Methodology

Data Sources

Models Used

Each asset uses one or more quantitative models. Scores range from 0-100 where higher values indicate historically more favorable conditions.

Macro Confluence adjusts model confidence using economic indicators. A positive alignment means macro conditions support the signal direction; a negative alignment means they oppose it. The adjustment is small (up to ±20% of base confidence) and is applied only to backtest-validated assets.

Enabled Assets & Indicators

How It Works

1. Compute z-score (value vs 2-year rolling mean) for each indicator.
2. Determine 3-month trend direction (rising / falling / flat) via linear fit.
3. Blend z-score and trend into a per-factor signal using asset-class rules.
4. Weighted sum of factor signals produces an alignment score (−1 to +1).
5. Confidence is adjusted: base × (1 + alignment × 0.20)

Factor weights were determined by walk-forward backtest with ablation testing on 70/30 train-test split. Only factors that improved score-outcome correlation were retained. Results are based on limited macro data history and should be treated as experimental.

Breakout Detection uses technical indicators validated via walk-forward backtest (70% train / 30% test, GO threshold: correlation > 0.05) on BTC data from 2014–2026.

Indicators & Timeframes

Production Strategy

• Daily signal: Volume Profile only (price vs. Value Area High/Low with volume confirmation)
• Weekly signal: S/R (55%) + RSI Divergence (45%)
• Combined: 50% daily + 50% weekly, mapped to 0–100 score

Indicators are timeframe-specific based on empirical results. Forced combination across timeframes degrades performance. Currently enabled for BTC only.

On-chain metrics measure Bitcoin network fundamentals directly from the blockchain. Data is sourced from the CoinMetrics Community API (free, no rate limit). Metrics are normalized via 2-year rolling z-scores and combined into a 0–100 score using empirically validated weights from walk-forward backtest.

Metrics & Weights

How It Works

1. Fetch daily MVRV, active addresses, exchange flows, and hashrate from CoinMetrics.
2. Normalize each metric using a 730-day rolling z-score (clipped to ±3, mapped to −1 to +1).
3. Compute weighted composite: MVRV × 0.35 + Active Addresses × 0.65.
4. Map composite (−1 to +1) to score (0–100). Above 60 = bullish, below 40 = bearish.

Backtest Results

• Test period: Oct 2022 – Feb 2026 (1,222 samples)
• Composite correlation: r=0.089 (90d forward) — GO
• Monotonicity (90d): 0.75 (GOOD) — higher scores correlate with higher returns
• Score bins: 0–20 → −8%, 40–60 → +13%, 80–100 → +41% (90d forward)

Currently enabled for BTC only. Exchange netflow and reserves are shown for context but excluded from scoring due to negative backtest results. Ablation study confirmed removing exchange_reserves improves composite correlation from 0.024 to 0.071.

Sentiment analysis measures market crowd psychology using the Fear & Greed Index as a contrarian indicator. Extreme fear is interpreted as bullish (historically favorable buying conditions), while extreme greed is bearish. Binance Futures data (funding rate, open interest, long/short ratio) is shown for context but not scored.

Metrics & Weights

Contrarian Logic

1. Fear & Greed raw value (0–100) is mapped via contrarian formula: (50 − value) / 50
2. Extreme Fear (0) → +1.0 (bullish). Extreme Greed (100) → −1.0 (bearish).
3. Composite signal (−1 to +1) is mapped to a 0–100 score. Above 60 = bullish, below 40 = bearish.

Backtest Results

• Test period: Sep 2023 – Feb 2026 (883 samples, 70/30 walk-forward split)
• BTC Fear & Greed: r=0.116 (90d forward, p=0.001) — GO
• ETH Fear & Greed: r=0.063 (90d forward, p=0.077) — GO
• Funding Rate ablation: removing it improves BTC composite from 0.040 to 0.116

Enabled for all crypto assets (BTC, ETH, XRP, SOL, BNB). Backtested on BTC (r=0.116) and ETH (r=0.063). Altcoins use the same Fear & Greed signal since it measures overall crypto market sentiment. Binance Futures display-only metrics have limited history. Data sources: Alternative.me (Fear & Greed), Binance Futures API (funding rate, OI, ratios).

Microstructure analysis examines derivatives market positioning through liquidation data, open interest changes, and orderbook depth. Daily snapshots from Coinalyze provide raw data for informational display.

Metrics (All Display-Only)

BTC only. Data sources: Coinalyze free API (daily liquidation/OI history), Binance Futures API (orderbook depth snapshot). Data is collected daily for informational purposes only.

Macro Factors in AI Analysis

The AI report writer receives these indicators as context when generating daily reports and answering chat questions.

What Is Not Included

• Advanced on-chain metrics — SOPR, NUPL, Puell Multiple, and Reserve Risk require paid APIs or rate-limited free tiers. Basic on-chain data (MVRV, active addresses, exchange flows) is integrated.
• Social media sentiment — Social media volume and news sentiment (LunarCrush, CryptoPanic) are not tracked due to API cost/limitations. Fear & Greed and Binance funding rates are integrated.
• Whale tracking — Whale transactions and large wallet movements are out of scope. Orderbook depth and liquidation data are collected (BTC, display-only) but have no validated predictive signal.
• Geopolitical events — The system reacts to price effects, not to the events themselves.
• Price predictions — Scores reflect historical favorability, not future price targets.
• Real-time data — All data is end-of-day. Intraday moves are not captured until the next daily run.