cards**: select any metric to see percentile, similar historical levels, forward returns, example dates by market cycle, and highlighted chart periods.
+- **Score history chart** with BTC price overlay, range controls, and selected-metric overlay.
+- **Backtest dashboard** with current signal context, score bracket performance, and signal-crossing events.
+- **Quick vs full refresh**: quick refresh updates BTC price and Fear & Greed; full refresh re-scrapes on-chain sources.
+- **LLM settings UI** for Ollama, LM Studio, OpenAI, Anthropic, and OpenRouter.
 
 ## Metrics
 
 | # | Metric | Source | Accumulation Signal |
 |---|--------|--------|-------------------|
-| 1 | Fear & Greed Index | alternative.me API | Extreme Fear (< 10) |
+| 1 | Fear & Greed Index | alternative.me API | Extreme fear / capitulation sentiment |
-| 2 | Puell Multiple | LookIntoBitcoin (scraped) | Miner capitulation (< 0.5) |
+| 2 | Puell Multiple | LookIntoBitcoin | Miner revenue stress |
-| 3 | MVRV Z-Score | LookIntoBitcoin (scraped) | Below realized value (< 0) |
+| 3 | MVRV Z-Score | LookIntoBitcoin | Market near/below realized value |
-| 4 | Drawdown from ATH | Calculated | Deep correction (> 50%) |
+| 4 | Drawdown from ATH | Calculated from BTC price | Deep correction from cycle high |
-| 5 | Price vs 200W SMA | LookIntoBitcoin (scraped) | Below 200-week average |
+| 5 | Price vs 200W SMA | LookIntoBitcoin + BTC price | Price near/below long-term trend |
-| 6 | Reserve Risk | LookIntoBitcoin (scraped) | High holder confidence (< 0.002) |
+| 6 | Reserve Risk | LookIntoBitcoin | High holder confidence relative to price |
-| 7 | RHODL Ratio | LookIntoBitcoin (scraped) | Long-term holder dominance (< 100) |
+| 7 | RHODL Ratio | LookIntoBitcoin | Long-term holder dominance |
-| 8 | NUPL | LookIntoBitcoin (scraped) | Market capitulation (< 0) |
+| 8 | Net Unrealized Profit/Loss (NUPL) | LookIntoBitcoin | Capitulation / early recovery zones |
-| 9 | LTH Realized Price | LookIntoBitcoin (scraped) | Price below LTH cost basis |
+| 9 | LTH Realized Price | LookIntoBitcoin | Price near long-term holder cost basis |
-| 10 | Hash Ribbons | LookIntoBitcoin (scraped) | Miner capitulation recovery |
+| 10 | Hash Ribbons | LookIntoBitcoin | Miner capitulation/recovery signal |
+| 11 | SOPR | CheckOnChain | Spent outputs near loss / reset territory |
+| 12 | Sell-side Risk Ratio | CheckOnChain | Low realized profit/loss pressure |
+| 13 | Active Address Momentum | CheckOnChain | Network activity momentum extremes |
+| 14 | Transaction Count Momentum | CheckOnChain | Transaction activity momentum extremes |
+| 15 | NVT Price | CheckOnChain | Network-value valuation discount/premium |
+| 16 | VDD Multiple | CheckOnChain | Coin-days/velocity reset conditions |
+
+Informational cards may also appear when data is available, such as **Long-Term Holder Supply** and **Pi Cycle Bottom**. These are displayed for context and are not included in the composite score.
 
 ## Score Interpretation
 
-| Score | Assessment | Historical Outcome |
+| Score | Assessment | Interpretation |
-|-------|-----------|-------------------|
+|-------|-----------|----------------|
-| 85-100 | 🟢 Extreme Accumulation | Rare (~4x per decade). Historically: 200%+ 1yr returns |
+| 80-100 | 🟢 Extreme Accumulation Zone | Broad capitulation/value conditions across active metrics |
-| 70-84 | 🟢 Strong Accumulation | Excellent long-term entry point |
+| 65-79 | 🟢 Strong Accumulation Zone | Historically attractive long-term entry territory |
-| 55-69 | 🟡 Moderate Opportunity | Decent entry, DCA appropriate |
+| 50-64 | 🟡 Moderate Opportunity | DCA-friendly, but not maximum-signal conditions |
-| 40-54 | 🟡 Neutral | Hold — not compelling either way |
+| 35-49 | 🟡 Neutral | Mixed signals; not compelling either direction |
-| 25-39 | 🔴 Caution | Market heating up |
+| 20-34 | 🔴 Caution — Overheated | Market conditions becoming less favorable |
-| 0-24 | 🔴 Extreme Caution | Historically worst times to buy |
+| 0-19 | 🔴 Extreme Caution | Historically poor accumulation setup |
+
+Backtest tables provide actual historical forward-return statistics per score bracket, including 30d/90d/180d/1yr averages, win rate, max gain/loss, and average max drawdown.
+
+## ML-Optimized Scoring
+
+The ML mode uses a `GradientBoostingClassifier` trained on historical feature rows to predict whether a day was a good long-term buy based on 365-day forward return. Training features include:
+
+- Classic metric scores.
+- Raw metric values.
+- 30-day metric deltas.
+- Interaction features such as MVRV × NUPL and Puell × Reserve Risk.
+- Cycle-position context such as days since ATH.
+
+The resulting feature importances are aggregated back into transparent metric weights stored in `config/ml_weights.json`. The UI displays normalized weight and contribution for each active metric.
+
+Validation uses purged expanding time-series splits: because each label uses a 365-day forward-return window, training rows whose label windows overlap validation are removed before scoring validation folds.
 
 ## Tech Stack
 
 | Component | Technology |
 |-----------|-----------|
 | Backend | Python 3.13 + FastAPI |
-| Frontend | Inline HTML/CSS/JS (dark trading terminal theme) |
+| Frontend | Inline HTML/CSS/JS dark trading-terminal UI |
 | Charts | Chart.js |
-| Scraping | Playwright (headless Chromium) |
+| Scraping | requests + Playwright-style browser scraping where needed |
-| Data APIs | alternative.me (F&G), CoinGecko (price) |
+| Data APIs | alternative.me, CoinGecko, LookIntoBitcoin, CheckOnChain |
-| Process Manager | pm2 |
+| ML | NumPy + pandas + scikit-learn GradientBoostingClassifier |
-| Port | 3088 |
+| Process Manager | pm2 or uvicorn |
+| Default Port | 3088 |
 
 ## How Data Is Collected
 
-All data is scraped from free, public sources — **no API keys required**.
+Data is collected from free/public sources and cached locally under `data/`.
 
-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.
+- Fast live refreshes update BTC price, ATH/drawdown, 200D SMA/Mayer where possible, and Fear & Greed.
+- On-chain metrics are cached and reused because they update slowly.
+- Full refresh re-scrapes on-chain metrics from LookIntoBitcoin/CheckOnChain.
+- Historical backtest data lives in `data/history.json` and supports charting, backtests, and metric-context lookups.
+- Score history appends to `data/score_history.jsonl`.
 
 ## Project Structure
 
 ```
 ├── dashboard/
-│   └── server.py              # FastAPI server + inline dashboard HTML
+│   └── server.py                  # FastAPI server + inline dashboard/backtest/settings UI
 ├── scrapers/
-│   ├── lookintobitcoin.py     # Playwright scraper for on-chain charts
+│   ├── lookintobitcoin.py         # LookIntoBitcoin metric scraping
+│   ├── checkonchain.py            # CheckOnChain metric scraping
 │   ├── history_collector.py       # Full historical data collection
+│   ├── history_updater.py         # Incremental historical updates
 │   ├── fear_greed.py              # Fear & Greed Index API
-│   └── price.py               # BTC price API
+│   └── price.py                   # BTC price, ATH, drawdown, SMA helpers
 ├── scoring/
-│   └── engine.py              # Metric scoring logic (0-10 per metric)
+│   └── engine.py                  # Classic + ML-weighted scoring logic
 ├── backtesting/
 │   └── engine.py                  # Historical backtest engine
+├── ml/
+│   └── optimizer.py               # ML training, purged CV, weight export
+├── tests/
+│   ├── test_ml_optimizer_validation.py
+│   └── test_scoring_engine_ml.py
 ├── data/
-│   ├── cache.json             # Live metric cache (auto-generated)
+│   ├── cache.json                 # Live metric cache
-│   └── history.json           # Historical data (auto-generated)
+│   ├── history.json               # Historical metric/time-series data
+│   └── score_history.jsonl        # Live score history
 ├── config/
-│   └── thresholds.json        # Scoring thresholds (customizable)
+│   ├── thresholds.json            # Classic scoring thresholds
-├── screenshots/               # Dashboard screenshots
+│   ├── ml_weights.json            # Learned ML metric weights
-├── ARCHITECTURE.md            # Detailed architecture & scoring logic
+│   └── llm_settings.json          # Optional AI commentary provider config
+├── screenshots/                   # README screenshots
+├── scripts/run.sh                 # Locked local launcher with Playwright path
+├── .gitea/workflows/ci.yml        # Gitea Actions test/compile gates
+├── Dockerfile                     # Non-root Chromium-enabled image
+├── docker-compose.yml             # Port, healthcheck, restart, persistent volumes
+├── pyproject.toml                 # Runtime, ML, and development dependency groups
+├── uv.lock                        # Exact reproducible dependency resolution
+├── ARCHITECTURE.md
 └── README.md
 ```
 
+## Reproducible Setup
+
+Install [uv](https://docs.astral.sh/uv/) and use Python 3.11-3.13. Dependencies are declared in explicit `runtime`, `ml`, and `dev` groups in `pyproject.toml`; exact cross-platform resolutions are committed in `uv.lock`.
+
+```bash
+git clone <repository-url>
+cd btc-accumulation-monitor
+uv sync --locked --group runtime --group ml --group dev
+```
+
+Install the Chromium binary once for full on-chain refreshes. Keep its path explicit so installation and runtime use the same browser cache:
+
+```bash
+export PLAYWRIGHT_BROWSERS_PATH="$PWD/.playwright"
+uv run --frozen playwright install chromium
+```
+
+`requirements_vps.txt` is a lock-derived, hash-pinned compatibility export for pip-based hosts. `pyproject.toml` and `uv.lock` remain authoritative; regenerate the compatibility file after dependency changes with:
+
+```bash
+uv export --frozen --no-dev --group runtime --group ml \
+  --no-emit-project --no-header --output-file requirements_vps.txt
+```
+
 ## Running
 
+The executable launcher fixes `PYTHONPATH`, preserves an explicitly supplied `PLAYWRIGHT_BROWSERS_PATH`, and starts port 3088 from the locked environment:
+
 ```bash
-# Install dependencies
+./scripts/run.sh
-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
+Equivalent exact command:
-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
+```bash
+PLAYWRIGHT_BROWSERS_PATH="$PWD/.playwright" PYTHONPATH=. \
+  uv run --frozen --no-dev --group runtime --group ml \
+  python -m uvicorn dashboard.server:app --host 0.0.0.0 --port 3088
+```
 
-The backtest engine reconstructs the composite score for every historical day and compares against actual BTC forward returns.
+Then visit `http://localhost:3088`.
 
-**Key feature: Recency weighting** — Bitcoin's cycle returns diminish over time (100x → 30x → 8x → 3-4x). The backtest weights recent cycles more heavily:
+## Container Deployment
-- 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.
+The image uses a multi-architecture Python base, installs Playwright Chromium and its OS libraries during the build, and runs the application as non-root UID `10001`. Compose publishes port 3088, restarts unless stopped, and persists `/app/data` and `/app/config` in named volumes.
+
+```bash
+docker compose build
+docker compose up -d
+```
+
+The Docker and Compose healthchecks probe `GET /health/live`. The deployment must include the reliability revision that supplies that endpoint; without it, Docker correctly reports the container unhealthy even if the older application server is accepting requests.
+
+Named volumes are initialized from the image on first use. Back up both before replacing or deleting them:
+
+```bash
+docker volume inspect btc-accumulation-monitor_btc-monitor-data
+docker volume inspect btc-accumulation-monitor_btc-monitor-config
+```
+
+For bind-mounted deployments, ensure the host directories are writable by UID `10001` and do not replace `config/` with an empty directory.
+
+## First Run and Data Freshness
+
+1. Visit `http://localhost:3088` for the dashboard.
+2. Use **Quick Refresh** for price and Fear & Greed updates while retaining cached slow-moving on-chain metrics.
+3. Use **Full Refresh** when on-chain source data must be re-scraped; this requires the installed Playwright Chromium browser and external source availability.
+4. Visit `http://localhost:3088/backtest` for historical analysis.
+5. If historical data is missing, populate `data/history.json` through the existing collection flow.
+
+Freshness is metric-specific. Price and sentiment APIs can update frequently, while public on-chain chart sources commonly update daily and may be reused from cache. A successful refresh is not proof that every upstream metric has a new observation. Check source timestamps/status exposed by the running revision, and treat missing, stale, or scrape-failed metrics as unavailable rather than silently current. `data/` is operational state and should be persisted and backed up.
+
+## ML and Backtest Caveats
+
+ML weights and backtest output are research artifacts, not investment advice or evidence of future performance. Any reported ML result must retain its provenance: source-data snapshot/range, feature and label definitions, training window, purge/embargo policy, code revision, dependency lock, random seed (when applicable), and generated weight/config artifact.
+
+Model selection and threshold tuning must use training/validation data only. Report final performance on a genuinely untouched out-of-sample (OOS) period; do not describe in-sample fit, cross-validation used for selection, or the best result from repeated experiments as OOS. Forward-return labels require purging overlapping label horizons, but purged cross-validation alone does not create an untouched final test set. Results without reproducible provenance and a reserved OOS evaluation should be labeled exploratory.
+
+## Useful API Endpoints
+
+| Endpoint | Description |
+|----------|-------------|
+| `GET /api/data?mode=classic` | Current metrics using equal-weight scoring |
+| `GET /api/data?mode=ml` | Current metrics using ML-optimized weights |
+| `GET /api/history` | Recent live score history |
+| `POST /api/refresh` | Quick refresh |
+| `POST /api/refresh?full=true` | Full on-chain refresh |
+| `GET /api/backtest?mode=classic` | Historical backtest with classic scoring |
+| `GET /api/backtest?mode=ml` | Historical backtest with ML scoring |
+| `GET /api/metric-context?metric=mvrv_zscore&mode=ml` | Similar historical levels and forward returns for one metric |
+| `GET /api/settings` | Safe LLM settings payload |
+
+## Testing and CI
+
+Run the committed test suite and the same static compilation gate used by Gitea Actions:
+
+```bash
+uv sync --locked --group runtime --group ml --group dev
+uv run --frozen python -m compileall -q \
+  dashboard scrapers scoring backtesting ml ml_engine llm_client scripts orchestrator.py
+uv run --frozen pytest
+```
+
+`.gitea/workflows/ci.yml` runs lock validation/install, static compilation, and tests for pull requests and pushes to `main`.
 
 ## Architecture
 
-See [ARCHITECTURE.md](ARCHITECTURE.md) for detailed documentation of every metric's scoring logic, data pipeline, and backtest methodology.
+See [ARCHITECTURE.md](ARCHITECTURE.md) for deeper implementation details on scoring, data collection, and backtesting.
 
 ## License
 

backtesting/engine.py

@@ -1,12 +1,18 @@
 """Historical backtest engine for Bitcoin Accumulation Zone scoring."""
 
+import copy
 import json
 import logging
 import os
 import sys
+import threading
 from collections import defaultdict
 from datetime import datetime, timedelta
 
+from scoring.policy import SCORE_BRACKETS, SCORE_VERSION, score_in_bracket
+from ml.artifacts import validate_ml_artifact
+from backtesting.statistics import summarize_returns
+
 log = logging.getLogger(__name__)
 
 BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
@@ -14,16 +20,14 @@ sys.path.insert(0, BASE_DIR)
 
 HISTORY_PATH = os.path.join(BASE_DIR, "data", "history.json")
 CACHE_PATH = os.path.join(BASE_DIR, "data", "cache.json")
+ML_WEIGHTS_PATH = os.path.join(BASE_DIR, "config", "ml_weights.json")
+
+_BACKTEST_CACHE = {}
+_BACKTEST_CACHE_LOCK = threading.Lock()
+_BACKTEST_CACHE_LIMIT = 4
 
 # Score brackets matching the dashboard assessment levels
-BRACKETS = [
+BRACKETS = SCORE_BRACKETS
-    (0, 20, "Extreme Caution"),
-    (21, 40, "Caution"),
-    (41, 55, "Neutral"),
-    (56, 70, "Moderate Opportunity"),
-    (71, 85, "Strong Accumulation"),
-    (86, 100, "Extreme Accumulation"),
-]
 
 # Scoring thresholds — load from config/thresholds.json (single source of truth)
 import os as _os
@@ -58,6 +62,22 @@ RATIO_SCORERS = {
     },
 }
 
+BACKTEST_METRIC_PANEL = tuple(METRIC_SCORERS) + tuple(RATIO_SCORERS) + ("drawdown",)
+METRIC_MAX_AGE_DAYS = {
+    "fear_greed": 2,
+    "puell_multiple": 7,
+    "mvrv_zscore": 7,
+    "reserve_risk": 7,
+    "rhodl_ratio": 7,
+    "nupl": 7,
+    "btc_price": 3,
+    "btc_price_coingecko": 3,
+    "btc_price_sma": 3,
+    "btc_price_lth": 3,
+    "200w_sma": 7,
+    "lth_realized_price": 7,
+}
+
 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]])
 
 
@@ -94,8 +114,8 @@ def _get_all_dates(index):
     return sorted(all_dates)
 
 
-def _last_known_value(lookup, date, max_lookback=30):
+def _last_known_value(lookup, date, max_lookback=0):
-    """Get value for date, or most recent prior value within lookback window."""
+    """Get value for date, or a prior value within an explicit lookback."""
     if date in lookup:
         return lookup[date]
     d = datetime.strptime(date, "%Y-%m-%d")
@@ -106,6 +126,17 @@ def _last_known_value(lookup, date, max_lookback=30):
     return None
 
 
+def _metric_observation(lookup, date, metric_key):
+    """Return value, source date, and age under a metric-specific freshness rule."""
+    max_age = METRIC_MAX_AGE_DAYS.get(metric_key, 0)
+    target = datetime.strptime(date, "%Y-%m-%d")
+    for age in range(max_age + 1):
+        source_date = (target - timedelta(days=age)).strftime("%Y-%m-%d")
+        if source_date in lookup:
+            return lookup[source_date], source_date, age
+    return None, None, None
+
+
 def _compute_ath_series(price_lookup, dates):
     """Compute running ATH and drawdown for each date."""
     ath = 0
@@ -121,15 +152,70 @@ def _compute_ath_series(price_lookup, dates):
     return drawdowns
 
 
-def _load_ml_weights():
+def _load_ml_artifact():
-    """Load ML weights for ML-optimized scoring mode."""
+    """Load an ML artifact and return it with validation status."""
     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", {})
+        status = validate_ml_artifact(data)
-    except Exception:
+        if not status["valid"]:
-        return {}
+            log.error("Rejected invalid ML artifact: %s", ", ".join(status["errors"]))
+            return None, status
+        return data, status
+    except Exception as exc:
+        return None, {"valid": False, "errors": [f"load_error:{exc}"]}
+
+
+def _build_ml_backtest_plan(artifact):
+    """Choose OOS fold weights when available; otherwise mark final weights in-sample."""
+    status = validate_ml_artifact(artifact)
+    if not status["valid"]:
+        raise ValueError("invalid ML artifact: " + ", ".join(status["errors"]))
+
+    if status["has_oos_fold_weights"]:
+        folds = []
+        for fold in artifact["cv_results"]["folds"]:
+            start, separator, end = fold["date_ranges"]["validation"].partition(" to ")
+            if not separator:
+                raise ValueError("invalid validation date range")
+            folds.append({
+                "fold": fold.get("fold"),
+                "start": start,
+                "end": end,
+                "weights": fold["weights"],
+            })
+        return {
+            "evaluation_scope": "out_of_sample_validation_folds",
+            "is_out_of_sample": True,
+            "weighting_source": "fold_specific_weights",
+            "folds": folds,
+            "weights": None,
+        }
+
+    return {
+        "evaluation_scope": "in_sample_full_history_weights",
+        "is_out_of_sample": False,
+        "weighting_source": "final_full_history_weights",
+        "folds": [],
+        "weights": artifact["weights"],
+    }
+
+
+def _weights_for_backtest_date(date, plan):
+    """Return date-appropriate weights and fold number for an ML plan."""
+    if plan["is_out_of_sample"]:
+        for fold in plan["folds"]:
+            if fold["start"] <= date <= fold["end"]:
+                return fold["weights"], fold["fold"]
+        return None, None
+    return plan["weights"], None
+
+
+def _load_ml_weights():
+    """Compatibility helper returning valid final weights only."""
+    artifact, _ = _load_ml_artifact()
+    return artifact.get("weights", {}) if artifact else {}
 
 # ML weight key mapping (backtest metric keys -> ML weight keys)
 _BT_ML_KEY_MAP = {
@@ -145,39 +231,110 @@ _BT_ML_KEY_MAP = {
 }
 
 
+def _common_panel_current_score(scored, ml_weights=None):
+    """Recompute the current score using only metrics present historically."""
+    by_key = {
+        metric.get("key"): metric.get("score")
+        for metric in scored.get("metrics", [])
+        if metric.get("key") in BACKTEST_METRIC_PANEL and metric.get("score") is not None
+    }
+    available_keys = [key for key in BACKTEST_METRIC_PANEL if key in by_key]
+    coverage = {
+        "available_count": len(available_keys),
+        "panel_count": len(BACKTEST_METRIC_PANEL),
+        "available_keys": available_keys,
+    }
+    if not available_keys:
+        return None, coverage
+
+    if ml_weights:
+        weighted = [
+            (by_key[key], ml_weights.get(_BT_ML_KEY_MAP[key], 0.0))
+            for key in available_keys
+        ]
+        weight_total = sum(weight for _, weight in weighted)
+        if weight_total > 0:
+            return round(sum(score * weight for score, weight in weighted) / weight_total * 10, 1), coverage
+
+    return round(sum(by_key[key] for key in available_keys) / len(available_keys) * 10, 1), coverage
+
+
+def _backtest_data_quality_metadata(metric_counts):
+    """Describe historical panel, coverage, and freshness assumptions."""
+    coverage = {
+        "minimum_metrics": min(metric_counts),
+        "maximum_metrics": max(metric_counts),
+        "average_metrics": round(sum(metric_counts) / len(metric_counts), 1),
+        "panel_count": len(BACKTEST_METRIC_PANEL),
+    } if metric_counts else {
+        "minimum_metrics": 0,
+        "maximum_metrics": 0,
+        "average_metrics": 0,
+        "panel_count": len(BACKTEST_METRIC_PANEL),
+    }
+    return {
+        "metric_panel": {
+            "id": "historical-common-v1",
+            "keys": list(BACKTEST_METRIC_PANEL),
+            "count": len(BACKTEST_METRIC_PANEL),
+        },
+        "coverage": coverage,
+        "staleness_days": dict(METRIC_MAX_AGE_DAYS),
+    }
+
+
 def score_day(date, index, drawdowns, ml_weights=None):
-    """Score a single day using all available metrics. Returns (composite_score, individual_scores, n_metrics).
+    """Score a single day using all available metrics. Returns (composite_score, details, n_metrics).
 
     If ml_weights is provided, uses ML-optimized weighting instead of equal weights.
+    details includes both "score" and "raw" (the actual metric value before scoring).
     """
     scores = []
     details = {}
 
     # Simple range-based metrics
     for metric_key, cfg in METRIC_SCORERS.items():
-        val = _last_known_value(index.get(metric_key, {}), date)
+        val, observed_date, age_days = _metric_observation(
+            index.get(metric_key, {}), date, metric_key
+        )
         if val is not None:
             s = _score_range(val, cfg["ranges"])
             if s is not None:
                 scores.append(s)
-                details[metric_key] = {"value": val, "score": s}
+                details[metric_key] = {
+                    "value": val,
+                    "score": s,
+                    "raw": val,
+                    "observed_date": observed_date,
+                    "age_days": age_days,
+                }
 
     # Ratio-based metrics (price vs reference)
     for metric_key, cfg in RATIO_SCORERS.items():
-        price_val = _last_known_value(index.get(cfg["price_key"], {}), date)
+        price_val, price_date, price_age = _metric_observation(
-        # Try alternate price keys
+            index.get(cfg["price_key"], {}), date, cfg["price_key"]
+        )
+        # Try alternate price keys, each with an explicit freshness rule.
         if price_val is None:
             for pk in ["btc_price_coingecko", "btc_price_sma", "btc_price_lth"]:
-                price_val = _last_known_value(index.get(pk, {}), date)
+                price_val, price_date, price_age = _metric_observation(index.get(pk, {}), date, pk)
                 if price_val is not None:
                     break
-        ref_val = _last_known_value(index.get(cfg["ref_key"], {}), date)
+        ref_val, ref_date, ref_age = _metric_observation(
+            index.get(cfg["ref_key"], {}), date, cfg["ref_key"]
+        )
         if price_val is not None and ref_val is not None and ref_val > 0:
             pct_above = ((price_val - ref_val) / ref_val) * 100
             s = _score_range(pct_above, cfg["ranges"])
             if s is not None:
                 scores.append(s)
-                details[metric_key] = {"value": pct_above, "score": s}
+                details[metric_key] = {
+                    "value": pct_above,
+                    "score": s,
+                    "raw": pct_above,
+                    "observed_date": min(price_date, ref_date),
+                    "age_days": max(price_age, ref_age),
+                }
 
     # Drawdown
     dd = drawdowns.get(date)
@@ -185,7 +342,7 @@ def score_day(date, index, drawdowns, ml_weights=None):
         s = _score_range(dd, DRAWDOWN_RANGES)
         if s is not None:
             scores.append(s)
-            details["drawdown"] = {"value": dd, "score": s}
+            details["drawdown"] = {"value": dd, "score": s, "raw": dd}
 
     if not scores:
         return None, details, 0
@@ -249,7 +406,62 @@ def compute_max_drawdown_forward(price_lookup, date, window=90):
     return round(max_dd, 2) if max_dd > 0 else 0
 
 
+def _file_signature(path):
+    """Return a cheap signature that invalidates when an input file changes."""
+    try:
+        stat = os.stat(path)
+        return path, stat.st_mtime_ns, stat.st_size
+    except OSError:
+        return path, None, None
+
+
+def clear_backtest_cache():
+    """Clear memoized backtest results (primarily for explicit refreshes/tests)."""
+    with _BACKTEST_CACHE_LOCK:
+        _BACKTEST_CACHE.clear()
+
+
+def _add_return_statistics(stats, period, returns):
+    """Add return summaries and a moving-block-bootstrap mean interval."""
+    horizon_days = int(period.removesuffix("d"))
+    summary = summarize_returns(
+        returns,
+        block_size=min(horizon_days, len(returns)),
+        n_resamples=400,
+    )
+    stats[f"avg_{period}"] = summary["mean"]
+    stats[f"median_{period}"] = summary["median"]
+    stats[f"win_rate_{period}"] = summary["win_rate"]
+    stats[f"avg_{period}_ci_low"] = summary["mean_ci_low"]
+    stats[f"avg_{period}_ci_high"] = summary["mean_ci_high"]
+    stats[f"max_gain_{period}"] = round(max(returns), 2)
+    stats[f"max_loss_{period}"] = round(min(returns), 2)
+    stats[f"n_{period}"] = summary["n"]
+
+
 def run_backtest(ml_mode=False):
+    """Return an isolated cached result keyed by all material input files."""
+    signature = (
+        bool(ml_mode),
+        _file_signature(HISTORY_PATH),
+        _file_signature(_THRESH_PATH),
+        _file_signature(ML_WEIGHTS_PATH),
+        _file_signature(CACHE_PATH),
+    )
+    with _BACKTEST_CACHE_LOCK:
+        cached = _BACKTEST_CACHE.get(signature)
+    if cached is not None:
+        return copy.deepcopy(cached)
+
+    result = _compute_backtest(ml_mode=ml_mode)
+    with _BACKTEST_CACHE_LOCK:
+        _BACKTEST_CACHE[signature] = copy.deepcopy(result)
+        while len(_BACKTEST_CACHE) > _BACKTEST_CACHE_LIMIT:
+            _BACKTEST_CACHE.pop(next(iter(_BACKTEST_CACHE)))
+    return copy.deepcopy(result)
+
+
+def _compute_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.
@@ -284,26 +496,47 @@ 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
+    # Build an explicit evaluation plan. Fold-specific validation weights are OOS;
-    ml_weights = _load_ml_weights() if ml_mode else None
+    # final weights fitted on full history are never represented as OOS.
-    if ml_mode and not ml_weights:
+    ml_plan = None
-        log.warning("ML mode requested but no weights found — falling back to equal weights")
+    ml_artifact = None
-        ml_weights = None
+    ml_artifact_status = None
+    if ml_mode:
+        ml_artifact, ml_artifact_status = _load_ml_artifact()
+        if ml_artifact:
+            ml_plan = _build_ml_backtest_plan(ml_artifact)
+        else:
+            log.warning("ML mode requested with invalid artifact — falling back to equal weights")
 
     # Score each day
     log.info("Scoring %d days...", len(all_dates))
     daily_scores = []
     for d in all_dates:
+        ml_weights = None
+        ml_fold = None
+        if ml_plan:
+            ml_weights, ml_fold = _weights_for_backtest_date(d, ml_plan)
+            if ml_plan["is_out_of_sample"] and ml_weights is None:
+                continue
         composite, details, n_metrics = score_day(d, index, drawdowns, ml_weights=ml_weights)
         if composite is not None and n_metrics >= 3:  # Require at least 3 metrics
             price = price_lookup.get(d)
+            # Collect raw metric values for per-metric historical exploration
+            metric_values = {}
+            for mk, info in details.items():
+                raw = info.get("raw")
+                if raw is not None:
+                    metric_values[mk] = round(raw, 6) if isinstance(raw, float) else raw
             entry = {
                 "date": d,
                 "score": composite,
                 "n_metrics": n_metrics,
                 "price": price,
                 "forward_returns": fwd_returns.get(d, {}),
+                "metric_values": metric_values,
             }
+            if ml_fold is not None:
+                entry["ml_fold"] = ml_fold
             daily_scores.append(entry)
 
     if not daily_scores:
@@ -314,7 +547,7 @@ def run_backtest(ml_mode=False):
     # --- Bracket statistics ---
     bracket_stats = []
     for low, high, label in BRACKETS:
-        days_in = [d for d in daily_scores if low <= d["score"] <= high]
+        days_in = [d for d in daily_scores if score_in_bracket(d["score"], (low, high, label))]
         if not days_in:
             bracket_stats.append({
                 "range": f"{low}-{high}", "label": label, "days": 0,
@@ -325,13 +558,7 @@ def run_backtest(ml_mode=False):
         for period in ["30d", "90d", "180d", "365d"]:
             returns = [d["forward_returns"][period] for d in days_in if period in d["forward_returns"]]
             if returns:
-                returns_sorted = sorted(returns)
+                _add_return_statistics(stats, period, returns)
-                stats[f"avg_{period}"] = round(sum(returns) / len(returns), 2)
-                stats[f"median_{period}"] = round(returns_sorted[len(returns_sorted) // 2], 2)
-                stats[f"win_rate_{period}"] = round(len([r for r in returns if r > 0]) / len(returns) * 100, 1)
-                stats[f"max_gain_{period}"] = round(max(returns), 2)
-                stats[f"max_loss_{period}"] = round(min(returns), 2)
-                stats[f"n_{period}"] = len(returns)
 
         # Average max drawdown within 90 days
         dd_list = []
@@ -375,23 +602,30 @@ def run_backtest(ml_mode=False):
     all_scores_list = [d["score"] for d in daily_scores]
     all_scores_list.sort()
 
-    # Get current score from cache
+    # Get current score from cache, recomputed on the common historical panel.
     current_score = None
     current_price = None
+    current_coverage = None
     if os.path.exists(CACHE_PATH):
         try:
             with open(CACHE_PATH) as f:
                 cache = json.load(f)
             scored = cache.get("_scored", {})
-            current_score = scored.get("composite_score")
+            current_ml_weights = ml_artifact.get("weights") if ml_mode and ml_artifact else None
+            current_score, current_coverage = _common_panel_current_score(scored, current_ml_weights)
             current_price = cache.get("price", {}).get("price")
         except Exception:
             pass
 
-    # If no cache, use latest daily score
+    # If no comparable cache panel is available, use latest historical score.
     if current_score is None and daily_scores:
         current_score = daily_scores[-1]["score"]
         current_price = daily_scores[-1].get("price")
+        current_coverage = {
+            "available_count": daily_scores[-1]["n_metrics"],
+            "panel_count": len(BACKTEST_METRIC_PANEL),
+            "available_keys": list(daily_scores[-1].get("metric_values", {})),
+        }
 
     current_context = None
     if current_score is not None:
@@ -451,6 +685,12 @@ def run_backtest(ml_mode=False):
         current_context = {
             "current_score": current_score,
             "current_price": current_price,
+            "score_version": SCORE_VERSION,
+            "metric_panel_id": "historical-common-v1",
+            "coverage": current_coverage,
+            "current_weighting_source": (
+                "final_full_history_weights" if ml_mode and ml_artifact else "equal_weight"
+            ),
             "percentile": percentile,
             "comparable_days": len(comparable),
             "avg_1yr_return": avg_1yr,
@@ -462,6 +702,7 @@ def run_backtest(ml_mode=False):
 
     # --- Build time series for charting ---
     # Smart downsampling: daily for last 2 years, weekly before that
+    # Include per-metric values so the frontend can plot any metric.
     chart_data = []
     import datetime as _dt
     try:
@@ -469,23 +710,61 @@ def run_backtest(ml_mode=False):
         cutoff_date = (last_date - _dt.timedelta(days=730)).strftime("%Y-%m-%d")
     except Exception:
         cutoff_date = "2024-01-01"
+
+    # Collect all metric keys that were ever scored (for per-metric series)
+    all_metric_keys = set()
+    for d in daily_scores:
+        all_metric_keys.update(d.get("metric_values", {}).keys())
+
     for i, d in enumerate(daily_scores):
         is_recent = d["date"] >= cutoff_date
         if is_recent or i % 7 == 0 or i == len(daily_scores) - 1:
-            chart_data.append({
+            entry = {
                 "date": d["date"],
                 "score": d["score"],
                 "price": d["price"],
-            })
+            }
+            # Include per-metric values (raw metric value, not score)
+            metric_vals = d.get("metric_values", {})
+            if metric_vals:
+                entry["metric_values"] = metric_vals
+            chart_data.append(entry)
 
+    if not ml_mode:
+        ml_evaluation = {"requested": False, "is_out_of_sample": False}
+    elif ml_plan:
+        ml_evaluation = {
+            "requested": True,
+            "evaluation_scope": ml_plan["evaluation_scope"],
+            "is_out_of_sample": ml_plan["is_out_of_sample"],
+            "weighting_source": ml_plan["weighting_source"],
+            "folds": len(ml_plan["folds"]),
+            "artifact": ml_artifact_status,
+        }
+    else:
+        ml_evaluation = {
+            "requested": True,
+            "evaluation_scope": "equal_weight_fallback",
+            "is_out_of_sample": False,
+            "weighting_source": "none_invalid_artifact",
+            "folds": 0,
+            "artifact": ml_artifact_status,
+        }
+
+    data_quality = _backtest_data_quality_metadata([day["n_metrics"] for day in daily_scores])
     result = {
         "date_range": {"start": daily_scores[0]["date"], "end": daily_scores[-1]["date"]},
         "total_days_scored": len(daily_scores),
+        "metric_panel": data_quality["metric_panel"],
+        "coverage": data_quality["coverage"],
+        "staleness_days": data_quality["staleness_days"],
         "bracket_stats": bracket_stats,
         "signal_events": signal_events,
         "current_context": current_context,
         "chart_data": chart_data,
         "ml_mode": ml_mode,
+        "ml_evaluation": ml_evaluation,
+        "score_version": SCORE_VERSION,
         "computed_at": datetime.utcnow().isoformat() + "Z",
     }
 

backtesting/statistics.py

@@ -0,0 +1,85 @@
+"""Statistical helpers for honest time-series backtest reporting."""
+
+from __future__ import annotations
+
+import math
+import random
+import statistics as stdlib_statistics
+from collections.abc import Iterable
+
+
+def _quantile(sorted_values: list[float], probability: float) -> float:
+    position = (len(sorted_values) - 1) * probability
+    lower = math.floor(position)
+    upper = math.ceil(position)
+    if lower == upper:
+        return sorted_values[lower]
+    fraction = position - lower
+    return sorted_values[lower] * (1 - fraction) + sorted_values[upper] * fraction
+
+
+def moving_block_bootstrap_ci(
+    values: Iterable[float],
+    *,
+    block_size: int = 30,
+    n_resamples: int = 1_000,
+    confidence: float = 0.95,
+    seed: int = 42,
+) -> dict[str, float | int]:
+    """Estimate a mean and CI while preserving local serial dependence."""
+    series = [float(value) for value in values]
+    if not series:
+        raise ValueError("values must not be empty")
+    if block_size < 1 or block_size > len(series):
+        raise ValueError("block_size must be between 1 and the number of values")
+    if n_resamples < 2:
+        raise ValueError("n_resamples must be at least 2")
+    if not 0 < confidence < 1:
+        raise ValueError("confidence must be between 0 and 1")
+
+    rng = random.Random(seed)
+    sample_means: list[float] = []
+    final_start = len(series) - block_size
+    for _ in range(n_resamples):
+        sample: list[float] = []
+        while len(sample) < len(series):
+            start = rng.randint(0, final_start)
+            sample.extend(series[start:start + block_size])
+        sample = sample[:len(series)]
+        sample_means.append(sum(sample) / len(sample))
+
+    sample_means.sort()
+    tail = (1 - confidence) / 2
+    return {
+        "estimate": sum(series) / len(series),
+        "ci_low": _quantile(sample_means, tail),
+        "ci_high": _quantile(sample_means, 1 - tail),
+        "n": len(series),
+    }
+
+
+def summarize_returns(
+    values: Iterable[float],
+    *,
+    block_size: int = 30,
+    n_resamples: int = 1_000,
+    confidence: float = 0.95,
+    seed: int = 42,
+) -> dict[str, float | int]:
+    """Summarize realized returns with an autocorrelation-aware mean CI."""
+    series = [float(value) for value in values]
+    interval = moving_block_bootstrap_ci(
+        series,
+        block_size=min(block_size, len(series)),
+        n_resamples=n_resamples,
+        confidence=confidence,
+        seed=seed,
+    )
+    return {
+        "n": len(series),
+        "mean": round(float(interval["estimate"]), 2),
+        "median": round(stdlib_statistics.median(series), 2),
+        "win_rate": round(sum(value > 0 for value in series) / len(series) * 100, 1),
+        "mean_ci_low": round(float(interval["ci_low"]), 2),
+        "mean_ci_high": round(float(interval["ci_high"]), 2),
+    }

config/best_config.json

@@ -27,6 +27,11 @@
       0.3,
       0.5
     ],
+    "return_scales_pct": [
+      10.0,
+      30.0,
+      60.0
+    ],
     "score_range": [
       0,
       100
@@ -61,7 +66,7 @@
     "rolling_test_size": 300,
     "walk_forward_windows": 5,
     "train_pct": 0.7,
-    "validation_pct": 0.15,
+    "validation_pct": 0.3,
     "test_pct": 0.15
   },
   "timeframe": "4h"

config/current_config.json

@@ -27,6 +27,11 @@
       0.3,
       0.5
     ],
+    "return_scales_pct": [
+      10.0,
+      30.0,
+      60.0
+    ],
     "score_range": [
       0,
       100
@@ -61,7 +66,7 @@
     "rolling_test_size": 300,
     "walk_forward_windows": 5,
     "train_pct": 0.7,
-    "validation_pct": 0.15,
+    "validation_pct": 0.3,
     "test_pct": 0.15
   },
   "timeframe": "4h"

config/initial_config.json

@@ -27,6 +27,11 @@
       0.3,
       0.5
     ],
+    "return_scales_pct": [
+      10.0,
+      30.0,
+      60.0
+    ],
     "score_range": [
       0,
       100
@@ -61,7 +66,7 @@
     "rolling_test_size": 300,
     "walk_forward_windows": 5,
     "train_pct": 0.7,
-    "validation_pct": 0.15,
+    "validation_pct": 0.3,
     "test_pct": 0.15
   },
   "timeframe": "4h"

config/llm_settings.example.json

@@ -0,0 +1,21 @@
+{
+  "provider": "ollama",
+  "model": "qwen3.5:27b",
+  "providers": {
+    "ollama": {
+      "base_url": "http://127.0.0.1:11434"
+    },
+    "lmstudio": {
+      "base_url": "http://127.0.0.1:1234"
+    },
+    "openai": {
+      "api_key": ""
+    },
+    "anthropic": {
+      "api_key": ""
+    },
+    "openrouter": {
+      "api_key": ""
+    }
+  }
+}

config/llm_settings.json

@@ -1,21 +0,0 @@
-{
-  "provider": "openrouter",
-  "model": "minimax/minimax-m2.5",
-  "providers": {
-    "ollama": {
-      "base_url": "http://100.100.242.21:11434"
-    },
-    "lmstudio": {
-      "base_url": "http://100.100.242.21:1234"
-    },
-    "openai": {
-      "api_key": ""
-    },
-    "anthropic": {
-      "api_key": ""
-    },
-    "openrouter": {
-      "api_key": "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"
-    }
-  }
-}

config/ml_weights.json

@@ -1,159 +1,383 @@
 {
+  "artifact_schema_version": 2,
+  "score_version": "accumulation-score-v2",
   "weights": {
     "pct_above_200w_sma": 0.5075,
-    "drawdown": 0.1459,
+    "drawdown": 0.1716,
-    "pct_above_lth_rp": 0.1095,
+    "pct_above_lth_rp": 0.0934,
-    "rhodl_ratio": 0.089,
+    "rhodl_ratio": 0.08,
-    "fear_greed": 0.0515,
+    "fear_greed": 0.0459,
-    "reserve_risk": 0.046,
+    "puell_multiple": 0.0384,
-    "puell_multiple": 0.0255,
+    "reserve_risk": 0.0335,
-    "mvrv_zscore": 0.0182,
+    "mvrv_zscore": 0.018,
-    "nupl": 0.0068
+    "nupl": 0.0116
   },
   "feature_importances": {
-    "raw_pct_above_200w_sma": 0.436377,
+    "raw_pct_above_200w_sma": 0.448165,
-    "days_since_ath": 0.119405,
+    "days_since_ath": 0.138053,
-    "raw_pct_above_lth_rp": 0.109451,
+    "raw_pct_above_lth_rp": 0.093426,
-    "raw_rhodl_ratio": 0.088999,
+    "raw_rhodl_ratio": 0.080038,
-    "score_pct_above_200w_sma": 0.071148,
+    "score_pct_above_200w_sma": 0.059336,
-    "raw_fear_greed": 0.051475,
+    "raw_fear_greed": 0.04573,
-    "puell_x_reserve": 0.032886,
+    "puell_x_reserve": 0.034997,
-    "raw_drawdown": 0.026474,
+    "raw_drawdown": 0.033552,
-    "raw_reserve_risk": 0.021707,
+    "raw_puell_multiple": 0.02023,
-    "raw_mvrv_zscore": 0.012429,
+    "raw_reserve_risk": 0.012477,
-    "raw_puell_multiple": 0.008599,
+    "raw_mvrv_zscore": 0.011366,
-    "delta_30d_reserve_risk": 0.007865,
+    "mvrv_x_nupl": 0.008204,
-    "delta_30d_mvrv_zscore": 0.004263,
+    "raw_nupl": 0.003843,
-    "raw_nupl": 0.003271,
+    "delta_30d_nupl": 0.003624,
-    "mvrv_x_nupl": 0.002979,
+    "delta_30d_reserve_risk": 0.003541,
-    "delta_30d_nupl": 0.002056,
+    "delta_30d_mvrv_zscore": 0.002548,
-    "delta_30d_puell_multiple": 0.000473,
+    "delta_30d_puell_multiple": 0.000677,
-    "score_fear_greed": 6.8e-05,
+    "score_fear_greed": 0.000182,
-    "score_mvrv_zscore": 5.4e-05,
+    "score_rhodl_ratio": 1.1e-05,
-    "score_puell_multiple": 1e-05,
+    "score_puell_multiple": 0.0,
-    "score_pct_above_lth_rp": 6e-06,
+    "score_mvrv_zscore": 0.0,
-    "score_rhodl_ratio": 2e-06,
     "score_reserve_risk": 0.0,
     "score_nupl": 0.0,
-    "score_drawdown": 0.0
+    "score_drawdown": 0.0,
+    "score_pct_above_lth_rp": 0.0
   },
   "cv_results": {
-    "mean_auc": 0.6164,
+    "mean_auc": 0.7667,
-    "std_auc": 0.3317,
+    "std_auc": 0.1734,
-    "mean_f1": 0.6736,
+    "mean_f1": 0.4085,
-    "mean_precision": 0.8015,
+    "mean_precision": 0.3708,
-    "mean_recall": 0.7047
+    "mean_recall": 0.4898,
+    "validation_method": "purged_expanding_window",
+    "label_horizon_days": 365,
+    "folds": [
+      {
+        "fold": 1,
+        "weights": {
+          "pct_above_lth_rp": 0.7418,
+          "drawdown": 0.1523,
+          "reserve_risk": 0.0339,
+          "mvrv_zscore": 0.0211,
+          "puell_multiple": 0.0164,
+          "rhodl_ratio": 0.0141,
+          "nupl": 0.0128,
+          "pct_above_200w_sma": 0.0043,
+          "fear_greed": 0.0033
+        },
+        "metrics": {
+          "auc": 0.7583,
+          "f1": 0.0,
+          "precision": 0.0,
+          "recall": 0.0
+        },
+        "date_ranges": {
+          "train": "2018-02-01 to 2019-08-05",
+          "validation": "2020-08-04 to 2021-10-31"
+        },
+        "n_train": 548,
+        "n_validation": 454
+      },
+      {
+        "fold": 2,
+        "weights": {
+          "reserve_risk": 0.4125,
+          "puell_multiple": 0.3942,
+          "drawdown": 0.085,
+          "pct_above_lth_rp": 0.0429,
+          "pct_above_200w_sma": 0.0324,
+          "rhodl_ratio": 0.0135,
+          "nupl": 0.0131,
+          "mvrv_zscore": 0.0053,
+          "fear_greed": 0.001
+        },
+        "metrics": {
+          "auc": 0.8346,
+          "f1": 0.6582,
+          "precision": 0.4906,
+          "recall": 1.0
+        },
+        "date_ranges": {
+          "train": "2018-02-01 to 2020-11-01",
+          "validation": "2021-11-01 to 2023-01-28"
+        },
+        "n_train": 1002,
+        "n_validation": 454
+      },
+      {
+        "fold": 3,
+        "weights": {
+          "reserve_risk": 0.2919,
+          "puell_multiple": 0.2495,
+          "drawdown": 0.1619,
+          "pct_above_lth_rp": 0.1028,
+          "pct_above_200w_sma": 0.089,
+          "fear_greed": 0.0754,
+          "nupl": 0.0103,
+          "mvrv_zscore": 0.0099,
+          "rhodl_ratio": 0.0091
+        },
+        "metrics": {
+          "auc": 0.9755,
+          "f1": 0.9757,
+          "precision": 0.9926,
+          "recall": 0.9593
+        },
+        "date_ranges": {
+          "train": "2018-02-01 to 2022-01-29",
+          "validation": "2023-01-29 to 2024-04-26"
+        },
+        "n_train": 1456,
+        "n_validation": 454
+      },
+      {
+        "fold": 4,
+        "weights": {
+          "drawdown": 0.6216,
+          "reserve_risk": 0.1302,
+          "puell_multiple": 0.1161,
+          "fear_greed": 0.0554,
+          "rhodl_ratio": 0.0322,
+          "pct_above_lth_rp": 0.0163,
+          "mvrv_zscore": 0.0117,
+          "pct_above_200w_sma": 0.0094,
+          "nupl": 0.0071
+        },
+        "metrics": {
+          "auc": 0.4983,
+          "f1": 0.0,
+          "precision": 0.0,
+          "recall": 0.0
+        },
+        "date_ranges": {
+          "train": "2018-02-01 to 2023-04-28",
+          "validation": "2024-04-27 to 2025-07-26"
+        },
+        "n_train": 1910,
+        "n_validation": 454
+      }
+    ]
   },
   "training_info": {
-    "n_samples": 2601,
+    "n_samples": 2728,
-    "n_positive": 1553,
+    "n_positive": 1554,
-    "positive_rate": 0.5971,
+    "positive_rate": 0.5696,
     "n_features": 25,
     "target_threshold": 30.0,
-    "date_range": "2018-02-01 to 2025-03-21",
+    "date_range": "2018-02-01 to 2025-07-26",
     "model": "GradientBoostingClassifier"
   },
+  "provenance": {
+    "validation_method": "purged_expanding_window",
+    "label_horizon_days": 365,
+    "weight_scope": "full_history_fit",
+    "training_date_range": {
+      "start": "2018-02-01",
+      "end": "2025-07-26"
+    },
+    "trained_at": "2026-07-26T23:19:01.231759+00:00"
+  },
   "comparison": {
     "equal_weight": [
       {
         "range": "0-20",
-        "label": "Extreme Caution",
+        "label": "EXTREME CAUTION",
-        "days": 295,
+        "days": 286,
-        "avg_365d": -5.94,
+        "avg_365d": -7.25,
-        "median_365d": -11.99,
+        "median_365d": -13.89,
+        "win_rate_365d": 34.3
+      },
+      {
+        "range": "20-35",
+        "label": "CAUTION \u2014 OVERHEATED",
+        "days": 537,
+        "avg_365d": 8.85,
+        "median_365d": -21.49,
         "win_rate_365d": 35.6
       },
       {
-        "range": "21-40",
+        "range": "35-50",
-        "label": "Caution",
+        "label": "NEUTRAL",
-        "days": 587,
+        "days": 660,
-        "avg_365d": 23.84,
+        "avg_365d": 85.33,
-        "median_365d": -7.2,
+        "median_365d": 16.54,
-        "win_rate_365d": 45.3
+        "win_rate_365d": 60.8
       },
       {
-        "range": "41-55",
+        "range": "50-65",
-        "label": "Neutral",
+        "label": "MODERATE OPPORTUNITY",
-        "days": 697,
+        "days": 575,
-        "avg_365d": 108.96,
+        "avg_365d": 113.54,
-        "median_365d": 75.92,
+        "median_365d": 88.64,
-        "win_rate_365d": 70.4
+        "win_rate_365d": 88.5
       },
       {
-        "range": "56-70",
+        "range": "65-80",
-        "label": "Moderate Opportunity",
+        "label": "STRONG ACCUMULATION ZONE",
-        "days": 450,
+        "days": 339,
-        "avg_365d": 128.81,
+        "avg_365d": 183.9,
-        "median_365d": 109.03,
+        "median_365d": 128.5,
-        "win_rate_365d": 96.4
+        "win_rate_365d": 89.7
       },
       {
-        "range": "71-85",
+        "range": "80-100",
-        "label": "Strong Accumulation",
+        "label": "EXTREME ACCUMULATION ZONE",
-        "days": 275,
+        "days": 331,
-        "avg_365d": 175.76,
+        "avg_365d": 120.82,
-        "median_365d": 117.95,
+        "median_365d": 91.85,
-        "win_rate_365d": 86.9
+        "win_rate_365d": 99.7
-      },
-      {
-        "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",
+        "label": "EXTREME CAUTION",
-        "days": 577,
+        "days": 642,
-        "avg_365d": -6.17,
+        "avg_365d": -7.11,
-        "median_365d": -26.21,
+        "median_365d": -26.48,
-        "win_rate_365d": 27.0
+        "win_rate_365d": 25.1
       },
       {
-        "range": "21-40",
+        "range": "20-35",
-        "label": "Caution",
+        "label": "CAUTION \u2014 OVERHEATED",
-        "days": 855,
+        "days": 679,
-        "avg_365d": 77.5,
+        "avg_365d": 18.61,
-        "median_365d": 39.28,
+        "median_365d": 4.67,
-        "win_rate_365d": 72.7
+        "win_rate_365d": 53.2
       },
       {
-        "range": "41-55",
+        "range": "35-50",
-        "label": "Neutral",
+        "label": "NEUTRAL",
-        "days": 241,
+        "days": 462,
-        "avg_365d": 165.77,
+        "avg_365d": 138.2,
-        "median_365d": 124.05,
+        "median_365d": 95.1,
-        "win_rate_365d": 92.5
+        "win_rate_365d": 87.0
       },
       {
-        "range": "56-70",
+        "range": "50-65",
-        "label": "Moderate Opportunity",
+        "label": "MODERATE OPPORTUNITY",
-        "days": 328,
+        "days": 277,
-        "avg_365d": 144.47,
+        "avg_365d": 206.31,
-        "median_365d": 124.27,
+        "median_365d": 163.37,
-        "win_rate_365d": 89.6
+        "win_rate_365d": 87.7
       },
       {
-        "range": "71-85",
+        "range": "65-80",
-        "label": "Strong Accumulation",
+        "label": "STRONG ACCUMULATION ZONE",
-        "days": 201,
+        "days": 285,
-        "avg_365d": 210.2,
+        "avg_365d": 182.42,
-        "median_365d": 122.22,
+        "median_365d": 123.75,
-        "win_rate_365d": 99.0
+        "win_rate_365d": 99.3
       },
       {
-        "range": "86-100",
+        "range": "80-100",
-        "label": "Extreme Accumulation",
+        "label": "EXTREME ACCUMULATION ZONE",
-        "days": 287,
+        "days": 383,
-        "avg_365d": 113.92,
+        "avg_365d": 118.93,
-        "median_365d": 99.53,
+        "median_365d": 119.03,
         "win_rate_365d": 100.0
       }
     ]
   },
-  "trained_at": "2026-03-21T23:15:38.277703+00:00"
+  "out_of_sample_comparison": {
+    "folds": 4,
+    "validation_days": 1816,
+    "equal_weight": [
+      {
+        "range": "0-20",
+        "label": "EXTREME CAUTION",
+        "days": 281,
+        "avg_365d": -6.2,
+        "median_365d": -13.66,
+        "win_rate_365d": 34.9
+      },
+      {
+        "range": "20-35",
+        "label": "CAUTION \u2014 OVERHEATED",
+        "days": 432,
+        "avg_365d": 20.09,
+        "median_365d": -14.6,
+        "win_rate_365d": 43.1
+      },
+      {
+        "range": "35-50",
+        "label": "NEUTRAL",
+        "days": 419,
+        "avg_365d": 71.09,
+        "median_365d": -9.19,
+        "win_rate_365d": 48.9
+      },
+      {
+        "range": "50-65",
+        "label": "MODERATE OPPORTUNITY",
+        "days": 255,
+        "avg_365d": 101.47,
+        "median_365d": 103.63,
+        "win_rate_365d": 76.9
+      },
+      {
+        "range": "65-80",
+        "label": "STRONG ACCUMULATION ZONE",
+        "days": 237,
+        "avg_365d": 100.95,
+        "median_365d": 122.53,
+        "win_rate_365d": 85.2
+      },
+      {
+        "range": "80-100",
+        "label": "EXTREME ACCUMULATION ZONE",
+        "days": 192,
+        "avg_365d": 76.91,
+        "median_365d": 51.03,
+        "win_rate_365d": 99.5
+      }
+    ],
+    "ml_weighted": [
+      {
+        "range": "0-20",
+        "label": "EXTREME CAUTION",
+        "days": 371,
+        "avg_365d": 9.72,
+        "median_365d": -19.7,
+        "win_rate_365d": 31.3
+      },
+      {
+        "range": "20-35",
+        "label": "CAUTION \u2014 OVERHEATED",
+        "days": 372,
+        "avg_365d": 13.85,
+        "median_365d": -10.98,
+        "win_rate_365d": 42.2
+      },
+      {
+        "range": "35-50",
+        "label": "NEUTRAL",
+        "days": 263,
+        "avg_365d": 107.81,
+        "median_365d": 76.78,
+        "win_rate_365d": 59.7
+      },
+      {
+        "range": "50-65",
+        "label": "MODERATE OPPORTUNITY",
+        "days": 348,
+        "avg_365d": 64.05,
+        "median_365d": 99.55,
+        "win_rate_365d": 64.9
+      },
+      {
+        "range": "65-80",
+        "label": "STRONG ACCUMULATION ZONE",
+        "days": 223,
+        "avg_365d": 121.71,
+        "median_365d": 126.27,
+        "win_rate_365d": 93.7
+      },
+      {
+        "range": "80-100",
+        "label": "EXTREME ACCUMULATION ZONE",
+        "days": 239,
+        "avg_365d": 61.69,
+        "median_365d": 42.02,
+        "win_rate_365d": 89.1
+      }
+    ]
+  },
+  "trained_at": "2026-07-26T23:19:01.231759+00:00"
 }

dashboard/jobs.py

@@ -0,0 +1,111 @@
+"""Persistent, thread-safe background job state."""
+
+from __future__ import annotations
+
+import threading
+import uuid
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Callable
+
+from dashboard.persistence import atomic_write_json, load_json
+
+_ACTIVE = {"queued", "running"}
+
+
+def _now() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+class JobRegistry:
+    """Reserve jobs before spawning and persist their lifecycle."""
+
+    def __init__(self, path: str | Path, *, history_limit: int = 100):
+        self.path = Path(path)
+        self.history_limit = history_limit
+        self._lock = threading.RLock()
+        loaded = load_json(self.path, {"jobs": []}) or {"jobs": []}
+        self._jobs = {
+            job["id"]: dict(job)
+            for job in loaded.get("jobs", [])
+            if isinstance(job, dict) and job.get("id")
+        }
+        changed = False
+        for job in self._jobs.values():
+            if job.get("status") in _ACTIVE:
+                job.update(
+                    status="interrupted",
+                    finished_at=_now(),
+                    error="process restarted before job completed",
+                )
+                changed = True
+        if changed:
+            self._save_locked()
+
+    def _save_locked(self) -> None:
+        jobs = sorted(self._jobs.values(), key=lambda job: job.get("created_at", ""))
+        if len(jobs) > self.history_limit:
+            keep = jobs[-self.history_limit :]
+            self._jobs = {job["id"]: job for job in keep}
+            jobs = keep
+        atomic_write_json(self.path, {"jobs": jobs})
+
+    def reserve(self, kind: str, *, details: dict[str, Any] | None = None) -> dict[str, Any] | None:
+        with self._lock:
+            if any(
+                job.get("kind") == kind and job.get("status") in _ACTIVE
+                for job in self._jobs.values()
+            ):
+                return None
+            job = {
+                "id": uuid.uuid4().hex,
+                "kind": kind,
+                "status": "queued",
+                "created_at": _now(),
+                "started_at": None,
+                "finished_at": None,
+                "progress": {},
+                "details": details or {},
+                "result": None,
+                "error": None,
+            }
+            self._jobs[job["id"]] = job
+            self._save_locked()
+            return dict(job)
+
+    def get(self, job_id: str) -> dict[str, Any] | None:
+        with self._lock:
+            job = self._jobs.get(job_id)
+            return dict(job) if job else None
+
+    def active(self, kind: str) -> dict[str, Any] | None:
+        with self._lock:
+            for job in self._jobs.values():
+                if job.get("kind") == kind and job.get("status") in _ACTIVE:
+                    return dict(job)
+        return None
+
+    def update_progress(self, job_id: str, progress: dict[str, Any]) -> None:
+        with self._lock:
+            job = self._jobs[job_id]
+            job["progress"] = dict(progress)
+            self._save_locked()
+
+    def run(self, job_id: str, operation: Callable[[], Any]) -> Any:
+        with self._lock:
+            job = self._jobs[job_id]
+            if job["status"] != "queued":
+                raise RuntimeError(f"job {job_id} is not queued")
+            job.update(status="running", started_at=_now())
+            self._save_locked()
+        try:
+            result = operation()
+        except Exception as exc:
+            with self._lock:
+                job.update(status="error", error=str(exc), finished_at=_now())
+                self._save_locked()
+            raise
+        with self._lock:
+            job.update(status="complete", result=result, finished_at=_now())
+            self._save_locked()
+        return result

dashboard/persistence.py

@@ -0,0 +1,222 @@
+"""Small, dependency-free persistence primitives for dashboard state."""
+
+from __future__ import annotations
+
+import json
+import os
+import tempfile
+import threading
+from contextlib import contextmanager
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Iterator
+
+try:
+    import fcntl
+except ImportError:  # pragma: no cover - Windows fallback uses the process lock
+    fcntl = None
+
+
+_LOCKS: dict[str, threading.RLock] = {}
+_LOCKS_GUARD = threading.Lock()
+_METADATA_KEYS = {"observed_at", "source", "stale", "last_error", "error"}
+
+
+def _thread_lock(path: Path) -> threading.RLock:
+    key = str(path.resolve())
+    with _LOCKS_GUARD:
+        return _LOCKS.setdefault(key, threading.RLock())
+
+
+@contextmanager
+def file_lock(path: str | os.PathLike[str]) -> Iterator[None]:
+    """Serialize readers/writers across threads and, on POSIX, processes."""
+    target = Path(path)
+    target.parent.mkdir(parents=True, exist_ok=True)
+    lock_path = target.with_name(f".{target.name}.lock")
+    with _thread_lock(target):
+        with lock_path.open("a+b") as lock_file:
+            if fcntl is not None:
+                fcntl.flock(lock_file.fileno(), fcntl.LOCK_EX)
+            try:
+                yield
+            finally:
+                if fcntl is not None:
+                    fcntl.flock(lock_file.fileno(), fcntl.LOCK_UN)
+
+
+def atomic_write_json(path: str | os.PathLike[str], data: Any, *, indent: int = 2) -> None:
+    """Durably replace a JSON file without exposing a partial document."""
+    target = Path(path)
+    target.parent.mkdir(parents=True, exist_ok=True)
+    with file_lock(target):
+        fd, temporary = tempfile.mkstemp(
+            prefix=f".{target.name}.", suffix=".tmp", dir=target.parent
+        )
+        try:
+            with os.fdopen(fd, "w", encoding="utf-8") as handle:
+                json.dump(data, handle, indent=indent, default=str)
+                handle.write("\n")
+                handle.flush()
+                os.fsync(handle.fileno())
+            os.replace(temporary, target)
+            try:
+                directory_fd = os.open(target.parent, os.O_DIRECTORY)
+                try:
+                    os.fsync(directory_fd)
+                finally:
+                    os.close(directory_fd)
+            except (AttributeError, OSError):
+                pass
+        finally:
+            try:
+                os.unlink(temporary)
+            except FileNotFoundError:
+                pass
+
+
+def load_json(path: str | os.PathLike[str], default: Any = None) -> Any:
+    target = Path(path)
+    if not target.exists():
+        return default
+    with file_lock(target):
+        try:
+            with target.open(encoding="utf-8") as handle:
+                return json.load(handle)
+        except (OSError, ValueError):
+            return default
+
+
+def _tail_bytes(target: Path, *, line_hint: int, chunk_size: int) -> bytes:
+    with target.open("rb") as handle:
+        handle.seek(0, os.SEEK_END)
+        position = handle.tell()
+        blocks: list[bytes] = []
+        newlines = 0
+        while position > 0 and newlines <= line_hint:
+            size = min(chunk_size, position)
+            position -= size
+            handle.seek(position)
+            block = handle.read(size)
+            blocks.append(block)
+            newlines += block.count(b"\n")
+        return b"".join(reversed(blocks))
+
+
+def load_jsonl_tail(
+    path: str | os.PathLike[str], *, limit: int = 90, chunk_size: int = 8192
+) -> list[dict[str, Any]]:
+    """Read only enough of a JSONL file to return its last valid entries."""
+    if limit <= 0:
+        return []
+    target = Path(path)
+    if not target.exists():
+        return []
+    with file_lock(target):
+        raw = _tail_bytes(target, line_hint=limit + 8, chunk_size=max(chunk_size, 32))
+    entries: list[dict[str, Any]] = []
+    for line in raw.splitlines():
+        try:
+            value = json.loads(line)
+        except (UnicodeDecodeError, ValueError):
+            continue
+        if isinstance(value, dict):
+            entries.append(value)
+    return entries[-limit:]
+
+
+def _utc_day(timestamp: Any) -> str | None:
+    if not isinstance(timestamp, str):
+        return None
+    try:
+        parsed = datetime.fromisoformat(timestamp.replace("Z", "+00:00"))
+        if parsed.tzinfo is None:
+            parsed = parsed.replace(tzinfo=timezone.utc)
+        return parsed.astimezone(timezone.utc).date().isoformat()
+    except ValueError:
+        return None
+
+
+def append_daily_jsonl(path: str | os.PathLike[str], entry: dict[str, Any]) -> bool:
+    """Append at most one record per UTC day, inspecting only the bounded tail."""
+    target = Path(path)
+    target.parent.mkdir(parents=True, exist_ok=True)
+    entry_day = _utc_day(entry.get("timestamp"))
+    if entry_day is None:
+        raise ValueError("entry timestamp must be an ISO-8601 datetime")
+    with file_lock(target):
+        if target.exists():
+            raw = _tail_bytes(target, line_hint=8, chunk_size=4096)
+            for line in reversed(raw.splitlines()):
+                try:
+                    previous = json.loads(line)
+                except (UnicodeDecodeError, ValueError):
+                    continue
+                if _utc_day(previous.get("timestamp")) == entry_day:
+                    return False
+                break
+        payload = (json.dumps(entry, default=str) + "\n").encode("utf-8")
+        fd = os.open(target, os.O_WRONLY | os.O_CREAT | os.O_APPEND, 0o644)
+        try:
+            os.write(fd, payload)
+            os.fsync(fd)
+        finally:
+            os.close(fd)
+    return True
+
+
+def has_observation(payload: Any) -> bool:
+    if not isinstance(payload, dict):
+        return payload is not None
+    return any(value is not None for key, value in payload.items() if key not in _METADATA_KEYS)
+
+
+def merge_observation(
+    previous: Any,
+    observed: Any,
+    *,
+    source: str,
+    observed_at: str | None = None,
+    error: str | None = None,
+) -> dict[str, Any]:
+    """Annotate a fresh observation or retain the last-known-good value as stale."""
+    if has_observation(observed):
+        merged = dict(observed) if isinstance(observed, dict) else {"value": observed}
+        merged.update(
+            observed_at=observed_at or datetime.now(timezone.utc).isoformat(),
+            source=source,
+            stale=False,
+            last_error=None,
+        )
+        return merged
+
+    merged = dict(previous) if isinstance(previous, dict) else {}
+    observed_error = observed.get("error") if isinstance(observed, dict) else None
+    merged.update(
+        source=merged.get("source") or source,
+        stale=True,
+        last_error=observed_error or error or "metric was not observed",
+    )
+    merged.setdefault("observed_at", None)
+    return merged
+
+
+def onchain_refresh_due(
+    timestamp: Any,
+    *,
+    now: datetime | None = None,
+    ttl_seconds: int = 6 * 60 * 60,
+) -> bool:
+    """Return whether the last successful on-chain observation exceeded its TTL."""
+    if not isinstance(timestamp, str) or not timestamp:
+        return True
+    try:
+        observed = datetime.fromisoformat(timestamp.replace("Z", "+00:00"))
+        if observed.tzinfo is None:
+            observed = observed.replace(tzinfo=timezone.utc)
+    except ValueError:
+        return True
+    current = now or datetime.now(timezone.utc)
+    if current.tzinfo is None:
+        current = current.replace(tzinfo=timezone.utc)
+    return (current.astimezone(timezone.utc) - observed.astimezone(timezone.utc)).total_seconds() >= ttl_seconds

data/score_history.jsonl

@@ -1,112 +0,0 @@
-{"timestamp": "2026-03-20T22:26:50.475811+00:00", "composite_score": 32.5, "scored_count": 8, "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": 43.891180203045685}, "price_vs_200w_sma": {"score": null, "value": 0.0}, "reserve_risk": {"score": 0, "value": 69871.0}, "rhodl_ratio": {"score": 0, "value": 69871.0}, "nupl": {"score": 0, "value": 69871.0}, "lth_realized_price": {"score": null, "value": null}, "hash_ribbons": {"score": 3, "value": null}}}
-{"timestamp": "2026-03-20T22:30:13.547149+00:00", "composite_score": 51.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 11}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 43.910215736040605}, "price_vs_200w_sma": {"score": 3, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
-{"timestamp": "2026-03-20T22:46:34.952569+00:00", "composite_score": 51.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 11}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 43.931630710659896}, "price_vs_200w_sma": {"score": 3, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
-{"timestamp": "2026-03-20T22:51:27.724327+00:00", "composite_score": 54.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 11}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 43.94907994923858}, "price_vs_200w_sma": {"score": 6, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
-{"timestamp": "2026-03-20T23:07:48.303808+00:00", "composite_score": 51.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 11}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 43.942734771573605}, "price_vs_200w_sma": {"score": 3, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
-{"timestamp": "2026-03-20T23:21:39.705718+00:00", "composite_score": 54.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 11}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 44.07439720812183}, "price_vs_200w_sma": {"score": 6, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
-{"timestamp": "2026-03-20T23:27:15.835859+00:00", "composite_score": 54.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 11}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 44.07122461928934}, "price_vs_200w_sma": {"score": 6, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
-{"timestamp": "2026-03-20T23:29:40.370530+00:00", "composite_score": 54.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 11}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 44.099777918781726}, "price_vs_200w_sma": {"score": 6, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
-{"timestamp": "2026-03-20T23:32:26.885241+00:00", "composite_score": 54.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 11}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 44.099777918781726}, "price_vs_200w_sma": {"score": 6, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
-{"timestamp": "2026-03-20T23:47:27.138815+00:00", "composite_score": 54.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 11}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 44.07122461928934}, "price_vs_200w_sma": {"score": 6, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
-{"timestamp": "2026-03-21T00:02:27.412395+00:00", "composite_score": 54.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 12}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 44.06408629441624}, "price_vs_200w_sma": {"score": 6, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
-{"timestamp": "2026-03-21T00:17:27.737482+00:00", "composite_score": 54.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 7, "value": 12}, "puell_multiple": {"score": 5, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 5, "value": 0.5211180167687892}, "drawdown": {"score": 6, "value": 44.025222081218274}, "price_vs_200w_sma": {"score": 6, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 7, "value": 0.22243290955405431}, "lth_realized_price": {"score": 1, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
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-{"timestamp": "2026-03-21T01:17:28.812131+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": 43.964149746192895}, "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": "2026-03-21T02:17:30.019509+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": 44.07201776649746}, "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": "2026-03-21T22:40:38.516771+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": 44.20685279187817}, "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": "2026-03-21T22:41:18.262393+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": 44.20685279187817}, "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": "2026-03-21T22:41:46.036660+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": 44.21875}, "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": "2026-03-21T22:42:33.632103+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": "2026-03-21T22:51:08.461576+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": 44.24968274111675}, "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": "2026-03-21T22:53:45.530567+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}}}

docker-compose.yml

@@ -0,0 +1,27 @@
+services:
+  btc-monitor:
+    build:
+      context: .
+    image: btc-accumulation-monitor:local
+    container_name: btc-accumulation-monitor
+    init: true
+    ports:
+      - "10.0.10.20:3088:3088"
+    restart: unless-stopped
+    environment:
+      PLAYWRIGHT_BROWSERS_PATH: /ms-playwright
+    volumes:
+      # Persist live history/cache and user-editable settings on the Mac Mini.
+      - ./data:/app/data
+      - ./config:/app/config
+    healthcheck:
+      test:
+        - CMD
+        - /app/.venv/bin/python
+        - -c
+        - "import urllib.request; urllib.request.urlopen('http://127.0.0.1:3088/health/live', timeout=3)"
+      interval: 30s
+      timeout: 5s
+      start_period: 30s
+      retries: 3
+    stop_grace_period: 30s

ml/artifacts.py

@@ -0,0 +1,91 @@
+"""Validation for persisted ML scoring artifacts."""
+
+import math
+
+from scoring.policy import SCORE_VERSION
+
+ML_ARTIFACT_SCHEMA_VERSION = 2
+REQUIRED_WEIGHT_KEYS = frozenset({
+    "puell_multiple",
+    "mvrv_zscore",
+    "reserve_risk",
+    "rhodl_ratio",
+    "nupl",
+    "fear_greed",
+    "drawdown",
+    "pct_above_200w_sma",
+    "pct_above_lth_rp",
+})
+
+
+def _weights_valid(weights):
+    if not isinstance(weights, dict) or not REQUIRED_WEIGHT_KEYS.issubset(weights):
+        return False
+    values = [weights[key] for key in REQUIRED_WEIGHT_KEYS]
+    return all(
+        isinstance(value, (int, float))
+        and not isinstance(value, bool)
+        and math.isfinite(value)
+        and value >= 0
+        for value in values
+    ) and sum(values) > 0
+
+
+def _has_oos_fold_weights(artifact):
+    folds = artifact.get("cv_results", {}).get("folds", [])
+    if not isinstance(folds, list) or not folds:
+        return False
+    for fold in folds:
+        validation_range = fold.get("date_ranges", {}).get("validation")
+        if not validation_range or not _weights_valid(fold.get("weights")):
+            return False
+    return True
+
+
+def validate_ml_artifact(artifact):
+    """Return machine-readable validity and provenance for an ML artifact."""
+    errors = []
+    if not isinstance(artifact, dict):
+        artifact = {}
+        errors.append("artifact_object")
+
+    schema_version = artifact.get("artifact_schema_version")
+    if schema_version != ML_ARTIFACT_SCHEMA_VERSION:
+        errors.append("artifact_schema_version")
+
+    score_version = artifact.get("score_version")
+    if score_version != SCORE_VERSION:
+        errors.append("score_version")
+
+    if not _weights_valid(artifact.get("weights")):
+        errors.append("weights")
+
+    provenance = artifact.get("provenance")
+    if not isinstance(provenance, dict):
+        provenance = {}
+        errors.append("provenance")
+    else:
+        required_provenance = {
+            "validation_method",
+            "label_horizon_days",
+            "weight_scope",
+            "training_date_range",
+            "trained_at",
+        }
+        if not required_provenance.issubset(provenance):
+            errors.append("provenance")
+        if provenance.get("validation_method") != "purged_expanding_window":
+            errors.append("purged_validation")
+        if provenance.get("label_horizon_days") != 365:
+            errors.append("label_horizon_days")
+        if provenance.get("weight_scope") != "full_history_fit":
+            errors.append("weight_scope")
+
+    return {
+        "valid": not errors,
+        "schema_version": schema_version,
+        "score_version": score_version,
+        "weight_scope": provenance.get("weight_scope"),
+        "has_oos_fold_weights": _has_oos_fold_weights(artifact),
+        "errors": list(dict.fromkeys(errors)),
+    }

ml/optimizer.py

@@ -26,6 +26,9 @@ from sklearn.metrics import (
 from sklearn.model_selection import TimeSeriesSplit
 from sklearn.preprocessing import StandardScaler
 
+from scoring.policy import SCORE_BRACKETS, SCORE_VERSION, score_in_bracket
+from ml.artifacts import ML_ARTIFACT_SCHEMA_VERSION
+
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s [%(name)s] %(levelname)s: %(message)s",
@@ -43,6 +46,9 @@ START_DATE = "2018-02-01"
 TRAIN_CUTOFF_DAYS = 365
 # Target: forward 365d return > 30% = "good time to buy"
 GOOD_BUY_THRESHOLD = 30.0
+# Validation embargo/purge horizon: labels use 365-day forward returns.
+LABEL_HORIZON_DAYS = 365
+VALIDATION_SPLITS = 5
 
 # The 8 core metrics we score
 METRIC_KEYS = [
@@ -95,6 +101,121 @@ def score_range(value, ranges):
     return 0
 
 
+SCORE_KEYS = [
+    "puell_multiple", "mvrv_zscore", "reserve_risk", "rhodl_ratio",
+    "nupl", "fear_greed", "drawdown", "pct_above_200w_sma", "pct_above_lth_rp",
+]
+
+SCORE_FEATURES = [f"score_{k}" for k in SCORE_KEYS]
+RAW_FEATURES = [
+    "raw_puell_multiple", "raw_mvrv_zscore", "raw_reserve_risk",
+    "raw_rhodl_ratio", "raw_nupl", "raw_fear_greed",
+    "raw_pct_above_200w_sma", "raw_pct_above_lth_rp", "raw_drawdown",
+]
+DELTA_FEATURES = [
+    "delta_30d_mvrv_zscore", "delta_30d_nupl",
+    "delta_30d_puell_multiple", "delta_30d_reserve_risk",
+]
+INTERACTION_FEATURES = ["mvrv_x_nupl", "puell_x_reserve"]
+CYCLE_FEATURES = ["days_since_ath"]
+FEATURE_COLS = SCORE_FEATURES + RAW_FEATURES + DELTA_FEATURES + INTERACTION_FEATURES + CYCLE_FEATURES
+
+BRACKETS = SCORE_BRACKETS
+
+
+def _row_date(row):
+    return datetime.strptime(row["date"], "%Y-%m-%d")
+
+
+def purged_time_series_splits(rows, n_splits=VALIDATION_SPLITS,
+                              label_horizon_days=LABEL_HORIZON_DAYS,
+                              embargo_days=0):
+    """Yield expanding-window splits with overlapping forward-label windows removed.
+
+    A row dated T with a 365-day forward-return label consumes information up to
+    T+365. For validation beginning at V, any training row whose label window
+    reaches V is removed. This keeps validation metrics out-of-sample for the
+    forward-return label, not just for features.
+    """
+    base_splitter = TimeSeriesSplit(n_splits=n_splits)
+    row_dates = [_row_date(r) for r in rows]
+    horizon = timedelta(days=label_horizon_days)
+    embargo = timedelta(days=embargo_days)
+
+    for train_idx, val_idx in base_splitter.split(np.arange(len(rows))):
+        val_start = row_dates[val_idx[0]]
+        val_end = row_dates[val_idx[-1]]
+        purged_train = []
+        for idx in train_idx:
+            label_end = row_dates[idx] + horizon
+            before_validation_label_window = label_end <= val_start - embargo
+            after_validation_embargo = row_dates[idx] > val_end + embargo
+            if before_validation_label_window or after_validation_embargo:
+                purged_train.append(idx)
+        if purged_train:
+            yield np.array(purged_train, dtype=int), np.array(val_idx, dtype=int)
+
+
+def viable_classification_splits(y, splits):
+    """Yield only folds whose training window contains both target classes."""
+    for train_idx, val_idx in splits:
+        if len(np.unique(y[train_idx])) < 2:
+            continue
+        yield train_idx, val_idx
+
+
+def artifact_fold_results(fold_results):
+    """Strip training-only row indexes from the persisted ML artifact."""
+    return [
+        {key: value for key, value in fold.items() if key not in {"train_idx", "val_idx"}}
+        for fold in fold_results
+    ]
+
+
+def _build_model():
+    return GradientBoostingClassifier(
+        n_estimators=300,
+        learning_rate=0.05,
+        max_depth=4,
+        subsample=0.8,
+        min_samples_leaf=20,
+        random_state=42,
+    )
+
+
+def derive_metric_weights(feature_cols, importances):
+    """Aggregate feature importances back to transparent score metric weights."""
+    metric_names = list(SCORE_KEYS)
+    feature_to_metric = {}
+    for m in metric_names:
+        feature_to_metric[f"score_{m}"] = m
+        feature_to_metric[f"raw_{m}"] = m
+    feature_to_metric["delta_30d_mvrv_zscore"] = "mvrv_zscore"
+    feature_to_metric["delta_30d_nupl"] = "nupl"
+    feature_to_metric["delta_30d_puell_multiple"] = "puell_multiple"
+    feature_to_metric["delta_30d_reserve_risk"] = "reserve_risk"
+
+    metric_importances = {m: 0.0 for m in metric_names}
+    for name, imp in zip(feature_cols, importances):
+        if name in feature_to_metric:
+            metric_importances[feature_to_metric[name]] += float(imp)
+        elif name == "mvrv_x_nupl":
+            metric_importances["mvrv_zscore"] += float(imp) / 2
+            metric_importances["nupl"] += float(imp) / 2
+        elif name == "puell_x_reserve":
+            metric_importances["puell_multiple"] += float(imp) / 2
+            metric_importances["reserve_risk"] += float(imp) / 2
+        elif name == "days_since_ath":
+            metric_importances["drawdown"] += float(imp)
+
+    total_imp = sum(metric_importances.values())
+    if total_imp > 0:
+        weights = {k: round(v / total_imp, 4) for k, v in metric_importances.items()}
+    else:
+        weights = {k: round(1 / len(metric_importances), 4) for k in metric_importances}
+    return dict(sorted(weights.items(), key=lambda x: x[1], reverse=True))
+
+
 def build_dataset(index, thresholds):
     """Build aligned training dataset: metric scores + forward returns."""
     # Get all dates from 2018-02-01 onward
@@ -257,39 +378,33 @@ def train_model(rows):
     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
+    feature_cols = FEATURE_COLS
-    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)
+    # Purged time-series cross-validation. Standard TimeSeriesSplit is not
-    tscv = TimeSeriesSplit(n_splits=5)
+    # enough here because each label consumes the next 365 days of returns.
     cv_scores = []
     cv_f1 = []
     cv_precision = []
     cv_recall = []
+    fold_results = []
 
-    for fold, (train_idx, val_idx) in enumerate(tscv.split(X)):
+    splits = list(viable_classification_splits(y, purged_time_series_splits(
+        labeled,
+        n_splits=VALIDATION_SPLITS,
+        label_horizon_days=LABEL_HORIZON_DAYS,
+        embargo_days=0,
+    )))
+    if not splits:
+        log.error("No viable purged validation splits. Need more history for %dd label horizon.",
+                  LABEL_HORIZON_DAYS)
+        return None
+
+    for fold, (train_idx, val_idx) in enumerate(splits):
         X_train, X_val = X[train_idx], X[val_idx]
         y_train, y_val = y[train_idx], y[val_idx]
 
@@ -297,14 +412,7 @@ def train_model(rows):
         X_train_s = scaler.fit_transform(X_train)
         X_val_s = scaler.transform(X_val)
 
-        model = GradientBoostingClassifier(
+        model = _build_model()
-            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)
@@ -320,27 +428,40 @@ def train_model(rows):
         cv_precision.append(prec)
         cv_recall.append(rec)
 
-        train_dates = f"{labeled[train_idx[0]]['date']} to {labeled[train_idx[-1]]['date']}"
+        fold_weights = derive_metric_weights(feature_cols, model.feature_importances_)
-        val_dates = f"{labeled[val_idx[0]]['date']} to {labeled[val_idx[-1]]['date']}"
+        fold_results.append({
+            "fold": fold + 1,
+            "train_idx": train_idx.tolist(),
+            "val_idx": val_idx.tolist(),
+            "weights": fold_weights,
+            "metrics": {
+                "auc": round(float(auc), 4),
+                "f1": round(float(f1), 4),
+                "precision": round(float(prec), 4),
+                "recall": round(float(rec), 4),
+            },
+            "date_ranges": {
+                "train": f"{labeled[train_idx[0]]['date']} to {labeled[train_idx[-1]]['date']}",
+                "validation": f"{labeled[val_idx[0]]['date']} to {labeled[val_idx[-1]]['date']}",
+            },
+            "n_train": len(train_idx),
+            "n_validation": len(val_idx),
+        })
+
+        train_dates = fold_results[-1]["date_ranges"]["train"]
+        val_dates = fold_results[-1]["date_ranges"]["validation"]
         log.info("Fold %d: Train %s | Val %s | AUC=%.3f F1=%.3f P=%.3f R=%.3f",
                  fold + 1, train_dates, val_dates, auc, f1, prec, rec)
 
-    log.info("CV Mean AUC: %.3f (+/- %.3f)", np.mean(cv_scores), np.std(cv_scores))
+    log.info("Purged 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))
+    log.info("Purged CV Mean F1:  %.3f (+/- %.3f)", np.mean(cv_f1), np.std(cv_f1))
 
     # Train final model on all labeled data
     log.info("Training final model on all %d labeled samples...", len(labeled))
     scaler = StandardScaler()
     X_scaled = scaler.fit_transform(X)
 
-    final_model = GradientBoostingClassifier(
+    final_model = _build_model()
-        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
@@ -357,48 +478,7 @@ def train_model(rows):
         bar = "#" * int(imp * 200)
         log.info("  %-30s %.4f %s", name, imp, bar)
 
-    # Extract optimal weights by aggregating importance per metric
+    weights = derive_metric_weights(feature_cols, importances)
-    # 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)
@@ -413,9 +493,14 @@ def train_model(rows):
     log.info("COMPARISON BACKTEST: ML-Weighted vs Equal-Weight")
     log.info("=" * 60)
     comparison = run_comparison(rows, weights)
+    out_of_sample_comparison = run_out_of_sample_comparison(labeled, fold_results)
 
-    # Build output
+    # Build output. Final weights are fitted on all labeled history for live use;
+    # only the fold weights below are valid for OOS comparisons.
+    trained_at = datetime.now(tz=__import__('datetime').timezone.utc).isoformat()
     result = {
+        "artifact_schema_version": ML_ARTIFACT_SCHEMA_VERSION,
+        "score_version": SCORE_VERSION,
         "weights": weights,
         "feature_importances": {name: round(float(imp), 6) for name, imp in feat_imp},
         "cv_results": {
@@ -424,6 +509,9 @@ def train_model(rows):
             "mean_f1": round(float(np.mean(cv_f1)), 4),
             "mean_precision": round(float(np.mean(cv_precision)), 4),
             "mean_recall": round(float(np.mean(cv_recall)), 4),
+            "validation_method": "purged_expanding_window",
+            "label_horizon_days": LABEL_HORIZON_DAYS,
+            "folds": artifact_fold_results(fold_results),
         },
         "training_info": {
             "n_samples": len(labeled),
@@ -434,67 +522,58 @@ def train_model(rows):
             "date_range": f"{labeled[0]['date']} to {labeled[-1]['date']}",
             "model": "GradientBoostingClassifier",
         },
+        "provenance": {
+            "validation_method": "purged_expanding_window",
+            "label_horizon_days": LABEL_HORIZON_DAYS,
+            "weight_scope": "full_history_fit",
+            "training_date_range": {
+                "start": labeled[0]["date"],
+                "end": labeled[-1]["date"],
+            },
+            "trained_at": trained_at,
+        },
         "comparison": comparison,
-        "trained_at": datetime.now(tz=__import__('datetime').timezone.utc).isoformat(),
+        "out_of_sample_comparison": out_of_sample_comparison,
+        "trained_at": trained_at,
     }
 
     return result
 
 
-def run_comparison(rows, ml_weights):
+def _composite_score(row, mode, ml_weights=None):
-    """Compare ML-weighted scoring vs equal-weight scoring across score brackets."""
+    scores = [row[f"score_{k}"] for k in SCORE_KEYS]
-    # Metrics used in scoring (maps to score_* columns)
+    if mode == "equal_weight" or not ml_weights:
-    score_keys = [
+        return sum(scores) / len(SCORE_KEYS) * 10
-        "puell_multiple", "mvrv_zscore", "reserve_risk", "rhodl_ratio",
+    equal_weight = 1.0 / len(SCORE_KEYS)
-        "nupl", "fear_greed", "drawdown", "pct_above_200w_sma", "pct_above_lth_rp",
+    weighted_sum = sum(row[f"score_{k}"] * ml_weights.get(k, equal_weight) for k in SCORE_KEYS)
-    ]
+    return weighted_sum * 10
-    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
+def _summarize_brackets(scored_rows, score_key):
-    scored_rows = [r for r in rows if "fwd_365d" in r]
+    results = []
-
+    for low, high, label in BRACKETS:
-    results = {"equal_weight": [], "ml_weighted": []}
+        days_in = [r for r in scored_rows if score_in_bracket(r[score_key], (low, high, label))]
-
-    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({
+            results.append({
                 "range": f"{low}-{high}", "label": label,
                 "days": 0, "avg_365d": None,
             })
             continue
         returns_365 = [r["fwd_365d"] for r in days_in]
+        returns_sorted = sorted(returns_365)
         win_rate = len([r for r in returns_365 if r > 0]) / len(returns_365) * 100
-            results[mode].append({
+        results.append({
             "range": f"{low}-{high}",
             "label": label,
             "days": len(days_in),
             "avg_365d": round(sum(returns_365) / len(returns_365), 2),
-                "median_365d": round(sorted(returns_365)[len(returns_365) // 2], 2),
+            "median_365d": round(returns_sorted[len(returns_sorted) // 2], 2),
             "win_rate_365d": round(win_rate, 1),
         })
+    return results
 
-    # Print comparison
+
+def _log_comparison_table(results):
     log.info("\n%-18s | %-8s %-8s %-8s | %-8s %-8s %-8s",
              "Bracket", "EQ Avg", "EQ Med", "EQ Win%", "ML Avg", "ML Med", "ML Win%")
     log.info("-" * 80)
@@ -508,6 +587,47 @@ def run_comparison(rows, ml_weights):
         log.info("%-18s | %-8s %-8s %-8s | %-8s %-8s %-8s",
                  eq["label"], eq_avg, eq_med, eq_win, ml_avg, ml_med, ml_win)
 
+
+def run_comparison(rows, ml_weights):
+    """Compare final ML-weighted scoring vs equal-weight scoring across all labeled rows.
+
+    This is retained for backwards compatibility with existing output. It is an
+    in-sample/full-history comparison; prefer out_of_sample_comparison for model
+    selection decisions.
+    """
+    scored_rows = [dict(r) for r in rows if "fwd_365d" in r]
+    for r in scored_rows:
+        r["composite_equal_weight"] = _composite_score(r, "equal_weight")
+        r["composite_ml_weighted"] = _composite_score(r, "ml_weighted", ml_weights)
+
+    results = {
+        "equal_weight": _summarize_brackets(scored_rows, "composite_equal_weight"),
+        "ml_weighted": _summarize_brackets(scored_rows, "composite_ml_weighted"),
+    }
+    _log_comparison_table(results)
+    return results
+
+
+def run_out_of_sample_comparison(rows, fold_results):
+    """Compare fold-specific ML weights on validation rows only."""
+    validation_rows = []
+    for fold in fold_results:
+        weights = fold.get("weights", {})
+        for idx in fold.get("val_idx", []):
+            if idx >= len(rows) or "fwd_365d" not in rows[idx]:
+                continue
+            r = dict(rows[idx])
+            r["fold"] = fold.get("fold")
+            r["composite_equal_weight"] = _composite_score(r, "equal_weight")
+            r["composite_ml_weighted"] = _composite_score(r, "ml_weighted", weights)
+            validation_rows.append(r)
+
+    results = {
+        "folds": len(fold_results),
+        "validation_days": len(validation_rows),
+        "equal_weight": _summarize_brackets(validation_rows, "composite_equal_weight"),
+        "ml_weighted": _summarize_brackets(validation_rows, "composite_ml_weighted"),
+    }
     return results
 
 

ml_engine/train_and_backtest.py

@@ -161,8 +161,8 @@ def compute_features(df, config):
 def create_accumulation_target(df, config):
     """Create accumulation score target based on forward returns.
 
-    For each candle, compute actual forward returns at multiple horizons,
+    For each candle, compute actual forward returns at multiple horizons and
-    rank them, and create a weighted accumulation score (0-100).
+    map them through fixed, configured return scales to a weighted 0-100 score.
     Times when buying led to the best long-term returns get highest scores.
     """
     tgt = config.get("target", {})
@@ -190,30 +190,22 @@ def create_accumulation_target(df, config):
             fwd[i] = (close[i + period] - close[i]) / close[i] * 100
         forward_returns.append(fwd)
 
-    # Rank each forward return (percentile rank, 0-1)
+    # Convert each return to a deterministic 0-100 quality score. Global
-    # Higher rank = better buy point (higher future return)
+    # percentile ranks leak the distribution of future validation/test rows into
-    ranked = []
+    # earlier training labels; a fixed tanh transform is invariant to rows added
-    for fwd in forward_returns:
+    # outside the observation's own forward horizons.
-        valid_mask = ~np.isnan(fwd)
+    scales = tgt.get("return_scales_pct", [10.0, 30.0, 60.0])
-        ranks = np.full(n, np.nan)
+    if len(scales) != len(forward_periods) or any(scale <= 0 for scale in scales):
-        valid_vals = fwd[valid_mask]
+        raise ValueError("target.return_scales_pct must contain one positive scale per forward period")
-        if len(valid_vals) > 0:
-            from scipy.stats import rankdata
-            r = rankdata(valid_vals, method="average") / len(valid_vals)
-            ranks[valid_mask] = r
-        ranked.append(ranks)
 
-    # Weighted combination of ranks -> accumulation score (0-100)
     score = np.zeros(n)
     valid = np.ones(n, dtype=bool)
-    for r, w in zip(ranked, weights):
+    for fwd, weight, scale in zip(forward_returns, weights, scales):
-        nan_mask = np.isnan(r)
+        nan_mask = np.isnan(fwd)
         valid &= ~nan_mask
-        r_filled = np.where(nan_mask, 0, r)
+        quality = 50.0 + 50.0 * np.tanh(np.where(nan_mask, 0.0, fwd) / scale)
-        score += w * r_filled
+        score += weight * quality
 
-    # Scale to 0-100
-    score = score * 100
     score[~valid] = np.nan
 
     return pd.Series(score, index=df.index, name="target")
@@ -488,6 +480,27 @@ def apply_scaling_pca(X_train, X_val, X_test, config):
     return X_train, X_val, X_test, scaler, pca
 
 
+# ---------------------------------------------------------------------------
+# Walk-forward split helpers
+# ---------------------------------------------------------------------------
+
+def _max_forward_horizon(config):
+    """Return the longest forward-label horizon in candles."""
+    target = config.get("target", {})
+    key = "forward_periods_1h" if config.get("timeframe", "4h") == "1h" else "forward_periods_4h"
+    periods = target.get(key, [168, 720, 2160] if key.endswith("1h") else [42, 180, 540])
+    return max(int(period) for period in periods)
+
+
+def _purge_label_overlap(frame, horizon):
+    """Remove rows whose forward-return labels cross the next split boundary."""
+    if horizon <= 0:
+        return frame
+    if len(frame) <= horizon:
+        return frame.iloc[0:0]
+    return frame.iloc[:-horizon]
+
+
 # ---------------------------------------------------------------------------
 # Rolling Window Validation
 # ---------------------------------------------------------------------------
@@ -499,6 +512,7 @@ def rolling_window_train_test(df, feature_cols, config):
     test_size = training_cfg.get("rolling_test_size", 300)
     val_pct = training_cfg.get("validation_pct", 0.15)
     model_type = config.get("model_type", "xgboost")
+    purge_horizon = _max_forward_horizon(config)
 
     n = len(df)
     all_predictions = []  # list of (predicted_score, actual_score, close_price)
@@ -527,10 +541,16 @@ def rolling_window_train_test(df, feature_cols, config):
             start += test_size
             continue
 
-        # Split train into train/val
+        # Purge labels whose longest forward-return horizon overlaps the next
+        # split. Without this embargo, training and validation targets consume
+        # prices from the following validation/test partition.
         val_split = int(len(train_full) * (1.0 - val_pct))
-        train_df = train_full.iloc[:val_split]
+        train_df = _purge_label_overlap(train_full.iloc[:val_split], purge_horizon)
-        val_df = train_full.iloc[val_split:]
+        val_df = _purge_label_overlap(train_full.iloc[val_split:], purge_horizon)
+
+        if len(train_df) < 10 or len(val_df) < 1:
+            start += test_size
+            continue
 
         X_train = train_df[feature_cols].values
         y_train = train_df["target"].values
@@ -632,6 +652,7 @@ def walk_forward_train_test(df, feature_cols, config):
     n_windows = training_cfg.get("walk_forward_windows", 5)
     train_pct = training_cfg.get("train_pct", 0.7)
     val_pct = training_cfg.get("validation_pct", 0.15)
+    purge_horizon = _max_forward_horizon(config)
 
     n = len(df)
     window_size = n // n_windows
@@ -655,8 +676,8 @@ def walk_forward_train_test(df, feature_cols, config):
         train_end = int(wn * train_pct)
         val_end = int(wn * (train_pct + val_pct))
 
-        train_df = window_data.iloc[:train_end]
+        train_df = _purge_label_overlap(window_data.iloc[:train_end], purge_horizon)
-        val_df = window_data.iloc[train_end:val_end]
+        val_df = _purge_label_overlap(window_data.iloc[train_end:val_end], purge_horizon)
         test_df = window_data.iloc[val_end:]
 
         if len(test_df) < 10:
@@ -842,6 +863,46 @@ def _extract_feature_importances(model, n_features):
 # Results Compilation
 # ---------------------------------------------------------------------------
 
+def simulate_periodic_accumulation(predicted_scores, close_prices, buy_threshold, contribution=1.0):
+    """Compare DCA and signal strategies with equal periodic contributions.
+
+    Both strategies receive the same cash on every observation. DCA invests the
+    contribution immediately; the signal strategy retains cash until a buy
+    signal, then deploys its available balance. Terminal wealth includes cash.
+    """
+    scores = np.asarray(predicted_scores, dtype=float)
+    prices = np.asarray(close_prices, dtype=float)
+    if len(scores) != len(prices):
+        raise ValueError("predicted_scores and close_prices must have equal length")
+    if len(prices) == 0 or contribution <= 0 or np.any(prices <= 0):
+        raise ValueError("prices must be positive and contribution must be greater than zero")
+
+    dca_btc = float(np.sum(contribution / prices))
+    model_btc = 0.0
+    model_cash = 0.0
+    for score, price in zip(scores, prices):
+        model_cash += contribution
+        if score >= buy_threshold:
+            model_btc += model_cash / price
+            model_cash = 0.0
+
+    contributed = float(len(prices) * contribution)
+    terminal_price = float(prices[-1])
+    dca_terminal = dca_btc * terminal_price
+    model_terminal = model_btc * terminal_price + model_cash
+    improvement = (model_terminal - dca_terminal) / dca_terminal * 100 if dca_terminal else 0.0
+    return {
+        "dca_contributed": contributed,
+        "model_contributed": contributed,
+        "dca_btc": dca_btc,
+        "model_btc": model_btc,
+        "model_cash": model_cash,
+        "dca_terminal_value": dca_terminal,
+        "model_terminal_value": model_terminal,
+        "terminal_wealth_improvement_pct": improvement,
+    }
+
+
 def compile_results(predictions, per_window_cost_improvement,
                     fi_sum, fi_count, feature_cols, config):
     """Compile accumulation signal results into output JSON."""
@@ -870,10 +931,8 @@ def compile_results(predictions, per_window_cost_improvement,
     else:
         avg_actual_strong = 0.0
 
-    # We need forward return info. Since actual_score is a rank-based measure (0-100),
+    # The target is a bounded return-quality score, not a realized return.
-    # and we want to report real forward returns, we approximate:
+    # Report it explicitly as quality rather than approximating a return.
-    # actual_score > 80 means the buy was in the top 20% of quality.
-    # For actual forward return stats, we use actual score as a proxy.
 
     # Profitable signals: those where actual score is also above median (50)
     if strong_buy_count > 0:
@@ -896,18 +955,25 @@ def compile_results(predictions, per_window_cost_improvement,
         model_avg = dca_avg
         cost_basis_improvement = 0.0
 
+    portfolio = simulate_periodic_accumulation(
+        pred_scores,
+        close_prices,
+        buy_threshold=good_threshold,
+        contribution=1.0,
+    )
+
     # --- Signal Frequency ---
     signal_frequency = strong_buy_count / total_candles * 100 if total_candles > 0 else 0
 
     # --- Score at actual extremes ---
-    # "Actual bottoms" = candles with actual score > 85 (top 15% buy opportunities)
+    # "Actual bottoms" = candles with a high realized return-quality score.
     actual_bottom_mask = actual_scores > 85
     if np.any(actual_bottom_mask):
         avg_score_at_bottoms = float(np.mean(pred_scores[actual_bottom_mask]))
     else:
         avg_score_at_bottoms = 0.0
 
-    # "Actual tops" = candles with actual score < 15 (worst 15% buy times)
+    # "Actual tops" = candles with a low realized return-quality score.
     actual_top_mask = actual_scores < 15
     if np.any(actual_top_mask):
         avg_score_at_tops = float(np.mean(pred_scores[actual_top_mask]))
@@ -932,10 +998,7 @@ def compile_results(predictions, per_window_cost_improvement,
         count = int(np.sum((pred_scores >= lo) & (pred_scores < (hi if hi < 100 else 101))))
         score_distribution[key] = count
 
-    # --- Forward return approximation from actual scores ---
+    # --- Realized return-quality summary ---
-    # Map actual score to approximate return quality
-    # Score 90+ = historically best 10% buys, score 10- = worst 10%
-    # Use actual score as proxy for "quality rank"
     if strong_buy_count > 0:
         # Average actual quality score for strong buy signals
         avg_quality_strong = float(np.mean(actual_scores[strong_buy_mask]))
@@ -946,7 +1009,14 @@ def compile_results(predictions, per_window_cost_improvement,
         quality_good = False
 
     return {
+        # Retained for backward compatibility; model selection uses the equal-
+        # capital terminal wealth metric below.
         "cost_basis_improvement_pct": round(cost_basis_improvement, 2),
+        "terminal_wealth_improvement_pct": round(portfolio["terminal_wealth_improvement_pct"], 2),
+        "model_terminal_value": round(portfolio["model_terminal_value"], 6),
+        "dca_terminal_value": round(portfolio["dca_terminal_value"], 6),
+        "model_cash": round(portfolio["model_cash"], 6),
+        "backtest_objective": "equal_periodic_contribution_terminal_wealth",
         "avg_cost_basis_model": round(model_avg, 2),
         "avg_cost_basis_dca": round(dca_avg, 2),
         "strong_buy_signal_count": strong_buy_count,
@@ -968,6 +1038,11 @@ def compile_results(predictions, per_window_cost_improvement,
 def _empty_results(per_window):
     return {
         "cost_basis_improvement_pct": 0.0,
+        "terminal_wealth_improvement_pct": 0.0,
+        "model_terminal_value": 0.0,
+        "dca_terminal_value": 0.0,
+        "model_cash": 0.0,
+        "backtest_objective": "equal_periodic_contribution_terminal_wealth",
         "avg_cost_basis_model": 0.0,
         "avg_cost_basis_dca": 0.0,
         "strong_buy_signal_count": 0,

orchestrator.py

@@ -28,7 +28,7 @@ MAC_MINI_HOST = "bizzle@bizzles-mac-mini-1"
 MAX_ITERATIONS = 50
 CONVERGENCE_WINDOW = 5
 CONVERGENCE_THRESHOLD = 0.01  # 1% improvement
-TARGET_COST_IMPROVEMENT = 20.0  # 20% cost basis improvement = exceptional
+TARGET_COST_IMPROVEMENT = 20.0  # Backward-compatible name: terminal wealth objective
 MIN_SIGNAL_COUNT = 30  # Minimum strong buy signals for valid results
 ML_TIMEOUT = 600  # 10 minutes
 
@@ -49,6 +49,11 @@ def log(msg, color=""):
     print(f"{C.DIM}[{ts}]{C.RESET} {color}{msg}{C.RESET}")
 
 
+def objective_score(results):
+    """Return the equal-capital portfolio objective used for model selection."""
+    return float(results.get("terminal_wealth_improvement_pct", 0.0))
+
+
 def run_cmd(cmd, timeout=120, check=True):
     """Run a shell command and return stdout."""
     result = subprocess.run(
@@ -160,11 +165,12 @@ def print_header():
 
 
 def print_results(results, iteration):
-    cost_imp = results.get("cost_basis_improvement_pct", 0)
+    objective = objective_score(results)
-    color = C.GREEN if cost_imp > 15 else C.YELLOW if cost_imp > 10 else C.RED
+    color = C.GREEN if objective > 15 else C.YELLOW if objective > 10 else C.RED
     print(f"""
 {C.BOLD}--- Iteration {iteration} Results ---{C.RESET}
-  Cost Improvement: {color}{C.BOLD}{cost_imp:.1f}%{C.RESET}
+  Terminal Wealth vs DCA: {color}{C.BOLD}{objective:.1f}%{C.RESET}
+  Legacy Cost Basis Delta: {results.get('cost_basis_improvement_pct', 0):.1f}%
   Avg Cost (Model): ${results.get('avg_cost_basis_model', 0):,.2f}
   Avg Cost (DCA):   ${results.get('avg_cost_basis_dca', 0):,.2f}
   Strong Signals:   {results.get('strong_buy_signal_count', 0)}
@@ -244,7 +250,7 @@ def main():
 
         print_results(results, iteration)
 
-        current_score = results.get("cost_basis_improvement_pct", 0)
+        current_score = objective_score(results)
         signal_count = results.get("strong_buy_signal_count", 0)
         is_best = current_score > best_score and signal_count >= MIN_SIGNAL_COUNT
 
@@ -252,12 +258,14 @@ def main():
             best_score = current_score
             with open(best_config_path, "w") as f:
                 json.dump(config, f, indent=2)
-            log(f"NEW BEST! Cost Improvement: {best_score:.1f}%", f"{C.BOLD}{C.GREEN}")
+            log(f"NEW BEST! Terminal Wealth Improvement: {best_score:.1f}%", f"{C.BOLD}{C.GREEN}")
 
         iter_data = {
             "iteration": iteration,
             "timestamp": datetime.now(timezone.utc).isoformat(),
             "cost_improvement": current_score,
+            "objective_improvement": current_score,
+            "objective": "equal_periodic_contribution_terminal_wealth",
             "avg_30d_return": results.get("avg_quality_score_strong_buy", 0),
             "avg_90d_return": results.get("pct_quality_strong_buy", 0),
             "signal_count": signal_count,
@@ -312,7 +320,7 @@ def main():
 ========================================================{C.RESET}
 
   Total Iterations:     {len(history)}
-  Best Cost Improvement: {C.BOLD}{best_score:.1f}%{C.RESET}
+  Best Terminal Wealth Improvement: {C.BOLD}{best_score:.1f}%{C.RESET}
   Best Config:          {best_config_path}
   Iteration Log:        {ITERATIONS_LOG}
 """)
@@ -405,7 +413,7 @@ def run_optimization_loop(callback=None, config_override=None):
             with open(results_local) as f:
                 results = json.load(f)
 
-            current_score = results.get("cost_basis_improvement_pct", 0)
+            current_score = objective_score(results)
             signal_count = results.get("strong_buy_signal_count", 0)
             is_best = current_score > best_score and signal_count >= MIN_SIGNAL_COUNT
 
@@ -419,6 +427,8 @@ def run_optimization_loop(callback=None, config_override=None):
                 "iteration": iteration,
                 "timestamp": datetime.now(timezone.utc).isoformat(),
                 "cost_improvement": current_score,
+                "objective_improvement": current_score,
+                "objective": "equal_periodic_contribution_terminal_wealth",
                 "signal_count": signal_count,
                 "signal_frequency": results.get("signal_frequency_pct", 0),
                 "r2_score": results.get("model_r2_score", 0),

pyproject.toml

@@ -0,0 +1,34 @@
+[project]
+name = "btc-accumulation-monitor"
+version = "0.1.0"
+description = "Bitcoin accumulation metrics dashboard and historical scoring tools"
+readme = "README.md"
+requires-python = ">=3.11,<3.14"
+
+[dependency-groups]
+runtime = [
+  "fastapi>=0.116,<1",
+  "playwright>=1.54,<2",
+  "requests>=2.32,<3",
+  "uvicorn[standard]>=0.35,<1",
+]
+ml = [
+  "ccxt>=4.4,<5",
+  "numpy>=2.2,<3",
+  "pandas>=2.2,<3",
+  "scikit-learn>=1.6,<2",
+  "ta>=0.11,<1",
+]
+dev = [
+  "pytest>=8.4,<9",
+]
+
+[tool.uv]
+package = false
+
+default-groups = ["runtime", "ml", "dev"]
+
+[tool.pytest.ini_options]
+addopts = "-q"
+testpaths = ["tests"]
+pythonpath = ["."]

scoring/engine.py

@@ -4,6 +4,9 @@ import json
 import os
 import logging
 
+from scoring.policy import SCORE_VERSION, assessment_for_score
+from ml.artifacts import validate_ml_artifact
+
 log = logging.getLogger(__name__)
 
 THRESHOLDS_PATH = os.path.join(
@@ -259,6 +262,65 @@ def score_hash_ribbons(data, thresholds=None):
     return 3, "Normal mining activity"
 
 
+def score_sopr(value, thresholds=None):
+    if value is None:
+        return None, "No data"
+    if value < 0.98:
+        return 10, "Deep loss realization — capitulation, strong accumulation"
+    if value < 1.0:
+        return 8, "Below breakeven — capitulation, good accumulation"
+    if value < 1.02:
+        return 5, "Near breakeven — neutral"
+    if value < 1.05:
+        return 2, "Moderate profit taking"
+    return 0, "Elevated profit taking — caution"
+
+
+def score_sellside_risk(value, thresholds=None):
+    if value is None:
+        return None, "No data"
+    if value < 0.001:
+        return 10, "Very low sell-side risk — strong accumulation"
+    if value < 0.002:
+        return 8, "Low sell-side risk — good accumulation"
+    if value < 0.005:
+        return 5, "Moderate sell-side risk"
+    if value < 0.01:
+        return 2, "Elevated sell-side risk"
+    return 0, "High sell-side risk"
+
+
+def score_momentum_pct(value):
+    if value is None:
+        return None, "No data"
+    pct = value * 100
+    if pct >= 20:
+        return 10, f"Strong positive momentum (+{pct:.0f}%)"
+    if pct >= 0:
+        return 6, f"Mild positive momentum (+{pct:.0f}%)"
+    if pct >= -10:
+        return 4, f"Slightly negative momentum ({pct:.0f}%)"
+    if pct >= -25:
+        return 2, f"Weak momentum ({pct:.0f}%)"
+    return 1, f"Strong negative momentum ({pct:.0f}%)"
+
+
+def score_nvt_price(nvt_price, spot_price):
+    if nvt_price is None or spot_price is None or spot_price <= 0:
+        return None, "No data"
+    premium = (nvt_price - spot_price) / spot_price * 100
+    if premium < -25:
+        return 10, f"NVT price {abs(premium):.0f}% below spot — deep value"
+    if premium < -10:
+        return 8, f"NVT price {abs(premium):.0f}% below spot — undervalued"
+    if premium < 10:
+        relation = "below" if premium < 0 else "above"
+        return 5, f"NVT price {abs(premium):.0f}% {relation} spot — fair value"
+    if premium < 30:
+        return 2, f"NVT price {premium:.0f}% above spot — extended"
+    return 0, f"NVT price {premium:.0f}% above spot — overheated"
+
+
 def score_all(metrics):
     """Score all metrics and return individual + composite scores."""
     thresholds = load_thresholds()
@@ -399,6 +461,85 @@ def score_all(metrics):
         "recent": [],
     })
 
+    # SOPR
+    sopr = metrics.get("sopr", {})
+    sopr_score, sopr_desc = score_sopr(sopr.get("value"), thresholds)
+    results.append({
+        "name": "SOPR",
+        "key": "sopr",
+        "value": sopr.get("value"),
+        "display_value": f"{sopr.get('value', 'N/A'):.4f}" if sopr.get("value") is not None else "N/A",
+        "score": sopr_score,
+        "description": sopr_desc,
+        "recent": sopr.get("recent", []),
+    })
+
+    # Sell-side Risk Ratio
+    ssr = metrics.get("sellside_risk", {})
+    ssr_score, ssr_desc = score_sellside_risk(ssr.get("value"), thresholds)
+    results.append({
+        "name": "Sell-side Risk Ratio",
+        "key": "sellside_risk",
+        "value": ssr.get("value"),
+        "display_value": f"{ssr.get('value', 'N/A'):.6f}" if ssr.get("value") is not None else "N/A",
+        "score": ssr_score,
+        "description": ssr_desc,
+        "recent": ssr.get("recent", []),
+    })
+
+    # Active Address Momentum
+    aam = metrics.get("active_address_momentum", {})
+    aam_score, aam_desc = score_momentum_pct(aam.get("value"))
+    results.append({
+        "name": "Active Address Momentum",
+        "key": "active_address_momentum",
+        "value": aam.get("value"),
+        "display_value": f"{aam.get('value') * 100:.1f}%" if aam.get("value") is not None else "N/A",
+        "score": aam_score,
+        "description": aam_desc,
+        "recent": aam.get("recent", []),
+    })
+
+    # Transaction Count Momentum
+    txm = metrics.get("txcount_momentum", {})
+    txm_score, txm_desc = score_momentum_pct(txm.get("value"))
+    results.append({
+        "name": "Transaction Count Momentum",
+        "key": "txcount_momentum",
+        "value": txm.get("value"),
+        "display_value": f"{txm.get('value') * 100:.1f}%" if txm.get("value") is not None else "N/A",
+        "score": txm_score,
+        "description": txm_desc,
+        "recent": txm.get("recent", []),
+    })
+
+    # NVT Price
+    nvt = metrics.get("nvt_price", {})
+    nvt_score, nvt_desc = score_nvt_price(nvt.get("value"), current_price)
+    results.append({
+        "name": "NVT Price",
+        "key": "nvt_price",
+        "value": nvt.get("value"),
+        "display_value": f"${nvt.get('value'):,.0f}" if nvt.get("value") is not None else "N/A",
+        "score": nvt_score,
+        "description": nvt_desc,
+        "recent": nvt.get("recent", []),
+    })
+
+    # VDD Multiple
+    vdd = metrics.get("vdd_multiple", {})
+    vdd_score, vdd_desc = score_momentum_pct(vdd.get("value"))
+    results.append({
+        "name": "VDD 30-Period Momentum",
+        "key": "vdd_multiple",
+        "transform": "30_period_return",
+        "value": vdd.get("value"),
+        "display_value": f"{vdd.get('value') * 100:.1f}%" if vdd.get("value") is not None else "N/A",
+        "score": vdd_score,
+        "description": vdd_desc,
+        "recent": vdd.get("recent", []),
+    })
+
     # Compute composite
     valid_scores = [r["score"] for r in results if r["score"] is not None]
     if valid_scores:
@@ -407,19 +548,7 @@ def score_all(metrics):
     else:
         composite = 0
 
-    # Assessment text — calibrated for cycle-aware scoring
+    assessment = assessment_for_score(composite)
-    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,
@@ -427,6 +556,17 @@ def score_all(metrics):
         "assessment": assessment,
         "scored_count": len(valid_scores),
         "total_count": len(results),
+        "score_version": SCORE_VERSION,
+        "metric_panel": {
+            "id": "live-all-v1",
+            "keys": [result["key"] for result in results],
+            "count": len(results),
+        },
+        "coverage": {
+            "available_count": len(valid_scores),
+            "panel_count": len(results),
+            "ratio": len(valid_scores) / len(results),
+        },
     }
 
 
@@ -452,14 +592,28 @@ _ML_KEY_MAP = {
 }
 
 
+_ml_artifact_status = {"valid": False, "errors": ["not_loaded"]}
+
+
 def load_ml_weights():
-    """Load ML-optimized weights from config."""
+    """Load weights only when their schema and training provenance are valid."""
+    global _ml_artifact_status
     try:
         with open(ML_WEIGHTS_PATH) as f:
             data = json.load(f)
-        return data.get("weights", {})
+        _ml_artifact_status = validate_ml_artifact(data)
-    except Exception:
+        if not _ml_artifact_status["valid"]:
+            log.error("Rejected invalid ML artifact: %s", ", ".join(_ml_artifact_status["errors"]))
             return {}
+        return data.get("weights", {})
+    except Exception as exc:
+        _ml_artifact_status = {"valid": False, "errors": [f"load_error:{exc}"]}
+        return {}
+
+
+def get_ml_artifact_status():
+    """Return the status from the most recent artifact load attempt."""
+    return dict(_ml_artifact_status)
 
 
 def score_all_ml(metrics):
@@ -476,49 +630,45 @@ def score_all_ml(metrics):
     if not ml_weights:
         # Fallback to classic if no ML weights available
         classic["ml_mode"] = False
+        status = get_ml_artifact_status()
+        if status.get("errors") and status["errors"] != ["not_loaded"]:
+            classic["ml_error"] = "ML artifact invalid: " + ", ".join(status["errors"])
+        else:
             classic["ml_error"] = "ML weights not found — run ml/optimizer.py"
+        classic["ml_artifact"] = status
         return classic
 
     results = classic["metrics"]
 
-    # Compute ML-weighted composite
+    # Compute raw ML weights first, then normalize across only the currently
-    weighted_sum = 0.0
+    # scored metrics. This keeps the dashboard's displayed per-metric weights and
-    weight_total = 0.0
+    # contribution points consistent with the normalized composite score even
-
+    # when optional metrics are missing or hash ribbons receives its fallback.
+    weighted_metrics = []
     for m in results:
         if m["score"] is None:
             continue
         ml_key = _ML_KEY_MAP.get(m["key"])
         if ml_key is None:
             # Hash ribbons or unknown metric — use small default weight
-            w = 0.01
+            raw_weight = 0.01
         else:
-            w = ml_weights.get(ml_key, 0.0)
+            raw_weight = ml_weights.get(ml_key, 0.0)
+        weighted_metrics.append((m, raw_weight))
 
-        m["ml_weight"] = round(w, 4)
+    weight_total = sum(raw_weight for _, raw_weight in weighted_metrics)
-        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
+        composite = sum(m["score"] * raw_weight for m, raw_weight in weighted_metrics) / weight_total * 10
     else:
         composite = 0
 
-    # Assessment text (same thresholds as classic)
+    for m, raw_weight in weighted_metrics:
-    if composite >= 80:
+        effective_weight = raw_weight / weight_total if weight_total > 0 else 0.0
-        assessment = "EXTREME ACCUMULATION ZONE"
+        m["ml_raw_weight"] = round(raw_weight, 4)
-    elif composite >= 65:
+        m["ml_weight"] = round(effective_weight, 4)
-        assessment = "STRONG ACCUMULATION ZONE"
+        m["ml_contribution"] = round(m["score"] * effective_weight * 10, 2)
-    elif composite >= 50:
+
-        assessment = "MODERATE OPPORTUNITY"
+    assessment = assessment_for_score(composite)
-    elif composite >= 35:
-        assessment = "NEUTRAL"
-    elif composite >= 20:
-        assessment = "CAUTION — OVERHEATED"
-    else:
-        assessment = "EXTREME CAUTION"
 
     return {
         "metrics": results,
@@ -528,4 +678,8 @@ def score_all_ml(metrics):
         "total_count": classic["total_count"],
         "ml_mode": True,
         "classic_score": classic["composite_score"],
+        "ml_weight_total": round(weight_total, 4),
+        "score_version": SCORE_VERSION,
+        "metric_panel": classic["metric_panel"],
+        "coverage": classic["coverage"],
     }

scoring/policy.py

@@ -0,0 +1,35 @@
+"""Canonical score version, brackets, and assessment semantics."""
+
+SCORE_VERSION = "accumulation-score-v2"
+
+# Half-open intervals [low, high), except the final bracket includes 100.
+# Keep labels canonical because they are persisted in live and backtest output.
+SCORE_BRACKETS = [
+    (0, 20, "EXTREME CAUTION"),
+    (20, 35, "CAUTION — OVERHEATED"),
+    (35, 50, "NEUTRAL"),
+    (50, 65, "MODERATE OPPORTUNITY"),
+    (65, 80, "STRONG ACCUMULATION ZONE"),
+    (80, 100, "EXTREME ACCUMULATION ZONE"),
+]
+
+
+def score_in_bracket(score, bracket):
+    """Return whether a 0-100 score belongs to a canonical bracket."""
+    low, high, _ = bracket
+    if not 0 <= score <= 100:
+        return False
+    return low <= score < high or (high == 100 and score == 100)
+
+
+def bracket_for_score(score):
+    """Return the one canonical bracket for a 0-100 score."""
+    for bracket in SCORE_BRACKETS:
+        if score_in_bracket(score, bracket):
+            return bracket
+    raise ValueError(f"score must be between 0 and 100, got {score!r}")
+
+
+def assessment_for_score(score):
+    """Return the canonical assessment label for a score."""
+    return bracket_for_score(score)[2]

scrapers/checkonchain.py

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

scrapers/history_collector.py

@@ -114,8 +114,15 @@ def collect_onchain_history(progress_cb=None):
 
             for metric_key, trace_name in cfg["traces"].items():
                 if trace_name is None:
-                    # Grab first trace with numeric data
+                    if metric_key == "lth_supply":
-                    for candidate in traces:
+                        from scrapers.lookintobitcoin import _find_lth_supply_trace
+                        candidates = [_find_lth_supply_trace(traces)]
+                    else:
+                        candidates = traces
+                    # Grab the first validated trace with numeric data.
+                    for candidate in candidates:
+                        if not candidate:
+                            continue
                         y = candidate.get("y", [])
                         if y and any(v is not None for v in y[-10:]):
                             dates, values = _extract_series(candidate)

scrapers/lookintobitcoin.py

@@ -2,6 +2,7 @@
 
 import logging
 import traceback
+from contextlib import contextmanager
 
 log = logging.getLogger(__name__)
 
@@ -51,15 +52,26 @@ CHARTS = {
 }
 
 
-def scrape_chart(chart_path, timeout=25000):
+@contextmanager
-    """Scrape a single chart from LookIntoBitcoin. Returns list of trace dicts or None."""
+def browser_page():
+    """Open one headless browser page for a batch of chart requests."""
     from playwright.sync_api import sync_playwright
 
-    store = {"data": None}
+    with sync_playwright() as playwright:
+        browser = playwright.chromium.launch(headless=True)
+        try:
+            yield browser.new_page()
+        finally:
+            browser.close()
 
-    with sync_playwright() as p:
+
-        browser = p.chromium.launch(headless=True)
+def scrape_chart(chart_path, timeout=25000, page=None):
-        page = browser.new_page()
+    """Scrape one chart, optionally reusing a caller-owned browser page."""
+    if page is None:
+        with browser_page() as owned_page:
+            return scrape_chart(chart_path, timeout=timeout, page=owned_page)
+
+    store = {"data": None}
 
     def handle_response(response):
         if "_dash-update-component" in response.url:
@@ -72,10 +84,10 @@ def scrape_chart(chart_path, timeout=25000):
     try:
         page.goto(f"{BASE_URL}{chart_path}", timeout=timeout)
         page.wait_for_timeout(6000)
-        except Exception as e:
+    except Exception as exc:
-            log.warning("Navigation error for %s: %s", chart_path, e)
+        log.warning("Navigation error for %s: %s", chart_path, exc)
     finally:
-            browser.close()
+        page.remove_listener("response", handle_response)
 
     if store["data"]:
         try:
@@ -115,6 +127,30 @@ def _find_trace(traces, name):
     return None
 
 
+def _trace_signal_is_active(trace):
+    """Return true only when the signal trace is active at its latest point."""
+    if not trace:
+        return False
+    values = trace.get("y", [])
+    if not values:
+        return False
+    latest = values[-1]
+    try:
+        return latest is not None and float(latest) != 0
+    except (TypeError, ValueError):
+        return bool(latest)
+
+
+def _find_lth_supply_trace(traces):
+    """Select an explicitly named LTH supply series and never a price fallback."""
+    for trace in traces or []:
+        name = str(trace.get("name", "")).lower()
+        is_lth = "long-term holder" in name or "long term holder" in name or "lth" in name
+        if is_lth and "supply" in name and "price" not in name:
+            return trace
+    return None
+
+
 def _get_latest_value(trace):
     """Get the most recent non-null y value from a trace."""
     if not trace:
@@ -148,13 +184,18 @@ def _get_recent_values(trace, n=30):
 
 
 def scrape_all():
-    """Scrape all charts and return parsed metric values."""
+    """Scrape all charts while reusing one browser process and page."""
+    with browser_page() as page:
+        return _scrape_all_with_page(page)
+
+
+def _scrape_all_with_page(page):
     results = {}
 
     for metric_key, chart_info in CHARTS.items():
         log.info("Scraping %s ...", metric_key)
         try:
-            traces = scrape_chart(chart_info["path"])
+            traces = scrape_chart(chart_info["path"], page=page)
             if not traces:
                 log.warning("No data for %s", metric_key)
                 results[metric_key] = {"value": None, "error": "No data returned"}
@@ -210,21 +251,16 @@ def scrape_all():
                     ],
                     "value": None,
                 }
-                # Try to detect buy signal from trace names/colors
+                # A named signal trace is not itself proof that the signal is active.
                 for t in traces:
                     name = t.get("name", "").lower()
-                    if "buy" in name or "signal" in name:
+                    if ("buy" in name or "signal" in name) and _trace_signal_is_active(t):
                         results[metric_key]["buy_signal"] = True
                         break
 
             elif metric_key == "lth_supply":
-                # Get main supply trace
+                # Require an explicitly named LTH supply trace; price is not supply.
-                t = traces[0] if traces else None
+                t = _find_lth_supply_trace(traces)
-                for candidate in traces:
-                    name = candidate.get("name", "").lower()
-                    if "supply" in name or "lth" in name:
-                        t = candidate
-                        break
                 recent = _get_recent_values(t, 60)
                 # Determine trend: compare recent avg to older avg
                 trend = None

scripts/run.sh

@@ -0,0 +1,11 @@
+#!/bin/sh
+set -eu
+
+ROOT=$(CDPATH= cd -- "$(dirname -- "$0")/.." && pwd)
+cd "$ROOT"
+
+export PYTHONPATH="${PYTHONPATH:-.}"
+export PLAYWRIGHT_BROWSERS_PATH="${PLAYWRIGHT_BROWSERS_PATH:-$ROOT/.playwright}"
+
+exec uv run --frozen --no-dev --group runtime --group ml \
+  python -m uvicorn dashboard.server:app --host 0.0.0.0 --port "${PORT:-3088}"

tests/test_backtest_cache.py

@@ -0,0 +1,56 @@
+import json
+import os
+
+from backtesting import engine
+
+
+def test_run_backtest_caches_by_input_file_signature(monkeypatch, tmp_path):
+    history = tmp_path / "history.json"
+    thresholds = tmp_path / "thresholds.json"
+    weights = tmp_path / "weights.json"
+    cache = tmp_path / "cache.json"
+    for path in (history, thresholds, weights, cache):
+        path.write_text("{}")
+
+    monkeypatch.setattr(engine, "HISTORY_PATH", str(history))
+    monkeypatch.setattr(engine, "_THRESH_PATH", str(thresholds))
+    monkeypatch.setattr(engine, "ML_WEIGHTS_PATH", str(weights))
+    monkeypatch.setattr(engine, "CACHE_PATH", str(cache))
+    calls = []
+    monkeypatch.setattr(
+        engine, "_compute_backtest",
+        lambda ml_mode=False: calls.append(ml_mode) or {"ml_mode": ml_mode, "calls": len(calls)},
+    )
+    engine.clear_backtest_cache()
+
+    first = engine.run_backtest()
+    second = engine.run_backtest()
+    ml_first = engine.run_backtest(ml_mode=True)
+    ml_second = engine.run_backtest(ml_mode=True)
+    classic_after_ml = engine.run_backtest()
+
+    assert first == second == {"ml_mode": False, "calls": 1}
+    assert ml_first == ml_second == {"ml_mode": True, "calls": 2}
+    assert classic_after_ml == first
+    assert calls == [False, True]
+
+    history.write_text('{"changed": true}')
+    os.utime(history, None)
+    invalidated = engine.run_backtest()
+    assert invalidated == {"ml_mode": False, "calls": 3}
+
+
+def test_cached_backtest_results_are_isolated_from_caller_mutation(monkeypatch, tmp_path):
+    history = tmp_path / "history.json"
+    history.write_text("{}")
+    monkeypatch.setattr(engine, "HISTORY_PATH", str(history))
+    monkeypatch.setattr(engine, "_THRESH_PATH", str(tmp_path / "missing-thresholds.json"))
+    monkeypatch.setattr(engine, "ML_WEIGHTS_PATH", str(tmp_path / "missing-weights.json"))
+    monkeypatch.setattr(engine, "CACHE_PATH", str(tmp_path / "missing-cache.json"))
+    monkeypatch.setattr(engine, "_compute_backtest", lambda ml_mode=False: {"chart_data": [{"score": 10}]})
+    engine.clear_backtest_cache()
+
+    first = engine.run_backtest()
+    first["chart_data"][0]["score"] = 99
+
+    assert engine.run_backtest()["chart_data"][0]["score"] == 10

tests/test_backtest_data_quality.py

@@ -0,0 +1,61 @@
+from backtesting import engine as backtest
+from scoring import engine as scoring
+
+
+def test_metric_specific_staleness_does_not_apply_generic_30_day_fill():
+    lookup = {"2024-01-01": 42}
+
+    assert backtest._metric_observation(lookup, "2024-01-03", "fear_greed") == (42, "2024-01-01", 2)
+    assert backtest._metric_observation(lookup, "2024-01-04", "fear_greed") == (None, None, None)
+    assert backtest._metric_observation(lookup, "2024-01-08", "200w_sma") == (42, "2024-01-01", 7)
+    assert backtest._metric_observation(lookup, "2024-01-02", "unknown_metric") == (None, None, None)
+
+
+def test_current_context_score_uses_only_the_common_backtest_panel():
+    cached_scored = {
+        "composite_score": 100,
+        "metrics": [
+            {"key": "fear_greed", "score": 10},
+            {"key": "puell_multiple", "score": 0},
+            {"key": "sopr", "score": 10},
+            {"key": "vdd_multiple", "score": 10},
+        ],
+    }
+
+    score, coverage = backtest._common_panel_current_score(cached_scored)
+
+    assert score == 50.0
+    assert coverage == {
+        "available_count": 2,
+        "panel_count": len(backtest.BACKTEST_METRIC_PANEL),
+        "available_keys": ["fear_greed", "puell_multiple"],
+    }
+
+
+def test_live_and_backtest_outputs_publish_panel_and_coverage_metadata():
+    live = scoring.score_all({"fear_greed": {"value": 10}})
+
+    assert live["metric_panel"]["id"] == "live-all-v1"
+    assert live["metric_panel"]["count"] == live["total_count"]
+    assert live["coverage"]["available_count"] == live["scored_count"]
+    assert live["coverage"]["ratio"] == live["scored_count"] / live["total_count"]
+    assert "score_version" in live
+
+    metadata = backtest._backtest_data_quality_metadata([3, 5, 9])
+    assert metadata["metric_panel"]["id"] == "historical-common-v1"
+    assert metadata["metric_panel"]["keys"] == list(backtest.BACKTEST_METRIC_PANEL)
+    assert metadata["coverage"] == {
+        "minimum_metrics": 3,
+        "maximum_metrics": 9,
+        "average_metrics": 5.7,
+        "panel_count": 9,
+    }
+    assert metadata["staleness_days"] == backtest.METRIC_MAX_AGE_DAYS
+
+
+def test_chart_data_exposes_metric_values_under_frontend_contract():
+    result = backtest.run_backtest()
+    entries = [entry for entry in result["chart_data"] if entry.get("metric_values")]
+
+    assert entries
+    assert all("metrics" not in entry for entry in entries)

tests/test_backtest_ml_scope.py

@@ -0,0 +1,64 @@
+from backtesting import engine
+from ml.artifacts import ML_ARTIFACT_SCHEMA_VERSION, REQUIRED_WEIGHT_KEYS
+from scoring.policy import SCORE_VERSION
+
+
+def _weights(focus):
+    weights = {key: 0.0 for key in REQUIRED_WEIGHT_KEYS}
+    weights[focus] = 1.0
+    return weights
+
+
+def _artifact(with_folds=True):
+    artifact = {
+        "artifact_schema_version": ML_ARTIFACT_SCHEMA_VERSION,
+        "score_version": SCORE_VERSION,
+        "weights": _weights("fear_greed"),
+        "provenance": {
+            "validation_method": "purged_expanding_window",
+            "label_horizon_days": 365,
+            "weight_scope": "full_history_fit",
+            "training_date_range": {"start": "2018-01-01", "end": "2024-01-01"},
+            "trained_at": "2026-07-01T00:00:00+00:00",
+        },
+        "cv_results": {"folds": []},
+    }
+    if with_folds:
+        artifact["cv_results"]["folds"] = [
+            {
+                "fold": 1,
+                "weights": _weights("drawdown"),
+                "date_ranges": {"validation": "2020-01-01 to 2020-12-31"},
+            },
+            {
+                "fold": 2,
+                "weights": _weights("nupl"),
+                "date_ranges": {"validation": "2021-01-01 to 2021-12-31"},
+            },
+        ]
+    return artifact
+
+
+def test_ml_backtest_plan_prefers_fold_weights_and_marks_them_oos():
+    plan = engine._build_ml_backtest_plan(_artifact(with_folds=True))
+
+    weights, fold = engine._weights_for_backtest_date("2021-06-01", plan)
+
+    assert weights == _weights("nupl")
+    assert fold == 2
+    assert plan["evaluation_scope"] == "out_of_sample_validation_folds"
+    assert plan["is_out_of_sample"] is True
+    assert plan["weighting_source"] == "fold_specific_weights"
+    assert engine._weights_for_backtest_date("2019-12-31", plan) == (None, None)
+
+
+def test_full_history_weights_are_explicitly_not_oos():
+    plan = engine._build_ml_backtest_plan(_artifact(with_folds=False))
+
+    weights, fold = engine._weights_for_backtest_date("2021-06-01", plan)
+
+    assert weights == _weights("fear_greed")
+    assert fold is None
+    assert plan["evaluation_scope"] == "in_sample_full_history_weights"
+    assert plan["is_out_of_sample"] is False
+    assert plan["weighting_source"] == "final_full_history_weights"

tests/test_backtest_statistics.py

@@ -0,0 +1,61 @@
+from backtesting import engine, statistics
+
+
+def test_moving_block_bootstrap_is_deterministic_and_handles_constant_series():
+    first = statistics.moving_block_bootstrap_ci(
+        [12.5] * 120,
+        block_size=15,
+        n_resamples=200,
+        seed=7,
+    )
+    second = statistics.moving_block_bootstrap_ci(
+        [12.5] * 120,
+        block_size=15,
+        n_resamples=200,
+        seed=7,
+    )
+
+    assert first == second
+    assert first == {"estimate": 12.5, "ci_low": 12.5, "ci_high": 12.5, "n": 120}
+
+
+def test_summarize_returns_reports_observations_and_block_bootstrap_interval():
+    summary = statistics.summarize_returns(
+        [10.0, -5.0, 20.0, -10.0],
+        block_size=2,
+        n_resamples=200,
+        seed=3,
+    )
+
+    assert summary["n"] == 4
+    assert summary["mean"] == 3.75
+    assert summary["median"] == 2.5
+    assert summary["win_rate"] == 50.0
+    assert summary["mean_ci_low"] <= summary["mean"] <= summary["mean_ci_high"]
+
+
+def test_backtest_brackets_publish_bootstrap_confidence_intervals():
+    stats = {}
+
+    engine._add_return_statistics(stats, "90d", [10.0, -5.0, 20.0, -10.0])
+
+    assert stats["avg_90d"] == 3.75
+    assert stats["median_90d"] == 2.5
+    assert stats["win_rate_90d"] == 50.0
+    assert stats["avg_90d_ci_low"] <= stats["avg_90d"] <= stats["avg_90d_ci_high"]
+
+
+def test_long_horizon_returns_use_a_matching_dependence_block(monkeypatch):
+    observed = {}
+
+    def fake_summary(values, *, block_size, n_resamples):
+        observed.update(block_size=block_size, n_resamples=n_resamples)
+        return {
+            "mean": 1.0, "median": 1.0, "win_rate": 100.0,
+            "mean_ci_low": 0.5, "mean_ci_high": 1.5, "n": len(values),
+        }
+
+    monkeypatch.setattr(engine, "summarize_returns", fake_summary)
+    engine._add_return_statistics({}, "365d", [1.0] * 500)
+
+    assert observed == {"block_size": 365, "n_resamples": 400}

tests/test_job_registry.py

@@ -0,0 +1,53 @@
+import threading
+
+from dashboard.jobs import JobRegistry
+
+
+def test_job_registry_atomically_reserves_only_one_job_per_kind(tmp_path):
+    registry = JobRegistry(tmp_path / "jobs.json")
+    barrier = threading.Barrier(10)
+    results = []
+
+    def reserve():
+        barrier.wait()
+        results.append(registry.reserve("refresh", details={"full": False}))
+
+    threads = [threading.Thread(target=reserve) for _ in range(10)]
+    for thread in threads:
+        thread.start()
+    for thread in threads:
+        thread.join()
+
+    reserved = [job for job in results if job is not None]
+    assert len(reserved) == 1
+    assert reserved[0]["id"]
+    assert reserved[0]["status"] == "queued"
+    assert registry.active("refresh")["id"] == reserved[0]["id"]
+
+
+def test_job_registry_tracks_completion_and_result(tmp_path):
+    registry = JobRegistry(tmp_path / "jobs.json")
+    job = registry.reserve("history")
+
+    result = registry.run(job["id"], lambda: {"records": 42})
+
+    assert result == {"records": 42}
+    saved = registry.get(job["id"])
+    assert saved["status"] == "complete"
+    assert saved["result"] == {"records": 42}
+    assert saved["started_at"]
+    assert saved["finished_at"]
+    assert registry.active("history") is None
+
+
+def test_job_registry_marks_abandoned_active_jobs_interrupted_on_restart(tmp_path):
+    path = tmp_path / "jobs.json"
+    first = JobRegistry(path)
+    job = first.reserve("refresh")
+
+    restarted = JobRegistry(path)
+
+    recovered = restarted.get(job["id"])
+    assert recovered["status"] == "interrupted"
+    assert recovered["finished_at"]
+    assert restarted.active("refresh") is None

tests/test_legacy_ml_validation.py

@@ -0,0 +1,103 @@
+import numpy as np
+import pandas as pd
+
+import orchestrator
+from ml_engine import train_and_backtest as legacy
+from ml_engine.train_and_backtest import create_accumulation_target
+
+
+def _frame(prices):
+    return pd.DataFrame({"close": prices})
+
+
+def test_accumulation_target_for_existing_row_is_invariant_to_unrelated_future_rows():
+    config = {
+        "timeframe": "4h",
+        "target": {
+            "forward_periods_4h": [1, 2, 3],
+            "weights": [0.2, 0.3, 0.5],
+            "return_scales_pct": [5, 10, 20],
+        },
+    }
+    base = _frame([100, 102, 104, 106, 108, 110, 112, 114])
+    extended = _frame([100, 102, 104, 106, 108, 110, 112, 114, 1000, 1, 2000])
+
+    base_target = create_accumulation_target(base, config)
+    extended_target = create_accumulation_target(extended, config)
+
+    assert np.isclose(base_target.iloc[0], extended_target.iloc[0])
+    assert 0 <= base_target.iloc[0] <= 100
+
+
+def test_rolling_validation_purges_forward_label_horizon_at_train_boundaries(monkeypatch):
+    rows = 200
+    frame = pd.DataFrame({
+        "feature": np.linspace(0, 1, rows),
+        "target": np.arange(rows, dtype=float) % 100,
+        "close": np.linspace(10_000, 20_000, rows),
+    })
+    observed = []
+
+    def fake_train(X_train, y_train, X_val, y_val, X_test, *args):
+        observed.append((len(X_train), len(X_val), len(X_test)))
+        return np.full(len(X_test), 50.0), np.array([1.0])
+
+    monkeypatch.setattr(legacy, "_train_and_predict_window", fake_train)
+    config = {
+        "model_type": "xgboost",
+        "target": {"forward_periods_4h": [1, 2, 3]},
+        "training": {
+            "rolling_train_size": 120,
+            "rolling_test_size": 40,
+            "validation_pct": 0.25,
+        },
+        "features": {"use_scaler": False, "use_pca": False},
+        "strategy": {},
+    }
+
+    legacy.rolling_window_train_test(frame, ["feature"], config)
+
+    assert observed[0] == (87, 27, 40)
+
+
+def test_periodic_accumulation_compares_equal_contributions_and_retains_cash():
+    result = legacy.simulate_periodic_accumulation(
+        predicted_scores=np.array([90, 10, 90, 10], dtype=float),
+        close_prices=np.array([100, 300, 100, 200], dtype=float),
+        buy_threshold=70,
+        contribution=100,
+    )
+
+    assert np.isclose(result["dca_contributed"], 400)
+    assert np.isclose(result["model_contributed"], 400)
+    assert np.isclose(result["model_cash"], 100)
+    assert np.isclose(result["model_btc"], 3)
+    assert result["model_terminal_value"] > result["dca_terminal_value"]
+
+
+def test_compiled_results_publish_equal_capital_terminal_wealth_metric():
+    predictions = [
+        {"predicted": score, "actual": 50.0, "close": price}
+        for score, price in zip([90, 10, 90, 10], [100, 300, 100, 200])
+    ]
+
+    result = legacy.compile_results(
+        predictions,
+        per_window_cost_improvement=[],
+        fi_sum=np.array([1.0]),
+        fi_count=1,
+        feature_cols=["feature"],
+        config={"model_type": "xgboost", "strategy": {"good_buy_threshold": 70}},
+    )
+
+    assert result["terminal_wealth_improvement_pct"] > 0
+    assert result["backtest_objective"] == "equal_periodic_contribution_terminal_wealth"
+
+
+def test_orchestrator_selects_models_by_terminal_wealth_not_cost_basis():
+    results = {
+        "terminal_wealth_improvement_pct": 4.5,
+        "cost_basis_improvement_pct": 99.0,
+    }
+
+    assert orchestrator.objective_score(results) == 4.5

tests/test_ml_artifacts.py

@@ -0,0 +1,60 @@
+import json
+from pathlib import Path
+
+from ml import artifacts
+from scoring import engine
+
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+
+
+def _valid_artifact():
+    return {
+        "artifact_schema_version": artifacts.ML_ARTIFACT_SCHEMA_VERSION,
+        "score_version": artifacts.SCORE_VERSION,
+        "weights": {key: 1 / len(artifacts.REQUIRED_WEIGHT_KEYS) for key in artifacts.REQUIRED_WEIGHT_KEYS},
+        "provenance": {
+            "validation_method": "purged_expanding_window",
+            "label_horizon_days": 365,
+            "weight_scope": "full_history_fit",
+            "training_date_range": {"start": "2018-02-01", "end": "2025-03-21"},
+            "trained_at": "2026-07-01T00:00:00+00:00",
+        },
+    }
+
+
+def test_repository_artifact_has_current_schema_and_purged_provenance():
+    artifact_path = REPO_ROOT / "config" / "ml_weights.json"
+    artifact = json.loads(artifact_path.read_text())
+
+    status = artifacts.validate_ml_artifact(artifact)
+
+    assert status["valid"] is True
+    assert status["schema_version"] == artifacts.ML_ARTIFACT_SCHEMA_VERSION
+    assert status["score_version"] == artifacts.SCORE_VERSION
+    assert status["has_oos_fold_weights"] is True
+    assert status["errors"] == []
+
+
+def test_valid_artifact_requires_schema_score_version_and_purged_provenance():
+    artifact = _valid_artifact()
+
+    status = artifacts.validate_ml_artifact(artifact)
+
+    assert status == {
+        "valid": True,
+        "schema_version": artifacts.ML_ARTIFACT_SCHEMA_VERSION,
+        "score_version": artifacts.SCORE_VERSION,
+        "weight_scope": "full_history_fit",
+        "has_oos_fold_weights": False,
+        "errors": [],
+    }
+
+
+def test_live_scoring_refuses_schema_less_weights(tmp_path, monkeypatch):
+    path = tmp_path / "ml_weights.json"
+    path.write_text(json.dumps({"weights": {"fear_greed": 1.0}}))
+    monkeypatch.setattr(engine, "ML_WEIGHTS_PATH", str(path))
+
+    assert engine.load_ml_weights() == {}
+    assert engine.get_ml_artifact_status()["valid"] is False

tests/test_ml_optimizer_validation.py

@@ -0,0 +1,107 @@
+from datetime import datetime, timedelta
+
+import numpy as np
+
+from ml import optimizer
+
+
+def _row(date, returns=10.0, score_200w=10, score_drawdown=0):
+    row = {
+        "date": date,
+        "price": 100.0,
+        "fwd_365d": returns,
+        "score_puell_multiple": 0,
+        "score_mvrv_zscore": 0,
+        "score_reserve_risk": 0,
+        "score_rhodl_ratio": 0,
+        "score_nupl": 0,
+        "score_fear_greed": 0,
+        "score_drawdown": score_drawdown,
+        "score_pct_above_200w_sma": score_200w,
+        "score_pct_above_lth_rp": 0,
+    }
+    return row
+
+
+def test_purged_time_series_splits_remove_overlapping_forward_label_windows():
+    start = datetime(2020, 1, 1)
+    rows = [_row((start + timedelta(days=i)).strftime("%Y-%m-%d")) for i in range(900)]
+
+    splits = list(
+        optimizer.purged_time_series_splits(
+            rows,
+            n_splits=3,
+            label_horizon_days=365,
+            embargo_days=0,
+        )
+    )
+
+    assert splits, "expected at least one viable split"
+    for train_idx, val_idx in splits:
+        val_start = datetime.strptime(rows[val_idx[0]]["date"], "%Y-%m-%d")
+        latest_allowed_train_date = val_start - timedelta(days=365)
+        assert len(train_idx) > 0, "purging should keep non-overlapping expanding-window training rows"
+        for idx in train_idx:
+            train_date = datetime.strptime(rows[idx]["date"], "%Y-%m-%d")
+            assert train_date <= latest_allowed_train_date
+
+
+def test_run_out_of_sample_comparison_scores_only_validation_rows_with_fold_weights():
+    rows = [
+        _row("2020-01-01", returns=-10, score_200w=0, score_drawdown=10),
+        _row("2020-01-02", returns=-5, score_200w=0, score_drawdown=10),
+        _row("2020-01-03", returns=100, score_200w=10, score_drawdown=10),
+        _row("2020-01-04", returns=120, score_200w=10, score_drawdown=10),
+    ]
+    fold_results = [
+        {
+            "fold": 1,
+            "val_idx": [2, 3],
+            "weights": {"pct_above_200w_sma": 1.0, "drawdown": 0.0},
+        }
+    ]
+
+    comparison = optimizer.run_out_of_sample_comparison(rows, fold_results)
+
+    assert comparison["validation_days"] == 2
+    assert comparison["folds"] == 1
+    assert sum(bucket["days"] for bucket in comparison["ml_weighted"]) == 2
+    assert sum(bucket["days"] for bucket in comparison["equal_weight"]) == 2
+
+    extreme_ml = next(bucket for bucket in comparison["ml_weighted"] if bucket["label"] == "EXTREME ACCUMULATION ZONE")
+    assert extreme_ml["days"] == 2
+    assert extreme_ml["avg_365d"] == 110.0
+
+    caution_equal = next(bucket for bucket in comparison["equal_weight"] if bucket["label"] == "CAUTION — OVERHEATED")
+    assert caution_equal["days"] == 2
+
+
+def test_classification_splits_skip_training_windows_with_one_class():
+    y = np.array([1, 1, 1, 0, 1, 0])
+    splits = [
+        (np.array([0, 1]), np.array([2, 3])),
+        (np.array([0, 1, 3, 4]), np.array([5])),
+    ]
+
+    viable = list(optimizer.viable_classification_splits(y, splits))
+
+    assert len(viable) == 1
+    assert viable[0][0].tolist() == [0, 1, 3, 4]
+
+
+def test_artifact_folds_omit_large_internal_index_arrays():
+    folds = [{
+        "fold": 1,
+        "train_idx": [0, 1],
+        "val_idx": [2, 3],
+        "weights": {"fear_greed": 1.0},
+        "date_ranges": {"validation": "2024-01-01 to 2024-01-02"},
+    }]
+
+    saved = optimizer.artifact_fold_results(folds)
+
+    assert saved == [{
+        "fold": 1,
+        "weights": {"fear_greed": 1.0},
+        "date_ranges": {"validation": "2024-01-01 to 2024-01-02"},
+    }]

tests/test_reliability_persistence.py

@@ -0,0 +1,121 @@
+import json
+import threading
+from datetime import datetime, timedelta, timezone
+
+import pytest
+
+from dashboard.persistence import (
+    append_daily_jsonl,
+    atomic_write_json,
+    load_jsonl_tail,
+    merge_observation,
+    onchain_refresh_due,
+)
+
+
+def test_atomic_write_json_remains_readable_under_concurrent_writers(tmp_path):
+    path = tmp_path / "cache.json"
+
+    threads = [
+        threading.Thread(target=atomic_write_json, args=(path, {"writer": i, "values": list(range(100))}))
+        for i in range(12)
+    ]
+    for thread in threads:
+        thread.start()
+    for thread in threads:
+        thread.join()
+
+    saved = json.loads(path.read_text())
+    assert saved["writer"] in range(12)
+    assert saved["values"] == list(range(100))
+    assert not list(tmp_path.glob(".cache.json.*.tmp"))
+
+
+def test_append_daily_jsonl_writes_at_most_one_entry_per_utc_day(tmp_path):
+    path = tmp_path / "scores.jsonl"
+    first = {"timestamp": "2026-07-26T01:00:00+00:00", "score": 10}
+    duplicate_day = {"timestamp": "2026-07-26T23:59:00+00:00", "score": 20}
+    next_day = {"timestamp": "2026-07-27T00:01:00+00:00", "score": 30}
+
+    assert append_daily_jsonl(path, first) is True
+    assert append_daily_jsonl(path, duplicate_day) is False
+    assert append_daily_jsonl(path, next_day) is True
+
+    assert load_jsonl_tail(path, limit=90) == [first, next_day]
+
+
+def test_load_jsonl_tail_is_bounded_and_ignores_malformed_lines(tmp_path):
+    path = tmp_path / "scores.jsonl"
+    path.write_text("".join(json.dumps({"n": i}) + "\n" for i in range(200)) + "partial{")
+
+    assert load_jsonl_tail(path, limit=3, chunk_size=64) == [{"n": 197}, {"n": 198}, {"n": 199}]
+
+
+def test_merge_observation_preserves_last_known_good_with_stale_metadata():
+    old = {
+        "value": 1.25,
+        "observed_at": "2026-07-25T12:00:00+00:00",
+        "source": "lookintobitcoin",
+        "stale": False,
+        "last_error": None,
+    }
+
+    merged = merge_observation(old, None, source="lookintobitcoin", error="timeout")
+
+    assert merged == {
+        "value": 1.25,
+        "observed_at": "2026-07-25T12:00:00+00:00",
+        "source": "lookintobitcoin",
+        "stale": True,
+        "last_error": "timeout",
+    }
+
+
+def test_merge_observation_records_metadata_for_fresh_value():
+    observed_at = "2026-07-26T12:00:00+00:00"
+
+    merged = merge_observation(
+        {"value": 1.0}, {"value": 2.0, "trend": "up"},
+        source="checkonchain", observed_at=observed_at,
+    )
+
+    assert merged["value"] == 2.0
+    assert merged["trend"] == "up"
+    assert merged["observed_at"] == observed_at
+    assert merged["source"] == "checkonchain"
+    assert merged["stale"] is False
+    assert merged["last_error"] is None
+
+
+def test_merge_observation_rejects_error_only_payload_as_fresh_data():
+    old = {
+        "value": 1.25,
+        "observed_at": "2026-07-25T12:00:00+00:00",
+        "source": "lookintobitcoin",
+        "stale": False,
+        "last_error": None,
+    }
+
+    merged = merge_observation(
+        old,
+        {"value": None, "error": "No data returned"},
+        source="lookintobitcoin",
+        error="metric missing from scrape",
+    )
+
+    assert merged["value"] == 1.25
+    assert merged["observed_at"] == old["observed_at"]
+    assert merged["stale"] is True
+    assert merged["last_error"] == "No data returned"
+
+
+@pytest.mark.parametrize("timestamp", [None, "", "not-a-time"])
+def test_onchain_refresh_due_when_timestamp_is_missing_or_invalid(timestamp):
+    assert onchain_refresh_due(timestamp, now=datetime(2026, 7, 26, tzinfo=timezone.utc)) is True
+
+
+def test_onchain_refresh_due_after_ttl():
+    now = datetime(2026, 7, 26, 12, tzinfo=timezone.utc)
+
+    assert onchain_refresh_due((now - timedelta(hours=5)).isoformat(), now=now, ttl_seconds=21600) is False
+    assert onchain_refresh_due((now - timedelta(hours=7)).isoformat(), now=now, ttl_seconds=21600) is True

tests/test_score_policy.py

@@ -0,0 +1,38 @@
+import pytest
+
+from backtesting import engine as backtest_engine
+from ml import optimizer
+from scoring import engine as scoring_engine
+from scoring import policy
+
+
+def test_score_brackets_are_contiguous_and_shared_by_all_scoring_paths():
+    assert backtest_engine.BRACKETS is policy.SCORE_BRACKETS
+    assert optimizer.BRACKETS is policy.SCORE_BRACKETS
+
+    for left, right in zip(policy.SCORE_BRACKETS, policy.SCORE_BRACKETS[1:]):
+        assert left[1] == right[0]
+
+    for tenth in range(0, 1001):
+        score = tenth / 10
+        matches = [bracket for bracket in policy.SCORE_BRACKETS if policy.score_in_bracket(score, bracket)]
+        assert len(matches) == 1, f"score {score} matched {matches}"
+        assert policy.assessment_for_score(score) == matches[0][2]
+
+
+@pytest.mark.parametrize(
+    ("score", "assessment"),
+    [
+        (0, "EXTREME CAUTION"),
+        (19.999, "EXTREME CAUTION"),
+        (20, "CAUTION — OVERHEATED"),
+        (35, "NEUTRAL"),
+        (50, "MODERATE OPPORTUNITY"),
+        (65, "STRONG ACCUMULATION ZONE"),
+        (80, "EXTREME ACCUMULATION ZONE"),
+        (100, "EXTREME ACCUMULATION ZONE"),
+    ],
+)
+def test_assessment_boundaries_match_live_scoring(score, assessment):
+    assert policy.assessment_for_score(score) == assessment
+    assert scoring_engine.assessment_for_score(score) == assessment

tests/test_scoring_engine_ml.py

@@ -0,0 +1,64 @@
+import math
+
+from scoring import engine
+
+
+def _complete_metrics():
+    return {
+        "fear_greed": {"value": 10, "classification": "Extreme Fear"},
+        "puell_multiple": {"value": 0.3},
+        "mvrv_zscore": {"value": -0.1},
+        "drawdown": {"value": 60.0, "ath": 250.0},
+        "price": {"price": 100.0},
+        "200w_sma": {"value": 120.0},
+        "reserve_risk": {"value": 0.001},
+        "rhodl_ratio": {"value": 50.0},
+        "nupl": {"value": -0.1},
+        "lth_realized_price": {"value": 120.0},
+        "hash_ribbons": {"buy_signal": False},
+    }
+
+
+def test_score_all_ml_normalizes_displayed_weights_and_contributions(monkeypatch):
+    monkeypatch.setattr(
+        engine,
+        "load_ml_weights",
+        lambda: {
+            "fear_greed": 0.40,
+            "puell_multiple": 0.20,
+            "mvrv_zscore": 0.15,
+            "drawdown": 0.10,
+            "pct_above_200w_sma": 0.05,
+            "reserve_risk": 0.04,
+            "rhodl_ratio": 0.03,
+            "nupl": 0.02,
+            "pct_above_lth_rp": 0.01,
+        },
+    )
+
+    scored = engine.score_all_ml(_complete_metrics())
+
+    assert scored["ml_mode"] is True
+    valid_metrics = [m for m in scored["metrics"] if m["score"] is not None]
+    assert scored["ml_weight_total"] == 1.01  # trained weights + small hash-ribbons fallback
+    assert math.isclose(sum(m["ml_weight"] for m in valid_metrics), 1.0, abs_tol=0.001)
+    assert math.isclose(
+        sum(m["ml_contribution"] for m in valid_metrics),
+        scored["composite_score"],
+        abs_tol=0.05,
+    )
+
+    hash_ribbons = next(m for m in valid_metrics if m["key"] == "hash_ribbons")
+    assert hash_ribbons["ml_raw_weight"] == 0.01
+    assert hash_ribbons["ml_weight"] == round(0.01 / 1.01, 4)
+
+
+def test_score_all_ml_preserves_classic_fallback_when_weights_missing(monkeypatch):
+    monkeypatch.setattr(engine, "load_ml_weights", lambda: {})
+
+    scored = engine.score_all_ml(_complete_metrics())
+
+    assert scored["ml_mode"] is False
+    assert scored["ml_error"]
+    assert scored["ml_artifact"]["valid"] is False
+    assert "classic_score" not in scored

tests/test_scraper_semantics.py

@@ -0,0 +1,56 @@
+from contextlib import contextmanager
+
+from scrapers import lookintobitcoin
+from scoring import engine
+
+
+def test_hash_ribbon_signal_requires_a_current_truthy_marker():
+    named_but_inactive = {"name": "Buy Signal", "y": [1, None, None]}
+    active = {"name": "Buy Signal", "y": [None, 0, 1]}
+
+    assert lookintobitcoin._trace_signal_is_active(named_but_inactive) is False
+    assert lookintobitcoin._trace_signal_is_active(active) is True
+
+
+def test_lth_supply_trace_selection_never_falls_back_to_price():
+    traces = [
+        {"name": "BTC Price", "y": [60000, 61000]},
+        {"name": "Long-Term Holder Supply", "y": [14_000_000, 14_100_000]},
+    ]
+
+    assert lookintobitcoin._find_lth_supply_trace(traces)["name"] == "Long-Term Holder Supply"
+    assert lookintobitcoin._find_lth_supply_trace(traces[:1]) is None
+
+
+def test_vdd_derived_return_is_labeled_as_momentum_not_raw_multiple():
+    result = engine.score_all({"vdd_multiple": {"value": 0.12}})
+    vdd = next(metric for metric in result["metrics"] if metric["key"] == "vdd_multiple")
+
+    assert vdd["name"] == "VDD 30-Period Momentum"
+    assert vdd["transform"] == "30_period_return"
+
+
+def test_scrape_all_reuses_one_browser_page(monkeypatch):
+    page = object()
+    seen_pages = []
+
+    @contextmanager
+    def fake_browser_page():
+        yield page
+
+    def fake_scrape_chart(_path, timeout=25000, page=None):
+        seen_pages.append(page)
+        return [{"name": "metric", "y": [1.0]}]
+
+    monkeypatch.setattr(lookintobitcoin, "CHARTS", {
+        "first": {"path": "/first", "traces": ["metric"]},
+        "second": {"path": "/second", "traces": ["metric"]},
+    })
+    monkeypatch.setattr(lookintobitcoin, "browser_page", fake_browser_page)
+    monkeypatch.setattr(lookintobitcoin, "scrape_chart", fake_scrape_chart)
+
+    result = lookintobitcoin.scrape_all()
+
+    assert seen_pages == [page, page]
+    assert result["first"]["value"] == 1.0
+    assert result["second"]["value"] == 1.0

tests/test_server_reliability.py

@@ -0,0 +1,194 @@
+import importlib
+import json
+import sys
+import types
+from datetime import datetime, timedelta, timezone
+
+import pytest
+
+
+@pytest.fixture
+def server(monkeypatch):
+    started = []
+    monkeypatch.setattr("threading.Thread.start", lambda self: started.append(self))
+    sys.modules.pop("dashboard.server", None)
+    module = importlib.import_module("dashboard.server")
+    module._threads_started_during_import = started
+    return module
+
+
+def test_server_import_does_not_start_scheduler_threads(server):
+    assert server._threads_started_during_import == []
+
+
+def test_frontend_uses_backtest_metric_values_contract(server):
+    assert ".filter(d => d.metric_values && d.metric_values[metricKey] != null)" in server.DASHBOARD_HTML
+    assert ".map(d => ({ date: d.date, value: d.metric_values[metricKey]" in server.DASHBOARD_HTML
+
+
+def test_dashboard_does_not_issue_duplicate_initial_backtest_request(server):
+    assert "const br = await fetch('/api/backtest');" not in server.DASHBOARD_HTML
+    assert server.DASHBOARD_HTML.count("fetch('/api/backtest?mode=' + currentMode)") == 1
+
+
+def test_health_endpoints_distinguish_process_liveness_from_data_readiness(server, monkeypatch):
+    assert server.health_live() == {"status": "ok"}
+
+    monkeypatch.setattr(server, "load_cache", lambda: {})
+    unavailable = server.health_ready()
+    assert unavailable.status_code == 503
+
+    monkeypatch.setattr(
+        server,
+        "load_cache",
+        lambda: {"_scored": {"composite_score": 72, "scored_count": 8}},
+    )
+    assert server.health_ready() == {
+        "status": "ready",
+        "score": 72,
+        "scored_metrics": 8,
+    }
+
+
+def test_server_cache_and_history_use_reliable_persistence(server, monkeypatch, tmp_path):
+    monkeypatch.setattr(server, "CACHE_PATH", str(tmp_path / "cache.json"))
+    monkeypatch.setattr(server, "HISTORY_PATH", str(tmp_path / "scores.jsonl"))
+
+    server.save_cache({"metric": {"value": 1}})
+    server.append_history({
+        "composite_score": 50,
+        "scored_count": 1,
+        "metrics": [{"key": "metric", "score": 5, "value": 1}],
+    })
+    server.append_history({
+        "composite_score": 60,
+        "scored_count": 1,
+        "metrics": [{"key": "metric", "score": 6, "value": 2}],
+    })
+
+    assert server.load_cache() == {"metric": {"value": 1}}
+    assert len(server.load_history()) == 1
+
+
+def test_partial_fast_scrape_preserves_last_known_good_metric(server, monkeypatch, tmp_path):
+    cache_path = tmp_path / "cache.json"
+    history_path = tmp_path / "scores.jsonl"
+    now = datetime.now(timezone.utc)
+    cache_path.write_text(json.dumps({
+        "price": {
+            "price": 65000,
+            "observed_at": (now - timedelta(minutes=15)).isoformat(),
+            "source": "coingecko",
+            "stale": False,
+            "last_error": None,
+        },
+        "puell_multiple": {"value": 1.2},
+        "_onchain_timestamp": now.isoformat(),
+    }))
+    monkeypatch.setattr(server, "CACHE_PATH", str(cache_path))
+    monkeypatch.setattr(server, "HISTORY_PATH", str(history_path))
+    monkeypatch.setattr(server.fear_greed, "fetch", lambda: {"value": 25})
+    monkeypatch.setattr(server.price, "fetch_current", lambda: (_ for _ in ()).throw(RuntimeError("price timeout")))
+    monkeypatch.setattr(server.price, "fetch_ath", lambda: (_ for _ in ()).throw(RuntimeError("ATH timeout")))
+    monkeypatch.setattr(server.price, "fetch_historical", lambda: (_ for _ in ()).throw(RuntimeError("history timeout")))
+    monkeypatch.setattr(server.engine, "score_all", lambda metrics: {"composite_score": 50, "scored_count": 1, "metrics": []})
+    monkeypatch.setattr(server.engine, "score_all_ml", lambda metrics: {"composite_score": 50, "scored_count": 1, "metrics": []})
+    fake_updater = types.ModuleType("scrapers.history_updater")
+    fake_updater.update_history = lambda: None
+    monkeypatch.setitem(sys.modules, "scrapers.history_updater", fake_updater)
+
+    server.run_scrape()
+
+    saved = json.loads(cache_path.read_text())
+    assert saved["price"]["price"] == 65000
+    assert saved["price"]["stale"] is True
+    assert "price timeout" in saved["price"]["last_error"]
+    assert saved["fear_greed"]["value"] == 25
+    assert saved["fear_greed"]["stale"] is False
+    assert saved["fear_greed"]["source"] == "alternative.me"
+
+
+def test_onchain_sources_fail_independently(server, monkeypatch):
+    fake_lib = types.ModuleType("scrapers.lookintobitcoin")
+    setattr(fake_lib, "scrape_all", lambda: (_ for _ in ()).throw(RuntimeError("LIB down")))
+    fake_coc = types.ModuleType("scrapers.checkonchain")
+    setattr(fake_coc, "scrape_all", lambda: {"sopr": {"value": 0.99}})
+    monkeypatch.setitem(sys.modules, "scrapers.lookintobitcoin", fake_lib)
+    monkeypatch.setitem(sys.modules, "scrapers.checkonchain", fake_coc)
+
+    observations, errors, successful_sources = server._scrape_onchain_sources()
+
+    assert observations["sopr"]["value"] == 0.99
+    assert successful_sources == 1
+    assert any("LookIntoBitcoin" in error for error in errors)
+
+
+def test_expired_onchain_timestamp_triggers_real_refresh(server, monkeypatch, tmp_path):
+    old = datetime.now(timezone.utc) - timedelta(hours=7)
+    cache_path = tmp_path / "cache.json"
+    cache_path.write_text(json.dumps({
+        "puell_multiple": {"value": 1.2},
+        "_onchain_timestamp": old.isoformat(),
+    }))
+    monkeypatch.setattr(server, "CACHE_PATH", str(cache_path))
+    monkeypatch.setattr(server, "HISTORY_PATH", str(tmp_path / "scores.jsonl"))
+    monkeypatch.setattr(server.fear_greed, "fetch", lambda: {"value": 25})
+    monkeypatch.setattr(server.price, "fetch_current", lambda: {"price": 65000})
+    monkeypatch.setattr(server.price, "fetch_ath", lambda: {"ath": 70000})
+    monkeypatch.setattr(server.price, "fetch_historical", lambda: [])
+    monkeypatch.setattr(server.engine, "score_all", lambda metrics: {"composite_score": 50, "scored_count": 1, "metrics": []})
+    monkeypatch.setattr(server.engine, "score_all_ml", lambda metrics: {"composite_score": 50, "scored_count": 1, "metrics": []})
+
+    calls = []
+    fake_lib = types.ModuleType("scrapers.lookintobitcoin")
+    fake_lib.scrape_all = lambda: calls.append("lib") or {"puell_multiple": {"value": 0.8}}
+    fake_coc = types.ModuleType("scrapers.checkonchain")
+    fake_coc.scrape_all = lambda: calls.append("coc") or {"sopr": {"value": 0.99}}
+    fake_updater = types.ModuleType("scrapers.history_updater")
+    fake_updater.update_history = lambda: None
+    import scrapers
+    monkeypatch.setattr(scrapers, "lookintobitcoin", fake_lib, raising=False)
+    monkeypatch.setattr(scrapers, "checkonchain", fake_coc, raising=False)
+    monkeypatch.setitem(sys.modules, "scrapers.lookintobitcoin", fake_lib)
+    monkeypatch.setitem(sys.modules, "scrapers.checkonchain", fake_coc)
+    monkeypatch.setitem(sys.modules, "scrapers.history_updater", fake_updater)
+
+    server.run_scrape()
+
+    assert calls == ["lib", "coc"]
+    saved = json.loads(cache_path.read_text())
+    assert saved["puell_multiple"]["value"] == 0.8
+    assert saved["puell_multiple"]["source"] == "lookintobitcoin"
+    assert saved["sopr"]["source"] == "checkonchain"
+    assert saved["_onchain_timestamp"] != old.isoformat()
+
+
+def test_refresh_job_is_reserved_before_thread_start(server, monkeypatch, tmp_path):
+    from dashboard.jobs import JobRegistry
+
+    registry = JobRegistry(tmp_path / "jobs.json")
+    monkeypatch.setattr(server, "_jobs", registry, raising=False)
+    monkeypatch.setattr(server, "_scraper_running", False)
+
+    started = server.api_refresh(full=False)
+    duplicate = server.api_refresh(full=False)
+
+    assert started["job_id"]
+    assert started["status"] == "queued"
+    assert registry.get(started["job_id"])["status"] == "queued"
+    assert duplicate.status_code == 409
+
+
+def test_history_collection_has_job_id_and_job_scoped_progress(server, monkeypatch, tmp_path):
+    from dashboard.jobs import JobRegistry
+
+    registry = JobRegistry(tmp_path / "jobs.json")
+    monkeypatch.setattr(server, "_jobs", registry, raising=False)
+
+    started = server.api_backtest_collect()
+    job = registry.get(started["job_id"])
+
+    assert started["status"] == "queued"
+    assert job["kind"] == "history"
+    assert job["progress"] == {"status": "starting", "current": "", "step": 0, "total": 0}
+    assert server.api_job_status(started["job_id"])["id"] == started["job_id"]