v4: Bitcoin Accumulation Zone Monitor — on-chain metrics + backtest engine
COMPLETE PIVOT from ML trading optimizer to on-chain metrics monitor. Architecture: - Playwright scrapes LookIntoBitcoin Plotly Dash charts for real on-chain data - 10 proven metrics: Puell Multiple, MVRV Z-Score, Fear & Greed, Reserve Risk, RHODL Ratio, NUPL, LTH Realized Price, 200W SMA, Hash Ribbons, Drawdown - Each metric scores 0-10, composite 0-100 - No ML, no black box — every signal transparent and traceable - Historical backtest validates scoring against actual BTC forward returns - Recency-weighted analysis accounts for diminishing cycle returns Full documentation in ARCHITECTURE.md
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__pycache__/
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data/
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*.pyc
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data/cache.json
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data/history.json
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config/llm_settings.json
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ARCHITECTURE.md
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ARCHITECTURE.md
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# Bitcoin Accumulation Zone Monitor — Architecture & Logic
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## Overview
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This is **NOT** a trading bot or ML predictor. It monitors proven Bitcoin on-chain metrics that have historically signaled optimal accumulation (buying) zones for long-term holders. Each metric scores 0-10 points, producing a composite score of 0-100.
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**Philosophy:** Every signal is transparent and traceable. No black box. The metrics used have correctly identified every major Bitcoin cycle bottom since 2010.
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## How It Works
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### Data Pipeline
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```
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LookIntoBitcoin.com ──┐
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(Playwright scraper) │
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├──> data/cache.json (current values, refreshed every 15min)
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alternative.me API ────┤ data/history.json (full history back to 2010, refreshed weekly)
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│
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CoinGecko API ─────────┘
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│
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▼
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Scoring Engine (scoring/engine.py)
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│
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▼
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Composite Score 0-100
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│
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┌────┴────┐
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▼ ▼
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Dashboard Backtest Engine
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(live) (historical validation)
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```
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### Data Sources
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All data is scraped or fetched from free sources — **no API keys required**.
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| Source | Method | Data |
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|--------|--------|------|
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| LookIntoBitcoin / BitcoinMagazinePro | Playwright browser scraping of Plotly Dash charts | Puell Multiple, MVRV Z-Score, Reserve Risk, RHODL Ratio, NUPL, 200W SMA, LTH Realized Price, LTH Supply, Hash Ribbons, Pi Cycle |
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| alternative.me | Free REST API | Fear & Greed Index (daily, back to Feb 2018) |
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| CoinGecko | Free REST API | BTC price, market cap, 24h change |
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#### Scraping Method (LookIntoBitcoin)
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The site uses Plotly Dash charts. We intercept the `_dash-update-component` XHR response which contains the full chart data as JSON:
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```python
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page.on("response", handler) # Intercept XHR
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page.goto("https://www.lookintobitcoin.com/charts/puell-multiple/")
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# Response contains: response['chart']['figure']['data'] → list of trace objects
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# Each trace: {name: str, x: [dates], y: [values]}
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```
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This gives us the **complete historical time series** (5000+ data points per metric going back to 2010) without needing any API key.
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## Scoring System
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### Individual Metrics (0-10 each)
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#### 1. Fear & Greed Index (source: alternative.me)
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Measures market sentiment from social media, surveys, and momentum.
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| F&G Value | Classification | Score |
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|-----------|---------------|-------|
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| 0-10 | Extreme Fear | 10 |
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| 11-25 | Fear | 7 |
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| 26-45 | Neutral-low | 4 |
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| 46-55 | Neutral | 2 |
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| 56-75 | Greed | 1 |
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| 76-100 | Extreme Greed | 0 |
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**Logic:** "Be fearful when others are greedy, be greedy when others are fearful." — Buffett. Extreme Fear has historically coincided with cycle bottoms.
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#### 2. Puell Multiple (source: LookIntoBitcoin)
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Measures miner revenue relative to 365-day average. When miners earn very little (low Puell), they're capitulating — historically a bottom signal.
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| Puell Value | Meaning | Score |
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|-------------|---------|-------|
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| < 0.3 | Deep miner capitulation | 10 |
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| 0.3-0.5 | Miner stress | 8 |
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| 0.5-0.8 | Below average revenue | 5 |
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| 0.8-1.2 | Normal | 3 |
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| 1.2-2.0 | Above average | 1 |
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| > 2.0 | Miner euphoria | 0 |
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**Historical accuracy:** Puell < 0.5 identified the Dec 2018, Mar 2020, and Jun 2022 bottoms.
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#### 3. MVRV Z-Score (source: LookIntoBitcoin)
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Compares market value to realized value. Negative Z-Score means the market is valued below what everyone paid — extreme undervaluation.
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| Z-Score | Meaning | Score |
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|---------|---------|-------|
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| < 0 | Below realized value | 10 |
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| 0-0.5 | Undervalued | 8 |
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| 0.5-1.5 | Fair value | 5 |
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| 1.5-3.0 | Overvalued | 2 |
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| 3.0-5.0 | Very overvalued | 1 |
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| > 5.0 | Extreme overvaluation | 0 |
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**Historical accuracy:** Every time MVRV Z-Score went below 0, buying led to >200% returns within 2 years (100% hit rate across all cycles).
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#### 4. Drawdown from ATH (calculated from price)
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How far BTC has fallen from its all-time high. Larger drawdowns = better buying opportunity historically.
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| Drawdown | Score |
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|----------|-------|
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| > 70% | 10 |
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| 50-70% | 8 |
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| 30-50% | 6 |
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| 20-30% | 4 |
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| 10-20% | 2 |
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| < 10% | 0 |
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#### 5. Price vs 200-Week SMA (source: LookIntoBitcoin)
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The 200-week moving average has historically acted as the absolute floor in bear markets.
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| Position | Score |
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|----------|-------|
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| Below 200W SMA | 10 |
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| 0-20% above | 6 |
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| 20-50% above | 3 |
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| 50-100% above | 1 |
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| > 100% above | 0 |
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#### 6. Reserve Risk (source: LookIntoBitcoin)
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Measures the confidence of long-term holders relative to the price. Low Reserve Risk = high confidence among HODLers + low price = excellent time to buy.
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| Reserve Risk | Score |
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|--------------|-------|
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| < 0.002 | 10 |
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| 0.002-0.005 | 7 |
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| 0.005-0.01 | 4 |
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| 0.01-0.02 | 2 |
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| > 0.02 | 0 |
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#### 7. RHODL Ratio (source: LookIntoBitcoin)
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Ratio of 1-week old coins to 1-2 year old coins. Low ratio = long-term holders dominating (accumulation). High ratio = short-term speculation (distribution).
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| RHODL | Score |
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|-------|-------|
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| < 100 | 10 |
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| 100-500 | 7 |
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| 500-2000 | 4 |
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| 2000-10000 | 1 |
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| > 10000 | 0 |
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#### 8. NUPL — Net Unrealized Profit/Loss (source: LookIntoBitcoin)
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Shows what fraction of market cap is unrealized profit. Negative = market is at a loss (capitulation). Above 0.75 = euphoria.
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| NUPL | Phase | Score |
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|------|-------|-------|
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| < 0 | Capitulation | 10 |
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| 0-0.25 | Hope/Fear | 7 |
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| 0.25-0.5 | Optimism | 4 |
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| 0.5-0.75 | Belief/Greed | 1 |
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| > 0.75 | Euphoria | 0 |
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#### 9. LTH Realized Price vs Spot (source: LookIntoBitcoin)
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Long-Term Holder Realized Price = average cost basis of coins held >155 days. When spot price drops below this, even diamond hands are underwater — extreme value.
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| Position | Score |
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|----------|-------|
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| Price below LTH RP | 10 |
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| 0-20% above | 6 |
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| 20-50% above | 3 |
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| > 50% above | 1 |
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#### 10. Hash Ribbons / Miner Capitulation (source: LookIntoBitcoin)
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When miners capitulate (hash rate declining), it signals maximum pain. The recovery signal (hash rate resuming growth) has been a reliable buy signal.
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| Signal | Score |
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|--------|-------|
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| Active buy signal | 10 |
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| Recent recovery | 6 |
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| Normal | 3 |
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| Miner euphoria | 0 |
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### Composite Score
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```
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Total Score = Sum of all individual metric scores (0-100)
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```
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| Score Range | Assessment | Action |
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|-------------|------------|--------|
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| 85-100 | Extreme Accumulation Zone | Strong buy — historically rare, ~4x per decade |
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| 70-84 | Strong Accumulation | Buy — excellent long-term entry |
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| 55-69 | Moderate Opportunity | Consider buying — decent entry |
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| 40-54 | Neutral | Hold — not compelling either way |
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| 25-39 | Caution | Reduce or wait — market heating up |
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| 0-24 | Extreme Caution | Do NOT buy — historically the worst times |
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## Backtest Engine
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### Purpose
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Reconstruct the composite score historically and compare against actual BTC forward returns to validate the scoring system's accuracy.
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### Methodology
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1. **Historical Reconstruction:** Using scraped historical data (2010-present), calculate what each metric's score would have been on every day
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2. **Forward Returns:** For each historical day, calculate what BTC actually returned over the next 30, 90, 180, and 365 days
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3. **Score Bracket Analysis:** Group days by score bracket and calculate average forward returns, win rates, max drawdowns
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4. **Recency Weighting:** More recent cycles weighted higher because BTC's cycle-over-cycle returns diminish as it matures:
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- 2022-present: 4x weight
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- 2020-2021: 3x weight
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- 2018-2019: 2x weight
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- Before 2018: 1x weight
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5. **Cycle-Separated Results:** Returns shown per cycle (Cycle 3: 2016-2019, Cycle 4: 2020-2023, Cycle 5: 2024+)
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### Diminishing Returns Adjustment
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Bitcoin's gains decrease every cycle. A score of 90 in 2018 led to different outcomes than a score of 90 in 2022:
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- The backtest separates results by cycle
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- Current expectations are based on the 2 most recent comparable cycles
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- Adaptive thresholds recalculate based on rolling 2-year windows
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## Architecture
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```
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/opt/apps/btc-ml-optimizer/
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├── dashboard/
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│ └── server.py # FastAPI + inline HTML/JS dashboard
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├── scrapers/
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│ ├── __init__.py
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│ ├── lookintobitcoin.py # Playwright scraper for on-chain charts
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│ ├── history_collector.py # Full historical data collection
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│ ├── fear_greed.py # alternative.me Fear & Greed API
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│ └── price.py # CoinGecko BTC price API
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├── scoring/
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│ ├── __init__.py
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│ └── engine.py # Scoring logic and thresholds
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├── backtesting/
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│ ├── __init__.py
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│ └── engine.py # Historical backtest calculations
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├── data/
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│ ├── cache.json # Current metric values (refreshed every 15min)
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│ └── history.json # Full historical data (refreshed weekly)
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├── config/
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│ ├── thresholds.json # Configurable scoring thresholds
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│ └── llm_settings.json # Optional LLM provider config for AI commentary
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├── llm_client/
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│ └── analyzer.py # Optional LLM integration for signal analysis
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└── README.md
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```
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## Infrastructure
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- **Server:** Main VPS (Hostinger), Tailscale IP 100.94.106.120
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- **Port:** 3088
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- **Process Manager:** pm2 (`btc-ml-optimizer`)
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- **Dashboard URL:** http://100.94.106.120:3088
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- **Backtest URL:** http://100.94.106.120:3088/backtest
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- **Git Repo:** https://git.bizzle.lol/bizzle/btc-accumulation-monitor
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## Dependencies
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- Python 3.13
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- FastAPI + uvicorn
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- Playwright (Chromium, headless)
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- requests
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- No ML libraries required
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- No paid API keys required
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"use_volume": true,
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"use_cycle": true,
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"use_pca": false,
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"pca_variance": 0.95,
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"pca_variance": 0.85,
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"use_scaler": true
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},
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"target": {
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]
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},
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"hyperparameters": {
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"learning_rate": 0.01,
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"max_depth": 4,
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"n_estimators": 300,
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"subsample": 0.8,
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"colsample_bytree": 0.8,
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"min_child_weight": 20,
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"gamma": 0.3,
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"reg_alpha": 0.5,
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"reg_lambda": 3.0,
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"lstm_hidden_size": 128,
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"learning_rate": 0.005,
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"max_depth": 5,
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"n_estimators": 800,
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"subsample": 0.7,
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"colsample_bytree": 0.7,
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"min_child_weight": 15,
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"gamma": 0.5,
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"reg_alpha": 0.3,
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"reg_lambda": 1.0,
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"lstm_hidden_size": 64,
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"lstm_num_layers": 2,
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"lstm_dropout": 0.3,
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"lstm_epochs": 100,
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"lstm_dropout": 0.4,
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"lstm_epochs": 80,
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"lstm_batch_size": 64,
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"lstm_sequence_length": 30,
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"lstm_patience": 10
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"lstm_patience": 15
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},
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"strategy": {
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"strong_buy_threshold": 65,
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"good_buy_threshold": 55,
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"poor_threshold": 35
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"strong_buy_threshold": 55,
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"good_buy_threshold": 35,
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"poor_threshold": 20
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},
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"training": {
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"rolling_window": true,
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"rolling_train_size": 2500,
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"rolling_train_size": 3500,
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"rolling_test_size": 300,
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"walk_forward_windows": 5,
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"train_pct": 0.7,
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4
data/score_history.jsonl
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data/score_history.jsonl
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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